33  Demographic pyramids and Likert-scales

Demographic pyramids are useful to show distributions of age and gender. Similar code can be used to visualize the results of Likert-style survey questions (e.g. “Strongly agree”, “Agree”, “Neutral”, “Disagree”, “Strongly disagree”). In this page we cover the following:

33.1 Preparation

Load packages

This code chunk shows the loading of packages required for the analyses. In this handbook we emphasize p_load() from pacman, which installs the package if necessary and loads it for use. You can also load installed packages with library() from base R. See the page on R basics for more information on R packages.

pacman::p_load(rio,       # to import data
               here,      # to locate files
               tidyverse, # to clean, handle, and plot the data (includes ggplot2 package)
               apyramid,  # a package dedicated to creating age pyramids
               janitor,   # tables and cleaning data
               stringr)   # working with strings for titles, captions, etc.

Import data

To begin, we import the cleaned linelist of cases from a simulated Ebola epidemic. If you want to follow along, click to download the “clean” linelist (as .rds file). Import data with the import() function from the rio package (it handles many file types like .xlsx, .csv, .rds - see the Import and export page for details).

# import case linelist 
linelist <- import("linelist_cleaned.rds")

The first 50 rows of the linelist are displayed below.


To make a traditional age/gender demographic pyramid, the data must first be cleaned in the following ways:

  • The gender column must be cleaned.
  • Depending on your method, age should be stored as either a numeric or in an age category column.

If using age categories, the column values should be corrected ordered, either by default alpha-numeric or intentionally set by converting to class factor.

Below we use tabyl() from janitor to inspect the columns gender and age_cat5.

linelist %>% 
  tabyl(age_cat5, gender)
 age_cat5   f   m NA_
      0-4 640 416  39
      5-9 641 412  42
    10-14 518 383  40
    15-19 359 364  20
    20-24 305 316  17
    25-29 163 259  13
    30-34 104 213   9
    35-39  42 157   3
    40-44  25 107   1
    45-49   8  80   5
    50-54   2  37   1
    55-59   0  30   0
    60-64   0  12   0
    65-69   0  12   1
    70-74   0   4   0
    75-79   0   0   1
    80-84   0   1   0
      85+   0   0   0
     <NA>   0   0  86

We also run a quick histogram on the age column to ensure it is clean and correctly classified:


33.2 apyramid package

The package apyramid is a product of the R4Epis project. You can read more about this package here. It allows you to quickly make an age pyramid. For more nuanced situations, see the section below using ggplot(). You can read more about the apyramid package in its Help page by entering ?age_pyramid in your R console.

Linelist data

Using the cleaned linelist dataset, we can create an age pyramid with one simple age_pyramid() command. In this command:

  • The data = argument is set as the linelist data frame
  • The age_group = argument (for y-axis) is set to the name of the categorical age column (in quotes)
  • The split_by = argument (for x-axis) is set to the gender column
apyramid::age_pyramid(data = linelist,
                      age_group = "age_cat5",
                      split_by = "gender")

The pyramid can be displayed with percent of all cases on the x-axis, instead of counts, by including proportional = TRUE.

apyramid::age_pyramid(data = linelist,
                      age_group = "age_cat5",
                      split_by = "gender",
                      proportional = TRUE)

When using agepyramid package, if the split_by column is binary (e.g. male/female, or yes/no), then the result will appear as a pyramid. However if there are more than two values in the split_by column (not including NA), the pyramid will appears as a faceted bar plot with grey bars in the “background” indicating the range of the un-faceted data for that age group. In this case, values of split_by = will appear as labels at top of each facet panel. For example, below is what occurs if the split_by = is assigned the column hospital.

apyramid::age_pyramid(data = linelist,
                      age_group = "age_cat5",
                      split_by = "hospital")  

Missing values

Rows that have NA missing values in the split_by = or age_group = columns, if coded as NA, will not trigger the faceting shown above. By default these rows will not be shown. However you can specify that they appear, in an adjacent barplot and as a separate age group at the top, by specifying na.rm = FALSE.

apyramid::age_pyramid(data = linelist,
                      age_group = "age_cat5",
                      split_by = "gender",
                      na.rm = FALSE)         # show patients missing age or gender

Proportions, colors, & aesthetics

By default, the bars display counts (not %), a dashed mid-line for each group is shown, and the colors are green/purple. Each of these parameters can be adjusted, as shown below:

You can also add additional ggplot() commands to the plot using the standard ggplot() “+” syntax, such as aesthetic themes and label adjustments:

  data = linelist,
  age_group = "age_cat5",
  split_by = "gender",
  proportional = TRUE,              # show percents, not counts
  show_midpoint = FALSE,            # remove bar mid-point line
  #pal = c("orange", "purple")      # can specify alt. colors here (but not labels)
  # additional ggplot commands
  theme_minimal()+                               # simplfy background
  scale_fill_manual(                             # specify colors AND labels
    values = c("orange", "purple"),              
    labels = c("m" = "Male", "f" = "Female"))+
  labs(y = "Percent of all cases",              # note x and y labs are switched
       x = "Age categories",                          
       fill = "Gender", 
       caption = "My data source and caption here",
       title = "Title of my plot",
       subtitle = "Subtitle with \n a second line...")+
    legend.position = "bottom",                          # legend to bottom
    axis.text = element_text(size = 10, face = "bold"),  # fonts/sizes
    axis.title = element_text(size = 12, face = "bold"))

Aggregated data

The examples above assume your data are in a linelist format, with one row per observation. If your data are already aggregated into counts by age category, you can still use the apyramid package, as shown below.

For demonstration, we aggregate the linelist data into counts by age category and gender, into a “wide” format. This will simulate as if your data were in counts to begin with. Learn more about Grouping data and Pivoting data in their respective pages.

demo_agg <- linelist %>% 
  count(age_cat5, gender, name = "cases") %>% 
    id_cols = age_cat5,
    names_from = gender,
    values_from = cases) %>% 
  rename(`missing_gender` = `NA`)

…which makes the dataset looks like this: with columns for age category, and male counts, female counts, and missing counts.

To set-up these data for the age pyramid, we will pivot the data to be “long” with the pivot_longer() function from dplyr. This is because ggplot() generally prefers “long” data, and apyramid is using ggplot().

# pivot the aggregated data into long format
demo_agg_long <- demo_agg %>% 
    col = c(f, m, missing_gender),            # cols to elongate
    names_to = "gender",                # name for new col of categories
    values_to = "counts") %>%           # name for new col of counts
    gender = na_if(gender, "missing_gender")) # convert "missing_gender" to NA

Then use the split_by = and count = arguments of age_pyramid() to specify the respective columns in the data:

apyramid::age_pyramid(data = demo_agg_long,
                      age_group = "age_cat5",# column name for age category
                      split_by = "gender",   # column name for gender
                      count = "counts")      # column name for case counts

Note in the above, that the factor order of “m” and “f” is different (pyramid reversed). To adjust the order you must re-define gender in the aggregated data as a Factor and order the levels as desired. See the Factors page.

33.3 ggplot()

Using ggplot() to build your age pyramid allows for more flexibility, but requires more effort and understanding of how ggplot() works. It is also easier to accidentally make mistakes.

To use ggplot() to make demographic pyramids, you create two bar plots (one for each gender), convert the values in one plot to negative, and finally flip the x and y axes to display the bar plots vertically, their bases meeting in the plot middle.


This approach uses the numeric age column, not the categorical column of age_cat5. So we will check to ensure the class of this column is indeed numeric.

[1] "numeric"

You could use the same logic below to build a pyramid from categorical data using geom_col() instead of geom_histogram().

Constructing the plot

First, understand that to make such a pyramid using ggplot() the approach is as follows:

  • Within the ggplot(), create two histograms using the numeric age column. Create one for each of the two grouping values (in this case genders male and female). To do this, the data for each histogram are specified within their respective geom_histogram() commands, with the respective filters applied to linelist.

  • One graph will have positive count values, while the other will have its counts converted to negative values - this creates the “pyramid” with the 0 value in the middle of the plot. The negative values are created using a special ggplot2 term ..count.. and multiplying by -1.

  • The command coord_flip() switches the X and Y axes, resulting in the graphs turning vertical and creating the pyramid.

  • Lastly, the counts-axis value labels must be altered so they appear as “positive” counts on both sides of the pyramid (despite the underlying values on one side being negative).

A simple version of this, using geom_histogram(), is below:

  # begin ggplot
  ggplot(mapping = aes(x = age, fill = gender)) +
  # female histogram
  geom_histogram(data = linelist %>% filter(gender == "f"),
                 breaks = seq(0,85,5),
                 colour = "white") +
  # male histogram (values converted to negative)
  geom_histogram(data = linelist %>% filter(gender == "m"),
                 breaks = seq(0,85,5),
                 mapping = aes(y = ..count..*(-1)),
                 colour = "white") +
  # flip the X and Y axes
  coord_flip() +
  # adjust counts-axis scale
  scale_y_continuous(limits = c(-600, 900),
                     breaks = seq(-600,900,100),
                     labels = abs(seq(-600, 900, 100)))

DANGER: If the limits of your counts axis are set too low, and a counts bar exceeds them, the bar will disappear entirely or be artificially shortened! Watch for this if analyzing data which is routinely updated. Prevent it by having your count-axis limits auto-adjust to your data, as below.

There are many things you can change/add to this simple version, including:

  • Auto adjust counts-axis scale to your data (avoid errors discussed in warning below)
  • Manually specify colors and legend labels

Convert counts to percents

To convert counts to percents (of total), do this in your data prior to plotting. Below, we get the age-gender counts, then ungroup(), and then mutate() to create new percent columns. If you want percents by gender, skip the ungroup step.

# create dataset with proportion of total
pyramid_data <- linelist %>%
        name = "counts") %>% 
  ungroup() %>%                 # ungroup so percents are not by group
  mutate(percent = round(100*(counts / sum(counts, na.rm=T)), digits = 1), 
         percent = case_when(
            gender == "f" ~ percent,
            gender == "m" ~ -percent,     # convert male to negative
            TRUE          ~ NA_real_))    # NA val must by numeric as well

Importantly, we save the max and min values so we know what the limits of the scale should be. These will be used in the ggplot() command below.

max_per <- max(pyramid_data$percent, na.rm=T)
min_per <- min(pyramid_data$percent, na.rm=T)

[1] 10.9
[1] -7.1

Finally we make the ggplot() on the percent data. We specify scale_y_continuous() to extend the pre-defined lengths in each direction (positive and “negative”). We use floor() and ceiling() to round decimals the appropriate direction (down or up) for the side of the axis.

# begin ggplot
  ggplot()+  # default x-axis is age in years;

  # case data graph
  geom_col(data = pyramid_data,
           mapping = aes(
             x = age_cat5,
             y = percent,
             fill = gender),         
           colour = "white")+       # white around each bar
  # flip the X and Y axes to make pyramid vertical

  # adjust the axes scales
  # scale_x_continuous(breaks = seq(0,100,5), labels = seq(0,100,5)) +
    limits = c(min_per, max_per),
    breaks = seq(from = floor(min_per),                # sequence of values, by 2s
                 to = ceiling(max_per),
                 by = 2),
    labels = paste0(abs(seq(from = floor(min_per),     # sequence of absolute values, by 2s, with "%"
                            to = ceiling(max_per),
                            by = 2)),

  # designate colors and legend labels manually
    values = c("f" = "orange",
               "m" = "darkgreen"),
    labels = c("Female", "Male")) +
  # label values (remember X and Y flipped now)
    title = "Age and gender of cases",
    x = "Age group",
    y = "Percent of total",
    fill = NULL,
    caption = stringr::str_glue("Data are from linelist \nn = {nrow(linelist)} (age or sex missing for {sum(is.na(linelist$gender) | is.na(linelist$age_years))} cases) \nData as of: {format(Sys.Date(), '%d %b %Y')}")) +
  # display themes
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    panel.background = element_blank(),
    axis.line = element_line(colour = "black"),
    plot.title = element_text(hjust = 0.5), 
    plot.caption = element_text(hjust=0, size=11, face = "italic")

Compare to baseline

With the flexibility of ggplot(), you can have a second layer of bars in the background that represent the “true” or “baseline” population pyramid. This can provide a nice visualization to compare the observed with the baseline.

Import and view the population data (see Download handbook and data page):

# import the population demographics data
pop <- rio::import("country_demographics.csv")

First some data management steps:

Here we record the order of age categories that we want to appear. Due to some quirks the way the ggplot() is implemented, in this specific scenario it is easiest to store these as a character vector and use them later in the plotting function.

# record correct age cat levels
age_levels <- c("0-4","5-9", "10-14", "15-19", "20-24",
                "25-29","30-34", "35-39", "40-44", "45-49",
                "50-54", "55-59", "60-64", "65-69", "70-74",
                "75-79", "80-84", "85+")

Combine the population and case data through the dplyr function bind_rows():

  • First, ensure they have the exact same column names, age categories values, and gender values
  • Make them have the same data structure: columns of age category, gender, counts, and percent of total
  • Bind them together, one on-top of the other (bind_rows())
# create/transform populaton data, with percent of total
pop_data <- pop %>% 
  pivot_longer(      # pivot gender columns longer
    cols = c(m, f),
    names_to = "gender",
    values_to = "counts") %>% 
    percent  = round(100*(counts / sum(counts, na.rm=T)),1),  # % of total
    percent  = case_when(                                                        
     gender == "f" ~ percent,
     gender == "m" ~ -percent,               # if male, convert % to negative
     TRUE          ~ NA_real_))

Review the changed population dataset

Now implement the same for the case linelist. Slightly different because it begins with case-rows, not counts.

# create case data by age/gender, with percent of total
case_data <- linelist %>%
  count(age_cat5, gender, name = "counts") %>%  # counts by age-gender groups
  ungroup() %>% 
    percent = round(100*(counts / sum(counts, na.rm=T)),1),  # calculate % of total for age-gender groups
    percent = case_when(                                     # convert % to negative if male
      gender == "f" ~ percent,
      gender == "m" ~ -percent,
      TRUE          ~ NA_real_))

Review the changed case dataset

Now the two data frames are combined, one on top of the other (they have the same column names). We can “name” each of the data frame, and use the .id = argument to create a new column “data_source” that will indicate which data frame each row originated from. We can use this column to filter in the ggplot().

# combine case and population data (same column names, age_cat values, and gender values)
pyramid_data <- bind_rows("cases" = case_data, "population" = pop_data, .id = "data_source")

Store the maximum and minimum percent values, used in the plotting function to define the extent of the plot (and not cut short any bars!)

# Define extent of percent axis, used for plot limits
max_per <- max(pyramid_data$percent, na.rm=T)
min_per <- min(pyramid_data$percent, na.rm=T)

Now the plot is made with ggplot():

  • One bar graph of population data (wider, more transparent bars)
  • One bar graph of case data (small, more solid bars)
# begin ggplot
ggplot()+  # default x-axis is age in years;

  # population data graph
    data = pyramid_data %>% filter(data_source == "population"),
    mapping = aes(
      x = age_cat5,
      y = percent,
      fill = gender),
    colour = "black",                               # black color around bars
    alpha = 0.2,                                    # more transparent
    width = 1)+                                     # full width
  # case data graph
    data = pyramid_data %>% filter(data_source == "cases"), 
    mapping = aes(
      x = age_cat5,                               # age categories as original X axis
      y = percent,                                # % as original Y-axis
      fill = gender),                             # fill of bars by gender
    colour = "black",                               # black color around bars
    alpha = 1,                                      # not transparent 
    width = 0.3)+                                   # half width
  # flip the X and Y axes to make pyramid vertical
  # manually ensure that age-axis is ordered correctly
  scale_x_discrete(limits = age_levels)+     # defined in chunk above
  # set percent-axis 
    limits = c(min_per, max_per),                                          # min and max defined above
    breaks = seq(floor(min_per), ceiling(max_per), by = 2),                # from min% to max% by 2 
    labels = paste0(                                                       # for the labels, paste together... 
              abs(seq(floor(min_per), ceiling(max_per), by = 2)), "%"))+                                                  

  # designate colors and legend labels manually
    values = c("f" = "orange",         # assign colors to values in the data
               "m" = "darkgreen"),
    labels = c("f" = "Female",
               "m"= "Male"),      # change labels that appear in legend, note order
  ) +

  # plot labels, titles, caption    
    title = "Case age and gender distribution,\nas compared to baseline population",
    subtitle = "",
    x = "Age category",
    y = "Percent of total",
    fill = NULL,
    caption = stringr::str_glue("Cases shown on top of country demographic baseline\nCase data are from linelist, n = {nrow(linelist)}\nAge or gender missing for {sum(is.na(linelist$gender) | is.na(linelist$age_years))} cases\nCase data as of: {format(max(linelist$date_onset, na.rm=T), '%d %b %Y')}")) +
  # optional aesthetic themes
    legend.position = "bottom",                             # move legend to bottom
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    panel.background = element_blank(),
    axis.line = element_line(colour = "black"),
    plot.title = element_text(hjust = 0), 
    plot.caption = element_text(hjust=0, size=11, face = "italic"))

33.4 Likert scale

The techniques used to make a population pyramid with ggplot() can also be used to make plots of Likert-scale survey data.

Import the data (see Download handbook and data page if desired).

# import the likert survey response data
likert_data <- rio::import("likert_data.csv")

Start with data that looks like this, with a categorical classification of each respondent (status) and their answers to 8 questions on a 4-point Likert-type scale (“Very poor”, “Poor”, “Good”, “Very good”).

First, some data management steps:

  • Pivot the data longer
  • Create new column direction depending on whether response was generally “positive” or “negative”
  • Set the Factor level order for the status column and the Response column
  • Store the max count value so limits of plot are appropriate
melted <- likert_data %>% 
    cols = Q1:Q8,
    names_to = "Question",
    values_to = "Response") %>% 
    direction = case_when(
      Response %in% c("Poor","Very Poor")  ~ "Negative",
      Response %in% c("Good", "Very Good") ~ "Positive",
      TRUE                                 ~ "Unknown"),
    status = fct_relevel(status, "Junior", "Intermediate", "Senior"),
    # must reverse 'Very Poor' and 'Poor' for ordering to work
    Response = fct_relevel(Response, "Very Good", "Good", "Very Poor", "Poor")) 

# get largest value for scale limits
melted_max <- melted %>% 
  count(status, Question) %>% # get counts
  pull(n) %>%                 # column 'n'
  max(na.rm=T)                # get max

Now make the plot. As in the age pyramids above, we are creating two bar plots and inverting the values of one of them to negative.

We use geom_bar() because our data are one row per observation, not aggregated counts. We use the special ggplot2 term ..count.. in one of the bar plots to invert the values negative (*-1), and we set position = "stack" so the values stack on top of each other.

# make plot
  # bar graph of the "negative" responses 
       data = melted %>% filter(direction == "Negative"),
       mapping = aes(
         x = status,
         y = ..count..*(-1),    # counts inverted to negative
         fill = Response),
       color = "black",
       closed = "left",
       position = "stack")+
     # bar graph of the "positive responses
       data = melted %>% filter(direction == "Positive"),
       mapping = aes(
         x = status,
         fill = Response),
       colour = "black",
       closed = "left",
       position = "stack")+
     # flip the X and Y axes
     # Black vertical line at 0
     geom_hline(yintercept = 0, color = "black", size=1)+
    # convert labels to all positive numbers
      # limits of the x-axis scale
      limits = c(-ceiling(melted_max/10)*11,    # seq from neg to pos by 10, edges rounded outward to nearest 5
      # values of the x-axis scale
      breaks = seq(from = -ceiling(melted_max/10)*10,
                   to = ceiling(melted_max/10)*10,
                   by = 10),
      # labels of the x-axis scale
      labels = abs(unique(c(seq(-ceiling(melted_max/10)*10, 0, 10),
                            seq(0, ceiling(melted_max/10)*10, 10))))) +
    # color scales manually assigned 
      values = c("Very Good"  = "green4", # assigns colors
                "Good"      = "green3",
                "Poor"      = "yellow",
                "Very Poor" = "red3"),
      breaks = c("Very Good", "Good", "Poor", "Very Poor"))+ # orders the legend
    # facet the entire plot so each question is a sub-plot
    facet_wrap( ~ Question, ncol = 3)+
    # labels, titles, caption
      title = str_glue("Likert-style responses\nn = {nrow(likert_data)}"),
      x = "Respondent status",
      y = "Number of responses",
      fill = "")+

     # display adjustments 
     theme(axis.text = element_text(size = 12),
           axis.title = element_text(size = 14, face = "bold"),
           strip.text = element_text(size = 14, face = "bold"),  # facet sub-titles
           plot.title = element_text(size = 20, face = "bold"),
           panel.background = element_rect(fill = NA, color = "black")) # black box around each facet

33.5 Resources

apyramid documentation