03 - Plots, Graphics, and tables

Layout

::: {#fig-dogs layout-ncol=2}

![Sleeping](images/boston-sleep.png){#fig-sleep}

![Happy](images/boston-terrier.png){#fig-awake}

The two states of Howard.

:::

Layout

(a) Sleeping

(b) Happy

Figure 1: The two states of Howard.

Figure panels

::: {layout-ncol=2}
![Howard on a couch](images/howard-comfy.png)

![Howard in a table](images/howard-table.png)
:::

Multiple rows

::: {layout-nrow=2}
![Howard on a couch](images/howard-comfy.png)

![Howard in a table](images/howard-table.png)

![Howard sleeping](images/boston-sleep.png)

![Howard awake](images/boston-terrier.png)
:::

Figure divs

You can treat any markdown content you want as a figure by enclosing it in Pandoc div block with an identifier prefaced with #fig-. For example, here we create a figure that includes an embedded iframe:

::: {#fig-elephant}

<iframe width="560" height="315" src="https://www.youtube.com/embed/SNggmeilXDQ"></iframe>

Elephant
:::

Figure 2: Elephant

Your Turn

  • Open quarto-workshop/07-static/figure-layout
  • Swap between row and col layout

Custom Layouts

Read [[1,1], [1]] as:

Row 1: two equal sized images each taking up half of the column

Row 2: one image, taking up the entire column

::: {layout="[[1,1], [1]]"}
![Howard sleeping](images/boston-sleep.png)

![Howard in a table](images/howard-table.png)

![Howard on a couch](images/howard-comfy.png)
:::

Custom Layouts

Read "[[70,30], [100]]" as:

Row 1: two images, taking up 70% and 30% of the column

Row 2: one image, taking up 100% of column

::: {layout="[[70,30], [100]]"}

![Howard on a couch](images/howard-comfy.png)

![Howard sleeping](images/boston-sleep.png)

![Howard in a table](images/howard-table.png)
:::

Custom layouts

You can also add negative values for “blank space”

[[40,-20,40], [100]]

Row 1: 40% image 1, 20% blank, 40% image 2

Row 2: 100% image 3

::: {layout="[[40,-20,40], [100]]"}
![Howard on a couch](images/howard-comfy.png)

![Howard sleeping](images/boston-sleep.png)

![Howard in a table](images/howard-table.png)
:::

Custom layouts, vertical

If images within a row are of differing heights, you can control vertical alignment.

{layout="[25,-2,10]" layout-valign="bottom"}

Row: 25

::: {layout="[25,-2,10]" layout-valign="bottom"}
![Howard on a couch](images/howard-comfy.png)

![Howard in a table](images/howard-table.png)
:::

Your Turn

  • Open quarto-workshop/07-visuals/figure-layout2.qmd

Figure layout

```{r}
#| eval: false
#| layout-ncol: 2
#| fig-cap:
#|   - "Speed and Stopping Distances of Cars"
#|   - "Engine displacement and fuel efficiency in Cars"

cars |> 
  ggplot(aes(x = speed, y = dist)) +
  geom_point()

mtcars |> 
  ggplot(aes(x = disp, y = mpg)) +
  geom_point()
```

Figure layout, subcaptions

```{r}
#| eval: false
#| label: fig-charts
#| fig-cap: Charts
#| fig-subcap:
#|   - "Cars"
#|   - "mtcars"
#| layout-ncol: 2

cars |> 
  ggplot(aes(x = speed, y = dist)) +
  geom_point()

mtcars |> 
  ggplot(aes(x = disp, y = mpg)) +
  geom_point()
```

Figure 3: Charts

Figure layout, custom

```{r}
#| eval: false
#| layout: [[45,-10, 45], [100]]

cars |> 
  ggplot(aes(x = speed, y = dist)) +
  geom_point()

mtcars |> 
  ggplot(aes(x = disp, y = mpg)) +
  geom_point()

mtcars |> 
  ggplot(aes(x = cyl, y = mpg, group = cyl, color = factor(cyl))) +
  geom_boxplot() +
  geom_jitter()
```

Your Turn

  • Open quarto-workshop/07-visuals/plot-layout.qmd
  • Explore, try it out and render

Block layout, arbitrary content

::: {layout-ncol=2}
## List One

- Item A
- Item B
- Item C

## List Two

- Item X
- Item Y
- Item Z
:::

Block layout, arbitrary content

List One

  • Item A
  • Item B
  • Item C

List Two

  • Item X
  • Item Y
  • Item Z

Block layout, arbitrary content

::: {layout-ncol=2}
- Item X
- Item Y
- Item Z

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Curabitur gravida eu erat et fring. Morbi congue augue vel eros ullamcorper, eget convallis tortor sagittis. Fusce sodales viverra mauris a fringilla. Donec feugiat, justo eu blandit placerat, enim dui volutpat turpis, eu dictum lectus urna eu urna. Mauris sed massa ornare, interdum ipsum a, semper massa. 
:::

Block layout, arbitrary content

  • Item X
  • Item Y
  • Item Z

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Curabitur gravida eu erat et fring. Morbi congue augue vel eros ullamcorper, eget convallis tortor sagittis. Fusce sodales viverra mauris a fringilla. Donec feugiat, justo eu blandit placerat, enim dui volutpat turpis, eu dictum lectus urna eu urna. Mauris sed massa ornare, interdum ipsum a, semper massa.

Grid, custom layout

The “grid” layout in Quarto is 12 units wide - you can break up the grid into different subsections as long as they add up to 12.

This column takes 1/3 of the page

This column takes 2/3 of the page

::: {.grid}

::: {.g-col-4}
This column takes 1/3 of the page
![](boston-terrier.png)
:::

::: {.g-col-8}
This column takes 2/3 of the page
![](boston-sleep.png)
:::

:::

Your Turn

  • Try out the grid layout in the previous slide, modifying the .g-col-8 to add up to a total of 12
  • Create a new Quarto doc

Article Layout

Quarto supports a variety of page layout options that enable you to author content that:

  • Fills the main content region .column-body
  • Overflows the content region .column-body-outset
  • Spans the entire page .column-page
  • Occupies the document margin .column-margin

Put it in a div, and assign a specific class

:::{.column-body-outset}
Outset content...
:::
:::{.column-margin}
Margin/aside figure
:::

Your Turn

  • Open quarto-workshop/07-visuals/plots.qmd
  • Try modifying the layout with ::: {.column-???} syntax

ggplot2

library(palmerpenguins)
library(ggplot2)
ggplot2::theme_set(ggplot2::theme_minimal())
penguins <- na.omit(penguins)

ggplot2

mass_flipper <- ggplot(data = penguins, 
                       aes(x = flipper_length_mm,
                           y = body_mass_g)) +
  geom_point(aes(color = species, 
                 shape = species),
             size = 3,
             alpha = 0.8) +
  scale_color_manual(values = c("darkorange","purple","cyan4")) +
  labs(title = "Penguin size, Palmer Station LTER",
       subtitle = "Flipper length and body mass for Adelie, Chinstrap and Gentoo Penguins",
       x = "Flipper length (mm)",
       y = "Body mass (g)",
       color = "Penguin species",
       shape = "Penguin species") +
  theme(legend.position = c(0.2, 0.7),
        plot.title.position = "plot",
        plot.caption = element_text(hjust = 0, face= "italic"),
        plot.caption.position = "plot")

ggplot2

mass_flipper

ggplot2

```{r}
#| fig-height: 2
mass_flipper
```

ggplot2

```{r}
#| fig-height: 4
#| fig-dpi: 600
mass_flipper
```

ggplot2

```{r}
#| fig-height: 10
#| fig-dpi: 600
mass_flipper
```

ggplot2

```{r}
#| fig-height: 10
#| fig-width: 16
#| fig-dpi: 400
mass_flipper
```

ggplot2

```{r}
#| fig-align: center
mass_flipper
```

Interactivity

Quarto supports JupyterWidgets (with Jupyter) or htmlwidgets in R/knitr

library(leaflet)
leaflet() %>%
  addTiles() %>%  # Add default OpenStreetMap map tiles
  addMarkers(lng=-77.0168, lat=38.7814, popup="You are here - in Maryland!")

Observable.js

Quarto includes native support for Observable JS, a set of enhancements to vanilla JavaScript created by Mike Bostock (also the author of D3). Observable JS is distinguished by its reactive runtime, which is especially well suited for interactive data exploration and analysis.

ggiraph

Example from Claus Wilke

library(tidyverse)
library(sf)
library(ggiraph)
library(glue)
library(cowplot)

texas_income <- readRDS(url("https://wilkelab.org/SDS375/datasets/Texas_income.rds")) %>%
  select(-county)
US_census <- read_csv("https://wilkelab.org/SDS375/datasets/US_census.csv")
tx_census <- US_census %>% 
  filter(state == "Texas") %>%
  select(FIPS, name, pop2010, area) %>%
  extract(name, "county", regex = "(.+) County") %>%
  mutate(
    FIPS = as.character(FIPS),
    popratio = pop2010/median(pop2010),
  )
texas_counties <- left_join(texas_income, tx_census, by = "FIPS")

ggiraph

texas_scatter <- texas_counties %>%
  ggplot(aes(pop2010, median_income)) +
  geom_point_interactive(
    aes(
      tooltip = county,
      data_id = county
    ),
    na.rm = TRUE, size = 3
  ) +
  scale_x_log10() +
  theme_bw()
# then make the map
texas_county_map <- texas_counties %>%
  ggplot() +
  geom_sf_interactive(
    aes(
      tooltip = county,
      data_id = county
    ),
    size = 0.2, color = "black"
  ) +
  theme_void()
girafe(
  ggobj = plot_grid(texas_scatter, texas_county_map),
  width_svg = 6,
  height_svg = 3
)

ggiraph

Tables

gt + gtExtras

With the gt package, anyone can make wonderful-looking tables using the R programming language. The gt philosophy: we can construct a wide variety of useful tables with a cohesive set of table parts. These include the table header, the stub, the column labels and spanner column labels, the table body, and the table footer.

gt, a Grammar of Tables

gt

library(dplyr, warn.conflicts = FALSE)
library(gt)
library(gtExtras)
library(gtsummary)

head(mtcars) |> 
  gt() |> 
  tab_options(
    table.font.size = px(40), #< because I've made the slides a lot bigger
    column_labels.font.size = px(45) #< normally don't have to increase this
  )
mpg cyl disp hp drat wt qsec vs am gear carb
21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
18.1 6 225 105 2.76 3.460 20.22 1 0 3 1

gt extension packages

gtExtras

  • gtExtras provides additional helper functions to gt, mainly themes and inline plotting

gtsummary

  • gtsummary extends gt to descriptive statistics and statistical summaries of models

gtsummary

  • Comes with built in example datasets like trial
trial2 <- trial %>% dplyr::select(trt, age, grade)
dplyr::glimpse(trial2)
Rows: 200
Columns: 3
$ trt   <chr> "Drug A", "Drug B", "Drug A", "Drug A", "Drug A", "Drug B", "Dru…
$ age   <dbl> 23, 9, 31, NA, 51, 39, 37, 32, 31, 34, 42, 63, 54, 21, 48, 71, 3…
$ grade <fct> II, I, II, III, III, I, II, I, II, I, III, I, III, I, I, III, II…
trial2 %>% tbl_summary(by = trt) %>% add_p()
Characteristic Drug A, N = 981 Drug B, N = 1021 p-value2
Age 46 (37, 59) 48 (39, 56) 0.7
Unknown 7 4
Grade 0.9
I 35 (36%) 33 (32%)
II 32 (33%) 36 (35%)
III 31 (32%) 33 (32%)
1 Median (IQR); n (%)
2 Wilcoxon rank sum test; Pearson's Chi-squared test

Create a summary

penguins <- palmerpenguins::penguins

penguin_sum <- penguins |>
  na.omit() |>
  group_by(species, sex) |>
  summarise(
    n = n(),
    bill_len = mean(bill_length_mm),
    plt_bill = list(bill_length_mm),
    mass = mean(body_mass_g),
    plt_mass = list(body_mass_g),
    .groups = "drop"
  ) |> 
  arrange(species, desc(bill_len))

penguin_sum
# A tibble: 6 × 7
  species   sex        n bill_len plt_bill    mass plt_mass  
  <fct>     <fct>  <int>    <dbl> <list>     <dbl> <list>    
1 Adelie    male      73     40.4 <dbl [73]> 4043. <int [73]>
2 Adelie    female    73     37.3 <dbl [73]> 3369. <int [73]>
3 Chinstrap male      34     51.1 <dbl [34]> 3939. <int [34]>
4 Chinstrap female    34     46.6 <dbl [34]> 3527. <int [34]>
5 Gentoo    male      61     49.5 <dbl [61]> 5485. <int [61]>
6 Gentoo    female    58     45.6 <dbl [58]> 4680. <int [58]>

gtExtras

```{r}
#| output-location: slide
#| fig-width: 10
gt(penguin_sum) |>
  gt_plt_dist(
    plt_bill, 
    line_color = "#447099", 
    fill = "#75aadb",
    fig_dim = c(15, 50)
    ) |> 
  gt_plt_dist(
    plt_mass, 
    line_color = "#447099", 
    fill = "#75aadb"
    ) |> 
  gt_add_divider(
    c(bill_len, mass), "left", 
    weight = px(1), color = "#d3d3d3"
    ) |> 
  cols_align("left", species) |> 
  text_transform(
    cells_body(sex),
    fn = function(x){
      sex_planet <- ifelse(x == "male", "mars", "venus")
      
      purrr::map(
        sex_planet, 
        ~fontawesome::fa(
          .x, height = "45px",
          fill = ifelse(.x =="mars", "#7fbf7b", "#af8dc3")))
                 }) |>
  gt_theme_nytimes() |>
  tab_options(data_row.padding = px(2),
              table.font.size = px(40),
              column_labels.font.size = px(35))
```

gtExtras

species sex n bill_len plt_bill mass plt_mass
Adelie 73 40.39041 4043.493
Adelie 73 37.25753 3368.836
Chinstrap 34 51.09412 3938.971
Chinstrap 34 46.57353 3527.206
Gentoo 61 49.47377 5484.836
Gentoo 58 45.56379 4679.741

gtExtras

Original: https://twitter.com/thomas_mock/status/1442541043019390982?s=20&t=0JewnMHkYrfPrYosfE2XIg

party_df <- tibble(
  Party = c("SPD", "CDU/CSU", "Greens", "FDP", "AfD", "Left", "Other"),
  Seats = c(206, 196, 118, 92, 83, 39, 1),
  `% of 2nd Votes` = c(25.7, 24.1, 14.8, 11.5, 10.3, 4.9, 8.7)
)

minimal_table <- gt(party_df) %>% 
  gtExtras::gt_plt_dot(column = Seats, category_column = Party,  max_value = 379,
    palette = c("#ec323f", "black", "#63d64a", "#fff24e", "#4fabf7", "#e956ad", "grey")) %>% 
  gtExtras::gt_theme_nytimes() %>% 
  gt::tab_header(title = "Results by Party in the Bundestag Election",
             subtitle = "Seats and votes are based on provisional official results.") %>% 
  gt::cols_width(Party ~ px(300), 3 ~ px(30))

gtExtras

Your Turn

  • Open quarto-workshop/07-static/gt-summary.qmd
  • Open quarto-workshop/07-static/stat-html.qmd
  • Explore and render