Lesson 7 Introduction to Tidyverse

This lesson covers some of the most basic Tidyverse functions. We talked about installing the Tidyverse package back in Section 1.7 Installing packages. Tidyverse functions are equivalent to some of the operations you learned about in earlier lessons. We are going to perform these functions using the iris dataset, which comes with base R.

Load the iris dataset:

data(iris)

We will also need to load the Tidyverse library:

library(tidyverse)

7.1 The filter function

This function removes data frame rows based on the values in one or more of the data frame columns.

# This is an example of filtering, where "x" is some 
# criteria based on a comparison operator, and "df" is a data frame
filter(df, x)

# The filter function above is equivalent to indexing the data frame, using 
# the which() function for the comparison criteria
df[which(x), , drop = FALSE]

# E.g., keep only sepal lengths > 6
#   ...using indexing
iris_long_sepals_indexing <- iris[which(iris$Sepal.Length > 6), , drop = F]
#   ...using the filter() function in Tidyverse
iris_long_sepals_tidyverse <- filter(iris, Sepal.Length > 6)

# E.g., keep only 'versicolor' species
#   ...using indexing
iris_versicolor_indexing <- iris[which(iris$Species == 
                                         'versicolor'), , drop = F]
#   OR
iris_not_versicolor_indexing <- iris[which(iris$Species != 
                                             'versicolor'), , drop = F]
#   ...using the filter() function in Tidyverse
iris_versicolor_tidyverse <- filter(iris, Species == 'versicolor')
#   OR
iris_not_versicolor_tidyverse <- filter(iris, Species != 'versicolor')

7.2 The select function

This function removes specified data frame columns.

# This is an example of selecting only the columns x and y from the 
# data frame df
select(df, x, y)

#  This is an example of the same operation using vector indexing
df[c("x", "y")] 

# E.g., keep only Species and Petal.Width
#   ...using indexing
iris_species_petals_indexing <- iris[c('Petal.Width', 'Species')]
#   ...using the select() function in Tidyverse
iris_species_petals_tidyverse <- select(iris, Petal.Width, Species)

7.3 The rename function

This function renames columns in your data frame.

# This is an example of renaming the column x in data frame 
# df with a new name, y
rename(df, y = x)
# This is an example of the same operation using vector indices
names(df)[names(df) == "x"] <- "y"

# E.g., rename 'Petal.Width' to 'PetalWidth_cm'
#   ...using indexing
iris_rename_indexing <- iris
names(iris_rename_indexing)[names(iris_rename_indexing) == 
                              'Petal.Width'] <- 'PetalWidth_cm'
#   ...using the rename() function in Tidyverse
iris_rename_tidyverse <- rename(iris, 'PetalWidth_cm' = 'Petal.Width')

7.4 The mutate function

The mutate function adds columns to a data frame by performing operations on one or more existing columns in the data frame.

# This creates a new column, z, which is the product of values in each row
# of columns x and y
mutate(df, z = x * y)

# This is the equivalent operation in base R
df$z <- df$x * df$y

# E.g., add new column for petal area
#   ...using indexing
iris_petal_area_indexing <- iris
iris_petal_area_indexing$PetalArea <- iris$Petal.Length * iris$Petal.Width
#   ...using the mutate() function in Tidyverse
iris_petal_area_tidyverse <- mutate(iris, PetalArea = 
                                      Petal.Length * Petal.Width)