Comparación Hospitales vs. Dirección Servicio
#evitar que ocupe curl
#options(renv.download.override = utils::download.file)
#arriba puse algunas opciones para que por defecto escondiera el código
#también cargue algunos estilo .css para que el texto me apareciera justificado, entre otras cosas.
local({r <- getOption("repos")
r["CRAN"] <- "http://cran.r-project.org"
options(repos=r)
})
clipboard <- function(x, sep="\t", row.names=FALSE, col.names=TRUE){
con <- pipe("xclip -selection clipboard -i", open="w")
write.table(x, con, sep=sep, row.names=row.names, col.names=col.names)
close(con)
}
`%>%` <- magrittr::`%>%`
copy_names <- function(x,row.names=FALSE,col.names=TRUE,dec=",",...) {
library(dplyr)
if(class(ungroup(x))[1]=="tbl_df"){
if(options()$OutDec=="."){
options(OutDec = dec)
write.table(format(data.frame(x)),"clipboard",sep="\t",row.names=FALSE,col.names=col.names,...)
options(OutDec = ".")
return(x)
} else {
options(OutDec = ",")
write.table(format(data.frame(x)),"clipboard",sep="\t",row.names=FALSE,col.names=col.names,...)
options(OutDec = ",")
return(x)
}
} else {
if(options()$OutDec=="."){
options(OutDec = dec)
write.table(format(x),"clipboard",sep="\t",row.names=FALSE,col.names=col.names,...)
options(OutDec = ".")
return(x)
} else {
options(OutDec = ",")
write.table(format(x),"clipboard",sep="\t",row.names=FALSE,col.names=col.names,...)
options(OutDec = ",")
return(x)
}
}
}
if(!require(pacman)){install.packages("pacman")}
pacman::p_unlock(lib.loc = .libPaths()) #para no tener problemas reinstalando paquetes
knitr::opts_chunk$set(
echo = TRUE,
message = FALSE,
warning = FALSE
)
#dejo los paquetes estadísticos que voy a utilizar
if(!require(plotly)){install.packages("plotly")}
if(!require(lubridate)){install.packages("lubridate")}
if(!require(htmlwidgets)){install.packages("htmlwidgets")}
#if(!require(tidyverse)){install.packages("tidyverse")}
if(!require(gganimate)){install.packages("gganimate")}
if(!require(readr)){install.packages("readr")}
if(!require(stringr)){install.packages("stringr")}
if(!require(data.table)){install.packages("data.table")}
if(!require(DT)){install.packages("DT")}
if(!require(ggplot2)){install.packages("ggplot2")}
if(!require(lattice)){install.packages("lattice")}
if(!require(forecast)){install.packages("forecast")}
if(!require(zoo)){install.packages("zoo")}
if(!require(panelView)){install.packages("panelView")}
if(!require(janitor)){install.packages("janitor")}
if(!require(rjson)){install.packages("rjson")}
if(!require(estimatr)){install.packages("estimatr")}
if(!require(CausalImpact)){install.packages("CausalImpact")}
if(!require(textreg)){install.packages("textreg")}
if(!require(sjPlot)){install.packages("sjPlot")}
if(!require(foreign)){install.packages("foreign")}
if(!require(tsModel)){install.packages("tsModel")}
if(!require(lmtest)){install.packages("lmtest")}
if(!require(Epi)){install.packages("Epi")}
if(!require(splines)){install.packages("splines")}
if(!require(vcd)){install.packages("vcd")}
if(!require(astsa)){install.packages("astsa")}
if(!require(MASS)){install.packages("MASS")}
if(!require(ggsci)){install.packages("ggsci")}
if(!require(Hmisc)){install.packages("Hmisc")}
if(!require(compareGroups)){install.packages("compareGroups")}
if(!require(dplyr)){install.packages("dplyr")}
if(!require(ggforce)){install.packages("ggforce")}
if(!require(doParallel)){install.packages("doParallel")}
if(!require(SCtools)){install.packages("SCtools")}
if(!require(rio)){install.packages("rio")}
if(!require(rbokeh)){install.packages("rbokeh")}
if(!require(altair)){install.packages("altair")}
if(!require(sqldf)){install.packages("sqldf")}
if(!require(devtools)){install.packages("devtools")}
if(!require(ExPanDaR)){install.packages("ExPanDaR")}
if(!require(skimr)){install.packages("skimr")}
if(!require(tm)){install.packages("tm")}
if(!require(wordcloud2)){install.packages("wordcloud2")}
if(!require(wordcloud)){install.packages("wordcloud")}
if(!require(RColorBrewer)){install.packages("RColorBrewer")}
if(!require(psych)){install.packages("psych")}
if(!require(GPArotation)){install.packages("GPArotation")}
if(!require(mvtnorm)){install.packages("mvtnorm")}
if(!require(polycor)){install.packages("polycor")}
if(!require(MVN)){install.packages("MVN")}
if(!require(ggcorrplot)){install.packages("ggcorrplot")}
if(!require(radiant)){install.packages("radiant")}
if(!require(homals)){install.packages("homals")}
if(!require(nFactors)){install.packages("nFactors")}
if(!require(ggiraph)){install.packages("ggiraph")}
if(!require(factoextra)){install.packages("factoextra")}
if(!require(tidyverse)){install.packages("tidyverse")}
if(!require(lubridate)){install.packages("lubridate")}
if(!require(ggfortify)){install.packages("ggfortify")}
if(!require(survminer)){install.packages("survminer")}
if(!require(finalfit)){install.packages("finalfit")}
if(!require(effects)){install.packages("effects")}
if(!require(lsmeans)){install.packages("lsmeans")}
if(!require(vcd)){install.packages("vcd")}
# Calculate the number of cores
#no_cores <- detectCores() - 1
##cl<-makeCluster(no_cores)
#registerDoParallel(cl)
# sudo apt -y install libfontconfig1-dev
# sudo apt-get install libxml2-dev
#Sys.setlocale(category = "LC_ALL", locale = "english")
#locale("es", decimal_mark = ",")
find_type <- function(x) {
case_when(
is.factor(x) ~ "factor",
is.character(x) ~ "character",
is.numeric(x) ~ "numeric",
TRUE ~ "not sure"
)
}
permute_icc <- function(x, y, n = 99) {
actual <- ICCbare(x, y)
nulls <- vector(length = length(n), mode = "numeric")
for(i in seq_along(1:n)) {
scrambled_x <- sample(x, length(x), replace = F)
nulls[i] <- ICCbare(scrambled_x, y)
}
(sum(abs(nulls) > ifelse(actual > 0, actual, -actual)) + 1) / (n+1)
}
permute_tau <- function(x, y, n = 99) {
actual <- GKtau(x, y)$tauxy
nulls <- vector(length = length(n), mode = "numeric")
for(i in seq_along(1:n)) {
scrambled_x <- sample(x, length(x), replace = F)
nulls[i] <- GKtau(scrambled_x, y)$tauxy
}
(sum(abs(nulls) > ifelse(actual > 0, actual, -actual)) + 1) / (n+1)
}
# to do:
## get p-values
eda <- function(x, plot = FALSE) {
x <- as.data.frame(x)
num_rows <- ncol(x)^2 - ncol(x)
df <- tibble(var1 = vector(mode = "character", length = 1),
var2 = vector(mode = "character", length = 1),
statistic = vector(mode = "character", length = 1),
value = vector(mode = "double", length = 1),
p_value = vector(mode = "double", length = 1),
n = vector(mode = "integer", length = 1))
for(i in seq_along(1:ncol(x)))
for(j in seq_along(1:ncol(x))) {
if(i < j){
# get type of columns i and j
var_1_type <- find_type(x[,i])
var_2_type <- find_type(x[,j])
#print(paste("var1 type: ", var_1_type, "\nvar2 type: ", var_2_type, "\n\n"))
x1 <- x[,i]
x2 <- x[,j]
# remove NAs for simplicity
if(any(is.na(x1))){
# get NA indicies
ind <- which(is.na(x1))
x1 <- x1[-ind]
x2 <- x2[-ind]
}
if(any(is.na(x2))){
# get NA indicies
ind <- which(is.na(x2))
x1 <- x1[-ind]
x2 <- x2[-ind]
}
# make sure x1 and x2 are the same length
stopifnot(length(x1) == length(x2))
n <- length(x1)
if(var_1_type == "numeric" & var_2_type == "numeric") {
# run a correlation
result <- cor.test(x1, x2)
df <- add_row(df,
var1 = names(x)[i],
var2 = names(x)[j],
statistic = "r",
value = result$estimate,
p_value = result$p.value,
n = n
)
} else if(var_1_type == "factor" & var_2_type == "numeric") {
# run an ANOVA or t-test, depending on number of levels
num_levels <- length(levels(x1))
require(ICC)
result <- ICCbare(x1, x2)
p <- permute_icc(x1, x2)
df <- add_row(df,
var1 = names(x)[i],
var2 = names(x)[j],
statistic = "ICC",
value = result,
p_value = p,
n = n
)
} else if(var_1_type == "numeric" & var_2_type == "factor") {
# run an ANOVA or t-test, depending on number of levels
num_levels <- length(levels(x2))
require(ICC)
result <- ICCbare(x2, x1)
p <- permute_icc(x2, x1)
df <- add_row(df,
var1 = names(x)[i],
var2 = names(x)[j],
statistic = "ICC",
value = result,
p_value = p,
n = n
)
} else if(var_1_type == "factor" & var_2_type == "factor") {
require("GoodmanKruskal")
# compute the GKtau statistic
stat1 <- GKtau(x1, x2)$tauxy
stat2 <- GKtau(x1, x2)$tauyx
p1 <- permute_tau(x1, x2)
p2 <- permute_tau(x2, x1)
df <- add_row(df,
var1 = names(x)[i],
var2 = names(x)[j],
statistic = "tau",
value = stat1,
p_value = p1,
n = n
)
df <- add_row(df,
var1 = names(x)[j],
var2 = names(x)[i],
statistic = "tau",
value = stat2,
p_value = p2,
n = n
)
} else{
# return an empty row
df <- add_row(df,
var1 = names(x)[i],
var2 = names(x)[j],
statistic = NA_character_,
value = NA_integer_,
p_value = NA_real_,
n = n
)
}
}
}
if(plot == TRUE) {
df[-1,] %>%
filter(!is.na(value)) %>%
unite(variables, var1, var2, sep = " by ") %>%
mutate(`possibly significant` = if_else(p_value < 0.05, "significant", "NS")) %>%
ggplot(aes(y = value, x = reorder(variables, value), color = `possibly significant`)) +
geom_point() +
coord_flip() +
facet_wrap(~statistic, scales = "free") +
theme_minimal() +
scale_color_manual(values = c("#37454B", "#E84F22"))
} else{
df[-1,]
}
}0.1 Exploración
#rowPerc(xtabs(n~sex+seat,data=
sexo<-
bd_comparacion %>%
dplyr::group_by(comparacion,p45_demo_sexo,tamizaje_ans) %>%
dplyr::summarise(n=n()) %>%
dplyr::filter(!is.na(comparacion),!is.na(p45_demo_sexo),!is.na(tamizaje_ans)) %>%
dplyr::ungroup() %>%
dplyr::group_by(comparacion, p45_demo_sexo) %>%
dplyr::mutate(perc=scales::percent(n/sum(n))) %>%
dplyr::filter(!grepl("Ausencia de",tamizaje_ans))%>%
dplyr::mutate(num_perc=readr::parse_number(perc, "\\d+"))
sexo_plot<-
ggplot(sexo, aes(y=num_perc, x=comparacion, fill=p45_demo_sexo))+
geom_bar(stat="identity", position=position_dodge())+
geom_text(aes(label=perc), position=position_dodge(width=1), vjust = -.5, size = 5) +
theme_sjplot() +
ylim(0,100)+
ylab("Porcentaje Sintomatología Ansiosa")+
xlab("Conjunto de Instituciones")+
scale_fill_manual("Sexo",values=c("grey30", "grey55")) +
theme(legend.position = "bottom")+
theme(axis.text.x=element_text(size=14),
axis.title.x = element_text(size=15),
axis.title.y = element_text(size=15),
legend.title = element_text(size=15),
legend.text = element_text(size=14))
sexo_plot
Figura 1a. Prevalencia Ansiedad según Sexo, por Agrupación de Instituciones
jpeg("_Fig0a_sexo.jpg", height = 7, width = 11.5, units = 'in', res = 600)
sexo_plot+ theme(plot.background = element_rect(fill=NA, color = NA))
dev.off()## png
## 2ggiraph(code = {print(sexo_plot)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )Figura 1a. Prevalencia Ansiedad según Sexo, por Agrupación de Instituciones
miedo_pand<-
bd_comparacion %>%
dplyr::group_by(comparacion,p11_exp_c19_miedo_pand_rec,tamizaje_ans) %>%
dplyr::summarise(n=n()) %>%
dplyr::filter(!is.na(comparacion),!is.na(p11_exp_c19_miedo_pand_rec),!is.na(tamizaje_ans)) %>%
dplyr::ungroup() %>%
dplyr::group_by(comparacion, p11_exp_c19_miedo_pand_rec) %>%
dplyr::mutate(perc=scales::percent(n/sum(n))) %>%
dplyr::filter(!grepl("Ausencia de",tamizaje_ans))%>%
dplyr::mutate(num_perc=readr::parse_number(perc, "\\d+"))
miedo_plot<-
ggplot(miedo_pand, aes(y=num_perc, x=comparacion, fill=p11_exp_c19_miedo_pand_rec))+
geom_bar(stat="identity", position=position_dodge())+
geom_text(aes(label=perc), position=position_dodge(width=1.), vjust = -.5, size = 5) +
theme_sjplot() +
ylim(0,100)+
ylab("Porcentaje Sintomatología Ansiosa")+
xlab("Conjunto de Instituciones")+
scale_fill_manual("Miedo Situación\nde Pandemia",values=c("grey30", "grey55")) +
theme(legend.position = "bottom")+
theme(axis.text.x=element_text(size=14),
axis.title.x = element_text(size=15),
axis.title.y = element_text(size=15),
legend.title = element_text(size=13),
legend.text = element_text(size=12))
miedo_plot
Figura 1b. Prevalencia Ansiedad según el Miedo a la Situación Pandemia, por Agrupación de Instituciones
jpeg("_Fig0b_miedo.jpg", height = 7, width = 11.5, units = 'in', res = 600)
miedo_plot+ theme(plot.background = element_rect(fill=NA, color = NA))
dev.off()## png
## 2ggiraph(code = {print(miedo_plot)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )Figura 1b. Prevalencia Ansiedad según el Miedo a la Situación Pandemia, por Agrupación de Instituciones
capacitacion<-
bd_comparacion %>%
dplyr::group_by(comparacion,p14_contr_cont_cap_epp_rec,tamizaje_ans) %>%
dplyr::summarise(n=n()) %>%
dplyr::filter(!is.na(comparacion),!is.na(p14_contr_cont_cap_epp_rec),!is.na(tamizaje_ans)) %>%
dplyr::ungroup() %>%
dplyr::group_by(comparacion, p14_contr_cont_cap_epp_rec) %>%
dplyr::mutate(perc=scales::percent(n/sum(n))) %>%
dplyr::filter(!grepl("Ausencia de",tamizaje_ans)) %>%
dplyr::mutate(num_perc=readr::parse_number(perc, "\\d+"))
capacitacion_plot<-
ggplot(capacitacion, aes(y=num_perc, x=comparacion, fill=p14_contr_cont_cap_epp_rec))+
geom_bar(stat="identity", position=position_dodge())+
geom_text(aes(label=perc), position=position_dodge(width=1.), vjust = -.5, size = 5) +
theme_sjplot() +
ylim(0,100)+
ylab("Porcentaje Sintomatología Ansiosa")+
xlab("Conjunto de Instituciones")+
scale_fill_manual("Capacitación\nen EPP's",values=c("grey30", "grey55")) +
theme(legend.position = "bottom")+
theme(axis.text.x=element_text(size=14),
axis.title.x = element_text(size=15),
axis.title.y = element_text(size=15),
legend.title = element_text(size=13),
legend.text = element_text(size=12))
capacitacion_plot
Figura 1c. Prevalencia Ansiedad según ha recibido capacitación en Elementos de Protección Personal, por Agrupación de Instituciones
jpeg("_Fig0c_capacitacion.jpg", height = 7, width = 11.5, units = 'in', res = 600)
capacitacion_plot+ theme(plot.background = element_rect(fill=NA, color = NA))
dev.off()## png
## 2ggiraph(code = {print(capacitacion_plot)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )Figura 1c. Prevalencia Ansiedad según ha recibido capacitación en Elementos de Protección Personal, por Agrupación de Instituciones
cultura<-
bd_comparacion %>%
dplyr::group_by(comparacion,p39_cond_trab_cult_org_rec,tamizaje_ans) %>%
dplyr::summarise(n=n()) %>%
dplyr::filter(!is.na(comparacion),!is.na(p39_cond_trab_cult_org_rec),!is.na(tamizaje_ans)) %>%
dplyr::ungroup() %>%
dplyr::group_by(comparacion, p39_cond_trab_cult_org_rec) %>%
dplyr::mutate(perc=scales::percent(n/sum(n))) %>%
dplyr::filter(!grepl("Ausencia de",tamizaje_ans)) %>%
dplyr::mutate(num_perc=readr::parse_number(perc, "\\d+"))
cultura_plot<-
ggplot(cultura, aes(y=num_perc, x=comparacion, fill=p39_cond_trab_cult_org_rec))+
geom_bar(stat="identity", position=position_dodge())+
geom_text(aes(label=perc), position=position_dodge(width=1.), vjust = -.5, size = 5) +
theme_sjplot() +
guides(fill=guide_legend(nrow=2,byrow=TRUE))+
ylim(0,100)+
ylab("Porcentaje Sintomatología Ansiosa")+
xlab("Conjunto de Instituciones")+
scale_fill_manual("Cultura Organizacional\nPerjudicial para la\nSalud Mental",values=c("grey30", "grey55")) +
theme(legend.position = "bottom")+
theme(axis.text.x=element_text(size=13),
axis.title.x = element_text(size=13),
axis.title.y = element_text(size=13),
legend.title = element_text(size=13),
legend.text = element_text(size=11))
cultura_plot
Figura 1d. Prevalencia Ansiedad según se Percibe la Cultura de su Organización como Perjudicial para la Salud Mental, por Agrupación de Instituciones
jpeg("_Fig0d_cultura.jpg", height = 7, width = 11.5, units = 'in', res = 600)
cultura_plot+ theme(plot.background = element_rect(fill=NA, color = NA))
dev.off()## png
## 2ggiraph(code = {print(cultura_plot)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )Figura 1d. Prevalencia Ansiedad según se Percibe la Cultura de su Organización como Perjudicial para la Salud Mental, por Agrupación de Instituciones
p42_res_total<-
bd_comparacion %>%
dplyr::group_by(comparacion,tamizaje_ans) %>%
dplyr::summarise(Mdn=median(p42_res_total,na.rm=T),p25=quantile(p42_res_total,.25,na.rm=T),p75=quantile(p42_res_total,.75,na.rm=T)) %>%
dplyr::filter(!is.na(tamizaje_ans)) %>%
dplyr::mutate(label=paste0("Mdn=",Mdn,"\n(Q1=",p25,"- Q3=",p75,")"))
resiliencia_plot<-
ggplot(p42_res_total, aes(y=Mdn, x=comparacion, fill=tamizaje_ans))+
geom_bar(stat="identity", position=position_dodge())+
ylab("Mediana (Puntaje del 50% del grupo) (Mdn)")+
xlab("Conjunto de Instituciones")+
ylim(0,11)+
geom_text(aes(label=label), position=position_dodge(width=1.), vjust = -1.5, size = 4) +
theme_sjplot() +
geom_errorbar(aes(ymin=p25, ymax=p75), width=.2,
position=position_dodge(.9))+
scale_fill_manual("Sintomatología\nAnsiosa",values=c("grey30", "grey55"),labels=c("Ausencia","Presencia"))
resiliencia_plot
Figura 1e. Prevalencia Ansiedad según Puntaje de Resiliencia, por Agrupación de Instituciones
jpeg("_Fig0e_resiliencia.jpg", height = 7, width = 11.5, units = 'in', res = 600)
resiliencia_plot+ theme(plot.background = element_rect(fill=NA, color = NA))
dev.off()## png
## 2ggiraph(code = {print(resiliencia_plot)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )Figura 1e. Prevalencia Ansiedad según Puntaje de Resiliencia, por Agrupación de Instituciones
ggplot(data = bd_comparacion, aes(x = comparacion, y = p42_res_total, color = tamizaje_ans)) +
geom_boxplot(outlier.shape = NA) +
geom_point(position=position_jitterdodge(jitter.width = .5,dodge.width=.7,jitter.height=.2)) +
# geom_jitter(aes(colour = tamizaje_ans, x = comparacion),position = position_jitterdodge(dodge.width = .25), alpha = 0.5) +
ylab("Puntaje de Resiliencia")+
xlab("Conjunto de Instituciones")+
theme_sjplot() +
scale_color_manual("Sintomatología\nAnsiosa",values=c("grey30", "#FF5252"),labels=c("Ausencia","Presencia"))+
theme(legend.position = "bottom")+
theme(axis.text.x=element_text(size=13),
axis.title.x = element_text(size=13),
axis.title.y = element_text(size=13),
legend.title = element_text(size=13),
legend.text = element_text(size=11))
Figura 1e. Prevalencia Ansiedad según Puntaje de Resiliencia, por Agrupación de Instituciones
sexo_ans_tot<-
bd_comparacion %>%
dplyr::group_by(p45_demo_sexo,tamizaje_ans) %>%
dplyr::summarise(n=n()) %>%
dplyr::filter(!is.na(p45_demo_sexo),!is.na(tamizaje_ans)) %>%
dplyr::ungroup() %>%
dplyr::group_by(p45_demo_sexo) %>%
dplyr::mutate(perc=scales::percent(n/sum(n))) %>%
dplyr::filter(!grepl("Ausencia de",tamizaje_ans))%>%
dplyr::mutate(num_perc=readr::parse_number(perc, "\\d+")) %>%
dplyr::rename("cat"="tamizaje_ans") %>%
dplyr::mutate(var="Sintomatología Ansiosa") %>%
dplyr::mutate(label=paste0(n, "(",perc,")"))
sexo_miedo_tot<-
bd_comparacion %>%
dplyr::group_by(p45_demo_sexo,p11_exp_c19_miedo_pand_rec) %>%
dplyr::summarise(n=n()) %>%
dplyr::filter(!is.na(p45_demo_sexo),!is.na(p11_exp_c19_miedo_pand_rec)) %>%
dplyr::ungroup() %>%
dplyr::group_by(p45_demo_sexo) %>%
dplyr::mutate(perc=scales::percent(n/sum(n))) %>%
dplyr::filter(!grepl("ausencia de",p11_exp_c19_miedo_pand_rec))%>%
dplyr::mutate(num_perc=readr::parse_number(perc, "\\d+"))%>%
dplyr::rename("cat"="p11_exp_c19_miedo_pand_rec") %>%
dplyr::mutate(var="Miedo Pandemia")%>%
dplyr::mutate(label=paste0(n, "(",perc,")"))
sexo_cap_epp_tot<-
bd_comparacion %>%
dplyr::group_by(p45_demo_sexo,p14_contr_cont_cap_epp_rec) %>%
dplyr::summarise(n=n()) %>%
dplyr::filter(!is.na(p45_demo_sexo),!is.na(p14_contr_cont_cap_epp_rec)) %>%
dplyr::ungroup() %>%
dplyr::group_by(p45_demo_sexo) %>%
dplyr::mutate(perc=scales::percent(n/sum(n))) %>%
dplyr::filter(!grepl("No ha",p14_contr_cont_cap_epp_rec))%>%
dplyr::mutate(num_perc=readr::parse_number(perc, "\\d+"))%>%
dplyr::rename("cat"="p14_contr_cont_cap_epp_rec")%>%
dplyr::mutate(var="Capacitación en uso de EPPs")%>%
dplyr::mutate(label=paste0(n, "(",perc,")"))
sexo_cultura_tot<-
bd_comparacion %>%
dplyr::group_by(p45_demo_sexo,p39_cond_trab_cult_org_rec) %>%
dplyr::summarise(n=n()) %>%
dplyr::filter(!is.na(p45_demo_sexo),!is.na(p39_cond_trab_cult_org_rec)) %>%
dplyr::ungroup() %>%
dplyr::group_by(p45_demo_sexo) %>%
dplyr::mutate(perc=scales::percent(n/sum(n))) %>%
dplyr::filter(!grepl("Neutral",p39_cond_trab_cult_org_rec))%>%
dplyr::mutate(num_perc=readr::parse_number(perc, "\\d+"))%>%
dplyr::rename("cat"="p39_cond_trab_cult_org_rec")%>%
dplyr::mutate(var="Cultura Perjudicial para la Salud")%>%
dplyr::mutate(label=paste0(n, "(",perc,")"))
sexo_ptje_res_tot<-
bd_comparacion %>%
dplyr::group_by(p45_demo_sexo) %>%
dplyr::summarise(Mdn=median(p42_res_total,na.rm=T),p25=quantile(p42_res_total,.25,na.rm=T),p75=quantile(p42_res_total,.75,na.rm=T), n=n()) %>%
dplyr::filter(!is.na(p45_demo_sexo)) %>%
dplyr::mutate(label=paste0("Mdn=",Mdn,"\n(Q1=",p25,"- Q3=",p75,") (n=",n,")")) %>%
dplyr::mutate(var="Resiliencia\n(Autoeficacia)") %>%
dplyr::mutate(cat="Puntaje")
#:#:#:#:#:#:#:#:#:#:#:
#Por estrato
comp_sexo_ans<-
bd_comparacion %>%
dplyr::group_by(comparacion,p45_demo_sexo,tamizaje_ans) %>%
dplyr::summarise(n=n()) %>%
dplyr::filter(!is.na(comparacion),!is.na(p45_demo_sexo),!is.na(tamizaje_ans)) %>%
dplyr::ungroup() %>%
dplyr::group_by(comparacion, p45_demo_sexo) %>%
dplyr::mutate(perc=scales::percent(n/sum(n))) %>%
dplyr::filter(!grepl("Ausencia de",tamizaje_ans))%>%
dplyr::mutate(num_perc=readr::parse_number(perc, "\\d+")) %>%
dplyr::rename("cat"="tamizaje_ans") %>%
dplyr::mutate(var="Sintomatología Ansiosa") %>%
dplyr::mutate(label=paste0(n, "(",perc,")"))
comp_sexo_miedo<-
bd_comparacion %>%
dplyr::group_by(comparacion,p45_demo_sexo,p11_exp_c19_miedo_pand_rec) %>%
dplyr::summarise(n=n()) %>%
dplyr::filter(!is.na(comparacion),!is.na(p45_demo_sexo),!is.na(p11_exp_c19_miedo_pand_rec)) %>%
dplyr::ungroup() %>%
dplyr::group_by(comparacion, p45_demo_sexo) %>%
dplyr::mutate(perc=scales::percent(n/sum(n))) %>%
dplyr::filter(!grepl("ausencia de",p11_exp_c19_miedo_pand_rec))%>%
dplyr::mutate(num_perc=readr::parse_number(perc, "\\d+"))%>%
dplyr::rename("cat"="p11_exp_c19_miedo_pand_rec") %>%
dplyr::mutate(var="Miedo Pandemia")%>%
dplyr::mutate(label=paste0(n, "(",perc,")"))
comp_sexo_cap_epp<-
bd_comparacion %>%
dplyr::group_by(comparacion,p45_demo_sexo,p14_contr_cont_cap_epp_rec) %>%
dplyr::summarise(n=n()) %>%
dplyr::filter(!is.na(comparacion),!is.na(p45_demo_sexo),!is.na(p14_contr_cont_cap_epp_rec)) %>%
dplyr::ungroup() %>%
dplyr::group_by(comparacion, p45_demo_sexo) %>%
dplyr::mutate(perc=scales::percent(n/sum(n))) %>%
dplyr::filter(!grepl("No ha",p14_contr_cont_cap_epp_rec))%>%
dplyr::mutate(num_perc=readr::parse_number(perc, "\\d+"))%>%
dplyr::rename("cat"="p14_contr_cont_cap_epp_rec")%>%
dplyr::mutate(var="Capacitación en uso de EPPs")%>%
dplyr::mutate(label=paste0(n, "(",perc,")"))
comp_sexo_cultura<-
bd_comparacion %>%
dplyr::group_by(comparacion,p45_demo_sexo,p39_cond_trab_cult_org_rec) %>%
dplyr::summarise(n=n()) %>%
dplyr::filter(!is.na(comparacion),!is.na(p45_demo_sexo),!is.na(p39_cond_trab_cult_org_rec)) %>%
dplyr::ungroup() %>%
dplyr::group_by(comparacion, p45_demo_sexo) %>%
dplyr::mutate(perc=scales::percent(n/sum(n))) %>%
dplyr::filter(!grepl("Neutral",p39_cond_trab_cult_org_rec))%>%
dplyr::mutate(num_perc=readr::parse_number(perc, "\\d+"))%>%
dplyr::rename("cat"="p39_cond_trab_cult_org_rec")%>%
dplyr::mutate(var="Cultura Perjudicial para la Salud")%>%
dplyr::mutate(label=paste0(n, "(",perc,")"))
comp_sexo_ptje_res<-
bd_comparacion %>%
dplyr::group_by(comparacion,p45_demo_sexo) %>%
dplyr::summarise(Mdn=median(p42_res_total,na.rm=T),p25=quantile(p42_res_total,.25,na.rm=T),p75=quantile(p42_res_total,.75,na.rm=T), n=n()) %>%
dplyr::filter(!is.na(p45_demo_sexo)) %>%
dplyr::mutate(label=paste0("Mdn=",Mdn,"\n(Q1=",p25,"- Q3=",p75,") (n=",n,")")) %>%
dplyr::mutate(var="Resiliencia\n(Autoeficacia)") %>%
dplyr::mutate(cat="Puntaje")
tabla_dos_vias<-
plyr::rbind.fill(comp_sexo_ans,comp_sexo_miedo,comp_sexo_cap_epp,comp_sexo_cultura,comp_sexo_ptje_res) %>% dplyr::select(comparacion, p45_demo_sexo,cat,var, label) %>%
group_by(p45_demo_sexo, comparacion) %>%
mutate(rn = row_number()) %>%
tidyr::pivot_wider(names_from = c("p45_demo_sexo","comparacion"), values_from = "label", names_sep = ".") %>%
dplyr::select(-rn) %>%
dplyr::select(var, everything())
tabla_dos_vias_tot<-
plyr::rbind.fill(sexo_ans_tot, sexo_miedo_tot, sexo_cap_epp_tot, sexo_cultura_tot, sexo_ptje_res_tot) %>%
dplyr::select(p45_demo_sexo,cat,var, label) %>%
group_by(p45_demo_sexo) %>%
mutate(rn = row_number()) %>%
tidyr::pivot_wider(names_from = c("p45_demo_sexo"), values_from = "label", names_sep = ".") %>%
dplyr::select(-rn,-var,-cat) %>%
dplyr::rename("Muestra.Total.Mujeres"="Mujer","Muestra.Total.Hombres"="Hombre")
#_#_#_#_#_
#Tabla final
cbind(
tabla_dos_vias,
Sig_dir=c("0.052","0.678","0.885","0.681","0.250"),
Sig_hosp=c("0.045", "0.251","0.193","1.000","0.856"),
tabla_dos_vias_tot,
Sig_tot=c("0.005","0.652","0.471","0.892","0.584")
) %>%
data.frame() %>%
dplyr::select(var,cat, `Hombre.Dirección.Servicio`,`Mujer.Dirección.Servicio`,
Sig_dir,`Hombre.Hospitales`,`Mujer.Hospitales`, Sig_hosp,
`Muestra.Total.Hombres`,`Muestra.Total.Mujeres`,Sig_tot) %>%
knitr::kable(format = "html", format.args = list(decimal.mark = ".", big.mark = ","),
caption="Tabla 1. Prevalencia de las Variables de Interés, según Sexo y Agrupación de Instituciones",
col.names=c("Variable", "Categoría", "Hombres", "Mujeres","Sig.","Hombres", "Mujeres","Sig.","Hombres", "Mujeres","Sig."),
align =rep('c', 101)) %>%
kableExtra::add_header_above(c(" "," ", "Dirección del Servicio" = 3, "Hospitales" = 3,"Muestra Total"=3)) %>%
kableExtra::kable_styling(bootstrap_options = c("striped", "hover"),font_size = 12) %>%
kableExtra::add_footnote(c("Nota. Porcentajes entre paréntesis representan los porcentajes de quienes presentan la categoría de interés, por Sexo y Agrupación;\nPara variable continuas, se presenta la Mediana (Mdn), Primer (Q1), Tercer (Q3) Cuartil y recuento (n)"), notation = "none") %>%
kableExtra::scroll_box(width = "100%", height = "400px") | Variable | Categoría | Hombres | Mujeres | Sig. | Hombres | Mujeres | Sig. | Hombres | Mujeres | Sig. |
|---|---|---|---|---|---|---|---|---|---|---|
| Sintomatología Ansiosa | Indicadores de Sintomatología Ansiosa | 10(29%) | 35(50.7%) | 0.052 | 13(26%) | 65(43%) | 0.045 | 23(27%) | 100(45.7%) | 0.005 |
| Miedo Pandemia | Predomina el miedo | 19(54.3%) | 33(47.8%) | 0.678 | 19(38%) | 73(48.7%) | 0.251 | 38(45%) | 106(48.4%) | 0.652 |
| Capacitación en uso de EPPs | Ha sido capacitado | 17(48.6%) | 31(45%) | 0.885 | 41(82%) | 107(71%) | 0.193 | 58(68%) | 138(63%) | 0.471 |
| Cultura Perjudicial para la Salud | De acuerdo/Totalmente de acuerdo | 15(43%) | 34(49.3%) | 0.681 | 22(44%) | 65(43%) | 1.000 | 37(44%) | 99(45.2%) | 0.892 |
| Resiliencia (Autoeficacia) | Puntaje | Mdn=9 (Q1=7- Q3=9) (n=35) | Mdn=8 (Q1=6- Q3=9) (n=69) | 0.250 | Mdn=8 (Q1=7- Q3=9) (n=50) | Mdn=8 (Q1=7- Q3=9) (n=150) | 0.856 | Mdn=8 (Q1=7- Q3=9) (n=85) | Mdn=8 (Q1=7- Q3=9) (n=219) | 0.584 |
|
Nota. Porcentajes entre paréntesis representan los porcentajes de quienes presentan la categoría de interés, por Sexo y Agrupación; Para variable continuas, se presenta la Mediana (Mdn), Primer (Q1), Tercer (Q3) Cuartil y recuento (n) |
library(compareGroups)
table1_dir <- suppressWarnings(compareGroups(p45_demo_sexo~
tamizaje_ans +
p11_exp_c19_miedo_pand_rec +
p14_contr_cont_cap_epp_rec +
p39_cond_trab_cult_org_rec +
p42_res_total,
method= c(
tamizaje_ans=3,
p11_exp_c19_miedo_pand_rec=3,
p14_contr_cont_cap_epp_rec=3,
p39_cond_trab_cult_org_rec=3,
p42_res_total=2
),
data = bd_comparacion %>% dplyr::filter(comparacion=="Dirección Servicio"), #Dirección Servicio Hospitales
include.miss = F, #baja el número de casos
p.corrected=T,
chisq.test.perm=T,
chisq.test.seed=1234,
var.equal=T)
)
#
restab1_dir <- createTable(table1_dir, show.p.overall = T)
table1_hosp <- suppressWarnings(compareGroups(p45_demo_sexo~
tamizaje_ans +
p11_exp_c19_miedo_pand_rec +
p14_contr_cont_cap_epp_rec +
p39_cond_trab_cult_org_rec +
p42_res_total,
method= c(
tamizaje_ans=3,
p11_exp_c19_miedo_pand_rec=3,
p14_contr_cont_cap_epp_rec=3,
p39_cond_trab_cult_org_rec=3,
p42_res_total=2
),
data = bd_comparacion %>% dplyr::filter(comparacion=="Hospitales"), #Dirección Servicio Hospitales
include.miss = F, #baja el número de casos
p.corrected=T,
chisq.test.perm=T,
chisq.test.seed=1234,
var.equal=T)
)
#
restab1_hosp <- createTable(table1_hosp, show.p.overall = T)
table1_tot <- suppressWarnings(compareGroups(p45_demo_sexo~
tamizaje_ans +
p11_exp_c19_miedo_pand_rec +
p14_contr_cont_cap_epp_rec +
p39_cond_trab_cult_org_rec +
p42_res_total,
method= c(
tamizaje_ans=3,
p11_exp_c19_miedo_pand_rec=3,
p14_contr_cont_cap_epp_rec=3,
p39_cond_trab_cult_org_rec=3,
p42_res_total=2
),
data = bd_comparacion, #Dirección Servicio Hospitales
include.miss = F, #baja el número de casos
p.corrected=T,
chisq.test.perm=T,
chisq.test.seed=1234,
var.equal=T)
)
#
restab1_tot <- createTable(table1_tot, show.p.overall = T)
export2md(cbind("Total Muestra" = restab1_tot, "Dirección Servicio" = restab1_dir, Hospitales = restab1_hosp)
, size=10, first.strip=T, hide.no="no", position="center",
format="html",caption= "Table 1. Descriptivo Aplicación Hospital SS Arauco",
# col.names=c("Variables", "SS Arauco", "N")
)%>%
kableExtra::add_footnote(c("Note. N= Número de participantes con al menos una respuesta válida en las variables seleccionadas; Variables continuas se presentan como medianas y percentiles 25 y 75;",
"Variables categóricas son presentadas como el recuento y el porcentaje"), notation = "none")%>%
kableExtra::scroll_box(width = "100%", height = "800px")| Hombre | Mujer | p.overall | Hombre | Mujer | p.overall | Hombre | Mujer | p.overall | |
|---|---|---|---|---|---|---|---|---|---|
| N=85 | N=219 | N=35 | N=69 | N=50 | N=150 | ||||
| Sintomatología ansiosa: | 0.005 | 0.052 | 0.045 | ||||||
| Ausencia de Indicadores de Sintomatología Ansiosa | 62 (72.9%) | 119 (54.3%) | 25 (71.4%) | 34 (49.3%) | 37 (74.0%) | 85 (56.7%) | |||
| Indicadores de Sintomatología Ansiosa | 23 (27.1%) | 100 (45.7%) | 10 (28.6%) | 35 (50.7%) | 13 (26.0%) | 65 (43.3%) | |||
| ¿Cómo califica su nivel de miedo de la situación de pandemia al día de hoy? (dicotomizado): | 0.652 | 0.678 | 0.251 | ||||||
| Predomina el miedo | 38 (44.7%) | 106 (48.4%) | 19 (54.3%) | 33 (47.8%) | 19 (38.0%) | 73 (48.7%) | |||
| Predomina la ausencia de miedo | 47 (55.3%) | 113 (51.6%) | 16 (45.7%) | 36 (52.2%) | 31 (62.0%) | 77 (51.3%) | |||
| ¿Ha participado en alguna capacitación sobre el uso correcto de EPPs? (dicotomizada): | 0.471 | 0.885 | 0.193 | ||||||
| Ha sido capacitado | 58 (68.2%) | 138 (63.0%) | 17 (48.6%) | 31 (44.9%) | 41 (82.0%) | 107 (71.3%) | |||
| No ha sido capacitado | 27 (31.8%) | 81 (37.0%) | 18 (51.4%) | 38 (55.1%) | 9 (18.0%) | 43 (28.7%) | |||
| ¿Cuán de acuerdo está con la siguiente afirmación?: La cultura de mi organización tolera comportamientos que perjudican la salud mental de los trabajadores (Dicotomizada): | 0.892 | 0.681 | 1.000 | ||||||
| De acuerdo/Totalmente de acuerdo | 37 (43.5%) | 99 (45.2%) | 15 (42.9%) | 34 (49.3%) | 22 (44.0%) | 65 (43.3%) | |||
| Neutral/En desacuerdo/Totalmente en desacuerdo | 48 (56.5%) | 120 (54.8%) | 20 (57.1%) | 35 (50.7%) | 28 (56.0%) | 85 (56.7%) | |||
| Suma puntajes de resilencia | 8.00 [7.00;9.00] | 8.00 [7.00;9.00] | 0.584 | 9.00 [7.00;9.00] | 8.00 [6.00;9.00] | 0.250 | 8.00 [7.00;9.00] | 8.00 [7.00;9.00] | 0.856 |
| Note. N= Número de participantes con al menos una respuesta válida en las variables seleccionadas; Variables continuas se presentan como medianas y percentiles 25 y 75; | |||||||||
| Variables categóricas son presentadas como el recuento y el porcentaje |
0.2 Asociaciones Multivariadas
Table = xtabs(n ~ tamizaje_ans+ p45_demo_sexo+ comparacion,
data=
bd_comparacion %>%
dplyr::group_by(comparacion,p45_demo_sexo,tamizaje_ans) %>%
dplyr::summarise(n=n()) %>%
dplyr::filter(!is.na(comparacion),!is.na(p45_demo_sexo),!is.na(tamizaje_ans))
)
### Note that the grouping variable is last in the xtabs function
data.frame(expand.grid(rev(attr(ftable(Table), "row.vars"))), unclass(ftable(Table))) %>%
knitr::kable(format = "html", format.args = list(decimal.mark = ".", big.mark = ","),
caption="Tabla 1. Recuento de Datos Según Sexo y Dirección",
col.names=c("Sexo", "Ansiedad", "Dir. Servicio", "Hospitales"),
align =rep('c', 101)) %>%
kableExtra::kable_styling(bootstrap_options = c("striped", "hover"),font_size = 12) %>%
kableExtra::scroll_box(width = "100%", height = "350px")#https://www.rdocumentation.org/packages/stats/versions/3.6.2/topics/mantelhaen.test
#Performs a Cochran-Mantel-Haenszel chi-squared test of the null that two nominal variables are conditionally independent in each stratum, assuming that there is no three-way interaction.
#Test for homogeneity on 2 x 2 x k tables over strata (i.e., whether the log odds ratios are the same in all strata).
vcd::woolf_test(Table)
#El test de wolf no es significativo, por lo que ORs comunes es al menos potencialmente rasonable para ambos estratos
DescTools::BreslowDayTest(x = Table, OR = 1)
#Hay homogeneidad, por lo que se puede utilizar M_H
# Me dan algo distinto
#The asymptotic distribution is only valid if there is no three-way interaction. In the classical 2 by 2 by K case, this is equivalent to the conditional odds ratios in each stratum being identical. Currently, no inference on homogeneity of the odds ratios is performed.
#The Breslow-Day test can be used to check the null hypothesis that all odds ratios are equal. The Cochran-Mantel-Haenszel test can be thought of as a special case of the Breslow-Day test wherein the common odds ratio is assumed to be 1
mantelhaen.test(Table)
mantelhaen.test(Table, exact = TRUE)
## Evidencia de la asociación entre sexo y ansiedad al ajustar por dirección del servicio
# Evidencia de que hay una asociación
# En cada nivel de la comparación (hosp y servicio), tamizaje ansiedad y sexo no son indpendientes.
# Hay una interacción de 3 vías
#apply(Table, 3, function(x) (x[1,1]*x[2,2])/(x[1,2]*x[2,1]))
# library(finalfit)
#explanatory = c("age.factor*sex.factor", "obstruct.factor", "perfor.factor")
#p40_istas_rel_su
#p40_istas_ex_psi
#p40_istas_rel_su
#p40_istas_con_rol
#table(bd_comparacion$comparacion)
bd_comparacion %>%
dplyr::filter(comparacion=="Dirección Servicio") %>%
dplyr::mutate(comparacion= ff_label(comparacion,"Agrupación Instituciones"),
p45_demo_sexo= ff_label(p45_demo_sexo, "Sexo\nMujer"),
p11_exp_c19_miedo_pand_rec= ff_label(forcats::fct_rev(p11_exp_c19_miedo_pand_rec), "Predomina el\nMiedo Pandemia"),
p14_contr_cont_cap_epp_rec= ff_label(p14_contr_cont_cap_epp_rec, "Capacitación uso de EPP's\n (Sin Capacitación)"),
p39_cond_trab_cult_org_rec= ff_label(forcats::fct_rev(p39_cond_trab_cult_org_rec), "Cultura Tolera \n Comportamientos Perjudiciales"),
p42_res_total= ff_label(p42_res_total, "Puntaje Resiliencia"),
tamizaje_ans= ff_label(tamizaje_ans, "Presencia Sint. Ansiosa"))%>%
#ff_interaction(p45_demo_sexo, p8_exp_c19_cerca_cont_rec) %>%
#ff_interaction(.data, ..., levels_sep = "|", var_sep = "__", label_sep = ":")
finalfit("tamizaje_ans", c("p45_demo_sexo","p11_exp_c19_miedo_pand_rec","p14_contr_cont_cap_epp_rec","p39_cond_trab_cult_org_rec","p42_res_total"),dependent_label_prefix = "") -> t_ss
bd_comparacion %>%
dplyr::filter(comparacion=="Hospitales") %>%
dplyr::mutate(comparacion= ff_label(comparacion,"Agrupación Instituciones"),
p45_demo_sexo= ff_label(p45_demo_sexo, "Sexo\nMujer"),
p11_exp_c19_miedo_pand_rec= ff_label(forcats::fct_rev(p11_exp_c19_miedo_pand_rec), "Predomina el\nMiedo Pandemia"),
p14_contr_cont_cap_epp_rec= ff_label(p14_contr_cont_cap_epp_rec, "Capacitación uso de EPP's\n (Sin Capacitación)"),
p39_cond_trab_cult_org_rec= ff_label(forcats::fct_rev(p39_cond_trab_cult_org_rec), "Cultura Tolera \n Comportamientos Perjudiciales"),
p42_res_total= ff_label(p42_res_total, "Puntaje Resiliencia"),
tamizaje_ans= ff_label(tamizaje_ans, "Presencia Sint. Ansiosa"))%>%
#ff_interaction(p45_demo_sexo, p8_exp_c19_cerca_cont_rec) %>%
#ff_interaction(.data, ..., levels_sep = "|", var_sep = "__", label_sep = ":")
finalfit("tamizaje_ans", c("p45_demo_sexo","p11_exp_c19_miedo_pand_rec","p14_contr_cont_cap_epp_rec","p39_cond_trab_cult_org_rec","p42_res_total"),dependent_label_prefix = "") -> t_hosp
bd_comparacion %>%
dplyr::mutate(comparacion= ff_label(comparacion,"Agrupación Instituciones"),
p45_demo_sexo= ff_label(p45_demo_sexo, "Sexo\nMujer"),
p11_exp_c19_miedo_pand_rec= ff_label(forcats::fct_rev(p11_exp_c19_miedo_pand_rec), "Predomina el\nMiedo Pandemia"),
p14_contr_cont_cap_epp_rec= ff_label(p14_contr_cont_cap_epp_rec, "Capacitación uso de EPP's\n (Sin Capacitación)"),
p39_cond_trab_cult_org_rec= ff_label(forcats::fct_rev(p39_cond_trab_cult_org_rec), "Cultura Tolera \n Comportamientos Perjudiciales"),
p42_res_total= ff_label(p42_res_total, "Puntaje Resiliencia"),
tamizaje_ans= ff_label(tamizaje_ans, "Presencia Sint. Ansiosa"))%>%
#ff_interaction(p45_demo_sexo, p8_exp_c19_cerca_cont_rec) %>%
#ff_interaction(.data, ..., levels_sep = "|", var_sep = "__", label_sep = ":")
finalfit("tamizaje_ans", c("p45_demo_sexo","p11_exp_c19_miedo_pand_rec","p14_contr_cont_cap_epp_rec","p39_cond_trab_cult_org_rec","p42_res_total"),dependent_label_prefix = "") -> t_tot
cbind.data.frame(t_tot[,c(5,6)],t_ss[,c(1,2,5,6)], t_hosp[,c(5,6)]) %>%
janitor::clean_names() %>%
dplyr::mutate(or_multivariable_2=dplyr::case_when(grepl("Contacto directo y estrecho con paciente",x)~"ICs no realistas",T~or_multivariable_2)) %>%
dplyr::mutate(x=dplyr::case_when(x=="Mean (SD)"~"",T~x)) %>%
dplyr::select(presencia_sint_ansiosa, x, or_univariable,or_multivariable,or_univariable_2,or_multivariable_2,or_univariable_3,or_multivariable_3) %>%
dplyr::rename("OR (Crudo) Total Muestra"="or_univariable") %>%
dplyr::rename("OR (Ajustado) Total Muestra"="or_multivariable") %>%
dplyr::rename("OR (Crudo) Dir. Servicio"="or_univariable_2") %>%
dplyr::rename("OR (Ajustado) Dir. Servicio"="or_multivariable_2") %>%
dplyr::rename("OR (Crudo) Hospitales"="or_univariable_3") %>%
dplyr::rename("OR (Ajustado) Hospitales"="or_multivariable_3") %>%
dplyr::rename("Categoría"="x","Var. de Exposición"="presencia_sint_ansiosa") %>%
knitr::kable(,format = "html", format.args = list(decimal.mark = ".", big.mark = ","),
caption="Tabla 2. Asociación entre Sintomatología Ansiosa y Variables de Interés por Dimensión",
align =rep('c', 101)) %>%
kableExtra::kable_styling(bootstrap_options = c("striped", "hover"),font_size = 10) %>%
kableExtra::scroll_box(width = "100%", height = "350px")| Var. de Exposición | Categoría | OR (Crudo) Total Muestra | OR (Ajustado) Total Muestra | OR (Crudo) Dir. Servicio | OR (Ajustado) Dir. Servicio | OR (Crudo) Hospitales | OR (Ajustado) Hospitales |
|---|---|---|---|---|---|---|---|
| Sexo Mujer | Hombre |
|
|
|
|
|
|
| Mujer | 2.27 (1.33-3.98, p=0.003) | 2.35 (1.32-4.31, p=0.005) | 2.57 (1.10-6.37, p=0.034) | 2.93 (1.13-8.12, p=0.031) | 2.18 (1.09-4.56, p=0.032) | 2.16 (1.02-4.78, p=0.049) | |
| Predomina el Miedo Pandemia | Predomina la ausencia de miedo |
|
|
|
|
|
|
| Predomina el miedo | 3.15 (2.07-4.84, p<0.001) | 4.29 (2.55-7.35, p<0.001) | 4.67 (2.21-10.24, p<0.001) | 5.09 (1.99-14.23, p=0.001) | 2.59 (1.56-4.36, p<0.001) | 3.85 (2.07-7.39, p<0.001) | |
| Capacitación uso de EPP’s (Sin Capacitación) | Ha sido capacitado |
|
|
|
|
|
|
| No ha sido capacitado | 1.24 (0.81-1.91, p=0.325) | 1.08 (0.63-1.82, p=0.784) | 1.10 (0.54-2.26, p=0.798) | 0.94 (0.38-2.31, p=0.885) | 1.19 (0.66-2.12, p=0.563) | 1.03 (0.50-2.09, p=0.942) | |
| Cultura Tolera Comportamientos Perjudiciales | Neutral/En desacuerdo/Totalmente en desacuerdo |
|
|
|
|
|
|
| De acuerdo/Totalmente de acuerdo | 1.88 (1.19-2.98, p=0.007) | 1.73 (1.05-2.88, p=0.033) | 3.72 (1.67-8.54, p=0.002) | 2.67 (1.09-6.66, p=0.033) | 1.32 (0.75-2.32, p=0.342) | 1.37 (0.74-2.55, p=0.323) | |
| Puntaje Resiliencia | 0.78 (0.66-0.91, p=0.002) | 0.72 (0.60-0.86, p<0.001) | 0.81 (0.60-1.07, p=0.135) | 0.74 (0.52-1.03, p=0.086) | 0.77 (0.63-0.93, p=0.008) | 0.72 (0.57-0.89, p=0.003) |
cbind.data.frame(t_tot[,c(5,6)],t_ss[,c(1,2,5,6)], t_hosp[,c(5,6)]) %>%
janitor::clean_names() %>%
#dplyr::mutate(or_multivariable_2=dplyr::case_when(grepl("Contacto directo y estrecho con paciente",x)~"ICs no realistas",T~or_multivariable_2)) %>%
dplyr::mutate(x=dplyr::case_when(x=="Mean (SD)"~"",T~x)) %>%
dplyr::select(presencia_sint_ansiosa, x, or_univariable,or_multivariable,or_univariable_2,or_multivariable_2,or_univariable_3,or_multivariable_3) %>%
dplyr::rename("Categoría"="x","Var. de Exposición"="presencia_sint_ansiosa") %>%
tidyr::separate(or_univariable, c("or_univariable_or", "or_univariable_ci"), " ", extra = "merge") %>%
tidyr::separate(or_univariable_ci, c("or_univariable_ci_lo", "or_univariable_ci_up"), "-", extra = "merge") %>%
tidyr::separate(or_univariable_ci_up, c("or_univariable_ci_up", "or_univariable_p"), ", p", extra = "merge") %>%
tidyr::separate(or_multivariable, c("or_multivariable_or", "or_multivariable_ci"), " ", extra = "merge") %>%
tidyr::separate(or_multivariable_ci, c("or_multivariable_ci_lo", "or_multivariable_ci_up"), "-", extra = "merge") %>%
tidyr::separate(or_multivariable_ci_up, c("or_multivariable_ci_up", "or_multivariable_p"), ", p", extra = "merge") %>%
tidyr::separate(or_univariable_2, c("or_univariable_or_2", "or_univariable_ci_2"), " ", extra = "merge") %>%
tidyr::separate(or_univariable_ci_2, c("or_univariable_ci_lo_2", "or_univariable_ci_up_2"), "-", extra = "merge") %>%
tidyr::separate(or_univariable_ci_up_2, c("or_univariable_ci_up_2", "or_univariable_p_2"), ", p", extra = "merge") %>%
tidyr::separate(or_multivariable_2, c("or_multivariable_or_2", "or_multivariable_ci_2"), " ", extra = "merge") %>%
tidyr::separate(or_multivariable_ci_2, c("or_multivariable_ci_lo_2", "or_multivariable_ci_up_2"), "-", extra = "merge") %>%
tidyr::separate(or_multivariable_ci_up_2, c("or_multivariable_ci_up_2", "or_multivariable_p_2"), ", p", extra = "merge") %>%
tidyr::separate(or_univariable_3, c("or_univariable_or_3", "or_univariable_ci_3"), " ", extra = "merge") %>%
tidyr::separate(or_univariable_ci_3, c("or_univariable_ci_lo_3", "or_univariable_ci_up_3"), "-", extra = "merge") %>%
tidyr::separate(or_univariable_ci_up_3, c("or_univariable_ci_up_3", "or_univariable_p_3"), ", p", extra = "merge") %>%
tidyr::separate(or_multivariable_3, c("or_multivariable_or_3", "or_multivariable_ci_3"), " ", extra = "merge") %>%
tidyr::separate(or_multivariable_ci_3, c("or_multivariable_ci_lo_3", "or_multivariable_ci_up_3"), "-", extra = "merge") %>%
tidyr::separate(or_multivariable_ci_up_3, c("or_multivariable_ci_up_3", "or_multivariable_p_3"), ", p", extra = "merge") %>%
dplyr::mutate_at(vars(c("or_univariable_p","or_multivariable_p","or_univariable_p_2","or_multivariable_p_2","or_univariable_p_3","or_multivariable_p_3")),list(`sig`=~ifelse(grepl("<",.),1,0))) %>%
#dplyr::mutate_at(vars(c("or_univariable_p","or_multivariable_p","or_univariable_p_2","or_multivariable_p_2","or_univariable_p_3","or_multivariable_p_3")),~str_replace(., "=", "")) %>%
dplyr::mutate_at(vars(
c("or_univariable_or", "or_univariable_ci_lo","or_univariable_ci_up","or_univariable_p",
"or_multivariable_or", "or_multivariable_ci_lo","or_multivariable_ci_up","or_multivariable_p",
"or_univariable_or_2", "or_univariable_ci_lo_2","or_univariable_ci_up_2","or_univariable_p_2",
"or_multivariable_or_2", "or_multivariable_ci_lo_2","or_multivariable_ci_up_2","or_multivariable_p_2",
"or_univariable_or_3", "or_univariable_ci_lo_3","or_univariable_ci_up_3","or_univariable_p_3",
"or_multivariable_or_3", "or_multivariable_ci_lo_3","or_multivariable_ci_up_3","or_multivariable_p_3")),~ readr::parse_number(., "\\d+")) %>% #or_univariable_p_2
#copy_names()
dplyr::rename("OR (Crudo)-Total Muestra"="or_univariable_or") %>%
dplyr::rename("OR (Crudo) [IC95% Bajo]-Total Muestra"="or_univariable_ci_lo") %>%
dplyr::rename("OR (Crudo) [IC95% Alto]-Total Muestra"="or_univariable_ci_up") %>%
dplyr::rename("OR (Crudo) Sig.-Total Muestra"="or_univariable_p") %>%
dplyr::rename("OR (Crudo) Sig.<001-Total Muestra"="or_univariable_p_sig") %>%
dplyr::rename("OR (Ajustado)-Total Muestra"="or_multivariable_or") %>%
dplyr::rename("OR (Ajustado) [IC95% Bajo]-Total Muestra"="or_multivariable_ci_lo") %>%
dplyr::rename("OR (Ajustado) [IC95% Alto]-Total Muestra"="or_multivariable_ci_up") %>%
dplyr::rename("OR (Ajustado) Sig.-Total Muestra"="or_multivariable_p") %>%
dplyr::rename("OR (Ajustado) Sig.<001-Total Muestra"="or_multivariable_p_sig") %>%
dplyr::rename("OR (Crudo)-Dir. Servicio"="or_univariable_or_2") %>%
dplyr::rename("OR (Crudo) [IC95% Bajo]-Dir. Servicio"="or_univariable_ci_lo_2") %>%
dplyr::rename("OR (Crudo) [IC95% Alto]-Dir. Servicio"="or_univariable_ci_up_2") %>%
dplyr::rename("OR (Crudo) Sig.-Dir. Servicio"="or_univariable_p_2") %>%
dplyr::rename("OR (Crudo) Sig.<001-Dir. Servicio"="or_univariable_p_2_sig") %>%
dplyr::rename("OR (Ajustado)-Dir. Servicio"="or_multivariable_or_2") %>%
dplyr::rename("OR (Ajustado) [IC95% Bajo]-Dir. Servicio"="or_multivariable_ci_lo_2") %>%
dplyr::rename("OR (Ajustado) [IC95% Alto]-Dir. Servicio"="or_multivariable_ci_up_2") %>%
dplyr::rename("OR (Ajustado) Sig.-Dir. Servicio"="or_multivariable_p_2") %>%
dplyr::rename("OR (Ajustado) Sig.<001-Dir. Servicio"="or_multivariable_p_2_sig") %>%
dplyr::rename("OR (Crudo)-Hospitales"="or_univariable_or_3") %>%
dplyr::rename("OR (Crudo) [IC95% Bajo]-Hospitales"="or_univariable_ci_lo_3") %>%
dplyr::rename("OR (Crudo) [IC95% Alto]-Hospitales"="or_univariable_ci_up_3") %>%
dplyr::rename("OR (Crudo) Sig.-Hospitales"="or_univariable_p_3") %>%
dplyr::rename("OR (Crudo) Sig.<001-Hospitales"="or_univariable_p_3_sig") %>%
dplyr::rename("OR (Ajustado)-Hospitales"="or_multivariable_or_3") %>%
dplyr::rename("OR (Ajustado) [IC95% Bajo]-Hospitales"="or_multivariable_ci_lo_3") %>%
dplyr::rename("OR (Ajustado) [IC95% Alto]-Hospitales"="or_multivariable_ci_up_3") %>%
dplyr::rename("OR (Ajustado) Sig.-Hospitales"="or_multivariable_p_3") %>%
dplyr::rename("OR (Ajustado) Sig.<001-Hospitales"="or_multivariable_p_3_sig") %>%
assign("ORs_table",.,envir=.GlobalEnv)
melt(ORs_table) %>%
tidyr::separate(variable, c("indicador", "población"), "-", extra = "merge") %>%
dplyr::mutate(`Var. de Exposición_cor`=ifelse(`Var. de Exposición`=="",lag(`Var. de Exposición`),`Var. de Exposición`)) %>%
dplyr::mutate(conservar=dplyr::case_when(`Var. de Exposición`=="" & `Categoría`!=""~1,
grepl("Resiliencia",`Var. de Exposición`) & `Categoría`==""~1,
T~0)) %>%
dplyr::filter(conservar==1) %>%
dplyr::mutate(`Var. de Exposición`=ifelse(`Var. de Exposición`=="",lag(`Var. de Exposición`),`Var. de Exposición`)) %>%
dplyr::select(-`Var. de Exposición`,-conservar) %>%
dplyr::rename(`Var. de Exposición`=`Var. de Exposición_cor`) %>%
dplyr::select(`Var. de Exposición`,everything()) %>%
assign("ORs_table_long",.,envir=.GlobalEnv) %>%
tidyr::pivot_wider(names_from = "indicador", values_from = c("value"), names_sep = ".") %>%
#glimpse()
dplyr::mutate(`OR (Crudo) Sig.<001`=dplyr::case_when(`OR (Crudo) Sig.<001`==1 & `OR (Crudo) Sig.`==.001~"p<.001",
`OR (Crudo) Sig.<001`==0 & `OR (Crudo) Sig.`==.001~"p=.001",
`OR (Crudo) Sig.<001`==0 & `OR (Crudo) Sig.`<.01~"p<.01",
`OR (Crudo) Sig.<001`==0 & `OR (Crudo) Sig.`<.05~"p<.05",
T~"NS")) %>%
dplyr::mutate(`OR (Ajustado) Sig.<001`=dplyr::case_when(`OR (Ajustado) Sig.<001`==1 & `OR (Ajustado) Sig.`==.001~"p<.001",
`OR (Ajustado) Sig.<001`==0 & `OR (Ajustado) Sig.`==.001~"p=.001",
`OR (Ajustado) Sig.<001`==0 & `OR (Ajustado) Sig.`<.01~"p<.01",
`OR (Ajustado) Sig.<001`==0 & `OR (Ajustado) Sig.`<.05~"p<.05",
T~"NS")) %>%
#dplyr::select(-`OR (Crudo) Sig.<001`,-`OR (Ajustado) Sig.<001`) %>%
assign("ORs_table_long_wide",.,envir=.GlobalEnv)
p1<-
ggplot2::ggplot(ORs_table_long_wide,aes(x = `Var. de Exposición`, y = `OR (Crudo)`, fill = población)) +
ylim(0,15)+
geom_hline(yintercept = 1, alpha=0.8, colour = "gray10",linetype = 2) +
geom_bar(stat="identity", position=position_dodge())+
geom_text(aes(label=`OR (Crudo) Sig.<001`), position=position_dodge(width=1.1), vjust = -.5, size = 5) +
geom_errorbar(aes(ymin=`OR (Crudo) [IC95% Bajo]`, ymax=`OR (Crudo) [IC95% Alto]`), width=.2,
position=position_dodge(.9)) +
theme_sjplot() +
scale_fill_manual(values=c("grey30", "grey55", "grey80")) +
theme(legend.position = "bottom")+
theme(axis.text.x=element_text(size=13),
axis.title.x = element_text(size=15),
axis.title.y = element_text(size=15),
legend.title = element_text(size=15),
legend.text = element_text(size=13))
p1
Figura 2. Chances de Presentar Ansiedad (Crudas)
jpeg("_Fig1.jpg", height = 7, width = 11.5, units = 'in', res = 600)
p1+ theme(plot.background = element_rect(fill=NA, color = NA))
dev.off()## png
## 2 p2<-
ggplot2::ggplot(ORs_table_long_wide,aes(x = `Var. de Exposición`, y = `OR (Ajustado)`, fill = población)) +
ylim(0,15)+
geom_hline(yintercept = 1, alpha=0.4, colour = "gray10",linetype = 2) +
geom_bar(stat="identity", position=position_dodge())+
geom_text(aes(label=`OR (Ajustado) Sig.<001`), position=position_dodge(width=1.1), vjust = -.5, size = 5) +
geom_errorbar(aes(ymin=`OR (Ajustado) [IC95% Bajo]`, ymax=`OR (Ajustado) [IC95% Alto]`), width=.2,
position=position_dodge(.9)) +
theme_sjplot() +
scale_fill_manual(values=c("grey30", "grey55", "grey80")) +
theme(legend.position = "bottom")+
theme(axis.text.x=element_text(size=14),
axis.title.x = element_text(size=15),
axis.title.y = element_text(size=15),
legend.title = element_text(size=15),
legend.text = element_text(size=14))
p2
Figura 3. Chances de Presentar Ansiedad (Ajustado)
jpeg("_Fig2.jpg", height = 7, width = 11.5, units = 'in', res = 600)
p2+ theme(plot.background = element_rect(fill=NA, color = NA))
dev.off()## png
## 2Por cada hombre que presenta sintomatología ansiosa, hay al menos 2 mujeres que la reportan, incluso manteniendo estables las diferencias por capacitación en EPP, miedo a la pandemia, cultura organizacional percibida y puntaje de resiliencia. Es decir, por sí mismo el hecho de ser mujer constituye un factor de riesgo de presentar sintomatología ansiosa.
A nivel general, el incremento de los puntajes de Resiliencia se vinculan con chances 28% menores (CI95%= 0.60-0.86, p<.001) de presentar sintomatología ansiosa, incluso manteniendo estables el resto de las variables. Descirptivamente, esta diferencia se aprecia mayormente en hospitales.
Aquellos(as) trabajadores(as) en los que predomina el miedo a la situación pandemia tienen 2.6 mayores chances de presentar sintomatología ansiosa, en comparación a a los(as) trabajadores(as) en los que no predomina el miedo a la situación pandemia.
No se observaron asociaciones con la presencia de sintomatología ansiosa entre aquellos(as) que recibieron capacitación en elementos de protección personal (EPP).
0.2.1 Interacción
¿La asociación entre las variables de interés y la sintomatología ansiosa varía según se pertenezca a Hospitales o a Dirección?
library(finalfit)
#explanatory = c("age.factor*sex.factor", "obstruct.factor", "perfor.factor")
#p40_istas_rel_su
#p40_istas_ex_psi
#p40_istas_rel_su
#p40_istas_con_rol
#table(bd_comparacion$comparacion)
datalist <- data.frame()
# CREA LA VARIABLE CON EL AJUSTE DEL MODELO CRUDO
for(scale in c("p45_demo_sexo", "p11_exp_c19_miedo_pand_rec", "p14_contr_cont_cap_epp_rec", "p39_cond_trab_cult_org_rec", "p42_res_total")){
for(var in c("tamizaje_ans")){
# dynamically generate formula
fmla <- as.formula(paste0(var, " ~ ","comparacion" ," * ", factor(scale)))
# fit glm model
assign(paste0("svyglm2_M_",as.character(scale),"_",as.character(var)), glm(fmla, data=bd_comparacion, family=binomial()))
fit <- glm(fmla, data=bd_comparacion, family=binomial())
datalist <- rbind.data.frame(datalist, data.table(round(Epi::ci.lin(fit, Exp=T),3),keep.rownames = T))
# print(data.table(round(Epi::ci.lin(fit, Exp=T),3),keep.rownames = T))
}
}
datalist_cons<-
datalist %>%
dplyr::select(-Estimate, -StdErr,-z) %>%
dplyr::filter(grepl("comparacionHospitales:",rn)) %>%
dplyr::rename("Interacciones"="rn")
datalist2 <- data.frame()
# CREA LA VARIABLE CON EL AJUSTE DEL MODELO CONTROLANDO POR OTRAS VAR
for(scale in c("p45_demo_sexo", "p11_exp_c19_miedo_pand_rec", "p14_contr_cont_cap_epp_rec", "p39_cond_trab_cult_org_rec", "p42_res_total")){
for(var in c("tamizaje_ans")){
# dynamically generate formula
fmla <- as.formula(paste0(var, " ~ ","comparacion" ," * ", scale, " + p45_demo_sexo + p11_exp_c19_miedo_pand_rec + p14_contr_cont_cap_epp_rec + p39_cond_trab_cult_org_rec + p42_res_total"))
# fit glm model
initial <- c(log(mean(bd_comparacion[var]==1, na.rm=T)),rep(0,12))
assign(paste0("svyglm2_M_",as.character(scale),"_",as.character(var)), glm(fmla, data=bd_comparacion, family=binomial()))
fit <- glm(fmla, data=bd_comparacion, family=binomial())
datalist2 <- rbind.data.frame(datalist2, data.table(round(Epi::ci.lin(fit, Exp=T),3),keep.rownames = T))
#print(round(Epi::ci.lin(fit, Exp=T),3))
}
}
datalist2_cons<-
datalist2 %>%
dplyr::select(-Estimate, -StdErr,-z) %>%
dplyr::filter(grepl("comparacionHospitales:",rn)) %>%
dplyr::rename("Interacciones"="rn")
cbind.data.frame(datalist_cons,datalist2_cons[,2:5]) %>%
janitor::clean_names() %>%
dplyr::mutate(interacciones=dplyr::case_when(grepl("Mujer",interacciones)~"Inst. x Sexo",
grepl("miedo",interacciones)~"Inst. x Miedo",
grepl("capacitado",interacciones)~"Inst. x Sin capacitación",
grepl("cult_org_rec",interacciones)~"Inst. x Cultura Perjudicial",
grepl("p42_res_total",interacciones)~"Inst. x Ptje. Resiliencia")) %>%
dplyr::select(interacciones, exp_est, x2_5_percent, x97_5_percent, p, exp_est_2, x2_5_percent_2, x97_5_percent_2, p_2) %>%
knitr::kable(,format = "html", format.args = list(decimal.mark = ".", big.mark = ","),
caption="Tabla 3. Asociación entre Sintomatología Ansiosa y Variables de Interés, + prueba de Diferencias entre Hospitales y Dirección",
col.names=c("Interacciones", "OR (Crudo)", "OR (Crudo) CI 95% Bajo","OR (Crudo) CI 95% Alto","Sig.","OR (Ajustado)", "OR (Ajustado) CI 95% Bajo","OR (Ajustado) CI 95% Alto", "Sig."),
align =rep('c', 101)) %>%
kableExtra::kable_styling(bootstrap_options = c("striped", "hover"),font_size = 10) %>%
kableExtra::scroll_box(width = "100%", height = "350px")| Interacciones | OR (Crudo) | OR (Crudo) CI 95% Bajo | OR (Crudo) CI 95% Alto | Sig. | OR (Ajustado) | OR (Ajustado) CI 95% Bajo | OR (Ajustado) CI 95% Alto | Sig. |
|---|---|---|---|---|---|---|---|---|
| Inst. x Sexo | 0.846 | 0.275 | 2.603 | 0.770 | 0.776 | 0.230 | 2.619 | 0.683 |
| Inst. x Miedo | 1.804 | 0.719 | 4.528 | 0.209 | 1.371 | 0.465 | 4.040 | 0.567 |
| Inst. x Sin capacitación | 1.081 | 0.429 | 2.724 | 0.869 | 1.057 | 0.342 | 3.266 | 0.924 |
| Inst. x Cultura Perjudicial | 2.822 | 1.049 | 7.594 | 0.040 | 1.963 | 0.667 | 5.777 | 0.221 |
| Inst. x Ptje. Resiliencia | 0.951 | 0.675 | 1.340 | 0.774 | 0.948 | 0.649 | 1.383 | 0.780 |
- Quienes se desempeñan en la Dirección del Servicio y perciben que la cultura de la organización tolera comportamientos perjudiciales, tienen mayores chances (OR= 2.8, CI95%= 1.05-7.59, p<.05) mayores que los hospitales de presentar sintomatología ansiosa. Sin embargo, al controlar por el resto de las variables, no existen tales diferencias (OR=1.96, CI95% 0.67-5.78, p=0.22).
0.3 Información de la Sesión
Sys.getenv("R_LIBS_USER")## [1] "C:/Users/andre/Google Drive/AccionSaludUDP_win/renv/library/R-4.0/x86_64-w64-mingw32;C:/Users/andre/AppData/Local/Temp/RtmpAXjXrN/renv-system-library"rstudioapi::getSourceEditorContext()## Document Context:
## - id: 'F5BC9F60'
## - path: 'C:/Users/andre/Google Drive/AccionSaludUDP_win/comparacion_ss_arauco_vs_hospitales.Rmd'
## - contents: <1227 rows>
## Document Selection:
## - [855, 27] -- [855, 27]: ''sessionInfo()## R version 4.0.2 (2020-06-22)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19042)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252 LC_CTYPE=Spanish_Chile.1252
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C
## [5] LC_TIME=Spanish_Chile.1252
##
## attached base packages:
## [1] parallel grid splines stats graphics grDevices datasets
## [8] utils methods base
##
## other attached packages:
## [1] gdtools_0.2.2 lsmeans_2.30-0
## [3] emmeans_1.5.3 effects_4.2-0
## [5] carData_3.0-4 finalfit_1.0.2
## [7] survminer_0.4.8 ggpubr_0.4.0
## [9] ggfortify_0.4.11 forcats_0.5.0
## [11] purrr_0.3.4 tibble_3.0.4
## [13] tidyverse_1.3.0 factoextra_1.0.7
## [15] ggiraph_0.7.8 nFactors_2.4.1
## [17] homals_1.0-8 radiant_1.3.2
## [19] radiant.multivariate_1.3.5 radiant.model_1.3.14
## [21] radiant.basics_1.3.4 radiant.design_1.3.5
## [23] radiant.data_1.3.12 tidyr_1.1.2
## [25] magrittr_2.0.1 ggcorrplot_0.1.3
## [27] MVN_5.8 polycor_0.7-10
## [29] mvtnorm_1.1-1 GPArotation_2014.11-1
## [31] psych_2.0.12 wordcloud_2.6
## [33] RColorBrewer_1.1-2 wordcloud2_0.2.1
## [35] skimr_2.1.2 ExPanDaR_0.5.3
## [37] devtools_2.3.2 usethis_2.0.0
## [39] sqldf_0.4-11 RSQLite_2.2.1
## [41] gsubfn_0.7 proto_1.0.0
## [43] altair_4.0.1 rbokeh_0.5.1
## [45] rio_0.5.16 SCtools_0.3.1
## [47] future_1.21.0 doParallel_1.0.16
## [49] iterators_1.0.13 foreach_1.5.1
## [51] ggforce_0.3.2 dplyr_1.0.2
## [53] compareGroups_4.4.6 Hmisc_4.4-2
## [55] Formula_1.2-4 survival_3.1-12
## [57] ggsci_2.9 astsa_1.12
## [59] vcd_1.4-8 Epi_2.42
## [61] lmtest_0.9-38 tsModel_0.6
## [63] foreign_0.8-80 sjPlot_2.8.6
## [65] textreg_0.1.5 CausalImpact_1.2.5
## [67] bsts_0.9.5 xts_0.12.1
## [69] BoomSpikeSlab_1.2.3 Boom_0.9.6
## [71] MASS_7.3-51.6 estimatr_0.28.0
## [73] rjson_0.2.20 janitor_2.1.0
## [75] panelView_1.1.2 zoo_1.8-8
## [77] forecast_8.13 lattice_0.20-41
## [79] DT_0.16 data.table_1.13.6
## [81] stringr_1.4.0 readr_1.4.0
## [83] gganimate_1.0.7 htmlwidgets_1.5.3
## [85] lubridate_1.7.9.2 plotly_4.9.2.2
## [87] ggplot2_3.3.3 pacman_0.5.1
## [89] tm_0.7-8 NLP_0.2-1
##
## loaded via a namespace (and not attached):
## [1] mitools_2.4 haven_2.3.1 tcltk_4.0.2
## [4] vctrs_0.3.6 mgcv_1.8-31 prodlim_2019.11.13
## [7] blob_1.2.1 later_1.1.0.1 nloptr_1.2.2.2
## [10] DBI_1.1.0 jpeg_0.1-8.1 sjmisc_2.8.5
## [13] pls_2.7-3 tseries_0.10-48 laeken_0.5.1
## [16] sROC_0.1-2 markdown_1.1 DEoptimR_1.0-8
## [19] Rcpp_1.0.5 KernSmooth_2.23-17 promises_1.1.1
## [22] ggeffects_1.0.1 pkgload_1.1.0 fs_1.5.0
## [25] mnormt_2.0.2 ranger_0.12.1 digest_0.6.27
## [28] png_0.1-7 crul_1.0.0 pkgconfig_2.0.3
## [31] gower_0.2.2 estimability_1.3 minqa_1.2.4
## [34] reticulate_1.18 gistr_0.9.0 modeltools_0.2-23
## [37] nortest_1.0-4 xfun_0.19 tidyselect_1.1.0
## [40] performance_0.6.1 reshape2_1.4.4 kernlab_0.9-29
## [43] randomizr_0.20.0 pcaPP_1.9-73 viridisLite_0.3.0
## [46] pkgbuild_1.2.0 rlang_0.4.10 AlgDesign_1.2.0
## [49] manipulateWidget_0.10.1 HardyWeinberg_1.7.1 hexbin_1.28.1
## [52] glue_1.4.2 pryr_0.1.4 modelr_0.1.8
## [55] fpc_2.2-9 sgeostat_1.0-27 lava_1.6.8.1
## [58] tictoc_1.0 ggsignif_0.6.0 bayestestR_0.8.0
## [61] recipes_0.1.15 kableExtra_1.3.1 labeling_0.4.2
## [64] GGally_2.0.0 import_1.2.0 httpuv_1.5.4
## [67] class_7.3-17 TH.data_1.0-10 webshot_0.5.2
## [70] jsonlite_1.7.2 tmvnsim_1.0-2 bit_4.0.4
## [73] shinyFiles_0.9.0 mime_0.9 systemfonts_0.3.2
## [76] fracdiff_1.5-1 gridExtra_2.3 zCompositions_1.3.4
## [79] stringi_1.5.3 insight_0.11.1 processx_3.4.5
## [82] survey_4.0 quadprog_1.5-8 bitops_1.0-6
## [85] cli_2.2.0 maps_3.3.0 rrcov_1.5-5
## [88] mvoutlier_2.0.9 energy_1.7-7 officer_0.3.16
## [91] rstudioapi_0.13 nlme_3.1-148 ks_1.11.7
## [94] listenv_0.8.0 miniUI_0.1.1.1 survMisc_0.5.5
## [97] httpcode_0.3.0 prabclus_2.3-2 NADA_1.6-1.1
## [100] dbplyr_2.0.0 sessioninfo_1.1.1 TTR_0.24.2
## [103] readxl_1.3.1 lifecycle_0.2.0 quantmod_0.4.18
## [106] timeDate_3043.102 munsell_0.5.0 cellranger_1.1.0
## [109] robCompositions_2.3.0 moments_0.14 ggalluvial_0.12.3
## [112] vegawidget_0.3.1 visNetwork_2.0.9 codetools_0.2-16
## [115] htmlTable_2.1.0 xtable_1.8-4 diptest_0.75-7
## [118] scatterplot3d_0.3-41 abind_1.4-5 km.ci_0.5-2
## [121] farver_2.0.3 parallelly_1.23.0 askpass_1.1
## [124] sjstats_0.18.0 rgl_0.103.5 patchwork_1.1.1
## [127] cluster_2.1.0 fda_5.1.9 Matrix_1.2-18
## [130] ellipsis_0.3.1 prettyunits_1.1.1 ggridges_0.5.2
## [133] reprex_0.3.0 mclust_5.4.7 flextable_0.6.1
## [136] sjlabelled_1.1.7 pdp_0.7.0 remotes_2.2.0
## [139] slam_0.1-48 etm_1.1.1 VIM_6.0.0
## [142] parameters_0.10.1 testthat_3.0.1 htmltools_0.5.0
## [145] yaml_2.2.1 ModelMetrics_1.2.2.2 fds_1.8
## [148] e1071_1.7-4 hdrcde_3.3 withr_2.3.0
## [151] xgboost_1.3.1.1 bit64_4.0.5 effectsize_0.4.1
## [154] multcomp_1.4-15 robustbase_0.93-7 data.tree_1.0.0
## [157] memoise_1.1.0 evaluate_0.14 pwr_1.3-0
## [160] callr_3.5.1 caret_6.0-86 ps_1.5.0
## [163] curl_4.3 DiagrammeR_1.0.6.1 fansi_0.4.1
## [166] highr_0.8 checkmate_2.0.0 renv_0.11.0
## [169] desc_1.2.0 cmprsk_2.2-10 truncnorm_1.0-8
## [172] openxlsx_4.2.3 rstatix_0.6.0 ggrepel_0.9.0
## [175] easyalluvial_0.2.3 shinycssloaders_1.0.0 rprojroot_2.0.2
## [178] tools_4.0.2 rainbow_3.6 sandwich_3.0-0
## [181] Rsolnp_1.16 RCurl_1.98-1.2 mice_3.12.0
## [184] ape_5.4-1 car_3.0-10 xml2_1.3.2
## [187] httr_1.4.2 assertthat_0.2.1 rmarkdown_2.6
## [190] boot_1.3-25 globals_0.14.0 R6_2.5.0
## [193] nnet_7.3-14 progress_1.2.2 statmod_1.4.35
## [196] repr_1.1.0 urca_1.3-0 clustMixType_0.2-9
## [199] snakecase_0.11.0 colorspace_2.0-0 generics_0.1.0
## [202] stats4_4.0.2 base64enc_0.1-3 chron_2.3-56
## [205] pillar_1.4.7 tweenr_1.0.1 sp_1.4-4
## [208] uuid_0.1-4 plyr_1.8.6 gtable_0.3.0
## [211] rvest_0.3.6 zip_2.1.1 knitr_1.30
## [214] latticeExtra_0.6-29 fastmap_1.0.1 crosstalk_1.1.0.1
## [217] flexmix_2.3-17 NeuralNetTools_1.5.2 broom_0.7.3
## [220] openssl_1.4.3 scales_1.1.1 backports_1.2.1
## [223] writexl_1.3.1 ipred_0.9-9 lme4_1.1-26
## [226] hms_0.5.3 shiny_1.5.0 KMsurv_0.1-5
## [229] polyclip_1.10-0 numDeriv_2016.8-1.1 lazyeval_0.2.2
## [232] cvTools_0.3.2 crayon_1.3.4 shinyAce_0.4.1
## [235] pROC_1.16.2 reshape_0.8.8 rpart_4.1-15
## [238] compiler_4.0.2