Lab 03: CS631

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# Lab 03: CS631
## Working with Data
### Alison Hill

---

# Data for today

We'll use data from [Wordbank](http://wordbank.stanford.edu)- an open source database of children's vocabulary development. The tool used to measure children's language and communicative development in this database is the [MacArthur-Bates Communicative Development Inventories (MB-CDI)](http://mb-cdi.stanford.edu). The MB-CDI is a parent-reported questionnaire.

- R package [`wordbankr`](https://cran.r-project.org/web/packages/wordbankr/index.html)
- [`wordbankr` vignette](https://cran.r-project.org/web/packages/wordbankr/vignettes/wordbankr.html)
- More about [Wordbank](http://wordbank.stanford.edu)
- More about [MB-CDI](http://mb-cdi.stanford.edu)

---
# Get the data

Use this code chunk to import my cleaned CSV file:

```r
library(readr)
sounds <- read_csv("http://bit.ly/cs631-meow")
```

---
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## RStudio Base R Cheatsheet

https://github.com/rstudio/cheatsheets/blob/master/base-r.pdf

---

## Know your data types

* Numeric (2 subtypes)
    - Integers (`1, 50`)
    - Double (`1.5, 50.25`, `?double`)
* Character (`"hello"`)
* Factor (`grade = "A" | grade = "B"`)
* Logical (`TRUE | FALSE`)

```r
typeof(sounds$age)
```

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

```r
typeof(sounds$sound)
```

```
[1] "character"
```

```r
typeof(sounds$sound == "meow")
```

```
[1] "logical"
```

---
# Even better: `glimpse`

```r
glimpse(sounds)
```

```
Observations: 33
Variables: 4
$ age          <dbl> 8, 8, 8, 9, 9, 9, 10, 10, 10, 11, 11, 11, 12, 12,...
$ sound        <chr> "cockadoodledoo", "meow", "woof woof", "cockadood...
$ kids_produce <dbl> 1, 0, 3, 0, 2, 2, 0, 5, 4, 0, 5, 12, 0, 12, 28, 9...
$ kids_respond <dbl> 35, 35, 35, 91, 93, 93, 139, 145, 143, 94, 94, 94...
```

---
# `sounds` (a subset)

- `age`: child age in months
- `sound`: a string describing a type of animal sound
- `kids_produce`: the number of parents who answered "yes, my child produces this animal sound" 
- `kids_respond`: the number of parents who responded to this question at all

<table>
 <thead>
  <tr>
   <th style="text-align:right;"> age </th>
   <th style="text-align:left;"> sound </th>
   <th style="text-align:right;"> kids_produce </th>
   <th style="text-align:right;"> kids_respond </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:right;"> 8 </td>
   <td style="text-align:left;"> cockadoodledoo </td>
   <td style="text-align:right;"> 1 </td>
   <td style="text-align:right;"> 35 </td>
  </tr>
  <tr>
   <td style="text-align:right;"> 8 </td>
   <td style="text-align:left;"> meow </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> 35 </td>
  </tr>
  <tr>
   <td style="text-align:right;"> 8 </td>
   <td style="text-align:left;"> woof woof </td>
   <td style="text-align:right;"> 3 </td>
   <td style="text-align:right;"> 35 </td>
  </tr>
  <tr>
   <td style="text-align:right;"> 9 </td>
   <td style="text-align:left;"> cockadoodledoo </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> 91 </td>
  </tr>
  <tr>
   <td style="text-align:right;"> 9 </td>
   <td style="text-align:left;"> meow </td>
   <td style="text-align:right;"> 2 </td>
   <td style="text-align:right;"> 93 </td>
  </tr>
  <tr>
   <td style="text-align:right;"> 9 </td>
   <td style="text-align:left;"> woof woof </td>
   <td style="text-align:right;"> 2 </td>
   <td style="text-align:right;"> 93 </td>
  </tr>
</tbody>
</table>

---
# Data types

---
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# ⌛️

## Let's review

---

# Data wrangling with `dplyr`

.pull-left[
From DataCamp Chapter 3

- `group_by`

- `summarize`

]

.pull-right[
Adding onto your arsenal of...

- `filter`

- `arrange`

- `mutate`

- `glimpse`

- `distinct`

- `count`

- `tally`

- `pull`

- `top_n`
]

---
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# 😈

## More on `mutate`

---
# 3 ways to `mutate`

1. <font color="#ED1941">Create a new variable with a specific value</font>

1. Create a new variable based on other variables

1. Change an existing variable

```r
sounds %>% 
  mutate(form = "WS")
```

```
# A tibble: 33 x 5
     age sound          kids_produce kids_respond form 
   <dbl> <chr>                 <dbl>        <dbl> <chr>
 1    8. cockadoodledoo           1.          35. WS   
 2    8. meow                     0.          35. WS   
 3    8. woof woof                3.          35. WS   
 4    9. cockadoodledoo           0.          91. WS   
 5    9. meow                     2.          93. WS   
 6    9. woof woof                2.          93. WS   
 7   10. cockadoodledoo           0.         139. WS   
 8   10. meow                     5.         145. WS   
 9   10. woof woof                4.         143. WS   
10   11. cockadoodledoo           0.          94. WS   
# ... with 23 more rows
```

---
# 3 ways to `mutate`

1. Create a new variable with a specific value

1. <font color="#ED1941">Create a new variable based on other variables</font>

1. Change an existing variable

```r
sounds %>% 
  mutate(prop_produce = kids_produce / kids_respond)
```

```
# A tibble: 33 x 5
     age sound          kids_produce kids_respond prop_produce
   <dbl> <chr>                 <dbl>        <dbl>        <dbl>
 1    8. cockadoodledoo           1.          35.       0.0286
 2    8. meow                     0.          35.       0.    
 3    8. woof woof                3.          35.       0.0857
 4    9. cockadoodledoo           0.          91.       0.    
 5    9. meow                     2.          93.       0.0215
 6    9. woof woof                2.          93.       0.0215
 7   10. cockadoodledoo           0.         139.       0.    
 8   10. meow                     5.         145.       0.0345
 9   10. woof woof                4.         143.       0.0280
10   11. cockadoodledoo           0.          94.       0.    
# ... with 23 more rows
```

---
# 3 ways to `mutate`

1. Create a new variable with a specific value

1. Create a new variable based on other variables

1. <font color="#ED1941">Change an existing variable</font>

```r
sounds %>% 
  mutate(prop_produce = prop_produce * 100)
```

```
# A tibble: 33 x 5
     age sound          kids_produce kids_respond prop_produce
   <dbl> <chr>                 <dbl>        <dbl>        <dbl>
 1    8. cockadoodledoo           1.          35.         2.86
 2    8. meow                     0.          35.         0.  
 3    8. woof woof                3.          35.         8.57
 4    9. cockadoodledoo           0.          91.         0.  
 5    9. meow                     2.          93.         2.15
 6    9. woof woof                2.          93.         2.15
 7   10. cockadoodledoo           0.         139.         0.  
 8   10. meow                     5.         145.         3.45
 9   10. woof woof                4.         143.         2.80
10   11. cockadoodledoo           0.          94.         0.  
# ... with 23 more rows
```

---
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# ⌛️

## Let's review some helpful functions for `mutate` + `summarize`

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background-image: url("../images/peapod.png")
background-size: 25%

## Remember:

## Base R + Tidyverse

---
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#💡

## First:

## Arithmetic

*especially useful for* `mutate`

See:

http://r4ds.had.co.nz/transform.html#mutate-funs

---

```r
?Arithmetic
```

<table>
 <thead>
  <tr>
   <th style="text-align:left;"> Operator </th>
   <th style="text-align:left;"> Description </th>
   <th style="text-align:left;"> Usage </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> + </td>
   <td style="text-align:left;"> addition </td>
   <td style="text-align:left;"> x + y </td>
  </tr>
  <tr>
   <td style="text-align:left;"> - </td>
   <td style="text-align:left;"> subtraction </td>
   <td style="text-align:left;"> x - y </td>
  </tr>
  <tr>
   <td style="text-align:left;"> * </td>
   <td style="text-align:left;"> multiplication </td>
   <td style="text-align:left;"> x * y </td>
  </tr>
  <tr>
   <td style="text-align:left;"> / </td>
   <td style="text-align:left;"> division </td>
   <td style="text-align:left;"> x / y </td>
  </tr>
  <tr>
   <td style="text-align:left;"> ^ </td>
   <td style="text-align:left;"> raised to the power of </td>
   <td style="text-align:left;"> x ^ y </td>
  </tr>
  <tr>
   <td style="text-align:left;"> abs </td>
   <td style="text-align:left;"> absolute value </td>
   <td style="text-align:left;"> abs(x) </td>
  </tr>
  <tr>
   <td style="text-align:left;"> %/% </td>
   <td style="text-align:left;"> integer division </td>
   <td style="text-align:left;"> x %/% y </td>
  </tr>
  <tr>
   <td style="text-align:left;"> %% </td>
   <td style="text-align:left;"> remainder after division </td>
   <td style="text-align:left;"> x %% y </td>
  </tr>
</tbody>
</table>

```r
5 %/% 2 # 2 goes into 5 two times with...
```

```
[1] 2
```

```r
5 %% 2 # 1 left over
```

```
[1] 1
```

---
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#💡

## Second:

## Summaries

*especially useful for* `summarize`

*even more useful after a* `group_by`

See:

http://r4ds.had.co.nz/transform.html#summarise-funs

---

<table>
 <thead>
  <tr>
   <th style="text-align:left;"> Description </th>
   <th style="text-align:left;"> Usage </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> sum </td>
   <td style="text-align:left;"> sum(x) </td>
  </tr>
  <tr>
   <td style="text-align:left;"> minimum </td>
   <td style="text-align:left;"> min(x) </td>
  </tr>
  <tr>
   <td style="text-align:left;"> maximum </td>
   <td style="text-align:left;"> max(x) </td>
  </tr>
  <tr>
   <td style="text-align:left;"> mean </td>
   <td style="text-align:left;"> mean(x) </td>
  </tr>
  <tr>
   <td style="text-align:left;"> median </td>
   <td style="text-align:left;"> mean(x) </td>
  </tr>
  <tr>
   <td style="text-align:left;"> standard deviation </td>
   <td style="text-align:left;"> sd(x) </td>
  </tr>
  <tr>
   <td style="text-align:left;"> variance </td>
   <td style="text-align:left;"> var(x) </td>
  </tr>
  <tr>
   <td style="text-align:left;"> rank </td>
   <td style="text-align:left;"> rank(x) </td>
  </tr>
</tbody>
</table>

* All allow for `na.rm` argument to remove `NA` values before summarizing. The default setting for this argument is *always* `na.rm = FALSE`, so if there is one `NA` value the summary will be `NA`.

* See "Maths Functions" in the RStudio Base R Cheatsheet:
https://github.com/rstudio/cheatsheets/blob/master/base-r.pdf

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![](../images/alicedata-lego-colors.jpg)
## <small>"Spent day pondering grayscale vs colourscale using `ggplot`"</small>

*photo and caption courtesy [@alice-data](https://twitter.com/alice_data)*

---
# Today's lab: COLORS

Specifically, discrete colors.

At the end of today's lab, you'll see an extra section on continuous colors.

---
## But first: `shape`

---
## Shapes with `color = "hotpink"`

---
## Shapes with `fill = "gold"`

---
## Default shape for `geom_point`

🕵🏽 Requires spelunking into the dark corners of the `ggplot2` code on [GitHub](https://github.com/tidyverse/ggplot2/blob/master/R/geom-point.r):

```r
default_aes = aes(
    shape = 19, colour = "black", size = 1.5, fill = NA,
    alpha = NA, stroke = 0.5
  )
```

So, the default for `geom_point(shape = 19)`! This is important to remember: this shape only "understands" the *color* aesthetic, but not the *fill* aesthetic.

---

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# 👇🏽

## R Markdown:

https://www.markdowntutorial.com

https://andrewbtran.github.io/NICAR/2018/workflow/docs/02-rmarkdown.html

https://yihui.name/tinytex/ *(install!)*

https://github.com/rstudio/cheatsheets/blob/master/rmarkdown-2.0.pdf

https://rmarkdown.rstudio.com/html_document_format.html

https://rmarkdown.rstudio.com/pdf_document_format.html