mdsr2exercises

The goal of the mdsr2exercises packages is to provide exercises for the 2nd edition of Modern Data Science with R.

Installation

Install from GitHub:

#install.packages("remotes")
remotes::install_github("beanumber/etude")
remotes::install_github("mdsr-book/mdsr2exercises")

Summary

This package contains the following set of exercises:

library(mdsr2exercises)
library(dplyr)
library(tidyr)

summary_table <- mdsr_exercise_all() %>%
  group_by(chapter, difficulty) %>%
  filter(!grepl("onlineonly", tags)) %>%
  count() %>%
  pivot_wider(names_from = difficulty, values_from = n) %>%
  rowwise() %>%
  mutate(N = sum(c_across(where(is.numeric)), na.rm = TRUE)) %>%
  arrange(desc(N)) %>%
  relocate(chapter, Easy, Medium) %>%
  janitor::adorn_totals("row") 
knitr::kable(summary_table)

chapter	Easy	Medium	Hard	N
data-I	6	8	1	15
appR	11	2	NA	13
reproducible	10	2	NA	12
text	6	5	1	12
data-II	3	8	NA	11
sqlI	4	6	1	11
dataviz-II	2	7	1	10
dataviz-III	1	7	2	10
ethics	1	8	1	10
simulation	NA	9	NA	9
algorithmic	1	6	1	8
sqlII	1	2	5	8
foundations	2	5	NA	7
learning-II	NA	4	3	7
dataviz-I	1	5	NA	6
iteration	2	3	1	6
join	1	4	1	6
learning-I	1	4	1	6
modeling	2	3	1	6
regression	1	4	NA	5
spatial-I	1	1	3	5
spatial-II	NA	2	2	4
netsci	NA	1	2	3
not working	NA	1	NA	1
Total	57	107	27	191

Here is the summary of the online only exercises:

summary_table <- mdsr_exercise_all() %>%
  group_by(chapter, difficulty) %>%
  filter(grepl("onlineonly", tags)) %>%
  count() %>%
  pivot_wider(names_from = difficulty, values_from = n) %>%
  rowwise() %>%
  mutate(N = sum(c_across(where(is.numeric)), na.rm = TRUE)) %>%
  arrange(desc(N)) %>%
  relocate(chapter, Easy, Medium) %>%
  janitor::adorn_totals("row") 
knitr::kable(summary_table)

chapter	Easy	Medium	Hard	N
data-I	6	NA	NA	6
dataviz-I	3	2	NA	5
dataviz-II	4	NA	1	5
join	3	NA	1	4
algorithmic	2	1	NA	3
data-II	3	NA	NA	3
text	NA	2	1	3
dataI	1	NA	1	2
foundations	NA	2	NA	2
simulation	NA	1	1	2
sqlII	1	NA	1	2
dataviz-III	NA	1	NA	1
ethics	NA	1	NA	1
not-working	NA	NA	1	1
regression	NA	1	NA	1
spatial	NA	NA	1	1
sqlI	1	NA	NA	1
Total	24	11	8	43

Example

We can build a tibble of exercises that you want to show. Also specify any options, as documented in etude::etude_list(). Here, we show how to display the first two exercises associated with a particular chapter.

exercises <- mdsr_exercise_ls(chapter == "data-I") %>%
  select(-author, -date) %>%
  head(2)
knitr::kable(exercises)

chapter	difficulty	version	tags	id	status	file	output	depends	editor_options
data-I	Easy	0.1	onlineonly:::dplyr	cow-make-vase	OK	/Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/library/mdsr2exercises/Exercises/cow-make-vase.Rmd	NA	NA	NA
data-I	Easy	0.1	babynames:::join:::dplyr	crow-burn-sheet	OK	/Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/library/mdsr2exercises/Exercises/crow-burn-sheet.Rmd	NA	NA	NA

Second, give the in-line command knit_mdsr_exercises(exercises):

Exercises

Problem 1 (Easy): Which dplyr operation is depicted below?

Problem 2 (Easy): Here is a random subset of the babynames data frame in the babynames package:

Random_subset
#> # A tibble: 10 × 5
#>     year sex   name           n      prop
#>    <dbl> <chr> <chr>      <int>     <dbl>
#>  1  2003 M     Bilal        146 0.0000695
#>  2  1999 F     Terria        23 0.0000118
#>  3  2010 F     Naziyah       45 0.0000230
#>  4  1989 F     Shawana       41 0.0000206
#>  5  1989 F     Jessi        210 0.000105 
#>  6  1928 M     Tillman       43 0.0000377
#>  7  1981 F     Leslee        83 0.0000464
#>  8  1981 F     Sherise       27 0.0000151
#>  9  1920 F     Marquerite    26 0.0000209
#> 10  1941 M     Lorraine      24 0.0000191

For each of the following tables wrangled from Random_subset, figure out what dplyr wrangling statement will produce the result.

1. Hint: Both rows and variables are missing from the original

#> # A tibble: 4 × 4
#>    year sex   name        n
#>   <dbl> <chr> <chr>   <int>
#> 1  2010 F     Naziyah    45
#> 2  1989 F     Shawana    41
#> 3  1928 M     Tillman    43
#> 4  1981 F     Leslee     83

2. Hint: the nchar() function is used in the statement.

#> # A tibble: 2 × 5
#>    year sex   name       n      prop
#>   <dbl> <chr> <chr>  <int>     <dbl>
#> 1  1999 F     Terria    23 0.0000118
#> 2  1981 F     Leslee    83 0.0000464

3. Hint: Note the new column, which is constructed from n and prop.

#> # A tibble: 2 × 6
#>    year sex   name        n      prop    total
#>   <dbl> <chr> <chr>   <int>     <dbl>    <dbl>
#> 1  1989 F     Shawana    41 0.0000206 1992225.
#> 2  1989 F     Jessi     210 0.000105  1991843.

4. Hint: All the years are still there, but there are only 8 rows as opposed to the original 10 rows.

#> # A tibble: 8 × 2
#>    year total
#>   <dbl> <int>
#> 1  1920    26
#> 2  1928    43
#> 3  1941    24
#> 4  1981   110
#> 5  1989   251
#> 6  1999    23
#> 7  2003   146
#> 8  2010    45

Exercises with answers

To display answers, add the show_answers = TRUE option.

Creating new exercises

Exercises are stored in inst/Exercises. To create a new exercise, call mdsr_exercise_new().

Please also see the etude template vignette

Quizzes

mdsr2exercises also contains quiz and exam questions in the exams format.

The write_moodle() function wraps exams::exams2moodle() to create XML files that can be imported into Moodle.

write_moodle(
  pattern = "wrangling", 
  quiz_name = "wrangling", 
  dir = tempdir()
)

Version and last updated

citation(package = "mdsr2exercises")
#> To cite package 'mdsr2exercises' in publications use:
#> 
#>   Baumer B, Horton N, Kaplan D (2023). _mdsr2exercises: Exercises for
#>   the 2nd edition of MDSR_. R package version 0.7.3.9018.
#> 
#> A BibTeX entry for LaTeX users is
#> 
#>   @Manual{,
#>     title = {mdsr2exercises: Exercises for the 2nd edition of MDSR},
#>     author = {Benjamin S. Baumer and Nicholas Horton and Daniel Kaplan},
#>     year = {2023},
#>     note = {R package version 0.7.3.9018},
#>   }

The file was last updated Mon Dec 4 08:35:13 2023 GMT.