\documentclass[conference,a4paper]{IEEEtran}

\usepackage{graphicx}   % for including figures
\usepackage{booktabs}   % for nicer tables

\begin{document}

\section{Abstract}\label{abstract}

\section{Introduction}\label{introduction}

\section{Keyboard Designs}\label{keyboard-designs}

\section{Experiment}\label{experiment}

\subsection{Participants}\label{participants}

\subsection{Apparatus}\label{apparatus}

\subsection{Procedure}\label{procedure}

\section{Results}\label{results}

\subsection{Descriptive Statistics}\label{descriptive-statistics}

\subsubsection{Objective Measures}\label{objective-measures}

<<echo=FALSE>>=
library(knitr)

# Read the results CSV
results <- read.csv("../data/results.csv", sep=",", header=TRUE)

# Summarize TER and WPM
ter <- summary(results[, c("qwerty_ter", "dvorak_ter", "circle_ter")])
wpm <- summary(results[, c("qwerty_wpm", "dvorak_wpm", "circle_wpm")])
@

% TER table
\begin{table}[h]
\centering
\scriptsize
<<results='asis', echo=FALSE>>=
kable(ter, format="latex", booktabs=TRUE)
@
\caption{Summary of Total Error Rate (TER)}
\end{table}

% WPM table
\begin{table}[h]
\centering
\scriptsize
<<results='asis', echo=FALSE>>=
kable(wpm, format="latex", booktabs=TRUE)
@
\caption{Summary of Words per Minute (WPM)}
\end{table}

<<echo=FALSE>>=
# Create figures directory if it doesn't exist
dir.create("../figures", showWarnings=FALSE)

# Helper functions for standard deviation and confidence intervals
mean_sd <- function(x) { 
  m <- mean(x) 
  s <- sd(x) 
  c(mean=m, lower=m-s, upper=m+s) 
}

mean_ci <- function(x) {
  m <- mean(x)
  se <- sd(x)/sqrt(length(x))
  ci <- qt(0.975, df=length(x)-1)*se
  c(mean=m, lower=m-ci, upper=m+ci)
}

# TER stats
ter_stats <- rbind(
  mean_ci(results$qwerty_ter),
  mean_ci(results$dvorak_ter),
  mean_ci(results$circle_ter)
)

# Save TER barplot as PDF using LaTeX-compatible fonts
pdf("../figures/ter_plot.pdf")
bar_pos <- barplot(
  ter_stats[,"mean"],
  names.arg=c("QWERTY","DVORAK","CIRCLE"),
  ylab="Total Error Rate (TER)",
  main="TER of layouts",
  ylim=c(0, max(ter_stats[,"upper"])*1.1)
)
# Add confidence intervals
arrows(
  x0=bar_pos, y0=ter_stats[,"lower"],
  x1=bar_pos, y1=ter_stats[,"upper"],
  angle=90, code=3, length=0.05
)
dev.off()

# WPM stats
wpm_stats <- rbind(
  mean_sd(results$qwerty_wpm),
  mean_sd(results$dvorak_wpm),
  mean_sd(results$circle_wpm)
)

# Save WPM barplot as PDF using LaTeX-compatible fonts
pdf("../figures/wpm_plot.pdf")
bar_pos <- barplot(
  wpm_stats[,"mean"],
  names.arg=c("QWERTY","DVORAK","CIRCLE"),
  ylab="Words per minute (WPM)",
  main="WPM of layouts",
  ylim=c(0, max(wpm_stats[,"upper"])*1.1)
)
arrows(
  x0=bar_pos, y0=wpm_stats[,"lower"],
  x1=bar_pos, y1=wpm_stats[,"upper"],
  angle=90, code=3, length=0.05
)
dev.off()
@

% Include TER plot
\begin{figure}[h]
\centering
\includegraphics[width=\columnwidth]{../figures/ter_plot.pdf}
\caption{Total Error Rate (TER) by Keyboard Layout}
\end{figure}

% Include WPM plot
\begin{figure}[h]
\centering
\includegraphics[width=\columnwidth]{../figures/wpm_plot.pdf}
\caption{Words per Minute (WPM) by Keyboard Layout}
\end{figure}

\subsubsection{Subjective Measures}\label{subjective-measures}

<<echo=FALSE>>=
# Read NASA-TLX data
nasa <- read.csv("../data/nasaTLX.csv")
nasa$layout <- factor(nasa$layout)

# Save boxplots as PDF using LaTeX-compatible fonts
pdf("../figures/nasa_boxplots.pdf")
par(mfrow=c(2,3))  # Arrange plots in 2 rows x 3 columns
boxplot(mental_demand ~ layout, data=nasa, main="Mental Demand")
boxplot(physical_demand ~ layout, data=nasa, main="Physical Demand")
boxplot(performance ~ layout, data=nasa, main="Performance")
boxplot(effort ~ layout, data=nasa, main="Effort")
boxplot(frustration ~ layout, data=nasa, main="Frustration")
par(mfrow=c(1,1))
dev.off()
@

% Include NASA-TLX boxplots
\begin{figure}[h]
\centering
\includegraphics[width=\columnwidth]{../figures/nasa_boxplots.pdf}
\caption{NASA-TLX Scores by Keyboard Layout}
\end{figure}

\subsection{Inferential Statistics}\label{inferential-statistics}
Independent var: 
- QWERTY
- DVORAK
- Circle

Dependent var:
- WPM
- TER
- Nasa-TLX

%Anova RM for WPM
<<echo=FALSE>>=
library(tidyr)
library(dplyr)

# Add participant ID
results$id <- 1:nrow(results)

# --- WPM Long Format ---
wpm_long <- results %>%
  select(id, qwerty_wpm, dvorak_wpm, circle_wpm) %>%
  pivot_longer(
    cols = -id,
    names_to = "layout",
    values_to = "wpm"
  )

wpm_long$id <- factor(wpm_long$id)

wpm_long$layout <- factor(wpm_long$layout,
                         levels=c("qwerty_wpm","dvorak_wpm","circle_wpm"),
                         labels=c("QWERTY","DVORAK","CIRCLE"))

# --- RM ANOVA for WPM ---
anova_wpm <- aov(wpm ~ layout + Error(id/layout), data=wpm_long)

# Print ANOVA table
summary(anova_wpm)
@

%Anova RM for TER
<<echo=FALSE>>=

# --- TER Long Format ---
ter_long <- results %>%
  select(id, qwerty_ter, dvorak_ter, circle_ter) %>%
  pivot_longer(
    cols = -id,
    names_to = "layout",
    values_to = "ter"
  )

ter_long$id <- factor(ter_long$id)

ter_long$layout <- factor(ter_long$layout,
                         levels=c("qwerty_ter","dvorak_ter","circle_ter"),
                         labels=c("QWERTY","DVORAK","CIRCLE"))

# --- RM ANOVA for TER ---
anova_ter <- aov(ter ~ layout + Error(id/layout), data=ter_long)

summary(anova_ter)
@

% Post-Hoc analysis with bonferroni correction for WPM
<<echo=FALSE>>=
suppressMessages(library(emmeans))

suppressMessages(emm_wpm <- emmeans(anova_wpm, ~ layout))

posthoc <- pairs(emm_wpm, adjust = "bonferroni")

print(posthoc)
@

\subsubsection{Objective Measures}\label{objective-measures-1}

\subsubsection{Subjective Measures}\label{subjective-measures-1}

\section{Discussion}\label{discussion}

\end{document}