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README.Rmd
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README.Rmd
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---
title: "Stimulus Sampling 360 Dataset"
output: github_document
---
```{r echo=FALSE, message=FALSE}
library(tidyverse)
knitr::opts_chunk$set(echo=F,fig.width=5, fig.height=3, cache=T)
psych_360 <- read_csv('psych-360.csv')
```
# Files
CSV of data: https://github.com/vhilab/psych-360/blob/master/psych-360.csv
Video Files: https://vhil.stanford.edu/mm/psych-360-videos.zip. *Note that the permission to use these videos has been given only for research purposes.*
# CSV File Information
Each row represents a sample, which in this case is a participant watching a video. Each participant watched five videos, so each participant has five rows associated with it.
- [`location`](#identification-location-pid-and-order): Locations of the study. Lab 1 and Lab2 are the rooms of our lab and Museum is our booth at The Tech Interactive in San Jose.
- [`pid`](#identification-location-pid-and-order): The IDs of the participants. They are unique per location not across locations.
- [`video`](#video): The video the participants watched.
- [`order`](#identification-location-pid-and-order): The order of the video from the participant's perspective.
- [`age`](#age): The age of the participant. (19-/19-25/26-45/45+)
- [`gender`](#gender): The gender of the participant. (Female/Male/Other)
- [`race`](#race): The race of the participant.
- [`experience`](#experience): Whether the participant had prior VR experience.
- [`arousal`](#arousal): The arousal level the participant reported after watching the video.
- [`presence`](#presence): The presence level the participant reported after watching the video.
- [`sickness`](#sickness): The simulator sickess level the participant reported after watching the video.
- [`preference`](#preference): The preference level the participant reported after watching the video.
- [`exploration`](#exploration): The proportion of a full 360-degree horizontal sweep participant made watching the video
The first ten results are show below.
```{r}
psych_360 %>% head(10) %>% knitr::kable()
```
Each variable is shown descriptively and explored further below.
## Identification (Location, PID, and Order)
Note that the unique identifier for a participant is the pair of variables (location, pid).
```{r echo=TRUE}
# When grouping by PID only, how many PIDs have more than 5 rows associated with them?
psych_360 %>%
group_by(pid) %>%
count() %>%
filter(n != 5) %>%
nrow
# Instead, it's better to group by PID and Location if you want participants to be uniquely grouped
psych_360 %>%
group_by(pid, location) %>%
count() %>%
filter(n != 5) %>%
nrow()
```
## Video
```{r fig.height=10, fig.width=8}
psych_360 %>%
ggplot(aes(x=video)) +
geom_bar() +
coord_flip()
```
## Age
```{r}
psych_360 %>%
ggplot(aes(x=age)) +
geom_bar()
```
## Gender
```{r}
psych_360 %>%
ggplot(aes(x=gender)) +
geom_bar()
```
## Race
```{r}
psych_360 %>%
ggplot(aes(x=race)) +
geom_bar() +
coord_flip()
```
## Experience
```{r}
psych_360 %>%
ggplot(aes(x=experience)) +
geom_bar()
```
## Arousal
```{r}
psych_360 %>%
ggplot(aes(x=arousal)) +
geom_bar()
```
## Presence
```{r}
psych_360 %>%
ggplot(aes(x=presence)) +
geom_bar()
```
## Sickness
```{r}
psych_360 %>%
ggplot(aes(x=sickness)) +
geom_bar()
```
## Preference
```{r}
psych_360 %>%
ggplot(aes(x=preference)) +
geom_bar()
```
## Exploration
```{r}
psych_360 %>%
ggplot(aes(x=exploration)) +
geom_histogram(bins=20)
```