Data Analysis with R Competency

  • 20m
  • 20 questions
The Data Analysis with R Competency benchmark measures whether a learner has had some exposure and experience in R programming and the libraries used for data analysis operations. A learner who scores high on this benchmark demonstrates a knowledge of the major areas of R for data analysis, but requires input and oversight from a more advanced R data analytics expert.

Topics covered

  • add, edit, and remove the names and values in lists
  • create an R5 reference class with various member variables and member functions
  • create crosstabs and view the aggregate statistics of data frames
  • create custom classes using the class(), attr(), and structure() functions
  • create lists and perform indexing operations
  • create variables using different techniques
  • export data to various text, CSV, JSON, and Excel files
  • format strings using formatC() and print data with placeholders using sprintf()
  • implement if statements and nested for loops within outer for loops
  • import data from XML, Excel, and JSON files
  • perform logical and filter operations on elements in vectors
  • perform operations on strings, such as combining and splitting strings
  • recall how built-in functions can be viewed and new functions created
  • recall the different basic or atomic data types
  • specify functions as input arguments to other functions
  • use recycling for operations with two different sized vectors
  • use repeat loops to repeat an operation until a break statement is reached
  • use the stack() and unstack() functions to reformat data frames
  • use the transform() function to transform data in data frames
  • visualize data using scatter plots, box plots, and line charts