Data Visualization in Rcustomizing ggplot2 
2020-08-221 / 54

Scales2 / 54

Scalesposition scales2 / 54

Scalesposition scalesscale_x_continuous()scale_y_date()scale_x_log10()2 / 54

Scalesaesthetic scales3 / 54

Scalesaesthetic scalesscale_color_hue()scale_fill_brewer()scale_shape_manual()3 / 54

mtcars %>% 
ggplot(aes(hp, mpg, col = factor(cyl))) + 
  geom_point() + 
  scale_x_log10() + 
  scale_color_brewer(palette = "Set2")

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mtcars %>% 
ggplot(aes(hp, mpg, col = factor(cyl))) + 
  geom_point() + 
  scale_x_log10() +
  scale_color_brewer(palette = "Set2")

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mtcars %>% 
ggplot(aes(hp, mpg, col = factor(cyl))) + 
  geom_point() + 
  scale_x_log10() +
  scale_color_brewer(palette = "Set2")

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mtcars %>% 
ggplot(aes(hp, mpg, col = factor(cyl))) + 
  geom_point() + 
  scale_x_log10() + 
  scale_color_brewer(palette = "Set2")

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Your Turn 10

1. Change the color scale by adding a scale layer. Experiment with `scale_color_distiller()` and `scale_color_viridis_c()`. Check the help pages for different palette options.

2. Set the color aesthetic to `gender`. Try `scale_color_brewer()`.

3. Set the colors manually with `scale_color_manual()`. Use `values = c("#E69F00", "#56B4E9")` in the function call.

4. Change the legend title for the color legend. Use the `name` argument in whatever scale function you're using.

diabetes %>% 
  ggplot(aes(waist, hip, col = weight)) + 
  geom_point()

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diabetes %>% 
  ggplot(aes(waist, hip, col = weight)) + 
  geom_point() + 
  scale_color_viridis_c()

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diabetes %>% 
  ggplot(aes(waist, hip, col = gender)) + 
  geom_point() + 
  scale_color_brewer()

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diabetes %>% 
  ggplot(aes(waist, hip, col = gender)) + 
  geom_point() + 
  scale_color_manual(values = c("#E69F00", "#56B4E9"))

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diabetes %>% 
  ggplot(aes(waist, hip, col = gender)) + 
  geom_point() + 
  scale_color_manual(name = "Sex", values = c("#E69F00", "#56B4E9"))

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Color Palettes

https://github.com/EmilHvitfeldt/r-color-palettes

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Themes14 / 54

ThemesNon-data ink (text, background, etc)14 / 54

ThemesNon-data ink (text, background, etc)Pre-specified themes: theme_gray() (default), theme_minimal(), theme_light(), etc.15 / 54

ThemesNon-data ink (text, background, etc)Pre-specified themes: theme_gray() (default), theme_minimal(), theme_light(), etc.theme()16 / 54

mtcars %>%
ggplot(aes(hp, mpg, col = factor(cyl))) +
  geom_point(size = 3) +
  scale_x_log10() + 
  scale_colour_brewer(name = "Cylinders", palette = "Set2") + 
  theme_minimal() +
  theme(
    axis.text = element_text(size = 16),
    legend.text = element_text(size = 8, face = "bold"),
    legend.direction = "horizontal"
  )

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mtcars %>% 
ggplot(aes(hp, mpg, col = factor(cyl))) + 
  geom_point(size = 3) + 
  scale_x_log10() +
  scale_colour_brewer(name = "Cylinders", palette = "Set2") +
  theme_minimal() +
  theme(
    axis.text = element_text(size = 16),
    legend.text = element_text(size = 8, face = "bold"),
    legend.direction = "horizontal"
  )

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mtcars %>% 
ggplot(aes(hp, mpg, col = factor(cyl))) + 
  geom_point(size = 3) + 
  scale_x_log10() + 
  scale_colour_brewer(name = "Cylinders", palette = "Set2") +
  theme_minimal() +
  theme(
    axis.text = element_text(size = 16),
    legend.text = element_text(size = 8, face = "bold"),
    legend.direction = "horizontal"
  )

19 / 54

mtcars %>% 
ggplot(aes(hp, mpg, col = factor(cyl))) + 
  geom_point(size = 3) + 
  scale_x_log10() + 
  scale_colour_brewer(name = "Cylinders", palette = "Set2") +
  theme_minimal() +
  theme(
    axis.text = element_text(size = 16),
    legend.text = element_text(size = 8, face = "bold"),
    legend.direction = "horizontal"
  )

20 / 54

mtcars %>% 
ggplot(aes(hp, mpg, col = factor(cyl))) + 
  geom_point(size = 3) + 
  scale_x_log10() + 
  scale_colour_brewer(name = "Cylinders", palette = "Set2") +
  theme_minimal() +
  theme(
    axis.text = element_text(size = 16),
    legend.text = element_text(size = 8, face = "bold"),
    legend.direction = "horizontal"
  )

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mtcars %>% 
ggplot(aes(hp, mpg, col = factor(cyl))) + 
  geom_point(size = 3) + 
  scale_x_log10() + 
  scale_colour_brewer(name = "Cylinders", palette = "Set2") +
  theme_minimal() +
  theme(
    axis.text = element_text(size = 16),
    legend.text = element_text(size = 8, face = "bold"),
    legend.direction = "horizontal"
  )

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23 / 54

theme elements

element
draws


element_blank()
nothing (remove element)

element_line()
lines

element_rect()
borders and backgrounds

element_text()
text

24 / 54

element	draws
element_blank()	nothing (remove element)
element_line()	lines
element_rect()	borders and backgrounds
element_text()	text

Your Turn 11

1. Change the theme using one of the built-in theme functions.

2. Use `theme()` to change the legend to the bottom with `legend.position = "bottom"`.

3. Remove the axis ticks by setting the `axis.ticks` argument to `element_blank()`

4. Change the font size for the axis titles. Use `element_text()`. Check the help page if you don't know what option to change.

diabetes %>% 
  ggplot(aes(waist, hip, col = weight)) + 
  geom_point() +
  scale_color_viridis_c()

25 / 54

diabetes %>% 
  ggplot(aes(waist, hip, col = weight)) + 
  geom_point() +
  scale_color_viridis_c() +
  theme_minimal() +
  theme(
    legend.position = "bottom",
    axis.ticks = element_blank(),
    axis.title = element_text(size = 16)
  )

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High-density plots

`?rnorm`, `?Distributions`

big_data <- tibble(x = rnorm(10000), y = rnorm(10000))

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High-density plots

big_data %>% 
  ggplot(aes(x, y)) + 
  geom_point()

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High-density plots

30 / 54

High-density plots

Transparency
Binning

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big_data %>% 
  ggplot(aes(x, y)) + 
  geom_point(alpha = .05)

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big_data %>% 
  ggplot(aes(x, y)) + 
  geom_bin2d()

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big_data %>% 
  ggplot(aes(x, y)) + 
  geom_hex()

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Your Turn 12

Take a look at the `diamonds` data set from ggplot2. How many rows does it have?

diamonds

1. Make a scatterplot of `carat` vs. `price`. How's it look?

2. Try adjusting the transparency.

3. Replace `geom_point()` with 2d bins.

4. Try hex bins.

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diamonds %>% 
  ggplot(aes(x = carat, price)) + 
  geom_point()

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diamonds %>% 
  ggplot(aes(x = carat, price)) + 
  geom_point(alpha = .05)

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diamonds %>% 
  ggplot(aes(x = carat, price)) + 
  geom_bin2d()

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diamonds %>% 
  ggplot(aes(x = carat, price)) + 
  geom_hex()

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Labels, titles, and legends40 / 54

Labels, titles, and legendsAdd a title:ggtitle()labs(title = "My Awesome Plot")41 / 54

Labels, titles, and legendsChange a label:xlab(), ylab()labs(x = "X Label", y = "Y Label")42 / 54

Labels, titles, and legendsChange a legend:scale_*() functionslabs(color = "Wow, labs does everything", fill = "Yup")43 / 54

Labels, titles, and legendsChange a legend:scale_*() functionslabs(color = "Wow, labs does everything", fill = "Yup")Remove the legend: theme(legend.position = "none")44 / 54

Your Turn 13

1. Add a title.

2. Change the x and y axis labels to include the unites (inches for `hip` and pounds for `weight`). You can use either `labs()` or `xlab()` and `ylab()`

3. Add `scale_linetype()` and set the `name` argument to "Sex".

ggplot(diabetes, aes(weight, hip, linetype = gender)) +
  geom_jitter(alpha = .2, size = 2.5) +
  geom_smooth(color = "black", se = FALSE) + 
  theme_bw(base_size = 12)

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ggplot(diabetes, aes(weight, hip, linetype = gender)) +
  geom_jitter(alpha = .2, size = 2.5) +
  geom_smooth(color = "black", se = FALSE) + 
  theme_bw(base_size = 12) +
  labs(x = "Weight (lbs)", y = "Hip (inches)") +
  ggtitle("Hip and Weight by Sex") +
  scale_linetype(name = "Sex")

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ggplot(diabetes, aes(weight, hip, linetype = gender)) +
  geom_jitter(alpha = .2, size = 2.5) +
  geom_smooth(color = "black", se = FALSE) + 
  theme_bw(base_size = 12) +
  labs(
    title = "Hip and Weight by Sex",
    x = "Weight (lbs)",
    y = "Hip (inches)",
    linetype = "Sex"
  )

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Saving plots49 / 54

Saving plotsggsave(filename = "figure_name.png", plot = last_plot(), dpi = 320)49 / 54

Your Turn 14Save the last plot and then locate it in the files pane.50 / 54

Your Turn 14

Save the last plot and then locate it in the files pane.

ggsave("diabetes_weight_hip.png", dpi = 320)

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Take aways:

You can use this code template to make thousands of graphs with ggplot2.

ggplot(data = <DATA>, mapping = aes(<MAPPINGS>)) +
  <GEOM_FUNCTION>() +
  <SCALE_FUNCTION>() + 
  <THEME_FUNCTION>()

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Data Visualization in R

customizing ggplot2

2020-08-22

Scales

Scales

position scales

Scales

position scales

scale_x_continuous()

scale_y_date()

scale_x_log10()

Scales

aesthetic scales

Scales

aesthetic scales

scale_color_hue()

scale_fill_brewer()

scale_shape_manual()

Your Turn 10

1. Change the color scale by adding a scale layer. Experiment with scale_color_distiller() and scale_color_viridis_c(). Check the help pages for different palette options.

2. Set the color aesthetic to gender. Try scale_color_brewer().

3. Set the colors manually with scale_color_manual(). Use values = c("#E69F00", "#56B4E9") in the function call.

4. Change the legend title for the color legend. Use the name argument in whatever scale function you're using.

Color Palettes

https://github.com/EmilHvitfeldt/r-color-palettes

Themes

Themes

Non-data ink (text, background, etc)

Themes

Non-data ink (text, background, etc)

Pre-specified themes: theme_gray() (default), theme_minimal(), theme_light(), etc.

Themes

Non-data ink (text, background, etc)

Pre-specified themes: theme_gray() (default), theme_minimal(), theme_light(), etc.

theme()

theme elements

Your Turn 11

1. Change the theme using one of the built-in theme functions.

2. Use theme() to change the legend to the bottom with legend.position = "bottom".

3. Remove the axis ticks by setting the axis.ticks argument to element_blank()

4. Change the font size for the axis titles. Use element_text(). Check the help page if you don't know what option to change.

High-density plots

?rnorm, ?Distributions

High-density plots

High-density plots

High-density plots

Your Turn 12

Take a look at the diamonds data set from ggplot2. How many rows does it have?

1. Make a scatterplot of carat vs. price. How's it look?

2. Try adjusting the transparency.

3. Replace geom_point() with 2d bins.

4. Try hex bins.

Labels, titles, and legends

Labels, titles, and legends

Add a title:

ggtitle()

labs(title = "My Awesome Plot")

Labels, titles, and legends

Change a label:

xlab(), ylab()

labs(x = "X Label", y = "Y Label")

Labels, titles, and legends

Change a legend:

scale_*() functions

labs(color = "Wow, labs does everything", fill = "Yup")

Labels, titles, and legends

Change a legend:

scale_*() functions

labs(color = "Wow, labs does everything", fill = "Yup")

Remove the legend: theme(legend.position = "none")

Your Turn 13

1. Add a title.

2. Change the x and y axis labels to include the unites (inches for hip and pounds for weight). You can use either labs() or xlab() and ylab()

3. Add scale_linetype() and set the name argument to "Sex".

Saving plots

Saving plots

ggsave(filename = "figure_name.png", plot = last_plot(), dpi = 320)

Your Turn 14

Save the last plot and then locate it in the files pane.

Your Turn 14

`scale_x_continuous()`

`scale_y_date()`

`scale_x_log10()`

`scale_color_hue()`

`scale_fill_brewer()`

`scale_shape_manual()`

1. Change the color scale by adding a scale layer. Experiment with `scale_color_distiller()` and `scale_color_viridis_c()`. Check the help pages for different palette options.

2. Set the color aesthetic to `gender`. Try `scale_color_brewer()`.

3. Set the colors manually with `scale_color_manual()`. Use `values = c("#E69F00", "#56B4E9")` in the function call.

4. Change the legend title for the color legend. Use the `name` argument in whatever scale function you're using.

Pre-specified themes: `theme_gray()` (default), `theme_minimal()`, `theme_light()`, etc.

Pre-specified themes: `theme_gray()` (default), `theme_minimal()`, `theme_light()`, etc.

`theme()`

2. Use `theme()` to change the legend to the bottom with `legend.position = "bottom"`.

3. Remove the axis ticks by setting the `axis.ticks` argument to `element_blank()`

4. Change the font size for the axis titles. Use `element_text()`. Check the help page if you don't know what option to change.

`?rnorm`, `?Distributions`

Take a look at the `diamonds` data set from ggplot2. How many rows does it have?

1. Make a scatterplot of `carat` vs. `price`. How's it look?

3. Replace `geom_point()` with 2d bins.

`ggtitle()`

`labs(title = "My Awesome Plot")`

`xlab()`, `ylab()`

`labs(x = "X Label", y = "Y Label")`

**`scale_*()`** functions

`labs(color = "Wow, labs does everything", fill = "Yup")`

`scale_*()` functions

`labs(color = "Wow, labs does everything", fill = "Yup")`

Remove the legend: `theme(legend.position = "none")`

2. Change the x and y axis labels to include the unites (inches for `hip` and pounds for `weight`). You can use either `labs()` or `xlab()` and `ylab()`

3. Add `scale_linetype()` and set the `name` argument to "Sex".

`ggsave(filename = "figure_name.png", plot = last_plot(), dpi = 320)`