library(rio)
data1= import("residuosPeru.xlsx")
data2= readxl::read_excel("Lima2022.xlsx")
## New names:
## • `` -> `...2`
library(rvest)
link = "https://es.wikipedia.org/wiki/Elecciones_municipales_de_Lima_de_2022"
path = '//*[@id="mw-content-text"]/div[1]/table[9]'
data3 <- read_html(link)%>%html_nodes(xpath = path)%>%html_table()%>% .[[1]]
head(data3)
## # A tibble: 6 × 17
## Distrito RP RP PP PP SP SP FE FE APP APP JP
## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 Distrito "" "" "" "" "" "" "" "" "" "" ""
## 2 Distrito "V" "%" "V" "%" "V" "%" "V" "%" "V" "%" "V"
## 3 Ancón "3,725" "13.2… "9,3… "33.… "5,9… "21.… "2,0… "7.2… "3,5… "12.… "1,6…
## 4 Ate "57,374" "17.4… "98,… "29.… "52,… "15.… "27,… "8.5… "26,… "7.9… "25,…
## 5 Barranco "11,604" "36.9… "5,7… "18.… "6,4… "20.… "2,7… "8.8… "2,1… "6.9… "1,6…
## 6 Breña "22,721" "31.1… "18,… "25.… "14,… "19.… "8,2… "11.… "2,4… "3.3… "3,8…
## # ℹ 5 more variables: JP <chr>, AvP <chr>, AvP <chr>, PL <chr>, PL <chr>
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
data1 = filter(data1,PROVINCIA == "LIMA" )
data1=data1[!duplicated(data1), ]
data1=filter(data1,PERIODO == 2021 )
data1 <- data1[, -c(1, 2,3,4,5,6,8,9,10,11,12,14,15)]
data3<- data3[-1, ]
data3<- data3[-1, ]
data3 <- data3[, -c(2,4,6,8,9,10,11,12,13,14,15,16,17)]
data3$Distrito = toupper(data3$Distrito)
table(data3$Distrito)
##
## ANCÓN ATE BARRANCO
## 1 1 1
## BREÑA CARABAYLLO CHACLACAYO
## 1 1 1
## CHORRILLOS CIENEGUILLA COMAS
## 1 1 1
## EL AGUSTINO INDEPENDENCIA JESÚS MARÍA
## 1 1 1
## LA MOLINA LA VICTORIA LIMA
## 1 1 1
## LINCE LOS OLIVOS LURIGANCHO-CHOSICA
## 1 1 1
## LURÍN MAGDALENA DEL MAR MIRAFLORES
## 1 1 1
## PACHACÁMAC PUCUSANA PUEBLO LIBRE
## 1 1 1
## PUENTE PIEDRA PUNTA HERMOSA PUNTA NEGRA
## 1 1 1
## RÍMAC SAN BARTOLO SAN BORJA
## 1 1 1
## SAN ISIDRO SAN JUAN DE LURIGANCHO SAN JUAN DE MIRAFLORES
## 1 1 1
## SAN LUIS SAN MARTÍN DE PORRES SAN MIGUEL
## 1 1 1
## SANTA ANITA SANTA MARÍA DEL MAR SANTA ROSA
## 1 1 1
## SANTIAGO DE SURCO SURQUILLO TOTAL
## 1 1 1
## VILLA EL SALVADOR VILLA MARÍA DEL TRIUNFO
## 1 1
data3$Distrito <- iconv(data3$Distrito, to = "ASCII//TRANSLIT")
data3[4, "Distrito"] <- "BREÑA"
data3[18, "Distrito"] <- "LURIGANCHO"
data2<-data2[,-c(2) ]
data2$Distrito<-gsub("^[0-9]+ ", "", data2$Distrito)
colnames(data1)[1] = "Distrito"
TOTADATA = merge(data1, data2, by = "Distrito", all.x = TRUE, all.y = TRUE)
TOTAL = merge(TOTADATA, data3, by = "Distrito")
library(dplyr)
TOTAL<- TOTAL %>%
mutate(Victoria_RP = ifelse(RP > pmax(PP, SP), 1, 0))
TOTAL1 <- na.omit(TOTAL)
modelo1=formula(Victoria_RP~Denuncias+QRESIDUOS_NO_DOM)
rlog1=glm(modelo1, data=TOTAL1,family = binomial)
summary(rlog1)
##
## Call:
## glm(formula = modelo1, family = binomial, data = TOTAL1)
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.657e+01 3.561e+05 0 1
## Denuncias110,1 5.313e+01 5.036e+05 0 1
## Denuncias111,4 -1.430e-13 5.036e+05 0 1
## Denuncias113,6 -3.291e-13 5.036e+05 0 1
## Denuncias120,2 -2.475e-13 5.036e+05 0 1
## Denuncias121,9 5.313e+01 5.036e+05 0 1
## Denuncias123,8 5.313e+01 5.036e+05 0 1
## Denuncias124,0 5.313e+01 5.036e+05 0 1
## Denuncias126,6 -2.251e-13 5.036e+05 0 1
## Denuncias127,2 -1.874e-13 5.036e+05 0 1
## Denuncias133,8 -2.227e-13 5.036e+05 0 1
## Denuncias148,0 5.313e+01 5.036e+05 0 1
## Denuncias151,0 5.313e+01 5.036e+05 0 1
## Denuncias153,1 5.313e+01 5.036e+05 0 1
## Denuncias153,9 -1.277e-13 5.036e+05 0 1
## Denuncias154,4 5.313e+01 5.036e+05 0 1
## Denuncias155,9 5.313e+01 5.036e+05 0 1
## Denuncias160,3 -1.579e-13 5.036e+05 0 1
## Denuncias160,5 -1.909e-13 5.036e+05 0 1
## Denuncias165,0 -1.331e-13 5.036e+05 0 1
## Denuncias169,8 5.313e+01 5.036e+05 0 1
## Denuncias170,4 -2.141e-13 5.036e+05 0 1
## Denuncias188,2 -1.465e-13 5.036e+05 0 1
## Denuncias200,4 5.313e+01 5.036e+05 0 1
## Denuncias210,3 5.313e+01 5.036e+05 0 1
## Denuncias215,1 -1.366e-13 5.036e+05 0 1
## Denuncias218,6 5.313e+01 5.036e+05 0 1
## Denuncias227,1 -2.113e-13 5.036e+05 0 1
## Denuncias251,3 -1.686e-13 5.036e+05 0 1
## Denuncias261,6 5.313e+01 5.036e+05 0 1
## Denuncias268,8 5.313e+01 5.036e+05 0 1
## Denuncias278,8 5.313e+01 5.036e+05 0 1
## Denuncias291,8 -1.551e-13 5.036e+05 0 1
## Denuncias389,8 -2.417e-13 5.036e+05 0 1
## Denuncias553,7 5.313e+01 5.036e+05 0 1
## Denuncias63,2 -1.922e-13 5.036e+05 0 1
## Denuncias642,7 5.313e+01 5.036e+05 0 1
## Denuncias78,9 -1.572e-13 5.036e+05 0 1
## Denuncias84,6 -1.047e-13 5.036e+05 0 1
## Denuncias90,1 -1.923e-13 5.036e+05 0 1
## Denuncias90,5 5.313e+01 5.036e+05 0 1
## Denuncias99,2 -2.389e-13 5.036e+05 0 1
## QRESIDUOS_NO_DOM NA NA NA NA
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 5.7843e+01 on 41 degrees of freedom
## Residual deviance: 2.4367e-10 on 0 degrees of freedom
## AIC: 84
##
## Number of Fisher Scoring iterations: 25
reg2=lm(modelo1,data=TOTAL1)
summary(reg2)
##
## Call:
## lm(formula = modelo1, data = TOTAL1)
##
## Residuals:
## ALL 42 residuals are 0: no residual degrees of freedom!
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.484e-16 NaN NaN NaN
## Denuncias110,1 1.000e+00 NaN NaN NaN
## Denuncias111,4 1.380e-15 NaN NaN NaN
## Denuncias113,6 3.840e-15 NaN NaN NaN
## Denuncias120,2 -2.879e-15 NaN NaN NaN
## Denuncias121,9 1.000e+00 NaN NaN NaN
## Denuncias123,8 1.000e+00 NaN NaN NaN
## Denuncias124,0 1.000e+00 NaN NaN NaN
## Denuncias126,6 -9.404e-16 NaN NaN NaN
## Denuncias127,2 -6.849e-16 NaN NaN NaN
## Denuncias133,8 -1.726e-16 NaN NaN NaN
## Denuncias148,0 1.000e+00 NaN NaN NaN
## Denuncias151,0 1.000e+00 NaN NaN NaN
## Denuncias153,1 1.000e+00 NaN NaN NaN
## Denuncias153,9 -7.062e-16 NaN NaN NaN
## Denuncias154,4 1.000e+00 NaN NaN NaN
## Denuncias155,9 1.000e+00 NaN NaN NaN
## Denuncias160,3 1.500e-16 NaN NaN NaN
## Denuncias160,5 -8.495e-16 NaN NaN NaN
## Denuncias165,0 1.772e-16 NaN NaN NaN
## Denuncias169,8 1.000e+00 NaN NaN NaN
## Denuncias170,4 -1.191e-16 NaN NaN NaN
## Denuncias188,2 -5.617e-16 NaN NaN NaN
## Denuncias200,4 1.000e+00 NaN NaN NaN
## Denuncias210,3 1.000e+00 NaN NaN NaN
## Denuncias215,1 -7.497e-16 NaN NaN NaN
## Denuncias218,6 1.000e+00 NaN NaN NaN
## Denuncias227,1 -5.055e-16 NaN NaN NaN
## Denuncias251,3 -5.746e-17 NaN NaN NaN
## Denuncias261,6 1.000e+00 NaN NaN NaN
## Denuncias268,8 1.000e+00 NaN NaN NaN
## Denuncias278,8 1.000e+00 NaN NaN NaN
## Denuncias291,8 -1.248e-15 NaN NaN NaN
## Denuncias389,8 -9.801e-16 NaN NaN NaN
## Denuncias553,7 1.000e+00 NaN NaN NaN
## Denuncias63,2 -5.638e-16 NaN NaN NaN
## Denuncias642,7 1.000e+00 NaN NaN NaN
## Denuncias78,9 -4.742e-16 NaN NaN NaN
## Denuncias84,6 -1.666e-15 NaN NaN NaN
## Denuncias90,1 -7.051e-16 NaN NaN NaN
## Denuncias90,5 1.000e+00 NaN NaN NaN
## Denuncias99,2 -9.421e-16 NaN NaN NaN
## QRESIDUOS_NO_DOM NA NA NA NA
##
## Residual standard error: NaN on 0 degrees of freedom
## Multiple R-squared: 1, Adjusted R-squared: NaN
## F-statistic: NaN on 41 and 0 DF, p-value: NA