森林图展示回归分析的结果

通过conda安装纯净环境的forestplot

conda create -n forestplot -c conda-forge r-base=4.1.3
conda activate forestplot
conda install -c conda-forge r-tidyverse=1.3.1 -y
conda install -c conda-forge r-irkernel -y
conda install -c conda-forge r-forestplot -y
Rscript -e “IRkernel::installspec(name=’forestplot’, displayname=’r-forestplot’)”

使用教程

require(forestplot)
f_forestplot <- function(df, xlab="XR", zero=0, lineheight=unit(10,'mm'), colgap=unit(2,'mm'), graphwidth=unit(60,'mm'), title="Forestplot"){
    df_labeltext <- df[,c('VarName', 'Pvalue')]
    df_labeltext[[paste0(xlab,'(95%CI)')]] <- paste0(sprintf("%0.2f", df$mean),'(',sprintf("%0.2f", df$lower),'~',sprintf("%0.2f", df$upper),')')
    df_labeltext[['Pvalue']] <- sprintf('%0.1e', df_labeltext[['Pvalue']])
    df_labeltext <- rbind(colnames(df_labeltext), df_labeltext)
    df <- rbind(rep(NaN, ncol(df)), df)
    forestplot(labeltext=as.matrix(df_labeltext[,c(1,3,2)]),
               mean=df$mean,
               lower=df$lower,
               upper=df$upper,
               zero=zero,
               boxsize=0.5,
               lineheight=lineheight,
               colgap=colgap,
               graphwidth=graphwidth,
               lwd.zero=2,
               lwd.ci=2, 
               col=fpColors(box='#458B00',
                            summary='#8B008B',
                            lines = 'black',
                            zero = '#7AC5CD'),
               xlab=xlab,
               lwd.xaxis =2,
               txt_gp = fpTxtGp(ticks = gpar(cex = 0.85),
                                xlab  = gpar(cex = 0.8),
                                cex = 0.9),
               lty.ci="solid",
               title=title, 
               line.margin = 1,
               graph.pos=2)
}

df <- data.frame(
    'VarName' = c('varA', 'varB', 'varC', 'varD', 'varE', 'varF', 'varG'),
    'mean' = c(0.2, 0.1, 0.4, 0.6, -0.3, 0.2, -0.2),
    'lower' = c(0.1, -0.1, 0.2, 0.3, -0.5, -0.1, -0.6),
    'upper' = c(0.6, 0.3, 0.5, 0.7, -0.2, 0.4, -0.1),
    'Pvalue' = c(0.6, 0.3, 0.5, 0.7, 0.2, 0.4, 0.1)
)
options(repr.plot.width=6, repr.plot.height=4)
f_forestplot(df)