In our previous article, we discussed the importance of data preprocessing and data management tasks in a data science pipeline. Also, we provided a brief explanation of the
dplyr R package. This article will focus on the power of this package to transform your datasets with ease in R.
dplyr package has five primary functions, commonly known as verbs. The verbs aids in performing most of the typical data manipulation operations, which we will discuss in the below sections.
glimpse method can be used to see the columns of data and display some portion of the data for each variable that can be fit on a single line.
select is used for choosing display variables based on the subset criteria. For instance,
select(mtcars,mpg) displays the MPG column from the
select(mtcars,mpg:disp) displays data in the columns from MPG to DISP, as shown in the below results:
select(mtcars, mpg:disp,-cyl) displays data in the columns from MPG to DISP without the CYL attribute:
pipe operator(%>%) is used to tie multiple operations together. This makes it easy, especially when we need to perform various operations on a dataset to derive the results.
We can read
mtcars %>% select(wt,mpg,disp) from left to right — from the
mtcars dataset, select WT, MPG, and DISP variables.
mutate is used to add new columns to a dataset. It is useful to create attributes that are functions of other attributes in the dataset. It’s one of the essential tools that can come handy for new feature creation in the data preprocessing stage.
mtcars %>% mutate(nv=wt+mpg) creates a new attribute NV by adding WT and MPG together.
filter method selects cases based on their values.
mtcars %>% filter(hp>123) displays data whose HP values are more than 123.
group_by is used to group data together based on one or more columns. It is often used along with a summarizing function to derive aggregated values:
mtcars %>% filter(hp>123) %>% group_by(am)
summarize is used to aggregate multiple values to a single value. It is most often used with the
group_by function, and the output has one row per group:
mtcars %>% filter(hp>123) %>% group_by(am) %>% summarize(avg_wt=mean(wt))
This command calculates the average WT for each unique value in the AM column for
mtcar data having HP > 123.
arrange is used to sort cases is ascending or descending order. The default is ascending order:
mtcars %>% filter(hp>123) %>% arrange(mpg)
As shown below, use
desc to order the data in descending order.
mtcars %>% filter(hp>123) %>% arrange(desc(mpg))
To learn more about
dplyr, see here.
Though we can perform these tasks using base R functions, the verbs in
dplyr are optimized for high performance, are easier to work with, and are consistent in the syntax. So, pick up a dataset, get started with
dplyr, and share your data preparation story on DZone for other people to understand.