R for the Rest of Us Cheatsheets

This is an in-progress version of cheatsheets for R in 3 Months participants and other people taking the R for the Rest of Us courses Fundamentals of R and Going Deeper with R.

General Syntax

Order	Syntax	Meaning	Example
1	<-	assignment operator	x <- 10
2	->	alternative assignment	10 -> x
3	=	assignment (mostly in function arguments)	mean(x = c(1,2,3))
4	!	logical NOT operator: returns FALSE if statement is TRUE	starwars |> filter(eye_color != “yellow”)
5	==	equal	x == y
6		greater than	x > y
7	<	less than	x < y
8	= =	greater than or equal to	x >= y
9	<=	less than or equal to	x <= y
10		logical OR operator: returns TRUE if one of the statements is TRUE	starwars |> filter(eye_color != “yellow” | height > 150)
11	&	logical AND operator: returns TRUE if both elements are TRUE	starwars |> filter(eye_color != “yellow” & height > 150)
12	:	creates a sequence of numbers	1:10
13		checks if a value is in a vector	3 %in% c(1,2,3,4)
14	( )	used for function calls and precedence	sum(1, 2, 3)
15	|>	pipe operator	mtcars |> select(mpg, cyl)
16	function()	defines a function	f <- function(x) x^2

Main functions used in our program

Getting Started with R

[R in 3 Months] Week 1

In this week we learn how to import/read our data and packages, and explore it by creating quick summaries.

Order	Function Name	Package	What it Does	Example	Category
1	install.packages()	base R	installs a package	install.packages(“tidyverse”)	Package Management
2	library()	base R	loads a package	library(tidyverse)	Package Management
3	read_csv()	{readr}	reads a csv file	data <- read_csv(“data.csv”)	Data Import/Export
4	glimpse()	{dplyr}	shows a summary of a data frame	glimpse(penguins)	Data Exploration
5	skim()	{skimr}	summary statistics about variables in data frames	skim(penguins)	Data Exploration
6	tbl_summary()	{gtsummary}	calculates descriptive statistics	tbl_summary(penguins)	Data Exploration
7	makeDataReport()	{dataReporter}	makes a data overview report	makeDataReport(penguins)	Data Exploration
8	scan_data()	{pointblank}	makes a data overview report	scan_data(penguins)	Data Exploration

Fundamentals of R

Data Wrangling and Analysis

[R in 3 Months] Week 2

These lessons teach us how to reduce our dataset to contain only variables and occurrences of interest, create new variables and summarise them.

Order	Function Name	Package	What it Does	Example	Category
9	select()	{dplyr}	keep or drop columns	starwars |> select(species, starships)	Data Wrangling and Analysis
10	mutate()	{dplyr}	create, modify, and delete columns	starwars |> mutate(body_ratio = mass / height)	Data Wrangling and Analysis
11	filter()	{dplyr}	keep or drop rows that match a condition	starwars |> filter(eye_color == “yellow”)	Data Wrangling and Analysis
12	summarize()	{dplyr}	summarize a variable	starwars |> summarize(avg_height = mean(height))	Data Wrangling and Analysis
13	group_by()	{dplyr}	group by one or more variables	starwars |> group_by(species) |> summarize(avg_height = mean(height))	Data Wrangling and Analysis
14	arrange()	{dplyr}	order the rows of a data frame	starwars |> arrange(desc(height))	Data Wrangling and Analysis

Data Visualization

[R in 3 Months] Week 3

In these lessons, we learned the basics of the grammar of graphics and how to make bar plots, scatterplots, and set some colours.

Order	Function Name	Package	What it Does	Example	Category
15	ggplot()	{ggplot2}	initializes a ggplot object	ggplot(data = starwars, aes(x = height, y = mass))	Data Visualization
16	aes()	{ggplot2}	defines aesthetic mappings	aes(x = height, y = mass, color = gender)	Data Visualization
17	geom_point()	{ggplot2}	creates a scatterplot	ggplot(starwars, aes(x = height, y = mass)) + geom_point()	Data Visualization
18	geom_bar()	{ggplot2}	creates a bar chart	ggplot(starwars, aes(x = species)) + geom_bar()	Data Visualization
19	geom_histogram()	{ggplot2}	creates a histogram	ggplot(starwars, aes(x = height)) + geom_histogram(binwidth = 10)	Data Visualization
20	geom_boxplot()	{ggplot2}	creates a boxplot	ggplot(starwars, aes(x = gender, y = height)) + geom_boxplot()	Data Visualization
21	facet_wrap()	{ggplot2}	splits the plot into multiple panels	ggplot(starwars, aes(x = height, y = mass)) + geom_point() + facet_wrap(vars(species))	Data Visualization
22	facet_grid()	{ggplot2}	creates a grid of plots	ggplot(starwars, aes(x = height, y = mass)) + geom_point() + facet_grid(rows = vars(gender), cols = vars(species))	Data Visualization
23	labs()	{ggplot2}	adds labels to the plot	ggplot(starwars, aes(x = height, y = mass)) + geom_point() + labs(title = “Height vs Mass in Star Wars”)	Data Visualization
24	theme()	{ggplot2}	customizes plot appearance	ggplot(starwars, aes(x = height, y = mass)) + geom_point() + theme_minimal()	Data Visualization
25	scale_color_manual()	{ggplot2}	manually sets color scale	ggplot(starwars, aes(x = height, y = mass, color = gender)) + geom_point() + scale_color_manual(values = c(“blue”, “pink”, “purple”))	Data Visualization
26	ggsave()	{ggplot2}	saves plots to a file	ggsave(filename = “plots/starwars-plot.pdf”, height = 8, width = 11, units = “in”)	Data Visualization

Quarto

[R in 3 Months] Week 4

On week 4, we dive into Quarto and learn about code chunks, how to format text using Markdown, and how to render basic reports in PDF, Word and HTML.

Order	Function Name	Package	What it Does	Example	Category
27	format: html	Quarto	sets document output format to HTML	format: html	Quarto
28	format: pdf	Quarto	sets document output format to PDF	format: pdf	Quarto
29	format: docx	Quarto	sets document output format to Word	format: docx	Quarto
30	toc: true	Quarto	adds a table of contents	toc: true	Quarto
31	Heading Heading	Quarto	creates a heading	Heading Heading	Quarto
32	bold	Quarto	formats text as bold	**bold**	Quarto
33	italic	Quarto	formats text as italic	*italic*	Quarto
34	Link	Quarto	creates a hyperlink	[Google](https://www.google.com)	Quarto
35	List item	Quarto	creates an unordered list	- Item 1 - Item 2 - Item 3	Quarto
36	Ordered item Ordered item	Quarto	creates an ordered list	FirstSecondThird First Second Third	Quarto
37	Blockquote	Quarto	creates a blockquote	> This is a blockquote	Quarto
38	Inline code	Quarto	formats text as code	The top response was `r top_response`	Quarto
39	Code block	Quarto	creates a chunk of R code in a Quarto document	```{r} library(tidyverse) ```	Quarto
40	Horizontal line	Quarto	creates a horizontal line	---	Quarto
41	Image	Quarto	inserts an image		Quarto
42	#| echo: false	Quarto	code chunk option to hide code but shows output	echo: false	Quarto
43	#| eval: false	Quarto	code chunk option to prevent code execution	#| eval: false	Quarto
44	#| warning: false	Quarto	code chunk option to hide warnings	#| warning: false	Quarto
45	#| message: false	Quarto	code chunk option to hide messages	#| message: false	Quarto
46	#| cache:	Quarto	code chunk option to cache code output	#| cache: true	Quarto
47	#| fig-align: ‘center’	Quarto	code chunk option to align figure in the center	#| fig-align: “center”	Quarto
48	#| fig-cap: ‘Caption’	Quarto	code chunk option to add a caption to a figure	#| fig-cap: “Scatterplot”	Quarto
49	#| fig-width:	Quarto	code chunk option to set figure width	#| fig-width: 6	Quarto
50	#| fig-height:	Quarto	code chunk option to set figure height	#| fig-height: 4	Quarto

Going Deeper with R

Advanced Data Wrangling

[R in 3 Months] Weeks 6, 7 and 8

In the advanced data wrangling section, we learn how to reshape our datasets and how to combine them. We also learn about the tidy principles and how to make a messy dataset more tidy.

Order	Function Name	Package	What it Does	Example	Category
51	download.file()	Base R	downloads a file from the Internet	download.file(“url”, destfile = “my_path/my_file.csv”)	Data Import/Export
52	read_excel()	{readxl}	reads an Excel file into R	read_excel(“data.xlsx”, sheet = 1)	Data Import/Export
53	write_csv()	{readr}	writes a dataframe to a CSV file	write_csv(data, “output.csv”)	Data Import/Export
54	pivot_longer()	{tidyr}	converts wide data into long format	pivot_longer(data, cols = c(var1, var2), names_to = “variable”, values_to = “value”)	Data Wrangling and Analysis
55	pivot_wider()	{tidyr}	converts long data into wide format	pivot_wider(data, names_from = “variable”, values_from = “value”)	Data Wrangling and Analysis
56	separate_wider_delim()	{tidyr}	separates one column into multiple columns	separate_wider_delim(data, col = “variable”, into = c(“var1”, “var2”), sep = “_“)	Data Wrangling and Analysis
57	separate_longer_delim()	{tidyr}	splits one column into multiple rows by a delimiter	separate_longer_delim(data, col = “tags”, delim = “,”)	Data Wrangling and Analysis
58	count()	{dplyr}	counts instances of unique values in a column	data |> count(category)	Data Wrangling and Analysis
59	distinct()	{dplyr}	returns unique rows in a dataset	data |> distinct()	Data Wrangling and Analysis
60	parse_number()	{readr}	extracts numbers from character strings	parse_number(“$1,234.56”)	Data Wrangling and Analysis
61	as.numeric()	{base}	converts a column to numeric type	as.numeric(c(“1”, “2”, “3”))	Data Wrangling and Analysis
62	case_when()	{dplyr}	performs conditional value assignment	mutate(data, category = case_when(value > 10 ~ “High”, .default ~ “Low”))	Data Wrangling and Analysis
63	case_match()	{dplyr}	maps values to new categories	mutate(data, category = case_match(x, “A” ~ “Alpha”, “B” ~ “Beta”))	Data Wrangling and Analysis
64	na_if()	{dplyr}	replaces a specific value with NA	mutate(data, var = na_if(var, “Unknown”))	Data Wrangling and Analysis
65	contains()	{dplyr}	selects columns that contain a string	select(data, contains(“score”))	Data Wrangling and Analysis
66	starts_with()	{dplyr}	selects columns that start with a string	select(data, starts_with(“age”))	Data Wrangling and Analysis
67	str_detect()	{stringr}	checks if a string contains a pattern	filter(data, str_detect(name, “John”))	Data Wrangling and Analysis
68	str_replace()	{stringr}	replaces text patterns in a string	mutate(data, name = str_replace(name, “Mr.”, ““))	Data Wrangling and Analysis
69	bind_rows()	{dplyr}	binds multiple dataframes by rows	bind_rows(df1, df2)	Data Wrangling and Analysis
70	inner_join()	{dplyr}	joins two datasets, keeping only matching rows	inner_join(df1, df2, join_by(“id”))	Data Wrangling and Analysis
71	left_join()	{dplyr}	joins two datasets, keeping all rows from the left	left_join(df1, df2, join_by(“id”))	Data Wrangling and Analysis
72	right_join()	{dplyr}	joins two datasets, keeping all rows from the right	right_join(df1, df2, join_by(“id”))	Data Wrangling and Analysis
73	full_join()	{dplyr}	joins two datasets, keeping all rows from both	full_join(df1, df2, join_by(“id”))	Data Wrangling and Analysis

Advanced Data Visualization

[R in 3 Months] Weeks 9, 10 and 11

This section teaches us how to make our plots look better. We learn how to tweak themes to declutter, highlight and explain.

Order	Function Name	Package	What it Does	Example	Category
74	read_rds()	{readr}	reads an RDS file into R	data <- read_rds(“data.rds”)	Data Import/Export
75	write_rds()	{readr}	writes a data frame to an RDS file	data |> write_rds(“data.rds”)	Data Import/Export
76	ungroup()	{dplyr}	removes grouping structure from a dataset	data |> ungroup()	Data Wrangling and Analysis
77	is.nan()	base R	checks if a value is NaN (Not a Number)	is.nan(c(1, NaN, 3))	Data Exploration
78	is.na()	base R	checks if a value is NA (Not Available)	is.na(c(1, NA, 3))	Data Exploration
79	fct_reorder()	{forcats}	reorders a factor based on another variable	data |> mutate(category = fct_reorder(category, value))	Data Wrangling and Analysis
80	geom_line()	{ggplot2}	adds a line plot layer to ggplot	ggplot(data, aes(x, y)) + geom_line()	Data Visualization
81	slice_max()	{dplyr}	selects rows with the highest values of a column	data |> slice_max(order_by = value, n = 5)	Data Wrangling and Analysis
82	pull()	{dplyr}	extracts a single column as a vector	vector <- data |> pull(column_name)	Data Wrangling and Analysis
83	fct_relevel()	{forcats}	relevels a factor, moving specified levels first	data |>mutate(category = fct_relevel(category, “High”))	Data Wrangling and Analysis
84	lag()	{dplyr}	shifts values down by one or more rows	data |> mutate(prev_value = lag(value))	Data Wrangling and Analysis
85	geom_text()	{ggplot2}	adds text labels to a ggplot	ggplot(data, aes(x, y, label = label)) + geom_text()	Data Visualization
86	geom_text_repel()	{ggrepel}	adds text labels that avoid overlapping	ggplot(data, aes(x, y, label = label)) + geom_text_repel()	Data Visualization
87	scale_y_continuous()	{ggplot2}	adjusts the y-axis scale	ggplot(data, aes(x, y)) + scale_y_continuous(labels = scales::percent)	Data Visualization
88	rename()	{dplyr}	renames columns in a dataset	data |> rename(new_name = old_name)	Data Wrangling and Analysis
89	drop_na()	{tidyr}	removes rows with missing values	drop_na(data, column_name)	Data Wrangling and Analysis
90	str_glue()	{glue}	creates formatted strings using variables	data |> mutate(full_name = str_glue(“My name is {first_name} {last_name}”))	Data Wrangling and Analysis
91	percent_format()	{scales}	formats numbers as percentages	ggplot(data, aes(x, y)) + scale_y_continuous(labels = percent_format())	Data Visualization
92	annotate()	{ggplot2}	adds text or shapes to a ggplot	ggplot(data, aes(x, y)) + annotate(“text”, x = 5, y = 10, label = “Note”)	Data Visualization

Advanced Quarto

[R in 3 Months] Week 12

In these option advanced Quarto lessons we learn how to make parameterized reports, how to use inline R code to print variables as text, and how to publish your reports.

Order	Function Name	Package	What it Does	Example	Category
93	flextable()	{flextable}	creates a customizable table	flextable(data)	Tables
94	gt()	{gt}	creates a gt table for formatted output	gt(data)	Tables
95	cols_label()	{gt}	renames columns in a gt table	data |> gt() |> cols_label(column_name = “New Label”)	Tables
96	cols_width()	{gt}	sets column widths in a gt table	data |> gt() |> cols_width(vars(column1) ~ px(100), vars(column2) ~ px(200))	Tables
97	cols_align()	{gt}	aligns columns in a gt table	data |> gt() |> cols_align(align = “center”, columns = vars(column_name))	Tables
98	tab_caption()	{gt}	adds a caption below a gt table	data |> gt() |> tab_caption(“This is a table caption.”)	Tables
99	opt_interactive()	{gt}	makes the table interactive (sortable, searchable)	data |> gt() |> opt_interactive()	Tables
100	quarto_render()	{quarto}	renders a Quarto document to the specified format	quarto_render(“report.qmd”, output_format = “html”)	Quarto
101	tibble()	{tibble}	creates a modern data frame	tibble(name = c(“Alice”, “Bob”), age = c(25, 30))	Quarto
102	pwalk()	{purrr}	iterates over multiple lists, applying a function	pwalk(list(names, ages), ~ print(paste(.x, “is”, .y, “years old”)))	Quarto