---
title: "Introduction to dataSDA"
author: "Po-Wei Chen"
date: "2023-06-13"
output: 
  rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Introduction to dataSDA}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

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

```{r setup, include = FALSE}
knitr::opts_chunk$set(echo = TRUE)
knitr::opts_chunk$set(warning = FALSE)
knitr::opts_chunk$set(message = FALSE)
knitr::opts_chunk$set(out.width = "100%")
knitr::opts_chunk$set(fig.align = 'center')
library(knitr)
library(dataSDA)
library(RSDA)
```

## Example of Interval-Valued Data
### Built-in Data

```{r}
data(mushroom)
head(mushroom)
```

### Set variable format

Changes the format of the set variables in the data to conform to the RSDA format.

+ `data` = the conventional dataframe
+ `location` = the location of the set variable in the data
+ `var` = the name of the set variable in the data

```{r}
mushroom.set <- set_variable_format(data = mushroom, location = 8, var = "Species")
head(mushroom.set)
```

### RSDA format

Changes the format of the data to conform to RSDA format.

+ `data` = the conventional dataframe
+ `sym_type1` = the labels I means an interval variable, and S means set variable in location
+ `location` = the location of the sym_type in the data
+ `sym_type2` = the labels I means an interval variable, and S means set variable in var
+ `var` = the name of the symbolic variable in the data
 
```{r}
mushroom.tmp <- RSDA_format(data = mushroom.set, sym_type1 = c("I", "S"),
                            location = c(25, 31), sym_type2 = c("S", "I", "I"),
                            var = c("Species", "Stipe.Length_min", "Stipe.Thickness_min"))
head(mushroom.tmp)
```

### Clean the column names

Clean up variable names to conform to the RSDA format.

+ `data` = the conventional dataframe

```{r}
mushroom.clean <- clean_colnames(data = mushroom.tmp)
head(mushroom.clean)
```

### Write a symbolic data table from a CSV data file

```{r, eval = FALSE}
write_csv_table(data = mushroom.clean, file = 'mushroom_interval.csv')
```

### Read the symbolic data table and check the format

```{r}
mushroom.int <- read.sym.table(file = 'mushroom_interval.csv', header = T, sep = ';', dec = '.', row.names = 1)
head(mushroom.int)
```

## Example of iGAP format Data
### Built-in Data

```{r}
data(Abalone.iGAP)
head(Abalone.iGAP)
```

### iGAP to MM

To convert iGAP files to CSV files.

+ `data` = the conventional dataframe(iGAP format)
+ `location` = the location of the symbolic variable in the data

```{r}
Abalone <- iGAP_to_MM(Abalone.iGAP, c(1, 2, 3, 4, 5, 6, 7))
head(Abalone)
```