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How to add confidence intervals to a line in ggplot2 (R)

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In this blog post, you’ll learn how to add confidence intervals to a line plot in R in the popular ggplot2 visualization package, part of the tidyverse.

First, let’s create some random data to work with. For demonstrational purposes, I’ve created two time series from two normally-distributed random variables. These are the steps I undertook:

• Series A will have a mean of 3 and a standard deviation of 1, series B will have a mean of 8 and a standard deviation of 2.
• I shuffle the data.
• I add an index and a lower and higher bound (fictional confidence intervals)
library(magrittr)
library(dplyr)
library(ggplot2)

two_timeseries <- c(rnorm(50, 3,1), rnorm(50,8,2)) # 100 observations, from two normal random variables
df <- tibble(series = c(rep("A", 50), rep("B",50)), data = two_timeseries)
df <- df[sample(100),] # shuffle them
df %<>% mutate(low = data * 0.75, high = data * 1.25, index = seq(1,100)) # add lower bounds, and an index

Add confidence intervals to a ggplot2 line plot

Next, let’s plot this data as a line, and add a ribbon (using geom_ribbon) that represents the confidence interval. By adding an alpha (opacity) you can give it a nice shaded effect.

ggplot(df, aes(x = index, y = data, group = 1)) +
geom_line(col='red') +
geom_ribbon(aes(ymin = low, ymax = high), alpha = 0.1)

This is the result:

Customize a ggplot2 ribbon

You should know that you can plot multiple lines and multiple confidence intervals, simply by setting multiple groups.

ggplot(df, aes(x = index, y = data, group=series)) +
geom_line(col="black") +
geom_point() +
geom_ribbon(aes(ymin = low, ymax = high, fill = series),
alpha=0.1,
linetype="dashed",
color="grey")

There are quite a lot of customization parameters that geom_ribbon supports. In the example above, I added a dashed border to the ribbon and set the fill color via the aesthetics.

By the way, if you’re having trouble understanding some of the code and concepts, I can highly recommend “An Introduction to Statistical Learning: with Applications in R”, which is the must-have data science bible. If you simply need an introduction into R, and less into the Data Science part, I can absolutely recommend this book by Richard Cotton. Hope it helps!

Good luck!

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