Data Sources: ENCOVI


Attaching package: <U+393C><U+3E31>dplyr<U+393C><U+3E32>

The following objects are masked from <U+393C><U+3E31>package:stats<U+393C><U+3E32>:

    filter, lag

The following objects are masked from <U+393C><U+3E31>package:base<U+393C><U+3E32>:

    intersect, setdiff, setequal, union
indicadores_desigualdad<- read_excel("indicadores_desigualdad.xlsx") 
names(indicadores_desigualdad) <- make.names(names(indicadores_desigualdad))
indicadores_desigualdad<- indicadores_desigualdad[complete.cases(indicadores_desigualdad), ]
str(indicadores_desigualdad)
Classes ‘tbl_df’, ‘tbl’ and 'data.frame':   22 obs. of  4 variables:
 $ Departamento    : chr  "Guatemala" "El Progreso" "Sacatepéquez" "Chimaltenango" ...
 $ Gini            : num  0.469 0.421 0.479 0.505 0.424 ...
 $ Atkinson..e...1.: num  0.325 0.267 0.337 0.364 0.266 ...
 $ Theil           : num  0.474 0.299 0.711 0.465 0.344 ...
head(indicadores_desigualdad)
summary(indicadores_desigualdad)
 Departamento            Gini        Atkinson..e...1.     Theil       
 Length:22          Min.   :0.3951   Min.   :0.2384   Min.   :0.2777  
 Class :character   1st Qu.:0.4538   1st Qu.:0.3153   1st Qu.:0.3760  
 Mode  :character   Median :0.4805   Median :0.3419   Median :0.4674  
                    Mean   :0.4862   Mean   :0.3535   Mean   :0.4924  
                    3rd Qu.:0.5069   3rd Qu.:0.3812   3rd Qu.:0.5318  
                    Max.   :0.6124   Max.   :0.5225   Max.   :0.9684  
indicadores_desigualdad <- indicadores_desigualdad %>% gather(indicadores.desigualdad, Penetracion, -Departamento)
indicadores_desigualdad$indicadores.desigualdad <- factor(indicadores_desigualdad$indicadores.desigualdad)
indicadores_desigualdad$Penetracion <- as.numeric(indicadores_desigualdad$Penetracion) / 100
str(indicadores_desigualdad)
Classes ‘tbl_df’, ‘tbl’ and 'data.frame':   66 obs. of  3 variables:
 $ Departamento           : chr  "Guatemala" "El Progreso" "Sacatepéquez" "Chimaltenango" ...
 $ indicadores.desigualdad: Factor w/ 3 levels "Atkinson..e...1.",..: 2 2 2 2 2 2 2 2 2 2 ...
 $ Penetracion            : num  0.00469 0.00421 0.00479 0.00505 0.00424 ...
head(indicadores_desigualdad)
summary(indicadores_desigualdad)
 Departamento           indicadores.desigualdad  Penetracion      
 Length:66          Atkinson..e...1.:22         Min.   :0.002384  
 Class :character   Gini            :22         1st Qu.:0.003575  
 Mode  :character   Theil           :22         Median :0.004406  
                                                Mean   :0.004440  
                                                3rd Qu.:0.004940  
                                                Max.   :0.009684  
ggplot(indicadores_desigualdad, aes(indicadores.desigualdad, Penetracion)) +
    geom_point() + theme(axis.text.x = element_text(angle = 90))

ggplot(indicadores_desigualdad, aes(indicadores.desigualdad, Penetracion)) +
    geom_boxplot() + theme(axis.text.x = element_text(angle = 90))

indicadores_desigualdad %>% filter(indicadores.desigualdad== "Gini") %>% arrange(desc(Penetracion)) %>% ggplot(aes(Departamento, Penetracion)) +
    geom_col(fill = "orange", color = "black") +
    labs(title = "Penetracion Gini") +
    theme(axis.text.x = element_text(angle = 90))

indicadores_desigualdad %>% ggplot(aes(Departamento, Penetracion)) +
    geom_col(aes(fill = indicadores.desigualdad, group = indicadores.desigualdad), position = "stack") +
    theme(axis.text.x = element_text(angle = 90))

indicadores_desigualdad %>% ggplot(aes(Departamento, Penetracion)) +
    geom_line(aes(color = indicadores.desigualdad, group = indicadores.desigualdad)) +
    theme(axis.text.x = element_text(angle = 90))

sc_pread <- spread(indicadores_desigualdad, indicadores.desigualdad, Penetracion)
sc_cor <- cor(select(sc_pread, -Departamento))

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