Practice 5 Answers

PUBH 523/623

Author

Nicky Wakim

Modified

July 29, 2026

pacman::p_load(
  tidyverse, 
  rio, 
  here
)

1hrs_data <- import(here("data", "hrs_data.rds"))

1: This file path is specific to my folder set-up.

Questions

Question 1: `dplyr` Basics

In Lesson 20, we learned that dplyr functions are “verbs” that take a data frame in and return a data frame out, and that they can be grouped into four categories based on what they operate on: rows, columns, groups, and joins.

Part A: Input and output

Task

In 1 sentence, describe the general input and output of most dplyr functions. What type of object typically goes in, and what type of object comes out?

Answer:

Not given

Part B: Sort the verbs

Task

For each function below, fill in the table with:

Whether it operates on rows, columns, or is part of the groups category (which we have not covered yet, but you saw it mentioned in Lesson 20)
What the function does, in your own words
What output you’d expect if you ran it on hrs_data (for example: the same number of rows but fewer columns, fewer rows but all the same columns, the rows reordered, etc.)

Function	Rows, Columns, or Groups (not yet covered)?	What does it do?	Expected output on `hrs_data`
`filter()`
`select()`
`mutate()`
`arrange()`
`distinct()`
`relocate()`
`rename()`
`group_by()`
`summarize()`

Answer:

Example of mutate:

Function	Rows, Columns, or Groups (not yet covered)?	What does it do?	Expected output on `hrs_data`
`filter()`
`select()`
`mutate()`	Columns	Creates a new column, or changes the values in an existing one	Same rows, same original columns plus any new column(s) added
`arrange()`
`distinct()`
`relocate()`
`rename()`
`group_by()`
`summarize()`

Question 2: Data transformation: rows

In Lesson 21, we learned three functions for transforming rows: filter(), arrange(), and distinct().

Part A: `filter()`

Using hrs_data:

Task 1

Use filter() to keep only respondents whose cond_count (total number of chronic conditions) is 5 or more. Assign this to hrs_01_filter_conditions, and use nrow() to report how many respondents that is.

Answer:

Here’s an example to show you the pattern — the rest of your answers should follow this same style, with code chunks and a #| ... chunk options as needed.

hrs_01_filter_conditions <- hrs_data |> 
  filter(cond_count >= 5)

nrow(hrs_01_filter_conditions)

[1] 97

tibble(hrs_01_filter_conditions)

# A tibble: 97 × 32
   HHID  pn    id    BIRTHYR BIRTHMO BIRTHDATE proxy coupled sex   age_mo age_yr
   <fct> <fct> <chr>   <dbl>   <dbl>     <dbl> <fct> <fct>   <fct>  <dbl>  <dbl>
 1 5511… 010   5511…    1940      10     -7170 Resp… Not a … Fema…    992     82
 2 5509… 020   5509…    1967       6      2722 Resp… Couple… Fema…    665     55
 3 5599… 010   5599…    1960      11       319 Resp… Not a … Fema…    757     63
 4 5501… 020   5501…    1953       6     -2361 Resp… Couple… Male     838     69
 5 5566… 010   5566…    1937       8     -7444 Resp… Couple… Fema…   1013     84
 6 5511… 020   5511…    1973       2      4763 Resp… Couple… Fema…    607     50
 7 5599… 010   5599…    1950       7     -3548 Resp… Not a … Fema…    868     72
 8 5510… 020   5510…    1957       3     -1022 Resp… Couple… Male     801     66
 9 5569… 010   5569…    1945       2     -5374 Resp… Not a … Male     935     77
10 5572… 010   5572…    1971       7      4213 Resp… Not a … Fema…    618     51
# ℹ 87 more rows
# ℹ 21 more variables: ed <dbl>, degree <fct>, race_original <fct>,
#   smoke_ever <fct>, smoke_now <fct>, drink <fct>, height <dbl>, srh <fct>,
#   act_vig <fct>, bp <fct>, diab <fct>, cancer <fct>, lung <fct>, hrt <fct>,
#   strk <fct>, psych <fct>, sleep <fct>, arth <fct>, cond_count <dbl>,
#   cesd <dbl>, income <dbl>

Task 2

Use filter() to keep only respondents whose srh (self-rated health) is exactly "Poor". Assign this to hrs_02_filter_srh.

Tip

Remember: if you misspell or mis-capitalize a value for a categorical/factor variable, filter() will not throw an error — it will just silently return 0 rows. Always check your output!

Answer:

Not given

Part B: `arrange()`

Task 1

Sort hrs_data by cesd in descending order first, and within ties, by age_yr in ascending order. Assign the result to hrs_03_arrange, and print it using tibble().

Answer:

Not given

Task 2

In 1 sentence, describe what would change about the output if you arranged by age_yr first and cesd second instead.

Answer:

Not given

Part C: `distinct()`

Task 1

Use distinct() on degree and race_original together to see how many unique combinations of these two variables exist in hrs_data. Assign this to hrs_04_distinct, and print it using tibble().

Answer:

The resulting distinct combinations would look like this:

                                degree          race_original
1                                 <NA>                   <NA>
2                  High school diploma        White/Caucasian
3                      Master's degree        White/Caucasian
4                      Master's degree Black/African American
5  Professional degree (Ph.D./M.D./JD)        White/Caucasian
6                                 None Black/African American
7                   Associate's degree Black/African American
8  Professional degree (Ph.D./M.D./JD)                  Other
9                                  GED        White/Caucasian
10                 High school diploma Black/African American
11                                None        White/Caucasian
12                  Associate's degree        White/Caucasian
13                                None                  Other
14                     Master's degree                  Other
15                  Associate's degree                  Other
16 Professional degree (Ph.D./M.D./JD) Black/African American
17                                 GED Black/African American
18                   Bachelor's degree        White/Caucasian
19                                None                   <NA>
20                   Bachelor's degree                  Other
21                   Bachelor's degree Black/African American
22                                 GED                  Other
23                 High school diploma                  Other
24                                <NA>                  Other
25                   Bachelor's degree                   <NA>
26                 High school diploma                   <NA>
27                     Master's degree                   <NA>
28                  Associate's degree                   <NA>
29                                <NA> Black/African American

Task 2

Use distinct() on degree again, but this time with .keep_all = TRUE, so you keep the full row (all columns) for the first respondent found at each unique degree value. Assign this to hrs_05_distinct_all.

Answer:

Not given

Question 3: Data transformation: columns

In Lesson 22, we learned four functions for transforming columns: select(), relocate(), mutate(), and rename().

Part A: `select()` and `relocate()`

Task 1

Use select() to keep only id, age_yr, ed, degree, income, cesd, and srh from hrs_data. Assign this to hrs_06_vars_subset.

Answer:

Not given

Task 2

Starting from hrs_06_vars_subset, use relocate() to move srh so that it comes right after id. Assign this to hrs_07_reloc, and print it using tibble().

Answer:

Not given

Part B: `mutate()`

Task

Using hrs_data, create a new variable called height_ft that converts height (recorded in meters) into feet, using the conversion 1 meter = 3.28084 feet. Round height_ft to 1 decimal place using round() inside your mutate() call. Assign the result to hrs_08_mutate, and use relocate() so that height_ft sits right next to height for easy comparison. Print it using tibble().

Tip

Remember, inside mutate() you use a single =, not <- or ==, to define your new column.

Answer:

Start with something like:

hrs_08_mutate <- hrs_data |> 
  mutate(height_ft = ______ )

Part C: `rename()`

Task

Using hrs_data, rename cesd to depression_score and srh to self_rated_health. Assign the result to hrs_09_rename, and print names(hrs_09_rename) to confirm the change.

Answer:

Not given

Question 4: Data transformation: advanced

In Lesson 23, we covered more advanced techniques: combining conditions with &, |, and %in%; checking our work; creating variables with case_when(); and working with factors.

Part A: Combining conditions

Task 1

Before writing any code: do you expect more respondents to have diabetes AND high blood pressure (bp), or diabetes OR high blood pressure? Explain your reasoning in 1 sentence.

Answer:

Not given

Task 2

Use filter() with & to find respondents who have diabetes and have high blood pressure. Assign this to hrs_10_filter_diab_bp.

Answer:

Start with something like:

hrs_10_filter_diab_bp <- hrs_data |> 
  filter(______ & _______)

Task 3

Use filter() with | to find respondents who have diabetes or have high blood pressure. Assign this to hrs_11_filter_diab_or_bp.

Answer:

Not given

Task 4

Print hrs_10_filter_diab_bp and hrs_11_filter_diab_or_bp using tibble(), and reference the row count shown at the top of each output to compare how many respondents are in each data frame. Then use distinct() on diab and bp within each object to see which combinations of diab and bp are actually present in each dataset. Does this match your prediction from Task 1?

Answer:

Not given

Task 5

Now combine multiple logic statements in a single filter(): find respondents who have diabetes and have high blood pressure, or who have more than 5 chronic conditions. Assign this to hrs_12_filter_combo. Think carefully about where your parentheses go.

Answer:

Not given

Part B: `case_when()`

Task

Using hrs_data, create a new variable called income_cat with 4 categories based on income:

"<25k" if income is less than $25,000
"25k-50k" if income is at least $25,000 but less than $50,000
"50k+" if income is $50,000 or more
NA if income is missing (use .default for this case)

Then use fct() to convert income_cat into a factor. Assign your final result to hrs_13_case_when, and check your work using count().

Tip

For the middle category, you’ll need an & statement inside case_when(), similar to how we combined conditions in Part A.

Answer:

Start with something like:

hrs_13_case_when <- hrs_data |> 
  mutate(
    income_cat = case_when(
      ______ ~ ______,
      ______ ~ ______,
      ______ ~ ______,
      .default = ______
    )
  )

Part C: Factors

Task 1

Check the current class of hrs_data$srh using class().

Answer:

Example:

class(hrs_data$srh)

[1] "factor"

Task 2

Use fct_relevel() to reorder the levels of srh into descending order from “Excellent” to “Poor.” Call the new column srh_releveled, and assign the result to hrs_14_relevel. Confirm the new order with levels().

Tip

If you misspell one of the levels inside fct_relevel(), R will not throw an error — it will silently drop that level to the bottom of the order (and print a warning in the console). Double-check your spelling against the original values in srh!

Answer:

Not given

Task 3

Now suppose you had instead written fct_relevel(srh, "Excellent", "Very good", "Good", "Fair", "Poor") — with a lowercase “g” in "Very good". Run this version and look at the warning message R gives you. In 1-2 sentences, explain what the warning is telling you and why it happens.

Answer:

Not given

Part D: Selecting columns by pattern or type

Task 1

Use select() with contains() to grab all columns in hrs_data whose name contains "smoke". Assign this to hrs_15_select_contains, and print it using tibble().

Answer:

Start with something like:

hrs_15_select_contains <- hrs_data |> 
  select(________)

Task 2

Use select() with where(is.factor) to grab all factor columns in hrs_data, and pipe the result into names() so you just see the column names.

Answer:

Not given

Questions

Question 1: dplyr Basics

Part A: Input and output

Part B: Sort the verbs

Question 2: Data transformation: rows

Part A: filter()

Part B: arrange()

Part C: distinct()

Question 3: Data transformation: columns

Part A: select() and relocate()

Part B: mutate()

Part C: rename()

Question 4: Data transformation: advanced

Part A: Combining conditions

Part B: case_when()

Part C: Factors

Part D: Selecting columns by pattern or type

Question 1: `dplyr` Basics

Part A: `filter()`

Part B: `arrange()`

Part C: `distinct()`

Part A: `select()` and `relocate()`

Part B: `mutate()`

Part C: `rename()`

Part B: `case_when()`