---
#############################################################
#                                                           #
# In RStudio click on "Run Document" to run this tutorial   #
#                                                           #
#############################################################
title: "Data and Data Frames"
author: "Luke Tierney"
output: learnr::tutorial
runtime: shiny_prerendered
---

```{r setup, include = FALSE}
library(learnr)
library(tidyverse)
knitr::opts_chunk$set(echo = FALSE, comment = "", warning = FALSE)
```

```{r stop_when_browser_closes, context = "server"}
# stop the app when the browser is closed (or, unfortunately, refreshed)
session$onSessionEnded(stopApp)
```

## Exercises


### Exercise 1

Which of the Stevens classifications (nominal, ordinal, interval, ratio)
best characterizes these variables:

```{r stevens_classes_temp, echo = FALSE}
question("Daily maximal temperatures in Iowa City.",
  answer("nominal"),
  answer("ordinal"),
  answer("interval", correct = TRUE),
  answer("ratio"),
  allow_retry = TRUE
)
```

```{r stevens_classes_pop, echo = FALSE}
question("Population counts for Iowa counties.",
  answer("nominal"),
  answer("ordinal"),
  answer("interval"),
  answer("ratio", correct = TRUE),
  allow_retry = TRUE
)
```

```{r stevens_classes_edlev, echo = FALSE}
question("Education level of job applicants using the [Bureau of Labor Statistics classification](https://www.bls.gov/careeroutlook/2014/article/education-level-and-jobs.htm).",
  answer("nominal"),
  answer("ordinal", correct = TRUE),
  answer("interval"),
  answer("ratio"),
  allow_retry = TRUE
)
```

```{r stevens_classes_major, echo = FALSE}
question("Major of UI students.",
  answer("nominal", correct = TRUE),
  answer("ordinal"),
  answer("interval"),
  answer("ratio"),
  allow_retry = TRUE
)
```


### Exercise 2

```{r tidy_or_not, echo = FALSE}
question("Which of these data sets are in tidy form?",
  answer("The builtin data set `co2`"),
  answer("The builtin data set `BOD`", correct = TRUE),
  answer("The `who` data set in package `tidyr` (`tidyr::who`)"),
  answer("The `mpg` data set in package `ggplot2` (`ggplot2::mpg`",
         correct = TRUE),
  allow_retry = TRUE
)
```
```{r tidy_or_not_workspace, exercise = TRUE}

```


### Exercise 3

The next exercises use the data in the variable `gapminder` in the
package `gapminder`. You can make it available with


```{r, echo = TRUE}
data(gapminder, package = "gapminder")
```

Use the function `str` to examine the value of the gapminder variable.
   
```{r gapminder-str, exercise = TRUE}

```
```{r gapminder-str-hint}
str(gapminder)
```
```{r gapminder_cases_question, echo = FALSE}
question_text("How many cases are there in the data set?",
  answer(as.character(nrow(gapminder)), correct = TRUE),
  allow_retry = TRUE
)
```
```{r gapminder_fct_question, echo = FALSE}
question_text("How many of the variables are factors?",
  answer(as.character(sum(sapply(gapminder, is.factor))), correct = TRUE),
  allow_retry = TRUE
)
```


### Exercise 4

Use the functions `class` and `names` to find the class and variable
names in the `gapminder` data.

```{r gapminder_class_names, exercise = TRUE}

```


### Exercise 5

Use `summary` to compute summary information for the variables.

```{r gapminder_summary, exercise = TRUE}

```


### Exercise 6

Fill in the values for `---` needed to produce plots of life
   expectancy against year for the countries in continent Oceania.

```{r gapminder_plot, exercise = TRUE, eval = FALSE}
library(dplyr)
library(ggplot2)
data(gapminder, package = "gapminder")
ggplot(filter(gapminder, continent == "Oceania"),
       aes(x = ---, y = ---, color = country)) +
    geom_line()
```