Click for the solution. Only click if you are struggling or are out of time.
"user_[1-9][0-9]?"
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8 Processing and joining datasets for cleaning
Here we will continue using the “Workflow” block and start moving over to the third block, “Create project data”, in Figure 8.1.
8.1 Learning objectives
- Learn what regular expressions are and how to use them on character data.
- Learn about and apply the various ways data can be joined.
- Apply functionals when repeatedly joining multiple datasets.
- Apply the function
case_when()when you need nested conditionals for cleaning. - Use the
usethis::use_data()function to save the final, fully joined dataset as an.Rdafile indata/.
8.2 Processing character data
8.3 Exercise: Brainstorm a regex that will match for the user ID
Time: 10 minutes.
In your groups do these tasks. Try not to look ahead, nor in the solution section 😉! When the time is up, we’ll share some ideas and go over what the regex will be.
- Looking at the
file_path_idcolumn, list what is similar in the user ID between rows and what is different. - Discuss and verbally describe (in English, not regex) what text pattern you might use to extract the user ID.
- Use the list below to think about how you might convert the English description of the text pattern to a regex. This will probably be very hard, but try anyway.
- When characters are written as is, regex will find those characters, e.g.
userwill find onlyuser. - Use
[]to find one possible character of the several between the brackets. E.g.[12]means 1 or 2 or[ab]means “a” or “b”. To find a range of numbers or letters, use-in between the start and end ranges, e.g.[1-3]means 1 to 3 or[a-c]means “a” to “c”. - Use
?if the character might be there or not. E.g.ab?means “a” and maybe “b” follows it or1[1-2]?means 1 and maybe 1 or 2 will follow it.
- When characters are written as is, regex will find those characters, e.g.
Once you’ve done these tasks, we’ll discuss all together and go over what the regex would be to extract the user ID.
8.4 Using regular expressions to extract text
Now that we’ve identified a possible regex to use to extract the user ID, let’s test it out on the user_info_df data. Once it works, we will convert it into a function and move (cut and paste) it into the R/functions.R file.
Since we will create a new column for the user ID, we will use the mutate() function from the dplyr package. We’ll use the str_extract() function from the stringr package to “extract a string” by using the regex user_[1-9][0-9]? that we discussed from the exercise. Since we’re going to use stringr, so let’s add it as a package dependency:
usethis::use_package("stringr")We’re also using an argument to mutate() you might not have seen previously, called .before. This will insert the new user_id column before the column we use and we do this entirely for visual reasons, since it is easier to see the newly created column when we run the code. In your doc/learning.qmd file, create a new header called ## Using regex for user ID at the bottom of the document, and create a new code chunk below that.
user_info_df <- import_multiple_files("user_info.csv", import_user_info)
# Note: your file paths and data may look slightly different.
user_info_df %>%
mutate(
user_id = str_extract(
file_path_id,
"user_[1-9][0-9]?"
),
.before = file_path_id
)#> # A tibble: 22 × 6
#> user_id file_path_id gender weight height age
#> <chr> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 user_1 data-raw/mmash/user_1/user_in… M 65 169 29
#> 2 user_10 data-raw/mmash/user_10/user_i… M 85 180 27
#> 3 user_11 data-raw/mmash/user_11/user_i… M 115 186 27
#> 4 user_12 data-raw/mmash/user_12/user_i… M 67 170 27
#> 5 user_13 data-raw/mmash/user_13/user_i… M 74 180 25
#> 6 user_14 data-raw/mmash/user_14/user_i… M 64 171 27
#> 7 user_15 data-raw/mmash/user_15/user_i… M 80 180 24
#> 8 user_16 data-raw/mmash/user_16/user_i… M 67 176 27
#> 9 user_17 data-raw/mmash/user_17/user_i… M 60 175 24
#> 10 user_18 data-raw/mmash/user_18/user_i… M 80 180 0
#> # ℹ 12 more rowsSince we don’t need the file_path_id column anymore, let’s drop it using select() and -.
user_info_df %>%
mutate(
user_id = str_extract(
file_path_id,
"user_[1-9][0-9]?"
),
.before = file_path_id
) %>%
select(-file_path_id)#> # A tibble: 22 × 5
#> user_id gender weight height age
#> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 user_1 M 65 169 29
#> 2 user_10 M 85 180 27
#> 3 user_11 M 115 186 27
#> 4 user_12 M 67 170 27
#> 5 user_13 M 74 180 25
#> 6 user_14 M 64 171 27
#> 7 user_15 M 80 180 24
#> 8 user_16 M 67 176 27
#> 9 user_17 M 60 175 24
#> 10 user_18 M 80 180 0
#> # ℹ 12 more rows8.5 Exercise: Convert ID extractor code into a function
Time: 15 minutes.
We now have code that takes the data that has the file_path_id column and extracts the user ID from it. First step: While in the doc/learning.qmd file, convert this code into a function, using the same process you’ve done previously.
- Call the new function
extract_user_idand add one argument calledimported_data.- Remember to output the code into an object and
return()it at the end of the function. - Include Roxygen documentation.
- Remember to output the code into an object and
- After writing it and testing that the function works, move (cut and paste) the function into
R/functions.R. - Run styler while in the
R/functions.Rfile with with the Palette (, then type “style file”). - Replace the code in the
doc/learning.qmdfile with the function name so it looks likeextract_user_id(user_info_df), restart the R session, source everything withsource()with or with the Palette (, then type “source”), and run the new function in the code chunk inside thedoc/learning.qmdto test that it works. This should automatically run thesetupcode chunk, otherwise, run that chunk if it doesn’t. - Knit / render the
doc/learning.qmdfile to make sure things remain reproducible with or with the Palette (, then type “render”). - Add and commit the changes to the Git history with or with the Palette (, then type “commit”).
Use this code as a guide to help complete this exercise:
extract_user_id <- ___(___) {
___ <- ___ %>%
___mutate(
user_id = ___str_extract(file_path_id,
"user_[0-9][0-9]?"),
.before = file_path_id
) %>%
___select(-file_path_id)
return(___)
}
# This tests that it works:
# extract_user_id(user_info_df)Click for the solution. Only click if you are struggling or are out of time.
#' Extract user ID from data with file path column.
#'
#' @param imported_data Data with `file_path_id` column.
#'
#' @return A data.frame/tibble.
#'
extract_user_id <- function(imported_data) {
extracted_id <- imported_data %>%
dplyr::mutate(
user_id = stringr::str_extract(
file_path_id,
"user_[0-9][0-9]?"
),
.before = file_path_id
) %>%
dplyr::select(-file_path_id)
return(extracted_id)
}
# This tests that it works:
extract_user_id(user_info_df)8.6 Modifying existing functions as part of the processing workflow
Now that we’ve created a new function to extract the user ID from the file path variable, we need to actually use it within our processing pipeline. Since we want this function to work on all the datasets that we will import, we need to add it to the import_multiple_files() function. We’ll go to the import_multiple_files() function in R/functions.R and use the %>% to add it after using the list_rbind() function. The code should look something like:
import_multiple_files <- function(file_pattern, import_function) {
data_files <- fs::dir_ls(here::here("data-raw/mmash/"),
regexp = file_pattern,
recurse = TRUE
)
combined_data <- purrr::map(data_files, import_function) %>%
purrr::list_rbind(names_to = "file_path_id") %>%
extract_user_id() # Add the function here.
return(combined_data)
}We’ll re-source the functions with source() using or with the Palette (, then type “source”). Then re-run these pieces of code you wrote during the exercise in Section 7.3 to update them based on the new code in the import_multiple_files() function. Add this to your doc/learning.qmd file for now.
user_info_df <- import_multiple_files("user_info.csv", import_user_info)
saliva_df <- import_multiple_files("saliva.csv", import_saliva)
rr_df <- import_multiple_files("RR.csv", import_rr)
actigraph_df <- import_multiple_files("Actigraph.csv", import_actigraph)As well as adding the summarised_rr_df and summarised_actigraph_df to use user_id instead of file_path_id:
summarised_rr_df <- rr_df %>%
group_by(user_id, day) %>%
summarise(across(ibi_s, list(
mean = ~ mean(.x, na.rm = TRUE),
sd = ~ sd(.x, na.rm = TRUE)
))) %>%
ungroup()
summarised_actigraph_df <- actigraph_df %>%
group_by(user_id, day) %>%
# These statistics will probably be different for you
summarise(across(hr, list(
mean = ~ mean(.x, na.rm = TRUE),
sd = ~ sd(.x, na.rm = TRUE)
))) %>%
ungroup()Let’s render the doc/learning.qmd document using or with the Palette (, then type “render”)to make sure everything still runs fine. Then, add and commit all the changed files into the Git history with or with the Palette (, then type “commit”).
8.7 Join datasets together
Reading task: ~10 minutes
The ability to join datasets together is a fundamental component of data processing and transformation. In our case, we want to add the datasets together so we eventually have preferably one main dataset to work with.
There are many ways to join datasets in dplyr that are described in ?dplyr::join. The more common ones that are implemented in the dplyr package are:
-
left_join(x, y): Join all rows and columns inythat match rows and columns inx. Columns that exist inybut notxare joined tox.
D in column A in the blue data is not included in the outputted data on the right. Modified from the RStudio dplyr cheatsheet.-
right_join(x, y): The opposite ofleft_join(). Join all rows and columns inxthat match rows and columns iny. Columns that exist inxbut notyare joined toy.
C in column A in the green data is not included in the outputted data on the right. Modified from the RStudio dplyr cheatsheet.-
full_join(x, y): Join all rows and columns inythat match rows and columns inx. Columns and rows that exist inybut notxare joined tox.
In our case, we want to use full_join(), since we want all the data from both datasets. This function takes two datasets and lets you indicate which column to join by using the by argument. Here, both datasets have the column user_id so we will join by them.
full_join(user_info_df, saliva_df, by = "user_id")#> # A tibble: 43 × 8
#> user_id gender weight height age samples cortisol_norm
#> <chr> <chr> <dbl> <dbl> <dbl> <chr> <dbl>
#> 1 user_1 M 65 169 29 before sleep 0.0341
#> 2 user_1 M 65 169 29 wake up 0.0779
#> 3 user_10 M 85 180 27 before sleep 0.0370
#> 4 user_10 M 85 180 27 wake up 0.0197
#> 5 user_11 M 115 186 27 before sleep 0.0406
#> 6 user_11 M 115 186 27 wake up 0.0156
#> 7 user_12 M 67 170 27 before sleep 0.156
#> 8 user_12 M 67 170 27 wake up 0.145
#> 9 user_13 M 74 180 25 before sleep 0.0123
#> 10 user_13 M 74 180 25 wake up 0.0342
#> # ℹ 33 more rows
#> # ℹ 1 more variable: melatonin_norm <dbl>full_join() is useful if we want to include all values from both datasets, as long as each participant (“user”) had data collected from that dataset. When the two datasets have rows that don’t match, we will get missingness in that row, but that’s ok in this case.
We also eventually have other datasets to join together later on. Since full_join() can only take two datasets at a time, do we then just keep using full_join() until all the other datasets are combined? What if we get more data later on? Well, that’s where more functional programming comes in. Again, we have a simple goal: For a set of data frames, join them all together. Here we use another functional programming concept called reduce(). Like map(), which “maps” a function onto a set of items, reduce() applies a function to each item of a vector or list, each time reducing the set of items down until only one remains: the output. Let’s use an example with our simple function add_numbers() we had created before (but later deleted) and add up 1 to 5. Since add_numbers() only takes two numbers, we have to give it two numbers at a time and repeat until we reach 5.
# Add from 1 to 5
first <- add_numbers(1, 2)
second <- add_numbers(first, 3)
third <- add_numbers(second, 4)
add_numbers(third, 5)#> [1] 15Instead, we can use reduce to do the same thing:
reduce(1:5, add_numbers)#> [1] 15Figure 8.5 visually shows what is happening within reduce().
func() function is placed in the first position of func() in the next iteration, and so on. Modified from the RStudio purrr cheatsheet.If we look at ?reduce, we see that reduce(), like map(), takes either a vector or a list as an input. Since data frames can only be put together as a list and not as a vector (a data frame has vectors for columns and so can’t be a vector itself), we need to combine the datasets together in a list() and reduce them with full_join().
Let’s code this together, using reduce(), full_join(), and list() while in the doc/learning.qmd file.
#> Joining with `by = join_by(user_id)`#> # A tibble: 43 × 8
#> user_id gender weight height age samples cortisol_norm
#> <chr> <chr> <dbl> <dbl> <dbl> <chr> <dbl>
#> 1 user_1 M 65 169 29 before sleep 0.0341
#> 2 user_1 M 65 169 29 wake up 0.0779
#> 3 user_10 M 85 180 27 before sleep 0.0370
#> 4 user_10 M 85 180 27 wake up 0.0197
#> 5 user_11 M 115 186 27 before sleep 0.0406
#> 6 user_11 M 115 186 27 wake up 0.0156
#> 7 user_12 M 67 170 27 before sleep 0.156
#> 8 user_12 M 67 170 27 wake up 0.145
#> 9 user_13 M 74 180 25 before sleep 0.0123
#> 10 user_13 M 74 180 25 wake up 0.0342
#> # ℹ 33 more rows
#> # ℹ 1 more variable: melatonin_norm <dbl>We now have the data in a form that would make sense to join it with the other datasets. So lets try it:
#> Joining with `by = join_by(user_id)`
#> Joining with `by = join_by(user_id)`#> # A tibble: 86 × 11
#> user_id gender weight height age samples cortisol_norm
#> <chr> <chr> <dbl> <dbl> <dbl> <chr> <dbl>
#> 1 user_1 M 65 169 29 before sleep 0.0341
#> 2 user_1 M 65 169 29 before sleep 0.0341
#> 3 user_1 M 65 169 29 wake up 0.0779
#> 4 user_1 M 65 169 29 wake up 0.0779
#> 5 user_10 M 85 180 27 before sleep 0.0370
#> 6 user_10 M 85 180 27 before sleep 0.0370
#> 7 user_10 M 85 180 27 wake up 0.0197
#> 8 user_10 M 85 180 27 wake up 0.0197
#> 9 user_11 M 115 186 27 before sleep 0.0406
#> 10 user_11 M 115 186 27 before sleep 0.0406
#> # ℹ 76 more rows
#> # ℹ 4 more variables: melatonin_norm <dbl>, day <dbl>,
#> # ibi_s_mean <dbl>, ibi_s_sd <dbl>Hmm, but wait, we now have four rows of each user, when we should have only two, one for each day. By looking at each dataset we joined, we can find that the saliva_df doesn’t have a day column and instead has a samples column. We’ll need to add a day column in order to join properly with the RR dataset. For this, we’ll learn about using nested conditionals.
8.8 Cleaning with nested conditionals
While still in the doc/learning.qmd file, we can use the case_when() function to set "before sleep" as day 1 and "wake up" as day 2 by creating a new column called day. (We will use NA_real_ because the other day columns are numeric, not integer.)
saliva_with_day_df <- saliva_df %>%
mutate(day = case_when(
samples == "before sleep" ~ 1,
samples == "wake up" ~ 2
))
saliva_with_day_df#> # A tibble: 42 × 5
#> user_id samples cortisol_norm melatonin_norm day
#> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 user_1 before sleep 0.0341 0.0000000174 1
#> 2 user_1 wake up 0.0779 0.00000000675 2
#> 3 user_10 before sleep 0.0370 0.00000000867 1
#> 4 user_10 wake up 0.0197 0.00000000257 2
#> 5 user_11 before sleep 0.0406 0.00000000204 1
#> 6 user_11 wake up 0.0156 0.00000000965 2
#> 7 user_12 before sleep 0.156 0.00000000354 1
#> 8 user_12 wake up 0.145 0.00000000864 2
#> 9 user_13 before sleep 0.0123 0.00000000190 1
#> 10 user_13 wake up 0.0342 0.00000000230 2
#> # ℹ 32 more rows…Now, let’s use the reduce() with full_join() again:
#> Joining with `by = join_by(user_id)`
#> Joining with `by = join_by(user_id)`
#> Joining with `by = join_by(user_id, day)`#> # A tibble: 86 × 13
#> user_id gender weight height age samples cortisol_norm
#> <chr> <chr> <dbl> <dbl> <dbl> <chr> <dbl>
#> 1 user_1 M 65 169 29 before sleep 0.0341
#> 2 user_1 M 65 169 29 before sleep 0.0341
#> 3 user_1 M 65 169 29 wake up 0.0779
#> 4 user_1 M 65 169 29 wake up 0.0779
#> 5 user_10 M 85 180 27 before sleep 0.0370
#> 6 user_10 M 85 180 27 before sleep 0.0370
#> 7 user_10 M 85 180 27 wake up 0.0197
#> 8 user_10 M 85 180 27 wake up 0.0197
#> 9 user_11 M 115 186 27 before sleep 0.0406
#> 10 user_11 M 115 186 27 before sleep 0.0406
#> # ℹ 76 more rows
#> # ℹ 6 more variables: melatonin_norm <dbl>, day <dbl>,
#> # ibi_s_mean <dbl>, ibi_s_sd <dbl>, hr_mean <dbl>, hr_sd <dbl>We now have two rows per participant! Let’s add and commit the changes to the Git history with or with the Palette (, then type “commit”).
8.9 Wrangling data into final form
Now that we’ve got several datasets processed and joined, its now time to bring it all together and put it into the data-raw/mmash.R script so we can create a final working dataset.
Open up the data-raw/mmash.R file and the top of the file, add the tidyverse package to the end of the list of other packages if it isn’t there already. Move the code library(fs) to go with the other packages as well. It should look something like this now:
Then, we will comment out the download.file(), unzip(), file_delete(), and file_move() code, since we don’t want to download and unzip the data every single time we run this script. It should look like this:
# Download
mmash_link <- "https://physionet.org/static/published-projects/mmash/multilevel-monitoring-of-activity-and-sleep-in-healthy-people-1.0.0.zip"
# download.file(mmash_link, destfile = here("data-raw/mmash-data.zip"))
# Unzip
# unzip(here("data-raw/mmash-data.zip"),
# exdir = here("data-raw"),
# junkpaths = TRUE)
# unzip(here("data-raw/MMASH.zip"),
# exdir = here("data-raw"))
# Remove/tidy up left over files
# file_delete(here(c("data-raw/MMASH.zip",
# "data-raw/SHA256SUMS.txt",
# "data-raw/LICENSE.txt")))
# file_move(here("data-raw/DataPaper"), here("data-raw/mmash"))Go into the doc/learning.qmd and cut the code used to create the saliva_with_day_df as well as the code to full_join() all the datasets together with reduce() and paste it at the bottom of the data-raw/mmash.R script. Assign the output into a new variable called mmash, like this:
saliva_with_day_df <- saliva_df %>%
mutate(day = case_when(
samples == "before sleep" ~ 1,
samples == "wake up" ~ 2,
TRUE ~ NA_real_
))
mmash <- list(
user_info_df,
saliva_df,
summarised_rr_df,
summarised_actigraph_df
) %>%
reduce(full_join)#> Joining with `by = join_by(user_id)`
#> Joining with `by = join_by(user_id)`
#> Joining with `by = join_by(user_id, day)`Lastly, we have to save this final dataset into the data/ folder. We’ll use the function usethis::use_data() to create the folder and save the data as an .rda file. We’ll add this code to the very bottom of the script:
usethis::use_data(mmash, overwrite = TRUE)#> ✔ Setting active project to '/home/runner/work/r-cubed-intermediate/r-cubed-intermediate'
#> ✔ Saving 'mmash' to 'data/mmash.rda'
#> • Document your data (see 'https://r-pkgs.org/data.html')We’re adding overwrite = TRUE so every time we re-run this script, the dataset will be saved. If the final dataset is going to be really large, we could (but won’t in this course) save it as a .csv file with:
vroom_write(mmash, here("data/mmash.csv"))And later load it in with vroom() (since it is so fast). Alright, we’re finished creating this dataset! Let’s generate it by:
- First running styler with with the Palette (, then type “style file”).
- Restarting the R session with or with the Palette (, then type “restart”).
- Sourcing the
data-raw/mmash.Rscript with or with the Palette (, then type “source”).
We now have a final dataset to start working on! The main way to load data is with load(here::here("data/mmash.rda")). Go into the doc/learning.qmd file and delete everything again, except for the YAML header and setup code chunk, so that we are ready for the next session. Lastly, add and commit all the changes, including adding the final mmash.rda data file, to the Git history by using or with the Palette (, then type “commit”).
8.10 Summary
- While very difficult to learn and use, regular expressions (regex or regexp) are incredibly powerfully at processing character data.
- Use
left_join(),right_join(), andfull_join()to join two datasets together. - Use the functional
reduce()to iteratively apply a function to a set of items in order to end up with one item (e.g. join more than two datasets into one final dataset). - Use
case_when()instead of nesting multiple “if else” conditions whenever you need to do slightly more complicated conditionals.