R-tistic Insights: Visualising your data in R

A word to the wise

Can anyone guess what statistical phenomenon is being observed here?

datasaurus_dozen %>% group_by(dataset) %>%
  summarize( mean(x), mean(y), var(x), var(y), cor(x,y)) %>%
  kable(digits=3) %>% kable_styling(bootstrap_options = c("striped"), font_size=17)

dataset	mean(x)	mean(y)	var(x)	var(y)	cor(x, y)
away	54.266	47.835	281.227	725.750	-0.064
bullseye	54.269	47.831	281.207	725.533	-0.069
circle	54.267	47.838	280.898	725.227	-0.068
dino	54.263	47.832	281.070	725.516	-0.064
dots	54.260	47.840	281.157	725.235	-0.060
h_lines	54.261	47.830	281.095	725.757	-0.062
high_lines	54.269	47.835	281.122	725.763	-0.069
slant_down	54.268	47.836	281.124	725.554	-0.069
slant_up	54.266	47.831	281.194	725.689	-0.069
star	54.267	47.840	281.198	725.240	-0.063
v_lines	54.270	47.837	281.232	725.639	-0.069
wide_lines	54.267	47.832	281.233	725.651	-0.067
x_shape	54.260	47.840	281.231	725.225	-0.066

Visualisation in key in understanding the underlying data and in key in telling a story.

Danger

Position is the most powerful way to demonstrate differences
Pie charts/ 3D charts are bad
Always design a visualization with a question in mind

ggplot2 holds the title of the most commonly used data visualization package in R. Its central function, ggplot(), forms the backbone of this package. The package, including its resulting figures, are often casually referred to as “ggplot” and the figures it produces as “ggplots”. The prefix “gg” stands for the “grammar of graphics”, which underpins the structure of the images generated. A host of additional R packages exists to expand the capabilities of ggplot2.

In contrast to the base R plotting syntax, ggplot2 introduces a unique syntax that might pose a learning challenge initially. It usually demands data to be organized in a way that aligns well with the tidyverse, making these packages effective when used together.

Over the next 2.5 hours we’ll walk through the basics of plotting with ggplot2.

Tip

This session will be a primer. Head on to this great resources to learn more:

There are several great tips on the R Epi Handbook
Cheat sheets are hacks. The invaluable ggplot data visualization cheat sheet can be downloaded from the RStudio website.
For those seeking innovative ideas for data visualization, we recommend exploring websites such as the R graph gallery and Data-to-viz.

Prepare for liftoff

Load packages

Lets get some important packages loaded

required_packages <- c("tidyverse", "rio", "here", "stringr", "lubridate", "ggforce")
not_installed <- required_packages[!(required_packages %in% installed.packages()[ , "Package"])]    
if(length(not_installed)) install.packages(not_installed)                                           
suppressWarnings(lapply(required_packages, require, character.only = TRUE))

Loading required package: rio

Loading required package: here

here() starts at C:/R/nih_training/StatsComputingR_sbdr2023

Loading required package: ggforce

Importing some data

For the purposes of this session lets utilise the Malaysian COVID-19 deaths linelinest maintained by the Ministry of Health on their Github page. Codes for each column are as follows:

date: yyyy-mm-dd format; date of death
date_announced: date on which the death was announced to the public (i.e. registered in the public linelist)
date_positive: date of positive sample
date_doseN: date of the individual’s first/second/third dose (if any)
brandN: p = Pfizer, s = Sinovac, a = AstraZeneca, c = Cansino, m = Moderna, h = Sinopharm, j = Janssen, u = unverified (pending sync with VMS)
state: state of residence
age: age as an integer; note that it is possible for age to be 0, denoting infants less than 6 months old
male: binary variable with 1 denoting male and 0 denoting female
bid: binary variable with 1 denoting brought-in-dead and 0 denoting an inpatient death
malaysian: binary variable with 1 denoting Malaysian and 0 denoting non-Malaysian
comorb: binary variable with 1 denoting that the individual has comorbidities and 0 denoting no comorbidities declared

Lets call in the data:

c19_df <- read.csv("https://raw.githubusercontent.com/MoH-Malaysia/covid19-public/main/epidemic/linelist/linelist_deaths.csv")

Check and clean

We can have a very quick look at the data structure using the skim function from skimr.

skimr::skim(c19_df)

Data summary
Name	c19_df
Number of rows	37152
Number of columns	15
_______________________
Column type frequency:
character	10
numeric	5
________________________
Group variables	None

Variable type: character

skim_variable	complete_rate	min	max	empty	n_unique
date	1	10	10	0	1004
date_announced	1	10	10	0	1000
date_positive	1	10	10	0	999
date_dose1	1	0	10	22437	298
date_dose2	1	0	10	28034	267
date_dose3	1	0	10	35719	161
brand1	1	0	16	22437	8
brand2	1	0	16	28034	7
brand3	1	0	16	35719	6
state	1	5	17	0	16

Variable type: numeric

skim_variable	complete_rate	mean	sd	p25	p50	p75	p100	hist
age	1	62.65	16.59	51	64	75	130	▁▃▇▃▁
male	1	0.58	0.49	0	1	1	1	▆▁▁▁▇
bid	1	0.21	0.41	0	0	0	1	▇▁▁▁▂
malaysian	1	0.89	0.31	1	1	1	1	▁▁▁▁▇
comorb	1	0.79	0.41	1	1	1	1	▂▁▁▁▇

So we can already see that unvaccinated individuals are coded here as EMPTY cells- lets change these dates to NA and their brands to Unvaccinated status. We can leave everything else as it is for now.

c19_df <- c19_df %>% 
  mutate(across(where(is.character), na_if, ""),#look at all character columns and change "" empty cells to NA
         across(contains("brand"), ~replace_na(na_if(.,"NA"),"unvaccinated")),#replace all NA cells in brand columns to unvaccinated
         across(contains("date"), ~as.Date(., format = "%Y-%m-%d")),#change character to dates\ fromat
  
         duration_toDeath=as.numeric(date-date_positive)) #create a rabdom second continuous variable

Lets also create a aggregated set counting deaths by epidemiologic weeks

week_df <- c19_df %>% select(state, date, age) %>%
  mutate(age_cat= ifelse(age<20, "<19", 
                         ifelse(age>59, ">60", "20-59"))) %>%
  select(-age) %>%
  group_by(state, age_cat, date) %>%
  summarise (deaths=n()) %>% ungroup()

`summarise()` has grouped output by 'state', 'age_cat'. You can override using
the `.groups` argument.

Any idea what is wrong with the dataset produced above- take a minute!

Try opening the dataset using View(week_df) .

Well if you still don’t know try and figure out what the code below is doing.

week_df <- week_df %>%
  group_by(state, age_cat) %>%
  complete(date = seq(min(date), max(date), by="day"), fill = list(deaths = 0))

“Grammar of Graphics” via ggplot2

ggplot2 plotting functions are structured in a layered approach, with different components of the plot added sequentially using the plus (+) symbol. This methodology results in a layered plot object which can be saved, altered, displayed, or exported as needed.

Even though ggplot objects can get intricate, the usual layering sequence is as follows:

Start with the fundamental ggplot() function - this initializes the ggplot and paves the way for other functions to be appended using the + symbol. Generally, the data set to be used is declared in this function.
Add geometry layers, denoted as “geom” layers - these functions represent the data in various visual forms (shapes), like a bar chart, line graph, scatter plot, histogram, or even a mix of these. These functions are recognizable by their prefix “geom_”.
Include design components to the plot such as labels for axes, title, fonts, sizes, color patterns, legends, or axis rotation.

An example of basic code structure is provided below. Detailed explanation of each segment will follow in the succeeding sections.

# plot data from my_data columns as red points
ggplot(data = my_data)+                   # use the dataset "my_data"
  geom_point(                             # add a layer of points (dots)
    mapping = aes(x = col1, y = col2),    # "map" data column to axes
    color = "red")+                       # other specification for the geom
  labs()+                                 # here you add titles, axes labels, etc.
  theme()                                 # here you adjust color, font, size etc of non-data plot elements (axes, title, etc.)

A breakdown of each part of the GG is as follows:

ggplot(): The ggplot2 visualization begins with the ggplot() command, acting as a blank canvas for subsequent layers. It usually incorporates the data = argument, setting the default dataset. The command ends with a +, remaining “open” for additions. The plot executes only when a final layer is added without a +.
geoms: A ggplot2 plot’s foundation, created with ggplot(), needs added geometry layers (“geoms”) to visualize data through shapes like bar plots or histograms. Functions starting with geom_, also known as geom_XXXX(), create these shapes. Common ones include

geom_histogram()- Histogram
geom_bar() or geom_col()- Bar charts
geom_boxplot()- Boxplots
geom_point()- Scatter plots
geom_line() or geom_path()- Line charts
geom_smooth()- Trend lines

Tip

With >40 geoms in ggplot2 and many more developed by users, various plots can be created, as seen in the ggplot2 gallery.

Mapping and aesthetics

In ggplot2, geometry functions require data mapping instructions for plot components like axes or shape colors, typically involving the x-axis and possibly the y-axis. This is done with the mapping = aes() argument, such as mapping = aes(x = col1, y = col2). For instance, the geom_point() function inherits the axis-column assignments from the preceding ggplot() command and visually represents those relationships as points on the canvas.

Important

Aesthetic mapping within mapping = aes() can be written in several places in your plotting commands and can even be written more than once. Just remember mapping at the top command leads to structure being inherited below it.

ggplot(data = c19_df, 
       mapping = aes(x = age, y = duration_toDeath))+
  geom_point()

or mapped in the geom

ggplot(data = c19_df)+
  geom_point(mapping = aes(x = age, y = duration_toDeath))

A range of aesthetics can be defined with each geom_XXXX with come not being available in others. Several common ones include:

shape = Display a point with geom_point() as a dot, star, triangle, or square…
fill = The interior color (e.g. of a bar or boxplot)
color = The exterior line of a bar, boxplot, etc., or the point color if using geom_point()
size = Size (e.g. line thickness, point size)
alpha = Transparency (1 = opaque, 0 = invisible)
binwidth = Width of histogram bins
width = Width of “bar plot” columns
linetype = Line type (e.g. solid, dashed, dotted)

Histograms & Density charts

Histograms, unlike bar charts, depict the distribution of a continuous variable with adjoining bars, representing grouped data ranges. Created using geom_histogram(), they require only one column input and divide the data into ‘bins’ based on numerical values. The number of bins or binwidth can be specified, otherwise, an optimal value is suggested.

# A) Regular histogram
ggplot(data = c19_df, aes(x = age))+  # provide x variable
  geom_histogram()+
  labs(title = "A) Default histogram (30 bins)")

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

# B) More bins
ggplot(data = c19_df, aes(x = age))+  # provide x variable
  geom_histogram(bins = 50)+
  labs(title = "B) Set to 50 bins")

# C) Fewer bins
ggplot(data = c19_df, aes(x = age))+  # provide x variable
  geom_histogram(bins = 5)+
  labs(title = "C) Set to 5 bins")


# D) More bins
ggplot(data = c19_df, aes(x = age))+  # provide x variable
  geom_histogram(binwidth = 1)+
  labs(title = "D) binwidth of 1")

To get smoothed proportions, you can use geom_density():

# Frequency with proportion axis, smoothed
ggplot(data = c19_df, mapping = aes(x = age)) +
  geom_density(size = 1, alpha = 0.2)+
  labs(title = "Proportional density")

Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.

# Stacked frequency with proportion axis, smoothed
ggplot(data = c19_df, mapping = aes(x = age, fill = factor(malaysian))) +
  geom_density(size = 1, alpha = 0.2, position = "stack")+
  labs(title = "'Stacked' proportional densities")

A “stacked” histogram of continuous data can be created either by using geom_histogram() with the fill = argument within aes() assigned to the grouping column, or through geom_freqpoly(). Setting y = after_stat(density) allows proportion representation of all values per group. Note the difference between color = and fill =.

# "Stacked" histogram
ggplot(data = c19_df, mapping = aes(x = age, fill = factor(malaysian))) +
  geom_histogram(binwidth = 2)+
  labs(title = "'Stacked' histogram")

# Frequency 
ggplot(data = c19_df, mapping = aes(x = age, color = factor(malaysian))) +
  geom_freqpoly(binwidth = 2, size = 2)+
  labs(title = "Freqpoly")

# Frequency with proportion axis
ggplot(data = c19_df, mapping = aes(x = age, y = after_stat(density), color = factor(malaysian))) +
  geom_freqpoly(binwidth = 5, size = 2)+
  labs(title = "Proportional freqpoly")

# Frequency with proportion axis, smoothed
ggplot(data = c19_df, mapping = aes(x = age, y = after_stat(density), fill = factor(malaysian))) +
  geom_density(size = 2, alpha = 0.2)+
  labs(title = "Proportional, smoothed with geom_density()")

Box-plots

geom_boxplot() in ggplot2 creates box plots by mapping only one axis (x or y) within aes(). Box plots per group are made by assigning the grouping column to the unassigned axis.

Be aware, NA values appear as separate plots unless removed. Optional aesthetics like color can be used.

# A) Overall boxplot
ggplot(data = c19_df)+  
  geom_boxplot(mapping = aes(y = age))+   # only y axis mapped (not x)
  labs(title = "A) Overall boxplot")

# B) Box plot by group
ggplot(data = c19_df, mapping = aes(y = age, x = malaysian, fill = factor(malaysian))) + 
  geom_boxplot()+                     
  theme(legend.position = "none")+   # remove legend (redundant)
  labs(title = "B) Boxplot by nationality")

Violin, jitter and sina-plots

Below is code for creating violin plots (geom_violin) and jitter plots (geom_jitter) to show distributions. You can specify that the fill or color is also determined by the data, by inserting these options within aes().

# A) Jitter plot by group
ggplot(data = c19_df,      # remove missing values
       mapping = aes(y = age,                     # Continuous variable
           x = malaysian,                           # Grouping variable
           color = factor(malaysian)))+                     # Color variable
  geom_jitter()+                                  # Create the violin plot
  labs(title = "A) jitter plot by nationality")

# B) Violin plot by group
ggplot(data = c19_df,       # remove missing values
       mapping = aes(y = age,                      # Continuous variable
           x = malaysian,                            # Grouping variable
           fill = factor(malaysian)))+                       # fill variable (color)
  geom_violin()+                                   # create the violin plot
  labs(title = "B) violin plot by nationality")

i like the violin plot for distributions but Sina plots really take the cake.

# A) Sina plot by group
ggplot(
  data = c19_df, 
  aes(y = age,           # numeric variable
      x = malaysian)) +    # group variable
  geom_violin(
    aes(fill = factor(malaysian)), # fill (color of violin background)
    color = "white",     # white outline
    alpha = 0.2)+        # transparency
  geom_sina(
    size=1,                # Change the size of the jitter
    aes(color = factor(malaysian)))+ # color (color of dots)
  scale_fill_manual(       # Define fill for violin background by death/recover
    values = c("1" = "#bf5300", 
              "0" = "#11118c")) + 
  scale_color_manual(      # Define colours for points by death/recover
    values = c("1" = "#bf5300", 
              "0" = "#11118c")) + 
  theme_minimal() +                                # Remove the gray background
  theme(legend.position = "none") +                # Remove unnecessary legend
  labs(title = "B) violin and sina plot by nationality, with extra formatting")

Line chart

Simple line charts are great and can be used like scatterplots which we already know the basics and of course line charts- we can of course look at 2 continuous variables in this case.

ggplot(data=week_df) +
  geom_line(mapping=aes(x=date, y=deaths, col=state))

ggplot(data=week_df %>% filter(date>as.Date("2022-12-31"))) +
  geom_line(mapping=aes(x=date, y=deaths, col=state))

Heat maps

My favorite plot- this one is super informative and flexible being able to storytell upt to 2 continuous variables in one go. Lets try an example

First get the data long as we saw earlier and on information we wish to check:

bid_df <- c19_df %>% select(malaysian, bid, date) %>%
  group_by(malaysian, date) %>%
  summarise (deaths=n(),
             bid=sum(bid==1)) %>%
  mutate(bid_rate=(bid/deaths)*100) %>%
  complete(date = seq(min(date), max(date), by="day"), fill = list(bid_rate = 0))

`summarise()` has grouped output by 'malaysian'. You can override using the
`.groups` argument.

ggplot(data = bid_df) +       # use long data, with proportions as Freq
  geom_tile(                    # visualize it in tiles
    aes(
      x = date,         # x-axis is case age
      y = malaysian,     # y-axis is infector age
      fill = bid_rate))+            # color of the tile is the Freq column in the data
  scale_fill_gradient(          # adjust the fill color of the tiles
    low = "blue",
    high = "orange")+
  labs(                         # labels
    x = "Date",
    y = "Nationality",
    title = "Where are the deaths occuring",
    subtitle = "Frequency BID deaths by nationality",
    fill = "Proportion of\n all deaths"     # legend title
  )

Bar chart

geom_bar() in ggplot2 creates bar plots where the height represents the count of relevant rows. It needs only one axis assignment, typically the x-axis. Stacked bars can be achieved by adding a fill = column assignment within aes(). The unassigned axis, titled “count”, corresponds to row numbers. Plot orientation and factor levels order can be manipulated for better visuals.

# A) Outcomes in all cases
ggplot(c19_df) + 
  geom_bar(aes(y = fct_rev(state)), width = 0.7) +
  theme_minimal()+
  labs(title = "A) Number of deaths by state",
       y = "State")

# B) Outcomes in all cases by state
ggplot(c19_df) + 
  geom_bar(aes(y = fct_rev(state), fill = factor(malaysian)), width = 0.7) +
  theme_minimal()+
  theme(legend.position = "bottom") +
  labs(title = "B) Number of dead by nationality and state",
       y = "State")

geom_col() in ggplot2 creates bar plots with height reflecting pre-calculated data values, usually aggregated counts or proportions, using both axes assignments.

Lets calculate the proportions

state_outcomes <- c19_df %>% 
  mutate(status=ifelse(malaysian==1&bid==1, "Non-Malaysian BID",
                       ifelse(malaysian==0&bid==1, "Malaysian BID",
                              ifelse(malaysian==1&bid==0, "Non-Malaysian Hospital", "Malaysian Hospital")))) %>%
  count(state, status) %>%  # get counts by state and outcome
  group_by(state) %>%        # Group so proportions are out of state total
  mutate(proportion = n/sum(n)*100) # calculate proportions of state total

head(state_outcomes) # Preview data

# A tibble: 6 × 4
# Groups:   state [2]
  state status                     n proportion
  <chr> <chr>                  <int>      <dbl>
1 Johor Malaysian BID            162       3.42
2 Johor Malaysian Hospital       204       4.30
3 Johor Non-Malaysian BID        550      11.6 
4 Johor Non-Malaysian Hospital  3824      80.7 
5 Kedah Malaysian BID             28       1.02
6 Kedah Malaysian Hospital        42       1.52

We then create the ggplot with some added formatting:

Axis flip: Swapped the axis around with coord_flip() so that we can read the hospital names.
Columns side-by-side: Added a position = "dodge" argument so that the bars for death and recover are presented side by side rather than stacked. Note stacked bars are the default.
Column width: Specified ‘width’, so the columns are half as thin as the full possible width.
Column order: Reversed the order of the categories on the y axis so that ‘Other’ and ‘Missing’ are at the bottom, with scale_x_discrete(limits=rev). Note that we used that rather than scale_y_discrete because hospital is stated in the x argument of aes(), even if visually it is on the y axis. We do this because Ggplot seems to present categories backwards unless we tell it not to.
Other details: Labels/titles and colours added within labs and scale_fill_color respectively.

# Outcomes in all cases by hospital
ggplot(state_outcomes) +  
  geom_col(
    mapping = aes(
      x = proportion,                 # show pre-calculated proportion values
      y = fct_rev(state),          # reverse level order so missing/other at bottom
      fill = factor(status)),                # stacked by outcome
    width = 0.5)+                    # thinner bars (out of 1)
  theme_minimal() +                  # Minimal theme 
  theme(legend.position = "bottom")+
  labs(subtitle = "Number of COVID-19 deaths in Malaysia, by state",
       fill = "Outcome",             # legend title
       y = "Count",                  # y axis title
       x = "State")+ # x axis title
  scale_fill_manual(                 # adding colors manually
    values = c("Non-Malaysian BID" = "#CE0200",
               "Malaysian BID" = "#e5e500",
               "Non-Malaysian Hospital" = "#3B1c8C",
               "Malaysian Hospital" = "#21908D"))

Static vs Scaled

Aesthetics can be assigned values in two ways:

Assigned a static value (e.g. color = “blue”) to apply across all plotted observations
Assigned to a column of the data (e.g. color = hospital) such that display of each observation depends on its value in that column

Static

# scatterplot
ggplot(data = c19_df, 
       mapping = aes(x = age, y = duration_toDeath))+  # set data and axes mapping
  geom_point(color = "firebrick2", size = 0.5, alpha = 0.2)         # set static point aesthetics

Scaled

# scatterplot
ggplot(data = c19_df,   # set data
       mapping = aes(     # map aesthetics to column values
         x = age,           # map x-axis to age            
         y = duration_toDeath,         # map y-axis to weight
         color = state)
       )+     # map color to age
  geom_point(size = 0.5, alpha = 0.2)         # display data as points

Groups

Data can be conveniently grouped for plotting by assigning a “grouping” column to an appropriate aesthetic within mapping = aes(), effective for both continuous and categorical values.

ggplot(data = c19_df,
       mapping = aes(x = age, y = duration_toDeath, color = malaysian))+
  geom_point(alpha = 0.5)

Please note that different grouping variables are required for different geoms- for instance geom_bar requires the fill argument instead ofcolour

Facets

Faceting is done with one of the following ggplot2 functions:

facet_wrap() To show a different panel for each level of a single variable. One example of this could be showing a different epidemic curve for each hospital in a region. Facets are ordered alphabetically, unless the variable is a factor with other ordering defined.

You can invoke certain options to determine the layout of the facets, e.g. nrow = 1 or ncol = 1 to control the number of rows or columns that the faceted plots are arranged within.

facet_grid() This is used when you want to bring a second variable into the faceting arrangement. Here each panel of a grid shows the intersection between values in two columns. For example, epidemic curves for each hospital-age group combination with hospitals along the top (columns) and age groups along the sides (rows).

nrow and ncol are not relevant, as the subgroups are presented in a grid

Wrapping

# A plot with facets by district
ggplot(week_df, aes(x = date, y = deaths)) +
  geom_col(width = 1, fill = "darkred") +       # plot the count data as columns
  theme_minimal()+                              # simplify the background panels
  labs(                                         # add plot labels, title, etc.
    x = "Date of report",
    y = "COVID-19 deaths",
    title = "COVID-19 deaths by states") +
  facet_wrap(~state, scales = "free_y")                       # the facets are created

Gridding

# A plot with facets by district
ggplot(week_df, aes(x = date, y = deaths)) +
  geom_col(width = 1, fill = "darkred") +       # plot the count data as columns
  theme_minimal()+                              # simplify the background panels
  labs(                                         # add plot labels, title, etc.
    x = "Date of report",
    y = "COVID-19 deaths",
    title = "COVID-19 deaths by states") +
  facet_wrap(age_cat~state, scales = "free_y")                       # the facets are created

Themes

Complete themes

Add a complete theme theme_() function to make sweeping adjustments - these include theme_classic(), theme_minimal(), theme_dark(), theme_light() theme_grey(), theme_bw() among others

# scatterplot
age_death_plot <- 
  ggplot(data = c19_df,   # set data
       mapping = aes(     # map aesthetics to column values
         x = age,           # map x-axis to age            
         y = duration_toDeath,         # map y-axis to weight
         color = state)
       )+     # map color to age
  geom_point(size = 0.5, alpha = 0.2)   +
  theme_minimal()

Manual theme

Adjust each tiny aspect of the plot individually within theme()

Here are some especially common theme() arguments. You will recognize some patterns, such as appending .x or .y to apply the change only to one axis.

`theme()` argument	What it adjusts
`plot.title = element_text()`	The title
`plot.subtitle = element_text()`	The subtitle
`plot.caption = element_text()`	The caption (family, face, color, size, angle, vjust, hjust…)
`axis.title = element_text()`	Axis titles (both x and y) (size, face, angle, color…)
`axis.title.x = element_text()`	Axis title x-axis only (use `.y` for y-axis only)
`axis.text = element_text()`	Axis text (both x and y)
`axis.text.x = element_text()`	Axis text x-axis only (use `.y` for y-axis only)
`axis.ticks = element_blank()`	Remove axis ticks
`axis.line = element_line()`	Axis lines (colour, size, linetype: solid dashed dotted etc)
`strip.text = element_text()`	Facet strip text (colour, face, size, angle…)
`strip.background = element_rect()`	facet strip (fill, colour, size…)

An example

age_death_plot +                               # pre-defined theme adjustments
  theme(
    legend.position = "bottom",                    # move legend to bottom
    
    plot.title = element_text(size = 30),          # size of title to 30
    plot.caption = element_text(hjust = 0),        # left-align caption
    plot.subtitle = element_text(face = "italic"), # italicize subtitle
    
    axis.text.x = element_text(color = "red", size = 15, angle = 90), # adjusts only x-axis text
    axis.text.y = element_text(size = 15),         # adjusts only y-axis text
    
    axis.title = element_text(size = 20)           # adjusts both axes titles
    )

Piping

We can directly edit a dataset within a ggplot

ggplot(c19_df %>% filter(duration_toDeath<50),
       mapping=aes(x=age, y=duration_toDeath, colour=age)) +
  geom_point()

But they can developed into something much more complex

c19_df %>%                                                     # begin with linelist
  select(c(malaysian, bid)) %>%     # select columns
  mutate(status=ifelse(malaysian==1&bid==1, "Non-Malaysian BID",
                       ifelse(malaysian==0&bid==1, "Malaysian BID",
                              ifelse(malaysian==1&bid==0, "Non-Malaysian Hospital death", "Malaysian Hospital death"))),
         id=row_number()) %>%
  select(-c(malaysian, bid)) %>%
  pivot_longer(                                                  # pivot longer
    cols = -id,                                  
    names_to = "name",
    values_to = "status") %>%
  mutate(                                                        # replace missing values
    status = replace_na(status, "unknown")) %>% 
  
  ggplot(                                                        # begin ggplot!
    mapping = aes(x = name, fill = status))+
  geom_bar(position = "fill", col = "black") +                    
  theme_classic() +
  labs(
    x = "Nationality and Death Status",
    y = "Nationality and Death Status (proportion)"
  )

Labels

We can use labs() to provide character strings to these arguements:

x = and y = The x-axis and y-axis title (labels)
title = The main plot title
subtitle = The subtitle of the plot, in smaller text below the title
caption = The caption of the plot, in bottom-right by default

Here is a plot we made earlier, but with nicer labels:

# scatterplot
age_death_plot <- 
  ggplot(data = c19_df,   # set data
       mapping = aes(     # map aesthetics to column values
         x = age,           # map x-axis to age            
         y = duration_toDeath,         # map y-axis to weight
         color = state)
       )+     # map color to age
  geom_point(size = 0.5, alpha = 0.2)   +     
  labs(
    title = "Age and Duration to Death distribution",
    subtitle = "COVID-19 Pandemic in Malaysia, 2020-2023",
    x = "Age (years)",
    y = "Duration to death (days)",
    color = "State",
    caption = stringr::str_glue("Data as of {max(c19_df$date, na.rm=T)}"))

age_death_plot

Saving and exporting plots

By default when a ggplot is called it will print in the Rstudio pane or below the if a markdown is used ( as is here). A plot can (and should) be written to a object name (as is done with a dataset). This ensures it is transfered to the global environment.

# define plot
age_death_plot <- 
ggplot(data = c19_df, 
       mapping = aes(x = age, y = duration_toDeath))+  # set data and axes mapping
  geom_point(color = "firebrick2", size = 0.5, alpha = 0.2)         # set static point aesthetics

# print
age_death_plot

A saved object can also be modified

age_death_plot +
  geom_vline(xintercept = 60)

And exported using the ggsave() command. We do this by specifying the name of the plot object, then the file path and name with extension

For example: ggsave(my_plot, here("plots", "my_plot.png"))

Keep moving forward

Plenty of other really powerful viz to consider and we will hopefully get to cover these in another sessions. These include

Demographic pyramids
Likert Scalesm
Diagrams - Sankey
Phylogenetic trees
Network maps
Interactive plots
Choropleths and maps

Acknowledgements

Material for this lecture was borrowed and adopted from

Additional Resources

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