---
title: "Big Five Inventory (BFI-2)"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{bfi-2}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  eval = FALSE,
  comment = "#>"
)
options(rmarkdown.html_vignette.check_title = FALSE)
frag_path <- here::here("man", "fragments", "bfi")
paths <- list.files(frag_path, "Rmd", full.names = TRUE)
names(paths) <- gsub("\\.Rmd", "", basename(paths))
paths <- as.list(paths)
```

```{r setup, eval = TRUE}
library(questionnaires)
```

## Background

```{r child=paths$background, eval = TRUE} 
```

## Scoring
```{r child=paths$scoring, eval = TRUE} 
```


## Data requirements  

```{r child=paths$datareq, eval = TRUE} 
```

## Using the `bfi` functions

## Computing all domains and facets.

```{r}
# Making some test data
test_data <- tibble(
  id = rep(1:10, each = 60),
  name = rep(sprintf("bfi_%02d", 1:60), 10),
  value = lapply(1:10, function(x){
    sample(1:5, size = 60, replace = TRUE)
  }) %>% unlist()
) %>% 
  tidyr::pivot_wider()

test_data
```

To compute all the possible domains and facets, as long as the data is set up correctly, you can run a single function

```{r}
bfi_compute(test_data)
```

You also have some options in terms of prefixing the data, and if you want to keep all the original data in the output as well.

```{r}
bfi_compute(test_data, keep_all = TRUE)

bfi_compute(test_data, prefix = "bfi_")
```

### Domains

There are two main ways you can compute domains alone.
These two ways are equivalent, and take `prefix` and `keep_all` arguments.

```{r}
bfi_compute(test_data, type = "domains")

bfi_compute_domains(test_data)
```

You can also choose to compute only certain domains, either by calling on their own special function or by specifying a domain in the main domain function.

```{r}
## Computes all
bfi_compute_domains(test_data, domains = c("extraversion", 
                "agreeableness",
                "conscientiousness",
                "negative emotionality",
                "open-mindedness"))

## extraversion and agreeableness only
bfi_compute_domains(test_data, domains = c("extraversion", 
                "agreeableness"))

## only agreeableness, returned as a vector, not a data.frame
bfi_domain_agreeable(test_data)

## So it can be used in a mutate, but, awkwardly
mutate(test_data,
  agree = bfi_domain_agreeable(test_data)) %>% 
  select(agree, everything())
```

### Facets

There are two main ways you can compute facets alone.
These two ways are equivalent, and take `prefix` and `keep_all` arguments.

```{r}
bfi_compute(test_data, type = "facets")

bfi_compute_facets(test_data)
```

You can also choose to compute only certain domains, either by calling on their own special function or by specifying a domain in the main domain function.

```{r}
## Computes all
bfi_compute_facets(test_data, 
                   facets = c("sociability", 
                "assertiveness",
                "energy",
                "compassion",
                "respectful",
                "trust", 
                "organization",
                "productive",
                "responsibility",
                "anxiety",
                "depression", 
                "emotional volatility",
                "intellectual curiosity",
                "aesthetic sensebility",
                "creative imagination"))

## extraversion and agreeableness only
bfi_compute_facets(test_data, 
                    facets = c("anxiety",
                "depression"))

## only anxiety, returned as a vector, not a data.frame
bfi_facet_anxiety(test_data)

## So it can be used in a mutate, but, awkwardly
mutate(test_data,
  anx = bfi_domain_agreeable(test_data)) %>% 
  select(anx, everything())
```

## References

```{r child=paths$references, eval = TRUE} 
```