library(readxl)

misDatos <- read_excel("data/pavimentando.xlsx") # ponga el nombre de su archivo.


misDatos$consejocomunal=factor(misDatos$consejocomunal,
                               levels = c(0,1), # lo que hay en la data actual
                               labels = c("_no","_si"), # como aparecerá ahora
                               ordered = FALSE) # TRUE si fuera ordinal

misDatos$priorizado=factor(misDatos$priorizado,
                               levels = c(0,1),
                               labels = c("_no","_si"),
                               ordered = FALSE)

misDatos$uribista=factor(misDatos$uribista,
                               levels = c(0,1),
                               labels = c("_no","_si"),
                               ordered = FALSE)

misDatos$ejecucion=factor(misDatos$ejecucion,
                               levels = c(0,1),
                               labels = c("_no","_si"),
                               ordered = FALSE)

library(ggplot2)

media=round(mean(misDatos$apropiaciondolar),3)
de=round(sd(misDatos$apropiaciondolar),3)
cv=round(de/media,3)

part1=ggplot(data=misDatos)
histogram=part1 + geom_histogram(aes(x=apropiaciondolar))

lamedia=histogram + annotate("text",x=25,y=400,
                       label=paste0("Media: ",media)) 

lamedia + annotate("text",x=25,y=200,
                       label=paste0("CV: ",cv)) 

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

library("ggpubr")

ggscatter(misDatos, 
          x = "pctopo", y = "apropiaciondolar", 
          add = "reg.line", 
          conf.int = TRUE, 
          add.params = list(color = "blue", fill = "lightgray"),
          cor.coef = TRUE, cor.method = "pearson") #spearman?

## `geom_smooth()` using formula 'y ~ x'

boxplot(apropiaciondolar~ejecucion,data=misDatos)

hipotesis3=formula(apropiaciondolar~priorizado  +  consejocomunal +  pctopo + poblacioncienmil+ nbi)
regre3=lm(hipotesis3,data=misDatos)

#
models=list('Dinero entregado'=regre3)

#
library(modelsummary)
modelsummary(models,
             title = "Regresion",
             statistic = 'conf.int',
             stars = TRUE,
             output = "kableExtra")

Regresion

	Dinero entregado
(Intercept)	10.896***
	[8.597, 13.195]
priorizado_si	−1.538
	[−3.574, 0.499]
consejocomunal_si	12.328***
	[8.362, 16.295]
pctopo	−0.025
	[−0.060, 0.010]
poblacioncienmil	1.933***
	[1.557, 2.309]
nbi	−0.072**
	[−0.117, −0.026]
Num.Obs.	1096
R2	0.151
R2 Adj.	0.147
AIC	9027.1
BIC	9062.1
Log.Lik.	−4506.567
RMSE	14.77
+ p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001

library(jtools)
library(ggstance)

## 
## Attaching package: 'ggstance'

## The following objects are masked from 'package:ggplot2':
## 
##     geom_errorbarh, GeomErrorbarh

plot_summs(regre3)

## Loading required namespace: broom.mixed