---
title: "Lab: data wrangling"
format: html
---

## Reading and previewing data

Our data are usually stored as a `.csv` file and after loading a `.csv` file into RStudio, we will have a "data frame". A data frame can be considered a special case of matrix where each column represents a measurement or variable of interest for each observation which correspond to the rows of the dataset. After loading the `tidyverse` suite of packages, we use the [`read_csv()`](https://readr.tidyverse.org/reference/read_delim.html) function to load the 2025 NBA regular season stats dataset from yesterday:

```{r}
library(tidyverse)
nba_stats <- read_csv("https://raw.githubusercontent.com/36-SURE/2026/main/data/nba_stats.csv")
```

By default, `read_csv()` reads in the dataset as a `tbl` (aka `tibble`) object instead of a `data.frame` object. There are [some differences](https://cran.r-project.org/web/packages/tibble/vignettes/tibble.html), which you can read about if you are interested, but it's not that meaningful for our purposes.

We can use the functions `slice_head()` and `slice_tail()` to view a sample of the data. Use the `slice_head()` function to view the first 6 rows, then use the `slice_tail()` function to view the last 3 rows:

```{r}
# INSERT CODE HERE
```

View the dimensions of the data with `dim()`:

```{r}
# INSERT CODE HERE
```

Quickly view summary statistics for all variables with the `summary()` function:

```{r}
# Uncomment the following code by deleting the # at the front
# summary(nba_stats)
```

View the data structure types with `str()`:

```{r}
# str(nba_stats)
```

What's the difference between the output from the two functions?

## Data manipulation with `dplyr`

An easier way to manipulate the data frame is through the `dplyr` package, which is in the `tidyverse` suite of packages. The operations we can do include: selecting specific columns, filtering for rows, re-ordering rows, adding new columns and summarizing data. The "split-apply-combine" concept can be achieved by `dplyr`.

### Selecting columns with `select()`

The function `select()` can be use to select certain column with the column names. First create a new table called `nba_stats_pg` that only contains the `player` and `games` columns:

```{r}
# INSERT CODE HERE
```

To select all columns except a specific column, use the `-` (subtraction) operator. For example, view the output from uncommenting the following line of code:

```{r}
# select(nba_stats, -player)
```

To select a range of columns by name (that are in consecutive order), use the `:` (colon) operator. For example, view the output from uncommenting the following line of code:

```{r}
# select(nba_stats, player:games)
```

To select all columns that start with certain character strings, use the function `starts_with()`. Other matching options are:

1.  `ends_with()`: select columns that end with a character string

2.  `contains()`: select columns that contain a character string

3.  `matches()`: select columns that match a regular expression

4.  `one_of()`: select columns names that are from a group of names

```{r}
# Uncomment the following lines of code
# select(nba_stats, starts_with("three"))
# select(nba_stats, contains("throw"))
```

### Extracting rows using `filter()`

We can also extract the rows/observations that satisfy certain criteria. Try extracting the rows with more than 500 assists:

```{r}
# INSERT CODE HERE
```

We can also filter on multiple criteria. Subset the rows with `age` above 30 and the team is either "HOU" or "GSW":

```{r}
# INSERT CODE HERE
```

### Arranging rows using `arrange()`

To arrange the data frame by a specific order we need to use the function `arrange()`. The default is by increasing order and the `desc()` function will provide the decreasing order. First arrange the `nba_stats` table by `personal_fouls` in ascending order:

```{r}
# INSERT CODE HERE
```

Next by descending order:

```{r}
# INSERT CODE HERE
```

Try combining a pipeline of `select()`, `filter()`, and `arrange()` steps together with the `|>` operator by:

1.  Selecting the `player`, `team`, `age`, and `games` columns,
2.  Filter to select only rows with `games` above 50,
3.  Sort by `age` in descending order

```{r}
# INSERT CODE HERE
```

### Creating new columns using `mutate()`

Sometimes the data does not include the variable that we are interested in and we need to manipulate the current variables to add new variables into the data frame. Create a new column `fouls_per_game` by taking the `personal_fouls` and dividing by `games` (reassign this output to the `nba_stats` table following the commented code chunk so this column is added to the table):

```{r}
# nba_stats <- nba_stats |>
#   mutate(INSERT CODE HERE)
```

### Creating summaries with `summarize()`

To create summary statistics for a given column in the data frame, we can use `summarize()` function. Compute the `mean`, `min`, and `max` number of `assists`:

```{r}
# INSERT CODE HERE
```

The advantage of `summarize()` is more obvious if we combine it with `group_by()`, the group operators. Since players at the different position tend to have very different statistics, first `group_by()` position and then compute the same summary statistics for `assists`:

```{r}
# INSERT CODE HERE
```

## Challenge exercises 

Finished early? We encourage you to review how to join dataframes in R by reading sections [19.3-19.5 of R for Data Science](https://r4ds.hadley.nz/joins.html#sec-mutating-joins) by Hadley Wickham, Mine Çetinkaya-Rundel, and Garrett Grolemund. Then, try the following exercises. We challenge you to not use AI---ask the TAs if you get stuck!

### Bonus exercise 1 (Paralympic Swimming)

Load the following dataset ([link to the codebook](https://github.com/rfordatascience/tidytuesday/blob/main/data/2021/2021-08-03/readme.md)). Create a dataframe with the number of athletes that have medaled in swimming since 2000 for each county. Create a second dataframe with the total number of medals that each country has won in swimming since 2000. Then, join the two dataframes. Arrange the joined dataframe from greatest number of medals to least number of medals. 

Check your work: Is the number of medals always greater than or equal to the number of athletes? 

```{r}
athletes <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-08-03/athletes.csv')
```

```{r}
# INSERT CODE HERE
```


### Bonus exercise 2 (Ultra running)

Load the following datasets that explore trends in ultra running ([link to the data dictionary](https://github.com/rfordatascience/tidytuesday/blob/main/data/2021/2021-10-26/readme.md)). Your task is to identify female runners who ran at least 10 ultra races between age 30-50. Create a dataset of their race results from this time in their life. The dataset should have additional columns: 

- the distance of the race (in miles)
- the average minutes per mile that runner the completed the race in

*There is a lot going on in this one, so don't feel discouraged if you get stuck!*

```{r}
ultra_rankings <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-10-26/ultra_rankings.csv')
race <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-10-26/race.csv')
```

```{r}
# INSERT CODE HERE
```


