Plot likert scales as centered stacked bars.

sjp.likert(items, title = NULL, legend.title = NULL, legend.labels = NULL,
  axis.titles = NULL, axis.labels = NULL, catcount = NULL,
  cat.neutral = NULL, sort.frq = NULL, = NULL,
  title.wtd.suffix = NULL, wrap.title = 50, wrap.labels = 30,
  wrap.legend.title = 30, wrap.legend.labels = 28, geom.size = 0.6,
  geom.colors = "BrBG", cat.neutral.color = "grey70",
  intercept.line.color = "grey50", reverse.colors = FALSE,
  values = "show", show.n = TRUE, show.legend = TRUE,
  show.prc.sign = FALSE, grid.range = 1, grid.breaks = 0.2,
  expand.grid = TRUE, digits = 1, coord.flip = TRUE, prnt.plot = TRUE)



Data frame, with each column representing one item.


character vector, used as plot title. Depending on plot type and function, will be set automatically. If title = "", no title is printed. For effect-plots, may also be a character vector of length > 1, to define titles for each sub-plot or facet.


character vector, used as title for the plot legend.


character vector with labels for the guide/legend.


character vector of length one or two, defining the title(s) for the x-axis and y-axis.


character vector with labels used as axis labels. Optional argument, since in most cases, axis labels are set automatically.


Optional, amount of categories of items (e.g. "strongly disagree", "disagree", "agree" and "strongly agree" would be catcount = 4). Note that this argument only applies to "valid" answers, i.e. if you have an additional neutral category (see cat.neutral) like "don't know", this won't count for catcount (e.g. "strongly disagree", "disagree", "agree", "strongly agree" and neutral category "don't know" would still mean that catcount = 4). See 'Note'.


If there's a neutral category (like "don't know" etc.), specify the index number (value) for this category. Else, set cat.neutral = NULL (default). The proportions of neutral category answers are plotted as grey bars on the left side of the figure.


Indicates whether the items of items should be ordered by total sum of positive or negative answers.


to order ascending by sum of positive answers


to order descending by sum of positive answers


for sorting ascending negative answers


for sorting descending negative answers


(default) for no sorting

Vector of weights that will be applied to weight all cases. Must be a vector of same length as the input vector. Default is NULL, so no weights are used.


Suffix (as string) for the title, if is specified, e.g. title.wtd.suffix=" (weighted)". Default is NULL, so title will not have a suffix when cases are weighted.


numeric, determines how many chars of the plot title are displayed in one line and when a line break is inserted.


numeric, determines how many chars of the value, variable or axis labels are displayed in one line and when a line break is inserted.


numeric, determines how many chars of the legend's title are displayed in one line and when a line break is inserted.


numeric, determines how many chars of the legend labels are displayed in one line and when a line break is inserted.


size resp. width of the geoms (bar width, line thickness or point size, depending on plot type and function). Note that bar and bin widths mostly need smaller values than dot sizes.


user defined color for geoms. See 'Details' in sjp.grpfrq.


Color of the neutral category, if plotted (see cat.neutral).


Color of the vertical intercept line that divides positive and negative values.


Logical, if TRUE, the color scale from geom.colors will be reversed, so positive and negative values switch colors.


Determines style and position of percentage value labels on the bars:


(default) shows percentage value labels in the middle of each category bar


hides the value labels, so no percentage values on the bars are printed


shows the sums of percentage values for both negative and positive values and prints them inside the end of each bar


shows the sums of percentage values for both negative and positive values and prints them outside the end of each bar


logical, if TRUE, adds total number of cases for each group or category to the labels.


logical, if TRUE, and depending on plot type and function, a legend is added to the plot.


Logical, if TRUE, %-signs for value labels are shown.


Numeric, limits of the x-axis-range, as proportion of 100. Default is 1, so the x-scale ranges from zero to 100% on both sides from the center. You can use values beyond 1 (100%) in case bar labels are not printed because they exceed the axis range. E.g. grid.range = 1.4 will set the axis from -140 to +140%, however, only (valid) axis labels from -100 to +100% are printed. Neutral categories are adjusted to the most left limit.


numeric; sets the distance between breaks for the axis, i.e. at every grid.breaks'th position a major grid is being printed.


logical, if TRUE, the plot grid is expanded, i.e. there is a small margin between axes and plotting region. Default is FALSE.


Numeric, amount of digits after decimal point when rounding estimates and values.


logical, if TRUE, the x and y axis are swapped.


logical, if TRUE (default), plots the results as graph. Use FALSE if you don't want to plot any graphs. In either case, the ggplot-object will be returned as value.


(Insisibily) returns the ggplot-object with the complete plot (plot) as well as the data frame that was used for setting up the ggplot-object (df.neg for the negative values, df.pos for the positive values and df.neutral for the neutral category values).


Note that only even numbers of categories are possible to plot, so the "positive" and "negative" values can be splitted into two halfs. A neutral category (like "don't know") can be used, but must be indicated by cat.neutral.

The catcount-argument indicates how many item categories are in the Likert scale. Normally, this argument can be ignored because the amount of valid categories is retrieved automatically. However, sometimes (for instance, if a certain category is missing in all items), auto-detection of the amount of categories fails. In such cases, specify the amount of categories with the catcount-argument.

See also


library(sjmisc) data(efc) # find all variables from COPE-Index, which all have a "cop" in their # variable name, and then plot that subset as likert-plot find_var(efc, pattern = "cop", out = "df") %>% sjp.likert()
sjp.likert( find_var(efc, pattern = "cop", out = "df"), grid.range = 1.2, expand.grid = FALSE, values = "sum.outside", show.prc.sign = TRUE )