Plot means

Function to plot means and confidence intervals for several independent variables

library(tidyverse)
library(ggpubr)

Attaching package: ‘ggpubr’

The following object is masked from ‘package:cowplot’:

    get_legend

library(broom)

calculate_t_ci <- function(.x) {
  t.test(.x, conf.int = TRUE) %>% 
    tidy() %>% 
    select(estimate, conf.low, conf.high)
}

plot_means <- function(.tbl, .resp, ...) {
  vars <- enquos(...)
  
  x <- vars[[1]]
  
  .tbl <- .tbl %>% 
    mutate(across(c(...), as.factor))
  
  ci <- .tbl %>% 
    group_by(...) %>% 
    summarise(calculate_t_ci({{.resp}}), .groups = "keep")
  
  gg <- ggplot(.tbl, aes(x = {{x}}, y = {{.resp}}, color = {{x}})) 
  
  
  if (length(vars) == 2) {
    y <- vars[[2]] 
    gg <- gg + facet_wrap(vars({{y}}), nrow = 1) 
    }
  
  if (length(vars) == 3) {
    y <- vars[[2]] 
    z <- vars[[3]] 
    gg <- gg + facet_wrap(vars({{y}}, {{z}}), nrow = 1) 
  }
  
  if (length(vars) == 4) {
    y <- vars[[2]] 
    z <- vars[[3]] 
    w <- vars[[4]]
    gg <- gg + facet_wrap(vars({{y}}, {{z}}, {{w}}), nrow = 2) 
  }
  
  gg +
    geom_sina(maxwidth = .5) +
    geom_crossbar(data = ci, 
                  aes(y = estimate, ymin= conf.low , ymax = conf.high))
    
}

You need to specify the data set, the response variable and then separate the independent variables by commas.

plot_means(mtcars, mpg, vs)

plot_means(mtcars, mpg, vs, am)