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Tables_code.R
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#Author: Cong Zhu
#Purpose: supplemental tables S2.1 - S2.6
library(readxl)
setwd("......")
df = read_excel('.....')
#var_list: list of variables to be tested,e.g., age, BMI, sex, DVH
#data; input data
dec_tab = function(data, var_list){
dec_sum_list = list()
n_var = dim(data)[2]
chratable_cluster = list()
cont_index = c()
cat_index = c()
for (k in c(1:dim(data)[2])){
if (length(table(data[[k]]))>5){
cont_index = append(cont_index, k)
}
else{
cat_index = append(cat_index, k)
}
}
#summary statistics of categorical variables
char_sum_list = list()
for (var_i in var_list){
chartable = c()
var_i_loc = grep(var_i, colnames(data))
cat_index2 = cat_index[!cat_index %in% var_i_loc]
for (i in cat_index2){
tb = table(data[[i]], data[[var_i]])
prop.tb = prop.table(tb,2)
p = round(chisq.test(tb)$p.value,3)
p = ifelse(p<=0.001, "<0.001",p)
P_value = rep("", dim(tb)[1])
P_value[1] = p
result_list = list()
for (j in c(1:dim(tb)[2])){
col = paste("col",1)
assign(col, paste(tb[,j],"(", round(prop.tb[,j],3)*100,"%",")"))
result_list[[j]] = eval(as.name(col))
result_temp = do.call(cbind,result_list)
}
tab_row_name = paste(names(data)[i],":",levels(as.factor(data[[i]])))
tab_col_name = names(table(data[[var_i]]))
result_temp = as.data.frame(result_temp)
names(result_temp) = tab_col_name
row.names(result_temp) = tab_row_name
chartable = rbind(chartable,cbind(result_temp,P_value))
}
char_sum_list = append(char_sum_list, list(as.data.frame(chartable)))
}
names(char_sum_list) = var_list
#summary statistics of continous variables
cont_sum_list = list()
for (var_i in var_list){
var_i_loc = grep(var_i, colnames(data))
cont_index2 = append(cont_index,var_i_loc)
df_cont = data[,cont_index2]
n_cont_col = dim(df_cont)[2]
mean_result = c()
sd_result = c()
p_value = c()
for (j in c(1:(n_cont_col-1))){
mean_1var = aggregate(df_cont[[j]]~ df_cont[[n_cont_col]], df_cont, function(x) mean = mean(x, na.rm = TRUE))
mean_1var = as.numeric(t(mean_1var)[2,])
mean_1var = round(mean_1var,2)
mean_result = rbind(mean_result,mean_1var)
sd_1var = aggregate(df_cont[[j]]~ df_cont[[n_cont_col]], df_cont, function(x) sd = sd(x, na.rm = TRUE))
sd_1var = as.numeric(t(sd_1var)[2,])
sd_1var = round(sd_1var,2)
sd_result = rbind(sd_result,sd_1var)
t_test = t.test(df_cont[[j]]~ df_cont[[n_cont_col]])
p = t_test$p.value
if (p <0.0001){
p = "<0.001"
}
else{
p = round(p,3)
}
p_value = rbind(p_value,p)
}
mean_sd_1col = paste(mean_result[,1]," (",sd_result[,1], ")")
mean_sd_2col = paste(mean_result[,2]," (",sd_result[,2], ")")
mean_sd_all = cbind(mean_sd_1col, mean_sd_2col,p_value)
mean_sd_all = as.data.frame(mean_sd_all)
rownames(mean_sd_all) = names(df_cont)[1:(n_cont_col-1)]
header = names(table(df_cont[[n_cont_col]]))
colnames(mean_sd_all) = c(header, "p-value")
cont_sum_list = append(cont_sum_list, list(mean_sd_all))
}
names(cont_sum_list) = var_list
#output tables as excel spreadsheets
for (var_i in var_list){
write.xlsx(cont_sum_list[[var_i]],file="....."
,sheetName=var_i
,append=T,row.names = T)
}
for (var_i in var_list){
write.xlsx(char_sum_list[[var_i]],file="....."
,sheetName=var_i
,append=T,row.names = T)
}
}
#list of variables for subgroup comparisons
#e.g., clusters, radiotherapy modality, toxicity status
var_list = c('var_name')
#call the function
dec_tab(df, var_list)