Data Visualisation with R Workshop Part 1

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<i class="fas fa-exclamation-circle"></i> These slides are viewed best by Chrome and occasionally need to be refreshed if elements did not load properly. See here for <a href=part1-session2.pdf>PDF <i class="fas fa-file-pdf"></i></a>. 
</div>

<br>

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.item.center[

# <span style="text-shadow: 2px 2px 30px white;">Data Visualisation with R<br>Workshop Part 1</span>

]

.center.shade_black.animated.bounceInUp.slower[
<br><br>
## Multiple layers, facetting and tidying your data

<br>

Presented by Emi Tanaka

Department of Econometrics and Business Statistics

<img src="images/monash-one-line-reversed.png" style="width:500px"><br>
<span><i class="fas  fa-envelope faa-float animated "></i></span>  emi.tanaka@monash.edu
<span><i class="fab  fa-twitter faa-float animated faa-fast "></i></span>  @statsgen

.bottom_abs.width100.bg-black[
6th Dec 2021 @ Statistical Society of Australia NSW Branch | Online
]

]

</div>

---

# Add multiple layers

.grid.font_smaller[

Each layer inherits mapping and data from `ggplot` by default.

```r
ggplot(penguins, aes(x = species, y = bill_length_mm)) +
  geom_violin() + 
  geom_boxplot() + 
  geom_point()
```
]

]

---

# Order of the layers matters!

Boxplot and violin plot order are switched around.

```r
ggplot(penguins, aes(species, bill_length_mm))  +
* geom_violin() +
* geom_boxplot() +
  geom_point()
```

]

]

```r
ggplot(penguins, aes(species, bill_length_mm)) +
* geom_boxplot() +
* geom_violin() +
  geom_point()
```

]

]

</div>

---

# Layer-specific data and aesthestic mappings

For each layer, aesthestic and/or data can be overwritten.

```r
ggplot(penguins, aes(species, bill_length_mm)) +
  geom_violin(aes(fill = species)) +
  geom_boxplot(data = filter(penguins, species=="Adelie")) +
  geom_point(data = filter(penguins, species=="Gentoo"),
             aes(y = bill_depth_mm))
```

]

]

---

# Aesthestic or Attribute?

Not what you want

```r
ggplot(penguins) + 
  geom_point(aes(body_mass_g, 
                 bill_depth_mm, 
                 color = "blue"))
```

<img src="images/part1-session2/unnamed-chunk-3-1.png" style="display: block; margin: auto;" />
]
.item[
{{content}}
]
]

What you want

```r
ggplot(penguins) + 
  geom_point(aes(body_mass_g, 
                 bill_depth_mm), 
             color = "blue")
```

Tip: the `I` operator will yield the same as above.

```r
ggplot(penguins) + 
  geom_point(aes(body_mass_g, 
                 bill_depth_mm, 
                 color = I("blue")))
```

---

# `group` in `ggplot`

```r
ggplot(penguins, 
       aes(body_mass_g, 
           bill_depth_mm)) + 
  geom_point(aes(color = species)) +
  geom_smooth(method = "lm")
```

<img src="images/part1-session2/unnamed-chunk-5-1.png" style="display: block; margin: auto;" />
* This is an obvious case of Simpson's paradox.
* What if we wanted to draw the fit of a simple linear model for each cluster?
]
.item[

]
]

```r
ggplot(penguins, 
       aes(body_mass_g, 
           bill_depth_mm)) + 
  geom_point(aes(color = species)) +
  geom_smooth(method = "lm", 
              aes(group = species=="Gentoo"))
```

---

# Facetting

```r
g <- ggplot(penguins, aes(bill_length_mm, bill_depth_mm, color = species)) + geom_point()
```

---

```r
g
```

```r
g + facet_wrap(~sex)
```

]

```r
g + facet_grid(island ~ sex)
```

]

---

# `facet_wrap` and `facet_grid`

```r
g + facet_wrap( ~ sex)
```

```r
g + facet_wrap( ~ sex, ncol = 1)
```

]

```r
g + facet_grid(. ~ sex)
```

```r
g + facet_grid(sex ~ .)
```

]

---

background-color: #e5e5e5

]

HELP!

* RStudio > Help > Cheatsheets
* [R4DS Community Slack](https://www.rfordatasci.com/)
* [Twitter with hastag #rstats](https://twitter.com/search?q=%23rstats)
* [RStudio Community](https://community.rstudio.com/)
* [Stackoverflow](https://stackoverflow.com/questions/tagged/ggplot)

]

</div>

---

# .circle-big[2]

# Tidying your data

.footnote.monash-bg-blue[
Wickham (2014) Tidy Data. *Journal of Statistical Software* 59 (10): 1–23.
]

---

# Weight gain in pigs for different treatments

The `crampton.pig` is from the `agridat` 📦

```r
library(agridat)
glimpse(crampton.pig)
```

```
## Rows: 50
## Columns: 5
## $ treatment <fct> T1, T1, T1, T1, T1, T1, T1, T1, T1, T1, T2, T2, T2, T2, T2, …
## $ rep       <fct> R1, R2, R3, R4, R5, R6, R7, R8, R9, R10, R1, R2, R3, R4, R5,…
## $ weight1   <int> 30, 21, 21, 33, 27, 24, 20, 29, 28, 26, 26, 24, 20, 35, 25, …
## $ feed      <int> 674, 628, 661, 694, 713, 585, 575, 638, 632, 637, 699, 626, …
## $ weight2   <int> 195, 177, 180, 200, 197, 170, 150, 180, 192, 184, 194, 204, …
```

`weight1` is initial weight and `weight2` is final weight

.footnote[
Wright (2018). agridat: Agricultural Datasets. R package version 1.16. https://CRAN.R-project.org/package=agridat

Crampton and Hopkins (1934). The Use of the Method of Partial Regression in the Analysis of Comparative Feeding Trial Data, Part II. *The Journal of Nutrition* 8 113-123.
]

---

<br>

```r
names(crampton.pig)
```

```
## [1] "treatment" "rep"       "weight1"   "feed"      "weight2"
```

What are the mappings to get the above graph? 🤔

```r
ggplot(crampton.pig, aes(x = ???, y = ???)) + 
  geom_point() + 
  geom_line() + 
  facet_grid(. ~ treatment)
```
.center[
🤨
]

---

# Getting the data in the right form

<img src="images/part1-session2/pig-plot-1.png" style="display: block; margin: auto;" />
.grid[.item.border-right[
* The x-axis is the time when pig was weighed 
* The y-axis is the weight
* The facetting is by treatment

```
## # A tibble: 100 × 5
##    treatment  feed id    when    weight
##    <fct>     <int> <chr> <fct>    <int>
##  1 T1          674 pig1  initial     30
##  2 T1          674 pig1  final      195
##  3 T1          628 pig2  initial     21
##  4 T1          628 pig2  final      177
##  5 T1          661 pig3  initial     21
##  6 T1          661 pig3  final      180
##  7 T1          694 pig4  initial     33
##  8 T1          694 pig4  final      200
##  9 T1          713 pig5  initial     27
## 10 T1          713 pig5  final      197
## # … with 90 more rows
```
]
.item[

]]

How I wrangled this data

```r
pig_df <- crampton.pig %>% 
 mutate(id = paste0("pig", 1:n())) %>% 
 pivot_longer(c(weight1, weight2),
            names_to = "when",
            values_to = "weight") %>% 
 mutate(when = factor(when, 
         levels = c("weight1", "weight2"),
         labels = c("initial", "final")))
```

<div class="font_small">
(note: teaching wrangling is not part of this workshop, please see <a href="http://emitanaka.org/datawrangle-workshop-ssavic" target="_blank">here</a> if you want to learn more)
</div>

---

# Putting it all together

```r
ggplot(pig_df, aes(when, weight)) + # tidying your data for plotting
  geom_point(size = 3) + # attribute not aesthestic
  geom_line(aes(group = id)) + # grouping 
  facet_grid(. ~ treatment) + # facetting
  labs(x = "") # we'll learn this in the last session
```

---

# Meaningfully order categorical variables

```r
ggplot(crampton.pig, 
  aes(treatment, weight2 - weight1)) + 
  geom_point(size = 3)
```

* Treatments are ordered alphabetically by default
* It's better to order categorical variables meaningfully
]
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{{content}}
]
]

Order factor levels by the mean of the weight difference.

```r
library(forcats) #  for easy factor manipulation
crampton.pig2 <- crampton.pig %>% 
 mutate(
  treatment = fct_reorder(treatment, 
                          weight2 - weight1,
                          mean))
ggplot(crampton.pig2, 
       aes(treatment, weight2 - weight1)) + 
  geom_point(size = 3)
```

---

# Plotting auxilliary data

Plot you may want:

]
.item[

]
]

One way to do this:

```r
fig <- ggplot(crampton.pig2, 
              aes(treatment, 
                  weight2 - weight1)) + 
  geom_point(size = 3) + 
  stat_summary(fun.data = mean_se, 
               geom = "pointrange", fatten = 2,
               color = "#027EB6", size = 3) +
  stat_summary(fun = mean, geom = "line", 
               group = 1, color = "#027EB6",
               size = 2)
```

---

# Plotting annotations

```r
fig + 
  geom_text(data = data.frame(treatment = 4.5,
                              weight2 = 185,
                              weight1 = 0),
            label = "Treatment\n means",
            size = 3,
            color = "#027EB6", 
            fontface = "bold") 
```

<img src="images/part1-session2/unnamed-chunk-20-1.png" style="display: block; margin: auto;" />
]
.item[
{{content}}
]
]

But it might be just easier to:

```r
fig + 
  annotate("text",
           x = 4.5, y = 185,
           label = "Treatment\n means",
           size = 3,
           color = "#027EB6", 
           fontface = "bold") 
```

---

# <i class="fas fa-code"></i> Open `part1-exercise-02.Rmd`

---

# Session Information

```r
devtools::session_info()
```

```
## ─ Session info  🍼  🏖️  🦸🏽   ──────────────────────────────────────────────────
##  hash: baby bottle, beach with umbrella, superhero: medium skin tone
## 
##  setting  value
##  version  R version 4.1.2 (2021-11-01)
##  os       macOS Big Sur 10.16
##  system   x86_64, darwin17.0
##  ui       X11
##  language (EN)
##  collate  en_AU.UTF-8
##  ctype    en_AU.UTF-8
##  tz       Australia/Melbourne
##  date     2021-12-06
##  pandoc   2.11.4 @ /Applications/RStudio.app/Contents/MacOS/pandoc/ (via rmarkdown)
## 
## ─ Packages ───────────────────────────────────────────────────────────────────
##  package        * version date (UTC) lib source
##  agridat        * 1.18    2021-01-12 [1] CRAN (R 4.1.0)
##  anicon           0.1.0   2021-11-30 [1] Github (emitanaka/anicon@0b756df)
##  assertthat       0.2.1   2019-03-21 [1] CRAN (R 4.1.0)
##  backports        1.3.0   2021-10-27 [1] CRAN (R 4.1.0)
##  broom            0.7.10  2021-10-31 [1] CRAN (R 4.1.0)
##  bslib            0.3.1   2021-10-06 [1] CRAN (R 4.1.0)
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##  callr            3.7.0   2021-04-20 [1] CRAN (R 4.1.0)
##  cellranger       1.1.0   2016-07-27 [1] CRAN (R 4.1.0)
##  cli              3.1.0   2021-10-27 [1] CRAN (R 4.1.0)
##  colorspace       2.0-2   2021-06-24 [1] CRAN (R 4.1.0)
##  countdown        0.3.5   2021-11-30 [1] Github (gadenbuie/countdown@a544fa4)
##  crayon           1.4.2   2021-10-29 [1] CRAN (R 4.1.0)
##  DBI              1.1.1   2021-01-15 [1] CRAN (R 4.1.0)
##  dbplyr           2.1.1   2021-04-06 [1] CRAN (R 4.1.0)
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##  devtools         2.4.2   2021-06-07 [1] CRAN (R 4.1.0)
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##  dplyr          * 1.0.7   2021-06-18 [1] CRAN (R 4.1.0)
##  ellipsis         0.3.2   2021-04-29 [1] CRAN (R 4.1.0)
##  evaluate         0.14    2019-05-28 [1] CRAN (R 4.1.0)
##  fansi            0.5.0   2021-05-25 [1] CRAN (R 4.1.0)
##  farver           2.1.0   2021-02-28 [1] CRAN (R 4.1.0)
##  fastmap          1.1.0   2021-01-25 [1] CRAN (R 4.1.0)
##  forcats        * 0.5.1   2021-01-27 [1] CRAN (R 4.1.0)
##  fs               1.5.0   2020-07-31 [1] CRAN (R 4.1.0)
##  generics         0.1.1   2021-10-25 [1] CRAN (R 4.1.0)
##  ggplot2        * 3.3.5   2021-06-25 [1] CRAN (R 4.1.0)
##  glue             1.5.0   2021-11-07 [1] CRAN (R 4.1.0)
##  gtable           0.3.0   2019-03-25 [1] CRAN (R 4.1.0)
##  haven            2.4.3   2021-08-04 [1] CRAN (R 4.1.0)
##  highr            0.9     2021-04-16 [1] CRAN (R 4.1.0)
##  hms              1.1.1   2021-09-26 [1] CRAN (R 4.1.0)
##  htmltools        0.5.2   2021-08-25 [1] CRAN (R 4.1.0)
##  httr             1.4.2   2020-07-20 [1] CRAN (R 4.1.0)
##  icon             0.1.0   2021-11-30 [1] Github (emitanaka/icon@8458546)
##  jquerylib        0.1.4   2021-04-26 [1] CRAN (R 4.1.0)
##  jsonlite         1.7.2   2020-12-09 [1] CRAN (R 4.1.0)
##  knitr            1.36    2021-09-29 [1] CRAN (R 4.1.0)
##  labeling         0.4.2   2020-10-20 [1] CRAN (R 4.1.0)
##  lattice          0.20-45 2021-09-22 [1] CRAN (R 4.1.2)
##  lifecycle        1.0.1   2021-09-24 [1] CRAN (R 4.1.0)
##  lubridate        1.8.0   2021-10-07 [1] CRAN (R 4.1.0)
##  magrittr         2.0.1   2020-11-17 [1] CRAN (R 4.1.0)
##  Matrix           1.3-4   2021-06-01 [1] CRAN (R 4.1.2)
##  memoise          2.0.0   2021-01-26 [1] CRAN (R 4.1.0)
##  mgcv             1.8-38  2021-10-06 [1] CRAN (R 4.1.2)
##  modelr           0.1.8   2020-05-19 [1] CRAN (R 4.1.0)
##  munsell          0.5.0   2018-06-12 [1] CRAN (R 4.1.0)
##  nlme             3.1-153 2021-09-07 [1] CRAN (R 4.1.2)
##  palmerpenguins * 0.1.0   2020-07-23 [1] CRAN (R 4.1.0)
##  pillar           1.6.4   2021-10-18 [1] CRAN (R 4.1.0)
##  pkgbuild         1.2.0   2020-12-15 [1] CRAN (R 4.1.0)
##  pkgconfig        2.0.3   2019-09-22 [1] CRAN (R 4.1.0)
##  pkgload          1.2.3   2021-10-13 [1] CRAN (R 4.1.0)
##  prettyunits      1.1.1   2020-01-24 [1] CRAN (R 4.1.0)
##  processx         3.5.2   2021-04-30 [1] CRAN (R 4.1.0)
##  ps               1.6.0   2021-02-28 [1] CRAN (R 4.1.0)
##  purrr          * 0.3.4   2020-04-17 [1] CRAN (R 4.1.0)
##  R6               2.5.1   2021-08-19 [1] CRAN (R 4.1.0)
##  Rcpp             1.0.7   2021-07-07 [1] CRAN (R 4.1.0)
##  readr          * 2.1.0   2021-11-11 [1] CRAN (R 4.1.0)
##  readxl           1.3.1   2019-03-13 [1] CRAN (R 4.1.0)
##  remotes          2.4.1   2021-09-29 [1] CRAN (R 4.1.0)
##  reprex           2.0.1   2021-08-05 [1] CRAN (R 4.1.0)
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##  sessioninfo      1.2.1   2021-11-02 [1] CRAN (R 4.1.0)
##  stringi          1.7.5   2021-10-04 [1] CRAN (R 4.1.0)
##  stringr        * 1.4.0   2019-02-10 [1] CRAN (R 4.1.0)
##  testthat         3.1.0   2021-10-04 [1] CRAN (R 4.1.0)
##  tibble         * 3.1.6   2021-11-07 [1] CRAN (R 4.1.0)
##  tidyr          * 1.1.4   2021-09-27 [1] CRAN (R 4.1.0)
##  tidyselect       1.1.1   2021-04-30 [1] CRAN (R 4.1.0)
##  tidyverse      * 1.3.1   2021-04-15 [1] CRAN (R 4.1.0)
##  tzdb             0.2.0   2021-10-27 [1] CRAN (R 4.1.0)
##  usethis          2.1.3   2021-10-27 [1] CRAN (R 4.1.0)
##  utf8             1.2.2   2021-07-24 [1] CRAN (R 4.1.0)
##  vctrs            0.3.8   2021-04-29 [1] CRAN (R 4.1.0)
##  whisker          0.4     2019-08-28 [1] CRAN (R 4.1.0)
##  withr            2.4.2   2021-04-18 [1] CRAN (R 4.1.0)
##  xaringan         0.22    2021-06-23 [1] CRAN (R 4.1.0)
##  xfun             0.28    2021-11-04 [1] CRAN (R 4.1.0)
##  xml2             1.3.2   2020-04-23 [1] CRAN (R 4.1.0)
##  yaml             2.2.1   2020-02-01 [1] CRAN (R 4.1.0)
## 
##  [1] /Library/Frameworks/R.framework/Versions/4.1/Resources/library
## 
## ──────────────────────────────────────────────────────────────────────────────
```
]

These slides are licensed under <br><center><a href="https://creativecommons.org/licenses/by-sa/3.0/au/"><img src="images/cc.svg" style="height:2em;"/><img src="images/by.svg" style="height:2em;"/><img src="images/sa.svg" style="height:2em;"/></a></center>