131 lines
3.0 KiB
Plaintext
131 lines
3.0 KiB
Plaintext
---
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title: "On-Screen Keyboard Layout Study"
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subtitle: "Human-Computer Interfaces"
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date: "`r Sys.Date()`"
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output: beamer_presentation
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---
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```{r setup, include=FALSE}
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knitr::opts_chunk$set(echo = FALSE)
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library(tidyr)
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library(dplyr)
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results <- read.csv("../data/results.csv", sep=",", header=TRUE)
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```
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## Implementation
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## Keyboard Design - Qwerty
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## Keyboard Design - Dvorak
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## Keyboard Design - Circle
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## Descriptive Stats - Objective
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---
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## Descriptive Stats- Subjective
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```{r, echo=FALSE}
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# Read NASA-TLX data
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nasa <- read.csv("../data/nasaTLX.csv")
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nasa$layout <- factor(nasa$layout)
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# Save boxplots as PDF using LaTeX-compatible fonts
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par(mfrow=c(1,2))
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boxplot(mental_demand ~ layout, data=nasa, main="Mental Demand")
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boxplot(physical_demand ~ layout, data=nasa, main="Physical Demand")
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```
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## Descriptive Stats- Subjective
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```{r, echo=FALSE}
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par(mfrow=c(1,3))
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boxplot(performance ~ layout, data=nasa, main="Performance")
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boxplot(effort ~ layout, data=nasa, main="Effort")
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boxplot(frustration ~ layout, data=nasa, main="Frustration")
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par(mfrow=c(1,1))
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```
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## Inferential Stats
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WPM:
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```{r, echo=FALSE}
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# Add participant ID
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results$id <- 1:nrow(results)
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# --- WPM Long Format ---
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wpm_long <- results %>%
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select(id, qwerty_wpm, dvorak_wpm, circle_wpm) %>%
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pivot_longer(
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cols = -id,
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names_to = "layout",
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values_to = "wpm"
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)
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wpm_long$id <- factor(wpm_long$id)
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wpm_long$layout <- factor(wpm_long$layout,
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levels=c("qwerty_wpm","dvorak_wpm","circle_wpm"),
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labels=c("QWERTY","DVORAK","CIRCLE"))
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# --- RM ANOVA for WPM ---
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anova_wpm <- aov(wpm ~ layout + Error(id/layout), data=wpm_long)
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wpm_tab <- summary(anova_wpm)[[2]][[1]]
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wpm_effect <- wpm_tab["layout", , drop=FALSE]
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wpm_effect$`Pr(>F)` <- "$p< .001$"
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colnames(wpm_effect) <- c("Df", "Sum Sq", "Mean Sq", "F value", "p-value")
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wpm_effect
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```
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TER:
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```{r, echo=FALSE}
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# --- TER Long Format ---
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ter_long <- results %>%
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select(id, qwerty_ter, dvorak_ter, circle_ter) %>%
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pivot_longer(
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cols = -id,
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names_to = "layout",
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values_to = "ter"
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)
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ter_long$id <- factor(ter_long$id)
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ter_long$layout <- factor(ter_long$layout,
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levels=c("qwerty_ter","dvorak_ter","circle_ter"),
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labels=c("QWERTY","DVORAK","CIRCLE"))
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# --- RM ANOVA for TER ---
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anova_ter <- aov(ter ~ layout + Error(id/layout), data=ter_long)
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ter_tab <- summary(anova_ter)[[2]][[1]]
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ter_effect <- ter_tab["layout", , drop=FALSE]
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colnames(ter_effect) <- c("Df", "Sum Sq", "Mean Sq", "F value", "p-value")
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ter_effect
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```
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## Post-Hoc
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```{r, echo=FALSE, message=FALSE}
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library(emmeans)
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emm_wpm <- emmeans(anova_wpm, ~ layout)
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posthoc <- pairs(emm_wpm, adjust = "bonferroni")
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posthoc_df <- as.data.frame(posthoc)
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posthoc_df <- posthoc_df %>%
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mutate(p.value = ifelse(p.value < 0.001, "$<0.001$", sprintf("%.3f", p.value)))
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posthoc_df
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``` |