In TanguyPerennec/Autostats: Automated medical science statistics
knitr::opts_chunk$set(echo = TRUE)
DF <- readxl::read_xlsx('~/MEGA/ARTICLES/Loge prostate > 75 ans/data/Base modifiée.xlsx', na=c(".",","),n_max = 70)
colnames(DF)
DF$HTA <- relevel(factor(DF$HTA),"Oui")
DF$Diabète <- relevel(factor(DF$Diabète),"Oui")
DF$Tabagisme <- relevel(factor(DF$Tabagisme),"Oui")
DF$`Chirurgie ano-rectale` <- relevel(factor(DF$`Chirurgie ano-rectale`),"Oui")
DF$`ATCD psy` <- relevel(factor(DF$`ATCD psy`),"Oui")
DF$`ATCD Cardio` <- relevel(factor(DF$`ATCD Cardio`),"Oui")
DF$`Score de Gleason total`<- sapply(DF["Score de Gleason total"],FUN=as.character)
DF$Gleason <- sapply(DF["Score de Gleason (pièce obligatoire 1)"],FUN=as.character)
DF$Gleason2 <- sapply(DF["Score de Gleason (pièce obligatoire 2)"],FUN=as.character)
for(i in seq(nrow(DF))){
if (DF$`Score de Gleason total`[i] == "7"){
DF$Gleason[i] <- paste(DF$Gleason[i,], DF["Score de Gleason (pièce obligatoire 2)"][i,],sep=" + ")
}else{
DF$Gleason[i] <- DF$`Score de Gleason total`[i]
}
}
DF$Gleason <- relevel(factor(DF$`Gleason`),"6")
DF["PSA pré-prostatectomie"] <- sapply(DF["PSA pré-prostatectomie"],FUN=as.numeric)
table1(DF[c('bras',"Age à la randomisation","HTA","Diabète","Tabagisme","Chirurgie ano-rectale", "globalqol","score_IADL_0","ATCD Cardio","ATCD psy","Gleason","pT (TNM 2005)","PSA pré-prostatectomie")],'bras')
library(survival)
library(survminer)
# Date censure = progression ou PDV ou décès
DF$censure<- DF$`Date recidive 1`
DF$censure[is.na(DF$censure)] <- DF$`Date dernières nouvelles ou décès`
DF$t <- (DF$censure - DF$DATVIS...18)/365
DF$Recidive1 <- DF$CENTRE...1
DF$Recidive1[!is.na(DF$`Date recidive 1`)] <- 1
DF$Recidive1[is.na(DF$`Date recidive 1`)] <- 0
km_global <- survfit(Surv(DF$t,DF$Recidive1)~bras,data=DF)
ggsurvplot(km_global,risk.table = T,
xlab='Time (year)',
ylab='Survival probability',
break.time.by=1,
ggtheme = theme_classic(),
palette = "lancet",
tables.theme = theme_cleantable(),
title="Biochemical free survival",
pval = TRUE,
conf.int = FALSE,
legend="bottom",
legend.title='Use of Hormon therapy',
legend.labs=c('No','Yes'),
xlim=c(0,8)
) -> plot2
km_global
plot2
plot(km_global,fun = "cloglog")
DF$Recidive1 -> DF$Recidive2
DF$Recidive2[DF$`Statut vital du patient` == "Décédé"] <- 1
km_global <- survfit(Surv(DF$t,DF$Recidive2)~bras,data=DF)
ggsurvplot(km_global,risk.table = T,
xlab='Time (year)',
ylab='Survival probability',
break.time.by=1,
ggtheme = theme_classic(),
palette = "lancet",
tables.theme = theme_cleantable(),
title="Biochemical free and alive survival",
pval = TRUE,
conf.int = F,
legend="bottom",
legend.title='Use of Hormon therapy',
legend.labs=c('No','Yes'),
xlim=c(0,12)
) -> plot2
plot2
plot(km_global,fun = "cloglog")
DF$GleasonT <- DF$`Score de Gleason total`
km_global <- survfit(Surv(DF$t,DF$Recidive2)~bras + GleasonT,data=DF)
ggsurvplot(km_global,risk.table = T,
xlab='Time (year)',
ylab='Survival probability',
break.time.by=1,
ggtheme = theme_classic(),
tables.theme = theme_cleantable(),
title="Biochemical free and alive survival",
conf.int = F,
xlim=c(0,12)
) -> plot2
plot2$plot + theme_bw()+facet_wrap(~GleasonT)
DF$`Statut vital du patient` -> DF$décès
DF$décès[DF$décès == "vivant"] <- 0
DF$décès[DF$décès != 0] <- 1
DF$décès <- as.numeric(DF$décès)
DF$t2 <- ( DF$`Date dernières nouvelles ou décès` - DF$DATVIS...18)/365
km_global <- survfit(Surv(DF$t2,DF$décès)~bras,data=DF)
ggsurvplot(km_global,risk.table = T,
xlab='Time (year)',
ylab='OS',
break.time.by=1,
ggtheme = theme_classic(),
palette = "lancet",
tables.theme = theme_cleantable(),
title="Overall survival",
pval = TRUE,
conf.int = F,
legend="bottom",
legend.title='Use of Hormon therapy',
legend.labs=c('No','Yes'),
xlim=c(0,6)
) -> plot2
plot2
DF$`Statut vital du patient` -> DF$décès
DF$décès[DF$décès == "vivant"] <- 0
DF$décès[DF$décès != 0] <- 1
DF$décès <- as.numeric(DF$décès)
DF$t2 <- ( DF$`Date dernières nouvelles ou décès` - DF$DATVIS...18)/365
km_global <- survfit(Surv(DF$t2,DF$décès)~bras,data=DF)
ggsurvplot(km_global,risk.table = T,
xlab='Time (year)',
ylab='OS',
break.time.by=1,
ggtheme = theme_classic(),
palette = "lancet",
tables.theme = theme_cleantable(),
title="Overall survival",
pval = TRUE,
conf.int = F,
legend="bottom",
legend.title='Use of Hormon therapy',
legend.labs=c('No','Yes'),
xlim=c(0,12)
) -> plot2
plot2
plot(km_global,fun = "cloglog")
DF$Cardiaque = DF$`ATCD Cardio`
km_global <- survfit(Surv(DF$t2,DF$décès)~bras + Cardiaque,data=DF)
ggsurvplot(km_global,risk.table = T,
xlab='Time (year)',
ylab='OS',
break.time.by=1,
ggtheme = theme_classic(),
palette = "lancet",
tables.theme = theme_cleantable(),
title="Overall survival",
pval = FALSE,
conf.int = F,
xlim=c(0,12),
data = DF
) -> plot2
plot2$plot + theme_bw()+facet_wrap(~Cardiaque)
explicatives = c("bras","Tabagisme","HTA","Diabète","ATCD Cardio","Score de Gleason total","pT (TNM 2005)")
DF$time=DF$t
DF$event=DF$Recidive2
time="time"
event="event"
verbose = TRUE
round=2
keep = FALSE
as.data.frame(DF) -> DF
DF_expl <- DF[,c(explicatives,time,event)]
NA_rm_for_glm(DF_expl,method_NA = "lessNA",EPV=1) -> DF_expl
DF_expl[,"surv"] <- Surv(as.numeric(DF_expl[ ,time]),as.numeric(DF_expl[ ,event]))
# Constructing 'vect_explicative'
#####
vect_explicative <- vector()
as.data.frame(DF_expl) -> DF_expl
n = 1
for (var in explicatives) {#making a vector with the name of the variable displayed as many times as (levels - 1)
if (is.numeric(DF_expl[, var])) {
vect_explicative[n] <- var
n <- n + 1
} else{
as.factor(DF_expl[, var]) -> DF_expl[,var]
levels_var <- length(levels(DF_expl[, var]))
vect_explicative[n:(n + levels_var - 2)] <- rep(var, (levels_var - 1))
n <- n + levels_var - 1
}
}
#####
if (verbose) cat(
"\n
\n
-----+-----------------------------+--------------------------------------
| |
| 1) UNIVARIATE MODEL |
| |
+-----------------------------+\n
")
rslt <- matrix(ncol = 7, nrow = (length(vect_explicative) + 1))
rownames(rslt) <- c("",vect_explicative)
getinfo_cox <- function(mod,k,var,cat) {
HR <- round(exp(as.numeric(mod$coefficients[k + 1])), round)
pval <- summary(mod)$coefficients[k + 1, 5]
IC <- paste0(round(suppressMessages(exp(confint(mod)))[k + 1, ], round),collapse = ";")
IC <- paste0("[",IC,"]")
name_var <- name_level<- var
if (cat != 'num') {
name_level <- stringr::str_split(rownames(summary(mod)$coefficients)[k+1], var)[[1]][2]
name_var <- ifelse(name_level == "",name_var,(paste0(name_var," (",name_level ,")")))
}
return(c(name_var,HR,IC,pval,name_level))
}
i = 0
for (var_uni in explicatives) {
progressbar(total = length(vect_explicative)-1,i,variable = var_uni)
mod_uni <- survival::coxph(as.formula(DF_expl[,c("surv",var_uni)]),data=DF_expl)
vector_var = vector()
k = 0
if (is.numeric(DF_expl[, var_uni])) {
i <- i + 1
ligneR <- getinfo_cox(mod_uni,k,var_uni,"num")
vector_var[i] <- var_uni
rslt[i + 1, ] <- c(ligneR[1:4], "-", "-", "-")
row.names(rslt)[i + 1] <- var_uni
} else {
while (k + 1 < length(levels(DF_expl[, var_uni]))) {
i <- i + 1
k <- k + 1
ligneR <- getinfo_cox(mod_uni,k-1,var_uni,'cat')
vector_var[i] <- var_uni
rslt[i + 1, ] <- c(ligneR[1:4], "-", "-", "-")
row.names(rslt)[i + 1] <- paste0(var_uni,ligneR[5])
}
} #else of is.numeric
} # end of loop
##################################################
##################################################
# MULTIVARIATE MODEL #
##################################################
if (verbose) cat("
\n
\n
-----+-----------------------------+--------------------------------------
| |
| 3) MULTIVARIATE MODEL |
| |
+-----------------------------+\n")
# Variable selection
#explicatives_multi <- multivariate_selection(DF_expl[,c(y,explicatives)],y,keep = keep2)
#explicatives_multi <- explicatives_multi$vars_multi
# Definitive model
step(survival::coxph(as.formula(DF_expl[,c("surv",explicatives)]),data=DF_expl)) -> mod_multi
survival::coxph(as.formula(DF_expl[,c("surv",attr(mod_multi$terms,"term.labels"))]),data=DF_expl) -> mod_multi
cox.zph(mod_multi) #test proportionnal hazards
##################################################
##################################################
# MATRICE DE RESULTATS #
##################################################
HR <- exp(mod_multi$coefficients)
pval <- summary(mod_multi)$coefficients[,5]
IC <- round(suppressMessages(exp(confint(mod_multi))),round)
i <- 0
for (HR_var in names(HR)) {
i <- i + 1
n_ligne <- match(HR_var,rownames(rslt))
p <- round(as.numeric(pval[i]), round)
p <- ifelse(p == 0, "<0.001", p)
IC_paste <- paste0("[",IC[i, 1], ";", IC[i, 2],"]")
rslt[n_ligne, 5:7] <- c(signif(HR[i], round), IC_paste, p)
}
for (n in 1:length(rslt[-1, 4])) {
p = as.numeric(rslt[n + 1, 4])
round(p, round) -> p
ifelse(p == 0, "<0.001", p) -> rslt[n + 1, 4]
}
rslt[1,] <- c("","HR","IC","pval","HR","IC","pval")
row.names(rslt) <- NULL
rslt <- as.data.frame(rslt[-1,])
colnames(rslt) <- c("variables","HR_univariate","IC_univariate","pval_univariate","HR_multivariate","IC_multivariate","pval_multivariate")
rslt <- flextable(rslt, col_keys = c("variables","HR_univariate","IC_univariate","pval_univariate","HR_multivariate","IC_multivariate","pval_multivariate"))
rslt <- delete_part(rslt,part = "header")
rslt <- add_header(x = rslt, variable = "variables",HR_univariate = "HR",IC_univariate = "IC95%",pval_univariate = "p-value",HR_multivariate = "HR",IC_multivariate = "IC95%",pval_multivariate = "p-value", top = FALSE)
rslt <- add_header(x = rslt, variable = "variables",HR_univariate = "univariate model",IC_univariate = "univariate model",pval_univariate = "univariate model",HR_multivariate = "multivariate model",IC_multivariate = "multivariate model",pval_multivariate = "multivariate model", top = TRUE)
rslt <- merge_at(rslt, part = "header",i = 1:1,j = 2:4)
rslt <- merge_at(rslt, part = "header",i = 1:1,j = 5:7)
rslt <- theme_booktabs(rslt)
rslt <- autofit(rslt)
rslt
TanguyPerennec/Autostats documentation built on Dec. 13, 2020, 10:43 a.m.
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knitr::opts_chunk$set(echo = TRUE) DF <- readxl::read_xlsx('~/MEGA/ARTICLES/Loge prostate > 75 ans/data/Base modifiée.xlsx', na=c(".",","),n_max = 70) colnames(DF)
DF$HTA <- relevel(factor(DF$HTA),"Oui") DF$Diabète <- relevel(factor(DF$Diabète),"Oui") DF$Tabagisme <- relevel(factor(DF$Tabagisme),"Oui") DF$`Chirurgie ano-rectale` <- relevel(factor(DF$`Chirurgie ano-rectale`),"Oui") DF$`ATCD psy` <- relevel(factor(DF$`ATCD psy`),"Oui") DF$`ATCD Cardio` <- relevel(factor(DF$`ATCD Cardio`),"Oui") DF$`Score de Gleason total`<- sapply(DF["Score de Gleason total"],FUN=as.character) DF$Gleason <- sapply(DF["Score de Gleason (pièce obligatoire 1)"],FUN=as.character) DF$Gleason2 <- sapply(DF["Score de Gleason (pièce obligatoire 2)"],FUN=as.character) for(i in seq(nrow(DF))){ if (DF$`Score de Gleason total`[i] == "7"){ DF$Gleason[i] <- paste(DF$Gleason[i,], DF["Score de Gleason (pièce obligatoire 2)"][i,],sep=" + ") }else{ DF$Gleason[i] <- DF$`Score de Gleason total`[i] } } DF$Gleason <- relevel(factor(DF$`Gleason`),"6") DF["PSA pré-prostatectomie"] <- sapply(DF["PSA pré-prostatectomie"],FUN=as.numeric) table1(DF[c('bras',"Age à la randomisation","HTA","Diabète","Tabagisme","Chirurgie ano-rectale", "globalqol","score_IADL_0","ATCD Cardio","ATCD psy","Gleason","pT (TNM 2005)","PSA pré-prostatectomie")],'bras')
library(survival) library(survminer) # Date censure = progression ou PDV ou décès DF$censure<- DF$`Date recidive 1` DF$censure[is.na(DF$censure)] <- DF$`Date dernières nouvelles ou décès` DF$t <- (DF$censure - DF$DATVIS...18)/365 DF$Recidive1 <- DF$CENTRE...1 DF$Recidive1[!is.na(DF$`Date recidive 1`)] <- 1 DF$Recidive1[is.na(DF$`Date recidive 1`)] <- 0 km_global <- survfit(Surv(DF$t,DF$Recidive1)~bras,data=DF) ggsurvplot(km_global,risk.table = T, xlab='Time (year)', ylab='Survival probability', break.time.by=1, ggtheme = theme_classic(), palette = "lancet", tables.theme = theme_cleantable(), title="Biochemical free survival", pval = TRUE, conf.int = FALSE, legend="bottom", legend.title='Use of Hormon therapy', legend.labs=c('No','Yes'), xlim=c(0,8) ) -> plot2 km_global plot2
plot(km_global,fun = "cloglog")
DF$Recidive1 -> DF$Recidive2 DF$Recidive2[DF$`Statut vital du patient` == "Décédé"] <- 1 km_global <- survfit(Surv(DF$t,DF$Recidive2)~bras,data=DF) ggsurvplot(km_global,risk.table = T, xlab='Time (year)', ylab='Survival probability', break.time.by=1, ggtheme = theme_classic(), palette = "lancet", tables.theme = theme_cleantable(), title="Biochemical free and alive survival", pval = TRUE, conf.int = F, legend="bottom", legend.title='Use of Hormon therapy', legend.labs=c('No','Yes'), xlim=c(0,12) ) -> plot2 plot2
plot(km_global,fun = "cloglog")
DF$GleasonT <- DF$`Score de Gleason total` km_global <- survfit(Surv(DF$t,DF$Recidive2)~bras + GleasonT,data=DF) ggsurvplot(km_global,risk.table = T, xlab='Time (year)', ylab='Survival probability', break.time.by=1, ggtheme = theme_classic(), tables.theme = theme_cleantable(), title="Biochemical free and alive survival", conf.int = F, xlim=c(0,12) ) -> plot2 plot2$plot + theme_bw()+facet_wrap(~GleasonT)
DF$`Statut vital du patient` -> DF$décès DF$décès[DF$décès == "vivant"] <- 0 DF$décès[DF$décès != 0] <- 1 DF$décès <- as.numeric(DF$décès) DF$t2 <- ( DF$`Date dernières nouvelles ou décès` - DF$DATVIS...18)/365 km_global <- survfit(Surv(DF$t2,DF$décès)~bras,data=DF) ggsurvplot(km_global,risk.table = T, xlab='Time (year)', ylab='OS', break.time.by=1, ggtheme = theme_classic(), palette = "lancet", tables.theme = theme_cleantable(), title="Overall survival", pval = TRUE, conf.int = F, legend="bottom", legend.title='Use of Hormon therapy', legend.labs=c('No','Yes'), xlim=c(0,6) ) -> plot2 plot2
DF$`Statut vital du patient` -> DF$décès DF$décès[DF$décès == "vivant"] <- 0 DF$décès[DF$décès != 0] <- 1 DF$décès <- as.numeric(DF$décès) DF$t2 <- ( DF$`Date dernières nouvelles ou décès` - DF$DATVIS...18)/365 km_global <- survfit(Surv(DF$t2,DF$décès)~bras,data=DF) ggsurvplot(km_global,risk.table = T, xlab='Time (year)', ylab='OS', break.time.by=1, ggtheme = theme_classic(), palette = "lancet", tables.theme = theme_cleantable(), title="Overall survival", pval = TRUE, conf.int = F, legend="bottom", legend.title='Use of Hormon therapy', legend.labs=c('No','Yes'), xlim=c(0,12) ) -> plot2 plot2
plot(km_global,fun = "cloglog")
DF$Cardiaque = DF$`ATCD Cardio` km_global <- survfit(Surv(DF$t2,DF$décès)~bras + Cardiaque,data=DF) ggsurvplot(km_global,risk.table = T, xlab='Time (year)', ylab='OS', break.time.by=1, ggtheme = theme_classic(), palette = "lancet", tables.theme = theme_cleantable(), title="Overall survival", pval = FALSE, conf.int = F, xlim=c(0,12), data = DF ) -> plot2 plot2$plot + theme_bw()+facet_wrap(~Cardiaque)
explicatives = c("bras","Tabagisme","HTA","Diabète","ATCD Cardio","Score de Gleason total","pT (TNM 2005)") DF$time=DF$t DF$event=DF$Recidive2 time="time" event="event" verbose = TRUE round=2 keep = FALSE as.data.frame(DF) -> DF DF_expl <- DF[,c(explicatives,time,event)] NA_rm_for_glm(DF_expl,method_NA = "lessNA",EPV=1) -> DF_expl DF_expl[,"surv"] <- Surv(as.numeric(DF_expl[ ,time]),as.numeric(DF_expl[ ,event])) # Constructing 'vect_explicative' ##### vect_explicative <- vector() as.data.frame(DF_expl) -> DF_expl n = 1 for (var in explicatives) {#making a vector with the name of the variable displayed as many times as (levels - 1) if (is.numeric(DF_expl[, var])) { vect_explicative[n] <- var n <- n + 1 } else{ as.factor(DF_expl[, var]) -> DF_expl[,var] levels_var <- length(levels(DF_expl[, var])) vect_explicative[n:(n + levels_var - 2)] <- rep(var, (levels_var - 1)) n <- n + levels_var - 1 } } ##### if (verbose) cat( "\n \n -----+-----------------------------+-------------------------------------- | | | 1) UNIVARIATE MODEL | | | +-----------------------------+\n ") rslt <- matrix(ncol = 7, nrow = (length(vect_explicative) + 1)) rownames(rslt) <- c("",vect_explicative) getinfo_cox <- function(mod,k,var,cat) { HR <- round(exp(as.numeric(mod$coefficients[k + 1])), round) pval <- summary(mod)$coefficients[k + 1, 5] IC <- paste0(round(suppressMessages(exp(confint(mod)))[k + 1, ], round),collapse = ";") IC <- paste0("[",IC,"]") name_var <- name_level<- var if (cat != 'num') { name_level <- stringr::str_split(rownames(summary(mod)$coefficients)[k+1], var)[[1]][2] name_var <- ifelse(name_level == "",name_var,(paste0(name_var," (",name_level ,")"))) } return(c(name_var,HR,IC,pval,name_level)) } i = 0 for (var_uni in explicatives) { progressbar(total = length(vect_explicative)-1,i,variable = var_uni) mod_uni <- survival::coxph(as.formula(DF_expl[,c("surv",var_uni)]),data=DF_expl) vector_var = vector() k = 0 if (is.numeric(DF_expl[, var_uni])) { i <- i + 1 ligneR <- getinfo_cox(mod_uni,k,var_uni,"num") vector_var[i] <- var_uni rslt[i + 1, ] <- c(ligneR[1:4], "-", "-", "-") row.names(rslt)[i + 1] <- var_uni } else { while (k + 1 < length(levels(DF_expl[, var_uni]))) { i <- i + 1 k <- k + 1 ligneR <- getinfo_cox(mod_uni,k-1,var_uni,'cat') vector_var[i] <- var_uni rslt[i + 1, ] <- c(ligneR[1:4], "-", "-", "-") row.names(rslt)[i + 1] <- paste0(var_uni,ligneR[5]) } } #else of is.numeric } # end of loop ################################################## ################################################## # MULTIVARIATE MODEL # ################################################## if (verbose) cat(" \n \n -----+-----------------------------+-------------------------------------- | | | 3) MULTIVARIATE MODEL | | | +-----------------------------+\n") # Variable selection #explicatives_multi <- multivariate_selection(DF_expl[,c(y,explicatives)],y,keep = keep2) #explicatives_multi <- explicatives_multi$vars_multi # Definitive model step(survival::coxph(as.formula(DF_expl[,c("surv",explicatives)]),data=DF_expl)) -> mod_multi survival::coxph(as.formula(DF_expl[,c("surv",attr(mod_multi$terms,"term.labels"))]),data=DF_expl) -> mod_multi cox.zph(mod_multi) #test proportionnal hazards ################################################## ################################################## # MATRICE DE RESULTATS # ################################################## HR <- exp(mod_multi$coefficients) pval <- summary(mod_multi)$coefficients[,5] IC <- round(suppressMessages(exp(confint(mod_multi))),round) i <- 0 for (HR_var in names(HR)) { i <- i + 1 n_ligne <- match(HR_var,rownames(rslt)) p <- round(as.numeric(pval[i]), round) p <- ifelse(p == 0, "<0.001", p) IC_paste <- paste0("[",IC[i, 1], ";", IC[i, 2],"]") rslt[n_ligne, 5:7] <- c(signif(HR[i], round), IC_paste, p) } for (n in 1:length(rslt[-1, 4])) { p = as.numeric(rslt[n + 1, 4]) round(p, round) -> p ifelse(p == 0, "<0.001", p) -> rslt[n + 1, 4] } rslt[1,] <- c("","HR","IC","pval","HR","IC","pval") row.names(rslt) <- NULL rslt <- as.data.frame(rslt[-1,]) colnames(rslt) <- c("variables","HR_univariate","IC_univariate","pval_univariate","HR_multivariate","IC_multivariate","pval_multivariate") rslt <- flextable(rslt, col_keys = c("variables","HR_univariate","IC_univariate","pval_univariate","HR_multivariate","IC_multivariate","pval_multivariate")) rslt <- delete_part(rslt,part = "header") rslt <- add_header(x = rslt, variable = "variables",HR_univariate = "HR",IC_univariate = "IC95%",pval_univariate = "p-value",HR_multivariate = "HR",IC_multivariate = "IC95%",pval_multivariate = "p-value", top = FALSE) rslt <- add_header(x = rslt, variable = "variables",HR_univariate = "univariate model",IC_univariate = "univariate model",pval_univariate = "univariate model",HR_multivariate = "multivariate model",IC_multivariate = "multivariate model",pval_multivariate = "multivariate model", top = TRUE) rslt <- merge_at(rslt, part = "header",i = 1:1,j = 2:4) rslt <- merge_at(rslt, part = "header",i = 1:1,j = 5:7) rslt <- theme_booktabs(rslt) rslt <- autofit(rslt) rslt
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