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Data Manipulation in R

Summary
15. Summary & Exercise 1

Instruction

In this part, we learned how to handle some errors and missing values in a data set. We've used the following functions:

  • hist() – to make a histogram to see the distribution of data. 
    hist(census2017$age)
  • is.na() – to find missing values. 
    is.na(census2017)
  • complete.cases() – to get TRUE if the given row is complete or else – FALSE. 
    complete.cases(census2018)
  • na.omit() – to filter out incomplete rows. 
    na.omit(census2018)

We have some employee data in memory. Let's use this data in a short quiz to test your understanding.

Exercise

Plot a histogram of employees' absences from work. Use the absence column from the employees data frame.