1. select() columns by variable name: front of cheatsheet, bottom right
2. filter() rows matching criteria: front of cheatsheet, bottom middle.
3. summarise() numerical variables that are group_by() categorical variables
4. mutate() existing variables to create new ones
5. arrange() rows

Piping allows you to take the output of one function and pipe it as the input of the next function. You can string along several pipes to form a single chain.

• R Command: %>%
• Described as: "then".

front of cheatsheet, bottom left

Ex: say you want to apply functions h() and g() and then f() on data x. You can do

• f(g(h(x))) OR
• x %>% h() %>% g() %>% f()

This

• saves you from confusing nested parentheses
• emphasizes the sequential breaking down of tasks, making it more readable
• i.e. Do this then do this then do this then …

Pipes are always directed to the first argument of any function. The following three bits of R code do the same thing: extract all january flights:

library(dplyr)
library(nycflights13)
data(flights)

# Bit 1: No piping
filter(flights, month == 1)

# Bit 2: Piping. Note no comma
flights %>% filter(month == 1)

# Bit 3: Piping across multiple lines (preferred for legibility)
flights %>% 
  filter(month==1)

1. select() columns by variable name: front of cheatsheet, bottom right
2. filter() rows matching criteria: front of cheatsheet, bottom middle
3. summarise() numerical variables that are group_by() categorical variables: back of cheatsheet, left-hand column, top and bottom
4. mutate() existing variables to create new ones
5. arrange() rows

Summary statistics are single numbers that summarise a vector (i.e. a sequence/list) of numerical values:

Load the following in your console

library(dplyr)

# Create data frame with two variables
test_data <- data_frame(
  name=c("Albert", "Albert", "Albert", "Virginia", "Virginia"),
  value=c(1, 2, 3, 4, 5)
)

# See contents in console
test_data

Run each of these 3 bits separately in your console:

# Bit 1: No group structure: overall sum
test_data %>% summarise(total=sum(value))

# Bit 2: Grouped by name: name-by-name sum "total"
test_data %>% group_by(name) %>% summarise(total=sum(value))

# Bit 3: Grouped by name: name-by-name sum "total" and mean "avg"
test_data %>% group_by(name) %>% 
  summarise(total=sum(value), avg=mean(value))