library(tidyverse)
library(mosaic) # Our trusted friend
library(skimr)
library(vcd) # Michael Friendly's package, Visualizing Categorical Data
library(vcdExtra) # Categorical Data Sets
library(resampledata) # More datasets
library(ggstats) # Likert Scale Plots
library(labelled) # Creating Labelled Data for Likert Plots
library(sjPlot) # Another package for Likert Plots
## Making Tables
library(kableExtra) # html styled tables
library(tinytable) # Elegant Tables for our data
library(DT) # Interactive Tables for our data
library(ggrepel)
library(marquee)
Extra Cheese with my 5-insect pizza, please!
2022-12-27
“It is not that we have so little time but that we lose so much…The life we receive is not short but we make it so; we are not ill provided but use what we have wastefully.”
— Seneca
Plot Fonts and Theme
Code
library(systemfonts)
library(showtext)
## Clean the slate
systemfonts::clear_local_fonts()
systemfonts::clear_registry()
##
showtext_opts(dpi = 96) # set DPI for showtext
sysfonts::font_add(
family = "Alegreya",
regular = "../../../../../../fonts/Alegreya-Regular.ttf",
bold = "../../../../../../fonts/Alegreya-Bold.ttf",
italic = "../../../../../../fonts/Alegreya-Italic.ttf",
bolditalic = "../../../../../../fonts/Alegreya-BoldItalic.ttf"
)
sysfonts::font_add(
family = "Roboto Condensed",
regular = "../../../../../../fonts/RobotoCondensed-Regular.ttf",
bold = "../../../../../../fonts/RobotoCondensed-Bold.ttf",
italic = "../../../../../../fonts/RobotoCondensed-Italic.ttf",
bolditalic = "../../../../../../fonts/RobotoCondensed-BoldItalic.ttf"
)
showtext_auto(enable = TRUE) # enable showtext
##
theme_custom <- function() {
theme_bw(base_size = 10) +
# theme(panel.widths = unit(11, "cm"),
# panel.heights = unit(6.79, "cm")) + # Golden Ratio
theme(
plot.margin = margin_auto(t = 1, r = 2, b = 1, l = 1, unit = "cm"),
plot.background = element_rect(
fill = "bisque",
colour = "black",
linewidth = 1
)
) +
theme_sub_axis(
title = element_text(
family = "Roboto Condensed",
size = 10
),
text = element_text(
family = "Roboto Condensed",
size = 8
)
) +
theme_sub_legend(
text = element_text(
family = "Roboto Condensed",
size = 6
),
title = element_text(
family = "Alegreya",
size = 8
)
) +
theme_sub_plot(
title = element_text(
family = "Alegreya",
size = 14, face = "bold"
),
title.position = "plot",
subtitle = element_text(
family = "Alegreya",
size = 10
),
caption = element_text(
family = "Alegreya",
size = 6
),
caption.position = "plot"
)
}
## Use available fonts in ggplot text geoms too!
ggplot2::update_geom_defaults(geom = "text", new = list(
family = "Roboto Condensed",
face = "plain",
size = 3.5,
color = "#2b2b2b"
))
ggplot2::update_geom_defaults(geom = "label", new = list(
family = "Roboto Condensed",
face = "plain",
size = 3.5,
color = "#2b2b2b"
))
ggplot2::update_geom_defaults(geom = "marquee", new = list(
family = "Roboto Condensed",
face = "plain",
size = 3.5,
color = "#2b2b2b"
))
ggplot2::update_geom_defaults(geom = "text_repel", new = list(
family = "Roboto Condensed",
face = "plain",
size = 3.5,
color = "#2b2b2b"
))
ggplot2::update_geom_defaults(geom = "label_repel", new = list(
family = "Roboto Condensed",
face = "plain",
size = 3.5,
color = "#2b2b2b"
))
## Set the theme
ggplot2::theme_set(new = theme_custom())
## tinytable options
options("tinytable_tt_digits" = 2)
options("tinytable_format_num_fmt" = "significant_cell")
options(tinytable_html_mathjax = TRUE)
## Set defaults for flextable
flextable::set_flextable_defaults(font.family = "Roboto Condensed")
This figure is from by the article Behind the Screens: Likert Scale Visualization by Orikami. The article discusses how to visualize Likert scale data, which is a common type of survey data used to measure attitudes or opinions.
The figure shows a Likert scale visualization of the Machiavellism personality trait, which is a measure of how manipulative and deceitful a person is. The visualization uses a horizontal bar chart to show the distribution of responses across different levels of Machiavellism, with the bars representing the percentage of respondents who selected each age-group level.
Are there any surprises, peasants of Gantiganahalli?
| Variable #1 | Variable #2 | Chart Names | “Chart Shape” |
|---|---|---|---|
| Qual | Qual | Likert Plots |  |
In many business and design situations, we perform say customer surveys to get Likert Scale data, where several respondents rate a product or a service on a scale of Very much like, somewhat like, neutral, Dislike and Very much dislike, for example. These are then plotted in a chart to get a distribution of opinions for each question in the survey. Some examples of Likert Scales are shown below.
As seen, we can use Likert Scale based questionnaire for a variety of aspects in our survey instruments.
A fictitious QuickEZ app
We are a start-up that has an app called QuickEZ for delivery of groceries. We conduct a survey of 200 people at a local store, with the following questions,
- “Have your heard of the QuickEZ app?”
- “Do you use the QuickEZ app?”
- “Do you find it easy to use the QuickEZ app?”
- “Will you continue to use the QuickEZ app?”
where each questions is to be answered on a scale of : “always”, “often”, “sometimes”,“rarely”, “never”.
Such data may look for example as follows:
| q1 | q2 | q3 | q4 |
|---|---|---|---|
| never | rarely | always | always |
| never | always | always | never |
| often | rarely | never | rarely |
| never | always | always | never |
| always | never | often | always |
| rarely | always | always | always |
| always | rarely | rarely | rarely |
| often | always | always | never |
| always | never | always | always |
| always | never | always | always |
tibble [200 × 4] (S3: tbl_df/tbl/data.frame)
$ q1: Factor w/ 4 levels "never","rarely",..: 1 1 3 1 4 2 4 3 4 4 ...
..- attr(*, "label")= chr "Have your heard of the QuickEZ app?"
$ q2: Factor w/ 4 levels "never","rarely",..: 2 4 2 4 1 4 2 4 1 1 ...
..- attr(*, "label")= chr "Do you use the QuickEZ app?"
$ q3: Factor w/ 4 levels "never","rarely",..: 4 4 1 4 3 4 2 4 4 4 ...
..- attr(*, "label")= chr "Do you find it easy to use the QuickEZ app?"
$ q4: Factor w/ 4 levels "never","rarely",..: 4 1 2 1 4 4 2 1 4 4 ...
..- attr(*, "label")= chr "Will you continue to use the QuickEZ app?"The columns here correspond to the 4 questions (q1-q4) and the rows contain the 200 responses, which have been coded as (1:4). Such data is also a form of Categorical data and we need to count and plot counts for each of the survey questions. Such a plot is called a Likert plot and it looks like this:
Based on this chart, since it looks like about 40% the survey respondents have not heard of our app, we need more publicity, and many do not find it easy to use 😿, so we have serious re-design and user testing to do !! But at least those who have managed to get past the hurdles are stating they will continue to use the app, so it does the job, but we can make it easier to use.
Here is another example of Likert data from the healthcare industry.
efc is a German data set from a European study titled EUROFAM study, on family care of older people. Following a common protocol, data were collected from national samples of approximately 1,000 family carers (i.e. caregivers) per country and clustered into comparable subgroups to facilitate cross-national analysis. The research questions in this EUROFAM study were:
To what extent do family carers of older people use support services or receive financial allowances across Europe? What kind of supports and allowances do they mainly use?
What are the main difficulties carers experience accessing the services used? What prevents carers from accessing unused supports that they need? What causes them to stop using still-needed services?
In order to improve support provision, what can be understood about the service characteristics considered crucial by carers, and how far are these needs met? and,
Which channels or actors can provide the greatest help in underpinning future policy efforts to improve access to services/supports?
We will select the variables from the efc data set that related to coping (on part of care-givers) and plot their responses after inspecting them:
Rows: 908
Columns: 26
$ c12hour <dbl> 16, 148, 70, 168, 168, 16, 161, 110, 28, 40, 100, 25, 25, 24,…
$ e15relat <dbl> 2, 2, 1, 1, 2, 2, 1, 4, 2, 2, 1, 8, 2, 1, 2, 2, 1, 1, 2, 1, 2…
$ e16sex <dbl> 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 2, 2, 1, 1, 1, 1, 2…
$ e17age <dbl> 83, 88, 82, 67, 84, 85, 74, 87, 79, 83, 68, 97, 80, 75, 82, 8…
$ e42dep <dbl> 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 3, 4, 3, 3, 3, 1, 3, 3, 4, 4…
$ c82cop1 <dbl> 3, 3, 2, 4, 3, 2, 4, 3, 3, 3, 3, 3, 3, 3, 2, 4, 3, 4, 3, 3, 3…
$ c83cop2 <dbl> 2, 3, 2, 1, 2, 2, 2, 2, 2, 2, 4, 3, 2, 2, 3, 2, 2, 2, 2, 2, 2…
$ c84cop3 <dbl> 2, 3, 1, 3, 1, 3, 4, 2, 3, 1, 4, 3, 2, 4, 3, 1, 1, 1, 1, 4, 3…
$ c85cop4 <dbl> 2, 3, 4, 1, 2, 3, 1, 1, 2, 2, 4, 1, 2, 4, 3, 3, 2, 2, 2, 2, 2…
$ c86cop5 <dbl> 1, 4, 1, 1, 2, 3, 1, 1, 2, 1, 4, 3, 2, 1, 2, 3, 1, 1, 2, 1, 1…
$ c87cop6 <dbl> 1, 1, 1, 1, 2, 2, 2, 1, 1, 1, 4, 1, 1, 1, 2, 1, 1, 1, 1, 3, 1…
$ c88cop7 <dbl> 2, 3, 1, 1, 1, 2, 4, 2, 3, 1, 4, 4, 2, 2, 1, 2, 2, 2, 3, 3, 1…
$ c89cop8 <dbl> 3, 2, 4, 2, 4, 1, 1, 3, 1, 1, 1, 3, 4, 4, 1, 1, 4, 3, 1, 2, 1…
$ c90cop9 <dbl> 3, 2, 3, 4, 4, 1, 4, 3, 3, 3, 1, 1, 4, 4, 1, 3, 4, 3, 4, 2, 3…
$ c160age <dbl> 56, 54, 80, 69, 47, 56, 61, 67, 59, 49, 66, 47, 58, 75, 49, 5…
$ c161sex <dbl> 2, 2, 1, 1, 2, 1, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 1, 2, 2…
$ c172code <dbl> 2, 2, 1, 2, 2, 2, 2, 2, NA, 2, 2, 2, 3, 1, 3, 2, 2, 2, 3, 3, …
$ c175empl <dbl> 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1…
$ barthtot <dbl> 75, 75, 35, 0, 25, 60, 5, 35, 15, 0, 25, 85, 15, 70, NA, 0, 9…
$ neg_c_7 <dbl> 12, 20, 11, 10, 12, 19, 15, 11, 15, 10, 28, 18, 13, 18, 16, 1…
$ pos_v_4 <dbl> 12, 11, 13, 15, 15, 9, 13, 14, 13, 13, 9, 8, 14, 14, 9, 14, 1…
$ quol_5 <dbl> 14, 10, 7, 12, 19, 8, 20, 20, 8, 15, 1, 19, 12, 8, 8, 6, 16, …
$ resttotn <dbl> 0, 4, 0, 2, 2, 1, 0, 0, 0, 1, 1, 1, 0, 0, 3, 0, 0, 0, 3, 0, 1…
$ tot_sc_e <dbl> 4, 0, 1, 0, 1, 3, 0, 1, 2, 1, 1, 1, 3, 0, 3, 2, 2, 0, 1, 7, 1…
$ n4pstu <dbl> 0, 0, 2, 3, 2, 2, 3, 1, 3, 3, 3, 1, 3, 1, 0, 0, 0, 2, 1, 2, 4…
$ nur_pst <dbl> NA, NA, 2, 3, 2, 2, 3, 1, 3, 3, 3, 1, 3, 1, NA, NA, NA, 2, 1,…'data.frame': 908 obs. of 9 variables:
$ c82cop1: num 3 3 2 4 3 2 4 3 3 3 ...
..- attr(*, "label")= chr "do you feel you cope well as caregiver?"
..- attr(*, "labels")= Named num [1:4] 1 2 3 4
.. ..- attr(*, "names")= chr [1:4] "never" "sometimes" "often" "always"
$ c83cop2: num 2 3 2 1 2 2 2 2 2 2 ...
..- attr(*, "label")= chr "do you find caregiving too demanding?"
..- attr(*, "labels")= Named num [1:4] 1 2 3 4
.. ..- attr(*, "names")= chr [1:4] "Never" "Sometimes" "Often" "Always"
$ c84cop3: num 2 3 1 3 1 3 4 2 3 1 ...
..- attr(*, "label")= chr "does caregiving cause difficulties in your relationship with your friends?"
..- attr(*, "labels")= Named num [1:4] 1 2 3 4
.. ..- attr(*, "names")= chr [1:4] "Never" "Sometimes" "Often" "Always"
$ c85cop4: num 2 3 4 1 2 3 1 1 2 2 ...
..- attr(*, "label")= chr "does caregiving have negative effect on your physical health?"
..- attr(*, "labels")= Named num [1:4] 1 2 3 4
.. ..- attr(*, "names")= chr [1:4] "Never" "Sometimes" "Often" "Always"
$ c86cop5: num 1 4 1 1 2 3 1 1 2 1 ...
..- attr(*, "label")= chr "does caregiving cause difficulties in your relationship with your family?"
..- attr(*, "labels")= Named num [1:4] 1 2 3 4
.. ..- attr(*, "names")= chr [1:4] "Never" "Sometimes" "Often" "Always"
$ c87cop6: num 1 1 1 1 2 2 2 1 1 1 ...
..- attr(*, "label")= chr "does caregiving cause financial difficulties?"
..- attr(*, "labels")= Named num [1:4] 1 2 3 4
.. ..- attr(*, "names")= chr [1:4] "Never" "Sometimes" "Often" "Always"
$ c88cop7: num 2 3 1 1 1 2 4 2 3 1 ...
..- attr(*, "label")= chr "do you feel trapped in your role as caregiver?"
..- attr(*, "labels")= Named num [1:4] 1 2 3 4
.. ..- attr(*, "names")= chr [1:4] "Never" "Sometimes" "Often" "Always"
$ c89cop8: num 3 2 4 2 4 1 1 3 1 1 ...
..- attr(*, "label")= chr "do you feel supported by friends/neighbours?"
..- attr(*, "labels")= Named num [1:4] 1 2 3 4
.. ..- attr(*, "names")= chr [1:4] "never" "sometimes" "often" "always"
$ c90cop9: num 3 2 3 4 4 1 4 3 3 3 ...
..- attr(*, "label")= chr "do you feel caregiving worthwhile?"
..- attr(*, "labels")= Named num [1:4] 1 2 3 4
.. ..- attr(*, "names")= chr [1:4] "never" "sometimes" "often" "always"The coping related variables have responses on the Likert Scale (1,2,3,4) which correspond to (never, sometimes, often, always), and each variable also has a label defining each variable. The labels are actually ( and perhaps usually ) the questions in the survey.
We can plot this data using the gglikert function from package ggstats:
efc %>%
select(dplyr::contains("cop")) %>%
gglikert(labels_size = 3, width = 0.75) +
labs(title = "Caregiver Survey from EUROFAM") +
scale_fill_brewer(
name = "Responses",
labels = c("never", "sometimes", "often", "always"),
palette = "Set3", direction = -1
) + theme_custom() +
theme(legend.position = "bottom")
Many questions here have strong negative responses. This may indicate that policy and publicity related efforts may be required.
Note how the y-axis has been populated with Survey Questions: this is an example of a labelled dataset, where not only do the variables have names i.e. column names, but also have longish text labels that add information to the data variables. The data values ( i.e scores) in the columns is also labelled as per the the Likert scale (Like/Dislike/Strongly Dislike OR never/sometimes/often/always) etc. These Likert scores are usually a set of contiguous integers.
Variable Labels and Value Labels
Variable label is human readable description of the variable. R supports rather long variable names and these names can contain even spaces and punctuation but short variables names make coding easier. Variable label can give a nice, long description of variable. With this description it is easier to remember what those variable names refer to.
Value labels are similar to variable labels, but value labels are descriptions of the values a variable can take. Labeling values means we don’t have to remember if 1=Extremely poor and 7=Excellent or vice-versa. We can easily get dataset description and variables summary with info function.
Let us manually create one such dataset, since this is a common-enough situation1 that we have survey data and then have to label the variables and the values before plotting. We will use the R package labelled to label our data.2.
It is also possible to label the tibble, the columns, and the values in similar fashion using the sjlabelled package3 and the labelr package4.
set.seed(42)
variable_labels <- c(
"Do you practice Analytics?",
"Do you code in R?",
"Have you published your R Code?",
"Do you use Quarto as your Workflow in R?",
"Will you use R at Work?"
)
##
value_labels <- c("never", "sometimes", "often", "always")
##
my_survey_data <-
# Create toy survey data
# 200 responses to 5 questions
# responses on Likert Scale
# 1:4 = "never", "sometimes","often","always")
tibble(
q1 = mosaic::sample(value_labels,
replace = TRUE, size = 200,
prob = c(0.2, 0.2, 0.5, 0.1)
),
q2 = mosaic::sample(value_labels,
replace = TRUE, size = 200,
prob = c(0.3, 0.3, 0.3, 0.1)
),
q3 = mosaic::sample(value_labels,
replace = TRUE, size = 200,
prob = c(0.2, 0.1, 0.1, 0.6)
),
q4 = mosaic::sample(value_labels,
replace = TRUE, size = 200,
prob = c(0.4, 0.2, 0.1, 0.3)
),
q5 = mosaic::sample(value_labels,
replace = TRUE, size = 200,
prob = c(0.1, 0.2, 0.5, 0.2)
)
) %>%
# Set VARIABLE labels
labelled::set_variable_labels(
.data = .,
q1 = variable_labels[1],
q2 = variable_labels[2],
q3 = variable_labels[3],
q4 = variable_labels[4],
q5 = variable_labels[5]
)
# Values within the variables are already labelled
###
head(my_survey_data, 6)tibble [200 × 5] (S3: tbl_df/tbl/data.frame)
$ q1: chr [1:200] "always" "always" "often" "sometimes" ...
..- attr(*, "label")= chr "Do you practice Analytics?"
$ q2: chr [1:200] "sometimes" "often" "sometimes" "often" ...
..- attr(*, "label")= chr "Do you code in R?"
$ q3: chr [1:200] "always" "always" "never" "always" ...
..- attr(*, "label")= chr "Have you published your R Code?"
$ q4: chr [1:200] "always" "never" "sometimes" "never" ...
..- attr(*, "label")= chr "Do you use Quarto as your Workflow in R?"
$ q5: chr [1:200] "never" "always" "often" "sometimes" ...
..- attr(*, "label")= chr "Will you use R at Work?"##
my_survey_data %>%
gglikert(labels_size = 3, width = 0.5) +
labs(
title = "Do you use R Survey",
subtitle = "Creating and Using Labelled Data",
caption = "Using gglikert from ggstats package"
) +
scale_fill_brewer(
palette = "Spectral",
name = "Responses",
labels = c("never", "sometimes", "often", "always"),
) +
geom_vline(xintercept = 0) +
theme_custom() +
theme(legend.position = "bottom")
It seems many people in the survey plan to use R at work!! And have published R code as well. But Quarto seems to have mixed results! But of course this is a toy dataset!!
So there we are with Survey data analysis and plots!
There are a few other plots with this type of data, which are useful in very specialized circumstances. One example of this is the agreement plot5 which captures the agreement between two (sets) of evaluators, on ratings given on a shared ordinal scale to a set of items. An example from the field of medical diagnosis is the opinions of two specialists on a common set of patients. However, that is for a more advanced course!
Larmarange, Joseph. 2025a. ggstats: Extension to “ggplot2” for Plotting Stats. https://doi.org/10.32614/CRAN.package.ggstats.
———. 2025b. labelled: Manipulating Labelled Data. https://doi.org/10.32614/CRAN.package.labelled.
Lüdecke, Daniel. 2025. sjPlot: Data Visualization for Statistics in Social Science. https://CRAN.R-project.org/package=sjPlot.