Learners' Reference

Last updated on 2026-04-23 | Edit this page

Cheat sheet of functions used in the workshops. This will be populated as we work through the workshops.

Workshop – Introduction to R

  • install.packages() # install R packages to your computer

Workshop – Introduction to R Packages,Markdown and Notebooks

  • library() # load R packages in each R session

Workshop – Starting with Data

  • <- # assignment arrow assigns inputs to a named object
  • read_csv() # import data in .csv files into R (readr, part of tidyverse)
  • class() # check the class of an object
  • dim() # returns a vector with the dimensions of the object
  • nrow() # returns the number of rows
  • ncol() # returns the number of columns
  • head() # shows the first 6 rows
  • tail() # shows the last 6 rows
  • names() # returns the column names
  • str() # structure of the object and information about the class, length and content of each column
  • summary() # summary statistics for each column
  • glimpse() # returns the number of columns and rows of a tibble, the names and class of each column, and previews as many values will fit on the screen (dplyr, part of tidyverse)
  • c() # combine values into a vector or list
  • factor() # encode a vector as a factor
  • levels(0 # provides access to the levels attribute of a variable
  • nlevels() # provides number of levels of a factor
  • as.character() # create or coerce objects of type character
  • as.numeric() # create or coerce objects of type numeric
  • as.factor() # encode a vector as a factor
  • plot() # create a generic X-Y plot
  • day() # extract day value from a date (lubridate, part of tidyverse)
  • month() # extract month value from a date (lubridate, part of tidyverse)
  • year() # extract year value from a date (lubridate, part of tidyverse)
  • day() # extract day value from a date (lubridate, part of tidyverse)
  • as_date() # convert an object to a date or date-time (lubridate, part of tidyverse)
  • mdy(), ymd(), dmy() # parse dates (lubridate, part of tidyverse)

Workshop – Manipulating Data with dplyr and tidyr

  • select() # subset columns (dplyr, part of tidyverse)
  • filter() # subset rows on conditions (dplyr, part of tidyverse)
  • mutate() # create new columns by using information from other columns (dplyr, part of tidyverse)
  • group_by() and summarize() # create summary statistics on grouped data (dplyr, part of tidyverse)
  • arrange() # sort results (dplyr, part of tidyverse)
  • count() # count discrete values (dplyr, part of tidyverse)
  • %>% # a pipe, take the output of one function and send it directly to the next (tidyverse)
  • filter() # filter rows (dplyr, part of tidyverse)
  • select() # seclect columns (dplyr, part of tidyverse)
  • mutate() # create, modify, and delete columns (dplyr, part of tidyverse)
  • distinct() # keep distinct/unique rows (dplyr, part of tidyverse)
  • sample_n() # sample n rows from a table (dplyr, part of tidyverse)
  • pivot_wider() # pivot data from long to wide (tidyr, part of tidyverse)
  • pivot_longer() # pivot data from wide to long (tidyr, part of tidyverse)
  • separate_longer_delim() # split a string into rows (tidyr, part of tidyverse)
  • replace_na() # replace NAs with specified values (tidyr, part of tidyverse)
  • write_csv() # export data in .csv format (readr, part of tidyverse)