Intro to Visualizations

class: center, middle, inverse, title-slide

# Intro to Visualizations
### Daniel Anderson
### Week 2, Class 1

---

layout: true

---
# Agenda

* Quick note on projects and `here::here()`

### Discuss different visualizations
* Visualizing distributions
  + histograms
  + density plots
  + Empirical cumulative density plots
  + QQ plots
* Visualizing amounts
  + bar plots
  + dot plots
  + heatmaps

---
# Learning Objectives

* Understand various ways the same underlying data can be displayed

* Think through pros/cons of each

* Understand the basic structure of the code to produce the various plots

---
class: inverse-blue center middle

### What type of data do you have?

--
We'll focus primarily on standard continuous/categorical data

--
### What is your purpose?

--
Exploratory? Communication?

---
class: inverse-red center middle
# One continuous variable

---
# Histogram

![](w2p1_files/figure-html/histo-1.png)

---
# Density plot

![](w2p1_files/figure-html/dens-1.png)

---
# (Empirical) Cumulative Density

![](w2p1_files/figure-html/cum_dens-1.png)

---
# QQ Plot
Compare to theoretical quantiles (for normality)

![](w2p1_files/figure-html/qq-1.png)

---
# Empirical examples
I'll move fast, but if you want to (try to) follow along, or recreate anything here later, first run

```r
remotes::install_github("clauswilke/dviz.supp")
```

---

### Titanic data

```r
head(titanic)
```

```
##   class   age    sex survived
## 1   1st 29.00 female        1
## 2   1st  2.00 female        0
## 3   1st 30.00   male        0
## 4   1st 25.00 female        0
## 5   1st  0.92   male        1
## 6   1st 47.00   male        1
```

---
# Basic histogram

```r
ggplot(titanic, aes(x = age)) +
  geom_histogram()
```

```
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
```

![](w2p1_files/figure-html/age_hist-1.png)

---
# Make it a little prettier

```r
ggplot(titanic, aes(x = age)) +
  geom_histogram(fill = "#56B4E9",
                 color = "white",
                 alpha = 0.9) 
```

```
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
```

![](w2p1_files/figure-html/age_hist2-1.png)

---
# Change the number of bins

```r
ggplot(titanic, aes(x = age)) +
  geom_histogram(fill = "#56B4E9",
                 color = "white",
                 alpha = 0.9,
*                bins = 50)
```

![](w2p1_files/figure-html/age_hist50-1.png)

---
# Vary the number of bins

![](w2p1_files/figure-html/bins-1.png)

---
# Denisty plot
### ugly 😫

```r
ggplot(titanic, aes(age)) +
  geom_density()
```

![](w2p1_files/figure-html/dens-titanic-1.png)

---
# Denisty plot
### Change the fill 😌

```r
ggplot(titanic, aes(age)) +
  geom_density(fill = "#56B4E9")
```

![](w2p1_files/figure-html/dens-titanic-blue-1.png)

---
# Density plot estimation
* Kernal density estimation
  + Different kernal shapes can be selected
  + Bandwidth matters most
  + Smaller bands = bend more to the data

* Approximation of the underlying continuous probability function
  + Integrates to 1.0 (y-axis is somewhat difficult to interpret)

---
# Denisty plot
### change the bandwidth

```r
ggplot(titanic, aes(age)) +
  geom_density(fill = "#56B4E9", 
               bw = 5)
```

![](w2p1_files/figure-html/dens-titanic5-1.png)

---
class: middle

![](w2p1_files/figure-html/vary-bw-1.png)

---
# Quickly
How well does it approximate a normal distribution?

```r
ggplot(titanic, aes(sample = age)) +
  stat_qq_line(color = "#56B4E9") +
  geom_qq(color = "gray40") 
```

![](w2p1_files/figure-html/qq-titanic-1.png)

---
class: inverse-red center middle
# Grouped data
### Distributions

How do we display more than one distribution at a time?

---
# Boxplots

![](w2p1_files/figure-html/boxplots-1.png)

---
# Violin plots

![](w2p1_files/figure-html/violin-1.png)

---
# Jittered points

![](w2p1_files/figure-html/jittered-1.png)

---
# Sina plots

![](w2p1_files/figure-html/sina-1.png)

---
# Stacked histograms

![](w2p1_files/figure-html/stacked-histo-1.png)

---
# Overlapping densities

![](w2p1_files/figure-html/overlap-dens-1.png)

---
# Ridgeline densities

![](w2p1_files/figure-html/ridgeline-1.png)

---
class: inverse-orange center middle
# Quick empirical examples

---
# Boxplots

```r
ggplot(titanic, aes(sex, age)) +
  geom_boxplot(fill = "#A9E5C5")
```

![](w2p1_files/figure-html/boxplots-empirical-1.png)

---
# Violin plots

```r
ggplot(titanic, aes(sex, age)) +
  geom_violin(fill = "#A9E5C5")
```

![](w2p1_files/figure-html/violin-empirical-1.png)

---
# Jittered point plots

```r
ggplot(titanic, aes(sex, age)) +
  geom_jitter(width = 0.3, height = 0)
```

![](w2p1_files/figure-html/jittered-empirical-1.png)

---
# Sina plot

```r
ggplot(titanic, aes(sex, age)) +
  ggforce::geom_sina()
```

![](w2p1_files/figure-html/sina-empirical-1.png)

---
# Stacked histogram

```r
ggplot(titanic, aes(age)) +
  geom_histogram(aes(fill = sex))
```

![](w2p1_files/figure-html/stacked-histo-empirical-1.png)

.realbig[🤨]

---
# Dodged

```r
ggplot(titanic, aes(age)) +
  geom_histogram(aes(fill = sex), 
                 position = "dodge")
```

![](w2p1_files/figure-html/dodged-histo-empirical-1.png)

Note `position = "dodge"` does not go into `aes` (not accessing a variable in
your dataset)

---
# Better

```r
ggplot(titanic, aes(age)) +
  geom_histogram(fill = "#A9E5C5",
                 color = "white",
                 alpha = 0.9,) +
* facet_wrap(~sex)
```

![](w2p1_files/figure-html/wrapped-histo-empirical-1.png)

---
# Overlapping densities

```r
ggplot(titanic, aes(age)) +
  geom_density(aes(fill = sex),
               color = "white",
               alpha = 0.4)
```

![](w2p1_files/figure-html/overlap-dens-empirical-1.png)

--
Note the default colors really don't work well in most of these

---

```r
ggplot(titanic, aes(age)) +
  geom_density(aes(fill = sex),
               color = "white",
               alpha = 0.6) +
  scale_fill_manual(values = c("#009973", "#99ffe6"))
```

![](w2p1_files/figure-html/overlap-dens-empirical2-1.png)

---
# Ridgeline densities

```r
ggplot(titanic, aes(age, sex)) +
  ggridges::geom_density_ridges(color = "white",
                                fill = "#A9E5C5")
```

![](w2p1_files/figure-html/ridgeline-dens-empirical-1.png)

---
class: inverse-red center middle
# Visualizing amounts

---
# Bar plots

![](w2p1_files/figure-html/bars-1.png)

---
# Flipped bars

![](w2p1_files/figure-html/flipped_bars-1.png)

---
# Dotplot

![](w2p1_files/figure-html/dots-1.png)

---
# Heatmap

![](w2p1_files/figure-html/heatmap-1.png)

---
# Empirical examples
### How much does college cost?

```r
library(here)
library(rio)
tuition <- import(here("data", "us_avg_tuition.xlsx"),
                  setclass = "tbl_df")
head(tuition)
```

```
## # A tibble: 6 x 13
##   State      `2004-05` `2005-06` `2006-07` `2007-08` `2008-09` `2009-10`
##   <chr>          <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>
## 1 Alabama     5682.838  5840.550  5753.496  6008.169  6475.092  7188.954
## 2 Alaska      4328.281  4632.623  4918.501  5069.822  5075.482  5454.607
## 3 Arizona     5138.495  5415.516  5481.419  5681.638  6058.464  7263.204
## 4 Arkansas    5772.302  6082.379  6231.977  6414.900  6416.503  6627.092
## 5 California  5285.921  5527.881  5334.826  5672.472  5897.888  7258.771
## 6 Colorado    4703.777  5406.967  5596.348  6227.002  6284.137  6948.473
## # … with 6 more variables: `2010-11` <dbl>, `2011-12` <dbl>,
## #   `2012-13` <dbl>, `2013-14` <dbl>, `2014-15` <dbl>, `2015-16` <dbl>
```

---
# By state: 2015-16

```r
ggplot(tuition, aes(State, `2015-16`)) +
  geom_col()
```

![](w2p1_files/figure-html/state-tuition1-1.png)

.realbig[🤮🤮🤮]

---
# Two puke emoji version
.realbig[🤮🤮]

```r
ggplot(tuition, aes(State, `2015-16`)) +
  geom_col() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 10))
```

![](w2p1_files/figure-html/state-tuition2-1.png)

---
# One puke emoji version
.realbig[🤮]

```r
ggplot(tuition, aes(State, `2015-16`)) +
  geom_col() +
  coord_flip()
```

---
![](w2p1_files/figure-html/state-tuition3-eval-1.png)

---
# Kinda smiley version
.realbig[😏]

```r
ggplot(tuition, aes(fct_reorder(State, `2015-16`), `2015-16`)) +
  geom_col() +
  coord_flip() 
```

---
![](w2p1_files/figure-html/state-tuition4-eval-1.png)

---
# Highlight Oregon
.realbig[🙂]

```r
ggplot(tuition, aes(fct_reorder(State, `2015-16`), `2015-16`)) +
  geom_col() +
  geom_col(fill = "cornflowerblue", 
           data = filter(tuition, State == "Oregon")) +
  coord_flip() 
```

---

![](w2p1_files/figure-html/oregon-highlight-eval-1.png)

---
# Not always good to sort

![](w2p1_files/figure-html/income_df-sorted-1.png)

---
# Much better

![](w2p1_files/figure-html/income_df-1.png)

---
# Averages tuition by year
### How?

```r
head(tuition)
```

---
# Rearrange

```r
tuition %>%
  pivot_longer(`2004-05`:`2015-16`,
               names_to = "year", 
               values_to = "avg_tuition") 
```

```
## # A tibble: 600 x 3
##    State   year    avg_tuition
##    <chr>   <chr>         <dbl>
##  1 Alabama 2004-05    5682.838
##  2 Alabama 2005-06    5840.550
##  3 Alabama 2006-07    5753.496
##  4 Alabama 2007-08    6008.169
##  5 Alabama 2008-09    6475.092
##  6 Alabama 2009-10    7188.954
##  7 Alabama 2010-11    8071.134
##  8 Alabama 2011-12    8451.902
##  9 Alabama 2012-13    9098.069
## 10 Alabama 2013-14    9358.929
## # … with 590 more rows
```

---
# Compute summaries

```r
annual_means <- tuition %>%
  pivot_longer(`2004-05`:`2015-16`,
               names_to = "year", 
               values_to = "avg_tuition")  %>%
  group_by(year) %>%
  summarize(mean_tuition = mean(avg_tuition))

annual_means
```

```
## # A tibble: 12 x 2
##    year    mean_tuition
##  * <chr>          <dbl>
##  1 2004-05     6409.564
##  2 2005-06     6654.177
##  3 2006-07     6809.914
##  4 2007-08     7085.881
##  5 2008-09     7156.560
##  6 2009-10     7761.810
##  7 2010-11     8228.834
##  8 2011-12     8539.115
##  9 2012-13     8842.357
## 10 2013-14     8947.938
## 11 2014-15     9037.357
## 12 2015-16     9317.633
```

---
# Good

```r
ggplot(annual_means, aes(year, mean_tuition)) +
  geom_col() 
```

![](w2p1_files/figure-html/avg-tuition1-eval-1.png)

---
# Better?

```r
ggplot(annual_means, aes(year, mean_tuition)) +
  geom_col() +
  coord_flip()
```

![](w2p1_files/figure-html/avg-tuition2-1.png)

---
# Better still?

```r
ggplot(annual_means, aes(year, mean_tuition)) +
  geom_point() +
  coord_flip()
```

![](w2p1_files/figure-html/tuition3-1.png)

---
# Even better

```r
annual_means %>%
  mutate(year = readr::parse_number(year)) %>%
  ggplot(aes(year, mean_tuition)) +
    geom_line(color = "cornflowerblue") +
    geom_point()
```

![](w2p1_files/figure-html/tuition4-1.png)

--
Treat time (year) as a continuous variable

---
# Grouped points
Show change in tuition from 05-06 to 2015-16

```r
tuition %>% 
  select(State, `2005-06`, `2015-16`)
```

```
## # A tibble: 50 x 3
##    State       `2005-06` `2015-16`
##    <chr>           <dbl>     <dbl>
##  1 Alabama      5840.550  9751.101
##  2 Alaska       4632.623  6571.340
##  3 Arizona      5415.516 10646.28 
##  4 Arkansas     6082.379  7867.297
##  5 California   5527.881  9269.844
##  6 Colorado     5406.967  9748.188
##  7 Connecticut  8249.074 11397.34 
##  8 Delaware     8610.597 11676.22 
##  9 Florida      3924.234  6360.159
## 10 Georgia      4492.167  8446.961
## # … with 40 more rows
```

---

```r
lt <- tuition %>% 
  select(State, `2005-06`, `2015-16`) %>% 
  pivot_longer(`2005-06`:`2015-16`,
               names_to = "Year", 
               values_to = "Tuition")
lt
```

```
## # A tibble: 100 x 3
##    State      Year      Tuition
##    <chr>      <chr>       <dbl>
##  1 Alabama    2005-06  5840.550
##  2 Alabama    2015-16  9751.101
##  3 Alaska     2005-06  4632.623
##  4 Alaska     2015-16  6571.340
##  5 Arizona    2005-06  5415.516
##  6 Arizona    2015-16 10646.28 
##  7 Arkansas   2005-06  6082.379
##  8 Arkansas   2015-16  7867.297
##  9 California 2005-06  5527.881
## 10 California 2015-16  9269.844
## # … with 90 more rows
```

---
class: middle

```r
ggplot(lt, aes(State, Tuition)) +
  geom_line(aes(group = State), color = "gray40") +
  geom_point(aes(color = Year)) +
  coord_flip()
```

---

![](w2p1_files/figure-html/grouped_points3-eval-1.png)

---
# Extensions
* I know we're probably running short on time, but we definitely would want to keep going here:

+ Order states according to something more meaningful (starting tuition, ending tuition, or difference in tuition)

+ Meaningful title, e.g., "Change in average tuition over a decade"

+ Consider better color scheme for points

---
# Let's back up a bit
* Lets go back to our full data, but in a format that we can have a `year`
variable.

```r
tuition_l <- tuition %>%
    pivot_longer(-State,
                 names_to = "year", 
                 values_to = "avg_tuition")

tuition_l
```

---
# Heatmap

```r
ggplot(tuition_l, aes(year, State)) +
  geom_tile(aes(fill = avg_tuition)) 
```

![](w2p1_files/figure-html/heatmap2-eval-1.png)

---
# Better heatmap

```r
ggplot(tuition_l, aes(year, fct_reorder(State, avg_tuition))) +
  geom_tile(aes(fill = avg_tuition)) 
```

![](w2p1_files/figure-html/heatmap3-eval-1.png)

---
# Even better heatmap

```r
ggplot(tuition_l, aes(year, fct_reorder(State, avg_tuition))) +
  geom_tile(aes(fill = avg_tuition)) +
  scale_fill_viridis_c(option = "magma")
```

![](w2p1_files/figure-html/heatmap4-eval-1.png)

---
background-image: url(img/heatmap.png)
class: inverse-blue bottom
background-size:contain

---
# Quick aside
* Think about the data you have
* Given that these are state-level data, they have a geographic component

```r
#install.packages("maps")
state_data <- map_data("state") %>% # ggplot2::map_data
  rename(State = region)
```

---
# Join it
Obviously we'll talk more about joins later

```r
tuition <- tuition %>% 
  mutate(State = tolower(State))
states <- left_join(state_data, tuition)
head(states)
```

```
##        long      lat group order   State subregion  2004-05 2005-06  2006-07
## 1 -87.46201 30.38968     1     1 alabama      <NA> 5682.838 5840.55 5753.496
## 2 -87.48493 30.37249     1     2 alabama      <NA> 5682.838 5840.55 5753.496
## 3 -87.52503 30.37249     1     3 alabama      <NA> 5682.838 5840.55 5753.496
## 4 -87.53076 30.33239     1     4 alabama      <NA> 5682.838 5840.55 5753.496
## 5 -87.57087 30.32665     1     5 alabama      <NA> 5682.838 5840.55 5753.496
## 6 -87.58806 30.32665     1     6 alabama      <NA> 5682.838 5840.55 5753.496
##    2007-08  2008-09  2009-10  2010-11  2011-12  2012-13  2013-14  2014-15
## 1 6008.169 6475.092 7188.954 8071.134 8451.902 9098.069 9358.929 9496.084
## 2 6008.169 6475.092 7188.954 8071.134 8451.902 9098.069 9358.929 9496.084
## 3 6008.169 6475.092 7188.954 8071.134 8451.902 9098.069 9358.929 9496.084
## 4 6008.169 6475.092 7188.954 8071.134 8451.902 9098.069 9358.929 9496.084
## 5 6008.169 6475.092 7188.954 8071.134 8451.902 9098.069 9358.929 9496.084
## 6 6008.169 6475.092 7188.954 8071.134 8451.902 9098.069 9358.929 9496.084
##    2015-16
## 1 9751.101
## 2 9751.101
## 3 9751.101
## 4 9751.101
## 5 9751.101
## 6 9751.101
```

---
# Rearrange

```r
states <- states %>% 
  gather(year, tuition, `2004-05`:`2015-16`)
head(states)
```

```
##        long      lat group order   State subregion    year  tuition
## 1 -87.46201 30.38968     1     1 alabama      <NA> 2004-05 5682.838
## 2 -87.48493 30.37249     1     2 alabama      <NA> 2004-05 5682.838
## 3 -87.52503 30.37249     1     3 alabama      <NA> 2004-05 5682.838
## 4 -87.53076 30.33239     1     4 alabama      <NA> 2004-05 5682.838
## 5 -87.57087 30.32665     1     5 alabama      <NA> 2004-05 5682.838
## 6 -87.58806 30.32665     1     6 alabama      <NA> 2004-05 5682.838
```

---
# Plot

```r
ggplot(states) +
  geom_polygon(aes(long, lat, group = group, fill = tuition)) +
* coord_fixed(1.3) +
  scale_fill_viridis_c(option = "magma") +
  facet_wrap(~year) 
```

![](w2p1_files/figure-html/usa-plot-1.png)

---
background-image: url(img/states-heatmap.png)
class: inverse bottom
background-size:contain

---
class: inverse bottom right
background-image: url(img/states-heatmap-anim.gif)
background-size:cover

# Or animated

---
class: middle
# Wrapping up
* We've got a ways to go - today was just an introduction
* The geographic part in particular was too fast, and we'll talk about better ways later (note that Alaska/Hawaii were not even included)
* We basically didn't talk about multivariate data (not even scatter plots)
* Other types of plots will be embedded within the topics later in the class

---
class:inverse-green

# Next time

### Lab 2
git/GitHub collaboration

It's already posted - feel free to start working on it whenever.

* Must be completed as a group
* Will use elements of what we talked about today, while also asking you to create branches, submit pull requests, etc.