Data management in R: the tidyverse

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# Data management in R: the tidyverse

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## The `tidyverse`

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## Intro to the `tidyverse`

.pull-left-large[

`tidyverse` is a package bundling several other R packages:

- `ggplot2`, `dplyr`, `tidyr`, `purrr`, ...

- share common data representations and API, i.e. work well together

- from the [tidyverse manifesto](https://tidyverse.tidyverse.org/articles/manifesto.html):

1. Reuse existing data structures.

2. Compose simple functions with the pipe.

3. Embrace functional programming.

4. Design for humans.

- see https://github.com/hadley/tidyverse for more information
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.pull-right-small[

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## Common structure

All functions of the tidyverse have `data` as their first argument:
  - `function(data, arg2, ...)`

The subsequent arguments describe what to do with the data frame, using the variable names (without quotes)

- Important: do not use `$` notation for variables within these functions, e.g: `filter(fbi, Year>=2019, State=="Iowa")`

The output is a new data frame that will print or it can be saved with the assignment operator, `<-`:
 - e.g: `fbi_iowa19 <- filter(fbi, Year >= 2019, State == "Iowa")`

The common structure makes it easy to chain together multiple simple steps using the pipe function (`%>%`)

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## The pipe operator: `%>%`

Implemented in the package `magrittr`

Can be pronounced as "*and then*" or "*then do*"

The output of one function is used as input to the next function
 - `x %>% f(y)` is the same as `f(x, y)`
 
 - `f(x) %>% g(y)` is equivalent to `g(f(x), y)`
 
 - statements of the form `k(h(g(f(x, y), z), u), v, w)` become 
`x %>% f(y) %>% g(z) %>% h(u) %>% k(v, w)`

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## How the pipe (`%>%`) works

Consider the following sequence of actions:  
- find key, unlock car, start car, drive to school, park.

Expressed as a set of **nested functions** in R pseudocode this would look like:

```r
  park(drive(start_car(find("keys")), to = "campus"))
  ```

Writing it out **using pipes** give it a more natural (and easier to read) structure: 
  
  
  ```r
  find("keys") %>%  
    start_car() %>% 
    drive(to = "campus") %>%
    park()
  ```

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## Using the pipe (`%>%`)

```r
ggplot(data = filter(fbi, Type == "Murder.and.nonnegligent.Manslaughter"), 
       aes(x = Year, y = Count)) + 
  geom_point()
```

becomes

```r
fbi %>% 
  filter(Type == "Murder.and.nonnegligent.Manslaughter") %>%
  ggplot(aes(x = Year, y = Count)) + 
    geom_point()
```

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# Your turn

For this your turn use the fbi data.

Using the pipe, create a subset of the data for one type of crime in Iowa *and then* create a line chart (use `geom_line()`) that shows counts over time.

.footnote[
LPT: use the keyboard shortcut `Ctrl + Shift + M` / `Cmd + Shift + M` for the pipe
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## Resources

- reference/document: https://www.tidyverse.org/

- Artwork by [@allison_horst](https://twitter.com/allison_horst?ref_src=twsrc%5Egoogle%7Ctwcamp%5Eserp%7Ctwgr%5Eauthor)

- https://datasciencebox.org/