---
title: "Reliability of SAM threat and challenge and COPE"
output: html_notebook
---


```{r libraries, message=FALSE, warning=FALSE, include=FALSE, cache=FALSE}
library(conflicted)
library(here)
library(tidyverse)
library(magrittr)
library(lavaan)
library(kableExtra)

conflicts_prefer(
    readr::col_factor,
    purrr::discard,
    dplyr::filter,
    dplyr::lag,
    purrr::set_names,
    tidyr::extract,
    kableExtra::group_rows
)
```

```{r style, include=FALSE, cache=FALSE}
styler::style_file(
    here("statistical_analysis", "scale_reliability.Rmd"),
    scope = "tokens",
    indent_by = 4L
)
```

The data were preprocessed and cleaned using [expl_esm_labels.py](../exploration/expl_esm_labels.py) script and read as csv here.

```{r read_data}
COL_TYPES <- cols(
    .default = col_double(),
    participant_id = col_factor(),
    username = col_factor(),
    device_id = col_factor(),
    esm_trigger = col_factor(),
    esm_instructions = col_factor(),
    double_esm_user_answer_timestamp = col_double(),
    datetime_lj = col_datetime(format = ""),
    date_lj = col_date(format = ""),
    time = col_factor(),
    esm_user_answer = col_factor()
)
df_SAM <- read_csv(here("data", "raw", "df_esm_SAM_threat_challenge.csv"), col_types = COL_TYPES)
df_COPE <- read_csv(here("data", "raw", "df_esm_COPE.csv"), col_types = COL_TYPES)
```

Demonstrate factor analysis for a single participant.

```{r}
df_COPE %>%
	group_by(question_id, questionnaire_id) %>%
	count()
```
