FundRmentals 05

class: center, middle, inverse, title-slide

# FundRmentals 05
### Dr Danielle Evans | University of Sussex

---

# Setup & Q&A

1. Open RStudio

1. Open/create your fundRmentals R Project (click the blue cube in the top right corner)

+ To create one, go to **File**, **New Project**, **New Directory**, **New Project**, give your project a name, then click **Create**
  + Then create **data** & **r_docs** folders (in the **Files pane** click **New Folder**)
  
1. [Download the worksheet here...](https://fundrmentals.netlify.app/docs/week_04.Rmd) & save it to your **r_docs** folder

1. Run the code chunks where we load tidyverse & read in the data

.center[

### Any questions from the last tutorial?

]

---

# Overview

- `tidyverse`

- Wrangling data

- The pipe **%>%**

- Next steps

---

# `tidyverse` & `dplyr`

- The `tidyverse` is a collection of packages that all share a similar design philosophy, grammar, & data structure

- When we load `tidyverse`, it loads several different packages automatically:

+ `readr`, `dplyr`, `forcats`, `ggplot2`, `tidyr`, `purrr`, `tibble`, & `stringr`

- `dplyr` (like pliers..) is a useful package for wrangling/manipulating data & includes functions to:
  
  + `select()`,
  
  + `filter()`,
  
  + `mutate()` & 
  
  + `rename()` variables

---

# `dplyr::select()`

- `dplyr::select()` is for selecting or removing specific columns in a dataset, follows this structure:

```r
object_name <- dplyr::select(.data = object_name, column_name) # for selecting
```

```r
object_name <- dplyr::select(.data = object_name, -column_name) # for removing
```

#### Assignment <-

- Last week we learned that we can save the output of commands by creating an object with the assignment operator **<-**

- When manipulating our data sometimes it might be useful to create a new object (& keep the original) by using a different object name, and other times it might be more useful to overwrite the original object by using the same object name

**Task**: remove the **Experience** column & create a new object by calling it **emp_data_2**

---

# `dplyr::filter()`

- `dplyr::filter()` is for **keeping** rows of data based on some conditions like these:

.tiny[

To keep rows equal to some text:

```r
object_name <- dplyr::filter(.data = object_name, column_name == "some text") 
```

To keep rows not equal to some text:

```r
object_name <- dplyr::filter(.data = object_name, column_name != "some text") 
```

To keep rows where value is smaller than 50:

```r
object_name <- dplyr::filter(.data = object_name, column_name < 50)
```

To keep rows that meet BOTH conditions;

```r
object_name <- dplyr::filter(.data = object_name, column_name_1 < 2 & column_name_2 == FALSE) 
```

]

**Task**: filter the emp_data_2 object by keeping participants above **Age 29**

---

# `dplyr::mutate()`

- `dplyr::mutate()` is for creating new columns/overwriting columns from existing ones by **mutating** them in some way

Creates a new column by multiplying the old values by 500:

```r
object_name <- dplyr::mutate(.data = object_name, new_column = old_column*500) 
```

Creates a new column by squaring the old values:

```r
object_name <- dplyr::mutate(.data = object_name, new_column = old_column^2) 
```

Overwrites the old column by dividing the old values by 2:

```r
object_name <- dplyr::mutate(.data = object_name, old_column = old_column/2)
```

**Task**: using the emp_data_2 object create a new column called **Attractiveness** from the **Q15620** column, by multiplying the scores by 100

---

# `dplyr::rename()`

- `dplyr::rename()` is for renaming columns

To rename one variable:

```r
object_name <- dplyr::rename(.data = object_name, new_column_name = old_column_name)
```

To rename multiple variables:

```r
object_name <- dplyr::rename(.data = object_name, 
 new_column_name = old_column_name, 
 new_column_name_2 = old_column_name_2, 
 new_column_name_3 = old_column_name_3)
```

**Task**: using the emp_data_2 object rename the **Q14097** column to be called: **Empathy**

---

# The Pipe %>%

- The pipe **%>%** is part of the `magrittr` package & loads with `library(tidyverse)` or `library(magrittr)`

- We can use the pipe **%>%** to chain multiple commands together

- It pipes the output from the left into the functions/commands on the right:

```r
a_function(object_name) %>% another_function()
```

- It helps us avoid confusing nested functions which makes code easy to read & less prone to errors

- To insert a pipe **%>%** we can use the keyboard shortcut of **Ctrl + Shift + M** or **Command + Shift + M**

---

# The Pipe %>%

**Nested example ()**

```r
am_routine <- leave_house(get_dressed(get_ready(wake_up(person = me, time = "too_early"),
 existential_crisis = TRUE, breakfast = FALSE, coffee_cups = 50), clothing = "pyjamas", 
 footwear = "fluffy_slippers"), university = FALSE, zoomiversity = TRUE)
```

**Piped example %>% **

```r
am_routine <- me %>%
 wake_up(person = ., time = "too_early") %>% 
 get_ready(person = ., existential_crisis = TRUE, breakfast = FALSE, coffee_cups = 50) %>% 
 get_dressed(person = ., clothing = "pyjamas", footwear = "fluffy_slippers") %>% 
 leave_house(person = ., university = FALSE, zoomiversity = TRUE)
```

- Whenever you see **.** or **.,** it's just a placeholder for the function argument that's being piped through

**Task**: try putting all the previous steps (select, filter, mutate, rename) into one pipeline!

---

# Next Steps

- Save the RMarkdown document from today **Ctrl + S** or **Command + S**

- For next week, work through the discovr_05 tutorial by pasting the below code into the **Console** in RStudio & pressing **Enter**.

```r
learnr::run_tutorial("discovr_05", package = "discovr")
```

- This tutorial is pretty long, and generally visualising data isn't something we learn by heart (or need to), but you should aim to try go through the ggplot2 section as a minimum, then ideally complete the boxplots, plotting means, and scatterplots sections (you can ignore the faceting tasks if you want to)

- There's also an empty code chunk for you to have a go at creating a plot from the emp_data in the worksheet from today if you want to!