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CMU Libraries Open Data Science Workshops

Exporting data

Now that you have learned how to use dplyr to extract information from or summarize your raw data, you may want to export these new data sets to share them with your collaborators or for archival.

Similar to the read_csv() function used for reading CSV files into R, there is a write_csv() function that generates CSV files from data frames.

Before using write_csv(), we are going to create a new folder, data_output, in our working directory that will store this generated dataset. We don’t want to write generated datasets in the same directory as our raw data. It’s good practice to keep them separate. The data folder should only contain the raw, unaltered data, and should be left alone to make sure we don’t delete or modify it. In contrast, our script will generate the contents of the data_output directory, so even if the files it contains are deleted, we can always re-generate them.

Imagine you wanted to use the reduced size dataset from the piping exercise above in a new project. If you remember, we used the following code to filter only those records where the village name was “Chirodzo” and select only the variables village through respondent_wall_type, and stored the results in a new object called interviews_ch.

interviews_ch <- interviews %>% 
	filter(village == "Chirodzo") %>% 
	select(village:respondent_wall_type) 
interviews_ch 
# A tibble: 39 x 5 
village interview_date no_membrs years_liv respondent_wall_type 
<chr> <dttm> <dbl> <dbl> <chr> 
1 Chirodzo 2016-11-16 00:00:00 12 70 burntbricks 
2 Chirodzo 2016-11-16 00:00:00 8 6 burntbricks 
3 Chirodzo 2016-12-16 00:00:00 12 23 burntbricks 
4 Chirodzo 2016-11-17 00:00:00 8 18 burntbricks 
5 Chirodzo 2016-11-17 00:00:00 5 45 muddaub 
6 Chirodzo 2016-11-17 00:00:00 6 23 sunbricks 
7  Chirodzo  2016-11-17  00:00:00  3  8  burntbricks  
8  Chirodzo  2016-11-17  00:00:00  7  29  muddaub  
9  Chirodzo  2016-11-17  00:00:00  2  6  muddaub  
10  Chirodzo  2016-11-17  00:00:00  9  7  muddaub  
#  …  with  29  more  rows

Now we can save this data frame to our data_output directory.

write_csv(interviews_ch, file = "data_output/interviews_ch.csv")

Key Points

  • Use the dplyr package to manipulate data frames.
  • Use select() to choose variables from a data frame.
  • Use filter() to choose data based on values.
  • Use group_by() and summarize() to work with subsets of data.
  • Use mutate() to create new variables.