R Cheat Sheet by non_human_entity

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 corresponding quantile for a given cumulative probability

Normal Distribution

qnorm(probability, mean, sd)

T Distribution

qt(probability, df, lower.tail)

F Distribution

qf(probability, df1, df2, lower.tail)

Chi-Square Distribution

qchisq(probability, df, lower.tail)

Return the the corresponding cumulative probability 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)

https://style.tidyverse.org/

R Cheat Sheet

Operators

Basic R Functions

Data Frames

Filter Function

Subset Function

Library Functions

Distribution Functions

Plotting: library(ggplot2)

R Style Guide (from the Tidyverse)

Badges

Comments

R Cheat Sheet

Operators

Basic R Functions

Data Frames

Filter Function

Subset Function

Library Functions

Distri­bution Functions

Plotting: librar­y(g­gplot2)

R Style Guide (from the Tidyverse)

Badges

Comments

Distribution Functions

Plotting: library(ggplot2)