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ossStudy.Rmd
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ossStudy.Rmd
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---
title: "OSS License Comprehension Study"
output: html_notebook
---
# Read data
```{r}
library(dplyr)
library(clValid) # For Dunn's Index
library(dendextend)
library(tidyverse)
library(lsr)
userdata <- read.csv("OSSStudy6.csv", header=T)
userdata
```
# Eye Fixation data Clustering (MEL + LEL)
Groups:
[1] 1 3 4 5 9
[1] 2 6 7 8 10
```{r,}
df <- scale(dplyr::select(userdata, matches('melfix|lelfix|switches'), -matches('melm|lelm|mouse|mfix')))
colnames(df)
hc <- hclust(dist(df))
plot(hc)
groups <- cutree(hc,k =2)
groups <- cbind(userdata$ID, groups)
print(g1 <- groups[groups[,2] == 1,1])
print(g2 <- groups[groups[,2] == 2,1])
h <- heatmap(df, distfun = dist, hclustfun = hclust, scale="none")
```
# Eye Fixation data Clustering (MEL only)
Groups:
[1] 1 3 4 5 9
[1] 2 6 7 8 10
```{r,}
df <- scale(dplyr::select(userdata, matches('melfix|melswitches'), -matches('mouse|lel|mfix')))
colnames(df)
hc <- hclust(dist(df))
groups <- cutree(hc,k =2)
groups <- cbind(userdata$ID, groups)
print(g1 <- groups[groups[,2] == 1,1])
print(g2 <- groups[groups[,2] == 2,1])
h <- heatmap(df, distfun = dist, hclustfun = hclust, scale="none")
```
# Eye Fixation data Clustering (LEL only)
Groups:
[1] 1 2 3 4 5 9 10
[1] 6 7 8
```{r,}
df <- scale(dplyr::select(userdata, matches('lelfix|lelswitches'), -matches('mouse|mel|mfix')))
colnames(df)
hc <- hclust(dist(df))
groups <- cutree(hc,k =2)
groups <- cbind(userdata$ID, groups)
print(g1 <- groups[groups[,2] == 1,1])
print(g2 <- groups[groups[,2] == 2,1])
h <- heatmap(df, distfun = dist, hclustfun = hclust, scale="none")
```
# Mouse data Clustering (Mouse Data)
Groups:
[1] 1 2 3 4 5 9 10
[1] 6 7 8
```{r,}
df <- scale(dplyr::select(userdata, matches('melmouse|melm'), matches('lelmouse|lelm')))
colnames(df)
hc <- hclust(dist(df))
groups <- cutree(hc,k =2)
groups <- cbind(userdata$ID, groups)
print(g1 <- groups[groups[,2] == 1,1])
print(g2 <- groups[groups[,2] == 2,1])
h <- heatmap(df, distfun = dist, hclustfun = hclust, scale="none")
```
# Mouse data Clustering (MEL Mouse Data)
Groups:
[1] 1 3 4 5 7 8 9 10
[1] 2 6
```{r,}
df <- scale(dplyr::select(userdata, matches('melmouse|melm')))
colnames(df)
hc <- hclust(dist(df))
groups <- cutree(hc,k =2)
groups <- cbind(userdata$ID, groups)
print(g1 <- groups[groups[,2] == 1,1])
print(g2 <- groups[groups[,2] == 2,1])
h <- heatmap(df, distfun = dist, hclustfun = hclust, scale="none")
```
# Mouse data Clustering (LEL Mouse Data)
Groups:
[1] 1 3 5 9
[1] 2 4 6 7 8 10
```{r,}
df <- scale(dplyr::select(userdata, matches('lelmouse|lelm')))
colnames(df)
hc <- hclust(dist(df))
groups <- cutree(hc,k =2)
groups <- cbind(userdata$ID, groups)
print(g1 <- groups[groups[,2] == 1,1])
print(g2 <- groups[groups[,2] == 2,1])
h <- heatmap(df, distfun = dist, hclustfun = hclust, scale="none")
```
# All User Interaction Data
Groups:
[1] 1 2 3 4 5 9 10
[1] 6 7 8
```{r}
df <- scale(dplyr::select(userdata, matches('melfix|lelfix|switches|melm|lelm')))
colnames(df)
Dist <- dist(df, method="euclidean")
hc <- hclust(Dist, method ="complete")
plot(hc)
groups <- cutree(hc,k =3)
dunn(Dist, groups)
avg_dend_obj <- as.dendrogram(hc)
avg_col_dend <- color_branches(avg_dend_obj, k = 3)
plot(avg_col_dend, ylab="Height", xlab="Participants")
```
```{r}
#Plot Feature Dendrogram
hc2 <- hclust(dist(t(df)))
avg_dend_obj <- as.dendrogram(hc2)
avg_col_dend <- color_branches(avg_dend_obj, k = 3)
plot(avg_col_dend,horiz=T, ylab="Height", xlab="Participants", asp = 0.5)
#Identify groups
groups <- cbind(userdata$ID, groups)
print(g1 <- groups[groups[,2] == 1,1])
print(g2 <- groups[groups[,2] == 2,1])
print(g3 <- groups[groups[,2] == 3,1])
#Heatmap
h <- heatmap(df, distfun = dist, hclustfun = hclust, scale="none")
```
# Convergent Validity with K-means
* Same clusters reported as hierarchical clustering
```{r}
#K-means Clustering
set.seed(20)
clusters <- kmeans(df, 3)
glimpse(clusters$cluster)
userdata$kgroup <- as.factor(clusters$cluster)
```
# Compute Composite Score and Other Features
```{r}
# Add computed columns to userdata
userdata <- userdata %>%
mutate(learnability = SUS4 + SUS10,
usability = SUS1 + SUS2 + SUS3 + SUS5 + SUS6 + SUS7 + SUS8 + SUS9,
score = as.integer((melacc1 * ifelse(melconf1 == " h", 3, ifelse( melconf1 == " l", 2, 1)) +
melacc2 * ifelse(melconf2 == " h", 3, ifelse( melconf2 == " l", 2, 1)) +
lelacc1 * ifelse(lelconf1 == " h", 3, ifelse( lelconf1 == " l", 2, 1)) +
lelacc2 * ifelse(lelconf2 == " h", 3, ifelse( lelconf2 == " l", 2, 1)))/12*100),
conf = as.integer((ifelse(melconf1 == " h", 3, ifelse( melconf1 == " l", 2, 1)) +
ifelse(melconf2 == " h", 3, ifelse( melconf2 == " l", 2, 1)) +
ifelse(lelconf1 == " h", 3, ifelse( lelconf1 == " l", 2, 1)) +
ifelse(lelconf2 == " h", 3, ifelse( lelconf2 == " l", 2, 1)))/12*100),
rawscore = melacc1+melacc2+lelacc1+lelacc2)
# Total Eye Fixation Counts
userdata$totalfixcnt = rowSums(dplyr::select(userdata, matches('melfixcnt|lelfixcnt')))
userdata$totalfixdur = rowSums(dplyr::select(userdata, matches('melfixdur|lelfixdur')))
# Total Mouse Fixation Counts
userdata$totalmousefixcnt= rowSums(dplyr::select(userdata, matches('mousecnt'), -matches('melmouse|lelmouse')))
userdata$totalmousefixdur= rowSums(dplyr::select(userdata, matches('mfixdur'), -matches('melm|lelm')))
#Eye Tracking Fixation Duration by AOI
userdata$totaleDurHierarchy <- rowSums(dplyr::select(userdata, matches('durhierachy'), -matches("mouse|cnt|mfix")))
userdata$totaleDurText <- rowSums(dplyr::select(userdata, matches('durtext'), -matches("mouse|mfix")))
userdata$totaleDurModel <- rowSums(dplyr::select(userdata, matches('durmodel'), -matches("mouse|mfix")))
userdata$totaleDurTask <- rowSums(dplyr::select(userdata, matches('durtask'), -matches("mouse|mfix")))
#Eye Tracking Fixation Counts by AOI
userdata$totaleCntHierarchy <- rowSums(dplyr::select(userdata, matches('cnthierachy'), -matches("mouse")))
userdata$totaleCntText <- rowSums(dplyr::select(userdata, matches('cnttext'), -matches("mouse|mfix")))
userdata$totaleCntModel <- rowSums(dplyr::select(userdata, matches('cntmodel'), -matches("mouse|mfix")))
userdata$totaleCntTask <- rowSums(dplyr::select(userdata, matches('cnttask'), -matches("mouse|mfix")))
#Eye Tracking AOI switches
userdata$totaleAOIswitch <- rowSums(dplyr::select(userdata, matches('lswitches')))
#Mouse AOI switches
userdata$totalmAOIswitchG1 <- rowSums(dplyr::select(userdata, matches('mswitches')))
# Add group as a factor
group <- as.factor(groups[,2])
userdata <- cbind(userdata, group)
#glimpse(userdata)
```
#Dixon's test for outliers
* Source https://cran.r-project.org/web/packages/outliers/outliers.pdf
```{r}
library("outliers")
shapiro.test(userdata$score)
plot(density(userdata$score))
dixon.test(userdata$score, type=10, opposite = T, two.sided = TRUE)
```
```{r}
# Test for composite score difference between two groups
userdata <- userdata[userdata$score != 0,]
boxplot(userdata$score~userdata$group, names = c("G1", "G2", "G3"), ylab = "Total Score")
p <- ggplot(userdata, aes(x=group, y=score, fill = group)) +
geom_boxplot()
p <- p + labs(x = "Groups", y = "% of Max Score")
p <- p + stat_summary(fun.y=mean, geom="point", shape=4, size=4) +
geom_jitter(shape=23, position=position_jitter(0.1)) +
theme(legend.position = "none")
p
ggsave("totalscores.tiff", dpi=300)
shapiro.test(userdata$score)
res.aov <- aov(userdata$score~userdata$group)
summary(res.aov)
etaSquared(res.aov)
TukeyHSD(res.aov)
# Test for raw score difference between two groups
boxplot(userdata$rawscore~userdata$group, names = c("G1", "G2", "G3"), ylab = "Raw Score")
res.aov <- aov(userdata$rawscore~userdata$group)
summary(res.aov)
etaSquared(res.aov)
TukeyHSD(res.aov)
# Test for confidence difference between two groups
boxplot(userdata$conf~userdata$group, names = c("G1", "G2", "G3"), ylab = "Mean Confidence")
res.aov <- aov(userdata$conf~userdata$group)
summary(res.aov)
etaSquared(res.aov)
TukeyHSD(res.aov)
```
# Eye tracking fixation count and duration differences between groups
* All differences significant!
```{r}
# Test for eye fixation count difference between groups
p <- ggplot(userdata, aes(x=group, y=totalfixcnt, fill = group)) +
geom_boxplot()
p <- p + labs(x = "Groups", y = "Eye Fixation Count")
p <- p + stat_summary(fun.y=mean, geom="point", shape=4, size=4) +
geom_jitter(shape=23, position=position_jitter(0.1))
p
ggsave("eyefixcnt.tiff", dpi=300)
res.aov <- aov(userdata$totalfixcnt~userdata$group)
summary(res.aov)
etaSquared(res.aov)
TukeyHSD(res.aov)
# Test for eye fixation duration difference between groups
p <- ggplot(userdata, aes(x=group, y=totalfixdur, fill = group)) +
geom_boxplot()
p <- p + labs(x = "Groups", y = "Eye Fixation Duration")
p <- p + stat_summary(fun.y=mean, geom="point", shape=4, size=4) +
geom_jitter(shape=23, position=position_jitter(0.1))
p
#ggsave("eyefixdiff.tiff", dpi=300)
res.aov <- aov(userdata$totalfixdur~userdata$group)
summary(res.aov)
etaSquared(res.aov)
TukeyHSD(res.aov)
```
# Mouse fixation count and duration differences between groups
* All differences significant!
```{r}
# Test for mouse fixation count difference between groups
boxplot(userdata$totalmousefixcnt~userdata$group, names = c("G1", "G2", "G3"), ylab = "Mouse Fixation Count")
kruskal.test(userdata$totalmousefixcnt~userdata$group)
pairwise.wilcox.test(userdata$totalmousefixcnt, userdata$group, p.adjust.method = "BH")
# Test for mouse fixation duration difference between two groups
boxplot(userdata$totalmousefixdur~userdata$group, names = c("G1", "G2", "G3"), ylab = "Mouse Fixation Duration")
kruskal.test(userdata$totalmousefixdur~userdata$group)
pairwise.wilcox.test(userdata$totalmousefixdur, userdata$group, p.adjust.method = "BH")
```
# SUS and SUS subscales differences between groups
While no difference between the overall SUS score or Usability Subscale, the Learnability sub-scale shows a difference between the two groups.
* The group that performed poorly thought the interface was more learnable than the group that performed better.
```{r}
# Test for SUS difference between two groups
boxplot(userdata$SUS~userdata$group, names = c("G1", "G2", "G3"), ylab = "Total SUS")
kruskal.test(userdata$SUS~userdata$group)
pairwise.wilcox.test(userdata$SUS, userdata$group, p.adjust.method = "BH")
# Test for SUS Learnability subscale difference between two groups
boxplot(userdata$learnability~userdata$group, names = c("G1", "G2", "G3"), ylab = "SUS Learnability Subscale")
kruskal.test(userdata$learnability~userdata$group)
pairwise.wilcox.test(userdata$learnability, userdata$group, p.adjust.method = "BH")
# Test for SUS Usability subscale difference between two groups
boxplot(userdata$usability~userdata$group, names = c("G1", "G2", "G3"), ylab = "SUS Usability Subscale")
kruskal.test(userdata$usability~userdata$group)
pairwise.wilcox.test(userdata$usability, userdata$group, p.adjust.method = "BH")
```
# Eye-Tracking Behavior Across Groups
```{r}
par(mfrow=c(2,4))
boxplot(userdata$totaleCntHierarchy~userdata$group, ylab="Eye Fix. Cnt. Hier.")
boxplot(userdata$totaleCntText~userdata$group, ylab="Eye Fix. Cnt. Text")
boxplot(userdata$totaleCntModel~userdata$group, ylab="Eye Fix. Cnt. Model")
boxplot(userdata$totaleCntTask~userdata$group, ylab="Eye Fix. Cnt. Task")
boxplot(userdata$totaleDurHierarchy~userdata$group, ylab="Eye Fix. Dur. Hier.")
boxplot(userdata$totaleDurText~userdata$group, ylab="Eye Fix. Dur. Text")
boxplot(userdata$totaleDurModel~userdata$group, ylab="Eye Fix. Dur. Model")
boxplot(userdata$totaleDurTask~userdata$group, ylab="Eye Fix. Dur. Task")
boxplot(userdata$totaleAOIswitch~userdata$group, ylab="Eye Tot. AOI Switches")
```
```{r}
#boxplot(userdata$totaleCntText~userdata$group, ylab="Eye Fix. Cnt. Text")
p <- ggplot(userdata, aes(x=group, y=totaleCntText, fill = group)) +
geom_boxplot()
p <- p + labs(x = "Groups", y = "Eye Fix. Cnt. Text")
p <- p + stat_summary(fun=mean, geom="point", shape=4, size=4) +
geom_jitter(shape=23, position=position_jitter(0.1))
p1 <- p + ylim(0, 2000)+ theme(legend.position = "none")
#ggsave("EyeFixCntText.tiff", dpi=300)
#boxplot(userdata$totaleCntModel~userdata$group, ylab="Eye Fix. Cnt. Model")
p <- ggplot(userdata, aes(x=group, y=totaleCntModel, fill = group)) +
geom_boxplot()
p <- p + labs(x = "Groups", y = "Eye Fix. Cnt. Model")
p <- p + stat_summary(fun=mean, geom="point", shape=4, size=4) +
geom_jitter(shape=23, position=position_jitter(0.1))
p2 <- p + ylim(0, 2000)+ theme(legend.position = "none")
#ggsave("EyeFixCntModel.tiff", dpi=300)
#boxplot(userdata$totaleDurText~userdata$group, ylab="Eye Fix. Dur. Text")
p <- ggplot(userdata, aes(x=group, y=totaleDurText, fill = group)) +
geom_boxplot()
p <- p + labs(x = "Groups", y = "Eye Fix. Dur. Text")
p <- p + stat_summary(fun=mean, geom="point", shape=4, size=4) +
geom_jitter(shape=23, position=position_jitter(0.1))
p3 <- p + ylim(0.5, 1) + theme(legend.position = "none")
#ggsave("EyeFixDurText.tiff", dpi=300)
#boxplot(userdata$totaleDurModel~userdata$group, ylab="Eye Fix. Dur. Model")
p <- ggplot(userdata, aes(x=group, y=totaleDurModel, fill = group)) +
geom_boxplot()
p <- p + labs(x = "Groups", y = "Eye Fix. Dur. Model")
p <- p + stat_summary(fun=mean, geom="point", shape=4, size=4) +
geom_jitter(shape=23, position=position_jitter(0.1))
p4 <- p + ylim(0.5, 1)+ theme(legend.position = "none")
grid.arrange(p1, p2, p3, p4, nrow = 2)
ggsave("Eye.tiff", plot= grid.arrange(p1, p2, p3, p4, nrow = 2), dpi=300)
#boxplot(userdata$mousecnttext~userdata$group, ylab="Mouse Fix. Cnt. Text")
p <- ggplot(userdata, aes(x=group, y=mousecnttext, fill = group)) +
geom_boxplot()
p <- p + labs(x = "Groups", y = "Mouse Fix. Cnt. Text")
p <- p + stat_summary(fun=mean, geom="point", shape=4, size=4) +
geom_jitter(shape=23, position=position_jitter(0.1))
p5 <- p + ylim(0, 2000)+ theme(legend.position = "none")
#ggsave("MouseFixCntText.tiff", dpi=300)
#boxplot(userdata$mousecntmodel~userdata$group, ylab="Mouse Fix. Cnt. Model")
p <- ggplot(userdata, aes(x=group, y=mousecntmodel, fill = group)) +
geom_boxplot()
p <- p + labs(x = "Groups", y = "Mouse Fix. Cnt. Model")
p <- p + stat_summary(fun=mean, geom="point", shape=4, size=4) +
geom_jitter(shape=23, position=position_jitter(0.1))
p6 <- p + ylim(0, 2000)+ theme(legend.position = "none")
#ggsave("MouseFixCntModel.tiff", dpi=300)
#boxplot(userdata$mfixdurtext~userdata$group, ylab="Mouse Fix. Dur. Text")
p <- ggplot(userdata, aes(x=group, y=mfixdurtext, fill = group)) +
geom_boxplot()
p <- p + labs(x = "Groups", y = "Mouse Fix. Dur. Text")
p <- p + stat_summary(fun=mean, geom="point", shape=4, size=4) +
geom_jitter(shape=23, position=position_jitter(0.1))
p7 <- p + ylim(0.25, 0.6)+ theme(legend.position = "none")
#ggsave("MouseFixDurText.tiff", dpi=300)
#boxplot(userdata$mfixdurmodel~userdata$group, ylab="Mouse Fix. Dur. Model")
p <- ggplot(userdata, aes(x=group, y=mfixdurmodel, fill = group)) +
geom_boxplot()
p <- p + labs(x = "Groups", y = "Mouse Fix. Dur. Model")
p <- p + stat_summary(fun=mean, geom="point", shape=4, size=4) +
geom_jitter(shape=23, position=position_jitter(0.1))
p8 <- p + ylim(0.25, 0.6) + theme(legend.position = "none")
#ggsave("MouseFixDurModel.tiff", dpi=300)
grid.arrange(p5, p6, p7, p8, nrow = 2)
ggsave("Mouse.tiff", plot= grid.arrange(p5, p6, p7, p8, nrow = 2), dpi=300)
```
# Mouse Behavior Across Groups
```{r}
par(mfrow=c(2,4))
boxplot(userdata$mousecnthierachy~userdata$group, ylab="Mouse Fix. Cnt. Hier.")
boxplot(userdata$mousecnttext~userdata$group, ylab="Mouse Fix. Cnt. Text")
boxplot(userdata$mousecntmodel~userdata$group, ylab="Mouse Fix. Cnt. Model")
boxplot(userdata$mousecnttask~userdata$group, ylab="Mouse Fix. Cnt. Task")
boxplot(userdata$mfixdurhierachy~userdata$group, ylab="Mouse Fix. Dur. Hier.")
boxplot(userdata$mfixdurtext~userdata$group, ylab="Mouse Fix. Dur. Text")
boxplot(userdata$mfixdurmodel~userdata$group, ylab="Mouse Fix. Dur. Model")
boxplot(userdata$mfixdurtask~userdata$group, ylab="Mouse Fix. Dur. Task")
boxplot(userdata$totalmAOIswitchG1~userdata$group, ylab="Mouse Tot. AOI Switches")
cor(userdata$totaleCntText,userdata$mousecnttext)
cor(userdata$totaleCntModel,userdata$mousecntmodel)
cor(userdata$totaleCntTask,userdata$mousecnttask)
cor(userdata$totaleDurText,userdata$mfixdurtext)
cor(userdata$totaleDurModel,userdata$mfixdurmodel)
cor(userdata$totaleDurTask,userdata$mfixdurtask)
cor(userdata$totaleCntHierarchy,userdata$mousecnthierachy)
cor(userdata$totaleDurHierarchy,userdata$mfixdurhierachy)
mean(userdata$totaleCntHierarchy)
mean(userdata$mousecnthierachy)
mean(userdata$totaleDurHierarchy)
mean(userdata$mfixdurhierachy)
```
# Plot figure for Feature Clusters
```{r}
colnames(df) <- c("Hierarchy Eye MEL Fix Cnt",
"Model Eye MEL Fix Cnt",
"Task Eye MEL Fix Cnt",
"Text Eye MEL Fix Cnt",
"Hierarchy Eye LEL Fix Cnt",
"Model Eye LEL Fix Cnt",
"Task Eye LEL Fix Cnt",
"Text Eye LEL Fix Cnt",
"Hierarchy Eye MEL Fix Dur",
"Model Eye MEL Fix Dur",
"Task Eye MEL Fix Dur",
"Text Eye MEL Fix Dur",
"Hierarchy Eye LEL Fix Dur",
"Model Eye LEL Fix Dur",
"Task Eye LEL Fix Dur",
"Text Eye LEL Fix Dir",
"Eye MEL Switches",
"Eye LEL Switches" ,
"Hierarchy Mouse MEL Cnt",
"Model Mouse MEL Cnt",
"Task Mouse MEL Cnt",
"Text Mouse MEL Cnt",
"Hierarchy Mouse MEL Dur",
"Model Mouse MEL Dur",
"Task Mouse MEL Dur",
"Text Mouse MEL Dur",
"Hierarchy Mouse LEL Cnt",
"Model Mouse LEL Cnt",
"Task Mouse LEL Cnt",
"Text Mouse LEL Cnt",
"Hierarchy Mouse LEL Dur",
"Model Mouse LEL Dur",
"Task Mouse LEL Dur",
"Text Mouse LEL Dur",
"Mouse MEL Switches",
"Mouse LEL Switches")
hc2 <- hclust(dist(t(df)))
avg_dend_obj <- as.dendrogram(hc2)
avg_col_dend <- color_branches(avg_dend_obj, k = 3)
plot(avg_col_dend,horiz=T, ylab="Height", xlab="Participants", asp = 0.5)
```