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analysis.Rmd
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analysis.Rmd
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---
title: "OctoPocus in VR - Result Analysis"
output:
html_notebook:
toc: true
toc_float: true
theme: lumen
highlight: default
code_folding: hide
number_sections: TRUE
---
# Tools & Libraries
```{r}
library(scales)
library(ez)
library(MASS)
library(effsize)
library(rcompanion)
library(ARTool)
library(emmeans)
library(HH)
library(rstatix)
library(tidyverse)
library(DescTools)
normal_error <- function(d) {
n <- length(d)
qnorm(0.975) * (sd(d) /sqrt(n))
}
summary_with_ci <- function(d) {
tibble(
MeanValue = mean(d),
ErrorValue = normal_error(d),
LowerValue = MeanValue - ErrorValue,
UpperValue = MeanValue + ErrorValue
)
}
```
# Load data
```{r}
trials <- read_csv(
"./data1.csv",
col_types = cols(
Participant = col_factor(),
Guide = col_factor(),
Set = col_factor(),
Phase = col_factor(),
Block = col_factor(),
Trial = col_character(),
TrialName = col_factor(),
TriggeredName = col_factor(),
Result = col_logical(),
Mode = col_factor(),
RestartCount = col_double(),
DrawTime = col_double(),
NoviceTime = col_double(),
EndTime = col_double(),
TriggerDuration = col_double(),
GripDuration = col_double(),
IdleDuration = col_double()
)
) %>%
filter(Participant != "P3" & Participant != "P6") %>%
mutate(
Participant = droplevels(Participant),
Block = factor(Block),
InputTime = EndTime-NoviceTime,
IsOutlier = Phase == "Training" &
EndTime > mean(EndTime[Phase == "Training"]) + 3 * sd(EndTime[Phase == "Training"]))
trials
# Number of outliers
a = sum(trials$IsOutlier == TRUE)
b = sum(trials$Phase == "Training")
(a/b)*100
```
# TIME
## Trial Time
```{r}
trialtime_p_summary <- trials %>%
filter(Mode == "Novice", Phase == "Training" & Result == TRUE & IsOutlier == FALSE) %>%
group_by(Participant, Guide, Phase, Block) %>%
summarize(Mean = mean(EndTime), .groups="drop")
# because P4 has zero novice trial in B3, we averaged values between that of B1 and B2
newRow = c("P4", "Sheet", "Training", "B3", 12.201982)
trialtime_p_summary = rbind(trialtime_p_summary, newRow)
# because P11 has zero novice trial in B3, we averaged values between that of B1 and B2
newRow = c("P11", "Sheet", "Training", "B3", 9.1895835)
# merge new row into table
trialtime_p_summary = rbind(trialtime_p_summary, newRow)
# convert from character to numbers
trialtime_p_summary$Mean <- as.numeric(as.character(trialtime_p_summary$Mean))
trialtime_p_summary
trialtime_octo <- subset(trialtime_p_summary, Guide == "Octo", Mean, drop = TRUE)
trialtime_sheet <- subset(trialtime_p_summary, Guide == "Sheet", Mean, drop = TRUE)
MeanDiffCI(trialtime_sheet, trialtime_octo)
trialtime_b1 <- subset(trialtime_p_summary, Block == "B1", Mean, drop = TRUE)
trialtime_b2 <- subset(trialtime_p_summary, Block == "B2", Mean, drop = TRUE)
trialtime_b3 <- subset(trialtime_p_summary, Block == "B3", Mean, drop = TRUE)
MeanDiffCI(trialtime_b2, trialtime_b1)
MeanDiffCI(trialtime_b3, trialtime_b1)
trialtime_summary <- trialtime_p_summary %>%
group_by(Guide, Phase, Block) %>%
summarize(summary_with_ci(Mean), .groups="drop") %>%
mutate(LowerValue = if_else(LowerValue < 0, 0, LowerValue))
trialtime_summary
```
### Graph
```{r}
pd <- position_dodge(0.4)
ggplot(data=trialtime_summary, aes(x=Block, y=MeanValue, group=Guide)) +
geom_errorbar(aes(ymin=LowerValue, ymax=UpperValue), width=.3, position=pd) +
geom_line(aes(color=Guide), position=pd)+
geom_point(aes(color=Guide, shape=Guide), size=3, position=pd)+
geom_text(aes(label=format(round(MeanValue,1),nsmall=1), color=Guide), show.legend = FALSE,
size=4, nudge_x=ifelse(trialtime_summary$Guide=="Octo", -0.3, 0.3),
nudge_y=ifelse(trialtime_summary$Guide=="Octo", -0.4, 0.4)
)+
expand_limits(y=c(0,11.5))+
labs(x="Block", y="Time (s)")+
scale_x_discrete(labels=c("B1-Train", "B2-Train", "B3-Train"))+
scale_color_manual(values = c("#d35400", "#95a5a6"),labels=c("OCTOPOCUS","CRIBSHEET"))+
scale_shape_discrete(labels=c("OCTOPOCUS","CRIBSHEET"))+
scale_linetype(labels=c("OCTOPOCUS","CRIBSHEET"))+
facet_grid(scales = "free", space = "free")+
theme_bw(base_size = 12)+
theme(legend.background = element_blank(), legend.title = element_blank(),
legend.position = c(0.5, 0.1), legend.direction = "horizontal",
panel.border = element_blank(),
axis.line = element_line(color = 'grey'),
axis.text.x = element_text(size = 12), axis.text.y = element_text(size = 12),
axis.title.y = element_text(margin = margin(t = 0, r = 5, b = 0, l = 0)),
axis.title.x = element_text(margin = margin(t = 5, r = 0, b = 0, l = 0)))
```
### Assumptions
Shapiro test p < 0.05. The distribution of the residuals is not normal.
```{r}
m = aov(Mean ~ Block*Guide + Error(Participant), data = trialtime_p_summary)
shapiro.test(residuals(m$Within))
plotNormalHistogram(residuals(m$Within))
qqnorm(residuals(m$Within)); qqline(residuals(m$Within))
```
### Data Transformation
Shapiro test p > 0.05. The distribution of the residuals is now normal.
```{r}
trialtime_p_summary$TransVal = log(trialtime_p_summary$Mean)
m = aov(TransVal ~ Block*Guide + Error(Participant), data = trialtime_p_summary)
shapiro.test(residuals(m$Within))
plotNormalHistogram(residuals(m$Within))
qqnorm(residuals(m$Within)); qqline(residuals(m$Within))
```
### ANOVA
```{r}
ezANOVA(
trialtime_p_summary,
dv = log(Mean),
wid = Participant,
within = c(Guide, Block)
)
```
### Pairwise Comparison
```{r}
pairwise.t.test(trialtime_p_summary$TransVal,
trialtime_p_summary$Block,
paired=TRUE,
p.adj="bonf")
```
## Reaction Time
```{r}
reactiontime_p_summary <- trials %>%
filter(Mode == "Novice" & Phase == "Training" & Result == TRUE & IsOutlier == FALSE) %>%
group_by(Participant, Guide, Phase, Block) %>%
summarize(Mean = mean(NoviceTime), .groups="drop")
# because P4 has zero novice trial in B3, we averaged values between that of B1 and B2
newRow = c("P4", "Sheet", "Training", "B3", 0.93730355)
reactiontime_p_summary = rbind(reactiontime_p_summary, newRow)
# because P11 has zero novice trial in B3, we averaged values between that of B1 and B2
newRow = c("P11", "Sheet", "Training", "B3", 1.48358335)
# merge new row into table
reactiontime_p_summary = rbind(reactiontime_p_summary, newRow)
# convert from character to numbers
reactiontime_p_summary$Mean <- as.numeric(as.character(reactiontime_p_summary$Mean))
reactiontime_p_summary
reactiontime_octo <- subset(reactiontime_p_summary, Guide == "Octo", Mean, drop = TRUE)
reactiontime_sheet <- subset(reactiontime_p_summary, Guide == "Sheet", Mean, drop = TRUE)
MeanDiffCI(reactiontime_sheet, reactiontime_octo)
reactiontime_summary <- reactiontime_p_summary %>%
group_by(Guide, Phase, Block) %>%
summarize(summary_with_ci(Mean), .groups="drop") %>%
mutate(LowerValue = if_else(LowerValue < 0, 0, LowerValue))
reactiontime_summary
```
### Graph
```{r}
pd <- position_dodge(0.4)
ggplot(data=reactiontime_summary, aes(x=Block, y=MeanValue, group=Guide)) +
geom_errorbar(aes(ymin=LowerValue, ymax=UpperValue), width=.3, position=pd) +
geom_line(aes(color=Guide), position=pd)+
geom_point(aes(color=Guide, shape=Guide), size=3, position=pd)+
geom_text(aes(label=format(round(MeanValue,1),nsmall=1), color=Guide), show.legend = FALSE,
size=4, nudge_x=ifelse(reactiontime_summary$Guide=="Octo", -0.1, 0.1),
nudge_y=ifelse(reactiontime_summary$Guide=="Octo", 0.8, -0.8)
)+
expand_limits(y=c(0,11.5))+
labs(x="Block", y="Time (s)")+
scale_x_discrete(labels=c("B1-Train", "B2-Train", "B3-Train"))+
scale_color_manual(values = c("#d35400", "#95a5a6"))+
# facet_grid(scales = "free", space = "free")+
theme_bw(base_size = 12)+
theme(panel.border = element_blank(),
axis.line = element_line(color = 'grey'),
legend.position = "none",
# legend.position = c(0.8, 0.85), legend.direction = "vertical",
axis.text.x = element_text(size = 12), axis.text.y = element_text(size = 12),
axis.title.y = element_text(margin = margin(t = 0, r = 5, b = 0, l = 0)),
axis.title.x = element_text(margin = margin(t = 5, r = 0, b = 0, l = 0)))
```
### Assumptions
Shapiro test p > 0.05. The distribution of the residuals is normal.
```{r}
m = aov(Mean ~ Block*Guide + Error(Participant), data = reactiontime_p_summary)
shapiro.test(residuals(m$Within))
plotNormalHistogram(residuals(m$Within))
qqnorm(residuals(m$Within)); qqline(residuals(m$Within))
```
### ANOVA
```{r}
ezANOVA(
reactiontime_p_summary,
dv = Mean,
wid = Participant,
within = c(Guide, Block)
)
```
## Input Time
```{r}
inputtime_p_summary <- trials %>%
filter(Mode == "Novice", Phase == "Training" & Result == TRUE & IsOutlier == FALSE) %>%
group_by(Participant, Guide, Phase, Block) %>%
summarize(Mean = mean(InputTime), .groups="drop")
# because P4 has zero novice trial in B3, we averaged values between that of B1 and B2
newRow = c("P4", "Sheet", "Training", "B3", 11.2646785)
inputtime_p_summary = rbind(inputtime_p_summary, newRow)
# because P11 has zero novice trial in B3, we averaged values between that of B1 and B2
newRow = c("P11", "Sheet", "Training", "B3", 7.706)
# merge new row into table
inputtime_p_summary = rbind(inputtime_p_summary, newRow)
# convert from character to numbers
inputtime_p_summary$Mean <- as.numeric(as.character(inputtime_p_summary$Mean))
inputtime_p_summary
inputtime_octo <- subset(inputtime_p_summary, Guide == "Octo", Mean, drop = TRUE)
inputtime_sheet <- subset(inputtime_p_summary, Guide == "Sheet", Mean, drop = TRUE)
MeanDiffCI(inputtime_sheet, inputtime_octo)
inputtime_b1 <- subset(inputtime_p_summary, Block == "B1", Mean, drop = TRUE)
inputtime_b2 <- subset(inputtime_p_summary, Block == "B2", Mean, drop = TRUE)
inputtime_b3 <- subset(inputtime_p_summary, Block == "B3", Mean, drop = TRUE)
MeanDiffCI(inputtime_b2, inputtime_b1)
MeanDiffCI(inputtime_b3, inputtime_b1)
inputtime_summary <- inputtime_p_summary %>%
group_by(Guide, Phase, Block) %>%
summarize(summary_with_ci(Mean), .groups="drop") %>%
mutate(LowerValue = if_else(LowerValue < 0, 0, LowerValue))
inputtime_summary
```
### Graph
```{r}
pd <- position_dodge(0.4)
ggplot(data=inputtime_summary, aes(x=Block, y=MeanValue, group=Guide)) +
geom_errorbar(aes(ymin=LowerValue, ymax=UpperValue), width=.3, position=pd) +
geom_line(aes(color=Guide), position=pd)+
geom_point(aes(color=Guide, shape=Guide), size=3, position=pd)+
geom_text(aes(label=format(round(MeanValue,1),nsmall=1), color=Guide), show.legend = FALSE,
size=4, nudge_x=ifelse(inputtime_summary$Guide=="Octo", -0.3, 0.3),
nudge_y=ifelse(inputtime_summary$Guide=="Octo", -0.4, 0.4)
)+
expand_limits(y=c(0,11.5))+
labs(x="Block", y="Time (s)")+
scale_x_discrete(labels=c("B1-Train", "B2-Train", "B3-Train"))+
scale_color_manual(values = c("#d35400", "#95a5a6"))+
# facet_grid(scales = "free", space = "free")+
theme_bw(base_size = 12)+
theme(legend.position = "none",
#legend.position = c(0.8, 0.15), legend.direction = "vertical",
# legend.background = element_blank(),
panel.border = element_blank(),
axis.line = element_line(color = 'grey'),
axis.text.x = element_text(size = 12), axis.text.y = element_text(size = 12),
axis.title.y = element_text(margin = margin(t = 0, r = 5, b = 0, l = 0)),
axis.title.x = element_text(margin = margin(t = 5, r = 0, b = 0, l = 0)))
```
### Assumptions
Shapiro test p < 0.05. The distribution of the residuals is not normal.
```{r}
m = aov(Mean ~ Block*Guide + Error(Participant), data = inputtime_p_summary)
shapiro.test(residuals(m$Within))
plotNormalHistogram(residuals(m$Within))
qqnorm(residuals(m$Within)); qqline(residuals(m$Within))
```
### Data Transformation
Shapiro test p > 0.05. The distribution of the residuals is now normal.
```{r}
inputtime_p_summary$TransVal = sqrt(inputtime_p_summary$Mean)
m = aov(TransVal ~ Block*Guide + Error(Participant), data = inputtime_p_summary)
shapiro.test(residuals(m$Within))
plotNormalHistogram(residuals(m$Within))
qqnorm(residuals(m$Within)); qqline(residuals(m$Within))
```
### ANOVA
```{r}
ezANOVA(
inputtime_p_summary,
dv = TransVal,
wid = Participant,
within = c(Guide, Block)
)
```
### Pairwise Comparison
```{r}
pairwise.t.test(inputtime_p_summary$TransVal,
inputtime_p_summary$Block,
paired=TRUE,
p.adj="bonf")
```
# ACCURACY
## Overall Training
```{r}
accuracy_overalltraining_p_summary <- trials %>%
filter(Phase == "Training" & !IsOutlier) %>%
group_by(Participant, Guide, Phase, Block) %>%
summarize(Mean = mean(Result), .groups="drop")
accuracy_overalltraining_p_summary
overalltrainingacc_octo <- subset(accuracy_overalltraining_p_summary, Guide == "Octo", Mean, drop = TRUE)
overalltrainingacc_sheet <- subset(accuracy_overalltraining_p_summary, Guide == "Sheet", Mean, drop = TRUE)
MeanDiffCI(overalltrainingacc_sheet, overalltrainingacc_octo)
accuracy_overalltraining_summary <- accuracy_overalltraining_p_summary %>%
group_by(Guide, Phase, Block) %>%
summarize(summary_with_ci(Mean), .groups="drop") %>%
mutate(LowerValue = if_else(LowerValue < 0, 0, LowerValue))
accuracy_overalltraining_summary
```
### Graph
```{r}
pd <- position_dodge(0.4)
ggplot(data=accuracy_overalltraining_summary, aes(x=Block, y=MeanValue, group=Guide)) +
geom_errorbar(aes(ymin=LowerValue, ymax=UpperValue), width=.3, position=pd) +
geom_line(aes(color=Guide), position=pd)+
geom_point(aes(color=Guide, shape=Guide), size=3, position=pd)+
geom_text(aes(label=sprintf("%.0f", 100*MeanValue), color=Guide), show.legend = FALSE,
size=4, nudge_x=ifelse(accuracy_overalltraining_summary$Guide=="Octo", -0.3, 0.3),
nudge_y=ifelse(accuracy_overalltraining_summary$Guide=="Octo", 0.1, -0.1)
)+
scale_y_continuous(labels = function(x) paste0(x*100),breaks = seq(0,1,0.25))+
# scale_y_continuous(labels = label_percent(), breaks = seq(0,1,0.25))+
expand_limits(y=c(0,1))+
labs(x="Block", y="Accuracy (%)")+
scale_x_discrete(labels=c("B1-Train", "B2-Train", "B3-Train"))+
scale_color_manual(values = c("#d35400", "#95a5a6"),labels=c("OCTOPOCUS","CRIBSHEET"))+
scale_shape_discrete(labels=c("OCTOPOCUS","CRIBSHEET"))+
scale_linetype(labels=c("OCTOPOCUS","CRIBSHEET"))+
facet_grid(scales = "free", space = "free")+
theme_bw(base_size = 12)+
theme(legend.background = element_blank(), legend.title = element_blank(),
legend.position = c(0.5, 0.1), legend.direction = "horizontal",
panel.border = element_blank(),
axis.line = element_line(color = 'grey'),
axis.text.x = element_text(size = 12), axis.text.y = element_text(size = 12),
axis.title.y = element_text(margin = margin(t = 0, r = 5, b = 0, l = 0)),
axis.title.x = element_text(margin = margin(t = 5, r = 0, b = 0, l = 0)))
```
### Assumptions
Shapiro test p > 0.05. The distribution of the residuals is normal.
```{r}
m = aov(Mean ~ Block*Guide + Error(Participant), data = accuracy_overalltraining_p_summary)
shapiro.test(residuals(m$Within))
plotNormalHistogram(residuals(m$Within))
qqnorm(residuals(m$Within)); qqline(residuals(m$Within))
```
### ANOVA
```{r}
ezANOVA(
accuracy_overalltraining_p_summary,
dv = Mean,
wid = Participant,
within = c(Guide, Block)
)
```
## Expert Training
```{r}
accuracy_p_expert_summary <- trials %>%
filter(Phase == "Training" & Mode == "Expert" & !IsOutlier) %>%
group_by(Participant, Guide, Phase, Block) %>%
summarize(Mean = mean(Result), .groups="drop")
accuracy_p_expert_summary
expertacc_octo <- subset(accuracy_p_expert_summary, Guide == "Octo", Mean, drop = TRUE)
expertacc_sheet <- subset(accuracy_p_expert_summary, Guide == "Sheet", Mean, drop = TRUE)
MeanDiffCI(expertacc_sheet, expertacc_octo)
expertacc_b1 <- subset(accuracy_p_expert_summary, Block == "B1", Mean, drop = TRUE)
expertacc_b2 <- subset(accuracy_p_expert_summary, Block == "B2", Mean, drop = TRUE)
expertacc_b3 <- subset(accuracy_p_expert_summary, Block == "B3", Mean, drop = TRUE)
MeanDiffCI(expertacc_b3, expertacc_b2)
accuracy_expert_summary <- accuracy_p_expert_summary %>%
group_by(Guide, Phase, Block) %>%
summarize(summary_with_ci(Mean), .groups="drop") %>%
mutate(LowerValue = if_else(LowerValue < 0, 0, LowerValue))
accuracy_expert_summary
```
### Graph
```{r}
ggplot(data=accuracy_expert_summary, aes(x=Block, y=MeanValue, group=Guide)) +
geom_errorbar(aes(ymin=LowerValue, ymax=UpperValue), width=.3, position=pd) +
geom_line(aes(color=Guide), position=pd)+
geom_point(aes(color=Guide, shape=Guide), size=3, position=pd)+
geom_text(aes(label=sprintf("%.0f", 100*MeanValue), color=Guide), show.legend = FALSE,
size=4, nudge_x=ifelse(accuracy_expert_summary$Guide=="Octo", -0.3, 0.3),
nudge_y=ifelse(accuracy_expert_summary$Guide=="Octo", 0.1, -0.1)
)+
scale_y_continuous(labels = function(x) paste0(x*100),breaks = seq(0,1,0.25))+
# scale_y_continuous(labels = label_percent(), breaks = seq(0,1,0.25))+
expand_limits(y=c(0,1)) +
labs(x="Block", y="Accuracy (%)")+
scale_x_discrete(labels=c("B1-Train", "B2-Train", "B3-Train"))+
scale_color_manual(values = c("#d35400", "#95a5a6"))+
facet_grid(scales = "free", space = "free")+
theme_bw(base_size = 12)+
theme(panel.border = element_blank(),
axis.line = element_line(color = 'grey'),
legend.position = "none",
axis.text.x = element_text(size = 12), axis.text.y = element_text(size = 12),
axis.title.y = element_text(margin = margin(t = 0, r = 5, b = 0, l = 0)),
axis.title.x = element_text(margin = margin(t = 5, r = 0, b = 0, l = 0)))
```
### Assumptions
Shapiro test p > 0.05. The distribution of the residuals is normal.
```{r}
m = aov(Mean ~ Block*Guide + Error(Participant), data = accuracy_p_expert_summary)
shapiro.test(residuals(m$Within))
plotNormalHistogram(residuals(m$Within))
qqnorm(residuals(m$Within)); qqline(residuals(m$Within))
```
### ANOVA
```{r}
ezANOVA(
accuracy_p_expert_summary,
dv = Mean,
wid = Participant,
within = c(Guide, Block)
)
```
### Pairwise Comparison
```{r}
pairwise.t.test(accuracy_p_expert_summary$Mean,
accuracy_p_expert_summary$Block,
paired=TRUE,
p.adj="bonf")
```
## Novice Training
```{r}
accuracy_p_novice_summary <- trials %>%
filter(Phase == "Training" & Mode == "Novice" & !IsOutlier) %>%
group_by(Participant, Guide, Phase, Block) %>%
summarize(Mean = mean(Result), .groups="drop")
# because P11 has zero novice trial in B3, we averaged values between that of B1 and B2
newRow = c("P11", "Sheet", "Training", "B3", 0.67857145)
# merge new row into table
accuracy_p_novice_summary = rbind(accuracy_p_novice_summary, newRow)
# convert from character to numbers
accuracy_p_novice_summary$Mean <- as.numeric(as.character(accuracy_p_novice_summary$Mean))
accuracy_p_novice_summary
noviceacc_octo <- subset(accuracy_p_novice_summary, Guide == "Octo", Mean, drop = TRUE)
noviceacc_sheet <- subset(accuracy_p_novice_summary, Guide == "Sheet", Mean, drop = TRUE)
MeanDiffCI(noviceacc_sheet, noviceacc_octo)
accuracy_novice_summary <- accuracy_p_novice_summary %>%
group_by(Guide, Phase, Block) %>%
summarize(summary_with_ci(Mean), .groups="drop") %>%
mutate(LowerValue = if_else(LowerValue < 0, 0, LowerValue))
accuracy_novice_summary
```
### Graph
```{r}
pd <- position_dodge(0.4)
ggplot(data=accuracy_novice_summary, aes(x=Block, y=MeanValue, group=Guide)) +
geom_errorbar(aes(ymin=LowerValue, ymax=UpperValue), width=.3, position=pd) +
geom_line(aes(color=Guide), position=pd)+
geom_point(aes(color=Guide, shape=Guide), size=3, position=pd)+
geom_text(aes(label=sprintf("%.0f", 100*MeanValue), color=Guide), show.legend = FALSE,
size=4, nudge_x=ifelse(accuracy_novice_summary$Guide=="Octo", -0.3, 0.3),
nudge_y=ifelse(accuracy_novice_summary$Guide=="Octo", 0.05, -0.05)
)+
scale_y_continuous(labels = function(x) paste0(x*100),breaks = seq(0,1,0.25))+
# scale_y_continuous(labels = label_percent(), breaks = seq(0,1,0.25))+
expand_limits(y=c(0,1))+
labs(x="Block", y="Accuracy (%)")+
scale_x_discrete(labels=c("B1-Train", "B2-Train", "B3-Train"))+
scale_color_manual(values = c("#d35400", "#95a5a6"))+
facet_grid(scales = "free", space = "free")+
theme_bw(base_size = 12)+
theme(panel.border = element_blank(),
axis.line = element_line(color = 'grey'),
legend.position = "none",
axis.text.x = element_text(size = 12), axis.text.y = element_text(size = 12),
axis.title.y = element_text(margin = margin(t = 0, r = 5, b = 0, l = 0)),
axis.title.x = element_text(margin = margin(t = 5, r = 0, b = 0, l = 0)))
```
### Assumptions
Shapiro test p > 0.05. The distribution of the residuals is normal.
```{r}
m = aov(Mean ~ Block*Guide + Error(Participant), data = accuracy_p_novice_summary)
shapiro.test(residuals(m$Within))
plotNormalHistogram(residuals(m$Within))
qqnorm(residuals(m$Within)); qqline(residuals(m$Within))
```
### ANOVA
```{r}
ezANOVA(
accuracy_p_novice_summary,
dv = Mean,
wid = Participant,
within = c(Guide, Block)
)
```
## Guess (Pre-Test)
```{r}
accuracy_pretest_p_summary <- trials %>%
filter(Phase == "PreTest" & !IsOutlier) %>%
group_by(Participant, Guide, Phase, Block) %>%
summarize(Mean = mean(Result), .groups="drop")
accuracy_pretest_p_summary
accuracy_pretest_summary <- accuracy_pretest_p_summary %>%
group_by(Guide, Phase, Block) %>%
summarize(summary_with_ci(Mean), .groups="drop") %>%
mutate(LowerValue = if_else(LowerValue < 0, 0, LowerValue))
pretestacc_octo <- subset(accuracy_pretest_p_summary, Guide == "Octo", Mean, drop = TRUE)
pretestacc_sheet <- subset(accuracy_pretest_p_summary, Guide == "Sheet", Mean, drop = TRUE)
MeanDiffCI(pretestacc_sheet, pretestacc_octo)
accuracy_pretest_summary
```
### Graph
```{r}
ggplot(data=accuracy_pretest_summary, aes(x=Guide, y=MeanValue, fill = Guide)) +
geom_bar(size=3,stat="identity")+
geom_errorbar(aes(ymin=LowerValue, ymax=UpperValue), width=.3, position=pd) +
geom_text(aes(label=sprintf("%.0f", 100*MeanValue), color=Guide),
show.legend = FALSE,
size=4, nudge_y = 0.1
)+
scale_y_continuous(labels = function(x) paste0(x*100),breaks = seq(0,1,0.25))+
# scale_y_continuous(labels = label_percent(), breaks = seq(0,1,0.25))+
expand_limits(y=c(0,1))+
labs(x="Guide", y="Accuracy (%)")+
scale_x_discrete(labels=c("OCTOPOCUS","CRIBSHEET"))+
scale_fill_manual(values = c("#d35400", "#95a5a6"),labels=c("OCTOPOCUS","CRIBSHEET"))+
scale_color_manual(values = c("#d35400", "#95a5a6"),labels=c("OCTOPOCUS","CRIBSHEET"))+
scale_shape_discrete(labels=c("OCTOPOCUS","CRIBSHEET"))+
scale_linetype(labels=c("OCTOPOCUS","CRIBSHEET"))+
theme_bw(base_size = 12)+
theme(legend.position = "none", legend.direction = "horizontal",
panel.border = element_blank(),
axis.line = element_line(color = 'grey'),
axis.text.x = element_text(size = 12), axis.text.y = element_text(size = 12),
axis.title.y = element_text(margin = margin(t = 0, r = 5, b = 0, l = 0)),
axis.title.x = element_text(margin = margin(t = 5, r = 0, b = 0, l = 0)))
```
### Assumptions
Shapiro test p > 0.05. The distribution of the residuals is normal.
```{r}
m = aov(Mean ~ Guide + Error(Participant), data = accuracy_pretest_p_summary)
shapiro.test(residuals(m$Within))
plotNormalHistogram(residuals(m$Within))
qqnorm(residuals(m$Within)); qqline(residuals(m$Within))
```
### ANOVA
```{r}
ezANOVA(
accuracy_pretest_p_summary,
dv = Mean,
wid = Participant,
within = c(Guide)
)
```
## Recall (Test)
```{r}
accuracy_test_p_summary <- trials %>%
filter(Phase == "Test" & !IsOutlier) %>%
group_by(Participant, Guide, Phase, Block) %>%
summarize(Mean = mean(Result), .groups="drop")
accuracy_test_p_summary
maintest_octo <- subset(accuracy_test_p_summary, Guide == "Octo", Mean, drop = TRUE)
maintest_sheet <- subset(accuracy_test_p_summary, Guide == "Sheet", Mean, drop = TRUE)
MeanDiffCI(maintest_sheet, maintest_octo)
accuracy_test_summary <- accuracy_test_p_summary %>%
group_by(Guide, Phase, Block) %>%
summarize(summary_with_ci(Mean), .groups="drop") %>%
mutate(LowerValue = if_else(LowerValue < 0, 0, LowerValue))
acctest_b1 <- subset(accuracy_test_p_summary, Block == "B1", Mean, drop = TRUE)
acctest_b2 <- subset(accuracy_test_p_summary, Block == "B2", Mean, drop = TRUE)
acctest_b3 <- subset(accuracy_test_p_summary, Block == "B3", Mean, drop = TRUE)
MeanDiffCI(acctest_b2, acctest_b1)
MeanDiffCI(acctest_b3, acctest_b2)
accuracy_test_summary
```
### Graph
```{r}
pd <- position_dodge(0.4)
ggplot(data=accuracy_test_summary, aes(x=Block, y=MeanValue, group=Guide)) +
geom_errorbar(aes(ymin=LowerValue, ymax=UpperValue), width=.3, position=pd) +
geom_line(aes(color=Guide), position=pd)+
geom_point(aes(color=Guide, shape=Guide), size=3, position=pd)+
geom_text(aes(label=sprintf("%.0f", 100*MeanValue), color=Guide),
size=4, nudge_x=ifelse(accuracy_test_summary$Guide=="Octo", -0.4, 0.4),
nudge_y=ifelse(accuracy_test_summary$Guide=="Octo", 0.1, -0.1)
)+
scale_y_continuous(labels = function(x) paste0(x*100),breaks = seq(0,1,0.25))+
# scale_y_continuous(labels = label_percent(), breaks = seq(0,1,0.25))+
expand_limits(y=c(0,1))+
labs(x="Block", y="Accuracy (%)")+
scale_x_discrete(labels=c("B1-Test", "B2-Test", "B3-Test"))+
scale_color_manual(values = c("#d35400", "#95a5a6"),labels=c("OCTOPOCUS","CRIBSHEET"))+
scale_shape_discrete(labels=c("OCTOPOCUS","CRIBSHEET"))+
scale_linetype(labels=c("OCTOPOCUS","CRIBSHEET"))+
facet_grid(scales = "free", space = "free")+
theme_bw(base_size = 12)+
theme(legend.background = element_blank(), legend.title = element_blank(),
legend.position = c(0.5, 0.1), legend.direction = "horizontal",
panel.border = element_blank(),
axis.line = element_line(color = 'grey'),
axis.text.x = element_text(size = 12), axis.text.y = element_text(size = 12),
axis.title.y = element_text(margin = margin(t = 0, r = 5, b = 0, l = 0)),
axis.title.x = element_text(margin = margin(t = 5, r = 0, b = 0, l = 0)))
```
### Assumptions
Shapiro test p > 0.05. The distribution of the residuals is normal.
```{r}
m = aov(Mean ~ Block*Guide + Error(Participant), data = accuracy_test_p_summary)
shapiro.test(residuals(m$Within))
plotNormalHistogram(residuals(m$Within))
qqnorm(residuals(m$Within)); qqline(residuals(m$Within))
```
### ANOVA
```{r}
ezANOVA(
accuracy_test_p_summary,
dv = Mean,
wid = Participant,
within = c(Guide, Block)
)
```
### Pairwise Comparison
```{r}
pairwise.t.test(accuracy_test_p_summary$Mean,
accuracy_test_p_summary$Block,
paired=TRUE,
p.adj="bonf")
```
## Incidental Learning (Post-test)
```{r}
# excluding targets
accuracy_posttest_p_summary <- trials %>%
filter(Phase == "PostTest" & !IsOutlier) %>%
filter(TrialName != "Abu Dhabi" & TrialName != "Amsterdam" & TrialName != "Dubai" & TrialName != "Los Angeles" & TrialName != "Madrid" & TrialName != "Paris" & TrialName != "Singapore" & TrialName != "Tokyo" & TrialName != "Austin" & TrialName != "Doha" & TrialName != "Frankfurt" & TrialName != "Houston" & TrialName != "Milan" & TrialName != "Prague" & TrialName != "Seoul" & TrialName != "Toronto") %>%
group_by(Participant, Guide, Phase, Block) %>%
summarize(Mean = mean(Result), .groups="drop")
accuracy_posttest_p_summary
accuracyposttest_octo <- subset(accuracy_posttest_p_summary, Guide == "Octo", Mean, drop = TRUE)
accuracyposttest_sheet <- subset(accuracy_posttest_p_summary, Guide == "Sheet", Mean, drop = TRUE)
MeanDiffCI(accuracyposttest_octo, accuracyposttest_sheet)
accuracy_posttest_summary <- accuracy_posttest_p_summary %>%
group_by(Guide, Phase, Block) %>%
summarize(summary_with_ci(Mean), .groups="drop") %>%
mutate(LowerValue = if_else(LowerValue < 0, 0, LowerValue))
accuracy_posttest_summary
```
### Graph
```{r}
ggplot(data=accuracy_posttest_summary, aes(x=Guide, y=MeanValue, fill = Guide)) +
geom_bar(size=3,stat="identity")+
geom_errorbar(aes(ymin=LowerValue, ymax=UpperValue), width=.3, position=pd) +
geom_text(aes(label=sprintf("%.0f", 100*MeanValue), color=Guide),
show.legend = FALSE,
size=4, nudge_y = 0.05, nudge_x = 0.3
)+
scale_y_continuous(labels = function(x) paste0(x*100),breaks = seq(0,1,0.25))+
# scale_y_continuous(labels = label_percent(), breaks = seq(0,1,0.25))+
expand_limits(y=c(0,1))+
labs(x="Guide", y="Accuracy (%)")+
scale_x_discrete(labels=c("OCTOPOCUS","CRIBSHEET"))+
scale_fill_manual(values = c("#d35400", "#95a5a6"),labels=c("OCTOPOCUS","CRIBSHEET"))+
scale_color_manual(values = c("#d35400", "#95a5a6"),labels=c("OCTOPOCUS","CRIBSHEET"))+
scale_shape_discrete(labels=c("OCTOPOCUS","CRIBSHEET"))+
scale_linetype(labels=c("OCTOPOCUS","CRIBSHEET"))+
theme_bw(base_size = 12)+
theme(legend.position = "none", legend.direction = "horizontal",
panel.border = element_blank(),
axis.line = element_line(color = 'grey'),
axis.text.x = element_text(size = 12), axis.text.y = element_text(size = 12),
axis.title.y = element_text(margin = margin(t = 0, r = 5, b = 0, l = 0)),
axis.title.x = element_text(margin = margin(t = 5, r = 0, b = 0, l = 0)))
```
### Assumptions
Shapiro test p > 0.05. The distribution of the residuals is normal.
```{r}
m = aov(Mean ~ Guide + Error(Participant), data = accuracy_posttest_p_summary)
shapiro.test(residuals(m$Within))
plotNormalHistogram(residuals(m$Within))
qqnorm(residuals(m$Within)); qqline(residuals(m$Within))
```
### ANOVA
```{r}
ezANOVA(
accuracy_posttest_p_summary,
dv = Mean,
wid = Participant,
within = c(Guide)
)
```
## Learning Curve (combined, only 8 trained gestures)
```{r}
targets <- c("Abu Dhabi", "Amsterdam", "Dubai", "Los Angeles", "Madrid", "Paris", "Singapore", "Tokyo", "Austin", "Doha", "Frankfurt", "Houston", "Milan", "Prague", "Seoul", "Toronto")
curve_p_summary <- trials %>%
filter(TrialName %in% targets & Phase!="Training" & !IsOutlier) %>%
mutate(PhaseName = ifelse(Phase=="Test" & Block=="B1", "Test B1",
ifelse(Phase=="Test" & Block=="B2", "Test B2",
ifelse(Phase=="Test" & Block=="B3", "Test B3",
ifelse(Phase=="PreTest", "PreTest",
ifelse(Phase=="PostTest", "PostTest",NA)))))) %>%
mutate(PhaseName = fct_relevel(PhaseName,
"PreTest", "Test B1", "Test B2", "Test B3", "PostTest")) %>%
mutate(PhaseName = as.factor(PhaseName)) %>%
group_by(Participant, Guide, PhaseName) %>%
summarize(Mean = mean(Result), .groups="drop")
curve_p_summary
curve_summary <- curve_p_summary %>%
group_by(Guide, PhaseName) %>%
summarize(summary_with_ci(Mean), .groups="drop") %>%
mutate(LowerValue = if_else(LowerValue < 0, 0, LowerValue))
curve_summary
curve_testB3 <- subset(curve_p_summary, PhaseName == "Test B3", Mean, drop = TRUE)
curve_posttest <- subset(curve_p_summary, PhaseName == "PostTest", Mean, drop = TRUE)
MeanDiffCI(curve_posttest, curve_testB3)
```
### Graph
```{r}
pd <- position_dodge(0.4)
ggplot(data=curve_summary, aes(x=PhaseName, y=MeanValue, group=Guide)) +
geom_errorbar(aes(ymin=LowerValue, ymax=UpperValue), width=.3, position=pd) +
geom_line(aes(color=Guide), position=pd)+
geom_point(aes(color=Guide, shape=Guide), size=3, position=pd)+
geom_text(aes(label=sprintf("%.0f", 100*MeanValue), color=Guide),
size=4, nudge_x=ifelse(curve_summary$Guide=="Octo", -0.3, 0.3),
nudge_y=ifelse(curve_summary$Guide=="Octo", 0.1, -0.1),
show.legend = FALSE
)+
scale_y_continuous(labels = function(x) paste0(x*100),breaks = seq(0,1,0.25))+
# scale_y_continuous(labels = label_percent(), breaks = seq(0,1,0.25))+
expand_limits(y=c(0,1))+
labs(x="Phase", y="Accuracy (%)")+
# scale_x_discrete(labels=c("B1-Test", "B2-Test", "B3-Test"))+
scale_color_manual(values = c("#d35400", "#95a5a6"),labels=c("OCTOPOCUS","CRIBSHEET"))+
scale_shape_discrete(labels=c("OCTOPOCUS","CRIBSHEET"))+
scale_linetype(labels=c("OCTOPOCUS","CRIBSHEET"))+
facet_grid(scales = "free", space = "free")+
theme_bw(base_size = 12)+
theme(legend.background = element_blank(), legend.title = element_blank(),
legend.position = c(0.5, 0.1), legend.direction = "horizontal",
panel.border = element_blank(),
axis.line = element_line(color = 'grey'),
axis.text.x = element_text(size = 12), axis.text.y = element_text(size = 12),
axis.title.y = element_text(margin = margin(t = 0, r = 5, b = 0, l = 0)),
axis.title.x = element_text(margin = margin(t = 5, r = 0, b = 0, l = 0)))
```
### Assumptions
Shapiro test p > 0.05. The distribution of the residuals is normal.
```{r}
m = aov(Mean ~ PhaseName*Guide + Error(Participant), data = curve_p_summary)
shapiro.test(residuals(m$Within))
plotNormalHistogram(residuals(m$Within))
qqnorm(residuals(m$Within)); qqline(residuals(m$Within))
```
### ANOVA
```{r}
# curve_p_summary_new = filter(curve_p_summary, PhaseName=="Test B3" | PhaseName=="PostTest")
# curve_p_summary_new
ezANOVA(
curve_p_summary,
dv = Mean,
wid = Participant,
within = c(Guide, PhaseName)
)
```
### Pairwise Comparison
```{r}
pairwise.t.test(curve_p_summary$Mean,
curve_p_summary$PhaseName,
paired=TRUE,
p.adj="bonf")
```
# OTHERS
## Expert Mode Rate (during Training)
```{r}
expertproportion_p_summary <- trials %>%
filter(Phase == "Training" & !IsOutlier) %>%
group_by(Participant, Guide, Phase, Block) %>%
summarize(ExpertCount = sum(Mode=="Expert"),
Total = sum(Mode=="Novice") + sum(Mode=="Expert"),
ExpertPercent = (sum(Mode=="Expert")/Total),
.groups="drop")
expertproportion_p_summary
expertpro_octo <- subset(expertproportion_p_summary, Guide == "Octo", ExpertPercent, drop = TRUE)
expertpro_sheet <- subset(expertproportion_p_summary, Guide == "Sheet", ExpertPercent, drop = TRUE)
MeanDiffCI(expertpro_sheet, expertpro_octo)
expertpro_b1 <- subset(expertproportion_p_summary, Block == "B1", ExpertPercent, drop = TRUE)
expertpro_b2 <- subset(expertproportion_p_summary, Block == "B2", ExpertPercent, drop = TRUE)
expertpro_b3 <- subset(expertproportion_p_summary, Block == "B3", ExpertPercent, drop = TRUE)
MeanDiffCI(expertpro_b2, expertpro_b1)
MeanDiffCI(expertpro_b3, expertpro_b1)
expertproportion_summary <- expertproportion_p_summary %>%
group_by(Guide, Phase, Block) %>%
summarize(summary_with_ci(ExpertPercent), .groups="drop") %>%
mutate(LowerValue = if_else(LowerValue < 0, 0, LowerValue))
expertproportion_summary
```
### Graph
```{r}
pd <- position_dodge(0.4)
ggplot(data=expertproportion_summary, aes(x=Block, y=MeanValue, group=Guide)) +
geom_errorbar(aes(ymin=LowerValue, ymax=UpperValue), width=.3, position=pd) +
geom_line(aes(color=Guide), position=pd)+
geom_point(aes(color=Guide, shape=Guide), size=3, position=pd)+
geom_text(aes(label=sprintf("%.0f", 100*MeanValue), color=Guide),
size=4, nudge_x=ifelse(expertproportion_summary$Guide=="Octo", -0.35, 0.35),
nudge_y=ifelse(expertproportion_summary$Guide=="Octo", -0.15, 0.15)
)+
scale_y_continuous(labels = function(x) paste0(x*100),breaks = seq(0,1,0.25))+
# scale_y_continuous(labels = label_percent(), breaks = seq(0,1,0.25))+
expand_limits(y=c(0,1))+
labs(x="Block", y="Usage Rate (%)")+
scale_x_discrete(labels=c("B1-Train", "B2-Train", "B3-Train"))+
scale_color_manual(values = c("#d35400", "#95a5a6"),labels=c("OCTOPOCUS","CRIBSHEET"))+
scale_shape_discrete(labels=c("OCTOPOCUS","CRIBSHEET"))+
scale_linetype(labels=c("OCTOPOCUS","CRIBSHEET"))+
facet_grid(scales = "free", space = "free")+
theme_bw(base_size = 12)+
theme(legend.background = element_blank(), legend.title = element_blank(),
legend.position = c(0.5, 0.1), legend.direction = "horizontal",
panel.border = element_blank(),
axis.line = element_line(color = 'grey'),
axis.text.x = element_text(size = 12), axis.text.y = element_text(size = 12),