# Chapter 4 Dual Variable Graphs

Dual variable or bivariate graphs display the relationship between two variables. The type of graph will depend on the type of the variables; categorical or quantitative.

## 4.1 Categorical vs. Categorical

We normally use stacked, grouped, or segmented bar charts when plotting the relationship between two categorical variables.

### 4.1.1 Prepare data for more categories

Let us continue with the `vacn`

data frame. It has only `state`

as the category. We convert it to a long format so `state`

and `vaccination`

are the categories. We select the columns related to adult vaccination and ignore the vaccine types.

### 4.1.2 Stacked bar chart

We simply plot the new data frame `df`

to visualize the two categories.

Figure 4.1 is not too interesting since the `count`

on the y-axis just shows the number of data points for each `vaccination`

category separated by each `state`

category. Since the second dosage can only start some days (about 14) after the first dosage, all the data points related to the second dosage will be less compared to the first. Also, the `state`

distribution is about the same since even if no vaccination was administered at a state for any particular date, it is registered as `0`

but the `count`

in `geom_bar`

still counts it as a data point (row or occurrence) regardless of its value.

Perhaps Figure 4.2 is more meaningful where we can see the daily administration of doses per state.

```
df %>%
filter(vaccination %in% c("daily_partial", "daily_full", "daily")) %>%
ggplot(aes(x = vaccination,
y = doses,
fill = state)) +
geom_bar(stat = "identity", position = "stack")
```

From Figure 4.2, we can see that most of the doses were administered in Selangor, Sarawak, and W.P. Kuala Lumpur.

`(position = "stack")`

for stacked bars is the default, so the last line could have also been written as `geom_bar(stat = "identity")`

.

### 4.1.3 Grouped bar chart

Grouped bar charts place bars for the second categorical variable side-by-side. We use the `(position = "dodge")`

option.

```
df %>%
filter(vaccination %in% c("daily_partial", "daily_full", "daily")) %>%
ggplot(aes(x = vaccination,
y = doses,
fill = state)) +
geom_bar(stat = "identity", position = "dodge")
```

Compare Figure 4.3 and Figure 4.4 to see the difference between `doses`

and `count`

.

```
df %>%
filter(vaccination %in% c("daily_partial", "daily_full", "daily")) %>%
ggplot(aes(x = vaccination,
fill = state)) +
geom_bar(position = "dodge")
```

### 4.1.4 Segmented bar chart

A segmented bar plot is a stacked bar plot where each bar represents 100 percent. We use the `(position = "fill")`

option.

```
df %>%
filter(vaccination %in% c("daily_partial", "daily_full", "daily")) %>%
ggplot(aes(x = vaccination,
y = doses,
fill = state)) +
geom_bar(stat = "identity", position = "fill") +
labs(y = "Proportion")
```

The segmented bar chart is useful to compare the percentage of a category in one variable across each level of another variable. We redo Figure 4.5 with the `state`

on the x-axis.

```
df %>%
filter(vaccination %in% c("daily_partial", "daily_full", "daily")) %>%
ggplot(aes(x = state,
y = doses,
fill = vaccination)) +
geom_bar(stat = "identity", position = "fill") +
labs(y = "Proportion")
```

Why are the blue bars all of the same height in Figure 4.6?

### 4.1.5 Color and labeling

In Figure 4.7, `factor`

changes the order and the labels of the categories for the `vaccination`

column. We also flipped the co-ordinates.

We have

- a bar plot, with each bar representing 100%,
- reordered bars with better labels
- colors from a different palette with
`scale_fill_brewer(palette = "Dark2")`

```
df %>%
filter(vaccination %in% c("daily_partial", "daily_full")) %>%
ggplot(aes(x = state,
y = doses,
fill = factor(vaccination,
levels = c("daily_partial", "daily_full"),
labels = c("First dose", "Second dose")))) +
geom_bar(stat = "identity", position = "fill") +
scale_y_continuous(breaks = seq(0, 1, .2)) +
scale_fill_brewer(palette = "Dark2") +
labs(y = "Percent",
fill = "Vacination type",
x = "State",
title = "Vaccination by State") +
coord_flip() +
theme_minimal()
```

In Figure 4.7, we used the factor function to reorder and/or rename the levels of a category. We could also apply this to the original data frame, making these changes permanent. It would then apply to all future graphs using that data frame.

Next, we add labels to each segment. Follow the flow of modifications to the data shown by `%>%`

. We use `(x = reorder(state, pct)`

to nicely order Figure 4.8.

```
df %>%
filter(vaccination %in% c("daily_partial", "daily_full")) %>%
group_by(state, vaccination) %>%
summarize(n = n(), .groups = 'drop') %>%
mutate(pct = round((n/sum(n))*100, 1)) %>%
ggplot(aes(x = reorder(state, pct),
y = pct,
fill = factor(vaccination,
levels = c("daily_partial", "daily_full"),
labels = c("First dose", "Second dose")))) +
geom_bar(stat = "identity") +
geom_text(aes(label = pct),
size = 3,
position = position_stack(vjust = 0.5)) +
scale_fill_brewer(palette = "Dark2") +
labs(y = "Percent",
fill = "Vacination type",
x = "State",
title = "Vaccination by State") +
coord_flip() +
theme_minimal()
```

Figure 4.8 calculates the percentage of data points or `count`

as per `summarize(n = n()`

in the code. We see not much difference in all the states because of the way `count`

works. Figure 4.9 uses this same data frame to calculate the percentage of doses administered per state. Compare closely the `summarize(doses = sum(doses))`

option here and in the previous code chunk.

We use the `geom_text`

function to add labels to each bar segment.

```
df %>%
filter(vaccination %in% c("daily_partial", "daily_full")) %>%
group_by(state, vaccination) %>%
summarize(doses = sum(doses), .groups = 'drop') %>%
mutate(pct = round((doses/sum(doses))*100, 1)) %>%
ggplot(aes(x = reorder(state, pct),
y = pct,
fill = factor(vaccination,
levels = c("daily_partial", "daily_full"),
labels = c("First dose", "Second dose")))) +
geom_bar(stat = "identity") +
geom_text(aes(label = pct),
size = 3,
position = position_stack(vjust = 0.5)) +
scale_fill_brewer(palette = "Dark2") +
labs(y = "Percent",
fill = "Vacination type",
x = "State",
title = "Vaccination by State") +
coord_flip() +
theme_minimal()
```

Now we have a graph that is easy to read and interpret. Again note the difference between Figure 4.8 and Figure 4.9;

`summarize(n = n(), .groups = 'drop')`

and`summarize(doses = sum(doses), .groups = 'drop')`

the `.groups = 'drop')`

option is to avoid some warning signs due to default settings of the new version of `dplyr`

.^{12}

We purposely leave it to the reader to adjust the `geom_text`

parameters to make the labels look better.

## 4.2 Quantitative vs. Quantitative

We normally use scatter plots and line graphs to visualize the relationship between two quantitative variables.

### 4.2.1 Scatterplot

We will go through some examples using the `mys1`

dataset. It has many numeric `columns`

or `variables`

.

Again recall that we try to learn the basic visualization functions and techniques provided for by `ggplot`

using the datasets we downloaded without any modifications. So far we have only changed some of the datasets to a long format using the `gather`

function. We also showed a simple example of combining two data frames with a `left_join`

.

```
mys1 %>%
filter(date >= "2021-01-21") %>%
ggplot(aes(x = cluster_workplace,
y = cluster_community)) +
geom_point()
```

As we have seen in Chapter 2, `geom_point`

parameters can be used to change the

- color - point color
- size - point size
- shape - point shape
- alpha - point transparency, from 0 (transparent) to 1 (opaque), and is a useful parameter when points overlap.

The functions `scale_x_continuous`

and `scale_y_continuous`

control the scaling on x and y axes respectively.

We can use these parameters and functions to improve upon Figure 4.10.

### 4.2.2 A bit on color combinations

We suggest the reader finds the color pairs or combinations that deliver the required theme. We will use these two based on some cool recommendations.^{13}

- Blue and Orange: Balance and Strength. These two complimentary shades look amazing together. Punchy orange (#e54b22) and cool blue (#abd1ff) creates a perfectly balanced and stylish look.

- Blue and Deep Peach: Professional yet Sophisticated. Blue (#0f149a) and peach (#fd9b4d) make a dynamic color scheme. This mix of cool and warm tones works well in contemporary and traditional contexts.

```
mys1 %>%
filter(date >= "2021-01-01") %>%
ggplot(aes(x = cluster_workplace,
y = cluster_community)) +
geom_point(color="#e54b22",
size = 2,
alpha= 0.8) +
scale_y_continuous(limits = c(0, 1000),
breaks = seq(0, 1000, 100)) +
scale_x_continuous(limits = c(0, 3000),
breaks = seq(0, 3000, 500)) +
labs(x = "Cases at workplace",
y = "Cases at community",
title = "Workplace and Community Cases",
subtitle = "Daily starting 01 Jan 2021")
```

### 4.2.3 Best fit lines

We can display a best-fit line to show the relationship between the points. The `geom_smooth()`

function has a few line types like linear, polynomial, and non-parametric (loess). By default, 95% confidence limits for these lines are displayed.

```
mys1 %>%
filter(date >= "2021-01-01") %>%
ggplot(aes(x = cluster_workplace,
y = cluster_community)) +
geom_point(color="#e54b22",
size = 2,
alpha= 0.8) +
geom_smooth(method = "lm", color = "darkblue") +
scale_y_continuous(limits = c(0, 900),
breaks = seq(0, 1000, 100)) +
scale_x_continuous(limits = c(0, 2500),
breaks = seq(0, 3000, 500)) +
labs(x = "Cases at workplace",
y = "Cases at community",
title = "Workplace and Community Cases",
subtitle = "Daily starting 01 Jan 2021")
```

It seems the community cases increases with workplace cases. However, there seems to be a dip at the right end. A straight line does not capture this. A line with a bend will fit better here. We can try a polynomial regression line with either a quadratic (one bend), or cubic (two bends) option. Higher order(> 3) polynomials are seldom used. Figure 4.13 applies a quadratic fit.

```
mys1 %>%
filter(date >= "2021-01-01") %>%
ggplot(aes(x = cluster_workplace,
y = cluster_community)) +
geom_point(color="#e54b22",
size = 2,
alpha= 0.8) +
geom_smooth(method = "lm",
formula = y ~ poly(x, 2),
color = "#0f149a") +
scale_y_continuous(limits = c(0, 900),
breaks = seq(0, 1000, 100)) +
scale_x_continuous(limits = c(0, 2500),
breaks = seq(0, 3000, 500)) +
labs(x = "Cases at workplace",
y = "Cases at community",
title = "Workplace and Community Cases",
subtitle = "Daily starting 01 Jan 2021")
```

Figure 4.14 applies a smoothed non-parametric fit line. The default in `ggplot2`

is a `loess`

line.

```
mys1 %>%
filter(date >= "2021-01-01") %>%
ggplot(aes(x = cluster_workplace,
y = cluster_community)) +
geom_point(color="orange2",
size = 2,
alpha= 0.8) +
geom_smooth(color = "darkblue") +
scale_y_continuous(limits = c(0, 900),
breaks = seq(0, 1000, 100)) +
scale_x_continuous(limits = c(0, 2500),
breaks = seq(0, 3000, 500)) +
labs(x = "Cases at workplace",
y = "Cases at community",
title = "Workplace and Community Cases",
subtitle = "Daily starting 01 Jan 2021")
```

We can suppress the confidence bands by including the option `se = FALSE`

.

Figure 4.15 is a more complete plot.

```
mys1 %>%
filter(date >= "2021-01-01") %>%
ggplot(aes(x = cluster_workplace,
y = cluster_community)) +
geom_point(color="orange3",
size = 2,
alpha= 0.8) +
geom_smooth(size = 1.5,
color = "blue3") +
scale_y_continuous(limits = c(0, 900),
breaks = seq(0, 1000, 100)) +
scale_x_continuous(limits = c(0, 2500),
breaks = seq(0, 3000, 500)) +
labs(x = "Cases at workplace",
y = "Cases at community",
title = "Workplace and Community Cases",
subtitle = "Daily starting 01 Jan 2021") +
theme_minimal()
```

### 4.2.4 Line plot

Most of our datasets have a time (date) column. A line plot can be an effective method of displaying relationship between new cases, new deaths, and others with the `date`

. We may need to convert the `character date`

to `numeric date`

using `as.Date(date)`

. We have seen in Chapter 1 that the `date`

data from the source files have mixed formats for the date. We stick with the `mys1`

data frame for the following few examples.

```
mys1 %>%
filter(date >= "2021-01-01") %>%
ggplot(aes(x = as.Date(date),
y = cluster_community)) +
geom_line()
```

It is difficult to read individual values in the graph above. In Figure 4.17, we add the points as well.

```
mys1 %>%
filter(date >= "2021-01-01") %>%
ggplot(aes(x = as.Date(date),
y = cluster_community)) +
geom_line(size = 1.5,
color = "#fd9b4d") +
geom_point(size = 1,
color = "blue4") +
labs(y = "Number",
x = "Day",
title = "Cases for Community Cluster over time",
subtitle = "Malaysia starting 01 Jan 2021",
caption = "Source: https://github.com/MoH-Malaysia/covid19-public/blob/main/epidemic/cases_malaysia.csv")
```

## 4.3 Categorical and Quantitative

Several graph types are available to plot the relationship between a categorical variable and a quantitative variable. These include bar charts using summary statistics, grouped kernel density plots, side-by-side box plots, side-by-side violin plots, and Cleveland plots.

### 4.3.1 Bar chart (on summary statistics)

Before this, we used bar charts to display the number of counts or cases or percentages by category for a single variable or two variables. We can also use bar charts to display other summary statistics like the mean or median of a quantitative variable based on each level of a categorical variable.

```
# calculate mean cases for each cluster
plotdata <- mys1 %>%
filter(date >= "2021-01-01") %>%
gather(key = "cluster", value = "cases", 3:9) %>%
group_by(cluster) %>%
summarize(mean_cases = mean(cases),
median_cases = median(cases),
min_cases = min(cases),
max_cases = max(cases))
plotdata %>%
ggplot(aes(x = cluster,
y = mean_cases)) +
geom_bar(stat = "identity")
```

We improve upon Figure 4.18 with some options. Again we purposely try `fill = "salmon"`

to see how it looks like.

```
ggplot(plotdata,
aes(x = factor(cluster,
labels = c("Import", "Religious", "Community",
"HighRisk", "Education", "DetentionCentre",
"Workplace")),
y = mean_cases)) +
geom_bar(stat = "identity",
fill = "salmon") +
geom_text(aes(label = round(mean_cases, 0)),
size = 3,
vjust = -0.25) +
scale_y_continuous(breaks = seq(0, 1000, 100)) +
labs(title = "Mean Cases by Cluster",
subtitle = "Starting 01 Jan 2021",
x = "",
y = "Mean Cases")
```

### 4.3.2 Grouped kernel density plots

We can compare groups on a numeric variable by superimposing kernel density plots in a single graph as in Figure 4.20 which shows the distribution of cases by cluster.

```
mys1 %>%
filter(date >= "2021-01-01") %>%
gather(key = "cluster", value = "cases", 3:9) -> myslong
myslong %>%
ggplot(aes(x = cases,
fill = cluster)) +
geom_density(alpha = 0.4) +
labs(title = "Cases by Cluster")
```

This is actual data, it may not appear nice or correct but it shows the dominance of a few clusters based on the data. Figure 4.20 makes clear that the workplace and community clusters dominate.

### 4.3.3 Box plots

A boxplot displays the 25th percentile, median, and 75th percentile of a distribution. The vertical lines capture about 99% of a normal distribution, and observations outside this range are plotted as points representing outliers (see Figure 4.21).

Side-by-side box plots are useful for comparing groups (the levels of a categorical variable like cluster types or states) on a numerical variable like the number of cases.

Figure 4.21 shows the distribution of cases by cluster using box plots.

```
myslong %>%
ggplot(aes(x = factor(cluster,
labels = c("Import", "Religious", "Community",
"HighRisk", "Education", "DetentionCentre",
"Workplace")),
y = cases)) +
geom_boxplot() +
labs(title = "Box Plot of Cases Distribution by Cluster",
x = "Cluster",
y = "Daily cases")
```

We can set `geom_boxplot(notch = TRUE)`

to create notched box plots. They provide an approximate method for visualizing whether groups differ.

```
myslong %>%
ggplot(aes(x = factor(cluster,
labels = c("Import", "Religious", "Community",
"HighRisk", "Education", "DetentionCentre",
"Workplace")),
y = cases)) +
geom_boxplot(notch = TRUE,
fill = "salmon2",
alpha = .7) +
labs(title = "Box Plot of Cases Distribution by Cluster",
x = "Cluster",
y = "Daily cases")
```

Figure 4.21 and Figure 4.22 show that all clusters appear to have different distributions of data. Some have several outlier observations.

### 4.3.4 Violin plots

Violin plots allow us to compare multiple data distributions. With ordinary density curves, it is difficult to compare several distributions together because the lines and shaded areas visually interfere with each other, as we have seen in Figure 4.20. With a violin plot, they are placed side by side.

```
myslong %>%
ggplot(aes(x = cluster,
y = cases)) +
geom_violin() +
labs(title = "Cases distribution by Cluster")
```

Our `mys1`

data frame in its long format `myslong`

does not give a useful violin plot. We may have to look at other datasets with other variables. Sometimes we see box plots layered on top of violin plots.

```
myslong %>%
ggplot(aes(x = factor(cluster,
labels = c("Import", "Religious", "Community",
"HighRisk", "Education", "DetentionCentre",
"Workplace")),
y = cases)) +
geom_violin(fill = "darkblue") +
geom_boxplot(width = 0.2,
fill = "salmon2",
outlier.color = "salmon4",
outlier.size = 2) +
labs(title = "Box and Violin Plot of Cases Distribution by Cluster",
x = "Cluster",
y = "Daily cases")
```

### 4.3.5 Mean/SEM plots

Another method for comparing groups on a quantitative variable using mean plots with error bars. Error bars can represent different parameters like standard deviations, or confidence intervals. In Figure 4.25 we plot the mean and standard deviations. We first calculate the means, standard deviations, standard errors, and 95% confidence intervals by `cluster`

.

```
plotdata <- myslong %>%
group_by(cluster) %>%
summarize(n = n(),
mean = mean(cases),
sd = sd(cases),
se = sd / sqrt(n),
ci = qt(0.95, df = n - 1) * sd / sqrt(n))
ggplot(plotdata,
aes(x = factor(cluster,
labels = c("Import", "Religious", "Community",
"HighRisk", "Education", "Detention\nCentre",
"Workplace")),
y = mean,
group = 1)) +
geom_point(size = 2) +
geom_line() +
geom_errorbar(aes(ymin = mean - sd,
ymax = mean + sd),
width = .1) +
labs(title = "Mean and Standard Deviation of daily cases by cluster",
x = "Cluster Type",
y = "Mean cases")
```

Figure 4.26 adds some options to make Figure 4.25 nicer.

```
ggplot(plotdata,
aes(x = factor(cluster,
labels = c("Import", "Religious", "Community",
"HighRisk", "Education", "Detention\nCentre",
"Workplace")),
y = mean,
group = 1)) +
geom_point(size = 2, color = "salmon3") +
geom_line() +
geom_errorbar(aes(ymin = mean - sd,
ymax = mean + sd),
width = .1, color = "darkblue") +
labs(title = "Mean and Standard Deviation of daily cases by cluster",
subtitle = "(mean +/- standard deviation)",
x = "Cluster Type",
y = "")
```

### 4.3.6 Multiple dot plots for grouped data

The relationship between a group category and a quantitative variable can be displayed with a scatter plot. Figure 4.27 plots the distribution of cases by cluster using one-dimensional strip plots.

```
ggplot(myslong,
aes(y = cluster,
x = cases)) +
geom_point(alpha = 0.5) +
labs(title = "Cases distribution by cluster")
```

When there are too many points, overprinting of points makes interpretation difficult, even when we use `geom_point(alpha = 0.5)`

. We can `jitter`

the points using `geom_jitter()`

which jitters the points using a small random number within a limited range. Figure 4.28 plots the distribution of cases by cluster using jittering.

```
ggplot(myslong,
aes(y = cluster,
x = cases)) +
geom_jitter() +
labs(title = "Cases distribution by cluster")
```

We use colors for easier comparison.

```
ggplot(myslong,
aes(y = factor(cluster,
labels = c("Import", "Religious", "Community",
"HighRisk", "Education", "Detention\nCentre",
"Workplace")),
x = cases,
color = cluster)) +
geom_jitter(alpha = 0.7,
size = 1.5) +
labs(title = "Cases distribution by cluster",
subtitle = "Starting 01 Jan 2021",
x = "Daily cases",
y = "") +
theme_minimal() +
theme(legend.position = "none")
```

Jitter line plots allow us to nicely visualize the distribution of point data by category when the number of points are not that many.

### 4.3.7 Combining jitter and boxplots

We can combine box plots and jitter plots.

```
ggplot(myslong,
aes(y = factor(cluster,
labels = c("Import", "Religious", "Community",
"HighRisk", "Education", "Detention\nCentre",
"Workplace")),
x = cases,
color = cluster)) +
geom_boxplot(size = 1,
outlier.shape = 4,
outlier.color = "black",
outlier.size = 3) +
geom_jitter(alpha = 0.5,
width = 0.2) +
labs(title = "Cases distribution by cluster",
subtitle = "Starting 01 Jan 2021",
x = "Daily cases",
y = "Cluster Type") +
theme_minimal() +
theme(legend.position = "none")
```

We used several options in Figure 4.30.

For the boxplot

- size = 1 makes the lines thicker
- outlier.color = “black” makes outlier points black
- outlier.shape = 4 specifies X for outlier points
- outlier.size = 2 increases the size of the outlier symbol

For the jitter

- alpha = 0.5 makes the points more transparent
- width = 0.2 decreases the amount of jitter (0.4 is the default)

### 4.3.8 Cleveland dot plots

Cleveland plots are useful when we want to compare a quantitative statistic for many group categories. For example, we want to compare the mean number of new Covid cases per state using the `mysstates`

dataset.

```
plotdata <- mysstates %>%
group_by(state) %>%
summarize(n = n(),
mean = mean(cases_new))
ggplot(plotdata,
aes(x= mean, y = state)) +
geom_point()
```

We should sort the states along the y-axis.

Figure 4.33 uses some options to improve upon Figure 4.32.

```
ggplot(plotdata,
aes(x = mean,
y = reorder(state, mean))) +
geom_point(color = "darkblue",
size = 5) +
geom_segment(aes(x = 0,
xend = mean,
y = reorder(state, mean),
yend = reorder(state, mean)),
color = "salmon3",
size = 2) +
labs (x = "Mean Cases",
y = "State",
title = "Mean daily new cases by state") +
theme_minimal() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
```

Selangor clearly has the highest mean daily cases. This last plot is also called a lollipop graph.

## 4.4 Combining some visual tools learned

In the next few plots we will explore the `hospital`

data frame. We convert it to a long format so `state`

and `category`

are the categories.

Figure 4.34 uses `geom_col`

and `facet_wrap`

to simply visualize the two categories we have in the data frame. This is an exploratory visualization. Imagine if we have a large data frame with millions of rows. This short code will simply paint the data frame.

We introduce the use of `geom_col`

here instead of `geom_bar(stat = "identity", position = "stack")`

. It is simpler. We also increase the height of the plot.

```
ggplot(hosplong,
aes(x = as.Date(date), y = patients,
fill = category)) +
geom_col() +
facet_wrap(~ state)
```

In Figure 4.35 we filter some categories. The fixed scale shows the more demanding states.

```
hosplong %>%
filter(category %in% c("beds_covid", "admitted_covid",
"hosp_covid", "discharged_covid")) %>%
ggplot(aes(x = as.Date(date), y = patients,
fill = category)) +
geom_col() +
facet_wrap(~ state) +
labs(title = "Covid hospitalization by state",
caption = "Fixed y-axis",
x = "Date")
```

Figure 4.36 uses `scale="free_y"`

to see the visual trend by state.

```
hosplong %>%
filter(category %in% c("beds_covid", "admitted_covid",
"hosp_covid", "discharged_covid")) %>%
ggplot(aes(x = as.Date(date), y = patients,
fill = category)) +
geom_col() +
facet_wrap(~ state, scale="free_y") +
labs(title = "Covid hospitalization by state",
caption = "Free y-axis",
x = "Date")
```

Figure 4.37 shows the visual trend by state of only two categories.

```
hosplong %>%
filter(category %in% c("beds_covid", "beds")) %>%
ggplot(aes(x = as.Date(date), y = patients,
fill = category)) +
geom_col() +
facet_wrap(~ state, scale="free_y") +
labs(title = "Hospital bed allocation by state",
caption = "Free y-axis",
x = "Date")
```

The above visuals that combine bar charts and facets allow simple visual comparisons between the state categories.