There you have it! With the power of readr and dplyr in your hands, you can now:
- load data from a CSV into a data frame
- inspect the data frame with
head()andsummary() select()the columns you want to analyzefilter()the rows with comparison and logical operatorsarrange()rows in ascending or descending order
You’ve also been exposed to the pipe %>%, a powerful tool for chaining function calls, as well as the general principles of data manipulation.
Now that you are well on your way to being a dplyr master, let’s combine what you have learned together to perform an analysis and see the true power of the pipe!
Instructions
The code in notebook.Rmd completes a sequence of steps:
- columns are selected from
artistsand saved tochosen_cols chosen_colsis filtered and saved topopular_not_hip_hoppopular_not_hip_hopis arranged and saved toyoutube_desc
Notice that to arrive at this result, two intermediate variables chosen_cols and popular_not_hip_hop were created.
With the power of the pipe, we can clean up this code!
In the last code block, select() all columns except country,year_founded, and albums from artists using the pipe %>%. Save the result to artists and view the head().
Place a pipe %>% after the call to select(). This will pipe your selection to the next line, where you should filter() all rows where spotify_monthly_listeners is greater than 20000000 and genre is not equal to 'Hip Hop'. Keep this data frame saved to artists.
Place a pipe %>% after the call to filter(). This will pipe your filtered data frame to the next line, where you should arrange() the rows in descending order by youtube_subscribers. Keep this data frame saved to artists.
Did you get the same result as the previous code block?
Make sure you’ve called head(artists) to see the resulting data frame!