| 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]