Learning R With Education Datasets
This article is originally published at https://rviews.rstudio.com/
Ryan A. Estrellado is a public education leader and data scientist helping administrators use practical data analysis to improve the student experience.
Timothy Gallwey wrote in The Inner Game of Tennis:
…There is a natural learning process which operates within everyone, if it is allowed to. This process is waiting to be discovered by all those who do not know of its existence … It can be discovered for yourself, if it hasn’t been already. If it has been experienced, trust it.
Discovering a new R concept like a function or package is exciting. You never know if you’re about to learn something that fundamentally changes the way you code or solve data science problems. But I get even more excited when I see somebody use new R concepts. For example, I learned about random forest models when I read about them in An Introduction to Statistical Learning (ISL). Then I imagined myself using them when I watched Julia Silge fit a random forest model to predict attendance at NFL games. I need the reading to give me language for what I see data scientists do. Then I need to see what data scientists do for me to imagine myself doing what I’ve read.
Still, for most people using R in their jobs, there’s another step. They have to imagine how to apply what they’ve read and seen to the problems they’re solving at work. But what if we used education datasets to help them imagine using R on the job, just as the authors of ISL use words and code to teach about models and Julia Silge uses video to inspire coding?
We learned from writing Data Science in Education Using R (DSIEUR) that we can combine words, code, and professional context. Professional context includes scenarios, language, and data that readers will recognize in their education jobs. We wanted readers to feel motivated and engaged by seeing words and data that reminds them of their everyday work tasks. This connection to their professional lives is a hook for readers as they engage R syntax which is, if you’ve never used it, literally a foreign language.
Let’s use pivot_longer() as an example. We’ll describe this process in three steps: discovering the concept, seeing how the concept is used, and seeing how the concept is used in education.
Step 1: See the concept
When I read something like “Use pivot_longer() to transform a dataset from wide to long”, I can imagine the shape of a dataset changing. But it’s harder to imagine what happens with the variables and their contents as the dataset’s shape changes. I’ve been using R for over five years and I still struggle to visualize the contents of many columns rearranging themselves into one.
Step 2: See how the concept is used
The concept gets much clearer when you add an example—even one with little context—to the explanation. Here’s one from the pivot_longer() vignette, which you can view with vignette("pivot"):
library(tidyverse)# Simplest case where column names are character data
relig_income#> # A tibble: 18 x 11
#> religion `<$10k` `$10-20k` `$20-30k` `$30-40k` `$40-50k` `$50-75k` `$75-100k`
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Agnostic 27 34 60 81 76 137 122
#> 2 Atheist 12 27 37 52 35 70 73
#> 3 Buddhist 27 21 30 34 33 58 62
#> 4 Catholic 418 617 732 670 638 1116 949
#> 5 Don’t k… 15 14 15 11 10 35 21
#> 6 Evangel… 575 869 1064 982 881 1486 949
#> 7 Hindu 1 9 7 9 11 34 47
#> 8 Histori… 228 244 236 238 197 223 131
#> 9 Jehovah… 20 27 24 24 21 30 15
#> 10 Jewish 19 19 25 25 30 95 69
#> 11 Mainlin… 289 495 619 655 651 1107 939
#> 12 Mormon 29 40 48 51 56 112 85
#> 13 Muslim 6 7 9 10 9 23 16
#> 14 Orthodox 13 17 23 32 32 47 38
#> 15 Other C… 9 7 11 13 13 14 18
#> 16 Other F… 20 33 40 46 49 63 46
#> 17 Other W… 5 2 3 4 2 7 3
#> 18 Unaffil… 217 299 374 365 341 528 407
#> # … with 3 more variables: `$100-150k` <dbl>, `>150k` <dbl>, `Don't
#> # know/refused` <dbl>relig_income %>%
pivot_longer(-religion, names_to = "income", values_to = "count")#> # A tibble: 180 x 3
#> religion income count
#> <chr> <chr> <dbl>
#> 1 Agnostic <$10k 27
#> 2 Agnostic $10-20k 34
#> 3 Agnostic $20-30k 60
#> 4 Agnostic $30-40k 81
#> 5 Agnostic $40-50k 76
#> 6 Agnostic $50-75k 137
#> 7 Agnostic $75-100k 122
#> 8 Agnostic $100-150k 109
#> 9 Agnostic >150k 84
#> 10 Agnostic Don't know/refused 96
#> # … with 170 more rowsSharing an idea by pairing an abstract programming concept with a reproducible example is a common practice for experienced R programmers. Community guidelines for Stack Overflow posts and the {reprex} package are two artifacts of a popular R community norm: help folks understand an idea by using words and code.
Step 3: See how the concept is used in education
Combining the explanation with a reproducible example makes pivot_longer() more concrete by showing how it works. What happens when we connect the explanation and reproducible example to the everyday work of a data scientist in education?
In chapter seven of DSIEUR, we use pivot_longer() to transform a dataset of coursework survey responses from wide to long. Before using pivot_longer(), the dataset had a column for each survey question. When we use pivot_longer(), the name of each survey question moves to a new column called “question”. Another new column is added, “response”, which contains the corresponding response to each survey question.
To run this code, you’ll need the DSIEUR companion R package, {dataedu}:
# Install the {dataedu} package if you don't have it
# devtools::install_github("data-edu/dataedu")
library(dataedu)Here’s the survey data in its original, wide format:
# Wide format
pre_survey#> # A tibble: 1,102 x 12
#> opdata_username opdata_CourseID Q1Maincellgroup… Q1Maincellgroup…
#> <chr> <chr> <dbl> <dbl>
#> 1 _80624_1 FrScA-S116-01 4 4
#> 2 _80623_1 BioA-S116-01 4 4
#> 3 _82588_1 OcnA-S116-03 NA NA
#> 4 _80623_1 AnPhA-S116-01 4 3
#> 5 _80624_1 AnPhA-S116-01 NA NA
#> 6 _80624_1 AnPhA-S116-02 4 2
#> 7 _80624_1 AnPhA-T116-01 NA NA
#> 8 _80624_1 BioA-S116-01 5 3
#> 9 _80624_1 BioA-T116-01 NA NA
#> 10 _80624_1 PhysA-S116-01 4 4
#> # … with 1,092 more rows, and 8 more variables: Q1MaincellgroupRow3 <dbl>,
#> # Q1MaincellgroupRow4 <dbl>, Q1MaincellgroupRow5 <dbl>,
#> # Q1MaincellgroupRow6 <dbl>, Q1MaincellgroupRow7 <dbl>,
#> # Q1MaincellgroupRow8 <dbl>, Q1MaincellgroupRow9 <dbl>,
#> # Q1MaincellgroupRow10 <dbl>The third through eighth columns are named after each survey question—“Q1MaincellgroupRow1”, “Q1MaincellgroupRow2”, “Q1MaincellgroupRow3”, etc. These are the column names we’ll be moving to a single column called “question” when the dataset transforms from wide to long.
Here’s the new dataset, where a column called “question” contains the question names and a column called “response” contains the corresponding responses:
# Pivot the dataset from wide to long format
pre_survey %>%
pivot_longer(cols = Q1MaincellgroupRow1:Q1MaincellgroupRow10,
names_to = "question",
values_to = "response")#> # A tibble: 11,020 x 4
#> opdata_username opdata_CourseID question response
#> <chr> <chr> <chr> <dbl>
#> 1 _80624_1 FrScA-S116-01 Q1MaincellgroupRow1 4
#> 2 _80624_1 FrScA-S116-01 Q1MaincellgroupRow2 4
#> 3 _80624_1 FrScA-S116-01 Q1MaincellgroupRow3 4
#> 4 _80624_1 FrScA-S116-01 Q1MaincellgroupRow4 1
#> 5 _80624_1 FrScA-S116-01 Q1MaincellgroupRow5 5
#> 6 _80624_1 FrScA-S116-01 Q1MaincellgroupRow6 4
#> 7 _80624_1 FrScA-S116-01 Q1MaincellgroupRow7 1
#> 8 _80624_1 FrScA-S116-01 Q1MaincellgroupRow8 5
#> 9 _80624_1 FrScA-S116-01 Q1MaincellgroupRow9 5
#> 10 _80624_1 FrScA-S116-01 Q1MaincellgroupRow10 5
#> # … with 11,010 more rowsWhen you put it all together, the learning thought process is something like this:
- There’s a function called
pivot_longer(), which turns a wide dataset into a long dataset pivot_longer()does this by putting multiple column names into its own column, then creating a new column that pairs each column name with a value- I can use
pivot_longer()to change an education survey dataset that has question names for columns into one that has a “question” column and a “response” column
We’ll be back with the next post in about two weeks. Until then, do share with us about the people and tools that inspire you to work on collaborative projects. You can reach us on Twitter: Emily @ebovee09, Jesse @kierisi, Joshua @jrosenberg6432, Isabella @ivelasq3 and me @RyanEs.
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