Introduction to The Tidyverse
Lecture
R lab
The Tidyverse
This “meta-package” consists of several R packages, or as the Founding Father Hadley Wickahm says, “The tidyverse is an opinionated collection of R packages designed for data science.”
ggplot2, for plotting.dplyr, for transforming and summarizing dataframe contenttidyr, for transforming (“tidying”) data frame structurepurrr, an upgrade of base R functional programming tools. (Beyond scope of class.)tibble, an improved data frame. (Package will be used implicitly.)
Read about other packages that accompany the Tidyverse but aren’t technically packaged with it here. Click on ggplot2, dplyr, and tidyr, above for the comprehensive references for virtually all functionality of these packages. Most material within will be beyond the scope of this class, but everything you might need will be there.
This class will focus on dplyr and ggplot2, as well as RMarkdown. Next week we will continue to explore ggplot2 and introduce tidyr.
dplyr Functions
Commands can be strung together in order using the pipe %>% operator.*
| Function | Use |
|---|---|
filter() |
Filter data frame on row |
select() |
Filter data frame on column |
mutate() |
Add new column to data frame |
group_by() |
Establish a grouping for downstream operations. Remove with ungroup() |
tally() |
Count the number of observations per grouping |
summarize() |
Perform a summary statistic on a column. Also can spell summarise() |
arrange() |
Arrange a column |
dplyr Examples
### Install the packages ####
install.packages("tidyverse") # Only do this one time
### Load the packages ###
library(tidyverse) # Do this for every R session where you use the package(s)
###### Picking rows with filter() #######
### Base R equivalent
iris[iris$Species == "virginica",]
### using dplyr::filter()
filter(iris, Species == "virginica")
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 6.3 3.3 6.0 2.5 virginica
2 5.8 2.7 5.1 1.9 virginica
3 7.1 3.0 5.9 2.1 virginica
4 6.3 2.9 5.6 1.8 virginica
5 6.5 3.0 5.8 2.2 virginica
6 7.6 3.0 6.6 2.1 virginica
7 4.9 2.5 4.5 1.7 virginica
8 7.3 2.9 6.3 1.8 virginica
9 6.7 2.5 5.8 1.8 virginica
# Equivalent code with %>% pipe
iris %>% filter(Species == "virginica")
#### Separate "and" conditions with a comma ####
iris %>% filter(Species == "virginica", Sepal.Length > 7.5)
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 7.6 3.0 6.6 2.1 virginica
2 7.7 3.8 6.7 2.2 virginica
3 7.7 2.6 6.9 2.3 virginica
4 7.7 2.8 6.7 2.0 virginica
5 7.9 3.8 6.4 2.0 virginica
6 7.7 3.0 6.1 2.3 virginica
###### Picking rows with select() #######
iris %>% select(Species, Petal.Length)
Species Petal.Length
1 setosa 1.4
2 setosa 1.4
3 setosa 1.3
4 setosa 1.5
5 setosa 1.4
6 setosa 1.7
###### Removing rows with select(-) ######
iris %>% select(-Species)
Sepal.Length Sepal.Width Petal.Length Petal.Width
1 5.1 3.5 1.4 0.2
2 4.9 3.0 1.4 0.2
3 4.7 3.2 1.3 0.2
4 4.6 3.1 1.5 0.2
5 5.0 3.6 1.4 0.2
6 5.4 3.9 1.7 0.4
##### Combining filter() and select() with %>% #########
iris %>%
filter(Species == "virginica", Sepal.Width > 3.5) %>%
select(Petal.Width)
Petal.Width
1 2.5
2 2.2
3 2.0
##### Creating new columns with mutate() #####
iris %>% mutate(Sepal.Area = Sepal.Width * Sepal.Length)
Sepal.Length Sepal.Width Petal.Length Petal.Width Species Sepal.Area
1 5.1 3.5 1.4 0.2 setosa 17.85
2 4.9 3.0 1.4 0.2 setosa 14.70
3 4.7 3.2 1.3 0.2 setosa 15.04
4 4.6 3.1 1.5 0.2 setosa 14.26
5 5.0 3.6 1.4 0.2 setosa 18.00
6 5.4 3.9 1.7 0.4 setosa 21.06
#### Combining verbs: Which flowers have sepal areas less than 15, ordered by area? ####
iris %>%
mutate(Sepal.Area = Sepal.Width * Sepal.Length) %>%
filter(Sepal.Area < 15) %>%
arrange(Sepal.Area)
1 5.0 2.0 3.5 1.0 versicolor 10.00
2 4.5 2.3 1.3 0.3 setosa 10.35
3 5.0 2.3 3.3 1.0 versicolor 11.50
4 4.9 2.4 3.3 1.0 versicolor 11.76
5 4.9 2.5 4.5 1.7 virginica 12.25
6 5.5 2.3 4.0 1.3 versicolor 12.65
7 5.1 2.5 3.0 1.1 versicolor 12.75
#### Combining verbs: Which flowers have sepal areas less than 15, ordered by descending area? ####
iris %>%
mutate(Sepal.Area = Sepal.Width * Sepal.Length) %>%
filter(Sepal.Area < 15) %>%
arrange(desc(Sepal.Area))
Sepal.Length Sepal.Width Petal.Length Petal.Width Species Sepal.Area
1 4.8 3.1 1.6 0.2 setosa 14.88
2 5.7 2.6 3.5 1.0 versicolor 14.82
3 4.6 3.2 1.4 0.2 setosa 14.72
4 4.9 3.0 1.4 0.2 setosa 14.70
5 6.3 2.3 4.4 1.3 versicolor 14.49
##### Combining verbs: How many flowers have sepal areas less than 15? ####
iris %>%
mutate(Sepal.Area = Sepal.Width * Sepal.Length) %>%
filter(Sepal.Area < 15) %>%
tally()
n
1 29
##### Combining verbs: How many flowers of each species have sepal areas less than 15?
iris %>%
mutate(Sepal.Area = Sepal.Width * Sepal.Length) %>%
filter(Sepal.Area < 15) %>%
group_by(Species) %>%
tally()
Species n
1 setosa 11
2 versicolor 15
3 virginica 3
##### Summarizing data ####
iris %>% summarize(mean.sepal.width = mean(Sepal.Width))
mean.sepal.width
1 3.057333
iris %>%
group_by(Species) %>%
summarize(mean.sepal.width = mean(Sepal.Width))
Species mean.sepal.width
1 setosa 3.428
2 versicolor 2.770
3 virginica 2.974