all_manzanas <- read_csv("~/Desktop/BarriosSustentables/statistical_tests/all_manzanas_barrio_data_export.csv",
col_types = cols(MANZENT = col_character(),X1 = col_skip()))
all_manzanas_no_na <- all_manzanas %>% filter(!is.na(VALUE))
vars <- unique(all_manzanas_no_na$VAR)
stage_2_test_1 <- all_manzanas_no_na %>%
slice_rows("VAR") %>%
by_slice(~tidy(shapiro.test(.x$VALUE))) %>%
unnest()
stage_2_nonormal_test <- all_manzanas_no_na %>%
slice_rows(c("VAR")) %>%
by_slice(~tidy(kruskal.test(.x$VALUE~factor(.x$CLASS)))) %>%
unnest() %>% select(VAR,p.value,method)
for(x in vars){
print(x)
dunn.test(subset(all_manzanas_no_na,VAR==x)$VALUE,factor(subset(all_manzanas_no_na,VAR==x)$CLASS),list=T,table = F,kw=F)
}
## [1] "Empleados en la misma comuna"
##
## Comparison of x by group
## (No adjustment)
##
## List of pairwise comparisons: Z statistic (p-value)
## ----------------------------------------------
## Central - Peri-central : 2.459003 (0.0070)
## Central - Periférico : -13.45588 (0.0000)
## Peri-central - Periférico : -19.74629 (0.0000)
##
## [1] "Acceso a tecnologias de la información"
##
## Comparison of x by group
## (No adjustment)
##
## List of pairwise comparisons: Z statistic (p-value)
## ----------------------------------------------
## Central - Peri-central : 13.03238 (0.0000)
## Central - Periférico : 15.49025 (0.0000)
## Peri-central - Periférico : 4.661105 (0.0000)
##
## [1] "Mujeres trabajando"
##
## Comparison of x by group
## (No adjustment)
##
## List of pairwise comparisons: Z statistic (p-value)
## ----------------------------------------------
## Central - Peri-central : 1.601392 (0.0546)
## Central - Periférico : 10.11208 (0.0000)
## Peri-central - Periférico : 10.91080 (0.0000)
##
## [1] "Cercanía a Areas Verdes"
##
## Comparison of x by group
## (No adjustment)
##
## List of pairwise comparisons: Z statistic (p-value)
## ----------------------------------------------
## Central - Peri-central : 19.24831 (0.0000)
## Central - Periférico : 21.10089 (0.0000)
## Peri-central - Periférico : 4.609433 (0.0000)
##
## [1] "Viviendas de buena calidad"
##
## Comparison of x by group
## (No adjustment)
##
## List of pairwise comparisons: Z statistic (p-value)
## ----------------------------------------------
## Central - Peri-central : 13.46847 (0.0000)
## Central - Periférico : 4.581147 (0.0000)
## Peri-central - Periférico : -9.614306 (0.0000)
##################################################
stage_3_nonormal_test <- all_manzanas_no_na %>%
slice_rows(c("VAR")) %>%
by_slice(~tidy(kruskal.test(.x$VALUE~factor(.x$CLASS)))) %>%
unnest() %>% select(VAR,p.value,method)
for(x in vars){
print(x)
dunn.test(subset(all_manzanas_no_na,VAR==x)$VALUE,factor(subset(all_manzanas_no_na,VAR==x)$LOCATION),list=T,table = F,kw=F)
}
## [1] "Empleados en la misma comuna"
##
## Comparison of x by group
## (No adjustment)
##
## List of pairwise comparisons: Z statistic (p-value)
## -------------------------------------------
## Concepcion - Copiapo : -21.29350 (0.0000)
## Concepcion - Coquimbo : -13.52247 (0.0000)
## Copiapo - Coquimbo : 8.731342 (0.0000)
## Concepcion - La Serena : -14.49652 (0.0000)
## Copiapo - La Serena : 3.164301 (0.0008)
## Coquimbo - La Serena : -4.102057 (0.0000)
## Concepcion - Santiago : 13.23498 (0.0000)
## Copiapo - Santiago : 34.85175 (0.0000)
## Coquimbo - Santiago : 28.19208 (0.0000)
## La Serena - Santiago : 24.34901 (0.0000)
## Concepcion - Temuco : -21.98002 (0.0000)
## Copiapo - Temuco : 1.498391 (0.0670)
## Coquimbo - Temuco : -7.977240 (0.0000)
## La Serena - Temuco : -2.083200 (0.0186)
## Santiago - Temuco : -38.31724 (0.0000)
## Concepcion - Valdivia : -21.68099 (0.0000)
## Copiapo - Valdivia : -3.722354 (0.0001)
## Coquimbo - Valdivia : -11.26744 (0.0000)
## La Serena - Valdivia : -6.159416 (0.0000)
## Santiago - Valdivia : -31.93786 (0.0000)
## Temuco - Valdivia : -5.204391 (0.0000)
##
## [1] "Acceso a tecnologias de la información"
##
## Comparison of x by group
## (No adjustment)
##
## List of pairwise comparisons: Z statistic (p-value)
## -------------------------------------------
## Concepcion - Copiapo : 2.803120 (0.0025)
## Concepcion - Coquimbo : 4.339194 (0.0000)
## Copiapo - Coquimbo : 1.059034 (0.1448)
## Concepcion - La Serena : -0.951251 (0.1707)
## Copiapo - La Serena : -3.007830 (0.0013)
## Coquimbo - La Serena : -4.083854 (0.0000)
## Concepcion - Santiago : 0.913997 (0.1804)
## Copiapo - Santiago : -2.495048 (0.0063)
## Coquimbo - Santiago : -4.339467 (0.0000)
## La Serena - Santiago : 1.619360 (0.0527)
## Concepcion - Temuco : 4.021526 (0.0000)
## Copiapo - Temuco : 0.787287 (0.2156)
## Coquimbo - Temuco : -0.297728 (0.3830)
## La Serena - Temuco : 3.852639 (0.0001)
## Santiago - Temuco : 3.960584 (0.0000)
## Concepcion - Valdivia : -13.13246 (0.0000)
## Copiapo - Valdivia : -14.12789 (0.0000)
## Coquimbo - Valdivia : -15.83586 (0.0000)
## La Serena - Valdivia : -9.987727 (0.0000)
## Santiago - Valdivia : -14.93565 (0.0000)
## Temuco - Valdivia : -15.60400 (0.0000)
##
## [1] "Mujeres trabajando"
##
## Comparison of x by group
## (No adjustment)
##
## List of pairwise comparisons: Z statistic (p-value)
## -------------------------------------------
## Concepcion - Copiapo : 0.919368 (0.1790)
## Concepcion - Coquimbo : -1.036380 (0.1500)
## Copiapo - Coquimbo : -1.766307 (0.0387)
## Concepcion - La Serena : -2.405946 (0.0081)
## Copiapo - La Serena : -2.896534 (0.0019)
## Coquimbo - La Serena : -1.566605 (0.0586)
## Concepcion - Santiago : -10.50181 (0.0000)
## Copiapo - Santiago : -9.318282 (0.0000)
## Coquimbo - Santiago : -8.309599 (0.0000)
## La Serena - Santiago : -4.019784 (0.0000)
## Concepcion - Temuco : -5.075944 (0.0000)
## Copiapo - Temuco : -5.231353 (0.0000)
## Coquimbo - Temuco : -3.807841 (0.0001)
## La Serena - Temuco : -1.381302 (0.0836)
## Santiago - Temuco : 3.534485 (0.0002)
## Concepcion - Valdivia : -17.17754 (0.0000)
## Copiapo - Valdivia : -16.38665 (0.0000)
## Coquimbo - Valdivia : -15.87518 (0.0000)
## La Serena - Valdivia : -12.17797 (0.0000)
## Santiago - Valdivia : -12.26221 (0.0000)
## Temuco - Valdivia : -13.26871 (0.0000)
##
## [1] "Cercanía a Areas Verdes"
##
## Comparison of x by group
## (No adjustment)
##
## List of pairwise comparisons: Z statistic (p-value)
## -------------------------------------------
## Concepcion - Copiapo : 0.726961 (0.2336)
## Concepcion - Coquimbo : 9.811789 (0.0000)
## Copiapo - Coquimbo : 7.725627 (0.0000)
## Concepcion - La Serena : -0.166240 (0.4340)
## Copiapo - La Serena : -0.709992 (0.2389)
## Coquimbo - La Serena : -7.357462 (0.0000)
## Concepcion - Santiago : -7.273977 (0.0000)
## Copiapo - Santiago : -6.560411 (0.0000)
## Coquimbo - Santiago : -18.32364 (0.0000)
## La Serena - Santiago : -4.444575 (0.0000)
## Concepcion - Temuco : 1.452402 (0.0732)
## Copiapo - Temuco : 0.550970 (0.2908)
## Coquimbo - Temuco : -7.911839 (0.0000)
## La Serena - Temuco : 1.225497 (0.1102)
## Santiago - Temuco : 8.372124 (0.0000)
## Concepcion - Valdivia : -8.807579 (0.0000)
## Copiapo - Valdivia : -8.597281 (0.0000)
## Coquimbo - Valdivia : -15.59086 (0.0000)
## La Serena - Valdivia : -7.119909 (0.0000)
## Santiago - Valdivia : -5.115543 (0.0000)
## Temuco - Valdivia : -9.580864 (0.0000)
##
## [1] "Viviendas de buena calidad"
##
## Comparison of x by group
## (No adjustment)
##
## List of pairwise comparisons: Z statistic (p-value)
## -------------------------------------------
## Concepcion - Copiapo : 12.58871 (0.0000)
## Concepcion - Coquimbo : 22.86175 (0.0000)
## Copiapo - Coquimbo : 7.585880 (0.0000)
## Concepcion - La Serena : 0.495816 (0.3100)
## Copiapo - La Serena : -9.212719 (0.0000)
## Coquimbo - La Serena : -16.26905 (0.0000)
## Concepcion - Santiago : 7.901344 (0.0000)
## Copiapo - Santiago : -8.213079 (0.0000)
## Coquimbo - Santiago : -20.04154 (0.0000)
## La Serena - Santiago : 4.471816 (0.0000)
## Concepcion - Temuco : -0.372232 (0.3549)
## Copiapo - Temuco : -12.34070 (0.0000)
## Coquimbo - Temuco : -21.95757 (0.0000)
## La Serena - Temuco : -0.752124 (0.2260)
## Santiago - Temuco : -7.640765 (0.0000)
## Concepcion - Valdivia : -0.063858 (0.4745)
## Copiapo - Valdivia : -9.560131 (0.0000)
## Coquimbo - Valdivia : -16.49417 (0.0000)
## La Serena - Valdivia : -0.452428 (0.3255)
## Santiago - Valdivia : -4.964675 (0.0000)
## Temuco - Valdivia : 0.205509 (0.4186)
####################################################
#stage_4_nonormal_test <- all_manzanas_no_na %>%
# slice_rows(c("VAR")) %>%
# by_slice(~tidy(kruskal.test(.x$VALUE~factor(.x$CLASS)))) %>%
# unnest() %>% select(VAR,p.value,method)
#for(x in vars){
# print(x)
# dunn.test(subset(all_manzanas_no_na,VAR==x)$VALUE,factor(subset(all_manzanas_no_na,VAR==x)$TYPE),list=T,table = F,kw=F)
#}