Data Wrangling in R

Collecting and preparing data for analysis is the primary job of a data scientist. This experience is called data wrangling. In this post, we will look at an example of data wrangling using a simple artificial data set. You can create the table below in r or excel. If you created it in excel just save it as a csv and load it into r. Below is the initial code

library(readr)
apple <- read_csv("~/Desktop/apple.csv")
## # A tibble: 10 × 2
##        weight      location
##         <chr>         <chr>
## 1         3.2        Europe
## 2       4.2kg       europee
## 3      1.3 kg          U.S.
## 4  7200 grams           USA
## 5          42 United States
## 6         2.3       europee
## 7       2.1kg        Europe
## 8       3.1kg           USA
## 9  2700 grams          U.S.
## 10         24 United States

This a small dataset with the columns of “weight” and “location”. Here are some of the problems

  • Weights are in different units
  • Weights are written in different ways
  • Location is not consistent

In order to have any success with data wrangling you need to state specifically what it is you want to do. Here are our goals for this project

  • Convert the “Weight variable” to a numerical variable instead of character
  • Remove the text and have only numbers in the “weight variable”
  • Change weights in grams to kilograms
  • Convert the “location” variable to a factor variable instead of character
  • Have consistent spelling for Europe and United States in the “location” variable

We will begin with the “weight” variable. We want to convert it to a numerical variable and remove any non-numerical text. Below is the code for this

corrected.weight<-as.numeric(gsub(pattern = "[[:alpha:]]","",apple$weight))
corrected.weight
##  [1]    3.2    4.2    1.3 7200.0   42.0    2.3    2.1    3.1 2700.0   24.0

Here is what we did.

  1. We created a variable called “corrected.weight”
  2. We use the function “as.numeric” this makes whatever results inside it to be a numerical variable
  3. Inside “as.numeric” we used the “gsub” function which allows us to substitute one value for another.
  4. Inside “gsub” we used the argument pattern and set it to “[[alpha:]]” and “” this told r to look for any lower or uppercase letters and replace with nothing or remove it. This all pertains to the “weight” variable in the apple dataframe.

We now need to convert the weights in grams to kilograms so that everything is the same unit. Below is the code

gram.error<-grep(pattern = "[[:digit:]]{4}",apple$weight)
corrected.weight[gram.error]<-corrected.weight[gram.error]/1000
corrected.weight
##  [1]  3.2  4.2  1.3  7.2 42.0  2.3  2.1  3.1  2.7 24.0

Here is what we did

  1. We created a variable called “gram.error”
  2. We used the grep function to search are the “weight” variable in the apple data frame for input that is a digit and is 4 digits in length this is what the “[[:digit:]]{4}” argument means. We do not change any values yet we just store them in “gram.error”
  3. Once this information is stored in “gram.error” we use it as a subset for the “corrected.weight” variable.
  4. We tell r to save into the “corrected.weight” variable any value that is changeable according to the criteria set in “gram.error” and to divided it by 1000. Dividing it by 1000 converts the value from grams to kilograms.

We have completed the transformation of the “weight” and will move to dealing with the problems with the “location” variable in the “apple” dataframe. To do this we will first deal with the issues related to the values that relate to Europe and then we will deal with values related to United States. Below is the code.

europe<-agrep(pattern = "europe",apple$location,ignore.case = T,max.distance = list(insertion=c(1),deletions=c(2)))
america<-agrep(pattern = "us",apple$location,ignore.case = T,max.distance = list(insertion=c(0),deletions=c(2),substitutions=0))
corrected.location<-apple$location
corrected.location[europe]<-"europe"
corrected.location[america]<-"US"
corrected.location<-gsub(pattern = "United States","US",corrected.location)
corrected.location
##  [1] "europe" "europe" "US"     "US"     "US"     "europe" "europe"
##  [8] "US"     "US"     "US"

The code is a little complicated to explain but in short We used the “agrep” function to tell r to search the “location” to look for values similar to our term “europe”. The other arguments provide some exceptions that r should change because the exceptions are close to the term europe. This process is repeated for the term “us”. We then store are the location variable from the “apple” dataframe in a new variable called “corrected.location” We then apply the two objects we made called “europe” and “america” to the “corrected.location” variable. Next we have to make some code to deal with “United States” and apply this using the “gsub” function.

We are almost done, now we combine are two variables “corrected.weight” and “corrected.location” into a new data.frame. The code is below

cleaned.apple<-data.frame(corrected.weight,corrected.location)
names(cleaned.apple)<-c('weight','location')
cleaned.apple
##    weight location
## 1     3.2   europe
## 2     4.2   europe
## 3     1.3       US
## 4     7.2       US
## 5    42.0       US
## 6     2.3   europe
## 7     2.1   europe
## 8     3.1       US
## 9     2.7       US
## 10   24.0       US

If you use the “str” function on the “cleaned.apple” dataframe you will see that “location” was automatically converted to a factor.

This looks much better especially if you compare it to the original dataframe that is printed at the top of this post.

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