I am a second year PhD student in Linguistics at
+the University of Pennsylvania. I am a psycholinguist broadly
+interested in experimental approaches to studying meaning, of various
+flavors. I use computational and behavioral methods to study how
+comprehenders parse linguistic input, both in real time and at different
+stages of language development. My advisor is Anna Papafragou and I am a
+member of the Language &
+Cognition Lab.
+
I received my B.A. in Linguistics from Northwestern University, where
+I worked with Jennifer
+Cole, Masaya
+Yoshida, and Annette
+D’Onofrio. I also worked as a research assistant for (and still
+collaborate with) the Language, Education, and
+Reading Neuroscience Lab) in Communication Sciences and Disorders. I
+wrote my thesis on the role of prosodic focus in the processing of
+garden-path sentences.
+
Beyond linguistics research, I have interests in data visualization,
+science communication, and the R programming language. I read and blog about these topics
+in my spare time as a hobby. I’m especially passionate about improving
+explanatory data viz in academic research and strongly believe that
+style is just as important as content. I am also a Penn MindCORE student
+affiliate and work as a data science tutor for researchers at Penn.
I gave a talk
+at RLadies philly on using
+icon fonts for data viz! I also wrote a follow-up blog
+post that goes deeper into font rendering in R.
+
+
May
+
+
SnowGlobe, a
+project started in my undergrad, was featured in an article
+by the Northwestern University Library. We also had a workshop
+for SnowGlobe which drew participants from over a hundred
+universities!
I joined designer Will
+Chase on his stream to talk
+about the psycholinguistics of speech production for a data viz project
+on Michael’s speech errors in The Office. It was a very cool and unique
+opportunity to bring my two interests together!
I became a data science tutor at MindCORE to help researchers
+at Penn with data visualization and R programming.
+
+
September
+
+
I have moved to Philadelphia to start my PhD in
+Linguistics at the University of Pennsylvania!
+
I presented a poster at AMLaP on my undergrad
+thesis research with my advisors Jennifer Cole and Masaya
+Yoshida.
+
+
June
+
+
I graduated from Northwestern University with a
+B.A. in Linguistics (with honors)! I was also elected into Phi Beta
+Kappa and appointed as the Senior Marshal for Linguistics.
+
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+// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
+// be compatible with the behavior of Pandoc < 2.8).
+document.addEventListener('DOMContentLoaded', function(e) {
+ var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
+ var i, h, a;
+ for (i = 0; i < hs.length; i++) {
+ h = hs[i];
+ if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6
+ a = h.attributes;
+ while (a.length > 0) h.removeAttribute(a[0].name);
+ }
+});
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+June Choe: The Correlation Parameter in Mixed Effects Models
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What is Corr in the output of mixed-effects models?
+
When fitting mixed effects regression models, especially those that try to keep it “maximal” (as per Barr, Levy, Scheepers, & Tily 2013), the random effects in the output of the model sometimes displays a column named Corr where some rows have numbers that range from -1 to 1.
+
+
It’s easy to guess that Corr stands for correlation and that the numbers in the column are correlation coefficients. If there are multiple predictors and the random effects structure includes more than one of those terms (e.g., (1 + Effect_1 * Effect_2 | Subject)), we even get another clue for this from the way that the Corr values spread out in the shape of a right triangle, much like in a correlation matrix.
+
+
Despite the fact that we’re bound to have encountered this at some point when working with or reading about mixed effects models, I’ve found that there aren’t many beginner-friendly material explaining what they are - there are one-paragraph StackExchange answers and dense statistics papers, but not much in between in terms of comprehensiveness.
+
So here are my compiled notes on correlation parameters in linear mixed effects models that I’ve made for myself (with a basic knowledge of LMEMs).
+
Before we get started
+
Our toy data and model
+
+
+
+
For the purposes of this discussion, I have created a toy experiment data (the code used to generate it is attached at the bottom).
+
The dataset toydata has 1,920 rows with the following columns:
+
+
Subject: The subject ID, which ranges from 1 to 80
+
Item: The item ID, which ranges from 1 to 24
+
Condition: The experimental condition, which is either Control or Treatment
+
Response: A continuous observed variable
+
+
+
+
+
+
+
toydata
+
+
+
# A tibble: 1,920 x 4
+ Subject Item Condition Response
+ <fct> <fct> <fct> <dbl>
+ 1 1 1 Control 226.
+ 2 1 2 Treatment 300.
+ 3 1 3 Control 239.
+ 4 1 4 Treatment 262.
+ 5 1 5 Control 241.
+ 6 1 6 Treatment 264.
+ 7 1 7 Control 237.
+ 8 1 8 Treatment 230.
+ 9 1 9 Control 229.
+ 10 1 10 Treatment 283.
+ # ... with 1,910 more rows
+
+
Imagine toydata to be the results from a very simple experiment. In this imaginary experiment, there are 80 subjects and each subject is tested on 24 items, resulting in a total of 1,920 trials/observations. This is a within-partipant design, so each participant sees 12 of the items in the Control condition and the other 12 in the Treatment condition.
+
Let’s say that with our toy data, we want to know whether Condition has a positive effect on Response. Our goal by using mixed-effects modeling is to isolate the effect of Condition on Response (fixed effect), while controlling for by-item and by-subject variations (random effects). So let’s fit a simple linear mixed-effects model with the maximal random effects structure, with random intercepts and slopes for Condition by Subject and Item:
+
+
+
model<-lmer(Response~Condition+(1+Condition|Subject)+(1+Condition|Item),
+ REML =FALSE, control =lmerControl('bobyqa'), data =toydata)
+
Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
+ method [lmerModLmerTest]
+ Formula: Response ~ Condition + (1 + Condition | Subject) + (1 + Condition |
+ Item)
+ Data: toydata
+ Control: lmerControl("bobyqa")
+
+ AIC BIC logLik deviance df.resid
+ 13267 13317 -6624 13249 1911
+
+ Scaled residuals:
+ Min 1Q Median 3Q Max
+ -3.176 -0.627 -0.065 0.576 4.864
+
+ Random effects:
+ Groups Name Variance Std.Dev. Corr
+ Subject (Intercept) 637.1 25.24
+ ConditionTreatment 108.1 10.40 0.85
+ Item (Intercept) 44.4 6.66
+ ConditionTreatment 308.2 17.56 0.14
+ Residual 37.4 6.11
+ Number of obs: 1920, groups: Subject, 80; Item, 24
+
+ Fixed effects:
+ Estimate Std. Error df t value Pr(>|t|)
+ (Intercept) 209.64 3.14 100.84 66.8 < 2e-16 ***
+ ConditionTreatment 35.88 3.78 28.52 9.5 2.5e-10 ***
+ ---
+ Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
+
+ Correlation of Fixed Effects:
+ (Intr)
+ CndtnTrtmnt 0.291
+
+
The high t-statistic and low p-value of ConditionTreatment in the fixed effects output suggests that our data is extremely unlikely given the null hypothesis that Condition has no effect on Response (and the sign of the estimate further suggests a positive effect of the Treatment condition on Response compared to the Control condition). Therefore, this is strong evidence in support of our hypothesis.
+
Here’s a nicer-looking summary table made with tab_model() from the {sjPlot} package. I will keep using this format from this point on. Not only is this nicer to look at, the notations used here (like \(\tau_{00}\) and \(\rho_{01}\)) are from Barr et al. (2013), so it’s easier to connect the pieces IMO.
Let’s ignore the Corr column for a moment and talk about Std.Dev first.
+
The Std.Dev. values for the Subject random effects group suggest that the variation in the subject intercepts are fitted with a larger standard deviation of 25.24 and that the variation in subject slopes for Condition are fitted with a smaller standard deviation of 10.4.
+
Let’s plot the by-subject variation:
+
+
+
+
We see a clear variation in the intercepts, and a subtler variation in the slopes. This is overall pretty consistent with the stand deviation values for subject random effects that we found earlier.
+
Let’s plot the by-item variation as well:
+
+
+
+
Here, we see the opposite: a clear variation in the slopes, and a subtler variation in the intercepts. Again, this is overall pretty consistent given the item random effects output: a larger standard deviation for the item slopes and a smaller standard deviation for item intercepts, as we found earlier.
+
Now that we’ve reviewed Std.Dev., let’s talk about Corr, which is our main focus.
+
The Correlation Parameter
+
Looking at the Corr column now, we see two numbers: 0.85 within the Subject random effects group and 0.14 within the Item random effects group.
As you might have guessed, they have something to do with the correlation between random effects (intercept and slope) within each group (subject and item).
+
But if we extract the subject random effects, for example, and measure the correlation between subject intercepts and subject slopes, we get a slightly different number:
In fact, we get slightly different values for the standard deviation of the subject random intercepts and slopes as well:
+
+
+
# Calculate the standard deviation of by-subject intercepts and slopes
+summarize_all(ranef_subj, sd)
+
+
+
(Intercept) ConditionTreatment
+ 1 25.3 10.18
+
+
So it looks like what the model isn’t just taking the random effects in our toydata dataset and calculating their variations and correlations. So then what is it doing?
+
How the model estimates random effects
+
What we have to keep in mind when doing mixed effects modeling is that the model is fitting the random effects in the data, rather than just describing them. More specifically, the model is estimatingpopulation parameters that generated the sample of random effects that are seen in the data.1
+
And in fact that’s exactly what we want to do. We don’t care about how individual subjects or items behave in our experiment, in the sense that we don’t care how fast John Doe presses a button, for example. We don’t want to predict John Doe’s behavior, but we do want to estimate, using data from John Doe, Jane Doe, Average Joe, and other participants from our experiment, the overall distribution of people’s idiosyncratic tendencies so that we can statistically control for them to get a better estimate for the fixed effects that we care about.
+
Take the random intercepts by subject for example. The model estimated the distribution of subject intercepts to follow a normal distribution with a standard deviation of 25.24, which is a an estimate of the Population. The variation in subject intercepts in the data itself that we manually calculated above (25.3) is the Sample standard deviation. Of course, if the sample follows a normal distribution and if we also assume the population to be normally distributed, the sample variance should be the best estimate for the population variance. And in fact they do end up being very close!
+
So the numbers in the Std.Dev. colum are the model’s fit for the variation within each random effect.
+
With this, we now have a better understanding of the numbers in the Corr column: they are the model’s fit for the correlation between random effects.
+
To go more in depth with our discussion, let’s plot the intercepts and slopes for our 80 subjects:
+
+
+
+
Recall that when we manually calculated the correlation between subject intercepts and subject slopes within our sample of 80 subjects in the data, we got 0.88. That is in fact what is shown by the plot above.
+
And as we discussed earlier, the numbers in the Std.Dev. column are the model’s fit for the variation within each random effect. So the model is saying that the variation for subject intercepts follows a normal distribution with mean = 0 and standard deviation = 25.24. Likewise, the model is saying that the variation for subject slopes follows a normal distribution with mean = 0 and standard deviation = 10.4.2
+
So the model estimates these two distributions - one for subject slopes and one for subject intercepts - to capture the overall distribution of subject random effects.
+
This is illustrated below, where the normal curve at the top is the distribution of subject intercepts estimated by the model, and the normal curve to the right is the distribution of subject slopes estimated by the model. For every subject, their intercepts and slopes are understood to be generated from these two underlying parameters:
+
+
+
+
But is specifying each distribution for intercept and item enough to capture the overall distribution of subject random effects?
+
One way to test this is to work backwards and generate some observations from the model’s parameters. The idea here is this: if the two normal distributions (one for subject intercept and one for subject slope) can sufficiently capture the distribution of the subject random effects, then sampling from them should yield a distribution that is in the shape of the actual distribution in our data.
+
Let’s draw 80 samples of subject intercepts and subject slopes from their respective distributions and then plot them together. Here’s one result:
+
+
+
+
These points are consistent with what the two distributions predict: there are more points towards the center (the means of the distributions) and less points towards the corners (the tails of the distributions).
+
But this doesn’t look like the actual distribution of our subject random effects.
+
We can repeat this sampling procedure many times, but none of them look close to the distribution of the subject random effects in our data:
+
+
+
+
+
+
So what are we missing here?
+
Well, what’s missing here is the correlation between the intercepts and slopes that I conveniently left out to demonstrate that just specifying the individual distributions for subject intercepts and subject slopes poorly captures the actual distribution of subject random effects in our data.
+
In more technical terms, treating the two random effects as independently sampled from their respective distributions fails to fit the data well because the two random effects are highly correlated. They should instead be treated as being jointly sampled from a bivariate distribution
+
And that’s exactly what adding the correlation parameter does. Let’s break this down.
+
When we say that two variables are independently sampled from two distributions (as we just did above), then their joint distribution looks something like this, where most of the data is expected to fall within the grey shaded ellipse:
+
+
+
+
This is clearly a bad fit for the distribution of our subject random effects…
+
+
+
+
… because the distribution of the subject random effects actually takes the shape of a tilted ellipse instead (dotted outline):
+
+
+
+
In fact, we cannot generate any distribution of a tilted shape with just two independent distributions for each variable. We need to factor in covariation to capture the correlation between variables. Barr et al. (2013) illustrates this clearly in the supplementary materials to their paper. You can see from the plot below (originally Figure 1 on the linked page) that without the correlation parameter, you can only capture distributions that are symmetrical with respect to the axes (the darker ellipses). However, once you add in a correlation parameter (\(\rho\)), you can capture distributions that are in the “tilted” shape (the lighter ellipses) like the distribution of our highly correlated subject intercepts and subject slopes.
+
+
+
+
+Figure 1: Figure from Barr et al. (2013)
+
+
+
+
Putting it all together
+
Here’s the output of model again:
+
+
+
+
+
+
+
+Response
+
+
+
+
+Predictors
+
+
+Estimates
+
+
+CI
+
+
+p
+
+
+
+
+(Intercept)
+
+
+209.64
+
+
+203.49 – 215.80
+
+
+<0.001
+
+
+
+
+Condition [Treatment]
+
+
+35.88
+
+
+28.48 – 43.28
+
+
+<0.001
+
+
+
+
+Random Effects
+
+
+
+
+σ2
+
+
+37.37
+
+
+
+τ00Subject
+
+
+637.14
+
+
+
+τ00Item
+
+
+44.39
+
+
+
+τ11Subject.ConditionTreatment
+
+
+108.10
+
+
+
+τ11Item.ConditionTreatment
+
+
+308.21
+
+
+
+ρ01Subject
+
+
+0.85
+
+
+
+ρ01Item
+
+
+0.14
+
+
+
+ICC
+
+
+0.97
+
+
+
+N Subject
+
+
+80
+
+
+
+N Item
+
+
+24
+
+
+
+Observations
+
+
+1920
+
+
+
+
+Marginal R2 / Conditional R2
+
+
+0.216 / 0.975
+
+
+
+
+
And let’s keep focusing on the subject random effects for now.
+
There are three parameters that the model estimated to capture the by-subject variation:
+
+
The variation (Std.Dev.) for subject intercept
+
The variation (Std.Dev.) for subject slope
+
The correlation (Corr) between subject intercept and subject slope.
+
+
With these three parameters, the model is defining a bivariate normal distribution, from which subject intercepts (\(S_{0s}\)) and subject slopes (\(S_{1s}\)) are sampled from (Equation 3 from Barr et al., 2013):
For the variance-covariance matrix, we can substitute the standard deviation for the subject intercept \(\tau_{00}^2\) with 25.24, the standard deviation for the subject slope \(\tau_{11}\) with 10.4, and the correlation \(\rho\) with 0.85 to get the following:
If subject intercepts and subject slopes are jointly sampled from the above distribution, most observations should fall within this grey area:
+
+
+
+
+
Let’s again repeatedly sample from this new bivariate distribution (which you can do with mvrnorm() from the {MASS} package) to check:
+
+
+
+
Like we expected, this new distribution generates observations of subject slopes and subject intercepts that are highly correlated. But more importantly, the distribution of subject random effects in our data looks like it could be one of these samples, meaning that this bivariate normal distribution fits our data well.
+
Good thing that we had the model estimate this parameter by specifying the random effects structure for subjects as (1 + Condition | Subject) in our model formula!
+
What if we leave out this correlation parameter? Would it significantly worsen model fit?
+
We can check by building another model without the correlation term between the subject random effects and comparing it with our original model.3
+
The no_subj_cor_model below is a depleted model without the correlation parameter between the random intercepts and random slopes by subject. You can see that the subject group is missing a value in the Corr column.4
+
+
+
no_subj_cor_model<-lmer(Response~Condition+(1+model.matrix(model)[,2]||Subject)+(1+Condition|Item),
+ REML =FALSE, control =lmerControl('bobyqa'), data =toydata)
+
+tab_model(no_subj_cor_model)
+
+
+
+
+
+
+
+
+Response
+
+
+
+
+Predictors
+
+
+Estimates
+
+
+CI
+
+
+p
+
+
+
+
+(Intercept)
+
+
+209.64
+
+
+203.44 – 215.84
+
+
+<0.001
+
+
+
+
+Condition [Treatment]
+
+
+35.88
+
+
+28.43 – 43.33
+
+
+<0.001
+
+
+
+
+Random Effects
+
+
+
+
+σ2
+
+
+37.34
+
+
+
+τ00Subject
+
+
+649.20
+
+
+
+τ00Subject.1
+
+
+110.96
+
+
+
+τ00Item
+
+
+44.56
+
+
+
+τ11Item.ConditionTreatment
+
+
+311.76
+
+
+
+ρ01Item
+
+
+0.14
+
+
+
+ICC
+
+
+0.96
+
+
+
+N Subject
+
+
+80
+
+
+
+N Item
+
+
+24
+
+
+
+Observations
+
+
+1920
+
+
+
+
+Marginal R2 / Conditional R2
+
+
+0.263 / 0.970
+
+
+
+
+
Now let’s perform a likelihood ratio test using anova():
The first thing to notice is that no_subj_cor_model has one less Df, or degrees of freedom, than model. This is because every correlation between random effects is an additional parameter that the model is estimating. So removing the correlation between random effects for no_subj_cor_model leaves it with 8 parameters, one less than the original, full model. There’s a good discussion of what parameters are specified by different lmer() formulas in this StackExchange thread.
+
After performing this sanity check, the next thing to note is the very low number in Pr(>Chisq), telling us that the models are significantly different from one another. This might come off as weird if you’re only used to performing ANOVA comparisons to check whether a predictor is significant or not. In fact, there are no obvious differences between the output of no_subj_cor_model and the output of our original model other than the presence/absence of the correlation between subject random effects.
+
But clearly something major is going on behind the curtains, so we turn to the last term(s) of interest - AIC and BIC, which are scores for model fit. The numbers are hard to interpret on their own, but useful when comparing models. Here, both the AIC and the BIC of no_subj_cor_model are higher than model, suggesting that no_subj_cor_model has a worse fit, and a statistically significant one at that.
+
More specifically, we know that the only meaningful difference between no_subj_cor_model and model is the correlation parameter for the subject random effects, so no_subj_cor_model must be capturing the subject random effects relativelty poorly under its assumption that subject intercepts and subject slopes do not correlate with one another (i.e., that they are independent).
+
So let’s look at the random effects calculated by no_subj_cor_model and its poor attempt at fitting their distribution.
+
First, let’s plot the subject intercepts by subject slopes like we did for our original model:
+
+
+
+
You might notice that the no_subj_cor_model calculates subject random effects that are very similar to those calculated by our original mode. Here’s a side-by-side comparison of the subject random effects from model and no_subj_cor_model:
+
+
+
+
This illustrates a very important point. Removing the correlation parameter does not change the calculation of the random effects (barring any serious convergence failures, of course). This shouldn’t be surprising because random effects, like fixed effects, speak to facts (in the frequentist sense) about how the data that we observe is generated. It is literally the case here since I included these random effects explicitly in making toydata. But more importantly, the idea that there are random variations generated from underlying population-level parameters is an assumption that we are making when we use mixed-effects models.
+
The only meaningful difference between the two models here, then, is in their fit - e.g., how well the model captures the distribution of the subject random effects. We actually went over this above - we saw that our original model fits subject random effects using a bivariate normal distribution assuming a correlation, while no_subj_cor_model should be fitting subject random effects using two univariate normal distributions, assuming no (i.e., zero) correlation.
+
Here’s a visual comparison of model fit, with the plot for model at the top and the plot for no_subj_cor_model at the bottom:
+
+
+
+
+
+
+
+
+
+
Where the term \(\rho = 0\) in the plot for no_subj_cor_model indicates that the subject intercepts and subject slopes are generated from this bivariate distribution:
Which is the same as independetly sampling from these two univariate normal distributions:
+
\[S_{0s} \sim N(0, \tau_{00})\]
+
\[S_{1s} \sim N(0, \tau_{11})\]
+
Now, looking at the previous pair of plots, no_subj_cor_model (bottom) clearly fits the distribution of the subject random effects poorly compared to our original model model (top), and that appears to be the driving the significant decrease in fit that we found from the likelihood ratio test earlier. It seems to be the case that the inclusion of the correlation parameter between subject random intercepts is necessary to fit the data well.
+
Are correlation parameters always necessary?
+
The question of how “maximal” our models should be is very tricky, and especially so when it concerns the inclusion/exclusion of correlation parameters (see discussions here and here). For example, Bates, Kliegl, Vasishth, & Baayen (2015) and Matuschek, Kliegl, Vasishth, & Baayen (2017) have called for parsimonious models with stricter criteria for including terms in the model, beyond whether they cause the model to fail to converge.5
+
I’ll demonstrate one case here where it doesn’t seem like including a correlation parameter particularly improves model fit.
+
Let’s repeat the model comparison process above, except this time taking out the correlation parameter for item.
+
For context, here is the random effects output of model again:
Again, there are three parameters that the model estimated to capture the by-item variation:
+
+
The variation (Std.Dev.) for item intercept
+
The variation (Std.Dev.) for item slope
+
The correlation (Corr) between item intercept and item slope.
+
+
And here is what the distribution of item random effects from model look like:
+
+
+
+
Our model fitted a bivariate normal distribution with the standard deviation of item intercepts = 6.66, the standard deviation of item slopes = 10.4, and correlation = 0.14.
+
We can again visualize the fit of model to the distribution of the item random effects:
+
+
+
+
The model estimates a low correlation of 0.14, which is reflected in the small tilt of the ellipse. It looks like the model is capturing the distribution of the item random effects pretty well. But is the correlation parameter really that necessary here?
+
Let’s make another depleted model, no_item_cor_model, with the correlation between item random effects removed:
+
+
+
no_item_cor_model<-lmer(Response~Condition+(1+Condition|Subject)+(1+model.matrix(model)[,2]||Item),
+ REML =FALSE, control =lmerControl('bobyqa'), data =toydata)
+
+tab_model(no_item_cor_model)
+
+
+
+
+
+
+
+
+Response
+
+
+
+
+Predictors
+
+
+Estimates
+
+
+CI
+
+
+p
+
+
+
+
+(Intercept)
+
+
+209.64
+
+
+203.49 – 215.80
+
+
+<0.001
+
+
+
+
+Condition [Treatment]
+
+
+35.88
+
+
+28.45 – 43.31
+
+
+<0.001
+
+
+
+
+Random Effects
+
+
+
+
+σ2
+
+
+37.37
+
+
+
+τ00Subject
+
+
+636.98
+
+
+
+τ00Item
+
+
+44.55
+
+
+
+τ00Item.1
+
+
+310.22
+
+
+
+τ11Subject.ConditionTreatment
+
+
+108.08
+
+
+
+ρ01Subject
+
+
+0.85
+
+
+
+ICC
+
+
+0.96
+
+
+
+N Subject
+
+
+80
+
+
+
+N Item
+
+
+24
+
+
+
+Observations
+
+
+1920
+
+
+
+
+Marginal R2 / Conditional R2
+
+
+0.244 / 0.972
+
+
+
+
+
Again, the output of the depleted model printed here does not differ that much from the output of model. We can see also get a sense of this by visualizing the fit of no_item_cor_model to the distribution of item random effects:
+
+
+
+
We can see this more clearly with a side-by-side comparison of model fit by model (blue) and no_item_cor_model (red):
+
+
+
+
Doesn’t seem like there are big differences here, but we have to run some statistics to be sure. So let’s perform another log likelihood ratio test:
First, for sanity check, we see that no_item_cor_model has one less Df than model, which is what we’d expect if no_item_cor_model indeed lacks the correlation parameter for item random effects. Next, we see that the value of Pr(>Chisq) is very high, at 0.49, suggesting that the models are not significantly different. This is corroborated by the very small differences between the models’ AIC and BIC values. These small differences here are likely reducible to the fact that AIC and BIC penalize more number of parameters (as a way of balancing model fit with model complexity). In fact, the differences in AIC between the models is approximately 2, which is exactly what you’d expect if you added a redundant parameter with no additional explanatory power to the model.
+
In sum, we see that when modeling our dataset toydata, estimating a correlation parameter for subject random effects improves model fit, while doing so for item random effects doesn’t as much.
+
Conclusion and implications for model building
+
My main goal here was to simply go over what the correlation parameter in mixed-effects models is, so the question of whether we should be including certain correlation parameter(s) in our models are beyond the scope of this discussion (and should be handled on a case-by-case basis). It’s also something that I’m in the process of learning, so I don’t have a good answer to it yet. But my personal view (which may change later with more knowledge and experience) is to keep correlation parameters in the model unless they make the model fail to converge. So in the case of the correlation parameter for item random effects in model that we discussed above, I’d personally keep that in since we didn’t run into any convergence issues fitting the maximal model. In general, if there’s no meaningful difference either way, I err towards leaving the correlation parameter in there. In other words, I try to keep the model as maximal as possible without overparameterizing it (Barr et al., 2013).
+
I don’t think that the spirit of this message is really controversial, and the more challenging part of this is putting it into practice. We not only need to balance model fit with model complexity, but we also often need to navigate conflicts between important considerations from statistical theory and from whatever domain our research is in (linguistics, psychology, etc.).
+
To resolve this, a lot of people have streamlined different methods of reducing the complexity of the random effects structure in a statistically motivated way. One such example is using Principal Components Analysis (PCA) (suggested in Bates et al., 2015). In Section 3 of their paper, Bates and colleagues outline a procedure for iterative model reduction which involves PCA (now available as rePCA() in the lme4 package) to determine how many random effect terms are sufficient to capture the variance in the random effects. This is still not a perfect solution, of course, but it’s a good next step for putting this knowledge into practice. Or you can just do fancy Bayesian analyses and avoid all these problems, so I hear
+
Anyways, that’s it for my notes. Here’s the code that generated toydata:
+
+
+
###########
+## Setup ##
+###########
+
+# Load Packages (make sure dplyr::filter() isn't makes by MASS:filter())
+library(MASS)
+library(tidyverse)
+library(lme4)
+
+# Set seed
+set.seed(1234)
+
+# Set number of participants and items
+n_subjects<-80
+n_items<-24
+
+#################
+## Make trials ##
+#################
+
+# Generate levels
+Subject<-gl(n_subjects, n_items)
+Item<-rep(gl(n_items, 1), n_subjects)
+Condition<-factor(rep(c(rep(c("Control", "Treatment"), n_items/2),
+ rep(c("Treatment", "Control"), n_items/2)),
+ n_subjects/2))
+
+# Treatment coding
+Condition_coded<-ifelse(Condition=="Control", 0, 1)
+
+# Combine into trials
+Data<-tibble(Subject, Item, Condition, Condition_coded)
+
+#############################
+## Add Intercept and Slope ##
+#############################
+
+# Add intercept
+Data$Intercept<-200
+
+# Add slope
+Data$Slope<-ifelse(Data$Condition=="Treatment", 30, 0)
+
+########################
+## Add Random Effects ##
+########################
+
+# By-subject variation in intercept and slope (sampled from bivariate normal)
+sd_subj_intercept<-25
+sd_subj_slope<-10
+subj_ranef_cor<-0.8
+
+subj_ranef<-mvrnorm(n_subjects,
+ # means of two normals are both 0
+ c("Intercept"=0, "Slope"=0),
+ # 2x2 variance-covariance matrix
+ matrix(
+ c(sd_subj_intercept^2,
+ subj_ranef_cor*sd_subj_intercept*sd_subj_slope,
+ subj_ranef_cor*sd_subj_intercept*sd_subj_slope,
+ sd_subj_slope^2),
+ ncol =2)
+ )
+
+Data$Subj_intercept<-rep(subj_ranef[,"Intercept"], each =n_items)
+Data$Subj_slope<-rep(subj_ranef[,"Slope"], each =n_items)
+
+# By-item variation in intercept and slope (sampled independently)
+Data$Item_intercept<-rep(rnorm(n_items, sd =5), times =n_subjects)
+Data$Item_slope<-rep(rnorm(n_items, sd =15), times =n_subjects)
+
+# Random noise
+Data$Noise<-rnorm(nrow(Data), 0, 5)+rlnorm(nrow(Data), 0.5)
+
+###########################
+## Generate Observations ##
+###########################
+
+Data<-Data%>%
+ mutate(Response =
+ Intercept+
+ Slope*Condition_coded+
+ Subj_intercept+
+ Subj_slope*Condition_coded+
+ Item_intercept+
+ Item_slope*Condition_coded+
+ Noise)
+
+#################
+## Toy Dataset ##
+#################
+
+toydata<-Data%>%
+ select(Subject, Item, Condition, Response)
+
+
+
+
+
+
+
+
This distinction is also reflected in the fact that the notation for random effect standard deviation is tau (\(\tau\)), which is a Greek symbol. In statistics, Greek symbols (like \(\beta\), which we may be more familiar with) refers to population-level paramters.↩︎
+
But what if the distribution of random effects has a mean that is not equal to zero? Well that just shifts the fixed effects estimate, so the distribution of random effects can be fully characterized by just its variance/standard deviation. This is also why you should never remove a term from fixed effects without removing it from random effects like in Response ~ 1 + (1 + Condiiton | Subject) without a good reason, because the model will assume the fixed effect of Condition to be zero.↩︎
+
Removing a correlation term in lmer() turns out to be actually sort of tricky if you don’t explicitly numerically code your factors - sometimes just using the double bar syntax (||) doesn’t always work. I won’t go into the details of how to do that here, but there are good discussions of doing this using model.matrix() in this Rpubs post and Section 5.4 (also Appendix E) of Frossard and Renaud (2019). I could have done numeric coding with something like mutate(data, Condition = as.integer(Condition == "Treatment")), but I wanted to try this way out for myself↩︎
+
Here, \(\tau_{00\ Subject.1}\) is actually the same as the \(\tau_{00\ Subject.ConditionTreatment}\) term from the maximal model, model. I don’t know how to suppress this name change after dropping a correlation term - if you do, please let me know!↩︎
+
The (simplified) argument here is that having the model estimate superfluous variance components can make it more difficult for the model to detect an effect if it actually exists - i.e., can lead to a loss of power↩︎
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
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diff --git a/docs/posts/2020-06-25-indexing-tip-for-spacyr/2020-06-25-indexing-tip-for-spacyr_files/header-attrs-2.3/header-attrs.js b/docs/posts/2020-06-25-indexing-tip-for-spacyr/2020-06-25-indexing-tip-for-spacyr_files/header-attrs-2.3/header-attrs.js
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@@ -0,0 +1,12 @@
+// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
+// be compatible with the behavior of Pandoc < 2.8).
+document.addEventListener('DOMContentLoaded', function(e) {
+ var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
+ var i, h, a;
+ for (i = 0; i < hs.length; i++) {
+ h = hs[i];
+ if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6
+ a = h.attributes;
+ while (a.length > 0) h.removeAttribute(a[0].name);
+ }
+});
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+June Choe: Indexing tip for {spacyr}
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The {spacyr} package is an R wrapper for Python’s spaCy package, powered by {reticulate}. Although it’s been around for over 3 years, it doesn’t seem to have really been picked up by R users.1 I actually think this makes sense since what makes spaCy so great is its object-oriented approach to NLP (which Python is good at). But perhaps more importantly, a good portion of data wrangling in spaCy is reducible to operating on vectors of such tokens, and I think that comes pretty naturally for R users with a functional programming background.2 So my guess is that since spaCy is accessible to R users, {spacyr} isn’t that widely used.
+
But with that said, I like to make my workflow as R-centered as possible and I think there’s still value in {spacyr} at least for very simple, exploratory analysis of text. The results being returned in a tidy format is a huge plus, and it doesn’t seem to sacrifice much speed.
+
There’s a good guide to using {spacyr} in the CRAN vignette which covers pretty much everything you need to know if you’re already familiar with spaCy (and if you aren’t, there’s a great cheatsheet from DataCamp).
+
Everything I just said above was just a whole lot of background information. What I really want to do here to contribute to the discussion around {spacyr} by sharing a tip for analyzing dependency relations from the output of spacy_parse(), which is {spacyr}’s main function that combines both the model-loading and text-processing stages of spaCy.
+
+
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For illustration, I’ll be using the 8 State of the Union addresses by President Barack Obama from 2009-2016, which comes from the {sotu} package.
[1] "Madam Speaker, Mr. Vice President, Members of Congress, the First Lady of the United States--she's a"
+ [2] "Madam Speaker, Vice President Biden, Members of Congress, distinguished guests, and fellow Americans"
+ [3] "Mr. Speaker, Mr. Vice President, Members of Congress, distinguished guests, and fellow Americans: To"
+ [4] "Mr. Speaker, Mr. Vice President, Members of Congress, distinguished guests, and fellow Americans: La"
+ [5] "Please, everybody, have a seat. Mr. Speaker, Mr. Vice President, Members of Congress, fellow America"
+ [6] "The President. Mr. Speaker, Mr. Vice President, Members of Congress, my fellow Americans: Today in A"
+ [7] "The President. Mr. Speaker, Mr. Vice President, Members of Congress, my fellow Americans: We are 15 "
+ [8] "Thank you. Mr. Speaker, Mr. Vice President, Members of Congress, my fellow Americans: Tonight marks "
+
+
We can pass this document to spacy_parse() to get back a dataframe of tokens and their attributes in tidy format, where each row (observation) is a token.3
+
+
+
parsed<-spacy_parse(doc, dep =TRUE, entity =FALSE)
+
+head(parsed, 10)
+
+
+
+
+
+
+doc_id
+
+
+sentence_id
+
+
+token_id
+
+
+token
+
+
+lemma
+
+
+pos
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+
+head_token_id
+
+
+dep_rel
+
+
+
+
+
+
+text1
+
+
+1
+
+
+1
+
+
+Madam
+
+
+Madam
+
+
+PROPN
+
+
+2
+
+
+compound
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+
+
+
+text1
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+
+1
+
+
+2
+
+
+Speaker
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+Speaker
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+
+PROPN
+
+
+2
+
+
+ROOT
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+
+
+
+text1
+
+
+1
+
+
+3
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+
+,
+
+
+,
+
+
+PUNCT
+
+
+2
+
+
+punct
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+
+
+
+text1
+
+
+1
+
+
+4
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+
+Mr.
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+
+Mr.
+
+
+PROPN
+
+
+6
+
+
+compound
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+
+
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+text1
+
+
+1
+
+
+5
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+Vice
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+Vice
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+PROPN
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+6
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+compound
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+
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+text1
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+1
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+6
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+President
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+President
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+PROPN
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+2
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+appos
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+text1
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+1
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+7
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+,
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+,
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+PUNCT
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+6
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+punct
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+text1
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+1
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+8
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+Members
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+Members
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+PROPN
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+6
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+appos
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+text1
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+1
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+9
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+of
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+of
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+ADP
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+
+8
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+prep
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+
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+text1
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+1
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+10
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+Congress
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+Congress
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+
+PROPN
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+9
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+
+pobj
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+
+
+
+
This output format is great for plotting in R with the familiar packages. For example, we can make a bar plot of top adjectives used by Obama in his SOTU addresses with minimal changes to the output.
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+
+
# Load tidytext package for stopwords
+library(tidytext)
+
+parsed%>%
+ filter(pos=="ADJ",
+ str_detect(lemma, "^[:alpha:].*[:alpha:]$"),
+ !lemma%in%tidytext::stop_words$word)%>%
+ count(lemma)%>%
+ mutate(lemma =fct_reorder(str_to_title(lemma), n))%>%
+ top_n(15)%>%
+ ggplot(aes(lemma, n))+
+ geom_col()+
+ coord_flip()+
+ labs(title ="Top 15 Adjectives from President Obama's SOTU Addresses",
+ x ="Adjective", y ="Count")+
+ theme_classic()
+
+
+
+
+
The Challenge
+
But what if we want to dig a little deeper in our analysis of adjectives? What if, for example, we were interested in the adjectives that were used to describe “America”
+
Because we set dep = TRUE when we called spacy_parse() earlier, we have information about dependencies in the dep_rel column and the head_token_id column. To be more precise, dep_rel is the .dep_ attribute from spaCy and head_token_id is the row index of the head token (.head attribute from spaCy) that is unique to the spacy_parse() output.
+
For example, let’s look at the the 298th sentence from Obama’s third SOTU address:
[1] "Now, we've made great strides over the last 2 years in using technology and getting rid of waste."
+
+
And here’s a visualization of the dependency parse made with displaCy. Sadly, displaCy is not a part of {spacyr}, so I’m just calling Python here using {reticulate}.
Basically, the task here is to find words like “competitive” in the example sentence above where the token is an adjective and its head is the word “America”, but it turns out harder than it seems.
+
The output of spacy_parse is set up such that every sentence stands on their own. More specifically speaking, the indices stored in token_id and head_token_id are local indices relative to each sentence.4 So while there are a total of 62791 tokens in parsed, the max token_id is 99, which is the index of the last token in the longest sentence.
+
A strictly tidyverse approach (which has become a sort of a tunnel-vision for me) would be to split parsed by sentence and map a filter function to each sentence. There are two ways of going about this and both are pretty slow.
+
The first way is to explicitly split the dataframe into a list of dataframes at the sentence level then map the filter function, using group_split() then map_df():
What would really help here is if we had global indices for tokens and head tokens, so that we can directly index a head from a token without going through the trouble of figuring out how sentences are organized in the dataframe.
+
So here’s my take on doing this:
+
+
+
# Calculate global indices from local indices
+global_index<-parsed%>%
+ group_by(doc_id, sentence_id)%>%
+ # add token counts for each sentence
+ add_count()%>%
+ ungroup()%>%
+ select(doc_id, sentence_id, n)%>%
+ distinct()%>%
+ # take the cumulative sum and shift 1 to the right (fill first index with 0)
+ mutate(n =c(0, cumsum(n)[1:n()-1]))
+
+# Clean the output
+parsed2<-parsed%>%
+ inner_join(global_index, by =c("doc_id", "sentence_id"))%>%
+ mutate(token_id_global =token_id+n,
+ head_token_id_global =head_token_id+n)%>%
+ relocate(token_id_global, .after =token_id)%>%
+ relocate(head_token_id_global, .after =head_token_id)%>%
+ select(-n)
+
+
+
+
This adds two colums - token_id_global and head_token_id_global - that stores indices that range over the entire dataframe. Here’s a sample of the new dataframe to demonstrate:
+
+
+
sample_n(parsed2, 10)
+
+
+
+
+
+
+doc_id
+
+
+sentence_id
+
+
+token_id
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+
+token_id_global
+
+
+token
+
+
+lemma
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+
+pos
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+
+head_token_id
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+
+head_token_id_global
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+
+dep_rel
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+
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+text7
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+37
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+11
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+48388
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+to
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+to
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+ADP
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+10
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+48387
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+dative
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+text3
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+154
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+3
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+18253
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+the
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+the
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+DET
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+5
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+18255
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+det
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+text1
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+44
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+2
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+1036
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+of
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+of
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+ADP
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+1
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+1035
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+pcomp
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+text8
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+17
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+6
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+56007
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+to
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+to
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+ADP
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+5
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+56006
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+prep
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+text5
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+316
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+3
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+38631
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+this
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+this
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+DET
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+4
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+38632
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+nsubj
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+text6
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+340
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+15
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+46579
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+then
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+then
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+ADV
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+23
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+46587
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+advmod
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+text4
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+162
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+7
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+26488
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+SPACE
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+6
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+26487
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+text6
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+87
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+13
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+41466
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+to
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+to
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+PART
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+14
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+41467
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+aux
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+text8
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+260
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+17
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+60517
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+
+positioned
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+
+position
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+
+VERB
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+
+9
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+60509
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+
+conj
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+
+
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+text8
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+
+281
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+
+2
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+
+60856
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+
+is
+
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+be
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+
+AUX
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+11
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+60865
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+
+ccomp
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+
+
+
+
+
And since this process isn’t destructive, we actually don’t need to assign the output to a new object. This is great because we can flexibly incorporate it into the pipeline workflow.
There are less than 30 posts about it on StackOverflow, for example.↩︎
+
I personally found it very easy to pick up vector comprehension in Python after working with purrr::map, for example.↩︎
+
The argument entity = FALSE is the same as disable = ['ner'] in spacy.load() in Python. I did this to save computation time.↩︎
+
This format is shared across other NLP packages in R based on spacCy, like {cleanNLP}↩︎
+
This would need to be tweaked a bit if you want to use it for the output of {cleanNLP} because the column for the local index of token heads, tid_source, is 0 when the token is the ROOT, as opposed to its own token index, which is the case in {spacyr}. You could add something like mutate(tid_source = ifelse(tid_source == 0, tid, tid_source) to the beginning of the pipeline to address this.↩︎
+
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@@ -0,0 +1,12 @@
+// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
+// be compatible with the behavior of Pandoc < 2.8).
+document.addEventListener('DOMContentLoaded', function(e) {
+ var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
+ var i, h, a;
+ for (i = 0; i < hs.length; i++) {
+ h = hs[i];
+ if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6
+ a = h.attributes;
+ while (a.length > 0) h.removeAttribute(a[0].name);
+ }
+});
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+June Choe: Plotting treemaps with {treemap} and {ggplot2}
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To steal the definition from Wikipedia, a treemap is used for “displaying hierarchical data using nested figures, usually rectangles.” There are lots of ways to make one in R, but I didn’t find any one existing solution appealing.
+
For illustration, let’s take the pokemon dataset from {highcharter} and plot a treemap with it using different methods.
+
+
+
+
+
+
data("pokemon", package ="highcharter")
+
+# Cleaning up data for a treemap
+data<-pokemon%>%
+ select(pokemon, type_1, type_2, color_f)%>%
+ mutate(type_2 =ifelse(is.na(type_2), paste("only", type_1), type_2))%>%
+ group_by(type_1, type_2, color_f)%>%
+ count(type_1, type_2)%>%
+ ungroup()
+
+head(data, 5)
+
It actually doesn’t look too bad, but this package hasn’t been updated for 3 years and there aren’t a lot of options for customization. For the options that do exist, they’re a big list of additional arguments to the main workhorse function, treemap(), which feels a bit restrictive if you’re used to {ggplot}’s modular and layered grammar. So while it’s very simple to use, I’d probably use it only for exploring the data for myself.
+
+
2. {highcharter}
+
All the way on the other side of this ease<—>customizability spectrum is {highcharter} which is arguably the most powerful data visualization package in R.
+
With highcharter, you can turn the previous graph into the following:
+
+
This looks much better, and it’s even interactive (although this particular one isn’t because I just copy pasted the image from this blog post from 2018). I’d use {highcharter} except that there isn’t a great documentation on plotting treemaps, and it definitely doesn’t help that {highcharter} has a pretty steep learning curve, even if you have a lot of experience with {ggplot2}.
+
The main problem I ran into is that the function hc_add_series_treemap() that was used to create the above graph is now depreciated. It redirects you to use hctreemap() which itself is also depreciated. That finally redirects you to use hctreemap2() which is pretty sparse in documentation and use-cases, and overall not very transparent IMO.
+
+
3. {treemapify}
+
{treemapify} is a ggplot solution to plotting treemaps.
+
Here’s a plot of the pokemon dataset, adopting the example code from the vignette. Since it follows the layered grammar of ggplot, I figured I’d show what each of the four layers outlined in the code does:
geom_treemap() draws type_2 borders and fill colors
+
+
+
+
geom_treemap_subgroup_border() draws type_1 borders
+
+
+
+
geom_treemap_subgroup_text() prints type_1 text
+
+
+
+
geom_treemap_text() prints type_2 text
+
+
+
+
I find this the most appealing out of the three options and I do recommend this package, but I’m personally a bit hesistant to use it for three reasons:
+
+
I don’t want to learn a whole ’nother family of geom_*s just to plot treemaps.
+
Some of the ggplot “add-ons” that I like don’t really transfer over. For example, I can’t use geom_text_repel() from {ggrepel} because I have to use {treemapify}’s own text geoms like geom_treemap_subgroup_text() and geom_treemap_text().
+
Customization options are kind of a mouthful, and I’ve yet to see a nice-looking treemap that was plotted using this package. There are a couple example treemaps in the vignette but none of them look particularly good. An independently produced example here doesn’t look super great either.
+
+
+
A Mixed (Hack-ish?) Solution
+
Basically, I’m very lazy and I want to avoid learning any new packages or functions as much as possible.
+
I’ve come up with a very simple solution to my self-created problem, which is to draw treemaps using geom_rect() with a little help from the {treemap} package introduced earlier.
+
So apparently, there’s a cool feature in treemap::treemap() where you can extract the plotting data.
+
You can do this by pulling the tm object from the plot function side-effect, and the underlying dataframe used for plotting looks like this.1:
+
+
+
tm<-treemap(
+ dtf =data,
+ index =c("type_1", "type_2"),
+ vSize ="n",
+ vColor ="color_f",
+ type ='color'# {treemap}'s equivalent of scale_fill_identity()
+)
+
We can simply use this data to recreate the treemap that was made with {treemapify} - except this time we have more flexibility!
+
First, we do some data cleaning:
+
+
+
tm_plot_data<-tm$tm%>%
+ # calculate end coordinates with height and width
+ mutate(x1 =x0+w,
+ y1 =y0+h)%>%
+ # get center coordinates for labels
+ mutate(x =(x0+x1)/2,
+ y =(y0+y1)/2)%>%
+ # mark primary groupings and set boundary thickness
+ mutate(primary_group =ifelse(is.na(type_2), 1.2, .5))%>%
+ # remove colors from primary groupings (since secondary is already colored)
+ mutate(color =ifelse(is.na(type_2), NA, color))
+
+
+
+
Then we plot. It looks like I can recreate a lot of it with a little help from the {ggfittext} package that was in the source code2:
+
+
+
ggplot(tm_plot_data, aes(xmin =x0, ymin =y0, xmax =x1, ymax =y1))+
+ # add fill and borders for groups and subgroups
+ geom_rect(aes(fill =color, size =primary_group),
+ show.legend =FALSE, color ="black", alpha =.3)+
+ scale_fill_identity()+
+ # set thicker lines for group borders
+ scale_size(range =range(tm_plot_data$primary_group))+
+ # add labels
+ ggfittext::geom_fit_text(aes(label =type_2), min.size =1)+
+ # options
+ scale_x_continuous(expand =c(0, 0))+
+ scale_y_continuous(expand =c(0, 0))+
+ theme_void()
+
+
+
+
+
Now, I can be a lot more flexible with my customizations.
+
For example, let’s say I wanted to isolate and emphasize the secondary types that have unique type-combinations with steel, AND also provide the name of the corresponding pokemon.
+
I can do this by using geom_text_repel() for a subset of the labels while keeping the same geom_fit_text() setting for the rest of the labels.
+
+
+
tm_plot_data%>%
+ ggplot(aes(xmin =x0, ymin =y0, xmax =x1, ymax =y1))+
+ geom_rect(aes(fill =color, size =primary_group),
+ show.legend =FALSE, color ="black", alpha =.3)+
+ scale_fill_identity()+
+ scale_size(range =range(tm_plot_data$primary_group))+
+ ggfittext::geom_fit_text(data =filter(tm_plot_data, type_1!="steel"|vSize>1),
+ aes(label =type_2), min.size =1)+
+ # pick out observations of interest and annotate with geom_text_repel
+ ggrepel::geom_text_repel(
+ data =filter(tm_plot_data, vSize==1, type_1=="steel")%>%
+ inner_join(pokemon, by =c("type_1", "type_2")),
+ aes(x =x, y =y, label =glue::glue("{type_2} ({pokemon})")),
+ color ="black", xlim =c(1.02, NA), size =4,
+ direction ="y", vjust =.5, force =3
+ )+
+ # expand x-axis limits to make room for test annotations
+ scale_x_continuous(limits =c(0, 1.2), expand =c(0, 0))+
+ scale_y_continuous(expand =c(0, 0))+
+ theme_void()
+
+
+
+
+
And that’s our final product! This would’ve been pretty difficult to do with any of the three options I reviewed at the top!
+
tl;dr - Use treemap() from the {treemap} package to get positions for geom_rect()s and you’re 90% of the way there to plotting a treemap! Apply your favorite styles (especially _text() geoms) from the {ggplot2} ecosystem for finishing touches!
You might get a warning referencing something about data.table here. No worries if this happens. The outdated {treemap} source code is built on {data.table} and contains a deprecated argument.↩︎
+
I highly recommend checking {ggfittext} out! Here’s the github repo. Also, this is more of a note to myself but I had some trouble getting this to work at first because the min.size argument defaults to 4, meaning that all fitted text smaller than size 4 are simply not plotted (so I couldn’t get geom_fit_text() to print anything in my treemap at first). You can compare and see the threshold by looking at the geom_text_repel() texts in my second example which also has a size of 4.↩︎
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diff --git a/docs/posts/2020-07-13-geom-paired-raincloud/2020-07-13-geom-paired-raincloud_files/figure-html5/unnamed-chunk-3-1.png b/docs/posts/2020-07-13-geom-paired-raincloud/2020-07-13-geom-paired-raincloud_files/figure-html5/unnamed-chunk-3-1.png
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diff --git a/docs/posts/2020-07-13-geom-paired-raincloud/2020-07-13-geom-paired-raincloud_files/header-attrs-2.3/header-attrs.js b/docs/posts/2020-07-13-geom-paired-raincloud/2020-07-13-geom-paired-raincloud_files/header-attrs-2.3/header-attrs.js
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@@ -0,0 +1,12 @@
+// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
+// be compatible with the behavior of Pandoc < 2.8).
+document.addEventListener('DOMContentLoaded', function(e) {
+ var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
+ var i, h, a;
+ for (i = 0; i < hs.length; i++) {
+ h = hs[i];
+ if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6
+ a = h.attributes;
+ while (a.length > 0) h.removeAttribute(a[0].name);
+ }
+});
diff --git a/docs/posts/2020-07-13-geom-paired-raincloud/index.html b/docs/posts/2020-07-13-geom-paired-raincloud/index.html
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+June Choe: geom_paired_raincloud()
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Geoms for rainplots (a.k.a. split violin plots) already exist, but you might have a very special case where you have pairs of rainplots and you want to track the change in individual datapoints between the rainplot distributions.
+
For example, say you want to track the height of a plant species across two timepoints and you want to communicate three information:
+
+
The change in the distribution of plant heights between timepoints.
+
The individual variation in height (“intercept”).
+
The individual variation in change of height between timepoints (“slope”).
# A tibble: 2 x 3
+ Timepoint Height_mean Height_sd
+ <chr> <dbl> <dbl>
+ 1 First 9.22 5.02
+ 2 Second 20.3 8.26
+
+
You can use geom_violhalf() from the {see} package to do this:
+
+
+
library(see)
+ggplot(plants, aes(Timepoint, Height, fill =Timepoint))+
+ geom_violinhalf()+
+ geom_point(aes(group =Plant),
+ position =position_nudge(-.05),
+ alpha =0.5, shape =16)+
+ geom_line(aes(group =Plant),
+ position =position_nudge(-.05))
+
+
+
+
+
But it’d look better if the lines don’t cross over the raincloud for the first timepoint.
+
geom_paired_raincloud() automatically flips the first raincloud for you! You do get a warining that there are overlapping points, but that’s because the x-axis is categorical and {ggplot2} thinks that flipping the raincloud intrudes into a different category. AFAIK you don’t lose any data despite this warning, but you should double check to be sure.
+
+
+
devtools::source_url("https://raw.githubusercontent.com/yjunechoe/geom_paired_raincloud/master/geom_paired_raincloud.R")
+
+ggplot(plants, aes(Timepoint, Height, fill =Timepoint))+
+ geom_paired_raincloud()
+
+
+
Warning: position_dodge requires non-overlapping x intervals
+
+
+
We can add individual points and lines onto this plot in a similar way, except you need to use a 2-length vector for position_dodge().
+
+
+
plants%>%
+ # arrange by individual plant
+ arrange(Plant)%>%
+ ggplot(aes(Timepoint, Height, fill =Timepoint))+
+ geom_paired_raincloud()+
+ geom_point(aes(group =Plant),
+ position =position_nudge(c(.05, -.05)),
+ alpha =0.5, shape =16,
+ show.legend =FALSE)+
+ geom_line(aes(group =Plant),
+ position =position_nudge(c(.05, -.05)))
+
+
+
+
+
+
geom_paired_raincloud works as long as the grouping is of length two (i.e., as long as you’re comparing distribution between two levels).
+
Let’s modify the plants dataset to include another species of plant:
But I think geom_paired_raincloud() works great if you have the right data. Here’s an example from my recent work, looking at the variation in how subjects respond to stimuli when they’re presented in one condition (Subject Accent) compared to the other (Verb Accent).
+
+
+
+
The above plot is a combination of geom_paired_raincloud(), geom_point(), geom_line(), and geom_boxplot(). I like that you can employ all these aesthetics at once without making the plot too overwhelming. I’ve included the important part of the code here and the full code is available at the github repo for this research project.
When I was an RA in the LEARN lab - a child language development lab at Northwestern - I worked on a shiny app that automates snowball search for meta-analysis research (relevant research poster). Long story short, I worked on it for a couple months, got it working, then stopped working on it for another couple months, and had the chance to revisit it just recently.
+
When I picked the project back up, I realized that my old code was poorly commented, somewhat inefficient, and even hackish at times. So I decided to re-write it from scratch. In this second time around, I learned a lot of useful functions/tricks that really helped streamline my code and I thought I’d document my three favorite ones here for future reference.
+
1. %||% from {rlang}
+
Basically, %||% is an infix operator that returns the left-hand side when it is not Null and the right-hand side when it is Null. It’s from the {rlang} package but you can also define the function yourself:
# note how the output is different when b is no longer null
+b<-11
+b%||%a
+
+
+
[1] 11
+
+
I found this operator to be extremely useful when displaying empty tables as placeholders when using DT::datatable(). It allows me to communicate to the user where a table is expected to appear rather than just not showing anything at all when no data is loaded (which is what happens by default).
+
For example, if you want to show an empty column with an empty row when the data (here, the reactive variable mydf) is null, you might do the following:
Another use-case for %||% is when I’m trying a sequence of function calls until one one of them succeeds and returns a non-null value. For example, say I want to scrape some information online and I have API wrappers for different websites that potentially have that information. I can chain them together using %||% like so:
This is much neater than nested if…else statements!
+
2. purrr::imap() and {shinybusy}
+
Using my shiny app involves a lot of waiting (querying online databases), so I looked into ways to show a progress bar similar to the family of *Modal() functions from {shiny}. The extension package {shinybusy} (project site) offers a very satisfying solution to this problem.
+
Basically, you initialize a progress bar with show_modal_progress_*() and increment its value inside whatever operation you’re doing. Here’s a pseudo code demonstrating how it works:
+
+
initialize a progress bar
+
+create a new_list of same size to store output
+
+for index inseq_along(list):
+new_list[index] <-calculations(list[index])
+ increment progress bar by index
+
+remove progress bar
+
+return new_list
+
+
But in my case, my “do stuff” part didn’t involve a big wall of code because I packed it into a single function in a separate file that I source at the beginning. This, coupled with my general aversion to for-loops, drove me to imap() and its variants from {purrr}. imap() is like map() except it also keeps track of the index of the element that you’re operating on (to put it another way, it’s like map2() where .y is the index).
+
Now, you don’t need an explicit for-loop to increment and the above code can be reduced to this:
+
+
initialize a progress bar
+
+new_list <-imap(list,
+~{
+calculations(.x)
+ increment progress bar by .y
+ })
+
+remove progress bar
+
+return new_list
+
+
In my opinion, this is much cleaner! For a more concrete example, here’s a template using actual code:
+
+
+
my_data<-eventReactive(input$my_button, {
+
+ # initialize a progress bar
+ show_modal_progress_line()
+
+ # do operation on elements of vector
+ result<-imap(my_reactive_var(),
+ ~{
+ update_modal_progress(value =.y/length(my_reactive_var()))
+ my_operation_on_element(.x)
+ })
+
+ # remove progress bar
+ remove_modal_progress()
+
+ # return output
+ return(result)
+
+})
+
+
+
+
3. User inputs inside modalDialog()
+
In {shiny}, you can show the user a pop-up message box by first laying out the content of the message in modalDialog() and then rendering it with showModal(). In the first version of my app, I used this to show simple messages like warnings, but did you know that you can include any *Input widgets too?
+
For example, this code renders a pop-up box for a file upload in response to a button click:
And you can access whatever is uploaded using input$UploadedFile like you would if the file upload widget was in the ui side of the app!
+
This took me a bit to get used to because you are defining the modal in the server side where the content of the modal looks like the ui side but can be accessed back at the server side. But this was life-changing and it opened up a lot of potential for my GUI to be less cluttered. Using this neat trick, I was able to move a large feature into a modal that would only be available upon a click of a button (it was a feature designed for a rare case scenario so I thought I’d save the user from having to see the entire interface for that if they don’t ask for it).
+
Ending note
+
The more I learn and use shiny, the less I feel like I know. I’m actually enjoying this stage of my progress because every new thing just absolutely wows me (and I hope to continue sharing what I learn - hence this being the “first set”). And very much looking forward to Hadley Wickham’s new book on shiny!
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diff --git a/docs/posts/2020-07-20-shiny-tips-1/preview.png b/docs/posts/2020-07-20-shiny-tips-1/preview.png
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diff --git a/docs/posts/2020-07-29-six-years-of-my-spotify-playlists/2020-07-29-six-years-of-my-spotify-playlists_files/header-attrs-2.3/header-attrs.js b/docs/posts/2020-07-29-six-years-of-my-spotify-playlists/2020-07-29-six-years-of-my-spotify-playlists_files/header-attrs-2.3/header-attrs.js
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@@ -0,0 +1,12 @@
+// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
+// be compatible with the behavior of Pandoc < 2.8).
+document.addEventListener('DOMContentLoaded', function(e) {
+ var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
+ var i, h, a;
+ for (i = 0; i < hs.length; i++) {
+ h = hs[i];
+ if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6
+ a = h.attributes;
+ while (a.length > 0) h.removeAttribute(a[0].name);
+ }
+});
diff --git a/docs/posts/2020-07-29-six-years-of-my-spotify-playlists/index.html b/docs/posts/2020-07-29-six-years-of-my-spotify-playlists/index.html
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+June Choe: Six years of my Spotify playlists
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One of my longest running habits is making monthly playlists. At the start of every month, I create a new playlist for that month and add songs that I like. Some songs are carried over from the previous month’s playlist and others are songs that I newly discover, but they’re all representative of the songs that I’m “into” for that month.
+
I’ve been doing this for many years, and have the best record of my monthly playlists for the past 6 years, which is how long I’ve been using spotify. So when I saw people talking about {spotifyr} - an R wrapper for Spotify’s web API - on twitter, I decided to take a stab at analyzing my monthly playlists (code here).
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Analysis #1 - Size of monthly playlists over time
+
When I first pulled information about my Spotify account, I noticed that I had some gaps in my monthly playlists. This was a special case of non-random missing data: when I didn’t make a new playlist for the month, it’s because I didn’t think that there was a substantial change in what I’m jamming to from the previous month. The {zoo} package, which I didn’t know about before, came in very handy here for dealing with this missingness with its na.locf() (Last Observation Carried Forward) function.
+
After some more cleaning steps, I first made a simple plot that counted the number of songs that each monthly playlist had.
+
+
There are some interesting things I notice, and here’s some context for them.
+
+
2014, the year I began using Spotify, has high number of songs per playlist. This makes sense because the transition period involved a lot of transferring-over-in-bulk of my favorite music from iTunes (which I used previously).
+
The three consecutive months of missing playlists in 2017 was over a summer camp. I had forgotten about that until I started this analysis, but for that summer I just kept one playlist called “Summer 2017.”
+
Both the playlist with the most songs (78) and the playlist with the least songs (1) are in 2017. That was the year when I was exposed to a lot with different music genres from going to parties my friends. That was also my Sophomore year, which was a hot mess, but that’s a story for another time. You can get a better sense of my music taste being all over the place in the next section.
+
May 2017 was the month of sad azn vibes. My playlist had a single song, which was American Girl by Mitski and I think that says everythihng.
+
In November 2017, the month with the largest playlist, a friend who was known for having a great taste in music shared with me their playlist of 200+ EDM songs. I never really got into EDM previously but that was a gamechanger. I kind of went through an EDM phase for a short while after that, and November 2017 playlist is still the playlist I go back to when I want to listen to some good EDM.
+
The missing playlist of March 2020 was at perhaps the busiest time of my life - I was juggling my thesis, finals, grad school visits/decisions, finishing up my graduation requirements, moving out of campus back to Korea, and, of course, dealing with the pandemic. Makes sense why that month is missing its playlist.
+
+
Analysis #2 - Audio features
+
The real deal of Spotify API is actually the audio features, which Spotify calculates using their special algorithms. Some of the features are listed in the table below (adopted from the documentation). Of these, I decided to narrow down to acousticness, danceability, energy, and valence because others didn’t really show much variation (e.g., I don’t listen to live-recorded music on Spotify, so liveness is always near zero).
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Feature
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Description
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acousticness
+
A confidence measure from 0.0 to 1.0 of whether the track is acoustic. 1.0 represents high confidence the track is acoustic.
+
+
+
danceability
+
Danceability describes how suitable a track is for dancing based on a combination of musical elements including tempo, rhythm stability, beat strength, and overall regularity. A value of 0.0 is least danceable and 1.0 is most danceable.
+
+
+
energy
+
Energy is a measure from 0.0 to 1.0 and represents a perceptual measure of intensity and activity. Typically, energetic tracks feel fast, loud, and noisy. For example, death metal has high energy, while a Bach prelude scores low on the scale. Perceptual features contributing to this attribute include dynamic range, perceived loudness, timbre, onset rate, and general entropy.
+
+
+
instrumentalness
+
Predicts whether a track contains no vocals. “Ooh” and “aah” sounds are treated as instrumental in this context. Rap or spoken word tracks are clearly “vocal”. The closer the instrumentalness value is to 1.0, the greater likelihood the track contains no vocal content. Values above 0.5 are intended to represent instrumental tracks, but confidence is higher as the value approaches 1.0.
+
+
+
liveness
+
Detects the presence of an audience in the recording. Higher liveness values represent an increased probability that the track was performed live. A value above 0.8 provides strong likelihood that the track is live.
+
+
+
loudness
+
The overall loudness of a track in decibels (dB). Loudness values are averaged across the entire track and are useful for comparing relative loudness of tracks. Loudness is the quality of a sound that is the primary psychological correlate of physical strength (amplitude). Values typical range between -60 and 0 db.
+
+
+
speechiness
+
Speechiness detects the presence of spoken words in a track. The more exclusively speech-like the recording (e.g. talk show, audio book, poetry), the closer to 1.0 the attribute value. Values above 0.66 describe tracks that are probably made entirely of spoken words. Values between 0.33 and 0.66 describe tracks that may contain both music and speech, either in sections or layered, including such cases as rap music. Values below 0.33 most likely represent music and other non-speech-like tracks.
+
+
+
tempo
+
The overall estimated tempo of a track in beats per minute (BPM). In musical terminology, tempo is the speed or pace of a given piece and derives directly from the average beat duration.
+
+
+
valence
+
A measure from 0.0 to 1.0 describing the musical positiveness conveyed by a track. Tracks with high valence sound more positive (e.g. happy, cheerful, euphoric), while tracks with low valence sound more negative (e.g. sad, depressed, angry).
+
+
+
+
+
+
+
I was interested in looking at how my music taste changed over time, so for each monthly playlist, I calculated the mean values for these four features and made a line plot:
+
+
Some things that pop out:
+
+
Overall, I tend to listen to bright music (high dancibility and energy) but there is still a lot of variation (especially in valence).
+
Acousticness stays pretty low and doesn’t seem to correlate much with time. This makes sense because I primarily listen to pop and electric songs, which are both very far from sounding acoustic.
+
The one dip in May 2017 is, you guessed it, that playlist with just that one Mitski song. That was the only playlist where the mean danceability and energy values are below 0.5.
+
It also looks like I listen to brighter music with every passing year, though this trend is subtle. This it’s a bit easier to see in the animation below (especially for energy and valence).
+
+
+
Analysis #3 - Songs during college
+
Next, I wanted to focus on my years in college, from Fall 2016 to Spring 2020. For this analysis, I defined time in terms of school years and quarters. While I was a college student, I often felt like the passage of time was defined in terms of quarters, so this scale felt appropriate.
+
Here is the same line plot, except the feature values are averaged by quarter instead of month, and the plot is now faceted by school year:
+
+
Observations:
+
+
The trend in increasing positivity can be observed in this plot as well (with my Junior year coming out on top).
+
I was actually wondering whether I’d see a pattern by quarter, but there doesn’t seem to be any strong ones.
+
+
Analysis #4 - “My Top 100” playlists
+
In my last analysis, I move from my monthly playlists to the end-of-the-year playlists that Spotify makes for you every year.
+
For this, I grabbed audio features of songs in my yearly top 100 playlists from 2016-2019. In this graph, each line represents a song and the top 10 most listened to song of each year are emphasized in black. The thick red line in each panel represents the average of the songs for that year.
+
+
Some observations on the variation in audio features among my top 100 playlists:
+
+
Although the monthly averages in the previous graphs showed extremely low acousticness values (never going above 0.5), that actually hid a lot of variation. You can see that a good number of songs with high acousticness make it to my top 100 (and sometimes even top 10) songs, but that number seems to gradually decline over time. The song with highest acousticness, Ripchord by Rilo Kiley comes from my 2016 playlist. Rilo Kiley is an indie rock band that had a strong influence on my music taste during high school, so it’s no surprise that this song made it there.
+
There’s also a trend of decreasing overall variability in the audio features of my top 100 songs. Perhaps this means that I’m narrowing in on my true music taste? Or maybe that I’m going through a phase in music taste? It’s kind of hard to tell but interesting nonetheless.
+
+
Conclusion
+
I didn’t really dig too deeply into the acoustic profile of the songs I listen to in this post, and I doubt that Spotify’s list of audio features are comprehensive enough to describe my music taste, but this was a cool exercise!
+
And although I ignored several of the audio features because they weren’t very informative for the songs I listen to, I thought I should at least leave a summary table showing the mean values for all features that I gave in the table above!
+
+
+
+
+
+
+
Feature
+
Description
+
Average
+
+
+
+
+
acousticness
+
A confidence measure from 0.0 to 1.0 of whether the track is acoustic. 1.0 represents high confidence the track is acoustic.
+
0.16
+
+
+
danceability
+
Danceability describes how suitable a track is for dancing based on a combination of musical elements including tempo, rhythm stability, beat strength, and overall regularity. A value of 0.0 is least danceable and 1.0 is most danceable.
+
0.64
+
+
+
energy
+
Energy is a measure from 0.0 to 1.0 and represents a perceptual measure of intensity and activity. Typically, energetic tracks feel fast, loud, and noisy. For example, death metal has high energy, while a Bach prelude scores low on the scale. Perceptual features contributing to this attribute include dynamic range, perceived loudness, timbre, onset rate, and general entropy.
+
0.75
+
+
+
instrumentalness
+
Predicts whether a track contains no vocals. “Ooh” and “aah” sounds are treated as instrumental in this context. Rap or spoken word tracks are clearly “vocal”. The closer the instrumentalness value is to 1.0, the greater likelihood the track contains no vocal content. Values above 0.5 are intended to represent instrumental tracks, but confidence is higher as the value approaches 1.0.
+
0.02
+
+
+
liveness
+
Detects the presence of an audience in the recording. Higher liveness values represent an increased probability that the track was performed live. A value above 0.8 provides strong likelihood that the track is live.
+
0.19
+
+
+
loudness
+
The overall loudness of a track in decibels (dB). Loudness values are averaged across the entire track and are useful for comparing relative loudness of tracks. Loudness is the quality of a sound that is the primary psychological correlate of physical strength (amplitude). Values typical range between -60 and 0 db.
+
-4.95
+
+
+
speechiness
+
Speechiness detects the presence of spoken words in a track. The more exclusively speech-like the recording (e.g. talk show, audio book, poetry), the closer to 1.0 the attribute value. Values above 0.66 describe tracks that are probably made entirely of spoken words. Values between 0.33 and 0.66 describe tracks that may contain both music and speech, either in sections or layered, including such cases as rap music. Values below 0.33 most likely represent music and other non-speech-like tracks.
+
0.08
+
+
+
tempo
+
The overall estimated tempo of a track in beats per minute (BPM). In musical terminology, tempo is the speed or pace of a given piece and derives directly from the average beat duration.
+
120.25
+
+
+
valence
+
A measure from 0.0 to 1.0 describing the musical positiveness conveyed by a track. Tracks with high valence sound more positive (e.g. happy, cheerful, euphoric), while tracks with low valence sound more negative (e.g. sad, depressed, angry).
You can rename the new column of counts (n by default) using the name argument:
+
+
+
##### Long Form #####
+# penguins %>%
+# count(species) %>%
+# rename(total = n)
+
+penguins%>%
+ count(species, name ="total")
+
+
+
# A tibble: 3 x 2
+ species total
+ <fct> <int>
+ 1 Adelie 146
+ 2 Chinstrap 68
+ 3 Gentoo 119
+
+
You can also rename the column(s) that are selected for counting in the same way as shown in the select() examples above:
+
+
+
##### Long Form #####
+# penguins %>%
+# count(species) %>%
+# rename(total = n,
+# penguin_species = species)
+
+penguins%>%
+ count(penguin_species =species, name ="total")
+
+
+
# A tibble: 3 x 2
+ penguin_species total
+ <fct> <int>
+ 1 Adelie 146
+ 2 Chinstrap 68
+ 3 Gentoo 119
+
+
Note that the new name passed into the name argument must be quoted, but the new name for selected column needs not to be unquoted:
+
+
+
identical(
+ # Method 1: new column name UNQUOTED
+ penguins%>%
+ count(penguin_species =species, name ="total"),
+ # Method 2: new column name QUOTED
+ penguins%>%
+ count("penguin_species"=species, name ="total")
+)
+
+
+
[1] TRUE
+
+
I prefer to unquote the new column names to keep it consistent with the recommended style for rename()
+
This feature of select() may seem weird and hackish (and I guess it sort of is in this demonstration) but it’s explicitly documented here if you want to read more on it.
+
3. mutate() inside count()
+
You can also create a new column to count by inside count(). This works very similarly to the above, but I think it’s worth its own mention.
+
It’s pretty simple - you just do what you’d do for mutate() inside count():
I’ve rarely used transmute() in the past because I thought it could only return modified columns, which would be very limiting (like in the above example, what good is a single column of penguin body mass in kilograms?)
+
But actually you can just name the columns you want to include in transmute() like you would in select() to carry over columns that you aren’t modifying. And of course, you can “rename” them as you do it1:
+
+
+
##### Long Form #####
+# penguins %>%
+# mutate(body_mass_kg = body_mass_g/1000) %>%
+# select(species, island, body_mass_kg) %>%
+# rename(penguin_species = species)
+
+penguins%>%
+ transmute(penguin_species =species,
+ island,
+ body_mass_kg =body_mass_g/1000)
+
I always found using ungroup() after summarize() to be extremely ugly, but I found myself using it a lot to remove left-over groupings after a summarize call:
# A tibble: 5 x 3
+ island species mean_mass
+ <fct> <fct> <dbl>
+ 1 Biscoe Adelie 3710.
+ 2 Biscoe Gentoo 5092.
+ 3 Dream Adelie 3701.
+ 4 Dream Chinstrap 3733.
+ 5 Torgersen Adelie 3709.
+
+
… because summarize() only drops the last grouping variable by defaut, meaning that the output is still grouped by the island variable if ungroup() isn’t called:
# A tibble: 5 x 3
+ island species mean_mass
+ <fct> <fct> <dbl>
+ 1 Biscoe Adelie 3710.
+ 2 Biscoe Gentoo 5092.
+ 3 Dream Adelie 3701.
+ 4 Dream Chinstrap 3733.
+ 5 Torgersen Adelie 3709.
+
+
But ungroup() still remains relevant as you can now selectively remove grouping variables in {dplyr} 1.0.0.
+
6. arrange() + other features inside slice()
+
In past versions of {dplyr}, if you wanted to grab the top n rows sorted by a column, you’d use top_n(), which provides a simpler way of doing slice() + arrange():
+
+
+
##### Long Form #####
+# penguins %>%
+# arrange(desc(body_mass_g)) %>%
+# slice(1:5)
+
+penguins%>%
+ top_n(5, wt =body_mass_g)
+
+
+
# A tibble: 6 x 8
+ species island bill_length_mm bill_depth_mm flipper_length_~ body_mass_g sex
+ <fct> <fct> <dbl> <dbl> <int> <int> <fct>
+ 1 Gentoo Biscoe 49.2 15.2 221 6300 male
+ 2 Gentoo Biscoe 59.6 17 230 6050 male
+ 3 Gentoo Biscoe 51.1 16.3 220 6000 male
+ 4 Gentoo Biscoe 45.2 16.4 223 5950 male
+ 5 Gentoo Biscoe 49.8 15.9 229 5950 male
+ 6 Gentoo Biscoe 48.8 16.2 222 6000 male
+ # ... with 1 more variable: year <int>
+
+
But the recent {dplyr} 1.0.0 augmented slice() with variants like slice_min() and slice_max() that now supresede top_n():
# A tibble: 6 x 8
+ species island bill_length_mm bill_depth_mm flipper_length_~ body_mass_g sex
+ <fct> <fct> <dbl> <dbl> <int> <int> <fct>
+ 1 Gentoo Biscoe 49.2 15.2 221 6300 male
+ 2 Gentoo Biscoe 59.6 17 230 6050 male
+ 3 Gentoo Biscoe 51.1 16.3 220 6000 male
+ 4 Gentoo Biscoe 48.8 16.2 222 6000 male
+ 5 Gentoo Biscoe 45.2 16.4 223 5950 male
+ 6 Gentoo Biscoe 49.8 15.9 229 5950 male
+ # ... with 1 more variable: year <int>
+
+
Note that the order of arguments is different for slice_min/max() - the first argument after piping is where you specify the variable for ordering rather than the number of rows to return, like in top_n().
+
This is because slice_min/max() gives you an option to either specify a certain number of rows n or a proportion of rows prop:
+
+
+
penguins%>%
+ slice_max(body_mass_g, prop =.01)
+
+
+
# A tibble: 4 x 8
+ species island bill_length_mm bill_depth_mm flipper_length_~ body_mass_g sex
+ <fct> <fct> <dbl> <dbl> <int> <int> <fct>
+ 1 Gentoo Biscoe 49.2 15.2 221 6300 male
+ 2 Gentoo Biscoe 59.6 17 230 6050 male
+ 3 Gentoo Biscoe 51.1 16.3 220 6000 male
+ 4 Gentoo Biscoe 48.8 16.2 222 6000 male
+ # ... with 1 more variable: year <int>
+
+
And actually, the most significant change with the new slice_*() functions is from adding appropriate behavior for grouped dataframes.
+
So for example, this example below returns the top 5% of penguins by weight for each species:
But note that slice_*() functions do not modify groups in the result if the input is a grouped dataframe, so you need to explicitly add a call to ungroup() if you want to drop groups after slicing.
+
7. count and sum by group with add_count()
+
Saving my favorite lesser-known {dplyr} function for last!
+
add_count() adds a column with the counts of each group (or combination of groups):
+
+
+
##### Long Form #####
+# penguins %>%
+# group_by(species) %>%
+# mutate(count_by_species = n()) %>%
+# ungroup()
+
+penguins%>%
+ add_count(species, name ="count_by_species")%>%
+ # cutting down some columns to show the new column
+ select(-contains("mm"))
+
+
+
# A tibble: 333 x 6
+ species island body_mass_g sex year count_by_species
+ <fct> <fct> <int> <fct> <int> <int>
+ 1 Adelie Torgersen 3750 male 2007 146
+ 2 Adelie Torgersen 3800 female 2007 146
+ 3 Adelie Torgersen 3250 female 2007 146
+ 4 Adelie Torgersen 3450 female 2007 146
+ 5 Adelie Torgersen 3650 male 2007 146
+ 6 Adelie Torgersen 3625 female 2007 146
+ 7 Adelie Torgersen 4675 male 2007 146
+ 8 Adelie Torgersen 3200 female 2007 146
+ 9 Adelie Torgersen 3800 male 2007 146
+ 10 Adelie Torgersen 4400 male 2007 146
+ # ... with 323 more rows
+
+
You can use the wt to effectively get sums by group (perhaps hackish but very very useful):
+
+
+
##### Long Form #####
+# penguins %>%
+# group_by(species) %>%
+# mutate(total_weight_by_species = sum(body_mass_g)) %>%
+# ungroup()
+
+
+penguins%>%
+ add_count(species, wt =body_mass_g, name ="total_weight_by_species")%>%
+ # cutting down some columns to show the new column
+ select(-contains("mm"))
+
+
+
# A tibble: 333 x 6
+ species island body_mass_g sex year total_weight_by_species
+ <fct> <fct> <int> <fct> <int> <int>
+ 1 Adelie Torgersen 3750 male 2007 541100
+ 2 Adelie Torgersen 3800 female 2007 541100
+ 3 Adelie Torgersen 3250 female 2007 541100
+ 4 Adelie Torgersen 3450 female 2007 541100
+ 5 Adelie Torgersen 3650 male 2007 541100
+ 6 Adelie Torgersen 3625 female 2007 541100
+ 7 Adelie Torgersen 4675 male 2007 541100
+ 8 Adelie Torgersen 3200 female 2007 541100
+ 9 Adelie Torgersen 3800 male 2007 541100
+ 10 Adelie Torgersen 4400 male 2007 541100
+ # ... with 323 more rows
+
+
Also check out its more primitive version add_tally().
+
By default, add_tally() adds a count of rows, which you can already do with mutate(n = n()), but it shines when you make use of its wt argument:
+
+
+
penguins%>%
+ add_count(species, wt =body_mass_g, name ="total_weight_by_species")%>%
+ add_tally(wt =body_mass_g, name ="total_weight_of_all_species")%>%
+ select(1:2, last_col(0):last_col(1))
+
What happens under the hood is actually copying of a sort, so this is probably not the best approach if you care about efficiency. As a case in point, you can’t use {tidyselect} helpers in transmute because you’re creating a new dataframe↩︎
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/docs/posts/2020-08-07-saving-a-line-of-piping/preview.png b/docs/posts/2020-08-07-saving-a-line-of-piping/preview.png
new file mode 100644
index 00000000..3eeca633
Binary files /dev/null and b/docs/posts/2020-08-07-saving-a-line-of-piping/preview.png differ
diff --git a/docs/posts/2020-08-07-saving-a-line-of-piping/saving-a-line-of-piping_files/header-attrs-2.3/header-attrs.js b/docs/posts/2020-08-07-saving-a-line-of-piping/saving-a-line-of-piping_files/header-attrs-2.3/header-attrs.js
new file mode 100644
index 00000000..dd57d92e
--- /dev/null
+++ b/docs/posts/2020-08-07-saving-a-line-of-piping/saving-a-line-of-piping_files/header-attrs-2.3/header-attrs.js
@@ -0,0 +1,12 @@
+// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
+// be compatible with the behavior of Pandoc < 2.8).
+document.addEventListener('DOMContentLoaded', function(e) {
+ var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
+ var i, h, a;
+ for (i = 0; i < hs.length; i++) {
+ h = hs[i];
+ if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6
+ a = h.attributes;
+ while (a.length > 0) h.removeAttribute(a[0].name);
+ }
+});
diff --git a/docs/posts/2020-08-07-saving-a-line-of-piping/saving-a-line-of-piping_files/htmlwidgets-1.5.1/htmlwidgets.js b/docs/posts/2020-08-07-saving-a-line-of-piping/saving-a-line-of-piping_files/htmlwidgets-1.5.1/htmlwidgets.js
new file mode 100644
index 00000000..6f3d672d
--- /dev/null
+++ b/docs/posts/2020-08-07-saving-a-line-of-piping/saving-a-line-of-piping_files/htmlwidgets-1.5.1/htmlwidgets.js
@@ -0,0 +1,903 @@
+(function() {
+ // If window.HTMLWidgets is already defined, then use it; otherwise create a
+ // new object. This allows preceding code to set options that affect the
+ // initialization process (though none currently exist).
+ window.HTMLWidgets = window.HTMLWidgets || {};
+
+ // See if we're running in a viewer pane. If not, we're in a web browser.
+ var viewerMode = window.HTMLWidgets.viewerMode =
+ /\bviewer_pane=1\b/.test(window.location);
+
+ // See if we're running in Shiny mode. If not, it's a static document.
+ // Note that static widgets can appear in both Shiny and static modes, but
+ // obviously, Shiny widgets can only appear in Shiny apps/documents.
+ var shinyMode = window.HTMLWidgets.shinyMode =
+ typeof(window.Shiny) !== "undefined" && !!window.Shiny.outputBindings;
+
+ // We can't count on jQuery being available, so we implement our own
+ // version if necessary.
+ function querySelectorAll(scope, selector) {
+ if (typeof(jQuery) !== "undefined" && scope instanceof jQuery) {
+ return scope.find(selector);
+ }
+ if (scope.querySelectorAll) {
+ return scope.querySelectorAll(selector);
+ }
+ }
+
+ function asArray(value) {
+ if (value === null)
+ return [];
+ if ($.isArray(value))
+ return value;
+ return [value];
+ }
+
+ // Implement jQuery's extend
+ function extend(target /*, ... */) {
+ if (arguments.length == 1) {
+ return target;
+ }
+ for (var i = 1; i < arguments.length; i++) {
+ var source = arguments[i];
+ for (var prop in source) {
+ if (source.hasOwnProperty(prop)) {
+ target[prop] = source[prop];
+ }
+ }
+ }
+ return target;
+ }
+
+ // IE8 doesn't support Array.forEach.
+ function forEach(values, callback, thisArg) {
+ if (values.forEach) {
+ values.forEach(callback, thisArg);
+ } else {
+ for (var i = 0; i < values.length; i++) {
+ callback.call(thisArg, values[i], i, values);
+ }
+ }
+ }
+
+ // Replaces the specified method with the return value of funcSource.
+ //
+ // Note that funcSource should not BE the new method, it should be a function
+ // that RETURNS the new method. funcSource receives a single argument that is
+ // the overridden method, it can be called from the new method. The overridden
+ // method can be called like a regular function, it has the target permanently
+ // bound to it so "this" will work correctly.
+ function overrideMethod(target, methodName, funcSource) {
+ var superFunc = target[methodName] || function() {};
+ var superFuncBound = function() {
+ return superFunc.apply(target, arguments);
+ };
+ target[methodName] = funcSource(superFuncBound);
+ }
+
+ // Add a method to delegator that, when invoked, calls
+ // delegatee.methodName. If there is no such method on
+ // the delegatee, but there was one on delegator before
+ // delegateMethod was called, then the original version
+ // is invoked instead.
+ // For example:
+ //
+ // var a = {
+ // method1: function() { console.log('a1'); }
+ // method2: function() { console.log('a2'); }
+ // };
+ // var b = {
+ // method1: function() { console.log('b1'); }
+ // };
+ // delegateMethod(a, b, "method1");
+ // delegateMethod(a, b, "method2");
+ // a.method1();
+ // a.method2();
+ //
+ // The output would be "b1", "a2".
+ function delegateMethod(delegator, delegatee, methodName) {
+ var inherited = delegator[methodName];
+ delegator[methodName] = function() {
+ var target = delegatee;
+ var method = delegatee[methodName];
+
+ // The method doesn't exist on the delegatee. Instead,
+ // call the method on the delegator, if it exists.
+ if (!method) {
+ target = delegator;
+ method = inherited;
+ }
+
+ if (method) {
+ return method.apply(target, arguments);
+ }
+ };
+ }
+
+ // Implement a vague facsimilie of jQuery's data method
+ function elementData(el, name, value) {
+ if (arguments.length == 2) {
+ return el["htmlwidget_data_" + name];
+ } else if (arguments.length == 3) {
+ el["htmlwidget_data_" + name] = value;
+ return el;
+ } else {
+ throw new Error("Wrong number of arguments for elementData: " +
+ arguments.length);
+ }
+ }
+
+ // http://stackoverflow.com/questions/3446170/escape-string-for-use-in-javascript-regex
+ function escapeRegExp(str) {
+ return str.replace(/[\-\[\]\/\{\}\(\)\*\+\?\.\\\^\$\|]/g, "\\$&");
+ }
+
+ function hasClass(el, className) {
+ var re = new RegExp("\\b" + escapeRegExp(className) + "\\b");
+ return re.test(el.className);
+ }
+
+ // elements - array (or array-like object) of HTML elements
+ // className - class name to test for
+ // include - if true, only return elements with given className;
+ // if false, only return elements *without* given className
+ function filterByClass(elements, className, include) {
+ var results = [];
+ for (var i = 0; i < elements.length; i++) {
+ if (hasClass(elements[i], className) == include)
+ results.push(elements[i]);
+ }
+ return results;
+ }
+
+ function on(obj, eventName, func) {
+ if (obj.addEventListener) {
+ obj.addEventListener(eventName, func, false);
+ } else if (obj.attachEvent) {
+ obj.attachEvent(eventName, func);
+ }
+ }
+
+ function off(obj, eventName, func) {
+ if (obj.removeEventListener)
+ obj.removeEventListener(eventName, func, false);
+ else if (obj.detachEvent) {
+ obj.detachEvent(eventName, func);
+ }
+ }
+
+ // Translate array of values to top/right/bottom/left, as usual with
+ // the "padding" CSS property
+ // https://developer.mozilla.org/en-US/docs/Web/CSS/padding
+ function unpackPadding(value) {
+ if (typeof(value) === "number")
+ value = [value];
+ if (value.length === 1) {
+ return {top: value[0], right: value[0], bottom: value[0], left: value[0]};
+ }
+ if (value.length === 2) {
+ return {top: value[0], right: value[1], bottom: value[0], left: value[1]};
+ }
+ if (value.length === 3) {
+ return {top: value[0], right: value[1], bottom: value[2], left: value[1]};
+ }
+ if (value.length === 4) {
+ return {top: value[0], right: value[1], bottom: value[2], left: value[3]};
+ }
+ }
+
+ // Convert an unpacked padding object to a CSS value
+ function paddingToCss(paddingObj) {
+ return paddingObj.top + "px " + paddingObj.right + "px " + paddingObj.bottom + "px " + paddingObj.left + "px";
+ }
+
+ // Makes a number suitable for CSS
+ function px(x) {
+ if (typeof(x) === "number")
+ return x + "px";
+ else
+ return x;
+ }
+
+ // Retrieves runtime widget sizing information for an element.
+ // The return value is either null, or an object with fill, padding,
+ // defaultWidth, defaultHeight fields.
+ function sizingPolicy(el) {
+ var sizingEl = document.querySelector("script[data-for='" + el.id + "'][type='application/htmlwidget-sizing']");
+ if (!sizingEl)
+ return null;
+ var sp = JSON.parse(sizingEl.textContent || sizingEl.text || "{}");
+ if (viewerMode) {
+ return sp.viewer;
+ } else {
+ return sp.browser;
+ }
+ }
+
+ // @param tasks Array of strings (or falsy value, in which case no-op).
+ // Each element must be a valid JavaScript expression that yields a
+ // function. Or, can be an array of objects with "code" and "data"
+ // properties; in this case, the "code" property should be a string
+ // of JS that's an expr that yields a function, and "data" should be
+ // an object that will be added as an additional argument when that
+ // function is called.
+ // @param target The object that will be "this" for each function
+ // execution.
+ // @param args Array of arguments to be passed to the functions. (The
+ // same arguments will be passed to all functions.)
+ function evalAndRun(tasks, target, args) {
+ if (tasks) {
+ forEach(tasks, function(task) {
+ var theseArgs = args;
+ if (typeof(task) === "object") {
+ theseArgs = theseArgs.concat([task.data]);
+ task = task.code;
+ }
+ var taskFunc = tryEval(task);
+ if (typeof(taskFunc) !== "function") {
+ throw new Error("Task must be a function! Source:\n" + task);
+ }
+ taskFunc.apply(target, theseArgs);
+ });
+ }
+ }
+
+ // Attempt eval() both with and without enclosing in parentheses.
+ // Note that enclosing coerces a function declaration into
+ // an expression that eval() can parse
+ // (otherwise, a SyntaxError is thrown)
+ function tryEval(code) {
+ var result = null;
+ try {
+ result = eval(code);
+ } catch(error) {
+ if (!error instanceof SyntaxError) {
+ throw error;
+ }
+ try {
+ result = eval("(" + code + ")");
+ } catch(e) {
+ if (e instanceof SyntaxError) {
+ throw error;
+ } else {
+ throw e;
+ }
+ }
+ }
+ return result;
+ }
+
+ function initSizing(el) {
+ var sizing = sizingPolicy(el);
+ if (!sizing)
+ return;
+
+ var cel = document.getElementById("htmlwidget_container");
+ if (!cel)
+ return;
+
+ if (typeof(sizing.padding) !== "undefined") {
+ document.body.style.margin = "0";
+ document.body.style.padding = paddingToCss(unpackPadding(sizing.padding));
+ }
+
+ if (sizing.fill) {
+ document.body.style.overflow = "hidden";
+ document.body.style.width = "100%";
+ document.body.style.height = "100%";
+ document.documentElement.style.width = "100%";
+ document.documentElement.style.height = "100%";
+ if (cel) {
+ cel.style.position = "absolute";
+ var pad = unpackPadding(sizing.padding);
+ cel.style.top = pad.top + "px";
+ cel.style.right = pad.right + "px";
+ cel.style.bottom = pad.bottom + "px";
+ cel.style.left = pad.left + "px";
+ el.style.width = "100%";
+ el.style.height = "100%";
+ }
+
+ return {
+ getWidth: function() { return cel.offsetWidth; },
+ getHeight: function() { return cel.offsetHeight; }
+ };
+
+ } else {
+ el.style.width = px(sizing.width);
+ el.style.height = px(sizing.height);
+
+ return {
+ getWidth: function() { return el.offsetWidth; },
+ getHeight: function() { return el.offsetHeight; }
+ };
+ }
+ }
+
+ // Default implementations for methods
+ var defaults = {
+ find: function(scope) {
+ return querySelectorAll(scope, "." + this.name);
+ },
+ renderError: function(el, err) {
+ var $el = $(el);
+
+ this.clearError(el);
+
+ // Add all these error classes, as Shiny does
+ var errClass = "shiny-output-error";
+ if (err.type !== null) {
+ // use the classes of the error condition as CSS class names
+ errClass = errClass + " " + $.map(asArray(err.type), function(type) {
+ return errClass + "-" + type;
+ }).join(" ");
+ }
+ errClass = errClass + " htmlwidgets-error";
+
+ // Is el inline or block? If inline or inline-block, just display:none it
+ // and add an inline error.
+ var display = $el.css("display");
+ $el.data("restore-display-mode", display);
+
+ if (display === "inline" || display === "inline-block") {
+ $el.hide();
+ if (err.message !== "") {
+ var errorSpan = $("").addClass(errClass);
+ errorSpan.text(err.message);
+ $el.after(errorSpan);
+ }
+ } else if (display === "block") {
+ // If block, add an error just after the el, set visibility:none on the
+ // el, and position the error to be on top of the el.
+ // Mark it with a unique ID and CSS class so we can remove it later.
+ $el.css("visibility", "hidden");
+ if (err.message !== "") {
+ var errorDiv = $("
").addClass(errClass).css("position", "absolute")
+ .css("top", el.offsetTop)
+ .css("left", el.offsetLeft)
+ // setting width can push out the page size, forcing otherwise
+ // unnecessary scrollbars to appear and making it impossible for
+ // the element to shrink; so use max-width instead
+ .css("maxWidth", el.offsetWidth)
+ .css("height", el.offsetHeight);
+ errorDiv.text(err.message);
+ $el.after(errorDiv);
+
+ // Really dumb way to keep the size/position of the error in sync with
+ // the parent element as the window is resized or whatever.
+ var intId = setInterval(function() {
+ if (!errorDiv[0].parentElement) {
+ clearInterval(intId);
+ return;
+ }
+ errorDiv
+ .css("top", el.offsetTop)
+ .css("left", el.offsetLeft)
+ .css("maxWidth", el.offsetWidth)
+ .css("height", el.offsetHeight);
+ }, 500);
+ }
+ }
+ },
+ clearError: function(el) {
+ var $el = $(el);
+ var display = $el.data("restore-display-mode");
+ $el.data("restore-display-mode", null);
+
+ if (display === "inline" || display === "inline-block") {
+ if (display)
+ $el.css("display", display);
+ $(el.nextSibling).filter(".htmlwidgets-error").remove();
+ } else if (display === "block"){
+ $el.css("visibility", "inherit");
+ $(el.nextSibling).filter(".htmlwidgets-error").remove();
+ }
+ },
+ sizing: {}
+ };
+
+ // Called by widget bindings to register a new type of widget. The definition
+ // object can contain the following properties:
+ // - name (required) - A string indicating the binding name, which will be
+ // used by default as the CSS classname to look for.
+ // - initialize (optional) - A function(el) that will be called once per
+ // widget element; if a value is returned, it will be passed as the third
+ // value to renderValue.
+ // - renderValue (required) - A function(el, data, initValue) that will be
+ // called with data. Static contexts will cause this to be called once per
+ // element; Shiny apps will cause this to be called multiple times per
+ // element, as the data changes.
+ window.HTMLWidgets.widget = function(definition) {
+ if (!definition.name) {
+ throw new Error("Widget must have a name");
+ }
+ if (!definition.type) {
+ throw new Error("Widget must have a type");
+ }
+ // Currently we only support output widgets
+ if (definition.type !== "output") {
+ throw new Error("Unrecognized widget type '" + definition.type + "'");
+ }
+ // TODO: Verify that .name is a valid CSS classname
+
+ // Support new-style instance-bound definitions. Old-style class-bound
+ // definitions have one widget "object" per widget per type/class of
+ // widget; the renderValue and resize methods on such widget objects
+ // take el and instance arguments, because the widget object can't
+ // store them. New-style instance-bound definitions have one widget
+ // object per widget instance; the definition that's passed in doesn't
+ // provide renderValue or resize methods at all, just the single method
+ // factory(el, width, height)
+ // which returns an object that has renderValue(x) and resize(w, h).
+ // This enables a far more natural programming style for the widget
+ // author, who can store per-instance state using either OO-style
+ // instance fields or functional-style closure variables (I guess this
+ // is in contrast to what can only be called C-style pseudo-OO which is
+ // what we required before).
+ if (definition.factory) {
+ definition = createLegacyDefinitionAdapter(definition);
+ }
+
+ if (!definition.renderValue) {
+ throw new Error("Widget must have a renderValue function");
+ }
+
+ // For static rendering (non-Shiny), use a simple widget registration
+ // scheme. We also use this scheme for Shiny apps/documents that also
+ // contain static widgets.
+ window.HTMLWidgets.widgets = window.HTMLWidgets.widgets || [];
+ // Merge defaults into the definition; don't mutate the original definition.
+ var staticBinding = extend({}, defaults, definition);
+ overrideMethod(staticBinding, "find", function(superfunc) {
+ return function(scope) {
+ var results = superfunc(scope);
+ // Filter out Shiny outputs, we only want the static kind
+ return filterByClass(results, "html-widget-output", false);
+ };
+ });
+ window.HTMLWidgets.widgets.push(staticBinding);
+
+ if (shinyMode) {
+ // Shiny is running. Register the definition with an output binding.
+ // The definition itself will not be the output binding, instead
+ // we will make an output binding object that delegates to the
+ // definition. This is because we foolishly used the same method
+ // name (renderValue) for htmlwidgets definition and Shiny bindings
+ // but they actually have quite different semantics (the Shiny
+ // bindings receive data that includes lots of metadata that it
+ // strips off before calling htmlwidgets renderValue). We can't
+ // just ignore the difference because in some widgets it's helpful
+ // to call this.renderValue() from inside of resize(), and if
+ // we're not delegating, then that call will go to the Shiny
+ // version instead of the htmlwidgets version.
+
+ // Merge defaults with definition, without mutating either.
+ var bindingDef = extend({}, defaults, definition);
+
+ // This object will be our actual Shiny binding.
+ var shinyBinding = new Shiny.OutputBinding();
+
+ // With a few exceptions, we'll want to simply use the bindingDef's
+ // version of methods if they are available, otherwise fall back to
+ // Shiny's defaults. NOTE: If Shiny's output bindings gain additional
+ // methods in the future, and we want them to be overrideable by
+ // HTMLWidget binding definitions, then we'll need to add them to this
+ // list.
+ delegateMethod(shinyBinding, bindingDef, "getId");
+ delegateMethod(shinyBinding, bindingDef, "onValueChange");
+ delegateMethod(shinyBinding, bindingDef, "onValueError");
+ delegateMethod(shinyBinding, bindingDef, "renderError");
+ delegateMethod(shinyBinding, bindingDef, "clearError");
+ delegateMethod(shinyBinding, bindingDef, "showProgress");
+
+ // The find, renderValue, and resize are handled differently, because we
+ // want to actually decorate the behavior of the bindingDef methods.
+
+ shinyBinding.find = function(scope) {
+ var results = bindingDef.find(scope);
+
+ // Only return elements that are Shiny outputs, not static ones
+ var dynamicResults = results.filter(".html-widget-output");
+
+ // It's possible that whatever caused Shiny to think there might be
+ // new dynamic outputs, also caused there to be new static outputs.
+ // Since there might be lots of different htmlwidgets bindings, we
+ // schedule execution for later--no need to staticRender multiple
+ // times.
+ if (results.length !== dynamicResults.length)
+ scheduleStaticRender();
+
+ return dynamicResults;
+ };
+
+ // Wrap renderValue to handle initialization, which unfortunately isn't
+ // supported natively by Shiny at the time of this writing.
+
+ shinyBinding.renderValue = function(el, data) {
+ Shiny.renderDependencies(data.deps);
+ // Resolve strings marked as javascript literals to objects
+ if (!(data.evals instanceof Array)) data.evals = [data.evals];
+ for (var i = 0; data.evals && i < data.evals.length; i++) {
+ window.HTMLWidgets.evaluateStringMember(data.x, data.evals[i]);
+ }
+ if (!bindingDef.renderOnNullValue) {
+ if (data.x === null) {
+ el.style.visibility = "hidden";
+ return;
+ } else {
+ el.style.visibility = "inherit";
+ }
+ }
+ if (!elementData(el, "initialized")) {
+ initSizing(el);
+
+ elementData(el, "initialized", true);
+ if (bindingDef.initialize) {
+ var result = bindingDef.initialize(el, el.offsetWidth,
+ el.offsetHeight);
+ elementData(el, "init_result", result);
+ }
+ }
+ bindingDef.renderValue(el, data.x, elementData(el, "init_result"));
+ evalAndRun(data.jsHooks.render, elementData(el, "init_result"), [el, data.x]);
+ };
+
+ // Only override resize if bindingDef implements it
+ if (bindingDef.resize) {
+ shinyBinding.resize = function(el, width, height) {
+ // Shiny can call resize before initialize/renderValue have been
+ // called, which doesn't make sense for widgets.
+ if (elementData(el, "initialized")) {
+ bindingDef.resize(el, width, height, elementData(el, "init_result"));
+ }
+ };
+ }
+
+ Shiny.outputBindings.register(shinyBinding, bindingDef.name);
+ }
+ };
+
+ var scheduleStaticRenderTimerId = null;
+ function scheduleStaticRender() {
+ if (!scheduleStaticRenderTimerId) {
+ scheduleStaticRenderTimerId = setTimeout(function() {
+ scheduleStaticRenderTimerId = null;
+ window.HTMLWidgets.staticRender();
+ }, 1);
+ }
+ }
+
+ // Render static widgets after the document finishes loading
+ // Statically render all elements that are of this widget's class
+ window.HTMLWidgets.staticRender = function() {
+ var bindings = window.HTMLWidgets.widgets || [];
+ forEach(bindings, function(binding) {
+ var matches = binding.find(document.documentElement);
+ forEach(matches, function(el) {
+ var sizeObj = initSizing(el, binding);
+
+ if (hasClass(el, "html-widget-static-bound"))
+ return;
+ el.className = el.className + " html-widget-static-bound";
+
+ var initResult;
+ if (binding.initialize) {
+ initResult = binding.initialize(el,
+ sizeObj ? sizeObj.getWidth() : el.offsetWidth,
+ sizeObj ? sizeObj.getHeight() : el.offsetHeight
+ );
+ elementData(el, "init_result", initResult);
+ }
+
+ if (binding.resize) {
+ var lastSize = {
+ w: sizeObj ? sizeObj.getWidth() : el.offsetWidth,
+ h: sizeObj ? sizeObj.getHeight() : el.offsetHeight
+ };
+ var resizeHandler = function(e) {
+ var size = {
+ w: sizeObj ? sizeObj.getWidth() : el.offsetWidth,
+ h: sizeObj ? sizeObj.getHeight() : el.offsetHeight
+ };
+ if (size.w === 0 && size.h === 0)
+ return;
+ if (size.w === lastSize.w && size.h === lastSize.h)
+ return;
+ lastSize = size;
+ binding.resize(el, size.w, size.h, initResult);
+ };
+
+ on(window, "resize", resizeHandler);
+
+ // This is needed for cases where we're running in a Shiny
+ // app, but the widget itself is not a Shiny output, but
+ // rather a simple static widget. One example of this is
+ // an rmarkdown document that has runtime:shiny and widget
+ // that isn't in a render function. Shiny only knows to
+ // call resize handlers for Shiny outputs, not for static
+ // widgets, so we do it ourselves.
+ if (window.jQuery) {
+ window.jQuery(document).on(
+ "shown.htmlwidgets shown.bs.tab.htmlwidgets shown.bs.collapse.htmlwidgets",
+ resizeHandler
+ );
+ window.jQuery(document).on(
+ "hidden.htmlwidgets hidden.bs.tab.htmlwidgets hidden.bs.collapse.htmlwidgets",
+ resizeHandler
+ );
+ }
+
+ // This is needed for the specific case of ioslides, which
+ // flips slides between display:none and display:block.
+ // Ideally we would not have to have ioslide-specific code
+ // here, but rather have ioslides raise a generic event,
+ // but the rmarkdown package just went to CRAN so the
+ // window to getting that fixed may be long.
+ if (window.addEventListener) {
+ // It's OK to limit this to window.addEventListener
+ // browsers because ioslides itself only supports
+ // such browsers.
+ on(document, "slideenter", resizeHandler);
+ on(document, "slideleave", resizeHandler);
+ }
+ }
+
+ var scriptData = document.querySelector("script[data-for='" + el.id + "'][type='application/json']");
+ if (scriptData) {
+ var data = JSON.parse(scriptData.textContent || scriptData.text);
+ // Resolve strings marked as javascript literals to objects
+ if (!(data.evals instanceof Array)) data.evals = [data.evals];
+ for (var k = 0; data.evals && k < data.evals.length; k++) {
+ window.HTMLWidgets.evaluateStringMember(data.x, data.evals[k]);
+ }
+ binding.renderValue(el, data.x, initResult);
+ evalAndRun(data.jsHooks.render, initResult, [el, data.x]);
+ }
+ });
+ });
+
+ invokePostRenderHandlers();
+ }
+
+
+ function has_jQuery3() {
+ if (!window.jQuery) {
+ return false;
+ }
+ var $version = window.jQuery.fn.jquery;
+ var $major_version = parseInt($version.split(".")[0]);
+ return $major_version >= 3;
+ }
+
+ /*
+ / Shiny 1.4 bumped jQuery from 1.x to 3.x which means jQuery's
+ / on-ready handler (i.e., $(fn)) is now asyncronous (i.e., it now
+ / really means $(setTimeout(fn)).
+ / https://jquery.com/upgrade-guide/3.0/#breaking-change-document-ready-handlers-are-now-asynchronous
+ /
+ / Since Shiny uses $() to schedule initShiny, shiny>=1.4 calls initShiny
+ / one tick later than it did before, which means staticRender() is
+ / called renderValue() earlier than (advanced) widget authors might be expecting.
+ / https://github.com/rstudio/shiny/issues/2630
+ /
+ / For a concrete example, leaflet has some methods (e.g., updateBounds)
+ / which reference Shiny methods registered in initShiny (e.g., setInputValue).
+ / Since leaflet is privy to this life-cycle, it knows to use setTimeout() to
+ / delay execution of those methods (until Shiny methods are ready)
+ / https://github.com/rstudio/leaflet/blob/18ec981/javascript/src/index.js#L266-L268
+ /
+ / Ideally widget authors wouldn't need to use this setTimeout() hack that
+ / leaflet uses to call Shiny methods on a staticRender(). In the long run,
+ / the logic initShiny should be broken up so that method registration happens
+ / right away, but binding happens later.
+ */
+ function maybeStaticRenderLater() {
+ if (shinyMode && has_jQuery3()) {
+ window.jQuery(window.HTMLWidgets.staticRender);
+ } else {
+ window.HTMLWidgets.staticRender();
+ }
+ }
+
+ if (document.addEventListener) {
+ document.addEventListener("DOMContentLoaded", function() {
+ document.removeEventListener("DOMContentLoaded", arguments.callee, false);
+ maybeStaticRenderLater();
+ }, false);
+ } else if (document.attachEvent) {
+ document.attachEvent("onreadystatechange", function() {
+ if (document.readyState === "complete") {
+ document.detachEvent("onreadystatechange", arguments.callee);
+ maybeStaticRenderLater();
+ }
+ });
+ }
+
+
+ window.HTMLWidgets.getAttachmentUrl = function(depname, key) {
+ // If no key, default to the first item
+ if (typeof(key) === "undefined")
+ key = 1;
+
+ var link = document.getElementById(depname + "-" + key + "-attachment");
+ if (!link) {
+ throw new Error("Attachment " + depname + "/" + key + " not found in document");
+ }
+ return link.getAttribute("href");
+ };
+
+ window.HTMLWidgets.dataframeToD3 = function(df) {
+ var names = [];
+ var length;
+ for (var name in df) {
+ if (df.hasOwnProperty(name))
+ names.push(name);
+ if (typeof(df[name]) !== "object" || typeof(df[name].length) === "undefined") {
+ throw new Error("All fields must be arrays");
+ } else if (typeof(length) !== "undefined" && length !== df[name].length) {
+ throw new Error("All fields must be arrays of the same length");
+ }
+ length = df[name].length;
+ }
+ var results = [];
+ var item;
+ for (var row = 0; row < length; row++) {
+ item = {};
+ for (var col = 0; col < names.length; col++) {
+ item[names[col]] = df[names[col]][row];
+ }
+ results.push(item);
+ }
+ return results;
+ };
+
+ window.HTMLWidgets.transposeArray2D = function(array) {
+ if (array.length === 0) return array;
+ var newArray = array[0].map(function(col, i) {
+ return array.map(function(row) {
+ return row[i]
+ })
+ });
+ return newArray;
+ };
+ // Split value at splitChar, but allow splitChar to be escaped
+ // using escapeChar. Any other characters escaped by escapeChar
+ // will be included as usual (including escapeChar itself).
+ function splitWithEscape(value, splitChar, escapeChar) {
+ var results = [];
+ var escapeMode = false;
+ var currentResult = "";
+ for (var pos = 0; pos < value.length; pos++) {
+ if (!escapeMode) {
+ if (value[pos] === splitChar) {
+ results.push(currentResult);
+ currentResult = "";
+ } else if (value[pos] === escapeChar) {
+ escapeMode = true;
+ } else {
+ currentResult += value[pos];
+ }
+ } else {
+ currentResult += value[pos];
+ escapeMode = false;
+ }
+ }
+ if (currentResult !== "") {
+ results.push(currentResult);
+ }
+ return results;
+ }
+ // Function authored by Yihui/JJ Allaire
+ window.HTMLWidgets.evaluateStringMember = function(o, member) {
+ var parts = splitWithEscape(member, '.', '\\');
+ for (var i = 0, l = parts.length; i < l; i++) {
+ var part = parts[i];
+ // part may be a character or 'numeric' member name
+ if (o !== null && typeof o === "object" && part in o) {
+ if (i == (l - 1)) { // if we are at the end of the line then evalulate
+ if (typeof o[part] === "string")
+ o[part] = tryEval(o[part]);
+ } else { // otherwise continue to next embedded object
+ o = o[part];
+ }
+ }
+ }
+ };
+
+ // Retrieve the HTMLWidget instance (i.e. the return value of an
+ // HTMLWidget binding's initialize() or factory() function)
+ // associated with an element, or null if none.
+ window.HTMLWidgets.getInstance = function(el) {
+ return elementData(el, "init_result");
+ };
+
+ // Finds the first element in the scope that matches the selector,
+ // and returns the HTMLWidget instance (i.e. the return value of
+ // an HTMLWidget binding's initialize() or factory() function)
+ // associated with that element, if any. If no element matches the
+ // selector, or the first matching element has no HTMLWidget
+ // instance associated with it, then null is returned.
+ //
+ // The scope argument is optional, and defaults to window.document.
+ window.HTMLWidgets.find = function(scope, selector) {
+ if (arguments.length == 1) {
+ selector = scope;
+ scope = document;
+ }
+
+ var el = scope.querySelector(selector);
+ if (el === null) {
+ return null;
+ } else {
+ return window.HTMLWidgets.getInstance(el);
+ }
+ };
+
+ // Finds all elements in the scope that match the selector, and
+ // returns the HTMLWidget instances (i.e. the return values of
+ // an HTMLWidget binding's initialize() or factory() function)
+ // associated with the elements, in an array. If elements that
+ // match the selector don't have an associated HTMLWidget
+ // instance, the returned array will contain nulls.
+ //
+ // The scope argument is optional, and defaults to window.document.
+ window.HTMLWidgets.findAll = function(scope, selector) {
+ if (arguments.length == 1) {
+ selector = scope;
+ scope = document;
+ }
+
+ var nodes = scope.querySelectorAll(selector);
+ var results = [];
+ for (var i = 0; i < nodes.length; i++) {
+ results.push(window.HTMLWidgets.getInstance(nodes[i]));
+ }
+ return results;
+ };
+
+ var postRenderHandlers = [];
+ function invokePostRenderHandlers() {
+ while (postRenderHandlers.length) {
+ var handler = postRenderHandlers.shift();
+ if (handler) {
+ handler();
+ }
+ }
+ }
+
+ // Register the given callback function to be invoked after the
+ // next time static widgets are rendered.
+ window.HTMLWidgets.addPostRenderHandler = function(callback) {
+ postRenderHandlers.push(callback);
+ };
+
+ // Takes a new-style instance-bound definition, and returns an
+ // old-style class-bound definition. This saves us from having
+ // to rewrite all the logic in this file to accomodate both
+ // types of definitions.
+ function createLegacyDefinitionAdapter(defn) {
+ var result = {
+ name: defn.name,
+ type: defn.type,
+ initialize: function(el, width, height) {
+ return defn.factory(el, width, height);
+ },
+ renderValue: function(el, x, instance) {
+ return instance.renderValue(x);
+ },
+ resize: function(el, width, height, instance) {
+ return instance.resize(width, height);
+ }
+ };
+
+ if (defn.find)
+ result.find = defn.find;
+ if (defn.renderError)
+ result.renderError = defn.renderError;
+ if (defn.clearError)
+ result.clearError = defn.clearError;
+
+ return result;
+ }
+})();
+
diff --git a/docs/posts/2020-08-07-saving-a-line-of-piping/saving-a-line-of-piping_files/reactable-binding-0.2.0/reactable.js b/docs/posts/2020-08-07-saving-a-line-of-piping/saving-a-line-of-piping_files/reactable-binding-0.2.0/reactable.js
new file mode 100644
index 00000000..308f474f
--- /dev/null
+++ b/docs/posts/2020-08-07-saving-a-line-of-piping/saving-a-line-of-piping_files/reactable-binding-0.2.0/reactable.js
@@ -0,0 +1,7 @@
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e.header?e.header(t):Object(n.hydrate)({Fragment:a.Fragment,WidgetContainer:Me},e.header)),o.a.isValidElement(r)?r:e.html?o.a.createElement("div",{dangerouslySetInnerHTML:{__html:r}}):null!=r?String(r):""}),e.align=e.align||"center",e.headerClassName=Q("rt-align-".concat(e.align),e.headerClassName)})),e}(u,c,{sortable:d,showSortIcon:z,showSortable:_,isExpanded:this.isExpanded,onExpanderClick:this.toggleExpand,theme:X,language:H}),null!=C&&(C=Math.max(C,1)),F=Q(F,T?"rt-outlined":"",j?"rt-bordered":"",E?"rt-borderless":"",A?"rt-compact":"",W?" rt-inline":"",D?"rt-nowrap":""),M=wt({width:K,height:B},M);var Z=l.a,re={};O&&(Z=Vt,re={isSelected:this.isSelected,toggleSelection:this.toggleSelection,toggleAll:this.toggleSelectionAll,selectType:"multiple"===O?"checkbox":"radio"});var ne=null==x,ae=Rt;(R||N||O||I||L)&&(ae=function(t,r){r=e.getRowInfo(r);var n,a=Rt(t,r);(R&&r&&(a.className=Q(a.className,r.viewIndex%2?null:"rt-tr-striped")),N&&r&&(a.className=Q(a.className,"rt-tr-highlight")),r&&r.selected&&(a.className=Q(a.className,"rt-tr-selected")),I)&&(n="function"==typeof I?I(r,t):I instanceof Array?r&&I[r.index]:I,a.className=Q(a.className,n));return L&&(a.style="function"==typeof L?L(r,t):L instanceof Array?r&&L[r.index]:L),a});var oe,ie=u.reduce((function(e,t){return e.concat(t.columns?t.columns:t)}),[]);ie.some((function(e){return e.details}))?(oe=function(t){var i=te(e.state.expanded,t.nestingPath),l=ie.find((function(e){return e.id===i})),s=l.details,u=l.html,c={};if("function"==typeof s){var f=s(e.getRowInfo(t));u&&(c.html=f),c.children=f}else if(s instanceof Array){var d=s[t.index];if(null==d)return null;u&&(c.html=d),c.children=Object(n.hydrate)({Reactable:r,Fragment:a.Fragment,WidgetContainer:Me},d)}return o.a.createElement(Jt,yt({key:i},c))},u=[{expander:!0,show:!1}].concat(nt(u))):oe=null;var le,se=function(){Object.keys(e.state.expanded).length>0&&e.setState({expanded:{}})},ue=Nt;k&&("select"===k?k=function(t,r){if(t&&(!r.pivoted||!t.aggregated))if(t.aggregated){if("single"===O)return;var n=t.subRows;if(!n||n.some((function(e){return e._aggregated})))return null;var a=n.map((function(e){return e._index})),o=a.every((function(t){return e.isSelected(t)}));e.toggleSelectionAll(a,!o)}else e.toggleSelection(t.index)}:"expand"===k&&(k=function(t,r){if(t){var n=ie.find((function(e){return e.details}));if(t.aggregated)r.pivoted||e.toggleExpand(t);else if(n){var a=n.details;if(a instanceof Array&&null==a[t.index])return;e.toggleExpand(t,n)}}}),ue=function(e,t,r){return wt({},Nt(e,t),{onClick:function(n,a){k(t,r,e),a&&a()}})});var ce=St;return h&&(le=Ut,ce=function(e,t,r,n){var a=e.filtered.find((function(t){return t.id===e.searchKey})),o=a?a.value:"";return wt({},St(e),{searchValue:o,onSearchChange:function(t){n.filterColumn({id:e.searchKey},t.target.value)},searchPlaceholder:e.language.searchPlaceholder,searchLabel:e.language.searchLabel,searchClassName:V(e.theme.searchInputStyle)})}),o.a.createElement(Z,yt({data:s,columns:u,pivotBy:f||[],sortable:d,resizable:p,filterable:g,searchable:h,searchKey:"__search__",defaultSortDesc:m,defaultSorted:b,defaultPageSize:y,pageSizeOptions:v,showPagination:x,showPageSizeOptions:S,PaginationComponent:me,paginationType:w,autoHidePagination:ne,showPageInfo:P,minRows:C,collapseOnSortingChange:!0,collapseOnPageChange:!0,collapseOnDataChange:!1,className:F,style:M,expanded:this.state.expanded,onExpandedChange:function(t){e.setState({expanded:t})},onPageChange:se,onSortedChange:se,getTableProps:ce,getTheadGroupTrProps:Ot,getTheadGroupThProps:Ct,getTheadTrProps:kt,getTheadThProps:Pt,getTheadFilterTrProps:Tt,getTheadFilterThProps:jt,getTbodyProps:It,getTrGroupProps:Et,getTrProps:ae,getTdProps:ue,getTfootTrProps:At,getTfootTdProps:Dt,TableComponent:le,SubComponent:oe},re,{theme:X,language:H,crosstalkGroup:U,crosstalkKey:J,crosstalkId:"__crosstalk__",key:"".concat(y),dataKey:q,ref:this.tableInstance,getProps:function(){return e.onTableUpdate(),{ref:e.tableElement,onMouseDown:function(){e.tableElement.current.classList.remove("rt-keyboard-active")},onKeyDown:function(){e.tableElement.current.classList.add("rt-keyboard-active")},onKeyUp:function(t){9===(t.which||t.keyCode)&&e.tableElement.current.classList.add("rt-keyboard-active")}}}}))}}]),r}(o.a.Component);$t.defaultProps={sortable:!0,resizable:!1,showPageSizeOptions:!1,showSortIcon:!0};var qt=$t;Object(n.reactWidget)("reactable","output",{Reactable:qt})}]);
+//# sourceMappingURL=reactable.js.map
\ No newline at end of file
diff --git a/docs/posts/2020-08-17-tidytuesday-2020-week-33/index.html b/docs/posts/2020-08-17-tidytuesday-2020-week-33/index.html
new file mode 100644
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+++ b/docs/posts/2020-08-17-tidytuesday-2020-week-33/index.html
@@ -0,0 +1,2695 @@
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+June Choe: TidyTuesday 2020 Week 33
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That you can get away with not doing the data part of data visualization for TidyTuesday
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Things to improve
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+
Should’ve picked a better color to represent air for Aang.
+
Not sure why it looks like white grid lines are there. All my attempts at getting rid of them failed, so I’ve just concluded that they’re likely an optical illusion.
For the last few weeks I’ve been reading about and experimenting with fonts for data visualization in my spare time.1 Out of that, I have found a couple fonts that I really like and wanted to do a small showcase of them here.
+
These fonts are all free and available for download at Google Fonts.2 Note that not only are they all large font families that come with many different styles, you can also adjust various theme settings like lineheight in {ggplot2}, so what I’m showing here isn’t the full extent of what you can make with these fonts.
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Arial
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It’s the default. It’s dull. It’s just here for comparison.
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Montserrat
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Simple design that can handle long lines of text. I like it for minimal plots.
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Roboto Mono
+
Monospaced member of the Roboto family. Very easy to read.
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+
Futura Bk BT
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A slender and bold member of the Futura family. Looks nice even in larger sizes.
+
+
Barlow
+
Also a slender font like Futura, but this has nicer ’j’s
+
+
Adelle
+
A serif font that doesn’t go overboard. I use it a lot for short paragraphs.
+
+
Merriweather
+
Similar to Adelle, but has a bit more pronounced hooks
+
+
+
+
Misc.
+
Why spend 3 minutes copy-pasting code when you can spend an hour automatizing it?
+
This was my first time using dynamic Rmarkdown reporting. The plots above and the text descriptions that went with them were generated in a for loop, which I learned about here.
+
Here is the single chunk of code that made this possible:
+
+
+
library(ggplot2)
+library(extrafont)
+knitr::opts_chunk$set(fig.width =7, dpi =600)
+
+theme_set(theme_classic(base_size =14))
+
+favorites<-c(
+ "Arial"="It's the default. It's dull. It's just here for comparison.",
+ "Montserrat"="Simple design that can handle long lines of text. I like it for minimal plots.",
+ "Roboto Mono"="Monospaced member of the Roboto family. Very easy to read.",
+ "Futura Bk BT"="A slender and bold member of the Futura family. Looks nice even in larger sizes.",
+ "Barlow"="Also a slender font like Futura, but this has nicer 'j's",
+ "Adelle"="A serif font that doesn't go overboard. I use it a lot for short paragraphs.",
+ "Merriweather"="Similar to Adelle, but has a bit more pronounced hooks"
+)
+
+
+for(fontinnames(favorites)){
+ cat("\n\n## ", font, "\n\n")
+ cat("", favorites[font], "\n\n")
+ plot<-qplot(data =mtcars, mpg, disp, color =factor(cyl))+
+ annotate("text", 28, 400, label =paste(letters, collapse =''), family =font)+
+ geom_curve(aes(x =28, y =380, xend =22, yend =260),
+ color ='black', curvature =-.3, arrow =arrow(), show.legend =FALSE)+
+ labs(title ="This is an interesting plot title",
+ subtitle ="Here's the subtitle 1234567890",
+ caption ="This is the plot caption")+
+ theme(text =element_text(family =font),
+ plot.title.position ='plot')
+ print(plot)
+}
+
+
+
+
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+
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+
I reference a lot this great collection of fonts used in profesional visualization here.↩︎
+
For how to import local fonts into R to use for plotting, check out {extrafont} and/or {showtext}.↩︎
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diff --git a/docs/posts/2020-09-06-fonts-for-graphs/preview.png b/docs/posts/2020-09-06-fonts-for-graphs/preview.png
new file mode 100644
index 00000000..d35fa446
Binary files /dev/null and b/docs/posts/2020-09-06-fonts-for-graphs/preview.png differ
diff --git a/docs/posts/2020-09-12-videos-in-reactable/2020-09-12-videos-in-reactable_files/header-attrs-2.3/header-attrs.js b/docs/posts/2020-09-12-videos-in-reactable/2020-09-12-videos-in-reactable_files/header-attrs-2.3/header-attrs.js
new file mode 100644
index 00000000..dd57d92e
--- /dev/null
+++ b/docs/posts/2020-09-12-videos-in-reactable/2020-09-12-videos-in-reactable_files/header-attrs-2.3/header-attrs.js
@@ -0,0 +1,12 @@
+// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
+// be compatible with the behavior of Pandoc < 2.8).
+document.addEventListener('DOMContentLoaded', function(e) {
+ var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
+ var i, h, a;
+ for (i = 0; i < hs.length; i++) {
+ h = hs[i];
+ if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6
+ a = h.attributes;
+ while (a.length > 0) h.removeAttribute(a[0].name);
+ }
+});
diff --git a/docs/posts/2020-09-12-videos-in-reactable/2020-09-12-videos-in-reactable_files/header-attrs-2.5/header-attrs.js b/docs/posts/2020-09-12-videos-in-reactable/2020-09-12-videos-in-reactable_files/header-attrs-2.5/header-attrs.js
new file mode 100644
index 00000000..dd57d92e
--- /dev/null
+++ b/docs/posts/2020-09-12-videos-in-reactable/2020-09-12-videos-in-reactable_files/header-attrs-2.5/header-attrs.js
@@ -0,0 +1,12 @@
+// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
+// be compatible with the behavior of Pandoc < 2.8).
+document.addEventListener('DOMContentLoaded', function(e) {
+ var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
+ var i, h, a;
+ for (i = 0; i < hs.length; i++) {
+ h = hs[i];
+ if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6
+ a = h.attributes;
+ while (a.length > 0) h.removeAttribute(a[0].name);
+ }
+});
diff --git a/docs/posts/2020-09-12-videos-in-reactable/2020-09-12-videos-in-reactable_files/header-attrs-2.6/header-attrs.js b/docs/posts/2020-09-12-videos-in-reactable/2020-09-12-videos-in-reactable_files/header-attrs-2.6/header-attrs.js
new file mode 100644
index 00000000..dd57d92e
--- /dev/null
+++ b/docs/posts/2020-09-12-videos-in-reactable/2020-09-12-videos-in-reactable_files/header-attrs-2.6/header-attrs.js
@@ -0,0 +1,12 @@
+// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
+// be compatible with the behavior of Pandoc < 2.8).
+document.addEventListener('DOMContentLoaded', function(e) {
+ var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
+ var i, h, a;
+ for (i = 0; i < hs.length; i++) {
+ h = hs[i];
+ if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6
+ a = h.attributes;
+ while (a.length > 0) h.removeAttribute(a[0].name);
+ }
+});
diff --git a/docs/posts/2020-09-12-videos-in-reactable/2020-09-12-videos-in-reactable_files/htmlwidgets-1.5.1/htmlwidgets.js b/docs/posts/2020-09-12-videos-in-reactable/2020-09-12-videos-in-reactable_files/htmlwidgets-1.5.1/htmlwidgets.js
new file mode 100644
index 00000000..6f3d672d
--- /dev/null
+++ b/docs/posts/2020-09-12-videos-in-reactable/2020-09-12-videos-in-reactable_files/htmlwidgets-1.5.1/htmlwidgets.js
@@ -0,0 +1,903 @@
+(function() {
+ // If window.HTMLWidgets is already defined, then use it; otherwise create a
+ // new object. This allows preceding code to set options that affect the
+ // initialization process (though none currently exist).
+ window.HTMLWidgets = window.HTMLWidgets || {};
+
+ // See if we're running in a viewer pane. If not, we're in a web browser.
+ var viewerMode = window.HTMLWidgets.viewerMode =
+ /\bviewer_pane=1\b/.test(window.location);
+
+ // See if we're running in Shiny mode. If not, it's a static document.
+ // Note that static widgets can appear in both Shiny and static modes, but
+ // obviously, Shiny widgets can only appear in Shiny apps/documents.
+ var shinyMode = window.HTMLWidgets.shinyMode =
+ typeof(window.Shiny) !== "undefined" && !!window.Shiny.outputBindings;
+
+ // We can't count on jQuery being available, so we implement our own
+ // version if necessary.
+ function querySelectorAll(scope, selector) {
+ if (typeof(jQuery) !== "undefined" && scope instanceof jQuery) {
+ return scope.find(selector);
+ }
+ if (scope.querySelectorAll) {
+ return scope.querySelectorAll(selector);
+ }
+ }
+
+ function asArray(value) {
+ if (value === null)
+ return [];
+ if ($.isArray(value))
+ return value;
+ return [value];
+ }
+
+ // Implement jQuery's extend
+ function extend(target /*, ... */) {
+ if (arguments.length == 1) {
+ return target;
+ }
+ for (var i = 1; i < arguments.length; i++) {
+ var source = arguments[i];
+ for (var prop in source) {
+ if (source.hasOwnProperty(prop)) {
+ target[prop] = source[prop];
+ }
+ }
+ }
+ return target;
+ }
+
+ // IE8 doesn't support Array.forEach.
+ function forEach(values, callback, thisArg) {
+ if (values.forEach) {
+ values.forEach(callback, thisArg);
+ } else {
+ for (var i = 0; i < values.length; i++) {
+ callback.call(thisArg, values[i], i, values);
+ }
+ }
+ }
+
+ // Replaces the specified method with the return value of funcSource.
+ //
+ // Note that funcSource should not BE the new method, it should be a function
+ // that RETURNS the new method. funcSource receives a single argument that is
+ // the overridden method, it can be called from the new method. The overridden
+ // method can be called like a regular function, it has the target permanently
+ // bound to it so "this" will work correctly.
+ function overrideMethod(target, methodName, funcSource) {
+ var superFunc = target[methodName] || function() {};
+ var superFuncBound = function() {
+ return superFunc.apply(target, arguments);
+ };
+ target[methodName] = funcSource(superFuncBound);
+ }
+
+ // Add a method to delegator that, when invoked, calls
+ // delegatee.methodName. If there is no such method on
+ // the delegatee, but there was one on delegator before
+ // delegateMethod was called, then the original version
+ // is invoked instead.
+ // For example:
+ //
+ // var a = {
+ // method1: function() { console.log('a1'); }
+ // method2: function() { console.log('a2'); }
+ // };
+ // var b = {
+ // method1: function() { console.log('b1'); }
+ // };
+ // delegateMethod(a, b, "method1");
+ // delegateMethod(a, b, "method2");
+ // a.method1();
+ // a.method2();
+ //
+ // The output would be "b1", "a2".
+ function delegateMethod(delegator, delegatee, methodName) {
+ var inherited = delegator[methodName];
+ delegator[methodName] = function() {
+ var target = delegatee;
+ var method = delegatee[methodName];
+
+ // The method doesn't exist on the delegatee. Instead,
+ // call the method on the delegator, if it exists.
+ if (!method) {
+ target = delegator;
+ method = inherited;
+ }
+
+ if (method) {
+ return method.apply(target, arguments);
+ }
+ };
+ }
+
+ // Implement a vague facsimilie of jQuery's data method
+ function elementData(el, name, value) {
+ if (arguments.length == 2) {
+ return el["htmlwidget_data_" + name];
+ } else if (arguments.length == 3) {
+ el["htmlwidget_data_" + name] = value;
+ return el;
+ } else {
+ throw new Error("Wrong number of arguments for elementData: " +
+ arguments.length);
+ }
+ }
+
+ // http://stackoverflow.com/questions/3446170/escape-string-for-use-in-javascript-regex
+ function escapeRegExp(str) {
+ return str.replace(/[\-\[\]\/\{\}\(\)\*\+\?\.\\\^\$\|]/g, "\\$&");
+ }
+
+ function hasClass(el, className) {
+ var re = new RegExp("\\b" + escapeRegExp(className) + "\\b");
+ return re.test(el.className);
+ }
+
+ // elements - array (or array-like object) of HTML elements
+ // className - class name to test for
+ // include - if true, only return elements with given className;
+ // if false, only return elements *without* given className
+ function filterByClass(elements, className, include) {
+ var results = [];
+ for (var i = 0; i < elements.length; i++) {
+ if (hasClass(elements[i], className) == include)
+ results.push(elements[i]);
+ }
+ return results;
+ }
+
+ function on(obj, eventName, func) {
+ if (obj.addEventListener) {
+ obj.addEventListener(eventName, func, false);
+ } else if (obj.attachEvent) {
+ obj.attachEvent(eventName, func);
+ }
+ }
+
+ function off(obj, eventName, func) {
+ if (obj.removeEventListener)
+ obj.removeEventListener(eventName, func, false);
+ else if (obj.detachEvent) {
+ obj.detachEvent(eventName, func);
+ }
+ }
+
+ // Translate array of values to top/right/bottom/left, as usual with
+ // the "padding" CSS property
+ // https://developer.mozilla.org/en-US/docs/Web/CSS/padding
+ function unpackPadding(value) {
+ if (typeof(value) === "number")
+ value = [value];
+ if (value.length === 1) {
+ return {top: value[0], right: value[0], bottom: value[0], left: value[0]};
+ }
+ if (value.length === 2) {
+ return {top: value[0], right: value[1], bottom: value[0], left: value[1]};
+ }
+ if (value.length === 3) {
+ return {top: value[0], right: value[1], bottom: value[2], left: value[1]};
+ }
+ if (value.length === 4) {
+ return {top: value[0], right: value[1], bottom: value[2], left: value[3]};
+ }
+ }
+
+ // Convert an unpacked padding object to a CSS value
+ function paddingToCss(paddingObj) {
+ return paddingObj.top + "px " + paddingObj.right + "px " + paddingObj.bottom + "px " + paddingObj.left + "px";
+ }
+
+ // Makes a number suitable for CSS
+ function px(x) {
+ if (typeof(x) === "number")
+ return x + "px";
+ else
+ return x;
+ }
+
+ // Retrieves runtime widget sizing information for an element.
+ // The return value is either null, or an object with fill, padding,
+ // defaultWidth, defaultHeight fields.
+ function sizingPolicy(el) {
+ var sizingEl = document.querySelector("script[data-for='" + el.id + "'][type='application/htmlwidget-sizing']");
+ if (!sizingEl)
+ return null;
+ var sp = JSON.parse(sizingEl.textContent || sizingEl.text || "{}");
+ if (viewerMode) {
+ return sp.viewer;
+ } else {
+ return sp.browser;
+ }
+ }
+
+ // @param tasks Array of strings (or falsy value, in which case no-op).
+ // Each element must be a valid JavaScript expression that yields a
+ // function. Or, can be an array of objects with "code" and "data"
+ // properties; in this case, the "code" property should be a string
+ // of JS that's an expr that yields a function, and "data" should be
+ // an object that will be added as an additional argument when that
+ // function is called.
+ // @param target The object that will be "this" for each function
+ // execution.
+ // @param args Array of arguments to be passed to the functions. (The
+ // same arguments will be passed to all functions.)
+ function evalAndRun(tasks, target, args) {
+ if (tasks) {
+ forEach(tasks, function(task) {
+ var theseArgs = args;
+ if (typeof(task) === "object") {
+ theseArgs = theseArgs.concat([task.data]);
+ task = task.code;
+ }
+ var taskFunc = tryEval(task);
+ if (typeof(taskFunc) !== "function") {
+ throw new Error("Task must be a function! Source:\n" + task);
+ }
+ taskFunc.apply(target, theseArgs);
+ });
+ }
+ }
+
+ // Attempt eval() both with and without enclosing in parentheses.
+ // Note that enclosing coerces a function declaration into
+ // an expression that eval() can parse
+ // (otherwise, a SyntaxError is thrown)
+ function tryEval(code) {
+ var result = null;
+ try {
+ result = eval(code);
+ } catch(error) {
+ if (!error instanceof SyntaxError) {
+ throw error;
+ }
+ try {
+ result = eval("(" + code + ")");
+ } catch(e) {
+ if (e instanceof SyntaxError) {
+ throw error;
+ } else {
+ throw e;
+ }
+ }
+ }
+ return result;
+ }
+
+ function initSizing(el) {
+ var sizing = sizingPolicy(el);
+ if (!sizing)
+ return;
+
+ var cel = document.getElementById("htmlwidget_container");
+ if (!cel)
+ return;
+
+ if (typeof(sizing.padding) !== "undefined") {
+ document.body.style.margin = "0";
+ document.body.style.padding = paddingToCss(unpackPadding(sizing.padding));
+ }
+
+ if (sizing.fill) {
+ document.body.style.overflow = "hidden";
+ document.body.style.width = "100%";
+ document.body.style.height = "100%";
+ document.documentElement.style.width = "100%";
+ document.documentElement.style.height = "100%";
+ if (cel) {
+ cel.style.position = "absolute";
+ var pad = unpackPadding(sizing.padding);
+ cel.style.top = pad.top + "px";
+ cel.style.right = pad.right + "px";
+ cel.style.bottom = pad.bottom + "px";
+ cel.style.left = pad.left + "px";
+ el.style.width = "100%";
+ el.style.height = "100%";
+ }
+
+ return {
+ getWidth: function() { return cel.offsetWidth; },
+ getHeight: function() { return cel.offsetHeight; }
+ };
+
+ } else {
+ el.style.width = px(sizing.width);
+ el.style.height = px(sizing.height);
+
+ return {
+ getWidth: function() { return el.offsetWidth; },
+ getHeight: function() { return el.offsetHeight; }
+ };
+ }
+ }
+
+ // Default implementations for methods
+ var defaults = {
+ find: function(scope) {
+ return querySelectorAll(scope, "." + this.name);
+ },
+ renderError: function(el, err) {
+ var $el = $(el);
+
+ this.clearError(el);
+
+ // Add all these error classes, as Shiny does
+ var errClass = "shiny-output-error";
+ if (err.type !== null) {
+ // use the classes of the error condition as CSS class names
+ errClass = errClass + " " + $.map(asArray(err.type), function(type) {
+ return errClass + "-" + type;
+ }).join(" ");
+ }
+ errClass = errClass + " htmlwidgets-error";
+
+ // Is el inline or block? If inline or inline-block, just display:none it
+ // and add an inline error.
+ var display = $el.css("display");
+ $el.data("restore-display-mode", display);
+
+ if (display === "inline" || display === "inline-block") {
+ $el.hide();
+ if (err.message !== "") {
+ var errorSpan = $("").addClass(errClass);
+ errorSpan.text(err.message);
+ $el.after(errorSpan);
+ }
+ } else if (display === "block") {
+ // If block, add an error just after the el, set visibility:none on the
+ // el, and position the error to be on top of the el.
+ // Mark it with a unique ID and CSS class so we can remove it later.
+ $el.css("visibility", "hidden");
+ if (err.message !== "") {
+ var errorDiv = $("
").addClass(errClass).css("position", "absolute")
+ .css("top", el.offsetTop)
+ .css("left", el.offsetLeft)
+ // setting width can push out the page size, forcing otherwise
+ // unnecessary scrollbars to appear and making it impossible for
+ // the element to shrink; so use max-width instead
+ .css("maxWidth", el.offsetWidth)
+ .css("height", el.offsetHeight);
+ errorDiv.text(err.message);
+ $el.after(errorDiv);
+
+ // Really dumb way to keep the size/position of the error in sync with
+ // the parent element as the window is resized or whatever.
+ var intId = setInterval(function() {
+ if (!errorDiv[0].parentElement) {
+ clearInterval(intId);
+ return;
+ }
+ errorDiv
+ .css("top", el.offsetTop)
+ .css("left", el.offsetLeft)
+ .css("maxWidth", el.offsetWidth)
+ .css("height", el.offsetHeight);
+ }, 500);
+ }
+ }
+ },
+ clearError: function(el) {
+ var $el = $(el);
+ var display = $el.data("restore-display-mode");
+ $el.data("restore-display-mode", null);
+
+ if (display === "inline" || display === "inline-block") {
+ if (display)
+ $el.css("display", display);
+ $(el.nextSibling).filter(".htmlwidgets-error").remove();
+ } else if (display === "block"){
+ $el.css("visibility", "inherit");
+ $(el.nextSibling).filter(".htmlwidgets-error").remove();
+ }
+ },
+ sizing: {}
+ };
+
+ // Called by widget bindings to register a new type of widget. The definition
+ // object can contain the following properties:
+ // - name (required) - A string indicating the binding name, which will be
+ // used by default as the CSS classname to look for.
+ // - initialize (optional) - A function(el) that will be called once per
+ // widget element; if a value is returned, it will be passed as the third
+ // value to renderValue.
+ // - renderValue (required) - A function(el, data, initValue) that will be
+ // called with data. Static contexts will cause this to be called once per
+ // element; Shiny apps will cause this to be called multiple times per
+ // element, as the data changes.
+ window.HTMLWidgets.widget = function(definition) {
+ if (!definition.name) {
+ throw new Error("Widget must have a name");
+ }
+ if (!definition.type) {
+ throw new Error("Widget must have a type");
+ }
+ // Currently we only support output widgets
+ if (definition.type !== "output") {
+ throw new Error("Unrecognized widget type '" + definition.type + "'");
+ }
+ // TODO: Verify that .name is a valid CSS classname
+
+ // Support new-style instance-bound definitions. Old-style class-bound
+ // definitions have one widget "object" per widget per type/class of
+ // widget; the renderValue and resize methods on such widget objects
+ // take el and instance arguments, because the widget object can't
+ // store them. New-style instance-bound definitions have one widget
+ // object per widget instance; the definition that's passed in doesn't
+ // provide renderValue or resize methods at all, just the single method
+ // factory(el, width, height)
+ // which returns an object that has renderValue(x) and resize(w, h).
+ // This enables a far more natural programming style for the widget
+ // author, who can store per-instance state using either OO-style
+ // instance fields or functional-style closure variables (I guess this
+ // is in contrast to what can only be called C-style pseudo-OO which is
+ // what we required before).
+ if (definition.factory) {
+ definition = createLegacyDefinitionAdapter(definition);
+ }
+
+ if (!definition.renderValue) {
+ throw new Error("Widget must have a renderValue function");
+ }
+
+ // For static rendering (non-Shiny), use a simple widget registration
+ // scheme. We also use this scheme for Shiny apps/documents that also
+ // contain static widgets.
+ window.HTMLWidgets.widgets = window.HTMLWidgets.widgets || [];
+ // Merge defaults into the definition; don't mutate the original definition.
+ var staticBinding = extend({}, defaults, definition);
+ overrideMethod(staticBinding, "find", function(superfunc) {
+ return function(scope) {
+ var results = superfunc(scope);
+ // Filter out Shiny outputs, we only want the static kind
+ return filterByClass(results, "html-widget-output", false);
+ };
+ });
+ window.HTMLWidgets.widgets.push(staticBinding);
+
+ if (shinyMode) {
+ // Shiny is running. Register the definition with an output binding.
+ // The definition itself will not be the output binding, instead
+ // we will make an output binding object that delegates to the
+ // definition. This is because we foolishly used the same method
+ // name (renderValue) for htmlwidgets definition and Shiny bindings
+ // but they actually have quite different semantics (the Shiny
+ // bindings receive data that includes lots of metadata that it
+ // strips off before calling htmlwidgets renderValue). We can't
+ // just ignore the difference because in some widgets it's helpful
+ // to call this.renderValue() from inside of resize(), and if
+ // we're not delegating, then that call will go to the Shiny
+ // version instead of the htmlwidgets version.
+
+ // Merge defaults with definition, without mutating either.
+ var bindingDef = extend({}, defaults, definition);
+
+ // This object will be our actual Shiny binding.
+ var shinyBinding = new Shiny.OutputBinding();
+
+ // With a few exceptions, we'll want to simply use the bindingDef's
+ // version of methods if they are available, otherwise fall back to
+ // Shiny's defaults. NOTE: If Shiny's output bindings gain additional
+ // methods in the future, and we want them to be overrideable by
+ // HTMLWidget binding definitions, then we'll need to add them to this
+ // list.
+ delegateMethod(shinyBinding, bindingDef, "getId");
+ delegateMethod(shinyBinding, bindingDef, "onValueChange");
+ delegateMethod(shinyBinding, bindingDef, "onValueError");
+ delegateMethod(shinyBinding, bindingDef, "renderError");
+ delegateMethod(shinyBinding, bindingDef, "clearError");
+ delegateMethod(shinyBinding, bindingDef, "showProgress");
+
+ // The find, renderValue, and resize are handled differently, because we
+ // want to actually decorate the behavior of the bindingDef methods.
+
+ shinyBinding.find = function(scope) {
+ var results = bindingDef.find(scope);
+
+ // Only return elements that are Shiny outputs, not static ones
+ var dynamicResults = results.filter(".html-widget-output");
+
+ // It's possible that whatever caused Shiny to think there might be
+ // new dynamic outputs, also caused there to be new static outputs.
+ // Since there might be lots of different htmlwidgets bindings, we
+ // schedule execution for later--no need to staticRender multiple
+ // times.
+ if (results.length !== dynamicResults.length)
+ scheduleStaticRender();
+
+ return dynamicResults;
+ };
+
+ // Wrap renderValue to handle initialization, which unfortunately isn't
+ // supported natively by Shiny at the time of this writing.
+
+ shinyBinding.renderValue = function(el, data) {
+ Shiny.renderDependencies(data.deps);
+ // Resolve strings marked as javascript literals to objects
+ if (!(data.evals instanceof Array)) data.evals = [data.evals];
+ for (var i = 0; data.evals && i < data.evals.length; i++) {
+ window.HTMLWidgets.evaluateStringMember(data.x, data.evals[i]);
+ }
+ if (!bindingDef.renderOnNullValue) {
+ if (data.x === null) {
+ el.style.visibility = "hidden";
+ return;
+ } else {
+ el.style.visibility = "inherit";
+ }
+ }
+ if (!elementData(el, "initialized")) {
+ initSizing(el);
+
+ elementData(el, "initialized", true);
+ if (bindingDef.initialize) {
+ var result = bindingDef.initialize(el, el.offsetWidth,
+ el.offsetHeight);
+ elementData(el, "init_result", result);
+ }
+ }
+ bindingDef.renderValue(el, data.x, elementData(el, "init_result"));
+ evalAndRun(data.jsHooks.render, elementData(el, "init_result"), [el, data.x]);
+ };
+
+ // Only override resize if bindingDef implements it
+ if (bindingDef.resize) {
+ shinyBinding.resize = function(el, width, height) {
+ // Shiny can call resize before initialize/renderValue have been
+ // called, which doesn't make sense for widgets.
+ if (elementData(el, "initialized")) {
+ bindingDef.resize(el, width, height, elementData(el, "init_result"));
+ }
+ };
+ }
+
+ Shiny.outputBindings.register(shinyBinding, bindingDef.name);
+ }
+ };
+
+ var scheduleStaticRenderTimerId = null;
+ function scheduleStaticRender() {
+ if (!scheduleStaticRenderTimerId) {
+ scheduleStaticRenderTimerId = setTimeout(function() {
+ scheduleStaticRenderTimerId = null;
+ window.HTMLWidgets.staticRender();
+ }, 1);
+ }
+ }
+
+ // Render static widgets after the document finishes loading
+ // Statically render all elements that are of this widget's class
+ window.HTMLWidgets.staticRender = function() {
+ var bindings = window.HTMLWidgets.widgets || [];
+ forEach(bindings, function(binding) {
+ var matches = binding.find(document.documentElement);
+ forEach(matches, function(el) {
+ var sizeObj = initSizing(el, binding);
+
+ if (hasClass(el, "html-widget-static-bound"))
+ return;
+ el.className = el.className + " html-widget-static-bound";
+
+ var initResult;
+ if (binding.initialize) {
+ initResult = binding.initialize(el,
+ sizeObj ? sizeObj.getWidth() : el.offsetWidth,
+ sizeObj ? sizeObj.getHeight() : el.offsetHeight
+ );
+ elementData(el, "init_result", initResult);
+ }
+
+ if (binding.resize) {
+ var lastSize = {
+ w: sizeObj ? sizeObj.getWidth() : el.offsetWidth,
+ h: sizeObj ? sizeObj.getHeight() : el.offsetHeight
+ };
+ var resizeHandler = function(e) {
+ var size = {
+ w: sizeObj ? sizeObj.getWidth() : el.offsetWidth,
+ h: sizeObj ? sizeObj.getHeight() : el.offsetHeight
+ };
+ if (size.w === 0 && size.h === 0)
+ return;
+ if (size.w === lastSize.w && size.h === lastSize.h)
+ return;
+ lastSize = size;
+ binding.resize(el, size.w, size.h, initResult);
+ };
+
+ on(window, "resize", resizeHandler);
+
+ // This is needed for cases where we're running in a Shiny
+ // app, but the widget itself is not a Shiny output, but
+ // rather a simple static widget. One example of this is
+ // an rmarkdown document that has runtime:shiny and widget
+ // that isn't in a render function. Shiny only knows to
+ // call resize handlers for Shiny outputs, not for static
+ // widgets, so we do it ourselves.
+ if (window.jQuery) {
+ window.jQuery(document).on(
+ "shown.htmlwidgets shown.bs.tab.htmlwidgets shown.bs.collapse.htmlwidgets",
+ resizeHandler
+ );
+ window.jQuery(document).on(
+ "hidden.htmlwidgets hidden.bs.tab.htmlwidgets hidden.bs.collapse.htmlwidgets",
+ resizeHandler
+ );
+ }
+
+ // This is needed for the specific case of ioslides, which
+ // flips slides between display:none and display:block.
+ // Ideally we would not have to have ioslide-specific code
+ // here, but rather have ioslides raise a generic event,
+ // but the rmarkdown package just went to CRAN so the
+ // window to getting that fixed may be long.
+ if (window.addEventListener) {
+ // It's OK to limit this to window.addEventListener
+ // browsers because ioslides itself only supports
+ // such browsers.
+ on(document, "slideenter", resizeHandler);
+ on(document, "slideleave", resizeHandler);
+ }
+ }
+
+ var scriptData = document.querySelector("script[data-for='" + el.id + "'][type='application/json']");
+ if (scriptData) {
+ var data = JSON.parse(scriptData.textContent || scriptData.text);
+ // Resolve strings marked as javascript literals to objects
+ if (!(data.evals instanceof Array)) data.evals = [data.evals];
+ for (var k = 0; data.evals && k < data.evals.length; k++) {
+ window.HTMLWidgets.evaluateStringMember(data.x, data.evals[k]);
+ }
+ binding.renderValue(el, data.x, initResult);
+ evalAndRun(data.jsHooks.render, initResult, [el, data.x]);
+ }
+ });
+ });
+
+ invokePostRenderHandlers();
+ }
+
+
+ function has_jQuery3() {
+ if (!window.jQuery) {
+ return false;
+ }
+ var $version = window.jQuery.fn.jquery;
+ var $major_version = parseInt($version.split(".")[0]);
+ return $major_version >= 3;
+ }
+
+ /*
+ / Shiny 1.4 bumped jQuery from 1.x to 3.x which means jQuery's
+ / on-ready handler (i.e., $(fn)) is now asyncronous (i.e., it now
+ / really means $(setTimeout(fn)).
+ / https://jquery.com/upgrade-guide/3.0/#breaking-change-document-ready-handlers-are-now-asynchronous
+ /
+ / Since Shiny uses $() to schedule initShiny, shiny>=1.4 calls initShiny
+ / one tick later than it did before, which means staticRender() is
+ / called renderValue() earlier than (advanced) widget authors might be expecting.
+ / https://github.com/rstudio/shiny/issues/2630
+ /
+ / For a concrete example, leaflet has some methods (e.g., updateBounds)
+ / which reference Shiny methods registered in initShiny (e.g., setInputValue).
+ / Since leaflet is privy to this life-cycle, it knows to use setTimeout() to
+ / delay execution of those methods (until Shiny methods are ready)
+ / https://github.com/rstudio/leaflet/blob/18ec981/javascript/src/index.js#L266-L268
+ /
+ / Ideally widget authors wouldn't need to use this setTimeout() hack that
+ / leaflet uses to call Shiny methods on a staticRender(). In the long run,
+ / the logic initShiny should be broken up so that method registration happens
+ / right away, but binding happens later.
+ */
+ function maybeStaticRenderLater() {
+ if (shinyMode && has_jQuery3()) {
+ window.jQuery(window.HTMLWidgets.staticRender);
+ } else {
+ window.HTMLWidgets.staticRender();
+ }
+ }
+
+ if (document.addEventListener) {
+ document.addEventListener("DOMContentLoaded", function() {
+ document.removeEventListener("DOMContentLoaded", arguments.callee, false);
+ maybeStaticRenderLater();
+ }, false);
+ } else if (document.attachEvent) {
+ document.attachEvent("onreadystatechange", function() {
+ if (document.readyState === "complete") {
+ document.detachEvent("onreadystatechange", arguments.callee);
+ maybeStaticRenderLater();
+ }
+ });
+ }
+
+
+ window.HTMLWidgets.getAttachmentUrl = function(depname, key) {
+ // If no key, default to the first item
+ if (typeof(key) === "undefined")
+ key = 1;
+
+ var link = document.getElementById(depname + "-" + key + "-attachment");
+ if (!link) {
+ throw new Error("Attachment " + depname + "/" + key + " not found in document");
+ }
+ return link.getAttribute("href");
+ };
+
+ window.HTMLWidgets.dataframeToD3 = function(df) {
+ var names = [];
+ var length;
+ for (var name in df) {
+ if (df.hasOwnProperty(name))
+ names.push(name);
+ if (typeof(df[name]) !== "object" || typeof(df[name].length) === "undefined") {
+ throw new Error("All fields must be arrays");
+ } else if (typeof(length) !== "undefined" && length !== df[name].length) {
+ throw new Error("All fields must be arrays of the same length");
+ }
+ length = df[name].length;
+ }
+ var results = [];
+ var item;
+ for (var row = 0; row < length; row++) {
+ item = {};
+ for (var col = 0; col < names.length; col++) {
+ item[names[col]] = df[names[col]][row];
+ }
+ results.push(item);
+ }
+ return results;
+ };
+
+ window.HTMLWidgets.transposeArray2D = function(array) {
+ if (array.length === 0) return array;
+ var newArray = array[0].map(function(col, i) {
+ return array.map(function(row) {
+ return row[i]
+ })
+ });
+ return newArray;
+ };
+ // Split value at splitChar, but allow splitChar to be escaped
+ // using escapeChar. Any other characters escaped by escapeChar
+ // will be included as usual (including escapeChar itself).
+ function splitWithEscape(value, splitChar, escapeChar) {
+ var results = [];
+ var escapeMode = false;
+ var currentResult = "";
+ for (var pos = 0; pos < value.length; pos++) {
+ if (!escapeMode) {
+ if (value[pos] === splitChar) {
+ results.push(currentResult);
+ currentResult = "";
+ } else if (value[pos] === escapeChar) {
+ escapeMode = true;
+ } else {
+ currentResult += value[pos];
+ }
+ } else {
+ currentResult += value[pos];
+ escapeMode = false;
+ }
+ }
+ if (currentResult !== "") {
+ results.push(currentResult);
+ }
+ return results;
+ }
+ // Function authored by Yihui/JJ Allaire
+ window.HTMLWidgets.evaluateStringMember = function(o, member) {
+ var parts = splitWithEscape(member, '.', '\\');
+ for (var i = 0, l = parts.length; i < l; i++) {
+ var part = parts[i];
+ // part may be a character or 'numeric' member name
+ if (o !== null && typeof o === "object" && part in o) {
+ if (i == (l - 1)) { // if we are at the end of the line then evalulate
+ if (typeof o[part] === "string")
+ o[part] = tryEval(o[part]);
+ } else { // otherwise continue to next embedded object
+ o = o[part];
+ }
+ }
+ }
+ };
+
+ // Retrieve the HTMLWidget instance (i.e. the return value of an
+ // HTMLWidget binding's initialize() or factory() function)
+ // associated with an element, or null if none.
+ window.HTMLWidgets.getInstance = function(el) {
+ return elementData(el, "init_result");
+ };
+
+ // Finds the first element in the scope that matches the selector,
+ // and returns the HTMLWidget instance (i.e. the return value of
+ // an HTMLWidget binding's initialize() or factory() function)
+ // associated with that element, if any. If no element matches the
+ // selector, or the first matching element has no HTMLWidget
+ // instance associated with it, then null is returned.
+ //
+ // The scope argument is optional, and defaults to window.document.
+ window.HTMLWidgets.find = function(scope, selector) {
+ if (arguments.length == 1) {
+ selector = scope;
+ scope = document;
+ }
+
+ var el = scope.querySelector(selector);
+ if (el === null) {
+ return null;
+ } else {
+ return window.HTMLWidgets.getInstance(el);
+ }
+ };
+
+ // Finds all elements in the scope that match the selector, and
+ // returns the HTMLWidget instances (i.e. the return values of
+ // an HTMLWidget binding's initialize() or factory() function)
+ // associated with the elements, in an array. If elements that
+ // match the selector don't have an associated HTMLWidget
+ // instance, the returned array will contain nulls.
+ //
+ // The scope argument is optional, and defaults to window.document.
+ window.HTMLWidgets.findAll = function(scope, selector) {
+ if (arguments.length == 1) {
+ selector = scope;
+ scope = document;
+ }
+
+ var nodes = scope.querySelectorAll(selector);
+ var results = [];
+ for (var i = 0; i < nodes.length; i++) {
+ results.push(window.HTMLWidgets.getInstance(nodes[i]));
+ }
+ return results;
+ };
+
+ var postRenderHandlers = [];
+ function invokePostRenderHandlers() {
+ while (postRenderHandlers.length) {
+ var handler = postRenderHandlers.shift();
+ if (handler) {
+ handler();
+ }
+ }
+ }
+
+ // Register the given callback function to be invoked after the
+ // next time static widgets are rendered.
+ window.HTMLWidgets.addPostRenderHandler = function(callback) {
+ postRenderHandlers.push(callback);
+ };
+
+ // Takes a new-style instance-bound definition, and returns an
+ // old-style class-bound definition. This saves us from having
+ // to rewrite all the logic in this file to accomodate both
+ // types of definitions.
+ function createLegacyDefinitionAdapter(defn) {
+ var result = {
+ name: defn.name,
+ type: defn.type,
+ initialize: function(el, width, height) {
+ return defn.factory(el, width, height);
+ },
+ renderValue: function(el, x, instance) {
+ return instance.renderValue(x);
+ },
+ resize: function(el, width, height, instance) {
+ return instance.resize(width, height);
+ }
+ };
+
+ if (defn.find)
+ result.find = defn.find;
+ if (defn.renderError)
+ result.renderError = defn.renderError;
+ if (defn.clearError)
+ result.clearError = defn.clearError;
+
+ return result;
+ }
+})();
+
diff --git a/docs/posts/2020-09-12-videos-in-reactable/2020-09-12-videos-in-reactable_files/htmlwidgets-1.5.2/htmlwidgets.js b/docs/posts/2020-09-12-videos-in-reactable/2020-09-12-videos-in-reactable_files/htmlwidgets-1.5.2/htmlwidgets.js
new file mode 100644
index 00000000..6f3d672d
--- /dev/null
+++ b/docs/posts/2020-09-12-videos-in-reactable/2020-09-12-videos-in-reactable_files/htmlwidgets-1.5.2/htmlwidgets.js
@@ -0,0 +1,903 @@
+(function() {
+ // If window.HTMLWidgets is already defined, then use it; otherwise create a
+ // new object. This allows preceding code to set options that affect the
+ // initialization process (though none currently exist).
+ window.HTMLWidgets = window.HTMLWidgets || {};
+
+ // See if we're running in a viewer pane. If not, we're in a web browser.
+ var viewerMode = window.HTMLWidgets.viewerMode =
+ /\bviewer_pane=1\b/.test(window.location);
+
+ // See if we're running in Shiny mode. If not, it's a static document.
+ // Note that static widgets can appear in both Shiny and static modes, but
+ // obviously, Shiny widgets can only appear in Shiny apps/documents.
+ var shinyMode = window.HTMLWidgets.shinyMode =
+ typeof(window.Shiny) !== "undefined" && !!window.Shiny.outputBindings;
+
+ // We can't count on jQuery being available, so we implement our own
+ // version if necessary.
+ function querySelectorAll(scope, selector) {
+ if (typeof(jQuery) !== "undefined" && scope instanceof jQuery) {
+ return scope.find(selector);
+ }
+ if (scope.querySelectorAll) {
+ return scope.querySelectorAll(selector);
+ }
+ }
+
+ function asArray(value) {
+ if (value === null)
+ return [];
+ if ($.isArray(value))
+ return value;
+ return [value];
+ }
+
+ // Implement jQuery's extend
+ function extend(target /*, ... */) {
+ if (arguments.length == 1) {
+ return target;
+ }
+ for (var i = 1; i < arguments.length; i++) {
+ var source = arguments[i];
+ for (var prop in source) {
+ if (source.hasOwnProperty(prop)) {
+ target[prop] = source[prop];
+ }
+ }
+ }
+ return target;
+ }
+
+ // IE8 doesn't support Array.forEach.
+ function forEach(values, callback, thisArg) {
+ if (values.forEach) {
+ values.forEach(callback, thisArg);
+ } else {
+ for (var i = 0; i < values.length; i++) {
+ callback.call(thisArg, values[i], i, values);
+ }
+ }
+ }
+
+ // Replaces the specified method with the return value of funcSource.
+ //
+ // Note that funcSource should not BE the new method, it should be a function
+ // that RETURNS the new method. funcSource receives a single argument that is
+ // the overridden method, it can be called from the new method. The overridden
+ // method can be called like a regular function, it has the target permanently
+ // bound to it so "this" will work correctly.
+ function overrideMethod(target, methodName, funcSource) {
+ var superFunc = target[methodName] || function() {};
+ var superFuncBound = function() {
+ return superFunc.apply(target, arguments);
+ };
+ target[methodName] = funcSource(superFuncBound);
+ }
+
+ // Add a method to delegator that, when invoked, calls
+ // delegatee.methodName. If there is no such method on
+ // the delegatee, but there was one on delegator before
+ // delegateMethod was called, then the original version
+ // is invoked instead.
+ // For example:
+ //
+ // var a = {
+ // method1: function() { console.log('a1'); }
+ // method2: function() { console.log('a2'); }
+ // };
+ // var b = {
+ // method1: function() { console.log('b1'); }
+ // };
+ // delegateMethod(a, b, "method1");
+ // delegateMethod(a, b, "method2");
+ // a.method1();
+ // a.method2();
+ //
+ // The output would be "b1", "a2".
+ function delegateMethod(delegator, delegatee, methodName) {
+ var inherited = delegator[methodName];
+ delegator[methodName] = function() {
+ var target = delegatee;
+ var method = delegatee[methodName];
+
+ // The method doesn't exist on the delegatee. Instead,
+ // call the method on the delegator, if it exists.
+ if (!method) {
+ target = delegator;
+ method = inherited;
+ }
+
+ if (method) {
+ return method.apply(target, arguments);
+ }
+ };
+ }
+
+ // Implement a vague facsimilie of jQuery's data method
+ function elementData(el, name, value) {
+ if (arguments.length == 2) {
+ return el["htmlwidget_data_" + name];
+ } else if (arguments.length == 3) {
+ el["htmlwidget_data_" + name] = value;
+ return el;
+ } else {
+ throw new Error("Wrong number of arguments for elementData: " +
+ arguments.length);
+ }
+ }
+
+ // http://stackoverflow.com/questions/3446170/escape-string-for-use-in-javascript-regex
+ function escapeRegExp(str) {
+ return str.replace(/[\-\[\]\/\{\}\(\)\*\+\?\.\\\^\$\|]/g, "\\$&");
+ }
+
+ function hasClass(el, className) {
+ var re = new RegExp("\\b" + escapeRegExp(className) + "\\b");
+ return re.test(el.className);
+ }
+
+ // elements - array (or array-like object) of HTML elements
+ // className - class name to test for
+ // include - if true, only return elements with given className;
+ // if false, only return elements *without* given className
+ function filterByClass(elements, className, include) {
+ var results = [];
+ for (var i = 0; i < elements.length; i++) {
+ if (hasClass(elements[i], className) == include)
+ results.push(elements[i]);
+ }
+ return results;
+ }
+
+ function on(obj, eventName, func) {
+ if (obj.addEventListener) {
+ obj.addEventListener(eventName, func, false);
+ } else if (obj.attachEvent) {
+ obj.attachEvent(eventName, func);
+ }
+ }
+
+ function off(obj, eventName, func) {
+ if (obj.removeEventListener)
+ obj.removeEventListener(eventName, func, false);
+ else if (obj.detachEvent) {
+ obj.detachEvent(eventName, func);
+ }
+ }
+
+ // Translate array of values to top/right/bottom/left, as usual with
+ // the "padding" CSS property
+ // https://developer.mozilla.org/en-US/docs/Web/CSS/padding
+ function unpackPadding(value) {
+ if (typeof(value) === "number")
+ value = [value];
+ if (value.length === 1) {
+ return {top: value[0], right: value[0], bottom: value[0], left: value[0]};
+ }
+ if (value.length === 2) {
+ return {top: value[0], right: value[1], bottom: value[0], left: value[1]};
+ }
+ if (value.length === 3) {
+ return {top: value[0], right: value[1], bottom: value[2], left: value[1]};
+ }
+ if (value.length === 4) {
+ return {top: value[0], right: value[1], bottom: value[2], left: value[3]};
+ }
+ }
+
+ // Convert an unpacked padding object to a CSS value
+ function paddingToCss(paddingObj) {
+ return paddingObj.top + "px " + paddingObj.right + "px " + paddingObj.bottom + "px " + paddingObj.left + "px";
+ }
+
+ // Makes a number suitable for CSS
+ function px(x) {
+ if (typeof(x) === "number")
+ return x + "px";
+ else
+ return x;
+ }
+
+ // Retrieves runtime widget sizing information for an element.
+ // The return value is either null, or an object with fill, padding,
+ // defaultWidth, defaultHeight fields.
+ function sizingPolicy(el) {
+ var sizingEl = document.querySelector("script[data-for='" + el.id + "'][type='application/htmlwidget-sizing']");
+ if (!sizingEl)
+ return null;
+ var sp = JSON.parse(sizingEl.textContent || sizingEl.text || "{}");
+ if (viewerMode) {
+ return sp.viewer;
+ } else {
+ return sp.browser;
+ }
+ }
+
+ // @param tasks Array of strings (or falsy value, in which case no-op).
+ // Each element must be a valid JavaScript expression that yields a
+ // function. Or, can be an array of objects with "code" and "data"
+ // properties; in this case, the "code" property should be a string
+ // of JS that's an expr that yields a function, and "data" should be
+ // an object that will be added as an additional argument when that
+ // function is called.
+ // @param target The object that will be "this" for each function
+ // execution.
+ // @param args Array of arguments to be passed to the functions. (The
+ // same arguments will be passed to all functions.)
+ function evalAndRun(tasks, target, args) {
+ if (tasks) {
+ forEach(tasks, function(task) {
+ var theseArgs = args;
+ if (typeof(task) === "object") {
+ theseArgs = theseArgs.concat([task.data]);
+ task = task.code;
+ }
+ var taskFunc = tryEval(task);
+ if (typeof(taskFunc) !== "function") {
+ throw new Error("Task must be a function! Source:\n" + task);
+ }
+ taskFunc.apply(target, theseArgs);
+ });
+ }
+ }
+
+ // Attempt eval() both with and without enclosing in parentheses.
+ // Note that enclosing coerces a function declaration into
+ // an expression that eval() can parse
+ // (otherwise, a SyntaxError is thrown)
+ function tryEval(code) {
+ var result = null;
+ try {
+ result = eval(code);
+ } catch(error) {
+ if (!error instanceof SyntaxError) {
+ throw error;
+ }
+ try {
+ result = eval("(" + code + ")");
+ } catch(e) {
+ if (e instanceof SyntaxError) {
+ throw error;
+ } else {
+ throw e;
+ }
+ }
+ }
+ return result;
+ }
+
+ function initSizing(el) {
+ var sizing = sizingPolicy(el);
+ if (!sizing)
+ return;
+
+ var cel = document.getElementById("htmlwidget_container");
+ if (!cel)
+ return;
+
+ if (typeof(sizing.padding) !== "undefined") {
+ document.body.style.margin = "0";
+ document.body.style.padding = paddingToCss(unpackPadding(sizing.padding));
+ }
+
+ if (sizing.fill) {
+ document.body.style.overflow = "hidden";
+ document.body.style.width = "100%";
+ document.body.style.height = "100%";
+ document.documentElement.style.width = "100%";
+ document.documentElement.style.height = "100%";
+ if (cel) {
+ cel.style.position = "absolute";
+ var pad = unpackPadding(sizing.padding);
+ cel.style.top = pad.top + "px";
+ cel.style.right = pad.right + "px";
+ cel.style.bottom = pad.bottom + "px";
+ cel.style.left = pad.left + "px";
+ el.style.width = "100%";
+ el.style.height = "100%";
+ }
+
+ return {
+ getWidth: function() { return cel.offsetWidth; },
+ getHeight: function() { return cel.offsetHeight; }
+ };
+
+ } else {
+ el.style.width = px(sizing.width);
+ el.style.height = px(sizing.height);
+
+ return {
+ getWidth: function() { return el.offsetWidth; },
+ getHeight: function() { return el.offsetHeight; }
+ };
+ }
+ }
+
+ // Default implementations for methods
+ var defaults = {
+ find: function(scope) {
+ return querySelectorAll(scope, "." + this.name);
+ },
+ renderError: function(el, err) {
+ var $el = $(el);
+
+ this.clearError(el);
+
+ // Add all these error classes, as Shiny does
+ var errClass = "shiny-output-error";
+ if (err.type !== null) {
+ // use the classes of the error condition as CSS class names
+ errClass = errClass + " " + $.map(asArray(err.type), function(type) {
+ return errClass + "-" + type;
+ }).join(" ");
+ }
+ errClass = errClass + " htmlwidgets-error";
+
+ // Is el inline or block? If inline or inline-block, just display:none it
+ // and add an inline error.
+ var display = $el.css("display");
+ $el.data("restore-display-mode", display);
+
+ if (display === "inline" || display === "inline-block") {
+ $el.hide();
+ if (err.message !== "") {
+ var errorSpan = $("").addClass(errClass);
+ errorSpan.text(err.message);
+ $el.after(errorSpan);
+ }
+ } else if (display === "block") {
+ // If block, add an error just after the el, set visibility:none on the
+ // el, and position the error to be on top of the el.
+ // Mark it with a unique ID and CSS class so we can remove it later.
+ $el.css("visibility", "hidden");
+ if (err.message !== "") {
+ var errorDiv = $("
").addClass(errClass).css("position", "absolute")
+ .css("top", el.offsetTop)
+ .css("left", el.offsetLeft)
+ // setting width can push out the page size, forcing otherwise
+ // unnecessary scrollbars to appear and making it impossible for
+ // the element to shrink; so use max-width instead
+ .css("maxWidth", el.offsetWidth)
+ .css("height", el.offsetHeight);
+ errorDiv.text(err.message);
+ $el.after(errorDiv);
+
+ // Really dumb way to keep the size/position of the error in sync with
+ // the parent element as the window is resized or whatever.
+ var intId = setInterval(function() {
+ if (!errorDiv[0].parentElement) {
+ clearInterval(intId);
+ return;
+ }
+ errorDiv
+ .css("top", el.offsetTop)
+ .css("left", el.offsetLeft)
+ .css("maxWidth", el.offsetWidth)
+ .css("height", el.offsetHeight);
+ }, 500);
+ }
+ }
+ },
+ clearError: function(el) {
+ var $el = $(el);
+ var display = $el.data("restore-display-mode");
+ $el.data("restore-display-mode", null);
+
+ if (display === "inline" || display === "inline-block") {
+ if (display)
+ $el.css("display", display);
+ $(el.nextSibling).filter(".htmlwidgets-error").remove();
+ } else if (display === "block"){
+ $el.css("visibility", "inherit");
+ $(el.nextSibling).filter(".htmlwidgets-error").remove();
+ }
+ },
+ sizing: {}
+ };
+
+ // Called by widget bindings to register a new type of widget. The definition
+ // object can contain the following properties:
+ // - name (required) - A string indicating the binding name, which will be
+ // used by default as the CSS classname to look for.
+ // - initialize (optional) - A function(el) that will be called once per
+ // widget element; if a value is returned, it will be passed as the third
+ // value to renderValue.
+ // - renderValue (required) - A function(el, data, initValue) that will be
+ // called with data. Static contexts will cause this to be called once per
+ // element; Shiny apps will cause this to be called multiple times per
+ // element, as the data changes.
+ window.HTMLWidgets.widget = function(definition) {
+ if (!definition.name) {
+ throw new Error("Widget must have a name");
+ }
+ if (!definition.type) {
+ throw new Error("Widget must have a type");
+ }
+ // Currently we only support output widgets
+ if (definition.type !== "output") {
+ throw new Error("Unrecognized widget type '" + definition.type + "'");
+ }
+ // TODO: Verify that .name is a valid CSS classname
+
+ // Support new-style instance-bound definitions. Old-style class-bound
+ // definitions have one widget "object" per widget per type/class of
+ // widget; the renderValue and resize methods on such widget objects
+ // take el and instance arguments, because the widget object can't
+ // store them. New-style instance-bound definitions have one widget
+ // object per widget instance; the definition that's passed in doesn't
+ // provide renderValue or resize methods at all, just the single method
+ // factory(el, width, height)
+ // which returns an object that has renderValue(x) and resize(w, h).
+ // This enables a far more natural programming style for the widget
+ // author, who can store per-instance state using either OO-style
+ // instance fields or functional-style closure variables (I guess this
+ // is in contrast to what can only be called C-style pseudo-OO which is
+ // what we required before).
+ if (definition.factory) {
+ definition = createLegacyDefinitionAdapter(definition);
+ }
+
+ if (!definition.renderValue) {
+ throw new Error("Widget must have a renderValue function");
+ }
+
+ // For static rendering (non-Shiny), use a simple widget registration
+ // scheme. We also use this scheme for Shiny apps/documents that also
+ // contain static widgets.
+ window.HTMLWidgets.widgets = window.HTMLWidgets.widgets || [];
+ // Merge defaults into the definition; don't mutate the original definition.
+ var staticBinding = extend({}, defaults, definition);
+ overrideMethod(staticBinding, "find", function(superfunc) {
+ return function(scope) {
+ var results = superfunc(scope);
+ // Filter out Shiny outputs, we only want the static kind
+ return filterByClass(results, "html-widget-output", false);
+ };
+ });
+ window.HTMLWidgets.widgets.push(staticBinding);
+
+ if (shinyMode) {
+ // Shiny is running. Register the definition with an output binding.
+ // The definition itself will not be the output binding, instead
+ // we will make an output binding object that delegates to the
+ // definition. This is because we foolishly used the same method
+ // name (renderValue) for htmlwidgets definition and Shiny bindings
+ // but they actually have quite different semantics (the Shiny
+ // bindings receive data that includes lots of metadata that it
+ // strips off before calling htmlwidgets renderValue). We can't
+ // just ignore the difference because in some widgets it's helpful
+ // to call this.renderValue() from inside of resize(), and if
+ // we're not delegating, then that call will go to the Shiny
+ // version instead of the htmlwidgets version.
+
+ // Merge defaults with definition, without mutating either.
+ var bindingDef = extend({}, defaults, definition);
+
+ // This object will be our actual Shiny binding.
+ var shinyBinding = new Shiny.OutputBinding();
+
+ // With a few exceptions, we'll want to simply use the bindingDef's
+ // version of methods if they are available, otherwise fall back to
+ // Shiny's defaults. NOTE: If Shiny's output bindings gain additional
+ // methods in the future, and we want them to be overrideable by
+ // HTMLWidget binding definitions, then we'll need to add them to this
+ // list.
+ delegateMethod(shinyBinding, bindingDef, "getId");
+ delegateMethod(shinyBinding, bindingDef, "onValueChange");
+ delegateMethod(shinyBinding, bindingDef, "onValueError");
+ delegateMethod(shinyBinding, bindingDef, "renderError");
+ delegateMethod(shinyBinding, bindingDef, "clearError");
+ delegateMethod(shinyBinding, bindingDef, "showProgress");
+
+ // The find, renderValue, and resize are handled differently, because we
+ // want to actually decorate the behavior of the bindingDef methods.
+
+ shinyBinding.find = function(scope) {
+ var results = bindingDef.find(scope);
+
+ // Only return elements that are Shiny outputs, not static ones
+ var dynamicResults = results.filter(".html-widget-output");
+
+ // It's possible that whatever caused Shiny to think there might be
+ // new dynamic outputs, also caused there to be new static outputs.
+ // Since there might be lots of different htmlwidgets bindings, we
+ // schedule execution for later--no need to staticRender multiple
+ // times.
+ if (results.length !== dynamicResults.length)
+ scheduleStaticRender();
+
+ return dynamicResults;
+ };
+
+ // Wrap renderValue to handle initialization, which unfortunately isn't
+ // supported natively by Shiny at the time of this writing.
+
+ shinyBinding.renderValue = function(el, data) {
+ Shiny.renderDependencies(data.deps);
+ // Resolve strings marked as javascript literals to objects
+ if (!(data.evals instanceof Array)) data.evals = [data.evals];
+ for (var i = 0; data.evals && i < data.evals.length; i++) {
+ window.HTMLWidgets.evaluateStringMember(data.x, data.evals[i]);
+ }
+ if (!bindingDef.renderOnNullValue) {
+ if (data.x === null) {
+ el.style.visibility = "hidden";
+ return;
+ } else {
+ el.style.visibility = "inherit";
+ }
+ }
+ if (!elementData(el, "initialized")) {
+ initSizing(el);
+
+ elementData(el, "initialized", true);
+ if (bindingDef.initialize) {
+ var result = bindingDef.initialize(el, el.offsetWidth,
+ el.offsetHeight);
+ elementData(el, "init_result", result);
+ }
+ }
+ bindingDef.renderValue(el, data.x, elementData(el, "init_result"));
+ evalAndRun(data.jsHooks.render, elementData(el, "init_result"), [el, data.x]);
+ };
+
+ // Only override resize if bindingDef implements it
+ if (bindingDef.resize) {
+ shinyBinding.resize = function(el, width, height) {
+ // Shiny can call resize before initialize/renderValue have been
+ // called, which doesn't make sense for widgets.
+ if (elementData(el, "initialized")) {
+ bindingDef.resize(el, width, height, elementData(el, "init_result"));
+ }
+ };
+ }
+
+ Shiny.outputBindings.register(shinyBinding, bindingDef.name);
+ }
+ };
+
+ var scheduleStaticRenderTimerId = null;
+ function scheduleStaticRender() {
+ if (!scheduleStaticRenderTimerId) {
+ scheduleStaticRenderTimerId = setTimeout(function() {
+ scheduleStaticRenderTimerId = null;
+ window.HTMLWidgets.staticRender();
+ }, 1);
+ }
+ }
+
+ // Render static widgets after the document finishes loading
+ // Statically render all elements that are of this widget's class
+ window.HTMLWidgets.staticRender = function() {
+ var bindings = window.HTMLWidgets.widgets || [];
+ forEach(bindings, function(binding) {
+ var matches = binding.find(document.documentElement);
+ forEach(matches, function(el) {
+ var sizeObj = initSizing(el, binding);
+
+ if (hasClass(el, "html-widget-static-bound"))
+ return;
+ el.className = el.className + " html-widget-static-bound";
+
+ var initResult;
+ if (binding.initialize) {
+ initResult = binding.initialize(el,
+ sizeObj ? sizeObj.getWidth() : el.offsetWidth,
+ sizeObj ? sizeObj.getHeight() : el.offsetHeight
+ );
+ elementData(el, "init_result", initResult);
+ }
+
+ if (binding.resize) {
+ var lastSize = {
+ w: sizeObj ? sizeObj.getWidth() : el.offsetWidth,
+ h: sizeObj ? sizeObj.getHeight() : el.offsetHeight
+ };
+ var resizeHandler = function(e) {
+ var size = {
+ w: sizeObj ? sizeObj.getWidth() : el.offsetWidth,
+ h: sizeObj ? sizeObj.getHeight() : el.offsetHeight
+ };
+ if (size.w === 0 && size.h === 0)
+ return;
+ if (size.w === lastSize.w && size.h === lastSize.h)
+ return;
+ lastSize = size;
+ binding.resize(el, size.w, size.h, initResult);
+ };
+
+ on(window, "resize", resizeHandler);
+
+ // This is needed for cases where we're running in a Shiny
+ // app, but the widget itself is not a Shiny output, but
+ // rather a simple static widget. One example of this is
+ // an rmarkdown document that has runtime:shiny and widget
+ // that isn't in a render function. Shiny only knows to
+ // call resize handlers for Shiny outputs, not for static
+ // widgets, so we do it ourselves.
+ if (window.jQuery) {
+ window.jQuery(document).on(
+ "shown.htmlwidgets shown.bs.tab.htmlwidgets shown.bs.collapse.htmlwidgets",
+ resizeHandler
+ );
+ window.jQuery(document).on(
+ "hidden.htmlwidgets hidden.bs.tab.htmlwidgets hidden.bs.collapse.htmlwidgets",
+ resizeHandler
+ );
+ }
+
+ // This is needed for the specific case of ioslides, which
+ // flips slides between display:none and display:block.
+ // Ideally we would not have to have ioslide-specific code
+ // here, but rather have ioslides raise a generic event,
+ // but the rmarkdown package just went to CRAN so the
+ // window to getting that fixed may be long.
+ if (window.addEventListener) {
+ // It's OK to limit this to window.addEventListener
+ // browsers because ioslides itself only supports
+ // such browsers.
+ on(document, "slideenter", resizeHandler);
+ on(document, "slideleave", resizeHandler);
+ }
+ }
+
+ var scriptData = document.querySelector("script[data-for='" + el.id + "'][type='application/json']");
+ if (scriptData) {
+ var data = JSON.parse(scriptData.textContent || scriptData.text);
+ // Resolve strings marked as javascript literals to objects
+ if (!(data.evals instanceof Array)) data.evals = [data.evals];
+ for (var k = 0; data.evals && k < data.evals.length; k++) {
+ window.HTMLWidgets.evaluateStringMember(data.x, data.evals[k]);
+ }
+ binding.renderValue(el, data.x, initResult);
+ evalAndRun(data.jsHooks.render, initResult, [el, data.x]);
+ }
+ });
+ });
+
+ invokePostRenderHandlers();
+ }
+
+
+ function has_jQuery3() {
+ if (!window.jQuery) {
+ return false;
+ }
+ var $version = window.jQuery.fn.jquery;
+ var $major_version = parseInt($version.split(".")[0]);
+ return $major_version >= 3;
+ }
+
+ /*
+ / Shiny 1.4 bumped jQuery from 1.x to 3.x which means jQuery's
+ / on-ready handler (i.e., $(fn)) is now asyncronous (i.e., it now
+ / really means $(setTimeout(fn)).
+ / https://jquery.com/upgrade-guide/3.0/#breaking-change-document-ready-handlers-are-now-asynchronous
+ /
+ / Since Shiny uses $() to schedule initShiny, shiny>=1.4 calls initShiny
+ / one tick later than it did before, which means staticRender() is
+ / called renderValue() earlier than (advanced) widget authors might be expecting.
+ / https://github.com/rstudio/shiny/issues/2630
+ /
+ / For a concrete example, leaflet has some methods (e.g., updateBounds)
+ / which reference Shiny methods registered in initShiny (e.g., setInputValue).
+ / Since leaflet is privy to this life-cycle, it knows to use setTimeout() to
+ / delay execution of those methods (until Shiny methods are ready)
+ / https://github.com/rstudio/leaflet/blob/18ec981/javascript/src/index.js#L266-L268
+ /
+ / Ideally widget authors wouldn't need to use this setTimeout() hack that
+ / leaflet uses to call Shiny methods on a staticRender(). In the long run,
+ / the logic initShiny should be broken up so that method registration happens
+ / right away, but binding happens later.
+ */
+ function maybeStaticRenderLater() {
+ if (shinyMode && has_jQuery3()) {
+ window.jQuery(window.HTMLWidgets.staticRender);
+ } else {
+ window.HTMLWidgets.staticRender();
+ }
+ }
+
+ if (document.addEventListener) {
+ document.addEventListener("DOMContentLoaded", function() {
+ document.removeEventListener("DOMContentLoaded", arguments.callee, false);
+ maybeStaticRenderLater();
+ }, false);
+ } else if (document.attachEvent) {
+ document.attachEvent("onreadystatechange", function() {
+ if (document.readyState === "complete") {
+ document.detachEvent("onreadystatechange", arguments.callee);
+ maybeStaticRenderLater();
+ }
+ });
+ }
+
+
+ window.HTMLWidgets.getAttachmentUrl = function(depname, key) {
+ // If no key, default to the first item
+ if (typeof(key) === "undefined")
+ key = 1;
+
+ var link = document.getElementById(depname + "-" + key + "-attachment");
+ if (!link) {
+ throw new Error("Attachment " + depname + "/" + key + " not found in document");
+ }
+ return link.getAttribute("href");
+ };
+
+ window.HTMLWidgets.dataframeToD3 = function(df) {
+ var names = [];
+ var length;
+ for (var name in df) {
+ if (df.hasOwnProperty(name))
+ names.push(name);
+ if (typeof(df[name]) !== "object" || typeof(df[name].length) === "undefined") {
+ throw new Error("All fields must be arrays");
+ } else if (typeof(length) !== "undefined" && length !== df[name].length) {
+ throw new Error("All fields must be arrays of the same length");
+ }
+ length = df[name].length;
+ }
+ var results = [];
+ var item;
+ for (var row = 0; row < length; row++) {
+ item = {};
+ for (var col = 0; col < names.length; col++) {
+ item[names[col]] = df[names[col]][row];
+ }
+ results.push(item);
+ }
+ return results;
+ };
+
+ window.HTMLWidgets.transposeArray2D = function(array) {
+ if (array.length === 0) return array;
+ var newArray = array[0].map(function(col, i) {
+ return array.map(function(row) {
+ return row[i]
+ })
+ });
+ return newArray;
+ };
+ // Split value at splitChar, but allow splitChar to be escaped
+ // using escapeChar. Any other characters escaped by escapeChar
+ // will be included as usual (including escapeChar itself).
+ function splitWithEscape(value, splitChar, escapeChar) {
+ var results = [];
+ var escapeMode = false;
+ var currentResult = "";
+ for (var pos = 0; pos < value.length; pos++) {
+ if (!escapeMode) {
+ if (value[pos] === splitChar) {
+ results.push(currentResult);
+ currentResult = "";
+ } else if (value[pos] === escapeChar) {
+ escapeMode = true;
+ } else {
+ currentResult += value[pos];
+ }
+ } else {
+ currentResult += value[pos];
+ escapeMode = false;
+ }
+ }
+ if (currentResult !== "") {
+ results.push(currentResult);
+ }
+ return results;
+ }
+ // Function authored by Yihui/JJ Allaire
+ window.HTMLWidgets.evaluateStringMember = function(o, member) {
+ var parts = splitWithEscape(member, '.', '\\');
+ for (var i = 0, l = parts.length; i < l; i++) {
+ var part = parts[i];
+ // part may be a character or 'numeric' member name
+ if (o !== null && typeof o === "object" && part in o) {
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+ } else { // otherwise continue to next embedded object
+ o = o[part];
+ }
+ }
+ }
+ };
+
+ // Retrieve the HTMLWidget instance (i.e. the return value of an
+ // HTMLWidget binding's initialize() or factory() function)
+ // associated with an element, or null if none.
+ window.HTMLWidgets.getInstance = function(el) {
+ return elementData(el, "init_result");
+ };
+
+ // Finds the first element in the scope that matches the selector,
+ // and returns the HTMLWidget instance (i.e. the return value of
+ // an HTMLWidget binding's initialize() or factory() function)
+ // associated with that element, if any. If no element matches the
+ // selector, or the first matching element has no HTMLWidget
+ // instance associated with it, then null is returned.
+ //
+ // The scope argument is optional, and defaults to window.document.
+ window.HTMLWidgets.find = function(scope, selector) {
+ if (arguments.length == 1) {
+ selector = scope;
+ scope = document;
+ }
+
+ var el = scope.querySelector(selector);
+ if (el === null) {
+ return null;
+ } else {
+ return window.HTMLWidgets.getInstance(el);
+ }
+ };
+
+ // Finds all elements in the scope that match the selector, and
+ // returns the HTMLWidget instances (i.e. the return values of
+ // an HTMLWidget binding's initialize() or factory() function)
+ // associated with the elements, in an array. If elements that
+ // match the selector don't have an associated HTMLWidget
+ // instance, the returned array will contain nulls.
+ //
+ // The scope argument is optional, and defaults to window.document.
+ window.HTMLWidgets.findAll = function(scope, selector) {
+ if (arguments.length == 1) {
+ selector = scope;
+ scope = document;
+ }
+
+ var nodes = scope.querySelectorAll(selector);
+ var results = [];
+ for (var i = 0; i < nodes.length; i++) {
+ results.push(window.HTMLWidgets.getInstance(nodes[i]));
+ }
+ return results;
+ };
+
+ var postRenderHandlers = [];
+ function invokePostRenderHandlers() {
+ while (postRenderHandlers.length) {
+ var handler = postRenderHandlers.shift();
+ if (handler) {
+ handler();
+ }
+ }
+ }
+
+ // Register the given callback function to be invoked after the
+ // next time static widgets are rendered.
+ window.HTMLWidgets.addPostRenderHandler = function(callback) {
+ postRenderHandlers.push(callback);
+ };
+
+ // Takes a new-style instance-bound definition, and returns an
+ // old-style class-bound definition. This saves us from having
+ // to rewrite all the logic in this file to accomodate both
+ // types of definitions.
+ function createLegacyDefinitionAdapter(defn) {
+ var result = {
+ name: defn.name,
+ type: defn.type,
+ initialize: function(el, width, height) {
+ return defn.factory(el, width, height);
+ },
+ renderValue: function(el, x, instance) {
+ return instance.renderValue(x);
+ },
+ resize: function(el, width, height, instance) {
+ return instance.resize(width, height);
+ }
+ };
+
+ if (defn.find)
+ result.find = defn.find;
+ if (defn.renderError)
+ result.renderError = defn.renderError;
+ if (defn.clearError)
+ result.clearError = defn.clearError;
+
+ return result;
+ }
+})();
+
diff --git a/docs/posts/2020-09-12-videos-in-reactable/2020-09-12-videos-in-reactable_files/reactable-binding-0.2.0/reactable.js b/docs/posts/2020-09-12-videos-in-reactable/2020-09-12-videos-in-reactable_files/reactable-binding-0.2.0/reactable.js
new file mode 100644
index 00000000..308f474f
--- /dev/null
+++ b/docs/posts/2020-09-12-videos-in-reactable/2020-09-12-videos-in-reactable_files/reactable-binding-0.2.0/reactable.js
@@ -0,0 +1,7 @@
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e=this,t=this.props,s=t.data,u=t.columns,c=t.columnGroups,f=t.pivotBy,d=t.sortable,p=t.resizable,g=t.filterable,h=t.searchable,m=t.defaultSortDesc,b=t.defaultSorted,y=t.defaultPageSize,v=t.pageSizeOptions,w=t.paginationType,x=t.showPagination,S=t.showPageSizeOptions,P=t.showPageInfo,C=t.minRows,O=t.selection,k=t.onClick,T=t.outlined,j=t.bordered,E=t.borderless,R=t.striped,N=t.highlight,A=t.compact,D=t.nowrap,z=t.showSortIcon,_=t.showSortable,F=t.className,M=t.style,I=t.rowClassName,L=t.rowStyle,W=t.inline,K=t.width,B=t.height,H=t.language,U=t.crosstalkGroup,J=t.crosstalkKey,q=t.dataKey,X=t.theme;for(var Y in F=Q(F,V((X=G(X)||{}).style)),H=wt({},$,{},H))H[Y]=H[Y]||null;s=function(e){for(var t=Object.keys(e),r=new Array(e[t[0]].length),n=0;n2&&void 0!==arguments[2]?arguments[2]:{},l=r.sortable,s=r.showSortIcon,u=r.showSortable,c=r.isExpanded,f=r.onExpanderClick,d=r.theme,p=r.language;return e=e.map((function(e){var 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Array&&!e.aggregated&&(s=t.cell[e.index])&&(s=Object(n.hydrate)({Fragment:a.Fragment,WidgetContainer:Me},t.cell[e.index]))),null!=s&&""!==s||(s=""),r=o.a.isValidElement(s)?s:t.html?o.a.createElement("div",{style:{display:"inline"},dangerouslySetInnerHTML:{__html:s}}):String(s),t.details&&(t.details instanceof Array&&null==t.details[e.index]||(l=i.ReactTableDefaults.ExpanderComponent($e({},e,{isExpanded:c(e)})))),l?o.a.createElement(o.a.Fragment,null,l,r):r},t.PivotValue=function(e){var r=t.Cell(e);return o.a.createElement("span",null,r," ",e.subRows&&"(".concat(e.subRows.length,")"))},t.Aggregated=function(e){var r=null!=e.value?e.value:"";return t.format&&t.format.aggregated&&(r=et(r,t.format.aggregated)),t.aggregated&&(r=t.aggregated($e({},e,{value:r}))),t.html?o.a.createElement("div",{dangerouslySetInnerHTML:{__html:r}}):r},t.Header=function(e){var r,i=t.name;if(t.header&&(i="function"==typeof 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diff --git a/docs/posts/2020-09-12-videos-in-reactable/2020-09-12-videos-in-reactable_files/twitter-widget-0.0.1/widgets.js b/docs/posts/2020-09-12-videos-in-reactable/2020-09-12-videos-in-reactable_files/twitter-widget-0.0.1/widgets.js
new file mode 100644
index 00000000..b30be8c6
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+/*!
+ * @overview es6-promise - a tiny implementation of Promises/A+.
+ * @copyright Copyright (c) 2014 Yehuda Katz, Tom Dale, Stefan Penner and contributors (Conversion to ES6 API by Jake Archibald)
+ * @license Licensed under MIT license
+ * See https://raw.githubusercontent.com/stefanpenner/es6-promise/master/LICENSE
+ * @version v4.2.5+7f2b526d
+ */var r;r=function(){"use strict";function t(t){return"function"==typeof t}var e=Array.isArray?Array.isArray:function(t){return"[object Array]"===Object.prototype.toString.call(t)},n=0,r=void 0,i=void 0,o=function(t,e){l[n]=t,l[n+1]=e,2===(n+=2)&&(i?i(h):w())},s="undefined"!=typeof window?window:void 0,a=s||{},u=a.MutationObserver||a.WebKitMutationObserver,c="undefined"==typeof self&&"undefined"!=typeof process&&"[object process]"==={}.toString.call(process),d="undefined"!=typeof Uint8ClampedArray&&"undefined"!=typeof importScripts&&"undefined"!=typeof MessageChannel;function f(){var t=setTimeout;return function(){return t(h,1)}}var l=new Array(1e3);function h(){for(var t=0;t=0&&this._handlers[t].splice(n,1):this._handlers[t]=[])},trigger:function(t,e){var n=this._handlers&&this._handlers[t];(e=e||{}).type=t,n&&n.forEach(function(t){r.async(i(t,this,e))})}};t.exports={Emitter:o,makeEmitter:function(){return r.aug(function(){},o)}}},function(t,e,n){var 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r=n(85),i=n(21),o="tfw_horizon_tweet_embed_9555";t.exports=function(t,e){var n;t&&(n=t[o])&&n.bucket&&r(o,n.bucket,n.version,i.formatHorizonTweetData(e))}},function(t,e,n){var r=n(2);t.exports=function(t,e){var i=new r;return n.e(7).then(function(r){var o;try{o=n(92),i.resolve(new o(t,e))}catch(t){i.reject(t)}}.bind(null,n)).catch(function(t){i.reject(t)}),i.promise}},function(t,e,n){var r=n(2);t.exports=function(t,e){var i=new r;return Promise.all([n.e(0),n.e(8)]).then(function(r){var o;try{o=n(93),i.resolve(new o(t,e))}catch(t){i.reject(t)}}.bind(null,n)).catch(function(t){i.reject(t)}),i.promise}},function(t,e,n){var r=n(10),i=n(0),o=n(13),s=n(14)(),a=n(68),u=n(5),c="a.twitter-share-button, a.twitter-mention-button, a.twitter-hashtag-button",d="twitter-hashtag-button",f="twitter-mention-button";t.exports=function(t){return s(t,c).map(function(t){return a(function(t){var e=o(t),n={screenName:t.getAttribute("data-button-screen-name"),text:t.getAttribute("data-text"),type:t.getAttribute("data-type"),size:t.getAttribute("data-size"),url:t.getAttribute("data-url"),hashtags:t.getAttribute("data-hashtags"),via:t.getAttribute("data-via"),buttonHashtag:t.getAttribute("data-button-hashtag")};return i.forIn(n,function(t,n){var r=e[t];e[t]=u.hasValue(r)?r:n}),e.screenName=e.screenName||e.screen_name,e.buttonHashtag=e.buttonHashtag||e.button_hashtag||e.hashtag,r.present(t,d)&&(e.type="hashtag"),r.present(t,f)&&(e.type="mention"),e}(t),t.parentNode,t)})}},function(t,e,n){var r=n(2);t.exports=function(t,e){var i=new r;return n.e(3).then(function(r){var o;try{o=n(94),i.resolve(new o(t,e))}catch(t){i.reject(t)}}.bind(null,n)).catch(function(t){i.reject(t)}),i.promise}},function(t,e,n){var r=n(0);t.exports=r.aug({},n(158),n(159),n(160),n(161),n(162),n(163),n(164))},function(t,e,n){var r=n(59),i=n(17)(["userId"],{},r);t.exports={createDMButton:i}},function(t,e,n){var r=n(62),i=n(17)(["screenName"],{},r);t.exports={createFollowButton:i}},function(t,e,n){var r=n(63),i=n(17)(["momentId"],{},r);t.exports={createMoment:i}},function(t,e,n){var r=n(64),i=n(17)(["username"],{},r);t.exports={createPeriscopeOnAirButton:i}},function(t,e,n){var r=n(8),i=n(12),o=n(3),s=n(0),a=n(5),u=n(65),c=n(66),d=n(17)([],{},c),f=n(6),l="Embedded grids have been deprecated. Please use twttr.widgets.createTimeline instead. More info: https://twittercommunity.com/t/update-on-the-embedded-grid-display-type/119564.",h={createTimeline:p,createGridFromCollection:function(t){var e=s.toRealArray(arguments).slice(1),n={sourceType:"collection",id:t};return e.unshift(n),i.publicLog(l),p.apply(this,e)}};function p(t){var e,n=s.toRealArray(arguments).slice(1);return a.isString(t)||a.isNumber(t)?f.reject("Embedded timelines with widget settings have been deprecated. See https://twittercommunity.com/t/deprecating-widget-settings/102295."):s.isObject(t)?(t=t||{},n.forEach(function(t){s.isType("object",t)&&function(t){t.ariaLive=t.ariaPolite}(e=t)}),e||(e={},n.push(e)),t.lang=e.lang,t.tweetLimit=e.tweetLimit,t.showReplies=e.showReplies,e.dataSource=u(t),d.apply(this,n)):f.reject("data source must be an object.")}o.isTwitterURL(r.href)&&(h.createTimelinePreview=function(t,e,n){var r={previewParams:t,useLegacyDefaults:!0,isPreviewTimeline:!0};return r.dataSource=u(r),d(e,r,n)}),t.exports=h},function(t,e,n){var r,i=n(0),o=n(67),s=n(17),a=(r=s(["tweetId"],{},o),function(){return i.toRealArray(arguments).slice(1).forEach(function(t){i.isType("object",t)&&(t.hideCard="none"==t.cards||"hidden"==t.cards,t.hideThread="none"==t.conversation||"hidden"==t.conversation)}),r.apply(this,arguments)});t.exports={createTweet:a,createTweetEmbed:a,createVideo:a}},function(t,e,n){var r=n(0),i=n(68),o=n(17),s=o(["url"],{type:"share"},i),a=o(["buttonHashtag"],{type:"hashtag"},i),u=o(["screenName"],{type:"mention"},i);function c(t){return function(){return r.toRealArray(arguments).slice(1).forEach(function(t){r.isType("object",t)&&(t.screenName=t.screenName||t.screen_name,t.buttonHashtag=t.buttonHashtag||t.button_hashtag||t.hashtag)}),t.apply(this,arguments)}}t.exports={createShareButton:c(s),createHashtagButton:c(a),createMentionButton:c(u)}},function(t,e,n){var r,i,o,s=n(4),a=n(1),u=0,c=[],d=s.createElement("a");function f(){var t,e;for(u=1,t=0,e=c.length;t
+
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+June Choe: Embedding videos in {reactable} tables
+
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In a {reactable} table, you can have a row expand to reveal more details by supplying the details argument with a function returning an image, raw html, another reactable table, etc. There are many examples of this in the package vignette, and they give you a good sense of just how flexible and powerful this feature is.
+
My first reaction to this was that it seemed like just about anything that can be displayed on a web page can be embedded in the expandable details. So what about something very unusual like… videos? Can {reactable} handle it? Are there potential usecases of this?
+
Annotated #tidytuesday screencasts
+
While entertaining this idea, I remembered coming across a tweet by Alex Cookson with a link to a very detailed spreadsheet containing timestamped notes of David Robinson’s live #tidytuesday screencasts.
So I turned the spreadsheet into a {reactable} table with rows that can expand to reveal a Youtube video at the timestamp. I actually think this makes a really cool use case - it’s easier here than in Google Spreadsheet to navigate around the table with pagination and search bar, and you don’t need to constantly open and close Youtube videos in new windows (in fact, you can keep multiple videos open across rows here!).
+
Try it out for yourself!
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Code
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+
library(tidyverse)
+library(htmltools)
+library(reactable)
+
+# David Robinson's (@drob) #tidytuesday screencast annotations, made by Alex Cookson (@alexcookson)
+screencasts<-
+ gsheet::gsheet2tbl("docs.google.com/spreadsheets/d/1pjj_G9ncJZPGTYPkR1BYwzA6bhJoeTfY2fJeGKSbOKM")%>%
+ select(Screencast, Date, Timestamp =`Timestamp (sec)`, Link:Functions)%>%
+ mutate(Link =str_extract(Link, "(?<=v=).*(?=&)"))
+
+
+###############
+## The Table ##
+###############
+
+reactable(screencasts,
+
+ # Function to embed Youtube Video
+ details =function(index){
+
+ # Grab video info from hidden columns
+ link<-screencasts$Link[index]
+ time<-screencasts$Timestamp[index]
+
+ # Div container to add grey padding around the video
+ tags$div(style ="text-align:center; padding:10px; background:grey",
+
+ # The actual video
+ tags$iframe(
+ height ="640", width ="640", allow ="fullscreen",
+ src =glue::glue("https://www.youtube.com/embed/{link}?start={time}&autoplay=1")
+ )
+
+ )
+
+ },
+
+ # Column options
+ columns =list(
+ Link =colDef(show =F),
+ Timestamp =colDef(show =F),
+ Description =colDef(width =500)
+ ),
+
+ # Some theme options
+ searchable =TRUE,
+ bordered =TRUE,
+ fullWidth =TRUE,
+ theme =reactableTheme(
+ style =list(fontSize ='14px'),
+ searchInputStyle =list(width ="100%")
+ ),
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diff --git a/docs/posts/2020-09-12-videos-in-reactable/preview.png b/docs/posts/2020-09-12-videos-in-reactable/preview.png
new file mode 100644
index 00000000..018e5b7c
Binary files /dev/null and b/docs/posts/2020-09-12-videos-in-reactable/preview.png differ
diff --git a/docs/posts/2020-09-14-tidytuesday-2020-week-38/2020-09-14-tidytuesday-2020-week-38_files/header-attrs-2.3/header-attrs.js b/docs/posts/2020-09-14-tidytuesday-2020-week-38/2020-09-14-tidytuesday-2020-week-38_files/header-attrs-2.3/header-attrs.js
new file mode 100644
index 00000000..dd57d92e
--- /dev/null
+++ b/docs/posts/2020-09-14-tidytuesday-2020-week-38/2020-09-14-tidytuesday-2020-week-38_files/header-attrs-2.3/header-attrs.js
@@ -0,0 +1,12 @@
+// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
+// be compatible with the behavior of Pandoc < 2.8).
+document.addEventListener('DOMContentLoaded', function(e) {
+ var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
+ var i, h, a;
+ for (i = 0; i < hs.length; i++) {
+ h = hs[i];
+ if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6
+ a = h.attributes;
+ while (a.length > 0) h.removeAttribute(a[0].name);
+ }
+});
diff --git a/docs/posts/2020-09-14-tidytuesday-2020-week-38/index.html b/docs/posts/2020-09-14-tidytuesday-2020-week-38/index.html
new file mode 100644
index 00000000..59bc69ec
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+June Choe: TidyTuesday 2020 week 38
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This is the first installment of plot makeover where I take a plot in the wild and make very opinionated modifications to it.
+
Before
+
Our plot-in-the-wild comes from the recent AMLAP 2020 conference, where I presented my thesis research and had the opportunity to talk with and listen to expert psycholinguists around the world. The plot that I’ll be looking at here is Figure 3 from the abstract of a work by E. Matthew Husband and Nikole Patson (Husband and Patson 2020).
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+Figure 1: Plot from Husband and Patson (2020)
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+
What we have is 6 pairs of barplots with error bars, laid out in a 2-by-3 grid. The total of 12 bars are grouped at three levels which are mapped in the following way:
+
+
First level is mapped to the grid column.
+
Second level is mapped to the grid row.
+
Third level is mapped to the x-axis.
+
+
To get a better sense of what they did, and to make data for the plot makeover, I have recreated the original plot below:1
# A tibble: 12 x 4
+ level_1 level_2 level_3 barheight
+ <fct> <fct> <fct> <dbl>
+ 1 Within Some Strong 0.63
+ 2 Within Some Weak 0.35
+ 3 Within Number Strong 0.72
+ 4 Within Number Weak 0.55
+ 5 Within Or Strong 0.61
+ 6 Within Or Weak 0.15
+ 7 Between Some Strong 0.6
+ 8 Between Some Weak 0.55
+ 9 Between Number Strong 0.52
+ 10 Between Number Weak 0.63
+ 11 Between Or Strong 0.17
+ 12 Between Or Weak 0.16
Flatten the grid in some way so that everything is laid out left-to-right and you can make comparisons horizontally.
+
Cap the y axis to make it clear that the values (proportions) can only lie between 0 and 1.
+
+
Minor Changes:
+
+
Remove grid lines
+
Increase space between axis and axis titles.
+
Remove boxes around strip labels
+
Make strip (facet) labels larger and more readable.
+
Increase letter spacing (probably by changing font)
+
+
After
+
I actually couldn’t settle on one final product2 so here are two plots that incorporate the changes that I wanted to make. I think that both look nice and you may prefer one style over the other depending on what relationships/comparisons you want your graph to emphasize.
+
Point-line plot
+
I got a suggestion that the groups could additionally be mapped to shape for greater clarity, so I’ve incorporated that change.3
+Husband, E. Matthew, and Nikole Patson. 2020. Priming of Implicatures Within and Between Categories: The Case of or. AMLaP2020. https://amlap2020.github.io/a/272.pdf.
+
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But note that this is likely not how the original plot was generated: the authors were likely feeding ggplot2 with the raw data (involving 1s and 0s in this case), but here I am just grabbing the summary statistic that was mapped to the bar aesthetic (hence my decision to name the y variable barheight).↩︎
+
I ran the first plot by a friend who has a degree in design, and she recommended several changes that eventually ended up being the second plot. Some major pointers were removing border lines from the legend, removing x-axis tick marks, and applying color/shade.↩︎
UPDATE 10/5/20: This blog post was featured in the rweekly highlights podcast! Thanks to the rweekly team for a flattering review of my tutorial!
+
Introduction
+
(Feel free to skip the intro section if you want to get to the point!)
+
A powerful concept in the Grammar of Graphics is that variables are mapped onto aesthetics. In {ggplot2}, a class of objects called geom implements this idea. For example, geom_point(mapping = aes(x = mass, y = height)) would give you a plot of points (i.e. a scatter plot), where the x-axis represents the mass variable and the y axis represents the height variable.
+
Because geom_*()s1 are so powerful and because aesthetic mappings are easily understandable at an abstract level, you rarely have to think about what happens to the data you feed it. Take this simple histogram for example:
What’s going on here? You might say that the body_mass_g variable is represented in the x-axis. Sure, that’s not wrong. But a fuller explanation would require you to talk about these extra steps under the hood:
+
+
The variable mapped to x is divided into discrete bins
+
A count of observations within each bin is calculated
+
That new variable is then represented in the y axis
+
Finally, the provided x variable and the internally calculated y variable is represented by bars that have certain position and height
+
+
I don’t mean to say here that you are a total fool if you can’t give a paragraph-long explanation of geom_histogram(). Rather, my intention here is to emphasize that the data-to-aesthetic mapping in GEOM objects is not neutral, although it can often feel very natural, intuitive, and objective (and you should thank the devs for that!). Just think about the many ways in which you can change any of the internal steps above, especially steps 12 and 23, while still having the output look like a histogram.
+
This important point rarely crosses our mind, in part because of what we have gotten drilled into our heads when we first started learning ggplot. As beginners we’ve likely experienced the frustration of having all the data we need to plot something, but ggplot just won’t work. You could imagine a beginner today who’s getting frustrated because geom_point(aes(x = mass, y = height)) throws an error with the following data.
+
+
# A tibble: 2 x 4
+ variable subject1 subject2 subject3
+ <chr> <dbl> <dbl> <dbl>
+ 1 mass 75 70 55
+ 2 height 154 172 144
+
+
And what would StackOverflow you tell this beginner? You’d probably tell them to put the data in a tidy format4 first.
+
+
# A tibble: 3 x 3
+ subject mass height
+ <dbl> <dbl> <dbl>
+ 1 1 75 154
+ 2 2 70 172
+ 3 3 55 144
+
+
Now, that’s something you can tell a beginner for a quick and easy fix. But if you still simply think “the thing that makes ggplot work = tidy data”, it’s important that you unlearn this mantra in order to fully understand the motivation behind stat.
+
When and why should I use STAT?
+
You could be using ggplot every day and never even touch any of the two-dozen native stat_*() functions. In fact, because you’ve only used geom_*()s, you may find stat_*()s to be the esoteric and mysterious remnants of the past that only the developers continue to use to maintain law and order in the depths of source code hell.
+
If that describes you, you might wonder why you even need to know about all these stat_*() functions.
+
+
+
+
Well, the main motivation for stat is simply this:
+
+
“Even though the data is tidy it may not represent the values you want to display”5
+
+
The histogram discussion in the previous section was a good example to this point, but here I’ll introduce another example that I think will hit the point home.
+
Suppose you have a data simple_data that looks like this:
And suppose that you want to draw a bar plot where each bar represents group and the height of the bars corresponds to the mean of score for each group.
+
If you’re stuck in the mindset of “the data that I feed in to ggplot() is exactly what gets mapped, so I need to tidy it first and make sure it contains all the aesthetics that each geom needs”, you would need to transform the data before piping it in like this:
# A tibble: 2 x 2
+ group mean_score
+ <fct> <dbl>
+ 1 A 43.0
+ 2 B 57.5
+
+
Ok, not really a problem there. But what if we want to add in error bars too? Error bars also plot a summary statistic (the standard error), so we’d need make another summary of the data to pipe into ggplot().
+
Let’s first plot the error bar by itself, we’re again passing in a transformed data
# A tibble: 2 x 5
+ group mean_score se lower upper
+ <fct> <dbl> <dbl> <dbl> <dbl>
+ 1 A 43.0 4.37 38.7 47.4
+ 2 B 57.5 2.82 54.7 60.4
+
+
Ok, now let’s try combining the two. One way to do this is to save the data paseed in for the bar plot and the data passed in for the errorbar plot as two separate variables, and then call each in their respective geoms:
Yeah… that code is a mouthful. The above approach is not parsimonious because we keep repeating similar processes in different places.6 If you, like myself, don’t like how this looks, then let this be a lesson that this is the consequence of thinking that you must always prepare a tidy data containing values that can be DIRECTLY mapped to geometric objects.
+
And on a more theoretical note, simple_data_bar and simple_data_errorbar aren’t even really “tidy” in the original sense of the term. We need to remind ourselves here that tidy data is about the organization of observations in the data. Under this definition, values like bar height and the top and bottom of whiskers are hardly observations themselves. Rather, they’re abstractions or summaries of the actual observations in our data simple_data which, if you notice, we didn’t even use to make our final plot above!
+
+
+
+
+Figure 1: Tidy data is about the organization of observations.
+
+
+
+
So not only is it inefficient to create a transformed dataframe that suits the needs of each geom, this method isn’t even championing the principles of tidy data like we thought.7
+
What we should do instead is to take advantage of the fact that our original data simple_data is the common denominator of simple_data_bar and simple_data_errorbar!
+
Wouldn’t it be nice if you could just pass in the original data containing all observations (simple_data) and have each layer internally transform the data in appropriate ways to suit the needs of the geom for that layer?
In this section, I built up a tedious walkthrough of making a barplot with error bars using only geom_*()s just to show that two lines of stat_summary() with a single argument can achieve the same without even touching the data through any form of pre-processing.
+
So that was a taste of how powerful stat_*()s can be, but how do they work and how can you use them in practice?
+
Understanding STAT with stat_summary()
+
Let’s analyze stat_summary() as a case study to understand how stat_*()s work more generally. I think that stat_summary() is a good choice because it’s a more primitive version of many other stat_*()s and is likely to be the one that you’d end up using the most for visualizations in data science.
+
Before we start, let’s create a toy data to work with. Let’s call this data height_df because it contains data about a group and the height of individuals in that group.
We can visualize the data with a familiar geom, say geom_point():
+
+
+
height_df%>%
+ ggplot(aes(x =group, y =height))+
+ geom_point()
+
+
+
+
+
As a first step in our investigation, let’s just replace our familiar geom_point() with the scary-looking stat_summary() and see what happens:
+
+
+
height_df%>%
+ ggplot(aes(x =group, y =height))+
+ stat_summary()
+
+
+
+
+
Instead of points, we now see a point and a line through that point. And before you get confused, this is actually one geom, called pointrange, not two separate geoms.8 Now that that’s cleared up, we might ask: what data is being represented by the pointrange?
+
Answering this question requires us to zoom out a little bit and ask: what variables does pointrange map as a geom? By looking at the documentation with ?geom_pointrange we can see that geom_pointrange()requires the following aesthetics:
+
+
x or y
+
ymin or xmin
+
ymax or xmax
+
+
So now let’s look back at our arguments in aes(). We said that group is mapped to x and that height is mapped to y. But we never said anything about ymin/xmin or ymax/xmax anywhere. So how is stat_summary() drawing a pointrange if we didn’t give it the required aesthetic mappings?
+
Well, a good guess is that stat_summary() is transforming the data to calculate the necessary values to be mapped to pointrange. Here’s one reason for that guess - I’ve been suppressing message throughout this post but if you run the above code with stat_summary() yourself, you’d actually get this message:
+
+
No summary function supplied, defaulting to `mean_se()`
+
+
Huh, a summary function? That sounds promising. Maybe that’s the key to our mystery!
+
First, we see from the documentation of stat_summary() that this mean_se() thing is the default value for the fun.data argument (we’ll talk more on this later).
+
Next, let’s call it in the console to see what it is:
+
+
+
mean_se
+
+
+
function (x, mult = 1)
+ {
+ x <- stats::na.omit(x)
+ se <- mult * sqrt(stats::var(x)/length(x))
+ mean <- mean(x)
+ new_data_frame(list(y = mean, ymin = mean - se, ymax = mean +
+ se), n = 1)
+ }
+ <bytecode: 0x0000000019292b88>
+ <environment: namespace:ggplot2>
+
+
Ok, so it’s a function that takes some argument x and a second argument mult with the default value 1.
+
Let’s go over what it does by breaking down the function body line by line:
+
+
Remove NA values
+
Calculate variable se which is the standard error of the values in x using the equation \(SE = \sqrt{\frac{1}{N}\sum_{i=1}^N(x_i-\bar{x})^2}\)
+
Calculate the variable mean9 which is the mean of x
+
Create a new dataframe with one row, with columns y, ymin, and ymax, where y is the mean of x, ymin is one standard error below the mean, and ymax is one standard error above the mean.10
+
+
A cool thing about this is that although mean_se() seems to be exclusively used for internal operations, it’s actually available in the global environment from loading {ggplot2}. So let’s pass height_df to mean_se() and see what we get back!
+
+
+
mean_se(height_df)
+
+
+
Error: Elements must equal the number of rows or 1
+
+
+
+
+
Uhhh what?
+
Do you see what happened just now? This is actually really important: stat_summary() summarizes one dimension of the data.11mean_se() threw an error when we passed it our whole data because it was expecting just a vector of the variable to be summarized.
+
+
Ok now that we’ve went over that little mishap, let’s give mean_se() the vector it wants.
+
+
+
mean_se(height_df$height)
+
+
+
y ymin ymax
+ 1 171.8 170.3 173.3
+
+
And look at that, these look like they’re the same values that were being represented by the mid-point and the end-points of the pointrange plot that we drew with stat_summary() above!
+
You know how else we can check that this is the case? With this neat function called layer_data().
+
We can pull the data that was used to draw the pointrange by passing our plot object to layer_data() and setting the second argument to 112:
x group y ymin ymax PANEL flipped_aes colour size linetype shape fill
+ 1 1 1 171.8 170.3 173.3 1 FALSE black 0.5 1 19 NA
+ alpha stroke
+ 1 NA 1
+
+
Would ya look at that! There’s a lot of stuff in there, but it looks like the values for y, ymin, and ymax used for the actual plot match up with the values we calculated with mean_se() above!
+
We’ve solved our mystery of how the pointrange was drawn when we didn’t provide all the required mappings!
+
+
+
+
Interim Summary #2
+
To summarize this section (ha!), stat_summary() works in the following order:
+
+
The data that is passed into ggplot() is inherited if one is not provided
+
The function passed into the fun.data argument applies transformations to (a part of) that data (defaults to mean_se())
+
The result is passed into the geom provided in the geom argument (defaults to pointrange).
+
If the data contains all the required mapppings for the geom, the geom will be plotted.
+
+
And to make things extra clear & to make stat_summary() less mysterious, we can explicitly spell out the two arguments fun.data and geom that we went over in this section.
Here, I will demonstrate a few ways of modifying stat_summary() to suit particular visualization needs.
+
For this section, I will use a modified version of the penguins data that I loaded all the way up in the intro section (I’m just removing NA values here, nothing fancy).
At no point in this section will I be modifying the data being piped into ggplot(). That is the beauty and power of stat.
+
1. Error bars showing 95% confidence interval
+
Here, we’re plotting the mean body_mass_g of penguins for each sex, with error bars that show the 95% confidence interval (a range of approx 1.96 standard errors from the mean).
+
+
+
my_penguins%>%
+ ggplot(aes(sex, body_mass_g))+
+ stat_summary(
+ fun.data =~mean_se(., mult =1.96), # Increase `mult` value for bigger interval!
+ geom ="errorbar",
+ )
+
+
+
+
+
+
+
+
The transformed data used for the errorbar geom inside stat_summary():
+
+
+
bind_rows(
+ mean_se(my_penguins$body_mass_g[my_penguins$sex=="female"], mult =1.96),
+ mean_se(my_penguins$body_mass_g[my_penguins$sex=="male"], mult =1.96),
+)
+
+
+
y ymin ymax
+ 1 3862 3761 3964
+ 2 4546 4427 4665
+
+
2. A color-coded bar plot of medians
+
Here, we’re plotting the median bill_length_mm for each penguins species and coloring the groups with median bill_length_mm under 40 in pink.
Even though the data is tidy, it may not represent the values you want to display
+
The solution is not to transform your already-tidy data so that it contains those values
+
Instead, you should pass in your original tidy data into ggplot()as is and allow stat_*() functions to apply transformations internally
+
These stat_*() functions can be customized for both their geoms and their transformation functions, and works similarly to geom_*() functions in other regards
+
If you want to use your own custom function, make sure to check the documentation of that particular stat_*() function to check the variable/data type it requires.
+
If you want to use a different geom, make sure that your transformation function calculates all the required aesthetics for that geom
+
+
STAT vs. GEOM or STAT and GEOM?
+
Although I have talked about the limitations of geom_*()s to demonstrate the usefulness of stat_*()s, both have their place. It’s about knowing when to use which; it’s not a question of either-or. In fact, they require each other - just like how stat_summary() had a geom argument, geom_*()s also have a stat argument. At a higher level, stat_*()s and geom_*()s are simply convenient instantiations of the layer() function that builds up the layers of ggplot.
+
Because this is important, I’ll wrap up this post with a quote from Hadley explaining this false dichotomy:
+
+
Unfortunately, due to an early design mistake I called these either stat_() or geom_(). A better decision would have been to call them layer_() functions: that’s a more accurate description because every layer involves a stat and a geom.13
+
+
+
+
+
+
Just to clarify on notation, I’m using the star symbol * here to say that I’m referencing all the functions that start with geom_ like geom_bar() and geom_point(). This is called the Kleene star and it’s used a lot in regex, if you aren’t familiar.↩︎
+
You could have bins of that are not of equal size. Or, you could have bins that bleed into each other to create a rolling window summary.↩︎
+
You could calculate the sum of raw values that are in each bin, or calculate proportions instead of counts↩︎
+
If you aren’t familiar already, “tidy” is a specific term of art↩︎
Yes, you can still cut down on the code somewhat, but will it even get as succinct as what I show below with stat_summary()? (9/30 edit) Okay, I was kinda strawmaning, and Hadley(!) has correctly caught me on that. The bar-errorbar plot was not the best choice to demonstrate the benefits of stat_summary(), but I just wanted to get people excited about stat_*()! Sorry for the confusion/irritation!!↩︎
+
There’s actually one more argument against transforming data before piping it into ggplot. When you choose the variables to plot, say cyl and mpg in the mtcars dataset, do you call select(cyl, mpg) before piping mtcars into ggplot? No? Well then why would you transform your data beforehand if you can just have that be handled internally instead? It’s the same logic!↩︎
+
If you’re still skeptical, save the plot object to a variable like plot and call plot$layers to confirm that geom_pointrange was used to draw the plot.↩︎
+
I personally don’t agree with this naming choice since mean is also the name of the base function↩︎
+
The function new_data_frame() is from {vctrs}. That last line of code in the function body is doing the same thing as data.frame(y = mean, ymin = mean - se, ymax = mean + se), but there’s less room for error the way it’s done in the source code.↩︎
+
If you read the documentation, the very first line starts with “stat_summary() operates on unique x or y …” (emphasis mine)↩︎
+
This second argument specifies which layer to return. Here, the pointrange layer is the first and only layer in the plot so I actually could have left this argument out.↩︎
Admittedly, the plot itself is quite simple, but I learned a lot from this process. So, breaking from the usual format of my tidytuesday blogposts, I want to talk about the background and motivation behind the plot as this was a big step in a new (and exciting!) direction for me that I’d like to document.
+
Just for clarification, I’m using the term guiding aesthetics to refer to elements of the plot that do not represent a variable in the data, but serves to emphasize the overall theme or topic of the being visualized. So the mountains in my plot do not themselves contain any data, but it’s the thing that tells readers that the plot is about mountains (a valuable, but different kind of info!). But more on that later.
+
Why bother with guiding aesthetics?
+
This was my first time adding a huge element to a plot that wasn’t meaningful, in the sense of representing the data. As someone in academia working on obscure topics (as one does in academia), I’m a firm believer in making your plots as simple and minimal as possible. So I, like many others, think that it’s always a huge risk to add elements that are not absolutely necessary.
+
But as you might imagine, I first fell in love with data visualization not because of how objective and straightforward they are, but because of how eye-catching they can be. Like, when I was young I used to be really into insects. In fact, I read insect encyclopedias as a hobby. That TMI is relevant because those are full of data(!) visualizations that employ literal mappings of data, since those are easy to interpret for children. For example, consider the following diagram of the life cycle of the Japanese beetle from the USDA.
+
+
+
+
+Figure 1: Diagram of the life cycle of the Japanese beetle
+
+
+
+
As a child, I never appreciated/realized all the data that was seamlessly packed into diagrams like this. But with my Grammar of Graphics lens on, I can now see that:
+
+
The developmental stage at each month is mapped to x
+
The depth at which the developing beetle lives at each stage is mapped to y
+
The appearance of the beetle at each developmental stage is mapped to shape
+
The size of the beetle at each developmental stage is mapped to, well, size
+
+
But I could have easily plotted something like this instead:
Both visuals represent the data accurately, but of course the diagram looks better. And not just because it’s complex, but also because it exploits the associations between aesthetic dimensions and their meanings, as well as the strength of those associations.
+
For example, the shape dimension literally corresponds to shape, and the shape of the different developmental stages of the beetle are unique enough for there to be interesting within-stage variation and still be recognizable - e.g., the beetle looks different crawling out the ground in June and entering back in August, but it’s still recognizable as a beetle (and the same beetle at that!). This would’ve been difficult if the variable being mapped to shape was something arbitrary and abstract, like the type of protein that’s most produced at a particular stage. The diagram thus exploits the strength of the association between the shape dimension and the literal shapes of the beetle to represent the developmental stages. And it should be clear now that the same goes for y and size.
+
How about x? There’s no such strong/literal interpretation of the x-dimension - at best it just means something horizontal. So it’s actually fitting that a similarly abstract concept like the passage of time is mapped to x. We understand time linearly, and often see time as the x-axis in other plots, so it fits pretty naturally here.
+
Lastly, let’s talk about the color dimension. Even though no information was actually mapped to color, we certainly are getting some kind of information from the colors used in the plot. Literally put, we’re getting the information that the grass is green and the soil is brown. Now, that information is actually not representing any data that we care about so it’s technically visual noise, but it helps bring forward the overall theme of the diagram. While this worked out in the end, notice now that you have effectively thrown out color as a dimension that can convey meaningful information. That was a necessary tradeoff, but a well motivated one, since the information that the diagram is trying to convey doesn’t really needcolor.
+
Designing guiding aesthetics
+
I’m hardly the expert, but I found it helpful to think of the process as mediating the tug of war between the guiding aesthetic and the variables in the data as they fight over space in different mapping dimensions.
+
This meant I had to make some changes to my usual workflow for explanatory data visualization, which mostly goes something like this:
+
+
Take my response variable and map it to y
+
Figure out the distribution of my response variables and choose the geom (e.g., boxplot, histogram).
+
Map my dependent variables to other dimensions - this usually ends at either just x or x + facet groupings
+
+
But if I’m trying to incorporate guiding aesthetics, my workflow would look more like this:
+
+
Start with a couple ideas for a visual representation of the topic (scenes, objects, etc.)
+
Figure out the dimensions that the variables in the data can be mapped to
+
Figure out the dimensions that each visual representation would intrude in
+
Make compromises between (2) and (3) in a way that maximizes the quality of the data and the visual representation
+
+
Of course, this kind of flexibility is unique to exploratory data visualization, in particular to the kinds where none of the variable is significant or interesting a priori. Of course in real life there will be a lot more constraints, but because we can assume a great degree of naivety towards the data for #tidytuesday, I get to pick and choose what I want to plot (which makes #tidytuesday such a great place to practice baby steps)!
+
For illustration, here’s my actual thought process while I was making the #tidytuesday plot.
+
WARNING: a very non-linear journey ahead!
+
My thought process
+
The topic was about Himalayan climbing expeditions, so I wanted my visual to involve a mountain shape. The most obvious idea that came to mind was to draw a mountain where the peak of that mountain corresponded with their height_metres, a variable from the peaks data. It’s straightforward and intuitive! So I drew a sketch (these are the original rough sketches - please forgive the quality!)
+
+
+
+
+Figure 2: The first guiding aesthetic idea
+
+
+
+
But this felt… a bit empty. I threw in Mount Everest, but now what? I still had data on hundreds of more Himalayan peaks that were in the dataset. Just visualizing Mount Everest is not badly motivated per se (it’s the most famous peak afterall), but it wouldn’t make an interesting plot since there wasn’t much data on individual peaks. I wanted to add a few more mountain shapes, but I struggled to find a handful of peaks that formed a coherent group. I knew that if I wanted to go with the idea of mountain shapes as the guiding aesthetic, I could only manage to fit about a dozen or so without it looking too crowded.
+
I put that issue aside for the moment and moved on while trying to accommodate for the possibility that I may have to fit in many more peaks. I thought about having a single mountain shape just for Mount Everest, and a point with a vertical spikeline for all other peaks to emphasize the y-axis representing height_metres.
+
+
+
+
+Figure 3: The second guiding aesthetic idea
+
+
+
+
At this point I started thinking about the x-axis. If I do use points to represent peaks (specifically, peak height), where would I actually position each point? Just randomly along the x-axis? It really started hitting me at this point that the quality of my data was pretty abysmal. Even if I ended up with a pretty visualization, I didn’t think I could justify calling it a data visualization. I felt that it’d be a reach to use complex visuals just to communicate a single variable.
+
I toyed around with enriching the data being plotted. What if I use size of the dots to represent the average age of climbers who reached the peak, from the memmbers data? Or what if I used shape of country flags on top of the dots to represent the country that first reached the peak, from the expeditions data?
+
These were all cool ideas, but I kept coming back to the need to make the x-dimension meaningful. It just stood out too much. I didn’t think I could prevent the reader from expecting some sort of a meaning from the positioning of the dots along the x-axis.
+
So I went back to Step #2. I gathered up all the continuous variables across the three data in the #tidytuesday dataset (peaks, members, expeditions) and evaluated how good of a candidate each of them were for being mapped to x. This was the most time-consuming part of the process, and I narrowed it down to three candidates:
+
+
expeditions$members: looked okay at first, but once I started aggregating (averaging) by peak, the distribution became quite narrow. That made it less interesting and not very ideal for mountain shapes (the typical mountain shape is wider than they are tall).
+
members$age: has a nice distribution and a manageable range with no extreme outliers.
+
peaks$first_ascent_year: also has the above features + doesn’t need to be aggregated in some way, so the x-axis would have a very straight forward interpretation.
+
+
The first_ascent_year variable looked the most promising, so that’s what I pursued (and ended up ultimately adopting!).
+
+
+
+
+Figure 4: The third guiding aesthetic idea
+
+
+
+
Now I felt like I had more direction to tackle the very first issue that I ran into during this process: the problem of picking out a small set of peaks that were interesting and well-motivated. I played around more with several options, but I ultimately settled on something very simple - the top 10 most popular peaks. Sure it’s overused and not particularly exciting, but that was a sacrifice that my over-worked brain was willing to make at the time.
+
And actually, it turned out to be a great fit with my new x variable! It turns out that the top 10 most climbed peaks are also those that were among the first to be climbed (a correlation that sorta makes sense), so this set of peaks had an additional benefit of localizing the range of x to between the 1940s-1960s. And because 10 was a manageable number, I went ahead with my very first idea of having a mountain accompanying each point, where the peaks represent the peak of the guiding aesthetic (the mountain shape) as well as the height_metres and first_ascent_year.
+
Finally, it came time for me to polish up on the mountains. I needed to decide on features of the mountains like how wide the base is, how many valleys and peaks it has, how tall the peaks are relative to each other, etc. I had to be careful that these superfluous features do not encroach on the dimensions where I mapped my data to - the x and y. Here, I had concerns about two of the mountain features in particular: base width and smaller peaks:
+
+
The base width was troubling because how wide the base of the mountain stretches could be interpreted as representing another variable that has to do with year (like the first and last time it was climbed, for example). This was a bit difficult to deal with, but I settled on a solution which was to keep the base width constant. By not having that feature vary at all, I could suppress any expectation for it to carry some sort of meaning. It’s kind of like how when you make a scatterplot with variables mapped to x and y, you don’t imbue any special meaning to the fact that the observations are represented by a circle (point), beacuse all of them are that shape. If they varied in any way, say you also have some rectangles and triangles, then you’d start expecting the shape to represent something meaningful.
+
The smaller peaks of the mountain shapes were troubling because I was already using the peak to represent the height. It helped that the actual peaks representing the data were also marked by a point and a label of the peak name. But to make it extra clear that the they were pure noise, I decided to randomly generate peaks and valleys, and tried to make that obvious. In the code attached at the bottom of this post, several parameters of the mountain-generating function allowed me to do this. It also helped that I added a note saying that the mountains were randomly generated when I tweeted it, which is kind of cheating perhaps, but it worked!
+
+
+
+I've been feeling particularly inspired by this week's #TidyTuesday so I made another plot! This is a simple scatterplot of peak height by year of first ascent, but with a twist: each point is also represented by the peak of a randomly generated mountain! #rstatspic.twitter.com/CqNQjdMYXP
+
That wraps up my long rant on how I made my mountains plot! For more context, making the plot took about a half a day worth of work, which isn’t too bad for a first attempt! Definitely looking forward to getting more inspirations like this in the future.
I, along with nearly two-hundred thousand other people, follow @everycolorbot on twitter. @everycolorbot is a twitter bot that tweets an image of a random color every hour (more details on github). It’s 70% a source of inspiration for new color schemes, and 30% a comforting source of constant in my otherwise hectic life.
+
What I’ve been noticing about @everycolorbot’s tweets is that bright, highly saturated neon colors (yellow~green) tend to get less likes compared to cool blue colors and warm pastel colors. You can get a feel of this difference in the number of likes between the two tweets below, tweeted an hour apart:
This is actually not a big surprise. Bright pure colors are very harsh and straining to the eye, especially on a white background.1 For this reason bright colors are almost never used in professional web design, and are also discouraged in data visualization.
+
So here’s a mini experiment testing that claim: I’ll use @everycolorbot’s tweets (more specifically, the likes on the tweets) as a proxy for likeability/readability/comfortableness/etc. It’ll be a good exercise for getting more familiar with different colors! I’m also going to try a simple descriptive analysis using the HSV color representation, which is a psychologically-motivated mental model of color that I like a lot (and am trying to get a better feel for).
+
+
+
+
+Figure 1: HSV cylinder
+
+
+
+
Setup
+
Using {rtweet} requires authentication from twitter. The steps to do so are very well documented on the package website so I wouldn’t expect too much trouble setting it up if it’s your first time using it. But just for illustration, here’s what my setup looks like:
After authorizing, I queried the last 10,000 tweets made by @everycolorbot. It ended up only returning about a 1/3 of that because the twitter API only allows you to go back so far in time, but that’s plenty for my purposes here.
As you see above, I also got back 90 variables (columns). I only care about the time of the tweet, the number of likes it got, and the color it tweeted, so those are what I’m going to grab. I also want to clean things up a bit for plotting, so I’m going to grab just the hour from the time and just the hex code from the text.
Here is the link to this data if you’d like to replicate or extend this analysis yourself.
+
Analysis
+
Below is a bar plot of colors where the height corresponds to the number of likes. It looks cooler than your usual bar plot because I transformed the x dimension into polar coordinates. My intent in doing this was to control for the hour of day in my analysis and visualize it like a clock (turned out better than expected!)
I notice at least two interesting contrasts in this visualization:
+
+
Neon colors (yellow, green, pink) and dark brown and black seems to dominate the center (least liked colors) while warm red~blue pastel colors dominate around the edges (most liked colors)
+
There also seems to be a distinction between pure blue and red in the inner-middle circle vs. the green~blue pastel colors in the outer-middle circle.
+
+
So maybe we can say that there are four clusters here:
+
+
Least liked: Bright neon colors + highly saturated dark colors
+
Lesser liked: Bright pure/near-pure colors
+
More liked: Darker pastel RGB
+
Most liked: Lighter pastel mixed colors
+
+
Now’s let’s try to quantitatively describe each cluster.
+
First, as a sanity check, I’m just gonna eyeball the range of likes for each cluster using an un-transformed version of the above plot with units. I think we can roughly divide up the clusters at 100 likes, 200 likes, and 400 likes.
If our initial hypothesis about the four clusters are true, we should see these clusters having distinct profiles. Here, I’m going to use the HSV representation to quantitatively test this. To convert our hex values into HSV, I use the as.hsv() function from the {chroma} package - an R wrapper for the javascript library of the same name.
# A tibble: 4 x 4
+ cluster h s v
+ * <fct> <dbl> <dbl> <dbl>
+ 1 Center 121. 0.770 0.710
+ 2 Inner-Mid 191. 0.734 0.737
+ 3 Outer-Mid 197. 0.589 0.710
+ 4 Edge 249. 0.304 0.832
+
+
This actually matches up pretty nicely with our initial analysis! We find a general dislike for green colors (h value close to 120) over blue colors (h value close to 240), as well as a dislike for highly saturated colors (intense, bright) over those with low saturation (which is what gives off the “pastel” look). To help make the hue values more interpretable, here’s a color wheel with angles that correspond to the hue values in HSV.2
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+
+
+Figure 2: Hue color wheel
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+
+
+
But we also expect to find within-cluster variation along HSV. In particular, hue is kind of uninterpretable on a scale so it probably doesn’t make a whole lot of sense to take a mean of that. So back to the drawing plotting board!
+
Since saturation and valuedo make more sense on a continuous scale, let’s draw a scatterplot for each cluster with saturation on the x-axis and value on the y-axis. I’m also going to map hue to the color of each point, but since hue is abstract on its own, I’m actually just going to replace it with the hex values (i.e., the actual color).
saturation (how colorful a color is) is mapped to the X-dimension
+
value (how light a color is) is mapped to the Y-dimension
+
hex (the actual color itself) is mapped to the COLOR dimension
+
+
+
Our plot above reinforce what we’ve found before. Colors are more likeable (literally) the more they…
+
+
Move away from green: Neon-green dominates the least-liked cluster, and that’s a blatant fact. Some forest-greens survive to the lesser-liked cluster, but is practically absent in the more-liked cluster and most-liked cluster. It looks like the only way for green to be redeemable is to either mix in with blue to become cyan and turquoise, which dominates the more-liked cluster, or severly drop in saturation to join the ranks of other pastel colors in the most-liked cluster.
+
Increase in value and decrease in saturation: It’s clear that the top-left corner is dominated by the more-liked and the most-liked cluster. That region is, again, where pastel colors live. They’re calmer than the bright neon colors that plague the least-liked cluster, and are more liked than highly-saturated and intense colors like those in the top right of the Outer-Mid panel. So perhaps this is a lesson that being “colorful” can only get you so far.
+
+
Conclusion
+
Obviously, all of this should be taken with a grain of salt. We don’t know the people behind the likes - their tastes, whether they see color differently, what medium they saw the tweet through, their experiences, etc.
+
And of course, we need to remind ourselves that we rarely see a color just by itself in the world. It contrasts and harmonizes with other colors in the environment in very complex ways.
+
But that’s what kinda makes our analysis cool - despite all these complexities, we see evidence for many things that experts working with color emphasize: avoid pure neon, mix colors, etc. This dataset also opens us up to many more types of analyses (like an actual cluster analysis) that might be worth looking into.
While all color wheels look the same, they aren’t all oriented the same. When using HSV, make sure to reference the color wheel where the red is at 0, green is as 120, and blue is at 240.↩︎
Using {magick} for animation composition, thanks to the {gganimate}wiki
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The very basics of working with spatial data with {rnaturalearth} and {sf}1
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A bit about color schemes for maps (I particularly love this color as a way of de-emphasizing territories in the background)
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Things to improve
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I couldn’t figure out how to add margins to the bottom, but I now realize that I could’ve just played around with expansion() for the y-axis of the bar animation plot.
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Image composition took a while to render, which was a bit frustrating. Need to find a way to speed that up.
If I don’t count all the convenient US-centric data/packages I’ve used to plot American maps before, this would be the first map I’ve made from scratch.↩︎
How to make waffle charts with {waffle} (finally!)
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Using {patchwork} for a large list of plots using wrap_plots() and theme styling inside plot_annotation()
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Working with a long canvas using the cairo_pdf() device
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Using {ggfittext} for dynamically re-sizing annotations.
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Things to improve
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Couldn’t figure out background color for the entire visual and white ended up looking a bit too harsh on the eye
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Ideally would like to replace the squares with icons. Maybe I could’ve pursued that if I only plotted a couple furnitures.
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The plot ended up being a bit too long. Again could’ve cut down a bit there, but I don’t mind it for this submission because I was more focused on learning how to make waffle charts at all.
This is the second installment of plot makeover where I take a plot in the wild and make very opinionated modifications to it.
+
Before
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Our plot-in-the-wild comes from (Yurovsky and Yu 2008), a paper on statistical word learning. The plot that I’ll be looking at here is Figure 2, a bar plot of accuracy in a 3-by-3 experimental design.
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+Figure 1: Plot from Yurovsky and Yu (2008)
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+
As you might notice, there’s something interesting going on in this bar plot. It looks like the red and green bars stack together but dodge from the blue bar. It’s looks a bit weird for me as someone who mainly uses {ggplot2} because this kind of a hybrid design is not explicitly supported in the API.
+
For this plot makeover, I’ll leave aside the issue of whether having a half-stacked, half-dodged bar plot is a good idea.1 In fact, I’m not even gonna focus much on the “makeover” part. Instead I’m just going to take a shot at recreating this plot (likely made in MATLAB with post-processing in PowerPoint) in {ggplot2}.
+
My Plan
+
Again, my primary goal here is replication. But I do want to touch up on some aesthetics while I’m at it.
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Major Changes:
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Move the title to above the plot
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Move the legend inside the plot
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Move/remove the y-axis title so it’s not vertically aligned
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Minor Changes:
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Remove grid lines
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Put y-axis in percentages
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Add white borders around the bars for clearer color contrast
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After
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First draft
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For a first pass on the makeover, I wanted to get the hybrid design right.
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The plot below isn’t quite there in terms of covering everything I laid out in my plan, but it does replicate the bar plot design specifically.
As you might guess from my two calls to geom_col() and geom_errorbar(), I actually split the plotting of the bars into two parts. First I drew the blue bars and their errorbars, then I drew the green and red bars and their errorbars.
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Effectively, the above plot is a combination of these two:2
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