ratingScaleSummary provides tools for quick analysis and visualisation of rating scales, consisting of equally coded sub-scales. These procedures might be useful for social scientists and psychologists and everyone else working with data collected via surveys making use of the the concept of multiple indicators for the measurment of dimensions of abstract or complex social concepts and terms.

In addition there are functions for calculating the interpolated median and percentiles of a grouped frequency distribution, the inter quartile range, and relative frequency and cross tables. The functions in this package produce output that is supposed to be easily integrated into RMarkdown and knitr files. All tabular outputs are data frames which integrate nicely into your workflow.

The plots created by this package could be called 'vertical-line-(dot) plots' (If there is another or a better name, please let me know!). Vertical-line plots can be understood intuitivly, since they resemble the structure of a typical rating scale layout, as it is presented to the survey participants. When used for group comparison vertical-line plots give a quick insight into the variables that 'stir controversy' between the groups, or which don't.

For now the ratingScaleSummary package is not available on CRAN, but it can be installed directly from github within R using the devtools package:

devtools::install_github('ratingScaleSummary/tilltnet')

or by using the source package for a local install.

We start with creating some sample data, that will be used throughout this vignette. We'll need a data.frame consisting of several variables, all using the same categories. In addition we create a grouping variable, that is used for the examples that incorporate group comparison.

df <- data.frame(replicate(6,sample(as.character(1:7), 2000, replace = T, prob = sample(1:100/100, 7))))
var_names <- lapply(1:6, FUN = function(x) paste(sample(letters, sample(6:13, 1)), collapse = ""))
names(df) <- var_names

g <- sample(c("G1", "G2", "G3"), 2000, replace = T)

rs_summary() returns a data.frame where each row represents one variable. It provides categories' frequencies (in %), N, median, and the interquartile range.

library(ratingScaleSummary)
res <- rs_summary(df = df)
knitr::kable(res)

The rs_summary() command gives a summary of the

vert_line_base() and vert_line_gg() plots a vertical line dot plot using base plotting or ggplot2.

## Using base plot.
vert_line_base(x = res["Median"], max_val = 7)

## Using ggplot2.
vert_line_gg(x = res["Median"], max_val = 7)

Returns a data.frame where each row represents one variable and the columns show the median values of the comparison groups. An additional colum shows the significance levels, based on the Kruskal-Wallis Rank Sum Test.

## Compare medians between groups accross several variables.
res_comp <- rs_compare_groups(df = df, groups = g)

### Warning in rs_compare_groups(df = df, groups = g): 'groups' is not a
### factor. Converting to factor.

knitr::kable(res_comp)

vert_line_base() and vert_line_gg() also produce plots for group comparison.

## Using base plot.
vert_line_base(x = res_comp, max_val = 7)

## Using ggplot2.
vert_line_gg(x = res_comp, max_val = 7)

library(ggthemes)
vert_line_gg(x = res["Median"], max_val = 7) +  theme_hc()

vert_line_gg(x = res_comp, max_val = 7) + theme_wsj()

• gmedian() calculates the interpolated median of a grouped frequency distribution.
• intQR() calculates interquartile range of a grouped frequency distribution.
• mQ() Calculates halfed interquartile range [german: mittlerer Quartilsabstand] of a grouped frequency distribution
• f_tab() returns a data frame where each row shows the frequencies of unique values in a vector. Weighting is supported.
• x_tab() returns a data frame as a crosstable with indication of statistic significance using chi-square. Weighting is supported.