cleveland_dot_plots.qmd

# Cleveland dot plots {#sec-cleveland-dot-plots}

```{r}
#| label: setup
#| message: false
#| warning: false
#| include: false
library(tidyverse)
library(lubridate)
library(scales)
library(knitr)
library(kableExtra)
library(colorblindr)
library(downlit)
# fonts ----
library(extrafont)
library(sysfonts)
source("_common.R")
# use font
showtext::showtext_auto()
# set theme
ggplot2::theme_set(theme_ggp2g(
    base_size = 15))
```

:::: {.callout-note collapse="false" icon=false}

<br>

::: {style="font-size: 1.10em; color: #02577A;"}
**This graph requires:**
:::

::: {style="font-size: 1.05em; color: #282b2d;"}

**`r emo::ji("check")` a numeric (continuous) variable**

**`r emo::ji("check")` at least one categorical variable (with two levels)**  

:::

```{r}
#| label: full_code_display
#| eval: true
#| echo: false
#| warning: false
#| message: false
#| out-height: '60%'
#| out-width: '60%'
#| fig-align: right
library(palmerpenguins) 
library(ggplot2)
peng_clev_dots <- palmerpenguins::penguins |>
    dplyr::filter(!is.na(sex) & !is.na(flipper_length_mm)) |>
    dplyr::group_by(sex, island) |>
    dplyr::summarise(
        med_flip_length_mm = median(flipper_length_mm)
        ) |>
    dplyr::ungroup()
labs_clev_dots <- labs(
    title = "Flipper Length Differences",
    subtitle = "Male and female penguins",
    x = "Median Flipper Length",
    y = "Island",
    color = "Sex"
)

ggp2_clev_dots <- ggplot(data = peng_clev_dots,
    mapping = aes(x = med_flip_length_mm, y = fct_rev(island))) +
        geom_line(aes(group = island), linewidth = 0.75) + 
        geom_point(aes(color = sex), size = 2.25) 
# glimpse(peng_clev_dots)
ggp2_clev_dots +
    labs_clev_dots
```

::::

## Description 

Cleveland dot plots compare numbers with dots on a line and are more efficient than bar graphs. The graph lists the categories on the side and shows the data with dots along a line.

Typically, the graph contains two points representing the numerical value on the `y` axis, differentiated by color. A line connecting the two points represents the difference between the two categorical levels (the width of the line is the size of the difference).

## Set up

::: {style="font-size: 1.10em; color: #1e83c8;"}
**PACKAGES:**
:::

Install packages.

::: {style="font-size: 0.85em;"}
```{r}
#| label: pkg_code_clev_dots
#| code-fold: show
#| eval: false
#| echo: true
#| warning: false
#| message: false
#| results: hide
install.packages("palmerpenguins")
library(palmerpenguins)
library(ggplot2)
```
:::



::: {style="font-size: 1.10em; color: #1e83c8;"}
**DATA:**
:::

::: {.column-margin}
![Artwork by Allison Horst](www/lter_penguins.png){fig-align="right" width="100%" height="100%"}
:::


Remove missing values from `sex` and `flipper_length_mm` and group on `sex` and `island` to the calculate the median flipper length (`med_flip_length_mm`).

::: {style="font-size: 0.85em;"}
```{r}
#| label: data_code_clev_dots
#| code-fold: show
#| eval: true
#| echo: true
peng_clev_dots <- palmerpenguins::penguins |>
    dplyr::filter(!is.na(sex) & !is.na(flipper_length_mm)) |>
    dplyr::group_by(sex, island) |>
    dplyr::summarise(
        med_flip_length_mm = median(flipper_length_mm)
        ) |>
    dplyr::ungroup()
glimpse(peng_clev_dots)
```
:::
::::

## Grammar

::: {style="font-size: 1.10em; color: #1e83c8;"}
**CODE:**
:::

  -   Create labels with `labs()`

  -   Initialize the graph with `ggplot()` and provide `data`

  -   Map the `med_flip_length_mm` to the `x` axis, and `island` to the `y` axis, but wrap `island` in `forcats::fct_rev()`.

  -   Add `geom_line()`, and map `island` to the `group` aesthetic. Set the `linewidth` to `0.75`

  -   Add `geom_point()` and map `sex` to `color` aesthetic. Set the `size` to `2.25`

::: {style="font-size: 0.85em;"}
```{r}
#| label: code_graph_clev_dots
#| code-fold: show
#| eval: false
#| echo: true 
#| warning: false
#| message: false
labs_clev_dots <- labs(
    title = "Flipper Length Differences",
    subtitle = "Male and female penguins",
    x = "Median Flipper Length",
    y = "Island",
    color = "Sex")

ggp2_clev_dots <- ggplot(data = peng_clev_dots,
                mapping = aes(x = med_flip_length_mm, 
                              y = fct_rev(island))) +
                geom_line(aes(group = island), 
                    linewidth = 0.75) + 
                geom_point(aes(color = sex), 
                    size = 2.25) 

ggp2_clev_dots + 
    labs_clev_dots
```
:::



::: {style="font-size: 1.10em; color: #1e83c8;"}
**GRAPH:**
:::

```{r}
#| label: create_graph_clev_dots
#| eval: true
#| echo: false
#| warning: false
#| message: false
#| layout-nrow: 1
#| column: page-inset-right
labs_clev_dots <- labs(
    title = "Flipper Length Differences",
    subtitle = "Male and female penguins",
    x = "Median Flipper Length",
    y = "Island",
    color = "Sex"
)

ggp2_clev_dots <- ggplot(data = peng_clev_dots,
                mapping = aes(x = med_flip_length_mm, 
                              y = fct_rev(island))) +
                geom_line(aes(group = island), 
                    linewidth = 0.75) + 
                geom_point(aes(color = sex), 
                    size = 2.25) 

ggp2_clev_dots + 
    labs_clev_dots
```

::::

## More info

Cleveland dot plots are also helpful when comparing multiple differences on a common scale.

### Common scale

::: {style="font-size: 1.10em; color: #1e83c8;"}
**SCALE:**
:::

  -   Remove missing values from `sex`, `bill_length_mm` and `bill_depth_mm`, and group on `sex` and `island` to the calculate the median bill length and median bill depth. These variables need to have 'showtime-ready' names because they'll be used in our facets. 

  -   After un-grouping the data, pivot the new columns into a long (tidy) format with `median_measure` containing the name of the variable, and `median_value` containing the numbers. 

  -   Finally, convert `median_measure` into a factor. 

::: {style="font-size: 0.85em;"}
```{r}
#| label: data_code_clev_dots2
#| eval: true
#| echo: true
peng_clev_dots2 <- palmerpenguins::penguins |>
    dplyr::filter(!is.na(sex) & 
            !is.na(bill_length_mm) & 
                !is.na(bill_depth_mm)) |>
    dplyr::group_by(sex, island) |>
    dplyr::summarise(
        `Median Bill Length` = median(bill_length_mm),
        `Median Bill Depth` = median(bill_depth_mm)) |>
    dplyr::ungroup() |> 
    tidyr::pivot_longer(cols = starts_with("Med"), 
        names_to = "median_measure", 
        values_to = "median_value") |> 
    dplyr::mutate(median_measure = factor(median_measure))
glimpse(peng_clev_dots2)
```
:::

### Scales

::: {style="font-size: 1.10em; color: #1e83c8;"}
**`scales`:**
:::

  -   Re-create labels

  -   Initialize the graph with `ggplot()` and provide `data`

  -   Map the `median_value` to the `x` axis, and `island` to the `y` axis, but wrap `island` in `forcats::fct_rev()`.

  -   Add `geom_line()`, and map `island` to the `group` aesthetic. Set the `linewidth` to `0.75`

  -   Add `geom_point()` and map `sex` to `color` aesthetic. Set the `size` to `2.25`

  -   Add `facet_wrap()` and facet by `median_measure`, setting `shrink` to `TRUE` and `scales` to `"free_x"`

  -   Move the legend with `theme(legend.position = "top")`

::: {style="font-size: 0.85em;"}
```{r}
#| label: graph_run_clev_dots_nrow_2
#| eval: true
#| echo: true
#| out-height: '100%'
#| out-width: '100%'
#| layout-nrow: 1
#| column: page-inset-right
labs_clev_dots2 <- labs(
    title = "Penguin Measurements Differences",
    subtitle = "Male and female penguins",
    x = "Median Bill Length/Depth (mm)",
    y = "Island",
    color = "Sex")

ggp2_clev_dots2 <- ggplot(data = peng_clev_dots2,
    mapping = aes(x = median_value,
        y = fct_rev(island))) +
    geom_line(aes(group = island),
        linewidth = 0.55) +
    geom_point(aes(color = sex),
        size = 2) +
    facet_wrap(. ~ median_measure,
        shrink = TRUE, nrow = 2) +
    theme(legend.position = "top")

ggp2_clev_dots2 +
    labs_clev_dots2
```
:::

::: {style="font-size: 1.00em; color: #E74A2F;"}
**CAUTION when using `scales = "free_x"`:**
The graph below shows that the median bill length and depth is larger for male penguins on all three islands, but the magnitude of the differences should be interpreted with caution because the length of the lines can't be directly compared! 
:::

::: {style="font-size: 0.85em;"}
```{r}
#| label: graph_run_clev_dots2_free_x
#| eval: true
#| echo: true
#| out-height: '100%'
#| out-width: '100%'
#| layout-nrow: 1
#| column: page-inset-right
labs_clev_dots2 <- labs(
    title = "Penguin Measurements Differences",
    subtitle = "Male and female penguins",
    x = "Median Bill Length/Depth (mm)",
    y = "Island",
    color = "Sex")

ggp2_clev_dots2_free_x <- ggplot(data = peng_clev_dots2,
    mapping = aes(x = median_value,
        y = fct_rev(island))) +
    geom_line(aes(group = island),
        linewidth = 0.55) +
    geom_point(aes(color = sex),
        size = 2) +
    facet_wrap(. ~ median_measure,
        shrink = TRUE, nrow = 2,
        scales = "free_x") +
    theme(legend.position = "top")

ggp2_clev_dots2_free_x +
    labs_clev_dots2
```
:::