# Data wrangling : Transforming (1/3)

**R-exercises**, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)

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Data wrangling is a task of great importance in data analysis. Data wrangling, is the process of importing, cleaning and transforming raw data into actionable information for analysis. It is a time-consuming process which is estimated to take about 60-80% of analyst’s time. In this series we will go through this process. It will be a brief series with goal to craft the reader’s skills on the data wrangling task. This is the third part of the series and it aims to cover the transforming of data used.This can include filtering, summarizing, and ordering your data by different means. This also includes combining various data sets, creating new variables, and many other manipulation tasks. At this post, we will go through the most basic tasks including slicing, and filtering on the famous `mtcars`

data set.

Before proceeding, it might be helpful to look over the help pages for the `select`

, `rename`

, `sample_frac`

, `slice`

, `distinct`

, `filter`

, `rownames`

, `%in%`

.

Moreover please load the following libraries.

`install.packages("dplyr")`

`library(dplyr)`

Answers to the exercises are available here.

If you obtained a different (correct) answer than those listed on the solutions page, please feel free to post your answer as a comment on that page.

Exercise 1

Print out the *hp* column using the `select`

function.

Exercise 2

Print out the all **but** *hp* column using the `select`

function.

Exercise 3

Print out the *mpg, hp, vs, am, gear* columns. Consider using the colon (:) symbol.

Exercise 4

Create the object **cars_m_h** containing the columns *mpg, hp* columns but let the column names be ‘miles_per_gallon’, and ‘horse_power’ respectively.

Exercise 5

Change the column names of **cars_m_h** from ‘miles_per_gallon’, and ‘horse_power’ to ‘mpg’ and ‘hp’ respectively.

Exercise 6

Print out a randomly half the observations of **cars_m_h**.

**Hint :** consider using the `sample_frac`

function

Exercise 7

Create a cars_m_h_s object, containing from 10th to 35th row of **cars_m_h**.

Hint : Consider using the `slice`

function.

Exercise 8

Print out the **cars_m_h_s** object without any duplicates.

**Hint :** Consider using the `distinct`

function.

Exercise 9

Print out from **cars_m_h_s** object all the observations which have mpg>20 and hp>100.

Exercise 10

Select the ‘Lotus Europa’ car.

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