R Quick Reference {.unnumbered}

Every R function used in this workbook, organized by task. Use this as a lookup table when you’re working on assignments.

Importing Data

Function	Package	What It Does	Example
`read_csv()`	tidyverse	Imports a CSV file into R	`df <- read_csv("data.csv")`
`readRDS()`	base R	Loads a saved .RDS file	`df <- readRDS("data.RDS")`
`clean_names()`	janitor	Converts column names to lowercase_with_underscores	`df <- df \|> clean_names()`

Exploring Data

Function	Package	What It Does	Example
`glimpse()`	tidyverse	Shows every column with data type and preview	`glimpse(df)`
`summary()`	base R	Summary statistics for all columns	`summary(df)`
`dim()`	base R	Number of rows and columns	`dim(df)`
`names()`	base R	List column names	`names(df)`
`nrow()`	base R	Count rows	`nrow(df)`
`head()`	base R	Show first 6 rows	`head(df)`

Cleaning Data

Function	Package	What It Does	Example
`distinct()`	tidyverse	Remove duplicate rows	`df \|> distinct()`
`filter()`	tidyverse	Keep rows matching a condition	`df \|> filter(age > 18)`
`mutate()`	tidyverse	Create or modify columns	`df \|> mutate(new = old * 2)`
`case_when()`	tidyverse	Recode values with if/then logic	`case_when(x == 1 ~ "yes", TRUE ~ "no")`
`str_to_lower()`	tidyverse	Convert text to lowercase	`str_to_lower("POP")` → `"pop"`
`is.na()`	base R	Check for missing values	`filter(!is.na(column))`
`factor()`	base R	Convert column to a categorical factor	`factor(df$genre)`
`levels()`	base R	Show factor levels	`levels(df$genre)`

Counting and Summarizing

Function	Package	What It Does	Example
`count()`	tidyverse	Count rows by group	`df \|> count(genre, sort = TRUE)`
`group_by()`	tidyverse	Group data for per-group calculations	`df \|> group_by(genre)`
`summarize()`	tidyverse	Calculate summary statistics per group	`summarize(mean_val = mean(x))`
`ungroup()`	tidyverse	Remove grouping	`df \|> ungroup()`
`pivot_wider()`	tidyverse	Reshape long → wide	`pivot_wider(names_from = col, values_from = n)`

Visualization (ggplot2)

Function	What It Does	Example
`ggplot()`	Initialize a plot	`ggplot(df, aes(x = genre))`
`aes()`	Map variables to visual properties	`aes(x = genre, fill = mode)`
`geom_col()`	Bar chart (from pre-counted data)	`+ geom_col()`
`geom_bar()`	Bar chart (counts from raw data)	`+ geom_bar()`
`geom_boxplot()`	Boxplot	`+ geom_boxplot()`
`geom_text()`	Add text labels to a plot	`+ geom_text(aes(label = n))`
`coord_flip()`	Flip x and y axes (horizontal bars)	`+ coord_flip()`
`fct_reorder()`	Sort factor levels by a variable	`fct_reorder(genre, n)`
`fct_infreq()`	Sort factor levels by frequency	`fct_infreq(genre)`
`scale_fill_brewer()`	Colorblind-friendly color palette	`+ scale_fill_brewer(palette = "Set2")`
`labs()`	Add title, subtitle, axis labels, caption	`+ labs(title = "My Chart")`
`theme_minimal()`	Clean, minimal chart theme	`+ theme_minimal()`

Statistical Testing

Function	Package	What It Does	Example
`table()`	base R	Build a contingency table	`table(df$var1, df$var2)`
`prop.table()`	base R	Convert counts to proportions	`prop.table(tbl, margin = 1)`
`chisq.test()`	base R	Chi-square test of independence	`chisq.test(tbl)`
`$statistic`	—	Extract χ² value from test result	`result$statistic`
`$parameter`	—	Extract degrees of freedom	`result$parameter`
`$p.value`	—	Extract p-value	`result$p.value`
`$stdres`	—	Extract standardized residuals	`result$stdres`

Exporting Data

Function	What It Does	Example
`saveRDS()`	Save data as .RDS (preserves factors)	`saveRDS(df, "clean.RDS")`
`write_csv()`	Save data as CSV	`write_csv(df, "output.csv")`

Publishing (Quarto)

Command	What It Does
`quarto render`	Render all chapters into PDF + HTML
`knitr::kable()`	Format a data frame as a publication-ready table
`@citationkey`	Insert an in-text citation from `references.bib`

The Pipe: `|>`

The pipe passes the result from the left to the first argument of the function on the right:

clean_names(read_csv("data.csv"))

read_csv("data.csv") |> clean_names()

music |>
  filter(!is.na(mode)) |>
  count(genre, sort = TRUE) |>
  mutate(pct = n / sum(n) * 100)

Read |> as “and then.”