Last updated on 2024-06-04 | Edit this page

Glossary


Cheat sheet of functions used in the lessons

Lesson 1 – Introduction to R

  • sqrt() # calculate the square root
  • round() # round a number
  • args() # find what arguments a function takes
  • length() # how many elements are in a particular vector
  • class() # the class (the type of element) of an object
  • str() # an overview of the object and the elements it contains
  • typeof # determines the (R internal) type or storage mode of any object
  • c() # create vector; add elements to vector
  • [ ] # extract and subset vector
  • %in% # to test if a value is found in a vector
  • is.na() # test if there are missing values
  • na.omit() # Returns the object with incomplete cases removed
  • complete.cases()# elements which are complete cases

Lesson 2 – Starting with Data

  • download.file() # download files from the internet to your computer
  • read_csv() # load CSV file into R memory
  • head() # shows the first 6 rows
  • view() # invoke a spreadsheet-style data viewer
  • read_delim() # load a file in table format into R memory
  • str() # check structure of the object and information about the class, length and content of each column
  • dim() # check dimension of data frame
  • nrow() # returns the number of rows
  • ncol() # returns the number of columns
  • tail() # shows the last 6 rows
  • names() # returns the column names (synonym of colnames() for data frame objects)
  • rownames() # returns the row names
  • summary() # summary statistics for each column
  • glimpse # like str() applied to a data frame but tries to show as much data as possible
  • factor() # create factors
  • levels() # check levels of a factor
  • nlevels() # check number of levels of a factor
  • as.character() # convert an object to a character vector
  • as.numeric() # convert an object to a numeric vector
  • as.numeric(as.character(x)) # convert factors where the levels appear as characters to a numeric vector
  • as.numeric(levels(x))[x] # convert factors where the levels appear as numbers to a numeric vector
  • plot() # plot an object
  • addNA() # convert NA into a factor level
  • data.frame() # create a data.frame object
  • ymd() # convert a vector representing year, month, and day to a Date vector
  • paste() # concatenate vectors after converting to character

Lesson 3 – Data Wrangling with dplyr and tidyr

  • str() # check structure of the object and information about the class, length and content of each column
  • view() # invoke a spreadsheet-style data viewer
  • select() # select columns of a data frame
  • filter() # allows you to select a subset of rows in a data frame
  • %>% # pipes to select and filter at the same time
  • mutate() # create new columns based on the values in existing columns
  • head() # shows the first 6 rows
  • group_by() # split the data into groups, apply some analysis to each group, and then combine the results.
  • summarize() # collapses each group into a single-row summary of that group
  • mean() # calculate the mean value of a vector
  • !is.na() # test if there are no missing values
  • print() # print values to the console
  • min() # return the minimum value of a vector
  • arrange() # arrange rows by variables
  • desc() # transform a vector into a format that will be sorted in descending order
  • count() # counts the total number of records for each category
  • pivot_wider() # reshape a data frame by a key-value pair across multiple columns
  • pivot_longer() # reshape a data frame by collapsing into a key-value pair
  • replace_na() # Replace NAs with specified values
  • n_distinct() # get a count of unique values
  • write_csv() # save to a csv formatted file

Lesson 4 – Data Visualization with ggplot2

  • read_csv() # load a csv formatted file into R memory
  • ggplot2(data= , aes(x= , y= )) + geom_point( ) + facet_wrap () + theme_bw() + theme() # skeleton for creating plot layers
  • aes() # by selecting the variables to be plotted and the variables to define the presentation such as plotting size, shape color, etc.
  • geom_ # graphical representation of the data in the plot (points, lines, bars). To add a geom to the plot use + operator
  • facet_wrap() # allows to split one plot into multiple plots based on a factor included in the dataset
  • labs() # set labels to plot
  • theme_bw() # set the background to white
  • theme() # used to locally modify one or more theme elements in a specific ggplot object
  • + # arrange ggplots horizontally
  • / # arrange ggplots vertically
  • plot_layout() # set width and height of individual plots in a patchwork of plots
  • ggsave() # save a ggplot

Lesson 5 – Processing JSON data

  • read_json() # load json object to an R object