An Introduction to Data Science. First Edition by Jeffrey S. Saltz & Jeffrey M. Stanton

Author:Jeffrey S. Saltz & Jeffrey M. Stanton
Language: eng
Format: epub
Published: 2017-09-02T13:30:11.154774+00:00

Next, we can change formats and data types as needed. We can remove the dots from in front of the state names very easily with str_replace():

> testFrame$stateName <-

+  str_replace(testFrame$stateName,“\\.”,“”)

The function str_replace() is part of the stringr package, and you will have to use install.packages() and library() to load it if it is not already in place. We previously used gsub, which is a similar function, but gsub replaces each occurrence, whereas str_replace replaces just the first occurrence (of the dot character [.] in our example). The two backslashes in the string expression above are called escape characters, and they force the dot that follows to be treated as a literal dot rather than as a wildcard character. The dot on its own is a wildcard that matches one instance of any character.

Next, let’s use the Numberize function. If you remember from Chapter 9, this function handles both unwanted commas and spaces and will convert strings into numbers whether or not they are integers (i.e., possibly with digits after the decimal point).

# Numberize() - Gets rid of commas and other junk and

# converts to numbers

# Assumes that the inputVector is a list of data that

# can be treated as character strings

Numberize <- function(inputVector)

{

# Get rid of commas

inputVector<-gsub(“,”,“”, inputVector)

# Get rid of spaces

inputVector<-gsub(“ ”,“”, inputVector)

return(as.numeric(inputVector))

}

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