---
title: "Functions for descriptive statistics"
author: "Keon-Woong Moon"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{descStatistics}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(echo = TRUE,comment = NA,message=FALSE,warning=FALSE,fig.width=6,fig.height = 6, fig.align='center',out.width="70%")
```

You can make tables summarizing descriptive statistics easily with webr package.  

## Installation of packages

You have to install the latest versions of "webr" and "moonBook" packages from github. 

```{r,eval=FALSE}
if(!require(devtools)) install.packages("devtools")
devtools::install_github("cardiomoon/webr")
devtools::install_github("cardiomoon/moonBook")   # For examples
devtools::install_github("cardiomoon/rrtable")    # For reproducible research
```

## Load packages

```{r,message=FALSE}
require(webr)
require(moonBook) # For data acs
```

## Summarizing Frequencies

You can summmarize the frequencies easily with freqSummary() function. Also you can make a table summarizng frequencies with freqTable() function. 

```{r}
freqSummary(acs$Dx)
freqTable(acs$Dx)
```


### Ready for reproducible research

The freqTable() function returns an object of class "flextable". With this object, you can make html, pdf, docx, pptx file easily.    

```{r}
result=freqTable(acs$Dx)
class(result)
```

### Frequency table for a continuous variable

You can make the frequency table for a continuous variable. In this time, you can get a long table.

```{r}
freqTable(mtcars$mpg)
```

## Frequency table for two categorical variables

You can make a table summarizing the independency of two categorical variables.

```{r}
x2Table(acs,Dx,sex)
```


You can make a table with columnwise percentages.

```{r}
x2Table(acs,Dx,sex,margin=2)
```

You can hide pecentages.

```{r}
x2Table(acs,Dx,sex,show.percent=FALSE)
```


## Numerical summary

### Numerical summary of a vector

You can make a numerical summary table with numSummary() function. If you use the numSummary() function to a continuous vector, you can get the following summary. This function uses psych::describe function

```{r,message=FALSE}
require(dplyr)
numSummary(acs$age)
numSummaryTable(acs$age)
```



### Numerical summary of a data.frame or a tibble 

You can make a numerical summary of a data.frame. The numSummary function uses is.numeric function to select numeric columns and make a numeric summary.

```{r}
numSummary(acs)
numSummaryTable(acs)
```

### Use of dplyr::group_by() and dplyr::select() function to summarize

You can use dplyr::select() function to select variables to summarize.

```{r}
acs %>% select(age,EF) %>% numSummary
acs %>% select(age,EF) %>% numSummaryTable
```

You can use dplyr::group_by() and dplyr::select() function to select variables to summarize by group.

```{r}
acs %>% group_by(sex) %>% select(age,EF) %>% numSummary
acs %>% group_by(sex) %>% select(age,EF) %>% numSummaryTable
```

You can summarize by multiple groups.

```{r}
acs %>% group_by(sex,Dx) %>% select(age,EF) %>% numSummary
acs %>% group_by(sex,Dx) %>% select(age,EF) %>% numSummaryTable
```

## For reproducible research

You can use package `rrtable` for reproducible research. 

```{r}
require(rrtable)
type=c("table","table")
title=c("Frequency Table","Numerical Summary")
code=c("freqTable(acs$Dx)","acs %>% group_by(sex) %>% select(EF,age) %>% numSummaryTable")
data=data.frame(type,title,code,stringsAsFactors = FALSE)
data2pptx(data)
data2docx(data)

```