DSBA 5122: Visual Analytics

# DSBA 5122: Visual Analytics
## Class 3: Visual Representations Basics II
### Ryan Wesslen
### February 4, 2019

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# Basic Principles of Visualization: Cairo, Chapter 5

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What if we want to show "high" and "low" levels?

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#      Directory of Visualizations: Wilke, Chapter 5 (with tidyverse)

```r
library(tidyverse)
```

```
## ── Attaching packages ────────────────────────────── tidyverse 1.2.1 ──
```

```
## ✔ ggplot2 3.1.0     ✔ purrr   0.3.0
## ✔ tibble  2.0.1     ✔ dplyr   0.7.8
## ✔ tidyr   0.8.2     ✔ stringr 1.3.1
## ✔ readr   1.3.1     ✔ forcats 0.3.0
```

```
## Warning: package 'tibble' was built under R version 3.5.2
```

```
## Warning: package 'purrr' was built under R version 3.5.2
```

```
## ── Conflicts ───────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
```

---

For this section, I'm going to use the `mpg` dataset.

```r
head(mpg,n=5)
```

```
## # A tibble: 5 x 11
##   manufacturer model displ  year   cyl trans  drv     cty   hwy fl    class
##   <chr>        <chr> <dbl> <int> <int> <chr>  <chr> <int> <int> <chr> <chr>
## 1 audi         a4      1.8  1999     4 auto(… f        18    29 p     comp…
## 2 audi         a4      1.8  1999     4 manua… f        21    29 p     comp…
## 3 audi         a4      2    2008     4 manua… f        20    31 p     comp…
## 4 audi         a4      2    2008     4 auto(… f        21    30 p     comp…
## 5 audi         a4      2.8  1999     6 auto(… f        16    26 p     comp…
```

```r
# glimpse is from dplyr
glimpse(mpg)
```

```
## Observations: 234
## Variables: 11
## $ manufacturer <chr> "audi", "audi", "audi", "audi", "audi", "audi", "au…
## $ model        <chr> "a4", "a4", "a4", "a4", "a4", "a4", "a4", "a4 quatt…
## $ displ        <dbl> 1.8, 1.8, 2.0, 2.0, 2.8, 2.8, 3.1, 1.8, 1.8, 2.0, 2…
## $ year         <int> 1999, 1999, 2008, 2008, 1999, 1999, 2008, 1999, 199…
## $ cyl          <int> 4, 4, 4, 4, 6, 6, 6, 4, 4, 4, 4, 6, 6, 6, 6, 6, 6, …
## $ trans        <chr> "auto(l5)", "manual(m5)", "manual(m6)", "auto(av)",…
## $ drv          <chr> "f", "f", "f", "f", "f", "f", "f", "4", "4", "4", "…
## $ cty          <int> 18, 21, 20, 21, 16, 18, 18, 18, 16, 20, 19, 15, 17,…
## $ hwy          <int> 29, 29, 31, 30, 26, 26, 27, 26, 25, 28, 27, 25, 25,…
## $ fl           <chr> "p", "p", "p", "p", "p", "p", "p", "p", "p", "p", "…
## $ class        <chr> "compact", "compact", "compact", "compact", "compac…
```

---

# Amounts

Descriptive statistics like averages and counts by one or two categorical groups (covariates or features). These use **absolute values**, rather than *relative* values, therefore **scale matters**.

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# Amounts

```r
ggplot(mpg, aes(x = class)) +
  geom_bar()
```
]
.pull-right[
![](03-class_files/figure-html/unnamed-chunk-15-1.png)
]

---

# Amounts

```r
ggplot(mpg, aes(x = class, `fill = class`)) +
  geom_bar() +
  `coord_flip() +`
  labs(x = " ", y = "Car count",
       title = "Cars by type") +
  `theme(legend.position = "none")`
```

]
.pull-right[
![](03-class_files/figure-html/unnamed-chunk-18-1.png)
]

---

# Amounts

```r
l <- c("2seater","minivan","pickup",
       "subcompact","midsize","compact","suv")

mpg %>%
  `mutate(class = factor(class, levels = l))` %>%
  ggplot(aes(x = class, fill = class)) +
  geom_bar() +
  coord_flip() +
  labs(x = " ", y = "Car count",
       title = "Cars by type") +
  theme(legend.position = "none")
```
]
.pull-right[
![](03-class_files/figure-html/unnamed-chunk-21-1.png)
]

---

# Amounts

```r
l <- c("2seater","minivan","pickup",
       "subcompact","midsize","compact","suv")

mpg %>%
  mutate(class = factor(class, levels = l)) %>%
  ggplot(aes(x = class, `fill = drv`)) +
  geom_bar() +
  coord_flip() +
  labs(x = " ", y = "Car count",
       title = "Cars by type") +
  theme(legend.position = c(0.8,0.2))
```
]
.pull-right[
![](03-class_files/figure-html/unnamed-chunk-24-1.png)
]

---

# Proportions

Relative values to compare sizes of categories.

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# Proportions

```r
p <- mpg %>%
  count(class) %>%
  mutate(pct = n / sum(n)) %>%
  ggplot(aes(x = "", y = pct, fill = class)) +
  geom_bar(width = 1, stat = "identity")

p
```
]
.pull-right[
![](03-class_files/figure-html/unnamed-chunk-29-1.png)
]

---

# Proportions

```r
p <- mpg %>%
  count(class) %>%
  mutate(pct = n / sum(n)) %>%
  ggplot(aes(x = "", y = pct, fill = class)) +
  geom_bar(width = 1, stat = "identity")

p + `coord_polar("y", start=0) +`
  theme_minimal() +
  labs(x = " ", y = "Proportion by class")
```
]
.pull-right[
![](03-class_files/figure-html/unnamed-chunk-32-1.png)
]

---

# Proportions

```r
library(treemapify)
mpg %>%
  filter(year == 1999) %>%
  `count(manufacturer) %>%`
  ggplot(aes(`area = n,`
             `fill = manufacturer`, 
             `label = manufacturer`)) +
  geom_treemap() +`
  geom_treemap_text() +
  theme(legend.position = "none")
```
]
.pull-right[
![](03-class_files/figure-html/unnamed-chunk-35-1.png)
]

---

# Proportions

```r
library(ggalluvial)

data(vaccinations)

ggplot(vaccinations,
       aes(x = survey, y = freq,  
           `alluvium = subject, stratum = response,`
           fill = response, label = response)) +
  scale_x_discrete(expand = c(.1, .1)) +
  geom_flow() +
  geom_stratum(alpha = .5) +
  `geom_text(stat = "stratum", size = 3) +`
  theme(legend.position = "none") +
  labs(title = "Vaccination survey response at three times")
```
]
.pull-right[
![](03-class_files/figure-html/unnamed-chunk-38-1.png)
]

---

# Distributions

What is the variance? How evenly spread are the values?

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# Distributions

```r
ggplot(mpg, aes(x = hwy)) +
  geom_histogram()
```
]
.pull-right[
![](03-class_files/figure-html/unnamed-chunk-43-1.png)
]

---

# Distributions

```r
ggplot(mpg, aes(x = hwy)) +
  geom_`density()`
```
]
.pull-right[
![](03-class_files/figure-html/unnamed-chunk-46-1.png)
]

---

# Distributions

```r
ggplot(mpg, aes(x = hwy)) +
  geom_density(`adjust = 0.2`) # adjust kernel
```
]
.pull-right[
![](03-class_files/figure-html/unnamed-chunk-49-1.png)
]
---

# Distributions

```r
ggplot(mpg, aes(x = hwy, `fill = drv`)) +
  geom_density(alpha = 0.4) +
  `theme(legend.position = c(0.8,0.8))`
```
]
.pull-right[
![](03-class_files/figure-html/unnamed-chunk-52-1.png)
]

---

# Distributions

```r
library(ggridges)
library(ggthemes)

l2 <- c("subcompact","midsize","compact",
       "2seater","minivan","pickup","suv")

mpg %>%
  mutate(class = factor(class, levels = l2)) %>%
  ggplot(aes(`x = hwy, y = class, fill = class`)) +
  `geom_density_ridges(alpha = 0.4) +`
  `theme_tufte() +`
  theme(legend.position = "none")
```
]
.pull-right[
![](03-class_files/figure-html/unnamed-chunk-55-1.png)
]

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# x-y relationships

What is the relationship between two or more variables?

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# x-y relationships

```r
ggplot(mpg, aes(x = cty, y = hwy)) +
  geom_point()
```
]
.pull-right[
![](03-class_files/figure-html/unnamed-chunk-60-1.png)
]

---

# x-y relationships

```r
library(ggthemes)

ggplot(mpg, aes(x = cty, y = hwy)) +
  geom_point(`aes(color = trans)`, size = 0.5) +
  `facet_wrap(~trans) +`
  theme_fivethirtyeight() +
  theme(legend.position = "none",
        `text = element_text(size=10)`)
```
]
.pull-right[
![](03-class_files/figure-html/unnamed-chunk-63-1.png)
]

---

# x-y relationships

```r
library(nycflights13)

# break up by data manipulation
df <- flights %>%
  mutate(day=as.Date(time_hour)) %>%
  filter(day < "2013-02-01") %>%
  count(day,origin)

# and ggplot
ggplot(df, aes(x=day, y=n, color=origin)) +
  geom_line(aes(group=origin)) +
  geom_point() +
  theme(legend.position="bottom")
```
]
.pull-right[
![](03-class_files/figure-html/unnamed-chunk-66-1.png)
]

---

# x-y relationships

```r
filter(mpg, class != "2seater") %>%
  ggplot(aes(x = cty, y = hwy)) +
  geom_density_2d()
```
]
.pull-right[
![](03-class_files/figure-html/unnamed-chunk-69-1.png)
]

---

# x-y relationships

```r
filter(mpg, class != "2seater") %>%
  ggplot(aes(x = cty, y = hwy)) +
  geom_density_2d(`aes(color = class)`) + 
  `facet_wrap(~class) +`
  `theme(legend.position = "none")`
```
]
.pull-right[
![](03-class_files/figure-html/unnamed-chunk-72-1.png)
]

---

# x-y relationships

```r
filter(mpg, class != "2seater") %>%
  ggplot(aes(x = cty, y = hwy)) +
  `geom_hex(aes(color = class), bins = 10) +`
  facet_wrap(~class) +
  theme(legend.position = "none")
```
]
.pull-right[
![](03-class_files/figure-html/unnamed-chunk-75-1.png)
]

---

# Geospatial

```r
library(maps)

crimes <- USArrests %>%
  rownames_to_column(var = "state") %>%
  mutate(state = tolower(state)) %>%
  `gather("variable","value",-state)`

states_map <- map_data("state")

crimes %>%
  `filter(variable == "Assault")` %>%
  ggplot(aes(map_id = state)) +
  `geom_map(aes(fill = value), map = states_map)` +
  expand_limits(x = states_map$long, 
                y = states_map$lat) +
  theme(legend.position = "bottom")
```
]
.pull-right[
![](03-class_files/figure-html/unnamed-chunk-78-1.png)
]
---

# Uncertainty

```r
l3 <- c("compact","subcompact","midsize",
       "2seater","minivan","suv","pickup")

# avg highway mpg with boostrapped 95% CI 
mpg %>%
  mutate(class = factor(class, levels = l3)) %>%
  ggplot(aes(x = class, y = hwy, color = class)) +
  `stat_summary(fun.y = mean, geom = "point")` + 
  `stat_summary(fun.data = mean_cl_boot,` 
               `geom = "pointrange")` + 
  theme_bw() +
  coord_flip() +
  theme(legend.position = "none") +
  labs(x = " ", y = "Highway MPG with 95% CI")
```
]
.pull-right[
![](03-class_files/figure-html/unnamed-chunk-81-1.png)
]
---

# Uncertainty

```r
l3 <- c("compact","subcompact","midsize",
       "2seater","minivan","suv","pickup")

# avg highway mpg with boostrapped 95% CI 
mpg %>%
  mutate(class = factor(class, levels = l3)) %>%
  ggplot(aes(x = class, y = hwy, color = class)) +
  stat_summary(fun.y = mean, geom = "point") + 
  stat_summary(fun.data = mean_cl_boot, 
               `geom = "errorbar"`) + 
  theme_bw() +
  coord_flip() +
  theme(legend.position = "none") +
  labs(x = " ", y = "Highway MPG with 95% CI")
```
]
.pull-right[
![](03-class_files/figure-html/unnamed-chunk-84-1.png)
]

---

# Uncertainty