Crd with r script
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This document demonstrates how to conduct a complete randomized experimental design using R script. It generates hypothetical data across three training groups and performs an analysis of variance to test for systematic variation across the groups. Multiple comparison tests and plots are used to analyze differences between group means and check assumptions of the experimental design model.
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# ---Simple Calculations---
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names(mydata)
row.names(mydata)
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mydata[ , 6] #All obs, 6th var
mydata[ , 6][5:8] #6th var, obs 5:8
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# Example renaming gender to sex while
# creating a data frame (left as a comment)
#
# mydata <- data.frame(workshop, sex = gender,
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dim(mymatrix)
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# left as a comment so we keep
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#
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R is a very flexible and powerful programming language, as well as a.pdf
R is a very flexible and powerful programming language, as well as a package that is written
using that language (and others like C). The following program demonstrates many of its basic
features. You can cut and paste it into R, or download the file that includes it from here. If you
run it line by line, many of its features will become clear. Both editions of R for SAS and SPSS
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output and explaining what R is doing.
# Filename: ProgrammingBasics.R
# ---Simple Calculations---
2 + 3
x <- 2
y <- 3
x + y
x * y
# ---Data Structures---
# Vectors
workshop <- c(1, 2, 1, 2, 1, 2, 1, 2)
print(workshop)
workshop
gender <- c(\"f\", \"f\", \"f\", NA, \"m\", \"m\", \"m\", \"m\")
q1 <- c(1, 2, 2, 3, 4, 5, 5, 4)
q2 <- c(1, 1, 2, 1, 5, 4, 3, 5)
q3 <- c(5, 4, 4,NA, 2, 5, 4, 5)
q4 <- c(1, 1, 3, 3, 4, 5, 4, 5)
# Selecting Elements of Vectors
q1[5]
q1[ c(5, 6, 7, 8) ]
q1[5:8]
q1[gender == \"m\"]
mean( q1[ gender == \"m\" ], na.rm = TRUE)
# ---Factors---
# Numeric Factors
# First, as a vector
workshop <- c(1, 2, 1, 2, 1, 2, 1, 2)
workshop
table(workshop)
mean(workshop)
gender[workshop == 2]
# Now as a factor
workshop <- c(1, 2, 1, 2, 1, 2, 1, 2)
workshop <- factor(workshop)
workshop
table(workshop)
mean(workshop) #generates error now.
gender[workshop == 2]
gender[workshop == \"2\"]
# Recreate workshop, making it a factor
# including levels that don\'t yet exist.
workshop <- c(1, 2, 1, 2, 1, 2, 1, 2)
workshop <- factor(
workshop,
levels = c( 1, 2, 3, 4),
labels = c(\"R\", \"SAS\", \"SPSS\", \"Stata\")
)
# Recreate it with just the levels it
# curently has.
workshop <- c(1, 2, 1, 2, 1, 2, 1, 2)
workshop <- factor(
workshop,
levels = c( 1, 2),
labels = c(\"R\",\"SAS\")
)
workshop
table(workshop)
gender[workshop == 2]
gender[workshop == \"2\"]
gender[workshop == \"SAS\"]
# Character factors
gender <- c(\"f\", \"f\", \"f\", NA, \"m\", \"m\", \"m\", \"m\")
gender <- factor(
gender,
levels = c(\"m\", \"f\"),
labels = c(\"Male\", \"Female\")
)
gender
table(gender)
workshop[gender == \"m\"]
workshop[gender == \"Male\"]
# Recreate gender and make it a factor,
# keeping simpler m and f as labels.
gender <- c(\"f\", \"f\", \"f\", NA, \"m\", \"m\", \"m\", \"m\")
gender <- factor(gender)
gender
# Data Frames
mydata <- data.frame(workshop, gender, q1, q2, q3, q4)
mydata
names(mydata)
row.names(mydata)
# Selecting components by index number
mydata[8, 6] #8th obs, 6th var
mydata[ , 6] #All obs, 6th var
mydata[ , 6][5:8] #6th var, obs 5:8
# Selecting components by name
mydata$q1
mydata$q1[5:8]
# Example renaming gender to sex while
# creating a data frame (left as a comment)
#
# mydata <- data.frame(workshop, sex = gender,
# q1, q2, q3, q4)
# Matrices
# Creating from vectors
mymatrix <- cbind(q1, q2, q3, q4)
mymatrix
dim(mymatrix)
# Creating from matrix function
# left as a comment so we keep
# version with names q1, q2...
#
# mymatrix <- matrix(
# c(1, 1, 5, 1,
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# 2, 2, 4, 3.
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- 1. Complete Randomized Experimental Design using R script Dr. D. Dutta Roy Psychology Research Unit Indian Statistical Institute
- 2. Hypothetical data • a1=1:10 • a2=5:14 • a3=10:19 • a4=data.frame(a1=1:5,a2=5:14,a3=10:19) • a4 • summary(a4) • a5=stack(a4) • a5 • boxplot(a4, main="Systematic variation across three training groups", ylab="Score",xlab="Training groups",col=c(2:4)) • colnames(a4)=c("Theory","Practical","Theory and practical")
- 3. • a6=aov(a5$values~a5$ind) • summary(a6) • tapply(a5$values,a5$ind,mean) • tapply(a5$values,a5$ind,sd)
- 4. Fitted data and residual • paste(a5$values) • paste(a6$fitted.values) • paste(a6$residuals)
- 5. Multiple Range test • TukeyHSD(a6) • plot(TukeyHSD(a6)) • kruskal.test(a5$values~a5$ind) • barplot(tapply(a5$values,a5$ind,mean))
- 6. Assumptions • dependent variable should be measured at the continuous level (i.e., they are interval or ratio variables). • two independent variables should each consist of two or more categorical, independent groups. • You should have independence of observations, which means that there is no relationship between the observations in each group or between the groups themselves. • There should be no significant outliers. • Your dependent variable should be approximately normally distributed for each combination of the groups of the two independent variables. • There needs to be homogeneity of variances for each combination of the groups of the two independent variables.