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- set.seed(1)
- df <- data.frame(a=rnorm(10),b=rnorm(10))
- summarydist <- function(x) {
- y1 <- summary(x)
- y2 <- IQR(x)
- names(y2) <- "IQR"
- require(moments)
- y3 <- skewness(x)
- names(y3) <- "Skewness"
- y4 <- kurtosis(x)
- names(y4) <- "kurtosis"
- c(y1,y2,y3,y4)
- }
- sapply(df,summarydist)
- # a b
- #Min. -0.8356000 -2.2150000
- #1st Qu. -0.5462000 -0.0377500
- #Median 0.2566000 0.4919000
- #Mean 0.1322000 0.2488000
- #3rd Qu. 0.5537000 0.9132000
- #Max. 1.5950000 1.5120000
- #IQR 1.0998807 0.9509302
- #Skewness 0.2961938 -1.1871418
- #kurtosis 2.2752871 3.8598299
- # library(fBasics)
- basicStats(df)
- a b
- nobs 10.000000 10.000000
- NAs 0.000000 0.000000
- Minimum -0.835629 -2.214700
- Maximum 1.595281 1.511781
- 1. Quartile -0.546187 -0.037748
- 3. Quartile 0.553693 0.913182
- Mean 0.132203 0.248845
- Median 0.256576 0.491872
- Sum 1.322028 2.488450
- SE Mean 0.246843 0.338210
- LCL Mean -0.426195 -0.516240
- UCL Mean 0.690600 1.013930
- Variance 0.609314 1.143862
- Stdev 0.780586 1.069515
- Skewness 0.252895 -1.013599
- Kurtosis -1.157017 0.126462
- DF <- basicStats(df)[c(3:8,15:16),]
- rbind(DF, IQR=DF[4,]-DF[3,])
- a b
- Minimum -0.835629 -2.214700
- Maximum 1.595281 1.511781
- 1. Quartile -0.546187 -0.037748
- 3. Quartile 0.553693 0.913182
- Mean 0.132203 0.248845
- Median 0.256576 0.491872
- Skewness 0.252895 -1.013599
- Kurtosis -1.157017 0.126462
- IQR 1.099880 0.950930
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