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During this session we’ll be introducing concepts for both the “Download raw data” block as well as the “Workflow” block in Figure 5.1. You already downloaded and unzipped the raw data during the pre-course tasks and here we’ll continue the process by now importing it into R.
The overall learning outcome for this session is to:
Specific objectives are to:
The ultimate goal for the beginning phases of a data analysis project is to eventually save a version of the raw data that is specific to your research questions and enables you to conduct your analyses. The first step to processing data is to import it into R so we can work on it. So for now, we’ll open up the doc/learning.qmd file so we can start building and testing out the code. There should be a setup code chunk already be in the file, where we will put the library() code for loading the tidyverse package, which has readr bundled with it, as well as library(here). It should look like this:
This setup code chunk is a special, named code chunk that tells R to run this code chunk first whenever you open this Quarto file and run code inside of the file. It’s in this setup code chunk that we will add library() functions when we want to load other packages. After adding this code chunk, create a new header below the setup code chunk by typing out ## Importing raw data, followed by creating a new code chunk right below it using Ctrl-Alt-ICtrl-Alt-I or with the Palette (Ctrl-Shift-PCtrl-Shift-P, then type “new chunk”).
Let’s first start by creating an object that has the file path to the dataset, then we’ll use read_csv() to import that dataset.
doc/learning.qmd
#> New names:
#> Rows: 1 Columns: 5
#> ── Column specification
#> ──────────────────────────────────────────── Delimiter: "," chr
#> (1): Gender dbl (4): ...1, Weight, Height, Age
#> ℹ Use `spec()` to retrieve the full column specification for this
#> data. ℹ Specify the column types or set `show_col_types = FALSE` to
#> quiet this message.
#> • `` -> `...1`You’ll see the output mention using spec() to use in the argument col_types. And that it has 5 columns, one called ...1. If we look at the CSV file though, we see that there are only four columns with names, but that technically there is a first empty column without a column header. So, let’s figure out what this message means. Let’s go to the Console and type out:
In the documentation, we see that it says:
“extracts the full column specification from a tibble…”
Without seeing the output, it’s not clear what “specification” means. Use spec() on the dataset object. In the Console again:
#> cols(
#> ...1 = col_double(),
#> Gender = col_character(),
#> Weight = col_double(),
#> Height = col_double(),
#> Age = col_double()
#> )This shows that a specification is a list and description of which columns are imported into R and what data types they are given. For instance, col_double() means numeric (double is how computers represent non-integer numbers) and col_character() means a character data type. Next, let’s see what the message meant about col_types. Let’s check out the help documentation for read_csv() by typing in the Console:
And if we scroll down to the explanation of col_types:
“One of NULL, a cols() specification, or a string. See vignette(”readr”) for more details.”
It says to use a “cols() specification”, which is the output of spec(). Copy the output from spec() and paste it into the col_types argument of read_csv().
doc/learning.qmd
#> New names:
#> • `` -> `...1`We get a funny message though. We copied and pasted, so what’s going on? If you recall, the user_info.csv file has an empty column name. Looking at the data dictionary it doesn’t seem there is any reference to this column, so it likely isn’t important. More than likely, readr is complaining about this empty column name and the use of ...1 to represent it. We don’t need it, so we’ll get rid of it when we load in the dataset. But how? Look at the help documentation again. Go to the Console and type out:
Looking at the list of arguments, there is an argument called col_select that sounds like we could use that to keep or drop columns. It says that it is used similar to dplyr::select(), which normally is used with actual column names. Our column doesn’t have a name, that’s the problem. Next check the Example section of the help. Scrolling down, you’ll eventually see:
read_csv(input_file, col_select = c(1, 3, 11))
So, it takes numbers! With dplyr::select(), using the - before the column name (or number) means to drop the column, so in this case, we could drop the first column with col_select = -1!
doc/learning.qmd
#> New names:
#> • `` -> `...1`Hmm, we still have the message. You may or may not see this message depending on the version of your packages (the most updated packages will show this), but you can see it in the code chunk above.
This is read_csv() letting you know that a column was renamed because it found an empty column with no column name and is letting you know it had to rename it. Even though we excluded it with col_select = -1, that only excludes it after reading the column names, before importing the data.
To remove this new message, we need to tell read_csv() exactly how to handle renaming cases. Let’s look at the help docs of readr: ?read_csv. Scroll down and we see an argument called name_repair that handles naming of columns. It says that it treats problematic column names, of which a missing column name is definitely a problem. There are several options here, but the one I want to focus on is the comment about “function: apply custom name repair”. This is an important one because we eventually want to rename the columns to match the style guide by using snake_case, since there is a package to do that called snakecase.
In the Console, type out snakecase:: and hit Tab. You’ll see a list of possible functions to use. We want to use the snakecase one, so scroll down and find the to_snake_case(). That’s the one we want to use. So to remove the messages and convert the variable names to snake case, we would add name_repair = snakecase::to_snake_case to the code. Notice the lack of () when using the function. We’ll explain more about this in later sessions.
doc/learning.qmd
#> Warning: The following named parsers don't match the column names:
#> ...1, Gender, Weight, Height, AgeNow we have another warning though. It’s probably because the names are repaired to snake case before the col_types are used. Let’s try repeating the read_csv() to spec() to read_csv() workflow we just used to see if that fixes the problem.
doc/learning.qmd
#> Rows: 1 Columns: 4
#> ── Column specification ────────────────────────────────────────────
#> Delimiter: ","
#> chr (1): gender
#> dbl (3): weight, height, age
#>
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.#> cols(
#> col_skip(),
#> gender = col_character(),
#> weight = col_double(),
#> height = col_double(),
#> age = col_double()
#> )That fixed it! No more messages or warnings! We can now look at the data:
#> # A tibble: 1 × 4
#> gender weight height age
#> <chr> <dbl> <dbl> <dbl>
#> 1 M 65 169 29Why might we use spec() and col_types? It’s good practice, at least when reading from a larger dataset and not your project specific dataset, to read only the columns you need. Depending on the size of the dataset, it could take a long time to load everything, which may not be very efficient if you only intend to use some parts of the dataset and not all of it. And sometimes, spec() incorrectly guesses the column types, so using col_types = cols() can fix those problems.
If you have a lot of columns in your dataset, then you can make use of col_select or cols_only() to keep only the columns you want. Before moving on to the exercise, run styler with the Palette (Ctrl-Shift-PCtrl-Shift-P, then type “style file”), then add and commit the changes to the Git history through the RStudio Git interface with Ctrl-Alt-MCtrl-Alt-M or with the Palette (Ctrl-Shift-PCtrl-Shift-P, then type “commit”).
Time: 15 minutes.
Practice importing data files by doing the same process with the saliva data.
doc/learning.qmd file and call it ## Exercise: Import the saliva data.___ with the correct R functions or other information.doc/learning.qmd
user_1_saliva_file <- here("data-raw/mmash/user_1/saliva.csv")
user_1_saliva_data_prep <- read_csv(
user_1_saliva_file,
col_select = -1,
name_repair = snakecase::to_snake_case
)
spec(user_1_saliva_data_prep)
user_1_saliva_data <- read_csv(
user_1_saliva_file,
col_select = -1,
col_types = cols(
col_skip(),
samples = col_character(),
cortisol_norm = col_double(),
melatonin_norm = col_double()
),
name_repair = snakecase::to_snake_case
)Sometimes you may have a dataset that’s just a bit too large. Sometimes readr may not have enough information to guess the data type of the column. Or maybe there are hundreds or thousands of columns in your data and you only want to import specific columns. In these cases, we can do a trick: read in the first few lines of the dataset, use spec() and paste in the output into the col_type argument, and then keep only the columns you want to keep.
Let’s do this on the RR.csv file. We can see from the file size that it is bigger than most of the other files (~2Mb). So, we’ll use this technique to decide what we want to keep. First, create a new header ## Import larger datasets and a new code chunk below it using Ctrl-Alt-ICtrl-Alt-I or with the Palette (Ctrl-Shift-PCtrl-Shift-P, then type “new chunk”).
Do the same thing that we’ve been doing, but this time we are going to use the argument n_max, which tells readr how many rows to read into R. In this case, let’s read in 100, since that is the amount readr will guess until. This dataset, like the others, has an empty column that we will drop.
doc/learning.qmd
#> Rows: 100 Columns: 3
#> ── Column specification ────────────────────────────────────────────
#> Delimiter: ","
#> dbl (2): ibi_s, day
#> time (1): time
#>
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.#> cols(
#> col_skip(),
#> ibi_s = col_double(),
#> day = col_double(),
#> time = col_time(format = "")
#> )Some people may see time set as something like col_double() or col_character(). This is great example of why we would use spec() and col_types to explicitly tell R what the data type the variable should be. If yours is not col_time(), after you paste the spec output into the col_type argument of readr::read_csv(), fix it so it is col_time(format = "").
Like with last time, copy and paste the output into a new use of read_csv(). Add the name_repair argument with snakecase::to_snake_case. Don’t forget to also remove the last , at the end! Make sure to remove the n_max argument, since we want to import in the whole dataset.
There’s a new column type: col_time(). To see all the other types of column specifications, in the Console type out col_ and then hit the Tab key. You’ll see other types, like col_date, col_factor, and so on. Right, back to the data, what does it look like?
#> # A tibble: 91,858 × 3
#> ibi_s day time
#> <dbl> <dbl> <time>
#> 1 0.258 1 10:10:17
#> 2 0.319 1 10:10:18
#> 3 0.266 1 10:10:18
#> 4 0.401 1 10:10:18
#> 5 1.09 1 10:10:19
#> 6 0.752 1 10:10:20
#> 7 0.337 1 10:10:20
#> 8 0.933 1 10:10:21
#> 9 0.731 1 10:10:22
#> 10 0.454 1 10:10:23
#> # ℹ 91,848 more rowsTo make sure everything is so far reproducible within the doc/learning.qmd file, we will render the Quarto document to output an HTML file. Click the “Render” button at the top of the Source pane or by typing Ctrl-Shift-KCtrl-Shift-K or with the Palette (Ctrl-Shift-PCtrl-Shift-P, then type “render”). If it generates an HTML document without problems, we know our code is at least starting to be reproducible.
Time: 15 minutes.
Practice some more. Do the same thing with the Actigraph.csv dataset as we did for the RR.csv. But first:
doc/learning.qmd file and call it ## Exercise: Import the Actigraph data.Use the same technique as we used for the RR.csv data and read in the Actigraph.csv file from user_1/.
Set the file path to the dataset with here().
Read in a max of 100 rows for n_max and exclude the first column with col_select = -1.
Use spec() to output the column specification and paste the results into col_types().
Render the Quarto document with Ctrl-Shift-KCtrl-Shift-K or with the Palette (Ctrl-Shift-PCtrl-Shift-P, then type “render”) to regenerate the HTML document.
If everything works, run styler with the Palette (Ctrl-Shift-PCtrl-Shift-P, then type “style file”) and then add and commit the new changes to the Git history with
Ctrl-Alt-MCtrl-Alt-M or with the Palette (Ctrl-Shift-PCtrl-Shift-P, then type “commit”)
Use this template as a guide for completing this exercise.
doc/learning.qmd
# Use first 100 or so lines to get spec
user_1_actigraph_file <- here("data-raw/mmash/user_1/Actigraph.csv")
user_1_actigraph_data_prep <- read_csv(
user_1_actigraph_file,
n_max = 100,
col_select = -1,
name_repair = snakecase::to_snake_case
)
spec(user_1_actigraph_data_prep)
user_1_actigraph_data <- read_csv(
user_1_actigraph_file,
col_select = -1,
col_types = cols(
col_skip(),
axis_1 = col_double(),
axis_2 = col_double(),
axis_3 = col_double(),
steps = col_double(),
hr = col_double(),
inclinometer_off = col_double(),
inclinometer_standing = col_double(),
inclinometer_sitting = col_double(),
inclinometer_lying = col_double(),
vector_magnitude = col_double(),
day = col_double(),
time = col_time(format = "")
),
name_repair = snakecase::to_snake_case
).xlsx use readxl).read_csv() for fast importing of plain text type files (e.g. csv).spec() to diagnose importing issues.col_types = and col_select = arguments in read_csv() to import only the data you need.name_repair = argument in read_csv().n_max = to test that importing works fine before importing the full dataset.