---
title: "learningRlab"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Introduction to LearningRlab}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

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

There are three families of fuctions in LearningRlab:

1. Main functions: these functions return the result of performing the process represented with the function.

1. Explained fuctions: these funcions returns the process itself to get the result, with the result.

1. User Interactive Functions: these functions maintain an interactive contact with the user to guide him in the resolution of the represented function.

## Main Functions:

To explain the use of each function, we present a dataset to work with them:

```{r}
data <- c(1,1,2,3,4,7,8,8,8,10,10,11,12,15,20,22,25)
plot(data); 
data2 <- c(1,1,4,5,5,5,7,8,10,10,10,11,20,22,22,24,25)
plot(data2);

#Binomial variables
n = 3
x = 2
p = 0.7
    
#Poisson variables
lam = 2
k = 3

#Normal variables
nor = 0.1

#T-Student variables
xt = 290 
ut = 310
st = 50
nt = 16

```

The arithmetic mean calculus function:
```{r}
mean_(data)

```

The geometric mean calculus function:
```{r}
geometricMean_(data)

```

The mode calculus function:
```{r}
mode_(data)

```

The median calculus function:
```{r}
median_(data)

```

The standard deviation calculus function:
```{r}
standardDeviation_(data)

```

The average absolute deviation calculus function:
```{r}
averageDeviation_(data)

```

The variance calculus function:
```{r}
variance_(data)

```

The quartiles calculus function:
```{r}
quartile_(data)

```

The percentile calculus function:
```{r}
percentile_(data,0.3)

```

The absolute frecuency calculus function:
```{r}
frecuency_abs(data,1)

```

The relative frecuency calculus function:
```{r}
frecuency_relative(data,20)

```

The absolute acumulated frecuency calculus function:
```{r}
frecuency_absolute_acum(data,1)

```

The relative acumulated frecuency calculus function:
```{r}
frecuency_relative_acum(data,20)

```


The covariance calculus function:
```{r}
covariance_(data, data2)

```

The harmonic mean calculus funtion:
```{r}
harmonicMean_(data)

```

The pearson correlaction calculus funtion:
```{r}
pearson_(data,data2)


```

The coefficient of variation calculus funtion:
```{r}
cv_(data)


```

The Laplace rule calculus funtion:
```{r}
laplace_(data,data2)


```

The binomial distribution calculus funtion:
```{r}
binomial_(n,x,p)


```

The poisson distribution calculus funtion:
```{r}
poisson_(k,lam)


```

The normal distribution calculus funtion:
```{r}
normal_(nor)


```

The tstudent distribution calculus funtion:
```{r}
tstudent_(xt,ut,st,nt)


```

The chisquared distribution calculus funtion:
```{r}
chisquared_(data,data2)


```

The fisher distribution calculus funtion:
```{r}
fisher_(data,data2)


```


##Explained Functions:

For each main function, there are an explained function to see the calculus process:

- arithmetic mean:
```{r}
explain.mean(data)

```

- geometric mean:
```{r}
explain.geometricMean(data)

```

- mode:
```{r}
explain.mode(data)

```

- median:
```{r}
explain.median(data)

```

- standard deviation:
```{r}
explain.standardDeviation(data)

```

- average absolute deviation:
```{r}
explain.averageDeviation(data)

```

- variance:
```{r}
explain.variance(data)

```

- quartile:
```{r}
explain.quartile(data)

```

- percentile:
```{r}
explain.percentile(data)

```

- absolute frecuency:
```{r}
explain.absolute_frecuency(data,10)

```

- relative frecuency:
```{r}
explain.relative_frecuency(data,8)

```

- absolute acumulated frecuency:
```{r}
explain.absolute_acum_frecuency(data,10)

```

- relative acumulated frecuency:
```{r}
explain.relative_acum_frecuency(data,8)

```

- covariance:
```{r}
explain.covariance(data,data2)

```

- harmonic mean:
```{r}
explain.harmonicMean(data)


```

- pearson correlaction:
```{r}
explain.pearson(data,data2)


```

- coefficient of variation:
```{r}
explain.cv(data)


```

- Laplace rule:
```{r}
explain.laplace(data,data2)


```

- binomial distribution:
```{r}
explain.binomial(n,x,p)


```

- poisson distribution:
```{r}
explain.poisson(k,lam)


```

- normal distribution:
```{r}
explain.normal(nor)


```

- tstudent distribution:
```{r}
explain.tstudent(xt,ut,st,nt)


```

- chisquared distribution:
```{r}
explain.chisquared(data,data2)


```

- fisher distribution:
```{r}
explain.fisher(data,data2)


```


##User Interactive Functions:

These functions are designed for the user to practice with them, and they are the following:

- interactive.mean()
- interactive.geometricMean()
- interactive.mode()
- interactive.median()
- interactive.standardDeviation()
- interactive.averageDeviation()
- interactive.variance()
- interactive.quartile()
- interactive.percentile()
- interactive.absolute_frecuency()
- interactive.relative_frecuency()
- interactive.absolute_acum_frecuency()
- interactive.relative_acum_frecuency()
- interactive.covariance()
- interactive.harmonicMean()
- interactive.pearson()
- interactive.cv()
- interactive.laplace()
- interactive.binomial()
- interactive.poisson()
- interactive.normal()
- interactive.tstudent()
- interactive.chisquared()
- interactive.fisher()