¿Qué relación tiene el PIB por entidad federativa con la educación?

¿Contar con un alto índice de desarrollo en México es sinónimo de contar con una mejor educación?

¿Cuál es el promedio de asistencias y no asistencias en el país enfocado en el sector educativo?

• Librerias y paquetes

setwd("~/ESTADISTICA APLICADA/U1A16")
library(pacman) 
p_load("base64enc", "htmltools", "mime", "xfun", "prettydoc","readr", "knitr","DT","dplyr", "ggplot2","plotly", "gganimate","gifski","scales")
library(hpackedbubble)

## 
## Attaching package: 'hpackedbubble'

## The following object is masked from 'package:datasets':
## 
##     CO2

library(gplots)

## 
## Attaching package: 'gplots'

## The following object is masked from 'package:stats':
## 
##     lowess

library(psych)

## 
## Attaching package: 'psych'

## The following objects are masked from 'package:scales':
## 
##     alpha, rescale

## The following objects are masked from 'package:ggplot2':
## 
##     %+%, alpha

library(GGally)

## Registered S3 method overwritten by 'GGally':
##   method from   
##   +.gg   ggplot2

library(car)

## Loading required package: carData

## 
## Attaching package: 'car'

## The following object is masked from 'package:psych':
## 
##     logit

## The following object is masked from 'package:dplyr':
## 
##     recode

library(gvlma)
library(strucchange)

## Loading required package: zoo

## 
## Attaching package: 'zoo'

## The following objects are masked from 'package:base':
## 
##     as.Date, as.Date.numeric

## Loading required package: sandwich

#library(tidyverse)
#library(hexbin)
library(xtable)
library(extrafont)

## Registering fonts with R

library(normtest)

Gráfica 1

library("readxl")
mydata <- read_excel(file.choose(), sheet = 1)
head(mydata,n=20L)

## # A tibble: 20 x 7
##    PAIS    ANO   IDH    CF   PPC SALUD  EDUC
##    <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
##  1 ARG    2000 0.77      0  8224   696   377
##  2 ARG    2001 0.775     0  7776   670   376
##  3 ARG    2002 0.77      0  6854   526   275
##  4 ARG    2003 0.775     0  7380   526   261
##  5 ARG    2004 0.775     0  7962   557   278
##  6 ARG    2005 0.777     0  8578   654   331
##  7 ARG    2006 0.801     0  9175   694   379
##  8 ARG    2007 0.806     0  9902   754   442
##  9 ARG    2008 0.81      1 10201   782   494
## 10 ARG    2009 0.81      1  9502   856   526
## 11 ARG    2010 0.818     1 10386   894   521
## 12 ARG    2013 0.823     1 10883   914   576
## 13 ARG    2012 0.823     1 10650   895   569
## 14 ARG    2013 0.824     1 10785    14   586
## 15 ARG    2014 0.825     1 10399   857   558
## 16 ARG    2015 0.828     0 10568   924   610
## 17 ARG    2016 0.828     0 10239   773   571
## 18 ARG    2017 0.832     0 10404   785   573
## 19 ARG    2018 0.83      0 10044   758   553
## 20 COL    2000 0.662     0  4858   277   171

pais =  mydata$PAIS
ppc =  mydata$PPC

x = mydata[c(3,6,7)]
pairs.panels(x,gap=0,method="pearson",lm=FALSE,hist.col="pink")

Población de 3 años y más por entidad federativa según condición de asistencia escolar, 2020

library(readr)
educacion <- read_xlsx("educacion.xlsx")

fig <- plot_ly(educacion, x = ~Entidad, y =~Asiste, type = 'bar', name = 'Asiste')
fig <- fig %>% add_trace(y = ~Noasiste, name = 'Noasiste')
fig <- fig %>% layout(yaxis = list(title = 'Índice'), barmode = 'group')
fig

## Warning: Ignoring 2 observations

## Warning: Ignoring 2 observations

library(readr)
comparacion <- read_xlsx("comparacion.xlsx")

fig <- plot_ly(comparacion, x = ~Entidad, y =~seis, type = 'bar', name = '2016')
fig <- fig %>% add_trace(y = ~siete, name = '2017')
fig <- fig %>% add_trace(y = ~ocho, name = '2018')
fig <- fig %>% add_trace(y = ~nueve, name = '2019')
fig <- fig %>% add_trace(y = ~veinte, name = '2020')
fig <- fig %>% layout(yaxis = list(title = 'Esperanza de escolaridad'), barmode = 'group')
fig

library(readxl)
library(plotly)
library(ggplot2)
library(readxl)
PIB2 <- read_excel("PIB2.xlsx")
rel <- ggplot(data = PIB2) +
  geom_line(aes(PIB2$Fecha, PIB2$`Gasto Educacion Per Capita`, colour= "Gasto Educacion Per Capita"), color="green") +  
  xlab("Años") +
  ylab("Gasto de Educacón Per Capita en $") +
  ggtitle("") +
 theme_linedraw()
  geom_point 

## function (mapping = NULL, data = NULL, stat = "identity", position = "identity", 
##     ..., na.rm = FALSE, show.legend = NA, inherit.aes = TRUE) 
## {
##     layer(data = data, mapping = mapping, stat = stat, geom = GeomPoint, 
##         position = position, show.legend = show.legend, inherit.aes = inherit.aes, 
##         params = list(na.rm = na.rm, ...))
## }
## <bytecode: 0x00000000259bcb10>
## <environment: namespace:ggplot2>

  ggplotly(rel)

## Warning: Use of `PIB2$Fecha` is discouraged. Use `Fecha` instead.

## Warning: Use of `PIB2$`Gasto Educacion Per Capita`` is discouraged. Use `Gasto
## Educacion Per Capita` instead.

library(readxl)
library(hpackedbubble)
IDG_10_Y_19 <- read_excel("IDG-10 Y 19.xlsx")
hpackedbubble(IDG_10_Y_19$Estado, IDG_10_Y_19$`IDH(2019)`, IDG_10_Y_19$`IDH (2010)`,
              title = "IDH 2019 y 2010 según la metodología actual del PNUD",
              pointFormat = "<b>{point.name}:</b> {point.y}",
              dataLabelsFilter = 200,
              packedbubbleMinSize = "30%",
              packedbubbleMaxSize = "40%",
              theme = "sky",  
              packedbubbleZMin = 0,
              packedbubbleZmax = 1000, split = 1,
              gravitational = 0.02,
              parentNodeLimit = 1,
              dragBetweenSeries = 0,
              seriesInteraction = 0,
              width = "100%")