anony_implementation.Rmd

---
title: "anony_implementation"
author: "Yong Cha"
output: 
  html_document:
    toc: yes
    toc_depth: 2
    number_sections: true
    
---


# Package Loading
```{r package, message=FALSE}
require(devtools)
install_github('entroPD', 'yongcha')
require(entroPD)
```



# Implementation

## Data Import
```{r load, echo=FALSE}
load('./data/data_adult.Rdata')
```

```{r data}
str(adult)
```


## Partition Data

### Define Attributes

* 데이터를 파티션하기 위헤 변수의 속성을 정의해줌
    * __Attributes (or Identifier)__ : (확실한) 식별자
        * SSN, Address, Name 등
    * __Target Attributes (TA)__ : 분석할 때 반응변수에 해당되는 목표 변수 
    * __Quasi-Identifiers (QI)__ : 불분명한 식별자
        * zip code, birthdate, gender 등
    * __Sensitive Attirbutes (SA)__ : 개인과 관련된 민감한 정보
        * 질병질환 정보, 연봉 등
    * __Insensitive Attributes (IS)__ : 민감하지 않은 기타 정보
    

```{r part}
# TA (Target)
TA <- c('income')
# QI (Quasi-Identifiers)
QI <- c('age', 'workclass', 'education', 'marital.status', 'occupation', 'race', 'sex', 'native.country')
#QI <- c('age', 'workclass', 'education', 'marital.status')
# SA (Sensitive Attributes)
SA <- c('capital.gain', 'capital.loss')
# IS (Insensitive)
IS <- c('relationship', 'hours.per.week', 'education.num')
# Remove
RM <- c('fnlwgt')
```

### `parda` 함수 적용

```{r parda}
adult.QI <- parda(adult, QI, TA)
str(adult.QI)
```



## `cpselec` 함수 적용

```{r cpselec}
cp.val <- cpselec(10, 100, 2)
cp.val
```



## `rpaclass` 함수 적용

### `mclapply` 적용

```{r rpamc, eval=FALSE}
cp.res.mc <- rpaclass(adult.QI, cp.val, matrix(c(0,1,2,0), ncol=2), cval = 100, mc = TRUE)
```

### `lapply` 적용 

```{r rpal}
cp.res.la <- rpaclass(adult.QI, cp.val, matrix(c(0,1,2,0), ncol=2), cval = 100)
cp.res.la[[1]]
```


## `lanode` 함수 적용

```{r lanode}
node.res <- lanode(cp.res.la)
node.res[[30]]
```


## `qigrp` 함수 적용

```{r qigrp, results='asis'}
qigrp.res <- qigrp(adult.QI, node.res, mcs=8)
knitr::kable(head(qigrp.res[[30]], 20))
```


## `eclass` 함수 적용

```{r eclass, results='asis'}
equiclass <- eclass(qigrp.res, QI, TA)
knitr::kable(head(equiclass[[30]], 20))
```



## `resume` 함수 적용

```{r resume, results='asis'}
measure.res <- resume(equiclass, kanon = 5, maxsup = 0.01)
knitr::kable(head(measure.res, 20))
```


## `plot.resume` 함수 적용
```{r plot, message=FALSE, fig.align='center', fig.height=7, fig.width=11, fig.cap='Figure 1'}
plot(measure.res)
```