Prevalencia QA Carelmapu 2019

packages

library("tidyverse")

Loading tidyverse: ggplot2
Loading tidyverse: tibble
Loading tidyverse: tidyr
Loading tidyverse: readr
Loading tidyverse: purrr
Loading tidyverse: dplyr
package 'ggplot2' was built under R version 3.4.4package 'dplyr' was built under R version 3.4.2Conflicts with tidy packages ------------------------------------------------------------------------
filter(): dplyr, stats
lag():    dplyr, stats

abro el df

df <- read.csv("https://docs.google.com/spreadsheets/d/e/2PACX-1vS5tAKzrYRbmf0GiH_H-aWC7V7AwliLxUtb8A8uW84FDZgEToM8xag8X_jzZxNF6eSKH75SBfTSFdpB/pub?gid=1955655961&single=true&output=csv")

tabla QA x tacabo

table(df$QA,df$Tabaquismo)

             
              Ex_fumador Fumador No_fumador
  No presenta          8      35         34
  Presenta             6      30         12

diferencias ??

chisq.test(table(df$QA,df$Tabaquismo))

    Pearson's Chi-squared test

data:  table(df$QA, df$Tabaquismo)
X-squared = 4.718, df = 2, p-value = 0.09451

tabla QA x exposicion

table(df$QA,df$Exposicion_solar) 

             
              0-9 10-19 >20
  No presenta  18    10  49
  Presenta      1     4  43

diferencias ?

chisq.test(table(df$QA,df$Exposicion_solar))###existe significancia 

    Pearson's Chi-squared test

data:  table(df$QA, df$Exposicion_solar)
X-squared = 12.096, df = 2, p-value = 0.002362

tabla QA x genero

table(df$QA,df$Genero)

             
              Femenino Masculino
  No presenta        1        76
  Presenta           0        48

chisq.test(table(df$QA,df$Genero))###no tenemos significancia

Chi-squared approximation may be incorrect

    Pearson's Chi-squared test with Yates' continuity correction

data:  table(df$QA, df$Genero)
X-squared = 3.7821e-33, df = 1, p-value = 1

tabla QA x Medidas de proteccion

table(df$QA,df$Medidas_de_proteccion)

             
              Bloqueador_piel Ninguno Sombrero_o_gorro ambos
  No presenta              14      31               22    10
  Presenta                  4      29               12     3

diferencias ??

chisq.test(table(df$QA,df$Medidas_de_proteccion))###no tenemos significancia

Chi-squared approximation may be incorrect

    Pearson's Chi-squared test

data:  table(df$QA, df$Medidas_de_proteccion)
X-squared = 5.9235, df = 3, p-value = 0.1154

graficas

mosaicplot(table(df$QA,df$Medidas_de_proteccion), shade = T)

graficas

mosaicplot(table(df$QA,df$Exposicion_solar) , shade = T)

agrupo

df %>% 
  group_by(QA,Medidas_de_proteccion) %>% 
summarise(n=n()) %>% 
  ungroup()

graficas

df %>% 
  group_by(QA,Medidas_de_proteccion) %>% 
summarise(n=n()) %>% 
  ggplot(aes(x=QA, y=n, fill=Medidas_de_proteccion))+
  geom_col()+
  theme_minimal()

graficas

df%>% 
  group_by(QA,Edad) %>% 
summarise(n=n()) %>% 
  ggplot(aes(x=Edad, y=n, fill=QA))+
  geom_col()+
  theme_minimal()

agrupo

df %>% 
group_by(QA,Edad,Exposicion_solar) %>% 
summarise(n=n())

graficas

df %>% 
group_by(QA,Edad,Exposicion_solar) %>% 
summarise(n=n()) %>% 
 ggplot(aes(x=Exposicion_solar, y=n, fill=QA))+
  geom_col()+
  facet_grid(~Edad)+
  theme_minimal()

graficas

df %>% 
group_by(QA,Edad,Exposicion_solar) %>% 
summarise(n=n()) %>% 
 ggplot(aes(x=Exposicion_solar, y=n, fill=QA))+
  geom_col()+
  theme_minimal()