Summary of mean values by group — means_by

Computes mean, sd and se for each sub-group (indicated by grp) of dv.

means_by_group(
  x,
  dv,
  grp,
  weights = NULL,
  digits = 2,
  out = c("txt", "viewer", "browser"),
  encoding = "UTF-8",
  file = NULL
)

grpmean(
  x,
  dv,
  grp,
  weights = NULL,
  digits = 2,
  out = c("txt", "viewer", "browser"),
  encoding = "UTF-8",
  file = NULL
)

Arguments

x: A (grouped) data frame.
dv: Name of the dependent variable, for which the mean value, grouped by grp, is computed.
grp: Factor with the cross-classifying variable, where dv is grouped into the categories represented by grp. Numeric vectors are coerced to factors.
weights: Name of variable in x that indicated the vector of weights that will be applied to weight all observations. Default is NULL, so no weights are used.
digits: Numeric, amount of digits after decimal point when rounding estimates and values.
out: Character vector, indicating whether the results should be printed to console (out = "txt") or as HTML-table in the viewer-pane (out = "viewer") or browser (out = "browser"), of if the results should be plotted (out = "plot", only applies to certain functions). May be abbreviated.
encoding: Character vector, indicating the charset encoding used for variable and value labels. Default is "UTF-8". Only used when out is not "txt".
file: Destination file, if the output should be saved as file. Only used when out is not "txt".

Value

For non-grouped data frames, means_by_group() returns a data frame with following columns: term, mean, N, std.dev,

std.error and p.value. For grouped data frames, returns a list of such data frames.

Details

This function performs a One-Way-Anova with dv as dependent and grp as independent variable, by calling lm(count ~ as.factor(grp)). Then contrast is called to get p-values for each sub-group. P-values indicate whether each group-mean is significantly different from the total mean.

Examples

data(efc)
means_by_group(efc, c12hour, e42dep)
#> 
#> # Grouped Means for average number of hours of care per week by elder's dependency
#> 
#> Category             |  Mean |   N |    SD |   SE |      p
#> ----------------------------------------------------------
#> independent          |  9.91 |  66 |  8.01 | 0.99 | < .001
#> slightly dependent   | 17.54 | 225 | 17.74 | 1.18 | < .001
#> moderately dependent | 34.52 | 306 | 41.54 | 2.37 |  0.983
#> severely dependent   | 75.90 | 304 | 61.72 | 3.54 | < .001
#> Total                | 42.44 | 901 | 50.82 | 1.69 |       
#> 
#> Anova: R2=0.245; adj.R2=0.242; F=96.908; p=0.000

data(iris)
means_by_group(iris, Sepal.Width, Species)
#> 
#> # Grouped Means for Sepal.Width by Species
#> 
#> Category   | Mean |   N |   SD |   SE |      p
#> ----------------------------------------------
#> setosa     | 3.43 |  50 | 0.38 | 0.05 | < .001
#> versicolor | 2.77 |  50 | 0.31 | 0.04 | < .001
#> virginica  | 2.97 |  50 | 0.32 | 0.05 |  0.035
#> Total      | 3.06 | 150 | 0.44 | 0.04 |       
#> 
#> Anova: R2=0.401; adj.R2=0.393; F=49.160; p=0.000

# also works for grouped data frames
if (require("dplyr")) {
  efc %>%
    group_by(c172code) %>%
    means_by_group(c12hour, e42dep)
}
#> 
#> Grouped by:
#> carer's level of education: low level of education
#> 
#> # Grouped Means for average number of hours of care per week by elder's dependency
#> 
#> Category             |  Mean |   N |    SD |   SE |      p
#> ----------------------------------------------------------
#> independent          | 16.33 |  12 | 10.74 | 3.10 |  0.024
#> slightly dependent   | 15.38 |  42 |  9.55 | 1.47 | < .001
#> moderately dependent | 42.05 |  61 | 46.53 | 5.96 |  0.696
#> severely dependent   | 85.52 |  65 | 56.42 | 7.00 | < .001
#> Total                | 49.81 | 180 | 52.24 | 3.89 |       
#> 
#> Anova: R2=0.307; adj.R2=0.295; F=25.955; p=0.000
#> 
#> 
#> Grouped by:
#> carer's level of education: intermediate level of education
#> 
#> # Grouped Means for average number of hours of care per week by elder's dependency
#> 
#> Category             |  Mean |   N |    SD |   SE |      p
#> ----------------------------------------------------------
#> independent          |  7.96 |  45 |  3.91 | 0.58 | < .001
#> slightly dependent   | 17.12 | 135 | 16.52 | 1.42 | < .001
#> moderately dependent | 33.55 | 163 | 41.05 | 3.22 |  0.753
#> severely dependent   | 79.71 | 163 | 63.13 | 4.94 | < .001
#> Total                | 41.76 | 506 | 51.42 | 2.29 |       
#> 
#> Anova: R2=0.284; adj.R2=0.280; F=66.374; p=0.000
#> 
#> 
#> Grouped by:
#> carer's level of education: high level of education
#> 
#> # Grouped Means for average number of hours of care per week by elder's dependency
#> 
#> Category             |  Mean |   N |    SD |   SE |      p
#> ----------------------------------------------------------
#> independent          | 15.20 |   5 | 18.43 | 8.24 |  0.363
#> slightly dependent   | 18.08 |  39 | 12.98 | 2.08 |  0.146
#> moderately dependent | 28.42 |  62 | 35.64 | 4.53 |  0.670
#> severely dependent   | 63.38 |  50 | 62.69 | 8.87 | < .001
#> Total                | 36.62 | 156 | 46.38 | 3.71 |       
#> 
#> Anova: R2=0.167; adj.R2=0.151; F=10.155; p=0.000
#> 
#> 

# weighting
efc$weight <- abs(rnorm(n = nrow(efc), mean = 1, sd = .5))
means_by_group(efc, c12hour, e42dep, weights = weight)
#> 
#> # Grouped Means for average number of hours of care per week by elder's dependency
#> 
#> Category             |  Mean |   N |    SD |   SE |      p
#> ----------------------------------------------------------
#> independent          | 10.20 |  68 |  8.63 | 1.06 | < .001
#> slightly dependent   | 17.65 | 221 | 15.23 | 1.02 | < .001
#> moderately dependent | 36.89 | 305 | 44.42 | 2.54 |  0.577
#> severely dependent   | 77.04 | 316 | 61.73 | 3.54 | < .001
#> Total                | 44.18 | 901 | 51.90 | 1.73 |       
#> 
#> Anova: R2=0.242; adj.R2=0.239; F=95.336; p=0.000