I am analysing an RCT and I wish to report summary statistics (mean with 95%CI) for a number of variables at three time points stratified by treatment allocation. Below is my code so far which only yields this figure.

set.seed(42)
n <- 100
dat1 <- data.frame(id=1:n,
                   treat = factor(sample(c('Trt','Ctrl'), n, rep=TRUE, prob=c(.5, .5))),
                   time = factor("T1"),
                   outcome1=rbinom(n = 100, size = 1, prob = 0.3),
                   st=runif(n, min=24, max=60),
                   qt=runif(n, min=.24, max=.60),
                   zt=runif(n, min=124, max=360)
)
dat2 <- data.frame(id=1:n,
                   treat = dat1$treat,
                   time = factor("T2"),
                   outcome1=dat1$outcome1,
                   st=runif(n, min=34, max=80),
                   qt=runif(n, min=.44, max=.90),
                   zt=runif(n, min=214, max=460)
)
dat3 <- data.frame(id=1:n,
                   treat = dat1$treat,
                   time = factor("T3"),
                   outcome1=dat1$outcome1,
                   st=runif(n, min=44, max=90),
                   qt=runif(n, min=.74, max=1.60),
                   zt=runif(n, min=324, max=1760)
)

dat <- rbind(dat1,dat2, dat3)

ggplot(dat,aes(x=mean(zt), y=time))   geom_point(aes(colour=treat))   coord_flip()   geom_line(aes(colour=treat)) 

I have three questions

1. can a line be added connecting T1 to T2 to T3 showing the trend
2. can the 95%CI for the mean be added to each point without having to calculate a "ymin" and "ymax" for all my response variables
3. if I have multiple response variables (in this example "st", "qt" and "zt") is there a way to produce these all at one as some sort of facet?

CodePudding user response：

Pivot_longer should do most of what you need. Pivot your st, qt, and zt (and whatever other response variables you need). Here I've labeled them "response_variables" and their values as value. You can then facet_wrap by response_variable. Stat_summary will add a line and the mean and ci (se), after group and color by treat. I opted for scales = "free" in facet_wrap otherwise you won't see much going on as zt dominates with its larger range

library(dplyr)
library(ggplot2)
library(Hmisc)
library(tidyr)
dat %>% 
  pivot_longer(-(1:4), names_to = "response_variables") %>% 
  ggplot(.,aes(x=value, y=time, group = treat, color = treat))  
  facet_wrap(~response_variables, scales = "free")   
  coord_flip()  
  stat_summary(fun.data = mean_cl_normal,
                 geom = "errorbar")  
  stat_summary(fun = mean,
               geom = "line")  
    stat_summary(fun = mean,
               geom = "point")