CONJ620: CM 1.2

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# CONJ620: CM 1.2
## Introduction to the Tidyverse
### Alison Hill

---

## I'm assuming...

You have working versions of:

- R and

- RStudio

installed on your computer.

---
## Today's in-class lab

- Our notepad: http://bit.ly/conj620-cm012

- [Graphics and statistics for cardiology: comparing categorical and continuous variables](http://heart.bmj.com/content/early/2016/01/27/heartjnl-2015-308104.full) 
    - [Full text](http://faculty.washington.edu/kenrice/heartgraphs/effectivegraphs.pdf)
    - doi:10.1136/heartjnl-2015-308104  
    - Site: http://faculty.washington.edu/kenrice/heartgraphs/

- The data is from [NHANES](https://www.cdc.gov/nchs/nhanes/index.htm): the CDC's National Health and Nutrition Examination Survey

---
## Set-up

I provide this code in your [lab worksheet](../labs/cm012-worksheet.html)

```r
library(tidyverse)
heart <- read_csv("http://faculty.washington.edu/kenrice/heartgraphs/nhanesmedium.csv", 
                  na = ".") 
```

Some notes:

- I'm going to ask that you trust me with this worksheet! You haven't learned about this kind of document yet, called an R Markdown (`.Rmd`) file- please just go with it! I promise we'll actually explain it in a later lab 😇

- Don't forget the notepad!: http://bit.ly/conj620-cm012

---
## Data Dictionary

From the [data dictionary](http://faculty.washington.edu/kenrice/heartgraphs/):

* `BPXSAR`: systolic blood pressure (mmHg)
* `BPXDAR`: diastolic blood pressure (mmHg)
* `BPXDI1`, `BPXDI2`: two diastolic blood pressure readings
* `race_ethc`: race/ethnicity, coded as:
    - Hispanic, 
    - White non-Hispanic, 
    - Black non-Hispanic and 
    - Other
* `gender`: sex, coded as Male/Female
* `DR1TFOLA`: folate intake (μg/day)
* `RIAGENDR`: sex, coded as 1/2
* `BMXBMI`: body mass index (kg/m2)
* `RIDAGEY`: age (years)

---
## Chapter 1: Data Wrangling

.pull-left[

- print a tibble
  - `heart`

- install a package 
  - `install.packages("dplyr")`
  - do 1x per machine

- load an installed package
  - `library(dplyr)`
  - do 1x per work session

- assign a variable a name (`<-`) 
]

.pull-right[

- `dplyr::filter`

- `dplyr::arrange`

- `dplyr::mutate`

]

---
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![](images/rladylego-pipe.jpg)

## Plus: `%>%`

*image courtesy [@LegoRLady](https://twitter.com/LEGO_RLady/status/986661916855754752)*

---
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# `%>%`

## The pipe

*"dataframe first, dataframe once"*

```r
library(dplyr)
```

RStudio Keyboard Shortcuts:

OSX: `CMD` + `SHIFT` + `M`

Else: `CTRL` + `SHIFT` + `M`

---
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*Nesting* a dataframe inside a function is hard to read.

```r
slice(heart, 1)
```

```
# A tibble: 1 x 10
  BPXSAR BPXDAR BPXDI1 BPXDI2 race_ethc    gender DR1TFOLA RIAGENDR BMXBMI
   <dbl>  <dbl>  <dbl>  <dbl> <chr>        <chr>     <dbl>    <dbl>  <dbl>
1   129.   50.7    48.    48. White non-H… Male       334.       1.   19.7
# ... with 1 more variable: RIDAGEYR <dbl>
```

Here, the "sentence" starts with a <font color="#ED1941">verb</font>.

<hr>

*Piping* a dataframe into a function lets you read L to R

```r
heart %>% slice(1)
```

Now, the "sentence" starts with a <font color="#ED1941">noun</font>.

---
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Sequences of functions make you read *inside out*

```r
slice(filter(heart, gender == "Male"), 1)
```

<hr>

Chaining functions together lets you read *L to R*

```r
heart %>% filter(gender == "Male") %>% slice(1)
```

---
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## "dataframe first, dataframe once"

---
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```r
heart %>% filter(gender == "Male") %>% slice(1)
```

<hr>

This does the same thing:

```r
heart %>% filter(.data = ., gender == "Male") %>% slice(.data = ., 1)
```

<hr>

So does this:

```r
heart %>% filter(., gender == "Male") %>% slice(., 1)
```

---
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![](https://media.giphy.com/media/PD0V8W0JWcYPm/giphy.gif)

`attach()`

`heart$gender` or other variants

---
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# ⌛️

## Let's review some helpful functions for `filter`

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

## Base R + Tidyverse

---
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#💡

## First:

## Logical Operators

---

```r
?base::Logic
```

---

Logical or (`|`) is inclusive, so `x | y` really means:

* x or 
* y or 
* both x & y

Exclusive or (`xor`) is exclusive, so `xor(x, y)` really means:

* x or
* y...
* but not both x & y

```r
x <- c(0, 1, 0, 1)
y <- c(0, 0, 1, 1)
boolean_or <- x | y
exclusive_or <- xor(x, y)
cbind(x, y, boolean_or, exclusive_or)
```

```
     x y boolean_or exclusive_or
[1,] 0 0          0            0
[2,] 1 0          1            1
[3,] 0 1          1            1
[4,] 1 1          1            0
```

---
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#💡

## Second:

## Comparisons

---

```r
?Comparison
```

<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;"> &lt; </td>
   <td style="text-align:left;"> less than </td>
   <td style="text-align:left;"> x &lt; y </td>
  </tr>
  <tr>
   <td style="text-align:left;"> &lt;= </td>
   <td style="text-align:left;"> less than or equal to </td>
   <td style="text-align:left;"> x &lt;= y </td>
  </tr>
  <tr>
   <td style="text-align:left;"> &gt; </td>
   <td style="text-align:left;"> greater than </td>
   <td style="text-align:left;"> x &gt; y </td>
  </tr>
  <tr>
   <td style="text-align:left;"> &gt;= </td>
   <td style="text-align:left;"> greater than or equal to </td>
   <td style="text-align:left;"> x &gt;= y </td>
  </tr>
  <tr>
   <td style="text-align:left;"> == </td>
   <td style="text-align:left;"> exactly equal to </td>
   <td style="text-align:left;"> x == y </td>
  </tr>
  <tr>
   <td style="text-align:left;"> != </td>
   <td style="text-align:left;"> not equal to </td>
   <td style="text-align:left;"> x != y </td>
  </tr>
  <tr>
   <td style="text-align:left;"> %in% </td>
   <td style="text-align:left;"> group membership* </td>
   <td style="text-align:left;"> x %in% y </td>
  </tr>
  <tr>
   <td style="text-align:left;"> is.na </td>
   <td style="text-align:left;"> is missing </td>
   <td style="text-align:left;"> is.na(x) </td>
  </tr>
  <tr>
   <td style="text-align:left;"> !is.na </td>
   <td style="text-align:left;"> is not missing </td>
   <td style="text-align:left;"> !is.na(x) </td>
  </tr>
</tbody>
</table>

*(shortcut to using `|` repeatedly with `==`)

---
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# ⌛️

## Let's review `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
heart_bp <- heart %>% 
  select(BPXDI1, BPXDI2)

heart_bp %>% 
  mutate(year = 2015)
```

```
# A tibble: 200 x 3
   BPXDI1 BPXDI2  year
    <dbl>  <dbl> <dbl>
 1    48.    48. 2015.
 2    76.    78. 2015.
 3    76.    76. 2015.
 4    64.    56. 2015.
 5    54.    56. 2015.
 6    80.    78. 2015.
 7    52.    NA  2015.
 8    NA     80. 2015.
 9    76.    NA  2015.
10    90.    80. 2015.
# ... with 190 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
heart_bp %>% 
  mutate(bp_ratio = BPXDI1 / BPXDI2)
```

```
# A tibble: 200 x 3
   BPXDI1 BPXDI2 bp_ratio
    <dbl>  <dbl>    <dbl>
 1    48.    48.    1.00 
 2    76.    78.    0.974
 3    76.    76.    1.00 
 4    64.    56.    1.14 
 5    54.    56.    0.964
 6    80.    78.    1.03 
 7    52.    NA    NA    
 8    NA     80.   NA    
 9    76.    NA    NA    
10    90.    80.    1.12 
# ... with 190 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
heart_bp %>% 
  mutate(bp_ratio = bp_ratio * 100)
```

```
# A tibble: 200 x 3
   BPXDI1 BPXDI2 bp_ratio
    <dbl>  <dbl>    <dbl>
 1    48.    48.    100. 
 2    76.    78.     97.4
 3    76.    76.    100. 
 4    64.    56.    114. 
 5    54.    56.     96.4
 6    80.    78.    103. 
 7    52.    NA      NA  
 8    NA     80.     NA  
 9    76.    NA      NA  
10    90.    80.    112. 
# ... with 190 more rows
```

---
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## To make your `mutate` "stick"

```r
heart_bp <- heart_bp %>% 
  mutate(bp_ratio = BPXDI1 / BPXDI2)
```

---
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# ⌛️

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

---
class: inverse, bottom, center
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
```

---
## Chapter 2: Data Visualization

all `ggplot2`

- `aes(x = , y = )` (aesthetics)
- `aes(x = , y = , color = )` (add color)
- `aes(x = , y = , size = )` (add size)
- `+ facet_wrap(~ )` (facetting)

---
## Old School <sup>1</sup>

- Sketch the graphics below on paper, where the `x`-axis is variable `age_yrs` and the `y`-axis is variable `systolic_bp`

```
# A tibble: 4 x 4
  age_yrs systolic_bp bmi_z gender
    <dbl>       <dbl> <dbl> <chr> 
1      8.         80.    1. male  
2      9.         90.    2. male  
3     10.        100.    3. female
4     11.        110.    4. female
```

1. A scatter plot
1. A scatter plot where the `color` of the points corresponds to `gender`
1. A scatter plot where the `size` of the points corresponds to `bmi_z`
1. A scatter plot facetted by `gender`

.footnote[
[1] Shamelessly borrowed with much appreciation to [Chester Ismay](https://ismayc.github.io/talks/ness-infer/slide_deck.html)
]

---

## 1. A scatterplot

```r
library(ggplot2)
ggplot(nhanes, aes(age_yrs, systolic_bp)) + 
  geom_point()
```
--

---

## 2. `color` points by `gender`

```r
library(ggplot2)
ggplot(nhanes, aes(age_yrs, systolic_bp, color = gender)) + 
  geom_point()
```
--

---

## 3. `size` points by `bmi_z`

```r
library(ggplot2)
ggplot(nhanes, aes(age_yrs, systolic_bp, size = bmi_z)) +  
  geom_point()
```
--

---

## 4. `facet_wrap` by `gender`

```r
library(ggplot2)
ggplot(nhanes, aes(age_yrs, systolic_bp)) +  
  geom_point() +
  facet_wrap(~gender)
```
--

---
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## Work your way through the lab worksheet now

View it [here](../labs/cm012-worksheet.html)

Download the file on Sakai to work on locally

---
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# 🔨

## More Resources

- [RStudio `ggplot2` Cheatsheet](https://github.com/rstudio/cheatsheets/blob/master/data-visualization-2.1.pdf)

- [RStudio `dplyr` Cheatsheet](https://github.com/rstudio/cheatsheets/blob/master/data-transformation.pdf)

- [RStudio Base R Cheatsheet](https://github.com/rstudio/cheatsheets/blob/master/base-r.pdf)

- [Alison's OHSU Data Jamboree using `ggplot2`](https://alison.rbind.io/talk/code-your-graph/)