---
title: "Reordering Bar and Column Charts with ggplot2 in R"
description: "There are some useful tools for getting your plots exactly how you want them"
author: "Gus Lipkin"
date: "2022/04/14"
categories:
  - R
  - "R: tidyverse"
  - "R: ggplot2"
  - "R: tidytext"
format:
  html:
    toc: true
---

# Intro

Like grammar in English, the grammar of graphics (`ggplot2`) can be somewhat confusing at times. Usually you know what you want your chart to look like but you're not sure exactly how to get there. A common issue is trying to get your discrete axis to show up in the order you want, whether it's getting your days of the week in order or ordering columns in ascending or descending order. 

::: {#fig-days-monthly-ascending layout-ncol=2}

![In order by day of the week](../assets/post-assets/2022-04-14-reordering-bar-and-column-charts-with-ggplot-in-r/days_in_order.png){#fig-days-in-order}

![In ascending order grouped by month](../assets/post-assets/2022-04-14-reordering-bar-and-column-charts-with-ggplot-in-r/monthly_ascending.png){#fig-monthly-ascending}

Bar charts organized by day of the week and descending order, respectively
:::

# Creating the Data

As usual, we want to load the `tidyverse` first since it gives us `%>%` from `magrittr` and `ggplot2` and everything that comes with it.

```{r}
#| warning: false
#| message: false
#| echo: false
library(tidyverse)
```

The dummy data for this post is painting sales. You're a starving artist who hasn't taken any days off selling paintings in the past year and you're trying to start taking a day to yourself every week. Our data has the day of the month (only 28 days per month), the month, the day of the week, and the number of paintings you sold each day. R has a constant built in for the name of the month, `month.name`, but not one for the days of the week, so we'll have to make that ourselves. In addition, we start our week on a Sunday.

```{r}
set.seed(2022)
days_of_the_week <- c("Sunday", "Monday", "Tuesday", "Wednesday", 
                      "Thursday", "Friday", "Saturday")
sales <- data.frame("dayOfMonth" = rep(1:28, 12),
                    "month" = rep(month.name, each = 28),
                    "weekday" = rep(days_of_the_week, 12*4),
                    "paintings" = round(rnorm(28*12, c(sample(1:28, 7)))))

head(sales, 7)
```

The first week's data shows that we sold the bulk of our paintings on Monday, Wednesday, and Friday, with barely any sales on Sunday and Saturday.

# Getting the Days in Order

In the first chart we make, we want to take the average number of sales per day of the week. To do that, we `group_by(weekday)` and then `summarise` the `mean(paintings)` as a new variable, `weekdaySales`. In the `ggplot` chain, we set the chart and axis titles with `labs` and the color palette with `scale_fill_brewer`. In `aes` in `geom_bar`, we want the `weekday` on the `x` axis and `weekdaySales` on the `y` axis. To add some color, we set `fill = weekday`. Outside of the `aes` but still in `geom_bar`, we set `stat = "identity"` so that the `y` axis value is used and `show.legend = FALSE` to hide the legend.

:::{.callout-note}
I've organized the `ggplot()` chain so that the items being changed are towards the bottom. The `ggplot()`, `labs()`, and `scale_fill_brewer()` are the same in every example.

The only aspects of `geom_bar()` that will change are the values used for `x` and `y` in `aes()`.
:::


```{r, fig.alt="A column chart of mean painting sales vs days of the week with the days of the week out of order"}
sales %>%
  group_by(weekday) %>%
  summarise(weekdaySales = mean(paintings)) %>%
  ggplot() +
  labs(title = "Mean Painting Sales by Day of the Week",
       x = "Day of the Week",
       y = "Mean Painting Sales") +
  scale_fill_brewer(palette = "Set2") +
  geom_bar(aes(x = weekday, y = weekdaySales, fill = weekday), 
           stat = "identity", show.legend = FALSE)
```

Unfortunately, our days of the week are not in order. Lots of people would be very sad if Monday came directly after Friday.

```{r, fig.alt="A column chart of mean painting sales vs days of the week with the days of the week in order"}
sales %>%
  group_by(weekday) %>%
  summarise(weekdaySales = mean(paintings)) %>%
  ggplot() +
  labs(title = "Mean Weekly Painting Sales by Day of the Week",
       x = "Day of the Week",
       y = "Mean Painting Sales") + 
  scale_fill_brewer(palette = "Set2") +
  geom_bar(aes(x = factor(weekday, days_of_the_week), y = weekdaySales, 
               fill = weekday), stat = "identity", show.legend = FALSE)
```

By changing `x = weekday` to `x = factor(weekday, days_of_the_week)`, we can get the days in order. The key here is that `factor()` takes two arguments; a vector of data and a vector defining the levels of the data in order. In this case, `days_of_the_week` defines the levels in order.

# Ascending or Descending Order

While the graph we just got does the job, we want to see how big the jumps are in order between days of the week. Similarly to getting our days of the week in order, we need to change `x = weekday` to something else to get our `x` axis sorted in ascending or descending order. While `factor()` lets us reorder them by `factor` levels, `reorder` allows us to reorder by numeric value. The first argument is the vector we want to change, in this case, `weekday`, and we want to change it to match the order of the second argument, `weekdaySales`. In fact, `reorder(weekday, weekdaySales)` is the same as `sales$weekday[order(sales$weekdaySales)]`.

```{r, fig.alt="A column chart of mean painting sales vs days of the week with the days of the week in ascending order by mean painting sales"}
sales %>%
  group_by(weekday) %>%
  summarise(weekdaySales = mean(paintings)) %>%
  ggplot() +
  labs(title = "Mean Weekly Painting Sales in Ascending Order",
       x = "Day of the Week",
       y = "Mean Painting Sales") + 
  scale_fill_brewer(palette = "Set2") +
  geom_bar(aes(x = reorder(weekday, weekdaySales), y = weekdaySales, 
               fill = weekday), stat = "identity", show.legend = FALSE)
```

The default order is ascending order, smallest to largest. To reverse the order, we can add a minus sign before the second argument so that it reads `reorder(weekday, -weekdaySales)`.

```{r, fig.alt="A column chart of mean painting sales vs days of the week with the days of the week in descending order by mean painting sales"}
sales %>%
  group_by(weekday) %>%
  summarise(weekdaySales = mean(paintings)) %>%
  ggplot() +
  labs(title = "Mean Weekly Painting Sales in Descending Order",
       x = "Day of the Week",
       y = "Mean Painting Sales") + 
  scale_fill_brewer(palette = "Set2") +
  geom_bar(aes(x = reorder(weekday, -weekdaySales), y = weekdaySales, 
               fill = weekday), stat = "identity", show.legend = FALSE)
```

# Attempting to Order Within Facets

Before we get started on facets, I want to change the data a little so that the examples are a little clearer. By adding some variation into the `paintings` variable, the differences between days of different months will be more apparent.

```{r}
sales$paintings <- round(rnorm(28*12, c(sample(1:28, 28*12, replace = TRUE))))
```

Thinking that maybe we'd like to switch up our day off every month, we want to see how our sales change over the course of the year, but still `group_by(weekday)`. However, since we want to know the mean for each `weekday` for each `month`, we need to add that so we have `group_by(month, weekday)`. We also add `facet_wrap(~ month)` to the end of the `ggplot` chain. Essentially, this means that we want to have a mini plot for each `month`. In this case, we've ordered our `y` axis so that the days of the week are in order from top to bottom.

```{r, fig.alt="One column chart for each month in alphabetical order with days of the week in proper order"}
#| message: false
sales %>%
  group_by(month, weekday) %>%
  summarise(weekdaySales = mean(paintings)) %>%
  ggplot() +
  labs(title = "Daily Painting Sales by Month",
       x = "Mean Painting Sales",
       y = "Day of the Week") + 
  scale_fill_brewer(palette = "Set2") +
  geom_bar(aes(x = weekdaySales, y = factor(weekday, rev(days_of_the_week)), 
               fill = weekday), stat = "identity", show.legend = FALSE) +
  facet_wrap(~ month)
```

That's all well and good, but what if we want each month ordered in descending order like we had the chart before? We can try changing `y = factor()` to `y = reorder()`. We also add `scales = "free_y"` to `facet_wrap` so that each chart's `y` axis is calculated independently. It's quickly apparent that this doesn't work the way we were expecting it to. The facets are not sorted properly and all of them are sorted the same way.

```{r, fig.alt="One column chart for each month in alphabetical order with days of the week in an unknown order"}
#| message: false
sales %>%
  group_by(month, weekday) %>%
  summarise(weekdaySales = mean(paintings)) %>%
  ggplot() +
  labs(title = "Daily Painting Sales by Month but not in Descending Order",
       x = "Mean Painting Sales",
       y = "Day of the Week") + 
  scale_fill_brewer(palette = "Set2") +
  geom_bar(aes(x = weekdaySales, y = reorder(weekday, weekdaySales), 
               fill = weekday), stat = "identity", show.legend = FALSE) +
  facet_wrap(~ month, scales = "free_y")
```

Not sure what happened, we try to investigate. If we re-run the earlier *Mean Weekly Painting Sales in Descending Order* chart with the new data and grouped by `weekday`, we find that the `y` axis in each facet is, in fact, in order with Sunday with the highest sales and Friday with the lowest, but the order hasn't been recalculated for each month.

```{r, fig.alt="Column chart of mean painting sales vs day of the week in order of descending mean painting sales"}
#| echo: false
sales %>%
  group_by(weekday) %>%
  summarise(weekdaySales = mean(paintings)) %>%
  ggplot() +
  labs(title = "Mean Weekly Painting Sales in Descending Order",
       x = "Day of the Week",
       y = "Mean Painting Sales") + 
  scale_fill_brewer(palette = "Set2") +
  geom_bar(aes(x = reorder(weekday, -weekdaySales), y = weekdaySales, 
               fill = weekday), stat = "identity", show.legend = FALSE)
```

# Ordering Within Facets

As it turns out, there's a rather simple way to give your facets each their own ordering. The `tidytext` package provides the functionality with three functions that go hand-in-hand. `reorder_within()` does the heavy lifting while `scale_x_reordered()` and `scale_y_reordered()` make sure that the order set in `reorder_within()` is respected.

:::{.callout-tip}
Julia Silge writes about `reorder_within` on her blog [here](https://juliasilge.com/blog/reorder-within/) and has some information on its creation and how it came to live in `tidytext` in the first paragraph of the "Enter `reorder_within()`" section.
:::

```{r}
library(tidytext)
```

Instead of using `factor()` and `reorder()`, we change the `y` axis argument in `aes()` to use `reorder_within`: `y = reorder_within(weekday, weekdaySales, month)`. Our first argument, `weekday` is the vector we want to reorder. The second argument, `weekdaySales` is the variable we want to use for reordering. The last argument, `month`, is the variable we want to use for grouping. The third argument could be a vector of column names if we wanted to group by multiple variables. Like with `reorder()`, we can set the direction to ascending with a minus sign before the ordering argument, `weekdaySales`. We keep `scales = "free_y"` in `facet_wrap()` so that each facet has its own order, and then use `scale_y_reordered()` to make sure that the new order from `reorder_within()` is used.

```{r}
#| message: false
#| layout-ncol: 2
sales %>%
  group_by(month, weekday) %>%
  summarise(weekdaySales = mean(paintings)) %>%
  ggplot() +
  labs(title = "Daily Painting Sales in Descending Order by Month",
       x = "Mean Painting Sales",
       y = "Day of the Week") +
  scale_fill_brewer(palette = "Set2") +
  geom_bar(aes(x = weekdaySales, 
               y = reorder_within(weekday, weekdaySales, month), 
               fill = weekday), stat = "identity", show.legend = FALSE) +
  facet_wrap(~ month, scales = "free_y") +
  scale_y_reordered()

sales %>%
  group_by(month, weekday) %>%
  summarise(weekdaySales = mean(paintings)) %>%
  ggplot() +
  labs(title = "Daily Painting Sales in Ascending Order by Month",
       x = "Mean Painting Sales",
       y = "Day of the Week") +
  scale_fill_brewer(palette = "Set2") +
  geom_bar(aes(x = weekdaySales, 
               y = reorder_within(weekday, -weekdaySales, month), 
               fill = weekday), stat = "identity", show.legend = FALSE) +
  facet_wrap(~ month, scales = "free_y") +
  scale_y_reordered()
```

# Ordering Facets and an Easier Way to Order by Day

At this point, you might be staring at your screen in disbelief and thinking, "But Gus, the facets aren't in order! The months are in alphabetical order and not calendar order!" You're not wrong. When we were setting the order of `y` with `factor()`, we were doing something that we could have done much earlier in our data creation stage. If we redefine the `month` and `weekday` variables using the `factor(variable, levels)` syntax, we don't need to use `y = factor(weekday)` to make sure our days are in order. In addition, the facets will now be in calendar order like they appear in the `month.name` constant. I've chosen to reverse the `days_of_the_week` so that Sunday appears at the top, rather than at the bottom of the `y` axis.

```{r, fig.alt="Column charts of day of the week vs mean painting sales with both the months and days of the week in order"}
#| message: false
sales$month <- factor(sales$month, levels = month.name)
sales$weekday <- factor(sales$weekday, levels = rev(days_of_the_week))

sales %>%
  group_by(month, weekday) %>%
  summarise(weekdaySales = mean(paintings)) %>%
  ggplot() +
  labs(title = "Daily Painting Sales in Descending Order by Month",
       x = "Mean Painting Sales",
       y = "Day of the Week") +
  scale_fill_brewer(palette = "Set2") +
  geom_bar(aes(x = weekdaySales, y = weekday, fill = weekday), 
           stat = "identity", show.legend = FALSE) +
  facet_wrap(~ month)
```

----

All code used in this article is available [here](https://raw.githubusercontent.com/guslipkin/guslipkin.github.io/main/professional-blog/2022-04-14-reordering-bar-and-column-charts-with-ggplot-in-r.qmd). If you want to see more from me, check out [my GitHub](https://github.com/guslipkin) or [guslipkin.github.io](https://guslipkin.github.io). If you want to hear from me, I'm also on Twitter [@guslipkin](https://twitter.com/GusLipkin).


<center><em>Gus Lipkin is a Data Scientist, Business Analyst, and occasional bike mechanic</em></center>