Descriptive statistics in R with metan
Getting started
In this quick tip, I will show you how to compute descriptive statistics in R with the package metan. If the package is not yet installed, you can download the stable version from CRAN with:
install.packages("metan")
Then, load it with:
library(metan)
For the latest release notes see the NEWS file.
Statistics by levels of a factor
metan provides a simple and intuitive pipe-friendly framework for computing descriptive statistics. A
set of functions can be used to compute the most used descriptive statistics quickly. In this tutorial, we will use the data example data_ge2 to create motivating examples.
To compute the mean values for each level of the factor GEN we use the function means_by().
means_by(data_ge2, GEN)
# # A tibble: 13 x 16
# GEN PH EH EP EL ED CL CD CW KW NR NKR CDED
# <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 H1 2.62 1.50 0.570 15.1 51.2 30.1 15.7 26.7 184. 16.6 32.2 0.588
# 2 H10 2.31 1.26 0.545 15.1 48.4 28.4 15.9 22.8 164. 15.6 32.4 0.586
# 3 H11 2.39 1.27 0.527 15.2 48.8 28.3 16.0 22.6 167. 15.6 33 0.580
# 4 H12 2.44 1.28 0.519 14.3 48.6 28.2 14.8 22.6 157. 16.3 30.4 0.582
# 5 H13 2.54 1.35 0.532 15.0 50.6 29.4 15.8 26.0 180. 17.4 31.0 0.582
# 6 H2 2.60 1.38 0.525 15.3 50.9 29.3 16.0 25.7 187. 16.7 31.9 0.574
# 7 H3 2.59 1.41 0.538 14.5 49.4 28.5 15.7 22.9 169. 15.8 31.4 0.578
# 8 H4 2.58 1.43 0.546 15.7 49.2 28.6 16.5 25.7 184. 15.5 35 0.581
# 9 H5 2.57 1.37 0.530 15.6 49.9 29.4 16.6 27.7 184. 16.1 33.9 0.588
# 10 H6 2.56 1.41 0.553 15.8 51.5 30.3 16.6 27.3 188. 16.3 32.8 0.588
# 11 H7 2.40 1.32 0.547 15.4 49.5 29.5 16.2 25.9 171. 16.2 31.4 0.597
# 12 H8 2.33 1.21 0.514 15.0 48.4 28.7 15.9 23.4 160. 15.9 31.3 0.594
# 13 H9 2.36 1.27 0.532 15.0 47.6 28.6 15.9 23.2 153. 15.5 32.5 0.601
# # ... with 3 more variables: PERK <dbl>, TKW <dbl>, NKE <dbl>
The following _by() functions are available for computing the main descriptive statistics by levels of a factor.
cv_by()For computing coefficient of variation.max_by()For computing maximum values.means_by()For computing arithmetic means.min_by()For compuing minimum values.n_by()For getting the length.sd_by()For computing sample standard deviation.sem_by()For computing standard error of the mean .
Useful functions
Other useful functions are also implemented. All of them works naturally with %>%, handle grouped data with group_by() and multiple variables (all numeric variables from .data by default).
av_dev()computes the average absolute deviation.ci_mean()computes the confidence interval for the mean.cv()computes the coefficient of variation.freq_table()Computes frequency fable.hm_mean(),gm_mean()computes the harmonic and geometric means, respectively. The harmonic mean is the reciprocal of the arithmetic mean of the reciprocals. The geometric mean is the nth root of n products.kurt()computes the kurtosis like used in SAS and SPSS.range_data()Computes the range of the values.sd_amo(),sd_pop()Computes sample and populational standard deviation, respectively.sem()computes the standard error of the mean.skew()computes the skewness like used in SAS and SPSS.sum_dev()computes the sum of the absolute deviations.sum_sq_dev()computes the sum of the squared deviations.var_amo(),var_pop()computes sample and populational variance.valid_n()Return the valid (not NA) length of a data.
The wrapper function desc_stat()
To compute all statistics at once we can use desc_stat(). This is a wrapper function around the above ones and may be used to compute measures of central tendency, position, and dispersion. By default (stats = "main"), seven statistics (coefficient of variation, maximum, mean, median, minimum, sample standard deviation, standard error and confidence interval of the mean) are computed. Other allowed values are "all" to show all the statistics, "robust" to show robust statistics, "quantile" to show quantile statistics, or chose one (or more) statistics using a comma-separated vector with the statistic names, e.g., stats = c("mean, cv"). We can also use hist = TRUE to create a histogram for each variable. Here, select helpers can also be used in the argument ....
All statistics for all numeric variables
desc_stat(data_ge2, stats = "all")
# # A tibble: 15 x 29
# variable av.dev ci cv gmean hmean iqr kurt mad
# <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 CD 0.948 0.186 7.34 15.9 15.9 1.65 -0.352 1.27
# 2 CDED 0.0261 0.0053 5.71 0.585 0.584 0.0417 0.669 0.0312
# 3 CL 1.98 0.365 7.95 28.9 28.8 3.70 -0.748 2.63
# 4 CW 5.21 0.99 25.2 24.0 23.0 9.19 -0.662 6.83
# 5 ED 2.30 0.437 5.58 49.5 49.4 4.40 -0.783 3.14
# 6 EH 0.249 0.045 21.2 1.31 1.28 0.484 -1.08 0.337
# 7 EL 0.995 0.199 8.28 15.1 15.1 1.72 -0.0174 1.26
# 8 EP 0.0459 0.0089 10.5 0.534 0.531 0.082 -0.369 0.0619
# 9 KW 27.2 5.18 18.9 170. 166. 46.8 -0.768 35.0
# 10 NKE 56.0 11.5 14.2 507. 501. 85.6 0.179 63.6
# 11 NKR 2.73 0.548 10.7 32.1 31.9 4.85 -0.116 3.56
# 12 NR 1.30 0.259 10.2 16.0 16.0 2.4 0.240 1.78
# 13 PERK 1.55 0.300 2.17 87.4 87.4 2.81 0.0317 2.10
# 14 PH 0.293 0.0528 13.4 2.46 2.44 0.595 -1.17 0.431
# 15 TKW 36.7 7.44 13.9 335. 332. 57.8 0.0313 44.8
# # ... with 20 more variables: max <dbl>, mean <dbl>, median <dbl>, min <dbl>,
# # n <dbl>, q2.5 <dbl>, q25 <dbl>, q75 <dbl>, q97.5 <dbl>, range <dbl>,
# # sd.amo <dbl>, sd.pop <dbl>, se <dbl>, skew <dbl>, sum <dbl>, sum.dev <dbl>,
# # sum.sq.dev <dbl>, valid.n <dbl>, var.amo <dbl>, var.pop <dbl>
Robust statistics using select helpers
data_ge2 %>%
desc_stat(contains("N"),
stats = "robust")
# # A tibble: 3 x 4
# variable n median iqr
# <chr> <dbl> <dbl> <dbl>
# 1 NKE 156 509. 85.6
# 2 NKR 156 32 4.85
# 3 NR 156 16 2.4
Quantile functions choosing variable names
data_ge2 %>%
desc_stat(PH, EH, CD, ED,
stats = "quantile")
# # A tibble: 4 x 7
# variable n min q25 median q75 max
# <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 CD 156 12.9 15.1 16 16.8 18.6
# 2 ED 156 43.5 47.3 49.9 51.7 54.9
# 3 EH 156 0.752 1.09 1.41 1.57 1.88
# 4 PH 156 1.71 2.18 2.52 2.77 3.04
Create a histogram for each variable
data_ge2 %>%
desc_stat(EP, EL, CL,
hist = TRUE)
# # A tibble: 3 x 9
# variable cv max mean median min sd.amo se ci
# <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 CL 7.95 34.7 29.0 28.7 23.5 2.31 0.185 0.365
# 2 EL 8.28 17.9 15.2 15.1 11.5 1.26 0.101 0.199
# 3 EP 10.5 0.660 0.537 0.544 0.386 0.0564 0.0045 0.0089
Statistics by levels of factors
To compute the statistics for each level of a factor, use the argument by. In addition, it is possible to select the statistics to compute using the argument stats, that is a single statistic name, e.g., "mean", or a a comma-separated vector of names with " at the beginning and end of vector only. Note that the statistic names ARE NOT case sensitive, i.e., both "mean", "Mean", or "MEAN" are recognized. Comma or spaces can be used to separate the statistics’ names.
- All options bellow will work:
stats = c("mean, se, cv, max, min")stats = c("mean se cv max min")stats = c("MEAN, Se, CV max MIN")
desc_stat(data_ge2,
contains("C"),
stats = ("mean, se, cv, max, min"),
by = ENV)
# # A tibble: 16 x 7
# ENV variable mean se cv max min
# <fct> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 A1 CD 16.4 0.174 6.62 18.3 14.1
# 2 A1 CDED 0.576 0.0059 6.40 0.664 0.495
# 3 A1 CL 29.7 0.358 7.53 34.7 25.9
# 4 A1 CW 28.3 0.906 20.0 38.5 17.8
# 5 A2 CD 15.9 0.215 8.46 18.3 13.3
# 6 A2 CDED 0.584 0.0054 5.80 0.694 0.507
# 7 A2 CL 28.5 0.405 8.88 33.0 23.9
# 8 A2 CW 23.8 1.11 29.1 35.7 11.1
# 9 A3 CD 15.8 0.151 6.00 17.6 14
# 10 A3 CDED 0.595 0.0059 6.22 0.681 0.511
# 11 A3 CL 28.4 0.386 8.47 33.2 23.5
# 12 A3 CW 20.8 0.818 24.6 29.6 11.5
# 13 A4 CD 15.8 0.194 7.67 18.6 12.9
# 14 A4 CDED 0.589 0.0036 3.81 0.631 0.542
# 15 A4 CL 29.4 0.286 6.07 32.8 25.8
# 16 A4 CW 26.4 0.730 17.3 34.7 15.3
To compute the descriptive statistics by more than one grouping variable, we need to pass a grouped data to the argument .data with the function group_by(). Let’s compute the mean, the standard error of the mean and the sample size for the variables EP and EL for all combinations of the factors ENV and GEN.
data_ge2 %>%
group_by(ENV, GEN) %>%
desc_stat(EP, EL,
stats = c("mean, se, n"))
# # A tibble: 104 x 6
# ENV GEN variable mean se n
# <fct> <fct> <chr> <dbl> <dbl> <dbl>
# 1 A1 H1 EL 15.4 0.637 3
# 2 A1 H1 EP 0.626 0.0193 3
# 3 A1 H10 EL 16.1 0.600 3
# 4 A1 H10 EP 0.584 0.018 3
# 5 A1 H11 EL 16.6 0.475 3
# 6 A1 H11 EP 0.574 0.0147 3
# 7 A1 H12 EL 15.2 0.252 3
# 8 A1 H12 EP 0.575 0.0212 3
# 9 A1 H13 EL 14.8 0.0811 3
# 10 A1 H13 EP 0.568 0.026 3
# # ... with 94 more rows