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ggplot

Click on the tab buttons below for each section

Introduction

Label

You add labels with the labs() function.
- title: The purpose of a plot title is to summarise the main finding.
- subtitle: adds additional detail in a smaller font beneath the title.
- caption: adds text at the bottom right of the plot, often used to describe the source of the data.
- x: replaces x-axis name (sometimes you want more descriptive names, or units).
- y: replaces y-axis name (sometimes you want more descriptive names, or units).
- The legend titles are replaced using the aesthetic that made it - e.g. colour or fill.

ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(color = class)) +
  geom_smooth(se = FALSE) +
  labs(title = "Fuel efficiency generally decreases with engine size")

ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(colour = class)) +
  geom_smooth(se = FALSE) +
  labs(title = str_glue("Fuel efficiency generally decreases ",
                        "with engine size"),
       subtitle = str_glue("Two seaters (sports cars) are the ",
                           "exception because of their light weight"),
       caption = "Data from fueleconomy.gov")

ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(colour = class)) +
  geom_smooth(se = FALSE) +
  labs(
    x = "Engine displacement (L)",
    y = "Highway fuel economy (mpg)",
    colour = "Car type"
  )

It’s possible to use mathematical equations instead of text strings. Just switch "" out for quote() and read about the available options in ?plotmath.

df <- tibble(
  x = runif(10),
  y = runif(10)
)
ggplot(df, aes(x, y)) +
  geom_point() +
  labs(
    x = quote(sum(x[i]^2, i == 1, n)),
    y = quote(alpha + beta + frac(delta, theta))
  )

Exercises

Create one plot on the fuel economy data with customised title, subtitle, caption, x, y, and colour labels.

ggplot(mpg, aes(class, hwy)) +
  geom_boxplot() +
  geom_jitter(aes(colour = displ), width = 0.1, height = 0) +
  labs(title = "Fuel efficiency is worst for pickups and suv",
       subtitle = "These vehicle types have the bigger engine size",
       x = "Car Type",
       y = "Highway fuel economy (mpg)",
       colour = "Engine size"
  )

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.

ggplot(mpg, aes(displ, hwy, colour = class)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE) +
  scale_colour_disney("pan") +
  labs(title = str_glue("Fuel efficiency generally decreases ",
                        "with engine size"),
       subtitle = str_glue("Two seaters (sports cars) are the ",
                           "exception because of their light weight"),
       caption = "Data from fueleconomy.gov",
       x = "Engine displacement (L)",
       y = "Highway fuel economy (mpg)",
       colour = "Car type")

Take an exploratory graphic that you’ve created in the last month, and add informative titles to make it easier for others to understand.

Annotations

It’s also often useful to label individual observations or groups of observations. The first tool you have at your disposal is geom_text(). geom_text() is similar to geom_point(), but it has an additional aesthetic - label. This makes it possible to add textual labels to your plots.

a tibble that provides labels
?? find out in exercises

mpg %>% 
  select(class, displ, hwy, model) %>% 
  group_by(class) %>% 
  mutate(rn = row_number(desc(hwy))) %>% 
  filter(rn %in% c(1,2))
#> # A tibble: 14 x 5
#> # Groups:   class [7]
#>    class      displ   hwy model                rn
#>                         
#>  1 2seater      5.7    26 corvette              1
#>  2 2seater      6.2    26 corvette              2
#>  3 minivan      2.4    24 caravan 2wd           1
#>  4 minivan      3      24 caravan 2wd           2
#>  5 midsize      2.4    31 sonata                2
#>  6 midsize      2.5    32 altima                1
#>  7 suv          2.5    27 forester awd          1
#>  8 suv          2.5    26 forester awd          2
#>  9 compact      1.8    37 corolla               2
#> 10 pickup       2.7    20 toyota tacoma 4wd     2
#> 11 pickup       2.7    22 toyota tacoma 4wd     1
#> 12 compact      1.9    44 jetta                 1
#> 13 subcompact   1.9    44 new beetle            1
#> 14 subcompact   1.9    41 new beetle            2
         
# create a tibble for your label data
best_in_class <- mpg %>% 
  group_by(class) %>% 
  filter(row_number(desc(hwy)) == 1)

ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(colour = class)) +
  geom_text(aes(label = model), # the label should be the model in your tbl 
            # use your label data
            data = best_in_class)

It is sometimes hard to read, like here, where labels overlap with each other, and with the points. We can make things a little better by switching to geom_label() which draws a rectangle behind the text. We also use the nudge_y parameter to move the labels slightly above the corresponding points.

ggplot(data = mpg, aes(displ, hwy)) +
  geom_point(aes(colour = class)) +
  geom_label(aes(label = model), 
            data = best_in_class,
            nudge_y = 2,
            alpha = 0.5)

There is still a problem in that there are two labels on top of each other in the left corner. We can use the ggrepel package by Kamil Slowikowski which automatically adjusts labels so that they don’t overlap ❗ ❤️

ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(colour = class)) +
  # add a second layer of large, hollow points to 
  # highlight the points labelled
  geom_point(size = 3, shape = 1, 
             data = best_in_class)

ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(colour = class)) +
  geom_point(size = 3, shape = 1,
             data = best_in_class) +
  ggrepel::geom_label_repel(aes(label = model), 
                           data = best_in_class)

theme(legend.position = "none") turns the legend off.

# find the median displ, hwy for each class
class_avg <- mpg %>% 
  group_by(class) %>% 
  summarise(
    displ = median(displ),
    hwy = median(hwy)
  )

ggplot(mpg, aes(displ, hwy, 
                # move up here since used by both 
                # geom_label_repel - to colour label text
                # and geom_point to colour points
                colour = class)) +
  ggrepel::geom_label_repel(aes(label = class), # stick label in median place
                             data = class_avg,
                             size = 6,
                             label.size = 0,
                             segment.colour = NA) +
  geom_point() +
  # we've replaced our legend by labels on the plot
  # in the median position!
  theme(legend.position = "none")

You may want to add a single label to the plot, but you’ll still need to create a data frame.
Often, you want the label in the corner of the plot, so it’s convenient to create a new data frame using summarise() to compute the maximum values of x and y.

label <- mpg %>% 
  summarise(
    displ = max(displ),
    hwy = max(hwy),
    label = "Increasing engine size is \nrelated to decreasing fuel economy."
  )

ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  geom_text(aes(label = label),
            data = label, 
            vjust = "top", hjust = "right")

If you want to place the text exactly on the borders of the plot, you can use +Inf and -Inf. Note we use tibble here since we have more “static” data.

label <- tibble(
  displ = Inf,
  hwy = Inf,
  label = "Increasing engine size is \nrelated to decreasing fuel economy."
)

ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  geom_text(aes(label = label), data = label, 
            vjust = "top", hjust = "right")

We manually broke the label up into lines using "\n" but we may have used stringr::str_wrap() to automatically add line breaks.

"Increasing engine size is related to decreasing fuel economy." %>%
  # break the above text up automatically
  # I want 40 characters per line
  stringr::str_wrap(width = 40) %>%
  writeLines()
#> Increasing engine size is related to
#> decreasing fuel economy.

label <- tibble(
  displ = Inf,
  hwy = Inf,
  label = stringr::str_wrap("Increasing engine size is 
                            related to decreasing fuel economy.", 
                            width = 35)
)

ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  geom_text(aes(label = label), data = label, 
            vjust = "top", hjust = "right")

All nine combinations of hjust and vjust.

Exercises

Use geom_text() with infinite positions to place text at the four corners of the plot.

I am not sure if it is just me but I am unable to do it with Inf and -Inf.

So I used 0, 1 as the authors did for the hjust, vjust plot, and tweaked it for my case here.

vjust <- c("top", "top", "bottom", "bottom")
hjust <- c("left", "right", "left", "right")
label <- c("Top left", "Top right", "Bottom left", "Bottom right")
x <- c(0, 1, 0, 1)
y <- c(1, 1, 0, 0)
df <- tibble(x = x,
             y = y,
             label = label,
             vjust = vjust,
             hjust = hjust)

ggplot(df, aes(x, y)) +
  geom_text(aes(label = label),
            vjust = vjust,
            hjust = hjust) +
  scale_x_continuous(labels = NULL) +
  scale_y_continuous(labels = NULL) +
  labs(x = "",
       y = "")

Read the documentation for annotate(). How can you use it to add a text label to a plot without having to create a tibble?

Some examples from the help page below. Used when you want to add some static text. I like the use of it for things like \(italic(R) ^ 2 == 0.75\), and also seems useful for marking anomalies on a plot.

p <- ggplot(mtcars, aes(x = wt, y = mpg)) + geom_point()
p + annotate("text", x = 2:5, y = 25, label = "Some text")

p + annotate("rect", xmin = 3, xmax = 4.2, ymin = 12, ymax = 21,
  alpha = .2)

p + annotate("text", x = 2:3, y = 20:21, label = c("my label", "label 2"))

p + annotate("text", x = 4, y = 25, label = "italic(R) ^ 2 == 0.75",
  parse = TRUE)

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.)

# Got this example from this SO post
# https://stackoverflow.com/questions/15867263/ggplot2-geom-text-with-facet-grid
x <-runif(9, 0, 125) 
data <- as.data.frame(x) 
data$y <- runif(9, 0, 125) 
data$yy <- factor(c("a","b","c")) 

ggplot(data, aes(x, y)) + 
    geom_point(shape = 2) + 
    facet_grid(~yy) + 
    geom_text(aes(x, y, label=lab),
        data=data.frame(x=60, y=Inf, lab=c("this","is","the way"),
             yy=letters[1:3]), vjust=1)


ggplot(data, aes(x, y)) + 
    geom_point(shape = 2) + 
    facet_grid(~yy) + 
    geom_text(aes(x, y, label=lab),
        # text on one facet
        data=data.frame(x=60, y=Inf, lab=c("this is the only way"),
             yy=letters[1]), # one facet only
        vjust=1)

What arguments to geom_label() control the appearance of the background box?
- label.padding: Amount of padding around label. Defaults to 0.25 lines.
- label.r: Radius of rounded corners. Defaults to 0.15 lines.
- label.size: Size of label border, in mm.

What are the four arguments to arrow()? How do they work? Create a series of plots that demonstrate the most important options.

Tweaked from this Stack Overflow post.

ggplot(mtcars, aes(wt, mpg)) + 
  geom_point() +
  geom_segment(aes(x = 5, y = 30, xend = 3.5, yend = 25),
                  arrow = arrow(length = unit(0.5, "cm")))


ggplot(mtcars, aes(wt, mpg)) + 
  geom_point() +
  geom_segment(aes(x = 5, y = 30, xend = 3.5, yend = 25),
                  arrow = arrow(length = unit(0.5, "cm"),
                                type = "closed"))


ggplot(mtcars, aes(wt, mpg)) + 
  geom_point() +
  geom_segment(aes(x = 5, y = 30, xend = 3.5, yend = 25),
                  arrow = arrow(length = unit(0.5, "cm"),
                                type = "closed",
                                ends = "both"))


ggplot(mtcars, aes(wt, mpg)) + 
  geom_point() +
  geom_segment(aes(x = 5, y = 30, xend = 3.5, yend = 25),
                  arrow = arrow(angle = 60, # how narrow or wide the arrow head
                                length = unit(0.5, "cm"),
                                type = "closed",
                                ends = "both"))



ggplot(mtcars, aes(wt, mpg)) + 
  geom_point() +
  geom_segment(aes(x = 5, y = 30, xend = 3.5, yend = 25),
                  arrow = arrow(angle = 15, # how narrow or wide the arrow head
                                length = unit(0.5, "cm"),
                                type = "closed",
                                ends = "both"))

Scales

You can also make your plot better for communication by adjusting the scales. ggplot2 automatically adds sensible default scales behind the scenes for you.

ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(colour = class))

Note this is the same as above.

ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(colour = class)) +
  scale_x_continuous() +
  scale_y_continuous() +
  scale_colour_discrete()

Axis ticks and legend keys

Collectively axes and legends are called guides. Axes are used for x and y aesthetics; legends are used for everything else.

breaks controls the position of the axis ticks, or the values associated with the legend keys.
labels controls the text label associated with each tick/key. Supply a character vector the same length as breaks OR suppress labels by using NULL .
When is NULL appropriate? Useful for maps, or when you can’t share absolute numbers in a published plot.
Breaks and labels for datetimes is a bit different:
- date_labels takes a format specification, in the same form as parse_datetime().
- date_breaks, takes a string like “2 days” or “1 month”.

ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(colour = class)) +
  scale_y_continuous(breaks = seq(15, 40, by = 5))

ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  scale_x_continuous(labels = NULL) +
  scale_y_continuous(labels = NULL)

presidential %>% 
  # add the number of the president, 
  # e.g. Obama was the 44th president of USA
  mutate(id = 33 + row_number())
#> # A tibble: 11 x 5
#>    name       start      end        party         id
#>    <chr>      <date>     <date>     <chr>      <dbl>
#>  1 Eisenhower 1953-01-20 1961-01-20 Republican    34
#>  2 Kennedy    1961-01-20 1963-11-22 Democratic    35
#>  3 Johnson    1963-11-22 1969-01-20 Democratic    36
#>  4 Nixon      1969-01-20 1974-08-09 Republican    37
#>  5 Ford       1974-08-09 1977-01-20 Republican    38
#>  6 Carter     1977-01-20 1981-01-20 Democratic    39
#>  7 Reagan     1981-01-20 1989-01-20 Republican    40
#>  8 Bush       1989-01-20 1993-01-20 Republican    41
#>  9 Clinton    1993-01-20 2001-01-20 Democratic    42
#> 10 Bush       2001-01-20 2009-01-20 Republican    43
#> 11 Obama      2009-01-20 2017-01-20 Democratic    44

presidential %>% 
  # add the number of the president, 
  # e.g. Obama was the 44th president of USA
  mutate(id = 33 + row_number()) %>%  
  ggplot(aes(start, id)) +
  geom_point() +
  geom_segment(aes(xend = end, yend = id)) +
  scale_x_date(NULL,
               breaks = presidential$start,
               date_labels = "'%y") +
  ggrepel::geom_label_repel(aes(label = name),
                                nudge_x = 0.5,
                                nudge_y = 0.4)

Legend layout

To control the overall position of the legend, you need to use a theme()
setting. The theme setting legend.position controls where the legend is drawn. You can also use legend.position = "none" to hide the display of the legend.

base <- ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(colour = class))
base + theme(legend.position = "left")

base + theme(legend.position = "top")

base + theme(legend.position = "bottom")

base + theme(legend.position = "right") # the default

To control the display of individual legends, use guides() along with guide_legend() or guide_colourbar().
Control the number of rows the legend uses with nrow
Override one of the aesthetics to make the points bigger. Useful if you have used a low alpha to display many points on a plot.

ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(colour = class)) +
  geom_smooth(se = FALSE) +
  theme(legend.position = "bottom") +
  guides(colour = guide_legend(nrow = 1,
                               override.aes = list(size = 4)))


ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(colour = class)) +
  geom_smooth(se = FALSE) +
  theme(legend.position = "bottom") +
  guides(colour = guide_legend(nrow = 4, 
                               override.aes = list(size = 5)))

Replacing a scale

Sometimes you will want to replace a scale completely - we have done this before when we log transformed graphs.

Aesthetic mapping Transform

ggplot(diamonds, aes(carat, price)) +
  geom_bin2d()

# disadvantage of this transformation is that the axes are 
# now labelled with the transformed values, making it hard to interpret the plot
ggplot(diamonds, aes(log10(carat), log10(price))) +
  geom_bin2d()

Scale Transform

Visually identical to above, except the axes are labelled using the original data scale.

ggplot(diamonds, aes(carat, price)) +
  geom_bin2d() + 
  scale_x_log10() + 
  scale_y_log10()

Useful colour scale alternatives are the ColorBrewer scales for certain colour blindness - e.g. red-green colour blindness.

ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(color = drv))


ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(color = drv)) +
  scale_colour_brewer(palette = "Set1")

Adding a redundant shape mapping is also completely acceptable. It also ensures the plot is interpretable in black and white.

ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(color = drv, shape = drv)) +
  scale_colour_brewer(palette = "Set1")

In ColorBrewer the sequential (top) and diverging (bottom) are useful if your categorical values are ordered, or have a “middle”. Often the case if you’ve used cut() to make a continuous variable into a categorical variable.

All ColourBrewer scales.

Use scale_colour_manual() when you have a predefined mapping between values and colours.

presidential %>%
  mutate(id = 33 + row_number()) %>%
  ggplot(aes(start, id, colour = party)) +
    geom_point() +
    geom_segment(aes(xend = end, yend = id)) +
    scale_colour_manual(values = c(Republican = "red", Democratic = "blue"))

For continuous colour, use scale_colour_gradient() or scale_fill_gradient().
For a diverging scale, use scale_colour_gradient2(). Here positive and negative values may have different colours. Also useful if you want to distinguish points above or below the mean.
scale_colour_viridis() is also a good alternative.

df <- tibble(
  x = rnorm(10000),
  y = rnorm(10000)
)
ggplot(df, aes(x, y)) +
  geom_hex() +
  coord_fixed()


ggplot(df, aes(x, y)) +
  geom_hex() +
  viridis::scale_fill_viridis() +
  coord_fixed()

All colour scales have scale_colour_x() and scale_fill_x() for the colour and fill aesthetics.

Exercises

Why doesn’t the following code override the default scale?

The hex should use fill not colour

ggplot(df, aes(x, y)) +
  geom_hex() +
  scale_colour_gradient(low = "white", high = "red") +
  coord_fixed()

ggplot(df, aes(x, y)) +
  geom_hex() +
  scale_fill_gradient(low = "white", high = "red") +
  coord_fixed()

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

The ... argument which passes on to the particular scale function in the arguments. The first arg is aesthetics that this scale works with e.g. x, y, colour etc.

labs is similar but talks to the labels not the scale.

Change the display of the presidential terms by:

Combining the two variants shown above.

presidential %>%
  mutate(id = 33 + row_number()) %>%
  ggplot(aes(start, id, colour = party)) +
    geom_point() +
    geom_segment(aes(xend = end, yend = id)) +
    scale_colour_manual(values = c(Republican = "red", 
                                   Democratic = "blue")) +
    scale_x_date(NULL,
               breaks = presidential$start,
               date_labels = "'%y")

Improving the display of the y axis.

presidential_amend <- presidential %>%
  mutate(id = 33 + row_number(),
         id_label = str_c(id, c(rep("th ", 7), 
                          "st ", "nd ", "rd ", "th "), 
                          c(rep("President", 11))),
         id_label = fct_reorder(id_label, id)) 

presidential_amend %>%
  ggplot(aes(start, id, colour = party)) +
    geom_point() +
    geom_segment(aes(xend = end, yend = id)) +
    scale_colour_manual(values = c(Republican = "red", 
                                   Democratic = "blue")) +
    scale_x_date(NULL,
               breaks = presidential_amend$start,
               date_labels = "'%y") +
    scale_y_continuous(breaks = presidential_amend$id,
                       labels = presidential_amend$id_label) +
    ggrepel::geom_label_repel(aes(label = name),
                            nudge_x = 0.5,
                            nudge_y = 0.4) +
    labs(y = "",
         x = "Presidential term",
         title = str_wrap("Republicans
         seem to be winning the race in terms of time in office?", 
         width = 70))

Labelling each term with the name of the president.

Done above.
Adding informative plot labels.

Done above.

Placing breaks every 4 years (this is trickier than it seems!).

    presidential_amend <- presidential %>%
      mutate(id = 33 + row_number(),
             id_label = str_c(id, c(rep("th ", 7), 
                              "st ", "nd ", "rd ", "th "), 
                              c(rep("President", 11))),
             id_label = fct_reorder(id_label, id)) 

    breaks <- function(df){
      df %>% 
        mutate(yr_start = year(start),
               yr_end = year(end)) %>% 
        summarise(min_start = min(yr_start),
               max_end = max(yr_end))
    }

    break_df <- presidential_amend %>% 
     breaks() 

    presidential_amend %>%
      ggplot(aes(start, id, colour = party)) +
        geom_point() +
        geom_segment(aes(xend = end, yend = id)) +
        scale_colour_manual(values = c(Republican = "red", 
                                       Democratic = "blue")) +
        scale_x_date(NULL,
                     breaks = seq.Date(ymd(str_glue("{break_df$min_start}0101")), 
                                  ymd(str_glue("{break_df$max_end}0101")), 
                                  "4 years"),
                     date_labels = "'%y") +
        scale_y_continuous(breaks = presidential_amend$id,
                           labels = presidential_amend$id_label) +
        ggrepel::geom_label_repel(aes(label = name),
                                nudge_x = 0.5,
                                nudge_y = 0.4) +
        labs(y = "",
             x = "Presidential term",
             title = str_wrap("Republicans
             seem to be winning the race in terms of time in office?", 
             width = 70))

Use override.aes to make the legend on the following plot easier to see.

ggplot(diamonds, aes(carat, price)) +
  geom_point(aes(colour = cut), alpha = 1/20)

ggplot(diamonds, aes(carat, price)) +
  geom_point(aes(colour = cut), alpha = 1/20) +
  guides(colour = guide_legend(override.aes = list(alpha = 1)))

Zooming

You can control plot limits by:

Adjusting what data are plotted
Setting the limits in each scale
Setting xlim and ylim in coord_cartesian()

To zoom in on a region of the plot, use coord_cartesian().

ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(colour = class)) +
  geom_smooth() +
  # zoom in to a specific set of coords
  coord_cartesian(xlim = c(5,7), ylim = c(10,30))


mpg %>% 
  filter(displ >= 5 & displ <= 7,
         hwy >= 10 & hwy <= 30) %>% 
  ggplot(aes(displ, hwy)) +
  geom_point(aes(colour = class)) +
  # we draw a smoothing fn for the data
  # but we filtered obs to just the ones we want to see
  geom_smooth()

Notice above that coord_cartesian() is the true zoom in, the smoothing function considers all data. In the filtered graph the smoothing function considers only the filtered observations so it is a very different view of the data. Consider your use case for zooming then choose a method.

suv <- mpg %>% filter(class == "suv")
compact <- mpg %>% filter(class == "compact")
ggplot(suv, aes(displ, hwy, colour = drv)) +
  geom_point()

ggplot(compact, aes(displ, hwy, colour = drv)) +
  geom_point()

The scales above are however different so comparisons are not easy. You may share scales across multiple plots.

x_scale <- scale_x_continuous(limits = range(mpg$displ))
y_scale <- scale_y_continuous(limits = range(mpg$hwy))
col_scale <- scale_colour_discrete(limits = unique(mpg$drv))
ggplot(suv, aes(displ, hwy, colour = drv)) +
  geom_point() +
  x_scale +
  y_scale +
  col_scale

ggplot(compact, aes(displ, hwy, colour = drv)) +
  geom_point() +
  x_scale +
  y_scale +
  col_scale

We could have used faceting but this technique is useful if for instance, you want spread plots over multiple pages of a report.

Themes

Non-data elements of your plot can be amended with a theme.

ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(color = class)) +
  geom_smooth(se = FALSE) +
  theme_bw()


ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(colour = class)) +
  geom_smooth(se = FALSE) +
  theme_light()


ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(colour = class)) +
  geom_smooth(se = FALSE) +
  theme_dark()

sessionInfo()
#> R version 3.6.3 (2020-02-29)
#> Platform: x86_64-w64-mingw32/x64 (64-bit)
#> Running under: Windows 10 x64 (build 18363)
#> 
#> Matrix products: default
#> 
#> locale:
#> [1] LC_COLLATE=English_South Africa.1252  LC_CTYPE=English_South Africa.1252   
#> [3] LC_MONETARY=English_South Africa.1252 LC_NUMERIC=C                         
#> [5] LC_TIME=English_South Africa.1252    
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#>  [1] werpals_0.1.0   lubridate_1.7.9 magrittr_1.5    flair_0.0.2    
#>  [5] forcats_0.5.0   stringr_1.4.0   dplyr_1.0.2     purrr_0.3.4    
#>  [9] readr_1.4.0     tidyr_1.1.2     tibble_3.0.3    ggplot2_3.3.2  
#> [13] tidyverse_1.3.0 workflowr_1.6.2
#> 
#> loaded via a namespace (and not attached):
#>  [1] viridis_0.5.1      httr_1.4.2         viridisLite_0.3.0  jsonlite_1.7.1    
#>  [5] splines_3.6.3      modelr_0.1.8       assertthat_0.2.1   highr_0.8         
#>  [9] emo_0.0.0.9000     cellranger_1.1.0   yaml_2.2.1         ggrepel_0.8.2     
#> [13] pillar_1.4.6       backports_1.1.6    lattice_0.20-38    glue_1.4.2        
#> [17] digest_0.6.27      RColorBrewer_1.1-2 promises_1.1.0     rvest_0.3.6       
#> [21] colorspace_1.4-1   htmltools_0.5.0    httpuv_1.5.2       Matrix_1.2-18     
#> [25] pkgconfig_2.0.3    broom_0.7.2        haven_2.3.1        scales_1.1.0      
#> [29] whisker_0.4        later_1.0.0        git2r_0.26.1       mgcv_1.8-31       
#> [33] generics_0.0.2     farver_2.0.3       ellipsis_0.3.1     withr_2.2.0       
#> [37] hexbin_1.28.1      cli_2.1.0          crayon_1.3.4       readxl_1.3.1      
#> [41] evaluate_0.14      ps_1.3.2           fs_1.5.0           fansi_0.4.1       
#> [45] nlme_3.1-144       xml2_1.3.2         tools_3.6.3        hms_0.5.3         
#> [49] lifecycle_0.2.0    munsell_0.5.0      reprex_0.3.0       compiler_3.6.3    
#> [53] rlang_0.4.8        grid_3.6.3         rstudioapi_0.11    labeling_0.3      
#> [57] rmarkdown_2.4      gtable_0.3.0       DBI_1.1.0          R6_2.4.1          
#> [61] gridExtra_2.3      knitr_1.28         utf8_1.1.4         rprojroot_1.3-2   
#> [65] stringi_1.5.3      Rcpp_1.0.4.6       vctrs_0.3.2        dbplyr_2.0.0      
#> [69] tidyselect_1.1.0   xfun_0.13

Chapter 22 - Graphics for communication

Vebash Naidoo

09/11/2020

ggplot

Introduction

Label

Exercises

Annotations

Annotations

Exercises

Scales

Scales

Axis ticks and legend keys

Legend layout

Replacing a scale

Exercises

Zooming

Themes

Themes