slope_graphs.qmd

# Slope graphs {#sec-slope-graphs}

```{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 = 13))
```

```{r}
#| label: status
#| results: "asis"
#| echo: false
# status ----
# polish, dev, draft, complete
status("complete")
```

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

<br>

```{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_slope <- palmerpenguins::penguins |>
    dplyr::filter(year < 2009) |>
    dplyr::group_by(year, island) |>
    dplyr::summarise(across(
        .cols = contains("mm"), 
        .fns = mean, 
            na.rm = TRUE, 
        .names = "avg_{.col}")) |>
    dplyr::ungroup()
labs_slope <- labs(
        title = "Changes in Bill Depth of Palmer Penguins", 
        subtitle = "Years 2007 & 2008",
        x = "Year", y = "Bill Depth (mm)", 
        color = "Island")

ggp2_slope <- ggplot(data = peng_slope, 
    mapping = aes(x = year,
                  y = avg_bill_depth_mm, 
              group = island)) +
  geom_line(aes(color = island), alpha = 1, size = 2, 
      show.legend = FALSE) +
  geom_point(aes(color = island), alpha = 1, size = 4) + 
  scale_x_continuous(breaks = c(2007, 2008), position = "top")

ggp2_slope + 
    labs_slope
```


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

::: {style="font-size: 1.05em; color: #282b2d;"}
**`r emo::ji("check")` a categorical variable**      
:::

::: {style="font-size: 1.05em; color: #282b2d;"}
**`r emo::ji("check")` a numeric (continuous) variable**
:::

::: {style="font-size: 1.05em; color: #282b2d;"}
**`r emo::ji("check")` a numeric (date) variable**
:::

::::

## Description

Slope graphs show changes in a numeric value (displayed on the `y` axis) typically over two points in time (along the `x` axis). The values for each group or unit of measurement are connected by lines, and any differences between the two time points are represented by the slope of the lines (hence the name, 'slope chart').

We can build slope graphs in `ggplot2` using the `geom_line()` and `geom_point()` functions.

## Set up

::: {style="font-size: 1.15em; color: #1e83c8;"}
**PACKAGES:**
:::

::: {style="font-size: 0.95rem;"}
Install packages.
:::


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



::: {style="font-size: 1.15em; color: #1e83c8;"}
**DATA:**
:::

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


We'll be using the penguins data for this graph, but slightly restructured. 

::: {style="font-size: 0.85em;"}
```{r}
#| label: data_code_slope
#| code-fold: show
#| eval: true
#| echo: true
peng_slope <- palmerpenguins::penguins |>
    dplyr::filter(year < 2009) |>
    dplyr::group_by(year, island) |>
    dplyr::summarise(across(
        .cols = contains("mm"), 
        .fns = mean, 
            na.rm = TRUE, 
        .names = "avg_{.col}")) |>
    dplyr::ungroup()
glimpse(peng_slope)
```
:::


## Grammar 

::: {style="font-size: 1.15em; color: #1e83c8;"}
**CODE:**
:::

  - Create labels with `labs()`

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

  - Map `year` to the `x`, `avg_bill_depth_mm` to `y`, and `island` to `group`

  - Add a `geom_line()` layer, mapping `island` to `color`, and setting the `size` to `2`

  - Add a `geom_point()` layer, mapping `color` to `island`, and setting `size` to `4`

  - We'll adjust the `x` axis with `scale_x_continuous()`, manually setting the `breaks` and moving the `position` to the `"top"` of the graph

::: {style="font-size: 0.85em;"}
```{r}
#| label: code_graph_slope
#| code-fold: show
#| eval: false
#| echo: true 
#| warning: false
#| message: false
#| out-height: '100%'
#| out-width: '100%'
labs_slope <- labs(
        title = "Changes in Bill Depth of Palmer Penguins", 
        subtitle = "Years 2007 & 2008",
        x = "Year", y = "Bill Depth (mm)", 
        color = "Island")

ggp2_slope <- ggplot(data = peng_slope, 
    mapping = aes(x = year,
                y = avg_bill_depth_mm, 
                group = island)) +
  geom_line(aes(color = island), 
                size = 2, show.legend = FALSE) +
  geom_point(aes(color = island), 
                size = 4) + 
  scale_x_continuous(
                breaks = c(2007, 2008), 
                position = "top")

ggp2_slope + 
    labs_slope
```
:::



::: {style="font-size: 1.15em; color: #1e83c8;"}
**GRAPH:**
:::

```{r}
#| label: create_graph_slope
#| eval: true
#| echo: false
#| warning: false
#| message: false
#| out-height: '100%'
#| out-width: '100%'
labs_slope <- labs(
        title = "Changes in Bill Depth of Palmer Penguins", 
        subtitle = "Years 2007 & 2008",
        x = "Year", y = "Average Bill Depth (mm)", 
        color = "Island")

ggp2_slope <- ggplot(data = peng_slope, 
    mapping = aes(x = year,
                y = avg_bill_depth_mm, 
                group = island)) +
  geom_line(aes(color = island), 
                size = 2, show.legend = FALSE) +
  geom_point(aes(color = island), 
                size = 4) + 
  scale_x_continuous(
                breaks = c(2007, 2008), 
                position = "top")

ggp2_slope + 
    labs_slope
```

## More info

We can also use faceting with slope graphs to add a third categorical variable.

::: {style="font-size: 1.15em; color: #1e83c8;"}
**DATA:**
:::

We'll be using the `penguins` dataset again, but group remove the missing values and group it by `year`, `island`, and `sex`. 

::: {style="font-size: 0.85em;"}
```{r}
#| label: data_code_grp_slope
#| eval: true
#| echo: true
peng_grp_slope <- palmerpenguins::penguins |>
    dplyr::select(year, sex, island,
                  contains("mm")) |> 
    tidyr::drop_na() |> 
    dplyr::filter(year != 2007) |>
    dplyr::group_by(year, sex, island) |>
    dplyr::summarise(across(
        .cols = contains("mm"), 
        .fns = mean, 
            na.rm = TRUE, 
        .names = "avg_{.col}")) |>
    dplyr::ungroup()
glimpse(peng_grp_slope)
```

:::



::: {style="font-size: 1.15em; color: #1e83c8;"}
**GRAPH:**
:::

  - Create labels with `labs()` 

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

  - Map `year` to the `x`, `avg_bill_depth_mm` to `y`, and `island` to `group`

  - Add a `geom_line()` layer, mapping `island` to `color`, and setting the `size` to `2`

  - Add a `geom_point()` layer, mapping `color` to `island`, and setting `size` to `4`

  - We'll adjust the `x` axis with `scale_x_continuous()`, manually setting the `breaks` and moving the `position` to the `"top"` of the graph

  - We'll duplicate the the `y` axis with `sec.axis`, setting `dup_axis()` to the same `name` of the previous `y` label.

  - Finally, we facet the graph by `sex`, adjust the `size` of the text, and move the legend to the `"bottom"` of the graph.

::: {style="font-size: 0.85em;"}
```{r}
#| label: create_graph_grp_slope
#| eval: true
#| echo: true
#| warning: false
#| message: false
#| out-height: '100%'
#| out-width: '100%'
labs_grp_slope <- labs(
    title = "Changes in Bill Depth of Palmer Penguins",
    subtitle = "Years 2008 & 2009",
    x = "",
    color = "Island")

ggp2_grp_slope <- ggplot(data = peng_grp_slope,
    mapping = aes(x = year,
        y = avg_bill_depth_mm,
        group = island)) +
    geom_line(aes(color = island),
        size = 2, show.legend = FALSE) +
    geom_point(aes(color = island),
        size = 4) +
    scale_x_continuous(breaks = c(2008, 2009),
        position = "top") +
    scale_y_continuous(name = "Average Bill Depth (mm)",
        sec.axis = dup_axis(name = "Average Bill Depth (mm)")) +
    facet_wrap(. ~ sex,
        ncol = 2) +
    theme_minimal(base_size = 14) +
    theme(
        legend.position = "bottom",
        axis.text.x = element_text(size = 9),
        axis.text.y = element_text(size = 9),
        strip.text = element_text(size = 10))

ggp2_grp_slope +
    labs_grp_slope
```
:::