time_stats: Compute stats for time data
In darrellpenta/uxstats: Common data analysis tools for UX researchers

Description Usage Arguments Details Value See Also Examples

Following Sauro and Lewis (2012), time_stats() takes the sample size into account when computing stats on time data.

time_stats(.x, ...)

## S3 method for class 'numeric'
time_stats(.x, ..., .alpha = 0.05)

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

`.x`	A vector or long-format data frame with a named column of numeric values corresponding to task or response times.
`...`	(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.
`.var`	If `x` is a data frame, the unquoted name of column containing the values to use in the computations.

time_stats() returns several point estimates, including means, geometric means (better when sample size <= 25), and medians (better for larger sample sizes). It also returns confidence interval information based on log-transformed values (for small samples) or raw (untransformed) data (for larger samples).

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.
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(), ratings_stats(), success_stats()

time_stats(c(40, 36, 53, 56, 110, 48, 34, 44, 30, 40, 80))

.ux_data <-
data.frame(
 "id" = rep(seq(1,10,1),2),
 "group" = rep(c("A","B"),10),
 "task" = c(rep(1,10),rep(2,10)),
 "time"  = runif(20,200,1000)
)

time_stats(.ux_data, .var=time, task, .alpha = 0.001)