A - Setup

1. Open your dataanalytics R project. It should already have the folders 1_Data and 2_Code. Make sure that the data files listed in the Datasets section above are in your 1_Data folder

2. Open a new R script. At the top of the script, using comments, write your name and the date. Save it as a new file called wrangling_practical.R in the 2_Code folder.

3. Using library() load the tidyverse package (if you don’t have it, you’ll need to install it with install.packages())!

# Load packages necessary for this script
library(tidyverse)

4. Using the following template, load the trial_act.csv data into R and store it as a new object called trial_act

# Load trial_act.csv from the data folder in your 

trial_act <- read_csv(file = "XX/XX")

5. Using the same code structure, load the trial_act_demo_fake.csv data as a new dataframe called trial_act_demo.

# Load trial_act_demo_fake.csv from the data folder in your 

XX <- XX(XX = "XX/XX")

6. Take a look at the first few rows of the datasets by printing them to the console.

# Print trial_act object
trial_act

# Print trial_act_demo object
trial_act_demo

B - Change column names with rename()

1. Print the names of the trial_act data with names(XXX)

names(XXX)

2. One of the columns is currently called wtkg. Using rename(), change the name of this column to weight_kg. Be sure to assign the result back to trial_act to change it!

# Change the name to weight_kg from wtkg

trial_act <- trial_act %>%
  rename(XX = XX)   # NEW = OLD

3. Look at the names of your trial_act dataframe again using names(), do you now see the column weight_kg?

4. One of the columns is called age, change it to age_y (to specify that age is in years).

C - Select columns with select()

1. Using the select() function, select only the column age_y and print the result (but don’t assign it to anything). Do you see only the age_y column now?

XX %>%
  select(XX)

2. Using select() select the columns pidnum, age_y, gender, and weight_kg (but don’t assign the result to anything)

XX %>%
  select(XX, XX, XX, XX)

3. Imagine that a colleague of yours wants an anonymised dataframe called trial_act_anon_ that does not contain the columns pidnum and age_y. Create this dataframe by selecting all columns in trial_act except pidnum and age_y (hint: use the notation select(-XXX, -XXX)) to select everything except specified columns). Assign the result to a new object called trial_act_anon

XX <- XX %>%
  select(-XX, -XX)

4. Create a new dataframe called CD4_wide which contains the following columns: pidnum, arms, cd40, cd420, and cd496. The cd40, cd420, and cd496 columns show patient’s CD4 T cell counts at baseline, 20 weeks, and 96 weeks. After you create CD4_wide, print it to make sure it worked!

XX <- trial_act %>% 
  select(XX, XX, XX, XX, XX)

5. Did you know you can easily select all columns that start with specific characters using starts_with()? Try adapting the following code to get the same result you got before.

CD4_wide <- trial_act %>% 
  select(pidnum, arms, starts_with("XXX"))

D - Add new columns with mutate()

1. Using the mutate() function, add the column age_m which shows each patient’s age in months instead of years (Hint: Just multiply age_y by 12!)

trial_act <- trial_act %>%
  mutate(XX = XX * 12)

2. Using mutate, add the following new columns to trial_act. (Try combining these into one call to the mutate() function!)

• weight_lb: Weight in lbs instead of kilograms. You can do this by multiplying weight_kg by 2.2.
• cd_change_20: Change in CD4 T cell count from baseline to 20 weeks. You can do this by taking cd420 - cd40
• cd_change_960: Change in CD4 T cell count from baseline to 96 weeks. You can do this by taking cd496 - cd40

XXX <- XXX %>% 
  mutate(weight_lb = XXX,
         cd_change_20 = XXX,
         XXX = XXX)

3. If you look at the gender column, you will see that it is numeric (0s and 1s). Using the mutate() and case_when() functions, create a new column called gender_char which shows the gender as a string, where 0 = “female” and 1 = “male”:

# Create gender_char which shows gender as a string
trial_act <- trial_act %>%
  mutate(
  gender_char = case_when(
    gender == XX ~ "XX",
    gender == XX ~ "XX"))

4. The column arms is also numeric and not very meaningful. Create a new column arms_char contains the names of the arms. Here is a table of the mapping

5. If you haven’t already, try putting all the code for your previous questions in one call to mutate(). That is, in one block of code, create age_m, weight_lb, cd_change_20, cd_change_960 and gender_char and arms_char using the mutate() function only once. Here’s how your code should look:

trial_act <- trial_act %>%
  mutate(
    age_m = XXX,
    weight_lb = XXX,
    cd_change_20 = XXX,
    cd_change_960 = XXX,
    gender_char = XXX,
    arms_char = XXX
  )

E - Arrange rows with arrange()

1. Using the arrange() function, arrange the trial_act data in ascending order of age_y (from lowest to highest). After you do, print the data to make sure it worked!

trial_act <- trial_act %>% 
 arrange(XXX)

2. Now arrange the data in descending order of age_y (from highest to lowest). After, look the data to make sure it worked. To arrange data in descending order, just include desc() around the variable

trial_act <- trial_act %>% 
 arrange(desc(XXX))

3. You can sort the rows of dataframes with multiple columns by including many arguments to arrange(). Now sort the data by arms (arms) and then age_y (age_y). Print the result to make sure it looks right!

trial_act <- trial_act %>% 
 arrange(XXX, XXX)

F - Filter specific rows with filter()

1. Using the filter() function, filter only the rows from males but don’t save the result (Hint: gender_char == "male")

trial_act %>%
  filter(XXX == "XXX")

2. Create a new dataframe called trial_act_male that only contains rows from males (hint: just assign what you did in the previous question to a new object!). After you create the object, print it to make sure it looks right.

3. Filter only rows for patients under the age of 60 and save the result to trial_act_young. After you create it, print the object to make sure it looks right.

4. A colleague of yours named Tracy wants a dataframe only containing data from females over the age of 40. Create this dataframe with filter() and call it trial_act_tracy. After you create the object, print it to make sure it looks right.

trial_act_tracy <- XXX %>%
  filter(XXX > XXX & XXX == XXX)

G - Combine dataframes with left_join()

The trial_act_demo.csv file contains additional (fictional) demographic data about the patients, namely the number of days of exercise they get per week, and their highest level of education. Our goal is to add the demographic information to our trial_act data.

1. In order to combine the two dataframes, we need to find one ‘key’ column that we can use to match rows. Look at both the trial_act and trial_act_demo dataframes. Which column can we use as the ‘key’ column?

2. Use the left_join() function to combine the trial_act and trial_act_demo datasets, set the by argument to the name of the key column that is common in both data sets. Assign the result to trial_act.

trial_act <- trial_act %>%
  left_join(XX, by = "XX")

3. Print your new trial_act dataframe. Do you now see the demographic data?

H - Reshaping with pivot_longer() and pivot_wider()

Remember the CD4_wide dataframe you created before? Currently it is in the wide format, where each row is a patient, where key data (different CD4 T cell counts) are in different columns like this:

# Data is in a 'wide' format
CD4_wide

# A tibble: 2,139 x 5
   pidnum  arms  cd40 cd420 cd496
    <dbl> <dbl> <dbl> <dbl> <dbl>
 1  10056     2   422   477   660
 2  10059     3   162   218    NA
 3  10089     3   326   274   122
 4  10093     3   287   394    NA
 5  10124     0   504   353   660
 6  10140     1   235   339   264
 7  10165     0   244   225   106
 8  10190     0   401   366   453
 9  10198     3   214   107     8
10  10229     0   221   132    NA
# … with 2,129 more rows

Our goal is to convert this data to a ‘long’ format, where each row represents a single CD4 T cell count for a specific patient, like this:

# This is the same data in 'long' format
CD4_long

# A tibble: 6,417 x 4
   pidnum  arms name  value
    <dbl> <dbl> <chr> <dbl>
 1  10056     2 cd40    422
 2  10056     2 cd420   477
 3  10056     2 cd496   660
 4  10059     3 cd40    162
 5  10059     3 cd420   218
 6  10059     3 cd496    NA
 7  10089     3 cd40    326
 8  10089     3 cd420   274
 9  10089     3 cd496   122
10  10093     3 cd40    287
# … with 6,407 more rows

1. Using the pivot_longer() function, create a new dataframe called CD4_long that shows the CD4_wide data in the ‘long’ format. To do this, use the following template.

CD4_long <- CD4_wide %>% 
  pivot_longer(starts_with("XX")) 

2. Print your CD4_long dataframe! Do you now see that each row is a specific observation for a patient?

3. Now use the pivot_wider() function to bring the data back into the wide format!

CD4_long %>%
  pivot_wider(names_from = XX, 
              values_from = XX)

I - Grouped aggregation

1. Now, apply the group_by() - summarize() idiom to the CD4_wide dataset to calculate the average (mean()) CD4 T cell counts for the various treament arms.

CD4_wide %>%
  group_by(XX) %>%
  summarize(XX = XX,
            XX = XX,
            XX = XX)

2. Handle the missing values by including na.rm = TRUE in the mean() functions.

3. Use n() to add another summary showing the number of cases per arm.

4. Now try to calculate the same values, but using the CD4_long dataset. How do you need to adjust the code?

5. Finally, use pivot_wider to change the format of the summarized results to the one obtained from CD4_wide.

trial_act.csv

First 5 rows of trial_act.csv

The trial_act.csv data set contains a randomized clinical trial to compare monotherapy with zidovudine or didanosine with combination therapy with zidovudine and didanosine or zidovudine and zalcitabine in adults infected with the human immunodeficiency virus type I whose CD4 T cell counts were between 200 and 500 per cubic millimeter.

trial_act_demo_fake.csv

First 5 rows of trial_act_demo_fake.csv

The trial_act_demo_fake.csv data set contains fake demogrpahic information corresponding to the patients in trial_act.csv

Packages

Functions

dplyr vignette

See https://cran.r-project.org/web/packages/dplyr/vignettes/dplyr.html for the full dplyr vignette with lots of wrangling tips and tricks.

Cheatsheets

from R Studio