---
title: "Cued Recall Example"
author: "Nicholas Maxwell, Erin Buchanan"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Cued Recall Example}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

# Libraries and Data

Please see manuscript for a long description of the following data. We will load the example data, and you can use the `?` with the dataset name to learn more about the data.  

```{r}
library(lrd)
data("cued_recall_manuscript")
head(cued_recall_manuscript)
#?cued_recall_manuscript
```

# Data Cleanup

Scoring in `lrd` is case sensitive, so we will use `tolower()` to lower case all correct answers and participant answers. 

```{r}
cued_recall_manuscript$Target <- tolower(cued_recall_manuscript$Target)
cued_recall_manuscript$Answer <- tolower(cued_recall_manuscript$Answer)
```

# Score the Data

You should define the following:

- data = dataframe of participant responses
- responses = column name of the participant answers
- key = column name of the answer key
- key.trial = column name of the trial id code
- id = column name of the participant id number
- id.trial = column name of the trial id within the participant data
- cutoff = the Levenshtein distance value you want to use for scoring (0 no changes exactly the same, higher numbers allow more variance in the word)
- flag = calculate z scores for outliers (TRUE/FALSE)
- group.by = column name(s) for grouping variables

Note that the answer key can be in a separate dataframe, use something like  `answer_key$answer` for the key argument and `answer_key$id_num` for the trial number. Fill in `answer_key` with your dataframe name and the column name for those columns after the `$`. 

```{r}
cued_output <- prop_correct_cued(data = cued_recall_manuscript,
                                 responses = "Answer",
                                 key = "Target",
                                 key.trial = "Trial_num",
                                 id = "Sub.ID",
                                 id.trial = "Trial_num",
                                 cutoff = 1,
                                 flag = TRUE,
                                 group.by = NULL)

str(cued_output)
```

# Output

We can use `DF_Scored` to see the original dataframe with our new scored column - also to check if our answer key and participant answers matched up correctly! The `DF_Participant` can be used to view a participant level summary of the data. Last, if a grouping variable is used, we can use `DF_Group` to see that output. 

```{r}
#Overall
cued_output$DF_Scored

#Participant
cued_output$DF_Participant
```