Practice 4 Answers

PUBH 523/623

Questions

Question 1: Import and Export

In Lesson 18, we learned how to import and export data using file-specific functions and the rio package.

Part A: Import the HRS dataset

WarningTask

Load the tidyverse, rio, and here packages. Then use import() together with here() to import hrs_data.rds from your data folder. Assign it to an object called hrs_data. Use glimpse() (or tibble()) to take a look at its structure.

Answer:

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.

Part B: Export and re-import

WarningTask

1. Use export() from rio to save hrs_data as a .csv file named hrs_data_export.csv in the same folder as the hrs_data.rds file.
2. Use import() from rio to read hrs_data_export.csv back into R as a new object called hrs_data_csv.
3. Use class() to check the class of the sex column in both hrs_data and hrs_data_csv. In 1-2 sentences, describe what you notice — is the class the same or different, and why might that be?

Answer:

Not given

Part C: When to use which file type

WarningTask

In 1-2 sentences, explain when you would prefer to save a dataset as .rds versus .csv, based on what we discussed in Lesson 18.

Answer:

Not given

Question 2: Piping

In Lesson 16, we learned that pipes (|>) let us write code that reads left to right, passing the output of one step into the next.

Part A: Nested to piped

WarningTask

Here is a chunk of nested code:

paste0("Mean CESD score: ", round(mean(hrs_data$cesd, na.rm = TRUE), digits = 1))

Rewrite this same calculation using the pipe (|>) instead of nesting. You will need the placeholder _ for at least one step, since paste0() does not take the piped input as its first argument. Assign the result to an object called cesd_summary and print it.

Answer:

cesd_summary <- hrs_data$cesd |> 
  mean(na.rm = TRUE) |> 
  round(digits = 1) |> 
  paste0("Mean CESD score: ", x = _)

cesd_summary

[1] "Mean CESD score: 1.8"

Part B: Write your own pipe

WarningTask

Using hrs_data$height, write a pipe that (1) calculates the mean while handling any missing values, and (2) rounds that mean to 1 decimal place. Assign the result to an object called height_summary and print it.

Answer:

Not given

Question 3: Key operators and functions

In Lesson 17, we expanded on boolean operators and were introduced to several new functions.

Part A: Boolean operators and %in%

WarningTask

Using hrs_data$sex, answer the following with R code (don’t just eyeball the data frame):

1. Is "Female" one of the values in hrs_data$sex? Use %in%.
2. Write one logical statement that uses & to check whether the first respondent (hrs_data$age_yr[1]) is over 65 and the first respondent’s sex is "Female".

Answer:

Not given

Part B: Statistical functions

WarningTask

Using hrs_data$cesd, calculate each of the following and print them: the standard deviation (sd()), the range (range()), and the mean rounded to the nearest whole number using round(). (Handle any missing values you find along the way.)

Answer:

Standard deviation result:

[1] 2.128984

Part C: seq(), rep(), and sample()

WarningTask

1. Use set.seed(523) and then sample() to randomly draw 5 ids (without replacement) from hrs_data$id. Assign this to sampled_ids and print it.
2. In 1 sentence, explain why we use set.seed() before sample().

Answer:

Sampled ID’s:

[1] "559315_010" "555653_010" "559806_020" "553623_010" "553793_010"

Question 4: Tidy data

In Lesson 19, we learned that tidy data follow three rules: each variable has its own column, each observation has its own row, and each value has its own cell.

Part A: Is it tidy?

WarningTask

Below is a small dataset showing the same information two different ways. For each table, state whether it is tidy or not, and explain why in 1-2 sentences (referencing the three rules above).

Table 1

id	age_yr	cesd
R1	55	2
R2	62	4

Table 2

id	variable	value
R1	age_yr	55
R1	cesd	2
R2	age_yr	62
R2	cesd	4

Answer:

Table 1 is tidy. Table is not tidy.

Part B: Is hrs_data tidy?

WarningTask

Look at hrs_data. In 1-2 sentences, explain whether it is tidy, and identify what one row and one column represent.

Answer:

Not given