Type: Package
Title: Descriptive Statistics
Version: 4.0
Date: 2019-07-07
Author: Emmanuel Arnhold
Maintainer: Emmanuel Arnhold <emmanuelarnhold@yahoo.com.br>
Description: Performs various analyzes of descriptive statistics, including correlations, graphics and tables.
Depends: R (≥ 3.0.0)
License: GPL-2
NeedsCompilation: no
Packaged: 2019-07-10 19:49:16 UTC; emmanuel
Repository: CRAN
Date/Publication: 2019-07-10 20:12:50 UTC

Descriptive Statistics

Description

The package performs various analyzes of descriptive statistics, including correlations

Details

Package: ds
Type: Package
Version: 4.0
Date: 2018-07-07
License: GPL-2

Author(s)

Emmanuel Arnhold

emmanuelarnhold@yahoo.com.br

References

KAPS, M. and LAMBERSON, W. R. Biostatistics for Animal Science: an introductory text. 2nd Edition. CABI Publishing, Wallingford, Oxfordshire, UK, 2009. 504p.

Examples

	# Example of weights and heart girths of cows. 
	# Weight was measured in kg and heart girth in cm on 10 cows (Kaps and Lamberson, 2009).
	Weight=c(641, 620, 633, 651, 640, 666, 650, 688, 680, 670) 
	Heart_girth=c(205, 212, 213, 216, 216, 217, 218, 219, 221, 226)

	data=data.frame(Weight,Heart_girth)

	r1<-dscor(data)
	r1

	r2<-dscor(data, option=2)
	r2

	r3<-dscor(data, method=2, option=1)
	r3

	r4<-dscor(data, method=2, option=2)
	r4

	r5<-gds(data)
	r5

X function

Description

The function performs input tables of the environment R

Usage

X(x)

Arguments

x

x is NULL

Details

insert

X ()

select the desired table and press enter

observation: the mouse cursor should be in front of X ()

Value

returns a data.frame

Author(s)

Emmanuel Arnhold

emmanuelarnhold@yahoo.com.br

See Also

gds, dscor

Examples

#X()

Dispersion Plot

Description

Plot dispersion of first column of data in relation other columns

Usage

dplot(data, xlab = "Variable x", ylab = "Variable y", position = 1, colors = TRUE, 
type = "o", mean=TRUE)

Arguments

data

data is a data.frame

xlab

x-axis title

ylab

y-axis title

position

position of legend

top=1 (default)

bottomright=2

bottom=3

bottomleft=4

left=5

topleft=6

topright=7

right=8

center=9

colors

colors lines =TRUE (default) or black lines =FALSE

type

type of plot (see the plot function)

mean

plot means = TRUE (default) or plot original data = FALSE

Author(s)

Emmanuel Arnhold

emmanuelarnhold@yahoo.com.br

See Also

dscor, gds, tables

Examples

Time=c(10,20,30,40,50,60,70)
x=c(1,3,5,6,7,9,6)
y=c(4,6,8,9,10,15,16)
z=c(1,5,18,19,22,20,15)

data=data.frame(Time,x,y,z)

dplot(data)

Descriptive Statistics (correlations)

Description

The function estimates and test correlations

Usage

   dscor(data, method = 1, option = 1)

Arguments

data

data is a data.frame or matrix

method

method = 1 Pearson (default)

method = 2 Spearman

option

option = 1 return data.frame (default)

option = 2 return matrix

Value

The function returns correlations (Pearson and Spearman) and probability values of the t test

In option = 2 (return matrix), diagonally above contains the correlations and diagonally below contains the p-values of t test

Author(s)

Emmanuel Arnhold

emmanuelarnhold@yahoo.com.br

References

KAPS, M. and LAMBERSON, W. R. Biostatistics for Animal Science: an introductory text. 2nd Edition. CABI Publishing, Wallingford, Oxfordshire, UK, 2009. 504p.

See Also

gds, cor, cor.test

Examples

	# Example of weights and heart girths of cows. 
	# Weight was measured in kg and heart girth in cm on 10 cows (Kaps and Lamberson, 2009).

	Weight=c(641, 620, 633, 651, 640, 666, 650, 688, 680, 670) 
	Heart_girth=c(205, 212, 213, 216, 216, 217, 218, 219, 221, 226)

	data=data.frame(Weight,Heart_girth)
	
	#Pearson (table)
	r1<-dscor(data)
	r1
	
	# Pearson (matrix)
	r2<-dscor(data, option=2)
	r2

	# Spearman (table)
	r3<-dscor(data, method=2, option=1)
	r3

	# Spearman (matrix)
	r4<-dscor(data, method=2, option=2)
	r4

	# fictional example

	var1=c(10,13,14,16,18,22,29,28,35)
	var2=c(0.5,1,1.5,2,2.5,3,3.5,4,4.5)
	var3=c(102,NA,106,91,109,108,120,101,NA)
	var4=c(500,456,423,378,312,263,200,120,50)
	var5=c(18,09,22,NA,26,59,10,NA,96)

	table=data.frame(var1,var2,var3,var4,var5)

	#Pearson 
	r5<-dscor(table)
	r5
	
	r6<-dscor(table, option=2)
	r6
		
	# Spearman
	r7<-dscor(table, method=2, option=1)
	r7
	
	r8<-dscor(table, method=2, option=2)
	r8

General Descriptive Statistics

Description

The function performs various analyzes of descriptive statistics

Usage

gds(data)

Arguments

data

data is a numeric vector, data.frame or matrix

Value

The function return mean, maximum, minimum, median, mean + or - standard deviation, quantiles, n, range, variance, standard deviation, standard error of the mean, coefficiente of variation, skewness, kurtosis, normality test (p-value of the Shapiro-Wilk test)

Author(s)

Emmanuel Arnhold

emmanuelarnhold@yahoo.com.br

References

KAPS, M. and LAMBERSON, W. R. Biostatistics for Animal Science: an introductory text. 2nd Edition. CABI Publishing, Wallingford, Oxfordshire, UK, 2009. 504p.

See Also

dscor, cor, cor.test, summary

Examples


	# Example of weights and heart girths of cows.
	# Weight was measured in kg and heart girth in cm on 10 cows (Kaps and Lamberson, 2009).

	Weight=c(641, 620, 633, 651, 640, 666, 650, 688, 680, 670) 
	Heart_girth=c(205, 212, 213, 216, 216, 217, 218, 219, 221, 226)

	r1<-gds(Weight)
	r1

	r2<-gds(Heart_girth)
	r2

	data=data.frame(Weight,Heart_girth)

	r3<-gds(data)
	r3

	# fictional example

	var1=c(10,13,14,16,18,22,29,28,35)
	var2=c(0.5,1,1.5,2,2.5,3,3.5,4,4.5)
	var3=c(102,NA,106,91,109,108,120,101,NA)
	var4=c(500,456,423,378,312,263,200,120,50)
	var5=c(18,09,22,NA,26,59,10,NA,96)

	table=data.frame(var1,var2,var3,var4,var5)

	r6=gds(table)
	r6

	#kurtosis
	r6[24,]
	r6[24,]-3

Tables of Categorical Variables

Description

Organizes various tables of categorical variables and tests tables (Chi-square and Fisher's exact test)

Usage

tables(data)

Arguments

data

data is a data.frame

Author(s)

Emmanuel Arnhold

emmanuelarnhold@yahoo.com.br

See Also

gds, dscor, dplot

Examples

treatments=gl(2, 30, labels = c("Control", "Treat"))
resultsA=rep(c("positive","negative", "positive","negative"),c(25,5,7,23))
resultsB=rep(c("positive","negative", "positive","negative"),c(28,2,8,22))
resultsC=rep(c("positive","negative", "positive","negative"),c(16,14,13,17))

data=data.frame(treatments,resultsA, resultsB, resultsC)

r=tables(data)

names(r)

r

r[1]
r[2]
r[6]