---
title: "Geriatric Depression Scale"
subtitle: "Original version: Yesavage J A, 1983 - Norwegian version: Knut Engedal"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{gds}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

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


## Background

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


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

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

```{r}
library(questionnaires)

gds_values(yes = 1, no = 0)
gds_values(yes = "yes", no = "no")
gds_values(yes = "ja", no = "nei")
```


## Using the `gds` functions

```{r}
library(dplyr)
library(tidyr)

# randomly create some data as example
gds_data <- expand_grid(ID = 1:5, 
            key = "gds", 
            question =  sprintf("%02d", 1:30))
gds_data <- gds_data %>% 
    mutate(value = sample(c(0,1), 
                          nrow(gds_data), 
                          replace = TRUE)) %>% 
  unite(key, c(key, question)) %>% 
  spread(key, value)

gds_data
```

### Calculating the GDS sum
The function to calculate the total GDS score is `gds_compute_sum`, and requires a data frame, a tidy-selector that selects all the GDS columns and a tidy-selector indicating which columns should be reversed.
If you have NOAS-like data, then the defaults should work, and as such you need not specify this.

```{r}
gds_data %>% 
  gds_compute_sum()
```

To add a column with total GDS to a data set, place the argument inside a `mutate`, here a column named `gds_total` will appear as the right-most column.

```{r}
gds_data %>% 
  mutate(
    gds_total = gds_compute_sum(gds_data)
  )
```

### Turning the sum into a factor
Most often, people will report not only the actual score, but also the accompanying depression classification based on that score.
This can be derived by using the `gds_factorise` function.


```{r}
gds_data %>% 
  gds_compute_sum() %>% 
  gds_factorise()
```

and this can also be added directly to the data through a `mutate``

```{r}
gds_data %>% 
  mutate(
    gds_total = gds_compute_sum(gds_data),
    gds_cat =  gds_factorise(gds_total)
  )
```

### Computing score and factor in one function
Lastly, since it is most common to want both the score and the factor added directly do data, a convenience function exists to assist in that.

```{r}
gds_data %>% 
  gds_compute()
```

alternatively, you can also `keep_all = FALSE` in that function, to only retain the two computed columns, while maintaining the remaining data structure.

```{r}
gds_data %>% 
  gds_compute(keep_all = FALSE)
```



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