---
title: "Custom table summarizing outcomes"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Custom summary statistics}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---
  
```{r setup, include=FALSE}
library(rmarkdown)
library(SmartEDA)
library(knitr)
library(ISLR)
library(scales)
library(gridExtra)
library(ggplot2)

```

## 1. Exploratory analysis - Custom tables, summary statistics

In this vignette we will discuss about how to customize the summary statistics using *ExpCustomStat* function from `SmartEDA`. The output of this function returns matrix object containing descriptive information on all input variables for each level/combination of levels in categorical/group variable. Also, while running the analysis we can filter row/cases of the data. We can apply the filters at individual variable level or complete data like base subsetting.

Function definition:
```
ExpCustomStat(data,Cvar=NULL,Nvar=NULL,stat=NULL,gpby=TRUE,filt=NULL,dcast=FALSE)
```
Key functionalities of `ExpCustomStat` are:

1. Categorical data descriptive statistics (Frequencies, Proportions)
2. Numerical data descriptive statistics (Mean, Median, Sum, Variance etc..)
3. Comparison of numerical data based on categorical data
4. Filter rows/cases where conditions are true. Options to apply filters at variable level or complete data set like base subsetting
5. Options to calculate basic statistics like Mean, Median, Std.Dev, Variance, Count, Proportions, Quantiles, IQR, Percentages of Shares (PS) for numerical data

### 1.1 Usage of `ExpCustomStat` function

Will open the carseats data from ISLR package and drive different types of use cases using `ExpCustomStat` function. 

In this vignette, we will be using a simulated data set containing sales of child car seats at 400 different stores. 

Data Source [ISLR package](https://www.rdocumentation.org/packages/ISLR/versions/1.2/topics/Carseats).

Function source [SmartEDA package](https://www.rdocumentation.org/packages/SmartEDA)

Carseats data from ISLR package:
```{r}
options(width = 150)
CData = ISLR::Carseats
head(CData,5)
```
## 2. Categorical summaries
Categorical summaries to describe the distribution for a qualitative variables.

* Count (Frequency) the number of subjects in each group
* The proportions in each group (Relative frequency)
* Tabulation or custom tables for more than two categorical variables

### 2.1. Frequency table
The number of observations for particular category

* Base: All carseats data
* Table: Calculating the count and proportions of `"US", "Urban" and "ShelveLoc"` variable
```{r}
ExpCustomStat(Carseats,Cvar=c("US","Urban","ShelveLoc","Education"),gpby=FALSE)
```

OR we can use similar analysis using `ExpCTable` function from same package, this functions includes cumulative percentages and Total

```{r}
ExpCTable(Carseats,Target=NULL,clim=5,nlim=15,round=2,bin=NULL,per=F)
```


* Base: All carseats data
* Table: Calculating the count and proportions of `"US", "Urban" and "ShelveLoc"` variable
```{r}
ExpCustomStat(Carseats,Cvar=c("US","Urban","ShelveLoc"),gpby=FALSE)
```

### 2.2. Crosstabulation (more than one categorical variable)
To produce cross tables which calculate counts and proportions for each combination of categorical variables we can use *ExpCustomStat*

NOTE: For crosstabulation change input `gpby`=TRUE

* Base: All carseats data
* Table: Calculating the count and proportions of `"US" Vs "Urban"`
```{r}
ExpCustomStat(Carseats,Cvar=c("US","Urban"),gpby=TRUE,filt=NULL)
```

We can also produce multidimensional tables based on three or more categorical variables

* Base: All carseats data
* Table: Calculating the count and proportions of `"US" Vs "Urban" Vs "ShelveLoc"`
```{r}
ExpCustomStat(Carseats,Cvar=c("US","Urban","ShelveLoc"),gpby=TRUE,filt=NULL)
```
### 2.3. Adding filters to tables
If we want to understand the number of stores in `US` and location is `Urban` for `Population` size greater than 150

* Base: Those stores having their average age of the local `Population` is > 150 
* Table: Calculating the count and proportions of `"US" Vs "Urban"`
```{r}
ExpCustomStat(Carseats,Cvar=c("US","Urban"),gpby=TRUE,filt="Population>150")
```

* Base: Those stores having their average age of the local `Population` > 150 and `Urban`=="Yes"
* Table: Calculating the count and proportions of `"US" Vs "ShelveLoc"`
```{r}
ExpCustomStat(Carseats,Cvar=c("US","ShelveLoc"),gpby=TRUE,filt="Urban=='Yes' & Population>150")
```

## 3. Numerical summaries
Numerical summaries to describe the distribution for quantitative variables.

* Measure of location (Mean, median)
* Measure of variation (Variance, Std.Dev, Range)
* Measure of shapes (Skewness and Kurtosis)
* Other summary statistics

### 3.1. Numerical variable summary 
* Base: All carseats data
* Table: Descriptive summary for Price, Population, Sales, CompPrice, Income

```{r}
options(width = 150)
ExpCustomStat(Carseats,Nvar=c("Population","Sales","CompPrice","Income"),stat = c('Count','mean','sum','var','sd','min','max','IQR'))
```

* Base: All carseats data
* Table: Tukey's five number summary (minimum, lower-hinge, median, upper-hinge, maximum) for Price, Population, Sales, CompPrice, Income

```{r}
ExpCustomStat(Carseats,Nvar=c("Population","Sales","CompPrice","Income"),stat = c('min','p0.25','median','p0.75','max'))
```

### 3.2. Adding filters to complete data (like base subset)
Filter rows/cases of complete dataset where conditions are true

* Base: Whether the store is in urban (Urban ==Yes)
* Table: Descriptive summary for Price, Population, Sales, CompPrice, Income. 

```{r}
options(width = 150)
ExpCustomStat(Carseats,Nvar=c("Population","Sales","CompPrice","Income"),stat = c('Count','mean','sum','var','min','median','max'),filt="Urban=='Yes'")
```

* Base: Those store having their average age of the local population > 150 and Urban=="Yes"
* Table: Descriptive summary for Price, Population, Sales, CompPrice, Income. 

```{r}
options(width=150)
ExpCustomStat(Carseats,Nvar=c("Population","Sales","CompPrice","Income"),stat = c('Count','mean','sum','median','IQR'),filt="Urban=='Yes' & Population>150")
```

### 3.3. Filter out unique value from all the numeric variables
This will be useful when we need to exclude redundant values like '999' or '9999' or '-9' or '-1111', or '888' etc from each selected variable.

Eg:dat = data.frame(x = c(23,24,34,999,12,12,23,999,45),
y = c(1,3,4,999,0,999,0,8,999,0)

Exclude 999: 

x = c(23,24,34,12,12,23,45)
y = c(1,3,4,0,0,8,0)

```{r}
data_sam = Carseats[,]
data_sam[sample(1:400,30),"Sales"] <- 999
data_sam[sample(1:400,20),"CompPrice"] <- -9
data_sam[sample(1:400,45),"Income"] <- 999
ExpCustomStat(data_sam,Nvar=c("Population","Sales","CompPrice","Income"),stat = c('Count','mean','sum','min'),filt="All %ni% c(999,-9)")
```

### 3.4. Adding filters at variable level
Different filters for each numeric variable. For example, below are the conditions (logic) for each variable summary analysis.

`"Population" - Consider only Good ShelveLoc (the quality of the shelving location for the car seats at each site) ShelveLoc=='Good'`

`"Sales" - Inculde only those store belongs to Urban location (Urban==Yes)`

`"CompPrice" - Exclude Price is greater than 150`

`"Education" - All stores`

`"Income" - Inculde only stores in US (US==Yes)`

Table: Descriptive summary for Price, Population, Sales, CompPrice, Income based on the filters.

```{r}
options(width = 150)
ExpCustomStat(Carseats,Nvar=c("Population","Sales","CompPrice","Education","Income"),stat = c('Count','mean','sum','var','sd','IQR','median'),filt=c("ShelveLoc=='Good'^Urban=='Yes'^Price>=150^All^US=='Yes'"))
```
## 4. Numerical summaries by category
Descriptive summary for numerical variable by group level. 

### 4.1. Variable summary report (One group variable)
* Base: All carseats data
* Table: Descriptive summary for Price, Population, Sales by group (Urban, ShelveLoc)

```{r}
options(width = 150)
ExpCustomStat(Carseats,Cvar = c("Urban","ShelveLoc"), Nvar=c("Population","Sales"), stat = c('Count','Prop','mean','min','P0.25','median','p0.75','max'),gpby=FALSE)
```
### 4.2. Variable summary report (More than One group variable)
* Base: All carseats data
* Table: Descriptive summary for CompPrice, Income by group (Urban){PS: Percentages of shares}

```{r}
options(width = 150)
ExpCustomStat(Carseats,Cvar = c("Urban","US","ShelveLoc"), Nvar=c("CompPrice","Income"), stat = c('Count','Prop','mean','sum','PS','min','max','IQR','sd'), gpby = TRUE)
```
### 4.3. Variable summary report (More than One group variable) with filter
* Base: Stores in Urban location only
* Table: Descriptive summary for CompPrice, Income by group (Urban){PS: Percentages of shares}

```{r}
options(width = 150)
ExpCustomStat(Carseats,Cvar = c("Urban","US","ShelveLoc"), Nvar=c("CompPrice","Income"), stat = c('Count','Prop','mean','sum','PS','median','IQR'), gpby = TRUE,filt="Urban=='Yes'")
```

* Base: Removing redundant values
* Table: Descriptive summary for CompPrice, Income by group (Urban){PS: Percentages of shares}

```{r}
options(width = 150)
data_sam = Carseats[,]
data_sam[sample(1:400,30),"Sales"] <- 888
data_sam[sample(1:400,20),"CompPrice"] <- 999
data_sam[sample(1:400,45),"Income"] <- 999
ExpCustomStat(data_sam,Cvar = c("Urban","US","ShelveLoc"), Nvar=c("Sales","CompPrice","Income"), stat = c('Count','Prop','mean','sum','PS'), gpby = TRUE,filt="All %ni% c(888,999)")
```
Different base for each numeric variable. 

`"Population" - Consider only Good ShelveLoc (the quality of the shelving location for the car seats at each site) ShelveLoc=='Good'`

`"Sales" - Inculde only those store belongs to Urban location (Urban==Yes)`

`"CompPrice" - Exclude Price is greater than 150`

```{r}
ExpCustomStat(Carseats,Cvar = c("Urban","US"), Nvar=c("Population","Sales","CompPrice"), stat = c('Count','Prop','mean','sum','var','IQR'), filt=c("ShelveLoc=='Good'^Urban=='Yes'^Price>=150"))
```

## 5. Resahpe data 
Reshapes a grouped data

```{r}
options(width = 150)
ExpCustomStat(Carseats,Cvar = c("Urban"), Nvar=c("Population","Sales"), stat = c('Count','Prop'),gpby=TRUE,dcast=TRUE)
```

##### Example scripts

```{r, warning=FALSE,eval=F,include=T}
##Frequency table for categorical variables
ExpCustomStat(Carseats,Cvar=c("US","Urban","ShelveLoc"),gpby=FALSE)

##Crosstabulation between categorical variables
ExpCustomStat(Carseats,Cvar=c("US","Urban"),gpby=TRUE,filt=NULL)
ExpCustomStat(Carseats,Cvar=c("US","Urban","ShelveLoc"),gpby=TRUE,filt=NULL)

##Adding filters for custom tables
ExpCustomStat(Carseats,Cvar=c("US","Urban"),gpby=TRUE,filt="Population>150")
ExpCustomStat(Carseats,Cvar=c("US","ShelveLoc"),gpby=TRUE,filt="Urban=='Yes' & Population>150")

## Numeric variable summary
ExpCustomStat(Carseats,Nvar=c("Population","Sales","CompPrice","Income"),stat = c('Count','mean','sum','var','min','max'))
ExpCustomStat(Carseats,Nvar=c("Population","Sales","CompPrice","Income"),stat = c('min','p0.25','median','p0.75','max'))

## Adding filters for complete data (like base Subset)
ExpCustomStat(Carseats,Nvar=c("Population","Sales","CompPrice","Income"),stat = c('Count','mean','sum','var'),filt="Urban=='Yes'")
ExpCustomStat(Carseats,Nvar=c("Population","Sales","CompPrice","Income"),stat = c('Count','mean','sum'),filt="Urban=='Yes' & Population>150")

## Filter unique value from all the numeric variables
ExpCustomStat(data_sam,Nvar=c("Population","Sales","CompPrice","Income"),stat = c('Count','mean','sum','min'),filt="All %ni% c(999,-9)")

## Adding filters at variable level
ExpCustomStat(Carseats,Nvar=c("Population","Sales","CompPrice","Education","Income"),stat = c('Count','mean','sum','var','sd','IQR','median'),filt=c("ShelveLoc=='Good'^Urban=='Yes'^Price>=150^ ^US=='Yes'"))

##Numerical summaries by category
##Variable summary report (One group variable)
ExpCustomStat(Carseats,Cvar = c("Urban","ShelveLoc"), Nvar=c("Population","Sales"), stat = c('Count','Prop','mean','min','P0.25','median','p0.75','max'),gpby=FALSE)

##Variable summary report (More than One group variable)
ExpCustomStat(Carseats,Cvar = c("Urban","US","ShelveLoc"), Nvar=c("CompPrice","Income"), stat = c('Count','Prop','mean','sum','PS','min','max','IQR','sd'), gpby = TRUE)

##Variable summary report (More than One group variable) with filter
ExpCustomStat(Carseats,Cvar = c("Urban","US","ShelveLoc"), Nvar=c("CompPrice","Income"), stat = c('Count','Prop','mean','sum','PS','P0.25','median','p0.75'), gpby = TRUE,filt="Urban=='Yes'")
ExpCustomStat(data_sam,Cvar = c("Urban","US","ShelveLoc"), Nvar=c("Sales","CompPrice","Income"), stat = c('Count','Prop','mean','sum','PS'), gpby = TRUE,filt="All %ni% c(888,999)")
ExpCustomStat(Carseats,Cvar = c("Urban","US"), Nvar=c("Population","Sales","CompPrice"), stat = c('Count','Prop','mean','sum','var','min','max'), filt=c("ShelveLoc=='Good'^Urban=='Yes'^Price>=150"))

```

##### References

* ‘data.table’ version 1.11.0 [data.table package](https://www.rdocumentation.org/packages/data.table).
