R Dataset / Package DAAG / socsupport

How To Create a Barplot

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Visual
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How To Create a Stacked Barplot

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How To Create a Pie Chart

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How To Compute the Mean

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How To Create a Plot

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How to Compute the Median

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Boxplot

Submitted by pmagunia on April 22, 2018 - 3:07 PM
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Correlation Coefficient

Submitted by pmagunia on April 22, 2018 - 3:08 PM
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Cumulative Frequency Histogram

Submitted by pmagunia on April 22, 2018 - 3:09 PM
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Dotplot

Submitted by pmagunia on April 22, 2018 - 3:10 PM
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Hollow Histogram

Submitted by pmagunia on April 22, 2018 - 3:10 PM
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Numerical Summaries

Submitted by pmagunia on April 22, 2018 - 2:51 PM
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Pie Chart

Submitted by pmagunia on April 22, 2018 - 3:11 PM
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Plot

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Regression

Submitted by pmagunia on April 22, 2018 - 3:12 PM

Stem and Leaf Plots

Submitted by pmagunia on April 22, 2018 - 3:12 PM
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Visual Summaries

Submitted by pmagunia on April 22, 2018 - 3:13 PM
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Submitted by pmagunia on March 9, 2018 - 1:06 PM
<iframe src="https://embed.picostat.com/r-dataset-package-daag-socsupport.html" frameBorder="0" width="100%" height="307px" />
Attachment Size
dataset-30455.csv 11.38 KB
Dataset License
GNU General Public License v2.0
Documentation License
GNU General Public License v2.0
Documentation

On this Picostat.com statistics page, you will find information about the socsupport data set which pertains to Social Support Data. The socsupport data set is found in the DAAG R package. You can load the socsupport data set in R by issuing the following command at the console data("socsupport"). This will load the data into a variable called socsupport. If R says the socsupport data set is not found, you can try installing the package by issuing this command install.packages("DAAG") and then attempt to reload the data. If you need to download R, you can go to the R project website. You can download a CSV (comma separated values) version of the socsupport R data set. The size of this file is about 11,652 bytes.


Social Support Data

Description

Data from a survey on social and other kinds of support.

Usage

socsupport

Format

This data frame contains the following columns:

gender

a factor with levels female, male

age

age, in years, with levels 18-20, 21-24, 25-30, 31-40,40+

country

a factor with levels australia, other

marital

a factor with levels married, other, single

livewith

a factor with levels alone, friends, other, parents, partner, residences

employment

a factor with levels employed fulltime, employed part-time, govt assistance, other, parental support

firstyr

a factor with levels first year, other

enrolment

a factor with levels full-time, part-time, <NA>

emotional

summary of 5 questions on emotional support availability

emotionalsat

summary of 5 questions on emotional support satisfaction

tangible

summary of 4 questions on availability of tangible support

tangiblesat

summary of 4 questions on satisfaction with tangible support

affect

summary of 3 questions on availability of affectionate support sources

affectsat

summary of 3 questions on satisfaction with affectionate support sources

psi

summary of 3 questions on availability of positive social interaction

psisat

summary of 3 questions on satisfaction with positive social interaction

esupport

summary of 4 questions on extent of emotional support sources

psupport

summary of 4 questions on extent of practical support sources

supsources

summary of 4 questions on extent of social support sources (formerly, socsupport)

BDI

Score on the Beck depression index (summary of 21 questions)

Source

Melissa Manning, Psychology, Australian National University

Examples

attach(socsupport)not.na <- apply(socsupport[,9:19], 1, function(x)!any(is.na(x)))
ss.pr1 <- princomp(as.matrix(socsupport[not.na, 9:19]), cor=TRUE)  
pairs(ss.pr1$scores[,1:3])
sort(-ss.pr1$scores[,1])        # Minus the largest value appears first
pause()not.na[36] <- FALSE
ss.pr <- princomp(as.matrix(socsupport[not.na, 9:19]), cor=TRUE)  
summary(ss.pr)          # Examine the contribution of the components
pause()# We now regress BDI on the first six principal components:
ss.lm <- lm(BDI[not.na] ~ ss.pr$scores[, 1:6], data=socsupport)
summary(ss.lm)$coef
pause()ss.pr$loadings[,1]
plot(BDI[not.na] ~  ss.pr$scores[ ,1], col=as.numeric(gender), 
pch=as.numeric(gender),  xlab ="1st principal component", ylab="BDI")
topleft <- par()$usr[c(1,4)]
legend(topleft[1], topleft[2], col=1:2, pch=1:2, legend=levels(gender))

--

Dataset imported from https://www.r-project.org.
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