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Visaluzing Data:
Part II

Dr. Mine Dogucu

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Review

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Outline

  • More ggplot

  • ggplot extensions

  • Accessible Data Visualization

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  • Using the penguins data,
  • Map bill depth to x-axis, bill length to y-axis, species to shape and color.
  • Add a layer of points and set the size of the points to 4.
ggplot(penguins,
       aes(x = bill_depth_mm,
           y = bill_length_mm,
           shape = species,
           color = species)) +
  geom_point(size = 4)
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Labs

  • Using the penguins data,
  • Map bill depth to x-axis, bill length to y-axis, species to shape.
  • Add a layer of points and set the size of the points to 4.
  • Add labels to x-axis (Bill Depth(mm)), y-axis (Bill Length(mm)), and the title of the plot (Palmer Penguins).
ggplot(penguins,
       aes(x = bill_depth_mm,
           y = bill_length_mm,
           shape = species,
           color = species)) +
  geom_point(size = 4) +
  labs(x = "Bill Depth (mm)",
       y = "Bill Length (mm)",
       title = "Palmer Penguins")
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ggplot(penguins,
       aes(x = bill_depth_mm,
           y = bill_length_mm,
           shape = species,
           color = species)) +
  geom_point() +
  labs(x = "Bill Depth (mm)", 
       y = "Bill Length (mm)", 
       title = "Palmer Penguins") +
  theme_bw()

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Themes

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ggplot(penguins,
       aes(x = bill_depth_mm,
           y = bill_length_mm,
           shape = species,
           color = species)) +
  geom_point() +
  labs(x = "Bill Depth (mm)", 
       y = "Bill Length (mm)", 
       title = "Palmer Penguins") +
  theme_bw() +
  theme(text = element_text(size=20))

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?theme

A blog post on theme elements

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ggplot(penguins,
       aes(x = bill_depth_mm,
           y = bill_length_mm,
           shape = species,
           color = species)) +
  geom_point(size = 4) +
  facet_grid(.~species)

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ggplot(penguins,
       aes(x = bill_depth_mm,
           y = bill_length_mm,
           shape = species,
           color = species)) +
  geom_point(size = 4) +
  facet_grid(species~.)

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ggplot(penguins,
       aes(x = bill_depth_mm,
           y = bill_length_mm)) +
  geom_point() +
  xlim(0, 30) +
  ylim(0, 70)

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Top Ten Dos and Don't for Charts and Graphs

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Check out the ggplot flipbook for some inspiration. Find your favorite new function/feature. Share it with your neighbor.

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library(maps)
Warning: package 'maps' was built under R version 4.1.1
usa <- map_data("usa")
glimpse(usa)
Rows: 7,243
Columns: 6
$ long      <dbl> -101.4078, -101.3906, -101.3620, -101.3505, -101.3219, -101.~
$ lat       <dbl> 29.74224, 29.74224, 29.65056, 29.63911, 29.63338, 29.64484, ~
$ group     <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ~
$ order     <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 1~
$ region    <chr> "main", "main", "main", "main", "main", "main", "main", "mai~
$ subregion <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, ~
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ggplot(data = usa,
       aes(x = long, 
           y = lat))

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ggplot(data = usa,
       aes(x = long, 
           y = lat)) + 
  geom_polygon()

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ggplot(data = usa,
       aes(x = long, 
           y = lat)) + 
  geom_polygon() + 
  coord_fixed(1.4)

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ggplot(data = usa,
       aes(x = long, 
           y = lat)) + 
  geom_polygon(fill = NA,
               color = "steelblue") + 
  coord_fixed(1.4)

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ggplot(data = usa,
       aes(x = long, 
           y = lat)) + 
  geom_polygon(fill = NA, 
               color = "steelblue",
               aes(group = group))  + 
  coord_fixed(1.4)

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location <- c("Northampton", "Columbus", "Sarasota","Irvine" )
long <- c(-72.6412, -82.998795, -82.538602, -117.824719)
lat <- c(42.3251, 39.961178, 27.336483, 33.685908)
pop <- c(28726, 892533, 57738, 282572)
home <- tibble::tibble(location = location,
                 lat = lat,
                 long = long,
                 pop = pop)
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home
# A tibble: 4 x 4
  location      lat   long    pop
  <chr>       <dbl>  <dbl>  <dbl>
1 Northampton  42.3  -72.6  28726
2 Columbus     40.0  -83.0 892533
3 Sarasota     27.3  -82.5  57738
4 Irvine       33.7 -118.  282572
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usa_map <- 
  ggplot(data = usa,
         aes(x = long, 
             y = lat)) + 
  geom_polygon(fill = NA, color = "steelblue",
               aes(group = group)) + 
  coord_fixed(1.4)
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usa_map +
  geom_point(data = home, 
             aes(x = long,
                 y = lat, 
                 color = pop))

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usa_map +
  geom_point(data = home, 
             aes(x = long,
                 y = lat, 
                 size = pop))

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states <- map_data("state")
ggplot(data = states,
       aes(x = long, 
           y = lat,
           fill = region)) + 
  geom_polygon(aes(group = group))  + 
  coord_fixed(1.4) +
  guides(fill = "none")

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library(sf)

Maps Chapter from ggplot2 book

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ggplot extensions

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  • patchwork combining plots into a single plot
  • gganimate animated graphics
  • ggthemes additional set of themes
  • ggtext improved text rendering support for ggplot2
  • ggdag Causal directed acyclic graphs (DAGs)
  • survminer survival analysis and visualization
  • gggenes gene arrow maps

There are more extensions.

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Making Accessible Visualizations

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Color blindness simulation: red-blind/protanopia

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Color blindness simulation: green-blind/deuteranopia

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Color blindness simulation: blue-blind/tritanopia

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palette.colors(palette = "Okabe-Ito")
        black        orange       skyblue   bluishgreen        yellow 
    "#000000"     "#E69F00"     "#56B4E9"     "#009E73"     "#F0E442" 
         blue    vermillion reddishpurple          gray 
    "#0072B2"     "#D55E00"     "#CC79A7"     "#999999"

Okabe-Ito color palette allows us to make figures that are colorblind friendly. You can use the HEX code printed for selecting colors.

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ggplot(penguins,
       aes(x = bill_depth_mm,
           y = bill_length_mm,
           shape = species,
           color = species)) +
  geom_point(size = 4) +
  facet_grid(species~.) +
  scale_color_manual(values =
                      c("#E69F00",
                        "#009E73",
                        "#56B4E9"))

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Detour: R Markdown chunk options

{r, echo=TRUE, message=FALSE}

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(some) Chunk Options in R Markdown

echo = FALSE hides the code
message = FALSE hides messages
warning = FALSE hides warning
error = TRUE renders despite errors and displays the error
fig.cap = "Some figure caption" creates a figure caption
fig.alt = "Some alternate text for figure" creates alternate text for figures
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Alternate Text

"Alt text" describes contents of an image. It is used in HTML pages such as the R Markdown outputs we have been writing. Screen-readers cannot read images but can read alt text. Alt text has to be provided and cannot magically appear.

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Potential Content of Alternate Text for Data Visualization

  1. Data source/frame
  2. Variables and axes information including units
  3. The plot type
  4. The message that the plot conveys.
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Please complete the in-class activity provided to you in lecture notes.

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Review

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