Take A Sad Plot & Make It Better

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# Take A Sad Plot & Make It Better
## A Case Study with R and ggplot2
### Alison Presmanes Hill
### April 6, 2018

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## About me

![](images/beatles-tidy.gif)

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![](images/aim-here.png)

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# `v1.ppt`

## Hey, Jude, don't make it bad...

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# Blameless postmortem

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![](images/version1.png)
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- 😀 Pink blobs are always smaller than green squares- always under-estimating.

- 😒 But, on R, there are more dots in the pink blob. The point is that more people are getting services, but...

- 😬 The "true" prevalence (total number of dots) looks different. It is actually the same at both time points!

- 😳 I actually like the dots! And I return to this later.
]

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# 💡

## What is the message?

Is there an autism epidemic? Based on the data available, we don't know!

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# 💡

## What is the medium?

A book chapter, but also presentations like this one!

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![](images/aim-here-v1.png)

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# `v2.doc`

## Take a sad (plot) and make it better...

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background-image: url("./images/version2.png")
background-size: 800px 550px

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![](images/beatles-face-palm.gif)

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# Full-of-blame postmortem

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![](images/version2.png)
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- 😬 The connection between the line and pie charts is totally unclear. They are also kind of redundant.

- 😬 There are pie charts. With some kind of reflective coating.
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![](images/aim-here-v2.png)

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# `v3 <- ggplot()`

## Then you'll begin to make it better...

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![](images/beatles-help.gif)

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# Asking for help

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![](images/help-me-viz-logo.jpg)
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Help Me Viz

[https://policyviz.com/helpmeviz/](https://policyviz.com/helpmeviz/)

*"A place to facilitate discussion, debate, and collaboration from the data visualization community"*
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![](images/help-me-newcat.png)

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background-image: url("./images/help-me-plea.png")
background-size: auto

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background-image: url("./images/help-me-response.png")
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# 💡

## I can use `ggplot2`!

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```r
set.seed(1000)
asdpop <- tibble::tibble(
  time1 = sample(1:100, 100, replace = F), 
  time2 = time1) %>% 
  tidyr::gather(x, y, time1:time2, factor_key = TRUE)  
asdpop
```

```
# A tibble: 200 x 2
   x         y
   <fct> <int>
 1 time1    33
 2 time1    76
 3 time1    12
 4 time1    68
 5 time1    50
 6 time1     7
 7 time1    70
 8 time1    55
 9 time1    20
10 time1    24
# ... with 190 more rows
```

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```r
asdpop <- asdpop %>% 
  mutate(services = as.factor(case_when(
    x == "time1" & y <= 30 ~ 1, 
    x == "time1" & y > 30 ~ 0, 
    x == "time2" & y <= 60 ~ 1, 
    TRUE ~ 0
    )))
asdpop
```

```
# A tibble: 200 x 3
   x         y services
   <fct> <int> <fct>   
 1 time1    33 0       
 2 time1    76 0       
 3 time1    12 1       
 4 time1    68 0       
 5 time1    50 0       
 6 time1     7 1       
 7 time1    70 0       
 8 time1    55 0       
 9 time1    20 1       
10 time1    24 1       
# ... with 190 more rows
```

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<img src="slides_files/figure-html/bar6-1.png" width="80%" style="display: block; margin: auto;" />

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![](images/aim-here-v3.png)

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# `rep("better", 40)`

## better, better, better, better, better, better, better, better, better, better, better, better, better, better, better, better, better, better, better, better, better, better, better, better, better, better, better, better, better, better, better, better, better, better, better, better, better, better, better, better...

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## Why I ❤️ `ggplot2`

![](images/beatles-rotation.gif)

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## Take CS631 with us!

![](images/beatles-hold-your-hand.gif)
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## CS631 Course Directors:

## Steven Bedrick, Jackie Wirz, me

![](images/beatles-abbey.gif)

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## Thanks!

Slides created via the R package [**xaringan**](https://github.com/yihui/xaringan).

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This work is licensed under a Creative Commons Attribution- ShareAlike 4.0 International License.

To view a copy of this license, visit:

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