ggplot version of plot(lmObject)

Check_assumptions.R

## function to do graphical checks in one hit, given a model
## assumes that broom and gridExtra are available
## and that the model object has tidiers available
check_assumptions <- function(x, ...){
  if (inherits(x,"gam")){
    stop("check_assumptions doesn't work with gam() objects")
  }
  
  my_theme <- ggplot2::theme_classic() + 
    ggplot2::theme(text=ggplot2::element_text(size=rel(4)))
  
  # get the residuals etc.
  rr <- broom::augment(x)
  ## plot 0 resid vs. fitted
  rr0 <- ggplot2::ggplot(rr, ggplot2::aes(x = .fitted, y = .resid)) + 
    ggplot2::geom_point() + 
    ggplot2::geom_smooth() + 
    ggplot2::geom_hline(yintercept = 0, linetype=2) + 
    my_theme
  ## plot 1 qq plot
  # get int and slope for qqline
  probs <- c(0.25,0.75)
  y <- quantile(rr$.std.resid, probs, names = FALSE, na.rm = TRUE)
  x <- qnorm(probs)
  slope <- diff(y)/diff(x)
  int <- y[1L] - slope * x[1L]
  rr1 <- ggplot2::ggplot(rr, ggplot2::aes(sample=.std.resid)) + 
    ggplot2::geom_qq(size=rel(4)) + 
    ggplot2::geom_abline(intercept = int, slope = slope, linetype = 2, size = 2) +
    my_theme
  
  ## plot 2 scale location plot
  rr2 <- ggplot2::ggplot(rr, ggplot2::aes(x = .fitted, y = sqrt(abs(.std.resid)))) + 
    ggplot2::geom_point() + 
    ggplot2::geom_smooth() + 
    ggplot2::geom_hline(yintercept  = 1) +
    my_theme
  ## plot 3 cooks distance plot
  rr3 <- ggplot2::ggplot(rr, ggplot2::aes(.hat, .std.resid)) +
    ggplot2::geom_vline(size = 2, xintercept = 0) +
    ggplot2::geom_hline(size = 2, yintercept = 0) +
    ggplot2::geom_point(ggplot2::aes(size = .cooksd)) + 
    ggplot2::geom_smooth(se = FALSE) + 
    my_theme
  
  plot(gridExtra::arrangeGrob(rr0, rr1, rr2, rr3, nrow=2))
  
}

Found a link to this somewhere. Seemed to work. You have an aes missing a ggplot2 prefix. Here it is in slightly edited form:

plot_residuals <- function(model, size = 1, linewidth = 1){  
    # extract residuals
    require(broom)
    d <- broom::augment(model) 

    # 1: residuals vs. fitted
    require(ggplot2)
    ggplot(data = d, aes(x = .fitted, y = .resid)) + 
        geom_point(size = size) + 
        geom_smooth() + 
        geom_hline(yintercept = 0, linetype = 2) -> p1

    ## 2: qq-plot
    probs <- c(0.25, 0.75)
    y <- quantile(d$.std.resid, probs, names = FALSE, na.rm = TRUE)
    x <- qnorm(probs)
    slope <- diff(y)/diff(x)
    intercept <- y[1L] - slope * x[1L]
    ggplot(data = d, aes(sample = .std.resid)) + 
        geom_qq(size = size) + 
        geom_abline(intercept = intercept, slope = slope, 
                    linetype = 2, size = linewidth) -> p2

    ## 3: scale location plot
    ggplot(data = d, aes(x = .fitted, y = sqrt(abs(.std.resid)))) + 
        geom_point(size = size) + 
        geom_smooth() + 
        geom_hline(yintercept = 1) -> p3 

    ## 4: Cooks distance plot
    ggplot(data = d, aes(.hat, .std.resid)) +
        geom_vline(size = linewidth, xintercept = 0) +
        geom_hline(size = linewidth, yintercept = 0) +
        geom_point(aes(size = .cooksd)) + 
        geom_smooth(se = FALSE) -> p4 
    require(gridExtra)
    return(plot(arrangeGrob(p1, p2, p3, p4, nrow = 2))) 
}

plot_residuals(m)