28-graphics-for-communication.Rmd

*Make sure the following packages are installed:*  

```{r setup28, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, cache = TRUE, message = FALSE, warning = FALSE)

library(ggplot2)
library(dplyr)
library(tidyr)
library(nycflights13)
library(babynames)
library(nasaweather)
library(lubridate)
library(ggrepel)
library(scales)
```


# Ch. 28: Graphics for communication
```{block2, type='rmdtip'}
**Functions and notes:**
```

* `labs()` to add labels
  * common args: `title`, `subtitle`, `caption`, `x`, `y`, `colour`, ...
  * for mathematical equations use `quote` and see `?plotmath`
      * e.g. within `labs()` could do `y = quote(alpha + beta + frac(delta, theta))`
* `geom_text()` similar to `geom_point()` but with argument `label` that adds text where the point would be
    * use `nudge_x` and `nudge_y` to move position around
    * use `vjust` ('top', 'center', or 'bottom') and `hjust` ('left', 'center', or 'right') to control alignment of text
    * can use `+Inf` and `-Inf` to put text in exact corners
    * use `stringr::str_wrap()` to automatically add line breaks
    * `geom_label()` is like `geom_text()` but draws a box around the data that makes easier to see (can adjust `alpha` and `fill` of background box)
    * `ggrepel::geom_label_repel()` is like `geom_label()` but prevents overlap of labels
* `geom_hline()` and `geom_vline` for reference lines (often use `size = 2` and `colour = white`)
* `geom_rect()` to draw rectangle around points (controlled by `xmin`, `xmax`, `ymin`, `ymax`)
* `geom_segment()` to draw attention to a point with an arrow, (common args: `arrow`, `x`, `y`, `xend`, `yend`)
* `annotate` can add in labels by hand (not from values of dataframe)
* `scale_x_continuous()`, `scale_y_continuous()`, `scale_colour_discrete()`, ... `scale_{aes}_{scale type}()`
    * `breaks` and `labels` are key args (can set `labels = NULL` to remove values)
    * `scale_colour_brewer(palette = "Set1")`for color blind people
    * `scale_colour_manual()` for definining colours with specific values, e.g. `scale_colour_manual(values = c(Republican = "red", Democratic = "blue"))`
    * for continuous scales try `scale_colour_gradient()`, `scale_fill_gradient()`, `scale_colour_gradient2()` (two colour gradient, e.g. + / - values), `viridis::scale_colour_viridis()`
    * date scales are a little different, e.g. `scale_x_date()` takes args `date_labels` (e.g. `date_labels = "'%y"`) and `date_breaks` (e.g. `date_breaks = "2 days"`)
    * `scale_x_log10()`, `scale_y_log10`... to substitute values with a particular transformation
* `theme()` customize any non-data components of plots
    * e.g. remove legend with `theme(legend.position = "none")` (could also have inputted "left", "top", "bottom", or "right")
* `guides()` to control display of individual legends -- use in conjunction with `guide_legend()` or `guide_colourbar()`
* `coord_cartesian()` to zoom using `xlim` and `ylim` args
* can customize your themes, e.g. `theme_bw()`, `theme_classic()`..., see `ggthemes` for a bunch of others
* `ggsave()` defaults to save most recent plot
    * key options: `fig.width`, `fig.height`, `fig.asp`, `out.width`, `out.height` (see chapter for details) 
    * other options:  `fig.align`, `fig.cap`, `dev` (e.g. `dev = "png"`)

## 28.2: Label

### 28.2.1

1.  Create one plot on the fuel economy data with customised `title`,
    `subtitle`, `caption`, `x`, `y`, and `colour` labels.
    
    ```{r}
    mpg %>% 
      ggplot(aes(x = hwy, displ))+
      geom_count(aes(colour = class))+
      labs(title = "Larger displacement has lower gas mileage efficiency",
           subtitle = "SUV and pickup classes` tend to be highest on disp",
           caption = "Data is for cars made in either 1999 or 2008",
           colour = "Car class")
    ```
    

1.  The `geom_smooth()` is somewhat misleading because the `hwy` for
    large engines is skewed upwards due to the inclusion of lightweight
    sports cars with big engines. Use your modelling tools to fit and display
    a better model.

    ```{r}
    mpg %>% 
      ggplot(aes(x = hwy, displ))+
      geom_count(aes(colour = class))+
      labs(title = "Larger displacement has lower gas mileage efficiency",
           subtitle = "SUV and pickup classes` tend to be highest on disp",
           caption = "Data is for cars made in either 1999 or 2008",
           colour = "Car class")+
      geom_smooth()
    ```
    
    You could take into account the class of the car
    
    ```{r, warning=FALSE}
    mpg %>% 
      ggplot(aes(x = hwy, displ, colour = class))+
      geom_count()+
      labs(title = "Larger displacement has lower gas mileage efficiency",
           subtitle = "SUV and pickup classes` tend to be highest on disp",
           caption = "Data is for cars made in either 1999 or 2008",
           colour = "Car class")+
      geom_smooth()+
      facet_wrap(~class)
    ```

1.  Take an exploratory graphic that you've created in the last month, and add
    informative titles to make it easier for others to understand.
    
    Done seperately.
    
## 28.3: Annotations

### 28.3.1

1.  Use `geom_text()` with infinite positions to place text at the
    four corners of the plot.
    
    ```{r}
    data_label <- tibble(x = c(Inf, -Inf), 
           hjust = c("right", "left"), 
           y = c(Inf, -Inf),
           vjust = c("top", "bottom")) %>% 
      expand(nesting(x, hjust), nesting(y, vjust)) %>% 
      mutate(label = glue::glue("hjust: {hjust}; vjust: {vjust}"))

    mpg %>% 
      ggplot(aes(x = hwy, displ))+
      geom_count(aes(colour = class))+
      labs(title = "Larger displacement has lower gas mileage efficiency",
           subtitle = "SUV and pickup classes` tend to be highest on disp",
           caption = "Data is for cars made in either 1999 or 2008",
           colour = "Car class")+
      geom_text(aes(x = x, y = y, label = label, hjust = hjust, vjust = vjust),
                data = data_label)
    ```
    

1.  Read the documentation for `annotate()`. How can you use it to add a text
    label to a plot without having to create a tibble?
    
    * function adds geoms, but not mapped from variables of a dataframe, so can pass in small items or single labels

    ```{r}
    mpg %>% 
      ggplot(aes(x = hwy, displ))+
      geom_count(aes(colour = class))+
      labs(title = "Larger displacement has lower gas mileage efficiency",
           subtitle = "SUV and pickup classes` tend to be highest on disp",
           caption = "Data is for cars made in either 1999 or 2008",
           colour = "Car class")+
      annotate("text", x = Inf, y = Inf, label = paste0("Mean highway mpg: ", round(mean(mpg$hwy))), vjust = "top", hjust = "right")
    ```
    
    
1.  How do labels with `geom_text()` interact with faceting? How can you
    add a label to a single facet? How can you put a different label in
    each facet? (Hint: think about the underlying data.)
    
    ```{r, warning=FALSE}
    data_label_single <- tibble(x = Inf, y = Inf, label = paste0("Mean highway mpg: ", round(mean(mpg$hwy))))
    
    data_label <- mpg %>% 
      group_by(class) %>% 
      summarise(hwy = round(mean(hwy))) %>% 
      mutate(label = paste0("hwy mpg for ", class, ": ", hwy)) %>% 
      mutate(x = Inf, y = Inf)
    
    mpg %>% 
      ggplot(aes(x = hwy, displ))+
      geom_count(aes(colour = class))+
      labs(title = "Larger displacement has lower gas mileage efficiency",
           subtitle = "SUV and pickup classes` tend to be highest on disp",
           caption = "Data is for cars made in either 1999 or 2008",
           colour = "Car class")+
      facet_wrap(~class)+
      geom_smooth()+
      geom_text(aes(x = x, y = y, label = label), data = data_label, vjust = "top", hjust = "right")
    ```
    
    
1.  What arguments to `geom_label()` control the appearance of the background
    box?
    
    * `fill` argument controls background color
    * `alpha` controls it's relative brighness
    ```{r}
    best_in_class <- mpg %>%
      group_by(class) %>%
      filter(row_number(desc(hwy)) == 1)
    
    ggplot(mpg, aes(displ, hwy)) +
      geom_point(aes(colour = class)) +
      geom_label(aes(label = model), data = best_in_class, nudge_y = 2, alpha = 0.1, fill = "green")
    ```

1.  What are the four arguments to `arrow()`? How do they work? Create a series
    of plots that demonstrate the most important options.
    
    ```{r}
    b <- ggplot(mtcars, aes(wt, mpg)) +
      geom_point()
    
    df <- data.frame(x1 = 2.62, x2 = 3.57, y1 = 21.0, y2 = 15.0)
    
    b + geom_curve(
      aes(x = x1, y = y1, xend = x2, yend = y2),
      data = df,
      arrow = arrow(length = unit(0.03, "npc"))
    )
    ```
    
    * `angle` (in degrees), `length` (use `unit()` function to specify with number and type, e.g. "inches"), `ends` ("last", "first", or "both" -- specifying which end), `type` ("open" or "closed")
    * See [28.3.1.5] for more notes on line options (not specific to `arrow()`)
    
## 28.4: Scales

### 28.4.4

1.  Why doesn't the following code override the default scale?

    ```{r}
    df <- tibble(x = rnorm(100), y = rnorm(100))
    
    ggplot(df, aes(x, y)) +
      geom_hex() +
      scale_colour_gradient(low = "white", high = "red") +
      coord_fixed()
    ```

    * `geom_hex` uses `fill`, not `colour`

    ```{r}
    df <- tibble(x = rnorm(100), y = rnorm(100))
    
    ggplot(df, aes(x, y)) +
      geom_hex() +
      scale_fill_gradient(low = "white", high = "red") +
      coord_fixed()
    ```

1.  What is the first argument to every scale? How does it compare to `labs()`?

    * `name`, i.e. what the title will be for that axis/legend/... `labs` first argument is `...` so requires you to name the input

1.  Change the display of the presidential terms by:

    1. Combining the two variants shown above.
    1. Improving the display of the y axis.
    1. Labelling each term with the name of the president.
    1. Adding informative plot labels.
    1. Placing breaks every 4 years (this is trickier than it seems!).
    
    ```{r}
    presidential %>%
      mutate(id = 33L + row_number()) %>%
      ggplot(aes(start, id, colour = party)) +
      geom_point() +
      geom_segment(aes(xend = end, yend = id)) +
      geom_text(aes(label = name), vjust = "bottom", nudge_y = 0.2)+
      scale_colour_manual(values = c(Republican = "red", Democratic = "blue"))+
      scale_x_date("Year in 20th and 21st century", date_breaks = "4 years", date_labels = "'%y")+
      # scale_x_date(NULL, breaks = presidential$start, date_labels = "'%y")+
      scale_y_continuous(breaks = c(36, 39, 42), labels = c("36th", "39th", "42nd"))+
      labs(y = "President number", x = "Year")
    ```
    
1.  Use `override.aes` to make the legend on the following plot easier to see.

    ```{r}
    diamonds %>% 
      ggplot(aes(carat, price)) +
      geom_point(aes(colour = cut), alpha = 1/20)+
      guides(colour = guide_legend(override.aes = list(alpha = 1)))
    ```
    
## Appendix

### 28.3.1.5

Not `arrow()` function specifically, but other line end options
```{r}
ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(colour = class)) +
  geom_segment(aes(xend = displ +5, yend = hwy + 5), data = best_in_class, lineend = "round")
  
```

```{r}
b <- ggplot(mtcars, aes(wt, mpg)) +
  geom_point()

df <- data.frame(x1 = 2.62, x2 = 3.57, y1 = 21.0, y2 = 15.0)

b +
 geom_curve(aes(x = x1, y = y1, xend = x2, yend = y2, colour = "curve"), data = df) +
 geom_segment(aes(x = x1, y = y1, xend = x2, yend = y2, colour = "segment"), data = df)

b + geom_curve(aes(x = x1, y = y1, xend = x2, yend = y2), data = df, curvature = -0.2)

b + geom_curve(aes(x = x1, y = y1, xend = x2, yend = y2), data = df, curvature = 1)
```