Provide descriptive statistics for a dataset.
descriptives(x, ...)An object for which a method exists.
Additional arguments.
A data.frame with descriptive statistics for x.
Its elements are:
| name | Character | Variable name |
| type | character | Data type in R, as obtained by class(x)[1] |
| n | Integer | Number of valid observations |
| missing | Numeric | Proportion missing |
| unique | Integer | Number of unique values |
| mean | numeric | Mean value of non-missing entries, only defined for variables that can be coerced to numeric |
| median | numeric | Median value of non-missing entries, only defined for numeric variables |
| mode | Integer | For numeric variables: The mode value. For factors: The frequency of the mode value |
| mode_value | Character | For factors: value of the mode |
| sd | numeric | Standard deviation of non-missing entries, only defined for variables that can be coerced to numeric |
| v | numeric | Variability coefficient V for factor variables (Agresti, 1990). V is the probability that two independent observations fall in different categories |
| min | numeric | Minimum value for numeric variables |
| max | numeric | Maximum value for numeric variables |
| range | numeric | Range (distance between min and max) for numeric variables |
| skew | numeric | Skewness. The normalized third central moment of a numeric variable, which reflects its skewness. A symmetric distribution has a skewness of zero |
| skew_2se | numeric | Skewness, divided by two times its standard error. Values greater than one can be considered "significant" according to a Z-test with significance level of .05 |
| kurt | numeric | Kurtosis. The normalized fourth central moment of a numeric variable, which reflects its peakedness. A heavy-tailed distribution has high kurtosis, a light-tailed distribution has low kurtosis (sometimes called platykurtic). |
| kurt_2se | numeric | Kurtosis, divided by two times its standard error. Values greater than one can be considered "significant" according to a Z-test with significance level of .05 |
Agresti, A. (2012). Categorical data analysis (Vol. 792). John Wiley & Sons.
descriptives(iris)
#> name type n missing unique mean median mode mode_value
#> 1 Sepal.Length numeric 150 0 35 5.843333 5.80 5.80 <NA>
#> 2 Sepal.Width numeric 150 0 23 3.057333 3.00 3.00 <NA>
#> 3 Petal.Length numeric 150 0 43 3.758000 4.35 4.35 <NA>
#> 4 Petal.Width numeric 150 0 22 1.199333 1.30 1.30 <NA>
#> 5 Species factor 150 0 4 NA NA 50.00 setosa
#> sd v min max range skew skew_2se kurt kurt_2se
#> 1 0.8280661 NA 4.3 7.9 3.6 0.3117531 0.7871027 2.426432 3.082490
#> 2 0.4358663 NA 2.0 4.4 2.4 0.3157671 0.7972372 3.180976 4.041048
#> 3 1.7652982 NA 1.0 6.9 5.9 -0.2721277 -0.6870579 1.604464 2.038279
#> 4 0.7622377 NA 0.1 2.5 2.4 -0.1019342 -0.2573597 1.663933 2.113826
#> 5 NA 0.6666667 NA NA NA NA NA NA NA