dbplot

• Installation
• Connecting to a data source
• Example
• ggplot
  • Histogram
  • Raster
  • Bar Plot
  • Line plot
  • Boxplot
• Calculation functions
• db_bin()

Leverages dplyr to process the calculations of a plot inside a database. This package provides helper functions that abstract the work at three levels:

1. Functions that ouput a ggplot2 object
2. Functions that outputs a data.frame object with the calculations.
3. Creates the formula needed to calculate bins for a Histogram or a Raster plot

Installation

# You can install the released version from CRAN
install.packages("dbplot")

# Or the the development version from GitHub:
install.packages("devtools")
devtools::install_github("edgararuiz/dbplot")

Connecting to a data source

• For more information on how to connect to databases, including Hive, please visit http://db.rstudio.com

• To use Spark, please visit the sparklyr official website: http://spark.rstudio.com

Example

In addition to database connections, the functions work with sparklyr. A Spark DataFrame will be used for the examples in this README.

library(sparklyr)

conf <- spark_config()
sc <- spark_connect(master = "local", version = "2.1.0")

spark_flights <- copy_to(sc, nycflights13::flights, "flights")

ggplot

Histogram

By default dbplot_histogram() creates a 30 bin histogram

library(ggplot2)

spark_flights %>% 
  dbplot_histogram(sched_dep_time)

Use binwidth to fix the bin size

spark_flights %>% 
  dbplot_histogram(sched_dep_time, binwidth = 200)

Because it outputs a ggplot2 object, more customization can be done

spark_flights %>% 
  dbplot_histogram(sched_dep_time, binwidth = 300) +
  labs(title = "Flights - Scheduled Departure Time") +
  theme_bw()

Raster

To visualize two continuous variables, we typically resort to a Scatter plot. However, this may not be practical when visualizing millions or billions of dots representing the intersections of the two variables. A Raster plot may be a better option, because it concentrates the intersections into squares that are easier to parse visually.

A Raster plot basically does the same as a Histogram. It takes two continuous variables and creates discrete 2-dimensional bins represented as squares in the plot. It then determines either the number of rows inside each square or processes some aggregation, like an average.

• If no fill argument is passed, the default calculation will be count, n()

spark_flights %>%
  filter(!is.na(arr_delay)) %>%
  dbplot_raster(arr_delay, dep_delay) 

• Pass an aggregation formula that can run inside the database

spark_flights %>%
  filter(!is.na(arr_delay)) %>%
  dbplot_raster(arr_delay, dep_delay, mean(distance)) 

• Increase or decrease for more, or less, definition. The resolution argument controls that, it defaults to 100

spark_flights %>%
  filter(!is.na(arr_delay)) %>%
  dbplot_raster(arr_delay, dep_delay, mean(distance), resolution = 500)

Bar Plot

• dbplot_bar() defaults to a tally of each value in a discrete variable

spark_flights %>%
  dbplot_bar(origin)

• Pass a formula that will be operated for each value in the discrete variable

spark_flights %>%
  dbplot_bar(origin, mean(dep_delay))

Line plot

• dbplot_line() defaults to a tally of each value in a discrete variable

spark_flights %>%
  dbplot_line(month)

• Pass a formula that will be operated for each value in the discrete variable

spark_flights %>%
  dbplot_line(month, mean(dep_delay))

Boxplot

• It expect a discrete variable to group by, and a continuous variable to calculate the percentiles and IQR. It doesn't calculate outliers. Currently, this feature works with sparklyr and Hive connections.

spark_flights %>%
  dbplot_boxplot(origin, dep_delay)

Calculation functions

If a more customized plot is needed, the data the underpins the plots can also be accessed:

1. db_compute_bins() - Returns a data frame with the bins and count per bin
2. db_compute_count() - Returns a data frame with the count per discrete value
3. db_compute_raster() - Returns a data frame with the results per x/y intersection
4. db_compute_boxplot() - Returns a data frame with boxplot calculations

spark_flights %>%
  db_compute_bins(arr_delay) 

## # A tibble: 28 x 2
##     arr_delay  count
##         <dbl>  <dbl>
##  1   4.533333  79784
##  2 -40.733333 207999
##  3  95.066667   7890
##  4  49.800000  19063
##  5 819.333333      8
##  6 140.333333   3746
##  7 321.400000    232
##  8 230.866667    921
##  9 -86.000000   5325
## 10 185.600000   1742
## # ... with 18 more rows

The data can be piped to a plot

spark_flights %>%
  filter(arr_delay < 100 , arr_delay > -50) %>%
  db_compute_bins(arr_delay) %>%
  ggplot() +
  geom_col(aes(arr_delay, count, fill = count))

db_bin()

Uses 'rlang' to build the formula needed to create the bins of a numeric variable in an un-evaluated fashion. This way, the formula can be then passed inside a dplyr verb.

db_bin(var)

## (((max(var) - min(var))/(30)) * ifelse((as.integer(floor(((var) - 
##     min(var))/((max(var) - min(var))/(30))))) == (30), (as.integer(floor(((var) - 
##     min(var))/((max(var) - min(var))/(30))))) - 1, (as.integer(floor(((var) - 
##     min(var))/((max(var) - min(var))/(30))))))) + min(var)

spark_flights %>%
  group_by(x = !! db_bin(arr_delay)) %>%
  tally

## # Source:   lazy query [?? x 2]
## # Database: spark_connection
##             x      n
##         <dbl>  <dbl>
##  1   4.533333  79784
##  2 -40.733333 207999
##  3  95.066667   7890
##  4  49.800000  19063
##  5 819.333333      8
##  6 140.333333   3746
##  7 321.400000    232
##  8 230.866667    921
##  9 -86.000000   5325
## 10 185.600000   1742
## # ... with more rows

spark_flights %>%
  filter(!is.na(arr_delay)) %>%
  group_by(x = !! db_bin(arr_delay)) %>%
  tally %>%
  collect %>%
  ggplot() +
  geom_col(aes(x, n))