Lab 8: Searching for Efficiency

For this week’s lab, we will be revisiting questions from previous lab assignments, with the purpose of using functions from the map() family to iterate over certain tasks. To do this, we will need to load in the data from Lab 2, Lab 3, and Lab 7. I’ve included all the datasets in the data folder, so all you need to do is read them in. 🙃

Lab 2

First up, we’re going to revisit Question 3 from Lab 2. This question asked:

What are the data types of the variables in this dataset?

1. Using map_chr(), produce a nicely formatted table of the data type of each variable in the surveys dataset. Specifically, the table should have 15 columns, one for each variable, with the datatype of that variable immediately below it’s name.

Lab 3

Now, were on to Lab 3 where we will revisit two questions.

In the original version of Lab 3, Question 5 asked you to:

Change data types in whichever way you see fit (e.g., is the instructor ID really a numeric data type?)

2. Using map_at(), convert the teacher_id, weekday, academic_degree, seniority, and gender columns to factors. Hint: You will need to use bind_cols() to transform the list output back into a data frame.

Next up, we’re going revisit Question 7 which asked:

What are the demographics of the instructors in this study? Investigate the variables academic_degree, seniority, and sex and summarize your findings in ~3 complete sentences.

Many people created multiple tables of counts for each of these demographics, but in this exercise we are going to create one table with every demographic.

3. Using pivot_longer() and pivot_wider(), recreate the table below.

Same classification as Challenge 3

I’m using the sen_level classification from Challenge 3

"junior" = seniority is 4 or less (inclusive)
"senior" = seniority is more than 4

Extending you thinking…

If you are interested in exploring my table fomatting, I specifically used the kable() function from the knitr package to first get an HTML table. Then I styled that table using the kable_styling() function from the kableExtra package.

Lab 7

For our last problem, we will revisit a question from the most recent lab. Question 1 asked you to use across() to make a table which summarized:

What variable(s) have missing values present?
How many observations have missing values?

4. Using map_int(), produce a nicely formatted table of the number of missing values for each variable in the fish data.

--- title: "Lab 8: Searching for Efficiency" format: html: code-tools: true toc: true editor: source execute: error: true echo: false eval: false message: false warning: false --- ```{r} #| label: pacakges library(tidyverse) ``` For this week's lab, we will be revisiting questions from previous lab assignments, with the purpose of using functions from the `map()` family to iterate over certain tasks. To do this, we will need to load in the data from Lab 2, Lab 3, and Lab 7. I've included **all** the datasets in the `data` folder, so all you need to do is read them in. 🙃 ```{r} #| label: read-in-old-datad # Data from Lab 2 surveys <- read_csv(here::here("labs", "instructions", "data", "surveys.csv")) # Data from Lab 3 evals <- read_csv(here::here("labs", "instructions", "data", "teacher_evals.csv")) |> rename(sex = gender) # Data from Lab 7 fish <- read_csv(here::here("labs", "instructions", "data", "BlackfootFish.csv")) ``` ## Lab 2 First up, we're going to revisit Question 3 from Lab 2. This question asked: > What are the data types of the variables in this dataset? **1. Using `map_chr()`, produce a nicely formatted table of the data type of each variable in the `surveys` dataset.** Specifically, the table should have 15 columns, one for each variable, with the datatype of that variable immediately below it's name. ```{r} #| label: map-data-types-of-surveys ``` ## Lab 3 Now, were on to Lab 3 where we will revisit two questions. In the original version of Lab 3, Question 5 asked you to: > Change data types in whichever way you see fit (e.g., is the instructor ID > really a numeric data type?) **2. Using `map_at()`, convert the `teacher_id`, `weekday`, `academic_degree`, `seniority`, and `gender` columns to factors.** Hint: You will need to use `bind_cols()` to transform the list output back into a data frame. ```{r} #| label: map-to-mutate-columns ``` Next up, we're going revisit Question 7 which asked: > What are the demographics of the instructors in this study? Investigate the > variables `academic_degree`, `seniority`, and `sex` and summarize your > findings in ~3 complete sentences. Many people created **multiple** tables of counts for each of these demographics, but in this exercise we are going to create **one** table with every demographic. **3. Using `pivot_longer()` and `pivot_wider()`, recreate the table below.** ![](images/lab-8-table-to-recreate.png) ::: {.callout-tip} # Same classification as Challenge 3 I'm using the `sen_level` classification from Challenge 3 - `"junior"` = `seniority` is 4 or less (inclusive) - `"senior"` = `seniority` is more than 4 ::: ```{r} #| label: double-pivot-for-summary-table ``` ::: {.callout-tip} # Extending you thinking... If you are interested in exploring my table fomatting, I specifically used the `kable()` function from the **knitr** package to first get an HTML table. Then I styled that table using the `kable_styling()` function from the **kableExtra** package. ::: ## Lab 7 For our last problem, we will revisit a question from the most recent lab. Question 1 asked you to use `across()` to make a table which summarized: > What variable(s) have missing values present? > How many observations have missing values? **4. Using `map_int()`, produce a nicely formatted table of the number of missing values for each variable in the `fish` data.** ```{r} #| label: map-missing-values-of-fish ```