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	%in%	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 packages used in our program

Copy the code below to install all the packages used in our program:

install.packages(c('dataReporter', 'dplyr', 'flextable', 'forcats', 'fs', 'ggplot2', 'ggrepel', 'ggtext', 
'ggthemes', 'glue', 'gtsummary', 'gt', 'janitor', 'marquee', 'pointblank', 'purrr', 'quarto', 'readr', 
'readxl', 'scales', 'skimr', 'stringr', 'tibble', 'tidyr'), 
dependencies = TRUE)

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() and filter_out()	{dplyr}	keep or drop rows that match a condition	starwars \|> filter(eye_color == “yellow”)	Data Wrangling and Analysis
12	when_any()	{dplyr}	equivalent to x \| y \| z	penguins \|> filter(when_any(species == “Adelie”, species == “Chinstrap”))	Data Wrangling and Analysis
13	when_all()	{dplyr}	equivalent to x & y & z	penguins \|> filter(when_all(species %in% c(“Adelie”, “Chinstrap”), island == “Torgersen”))	Data Wrangling and Analysis
14	summarize()	{dplyr}	summarize a variable	starwars \|> summarize(avg_height = mean(height))	Data Wrangling and Analysis
15	view()	{tibble}	opens a data viewer in the IDE	view(df)	Data Exploration
16	group_by()	{dplyr}	group by one or more variables	starwars \|> group_by(species) \|> summarize(avg_height = mean(height))	Data Wrangling and Analysis
17	max() and min()	base R	Returns the maxima and minima of the input values.	df \|> filter(value > min(value))	Data Wrangling and Analysis
18	arrange()	{dplyr}	order the rows of a data frame	starwars \|> arrange(desc(height))	Data Wrangling and Analysis
19	c()	base R	combines values into a vector	c(1,2,3)	Data Wrangling and Analysis
20	ends_with()	{dplyr}	select variables that ends with an exact suffix	df \|> select(ends_with(“name”))	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
21	ggplot()	{ggplot2}	initializes a ggplot object	ggplot(data = starwars, aes(x = height, y = mass))	Data Visualization
22	aes()	{ggplot2}	defines aesthetic mappings	aes(x = height, y = mass, color = gender)	Data Visualization
23	coord_flip()	{ggplot2}	flips x and y axes	ggplot(df) + coord_flip()	Data Visualization
24	desc()	{dplyr}	sorts values in descending order	arrange(desc(x))	Data Wrangling and Analysis
25	geom_point()	{ggplot2}	creates a scatterplot	ggplot(starwars, aes(x = height, y = mass)) + geom_point()	Data Visualization
26	geom_bar()	{ggplot2}	creates a bar chart	ggplot(starwars, aes(x = species)) + geom_bar()	Data Visualization
27	geom_histogram()	{ggplot2}	creates a histogram	ggplot(starwars, aes(x = height)) + geom_histogram(binwidth = 10)	Data Visualization
28	geom_label()	{ggplot2}	adds labeled text to plots	geom_label(aes(label = x))	Data Visualization
29	geom_boxplot()	{ggplot2}	creates a boxplot	ggplot(starwars, aes(x = gender, y = height)) + geom_boxplot()	Data Visualization
30	geom_col()	{ggplot2}	creates a bar chart with precomputed values	ggplot(df, aes(x, y)) + geom_col()	Data Visualization
31	facet_wrap()	{ggplot2}	splits the plot into multiple panels	ggplot(starwars, aes(x = height, y = mass)) + geom_point() + facet_wrap(vars(species))	Data Visualization
32	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
33	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
34	mean()	base R	calculates the average of values	mean(x)	Data Wrangling and Analysis
35	n()	{dplyr}	counts rows in a group	summarize(n = n())	Data Wrangling and Analysis
36	number()	{scales}	low-level numeric formatter	number(1234567, big.mark = “,”, decimal.mark = “.”, accuracy = 1)	Data Visualization
37	read_tsv()	{readr}	reads a tab-separated file	read_tsv(‘file.tsv’)	Data Import/Export
38	theme()	{ggplot2}	customizes plot appearance	ggplot(starwars, aes(x = height, y = mass)) + geom_point() + theme_minimal()	Data Visualization
39	vars()	{dplyr}	tidy-select semantics for scoped verbs like mutate_at() and summarise_at(); superseded in favour of across()	facet_wrap(vars(x))	Data Wrangling and Analysis
40	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
41	scale_color_viridis_c()	{ggplot2}	applies continuous viridis color scale	ggplot(df) + scale_color_viridis_c()	Data Visualization
42	scale_color_viridis_d()	{ggplot2}	applies discrete viridis color scale	ggplot(df) + scale_color_viridis_d()	Data Visualization
43	scale_fill_manual()	{ggplot2}	manually sets fill colors	ggplot(df) + scale_fill_manual(values = c(‘red’,‘blue’))	Data Visualization
44	scale_fill_viridis_c()	{ggplot2}	applies continuous viridis fill scale	ggplot(df) + scale_fill_viridis_c()	Data Visualization
45	scale_fill_viridis_d()	{ggplot2}	applies discrete viridis fill scale	ggplot(df) + scale_fill_viridis_d()	Data Visualization
46	theme_economist()	{ggthemes}	applies Economist-style theme to plots	ggplot(df) + theme_economist()	Data Visualization
47	theme_fivethirtyeight()	{ggthemes}	applies FiveThirtyEight-style theme	ggplot(df) + theme_fivethirtyeight()	Data Visualization
48	theme_light()	{ggplot2}	applies a light theme to plots	ggplot(df) + theme_light()	Data Visualization
49	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
50	format: html	Quarto	sets document output format to HTML	format: html	Quarto
51	format: pdf	Quarto	sets document output format to PDF	format: pdf	Quarto
52	format: docx	Quarto	sets document output format to Word	format: docx	Quarto
53	toc: true	Quarto	adds a table of contents	toc: true	Quarto
54	Heading Heading	Quarto	creates a heading	Heading Heading	Quarto
55	bold	Quarto	formats text as bold	bold	Quarto
56	italic	Quarto	formats text as italic	italic	Quarto
57	Link	Quarto	creates a hyperlink	[Google](https://www.google.com)	Quarto
58	List item	Quarto	creates an unordered list	- Item 1 - Item 2 - Item 3	Quarto
59	Ordered item Ordered item	Quarto	creates an ordered list	FirstSecondThird First Second Third	Quarto
60	Blockquote	Quarto	creates a blockquote	> This is a blockquote	Quarto
61	Inline code	Quarto	formats text as code	The top response was `r top_response`	Quarto
62	Code block	Quarto	creates a chunk of R code in a Quarto document	```{r} library(tidyverse) ```	Quarto
63	Horizontal line	Quarto	creates a horizontal line	---	Quarto
64	Image	Quarto	inserts an image	![Quarto Logo](quarto.png)	Quarto
65	#\| echo: false	Quarto	code chunk option to hide code but shows output	echo: false	Quarto
66	#\| eval: false	Quarto	code chunk option to prevent code execution	#\| eval: false	Quarto
67	#\| warning: false	Quarto	code chunk option to hide warnings	#\| warning: false	Quarto
68	#\| message: false	Quarto	code chunk option to hide messages	#\| message: false	Quarto
69	#\| cache:	Quarto	code chunk option to cache code output	#\| cache: true	Quarto
70	#\| fig-align: ‘center’	Quarto	code chunk option to align figure in the center	#\| fig-align: “center”	Quarto
71	#\| fig-cap: ‘Caption’	Quarto	code chunk option to add a caption to a figure	#\| fig-cap: “Scatterplot”	Quarto
72	#\| fig-width:	Quarto	code chunk option to set figure width	#\| fig-width: 6	Quarto
73	#\| 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
74	download.file()	base R	downloads a file from the Internet	download.file(“url”, destfile = “my_path/my_file.csv”)	Data Import/Export
75	read_excel()	{readxl}	reads an Excel file into R	read_excel(“data.xlsx”, sheet = 1)	Data Import/Export
76	recode_values()	{dplyr}	map old values to new values and creates a new vector	recode_values(x, ‘A’ ~ ‘Alpha’, ‘B ~ ’Beta’)	Data Wrangling and Analysis
77	recode()	{dplyr}	recodes values; superseeded in favor of recode_values()	recode(x, A = ‘Alpha’)	Data Wrangling and Analysis
78	replace_na()	{tidyr}	replaces NA values	replace_na(x, 0)	Data Wrangling and Analysis
79	write_csv()	{readr}	writes a dataframe to a CSV file	write_csv(data, “output.csv”)	Data Import/Export
80	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
81	pivot_wider()	{tidyr}	converts long data into wide format	pivot_wider(data, names_from = “variable”, values_from = “value”)	Data Wrangling and Analysis
82	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
83	set_names()	{purrr}	sets names for a vector	set_names(x, c(‘a’,‘b’))	Data Wrangling and Analysis
84	separate_longer_delim()	{tidyr}	splits one column into multiple rows by a delimiter	separate_longer_delim(data, col = “tags”, delim = “,”)	Data Wrangling and Analysis
85	count()	{dplyr}	counts instances of unique values in a column	data \|> count(category)	Data Wrangling and Analysis
86	distinct()	{dplyr}	returns unique rows in a dataset	data \|> distinct()	Data Wrangling and Analysis
87	parse_number()	{readr}	extracts numbers from character strings	parse_number(“$1,234.56”)	Data Wrangling and Analysis
88	as.numeric()	base R	converts a column to numeric type	as.numeric(c(“1”, “2”, “3”))	Data Wrangling and Analysis
89	case_when()	{dplyr}	performs conditional value assignment	mutate(data, category = case_when(value > 10 ~ “High”, .default ~ “Low”))	Data Wrangling and Analysis
90	clean_names()	{janitor}	standardizes column names	clean_names(df)	Data Cleaning
91	case_match()	{dplyr}	maps values to new categories	mutate(data, category = case_match(x, “A” ~ “Alpha”, “B” ~ “Beta”))	Data Wrangling and Analysis
92	na_if()	{dplyr}	replaces a specific value with NA	mutate(data, var = na_if(var, “Unknown”))	Data Wrangling and Analysis
93	contains()	{dplyr}	selects columns that contain a string	select(data, contains(“score”))	Data Wrangling and Analysis
94	starts_with()	{dplyr}	selects columns that start with a string	select(data, starts_with(“age”))	Data Wrangling and Analysis
95	str_detect()	{stringr}	checks if a string contains a pattern	filter(data, str_detect(name, “John”))	Data Wrangling and Analysis
96	str_remove()	{stringr}	removes pattern from strings	str_remove(x, ‘a’)	Data Wrangling and Analysis
97	str_replace()	{stringr}	replaces text patterns in a string	mutate(data, name = str_replace(name, “Mr.”, ““))	Data Wrangling and Analysis
98	bind_rows()	{dplyr}	binds multiple dataframes by rows	bind_rows(df1, df2)	Data Wrangling and Analysis
99	inner_join()	{dplyr}	joins two datasets, keeping only matching rows	inner_join(df1, df2, join_by(“id”))	Data Wrangling and Analysis
100	join_by()	{dplyr}	specifies join conditions	left_join(df1, df2, join_by(id))	Data Wrangling and Analysis
101	left_join()	{dplyr}	joins two datasets, keeping all rows from the left	left_join(df1, df2, join_by(“id”))	Data Wrangling and Analysis
102	right_join()	{dplyr}	joins two datasets, keeping all rows from the right	right_join(df1, df2, join_by(“id”))	Data Wrangling and Analysis
103	full_join()	{dplyr}	joins two datasets, keeping all rows from both	full_join(df1, df2, join_by(“id”))	Data Wrangling and Analysis
104	if_else()	{dplyr}	vectorized conditional function	mutate(x = if_else(a > 0, ‘yes’,‘no’))	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
105	read_rds()	{readr}	reads an RDS file into R	data <- read_rds(“data.rds”)	Data Import/Export
106	write_rds()	{readr}	writes a data frame to an RDS file	data \|> write_rds(“data.rds”)	Data Import/Export
107	ungroup()	{dplyr}	removes grouping structure from a dataset	data \|> ungroup()	Data Wrangling and Analysis
108	is.nan()	base R	checks if a value is NaN (Not a Number)	is.nan(c(1, NaN, 3))	Data Exploration
109	is.na()	base R	checks if a value is NA (Not Available)	is.na(c(1, NA, 3))	Data Exploration
110	fct_reorder()	{forcats}	reorders a factor based on another variable	data \|> mutate(category = fct_reorder(category, value))	Data Wrangling and Analysis
111	geom_line()	{ggplot2}	adds a line plot layer to ggplot	ggplot(data, aes(x, y)) + geom_line()	Data Visualization
112	slice_max()	{dplyr}	selects rows with the highest values of a column	data \|> slice_max(order_by = value, n = 5)	Data Wrangling and Analysis
113	pull()	{dplyr}	extracts a single column as a vector	vector <- data \|> pull(column_name)	Data Wrangling and Analysis
114	fct_relevel()	{forcats}	relevels a factor, moving specified levels first	data \|>mutate(category = fct_relevel(category, “High”))	Data Wrangling and Analysis
115	lag()	{dplyr}	shifts values down by one or more rows	data \|> mutate(prev_value = lag(value))	Data Wrangling and Analysis
116	list()	base R	creates a list object	list(a = 1, b = 2)	Data Wrangling and Analysis
117	map()	{purrr}	applies a function to each element	map(x, ~ .x * 2)	Data Wrangling and Analysis
118	marquee_glue()	{marquee}	creates animated text labels	marquee_glue(‘text’)	Data Visualization
119	geom_text()	{ggplot2}	adds text labels to a ggplot	ggplot(data, aes(x, y, label = label)) + geom_text()	Data Visualization
120	geom_text_repel()	{ggrepel}	adds text labels that avoid overlapping	ggplot(data, aes(x, y, label = label)) + geom_text_repel()	Data Visualization
121	scale_y_continuous()	{ggplot2}	adjusts the y-axis scale	ggplot(data, aes(x, y)) + scale_y_continuous(labels = scales::percent)	Data Visualization
122	rename()	{dplyr}	renames columns in a dataset	data \|> rename(new_name = old_name)	Data Wrangling and Analysis
123	reorder()	{stats}	reorders factor levels	reorder(x, y)	Data Wrangling and Analysis
124	scale_x_discrete()	{ggplot2}	customizes discrete x scale	scale_x_discrete()	Data Visualization
125	drop_na()	{tidyr}	removes rows with missing values	drop_na(data, column_name)	Data Wrangling and Analysis
126	element_blank()	{ggplot2}	removes plot elements	theme(axis.text = element_blank())	Data Visualization
127	element_markdown()	{ggtext}	renders markdown in plot text	element_markdown()	Data Visualization
128	element_marquee()	{marquee}	adds animated text elements	element_marquee()	Data Visualization
129	element_text()	{ggplot2}	customizes text appearance	element_text(size = 12)	Data Visualization
130	everything()	{dplyr}	selects all columns	select(everything())	Data Wrangling and Analysis
131	expansion()	{ggplot2}	controls axis expansion	scale_x_continuous(expand = expansion())	Data Visualization
132	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
133	sum()	base R	sums values	sum(x)	Data Wrangling and Analysis
134	theme_minimal()	{ggplot2}	applies minimal theme	theme_minimal()	Data Visualization
135	percent_format()	{scales}	formats numbers as percentages	ggplot(data, aes(x, y)) + scale_y_continuous(labels = percent_format())	Data Visualization
136	percent()	{scales}	formats numbers as percentages	percent(0.25)	Data Visualization
137	annotate()	{ggplot2}	adds text or shapes to a ggplot	ggplot(data, aes(x, y)) + annotate(“text”, x = 5, y = 10, label = “Note”)	Data Visualization
138	as.character()	base R	converts to character	as.character(x)	Data Wrangling and Analysis
139	dir_create()	{fs}	creates directories	dir_create(‘data/’)	Data Import/Export

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
140	flextable()	{flextable}	creates a customizable table	flextable(data)	Tables
141	gt()	{gt}	creates a gt table for formatted output	gt(data)	Tables
142	cols_label()	{gt}	renames columns in a gt table	data \|> gt() \|> cols_label(column_name = “New Label”)	Tables
143	cols_width()	{gt}	sets column widths in a gt table	data \|> gt() \|> cols_width(vars(column1) ~ px(100), vars(column2) ~ px(200))	Tables
144	cols_align()	{gt}	aligns columns in a gt table	data \|> gt() \|> cols_align(align = “center”, columns = vars(column_name))	Tables
145	tab_caption()	{gt}	adds a caption below a gt table	data \|> gt() \|> tab_caption(“This is a table caption.”)	Tables
146	opt_interactive()	{gt}	makes the table interactive (sortable, searchable)	data \|> gt() \|> opt_interactive()	Tables
147	quarto_render()	{quarto}	renders a Quarto document to the specified format	quarto_render(“report.qmd”, output_format = “html”)	Quarto
148	tibble()	{tibble}	creates a modern data frame	tibble(name = c(“Alice”, “Bob”), age = c(25, 30))	Quarto
149	pwalk()	{purrr}	iterates over multiple lists, applying a function	pwalk(list(names, ages), ~ print(paste(.x, “is”, .y, “years old”)))	Quarto