Notes

Functions - 
1. seq() generates numbers from 
2. typeof() for details of the variable
3. starting a variable with "." makes it hidden
4. NA is not available. is.na 
    * The generic function is.na indicates which elements are missing.
    * The generic function is.na<- sets elements to NA.


Vector is a set of one type of data (numbers or letters)

for help -      > ?ls         e.g. ?function
                > ??model     to find a particular function??model
                > vignette is for help in the whole package
                > vignette(package = "tidyverse")
                > vignette("manifesto", "tidyverse")
                * www.cran.csiro.au CRAN Task Views to find package of interest
                * search google for help with the package name. 
                * www.stackoverflow.com search box -> "[R] change point color on R ggplot"

A tidy table
  1. Each variable in the data set is placed in its own column
  2. Each observation is placed in its own row
  3. Each value is placed in its own cell
  
  
A tidy table  header from a 96 well plate date. 
plate row column value


Packages function conflicts
In case of conflickts R would use the most recent lodaded package.
To force it for a particular function use the package name -> base::stat

select(data, arguments)
  arguments could be
    column names, 
    column numbers
    ranges
    -sign to remove
    start_with
    end_with
    contains etc
    
Renaming coulumn 
  select(data, new_name = old_name) #it will give you only remaned column back
  rename(data, new_name = old_name)
  
Fixing all column look package "janitor"

Filtering rows
  filter(data, column_head "column_value_to_be_Filtered" mathematical_function column_head "column_value_to_be_Filtered")
  
filter(gapminder, continent %in% c("Africa", "Asia", "Europe"))

#add or remove new columns using mutate
#create a new column called gdp by multiplying gdpPercep by pop
mutate(gapminder, gdp = gdpPercap * pop)
#create a new column to calculate the polupations in millions
mutate(gapminder, popMillion = pop/10000)
mutate(gapminder, popMillion = log(pop))

#to pull string from value of a column to a new column (to pull variables from a sample name)
mutate(gapminder, country_abbr = str_sub(country, start = 1, end = 4))
#using "-" minus sign, it would start from the end of the string
mutate(gapminder, country_end_letter = str_sub(country, start = -1))

Group_by
summarise(gapminder, mean_pop = mean(pop), median_pop = median(pop))
summarise(gapminder_by_country, mean_LE = mean(lifeExp), median_LE = median(lifeExp))

#Summarise and Grouping
#Calculate meand and median for the population for the gapminder dataframe
summarise(gapminder, mean_pop = mean(pop), median_pop = median(pop))

pipe
filter(gapminder, country == "Australia") %>% select(., "country", "year" , "pop")