Data Management Overview: Session 3

# Data Management Overview: Session 3
## Training for Schoen Research

----

## Crystal Lewis

Slides available on [<svg aria-hidden="true" role="img" viewBox="0 0 496 512" style="height:1em;width:0.97em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:white;overflow:visible;position:relative;"><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg>](https://cghlewis.github.io/schoen-data-mgmt-series-public/)

---

# Plan for this series

<br>

![](img/step5.PNG)
]

.pull-right[
Session 2
* ~~Creating instruments~~
* ~~Tracking data~~
* ~~Capturing and storing data~~
* ~~Preparing to clean and validate data~~
]

---

# Plan for this series

Session 3
* Why R?
* Getting acclimated with R and RStudio
* Understanding objects, functions, and packages
* Code writing best practices

Session 4
* Packages and functions for data wrangling
]

Session 5
* Setting up a reproducible syntax file
* Cleaning and validating data with R

Session 6
* Additional data wrangling with R

<img src="img/r-project.svg" width="300px" style="display: block; margin: auto;" />
]

---

---

---

# Why use R for Data Management?

.pull-left[
* Writing syntax: 
  - Automates your work
  - Allows your work to be reproducible
  - Facilitates collaboration
  - Allows others to check your work 
<img src="img/heartyr.gif" width="300px" style="display: block; margin: auto;" />
.center[Source: @allison_horst]

]

- **Free and open source**
  - Platform independent
  - Supportive community
  - Powerful packages that allow us to quickly manipulate data
  - It integrates well with other languages, file types, and applications
  - Becoming more ubiquitous in the world of education research
]

---

<table class="table table-striped" style="margin-left: auto; margin-right: auto;">
 <thead>
  <tr>
   <th style="text-align:right;"> id </th>
   <th style="text-align:left;"> Teach Years </th>
   <th style="text-align:left;"> Teach grade </th>
   <th style="text-align:left;"> School District ID </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:right;"> 123 </td>
   <td style="text-align:left;"> 12yrs </td>
   <td style="text-align:left;"> k </td>
   <td style="text-align:left;"> 50_100 </td>
  </tr>
  <tr>
   <td style="text-align:right;"> 234 </td>
   <td style="text-align:left;"> 15 </td>
   <td style="text-align:left;"> 2 </td>
   <td style="text-align:left;"> 50_100 </td>
  </tr>
  <tr>
   <td style="text-align:right;"> 345 </td>
   <td style="text-align:left;"> 22.5 </td>
   <td style="text-align:left;"> 1 </td>
   <td style="text-align:left;"> 60_102 </td>
  </tr>
  <tr>
   <td style="text-align:right;"> 456 </td>
   <td style="text-align:left;"> 4yrs </td>
   <td style="text-align:left;"> 2 </td>
   <td style="text-align:left;"> 60_102 </td>
  </tr>
  <tr>
   <td style="text-align:right;"> 567 </td>
   <td style="text-align:left;"> 5 </td>
   <td style="text-align:left;"> 3 </td>
   <td style="text-align:left;"> 60_102 </td>
  </tr>
  <tr>
   <td style="text-align:right;"> 678 </td>
   <td style="text-align:left;"> .5 </td>
   <td style="text-align:left;"> 4 </td>
   <td style="text-align:left;"> 50_100 </td>
  </tr>
</tbody>
</table>
]
.panel[.panel-name[cleaning_code]

```r
library(tidyverse)
library(stringr)
library(janitor)

clean <- mess %>%
  # fix names
  clean_names() %>%
  # remove "yrs" from teach_years
  mutate(teach_years = str_remove_all(teach_years, "yrs")) %>% 
  # make teach_years numeric and round up
  mutate(teach_years = ceiling(as.numeric(teach_years))) %>%
  # for teach_grade, recode k to 0 and make it a numeric variable
  mutate(teach_grade = as.numeric(recode(teach_grade, `k` = "0"))) %>%
  # separate school and district id
  separate(school_district_id, into = c("sch_id", "district_id"), sep = "_")
```
]

<table class="table table-striped" style="margin-left: auto; margin-right: auto;">
 <thead>
  <tr>
   <th style="text-align:right;"> id </th>
   <th style="text-align:right;"> teach_years </th>
   <th style="text-align:right;"> teach_grade </th>
   <th style="text-align:left;"> sch_id </th>
   <th style="text-align:left;"> district_id </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:right;"> 123 </td>
   <td style="text-align:right;"> 12 </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:left;"> 50 </td>
   <td style="text-align:left;"> 100 </td>
  </tr>
  <tr>
   <td style="text-align:right;"> 234 </td>
   <td style="text-align:right;"> 15 </td>
   <td style="text-align:right;"> 2 </td>
   <td style="text-align:left;"> 50 </td>
   <td style="text-align:left;"> 100 </td>
  </tr>
  <tr>
   <td style="text-align:right;"> 345 </td>
   <td style="text-align:right;"> 23 </td>
   <td style="text-align:right;"> 1 </td>
   <td style="text-align:left;"> 60 </td>
   <td style="text-align:left;"> 102 </td>
  </tr>
  <tr>
   <td style="text-align:right;"> 456 </td>
   <td style="text-align:right;"> 4 </td>
   <td style="text-align:right;"> 2 </td>
   <td style="text-align:left;"> 60 </td>
   <td style="text-align:left;"> 102 </td>
  </tr>
  <tr>
   <td style="text-align:right;"> 567 </td>
   <td style="text-align:right;"> 5 </td>
   <td style="text-align:right;"> 3 </td>
   <td style="text-align:left;"> 60 </td>
   <td style="text-align:left;"> 102 </td>
  </tr>
  <tr>
   <td style="text-align:right;"> 678 </td>
   <td style="text-align:right;"> 1 </td>
   <td style="text-align:right;"> 4 </td>
   <td style="text-align:left;"> 50 </td>
   <td style="text-align:left;"> 100 </td>
  </tr>
</tbody>
</table>
]
]

---

# What Else Can R Do?

Source: [<span style="color: white; ">rviews</span>](https://rviews.rstudio.com/2019/06/19/a-gentle-intro-to-tidymodels/)

---

# Analyze Data

.footer[Source: [statisticsglobe](https://statisticsglobe.com/extract-standard-error-t-and-p-value-from-regression-in-r)]

---

# Data Visualization

---

# Mapping

.footer[Source: [revolutionanalytics](https://blog.revolutionanalytics.com/2012/02/creating-beautiful-maps-with-r.html)]

---

---

# Applications

Source: [Ashley Edwards](https://wordreadinggrowth.shinyapps.io/exposures_to_mastery/)

---

# Slide Decks

---

# Websites

Source: [Meghan Hall](https://meghan.rbind.io/)

---

---

### R is a free, open-source programming language for statistics and data visualization
]

![](img/rstud.PNG)

### RStudio is an integrated development environment (IDE) for R
]

---

---

![](img/justR.PNG)
]

<img src="img/justRStud.PNG" width="540px" height="540px" style="display: block; margin: auto;" />
]

---

---

---

# Disclaimer

---

# Let's Get to Know R and RStudio

<style>

.exercise {

font-size: 2em;
font-style: bold

}
.tan{

color: #CEB888;

}
</style>

.exercise[<svg aria-hidden="true" role="img" viewBox="0 0 384 512" style="height:1em;width:0.75em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#CEB888;overflow:visible;position:relative;"><path d="M202.021 0C122.202 0 70.503 32.703 29.914 91.026c-7.363 10.58-5.093 25.086 5.178 32.874l43.138 32.709c10.373 7.865 25.132 6.026 33.253-4.148 25.049-31.381 43.63-49.449 82.757-49.449 30.764 0 68.816 19.799 68.816 49.631 0 22.552-18.617 34.134-48.993 51.164-35.423 19.86-82.299 44.576-82.299 106.405V320c0 13.255 10.745 24 24 24h72.471c13.255 0 24-10.745 24-24v-5.773c0-42.86 125.268-44.645 125.268-160.627C377.504 66.256 286.902 0 202.021 0zM192 373.459c-38.196 0-69.271 31.075-69.271 69.271 0 38.195 31.075 69.27 69.271 69.27s69.271-31.075 69.271-69.271-31.075-69.27-69.271-69.27z"/></svg> .tan[Exercises] <svg aria-hidden="true" role="img" viewBox="0 0 384 512" style="height:1em;width:0.75em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#CEB888;overflow:visible;position:relative;"><path d="M202.021 0C122.202 0 70.503 32.703 29.914 91.026c-7.363 10.58-5.093 25.086 5.178 32.874l43.138 32.709c10.373 7.865 25.132 6.026 33.253-4.148 25.049-31.381 43.63-49.449 82.757-49.449 30.764 0 68.816 19.799 68.816 49.631 0 22.552-18.617 34.134-48.993 51.164-35.423 19.86-82.299 44.576-82.299 106.405V320c0 13.255 10.745 24 24 24h72.471c13.255 0 24-10.745 24-24v-5.773c0-42.86 125.268-44.645 125.268-160.627C377.504 66.256 286.902 0 202.021 0zM192 373.459c-38.196 0-69.271 31.075-69.271 69.271 0 38.195 31.075 69.27 69.271 69.27s69.271-31.075 69.271-69.271-31.075-69.27-69.271-69.27z"/></svg>]

### 1. Open R

### 2. Open RStudio

### 3. Find what version of R and RStudio that you have

---

## Tour of RStudio

---

---

## Source Pane

---

---

# Let's Get to Know our Settings

### Tools -> Global Options

1. General Options

2. Code Options

3. Appearance Options

4. Pane Layout

]

![](img/options.PNG)
]

---

# Terminology

<style>

.yellow{

color: yellow;
font-style: bold;

}
</style>

Everything that exists in R is an **object**

Recall the assignment operator .yellow[`<-`]

```r
x <- 5

y <- 6
```

Everything that happens is a **function**

Consider the function .yellow[sum]

```r
sum(x, y)
```

```
[1] 11
```

---

# Objects

.pull-left[
1. There are 6 types of **objects** in R programming
  + **Vector**
  
  + Lists
  
  + Matrices
  
  + Array
  
  + Factors
  
  + **Data Frame** (Tibble)
  
]

![](img/objects.PNG)
]

---

# Objects

.pull-left[
* Data Frame
  - Simply put, this is your dataset. A two dimensional data structure, where each column is a variable and each row is a case.
  - In R you can create your own dataframe or read in a dataframe from your computer, a website, or a package.
  - Tibble is another term you might hear

```r
data <- data.frame(
  id = c(123, 234, 456), 
                   age = c(12, 10, 9))

data
```

```
   id age
1 123  12
2 234  10
3 456   9
```
]

.pull-right[
* Vector
  - This is the simplest object
  - It consists of one or more elements all of the same type (ex: all numeric, all character)
  - Think of vector as a variable outside of a dataframe

```r
id <- c(123, 234, 456)

id
```

```
[1] 123 234 456
```
]

---

# Best Practices for Object Naming

1. No spaces

2. Use all lower case

3. Use underscores to separate words

4. Descriptive

5. Not names of existing functions

6. Don't start with a number

7. No special characters

]

```r
# Good
day_one
day_1

# Bad
1_day
DayOne
day-one
x
```

```r
# Bad
T <- FALSE
c <- 10
sum <- x + y
```

Source: [Advanced R](http://adv-r.had.co.nz/Style.html)

]

---

# Every R Object has a Type and Class

#### **Type**: How an object is stored in memory

1. Character: **"apple"**, **"12_405"**

2. Double: **2**, **2.5**

3. Integer: **2L**

4. Logical: **TRUE**, **FALSE**

5. Complex: **1+4i**

]

#### **Class**: The abstract type

1. Character

1. Numeric

1. Integer

1. Factor

1. Date

1. POSIXct

1. Logical

]

.footnote[[The Carpentries](https://swcarpentry.github.io/r-novice-inflammation/13-supp-data-structures/)]

---

# Examples of Type and Class

```r
# Numeric vector

age <- c(12, 14)

class(age)
```

```
[1] "numeric"
```

```r
typeof(age)
```

```
[1] "double"
```
]

```r
# Date

birth_date <- as.Date(c("2005-01-14", 
                        "2006-07-22"))

class(birth_date)
```

```
[1] "Date"
```

```r
typeof(birth_date)
```

```
[1] "double"
```
]

---

# Examples of Type and Class

```r
# Dataframe

data <- data.frame(
  id = c(123, 234, 456), 
                   age = c(12, 10, 9))

print(data)
```

```
   id age
1 123  12
2 234  10
3 456   9
```

```r
class(data)
```

```
[1] "data.frame"
```

```r
typeof(data)
```

```
[1] "list"
```

---

# Functions

**Option 1:**

Write your own function

```r
my_sum <- function(x,y){
  x + y
}

my_sum(x=1, y=2)
```

```
[1] 3
```

]

**Option 2:**

Use an existing function (calling a function)

```r
x <- 1
y <- 2

sum(x, y)
```

```
[1] 3
```
]

---

# Anatomy of a Function Call

<style>

.large {

font-size: 2em;
font-style: bold;

}

</style>

---

# <svg aria-hidden="true" role="img" viewBox="0 0 384 512" style="height:1em;width:0.75em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#782F40;overflow:visible;position:relative;"><path d="M202.021 0C122.202 0 70.503 32.703 29.914 91.026c-7.363 10.58-5.093 25.086 5.178 32.874l43.138 32.709c10.373 7.865 25.132 6.026 33.253-4.148 25.049-31.381 43.63-49.449 82.757-49.449 30.764 0 68.816 19.799 68.816 49.631 0 22.552-18.617 34.134-48.993 51.164-35.423 19.86-82.299 44.576-82.299 106.405V320c0 13.255 10.745 24 24 24h72.471c13.255 0 24-10.745 24-24v-5.773c0-42.86 125.268-44.645 125.268-160.627C377.504 66.256 286.902 0 202.021 0zM192 373.459c-38.196 0-69.271 31.075-69.271 69.271 0 38.195 31.075 69.27 69.271 69.27s69.271-31.075 69.271-69.271-31.075-69.27-69.271-69.27z"/></svg> Let's Practice Some Base R Functions <svg aria-hidden="true" role="img" viewBox="0 0 384 512" style="height:1em;width:0.75em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#782F40;overflow:visible;position:relative;"><path d="M202.021 0C122.202 0 70.503 32.703 29.914 91.026c-7.363 10.58-5.093 25.086 5.178 32.874l43.138 32.709c10.373 7.865 25.132 6.026 33.253-4.148 25.049-31.381 43.63-49.449 82.757-49.449 30.764 0 68.816 19.799 68.816 49.631 0 22.552-18.617 34.134-48.993 51.164-35.423 19.86-82.299 44.576-82.299 106.405V320c0 13.255 10.745 24 24 24h72.471c13.255 0 24-10.745 24-24v-5.773c0-42.86 125.268-44.645 125.268-160.627C377.504 66.256 286.902 0 202.021 0zM192 373.459c-38.196 0-69.271 31.075-69.271 69.271 0 38.195 31.075 69.27 69.271 69.27s69.271-31.075 69.271-69.271-31.075-69.27-69.271-69.27z"/></svg>

| Task  |  Function   | Arguments   |
|-------|------------|-------|
|combine elements |   `c` | objects |
|check the class of an object | `class` | object |
|check the length of a vector | `length` | object | 
|get the mean of values | `mean`  | object, na.rm|
|create a data frame | `data.frame` | vectors of the same length |
|check internal structure of an object | `str`| object |

---
class: inverse

# Packages

Packages are a collection of functions.

They are written by a worldwide community of R users and can be downloaded for free from the internet.

.footnote[Source: [<span style="color: white;">Modern Dive</span>](https://moderndive.com/1-getting-started.html#r-rstudio)]

---

# Packages

.pull-left[
#### A huge collection of packages are hosted on the internet
1. CRAN
  + A central repository supported by the R Foundation
  + **C**omprehensive **R** **A**rchive **N**etwork
  + These packages must meet certain quality standards, and are regularly tested
  + Anyone can submit their package to CRAN and have it published for broad use
  + https://cran.r-project.org/

#### You may also find packages in other places
2. Bioconductor
3. GitHub
4. Your own personal computer or network drive
]

---

# How to Access Packages

Either install a package through code

```r
install.packages("package_name")
```

Or install manually

]

```r
library(package_name)

library(stringr)
library(dplyr)
library(readr)
library(haven)
```
]

---

# Getting Help

If you want to understand a package, you have several options:

![](img/documentation.PNG)
]

![](img/help.PNG)

]

---

# Getting Help in General

Google is your friend

![](img/google.gif)

]

Turn to the supportive community

![](img/get_help.jpg)

Source: [<span style="color: white;">Shannon Pileggi</span>](https://www.pipinghotdata.com/)

]

---

# Restart R Session

![](img/restart.PNG)
]

---

# <svg aria-hidden="true" role="img" viewBox="0 0 384 512" style="height:1em;width:0.75em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#782F40;overflow:visible;position:relative;"><path d="M202.021 0C122.202 0 70.503 32.703 29.914 91.026c-7.363 10.58-5.093 25.086 5.178 32.874l43.138 32.709c10.373 7.865 25.132 6.026 33.253-4.148 25.049-31.381 43.63-49.449 82.757-49.449 30.764 0 68.816 19.799 68.816 49.631 0 22.552-18.617 34.134-48.993 51.164-35.423 19.86-82.299 44.576-82.299 106.405V320c0 13.255 10.745 24 24 24h72.471c13.255 0 24-10.745 24-24v-5.773c0-42.86 125.268-44.645 125.268-160.627C377.504 66.256 286.902 0 202.021 0zM192 373.459c-38.196 0-69.271 31.075-69.271 69.271 0 38.195 31.075 69.27 69.271 69.27s69.271-31.075 69.271-69.271-31.075-69.27-69.271-69.27z"/></svg> Let's Use our First Package <svg aria-hidden="true" role="img" viewBox="0 0 384 512" style="height:1em;width:0.75em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#782F40;overflow:visible;position:relative;"><path d="M202.021 0C122.202 0 70.503 32.703 29.914 91.026c-7.363 10.58-5.093 25.086 5.178 32.874l43.138 32.709c10.373 7.865 25.132 6.026 33.253-4.148 25.049-31.381 43.63-49.449 82.757-49.449 30.764 0 68.816 19.799 68.816 49.631 0 22.552-18.617 34.134-48.993 51.164-35.423 19.86-82.299 44.576-82.299 106.405V320c0 13.255 10.745 24 24 24h72.471c13.255 0 24-10.745 24-24v-5.773c0-42.86 125.268-44.645 125.268-160.627C377.504 66.256 286.902 0 202.021 0zM192 373.459c-38.196 0-69.271 31.075-69.271 69.271 0 38.195 31.075 69.27 69.271 69.27s69.271-31.075 69.271-69.271-31.075-69.27-69.271-69.27z"/></svg>

2. Install a package
  + `readr`
  
3. Library a package
  + `library(readr)`
  
4. Use a function from that package
  + `read_csv()`
  + To read in this seattle pet names data from a website: https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-03-26/seattle_pets.csv
]

```r
# Install readr package

install.packages("readr")

# Library package

library(readr)

# Read in data using readr and assign to an object

pet_names <- read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-03-26/seattle_pets.csv")
```

]

---

# General Code Writing Best Practices

1. Comment your code
  + Comments begin with `#` and a space
  + Comments should explain what and why something is happening

```r
# Load raw data

# Drop duplicates cases from raw data
```

2\. Add sections to further refine your comments and make your file more searchable
  + Sections can be added by using 4 dashes after your comment `----`
  + You can also add sections by going to 
      - Code -> Insert Section

```r
# Load raw data ----

# (1) Read in the data

# (2) Review the data
```

---

# General Code Writing Best Practices

3\. Use spacing
  + Place spacing around all operators (**except** the colon)
  + Do not put spaces inside or outside parentheses for regular function calls

```r
# Good
average <- mean(feet / 12 + inches, na.rm = TRUE)
x <- 1:10
mean(x, na.rm = TRUE)

# Bad
average<-mean(feet/12+inches,na.rm=TRUE)
x <- 1 : 10
mean (x, na.rm = TRUE)
mean( x, na.rm = TRUE )
```

---

# General Code Writing Best Practices

4\. Function-indent long lines
  + Strive to limit your code to 80 characters per line 
  + If a function call is too long, place each argument on its own line

```r
do_something_very_complicated(
  something = "that",
  requires = many,
  arguments = "some of which may be long"
)
```

.footnote[Source: [Tidyverse Style Guide](https://style.tidyverse.org/syntax.html) and [Advanced R](http://adv-r.had.co.nz/Style.html)]

---

# Shortcuts

| Task  |  Windows   | Mac   |
|-------|------------|-------|
|Run current line of code | `Ctrl + Enter` |  `Cmd + Return` |
|Run all lines of code | ` Ctrl + A + Enter` | `Cmd + A + Enter` |
|Code completion | `Tab` | `Tab`|
|Insert assignment operator | `Alt + -` | `Option + -` |
|Insert pipe operator | `Ctrl + Shift + M` | `Cmd + Shift + M` |
|Comment or uncomment a line | `Ctrl + Shift + C` | `Cmd + Shift + C`|
|Restart R Session | `Ctrl + Shift + F10` | `Cmd + Shift + F10`
|Multi-line cursor| `Alt + click and drag` | `Option + click and drag`|
|Previous command in console | `up-arrow` | `up-arrow` |

---

# <svg aria-hidden="true" role="img" viewBox="0 0 384 512" style="height:1em;width:0.75em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#782F40;overflow:visible;position:relative;"><path d="M202.021 0C122.202 0 70.503 32.703 29.914 91.026c-7.363 10.58-5.093 25.086 5.178 32.874l43.138 32.709c10.373 7.865 25.132 6.026 33.253-4.148 25.049-31.381 43.63-49.449 82.757-49.449 30.764 0 68.816 19.799 68.816 49.631 0 22.552-18.617 34.134-48.993 51.164-35.423 19.86-82.299 44.576-82.299 106.405V320c0 13.255 10.745 24 24 24h72.471c13.255 0 24-10.745 24-24v-5.773c0-42.86 125.268-44.645 125.268-160.627C377.504 66.256 286.902 0 202.021 0zM192 373.459c-38.196 0-69.271 31.075-69.271 69.271 0 38.195 31.075 69.27 69.271 69.27s69.271-31.075 69.271-69.271-31.075-69.27-69.271-69.27z"/></svg> Let's Practice Shortcuts <svg aria-hidden="true" role="img" viewBox="0 0 384 512" style="height:1em;width:0.75em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#782F40;overflow:visible;position:relative;"><path d="M202.021 0C122.202 0 70.503 32.703 29.914 91.026c-7.363 10.58-5.093 25.086 5.178 32.874l43.138 32.709c10.373 7.865 25.132 6.026 33.253-4.148 25.049-31.381 43.63-49.449 82.757-49.449 30.764 0 68.816 19.799 68.816 49.631 0 22.552-18.617 34.134-48.993 51.164-35.423 19.86-82.299 44.576-82.299 106.405V320c0 13.255 10.745 24 24 24h72.471c13.255 0 24-10.745 24-24v-5.773c0-42.86 125.268-44.645 125.268-160.627C377.504 66.256 286.902 0 202.021 0zM192 373.459c-38.196 0-69.271 31.075-69.271 69.271 0 38.195 31.075 69.27 69.271 69.27s69.271-31.075 69.271-69.271-31.075-69.27-69.271-69.27z"/></svg>

---

# Questions?