README.md
In osoramirez/resumeR: The Most Complete Summary Statistic Ever!

resumeR

The goal of resumeR is to get a entire summary statistic.

You have installed resumeR on your R system, version 3.4.4 or later “https://cran.r-project.org/”. Works better in Rstudio “https://www.rstudio.com”

You can install the released version of resumeR from CRAN with:

install.packages("resumeR"). Not yet available

And the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("osoramirez/resumeR")

Activate the resumeR package:

library(resumeR)
#> Loading required package: e1071
#> Loading required package: car
#> Loading required package: carData

You could requiere “e1071”; “car” and “carData” package

This is a basic example which shows you how to solve a common problem:

set.seed(12345)
data<-rnorm(100)
resume(rnorm(100))
#> [1] "You got Normal distribution."
#> [1] "You got a good sample size (n>=30)"
#> [1] "Warning: You  have outlier"

Get a complete summary table, histogram and boxplot of your distribution data.

resume2data(data)

#> This function shows summary statistics.
#>                   It includes measures of central tendency,
#>                   measures of variability,
#>                   and measures of shape.
#> [[1]]
#> NULL
#> 
#> [[2]]
#>                Size (n)                 Missing                 Minimum 
#>               100.00000                 0.00000                -2.38000 
#>                  1st Qu                  Median                  3st Qu 
#>                -0.59000                 0.48400                 0.90000 
#>                     Max                    Mean                      sd 
#>                 2.47700                 0.24500                 1.11500 
#>                     Var                 SE Mean                  TrMean 
#>                 1.24300                 0.11200                 0.25800 
#>                     IQR                   Range                Kurtosis 
#>                 1.49000                 4.85700                -0.61000 
#>                Skewness                      CV                 CI.Mean 
#>                -0.14000                 4.55102                 0.00704 
#>                  lwr.ci                  upr.ci                     Sum 
#>                 0.02000                 0.47000                24.51972 
#>      Shapiro.test p-val 
#>                 0.24200

If you are interested in exploring the distribution of your data using a histogram, use “plothist”.

plothist(data)

#> This option performs a histogram that show your data has mean of  0.245  and  1.115  of standar desviation. Your median is  0.484  in  100  samples data. Check all your statistical data, using function: resume2data.

You can also embed boxplot, for example:

plotbox(data)

#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#>  -2.380  -0.590   0.484   0.245   0.900   2.477

A coefficient of variation (cv) is a statistical measure of the dispersion of data points in a data series around the mean.

cv(data)
#> [1] 4.55

The geometric mean is a mean or average, which indicates the central tendency

g_mean(data)
#> Warning in log(x): NaNs produced
#> [1] 0.751

The harmonic mean is a very specific type of average. It’s generally used when dealing with averages of units, like speed or other rates and ratios. Its a reciprocals of the numbers in your data set or it is calculated by dividing the number of observations by the reciprocal of each number in the series.

h_mean(data)
#> [1] 1.32

The "Mode" is the value that occurs most often. If no number in the list is repeated.

data2<-c(1,1,1,2,3,4,5,6,7,8,9)
Mode(data2)
#> [1] 1

Is as a measure of the precision of the sample mean, it is considered as a measures of spread.

se(data)
#> [1] 0.112
#> mean= 0.245±0.112 =standard error

summaries for data.frame

For your data.frame data use this functions.

data(iris)
resumendf(Petal.Width ~Species, data = iris)

#>             n  Mean    sd Median Min Max 1st Qu 3st Qu     se Missing
#> setosa     50 0.246 0.105    0.2 0.1 0.6    0.2     0.3 0.015       0
#> versicolor 50 1.326 0.198    1.3 1.0 1.8    1.2     1.5 0.028       0
#> virginica  50 2.026 0.275    2.0 1.4 2.5    1.8     2.3 0.039       0