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 a few more advanced transformation tasks on
mtcars data set.
Before proceeding, it might be helpful to look over the help pages for the
Moreover please load the following libraries.
Answers to the exercises are available here. For other parts, follow the tag data wrangling.
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.
Create a new object named cars_cyl and assign to it the mtcars data frame grouped by the variable cyl
Hint: be careful about the data type of the variable, in order to be used for grouping it has to be a factor.
Remove the grouping from the object cars_cyl
Print out the summary statistics of the mtcars data frame using the summary function and pipeline symbols %>%.
Make a more descriptive summary statistics output containing the 4 quantiles, the mean, the standard deviation and the count.
Print out the average *hp* for every cyl category
Print out the mtcars data frame sorted by hp (ascending oder)
Print out the mtcars data frame sorted by hp (descending oder)
Create a new object named cars_per containing the mtcars data frame along with a new variable called performance and calculated as
performance = hp/mpg
Print out the cars_per data frame, sorted by performance in descending order and create a new variable called rank indicating the rank of the cars in terms of performance.
To wrap everything up, we will use the iris data set. Print out the mean of every variable for every Species and create two new variables called Sepal.Density and Petal.Density being calculated as
Sepal.Density = Sepal.Length Sepal.Width and
Petal.Density = Sepal.Length Petal.Width respectively.
Hi. In ex_1 do you really have to factor first?
> cars_cyl % group_by(cyl) %>% summarize(res=sum(disp))
without factoring I obtain the same results.