Prueba_Estetica.R

# ==========================================================
#                   Estructura Base Proyectos
# ==========================================================
# Autor: Natalia Valeria Torrico Saavedra
# Fecha: 2024/08/2024
# Descripcion: Este script realiza una estructura base para
#              la elaboracion de los codigos.
# ==========================================================

# 1. Clean Global Enviroment -------------------------------

remove(list = ls())

# 2. Cargar Librerias --------------------------------------

library(tidyverse) # Conjunto de herramientas

## Warning: package 'tidyverse' was built under R version 4.3.3

## Warning: package 'ggplot2' was built under R version 4.3.3

## Warning: package 'tibble' was built under R version 4.3.3

## Warning: package 'tidyr' was built under R version 4.3.3

## Warning: package 'readr' was built under R version 4.3.3

## Warning: package 'purrr' was built under R version 4.3.3

## Warning: package 'dplyr' was built under R version 4.3.3

## Warning: package 'forcats' was built under R version 4.3.3

## Warning: package 'lubridate' was built under R version 4.3.3

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.5.1     ✔ tibble    3.2.1
## ✔ lubridate 1.9.3     ✔ tidyr     1.3.1
## ✔ purrr     1.0.2     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

#                    para la ciencia de datos

# 3. Set Working Directory ---------------------------------

setwd("C:/Users/natur/Desktop/Proyectos de Estadística")

# 4. Cargas Datos Necesarios -------------------------------

load("mydata2.RData")

# 5. Preparacion de Datos ----------------------------------

x <- mydata2$x2 #Variable
y <- mydata2$y2 #Variable

n <- length(x) #Tamaño de la muestra

# 6. Desarrollo del Ejercicio ------------------------------

  # 6.1. Calcular las medias de x e y
  mean.x <- sum(x)/n #Media aritmetica mediante formula
  mean(x) #Media aritmetica mediante RStudio

## [1] 9

  mean(x) == mean.x #Verificacion de igualdad

## [1] TRUE

  mean.y <- sum(y)/n #Media aritmetica mediante formula
  mean(y) #Media aritmetica mediante RStudio

## [1] 7.500909

  mean(y) == mean.y #Verificacion de igualdad

## [1] TRUE

  # 6.2. Calcular desviacion estandar
  sd.x <- sqrt((sum((x-mean.x)^2))/(n-1))
  sd(x)

## [1] 3.316625

  sd(x) == sd.x

## [1] TRUE

  sd.y <- sqrt((sum((y-mean.y)^2))/(n-1))
  sd(y)

## [1] 2.031657

  sd(y) == sd.y

## [1] TRUE

  # 6.3. Calculo del Coeficiente de Correlacion de Pearson
  cor <- (sum((x-mean.x)*(y-mean.y))/((n-1)*sd.x*sd.y))
  cor(x, y)

## [1] 0.8162365

  cor == cor(x, y)

## [1] TRUE

# 7. Conclusiones -----------------------------------------
  
# Si no vemos los datos realmente no comprendemos ningun
# patron util de los mismos.
  
# ==========================================================
#                     FIN
# ==========================================================