Common inputs / outputs of an R session are datasets or R scripts.
For this meeting we focus on datasets as inputs to the R session, loading data and saving data.
The file format (csv, RData, xls, stata …) and directory of the files are important keep in mind.
We will go over a few options, potential issues, and how to avoid the need to type in a long directory name when managing the input and output of the R session.
Working directories and R projects
Whenever we provide R with a file name, it can include the full path on the computer.
An alternative is to work on a specified directory.
Another alternative is to work within a ‘project’ that all paths are visible to the project scripts.
If we do not provide any path, R will use the current “working directory” for reading or writing files. It can be obtained by the command
You can view the data by clicking on it in the `Environment’ at the top-right of the workspace.
General way of importing and exporting of other data formats
Using the correct libraries for different data formats can be tedious.
R package rio is very convenient for data import and export. It figures out the type of data format from the file name extension, e.g. .csv for CSV, .dta for Stata, or *.sav for SPSS data sets
For a complete list of supported formats, see help(rio).
It calls an appropriate package to do the actual importing or exporting.
It is possible to automatically load data from a web source using APIs.
An API (Application Programming Interface) acts as a bridge between your R code and an external data source: a website, database, or an online platform with permissions.
Advantages: Automation, efficiency and real-time access.
Disadvantages: No offline access to data (can be important for replication), dependency on external services (API can go down).
Suggestion: Save the downloaded data in RData format to mitigate disadvantages.
Example: AAPL daily prices from Yahoo Finance
As an example we will download AAPL daily prices from Yahoo Finance.
Convert loaded data to a vector.
Save the data as an RData file for future offline access.
library(quantmod)# Get Apple Inc. (AAPL) stock data from Yahoo FinancegetSymbols("AAPL", src ="yahoo", from ="2024-01-01", to ="2025-06-13")# Save data as RData filesave.image("data/AAPL.RData")
Example: AAPL daily prices from Yahoo Finance (cont’d)
View and plot data.
Loading required package: xts
Loading required package: zoo
Attaching package: 'zoo'
The following objects are masked from 'package:base':
as.Date, as.Date.numeric
Loading required package: TTR
Registered S3 method overwritten by 'quantmod':
method from
as.zoo.data.frame zoo
plot(x =long_data$MONTH, y =long_data$TEMP)## Scatter plot wrt month
Multiple plots
par(mfrow =c(1,2))# multiple plots in a rowplot(long_data$TEMP)## Plotting a single variableplot(x =long_data$MONTH, y =long_data$TEMP)## Scatter plot wrt month
Function `dev.off()’is called after all the plotting, to save the file and return control to the screen.
load("data/climate_wide.Rdata")plot(wide_data$MAASTRICHT)# temperatures in Maastrichtlines(wide_data$EINDHOVEN)# temperatures in Eindhoven in lines
Customizing Graphics
The plot() function takes several many arguments that can change the layout of the plots. See ?par for all graphical options; there are many!
Some examples:
col: color of lines / points
lty, lwd: Line type and thickness
pch: Point type (1-16)
main, sub: Title, subtitle
xlab, ylab: x and y axis labels
log, xlog and ylog for logarithmic scales
xlim, ylim: x and y axis limits (for overriding R’s default choices)
mfcol, mfrow: Multiple plots in one graphics window (column-wise/row-wise)
Low-Level Graphic Functions
lines: Draw lines
abline: Quickly add horizontal, vertical lines, and lines using equation \(y = bx + a\)
points: Add points
arrows: Add arrows
title: Add a title
legend: Add a legend
text: Add text at \((x,y)\) coordinates
mtext: Add text with positional specification like side=1,...,4
Exercise 4.3: Plot temperatures for Maastricht
We want to visualize the daily temperatures in the climate data specifically for Maastricht. First, make a basic plot of temperatures in Maastricht then customise the plot in the following ways:
The title of the X-axis should say ‘Month’, the title of the Y-axis ‘Average Temperature’.
Make the plot a line plot with a blue line. (Hint: specifying the colour literally as "blue" works)
Make the tick marks appear on the inside of the figure rather than the outside.
Calculate the average temperature.
Add a horizontal line with the average maximum temperature
You will need to consult the help file for this exercise; see this therefore more as an exercise in how to navigate R’s help system, than an exercise in plotting (which we will cover in more detail later).
You may want to ask ChatGPT for help.
Manually saving R plots
Use the plot functions without creating a graph.
Use the `plots’ area to save image manually.
Different plot types
You can manually save graphs of several formats.
Best practice is to save a graph through a device such as pdf or similar:
jpeg("figures/MaasTemperature.jpeg")plot(x =wide_data$MAASTICHT, y =wide_data$MONTH)dev.off()
A more complex example for plotting data over time gradually
Especially when data is large, a gradual illustration of data over time can be handy.
In this exercise, we plot the temperatures in Maastricht as if the data are becoming available gradually.
For this, we will use a loop that iterates over time points (months).
The concept of a loop was only mentioned in Session 2.
wide_data$MAASTRICHT xlab ="Time", ylab ="Value", main ="Adding Data Over Time", type ='l')# Gradually plot more and more of the data using a `for loop`for (i in4:nrow(wide_data)) {plot(1:i, wide_data$MAASTRICHT[1:i], # notice index i is increasing the number of plotted pointsxlab ="Time", ylab ="Value", main ="Adding Data Over Time", type ='l')}
Exercise 4.4 Make a continuous plot of temperatures - Use the last loop example to plot temperatures gradually. - Start with an initial number of 3 observations, as in the example. - Make sure that the range of the x and y axes match with the whole dataset in the first plot. - Within the for loop, add lines to the first plot, instead of plotting the data again. - Pause the program within the for loop to simulate “gradual” effect. - You can use ChatGBT or help functions for ?ylim, ?Sys.sleep
Bonus: Advanced Graphics using ggplot
R has several advanced graphics packages such as ggplot2 (see book by Hadley Wickham), plotly, Rgnuplot,…
We will focus on ggplot2 as this is widely used.
The ggplot2 package in R enables to build complex plots from data in a structured and layered manner.
The plot is based on a specified data frame, aesthetic mappings (like x and y axes), and layers such as points, lines, or bars (geom_* functions).
Advantages: flexible, consistent syntax, and fancy graphics.
Disadvantages: Very different syntax e.g. compared to functions.
Typical ggplot help function sections
Section What it Gives You
Title Short description
Description What the function does
Usage The function’s syntax
Arguments What each argument means
Details Extra explanation and special behavior
Aesthetics Visual mappings (like x, y, color, etc.)
There is a lot of information in the help file as a result of flexibility, but the proper use is explained.
load("data/climate_wide.Rdata")# load datawide_data$index<-1:nrow(wide_data)# create data frameggplot(wide_data, aes(x =index, y =MAASTRICHT, color ="blue"))+geom_line(linewidth =1)# adds lines #
Example: Adding additional geometrics and aesthetics
Adding layers, such as a new set of points is simple:
load("data/climate_wide.Rdata")# load datawide_data$index<-1:nrow(wide_data)# create data frameggplot(wide_data, aes(x =index, y =MAASTRICHT))+geom_line(color ="blue", size =1, linewidth =2)+geom_line(aes(x =index, y =EINDHOVEN), linewidth =0.3)# Adds Eindhoven data to the last plot
Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.
Example: Adding more layers
Adding more layers for labels and legends
load("data/climate_wide.Rdata")# load datawide_data$index<-1:nrow(wide_data)# create data frameggplot(wide_data, aes(x =index, y =MAASTRICHT))+geom_line(color ="blue", size =1, linewidth =2)+geom_line(aes(x =index, y =EINDHOVEN), linewidth =0.3)+# Adds Eindhoven data to the last plotlabs( x ="X Axis", y ="Y Axis", color ="Legend Title", # Axis labels and legend title title ="Line Plot with Two Variables")+scale_color_manual(values =c("blue", "red"))+# Custom colorstheme_minimal()# Minimal theme for a clean look
Example ggplot: Color points for Maastricht and Eindhoven temperatures
For this example, we will use the long dataframe since we will plot values for both cities.
This is a more complex graph where aesthetics defined in geometrics create point colors according to a variable in the data frame.
library('ggplot2')load("data/climate_long.Rdata")# load datalong_data$index<-1:nrow(long_data)# create data frameggplot(data =long_data)+geom_point(aes(x =index, y =TEMP, color =NAME))
