Goals

We will understand how:

• Data are mapped to aesthetics
• Plots are built in layers by geoms
• Control aesthetics using scales
• Show small multiples using facets
• Control axes and legends using scales/guides
• Change plot appearance using themes and other options
• Save plots using ggsave()

Plan

1. General overview of key concepts: aesthetics, geoms, layering, scales, facets, themes
2. Step-by-step example, to demonstrate details

Note: This is not an exhaustive tutorial; it is an overview of the features I use most often. Much more at ggplot2.tidyverse.org!

The Grammar of Graphics

• Largely developed by Leland Wilkinson in The Grammar of Graphics
• Extended by Hadley Wickham (paper), original author of ggplot2

The Grammar of Graphics

• Largely developed by Leland Wilkinson in The Grammar of Graphics
• Extended by Hadley Wickham (paper), original author of ggplot2

The Grammar of Graphics

• Largely developed by Leland Wilkinson in The Grammar of Graphics
• Extended by Hadley Wickham (paper), original author of ggplot2

(compare this to trying to remember all the arguments to par)

Components of Statistical Graphics

• Data
• Aesthetic mappings of the data (eg, location or size)
• Geometric objects (ie, the shape of the data)
• Scales
• Statistical transformation
• Coordinates

Components of Statistical Graphics

• Data
• Aesthetic mappings of the data (eg, location or size)
• Geometric objects (ie, the shape of the data)
• Scales
• Statistical transformation
• Coordinates

ggplot(
  data = df,
  aes(x = xvar, y = yvar)
) +
  geom_point(stat = "identity") +
  scale_x_continuous(
    limits = c(min(df$xvar), max(df$xvar)),
    name = "X Axis"
  ) +
  scale_y_continuous(
    limits = c(min(df$yvar), max(df$yvar)),
    name = "Y Axis"
  )

with(df, plot(yvar ~ xvar))

ggplot(
  data = df,
  aes(x = xvar, y = yvar)
) +
  geom_point()

Notice: ggplot2 knows which stat, scales to use by default

Represent Data with aesthetics

• "Aesthetics" = how we map the values of data to the appearance of the plot
• Examples:
  • X, Y axis values
  • Color, shape, sizes of points
  • Opacity, fill of shapes

print(head(df, n = 3), digits = 2)

##    xvar  yvar
## 1 -0.34 -0.08
## 2 -1.64 -1.03
## 3 -1.06 -0.78

ggplot(
  data = df,
  aes(x = xvar, y = yvar)
)

print(head(df, n = 3), digits = 2)

##    xvar  yvar
## 1 -0.34 -0.08
## 2 -1.64 -1.03
## 3 -1.06 -0.78

ggplot(
  data = df,
  aes(x = xvar, y = yvar)
)

print(head(df, n = 3), digits = 2)

##    xvar  yvar
## 1 -0.34 -0.08
## 2 -1.64 -1.03
## 3 -1.06 -0.78

ggplot(
  data = df,
  aes(x = xvar, y = yvar)
)

print(head(df, n = 3), digits = 2)

##    xvar  yvar
## 1 -0.34 -0.08
## 2 -1.64 -1.03
## 3 -1.06 -0.78

ggplot(
  data = df,
  aes(x = xvar, y = yvar)
)

We haven't told it how to show the data!

Represent Data with geoms

geoms determine the shape of the data.

ggplot(
  data = df, aes(x = xvar, y = yvar)
) +
  geom_point()

Represent Data with geoms

geoms determine the shape of the data.

ggplot(
  data = df, aes(x = xvar, y = yvar)
) +
  geom_point()

Represent Data with geoms

geoms determine the shape of the data.

ggplot(
  data = df, aes(x = xvar, y = yvar)
) +
  geom_line()

Represent Data with geoms

geoms determine the shape of the data.

ggplot(
  data = df, aes(x = xvar, y = yvar)
) +
  geom_line()

The aesthetics you need depend on the geom you want to show.

Examples:

• geom_point, geom_line each need only X, Y values
• geom_ribbon needs X, but rather than a single Y, it needs ymin and ymax

The aesthetics you need depend on the geom you want to show.

Examples:

• geom_point, geom_line each need only X, Y values
• geom_ribbon needs X, but rather than a single Y, it needs ymin and ymax

Your Turn!

Look at the help files for these geoms and see what aesthetics each one needs.

library(ggplot2)
?geom_line
?geom_boxplot
?geom_bar
?geom_ribbon

Your Turn!

Using the gapminder dataset from the year 2007, show the relationship between gdpPercap and lifeExp using aesthetics and geoms.

# install.packages("gapminder")
library(gapminder)
head(gapminder)

## # A tibble: 6 x 6
##   country     continent  year lifeExp      pop gdpPercap
##   <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
## 1 Afghanistan Asia       1952    28.8  8425333       779
## 2 Afghanistan Asia       1957    30.3  9240934       821
## 3 Afghanistan Asia       1962    32.0 10267083       853
## 4 Afghanistan Asia       1967    34.0 11537966       836
## 5 Afghanistan Asia       1972    36.1 13079460       740
## 6 Afghanistan Asia       1977    38.4 14880372       786

gap2007 <- subset(gapminder, year == 2007)

Your Turn!

p <- ggplot(
  data = gap2007,
  aes(x = gdpPercap, y = lifeExp)
)

p +
  geom_point()

p +
  geom_line()

geoms + stats

Some geoms require the user to supply all the information needed to map each point.

• geom_point, geom_line, geom_ribbon

geoms + stats

Some geoms require the user to supply all the information needed to map each point.

• geom_point, geom_line, geom_ribbon

Others use stats behind the scenes to summarize the data.

ggplot(
  data = df, aes(x = 1, y = yvar)
) +
  geom_boxplot()

Often we won't need to call stats explicitly; geoms have excellent defaults that do much of the work for us!

Scenario: Raw Data + Regression Line

We want to represent the same data, using a summary as well as the raw data.

We can do this by layering geoms.

ggplot(
  data = gap2007,
  aes(x = gdpPercap, y = lifeExp)
) +
  geom_point()

Scenario: Raw Data + Regression Line

We want to represent the same data, using a summary as well as the raw data.

We can do this by layering geoms.

ggplot(
  data = gap2007,
  aes(x = gdpPercap, y = lifeExp)
) +
  geom_point() +
  geom_smooth()

Scenario: Raw Data + Regression Line

We want to represent the same data, using a summary as well as the raw data.

We can do this by layering geoms.

ggplot(
  data = gap2007,
  aes(x = gdpPercap, y = lifeExp)
) +
  geom_point() +
  geom_smooth() +
  geom_rug()

Your Turn!

Summarize and show raw data for each country's gross domestic product (gdpPercap).

For extra credit 😁, do this separately for each continent.

• What geoms would you use?
• What aesthetics would you use?

Your Turn!

Summarize and show raw data for each country's gross domestic product (gdpPercap).

For extra credit 😁, do this separately for each continent.

• What geoms would you use?
• What aesthetics would you use?

ggplot(
  data = gap2007,
  aes(x = continent, y = gdpPercap)
) +
  geom_boxplot() +
  geom_point()

Positions

positions help us change the position of data a bit. Positions are still based on aesthetics, but sometimes it is helpful to modify those values.

For example, you may have many single points with the same value, or several groups contained within one value.

Positions

positions help us change the position of data a bit. Positions are still based on aesthetics, but sometimes it is helpful to modify those values.

For example, you may have many single points with the same value, or several groups contained within one value.

Common position functions:

• position_dodge(): vertical position stays the same; horizontal changes
• position_stack(): stacks bars on top of one another
• position_fill(): stacks bars and standardizes each to the same height
• position_jitter(): adds random noise to values to avoid overplotting

Example: Barcharts

rct_df <- data.frame(
  trt = factor(c("A", "A", "B", "B")),
  sex = factor(rep(c("Male", "Female"), 2)),
  npts = c(52, 48, 65, 75)
)

ggplot(
  data = rct_df,
  aes(x = trt, y = npts,
      group = sex, fill = sex)
) +
  geom_bar(
    stat = "identity",
    position = position_dodge()
  )

Example: Barcharts

rct_df <- data.frame(
  trt = factor(c("A", "A", "B", "B")),
  sex = factor(rep(c("Male", "Female"), 2)),
  npts = c(52, 48, 65, 75)
)

ggplot(
  data = rct_df,
  aes(x = trt, y = npts,
      group = sex, fill = sex)
) +
  geom_bar(
    stat = "identity",
    position = position_stack()
  )

Example: Barcharts

rct_df <- data.frame(
  trt = factor(c("A", "A", "B", "B")),
  sex = factor(rep(c("Male", "Female"), 2)),
  npts = c(52, 48, 65, 75)
)

ggplot(
  data = rct_df,
  aes(x = trt, y = npts,
      group = sex, fill = sex)
) +
  geom_bar(
    stat = "identity",
    position = position_fill()
  )

Positions

Because positions are functions, we can add arguments to control them further.

ggplot(
  data = rct_df,
  aes(x = trt, y = npts,
      group = sex, fill = sex)
) +
  geom_bar(
    stat = "identity",
    position =
      position_dodge2(padding = 0.2)
  )

Positions

But if we want the default position settings, we can use shortcuts:

ggplot(
  data = rct_df,
  aes(x = trt, y = npts,
      group = sex, fill = sex)
) +
  geom_bar(
    stat = "identity",
    position = "dodge2"
  )

Your Turn!

Take the boxplot we made earlier and use a position to reduce the overplotting of the raw data.

ggplot(
  data = gap2007,
  aes(x = continent, y = gdpPercap)
) +
  geom_boxplot() +
  geom_point()

Your Turn!

ggplot(
  data = gap2007,
  aes(x = continent, y = gdpPercap)
) +
  geom_boxplot() +
  geom_point(
    position = "jitter"
  )

Your Turn!

ggplot(
  data = gap2007,
  aes(x = continent, y = gdpPercap)
) +
  geom_boxplot() +
  geom_point(
    position =
      position_jitter(width = 0.25)
  )

Inheritance

Did You Notice...

...so far, we have only explicitly specified our data and aesthetics in the initial ggplot() call?

Even when we had three separate layers!

Inheritance

Did You Notice...

...so far, we have only explicitly specified our data and aesthetics in the initial ggplot() call?

Even when we had three separate layers!

ggplot2 uses inheritance. This means that each layer uses the same data and aesthetics set in ggplot(...), unless we tell it otherwise.

• Simplicity: Inheritance means we don't have to specify data and aesthetics multiple times
• Power: We can use different datasets or aesthetics for each layer if we want to

Example of Inheritance: Marginal Effects Plots

ggplot(
  data = predvals,
  aes(x = pointest, y = adjvalue)
) +
  ## Use inherited data;
  ##  specify aesthetics
  geom_ribbon(
    aes(ymin = lcl, ymax = ucl)
  ) +
  ## Use inherited data + aesthetics
  geom_line() +
  ## Add raw data
  geom_point(
    aes(x = covar, y = orgvalue),
    data = orgdata
  )

Boxplot Example

ggplot(
  data = gap2007,
  aes(x = continent, y = gdpPercap)
) +
  geom_boxplot() +
  geom_point() +
  scale_y_continuous(
    limits = c(0, 5000),
    breaks = seq(0, 5000, 1025),
    labels = scales::comma,
    name = "GDP per Capita"
  )

Note: ggplot2 automatically set gridlines at our break points!

Barchart Example

Note how scales control the legend!

ggplot(
  data = rct_df,
  aes(x = trt, y = npts,
      group = sex, fill = sex)
) +
  geom_bar(
    stat = "identity",
    position = "dodge2"
  ) +
   scale_fill_hue(
     h = c(90, 270), l = 40,
       ## change *hues*, *lightness*
     name = "Patient Sex"
       ## change *name*
   )

Types of scales

Scales generally correspond to aesthetics. Some common scale types:

• scale_[x, y]_[continuous/discrete]
• scale_[colour, fill]_[many options!]
• scale_size_..., scale_shape_..., scale_alpha_...

This is not an exhaustive list. See the ggplot2 reference pages for more options.

Note: For color scales and aesthetics, you can use either color or colour.

Types of scales

Scales generally correspond to aesthetics. Some common scale types:

• scale_[x, y]_[continuous/discrete]
• scale_[colour, fill]_[many options!]
• scale_size_..., scale_shape_..., scale_alpha_...

This is not an exhaustive list. See the ggplot2 reference pages for more options.

Note: For color scales and aesthetics, you can use either color or colour.

scales have intelligent default values, but you can use different scale types to use specific values you choose. Examples:

• A particular color palette
• Beginning and ending sizes (maybe you want the smallest size to still be seen on a projector)
• Breaks on X, Y axes at clinically relevant points

Your Turn!

Using the boxplot we made earlier, use scales (and maybe aesthetics) to

1. Change the name of the X axis
2. Make each country's raw data a different color
3. Not include a legend

?scale_x_discrete

?scale_color_hue

Your Turn!

Using the boxplot we made earlier, use scales (and maybe aesthetics) to

1. Change the name of the X axis
2. Make each country's raw data a different color
3. Not include a legend

?scale_x_discrete

?scale_color_hue

ggplot(
  data = gap2007,
  aes(x = continent, y = gdpPercap)
) +
  geom_boxplot(outlier.shape = NA) +
  geom_point(
    aes(color = continent),
    position =
      position_jitter(width = 0.2),
    alpha = 0.6
  ) +
  scale_x_discrete(name="Continent") +
  scale_color_hue(guide = FALSE)

Choosing a color scale

By default, ggplot2 uses color scales that allow for the most difference between categories on a color wheel, or to show a spectrum of continuous values.

You can change these defaults in several ways, including:

• Tweak the defaults with scale_color_hue() or scale_color_gradient() - for example, change the gradient color from the default blue to green, or change the range of hues for a categorical variable
• Use one of the built-in ColorBrewer schemes, which are built to handle sequential, diverging, and qualitative color schemes (scale_color_brewer() for categorical, scale_color_distiller() for continuous)
• Supply a manual color scheme, using words like "blue" or hex colors (eg, #FAFAFA): scale_color_manual()
• I personally like the viridis color schemes. (These are not currently included in ggplot2 itself, but will be in the next version released to CRAN this summer. You can install the viridisLite package to use them now.) These scales print well, even in black & white, and are built to be perceived by people with color blindness.

(All scale options above also apply to scale_fill_xxxx)

Color Scale Examples: Default Hues

p <- ggplot(data = gap2007, aes(x = gdpPercap, y = lifeExp)) +
  geom_point(aes(color = country, size = pop), alpha = 0.5)

p + scale_color_hue(guide = FALSE)

Color Scale Examples: Manual Values

p + scale_color_manual(values = country_colors, guide = FALSE)

Color Scale Examples: viridis

library(viridisLite)
  ## or install development version of
  ## ggplot2 from Github
p + scale_color_viridis_d(guide = FALSE)

Example: GDP vs Life Expectancy

ggplot(data = gap2007, aes(x = gdpPercap, y = lifeExp)) +
  facet_wrap(~ continent) +
  geom_point() +
  geom_smooth()

Both have these arguments (among others):

• nrow, ncol: if you want a specific layout
• scales
  • fixed: default; same for every panel
  • free_y, free_x, free: allow X and/or Y axis to change for each panel

facet_grid() example

First + last years available for each continent in the gapminder data

ggplot(
  data = subset(
    gapminder,
    year %in% c(1952, 2007) &
      !(continent == "Oceania")
  ),
  aes(x = gdpPercap, y = lifeExp)
) +
  facet_grid(year ~ continent) +
  geom_point() +
  geom_smooth()

facet_grid() example

First + last years available for each continent in the gapminder data

ggplot(
  data = subset(
    gapminder,
    year %in% c(1952, 2007) &
      !(continent == "Oceania")
  ),
  aes(x = gdpPercap, y = lifeExp)
) +
  facet_grid(year ~ continent) +
  geom_point() +
  geom_smooth()

Leaving scales consistent between panels is great for comparisons between panels (like here). If we are more interested in informing than comparison, we may want to let the scales vary by panel.

facet_grid() example

ggplot(
  data = subset(
    gapminder,
    year %in% c(1952, 2007) &
      !(continent == "Oceania")
  ),
  aes(x = gdpPercap, y = lifeExp)
) +
  facet_grid(
    year ~ continent,
    scales = "free_x"
  ) +
  geom_point() +
  geom_smooth()

facet_grid() example

ggplot(
  data = subset(
    gapminder,
    year %in% c(1952, 2007) &
      !(continent == "Oceania")
  ),
  aes(x = gdpPercap, y = lifeExp)
) +
  facet_grid(
    year ~ continent,
    scales = "free_x"
  ) +
  geom_point() +
  geom_smooth()

The X axis scales for each column are still the same for the two rows, allowing us to see the differences between 1952 and 2007 clearly for each continent.

But the X axis scales are different from column to column; we can see the relationships for Africa without being affected by the larger GDPs found in Europe, for example.

Your Turn!

Using the original gapminder data for 1952 and 2007, update your boxplots of GDP by continent to show one column for each of those two years, and one row for each continent.

Hint: You can set the X aesthetic to always be 1.

Your Turn!

Using the original gapminder data for 1952 and 2007, update your boxplots of GDP by continent to show one column for each of those two years, and one row for each continent.

Hint: You can set the X aesthetic to always be 1.

ggplot(
  data = subset(
    gapminder, year %in% c(1952, 2007)
  ),
  aes(
    x = 1,
    y = gdpPercap
  )
) +
  facet_grid(
     continent ~ year,
     scales = "free_y"
   ) + 
  geom_boxplot() +
  geom_point()

Change the look of your plot

Using ggplot2 themes, it is easy to give your plots a different look with one line.

p <- ggplot(data = gap2007, aes(x = gdpPercap, y = lifeExp)) +
  geom_point()

p + theme_bw()

p + theme_minimal()

Changing Plot Elements with themes

We also use themes to change elements of the plot that are not related to data:

p + theme_minimal()

p + theme_minimal() +
  theme(
    panel.border = element_rect(
      fill = NA,
      color = "#b756b9",
      size = 5
    )
  )

elements

Pieces of the theme are controlled by the element functions (examples):

• element_line(): gridlines, axis lines
• element_text(): axis labels, titles, captions
• element_rect(): plot and panel backgrounds, facet strip backgrounds
• element_blank(): can be used for any element to "make it disappear"

Use these functions to set aspects like sizes, colors, font faces...

Your Turn!

Give a boxplot we made earlier a different overall theme, then customize at least one element. You might...

• Make the axis text bold or bigger
• Make the gridlines thicker or disappear
• Make the strip.background a different color

Your Turn!

Give a boxplot we made earlier a different overall theme, then customize at least one element. You might...

• Make the axis text bold or bigger
• Make the gridlines thicker or disappear
• Make the strip.background a different color

p +
  theme_minimal() +
  theme(
    axis.title.x = element_blank(),
    axis.text.x = element_blank(),
    strip.background =
      element_rect(fill = "gray90"),
    panel.background =
      element_rect(fill = NA, color = "gray90"),
    legend.position = "none"
  )

Adding Labels and Titles

We may want to add a title to our plot, or a caption to give additional information about how something was defined. Maybe we want to name an aesthetic "Patient Sex" instead of "sex" but with less typing than scale_x_discrete(name = "Patient Sex").

labs() allows us to set these elements:

• title
• subtitle
• caption
• titles for aesthetics (these will show up in the legend, or X/Y axis titles)

p +
  labs(x = "My X axis title")

Adding Labels and Titles

We may want to add a title to our plot, or a caption to give additional information about how something was defined. Maybe we want to name an aesthetic "Patient Sex" instead of "sex" but with less typing than scale_x_discrete(name = "Patient Sex").

labs() allows us to set these elements:

• title
• subtitle
• caption
• titles for aesthetics (these will show up in the legend, or X/Y axis titles)

p +
  labs(x = "My X axis title")

Your Turn!

Use labs() to add a plot title and change the Y axis title on the plot you just made.

Controlling Non-Data Elements

To control elements of the plot that represent data, but not in a way directly tied to our data, we can still use aesthetic qualities. However, we will set them outside the aes(...) function.

Example: We want all our points to be a certain color, or to make a line width thicker to be seen better in a presentation.

ggplot(
  data = gap2007,
  aes(x = gdpPercap, y = lifeExp)
) +
  geom_point(
    color = "#a7a9ac",
    alpha = 0.75,
    size = 1.25
  ) +
  geom_smooth(
    fill = "#532354",
    color = "#b756b9",
    alpha = 0.2,
    size = 2
  ) +
  geom_rug(
    alpha = 0.3, color = "#532354"
  )

Goal

Create a publication-quality chart showing the relationship between gross domestic product and life expectancy, 1952 and 2007.

Preparation

## Only look at 1952, 2007
gap_sub <- subset(
  gapminder,
  year %in% c(1952, 2007)
)

gdp_exp <- ggplot(
  data = gap_sub,
  aes(x = gdpPercap, y = lifeExp)
)

gdp_exp

facet by year and continent

gdp_exp <- gdp_exp +
  facet_grid(year ~ continent)

Add data points

We want to use a different color for each country, and make our point sizes vary according to the countries' population size.

gdp_exp <- gdp_exp +
  geom_point(
    aes(color = country, size = pop),
    alpha = 0.6 ## to help overplotting
  )

Add data points

We want to use a different color for each country, and make our point sizes vary according to the countries' population size.

gdp_exp <- gdp_exp +
  geom_point(
    aes(color = country, size = pop),
    alpha = 0.6 ## to help overplotting
  )

😱 Fix it! With scales

As we saw, a legend for color will not be helpful; there are too many countries.

However, a legend would be helpful for the population sizes.

We will use two scales to

1) Specify the colors we want, using scale_color_manual and country_colors, a named vector of hex colors that comes with the gapminder package

head(country_colors)

##          Nigeria            Egypt         Ethiopia Congo, Dem. Rep. 
##        "#7F3B08"        "#833D07"        "#873F07"        "#8B4107" 
##     South Africa            Sudan 
##        "#8F4407"        "#934607"

2) Format a legend for our population sizes

Modification of scales

gdp_exp <- gdp_exp +
  scale_color_manual(
    ## Manually specify colors
    values = country_colors,
    ## Turn off the legend for colors
    guide = FALSE
  ) +
  scale_size(
    range = c(3, 7),
    labels = function(x){
      scales::comma(x / 1000000)
    },
    name =
      "Population\n(x 1M)"
  )

Exploratory data analysis

We see that there is a lot of space on the X axis that may be unnecessary, possibly caused by one point. What point is it?

ggplot(
  data = gap_sub,
  aes(x = gdpPercap)
) +
  geom_histogram()

Exploratory data analysis

We see that there is a lot of space on the X axis that may be unnecessary, possibly caused by one point. What point is it?

ggplot(
  data = gap_sub,
  aes(x = gdpPercap)
) +
  geom_histogram()

The extreme outlier in 1952 is from Kuwait; looking at other years in the gapminder data, it seems to be a legitimate value, but including it is keeping us from seeing the other data as clearly.

We'll exclude it from our plot, but will make a note in a figure caption.

Modify X axis scale

## Save X axis title to a string so we can see the whole thing
xtitle <- "Gross Domestic Product per Capita\n(x $1,000 USD)"

gdp_exp <- gdp_exp +
  scale_x_continuous(
    limits = c(0, 55000),
    labels = function(x){
      scales::dollar(x / 1000)
    },
    name = xtitle
  )

Add Labels

plottitle <- "GDP vs Life Expectancy, 1952 and 2007"
captitle <- sprintf(
  "Kuwait's 1952 values were excluded due to its extremely high GDP of $%s.\nAverage life expectancy at that time was %s.",
  format(
    round(subset(gap_sub, year == 1952 & country == "Kuwait")$gdpPercap),
    big.mark = ","
  ),
  round(subset(gap_sub, year == 1952 & country == "Kuwait")$lifeExp, 1)
)

gdp_exp <- gdp_exp +
  labs(
    title = plottitle,
    subtitle =
      "Source: gapminder.org/data",
    caption = captitle,
    y = "Life Expectancy (Years)"
  )

Modify Theme Elements

gdp_exp <- gdp_exp +
  theme_bw() +
  theme(
    plot.title = element_text(
      face = "bold", size = 16
    ),
    axis.title.x = element_text(
      vjust = 0
    ),
    plot.caption = element_text(
      vjust = 0, face = "italic"
    ),
    strip.text = element_text(
      face = "bold", size = 12
    ),
    legend.position = "bottom",
    legend.direction = "horizontal"
  )

Final Result

Your Turn!

Using any of these strategies (or others!), make changes to the boxplots we've been working with.

Some ideas:

• Changing the color of the boxplots, or removing the inside altogether
• Changing the breaks of the Y axis
• Adding axis, plot titles
• Citing the source of our data

Any other ideas are welcome!

Saving Your Plots

The ggsave function allows us to easily save our figures in several formats. For example, you might want to create a PDF of the figure for a journal submission, but have a PNG for PowerPoint presentations.

ggsave(
  filename =
    "gapminder_gdplifeexp.pdf",
  gdp_exp,
  device = "pdf",
  path = "figures/",
  width = 10,
  height = 7,
  units = "in"
)

ggsave(
  filename =
    "gapminder_gdplifeexp.png",
  gdp_exp,
  device = "png",
  path = "figures/",
  width = 10,
  height = 7,
  units = "in"
)

Saving Your Plots

The ggsave function allows us to easily save our figures in several formats. For example, you might want to create a PDF of the figure for a journal submission, but have a PNG for PowerPoint presentations.

ggsave(
  filename =
    "gapminder_gdplifeexp.pdf",
  gdp_exp,
  device = "pdf",
  path = "figures/",
  width = 10,
  height = 7,
  units = "in"
)

ggsave(
  filename =
    "gapminder_gdplifeexp.png",
  gdp_exp,
  device = "png",
  path = "figures/",
  width = 10,
  height = 7,
  units = "in"
)

Your Turn!

Save the results of your plot to a figures/ directory in two different formats.

Helpful Resources

• ggplot2 reference page
• Cookbook for R
• Data visualization and graphics for communication, R for Data Science
• ggplot2 extensions, for geoms that are not included in ggplot2 itself
• ggthemr for building custom themes and color palettes (not currently on CRAN; install from Github)
• Color palettes:
  • ColorBrewer
  • colorhexa.com
• Theoretical basis:
  • ggplot2: Elegant Graphics for Data Analysis
  • Grammar of Graphics