C R Cheatsheet

This section gives a very brief overview of the most useful functions, and links to sections where those are discussed more in depth.

C.1 Rstudio Keyboard shortcuts

Note: some of these are system-dependent. See the complete list at Posit’s page, also available from menu Help -> Keyboard Shortcuts.

<-, the assignment operator: Alt - - (alt-minus).
%>%, the pipe operator: Ctrl + Shift + M
Open new script: Ctrl + Shift + N
zooming/focusing (see the View -> Panes menu)
- switch to source: Ctrl + 1
- zoom to source: Ctrl + Shift + 1
- switch to console: Ctrl + 2
- zoom to console: Ctrl + Shift + 2
- bring back the default 4-pane view: Ctrl + Shift + 0
Work with code
- indent: Ctrl - I
- comment/uncomment: Ctrl + Shift + C
run program (hover mouse over the buttons):
- source with echo: Ctrl + Shift + Enter
- source without echo: Ctrl + Shift + S
- run selection/statement: Ctrl + Enter
- save current file: Ctrl + S (asks for name if saving for the first time).
Rmarkdown
- insert code chunk: Ctrl + Alt + I
- knit: Ctrl - Shift - K

install.packages, e.g. install.packages("lubridate"): install the package (library). Do it once on each computer. See 2.7
library (library(tidyverse)): load the package. Once per R session. See 2.7

read_delim, e.g. titanic <- read_delim("titanic.csv"): load a csv (delimited) file, automatically detect the delimiter. See 3.7.
c, e.g. grades <- c(4.0, 3.5, 2.4): create a new variable (vector) that contains multiple values.
data.frame, e.g. students <- data.frame(name=c("Ah", "Yuval", "Shaykh"), grades=c(4.0, 3.5, 3.0)): create a new data frame

nrow (nrow(titanic)): number of rows (observations) in the dataset. See 6.2.1.
ncol (ncol(titanic)): number of columns (variables) in the dataset. See 6.2.1.
names (names(titanic)): list of variables names in the dataset. See 6.2.1.
head (titanic %>% head(3)): show the first few observations of the dataset. See 6.2.1.
tail (titanic %>% tail(3)): show the last few observations of the dataset. See 6.2.1.
summary (titanic %>% pull(age) %>% summary()): provide a brief description of the variables (or dataset). See 6.4.
counting missings titanic %>% pull(age) %>% is.na() %>% sum() tests if the values are missing, and counts all that are. See 6.4.
is.na tests if certain values are missing (but does not count them). See 6.4.
table (titanic %>% pull(sex) %>% table()) compute frequency table of a variable (or pivot table of two variables). See 6.4.

sample_n (titanic %>% sample_n(3)): show a random subset of the dataset. See 6.2.1.
pull (titanic %>% pull(fare)): extract a single variable from the data frame. See 6.2.2.

sum (titanic %>% pull(fare) %>% sum(); titanic %>% pull(age) %>% is.na() %>% sum()) compute sum, or count cases where a condition holds. See 6.4.
min, max, range (titanic %>% pull(fare) %>% min(); titanic %>% pull(age) %>% range(na.rm=TRUE)) compute minimum, maximum, or both. Extra argument na.rm=TRUE makes this to ignore NA-s. See 6.4.

See Section 5.3.

select: select desired columns (variables). Useful to avoid unnecessary clutter in your data.
filter: filter (keep) only desired rows (observations). In this way we narrowed our example analysis down to just male survivors.
arrange: order observations by descending/ascending order by some sort of value, e.g. by age.
mutate: compute new variables, or overwrite existing ones.
summarize: collapse data down to a small number of summary statistics.

See Section 5.3.2.2

==: equality, as filter(survived == 1)
>: greater than, as filter(age > 60)
>=: greater than or equal, e.g. filter(age >= 60).
<: less than, as filter(age < 60)
<=: less than or equal, e.g. filter(age <= 60).
!=: not equal. For instance, filter(embarked != "S").
%in% c(...): only keep observations where the value is in a given list of values, as filter(embarked %in% c("C", "Q")).
&: logical “AND”, as filter(sex == "male" & age >= 20) for adult male
|: logical “OR”, as filter(sex == "female" | age <15) for a woman or a child.
!: logical “NOT”, as filter(!(sex == "female" | age <15)) for someone who is neither woman nor a child. Note the double parenthesis.

as.numeric (harden %>% mutate(FG = as.numeric(FG))) converts a text (character) variable to numbers. If the column is already numeric then it does nothing.
as.character (harden %>% mutate(FG = as.numeric(FG))) converts a numeric (or factor) column into a character column. If it is already a text column then nothing happens.

separate (harden %>% separate(MP, into=c("min", "sec"), convert=TRUE)) separates a text column into multiple columns based on a separator (default: a non-letter/non-numeric character). See Section 11.5.