Coefficient plot in R

coef_plot_with_broom.R

# Libraries
library(dplyr)
library(broom)
library(ggplot2)

# Create data
fake.data <- data_frame(preg = rbinom(100, 1, prob=0.5),
                        trend = rnorm(100),
                        age = sample(15:55, 100, replace=TRUE),
                        parity = rbinom(100, 4, prob=0.5))

# Build model
model <- lm(preg ~ trend + age + parity, data=fake.data)
summary(model)

# Variables for the plot data
var.names <- c("Intercept", "Trend", "Age", "Parity")
multiplier <- qnorm(1 - 0.05 / 2)

# tidy() comes from the broom package and converts the model summary to a dataframe
plot.data <- tidy(model) %>%
  mutate(ymin = estimate - (multiplier * std.error),
         ymax = estimate + (multiplier * std.error),
         # Change the variable names into an ordered factor.
         # Reverse the order so that they display top->bottom when the plot is flipped.
         term = factor(term, levels=rev(term), 
                       labels=rev(var.names), ordered=TRUE))

# Get rid of the intercept if you want
plot.data <- plot.data %>% filter(term != "Intercept")


# Make a plot
p <- ggplot(plot.data, aes(x=term, y=estimate)) + 
  geom_hline(yintercept=0, colour="#8C2318", size=1) +  # Line at 0
  geom_pointrange(aes(ymin=ymin, ymax=ymax)) +  # Ranges for each coefficient
  labs(x="Coefficient", y="Estimate", title="Whatever") +  # Labels
  coord_flip() +  # Rotate the plot
  theme_bw()  # Nicer theme
p

# Save plot
ggsave(p, filename="~/Desktop/cool_plot.pdf", width=5, height=5, units="in")
ggsave(p, filename="~/Desktop/cool_plot.png", width=5, height=5, units="in")