用删减表创建一个ggplot2生存曲线

本文介绍了用删减表创建一个ggplot2生存曲线的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！ 问题描述 29岁程序员，3月因学历无情被辞！ $ （肺，{b1，b2，b3，b3，b3，b3，b3，b3，b3）的数据（肺，包=存活）b $ b sex }） ggthemes_data require（ （数据帧（x =肺$时间，y =肺$状态，z =肺$性）） .df .fit< - survival :: survfit（survival :: Surv（time = x，event = y，type =right）〜z， .df） .pval 函数（x）{ data.frame（x = 0，y = 0，df = 1， chisq = survival :: survdiff（ survival :: Surv（time = x，event = y，type =right）〜z，x ）$ chisq ）}） .pval $ label< - paste0（paste（italic（p），\=， signif（1 - pchisq（.p） val $ chisq，.pval $ df），3），\）） .fit< - data.frame（x = .fit $ time，y = .fit $ surv，nrisk = .fit $ n.risk，nevent = .fit $ n.event，ncensor = .fit $ n.censor，upper = .fit $ upper，lower = .fit $ lower） $ b $ .df .df< - .fit< - data.frame （.fit，.df [，c（z），drop = FALSE]） .med data.frame（ median = min（subset（x，y< （0.5 + .Machine $ double.eps ^ 0.5））$ x））}） .df< - .fit< - rbind（unique（data.frame（x = 0，y = 1，nrisk = NA，nevent = NA， ncensor = NA，upper = 1，lower = 1，.df [，c（z），drop = FALSE]）），.fit） .cens< - subset（.fit，ncensor == 1） .tmp1< - data.frame（as.table（by（.df，.df [，c（z）， （d） max（d $ nrisk，na.rm = TRUE）））） .tmp1 $ x .nrisk {.df data.frame（as.table（by（.df，.df [，c（z），drop = FALSE），函数（d）if （all is.na（d $ nrisk）））NA else min（d $ nrisk - d $ nevent - d $ ncensor，na.rm = TRUE））））; .tmp2 $ x< - 100 * i; .tmp2 $ Freq [is.na（.tmp2 $ Freq）] .tmp1< - .tmp2; .nrisk .nrisk $ y .plot RcmdrPlugin.KMggplot2 :: geom_stepribbon（data = .fit，aes（x = x，ymin = lower，ymax = upper，fill = z），alpha = 0.25，color =transparent，show.legend = FALSE，kmplot = TRUE）+ geom_step（size = 1.5）+ geom_linerange（data = .cens，aes（x = x，ymin = y， ymax = y + 0.02），size = 1.5）+ geom_text（data = .pval，aes（y = y，x = x， label = label），color =black，hjust = 0，vjust = -0.5，parse = TRUE，show.legend = FALSE，size = 14 * 0.282，family =sans） + geom_vline（data = .med，aes（xintercept = median），color =black，lty = 2）+ scale_x_continuous（breaks = seq（0，900，by = 100），limits = c（0,900））+ scale_y_continuous（limits = c（0,1），expand = c（0.01,0））+ scale_colour_brewer（palette =Set1）+ scale_f ill_brewer（palette =Set1）+ xlab（从入门开始的时间）+ ylab（生存比例）+ labs（color =sex）+ ggthemes :: theme_calc（base_size = 1，base_family =sans）+主题（legend.position = c（1,1），legend.justification = c（1,1）） .nrisk $ y .plot2 geom_text（size = 14 * 0.282，family =sans）+ scale_x_continuous（breaks = seq（0,900，by = 100），limits = c（0，900 ）+ scale_y_continuous（limits = c（0，1））+ scale_colour_brewer（palette =Set1）+ ylab（生存比例）+ RcmdrPlugin.KMggplot2 :: theme_natrisk（ggthemes :: theme_calc，14，sans）.plot3 geom_text（hjust = 0，size = 14 * 0.282，family =sans）+ scale_x_continuous（limits = c（-5，5 ））+ scale_y_continuous（limits = c（0，1））+ scale_colour_brewer（palette =Set1）+ RcmdrPlugin.KMggplot2 :: theme_natrisk21（ggthemes :: theme_calc，14，sans） .plotb RcmdrPlugin.KMggplot2 :: theme_natriskbg（ggthemes :: theme_calc，14，sans ） grid :: grid.newpage（）; grid :: pushViewport（grid :: viewport（layout = grid :: grid.layout（2，2，heights = unit（c（1,3），c（null，lines））， widths = unit（c（4，1），c（lines，null）））））; print（.plotb，vp = grid :: viewport（layout.pos.row = 1：2，layout.pos.col = 1：2））; print（.plot，vp = grid :: viewport（layout.pos.row = 1，layout.pos.col = 1：2））; print（.plot2，vp = grid :: viewport（layout.pos.row = 2，layout.pos.col = 1：2））; print（.plot3，vp = grid :: viewport（layout.pos.row = 2，layout.pos.col = 1））; .plot< - recordPlot（） print（.plot） I am trying to create a Kaplan-Meier plot with 95% confidence bands plus having the censored data in a table beneath it. I can create the plot, but not the table. I get the error message: Error in grid.draw(both) : object 'both' not found. library(survival) library(ggplot2) library(GGally) library(gtable) data(lung) sf.sex <- survfit(Surv(time, status) ~ sex, data = lung) pl.sex <- ggsurv(sf.sex) + geom_ribbon(aes(ymin=low,ymax=up,fill=group),alpha=0.3) + guides(fill=guide_legend("sex")) pl.sex tbl <- ggplot(df_nums, aes(x = Time, y = factor(variable), colour = variable,+label=value)) + geom_text() + theme_bw() + theme(panel.grid.major = element_blank(),+ legend.position = "none",+ plot.background = element_blank(), + panel.grid.major = element_blank(),+ panel.grid.minor = element_blank(),+ panel.border = element_blank(),+ legend.position="none",+ axis.line = element_blank(),+ axis.text.x = element_blank(),+ axis.text.y = element_text(size=15, face="bold", color = 'black'),+ axis.ticks=element_blank(),+ axis.title.x = element_blank(),+ axis.title.y = element_blank(),+ plot.title = element_blank()) + scale_y_discrete(breaks=c("Group.A", "Group.B"), labels=c("Group A", "Group B")) both = rbind(ggplotGrob(g), ggplotGrob(tbl), size="last") panels <- both$layout$t[grep("panel", both$layout$name)] both$heights[panels] <- list(unit(1,"null"), unit(2, "lines")) both <- gtable_add_rows(both, heights = unit(1,"line"), 8) both <- gtable_add_grob(both, textGrob("Number at risk", hjust=0, x=0), t=9, l=2, r=4) grid.newpage() grid.draw(both) 解决方案 I solved the problem by using the Rcmdrplugin KMggplot2 The code is generated by the plugin after selecting the data and variables. library(survival, pos=18) data(lung, package="survival") lung <- within(lung, { sex <- factor(sex, labels=c('male','female')) }) ggthemes_data <- ggthemes::ggthemes_data require("ggplot2") .df <- na.omit(data.frame(x = lung$time, y = lung$status, z = lung$sex)) .df <- .df[do.call(order, .df[, c("z", "x"), drop = FALSE]), , drop = FALSE] .fit <- survival::survfit(survival::Surv(time = x, event = y, type = "right") ~ z, .df) .pval <- plyr::ddply(.df, plyr::.(), function(x) { data.frame( x = 0, y = 0, df = 1, chisq = survival::survdiff( survival::Surv(time = x, event = y, type = "right") ~ z, x )$chisq )}) .pval$label <- paste0( "paste(italic(p), \" = ", signif(1 - pchisq(.pval$chisq, .pval$df), 3), "\")" ) .fit <- data.frame(x = .fit$time, y = .fit$surv, nrisk = .fit$n.risk, nevent = .fit$n.event, ncensor= .fit$n.censor, upper = .fit$upper, lower = .fit$lower) .df <- .df[!duplicated(.df[,c("x", "z")]), ] .df <- .fit <- data.frame(.fit, .df[, c("z"), drop = FALSE]) .med <- plyr::ddply(.fit, plyr::.(z), function(x) { data.frame( median = min(subset(x, y < (0.5 + .Machine$double.eps^0.5))$x) )}) .df <- .fit <- rbind(unique(data.frame(x = 0, y = 1, nrisk = NA, nevent = NA, ncensor = NA, upper = 1, lower = 1, .df[, c("z"), drop = FALSE])), .fit).cens <- subset(.fit, ncensor == 1).tmp1 <- data.frame(as.table(by(.df, .df[, c("z"), drop = FALSE], function(d) max(d$nrisk, na.rm = TRUE)))) .tmp1$x <- 0 .nrisk <- .tmp1 for (i in 1:9) {.df <- subset(.fit, x < 100 * i); .tmp2 <- data.frame(as.table(by(.df, .df[, c("z"), drop = FALSE], function(d) if (all(is.na(d$nrisk))) NA else min(d$nrisk - d$nevent - d$ncensor, na.rm = TRUE)))); .tmp2$x <- 100 * i; .tmp2$Freq[is.na(.tmp2$Freq)] <- .tmp1$Freq[is.na(.tmp2$Freq)]; .tmp1 <- .tmp2; .nrisk <- rbind(.nrisk, .tmp2)} .nrisk$y <- rep(seq(0.075, 0.025, -0.05), 10) .plot <- ggplot(data = .fit, aes(x = x, y = y, colour = z)) + RcmdrPlugin.KMggplot2::geom_stepribbon(data = .fit, aes(x = x, ymin = lower, ymax = upper, fill = z), alpha = 0.25, colour = "transparent", show.legend = FALSE, kmplot = TRUE) + geom_step(size = 1.5) +geom_linerange(data = .cens, aes(x = x, ymin = y, ymax = y + 0.02), size = 1.5) +geom_text(data = .pval, aes(y = y, x = x, label = label), colour = "black", hjust = 0, vjust = -0.5, parse = TRUE, show.legend = FALSE, size = 14 * 0.282, family = "sans") + geom_vline(data = .med, aes(xintercept = median), colour = "black", lty = 2) + scale_x_continuous(breaks = seq(0, 900, by = 100), limits = c(0, 900)) + scale_y_continuous(limits = c(0, 1), expand = c(0.01,0)) + scale_colour_brewer(palette = "Set1") + scale_fill_brewer(palette = "Set1") + xlab("Time from entry") + ylab("Proportion of survival") + labs(colour = "sex") + ggthemes::theme_calc(base_size = 14, base_family = "sans") + theme(legend.position = c(1, 1), legend.justification = c(1, 1)) .nrisk$y <- ((.nrisk$y - 0.025) / (max(.nrisk$y) - 0.025) + 0.5) * 0.5 .plot2 <- ggplot(data = .nrisk, aes(x = x, y = y, label = Freq, colour = z)) + geom_text(size = 14 * 0.282, family = "sans") + scale_x_continuous(breaks = seq(0,900, by = 100), limits = c(0, 900)) + scale_y_continuous(limits = c(0, 1)) + scale_colour_brewer(palette = "Set1") + ylab("Proportion of survival") + RcmdrPlugin.KMggplot2::theme_natrisk(ggthemes::theme_calc, 14, "sans") .plot3 <- ggplot(data = subset(.nrisk, x == 0), aes(x = x, y = y, label = z, colour = z)) + geom_text(hjust = 0, size = 14 * 0.282, family = "sans") + scale_x_continuous(limits = c(-5, 5)) + scale_y_continuous(limits = c(0, 1)) + scale_colour_brewer(palette = "Set1") + RcmdrPlugin.KMggplot2::theme_natrisk21(ggthemes::theme_calc, 14, "sans") .plotb <- ggplot(.df, aes(x = x, y = y)) + geom_blank() + RcmdrPlugin.KMggplot2::theme_natriskbg(ggthemes::theme_calc, 14, "sans") grid::grid.newpage(); grid::pushViewport(grid::viewport(layout = grid::grid.layout(2, 2, heights = unit(c(1, 3), c("null", "lines")), widths = unit(c(4, 1), c("lines", "null"))))); print(.plotb, vp = grid::viewport(layout.pos.row = 1:2, layout.pos.col = 1:2)); print(.plot , vp = grid::viewport(layout.pos.row = 1 , layout.pos.col = 1:2)); print(.plot2, vp = grid::viewport(layout.pos.row = 2 , layout.pos.col = 1:2)); print(.plot3, vp = grid::viewport(layout.pos.row = 2 , layout.pos.col = 1 )); .plot <- recordPlot() print(.plot) 这篇关于用删减表创建一个ggplot2生存曲线的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！ 上岸，阿里云！