2 Manipulating Rows

Select rows	`filter()`, `distinct()`, `slice()`
Arrange rows	`arrange()`
Add rows	`add_row()`, `bind_rows()`

Please copy the following code and paste them into a script in the RStudio. We will walk through them with the visual explanations from the dplyr cheat sheet.

Input

# make sure you loaded the tidyverse library
library(tidyverse)

# Get to know the data
?mtcars
mtcars

# Sample code from the dplyr cheat sheet
## 2.1 Select cases
over20=filter(mtcars, mpg > 20)
distinctmt=distinct(mtcars, gear)
slicemt=slice(mtcars, 10:15)
headmt=slice_head(mtcars, n = 5)
tailmt=slice_tail(mtcars, n = 5)

## 2.2 Arrange observations
arrmt=arrange(mtcars, mpg)
descmt=arrange(mtcars, desc(mpg))

## 2.3 Make dataframes and bind them together
### A showcase for bind_rows
x <- data.frame(
  A = c('a', 'b'), 
  B = c('t', 'u'),
  C = c(1, 2))
x
y <- data.frame(
  A = c('c', 'v'), 
  B = c('d', 'w'),
  D = c(TRUE, FALSE))
y
bind_rows(x, y)

Practice 1

iris is a data frame with 150 cases (rows) and 5 variables (columns) such as Petal.Width and Species. In the iris data set, the cases with the minimum and maximum petal width belong to what species?

Click here for solutions

# solution 1
arrange(iris, Petal.Width)
# solution 2
slice_min(iris, Petal.Width)
slice_max(iris, Petal.Width)

# The case with the minimum petal width belongs to setosa.
# The case with the maximum petal width belongs to virginica.