ratings_stats: Compute stats for ratings or continuously distributed data
In darrellpenta/uxstats: Common data analysis tools for UX researchers

Description Usage Arguments Details Value See Also Examples

For ratings data and other continuously distributed variables, ratings_stats() returns means; information about confidence intervals (based on the T distribution); standard deviations; medians; and other details.

ratings_stats(.x, ...)

## S3 method for class 'numeric'
ratings_stats(.x, ..., .alpha = 0.05, .limits = NULL)

## S3 method for class 'data.frame'
ratings_stats(.x, .var, ..., .alpha = 0.05, .limits = NULL)

`.x`	A vector of values, or a long-format data frame with a named column containing numeric ratings data.
`...`	(Optional) If `.x` is a long-format data frame, you can pass the name of one or more grouping variables as unquoted, comma-separated column names (without naming the `...` argument) to compute stats by groups.
`.alpha`	(Optional) A positive number (where 0 < `.alpha` < 1) specifying the significance level to be used. Defaults to `.alpha = 0.05`. To set a different significance level, the argument must be named (i.e., `.alpha=0.001`) or else the function may yield unexpected results.
`.limits`	(Optional) If you want to specify the end-points (limits) for the ratings scale, which will ensure that confidence interval values don't exceed the upper and lower bounds, you can supply a numeric vector of length two,indicating the limits (e.g., `.limits = c(1,7)`).
`.var`	If `.x` is a data frame, the unquoted name of the data frame column containing the values to use in the computations.

You can modify the alpha level to adjust confidence intervals by including .alpha as a named argument and providing a numeric value: e.g., .aplha = 0.001.
You can specific scale limits so that the output values have upper- and lower-bounds by including .limits and providing a numeric vector of length 2: e.g., .limits = c(1.5,6.5).
If you're passing a data frame to .x, you can optionally pass one or more grouping variables as unquoted, comma-separated column names (without naming the ... argument) to compute stats by groups.

Note that NAs are automatically dropped in all calculations.

A tibble with one or more means, confidence interval information, and other information.

Other descriptive stats for UX measures: problem_stats(), success_stats(), time_stats()

# Compare the difference between the output of:
ratings_stats(c(1,8,8)) # and:

ratings_stats(c(1,8,8), .limits = c(1,8))

.ux_data <-
 data.frame(
  "id" = rep(seq(1,10,1),2),
  "task" = c(rep(1,10),rep(2,10)),
  "easiness"  = sample(1:7,20,replace=TRUE))

ratings_stats(.ux_data, easiness,task,.alpha=0.01,.limits=c(1,7))