Data Visualization as Communication

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# Data Visualization as Communication
## bmRn CSM: Advanced graphing techniques with ggplot2
### Martin Frigaard
### 2020-12-13

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# `ggplot2` = a grammar for data visualization

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# Load the packages

```r
install.packages("tidyverse")
library(tidyverse)
```

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## Outline

.pull-left[

###  Recap `ggplot2`

###  Graphing preliminaries

- *Data Wrangling*
  - *Tidying*

### Variable Distributions

- *Histograms, density plots, violin plots*

### Line Graphs

]

.pull-right[

### Adding Text

- *Annotations, labeling values*

### Reference Lines

### Advanced Faceting

- *facet_wrap(), facet_wrap_paginate(), facet_geo()*

]

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# Materials

## Slides

https://mjfrigaard.github.io/data-viz-as-comm/Index.html

## Exercises

https://mjfrigaard.github.io/r-meetup-tutorials/gg01_data-viz-as-comm.html

## RStudio Project

https://rstudio.cloud/project/1983257

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# Recap of `ggplot2`

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# Recap of `ggplot2`

## In the previous lesson, we covered:

### 1) The grammar of graphics

- `ggplot2` is a language of *layers*, organized linearly

- `ggplot2`'s layers give us a "*linear ordering of phrases*" to build an infinite number of graphs "*which convey a gnarly network of ideas.*"

- "**Infinitely extensible**"

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# Recap of `ggplot2` (cont)

## In the previous lesson, we covered:

### 1) The grammar of graphics

### 2) Identifying graph aesthetics

- position (`x` and `y`), size, color, shape, etc.

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# Recap of `ggplot2` (cont)

## In the previous lesson, we covered:

### 1) The grammar of graphics

### 2) Identifying graph aesthetics

### 3) Recognizing and using `geoms`

- Scatter plot = `geom_point()`

- Box plot = `geom_boxplot()`

- Line graph = `geom_line()`

- Bar graph = `geom_histogram()`, `geom_bar()`, `geom_col()`

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# Recap of `ggplot2` (cont)

## In the previous lesson, we covered:

### 1) The grammar of graphics

### 2) Identifying graph aesthetics

### 3) Recognizing and using `geoms`

### 4) Labels and factes (exercises)

- **Build labels first!**

- Facet for subplots of levels in a grouping variable

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# Before we start...

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## Things to consider (1)

### Recognize the needs of your audience

#### *level of data literacy, subject matter expertise, etc.*

### Check and communicate data quality with stakeholders

#### *let them know the good and the bad news*

### Identify the correct data visualization (based on the data)

#### *single variable, bivariate, and multivariate graphs*

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## Things to consider (2)

<br>

### Incorporate feedback from stakeholders/audience into graphs

#### *ask them to be part of the process*

### Design visualizations with the appropriate detail and annotations

####  *inform (and do not mislead) the audience*

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## Getting started

### 1) Clearly define the question or problem

- Start with a general goal, broad question, or novel problem

- Move towards specific tasks

### 2) Matching the measurements to metrics

- *'Measurements'* are what we care about

- *'Metrics'* are the available data

### 3) Is a data visualization the best choice?

- Not always...

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# COVID and Transportation Habits

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## Example: How has COVID changed our modes of transportation?

### What kind of measurements would these be?

***how are people traveling (walk, drive, etc.)***

### What would these data look like?

***what would the columns and rows look like?***

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# Apple Mobility Data

## Fortunately, these data exist!

Apple released mobility data:

https://covid19.apple.com/mobility

Import these data below:

```r
AppleMobRaw <- readr::read_csv("https://bit.ly/36tTVpe")
```

*Use `Raw` as a prefix or suffix for data in it's most 'raw' state*

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# Raw Apple Mobility Data

```r
AppleMobRaw %>% View("Apple")
```

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# Wrangling Apple Mobility Data

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## Wrangling Apple Mobility Data

<br>

### What variables do we have?

### How are these variables formatted?

### How do we need to change them?

#### *...with a focus on answering our question*

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## View Apple Mobility Data (head)

```r
AppleMobRaw %>% head()
```

<div data-pagedtable="false">
  <script data-pagedtable-source type="application/json">
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---
class: left, top
background-image: url(img/ggplot2.png)
background-position: 95% 8%
background-size: 8%

## View Apple Mobility Data (tail)

```r
AppleMobRaw %>% tail()
```

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  </script>
</div>

---
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background-image: url(img/ggplot2.png)
background-position: 95% 8%
background-size: 8%

### Tidying Apple Mobility Data

Tidy dates and mobility into `date` and `dir_request` ('relative usage of directions')

```r
AppleMobRaw %>% 
  tidyr::pivot_longer(cols = -c(geo_type:country), 
    names_to = "date", values_to = "dir_request")
```

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background-image: url(img/ggplot2.png)
background-position: 95% 8%
background-size: 8%

### Manipulate Apple Mobility Data

Remove missing values in `country` and `sub-region` and `clean_names()`

```r
AppleMobRaw %>% 
  tidyr::pivot_longer(cols = -c(geo_type:country), 
    names_to = "date", values_to = "dir_request") %>% 
  # remove missing country data
  dplyr::filter(!is.na(country) & !is.na(`sub-region`)) %>% 
  # clean names
  janitor::clean_names() %>% View("TidyApple")
```

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background-image: url(img/ggplot2.png)
background-position: 95% 8%
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### New Tidy Apple Mobility Data!

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background-position: 95% 8%
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### Assign Apple Mobility Data to `TidyApple`

This looks good, so assign to `TidyApple`

---
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background-image: url(img/ggplot2.png)
background-position: 95% 8%
background-size: 8%

### `TidyApple`: Format Variables

Our `TidyApple` data still needs some attention.

```r
glimpse(TidyApple)
```

```
## Rows: 1,055,293
## Columns: 7
## $ geo_type            <chr> "city", "city", "city", "city…
## $ region              <chr> "Aachen", "Aachen", "Aachen",…
## $ transportation_type <chr> "driving", "driving", "drivin…
## $ sub_region          <chr> "North Rhine-Westphalia", "No…
## $ country             <chr> "Germany", "Germany", "German…
## $ date                <chr> "2020-01-13", "2020-01-14", "…
## $ dir_request         <dbl> 100.00, 100.73, 102.86, 102.6…
```

The `date` variable needs to be formatted as a `date`

```r
TidyApple %>% 
  mutate(date = lubridate::ymd(date)) -> TidyApple
```

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background-image: url(img/ggplot2.png)
background-position: 95% 8%
background-size: 8%

### `TidyApple`: Rename Variables

Let's rename `transportation_type` to `trans_type`

```r
TidyApple %>% 
  rename(trans_type = transportation_type) -> TidyApple
```

---
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background-image: url(img/ggplot2.png)
background-position: 95% 8%
background-size: 8%

### `TidyApple`: Check Formatted Variables

Re-check `TidyApple` data.

```r
glimpse(TidyApple)
```

```
## Rows: 1,055,293
## Columns: 7
## $ geo_type    <chr> "city", "city", "city", "city", "city…
## $ region      <chr> "Aachen", "Aachen", "Aachen", "Aachen…
## $ trans_type  <chr> "driving", "driving", "driving", "dri…
## $ sub_region  <chr> "North Rhine-Westphalia", "North Rhin…
## $ country     <chr> "Germany", "Germany", "Germany", "Ger…
## $ date        <date> 2020-01-13, 2020-01-14, 2020-01-15, …
## $ dir_request <dbl> 100.00, 100.73, 102.86, 102.65, 109.3…
```

Now we can see `date` is formatted correctly

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### `TidyApple`: Counting

> “*data science is mostly counting things*” - `tabyl` vignette

Count the `trans_type` variable with `dplyr::count()`

.pull-left[

<br>

```r
TidyApple %>% 
  count(trans_type)
```

```
## # A tibble: 3 x 2
##   trans_type      n
##   <chr>       <int>
## 1 driving    743682
## 2 transit    106512
## 3 walking    205099
```

]

.pull-right[

*Add the `sort = TRUE` argument*

```r
TidyApple %>% 
  count(trans_type, sort = TRUE)
```

```
## # A tibble: 3 x 2
##   trans_type      n
##   <chr>       <int>
## 1 driving    743682
## 2 walking    205099
## 3 transit    106512
```

]

---
class: inverse, center, top
background-image: url(img/ggplot2.png)
background-position: 50% 70%
background-size: 35%

# Visualizing Variable Distributions

---
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background-image: url(img/ggplot2.png)
background-position: 95% 8%
background-size: 8%

## What kinds of question(s) do graphs like these answer?

### What does the distribution of direction requests look like?

What is the distribution of `dir_request`? We will explore this with a histogram.

### Labels!!

```r
lab_hist <- labs(x = "Apple directions requests",
                 y = "Count",
     title = "Distribution of Direction Requests",
     subtitle = "source: https://covid19.apple.com/mobility")
```

---
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background-image: url(img/ggplot2.png)
background-position: 8% 95%
background-size: 8%

## Histogram = single variable distributions

Create a histogram of `dir_request` with the code below:

```r
TidyApple %>% ggplot() + 
* geom_histogram(aes(x = dir_request)) +
  lab_hist
```

---
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background-image: url(img/ggplot2.png)
background-position: 95% 8%
background-size: 8%

## Histograms: Changing the Y Axis

Fix the `y` axis numbers with help from the [`scales` package](https://scales.r-lib.org/)

```r
library(scales)
TidyApple %>% ggplot() + 
  geom_histogram(aes(x = dir_request)) + 
* scale_y_continuous(labels = scales::comma) +
  lab_hist
```

---
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background-image: url(img/ggplot2.png)
background-position: 95% 8%
background-size: 8%

## Histograms: Changing the Y Axis

<br>

---
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background-image: url(img/ggplot2.png)
background-position: 95% 8%
background-size: 8%

## Changing Histogram Shape

Adjust the shape of the histogram with the `bins` argument

```r
TidyApple %>% ggplot() + 
* geom_histogram(aes(x = dir_request), bins = 15) +
  scale_y_continuous(labels = scales::comma) +
  lab_hist
```

---
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background-image: url(img/ggplot2.png)
background-position: 95% 8%
background-size: 8%

## Changing Histogram Shape

Adjust the shape of the histogram with the `bins` argument

```r
TidyApple %>% ggplot() + 
* geom_histogram(aes(x = dir_request), bins = 45) +
  scale_y_continuous(labels = scales::comma) +
  lab_hist
```

---
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background-image: url(img/ggplot2.png)
background-position: 8% 95%
background-size: 6%

## Visualizing variable distributions across groups

### What questions do these graphs answer?

How does the distribution of `dir_request` across `trans_type`?

### We can view this with a density plot, violin plot, or ridgeline plot

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## Density Plots

### CREATE LABELS FIRST!!

We need a new set of labels

```r
lab_density <- labs(x = "Apple directions requests",
                    y = "Density",
     title = "Direction Requests vs. Transportation Type",
     subtitle = "source: https://covid19.apple.com/mobility")
```

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## Density Plots

Visualize the distribution of `dir_request` across `trans_type` with a `geom_density()`

```r
TidyApple %>% 
  ggplot() +
* geom_density(aes(x = dir_request,
*                  fill = trans_type)) +
  lab_density
```

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## Density Plots

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## Density Plots (`alpha`)

<br>

Adjust the `alpha` so we can see the overlap

```r
TidyApple %>% 
  ggplot() +
  geom_density(aes(x = dir_request, 
                   fill = trans_type), 
*              alpha = 1/3) +
  lab_density
```

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## Density Plots (`alpha`)

Now we can see where they overlap

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## Violin Plots

Violin plots are alternatives to boxplots.

```r
lab_violin <- labs(y = "Apple directions requests",
                   x = "Transportation Types",
     title = "Direction Requests by Transportation Type",
     subtitle = "source: https://covid19.apple.com/mobility")
```

These allow us to add a categorical variable to the `x` axis.

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## Violin Plots

```r
TidyApple %>% 
  ggplot() +
* geom_violin(aes(y = dir_request, x = trans_type,
*                 fill = trans_type)) +
  lab_violin
```

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## Violin Plots: *confused?*

Add a boxplot layer!

```r
TidyApple %>% 
  ggplot() +
* geom_violin(aes(y = dir_request, x = trans_type,
*                 fill = trans_type), alpha = 1/5)
```

```r
TidyApple %>% 
  ggplot() +
  geom_violin(aes(y = dir_request, x = trans_type, 
                  fill = trans_type), alpha = 1/5) + 
* geom_boxplot(aes(y = dir_request, x = trans_type,
*                  color = trans_type)) +
  lab_violin
```

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## Violin Plots *and* Boxpots

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## Rideline Plots

Another option is a ridgeline plot (from the `ggridges` package). These display multiple densities.

```r
lab_ridges <- labs(x = "Apple directions requests",
                   y = "Transportation Types",
     title = "Direction Requests by Transportation Type",
     subtitle = "source: https://covid19.apple.com/mobility")
```

```r
library(ggridges)
TidyApple %>%  
  ggplot() + 
  geom_density_ridges(aes(x = dir_request, 
                          y = trans_type, 
                          fill = trans_type), 
                      alpha = 1/5) + 
  lab_ridges
```

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## Rideline Plots

We can adjust the alpha on these just like the density plots.

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# Line Graphs

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### Narrowing date ranges

Filter the data to only us cities between `2020-02-01` and `2020-08-01`. Use `skimr::skim()` to make sure it works!

```r
TidyApple %>% 
  # us cities
  filter(geo_type == "city" &
         country == "United States", 
         # feb - aug
         date >= lubridate::as_date("2020-02-01") & 
         date <= lubridate::as_date("2020-08-01")) %>% 
         # check work!
         skimr::skim(date)
```

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### Check with `skimr` output

These are helpful if we're checking a filter on a numerical certain condition (`min`, `max`, `mean`, etc.)

<br>

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### Narrow to US Cities (Feb-Jul)

<br>

Create `USCitiesFebJul` data by filtering to US cities between Feb 1, 2020 and July 31, 2020.

<br>

```r
TidyApple %>% 
  filter(geo_type == "city" &
         country == "United States", 
         date >= lubridate::as_date("2020-02-01") & 
         date <= lubridate::as_date("2020-08-01")) -> USCitiesFebJul
```

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### Line Graph: Labels (1)

Ideally, our labels update whenever the data changes. We can do this with `paste0()`.

```r
paste0(min(USCitiesFebJul$date), 
                       " through ", 
                       max(USCitiesFebJul$date)) 
```

```
## [1] "2020-02-01 through 2020-08-01"
```

```r
 subtitle = paste0(
                    min(USCitiesFebJul$date), # first date
                    " through ", # plain text
                    max(USCitiesFebJul$date)  # last date
                    ), 
```

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### Line Graph: Labels (2)

<br>

We can then supply the `subtitle` to `labs`

```r
labs(x = "Date",
     y = "Direction Requests",
     title = "Direction Requests Over Time (US Cities Only)",
*    subtitle = paste0(min(USCitiesFebJul$date),
*                      " through ",
*                      max(USCitiesFebJul$date)),
     caption = "source: https://covid19.apple.com/mobility", 
     color = "Transit Type") -> lab_line_graph
```

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### Line Graph: Labels (3)

<br>

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### Line Graphs: Overlapping Lines

Consider our previous line graph--the lines overlap a bit.

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### Line Graph: Line Size (1)

We can minimize the size of the line with the `size` aesthetic.

```r
USCitiesFebJul %>% 
  ggplot() +
  geom_line(aes(x = date, y = dir_request, 
             group = trans_type, color = trans_type), 
            # make these slightly smaller
            size = 0.30) + 
  lab_line_graph
```

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### Line Graph: Line Size (2)

This makes the trends easier to see.

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# Adding Text

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## Labeling Missing Data

There is a gap in the direct request data (this is [documented in the data source](https://covid19.apple.com/mobility)).

> *"Data for May 11-12 is not available and will appear as blank columns in the data set."*

We should annotate this on a line graph!

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### Create Annotate Data

These data are `filter`ed to US cities between March 1, 2020 to June 30, 2020.

```r
USCitiesMarJun <-  TidyApple %>% 
  filter(geo_type == "city" & country == "United States", 
         date >= lubridate::as_date("2020-03-01") & 
         date <= lubridate::as_date("2020-07-01"))
```

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### Annotate Data

```r
USCitiesMarJun
```

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### `annotate`: Build Labels

Build our labels first!

```r
labs(x = "Date",
     y = "Direction Requests",
     title = "Spring Direction Requests (Mar-Jun) in US Cities",
*    subtitle = paste0(min(USCitiesMarJun$date),
                       " through ", 
*                      max(USCitiesMarJun$date)),
     caption = "source: https://covid19.apple.com/mobility", 
     color = "Transit Type") -> lab_annotation
```

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### `annotate`: Build Line Graph

Build a line graph layer (`gg_line_annotate`)

```r
USCitiesMarJun %>% 
    ggplot() +
    geom_line(aes(x = date, y = dir_request, 
             group = trans_type, color = trans_type), 
             size = 0.30) -> gg_line_annotate
```

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### `annotate`: build coordinate system

Add a coordinate system layer (`gg_coord_system`)

```r
coord_cartesian(
 xlim = c(min(USCitiesMarJun$date), 
          max(USCitiesMarJun$date)), 
 
 ylim = c(min(USCitiesMarJun$dir_request, na.rm = TRUE), 
          max(USCitiesMarJun$dir_request, na.rm = TRUE))) -> 
  gg_coord_system
```

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### `annotate`: build line segment

Build vertical line segment (`gg_line_segment`)

```r
annotate(geom = "segment", 
        size = 1, 
        color = "firebrick3",
        x = lubridate::as_date("2020-05-11"), 
        xend = lubridate::as_date("2020-05-11"), 
        y = 270, 
        yend = 100) -> gg_line_segment
```

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### `annotate`: Build Text Annotatation

Build text annotation (`gg_text_annotation`)

```r
annotate(geom = "text",
           color = "red", 
           hjust = 0.5, 
           size = 6,
           x = lubridate::as_date("2020-05-07"), 
           y = 300, 
           label = "Data not available") -> gg_text_annotation
```

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### `annotate`: Combine Layers

Now we can use the `ggplot2` syntax to combine these layers...

```r
gg_line_annotate + # line graph
  gg_coord_system + # coordinate system 
  gg_line_segment + # line annotation
  gg_text_annotation + # text annotation 
  lab_annotation # labels
```

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### `annotate`: Complete Graph

...and we see a complete graph!

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## Labeling Values (Review)

In the previous slides, we learned about labeling values with `ggrepel`.

Now we're going to extend this to plotting text and values on our graphs!

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### Highlighting Large US Cities

Filter `TidyApple` to the 5 largest US cities (by [population](https://en.wikipedia.org/wiki/List_of_United_States_cities_by_population)).

```r
TopUSCities <- TidyApple %>% 
  filter(country == "United States" & 
           region %in% c("New York City","Los Angeles", 
                         "Chicago", "Houston", "Phoenix")) 
```

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### View `TopUSCities`

```r
TopUSCities
```

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## Highlighting Peak Driving

Create a dataset with only the maximum direction request values for `"driving"` per `region`.

```r
TopUSCities %>% 
  filter(trans_type == "driving") %>% 
  group_by(region) %>% 
  slice_max(dir_request) %>% 
  ungroup() -> MaxUSCitiesDriving
```

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## View `MaxUSCitiesDriving`

```r
MaxUSCitiesDriving
```

<br>

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### Create graph labels

<br>

We know we want to see the max direction requests labeled, so we will update the labels for the graph.

```r
lab_line_max_drivers <- labs(
 x = "Date",
 y = "Direction Requests",
 title = "Peak Driving Direction Requests in Largest US Cities",
 subtitle = paste0(min(TopUSCities$date), 
                   " through ", 
                   max(TopUSCities$date)),
 caption = "source: https://covid19.apple.com/mobility", 
 color = "Transit Type")
```

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### Create Value Labels

We will also use `paste0()` here to create a variable for the labels that combines the city and date.

```r
MaxUSCitiesDriving <- MaxUSCitiesDriving %>% 
  mutate(max_driving_labels = paste0(region, ", ", date))
```

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### View Value Labels

Take a look at the labels we've created

```r
MaxUSCitiesDriving %>% 
  select(max_driving_labels)
```

<div data-pagedtable="false">
  <script data-pagedtable-source type="application/json">
{"columns":[{"label":["max_driving_labels"],"name":[1],"type":["chr"],"align":["left"]}],"data":[{"1":"Chicago, 2020-07-17"},{"1":"Houston, 2020-02-14"},{"1":"Los Angeles, 2020-02-14"},{"1":"New York City, 2020-09-04"},{"1":"Phoenix, 2020-02-29"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}}
  </script>
</div>

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### Create Line Layer + Label Layer

```r
library(ggrepel)
TopUSCities %>% 
    ggplot() + 
    geom_line(aes(x = date, y = dir_request, 
                  group = trans_type, 
                  color = trans_type), 
                  # make these slightly smaller again...
                  size = 0.15) + 
    geom_label_repel(data = MaxUSCitiesDriving,
              aes(x = date, y = dir_request, 
                  label = max_driving_labels), 
                  # set color and size...
                  color = "red", 
                  size = 3) + 
  lab_line_max_drivers
```

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### View plot with labels

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# Adding Reference Lines

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## Reference Line Data

```r
TopUSCities
```

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## Reference Line Labels

```r
labs(x = "Date", 
 y  = "Direction Requests", 
 title = "Trends of Relative Activity in Selected US Cities",
 subtitle = "New York, Los Angeles, Chicago, Houston, Phoenix",
 color = "Type") -> lab_ref_lines
```

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## Horizontal Reference Lines

Add the `geom_hline()` for a horizontal reference line (at `100`)

```r
TopUSCities %>% 
  ggplot(aes(x = date, y = dir_request, 
             group = trans_type, 
             color = trans_type)) + 
  geom_line(size = 0.1) + 
  geom_hline(yintercept = 100, size = 0.2, color = "gray20") +
  lab_ref_lines
```

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## Horizontal Reference Lines

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# Advanced Facetting

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## Faceting basics

Facets create subplots across levels of a categorical variable.

```r
TopUSCities %>% 
  ggplot(aes(x = date, y = dir_request, 
             group = trans_type, 
             color = trans_type)) + 
  geom_line(size = 0.1) + 
  geom_hline(yintercept = 100, 
             size = 0.2, 
             color = "gray20") +
* facet_wrap(~ region,  ncol = 2) +
  lab_ref_lines
```

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## Single Variable Facets

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## Multiple Variable Facets

```r
TopUSCities %>% 
  ggplot(aes(x = date, y = dir_request, 
             group = region, 
             color = trans_type)) + 
  geom_line(size = 0.1) + 
  geom_hline(yintercept = 100, 
             size = 0.2, 
             color = "gray20") +
* facet_wrap(region ~ trans_type, ncol = 5) +
  lab_ref_lines
```

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## Multiple Variable Facets

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### Advanced Faceting (`facet_wrap_paginate`)

Check out the exercises for more advanced faceting with `facet_wrap_paginate()` from the `ggforce` package.

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### Advanced Faceting (`facet_geo`)

Check out the exercises for more advanced faceting with `facet_geo()` from the `geofacet` package.

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# More Resources

### [Fundamentals of Data Visualization](https://clauswilke.com/dataviz/)

### [ggplot2 extensions gallery](https://exts.ggplot2.tidyverse.org/gallery/)

### [R Graphics Cookbook](https://r-graphics.org/)