Tidyverse Sample Exam v2.0
Solutions

Instructions

1. This exam covers material from R for Data Science. You may find the study guide useful. If you have any questions about scope, please get in touch.

2. You must complete the exam within 90 minutes.

3. You may use any books or digital resources you want during this examination, but you may not communicate with any person other than your examiner.

4. You are required to use the RStudio IDE for the practical portions of this exam. You may use either the desktop edition or rstudio.cloud as you prefer.

There is of course no one correct way to solve each of these questions. I’ve included the solutions that first came to my mind when I did this practice exam. If you find a mistake or a better solution, please feel free to submit a pull request on Github.

You can download the blank sample exam .rmd here. You can download this current .rmd containing the solutions here.

By default, the code for each solution is hidden. You can toggle the code on and off by clicking Code/Hide at the top right of each chunk or Code > Show All Code / Hide All Code at the top right of this document.

library(tidyverse)

Basic Operations

1. Read the file person.csv and store the result in a tibble called person.

(person <- read_csv(here::here("person.csv")))

2. Create a tibble containing only family and personal names, in that order. You do not need to assign this tibble or any others to variables unless explicitly asked to do so. However, as noted in the introduction, you must use the pipe operator %>% and code that follows the tidyverse style guide.

person %>% 
  select(family_name, personal_name)

3. Create a new tibble containing only the rows in which family names come before the letter M. Your solution should work for tables with more rows than the example, i.e., you cannot rely on row numbers or select specific names.

before_m <- letters[which(letters == "a"):which(letters == "m") - 1]

person %>% 
  mutate(family_name = tolower(family_name),
         family_first_letter = str_sub(family_name, 1, 1)) %>% 
  filter(family_first_letter %in% before_m) %>% 
  select(-family_first_letter)

Another, much more elegant solution, courtesy of Beatriz Milz:

person %>% filter(family_name < "M")

4. Display all the rows in person sorted by family name length with the longest name first.

person %>% 
  arrange(desc(str_length(family_name)))

Cleaning and Counting

1. Read the file measurements.csv to create a tibble called measurements. (The strings "rad", "sal", and "temp" in the quantity column stand for “radiation”, “salinity”, and “temperature” respectively.)

(measurements <- read_csv(here::here("measurements.csv")))

2. Create a tibble containing only rows where none of the values are NA and save in a tibble called cleaned.

(cleaned <- measurements %>% 
  drop_na())

3. Count the number of measurements of each type of quantity in cleaned. Your result should have one row for each quantity "rad", "sal", and "temp".

cleaned %>% 
  count(quantity)

4. Display the minimum and maximum value of reading separately for each quantity in cleaned. Your result should have one row for each quantity "rad", "sal", and "temp".

cleaned %>% 
  group_by(quantity) %>% 
  summarize(reading_min = min(reading),
            reading_max = max(reading))

Note: You could also use dplyr::across() and a named list of functions! 😎

cleaned %>% 
  group_by(quantity) %>% 
  summarize(across(reading, list(min = min, max = max)))

5. Create a tibble in which all salinity ("sal") readings greater than 1 are divided by 100. (This is needed because some people wrote percentages as numbers from 0.0 to 1.0, but others wrote them as 0.0 to 100.0.)

cleaned %>% 
  mutate(reading = case_when(
                      quantity == "sal" & reading > 1 ~ reading/100,
                      TRUE ~ reading)
         )

Combining Data

1. Read visited.csv and drop rows containing any NAs, assigning the result to a new tibble called visited.

(visited <- read_csv(here::here("visited.csv")) %>% 
  drop_na())

2. Use an inner join to combine visited with cleaned using the visit_id column for matches.

(combined <- inner_join(visited, cleaned, by = "visit_id"))

3. Find the highest radiation ("rad") reading at each site. (Sites are identified by values in the site_id column.)

(max_rad <- combined %>% 
  pivot_wider(names_from = quantity, values_from = reading) %>% 
  group_by(site_id) %>% 
  summarize(max_rad = max(rad, na.rm = TRUE)))

4. Find the date of the highest radiation reading at each site.

combined %>% 
  pivot_wider(names_from = quantity, values_from = reading) %>% 
  group_by(site_id, visit_date) %>% 
  summarize(max_rad = max(rad, na.rm = TRUE)) %>% 
  semi_join(max_rad) %>% 
  select(visit_date, everything())

Plotting

1. The code below is supposed to read the file home-range-database.csv to create a tibble called hra_raw, but contains a bug. Describe and fix the problem. (There are several ways to fix it: please use whichever you prefer.)

hra_raw <- read_csv(here::here("data", "home-range-database.csv"))

Note: The file home-range-database.csv is currently saved in the root directory of the project, not in a subdirectory called data, as the code above would suggest. You could either create the data folder and move the data file there, or you could update the code that imports the data (as I demonstrate below).

(hra_raw <- read_csv(here::here("home-range-database.csv")))

2. Convert the class column (which is text) to create a factor column class_fct and assign the result to a tibble hra. Use forcats to order the factor levels as:
   1. mammalia
   2. reptilia
   3. aves
   4. actinopterygii

(hra <- hra_raw %>% 
  mutate(class_fct = factor(class, levels = c("mammalia", 
                                              "reptilia", 
                                              "aves", 
                                              "actinopterygii"))) %>% 
   relocate(class_fct, .after = class))

3. Create a scatterplot showing the relationship between log10.mass and log10.hra in hra.

hra %>% 
  ggplot(aes(log10.mass, log10.hra)) + 
  geom_point(size = 2, alpha = 0.7) + 
  theme_minimal()

4. Colorize the points in the scatterplot by class_fct.

hra %>% 
  ggplot(aes(log10.mass, log10.hra, color = class_fct)) + 
  geom_point(size = 2, alpha = 0.7) + 
  scale_color_viridis_d(end = .9) + 
  theme_minimal()

5. Display a scatterplot showing only data for birds (class aves) and fit a linear regression to that data using the lm function.

hra %>% 
  filter(class == "aves") %>% 
  ggplot(aes(log10.mass, log10.hra)) + 
  geom_point(size = 2, alpha = 0.7) + 
  geom_smooth(method = "lm") + 
  labs(title = "Linear relationship between home range and mass for Aves") + 
  theme_minimal()

Functional Programming

1. Write a function called summarize_table that takes a title string and a tibble as input and returns a string that says something like, “title has # rows and # columns”. For example, summarize_table('our table', person) should return the string "our table has 5 rows and 3 columns".

summarize_table <- function(title, df) { 
  nrow <- nrow(df)
  ncol <- ncol(df) 
  
  glue::glue("{title} has {nrow} rows and {ncol} columns.")
}

summarize_table("our table", person)

## our table has 5 rows and 3 columns.

2. Write another function called show_columns that takes a string and a tibble as input and returns a string that says something like, “table has columns name, name, name”. For example, show_columns('person', person) should return the string "person has columns person_id, personal_name, family_name".

show_columns <- function(title, df) { 
  col_names <- names(df) %>% 
    str_c(collapse = ", ")

glue::glue("{title} has columns {col_names}")
}

show_columns('person', person)

## person has columns person_id, personal_name, family_name

3. The function rows_from_file returns the first N rows from a table in a CSV file given the file’s name and the number of rows desired. Modify it so that if no value is specified for the number of rows, a default of 3 is used.

rows_from_file <- function(filename, num_rows) {
      readr::read_csv(filename) %>% head(n = num_rows)
    }

rows_from_file("measurements.csv") # should show 3 rows

rows_from_file <- function(filename, num_rows = 3) {
  readr::read_csv(filename) %>% head(n = num_rows)
}

rows_from_file("measurements.csv")

4. The function long_name checks whether a string is longer than 4 characters. Use this function and a function from purrr to create a logical vector that contains the value TRUE where family names in the tibble person are longer than 4 characters, and FALSE where they are 4 characters or less.

long_name <- function(name) {
      stringr::str_length(name) > 4
    }

person %>% 
  mutate(long_family_name = map_lgl(family_name, long_name))

Wrapping Up

1. Modify the YAML header of this file so that a table of contents is automatically created each time this document is knit, and fix any errors that are preventing the document from knitting cleanly.

---
title: "Tidyverse Exam Version 2.0"
output:
html_document:
    theme: flatly
---

Corrected YAML header:

---
title: "Tidyverse Exam Version 2.0"
output:
  html_document:
    theme: flatly
    toc: TRUE
---

Note: You need to add an indentation after html_document: and add toc: TRUE

You can read more about the RStudio Instructor Training and Certification Program here. There is another sample exam available with solutions, courtesy of Marly Gotti. I wrote about my own experience with the training and shared some of my exam prep materials here. Feel free to reach out with any questions!