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Compute means, standard deviations and confidence intervals by sub-groups

Source: R/mean_sd.R
mean_sd.Rd

mean_sd() lets you quickly compute mean and standard deviation by sub-groups. Use .conf.int = TRUE to also return confidence intervals of the mean.

Usage

mean_sd(data, ...)

# S3 method for class 'data.frame'
mean_sd(
  data,
  ...,
  .by = NULL,
  .drop = FALSE,
  .drop_na_by = FALSE,
  .conf.int = FALSE,
  .conf.level = 0.95,
  .options = NULL
)

# S3 method for class 'survey.design'
mean_sd(
  data,
  ...,
  .by = NULL,
  .drop = FALSE,
  .drop_na_by = FALSE,
  .conf.int = FALSE,
  .conf.level = 0.95,
  .options = NULL
)

# Default S3 method
mean_sd(
  data,
  ...,
  .drop = FALSE,
  .conf.int = FALSE,
  .conf.level = 0.95,
  .options = NULL
)

Arguments

data

A vector, a data frame, data frame extension (e.g. a tibble), or a survey design object.

...

<data-masking> Variable(s) for which to compute mean and standard deviation.

.by

<tidy-select> Optional additional variables to group by (in addition to those eventually previously declared using dplyr::group_by()).

.drop

If TRUE, will remove empty groups from the output.

.drop_na_by

If TRUE, will remove any NA values observed in the .by variables (or variables defined with dplyr::group_by()).

.conf.int

If TRUE, will estimate confidence intervals.

.conf.level

Confidence level for the returned confidence intervals.

.options

Additional arguments passed to stats::t.test() or srvyr::survey_mean().

Value

A tibble. Column "n" reports the number of valid observations and "missing" the number of missing (NA) observations, unweighted for survey objects.

A tibble with one row per group.

Examples

# using a vector
iris$Petal.Length |> mean_sd()
#> # A tibble: 1 × 5
#>   x       mean    sd     n missing
#>   <chr>  <dbl> <dbl> <int>   <int>
#> 1 vector  3.76  1.77   150       0

# one variable
iris |> mean_sd(Petal.Length)
#> # A tibble: 1 × 5
#>   x             mean    sd     n missing
#>   <chr>        <dbl> <dbl> <int>   <int>
#> 1 Petal.Length  3.76  1.77   150       0
iris |> mean_sd(Petal.Length, .conf.int = TRUE)
#> # A tibble: 1 × 7
#>   x             mean mean_low mean_high    sd     n missing
#>   <chr>        <dbl>    <dbl>     <dbl> <dbl> <int>   <int>
#> 1 Petal.Length  3.76     3.47      4.04  1.77   150       0
iris |> mean_sd(Petal.Length, .by = Species)
#> # A tibble: 3 × 6
#>   x            Species     mean    sd     n missing
#>   <chr>        <fct>      <dbl> <dbl> <int>   <int>
#> 1 Petal.Length setosa      1.46 0.174    50       0
#> 2 Petal.Length versicolor  4.26 0.470    50       0
#> 3 Petal.Length virginica   5.55 0.552    50       0
mtcars |> mean_sd(mpg, .by = c(cyl, gear))
#> # A tibble: 8 × 7
#> # Groups:   cyl [3]
#>   x       cyl  gear  mean     sd     n missing
#>   <chr> <dbl> <dbl> <dbl>  <dbl> <int>   <int>
#> 1 mpg       4     3  21.5 NA         1       0
#> 2 mpg       4     4  26.9  4.81      8       0
#> 3 mpg       4     5  28.2  3.11      2       0
#> 4 mpg       6     3  19.8  2.33      2       0
#> 5 mpg       6     4  19.8  1.55      4       0
#> 6 mpg       6     5  19.7 NA         1       0
#> 7 mpg       8     3  15.0  2.77     12       0
#> 8 mpg       8     5  15.4  0.566     2       0

# two variables
iris |> mean_sd(Petal.Length, Petal.Width)
#> # A tibble: 2 × 5
#>   x             mean    sd     n missing
#>   <chr>        <dbl> <dbl> <int>   <int>
#> 1 Petal.Length  3.76 1.77    150       0
#> 2 Petal.Width   1.20 0.762   150       0
iris |> mean_sd(dplyr::pick(dplyr::starts_with("Petal")), .by = Species)
#> # A tibble: 6 × 6
#>   x            Species     mean    sd     n missing
#>   <chr>        <fct>      <dbl> <dbl> <int>   <int>
#> 1 Petal.Length setosa     1.46  0.174    50       0
#> 2 Petal.Length versicolor 4.26  0.470    50       0
#> 3 Petal.Length virginica  5.55  0.552    50       0
#> 4 Petal.Width  setosa     0.246 0.105    50       0
#> 5 Petal.Width  versicolor 1.33  0.198    50       0
#> 6 Petal.Width  virginica  2.03  0.275    50       0

# missing values
d <- iris
d$Petal.Length[1:10] <- NA
d |> mean_sd(Petal.Length)
#> # A tibble: 1 × 5
#>   x             mean    sd     n missing
#>   <chr>        <dbl> <dbl> <int>   <int>
#> 1 Petal.Length  3.92  1.71   140      10
d |> mean_sd(Petal.Length, .by = Species)
#> # A tibble: 3 × 6
#>   x            Species     mean    sd     n missing
#>   <chr>        <fct>      <dbl> <dbl> <int>   <int>
#> 1 Petal.Length setosa      1.46 0.187    40      10
#> 2 Petal.Length versicolor  4.26 0.470    50       0
#> 3 Petal.Length virginica   5.55 0.552    50       0

# \donttest{
## SURVEY DATA ------------------------------------------------------

ds <- srvyr::as_survey(iris)
ds |> mean_sd(Petal.Length, .by = Species, .conf.int = TRUE)
#> # A tibble: 3 × 8
#>   x            Species     mean mean_low mean_high     sd     n missing
#>   <chr>        <fct>      <dbl>    <dbl>     <dbl>  <dbl> <int>   <int>
#> 1 Petal.Length setosa      1.46     1.41      1.51 0.0244    50       0
#> 2 Petal.Length versicolor  4.26     4.13      4.39 0.0660    50       0
#> 3 Petal.Length virginica   5.55     5.40      5.71 0.0775    50       0
# }