In this part, we've learned some basic functions to clean our data. We know how to:

• Change the data type of a column using functions such as as.factor() or as.character():

  survey$email <- as.character(survey$email)

• Change columns' names using the colnames() function:

  colnames(survey)[1] <- c("first_name")

• Delete rows of data to remove duplicates from our dataset by subsetting the data frame with the output of the duplicated() function:

  duplicates <- duplicated(survey)
  survey <- survey[!duplicates, ]
  

• Separate and join columns with the separate() and unite() functions (from the tidyr package):

  library(tidyr)
  

  survey <- separate(
    survey,
    col = "name",
    sep = "_",
    into = c("first_name", "last_name"))
  

  united_names <- unite(
    survey,
    col = "name",
    sep = "_",
    first_name,
    last_name)
  

Let's review all this information with a short quiz.