Cheat sheet of functions used in the lessons

Lesson 1 – Introduction to R

  • 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
  • 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() # check the top (the first 6 lines) of an object including data frames
  • factor() # create factors
  • levels() # check levels of a factor
  • nlevels() # check number of levels of a factor
  • as.numeric(levels(x))[x] # convert factors where the levels appear as numbers to a numeric vector

Lesson 3 – Introducing data.frame

  • data.frame() # create a data frame
  • dim() # check dimension of data frame
  • 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 (synonym of colnames() for data frame objects)
  • rownames() # returns the row names
  • str() # check structure of the object and information about the class, length and content of each column
  • summary() # summary statistics for each column
  • seq() # generates a sequence of numbers

Lesson 4 – Aggregating and analyzing data with dplyr

  • install.packages() # install a CRAN package in R
  • library() # load installed package into the current session
  • 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
  • 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
  • tally() # counts the total number of records for each category.
  • write.csv() # save CSV file

Lesson 5 – Data visualization with ggplot2

  • ggplot2(data= , aes(x= , y= )) + geom_point( ) + facet_wrap () + theme_bw() + theme()
  • 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
  • theme_bw() # set the background to white
  • theme() # used to locally modify one or more theme elements in a specific ggplot object

  • Lesson 6 – R and SQL

  • src_sqlite # connect dplyr to a SQLite database file
  • tbl # connect to a table within a database
  • collect # retrieve all the results from the database
  • explain # show the SQL translation of a dplyr query
  • inner_join # perform an inner join between two tables
  • copy_to # copy a data frame as a table into a database

Data Carpentry, 2018. License. Contributing.
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