Week 7 (Workbook) - Piecing Plots Together

Piecing Plots Together

Packages

Code

# | code-line-numbers: "11-13"
library(RColorBrewer)
library(knitr)
library(kableExtra)
library(plyr)
library(broom)
library(modelr)
library(lme4)
library(broom.mixed)
library(tidyverse)
library(ggdist)
library(patchwork)
library(cowplot)
library(ggExtra)
library(distributional)
library(gganimate)

Custom Theme:

Code

my_theme <- function(){
  theme_bw() + 
  theme(
    legend.position = "bottom"
    , legend.title = element_text(face = "bold", size = rel(1))
    , legend.text = element_text(face = "italic", size = rel(1))
    , axis.text = element_text(face = "bold", size = rel(1.1), color = "black")
    , axis.title = element_text(face = "bold", size = rel(1.2))
    , plot.title = element_text(face = "bold", size = rel(1.2), hjust = .5)
    , plot.subtitle = element_text(face = "italic", size = rel(1.2), hjust = .5)
    , strip.text = element_text(face = "bold", size = rel(1.1), color = "white")
    , strip.background = element_rect(fill = "black")
    )
}

Review

• Over the last several weeks, we have talked about:
  • tidying data
  • ggplot2 logic
  • visualizing proportions
  • visualizing differences
  • visualizing time series
  • visualizing uncertainty

• For the rest of the course, we will pivot to taking everything we’ve learning and piecing it all together
  • Today: Piecing visualizations together
  • Next week: Polishing visualizations **
  • 03/05: Interactive Visualizations (shiny)

Today

• There are lots of packages for piecing visualizations together

• Over the years, I’ve tried:

  • ggExtra
  • cowplot
  • patchwork

• Although patchwork wins by a landslide (imo), each has helpful unique features, so I’ll show you elements of each

• Here is a short list of some core ggplot2 extensions: https://exts.ggplot2.tidyverse.org/gallery/

• We’ll cover:

  • ggExtra
  • patchwork
  • cowplot (and lots of assortments)

ggExtra

• We’ll start with ggExtra because it will help us create plots with distributions in the margins.
• After, we’ll move to patchwork, where there will be lots of little odds and ends to step through
• Remember these data?

Code

load(url("https://github.com/emoriebeck/psc290-data-viz-2022/blob/main/04-week4-associations/04-data/week4-data.RData?raw=true"))
pred_data

Let’s plot the association between conscientiousness and self-rated health across genders in Study 1:

Code

p <- pred_data %>% 
  filter(study == "Study1") %>%
  ggplot(aes(x = p_value, y = SRhealth, color = gender)) + 
    geom_point(
      , size = 2
      , alpha = .5
      ) + 
    scale_color_manual(
      values = c("cornflowerblue", "coral")
      , labels = c("Male", "Female")
      ) + 
    labs(
      x = "Conscientiousness (POMP, 0-10)"
      , y = "Self-Rated Health (POMP, 0-10)"
      , color = "Gender"
    ) + 
    my_theme()
p

Add a smoothed line:

Code

p <- p + 
  geom_smooth(
    method = "lm"
    ) 
p

To get marginal distributions, we can just use ggExtra::ggMarginal()

Code

ggMarginal(p)

This is fine, but we can do better!

Let’s try color:

Code

ggMarginal(p, color = "purple4")

Let’s try fill:

Code

ggMarginal(
  p
  , color = "purple4"
  , fill = "purple4"
  , alpha = .5
  )

Let’s try a histogram:

Code

ggMarginal(
  p
  , color = "purple4"
  , fill = "purple4"
  , alpha = .5
  , type = "histogram"
  )

Let’s color and fill based on groups in the data:

cowplot + pathwork

• Why cowplot or patchwork?
  • figure alignment
  • easier to choose relative values and layouts
  • can mix base R plots and ggplot2 plots
  • allows you to annotate plots (including stacking, as opposed to layering)
  • shared legends!
  • includes the themes from his book

Patchwork: Piecing the Plots Together

Code

px <- pred_data %>% 
  filter(study == "Study1") %>%
  ggplot(aes(x = p_value, fill = gender, color = gender)) + 
    geom_density(alpha = .5) + 
    scale_color_manual(
      values = c("cornflowerblue", "coral")
      , labels = c("Male", "Female")
      ) + 
    scale_fill_manual(
      values = c("cornflowerblue", "coral")
      , labels = c("Male", "Female")
      ) + 
    labs(fill = "Gender", color = "Gender") + 
    theme_void()
px

Code

py <- pred_data %>% 
  filter(study == "Study1") %>%
  ggplot(aes(x = SRhealth, fill = gender, color = gender)) + 
    geom_density(alpha = .5) + 
    scale_color_manual(
      values = c("cornflowerblue", "coral")
      , labels = c("Male", "Female")
      ) + 
    scale_fill_manual(
      values = c("cornflowerblue", "coral")
      , labels = c("Male", "Female")
      ) + 
    labs(fill = "Gender", color = "Gender") + 
    coord_flip() + 
    theme_void()
py

We can use the + and / operators to arrange them:

Code

px / (p + py)

That arrangement isn’t quite right. Let’s try a custom layout:

Code

layout <- "
AAAAAA##
BBBBBBCC
BBBBBBCC
BBBBBBCC
BBBBBBCC
BBBBBBCC
"

px + p + py +
  plot_layout(design = layout)

Those legends are messing us up! Let’s “collect” them and move them to the bottom.

Code

layout <- "
AAAAAA##
BBBBBBCC
BBBBBBCC
BBBBBBCC
BBBBBBCC
BBBBBBCC
"

px + p + py +
  plot_layout(
    design = layout
    , guides = "collect"
    ) & 
  theme(legend.position = "bottom")

Honestly, we don’t need the marginal legend, so let’s remove those legends all together.

Code

layout <- "
AAAAAA##
BBBBBBCC
BBBBBBCC
BBBBBBCC
BBBBBBCC
BBBBBBCC
BBBBBBCC
"

(px + theme(legend.position = "none")) + 
  p + 
  (py + theme(legend.position = "none")) +
  plot_layout(design = layout) 

We could also use this to add marginal boxplots, instead of the density distributions from ggextra:

Code

px <- pred_data %>% 
  filter(study == "Study1") %>%
  ggplot(aes(x = p_value, y = gender, fill = gender, color = gender)) + 
    geom_boxplot(alpha = .5) + 
    geom_jitter(aes(y = gender), alpha = .5) + 
    scale_color_manual(
      values = c("cornflowerblue", "coral")
      , labels = c("Male", "Female")
      ) + 
    scale_fill_manual(
      values = c("cornflowerblue", "coral")
      , labels = c("Male", "Female")
      ) + 
    labs(fill = "Gender", color = "Gender") + 
    theme_void() + 
    theme(legend.position = "none")
px

Code

py <- pred_data %>% 
  filter(study == "Study1") %>%
  ggplot(aes(x = SRhealth, y = gender, fill = gender, color = gender)) + 
    geom_boxplot(alpha = .5) + 
    geom_jitter(aes(y = gender), alpha = .5) + 
    scale_color_manual(
      values = c("cornflowerblue", "coral")
      , labels = c("Male", "Female")
      ) + 
    scale_fill_manual(
      values = c("cornflowerblue", "coral")
      , labels = c("Male", "Female")
      ) + 
    labs(fill = "Gender", color = "Gender") + 
    coord_flip() + 
    theme_void() + 
    theme(legend.position = "none")
py

And then put them together using our custom arrangement:

Code

layout <- "
AAAAAA##
BBBBBBCC
BBBBBBCC
BBBBBBCC
"

(px + theme(legend.position = "none")) + 
  p + 
  (py + theme(legend.position = "none")) +
  plot_layout(design = layout) 

Advanced Piecing Plots Together

• Marginal plots are great for lots of reasons
• But when it comes to piecing plots together, we are often interested for bringing together different kinds of figures together because you can’t bring them together with facets or other ways

Let me show you a couple of examples from my work that has used cowplot or patchwork

Beck & Jackson, 2022, JPSP

Beck et al., 2024, Alzheimer’s & Dementia

Beck & Jackson, 2023, Psych Science

Beck et al., under review

Example: Forest Plots

• Let’s use forest plots as an example. Why use forest plots:
  • Meta-analyses are common, and within-paper meta-analyses in multi-study papers are becoming more common
  • Not only will this let us practice piecing plots together, this is a particularly advanced case that will let us learn about new elements that we can creating (e.g., via grobs)
• Let’s build up our use cases incrementally!
• But first, we need some data to plot!

And remember these models?

Code

tidy_ci <- function(m) tidy(m, conf.int = T)

nested_m <- pred_data %>%
  group_by(study) %>%
  nest() %>%
  ungroup() %>%
  mutate(
    m = map(data
            , ~glm(
              o_value ~ p_value
              , data = .
              , family = binomial(link = "logit")
              )
            )
    , tidy = map(m, tidy_ci)
  )
nested_m

Run the models

• And remember these models?
  
• Let’s make two small changes:
  • Add the number of observations
  • Add the residual degrees of freedom
• Why? We usually include these in a plot as it’s relevant information

Code

m_fun <- function(d) {
  glm(o_value ~ p_value + married + married:p_value
      , data = d
      , family = binomial(link = "logit"))
}
tidy_ci <- function(m) tidy(m, conf.int = T) %>% mutate(df.resid = m$df.residual, n = nrow(m$data))

nested_m <- pred_data %>%
  group_by(study) %>%
  nest() %>%
  ungroup() %>%
  mutate(
    m = map(data, m_fun)
    , tidy = map(m, tidy_ci)
  )
nested_m

Here’s our unnested model terms:

Code

nested_m %>% 
  select(study, tidy) %>%
  unnest(tidy) %>%
  mutate_at(vars(estimate, conf.low, conf.high), exp)

But maybe we are particularly interested in the interaction between marital status and personality in predicting mortality, which we want to plot as a forest plot

Code

nested_m %>% 
  select(study, tidy) %>%
  unnest(tidy) %>%
  mutate_at(vars(estimate, conf.low, conf.high), exp) %>%
  filter(term == "p_value:married1")

• We could hack our way to a forest plot in a single figure, but it never looks as nice as if we do it in two
  • the forest plot itself
    
  • the table of values

Example Setup: Forest Plot (P1)

Code

p1 <- nested_m %>% 
  select(study, tidy) %>%
  unnest(tidy) %>%
  mutate_at(vars(estimate, conf.low, conf.high), exp) %>%
  filter(term == "p_value:married1") %>%
  ggplot(aes(x = estimate, y = fct_rev(study))) + 
    labs(
      x = "Model Estimated OR (CI)"
      , y = NULL
      ) + 
    my_theme()
p1

Let’s add our point estimates and uncertainty intervals

Code

p1 <- p1 + 
  stat_gradientinterval(
    aes(
      xdist = dist_student_t(
        df = df.resid
        , mu = estimate
        , sigma = std.error
        ))
    , .width = c(.95, .99)
    , shape = "square"
  ) 
p1

But we want to order the points by the effect sizes:

Code

p1 <- nested_m %>% 
  select(study, tidy) %>%
  unnest(tidy) %>%
  mutate_at(vars(estimate, conf.low, conf.high), exp) %>%
  filter(term == "p_value:married1") %>%
  arrange(desc(estimate)) %>%
  mutate(study = fct_inorder(study)) %>%
  ggplot(aes(x = estimate, y = study)) + 
    labs(
      x = "Model Estimated OR (CI)"
      , y = NULL
      ) + 
    my_theme()
p1

Let’s add our point estimates and uncertainty intervals back in

Code

p1 <- p1 + 
  stat_gradientinterval(
    aes(
      xdist = dist_student_t(
        df = df.resid
        , mu = estimate
        , sigma = std.error
        ))
    , .width = c(.95, .99)
    , shape = "square"
  ) 
p1

And add in a vertical line at OR = 1:

Code

p1 <- p1 + 
  geom_vline(
    aes(xintercept = 1)
    , linetype = "dashed"
    ) 
p1 

Example Setup: Forest Plot Table (P2)

• In a forest plot, we don’t just show estimates, we print them with the sample size
  

• Let’s build a table with those!

• There are packages to do this, but I like to build them myself because it helps them play nicer with cowplot or patchwork

• To figure out how to make it, it’s easiest to figure where you want to end up and work backward.

First, we need to set up the data for the plot:

Code

p2 <- nested_m %>% 
  select(study, tidy) %>%
  unnest(tidy) %>%
  mutate_at(vars(estimate, conf.low, conf.high), exp) %>%
  filter(term == "p_value:married1") %>%
  arrange(desc(estimate)) %>%
  mutate(
    study = fct_inorder(study)
    , study2 = 1:n()
    ) %>%
  mutate_at(vars(estimate, conf.low, conf.high), ~sprintf("%.2f", .)) %>%
  mutate(
    est = sprintf("%s [%s, %s]", estimate, conf.low, conf.high)
    , n = as.character(n)
    ) %>%
  select(study, study2, estimate, n, est) %>%
  pivot_longer(
    cols = c(est, n)
    , values_to = "lab"
    , names_to = "est"
  )
p2

Let’s build our base:

Code

p2 <- p2 %>%
  ggplot(aes(x = est, y = study2)) + 
    labs(
      x = NULL
      , y = NULL
      ) + 
    my_theme()
p2

Add in the text:

Code

p2 <- p2 + 
  geom_text(aes(label = lab))
p2

Set the theme

Code

p2 <- p2 + 
  theme_void()
p2

We’ll add a horizontal line at the top and bottom to match the forest plot:

Code

p2 <- p2 + 
  geom_hline(aes(yintercept = 6.5)) + 
  theme(axis.line.x = element_line(color = "black"))
p2

Add the column labels:

Code

p2 <- p2 + 
  annotate("text"
           , x = "est" , y = 7
           , label = "b [CI]"
           , fontface = "bold"
           ) + 
  annotate("text"
           , x = "n", y = 7
           , label = "N"
           , fontface = "bold"
           ) 
p2

We need a little margin on the top and bottom:

Code

p2 <- p2 + 
  scale_y_continuous(limits = c(.4,7.1))
p2

Example Setup: Back to the Forest Plot (P1)

• We added an extra row at the top of the table, so we need to do that for the forest plot, too.
• To do so, we will use the same trick we did for the table, which is “tricking” ggplot into thinking we have a continuous y-axis

Code

p1 <- nested_m %>% select(study, tidy) %>%
  unnest(tidy) %>%
  mutate_at(vars(estimate, conf.low, conf.high), exp) %>%
  filter(term == "p_value:married1") %>%
  arrange(desc(estimate)) %>%
  mutate(study = fct_inorder(study)
         , study2 = 1:n()) %>%
  ggplot(aes(x = estimate, y = study2)) + 
    labs(
      x = "Model Estimated OR (CI)"
      , y = NULL
      ) + 
    my_theme()
p1

Add our point estimates and uncertainty intervals, along with the vertical line at OR = 1

Code

p1 <- p1 + 
  stat_gradientinterval(
    aes(
      xdist = dist_student_t(
        df = df.resid
        , mu = estimate
        , sigma = std.error
        ))
    , .width = c(.95, .99)
    , shape = "square"
  ) + 
  geom_vline(aes(xintercept = 1), linetype = "dashed") 
p1

Change the y scale back

Code

p1 <- p1 + 
  scale_y_continuous(
    limits = c(.4,7.1) # identical to table
    , breaks = seq(1,6,1) # one per study
    , labels = levels(p1$data$study)
    )
p1 

Add in that top bar:

Code

p1 <- p1 + 
  geom_hline(aes(yintercept = 6.5))
p1 

Remove the y axis line

Code

p1 <- p1 + 
  theme(
    axis.line.x = element_line()
    , axis.ticks.y = element_blank()
    , panel.border = element_blank()
    )
p1 

• Remember that ggplot is layered.
• So sometimes, you have to hack ggplot and use annotate() rectangles to block out portions of the plot.
• Let’s block out where the dashed line touches the top:

Code

p1 <- p1 + 
  annotate(
    "rect"
    , xmin = -Inf
    , xmax = Inf
    , ymin = 6.51
    , ymax = Inf
    , fill = "white"
    )
p1

patchwork

• I know that was a lot, but such is the reality of ggplot – we have to hack it!

  • annotate() is a great tool for this
  • so are our scale_[map]_[type] functions, especially given the labels can be anything we want!
  • and our theme elements also let us hack many more parts!

• The biggest trick to ggplot2 is simply having lots of tricks up your sleeve, which come from knowledge (and StackOverflow)

• patchwork is great, and a little more intuitive for simple use cases

• (We’ll still talk some about cowplot and a more full demo of it is at the end of the slides and in the workbook)

• patchwork allows you to use the + to piece plots together and makes a lot of default assumptions about alignment

• It also let’s you continue to layer on top of figures that are pieced together, which cowplot doesn’t do (easily)

We can just use the + operator!

Code

p1 + p2

We can also add rows using the /

Code

p1 / p2

And change their arrangement using plot_layout(), adjust both/either the heights and/or widths:

Code

p1 / p2 + 
  plot_layout(heights = c(3,7))

Code

p1 + p2 + 
  plot_layout(widths = c(6,4))

We can add titles using plot_annotation()

Code

p1 + p2 + 
  plot_layout(widths = c(6,4)) + 
  plot_annotation(
    title = "Mortality Odds"
    , subtitle = "Conscientiousness x Marital Status"
    , theme = my_theme()
    ) 

We can add labels to plot using plot_annotation()

Code

p1 + p2 + 
  plot_layout(widths = c(6,4)) + 
  plot_annotation(
    title = "Mortality Odds"
    , subtitle = "Conscientiousness x Marital Status"
    , theme = my_theme()
    , tag_levels = 'A'
    ) 

And change various properties of those with additional arguments:

Code

p1 + p2 + 
  plot_layout(widths = c(6,4)) + 
  plot_annotation(
    title = "Mortality Odds"
    , subtitle = "Conscientiousness x Marital Status"
    , theme = my_theme()
    , tag_levels = 'A'
    , tag_prefix = 'Fig. '
    , tag_suffix = ':'
    ) & 
  theme(plot.tag = element_text(size = 8, face = "bold"))

Example 2 Setup: Simple Effects

To accompany our parameter estimates, we may want to couple that with simple effects plots that decompose the interaction. To do so, we’ll need to get model-based predictions for the C-mortality association across levels of marital status using the predict() function.

Code

pred_fun <- function(m){
  m$data %>%
    data_grid(married, p_value = seq_range(p_value, n = 100)) %>%
    drop_na() %>%
    augment(m
            , newdata = .
            , se_fit = T
            , type.predict = "response"
            )
}

nested_m <- nested_m %>%
  mutate(pred = map(m, pred_fun)) 
nested_m

Code

nested_m %>% 
  mutate(df.resid = map_dbl(m, df.residual)) %>%
  select(study, pred, df.resid) %>%
  unnest(pred) 

Let’s set up the core part of the simple effects plots:

Code

p3 <- nested_m %>% 
  mutate(df.resid = map_dbl(m, df.residual)) %>%
  select(study, pred, df.resid) %>%
  unnest(pred) %>%
  mutate(married = factor(married, c(0,1), c("Never Married", "Married"))) %>%
  ggplot(aes(x = p_value, y = .fitted, fill = study, color = study)) + 
  labs(x = "Conscientiousness (POMP, 0-10)"
       , y = "Predicted Odds Ratio\nof Mortality (95% CI)"
       , fill = NULL
       , color = NULL) + 
  facet_grid(~married) + 
  my_theme()  
p3

Add in our lineribbon:

Code

p3 <- p3 + 
  stat_lineribbon(
      aes(
        ydist = dist_student_t(
          df = df.resid
          , mu = .fitted
          , sigma = .se.fit
          ))
      , alpha = .25
      , .width = c(.95,.99)
      ) + 
    scale_fill_brewer(palette = "Set2") +
    scale_color_brewer(palette = "Dark2") 
p3

We can even put these back together to combine the pieces of information together, just like what I showed you from my A&D paper!

Code

(p1 + p2) / p3

But we need to add better labels

Code

(p1 + p2) / p3 + 
  plot_layout(widths = c(6,4)) + 
  plot_annotation(
    title = "Mortality Odds"
    , subtitle = "Conscientiousness x Marital Status"
    , theme = my_theme()
    ) 

And deal with the legends:

Code

(p1 + p2) / p3 + 
  plot_layout(widths = c(6,4)) + 
  plot_annotation(
    title = "Mortality Odds"
    , subtitle = "Conscientiousness x Marital Status"
    , theme = my_theme()
    ) + 
  plot_layout(guides = 'collect')

cowplot

New grobs for drawing on our plots

• Relative to patchwork, cowplot also adds some other new tools to our repertoire:
  • ggdraw()
  • draw_label()
  • draw_plot_label()
  • draw_grob()
  • draw_image()

ggdraw()

• ggdraw() is more or a setup function that allows us to add grobs on top
• We’ll use it with draw_label() to make our title (just some text to put on the plot)

It’d be nice if the title was centered, right?

Code

(p1 +
  labs(
    subtitle = "Conscientiousness x Marital Status"
    , title = "Mortality Odds"
    )) + 
  p2 + 
  plot_layout(widths = c(6,4))

We could use cowplot::draw_label() to add a title and subtitle to our plot:

Code

title <- ggdraw() + 
  draw_label(
    "Mortality Odds"
    , fontface = 'bold'
    , x = .5
    , hjust = .5
    , y = .8
  ) +
  draw_label(
    "Conscientiousness x Marital Status"
    , fontface = 'italic'
    , x = .5
    , hjust = .5
    , y = .2
  ) +
  theme(plot.margin = margin(0, 0, 0, 7))
title

Code

p <- title / (p1 + p2) + 
  plot_layout(
    widths = c(6,4)
    , heights = c(1,9)
    )
p

draw_label()

• draw_label() is meant to be a better wrapper for geom_text() that requires less customization
  
• Say for example, we want to put a wordmark on our plots (there are journals that require this!)
• Doing this with geom_text() would require 10+ arguments and has no easy application to figures put together with cowplot (or other packages for doing so)

Code

ggdraw(p) + 
  draw_label("Draft", color = "grey80", size = 100, angle = 45)

Imagine you want to put a plot inside of another

Code

inset <- 
  pred_data %>% 
  filter(study == "Study1") %>%
  ggplot(aes(y = gender, x = SRhealth, fill = gender)) + 
    scale_fill_manual(values = c("cornflowerblue", "coral")) + 
    scale_y_discrete(labels = c("Male", "Female")) + 
    stat_halfeye(alpha = .8) + 
    my_theme() + 
    theme(legend.position = "none") + 
    theme_half_open(12)

Code

p4 <- pred_data %>% 
  filter(study == "Study1") %>%
  ggplot(aes(x = p_value, SRhealth, fill = gender)) + 
    geom_point(
      shape = 21
      , color = "grey20"
      , size = 2
      , alpha = .5
      ) + 
    scale_fill_manual(
      values = c("cornflowerblue", "coral")
      , labels = c("Male", "Female")
      ) + 
    my_theme()

Code

ggdraw(p4) + 
  draw_plot(inset, .1, .2, .6, .4)

We can also add images!

Code

ggdraw() + 
  draw_plot(p) + 
  draw_image(
    "https://github.com/emoriebeck/psc290-data-viz-2022/raw/main/01-week1-intro/02-code/02-images/ucdavis_logo_blue.png"
    , x = 1
    , y = 0.05
    , hjust = 1
    , vjust = 1
    , halign = 1
    , valign = 1
    , width = 0.15
  )

Extra Slides: cowplot::plot_grid()

plot_grid()

• The core function of cowplot is plot_grid(), which allows us to place differnt figures within the same figure in a grid, and it has a lot of useful arguments
• It’s the alternative to +, / in `patchwork

• plotlist = NULL
• align = c("none", "h", "v", "hv")
• axis = c("none", "l", "r", "t", "b", "lr", "tb", "tblr")
• nrow = NULL
• ncol = NULL
• rel_widths = 1
• rel_heights = 1

• labels = NULL
• label_size = 14
• label_fontfamily = NULL
• label_fontface = "bold"
• label_colour = NULL
• label_x = 0
• label_y = 1

• hjust = -0.5
• vjust = 1.5
• scale = 1
• greedy = TRUE
• byrow = TRUE
• cols = NULL
• rows = NULL

• But now that we have our plot, we want to put it together! Remember these?

Code

plot_grid(
  p1, p2
)

Not bad, but we want to align our plots

Code

plot_grid(p1, p2, align = "h")

Code

plot_grid(p1, p2, align = "v")

Code

plot_grid(p1, p2, align = "hv")

Similar behavior, but "hv" leads to odd spacing

Code

plot_grid(p1, p2, axis = "t")

Code

plot_grid(p1, p2, axis = "b")

Code

plot_grid(p1, p2, axis = "tblr")

Doesn’t properly align our bottom because it’s not optimized for labels

Code

plot_grid(
  p1, p2
  , align = "h"
  , nrow = 1
  , rel_widths = c(.6, .4)
  )

Let our interval estimates shine

Code

plot_grid(
  p1, p2
  , align = "hv"
  , nrow = 2
  , rel_heights = c(.6, .4)
  )

We wouldn’t do this, but note that when we have rows, we use rel_heights

plot_grid(): Labels

Code

plot_grid(
  p1, p2
  , align = "h"
  , nrow = 1
  , rel_widths = c(.6, .4)
  , labels = "auto"
  )

Code

plot_grid(
  p1, p2
  , align = "h"
  , nrow = 1
  , rel_widths = c(.6, .4)
  , labels = "AUTO"
  , label_size = 18 # 14 default
  , label_fontface = "bold.italic"
  , label_fontfamily = "Times"
  , label_colour = "purple" # u is sensitive
  )

Code

plot_grid(
  p1, p2
  , align = "h"
  , nrow = 1
  , rel_widths = c(.6, .4)
  , labels = "AUTO"
  , label_size = 18 # 14 default
  , label_fontface = "bold.italic"
  , label_fontfamily = "Times"
  , label_colour = "purple" # u is sensitive
  , label_x = .5
  , label_y = .5
  )

Code

plot_grid(
  p1, p2
  , align = "h"
  , nrow = 1
  , rel_widths = c(.6, .4)
  , labels = "AUTO"
  , label_size = 18 # 14 default
  , label_fontface = "bold.italic"
  , label_fontfamily = "Times"
  , label_colour = "purple" # u is sensitive
  , label_x = c(.1,.85)
  , label_y = c(.95,.1)
  )

Code

plot_grid(
  p1, p2
  , align = "h"
  , nrow = 1
  , rel_widths = c(.6, .4)
  , labels = "AUTO"
  , label_size = 18 # 14 default
  , label_fontface = "bold.italic"
  , label_fontfamily = "Times"
  , label_colour = "purple" # u is sensitive
  , hjust = .5
  , vjust = .5
  )