1 Intro
Welcome!
For this workshop, we will be cleaning a dataset. It is a hands-on
approach to using the select(), filter(), and
mutate().
The assignment should be submitted individually, but you are encouraged to brainstorm with partners.
The final due date for the assignment is Tuesday, November 15th at 23:59 PM UTC+2.
2 Get the assignment repo
To get started, you should download and look through the assignment folder.
First download the repo to your local computer here.
You should ideally work on your local computer, but if you would rather work on RStudio Cloud, you can upload the zip file to RStudio Cloud through the Files pane. Consult one of the instructors for guidance on this.
Unzip/Extract the downloaded folder.
If you are on macOS, you can simply double-click on a file to unzip it.
If you are on Windows and are not sure how to “unzip” a file, see this image. You need to right-click on the file and then select “extract all”.
Once done, click on the RStudio Project file in the unzipped folder to open the project in RStudio.
In RStudio, navigate to the Files tab and open the “rmd” folder. The instructions for your exercise are outlined there (these are the same instructions you see here).
Open the “data” folder and observe its components. You will work with the “obesity.csv” file. (You can also open the “00_info_about_the_dataset” file to learn more about this dataset.)
3 Load and clean the data
Now that you understand the structure of the repo, you can load in and clean your dataset.
In the code section below, load in the needed packages.
Now, read the dataset into R. The data frame you
import should have 142 rows and 9 columns. Remember to use the
here() function to allow your Rmd to use project-relative
paths.
## Rows: 142 Columns: 9
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (7): sex, status, bmi, sedentary_ap_s_day, light_ap_s_day, mvpa_s_day, o...
## dbl (2): personal_id, household_id
##
## 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.3.1 Step 1: Verify the type of your variables
Before jumping into wrangling or plotting, you should take the time
to know what data types you are working with. You can look at these
types with summary() or typeof().
Some of your variables are numeric but they should be factors (i.e. categories), some are characters but should be factors, and some are characters but should be numeric. Having them in the correct type will be essential for the next manipulations and for plotting !
Use mutate() to convert your variables into the right
type.
## Warning in mask$eval_all_mutate(quo): NAs introduced by coercion
## Warning in mask$eval_all_mutate(quo): NAs introduced by coercion
## Warning in mask$eval_all_mutate(quo): NAs introduced by coercion
## Warning in mask$eval_all_mutate(quo): NAs introduced by coercion3.2 Step 2: Convert the physical activity variables
Currently, the variables of physical activity are in seconds per day.
There are 3 types of physical activity variables: sedentary physical
activity (sedentary_ap_s_day), light physical activity
(light_ap_s_day), and moderate to vigorous physical
activity (mvpa_s_day).
Please convert these numerical variables in seconds/day to minutes/week. As a kind reminder, 60 seconds = 1 minute and 7 days = 1 week.
(Hint: use mutate() to create new variables that are in
minutes per week. If you feel more comfortable changing the variabless
in-place, that’s also acceptable.)
Why do we perform this conversion? The WHO (known as OMS in French) recommendations are in minutes per week, so we want to align with these measures.
You will use this obesity_data_cleaned for all the
following subset making and plotting.
4 Plot 1: BMI distribution by sex
4.1 Extract: Make a subset
Make a subset with only the variables of interest for your plot. This is good practice to make a subset with the variables you need for plotting.
Print this subset in an elegant manner for your HTML (hint: use
reactable).
4.2 Plot with Esquisse: Violin Plot
Using esquisse and the subset you just made above, plot BMI distributions by sex, as a violin plot.
Violin plots are interesting because you can compare the density curves’ peaks, valleys, and tails to see where the groups are similar or different.
## Warning: Removed 3 rows containing non-finite values (`stat_ydensity()`).
5 Plot 2: Male respondents’ Light Physical Activity (LPA, in minutes per week)
5.1 Extract: Make a data subset
To make this subset, you will only male respondents.
Then keep only the variables useful for the plot.
Print this subset in an elegant manner for your HTML (hint: use
reactable).
5.2 Plot with Esquisse: Histogram of Light Physical Activity (LPA) of Male Respondents
Using esquisse and the subset you just made above, plot LPA distribution (minutes/week) for male respondents, as a histogram.
## Warning: Removed 6 rows containing non-finite values (`stat_bin()`).
6 Plot 3: Adults complying to OMS/WHO recommendations’ Moderate to Vigorous Physical Activity (MVPA, minutes per week)
6.1 Extract: Make a data subset
- To make this subset, you will only keep individuals in the dataset who have complied to OMS/WHO recommendations
(Hint 1: oms_recommendation should be equal to
Yes. Side-note: OMS is Organisation Mondiale de la Santé,
French for WHO.)
(Hint 2: The variable status is encoded in French as
well. “Adulte” means “Adult” and “Enfant” means “Child”.)
Then keep only the variables useful for the plot.
Print this subset in an elegant manner for your HTML (hint: use
reactable).
6.2 Plot with Esquisse: Boxplots of Moderate to Vigorous Physical Activity per Age Group
Using esquisse and the subset you just made above, plot MVPA distributions (minutes/week) by age groups, as boxplots.
7 Submission: Upload HTML
Once you have finished the tasks above, you should knit this Rmd into an HTML and upload it on the assignment page.