How to transform data and the dplyr function. For each scenario below, describe how you would transform the data and the dplyr function(s) you could use to achieve that.
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Question 10 pts
You are working with the Salaries dataset from the car data package, which includes salary information for a group of professors. You want to see how Salaries differ by sex.
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Question 20 pts
You are working with a parking dataset from the City of Seattle that contains street parking data for the entire city over multiple years, but you want to focus your analysis on only two neighborhoods in the past year.
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Question 30 pts
You are working with employee satisfaction survey data. It currently has a column for each question answered, but you would like to have a calculated summary score rather than looking at each question.
Although many fundamental data manipulation functions exist in R, they have been a bit convoluted to date and have lacked consistent coding and the ability to easily flow together. This leads to difficult-to-read nested functions and/or choppy code. R Studio is driving a lot of new packages to collate data management tasks and better integrate them with other analysis activities. As a result, a lot of data processing tasks are becoming packaged in more cohesive and consistent ways, which leads to:
dplyr is one such package which was built for the sole purpose of simplifying the process of manipulating, sorting, summarizing, and joining data frames. This tutorial serves to introduce you to the basic functions offered by the
dplyr package. These fundamental functions of data transformation that the dplyr package offers includes:
select() selects variables
filter() provides basic filtering capabilities
group_by() groups data by categorical levels
summarise() summarizes data by functions of choice
arrange() orders data
join() joins separate dataframes
mutate() creates new variables