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2 years ago 收藏贴-森林图绘图R包汇总 by 作图丫
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GUIDE ╲
森林图是以统计指标和统计分析方法为基础，用数值运算结果绘制出的图型。用以综合展示每个被纳入研究的效应量以及汇总的合并效应量。
library(grid)library(forestploter)# 读取实例数据dt <- read.csv(system.file("extdata", "example_data.csv", package = "forestploter"))# 筛选需要的列dt <- dt[,1:6]# 缩进dt$Subgroup <- ifelse(is.na(dt$Placebo),dt$Subgroup,paste0(" ", dt$Subgroup))# NA 为空白或 NA 将转换为字符dt$Treatment <- ifelse(is.na(dt$Treatment), "", dt$Treatment)dt$Placebo <- ifelse(is.na(dt$Placebo), "", dt$Placebo)dt$se <- (log(dt$hi) - log(dt$est))/1.96# 为森林图添加空白列以显示 CIdt$` ` <- paste(rep(" ", 20), collapse = " ")# 创建置信区间列dt$`HR (95% CI)` <- ifelse(is.na(dt$se), "",sprintf("%.2f (%.2f to %.2f)",dt$est, dt$low, dt$hi))head(dt)
# 设置主题tm <- forest_theme(base_size = 10,refline_col = "red",footnote_col = "#636363",footnote_fontface = "italic")##参数设置p <- forest(dt[,c(1:3, 8:9)],est = dt$est,lower = dt$low,upper = dt$hi,sizes = dt$se,ci_column = 4,ref_line = 1,arrow_lab = c("Placebo Better", "Treatment Better"),xlim = c(0, 4),ticks_at = c(0.5, 1, 2, 3),footnote = "This is the demo data. Please feel free to change\nanything you want.",theme = tm)plot(p)
library(forestplot)library(dplyr)# 来自“rmeta”包的 Cochrane 数据cochrane_from_rmeta <- structure(list(mean = c(NA, NA, 0.578, 0.165, 0.246, 0.700, 0.348, 0.139, 1.017, NA, 0.531),lower = c(NA, NA, 0.372, 0.018, 0.072, 0.333, 0.083, 0.016, 0.365, NA, 0.386),upper = c(NA, NA, 0.898, 1.517, 0.833, 1.474, 1.455, 1.209, 2.831, NA, 0.731)),.Names = c("mean", "lower", "upper"),row.names = c(NA, -11L),class = "data.frame")#数据处理tabletext <- cbind(c("", "Study", "Auckland", "Block", "Doran", "Gamsu", "Morrison", "Papageorgiou", "Tauesch", NA, "Summary"),c("Deaths", "(steroid)", "36", "1", "4", "14", "3", "1", "8", NA, NA),c("Deaths", "(placebo)", "60", "5", "11", "20", "7", "7", "10", NA, NA),c("", "OR", "0.58", "0.16", "0.25", "0.70", "0.35", "0.14", "1.02", NA, "0.53"))cochrane_from_rmeta %>%forestplot(labeltext = tabletext,is.summary = c(rep(TRUE, 2), rep(FALSE, 8), TRUE),clip = c(0.1, 2.5),xlog = TRUE,col = fpColors(box = "royalblue",line = "darkblue",summary = "royalblue"))
exdf <- cbind(OR=c(1.21,0.90,1.02,1.54,1.32,0.79,1.38,0.85,1.11,1.58,1.80,2.27),LCI=c(0.82,0.61,0.66,1.08,0.91,0.48,1.15,0.39,0.91,0.99,1.48,0.92),UCI=c(1.79,1.34,1.57,2.19,1.92,1.32,1.64,1.87,1.34,2.54,2.19,5.59),groups=c(1,1,1,2,2,2,2,2,2,3,3,3))exdf <- data.frame(exdf)rownames(exdf) <- c('Barry, 2005', 'Frances, 2000', 'Rowley, 1995','Biro, 2000', 'Crowe, 2010', 'Harvey, 1996','Johns, 2004', 'Parr, 2002', 'Zhang, 2011','Flint, 1989', 'Mac Vicar, 1993', 'Turnbull, 1996')knitr::kable(exdf)
forplo(exdf[,1:3],groups=exdf$groups,grouplabs=c('Low risk of bias','Some concerns','High risk of bias'))
library(metaviz)viz_forest(x = mozart[1:10, c("d", "se")], study_labels = mozart[1:10, c("study_name")],summary_label = "Summary effect", xlab = "Cohen d")
viz_forest(x = mozart[1:10, c("d", "se")], study_labels = mozart[1:10, c("study_name")],summary_label = "Summary effect", xlab = "Cohen d", variant = "rain")
library(ggforestplot)library(tidyverse)# 数据集df <-ggforestplot::df_linear_associations %>%filter(trait == "BMI",dplyr::row_number() <= 30)# 绘制横截面线性关联的森林图ggforestplot::forestplot(df = df,name = name,estimate = beta,se = se)
library(survminer)library(survival)
res.cox <- coxph(Surv(time, status) ~ ph.karno * age, data=lung)summary(res.cox, conf.int = FALSE)##画图ggforest()ggforest(res.cox, data = lung)
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