Tidyverse Sample Exam v2.0 Solutions

Instructions

Basic Operations

Cleaning and Counting

Combining Data

Plotting

Functional Programming

Wrapping Up

Instructions

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.
You must complete the exam within 90 minutes.
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.
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

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

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

ABCDEFGHIJ0123456789

person_id <chr>	personal_name <chr>	family_name <chr>
dyer	William	Dyer
pb	Frank	Pabodie
lake	Anderson	Lake
roe	Valentina	Roerich
danforth	Frank	Danforth

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)

ABCDEFGHIJ0123456789

family_name <chr>	personal_name <chr>
Dyer	William
Pabodie	Frank
Lake	Anderson
Roerich	Valentina
Danforth	Frank

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)

ABCDEFGHIJ0123456789

person_id <chr>	personal_name <chr>	family_name <chr>
dyer	William	dyer
lake	Anderson	lake
danforth	Frank	danforth

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

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

ABCDEFGHIJ0123456789

person_id <chr>	personal_name <chr>	family_name <chr>
dyer	William	Dyer
lake	Anderson	Lake
danforth	Frank	Danforth

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

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

ABCDEFGHIJ0123456789

person_id <chr>	personal_name <chr>	family_name <chr>
danforth	Frank	Danforth
pb	Frank	Pabodie
roe	Valentina	Roerich
dyer	William	Dyer
lake	Anderson	Lake

Cleaning and Counting

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")))

ABCDEFGHIJ0123456789

visit_id <dbl>	visitor <chr>	quantity <chr>	reading <dbl>
619	dyer	rad	9.82
619	dyer	sal	0.13
622	dyer	rad	7.80
622	dyer	sal	0.09
734	pb	rad	8.41
734	lake	sal	0.05
734	pb	temp	-21.50
735	pb	rad	7.22
735	NA	sal	0.06
735	NA	temp	-26.00

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

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

ABCDEFGHIJ0123456789

visit_id <dbl>	visitor <chr>	quantity <chr>	reading <dbl>
619	dyer	rad	9.82
619	dyer	sal	0.13
622	dyer	rad	7.80
622	dyer	sal	0.09
734	pb	rad	8.41
734	lake	sal	0.05
734	pb	temp	-21.50
735	pb	rad	7.22
751	pb	rad	4.35
751	pb	temp	-18.50

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)

ABCDEFGHIJ0123456789

quantity <chr>	n <int>
rad	8
sal	7
temp	3

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))

ABCDEFGHIJ0123456789

quantity <chr>	reading_min <dbl>	reading_max <dbl>
rad	1.46	11.25
sal	0.05	41.60
temp	-21.50	-16.00

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)))

ABCDEFGHIJ0123456789

quantity <chr>	reading_min <dbl>	reading_max <dbl>
rad	1.46	11.25
sal	0.05	41.60
temp	-21.50	-16.00

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)
         )

ABCDEFGHIJ0123456789

visit_id <dbl>	visitor <chr>	quantity <chr>	reading <dbl>
619	dyer	rad	9.820
619	dyer	sal	0.130
622	dyer	rad	7.800
622	dyer	sal	0.090
734	pb	rad	8.410
734	lake	sal	0.050
734	pb	temp	-21.500
735	pb	rad	7.220
751	pb	rad	4.350
751	pb	temp	-18.500

Combining Data

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())

ABCDEFGHIJ0123456789

visit_id <dbl>	site_id <chr>	visit_date <date>
619	DR-1	1927-02-08
622	DR-1	1927-02-10
734	DR-3	1930-01-07
735	DR-3	1930-01-12
751	DR-3	1930-02-26
837	MSK-4	1932-01-14
844	DR-1	1932-03-22

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

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

ABCDEFGHIJ0123456789

visit_id <dbl>	site_id <chr>	visit_date <date>	visitor <chr>	quantity <chr>	reading <dbl>
619	DR-1	1927-02-08	dyer	rad	9.82
619	DR-1	1927-02-08	dyer	sal	0.13
622	DR-1	1927-02-10	dyer	rad	7.80
622	DR-1	1927-02-10	dyer	sal	0.09
734	DR-3	1930-01-07	pb	rad	8.41
734	DR-3	1930-01-07	lake	sal	0.05
734	DR-3	1930-01-07	pb	temp	-21.50
735	DR-3	1930-01-12	pb	rad	7.22
751	DR-3	1930-02-26	pb	rad	4.35
751	DR-3	1930-02-26	pb	temp	-18.50

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)))

ABCDEFGHIJ0123456789

site_id <chr>	max_rad <dbl>
DR-1	11.25
DR-3	8.41
MSK-4	1.46

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())

ABCDEFGHIJ0123456789

visit_date <date>	site_id <chr>	max_rad <dbl>
1932-03-22	DR-1	11.25
1930-01-07	DR-3	8.41
1932-01-14	MSK-4	1.46

Plotting

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")))

ABCDEFGHIJ0123456789

taxon <chr>	common.name <chr>	class <chr>	order <chr>
lake fishes	american eel	actinopterygii	anguilliformes
river fishes	blacktail redhorse	actinopterygii	cypriniformes
river fishes	central stoneroller	actinopterygii	cypriniformes
river fishes	rosyside dace	actinopterygii	cypriniformes
river fishes	longnose dace	actinopterygii	cypriniformes
river fishes	muskellunge	actinopterygii	esociformes
marine fishes	pollack	actinopterygii	gadiformes
marine fishes	saithe	actinopterygii	gadiformes
marine fishes	lined surgeonfish	actinopterygii	perciformes
marine fishes	orangespine unicornfish	actinopterygii	perciformes

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))

ABCDEFGHIJ0123456789

taxon <chr>	common.name <chr>	class <chr>	class_fct <fctr>
lake fishes	american eel	actinopterygii	actinopterygii
river fishes	blacktail redhorse	actinopterygii	actinopterygii
river fishes	central stoneroller	actinopterygii	actinopterygii
river fishes	rosyside dace	actinopterygii	actinopterygii
river fishes	longnose dace	actinopterygii	actinopterygii
river fishes	muskellunge	actinopterygii	actinopterygii
marine fishes	pollack	actinopterygii	actinopterygii
marine fishes	saithe	actinopterygii	actinopterygii
marine fishes	lined surgeonfish	actinopterygii	actinopterygii
marine fishes	orangespine unicornfish	actinopterygii	actinopterygii

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()

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()

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

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.

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

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")

ABCDEFGHIJ0123456789

visit_id <dbl>	visitor <chr>	quantity <chr>	reading <dbl>
619	dyer	rad	9.82
619	dyer	sal	0.13
622	dyer	rad	7.80

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))

ABCDEFGHIJ0123456789

person_id <chr>	personal_name <chr>	family_name <chr>	long_family_name <lgl>
dyer	William	Dyer	FALSE
pb	Frank	Pabodie	TRUE
lake	Anderson	Lake	FALSE
roe	Valentina	Roerich	TRUE
danforth	Frank	Danforth	TRUE

Wrapping Up

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!

---
title: "Tidyverse Sample Exam v2.0 <br>Solutions"
output: 
  html_document:
    toc: TRUE
    toc_float: TRUE
    code_folding: hide
    code_download: true
    df_print: paged
    theme: flatly
    css: "css/custom.css"
author: "[Brendan Cullen](https://bcullen.rbind.io/)"
---

<br>
<center>
```{r out.width="50%", fig.pos="c", echo=FALSE}
knitr::include_graphics("img/tidyverse_key.png")
```
</center>
<br>

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE, rows.print = 10)
```

```{r echo=FALSE}
library(metathis)
meta() %>% 
  meta_description("A solution guide to the sample tidyverse exam version 2.0 for the RStudio Instructor Training and Certification Program") %>% 
  meta_name("github-repo" = "brendanhcullen/tidyverse-sample-exam-v2.0") %>% 
  meta_viewport() %>% 
  meta_social(
    title = "Tidyverse Sample Exam v2.0 Solutions",
    url = "https://tidyverse-exam-v2-solutions.netlify.app/",
    image = "https://images.unsplash.com/photo-1512314889357-e157c22f938d?ixlib=rb-1.2.1&ixid=eyJhcHBfaWQiOjEyMDd9&auto=format&fit=crop&w=1502&q=80",
    og_author = "Brendan Cullen",
    og_type = "article",
    twitter_card_type = "summary_large_image",
    twitter_creator = "@_bcullen")
```

### Instructions

1. This exam covers material from [R for Data Science](https://r4ds.had.co.nz/). You may find the [study guide](https://jrnold.github.io/r4ds-exercise-solutions/) 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.

<br>

:::alert
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](https://github.com/brendanhcullen/tidyverse-sample-exam-v2.0).
:::


:::download
<br>
You can download the blank sample exam `.rmd` [here](sample_exam_v2.0.Rmd). You can download this current `.rmd` containing the solutions [here](index.Rmd).
:::

:::toggle
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.
:::

<br>

```{r message=FALSE, class.source = "fold-show"}
library(tidyverse)
```


### Basic Operations

1.  Read the file [`person.csv`](./person.csv) and store the result in a tibble called `person`. 

```{r}
(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.

```{r}
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.

```{r}
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](https://education.rstudio.com/trainers/people/milz+beatriz/):

```{r}
person %>% filter(family_name < "M")
```

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

```{r}
person %>% 
  arrange(desc(str_length(family_name)))
```

### Cleaning and Counting

1.  Read the file [`measurements.csv`](./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.)

```{r}
(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`.

```{r}
(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"`.

```{r}
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"`.

```{r}
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!** `r emo::ji("sunglasses")`

```{r}
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.)

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

### Combining Data

1.  Read [`visited.csv`](./visited.csv) and drop rows containing any `NA`s, assigning the result to a new tibble called `visited`.

```{r}
(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.

```{r}
(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.)

```{r}
(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.

```{r}
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.)

```{r eval = FALSE, class.source = "fold-show"}
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).**

```{r}
(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
    
```{r}
(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`.

```{r}
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`.

```{r}
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.

```{r}
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"`.


```{r}
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)
```

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"`.


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

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

show_columns('person', person)
```


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.
    
```{r eval=FALSE, class.source = "fold-show"}
rows_from_file <- function(filename, num_rows) {
      readr::read_csv(filename) %>% head(n = num_rows)
    }

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


```{r}
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.
    
```{r class.source = "fold-show"}
long_name <- function(name) {
      stringr::str_length(name) > 4
    }
```


```{r}
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`**

<br>

:::book
You can read more about the RStudio Instructor Training and Certification Program [here](https://education.rstudio.com/trainers/). There is [another sample exam](https://education.rstudio.com/blog/2020/02/instructor-certification-exams/#tidyverse-certification-exam) available with [solutions](https://marlycormar.github.io/tidyverse_sample_exam/sample_exam_sols/sols.html), courtesy of [Marly Gotti](https://www.marlygotti.com/). I wrote about my own experience with the training and shared some of my exam prep materials [here](https://bcullen.rbind.io/post/2020-09-03-reflections-on-rstudio-instructor-training/). Feel free to [reach out](https://twitter.com/_bcullen) with any questions!
:::

<br>


Tidyverse Sample Exam v2.0 Solutions

Brendan Cullen

Instructions

Basic Operations

Cleaning and Counting

Combining Data

Plotting

Functional Programming

Wrapping Up

Tidyverse Sample Exam v2.0
Solutions