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evaluation_time.Rmd
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evaluation_time.Rmd
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---
title: "Evaluation for Efficiency (time)"
author: "Jonas Hurst"
date: "2023-06-24"
output:
pdf_document: default
html_document: default
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
setwd("~/ma")
library(gridExtra) # for putting two ggplots next two each other
library(tidyverse)
library(bootES)
library(ggplot2)
set.seed(12345)
# import data
source("./get_data.R")
```
# All data
```{r, echo=FALSE}
hist(evaluation_data$time, xlab = "time (s)", ylab = "number", main = "history for efficiency (all obs.)")
```
# Efficiency (time to Retrieve)
```{r, include=FALSE}
hist_breakpoints = seq(0, max(evaluation_data$time), by=5)
hist_breakpoints = append(hist_breakpoints, tail(hist_breakpoints, n=1)+5)
```
```{r, echo=FALSE}
ggplot(evaluation_data, aes(x = time, fill = method)) +
geom_histogram(breaks=hist_breakpoints, position="dodge", col="black", aes(y = after_stat(density*width)*100)) +
scale_fill_manual(values = c("P2F" = p2f_color, "F2P" = f2p_color)) +
scale_x_continuous(breaks = hist_breakpoints) +
labs(x="Efficiency (time (s) / polygon)", y="% of polygons in method") +
theme(legend.position = c(0.94, 0.92), legend.title = element_blank())
ggsave("./graphs/time_histogram.png", width=15, height=8, units = "cm")
evaluation_data %>%
mutate(bin = cut(time, breaks=hist_breakpoints)) %>%
group_by(method, bin) %>%
summarise(count = n(), .groups = "rowwise")%>%
print(n=Inf)
evaluation_data %>%
group_by(method)%>%
summarize(count = n(), mean = mean(time), median = median(time), std.dv. = sd(time))
p2f.time.bootES <- bootES(p2f$time, R=5000)
f2p.time.bootES <- bootES(f2p$time, R=5000)
time.bootESsummary <- data.frame(
method = c("P2F", "F2P"),
mean = c(p2f.time.bootES$t0, f2p.time.bootES$t0),
mean.min = c(p2f.time.bootES$bounds[1], f2p.time.bootES$bounds[1]),
mean.max = c(p2f.time.bootES$bounds[2], f2p.time.bootES$bounds[2])
)
```
\newpage
Mean with bootstrapped 95% CI
```{r, echo=FALSE, fig.asp=0.25}
ggplot(time.bootESsummary,
aes(y = method, x=mean, xmin = mean.min, xmax = mean.max, color = method)) +
geom_pointrange() +
scale_color_manual(values = c("P2F" = p2f_color, "F2P" = f2p_color)) +
scale_fill_discrete(breaks = c("P2F", "F2P")) +
theme(axis.title.x = element_blank(), axis.title.y = element_blank(), legend.position = "none")
ggsave("./graphs/time_means_all.png", width=12, height=3, units = "cm")
p2f.time.bootES
f2p.time.bootES
bootES(evaluation_data, data.col="time", group.col = "method", contrast=c("P2F", "F2P"), R=5000)
```
\newpage
## Comparison large/small beetween techniques
### Large polygons:
```{r, echo=FALSE}
p2f.time.large.bootES <- bootES(p2f_large$time, R=5000)
f2p.time.large.bootES <- bootES(f2p_large$time, R=5000)
time.large.bootESSummary <- data.frame(
method = c("P2F", "F2P"),
mean = c(p2f.time.large.bootES$t0, f2p.time.large.bootES$t0),
mean.min = c(p2f.time.large.bootES$bounds[1], f2p.time.large.bootES$bounds[1]),
mean.max = c(p2f.time.large.bootES$bounds[2], f2p.time.large.bootES$bounds[2])
)
```
Mean with bootstrapped 95% CI
```{r, echo=FALSE, fig.asp=0.25}
ggplot(time.large.bootESSummary, aes(y=method, x=mean, xmin = mean.min, xmax = mean.max, color=method))+
geom_pointrange() +
scale_color_manual(values = c("P2F" = p2f_color, "F2P" = f2p_color)) +
scale_fill_discrete(breaks = c("P2F", "F2P")) +
theme(axis.title.x = element_blank(), axis.title.y = element_blank(), legend.position = "none")
ggsave("./graphs/time_means_large.png", width=12, height=3, units = "cm")
p2f.time.large.bootES
f2p.time.large.bootES
bootES(evaluation_data_large, data.col="time", group.col = "method", contrast=c("P2F", "F2P"), R=5000)
```
\newpage
### Small polygons:
```{r, echo=FALSE}
p2f.time.small.bootES <- bootES(p2f_small$time, R=5000)
f2p.time.small.bootES <- bootES(f2p_small$time, R=5000)
time.small.bootESSummary <- data.frame(
method = c("P2F", "F2P"),
mean = c(p2f.time.small.bootES$t0, f2p.time.small.bootES$t0),
mean.min = c(p2f.time.small.bootES$bounds[1], f2p.time.small.bootES$bounds[1]),
mean.max = c(p2f.time.small.bootES$bounds[2], f2p.time.small.bootES$bounds[2])
)
```
Mean with bootstrapped 95% CI
```{r, echo=FALSE, fig.asp=0.25}
ggplot(time.small.bootESSummary, aes(y=method, x=mean, xmin = mean.min, xmax = mean.max, color=method))+
geom_pointrange() +
scale_color_manual(values = c("P2F" = p2f_color, "F2P" = f2p_color)) +
scale_fill_discrete(breaks = c("P2F", "F2P")) +
theme(axis.title.x = element_blank(), axis.title.y = element_blank(), legend.position = "none")
ggsave("./graphs/time_means_small.png", width=12, height=3, units = "cm")
p2f.time.small.bootES
f2p.time.small.bootES
bootES(evaluation_data_small, data.col="time", group.col = "method", contrast=c("P2F", "F2P"), R=5000)
```
Reason (maybe?): Unexpected behavior of how participants used the fine mechanism:
* In P2F they used it as a "break" on the pointer to not move it away while selecting.
As selection with coarse pointing was already quite good, few additional fine pointing was necessary
* In F2P, they used it to precisely pan the map, which took a lot of time
\newpage
## Comparison large/small within technique
### P2F
```{r, echo=FALSE}
bootES(p2f, data.col="time", group.col = "state_type", contrast=c("large", "small"), R=5000)
```
### F2P
```{r, echo=FALSE}
bootES(f2p, data.col="time", group.col = "state_type", contrast=c("large", "small"), R=5000)
```
\newpage
## Learnability
Large polygons
```{r, echo=FALSE}
time_by_polygon_large <- data.frame(matrix(ncol=5, nrow=0, dimnames=list(NULL, c("state_number", "method", "mean", "mean.min", "mean.max"))))
for (retrieve_nr in 1:3) {
bootresult_large_p2f <- bootES(filter(p2f, state_number == retrieve_nr)$time, R=5000)
bootresult_large_f2p <- bootES(filter(f2p, state_number == retrieve_nr)$time, R=5000)
bootresult_large <- data.frame(
state_number = c(retrieve_nr, retrieve_nr),
method = c("P2F", "F2P"),
mean = c(bootresult_large_p2f$t0, bootresult_large_f2p$t0),
mean.min = c(bootresult_large_p2f$bounds[1], bootresult_large_f2p$bounds[1]),
mean.max = c(bootresult_large_p2f$bounds[2], bootresult_large_f2p$bounds[2])
)
time_by_polygon_large <- rbind(time_by_polygon_large, bootresult_large)
}
scale_large_min <- min(time_by_polygon_large$mean.min)
scale_large_max <- max(time_by_polygon_large$mean.max)
ggplot(time_by_polygon_large, aes(x=state_number, y=mean, ymin=mean.min, ymax=mean.max, color=method)) +
geom_point() +
geom_line() +
geom_errorbar(aes(width=0.2)) +
scale_color_manual(values = c("P2F" = p2f_color, "F2P" = f2p_color))+
scale_x_continuous(breaks = 1:3) +
scale_y_continuous(breaks=seq(floor(scale_large_min), ceiling(scale_large_max), by=2)) +
xlab("Number of polygon") +
ylab("Retrieve time (s)") +
facet_grid(cols=vars(method)) +
theme(legend.position = "none")
ggsave("./graphs/learn_time_large.png", width=8, height=8, units = "cm")
```
Small polygons
```{r, echo=FALSE}
time_by_polygon_small <- data.frame(matrix(ncol=5, nrow=0, dimnames=list(NULL, c("state_number", "method", "mean", "mean.min", "mean.max"))))
for (retrieve_nr in 4:6) {
bootresult_small_p2f <- bootES(filter(p2f, state_number == retrieve_nr)$time, R=5000)
bootresult_small_f2p <- bootES(filter(f2p, state_number == retrieve_nr)$time, R=5000)
bootresult_small <- data.frame(
state_number = c(retrieve_nr, retrieve_nr),
method = c("P2F", "F2P"),
mean = c(bootresult_small_p2f$t0, bootresult_small_f2p$t0),
mean.min = c(bootresult_small_p2f$bounds[1], bootresult_small_f2p$bounds[1]),
mean.max = c(bootresult_small_p2f$bounds[2], bootresult_small_f2p$bounds[2])
)
time_by_polygon_small <- rbind(time_by_polygon_small, bootresult_small)
}
scale_small_min <- min(time_by_polygon_small$mean.min)
scale_small_max <- max(time_by_polygon_small$mean.max)
ggplot(time_by_polygon_small, aes(x=state_number, y=mean, ymin=mean.min, ymax=mean.max, color=method)) +
geom_point() +
geom_line() +
geom_errorbar(aes(width=0.2)) +
scale_color_manual(values = c("P2F" = p2f_color, "F2P" = f2p_color))+
ylim(scale_small_min, scale_small_max) +
scale_x_continuous(breaks = 4:6) +
xlab("Number of polygon") +
ylab("Retrieve time (s)") +
facet_grid(cols=vars(method)) +
theme(legend.position = "none")
ggsave("./graphs/learn_time_small.png", width=8, height=8, units = "cm")
```
Todo: test this with Linear Mixed Model