Overview
In this practical you’ll practice “data wrangling” with the dplyr and tidyr packages (part of the `tidyverse collection of packages).
By the end of this practical you will know how to:
- Change column names, select specific columns
- Create new columns
- Filter rows of data based on multiple criteria
- Combine (aka, ‘join’) multiple data sets through key columns
- Convert data between wide and long formats
Tasks
A - Setup
- Open your
BernRBootcampR project. It should already have the folders1_Dataand2_Code. Make sure that the data files listed in theDatasetssection above are in your1_Datafolder
# Done!- 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.Rin the2_Codefolder.
# Done!Using
library()load the set of packages for this practical listed in the packages section above.For this practical, we’ll use the
trial_actdata, this is the result of a randomized clinical trial comparing the effects of different medications on adults infected with the human immunodeficiency virus (You can learn more about the trial here). Using the following template, load the data into R and store it as a new object calledtrial_act.
# Load trial_act.csv from the data folder in your working directory
trial_act <- read_csv(file = "XX")trial_act <- read_csv(file = "1_Data/trial_act.csv")- Using the same code structure, load the
trial_act_demo_fake.csvdata as a new dataframe calledtrial_act_demo
trial_act_demo <- read_csv(file = "1_Data/trial_act_demo_fake.csv")- Take a look at the first few rows of the datasets by printing them to the console.
# Print trial_act object
trial_act# A tibble: 2,139 x 27
pidnum age wtkg hemo homo drugs karnof oprior z30 zprior preanti race
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10056 48 89.8 0 0 0 100 0 0 1 0 0
2 10059 61 49.4 0 0 0 90 0 1 1 895 0
3 10089 45 88.5 0 1 1 90 0 1 1 707 0
4 10093 47 85.3 0 1 0 100 0 1 1 1399 0
5 10124 43 66.7 0 1 0 100 0 1 1 1352 0
6 10140 46 88.9 0 1 1 100 0 1 1 1181 0
7 10165 31 73.0 0 1 0 100 0 1 1 930 0
8 10190 41 66.2 0 1 1 100 0 1 1 1329 0
9 10198 40 82.6 0 1 0 90 0 1 1 1074 0
10 10229 35 78.0 0 1 0 100 0 1 1 964 0
# … with 2,129 more rows, and 15 more variables: gender <dbl>, str2 <dbl>,
# strat <dbl>, symptom <dbl>, treat <dbl>, offtrt <dbl>, cd40 <dbl>,
# cd420 <dbl>, cd496 <dbl>, r <dbl>, cd80 <dbl>, cd820 <dbl>, cens <dbl>,
# days <dbl>, arms <dbl>- Use the the
summary()function to print more details on the columns of the datasets.
summary(trial_act) pidnum age wtkg hemo
Min. : 10056 Min. :12.0 Min. : 31.0 Min. :0.000
1st Qu.: 81446 1st Qu.:29.0 1st Qu.: 66.7 1st Qu.:0.000
Median :190566 Median :34.0 Median : 74.4 Median :0.000
Mean :248778 Mean :35.2 Mean : 75.1 Mean :0.084
3rd Qu.:280277 3rd Qu.:40.0 3rd Qu.: 82.6 3rd Qu.:0.000
Max. :990077 Max. :70.0 Max. :159.9 Max. :1.000
homo drugs karnof oprior z30
Min. :0.000 Min. :0.000 Min. : 70.0 Min. :0.000 Min. :0.00
1st Qu.:0.000 1st Qu.:0.000 1st Qu.: 90.0 1st Qu.:0.000 1st Qu.:0.00
Median :1.000 Median :0.000 Median :100.0 Median :0.000 Median :1.00
Mean :0.661 Mean :0.131 Mean : 95.4 Mean :0.022 Mean :0.55
3rd Qu.:1.000 3rd Qu.:0.000 3rd Qu.:100.0 3rd Qu.:0.000 3rd Qu.:1.00
Max. :1.000 Max. :1.000 Max. :100.0 Max. :1.000 Max. :1.00
zprior preanti race gender str2
Min. :1 Min. : 0 Min. :0.000 Min. :0.000 Min. :0.000
1st Qu.:1 1st Qu.: 0 1st Qu.:0.000 1st Qu.:1.000 1st Qu.:0.000
Median :1 Median : 142 Median :0.000 Median :1.000 Median :1.000
Mean :1 Mean : 379 Mean :0.288 Mean :0.828 Mean :0.586
3rd Qu.:1 3rd Qu.: 740 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:1.000
Max. :1 Max. :2851 Max. :1.000 Max. :1.000 Max. :1.000
strat symptom treat offtrt cd40
Min. :1.00 Min. :0.000 Min. :0.000 Min. :0.000 Min. : 0
1st Qu.:1.00 1st Qu.:0.000 1st Qu.:1.000 1st Qu.:0.000 1st Qu.: 264
Median :2.00 Median :0.000 Median :1.000 Median :0.000 Median : 340
Mean :1.98 Mean :0.173 Mean :0.751 Mean :0.363 Mean : 351
3rd Qu.:3.00 3rd Qu.:0.000 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.: 423
Max. :3.00 Max. :1.000 Max. :1.000 Max. :1.000 Max. :1199
cd420 cd496 r cd80 cd820
Min. : 49 Min. : 0 Min. :0.000 Min. : 40 Min. : 124
1st Qu.: 269 1st Qu.: 209 1st Qu.:0.000 1st Qu.: 654 1st Qu.: 632
Median : 353 Median : 321 Median :1.000 Median : 893 Median : 865
Mean : 371 Mean : 329 Mean :0.627 Mean : 987 Mean : 935
3rd Qu.: 460 3rd Qu.: 440 3rd Qu.:1.000 3rd Qu.:1207 3rd Qu.:1146
Max. :1119 Max. :1190 Max. :1.000 Max. :5011 Max. :6035
NA's :797
cens days arms
Min. :0.000 Min. : 14 Min. :0.00
1st Qu.:0.000 1st Qu.: 727 1st Qu.:1.00
Median :0.000 Median : 997 Median :2.00
Mean :0.244 Mean : 879 Mean :1.52
3rd Qu.:0.000 3rd Qu.:1091 3rd Qu.:3.00
Max. :1.000 Max. :1231 Max. :3.00
summary(trial_act_demo) pidnum exercise education
Min. : 10056 Min. :0.0 Length:2139
1st Qu.: 81446 1st Qu.:1.0 Class :character
Median :190566 Median :1.0 Mode :character
Mean :248778 Mean :2.2
3rd Qu.:280277 3rd Qu.:3.0
Max. :990077 Max. :7.0 B - Change column names with rename()
- Print the names of the
trial_actdata withnames(XXX)
names(XXX)names(trial_act) [1] "pidnum" "age" "wtkg" "hemo" "homo" "drugs" "karnof"
[8] "oprior" "z30" "zprior" "preanti" "race" "gender" "str2"
[15] "strat" "symptom" "treat" "offtrt" "cd40" "cd420" "cd496"
[22] "r" "cd80" "cd820" "cens" "days" "arms" - Using
rename(), change the column namewtkgin thetrial_actdataframe toweight_kg. Be sure to assign the result back totrial_actto change it!
# Change the name to weight_kg from wtkg
trial_act <- trial_act %>%
rename(XX = XX)trial_act <- trial_act %>%
rename(weight_kg = wtkg)- Look at the names of your
trial_actdataframe again, do you now see the columnweight_kg?
names(trial_act) [1] "pidnum" "age" "weight_kg" "hemo" "homo" "drugs"
[7] "karnof" "oprior" "z30" "zprior" "preanti" "race"
[13] "gender" "str2" "strat" "symptom" "treat" "offtrt"
[19] "cd40" "cd420" "cd496" "r" "cd80" "cd820"
[25] "cens" "days" "arms" - Change the column name
agetoage_y(to specify that age is in years).
trial_act <- trial_act %>%
rename(age_y = age)C - Select columns with select()
- Using the
select()function, select only the columnage_yand print the result (but don’t assign it to anything). Do you see only theage_ycolumn now?
XXX %>%
select(XXX)trial_act %>%
select(age_y)# A tibble: 2,139 x 1
age_y
<dbl>
1 48
2 61
3 45
4 47
5 43
6 46
7 31
8 41
9 40
10 35
# … with 2,129 more rowsUsing
select()select the columnspidnum,age_y,gender, andweight_kg(but don’t assign the result to anything)Now it’s time to create a new dataframe! Select the columns
pidnum,arms,cd40,cd420, andcd496and assign the result to a new object calledCD4_wide. Thecd40,cd420, andcd496columns show patient’s CD4 T cell counts at baseline, 20 weeks, and 96 weeks. After you createCD4_wide, print it to make sure it worked!
XX <- trial_act %>%
select(XX, XX, XX, XX, ...)CD4_wide <- trial_act %>%
select(`pidnum`, `arms`, `cd40`, `cd420`, `cd496`)- 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"))CD4_wide <- trial_act %>%
select(pidnum, arms, starts_with("cd"))- A colleague wants an anonymised dataframe that does not contain the columns
pidnumandage. Create this dataframe by selecting all columns exceptpidnumandage(hint: use the notationselect(-XXX, -XXX))to select everything except specified columns
D - Add new columns with mutate()
- Using the
mutate()function, add the columnage_mwhich shows each patient’s age in months instead of years (Hint: Just multiplyage_yby 12!)
trial_act <- trial_act %>%
mutate(XX = XX * 12)trial_act <- trial_act %>%
mutate(age_m = age_y * 12)- Using
mutate, add the following new columns totrial_act. (Try combining these into one call to themutate()function!)
weight_lb: Weight in lbs instead of kilograms. You can do this by multiplyingweight_kgby 2.2.cd_change_20: Change in CD4 T cell count from baseline to 20 weeks. You can do this by takingcd420 - cd40cd_change_960: Change in CD4 T cell count from baseline to 96 weeks. You can do this by takingcd496 - cd40
XXX <- XXX %>%
mutate(weight_lb = XXX,
cd_change_20 = XXX,
XXX = XXX)trial_act <- trial_act %>%
mutate(weight_lb = weight_kg * 2.2,
cd_change_20 = cd420 - cd40,
cd_change_960 = cd496 - cd40)- If you look at the
gendercolumn, you will see that it is numeric (0s and 1s). Using themutate()andcase_when()functions, create a new column calledgender_charwhich shows the gender as a string, where 0 = “female” and 1 = “male”:
# Create gender_char which shows gender as a stringh
trial_act <- trial_act %>%
mutate(
gender_char = case_when(
gender == XX ~ "XX",
gender == XX ~ "XX"))trial_act <- trial_act %>%
mutate(
gender_char = case_when(
gender == 0 ~ "female",
gender == 1 ~ "male"))- The column
armsis also numeric and not very meaningful. Create a new columnarms_charcontains the names of the arms. Here is a table of the mapping
| arms | arms_char |
|---|---|
| 0 | zidovudine |
| 1 | zidovudine and didanosine |
| 2 | zidovudine and zalcitabine |
| 3 | didanosine |
trial_act <- trial_act %>%
mutate(
arms_char = case_when(
arms == 0 ~ "zidovudine",
arms == 1 ~ "zidovudine and didanosine",
arms == 2 ~ "zidovudine and zalcitabine",
arms == 3 ~ "didanosine"))- 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, createage_m,weight_lb,cd_change_20,cd_change_960,gender_charandover50using themutate()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,
XXX = XXX,
XXX = case_when(XXX),
XXX = case_when(XXX)
)trial_act <- trial_act %>%
mutate(
agem = age_y * 12,
weight_lb = weight_kg * 2.2,
cd_change_20 = cd420 - cd40,
cd_change_960 = cd496 - cd40,
gender_char = case_when(
gender == 0 ~ "female",
gender == 1 ~ "male"),
arms_char = case_when(
arms == 0 ~ "zidovudine",
arms == 1 ~ "zidovudine and didanosine",
arms == 2 ~ "zidovudine and zalcitabine",
arms == 3 ~ "didanosine")
)E - Arrange rows with arrange()
- Using the
arrange()function, arrange thetrial_actdata in ascending order ofage_y(from lowest to highest). After you do, print the data to make sure it worked!
trial_act <- trial_act %>%
arrange(XXX)trial_act <- trial_act %>%
arrange(age_y)
trial_act# A tibble: 2,139 x 34
pidnum age_y weight_kg hemo homo drugs karnof oprior z30 zprior preanti
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 940533 12 41.4 1 0 0 100 0 0 1 0
2 950037 12 53.1 1 0 0 100 0 1 1 902
3 950056 12 31 1 0 0 100 0 1 1 2500
4 910034 13 32.7 1 0 0 100 0 1 1 553
5 940534 13 62.9 1 0 0 100 0 0 1 366
6 960014 13 48.5 1 0 0 100 0 0 1 0
7 310767 14 65 1 0 0 100 0 1 1 490
8 920050 14 54.2 1 0 0 100 0 1 1 610
9 940544 14 41.1 1 0 0 100 0 0 1 0
10 950061 14 64.3 1 0 0 100 0 1 1 387
# … with 2,129 more rows, and 23 more variables: race <dbl>, gender <dbl>,
# str2 <dbl>, strat <dbl>, symptom <dbl>, treat <dbl>, offtrt <dbl>,
# cd40 <dbl>, cd420 <dbl>, cd496 <dbl>, r <dbl>, cd80 <dbl>, cd820 <dbl>,
# cens <dbl>, days <dbl>, arms <dbl>, age_m <dbl>, weight_lb <dbl>,
# cd_change_20 <dbl>, cd_change_960 <dbl>, gender_char <chr>,
# arms_char <chr>, agem <dbl>- 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 includedesc()around the variable. E.g.;data %>% arrrange(desc(height))
trial_act <- trial_act %>%
arrange(desc(age_y))
trial_act# A tibble: 2,139 x 34
pidnum age_y weight_kg hemo homo drugs karnof oprior z30 zprior preanti
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 11438 70 73.9 0 1 0 100 0 1 1 674
2 211360 70 63.1 0 1 0 90 0 0 1 0
3 211284 69 81.6 0 1 0 100 0 0 1 0
4 50580 68 90.5 0 1 1 100 0 1 1 618
5 81127 68 70.8 0 1 0 90 0 1 1 1770
6 10924 67 71 0 1 0 100 0 0 1 0
7 241150 67 82.1 0 1 0 90 0 0 1 0
8 50662 66 84.4 0 1 0 100 0 0 1 0
9 11987 65 77.2 0 1 0 90 0 0 1 0
10 140797 65 60.5 0 1 0 90 0 0 1 0
# … with 2,129 more rows, and 23 more variables: race <dbl>, gender <dbl>,
# str2 <dbl>, strat <dbl>, symptom <dbl>, treat <dbl>, offtrt <dbl>,
# cd40 <dbl>, cd420 <dbl>, cd496 <dbl>, r <dbl>, cd80 <dbl>, cd820 <dbl>,
# cens <dbl>, days <dbl>, arms <dbl>, age_m <dbl>, weight_lb <dbl>,
# cd_change_20 <dbl>, cd_change_960 <dbl>, gender_char <chr>,
# arms_char <chr>, agem <dbl>- 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)trial_act <- trial_act %>%
arrange(arms, age_y)
trial_act# A tibble: 2,139 x 34
pidnum age_y weight_kg hemo homo drugs karnof oprior z30 zprior preanti
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 960014 13 48.5 1 0 0 100 0 0 1 0
2 960031 14 48.3 1 0 0 100 0 0 1 0
3 990071 14 60 1 0 0 100 0 0 1 0
4 980042 16 63 1 0 0 100 0 1 1 753
5 171040 17 51.3 0 0 0 90 0 0 1 0
6 990026 17 103. 1 0 0 100 0 1 1 417
7 310234 18 57.3 1 0 0 100 0 1 1 344
8 940519 18 56.8 1 0 0 100 0 1 1 805
9 211314 19 50.8 0 0 0 90 0 0 1 0
10 340767 19 74.8 0 1 0 100 0 0 1 0
# … with 2,129 more rows, and 23 more variables: race <dbl>, gender <dbl>,
# str2 <dbl>, strat <dbl>, symptom <dbl>, treat <dbl>, offtrt <dbl>,
# cd40 <dbl>, cd420 <dbl>, cd496 <dbl>, r <dbl>, cd80 <dbl>, cd820 <dbl>,
# cens <dbl>, days <dbl>, arms <dbl>, age_m <dbl>, weight_lb <dbl>,
# cd_change_20 <dbl>, cd_change_960 <dbl>, gender_char <chr>,
# arms_char <chr>, agem <dbl>F - Filter specific rows with filter()
- Using the
filter()function, filter only the rows from males (Hint:gender_char == "male")
trial_act %>%
filter(XXX == "XXX")trial_act %>%
filter(gender_char == "male")# A tibble: 1,771 x 34
pidnum age_y weight_kg hemo homo drugs karnof oprior z30 zprior preanti
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 960014 13 48.5 1 0 0 100 0 0 1 0
2 960031 14 48.3 1 0 0 100 0 0 1 0
3 990071 14 60 1 0 0 100 0 0 1 0
4 980042 16 63 1 0 0 100 0 1 1 753
5 990026 17 103. 1 0 0 100 0 1 1 417
6 940519 18 56.8 1 0 0 100 0 1 1 805
7 340767 19 74.8 0 1 0 100 0 0 1 0
8 211007 20 72.7 0 1 0 90 0 1 1 21
9 261065 20 57.3 0 1 0 100 0 0 1 0
10 490308 20 72 0 1 0 100 0 0 1 539
# … with 1,761 more rows, and 23 more variables: race <dbl>, gender <dbl>,
# str2 <dbl>, strat <dbl>, symptom <dbl>, treat <dbl>, offtrt <dbl>,
# cd40 <dbl>, cd420 <dbl>, cd496 <dbl>, r <dbl>, cd80 <dbl>, cd820 <dbl>,
# cens <dbl>, days <dbl>, arms <dbl>, age_m <dbl>, weight_lb <dbl>,
# cd_change_20 <dbl>, cd_change_960 <dbl>, gender_char <chr>,
# arms_char <chr>, agem <dbl>- Create a new dataframe called
trial_act_malethat only contains rows from males (hint: just assign what you did in the previous question to a new object!). After you createtrial_act_male, print it to make sure it only contains data from males!
trial_act_male <- trial_act %>%
filter(gender_char == "male")
trial_act_male# A tibble: 1,771 x 34
pidnum age_y weight_kg hemo homo drugs karnof oprior z30 zprior preanti
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 960014 13 48.5 1 0 0 100 0 0 1 0
2 960031 14 48.3 1 0 0 100 0 0 1 0
3 990071 14 60 1 0 0 100 0 0 1 0
4 980042 16 63 1 0 0 100 0 1 1 753
5 990026 17 103. 1 0 0 100 0 1 1 417
6 940519 18 56.8 1 0 0 100 0 1 1 805
7 340767 19 74.8 0 1 0 100 0 0 1 0
8 211007 20 72.7 0 1 0 90 0 1 1 21
9 261065 20 57.3 0 1 0 100 0 0 1 0
10 490308 20 72 0 1 0 100 0 0 1 539
# … with 1,761 more rows, and 23 more variables: race <dbl>, gender <dbl>,
# str2 <dbl>, strat <dbl>, symptom <dbl>, treat <dbl>, offtrt <dbl>,
# cd40 <dbl>, cd420 <dbl>, cd496 <dbl>, r <dbl>, cd80 <dbl>, cd820 <dbl>,
# cens <dbl>, days <dbl>, arms <dbl>, age_m <dbl>, weight_lb <dbl>,
# cd_change_20 <dbl>, cd_change_960 <dbl>, gender_char <chr>,
# arms_char <chr>, agem <dbl>- Filter only rows for patients under the age of 60.
trial_act %>%
filter(age_y < 60)# A tibble: 2,110 x 34
pidnum age_y weight_kg hemo homo drugs karnof oprior z30 zprior preanti
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 960014 13 48.5 1 0 0 100 0 0 1 0
2 960031 14 48.3 1 0 0 100 0 0 1 0
3 990071 14 60 1 0 0 100 0 0 1 0
4 980042 16 63 1 0 0 100 0 1 1 753
5 171040 17 51.3 0 0 0 90 0 0 1 0
6 990026 17 103. 1 0 0 100 0 1 1 417
7 310234 18 57.3 1 0 0 100 0 1 1 344
8 940519 18 56.8 1 0 0 100 0 1 1 805
9 211314 19 50.8 0 0 0 90 0 0 1 0
10 340767 19 74.8 0 1 0 100 0 0 1 0
# … with 2,100 more rows, and 23 more variables: race <dbl>, gender <dbl>,
# str2 <dbl>, strat <dbl>, symptom <dbl>, treat <dbl>, offtrt <dbl>,
# cd40 <dbl>, cd420 <dbl>, cd496 <dbl>, r <dbl>, cd80 <dbl>, cd820 <dbl>,
# cens <dbl>, days <dbl>, arms <dbl>, age_m <dbl>, weight_lb <dbl>,
# cd_change_20 <dbl>, cd_change_960 <dbl>, gender_char <chr>,
# arms_char <chr>, agem <dbl>- A colleague of yours named Tracy wants a datafame only containing data from females over the age of 40. Create this dataframe with
filter()and call ittrial_act_Tracy. After you create the dataframe, print it to make sure it looks correct!
trial_act_Tracy <- XXX %>%
filter(XXX > XXX & XXX == XXX)trial_act_Tracy <- trial_act %>%
filter(age_y > 40 & gender_char == "female")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.
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_actandtrial_act_demodataframes. Which column can we use as the ‘key’ column?Use the
left_join()function to combine thetrial_actandtrial_act_demodatasets, set thebyargument to the name of the key column that is common in both data sets. Assign the result totrial_act.
trial_act <- trial_act %>%
left_join(XX, by = "XX")trial_act <- trial_act %>%
left_join(trial_act_demo, by = "pidnum")- Print your new
trial_actdataframe. Do you now see the demographic data?
trial_act# A tibble: 2,139 x 36
pidnum age_y weight_kg hemo homo drugs karnof oprior z30 zprior preanti
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 960014 13 48.5 1 0 0 100 0 0 1 0
2 960031 14 48.3 1 0 0 100 0 0 1 0
3 990071 14 60 1 0 0 100 0 0 1 0
4 980042 16 63 1 0 0 100 0 1 1 753
5 171040 17 51.3 0 0 0 90 0 0 1 0
6 990026 17 103. 1 0 0 100 0 1 1 417
7 310234 18 57.3 1 0 0 100 0 1 1 344
8 940519 18 56.8 1 0 0 100 0 1 1 805
9 211314 19 50.8 0 0 0 90 0 0 1 0
10 340767 19 74.8 0 1 0 100 0 0 1 0
# … with 2,129 more rows, and 25 more variables: race <dbl>, gender <dbl>,
# str2 <dbl>, strat <dbl>, symptom <dbl>, treat <dbl>, offtrt <dbl>,
# cd40 <dbl>, cd420 <dbl>, cd496 <dbl>, r <dbl>, cd80 <dbl>, cd820 <dbl>,
# cens <dbl>, days <dbl>, arms <dbl>, age_m <dbl>, weight_lb <dbl>,
# cd_change_20 <dbl>, cd_change_960 <dbl>, gender_char <chr>,
# arms_char <chr>, agem <dbl>, exercise <dbl>, education <chr>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:
CD4_wide <- trial_act %>%
select(`pidnum`, `arms`, `cd40`, `cd420`, `cd496`)# 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 960014 0 283 271 NA
2 960031 0 481 428 519
3 990071 0 166 169 28
4 980042 0 299 214 124
5 171040 0 549 415 436
6 990026 0 373 218 NA
7 310234 0 445 371 338
8 940519 0 276 150 34
9 211314 0 298 267 NA
10 340767 0 503 452 NA
# … with 2,129 more rowsOur 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 time value
<dbl> <dbl> <chr> <dbl>
1 960014 0 cd40 283
2 960014 0 cd420 271
3 960014 0 cd496 NA
4 960031 0 cd40 481
5 960031 0 cd420 428
6 960031 0 cd496 519
7 990071 0 cd40 166
8 990071 0 cd420 169
9 990071 0 cd496 28
10 980042 0 cd40 299
# … with 6,407 more rows- Using the
pivot_longer()function, create a new dataframe calledCD4_longthat shows theCD4_widedata in the ‘long’ format. To do this, use the following template. Set the grouping column totimeand the new data column tovalue.
CD4_long <- CD4_wide %>%
pivot_longer(names_to = 'XXX', # New grouping column
values_to = 'XXX', # New data column
cols = c('XXX', 'XXX', 'XXX')) # Names of columns to replicate- Print your
CD4_longdataframe! Do you now see that each row is a specific observation for a patient?
CD4_long# A tibble: 6,417 x 4
pidnum arms time value
<dbl> <dbl> <chr> <dbl>
1 960014 0 cd40 283
2 960014 0 cd420 271
3 960014 0 cd496 NA
4 960031 0 cd40 481
5 960031 0 cd420 428
6 960031 0 cd496 519
7 990071 0 cd40 166
8 990071 0 cd420 169
9 990071 0 cd496 28
10 980042 0 cd40 299
# … with 6,407 more rows- Now use the
pivot_wider()function to convert the long data back into the wide format! To do this, make the first argumenttime, and the second argumentvalue
CD4_long %>%
pivot_wider(names_from = 'XXX', # Grouping column
values_from = 'XXX') # Value columnnCD4_long %>%
pivot_wider(names_from = 'time',
values_from = 'value')# A tibble: 2,139 x 5
pidnum arms cd40 cd420 cd496
<dbl> <dbl> <dbl> <dbl> <dbl>
1 960014 0 283 271 NA
2 960031 0 481 428 519
3 990071 0 166 169 28
4 980042 0 299 214 124
5 171040 0 549 415 436
6 990026 0 373 218 NA
7 310234 0 445 371 338
8 940519 0 276 150 34
9 211314 0 298 267 NA
10 340767 0 503 452 NA
# … with 2,129 more rowsPractice!
- Create a new dataframe called
trial_Afromtrial_actwith the following restrictions:
- Only patients who take intraveneous drugs (
drugs == 1) - Only patients younger than 60
- Only patients whose baseline CD4 T cell counts
cd40were greater than 250 and less than 400.
trial_A <- trial_act %>%
filter(drugs == 1,
age_y < 60,
cd40 > 250 & cd40 < 400)
trial_A# A tibble: 126 x 36
pidnum age_y weight_kg hemo homo drugs karnof oprior z30 zprior preanti
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 630023 26 86.2 0 0 1 90 0 0 1 0
2 10378 27 67.3 0 0 1 90 0 1 1 875
3 320475 27 66.6 0 0 1 90 0 0 1 0
4 71333 29 73.9 0 1 1 100 0 0 1 0
5 140809 29 70 0 1 1 100 0 0 1 0
6 630030 29 88.2 0 0 1 100 0 0 1 0
7 190395 33 68 0 0 1 100 0 1 1 842
8 320365 34 86.0 0 0 1 100 0 0 1 0
9 540015 34 72.6 0 0 1 100 0 0 1 0
10 180917 35 77.7 0 0 1 90 0 1 1 248
# … with 116 more rows, and 25 more variables: race <dbl>, gender <dbl>,
# str2 <dbl>, strat <dbl>, symptom <dbl>, treat <dbl>, offtrt <dbl>,
# cd40 <dbl>, cd420 <dbl>, cd496 <dbl>, r <dbl>, cd80 <dbl>, cd820 <dbl>,
# cens <dbl>, days <dbl>, arms <dbl>, age_m <dbl>, weight_lb <dbl>,
# cd_change_20 <dbl>, cd_change_960 <dbl>, gender_char <chr>,
# arms_char <chr>, agem <dbl>, exercise <dbl>, education <chr>- Add a new column to
trial_actcalleddrugs_charwhich is"User"when drugs == 1, and"Non User"when drugs == 0.
trial_act <- trial_act %>%
mutate(drugs_char = case_when(
drugs == 1 ~ "User",
drugs == 0 ~ "Non User"
))
trial_act# A tibble: 2,139 x 37
pidnum age_y weight_kg hemo homo drugs karnof oprior z30 zprior preanti
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 960014 13 48.5 1 0 0 100 0 0 1 0
2 960031 14 48.3 1 0 0 100 0 0 1 0
3 990071 14 60 1 0 0 100 0 0 1 0
4 980042 16 63 1 0 0 100 0 1 1 753
5 171040 17 51.3 0 0 0 90 0 0 1 0
6 990026 17 103. 1 0 0 100 0 1 1 417
7 310234 18 57.3 1 0 0 100 0 1 1 344
8 940519 18 56.8 1 0 0 100 0 1 1 805
9 211314 19 50.8 0 0 0 90 0 0 1 0
10 340767 19 74.8 0 1 0 100 0 0 1 0
# … with 2,129 more rows, and 26 more variables: race <dbl>, gender <dbl>,
# str2 <dbl>, strat <dbl>, symptom <dbl>, treat <dbl>, offtrt <dbl>,
# cd40 <dbl>, cd420 <dbl>, cd496 <dbl>, r <dbl>, cd80 <dbl>, cd820 <dbl>,
# cens <dbl>, days <dbl>, arms <dbl>, age_m <dbl>, weight_lb <dbl>,
# cd_change_20 <dbl>, cd_change_960 <dbl>, gender_char <chr>,
# arms_char <chr>, agem <dbl>, exercise <dbl>, education <chr>,
# drugs_char <chr>- Create a new dataframe called
trial_Bfromtrial_actwith the following restrictions:
- Only patients whose CD4 T cell counts increase more than 200 between baseline (
cd40) and after 96 weeks (cd496) - Only drug users
trial_B <- trial_act %>%
filter(
cd496 - cd40 > 200,
drugs == 1
)
trial_B# A tibble: 8 x 37
pidnum age_y weight_kg hemo homo drugs karnof oprior z30 zprior preanti
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 220489 45 70.9 0 0 1 100 0 0 1 0
2 140744 30 81.2 0 1 1 100 0 0 1 0
3 10962 41 81.9 0 0 1 90 0 0 1 0
4 50580 68 90.5 0 1 1 100 0 1 1 618
5 10881 36 81.6 0 1 1 100 0 1 1 86
6 250197 49 86.2 0 1 1 90 0 0 1 0
7 50572 28 84.4 0 1 1 90 0 0 1 15
8 50623 35 77 0 1 1 100 0 0 1 0
# … with 26 more variables: race <dbl>, gender <dbl>, str2 <dbl>, strat <dbl>,
# symptom <dbl>, treat <dbl>, offtrt <dbl>, cd40 <dbl>, cd420 <dbl>,
# cd496 <dbl>, r <dbl>, cd80 <dbl>, cd820 <dbl>, cens <dbl>, days <dbl>,
# arms <dbl>, age_m <dbl>, weight_lb <dbl>, cd_change_20 <dbl>,
# cd_change_960 <dbl>, gender_char <chr>, arms_char <chr>, agem <dbl>,
# exercise <dbl>, education <chr>, drugs_char <chr>Examples
# Wrangling with dplyr and tidyr ---------------------------
library(tidyverse) # Load tidyverse for dplyr and tidyr
# Load baselers data
baselers <- read_csv("https://raw.githubusercontent.com/therbootcamp/baselers/master/inst/extdata/baselers.txt")
# Perform many dplyr operations in a row
baselers %>%
# Change some names
rename(age_y = age,
swimming = rhine) %>%
# Only include people over 30
filter(age_y > 30) %>%
# Calculate some new columns
mutate(weight_lbs = weight * 2.22,
height_m = height / 100,
BMI = weight / height_m ^ 2,
# Make binary version of sex
sex_bin = case_when(
sex == "male" ~ 0,
sex == "female" ~ 1),
# Show when height is greater than 150
height_lt_150 = case_when(
height < 150 ~ 1,
height >= 150 ~ 0)) %>%
# Sort in ascending order of sex, then
# descending order of age
arrange(sex, desc(age_y)))
# TIPP -------
# In this practical you will do many operations on dataframes. Remember: when using `dplyr`, you can chain
# multiple functions together with the pipe `%>%`. When giving your answers to the questions in this practical,
# see how many operations you can chain with the pipe!
baselers <- read_csv("1_Data/baselers.csv")
# Method 1: Separate operations
baselers <- baselers %>%
rename(Age_y = age) # Change age to Age__y
baselers <- baselers %>%
mutate(food_p = food / income) # calculate food_p
baselers <- baselers %>%
filter(sex == "m") # Only include males
# Method 2: Chain with the pipe!
baselers <- read_csv("1_Data/baselers.csv")
baselers <- baselers %>%
rename(Age_y = age) %>% # Change age to Age__y
mutate(food_p = food / income) %>% # calculate food_p
filter(sex == "m") # Only include malesDatasets
| File | Rows | Columns |
|---|---|---|
| trial_act.csv | 2139 | 27 |
| trial_act_demo_fake.csv | 2139 | 3 |
The trial_act 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.
| File | Rows |
|---|---|
| pidnum | patient’s ID number |
| age | age in years at baseline |
| wtkg | weight in kg at baseline |
| hemo | hemophilia (0=no, 1=yes) |
| homo | homosexual activity (0=no, 1=yes) |
| drugs | history of intravenous drug use (0=no, 1=yes) |
| karnof | Karnofsky score (on a scale of 0-100) |
| oprior | non-zidovudine antiretroviral therapy prior to initiation of study treatment (0=no, 1=yes) |
| z30 | zidovudine use in the 30 days prior to treatment initiation (0=no, 1=yes) |
| zprior | zidovudine use prior to treatment initiation (0=no, 1=yes) |
| preanti | number of days of previously received antiretroviral therapy |
| race | race (0=white, 1=non-white) |
| gender | gender (0=female, 1=male) |
| str2 | antiretroviral history (0=naive, 1=experienced) |
| strat | antiretroviral history stratification (1=’antiretroviral naive’, 2=’> 1 but ≤ 52 weeks of prior antiretroviral therapy’, 3=’> 52 weeks’) |
| symptom | symptomatic indicator (0=asymptomatic, 1=symptomatic) |
| treat | treatment indicator (0=zidovudine only, 1=other therapies) |
| offtrt | indicator of off-treatment before 96±5 weeks (0=no,1=yes) |
| cd40 | CD4 T cell count at baseline |
| cd420 | CD4 T cell count at 20±5 weeks |
| cd496 | CD4 T cell count at 96±5 weeks (=NA if missing) |
| r | missing CD4 T cell count at 96±5 weeks (0=missing, 1=observed) |
| cd80 | CD8 T cell count at baseline |
| cd820 | CD8 T cell count at 20±5 weeks |
| cens | indicator of observing the event in days |
| days | number of days until the first occurrence of: (i) a decline in CD4 T cell count of at least 50 (ii) an event indicating progression to AIDS, or (iii) death. |
| arms | treatment arm (0=zidovudine, 1=zidovudine and didanosine, 2=zidovudine and zalcitabine, 3=didanosine). |
Functions
Packages
| Package | Installation |
|---|---|
tidyverse |
install.packages("tidyverse") |
Functions
| Function | Package | Description |
|---|---|---|
rename() |
dplyr |
Rename columns |
select() |
dplyr |
Select columns based on name or index |
filter() |
dplyr |
Select rows based on some logical criteria |
arrange() |
dplyr |
Sort rows |
mutate() |
dplyr |
Add new columns |
case_when() |
dplyr |
Recode values of a column |
group_by(), summarise() |
dplyr |
Group data and then calculate summary statistics |
left_join() |
dplyr |
Combine multiple data sets using a key column |
pivot_wider() |
tidyr |
Convert long data to wide format - from rows to columns |
pivot_longer() |
tidyr |
Convert wide data to long format - from columns to rows |
Resources
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