## Usage

```
gglikert(
data,
include = dplyr::everything(),
weights = NULL,
y = ".question",
variable_labels = NULL,
sort = c("none", "ascending", "descending"),
sort_method = c("prop", "prop_lower", "mean", "median"),
sort_prop_include_center = totals_include_center,
factor_to_sort = ".question",
exclude_fill_values = NULL,
cutoff = NULL,
data_fun = NULL,
add_labels = TRUE,
labels_size = 3.5,
labels_color = "auto",
labels_accuracy = 1,
labels_hide_below = 0.05,
add_totals = TRUE,
totals_size = labels_size,
totals_color = "black",
totals_accuracy = labels_accuracy,
totals_fontface = "bold",
totals_include_center = FALSE,
totals_hjust = 0.1,
y_reverse = TRUE,
y_label_wrap = 50,
reverse_likert = FALSE,
width = 0.9,
facet_rows = NULL,
facet_cols = NULL,
facet_label_wrap = 50
)
gglikert_data(
data,
include = dplyr::everything(),
weights = NULL,
variable_labels = NULL,
sort = c("none", "ascending", "descending"),
sort_method = c("prop", "prop_lower", "mean", "median"),
sort_prop_include_center = TRUE,
factor_to_sort = ".question",
exclude_fill_values = NULL,
cutoff = NULL,
data_fun = NULL
)
gglikert_stacked(
data,
include = dplyr::everything(),
weights = NULL,
y = ".question",
variable_labels = NULL,
sort = c("none", "ascending", "descending"),
sort_method = c("prop", "prop_lower", "mean", "median"),
sort_prop_include_center = FALSE,
factor_to_sort = ".question",
data_fun = NULL,
add_labels = TRUE,
labels_size = 3.5,
labels_color = "auto",
labels_accuracy = 1,
labels_hide_below = 0.05,
add_median_line = FALSE,
y_reverse = TRUE,
y_label_wrap = 50,
reverse_fill = TRUE,
width = 0.9
)
```

## Arguments

- data
a data frame

- include
variables to include, accepts tidy-select syntax

- weights
optional variable name of a weighting variable, accepts tidy-select syntax

- y
name of the variable to be plotted on

`y`

axis (relevant when`.question`

is mapped to "facets, see examples), accepts tidy-select syntax- variable_labels
a named list or a named vector of custom variable labels

- sort
should the factor defined by

`factor_to_sort`

be sorted according to the answers (see`sort_method`

)? One of "none" (default), "ascending" or "descending"- sort_method
method used to sort the variables:

`"prop"`

sort according to the proportion of answers higher than the centered level,`"prop_lower"`

according to the proportion lower than the centered level,`"mean"`

considers answer as a score and sort according to the mean score,`"median"`

used the median and the majority judgment rule for tie-breaking.- sort_prop_include_center
when sorting with

`"prop"`

and if the number of levels is uneven, should half of the central level be taken into account to compute the proportion?- factor_to_sort
name of the factor column to sort if

`sort`

is not equal to`"none"`

; by default the list of questions passed to`include`

; should be one factor column of the tibble returned by`gglikert_data()`

; accepts tidy-select syntax- exclude_fill_values
Vector of values that should not be displayed (but still taken into account for computing proportions), see

`position_likert()`

- cutoff
number of modalities to be displayed negatively (i.e. on the left of the x axis or the bottom of the y axis), could be a decimal value:

`2`

to display negatively the two first modalities,`2.5`

to display negatively the two first modalities and half of the third,`2.2`

to display negatively the two first modalities and a fifth of the third (see examples). By default (`NULL`

), it will be equal to the number of modalities divided by 2, i.e. it will be centered.- data_fun
for advanced usage, custom function to be applied to the generated dataset at the end of

`gglikert_data()`

- add_labels
should percentage labels be added to the plot?

- labels_size
size of the percentage labels

- labels_color
color of the percentage labels (

`"auto"`

to use`hex_bw()`

to determine a font color based on background color)- labels_accuracy
accuracy of the percentages, see

`scales::label_percent()`

- labels_hide_below
if provided, values below will be masked, see

`label_percent_abs()`

- add_totals
should the total proportions of negative and positive answers be added to plot?

**This option is not compatible with facets!**- totals_size
size of the total proportions

- totals_color
color of the total proportions

- totals_accuracy
accuracy of the total proportions, see

`scales::label_percent()`

- totals_fontface
font face of the total proportions

- totals_include_center
if the number of levels is uneven, should half of the center level be added to the total proportions?

- totals_hjust
horizontal adjustment of totals labels on the x axis

- y_reverse
should the y axis be reversed?

- y_label_wrap
number of characters per line for y axis labels, see

`scales::label_wrap()`

- reverse_likert
if

`TRUE`

, will reverse the default stacking order, see`position_likert()`

- width
bar width, see

`ggplot2::geom_bar()`

- facet_rows, facet_cols
A set of variables or expressions quoted by

`ggplot2::vars()`

and defining faceting groups on the rows or columns dimension (see examples)- facet_label_wrap
number of characters per line for facet labels, see

`ggplot2::label_wrap_gen()`

- add_median_line
add a vertical line at 50%?

- reverse_fill
if

`TRUE`

, will reverse the default stacking order, see`ggplot2::position_fill()`

## Details

Combines several factor variables using the same list of ordered levels (e.g. Likert-type scales) into a unique data frame and generates a centered bar plot.

You could use `gglikert_data()`

to just produce the dataset to be plotted.

If variable labels have been defined (see `labelled::var_label()`

), they will
be considered. You can also pass custom variables labels with the
`variable_labels`

argument.

## Examples

```
library(ggplot2)
library(dplyr)
#>
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
#>
#> filter, lag
#> The following objects are masked from ‘package:base’:
#>
#> intersect, setdiff, setequal, union
likert_levels <- c(
"Strongly disagree",
"Disagree",
"Neither agree nor disagree",
"Agree",
"Strongly agree"
)
set.seed(42)
df <-
tibble(
q1 = sample(likert_levels, 150, replace = TRUE),
q2 = sample(likert_levels, 150, replace = TRUE, prob = 5:1),
q3 = sample(likert_levels, 150, replace = TRUE, prob = 1:5),
q4 = sample(likert_levels, 150, replace = TRUE, prob = 1:5),
q5 = sample(c(likert_levels, NA), 150, replace = TRUE),
q6 = sample(likert_levels, 150, replace = TRUE, prob = c(1, 0, 1, 1, 0))
) %>%
mutate(across(everything(), ~ factor(.x, levels = likert_levels)))
gglikert(df)
gglikert(df, include = q1:3) +
scale_fill_likert(pal = scales::brewer_pal(palette = "PRGn"))
#> Scale for fill is already present.
#> Adding another scale for fill, which will replace the existing scale.
gglikert(df, sort = "ascending")
# \donttest{
gglikert(df, sort = "ascending", sort_prop_include_center = TRUE)
gglikert(df, sort = "ascending", sort_method = "mean")
gglikert(df, reverse_likert = TRUE)
gglikert(df, add_totals = FALSE, add_labels = FALSE)
gglikert(
df,
totals_include_center = TRUE,
totals_hjust = .25,
totals_size = 4.5,
totals_fontface = "italic",
totals_accuracy = .01,
labels_accuracy = 1,
labels_size = 2.5,
labels_hide_below = .25
)
gglikert(df, exclude_fill_values = "Neither agree nor disagree")
if (require("labelled")) {
df %>%
set_variable_labels(
q1 = "First question",
q2 = "Second question"
) %>%
gglikert(
variable_labels = c(
q4 = "a custom label",
q6 = "a very very very very very very very very very very long label"
),
y_label_wrap = 25
)
}
#> Loading required package: labelled
# Facets
df_group <- df
df_group$group <- sample(c("A", "B"), 150, replace = TRUE)
gglikert(df_group, q1:q6, facet_rows = vars(group))
gglikert(df_group, q1:q6, facet_cols = vars(group))
gglikert(df_group, q1:q6, y = "group", facet_rows = vars(.question))
# Custom function to be applied on data
f <- function(d) {
d$.question <- forcats::fct_relevel(d$.question, "q5", "q2")
d
}
gglikert(df, include = q1:q6, data_fun = f)
# }
gglikert_stacked(df, q1:q6)
gglikert_stacked(df, q1:q6, add_median_line = TRUE, sort = "asc")
# \donttest{
gglikert_stacked(df_group, q1:q6, y = "group", add_median_line = TRUE) +
facet_grid(rows = vars(.question))
# }
```