Present results for stressful events.

ml_pipeline
junos 2023-01-04 20:00:08 +01:00
parent af6843634c
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/data/30min* /data/30min*
/presentation/*scores.csv /presentation/*scores.csv
/presentation/Results.ods /presentation/Results.ods
.Rproj.user
/presentation/*.nb.html

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---
title: "Stressful event detection"
output: html_notebook
---
```{r chunk_options, include=FALSE}
knitr::opts_chunk$set(
comment = "#>", echo = FALSE, fig.width = 6
)
```
```{r libraries, include=FALSE}
library(knitr)
library(kableExtra)
library(RColorBrewer)
library(magrittr)
library(tidyverse)
```
```{r fig_setup, include=FALSE}
accent <- RColorBrewer::brewer.pal(7, "Accent")
```
```{r read_data, include=FALSE}
podatki <- read_csv("E:/STRAWresults/stressfulness_event_with_target_0_ver2/input_appraisal_stressfulness_event_mean.csv")
podatki %<>% mutate(pid = as_factor(pid))
```
# Event descriptions
Participants were asked "Was there a particular event that created tension in you?" with the following options:
- 0 - No
- 1 - Yes, slightly
- 2 - Yes, moderately
- 3 - Yes, considerably
- 4 - Yes, extremely
If they answered anything but "No", they were also asked about the event's perceived threat (e.g. "Did this event make you feel anxious?") and challenge (e.g. "How eager are you to tackle this event?").
We only consider general "stressfulness" in this presentation.
Most of the time, nothing stressful happened:
```{r target_table}
kable(table(podatki$target), col.names = c("stressfulness", "frequency")) %>%
kable_styling(full_width = FALSE)
```
Most participants had somewhere between 0 and 10 stressful events.
```{r target_distribution}
podatki %>%
group_by(pid) %>%
summarise(no_of_events = sum(target > 0)) %>%
ggplot(aes(no_of_events)) +
geom_histogram(binwidth = 1, fill = accent[1]) +
coord_cartesian(expand = FALSE) +
labs(x = "Number of events per participant") +
theme_classic()
```
When a stressful event occurred, participants mostly perceived it as slightly to moderately stressful on average.
```{r mean_stressfulness_distribution}
podatki %>%
filter(target > 0) %>%
group_by(pid) %>%
summarise(mean_stressfulness = mean(target)) %>%
ggplot(aes(mean_stressfulness)) +
geom_histogram(binwidth = 0.1, fill = accent[1]) +
coord_cartesian(expand = FALSE) +
labs(x = "Mean stressfulness per participant") +
theme_classic()
```
# Problem description
We are trying to predict whether a stressful event occurred, i.e. stressfulness > 0, or not (stressfulness == 0).
First

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