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R Cheat Sheet Cheat Sheet by

A brief cheat sheet for students learning R

Operators

=

<-
Assigns a value to an object
 
 >
greater than
 
<
less than
 
>=
greater than or equal to
 
<=
less than or equal to
 
==
exactly equal to
 
!=
not equal to
 
!x
not x
 
x | y
x OR y
 
x & y
x AND y
 
|>
Sends something (e.g., dataframe or function output) to something else. i.e., it is an elegant way to nest tidyverse functions.
 
Example:

library(tidyverse)
msleep |>
  ­fil­ter­(co­nse­rvation == 'domes­tic­ated') |>
    ­sum­mar­ise(m = mean(b­rainwt, na.rm = TRUE),
              s = sd(bra­inwt, na.rm = TRUE),
              med = median­(br­ainwt, na.rm = TRUE)
              )


Note that 
summar­ise()
is nested within 
filter()
, which is using info from the 
msleep
dataframe.

Basic R Functions

Access a function's help file
?[function name]
Load a csv file
read.csv( "­­fi­l­e­na­­me.c­­s­v­", header = TRUE )
Install a library
insta­­ll.p­­a­c­k­ag­­es(­­"­l­ibrary name")
Load an installed library
libra­­ry(­­li­brary name)
Resize images in Google Collab
optio­­ns(­­re­p­r.p­­lot.width = x, repr.p­­lo­t.h­eight = y)
Return the amount of values in x
lengt­h(x)
Return the number of rows in a dataframe
nrow(d­ata­frame)
Return the absolute value(s) in x
abs(x)
Return the sum of all the values in x
sum(x)
Return the square­­-root of the value(s) in x
sqrt(x)
Return the mean of the values in x with optional arguments for trimming and removing NAs
mean(x, tr = 0, na.rm = FALSE)
Return the median of the values in x with optional arguments removing NAs
median(x, na.rm = FALSE)
Return the sample standard deviation of values in x with optional argument for removing NAs
sd(x, na.rm = FALSE)
Return the sample variance of values in x with optional argument for removing NAs
var(x, na.rm = FALSE)
Return the quartiles for x with optional argument for removing NAs
quanti­le(x, na.rm = FALSE)
Sort the values of x into ascending order
sort(x)
Compute the median absolute deviation of x with optional argument to remove NAs
mad(x, na.rm = FALSE)
Find NA values in x (returns TRUE/F­ALSE)
is.na(x)
Paste things together into a single string
paste(x, y, z, sep = "­­")
Create a table of counts
Examples: 
table(x)

table(x, y)

Data Frames

Create a new data frame
col_1 <- c("A­", "­B", "­C")

col_2 <- c(21, 22, NA)

new_df <- data.f­ram­e(c­ol_1, col_2)
Add a column
new_df­$col_3 <- c(51, 52, 53)
Select a specific value (e.g., 52 = row 2, column 3)
new_df[2, 3]
Select a series of values (e.g., all of row 2)
new_df[2, c(1,2,3)]

or 
new_df[2, ]
Select an entire column (e.g., column 2)
new_df­$col_2

or 
new_df[ , 2]
Isolate values that are not NAs
new_df­$co­l_2­[!i­s.n­a(n­ew_­df$­col_2)]

Filter Function

Used to select specific observ­ations from a dataframe according to a rule you specify.
filter­(da­taf­rame, subset rule)
Example 1:
filter­(he­igh­tData, Father < 60.1 | Father > 75.3)
Example 2:
heightData |> filter­(Father < 60.1 | Father > 75.3)

Subset Function

Used to select specific observ­ations from a dataframe according to a rule you specify.
subset­(da­taf­rame, subset rule, select = ("co­lumns to keep"))
Example:
outliers <- subset­(he­igh­tData, Father < 60.1 | Father > 75.3, select = c("F­ath­er"))

Library Functions

librar­y(t­idy­verse) or librar­y(d­plyr)
Aggregate data sets into a new dataframe.
For example . . .
msleep %>%
  group_by(vore, conser­vation) %>%  ­  
  summarise(m = mean(b­rainwt, na.rm = TRUE),
            s = sd(bra­inwt, na.rm = TRUE)
            )
librar­y(r­com­panion)
Calculates lambda for Tukey's ladder of powers
transf­orm­Tuk­ey(x, plotit = FALSE, return­Lambda = TRUE)
librar­y(WRS2)
Winsorized variance of x
winvar(x, tr = .2)

Distri­bution Functions

Return the the corres­​po­nding quantile for a given cumulative probab­​ility
Normal Distri­bution
qnorm​­(pr­​ob­a​b­ility, mean, sd)
T Distri­bution
qt(pr​­oba­​bi­lity, df, lower.t­ail)
F Distri­bution
qf(pro­bab­ility, df1, df2, lower.t­ail)
Chi-Square Distri­bution
qchisq­(pr­oba­bility, df, lower.t­ail)
Return the the corres­​po­nding cumulative probab­​ility for a given quantile.
Normal Distri­bution
pnorm​­(qu­antile, mean, sd)
T Distri­bution
pt(qua­ntile, df, lower.t­ail)
F Distri­bution
pf(qua­ntile, df1, df2, lower.t­ail)
Chi-Square Distri­bution
pchisq­(qu­antile, df, lower.t­ail)
Note:
- z-scores and t-scores (e.g. critical T and test statis­tics) are types of quantiles.

- The calcul­ations are all performed from left to right by default unless you specify lower.tail = FALSE).

Plotting: librar­y(g­gplot2)

Histogram
ggplot­(da­taF­rame, aes(x = Dep_Var)) +

 ­ ­ ­ ­   ­geo­m_h­ist­ogr­am(­colour = "­bla­ck",

 ­ ­ ­ ­ ­ ­ ­ ­ ­ ­       ­ ­ ­ ­  fill = "­whi­te")
Density Plot
ggplot­(da­taF­rame, aes(x = Dep_Var)) +

 ­ ­ ­ ­   ­geo­m_d­ens­ity­(colour = "­bla­ck",

 ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­      fill = "­pin­k",

 ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­  adjust = 1)
Boxplot - for one sample
ggplot­(da­taF­rame, aes(y = Dep_Var,)) +

 ­ ­ ­ ­   ­geo­m_b­oxp­lot()
Boxplot - for two or more samples
ggplot­(da­taF­rame, aes(x = Indep_Var, y = Dep_Var)) +

 ­ ­ ­ ­   ­geo­m_b­oxp­lot()
Barplot with errorbars
ggplot­(pl­otData, aes(x = Indep_Var, y = Dep_Var,

 ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­       fill = Indep_­Var)) +

 ­  geom_b­ar(stat = "­ide­nti­ty", colour = "­bla­ck") +

 ­  geom_e­rro­rba­r(a­es(ymin = bottom­_va­lues,

 ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ymax = top_va­lues),

 ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­  width = .25)
Q-Q Plot For two indepe­ndent samples
Remove + facet_­wrap() for a single sample
ggplot­(da­taF­rame, aes(sample = Dep_Var)) +

 ­ ­ ­ ­ ­ ­     stat_qq() +

 ­ ­ ­ ­ ­ ­     stat_q­q_l­ine() +

 ­ ­ ­ ­ ­ ­  facet_­wrap(~ Indep_Var)
Line Plot of Means with Two Predictors
ggplot­(pl­otData, aes(x = Predic­torA, y = Means, group = Predic­torB, colour = Predic­torB)) + 
     geom_l­ine­(po­sition = positi­on_­dod­ge(­width = 0.4)) + 
     geom_p­oin­t(p­osition = positi­on_­dod­ge(­width = 0.4))
Scatte­rplot with Regression Line
ggplot­(da­taf­rame, aes(x = predictor, y = response)) + 
     geom_p­oint() + 
     geom_a­bli­ne(­int­ercept = b0, slope = b1)
Note:
Indep_Var = Indepe­ndent Variable
Dep_Var = Dependent Variable
plotData = Dataframe of aggregated values

R Style Guide (from the Tidyverse)
 

Metadata

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  • Published: 28th December, 2023
  • Last Updated: 22nd September, 2025

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