---
title: "gghdx"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{gghdx}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.showtext = TRUE,
  warning = FALSE,
  dpi = 300,
  fig.retina = 1,
  fig.align = "center"
)
```

## Using the package

The package is designed so the user just has to run `gghdx()` once a session
and mainly forget about it. This will automatically set your ggplot2
to use the HDX theme, palettes, fonts, and more by default. If you want more
control or want to better understand how the package works, please see the
details below!

### Theme

A quick and simple example would be plotting the `iris` dataset included in
base R.

```{r intro-plot, out.width = "90%", out.height = "10%", fig.height = 4, fig.width = 6}
library(ggplot2)

p <- ggplot(
  iris,
  aes(
    x = Sepal.Length,
    y = Petal.Length,
    color = Species
  )
) +
  geom_point() +
  labs(
    title = "Iris species distributed by sepal and petal lengths",
    y = "Petal length",
    x = "Sepal length"
  )

p
```

This output using the base ggplot style doesn't look particularly bad, but we
can use `theme_hdx()` to quickly adjust some of the styling to fit the style
guide.

```{r intro-hdx, out.width = "90%", out.height = "10%", fig.height = 4, fig.width = 6}
library(gghdx)

p + theme_hdx(base_family = "sans")
```

Now, axis lines have been cleaned up and the
plot better resembles recommendations from the visual guide with just that
single line of code.

### Color palettes

However, the color palette for the points is still using the base R palette. We
can use one of the many `scale_...hdx()` functions to use HDX colors. Let's just
use the primary discrete color scale that will align each species with one of
the 3 non-gray colorramps (sapphire, mint, and tomato).

```{r intro-ramp, out.width = "90%", out.height = "10%", fig.height = 4, fig.width = 6}
p + theme_hdx(base_family = "sans") + scale_color_hdx_discrete()
```

You can check the documentation of any of the `scale_...hdx()` functions to see
all available scales, or directly access the colors using `hdx_colors()` or
the raw list in `hdx_color_list`. The available palettes can be easily
visualized using `hdx_display_pal()`.

### Adding fonts

We also would like to use the HDX font family. Since Source Sans 3 is a free
Google font, it makes it relatively easy to access in R. gghdx uses the
[sysfonts](https://CRAN.R-project.org/package=sysfonts) package
to load the Google font and then [showtext](https://CRAN.R-project.org/package=showtext)
to include them in our plot. You can also use the
[extrafont](https://CRAN.R-project.org/package=extrafont) package as an alternative if you
have the font installed locally. This requires ghostscript to be installed
locally and can run into other issues, such as
[font names](https://github.com/wch/extrafont/issues/32) not being found.

Below, I use the showtext package because it's simpler.

```{r extrafont, out.width = "90%", out.height = "10%", fig.height = 4, fig.width = 6}
library(showtext)

font_add_google("Source Sans 3")
showtext_auto()

p + theme_hdx(base_family = "Source Sans 3") + scale_color_hdx_discrete()
```

### Streamlined plotting

As clear above, even though we have an HDX theme function, we still have to
separately call the scale function to adjust our colors. And we have to call
these every time we make a new plot. So, to make life simpler, `gghdx()` is
provided as a convenience function that sets ggplot to:

- automatically use the HDX theme by default;
- use default HDX sapphire for point and line colors and and HDX mint for fill 
when not an aesthetic;
- use `scale_fill_hdx_discrete()` and `scale_color_hdx_discrete()` as the default discrete fill
and color respectively;
- use `scale_fill_gradient_hdx_mint()` and `scale_color_gradient_hdx_sapphire()`
as the default continuous fill and color;
- loads the Source Sans 3 font from Google and activates its usage for the
current session.

You just have to run `gghdx()` once a session, and then our plots will already
be where we would like!

```{r gghdx, out.width = "90%", out.height = "10%", fig.height = 4, fig.width = 6}
gghdx()
p
```

And voíla, we have our graph without specifying the theme or color scale.

### COVID plots

As a final example, we can closely match the COVID plots referenced in the
visual guide using the theme and color scales in the package.

```{r example-plots, echo = FALSE, out.width = "45%", out.height = "20%", fig.show = "hold", fig.align = "default"}

knitr::include_graphics(
  c(
    here::here("man", "figures", "covid_blue.png"),
    here::here("man", "figures", "covid_red.png")
  )
)
```

The inbuilt data `gghdx::df_covid` has aggregated COVID data we can use to
mirror this plot. To make the data start at the y-axis, we can use
`scale_y_continuous_hdx()` which sets `expand = c(0, 0)` by default, and the
`label_number_hdx()` function to create custom labels.

```{r covid-match, fig.height = 5, fig.width = 6, out.width = "45%", fig.show = "hold", fig.align = "default"}
p_blue <- ggplot(
  df_covid,
  aes(
    x = date,
    y = cases_monthly
  )
) +
  geom_bar(
    stat = "identity",
    width = 6,
    fill = hdx_hex("sapphire-hdx") # use sapphire for fill
  ) +
  scale_y_continuous_hdx(
    labels = label_number_hdx()
  ) +
  scale_x_date(
    date_breaks = "1 month",
    labels = function(x) toupper(strftime(x, "%b"))
  ) +
  labs(
    title = "Monthly global COVID-19 confirmed cases in 2020",
    subtitle = "DATA | JUL 2022 | World Health Organization",
    x = "",
    y = ""
  )

p_blue

# create red plot
p_blue +
  geom_bar(
    aes(
      fill = flag
    ),
    width = 6,
    stat = "identity"
  ) +
  scale_fill_hdx_tomato() +
  theme(
    legend.position = "none"
  ) +
  labs(
    title = "Monthly COVID-19 # of cases surpasses 8 million"
  )
```

We've used relatively few lines of code to match fairly closely these examples
plots!