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Calculates descriptive statistics for a continuous variable

Source: R/overview.R
overview.Rd

This function calculates basic descriptive statistics for a numerical variable. It can calculate an overall summary, or broken down by the levels of a grouping variable. Inputs can be summary data, vectors, or a data frame.

Usage

overview(
  data = NULL,
  outcome_variable = NULL,
  grouping_variable = NULL,
  means = NULL,
  sds = NULL,
  ns = NULL,
  grouping_variable_levels = NULL,
  outcome_variable_name = "My Outcome Variable",
  grouping_variable_name = NULL,
  conf_level = 0.95,
  assume_equal_variance = FALSE
)

Arguments

data

  • for raw data, a data frame or tibble

outcome_variable

  • for raw data, either a vector containing numerical data or the name of a data-frame column containing a factor

grouping_variable

  • optional; for raw data either a vector containing a factor or the name of a data frame column containing a factor

means

For summary data - A vector of 1 or more numerical means

sds

For summary data - A vector of standard deviations, same length as means

ns

For summary data - A vector of sample sizes, same length as means

grouping_variable_levels

For summary data - An optional vector of group labels, same length as means. If not passed, auto-generated.

outcome_variable_name

Optional friendly name for the outcome variable. Defaults to 'My Outcome Variable'. Ignored if a data-frame is passed, this argument is ignored.

grouping_variable_name

Optional friendly name for the grouping variable. If a data frame is passed, this argument is ignored.

conf_level

The confidence level for the confidence interval. Given in decimal form. Defaults to 0.95.

assume_equal_variance

Defaults to FALSE

Value

Returns a table of descriptive statistics

  • overview

    • outcome_variable_name -

    • grouping_variable_name -

    • grouping_variable_level -

    • mean -

    • mean_LL -

    • mean_UL -

    • median -

    • median_LL -

    • median_UL -

    • sd -

    • min -

    • max -

    • q1 -

    • q3 -

    • n -

    • missing -

    • df -

    • mean_SE -

    • median_SE -

Details

If equal variance is not assumed, each group is treated independently. In that case, the estimated mean, CI, and SE is from statpsych::ci.mean(), and the estimated median, CI, and SE is from statpsych::ci.median(). If equal variance is assumed, each group CI is calculated as with respect to all group data, using statpsych::ci.lc.mean.bs() and statpsych::ci.lc.median.bs()

Examples

# example code
esci::overview(data_latimier_3groups, "Test%", "Group")
#>   outcome_variable_name grouping_variable_name grouping_variable_level     mean
#> 1                 Test%                  Group                  Reread 37.29323
#> 2                 Test%                  Group                    Quiz 42.75689
#> 3                 Test%                  Group                 Prequiz 37.14286
#>    mean_LL  mean_UL   median median_LL median_UL       sd      min      max
#> 1 34.17500 40.41146 33.33333  28.79785  37.86881 15.30716  9.52381 76.19048
#> 2 38.96548 46.54830 42.85714  36.05392  49.66036 18.61177 14.28571 95.23810
#> 3 33.88967 40.39604 33.33333  31.06559  35.60107 15.96965  9.52381 90.47619
#>         q1       q3  n missing df  mean_SE median_SE
#> 1 28.57143 47.61905 95       0 94 1.570482  2.314063
#> 2 28.57143 57.14286 95       0 94 1.909527  3.471095
#> 3 23.80952 42.85714 95       0 94 1.638451  1.157033