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licorice is an R package that eases the plotting of Likert-like data. It has been heavily inspired by the likert package from Bryer and Speerschneider.

licorice makes use of the ggplot2 plotting engine in such a way that interference by the licorice package in terms of theming is kept to a minimum (the graphs diplayed belowed are themed using the ggthemr package). When using the licorice function a ggplot2 object is returned which can then be added upon to your liking.


For now, no CRAN version exists and you'll have to install from GitHub using devtools.


Preparing the data

The licorice function expects a given structure of data. The example pisatest dataset can be used as a reference.

A minimal requirement is the presence of the question, response and count column. Additionally a group column can be added.


## Warning: replacing previous import by 'tidyr::%>%' when loading 'licorice'


##   question response         group count
## 1  ST24Q01    Agree        Canada  5623
## 2  ST24Q01    Agree        Mexico 12622
## 3  ST24Q01    Agree United States  1755
## 4  ST24Q01 Disagree        Canada  7938
## 5  ST24Q01 Disagree        Mexico 13872
## 6  ST24Q01 Disagree United States  1705

For example the gapsample dataset is not structured as it should be. Some simple preparations make it suited for licorice.


##   StudentId      question          response
## 1         1 Financial Aid             Agree
## 2         2 Financial Aid Strongly disagree
## 3         3 Financial Aid    Agree somewhat
## 4         4 Financial Aid    Strongly agree
## 5         5 Financial Aid    Strongly agree
## 6         6 Financial Aid             Agree

  gapsample %>%
  group_by(question, response) %>%
  summarise(count = n())


## Source: local data frame [6 x 3]
## Groups: question [1]
##        question          response count
##          (fctr)            (fctr) (int)
## 1 Financial Aid Strongly disagree     7
## 2 Financial Aid          Disagree     3
## 3 Financial Aid Disagree somewhat     2
## 4 Financial Aid         Undecided     3
## 5 Financial Aid    Agree somewhat     9
## 6 Financial Aid             Agree     7

Plotting the data

Three main plots are available. First a centered plot is shown; here the junction between two categories (which can be controlled by the middle_pos parameter) is centered. If the factor levels of the reponse variable are not set correctly, they can be specified using the answer_order parameter. If a middle_pos value of e.g. 2 is given, the results are centered at the junction between the second and third response type.

  c("Strongly disagree","Disagree", "Agree", "Strongly agree")

licorice(pisatest, answers_order = my_order, middle_pos = 2, type = "center", sort=T)

One can also fill the vertical space using a filled plot (also notice the sort argument).

licorice(pisatest, answers_order = my_order, type = "fill", sort=TRUE)

We can also have a look at the count data.

licorice(pisatest, answers_order = my_order, type = "count")

You can also show plots in combination with each other using existing functionality (the gridExtra library).


  licorice(pisatest, my_order, middle_pos = 2.5, type = "center", sort=TRUE),
  licorice(pisatest, my_order, type = "count", sort=TRUE) +
    theme(axis.text.y=element_blank()) +
  ncol = 2,
  widths = c(3/4,1/4)

Groups (as the countries in the graph above) are shown automatically when a group column is available in the data set. For example, when using the generated gap_fixed data set (where not group column is present), no group is shown;


## [1] "Strongly disagree" "Disagree"          "Disagree somewhat"
## [4] "Undecided"         "Agree somewhat"    "Agree"            
## [7] "Strongly agree"

licorice(gap_fixed, middle_pos = 4, sort=TRUE) +


plot Likert-like data using the ggplot2 plotting engine



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