R/surv_group.R
In IOBR/IOBR: Immune Oncology Biological Research
Defines functions
break_month
surv_group
Documented in
break_month
surv_group
#' Generate Kaplan-Meier Survival Plots for Categorical Groups
#'
#' This function creates Kaplan-Meier survival plots for data grouped by a categorical variable.
#' It handles both binary and multi-level categorical groups, adjusts follow-up times, and allows for
#' customizable plot aesthetics and output formats.
#'
#' @param input_pdata A data frame containing survival data and grouping variables.
#' @param target_group The name of the column in input_pdata that contains the grouping variable.
#' @param ID The name of the column in input_pdata that serves as a unique identifier for each row.
#' @param levels A vector of names for the levels of the target_group; used for labeling in the plot.
#' @param reference_group The reference level for comparison in survival analysis; used only for binary groups.
#' @param project An optional title for the plot; used in file naming and plot subtitles.
#' @param time The name of the column containing follow-up times.
#' @param status The name of the column containing event indicators (1=event occurred, 0=censored).
#' @param time_type Units of follow-up time ('month' or 'day'). Converts days to months if 'day' is selected.
#' @param break_month The interval for breaking time on the X-axis of the plot. If 'auto', it is calculated automatically.
#' @param cols A vector of colors for the plot lines; if NULL, uses a default palette based on 'palette' parameter.
#' @param palette The name of the color palette to use if 'cols' is NULL. Common choices include 'jama' and other palette names from the `ggsci` package.
#' @param mini_sig A prefix label for variables in the plot, typically indicating a scoring metric.
#' @param save_path The directory path where the plot should be saved.
#' @param fig.type The file format for the saved plot ('pdf' or 'png').
#' @param index A unique identifier for the plot file, typically used in file naming.
#' @param width The width of the output plot.
#' @param height The height of the output plot.
#' @param font.size.table The font size used in the plot's risk table.
#'
#' @return Saves the Kaplan-Meier plot to the specified path and returns the plot object.
#' @import dplyr
#' @import ggplot2
#' @import survival
#' @import survminer
#' @author Dongqiang Zeng
#'
#' @examples
#'data("tcga_stad_pdata", package = "IOBR")
#'surv_group(input_pdata = tcga_stad_pdata, target_group = "TMEscore_plus_binary", time = "time", status = "OS_status")
#' @export
surv_group <-function(input_pdata,
target_group,
ID = "ID",
levels = c("High","Low"),
reference_group = NULL,
project = NULL,
time = "time",
status = "status",
time_type = "month",
break_month = "auto",
cols = NULL,
palette ="jama",
mini_sig = "score",
save_path = paste0("KMplot"),
fig.type = "pdf",
index = 1,
width = 6,
height = 6.5,
font.size.table = 3){
if(!target_group%in%colnames(input_pdata)) stop("Target group must be in the column of input_pdata")
###############################
if (!file.exists(save_path)) dir.create(save_path)
abspath<-paste(getwd(),"/" ,save_path, "/",sep ="" )
###########################################
input_pd <- input_pdata[,c(ID, target_group, time, status)]
# if(time!="time" & "time"%in%colnames(input_pd)) stop(paste0("time already exists in column name of input_pd."))
# if(status!="status" & "status"%in%colnames(input_pd)) stop(paste0("status already exists in column name of input_pd."))
#filter data
colnames(input_pd)[which(colnames(input_pd)==time)]<-"time"
colnames(input_pd)[which(colnames(input_pd)==status)]<-"status"
colnames(input_pd)[which(colnames(input_pd)==ID)]<-"ID"
input_pd$time<-as.numeric(input_pd$time)
input_pd$status<-as.numeric(as.character(input_pd$status))
input_pd<- input_pd %>%
dplyr::filter(!is.na(time)) %>%
dplyr::filter(!is.na(status)) %>%
dplyr::filter(!.$time=="NA") %>%
dplyr::filter(!.$status=="NA") %>%
dplyr::filter(.$time > 0)
###################################
input_pd$time<-as.numeric(input_pd$time)
input_pd$status<-as.numeric(input_pd$status)
###################################
input_pd<-as.data.frame(input_pd[,colnames(input_pd)%in%c("ID","time","status",target_group)])
input_pd$ID<-as.character(input_pd$ID)
# input_pd$ProjectID<-project
##################################
#transform follow up time
if(time_type=="day") input_pd$time<-input_pd$time/30
###################################
#cut use quantile
#################################
message(paste(">>> Dataset's survival follow up time is range between",
paste(round(summary(input_pd$time),2)[c(1,6)],collapse = " to "),"months"))
###################################
save(input_pd,file = paste0(abspath,"0-",project,"-",target_group,"-survival-analysis-input.RData"))
#######################################
colnames(input_pd)[which(colnames(input_pd)==target_group)]<-"target_group"
input_pd<-input_pd[!is.na(input_pd$target_group),]
message(print(summary(as.factor(input_pd$target_group))))
####################################
# define the break time and colors
if(break_month == "auto"){
break_month<-break_month(input = input_pd$time,block = 6)
}
max_month<- break_month*6
###########################################
if(is.null(cols)) cols<- palettes(category = "box",palette = palette, show_col = F)
# print(unique(input_pd$target_group))
if(length(unique(input_pd$target_group))>2){
input_pd<-input_pd[!is.na(input_pd$target_group),]
# input_pd$target_group<-ifelse(input_pd$target_group=="High",3,ifelse(input_pd$target_group=="Middle",2,1))
# Sur <- Surv(input_pd$time,input_pd$status)
# pvalue<-getHRandCIfromCoxph(coxph(Surv(input_pd$time,input_pd$status)~input_pd$target_group,data = input_pd))
# HR <- paste("Hazard Ratio = ", round(pvalue[,2],2), sep = "")
# CI <- paste("95% CI: ", paste(round(pvalue[,3],2), round(pvalue[,4],2), sep = " - "), sep = "")
##########################################
sfit <- survminer:: surv_fit(Surv(input_pd$time,input_pd$status) ~ input_pd$target_group,data=input_pd)
# hack strata for better survival curve
names(sfit$strata) <- gsub("target_group=", "", names(sfit$strata))
#############################################
#############################################
pp<- survminer:: ggsurvplot(sfit,
data = input_pd,
censor = TRUE,
ncensor.plot = F,conf.int = F,
xlim = c(0,max_month),
break.time.by = break_month,
xlab = "Months after diagnosis",
# legend.labs = c(paste0('Low ',mini_sig),paste0("Middle ",mini_sig),paste0("High ",mini_sig)),
submain = paste0(target_group," ",project),
surv.median.line = "h", # draw horizontal line at median survival
risk.table = T,
tables.height = 0.25,
palette = cols,
pval.size = 8)
fitd <- survdiff(Surv(time,status)~ target_group,
data= input_pd,
na.action = na.exclude)
p.val <- 1 - pchisq(fitd$chisq, length(fitd$n) - 1)
# add nominal pvalue for log-rank test
p.lab <- paste0("Overall P",
ifelse(p.val < 0.001, " < 0.001",
paste0(" = ",round(p.val, 3))))
pp$plot <- pp$plot + annotate("text",
x = 0, y = 0.55,
hjust = 0,
fontface = 3,
# size = 1,
label = p.lab)
# calculate pair-wise survival comparison
ps <- pairwise_survdiff(Surv(time,status)~ target_group,
data= input_pd,
p.adjust.method = "none")
# add pair-wise comparison table
# options(stringsAsFactors = FALSE)
addTab <- as.data.frame(as.matrix(ifelse(round(ps$p.value, 3) < 0.001, "<0.001",
round(ps$p.value, 3))))
addTab[is.na(addTab)] <- "-"
# options(stringsAsFactors = TRUE)
df <- tibble(x = 0, y = 0, tb = list(addTab))
pp$plot <- pp$plot + ggpp::geom_table(data = df, aes(x = x, y = y, label = tb), table.rownames = TRUE, size = font.size.table)
}else{
if(is.null(reference_group)){
levels <- unique(input_pd$target_group)
levels <- levels[order(levels)]
#####################################
message(paste0('>>>--- Reference_group was not defined...'))
pvalue<-getHRandCIfromCoxph(coxph(Surv(time = input_pd$time, event = input_pd$status)~input_pd$target_group,data = input_pd))
HR <- paste("Hazard Ratio = ", round(pvalue[,2],2), sep = "")
CI <- paste("95% CI: ", paste(round(pvalue[,3],2), round(pvalue[,4],2), sep = " - "), sep = "")
# cut_off<-paste("cutoff = ",round(res.cut,3),sep = "")
###########################################
sfit <- surv_fit(Surv(time = input_pd$time, event = input_pd$status) ~ input_pd$target_group,data = input_pd)
# input_pd[,index]<-ifelse(input_pd[,index]==1,"High","LOW")
###########################################
pp<-survminer:: ggsurvplot(sfit,
data = input_pd,
censor = TRUE,
ncensor.plot = F,conf.int = F,
xlim = c(0,max_month),
break.time.by = break_month,
xlab = "Months after diagnosis",
legend.labs = c(paste0(levels[1]), paste0(levels[2])),
submain= paste0(target_group," ",project),
risk.table = T,
tables.height = 0.20,
palette = cols,
pval.size = 8,
pval = paste(pval = ifelse(pvalue[,1] < 0.0001, "P < 0.0001",
paste("P = ",round(pvalue[,1],4), sep = "")),
HR, CI,sep = "\n"))
}else{
# print(unique(input_pd$target_group))
levels <- unique(input_pd$target_group)
levels <- levels[order(levels)]
if(reference_group!=levels[1]){
levels <- c(levels[2], levels[1])
}
#####################################
if(! reference_group%in% c(input_pd$target_group)) stop(">>>--- Reference_group must be one of target...")
input_pd$target_group<-ifelse(input_pd$target_group == reference_group, 1, 0)
pvalue<-getHRandCIfromCoxph(coxph(Surv(time = input_pd$time, event = input_pd$status)~input_pd$target_group,data = input_pd))
HR <- paste("Hazard Ratio = ", round(pvalue[,2],2), sep = "")
CI <- paste("95% CI: ", paste(round(pvalue[,3],2), round(pvalue[,4],2), sep = " - "), sep = "")
# cut_off<-paste("cutoff = ",round(res.cut,3),sep = "")
###########################################
sfit <- surv_fit(Surv(time = input_pd$time, event = input_pd$status) ~ input_pd$target_group,data = input_pd)
# input_pd[,index]<-ifelse(input_pd[,index]==1,"High","LOW")
############################################
# define break time
###########################################
pp<-survminer:: ggsurvplot(sfit,
data = input_pd,
censor = TRUE,
ncensor.plot = F,conf.int = F,
xlim = c(0,max_month),
break.time.by = break_month,
xlab = "Months after diagnosis",
legend.labs = c(paste0(levels[2]), paste0(levels[1])),
submain= paste0(target_group," ",project),
risk.table = T,
tables.height = 0.20,
palette = cols,
pval.size = 8,
pval = paste(pval = ifelse(pvalue[,1] < 0.0001, "P < 0.0001",
paste("P = ",round(pvalue[,1],4), sep = "")),
HR, CI,sep = "\n"))
}
}
pp<-list(pp)
res <- arrange_ggsurvplots(pp, print = FALSE, ncol = 1, nrow = 1)
ggsave(res,filename = paste0(index,"-KMplot-",target_group,"-",project,".", fig.type),
width = width ,height = height,path = save_path)
return(res)
}
#' Break Time Into Blocks
#'
#' Divides a time duration into specified blocks. This function is useful for creating intervals or categories
#' within a given time period, such as months or days, for further analysis or visualization in studies where
#' time segmentation might be relevant.
#'
#' @param input A numeric vector representing time durations that need to be divided, typically in months or days.
#' @param block An integer specifying the number of blocks to divide the time into; default is 6.
#' @param time_type A character string specifying the units of the input time: "month" for months and "day" for days.
#' The default is "month". If "day" is specified, the function converts days into months by dividing by 30.
#'
#' @return A numeric vector representing the breakpoints for the time blocks, rounded to the nearest multiple of 5.
#' @export
#' @author Dongqiang Zeng
#'
#' @examples
#' # Example with time in months
#' time_data <- c(24, 36, 12, 48)
#' blocks <- break_month(input = time_data)
#' print(blocks)
#'
#' # Example with time in days
#' day_data <- c(720, 1080, 360, 1440) # Corresponding to 24, 36, 12, 48 months
#' blocks_days <- break_month(input = day_data, time_type = "day")
#' print(blocks_days)
break_month<-function(input, block = 6, time_type = "month"){
max_time <-max(input)
if(time_type=="month"){
max_time<-max_time
}else if(time_type=="day"){
max_time<-max_time/30
}
message(paste0(" Maximum of follow up time is ", max_time, " months; and will be divided into ", block, " sections;"))
round(c(max_time %/% block)/5,0)*5
}
IOBR/IOBR documentation built on April 3, 2025, 2:19 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Defines functions break_month surv_group
Documented in break_month surv_group
#' Generate Kaplan-Meier Survival Plots for Categorical Groups
#'
#' This function creates Kaplan-Meier survival plots for data grouped by a categorical variable.
#' It handles both binary and multi-level categorical groups, adjusts follow-up times, and allows for
#' customizable plot aesthetics and output formats.
#'
#' @param input_pdata A data frame containing survival data and grouping variables.
#' @param target_group The name of the column in input_pdata that contains the grouping variable.
#' @param ID The name of the column in input_pdata that serves as a unique identifier for each row.
#' @param levels A vector of names for the levels of the target_group; used for labeling in the plot.
#' @param reference_group The reference level for comparison in survival analysis; used only for binary groups.
#' @param project An optional title for the plot; used in file naming and plot subtitles.
#' @param time The name of the column containing follow-up times.
#' @param status The name of the column containing event indicators (1=event occurred, 0=censored).
#' @param time_type Units of follow-up time ('month' or 'day'). Converts days to months if 'day' is selected.
#' @param break_month The interval for breaking time on the X-axis of the plot. If 'auto', it is calculated automatically.
#' @param cols A vector of colors for the plot lines; if NULL, uses a default palette based on 'palette' parameter.
#' @param palette The name of the color palette to use if 'cols' is NULL. Common choices include 'jama' and other palette names from the `ggsci` package.
#' @param mini_sig A prefix label for variables in the plot, typically indicating a scoring metric.
#' @param save_path The directory path where the plot should be saved.
#' @param fig.type The file format for the saved plot ('pdf' or 'png').
#' @param index A unique identifier for the plot file, typically used in file naming.
#' @param width The width of the output plot.
#' @param height The height of the output plot.
#' @param font.size.table The font size used in the plot's risk table.
#'
#' @return Saves the Kaplan-Meier plot to the specified path and returns the plot object.
#' @import dplyr
#' @import ggplot2
#' @import survival
#' @import survminer
#' @author Dongqiang Zeng
#'
#' @examples
#'data("tcga_stad_pdata", package = "IOBR")
#'surv_group(input_pdata = tcga_stad_pdata, target_group = "TMEscore_plus_binary", time = "time", status = "OS_status")
#' @export
surv_group <-function(input_pdata,
target_group,
ID = "ID",
levels = c("High","Low"),
reference_group = NULL,
project = NULL,
time = "time",
status = "status",
time_type = "month",
break_month = "auto",
cols = NULL,
palette ="jama",
mini_sig = "score",
save_path = paste0("KMplot"),
fig.type = "pdf",
index = 1,
width = 6,
height = 6.5,
font.size.table = 3){
if(!target_group%in%colnames(input_pdata)) stop("Target group must be in the column of input_pdata")
###############################
if (!file.exists(save_path)) dir.create(save_path)
abspath<-paste(getwd(),"/" ,save_path, "/",sep ="" )
###########################################
input_pd <- input_pdata[,c(ID, target_group, time, status)]
# if(time!="time" & "time"%in%colnames(input_pd)) stop(paste0("time already exists in column name of input_pd."))
# if(status!="status" & "status"%in%colnames(input_pd)) stop(paste0("status already exists in column name of input_pd."))
#filter data
colnames(input_pd)[which(colnames(input_pd)==time)]<-"time"
colnames(input_pd)[which(colnames(input_pd)==status)]<-"status"
colnames(input_pd)[which(colnames(input_pd)==ID)]<-"ID"
input_pd$time<-as.numeric(input_pd$time)
input_pd$status<-as.numeric(as.character(input_pd$status))
input_pd<- input_pd %>%
dplyr::filter(!is.na(time)) %>%
dplyr::filter(!is.na(status)) %>%
dplyr::filter(!.$time=="NA") %>%
dplyr::filter(!.$status=="NA") %>%
dplyr::filter(.$time > 0)
###################################
input_pd$time<-as.numeric(input_pd$time)
input_pd$status<-as.numeric(input_pd$status)
###################################
input_pd<-as.data.frame(input_pd[,colnames(input_pd)%in%c("ID","time","status",target_group)])
input_pd$ID<-as.character(input_pd$ID)
# input_pd$ProjectID<-project
##################################
#transform follow up time
if(time_type=="day") input_pd$time<-input_pd$time/30
###################################
#cut use quantile
#################################
message(paste(">>> Dataset's survival follow up time is range between",
paste(round(summary(input_pd$time),2)[c(1,6)],collapse = " to "),"months"))
###################################
save(input_pd,file = paste0(abspath,"0-",project,"-",target_group,"-survival-analysis-input.RData"))
#######################################
colnames(input_pd)[which(colnames(input_pd)==target_group)]<-"target_group"
input_pd<-input_pd[!is.na(input_pd$target_group),]
message(print(summary(as.factor(input_pd$target_group))))
####################################
# define the break time and colors
if(break_month == "auto"){
break_month<-break_month(input = input_pd$time,block = 6)
}
max_month<- break_month*6
###########################################
if(is.null(cols)) cols<- palettes(category = "box",palette = palette, show_col = F)
# print(unique(input_pd$target_group))
if(length(unique(input_pd$target_group))>2){
input_pd<-input_pd[!is.na(input_pd$target_group),]
# input_pd$target_group<-ifelse(input_pd$target_group=="High",3,ifelse(input_pd$target_group=="Middle",2,1))
# Sur <- Surv(input_pd$time,input_pd$status)
# pvalue<-getHRandCIfromCoxph(coxph(Surv(input_pd$time,input_pd$status)~input_pd$target_group,data = input_pd))
# HR <- paste("Hazard Ratio = ", round(pvalue[,2],2), sep = "")
# CI <- paste("95% CI: ", paste(round(pvalue[,3],2), round(pvalue[,4],2), sep = " - "), sep = "")
##########################################
sfit <- survminer:: surv_fit(Surv(input_pd$time,input_pd$status) ~ input_pd$target_group,data=input_pd)
# hack strata for better survival curve
names(sfit$strata) <- gsub("target_group=", "", names(sfit$strata))
#############################################
#############################################
pp<- survminer:: ggsurvplot(sfit,
data = input_pd,
censor = TRUE,
ncensor.plot = F,conf.int = F,
xlim = c(0,max_month),
break.time.by = break_month,
xlab = "Months after diagnosis",
# legend.labs = c(paste0('Low ',mini_sig),paste0("Middle ",mini_sig),paste0("High ",mini_sig)),
submain = paste0(target_group," ",project),
surv.median.line = "h", # draw horizontal line at median survival
risk.table = T,
tables.height = 0.25,
palette = cols,
pval.size = 8)
fitd <- survdiff(Surv(time,status)~ target_group,
data= input_pd,
na.action = na.exclude)
p.val <- 1 - pchisq(fitd$chisq, length(fitd$n) - 1)
# add nominal pvalue for log-rank test
p.lab <- paste0("Overall P",
ifelse(p.val < 0.001, " < 0.001",
paste0(" = ",round(p.val, 3))))
pp$plot <- pp$plot + annotate("text",
x = 0, y = 0.55,
hjust = 0,
fontface = 3,
# size = 1,
label = p.lab)
# calculate pair-wise survival comparison
ps <- pairwise_survdiff(Surv(time,status)~ target_group,
data= input_pd,
p.adjust.method = "none")
# add pair-wise comparison table
# options(stringsAsFactors = FALSE)
addTab <- as.data.frame(as.matrix(ifelse(round(ps$p.value, 3) < 0.001, "<0.001",
round(ps$p.value, 3))))
addTab[is.na(addTab)] <- "-"
# options(stringsAsFactors = TRUE)
df <- tibble(x = 0, y = 0, tb = list(addTab))
pp$plot <- pp$plot + ggpp::geom_table(data = df, aes(x = x, y = y, label = tb), table.rownames = TRUE, size = font.size.table)
}else{
if(is.null(reference_group)){
levels <- unique(input_pd$target_group)
levels <- levels[order(levels)]
#####################################
message(paste0('>>>--- Reference_group was not defined...'))
pvalue<-getHRandCIfromCoxph(coxph(Surv(time = input_pd$time, event = input_pd$status)~input_pd$target_group,data = input_pd))
HR <- paste("Hazard Ratio = ", round(pvalue[,2],2), sep = "")
CI <- paste("95% CI: ", paste(round(pvalue[,3],2), round(pvalue[,4],2), sep = " - "), sep = "")
# cut_off<-paste("cutoff = ",round(res.cut,3),sep = "")
###########################################
sfit <- surv_fit(Surv(time = input_pd$time, event = input_pd$status) ~ input_pd$target_group,data = input_pd)
# input_pd[,index]<-ifelse(input_pd[,index]==1,"High","LOW")
###########################################
pp<-survminer:: ggsurvplot(sfit,
data = input_pd,
censor = TRUE,
ncensor.plot = F,conf.int = F,
xlim = c(0,max_month),
break.time.by = break_month,
xlab = "Months after diagnosis",
legend.labs = c(paste0(levels[1]), paste0(levels[2])),
submain= paste0(target_group," ",project),
risk.table = T,
tables.height = 0.20,
palette = cols,
pval.size = 8,
pval = paste(pval = ifelse(pvalue[,1] < 0.0001, "P < 0.0001",
paste("P = ",round(pvalue[,1],4), sep = "")),
HR, CI,sep = "\n"))
}else{
# print(unique(input_pd$target_group))
levels <- unique(input_pd$target_group)
levels <- levels[order(levels)]
if(reference_group!=levels[1]){
levels <- c(levels[2], levels[1])
}
#####################################
if(! reference_group%in% c(input_pd$target_group)) stop(">>>--- Reference_group must be one of target...")
input_pd$target_group<-ifelse(input_pd$target_group == reference_group, 1, 0)
pvalue<-getHRandCIfromCoxph(coxph(Surv(time = input_pd$time, event = input_pd$status)~input_pd$target_group,data = input_pd))
HR <- paste("Hazard Ratio = ", round(pvalue[,2],2), sep = "")
CI <- paste("95% CI: ", paste(round(pvalue[,3],2), round(pvalue[,4],2), sep = " - "), sep = "")
# cut_off<-paste("cutoff = ",round(res.cut,3),sep = "")
###########################################
sfit <- surv_fit(Surv(time = input_pd$time, event = input_pd$status) ~ input_pd$target_group,data = input_pd)
# input_pd[,index]<-ifelse(input_pd[,index]==1,"High","LOW")
############################################
# define break time
###########################################
pp<-survminer:: ggsurvplot(sfit,
data = input_pd,
censor = TRUE,
ncensor.plot = F,conf.int = F,
xlim = c(0,max_month),
break.time.by = break_month,
xlab = "Months after diagnosis",
legend.labs = c(paste0(levels[2]), paste0(levels[1])),
submain= paste0(target_group," ",project),
risk.table = T,
tables.height = 0.20,
palette = cols,
pval.size = 8,
pval = paste(pval = ifelse(pvalue[,1] < 0.0001, "P < 0.0001",
paste("P = ",round(pvalue[,1],4), sep = "")),
HR, CI,sep = "\n"))
}
}
pp<-list(pp)
res <- arrange_ggsurvplots(pp, print = FALSE, ncol = 1, nrow = 1)
ggsave(res,filename = paste0(index,"-KMplot-",target_group,"-",project,".", fig.type),
width = width ,height = height,path = save_path)
return(res)
}
#' Break Time Into Blocks
#'
#' Divides a time duration into specified blocks. This function is useful for creating intervals or categories
#' within a given time period, such as months or days, for further analysis or visualization in studies where
#' time segmentation might be relevant.
#'
#' @param input A numeric vector representing time durations that need to be divided, typically in months or days.
#' @param block An integer specifying the number of blocks to divide the time into; default is 6.
#' @param time_type A character string specifying the units of the input time: "month" for months and "day" for days.
#' The default is "month". If "day" is specified, the function converts days into months by dividing by 30.
#'
#' @return A numeric vector representing the breakpoints for the time blocks, rounded to the nearest multiple of 5.
#' @export
#' @author Dongqiang Zeng
#'
#' @examples
#' # Example with time in months
#' time_data <- c(24, 36, 12, 48)
#' blocks <- break_month(input = time_data)
#' print(blocks)
#'
#' # Example with time in days
#' day_data <- c(720, 1080, 360, 1440) # Corresponding to 24, 36, 12, 48 months
#' blocks_days <- break_month(input = day_data, time_type = "day")
#' print(blocks_days)
break_month<-function(input, block = 6, time_type = "month"){
max_time <-max(input)
if(time_type=="month"){
max_time<-max_time
}else if(time_type=="day"){
max_time<-max_time/30
}
message(paste0(" Maximum of follow up time is ", max_time, " months; and will be divided into ", block, " sections;"))
round(c(max_time %/% block)/5,0)*5
}
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.