R's summary function works really well on a dataframe, giving, for example:
> summary(fred)
sum.count count sum value
Min. : 1.000 Min. : 1.0 Min. : 1 Min. : 0.00
1st Qu.: 1.000 1st Qu.: 6.0 1st Qu.: 7 1st Qu.:35.82
Median : 1.067 Median : 9.0 Median : 10 Median :42.17
Mean : 1.238 Mean : 497.1 Mean : 6120 Mean :43.44
3rd Qu.: 1.200 3rd Qu.: 35.0 3rd Qu.: 40 3rd Qu.:51.31
Max. :40.687 Max. :64425.0 Max. :2621278 Max. :75.95
What I'd like to do is modify the function so it also gives, after 'Mean', an entry for the standard deviation, the kurtosis and the skew.
What's the best way to do this? I've researched this a bit, and adding a function with a method doesn't work for me:
> summary.class <- function(x)
{
return(sd(x))
}
The above is just ignored. I suppose that I need to understand how to define all classes to return.
How about using already existing solutions from the psych package?
my.dat <- cbind(norm = rnorm(100), pois = rpois(n = 100, 10))
library(psych)
describe(my.dat)
# vars n mean sd median trimmed mad min max range skew kurtosis se
# norm 1 100 -0.02 0.98 -0.09 -0.06 0.86 -3.25 2.81 6.06 0.13 0.74 0.10
# pois 2 100 9.91 3.30 10.00 9.95 4.45 3.00 17.00 14.00 -0.07 -0.75 0.33
Another choice is the Desc function from the DescTools package which produce both summary stats and plots.
library(DescTools)
Desc(iris3, plotit = TRUE)
#> -------------------------------------------------------------------------
#> iris3 (numeric)
#>
#> length n NAs unique 0s mean meanCI
#> 600 600 0 74 0 3.46 3.31
#> 100.0% 0.0% 0.0% 3.62
#>
#> .05 .10 .25 median .75 .90 .95
#> 0.20 1.10 1.70 3.20 5.10 6.20 6.70
#>
#> range sd vcoef mad IQR skew kurt
#> 7.80 1.98 0.57 2.52 3.40 0.13 -1.05
#>
#> lowest : 0.1 (5), 0.2 (29), 0.3 (7), 0.4 (7), 0.5
#> highest: 7.3, 7.4, 7.6, 7.7 (4), 7.9
Results from Desc can be redirected to a Microsoft Word file
### RDCOMClient package is needed
install.packages("RDCOMClient", repos = "http://www.omegahat.net/R")
# or
devtools::install_github("omegahat/RDCOMClient")
# create a new word instance and insert title and contents
wrd <- GetNewWrd(header = TRUE)
DescTools::Desc(iris3, plotit = TRUE, wrd = wrd)
The skim function from the skimr package is also a good one
library(skimr)
skim(iris)
Skim summary statistics
n obs: 150
n variables: 5
-- Variable type:factor --------------------------------------------------------
variable missing complete n n_unique
Species 0 150 150 3
top_counts ordered
set: 50, ver: 50, vir: 50, NA: 0 FALSE
-- Variable type:numeric -------------------------------------------------------
variable missing complete n mean sd p0 p25 p50
Petal.Length 0 150 150 3.76 1.77 1 1.6 4.35
Petal.Width 0 150 150 1.2 0.76 0.1 0.3 1.3
Sepal.Length 0 150 150 5.84 0.83 4.3 5.1 5.8
Sepal.Width 0 150 150 3.06 0.44 2 2.8 3
p75 p100 hist
5.1 6.9 ▇▁▁▂▅▅▃▁
1.8 2.5 ▇▁▁▅▃▃▂▂
6.4 7.9 ▂▇▅▇▆▅▂▂
3.3 4.4 ▁▂▅▇▃▂▁▁
Edit: probably off topic but it's worth to mention the DataExplorer package for Exploratory Data Analysis.
library(DataExplorer)
introduce(iris)
#> rows columns discrete_columns continuous_columns all_missing_columns
#> 1 150 5 1 4 0
#> total_missing_values total_observations memory_usage
#> 1 0 750 7256
plot_missing(iris)
plot_boxplot(iris, by = 'Species')
plot_histogram(iris)
plot_correlation(iris, cor_args = list("use" = "pairwise.complete.obs"))
Created on 2018-09-16 by the reprex package (v0.2.1.9000)
来源：https://stackoverflow.com/questions/52344047/how-to-extend-the-summary-function-to-include-sd-kurtosis-and-skew