Package 'webr'

Title:	Data and Functions for Web-Based Analysis
Description:	Several analysis-related functions for the book entitled "Web-based Analysis without R in Your Computer"(written in Korean, ISBN 978-89-5566-185-9) by Keon-Woong Moon. The main function plot.htest() shows the distribution of statistic for the object of class 'htest'.
Authors:	Keon-Woong Moon [aut, cre], Tommaso Martino [ctb]
Maintainer:	Keon-Woong Moon <[email protected]>
License:	GPL-3
Version:	0.1.6
Built:	2025-02-12 04:43:28 UTC
Source:	https://github.com/cardiomoon/webr

Help Index

Extract bivariate variables
Extract continuous variables
Cox-Stuart test for trend analysis The Cox-Stuart test is defined as a little powerful test (power equal to 0.78), but very robust for the trend analysis. It is therefore applicable to a wide variety of situations, to get an idea of the evolution of values obtained. The proposed method is based on the binomial distribution. This function was written by Tommaso Martino<[email protected]> (See 'References')
Extract labels
Make table summarizing frequency
Make flextable summarizing frequency
Make default palette
Extract categorical variables
Select word
Make subtitle
Make subcolors with main colors
My chisquare test
Numerical Summary
Make a table showing numerical summary
Draw a PieDonut plot
Plotting distribution of statistic for object "htest"
Renew dictionary Renew dictionary
replace vector with labels
Runs test for randomness
Make transparent theme
Extract x2 statistical result
Summarize chisquare result
Make a chisquare result table

Extract bivariate variables

Description

Extract bivariate variables

Usage

BiVar(df)
BiVar(df)

Arguments

`df`	a data.frame

Extract continuous variables

Description

Extract continuous variables

Usage

ContinuousVar(df)
ContinuousVar(df)

Arguments

`df`	a data.frame

Cox-Stuart test for trend analysis The Cox-Stuart test is defined as a little powerful test (power equal to 0.78), but very robust for the trend analysis. It is therefore applicable to a wide variety of situations, to get an idea of the evolution of values obtained. The proposed method is based on the binomial distribution. This function was written by Tommaso Martino<[email protected]> (See 'References')

Description

Cox-Stuart test for trend analysis The Cox-Stuart test is defined as a little powerful test (power equal to 0.78), but very robust for the trend analysis. It is therefore applicable to a wide variety of situations, to get an idea of the evolution of values obtained. The proposed method is based on the binomial distribution. This function was written by Tommaso Martino<[email protected]> (See 'References')

Usage

cox.stuart.test(x)
cox.stuart.test(x)

Arguments

`x`	A numeric vector

Value

A list with class "htest"

References

Original code: http://statistic-on-air.blogspot.kr/2009/08/trend-analysis-with-cox-stuart-test-in.html

Examples

customers = c(5, 9, 12, 18, 17, 16, 19, 20, 4, 3, 18, 16, 17, 15, 14)
cox.stuart.test(customers)
customers = c(5, 9, 12, 18, 17, 16, 19, 20, 4, 3, 18, 16, 17, 15, 14)
cox.stuart.test(customers)

Extract labels

Description

Extract labels

Usage

extractLabels(x)
extractLabels(x)

Arguments

x

a vector

Make table summarizing frequency

Description

Make table summarizing frequency

Usage

freqSummary(x, digits = 1, lang = "en")
freqSummary(x, digits = 1, lang = "en")

Arguments

`x`	A vector
`digits`	integer indicating the number of decimal places
`lang`	Language. choices are one of c("en","kor")

Examples

require(moonBook)
freqSummary(acs$Dx)
#freqSummary(acs$smoking,lang="kor")
require(moonBook)
freqSummary(acs$Dx)
#freqSummary(acs$smoking,lang="kor")

Make flextable summarizing frequency

Description

Make flextable summarizing frequency

Usage

freqTable(
  x,
  digits = 1,
  lang = getOption("freqTable.lang", "en"),
  vanilla = FALSE,
  ...
)
freqTable(
  x,
  digits = 1,
  lang = getOption("freqTable.lang", "en"),
  vanilla = FALSE,
  ...
)

Arguments

`x`	A vector
`digits`	integer indicating the number of decimal places
`lang`	Language. choices are one of c("en","kor")
`vanilla`	Logical. Whether make vanilla table or not
`...`	Further arguments to paseed to the df2flextable function

Value

An object of clss flextable

Examples

require(moonBook)
freqTable(acs$Dx)
#freqTable(acs$smoking,lang="kor",vanilla=TRUE,fontsize=12)
require(moonBook)
freqTable(acs$Dx)
#freqTable(acs$smoking,lang="kor",vanilla=TRUE,fontsize=12)

Make default palette

Description

Make default palette

Usage

gg_color_hue(n)
gg_color_hue(n)

Arguments

`n`	number of colors

Extract categorical variables

Description

Extract categorical variables

Usage

GroupVar(df, max.ylev = 20)
GroupVar(df, max.ylev = 20)

Arguments

`df`	a data.frame
`max.ylev`	maximal length of unique values of catergorical variables

Select word

Description

Select word

Usage

langchoice1(id, lang = "en")
langchoice1(id, lang = "en")

Arguments

`id`	data id
`lang`	language. Possible choices are c("en","kor")

Make subtitle

Description

Make subtitle

Usage

makeSub(x)
makeSub(x)

Arguments

`x`	An object of class "htest"

Make subcolors with main colors

Description

Make subcolors with main colors

Usage

makeSubColor(main, no = 3)
makeSubColor(main, no = 3)

Arguments

`main`	character. main colors
`no`	number of subcolors

My chisquare test

Description

My chisquare test

Usage

mychisq.test(x)
mychisq.test(x)

Arguments

x

a table

Numerical Summary

Description

Numerical Summary

Usage

numSummary(x, ..., digits = 2, lang = "en")

numSummary1(x, ..., digits = 2, lang = "en")

numSummary2(x, ..., digits = 2, lang = "en")
numSummary(x, ..., digits = 2, lang = "en")

numSummary1(x, ..., digits = 2, lang = "en")

numSummary2(x, ..., digits = 2, lang = "en")

Arguments

`x`	A numeric vector or a data.frame or a grouped_df
`...`	further arguments to be passed
`digits`	integer indicating the number of decimal places
`lang`	Language. choices are one of c("en","kor")

Functions

numSummary1: Numerical Summary of a data.frame or a vector
numSummary2: Numerical Summary of a grouped_df

Examples

require(moonBook)
require(magrittr)
require(dplyr)
require(rrtable)
require(webr)
require(tibble)
numSummary(acs)
numSummary(acs$age)
numSummary(acs,age,EF)
acs %>% group_by(sex) %>% numSummary(age,BMI)
acs %>% group_by(sex) %>% select(age) %>% numSummary
acs %>% group_by(sex) %>% select(age,EF) %>% numSummary
acs %>% group_by(sex,Dx) %>% select(age,EF) %>% numSummary
acs %>% group_by(sex,Dx) %>% select(age) %>% numSummary
#acs %>% group_by(sex,Dx) %>% numSummary(age,EF,lang="kor")
require(moonBook)
require(magrittr)
require(dplyr)
require(rrtable)
require(webr)
require(tibble)
numSummary(acs)
numSummary(acs$age)
numSummary(acs,age,EF)
acs %>% group_by(sex) %>% numSummary(age,BMI)
acs %>% group_by(sex) %>% select(age) %>% numSummary
acs %>% group_by(sex) %>% select(age,EF) %>% numSummary
acs %>% group_by(sex,Dx) %>% select(age,EF) %>% numSummary
acs %>% group_by(sex,Dx) %>% select(age) %>% numSummary
#acs %>% group_by(sex,Dx) %>% numSummary(age,EF,lang="kor")

Make a table showing numerical summary

Description

Make a table showing numerical summary

Usage

numSummaryTable(
  x,
  ...,
  lang = getOption("numSummaryTable.lang", "en"),
  vanilla = FALSE,
  add.rownames = NULL
)
numSummaryTable(
  x,
  ...,
  lang = getOption("numSummaryTable.lang", "en"),
  vanilla = FALSE,
  add.rownames = NULL
)

Arguments

`x`	A grouped_df or a data.frame or a vector
`...`	further argument to be passed
`lang`	Language. choices are one of c("en","kor")
`vanilla`	Logical. Whether make vanilla table or not
`add.rownames`	Logical. Whether or not add rownames

Examples

require(moonBook)
require(dplyr)
numSummaryTable(acs)
numSummaryTable(acs$age)
acs %>% group_by(sex) %>% select(age) %>% numSummaryTable
acs %>% group_by(sex) %>% select(age,EF) %>% numSummaryTable
acs %>% group_by(sex,Dx) %>% select(age,EF) %>% numSummaryTable(vanilla=FALSE)
acs %>% group_by(sex,Dx) %>% numSummaryTable(age,EF,add.rownames=FALSE)
require(moonBook)
require(dplyr)
numSummaryTable(acs)
numSummaryTable(acs$age)
acs %>% group_by(sex) %>% select(age) %>% numSummaryTable
acs %>% group_by(sex) %>% select(age,EF) %>% numSummaryTable
acs %>% group_by(sex,Dx) %>% select(age,EF) %>% numSummaryTable(vanilla=FALSE)
acs %>% group_by(sex,Dx) %>% numSummaryTable(age,EF,add.rownames=FALSE)

Draw a PieDonut plot

Description

Draw a PieDonut plot

Usage

PieDonut(
  data,
  mapping,
  start = getOption("PieDonut.start", 0),
  addPieLabel = TRUE,
  addDonutLabel = TRUE,
  showRatioDonut = TRUE,
  showRatioPie = TRUE,
  ratioByGroup = TRUE,
  showRatioThreshold = getOption("PieDonut.showRatioThreshold", 0.02),
  labelposition = getOption("PieDonut.labelposition", 2),
  labelpositionThreshold = 0.1,
  r0 = getOption("PieDonut.r0", 0.3),
  r1 = getOption("PieDonut.r1", 1),
  r2 = getOption("PieDonut.r2", 1.2),
  explode = NULL,
  selected = NULL,
  explodePos = 0.1,
  color = "white",
  pieAlpha = 0.8,
  donutAlpha = 1,
  maxx = NULL,
  showPieName = TRUE,
  showDonutName = FALSE,
  title = NULL,
  pieLabelSize = 4,
  donutLabelSize = 3,
  titlesize = 5,
  explodePie = TRUE,
  explodeDonut = FALSE,
  use.label = TRUE,
  use.labels = TRUE,
  family = getOption("PieDonut.family", "")
)
PieDonut(
  data,
  mapping,
  start = getOption("PieDonut.start", 0),
  addPieLabel = TRUE,
  addDonutLabel = TRUE,
  showRatioDonut = TRUE,
  showRatioPie = TRUE,
  ratioByGroup = TRUE,
  showRatioThreshold = getOption("PieDonut.showRatioThreshold", 0.02),
  labelposition = getOption("PieDonut.labelposition", 2),
  labelpositionThreshold = 0.1,
  r0 = getOption("PieDonut.r0", 0.3),
  r1 = getOption("PieDonut.r1", 1),
  r2 = getOption("PieDonut.r2", 1.2),
  explode = NULL,
  selected = NULL,
  explodePos = 0.1,
  color = "white",
  pieAlpha = 0.8,
  donutAlpha = 1,
  maxx = NULL,
  showPieName = TRUE,
  showDonutName = FALSE,
  title = NULL,
  pieLabelSize = 4,
  donutLabelSize = 3,
  titlesize = 5,
  explodePie = TRUE,
  explodeDonut = FALSE,
  use.label = TRUE,
  use.labels = TRUE,
  family = getOption("PieDonut.family", "")
)

Arguments

`data`	A data.frame
`mapping`	Set of aesthetic mappings created by aes or aes_.
`start`	offset of starting point from 12 o'clock in radians
`addPieLabel`	A logical value. If TRUE, labels are added to the Pies
`addDonutLabel`	A logical value. If TRUE, labels are added to the Donuts
`showRatioDonut`	A logical value. If TRUE, ratios are added to the DonutLabels
`showRatioPie`	A logical value. If TRUE, ratios are added to the PieLabels
`ratioByGroup`	A logical value. If TRUE, ratios ara calculated per group
`showRatioThreshold`	An integer. Threshold to show label as a ratio of total. default value is 0.02.
`labelposition`	A number indicating the label position
`labelpositionThreshold`	label position threshold. Default value is 0.1.
`r0`	Integer. start point of pie
`r1`	Integer. end point of pie
`r2`	Integer. end point of donut
`explode`	pies to explode
`selected`	donuts to explode
`explodePos`	explode position
`color`	color
`pieAlpha`	transparency of pie
`donutAlpha`	transparency of pie
`maxx`	maximum position of plot
`showPieName`	logical. Whether or not show Pie Name
`showDonutName`	logical. Whether or not show Pie Name
`title`	title of plot
`pieLabelSize`	integer. Pie label size
`donutLabelSize`	integer. Donut label size
`titlesize`	integer. Title size
`explodePie`	Logical. Whether or not explode pies
`explodeDonut`	Logical. Whether or not explode donuts
`use.label`	Logical. Whether or not use column label in case of labelled data
`use.labels`	Logical. Whether or not use value labels in case of labelled data
`family`	font family

Examples

require(moonBook)
require(ggplot2)
browser=c("MSIE","Firefox","Chrome","Safari","Opera")
share=c(50,21.9,10.8,6.5,1.8)
df=data.frame(browser,share)
PieDonut(df,aes(browser,count=share),r0=0.7,start=3*pi/2,labelpositionThreshold=0.1)

PieDonut(df,aes(browser,count=share),r0=0.7,explode=5,start=3*pi/2)
PieDonut(mtcars,aes(gear,carb),start=3*pi/2,explode=3,explodeDonut=TRUE,maxx=1.7)
PieDonut(mtcars,aes(carb,gear),r0=0)
PieDonut(acs,aes(smoking,Dx),title="Distribution of smoking status by diagnosis")
PieDonut(acs,aes(Dx,smoking),ratioByGroup=FALSE,r0=0)
PieDonut(acs,aes(Dx,smoking),selected=c(1,3,5,7),explodeDonut=TRUE)
PieDonut(acs,aes(Dx,smoking),explode=1,selected=c(2,4,6,8),labelposition=0,explodeDonut=TRUE)
PieDonut(acs,aes(Dx,smoking),explode=1)
PieDonut(acs,aes(Dx,smoking),explode=1,explodeDonut=TRUE,labelposition=0)
PieDonut(acs,aes(Dx,smoking),explode=1,explodePie=FALSE,explodeDonut=TRUE,labelposition=0)
PieDonut(acs,aes(Dx,smoking),selected=c(2,5,8), explodeDonut=TRUE,start=pi/2,labelposition=0)
PieDonut(acs,aes(Dx,smoking),r0=0.2,r1=0.9,r2=1.3,explode=1,start=pi/2,explodeDonut=TRUE)
PieDonut(acs,aes(Dx,smoking),r0=0.2,r1=0.9,r2=1.3,explode=1,start=pi/2,labelposition=0)
PieDonut(acs,aes(Dx,smoking),explode=1,start=pi,explodeDonut=TRUE,labelposition=0)
require(dplyr)
df=mtcars %>% group_by(gear,carb) %>% summarize(n=n())
PieDonut(df,aes(pies=gear,donuts=carb,count=n),ratioByGroup=FALSE)

require(moonBook)
require(ggplot2)
browser=c("MSIE","Firefox","Chrome","Safari","Opera")
share=c(50,21.9,10.8,6.5,1.8)
df=data.frame(browser,share)
PieDonut(df,aes(browser,count=share),r0=0.7,start=3*pi/2,labelpositionThreshold=0.1)

PieDonut(df,aes(browser,count=share),r0=0.7,explode=5,start=3*pi/2)
PieDonut(mtcars,aes(gear,carb),start=3*pi/2,explode=3,explodeDonut=TRUE,maxx=1.7)
PieDonut(mtcars,aes(carb,gear),r0=0)
PieDonut(acs,aes(smoking,Dx),title="Distribution of smoking status by diagnosis")
PieDonut(acs,aes(Dx,smoking),ratioByGroup=FALSE,r0=0)
PieDonut(acs,aes(Dx,smoking),selected=c(1,3,5,7),explodeDonut=TRUE)
PieDonut(acs,aes(Dx,smoking),explode=1,selected=c(2,4,6,8),labelposition=0,explodeDonut=TRUE)
PieDonut(acs,aes(Dx,smoking),explode=1)
PieDonut(acs,aes(Dx,smoking),explode=1,explodeDonut=TRUE,labelposition=0)
PieDonut(acs,aes(Dx,smoking),explode=1,explodePie=FALSE,explodeDonut=TRUE,labelposition=0)
PieDonut(acs,aes(Dx,smoking),selected=c(2,5,8), explodeDonut=TRUE,start=pi/2,labelposition=0)
PieDonut(acs,aes(Dx,smoking),r0=0.2,r1=0.9,r2=1.3,explode=1,start=pi/2,explodeDonut=TRUE)
PieDonut(acs,aes(Dx,smoking),r0=0.2,r1=0.9,r2=1.3,explode=1,start=pi/2,labelposition=0)
PieDonut(acs,aes(Dx,smoking),explode=1,start=pi,explodeDonut=TRUE,labelposition=0)
require(dplyr)
df=mtcars %>% group_by(gear,carb) %>% summarize(n=n())
PieDonut(df,aes(pies=gear,donuts=carb,count=n),ratioByGroup=FALSE)

Plotting distribution of statistic for object "htest"

Description

Plotting distribution of statistic for object "htest"

Usage

## S3 method for class 'htest'
plot(x, ...)
## S3 method for class 'htest'
plot(x, ...)

Arguments

`x`	object of class "htest"
`...`	further arguments to ggplot

Value

a ggplot or NULL

Examples


require(moonBook)
require(webr)
## chi-square test
x=chisq.test(table(mtcars$am,mtcars$cyl))
plot(x)

#Welch Two Sample t-test
x=t.test(mpg~am,data=mtcars)
plot(x)

x=t.test(BMI~sex,data=acs)
plot(x)

# F test to compare two variances
x=var.test(age~sex,data=acs,alternative="less")
plot(x)

# Paired t-test
x=t.test(iris$Sepal.Length,iris$Sepal.Width,paired=TRUE)
plot(x)

# One sample t-test
plot(t.test(acs$age,mu=63))

# Two sample t-test
x=t.test(age~sex, data=acs,conf.level=0.99,alternative="greater",var.equal=TRUE)
plot(x)

require(moonBook)
require(webr)
## chi-square test
x=chisq.test(table(mtcars$am,mtcars$cyl))
plot(x)

#Welch Two Sample t-test
x=t.test(mpg~am,data=mtcars)
plot(x)

x=t.test(BMI~sex,data=acs)
plot(x)

# F test to compare two variances
x=var.test(age~sex,data=acs,alternative="less")
plot(x)

# Paired t-test
x=t.test(iris$Sepal.Length,iris$Sepal.Width,paired=TRUE)
plot(x)

# One sample t-test
plot(t.test(acs$age,mu=63))

# Two sample t-test
x=t.test(age~sex, data=acs,conf.level=0.99,alternative="greater",var.equal=TRUE)
plot(x)

Renew dictionary Renew dictionary

Description

Renew dictionary Renew dictionary

Usage

renew_dic()
renew_dic()

replace vector with labels

Description

replace vector with labels

Usage

replaceWithLabels(x)
replaceWithLabels(x)

Arguments

x

A vector

Runs test for randomness

Description

Runs test for randomness

Usage

runs.test(
  y,
  plot.it = FALSE,
  alternative = c("two.sided", "positive.correlated", "negative.correlated")
)
runs.test(
  y,
  plot.it = FALSE,
  alternative = c("two.sided", "positive.correlated", "negative.correlated")
)

Arguments

`y`	A vector
`plot.it`	A logical. whether or not draw a plot
`alternative`	a character string specifying the alternative hypothesis, must be one of "two.sided" (default), "greater" or "less".

Value

A list with class "htest" containing the following components: statistic,p-value,method and data.name

Examples

y=c(1,2,2,1,1,2,1,2)
runs.test(y)
y=c("A","B","B","A","A","B","A","B")
runs.test(y,alternative="p")
y=c(1,2,2,1,1,2,1,2)
runs.test(y)
y=c("A","B","B","A","A","B","A","B")
runs.test(y,alternative="p")

Make transparent theme

Description

Make transparent theme

Usage

transparent(size = 0)
transparent(size = 0)

Arguments

size

border size. default value is 0

Extract x2 statistical result

Description

Extract x2 statistical result

Usage

x2result(x)
x2result(x)

Arguments

x

a table

Summarize chisquare result

Description

Summarize chisquare result

Usage

x2summary(
  data = NULL,
  x = NULL,
  y = NULL,
  a,
  b,
  margin = 1,
  show.percent = TRUE,
  show.label = TRUE
)
x2summary(
  data = NULL,
  x = NULL,
  y = NULL,
  a,
  b,
  margin = 1,
  show.percent = TRUE,
  show.label = TRUE
)

Arguments

`data`	A data.frame
`x`	a column name
`y`	a column name
`a`	a vector
`b`	a vector
`margin`	numeric If 1 row percent, if 2 col percent
`show.percent`	logical
`show.label`	logical

Examples

require(moonBook)
x2summary(acs,sex,DM)
require(moonBook)
x2summary(acs,sex,DM)

Make a chisquare result table

Description

Make a chisquare result table

Usage

x2Table(
  data,
  x,
  y,
  margin = 1,
  show.percent = TRUE,
  show.label = TRUE,
  show.stat = TRUE,
  vanilla = FALSE,
  fontsize = 12,
  ...
)
x2Table(
  data,
  x,
  y,
  margin = 1,
  show.percent = TRUE,
  show.label = TRUE,
  show.stat = TRUE,
  vanilla = FALSE,
  fontsize = 12,
  ...
)

Arguments

`data`	A data.frame
`x`	a column name
`y`	a column name
`margin`	numeric If 1 row percent, if 2 col percent
`show.percent`	logical
`show.label`	logical
`show.stat`	logical
`vanilla`	logical whether or not make vanilla table
`fontsize`	A numeric
`...`	Further arguments to be passed to df2flextable()

Examples

require(moonBook)
x2Table(acs,sex,DM)
require(moonBook)
x2Table(acs,sex,DM)