This tutorial uses
ggplot2 to create customized plots of time
series data. We will learn how to adjust x- and y-axis ticks using the
package, how to add trend lines to a scatter plot and how to customize plot
labels, colors and overall plot appearance using
After completing this tutorial, you will be able to:
- Create basic time series plots using
- Explain the syntax of
ggplot()and know how to find out more about the package.
- Plot data using scatter and bar plots.
Things You’ll Need To Complete This Tutorial
You will need the most current version of R and, preferably, RStudio loaded on your computer to complete this tutorial.
Install R Packages
install.packages("ggthemes", dependencies = TRUE)
More on Packages in R – Adapted from Software Carpentry.
The data used in this lesson were collected at the National Ecological Observatory Network's Harvard Forest field site. These data are proxy data for what will be available for 30 years on the NEON data portal for the Harvard Forest and other field sites located across the United States.
Set Working Directory: This lesson assumes that you have set your working directory to the location of the downloaded and unzipped data subsets.
R Script & Challenge Code: NEON data lessons often contain challenges that reinforce learned skills. If available, the code for challenge solutions is found in the downloadable R script of the entire lesson, available in the footer of each lesson page.
- Winston Chang's
Plotting Time Series Data
Plotting our data allows us to quickly see general patterns including
outlier points and trends. Plots are also a useful way to communicate the
results of our research.
ggplot2 is a powerful R package that we use to
create customized, professional plots.
Load the Data
We will use the
gridExtra packages in
Our data subset will be the daily meteorology data for 2009-2011 for the NEON
Harvard Forest field site
If this subset is not already loaded, please load it now.
# Remember it is good coding technique to add additional packages to the top of # your script library(lubridate) # for working with dates library(ggplot2) # for creating graphs library(scales) # to access breaks/formatting functions library(gridExtra) # for arranging plots # set working directory to ensure R can find the file we wish to import wd <- "~/Git/data/" # daily HARV met data, 2009-2011 harMetDaily.09.11 <- read.csv( file=paste0(wd,"NEON-DS-Met-Time-Series/HARV/FisherTower-Met/Met_HARV_Daily_2009_2011.csv"), stringsAsFactors = FALSE) # covert date to Date class harMetDaily.09.11$date <- as.Date(harMetDaily.09.11$date) # monthly HARV temperature data, 2009-2011 harTemp.monthly.09.11<-read.csv( file=paste0(wd,"NEON-DS-Met-Time-Series/HARV/FisherTower-Met/Temp_HARV_Monthly_09_11.csv"), stringsAsFactors=FALSE ) # datetime field is actually just a date #str(harTemp.monthly.09.11) # convert datetime from chr to date class & rename date for clarification harTemp.monthly.09.11$date <- as.Date(harTemp.monthly.09.11$datetime)
Plot with qplot
We can use the
qplot() function in the
ggplot2 package to quickly plot a
variable such as air temperature (
airt) across all three years of our daily
average time series data.
# plot air temp qplot(x=date, y=airt, data=harMetDaily.09.11, na.rm=TRUE, main="Air temperature Harvard Forest\n 2009-2011", xlab="Date", ylab="Temperature (°C)")
The resulting plot displays the pattern of air temperature increasing and
decreasing over three years. While
qplot() is a quick way to plot data, our
ability to customize the output is limited.
Plot with ggplot
ggplot() function within the
ggplot2 package gives us more control
over plot appearance. However, to use
ggplot we need to learn a slightly
different syntax. Three basic elements are needed for
ggplot() to work:
- The data_frame: containing the variables that we wish to plot,
aes(aesthetics): which denotes which variables will map to the x-, y- (and other) axes,
geom_XXXX(geometry): which defines the data's graphical representation (e.g. points (
geom_point), bars (
geom_bar), lines (
The syntax begins with the base statement that includes the
harMetDaily.09.11) and associated x (
date) and y (
airt) variables to be
ggplot(harMetDaily.09.11, aes(date, airt))
To successfully plot, the last piece that is needed is the
geometry type. In
this case, we want to create a scatterplot so we can add
Let's create an air temperature scatterplot.
# plot Air Temperature Data across 2009-2011 using daily data ggplot(harMetDaily.09.11, aes(date, airt)) + geom_point(na.rm=TRUE)
Customize A Scatterplot
We can customize our plot in many ways. For instance, we can change the size and
color of the points using
color= in the
geom_point(na.rm=TRUE, color="blue", size=1)
# plot Air Temperature Data across 2009-2011 using daily data ggplot(harMetDaily.09.11, aes(date, airt)) + geom_point(na.rm=TRUE, color="blue", size=3, pch=18)
Modify Title & Axis Labels
We can modify plot attributes by adding elements using the
For example, we can add a title by using
+ ggtitle="TEXT", and axis
+ xlab("TEXT") + ylab("TEXT").
# plot Air Temperature Data across 2009-2011 using daily data ggplot(harMetDaily.09.11, aes(date, airt)) + geom_point(na.rm=TRUE, color="blue", size=1) + ggtitle("Air Temperature 2009-2011\n NEON Harvard Forest Field Site") + xlab("Date") + ylab("Air Temperature (C)")
Name Plot Objects
We can create a
ggplot object by assigning our plot to an object name.
When we do this, the plot will not render automatically. To render the plot, we
need to call it in the code.
Assigning plots to an R object allows us to effectively add on to,
and modify the plot later. Let's create a new plot and call it
# plot Air Temperature Data across 2009-2011 using daily data AirTempDaily <- ggplot(harMetDaily.09.11, aes(date, airt)) + geom_point(na.rm=TRUE, color="purple", size=1) + ggtitle("Air Temperature\n 2009-2011\n NEON Harvard Forest") + xlab("Date") + ylab("Air Temperature (C)") # render the plot AirTempDaily
Format Dates in Axis Labels
We can adjust the date display format (e.g. 2009-07 vs. Jul 09) and the number
of major and minor ticks for axis date values using
format the axis ticks so they read "month year" (
%b %y). To do this, we will
use the syntax:
Rather than re-coding the entire plot, we can add the
to the plot object
AirTempDaily that we just created.
# format x-axis: dates AirTempDailyb <- AirTempDaily + (scale_x_date(labels=date_format("%b %y"))) AirTempDailyb
Adjust Date Ticks
We can adjust the date ticks too. In this instance, having 1 tick per year may
be enough. If we have the
scales package loaded, we can use
breaks=date_breaks("1 year") within the
scale_x_date element to create
a tick for every year. We can adjust this as needed (e.g. 10 days, 30 days, 1
From R HELP (
widthan interval specification, one of "sec", "min", "hour", "day", "week", "month", "year". Can be by an integer and a space, or followed by "s".
# format x-axis: dates AirTempDaily_6mo <- AirTempDaily + (scale_x_date(breaks=date_breaks("6 months"), labels=date_format("%b %y"))) AirTempDaily_6mo
# format x-axis: dates AirTempDaily_1y <- AirTempDaily + (scale_x_date(breaks=date_breaks("1 year"), labels=date_format("%b %y"))) AirTempDaily_1y
ggplot - Subset by Time
Sometimes we want to scale the x- or y-axis to a particular time subset without
subsetting the entire
data_frame. To do this, we can define start and end
times. We can then define the
limits in the
scale_x_date object as
# Define Start and end times for the subset as R objects that are the time class startTime <- as.Date("2011-01-01") endTime <- as.Date("2012-01-01") # create a start and end time R object start.end <- c(startTime,endTime) start.end ##  "2011-01-01" "2012-01-01" # View data for 2011 only # We will replot the entire plot as the title has now changed. AirTempDaily_2011 <- ggplot(harMetDaily.09.11, aes(date, airt)) + geom_point(na.rm=TRUE, color="purple", size=1) + ggtitle("Air Temperature\n 2011\n NEON Harvard Forest") + xlab("Date") + ylab("Air Temperature (C)")+ (scale_x_date(limits=start.end, breaks=date_breaks("1 year"), labels=date_format("%b %y"))) AirTempDaily_2011
We can use the
theme() element to adjust figure elements.
There are some nice pre-defined themes that we can use as a starting place.
# Apply a black and white stock ggplot theme AirTempDaily_bw<-AirTempDaily_1y + theme_bw() AirTempDaily_bw
theme_bw() we now have a white background rather than grey.
Import New Themes Bonus Topic
There are externally developed themes built by the R community that are worth
mentioning. Feel free to experiment with the code below to install
# install additional themes # install.packages('ggthemes', dependencies = TRUE) library(ggthemes) AirTempDaily_economist<-AirTempDaily_1y + theme_economist() AirTempDaily_economist
AirTempDaily_strata<-AirTempDaily_1y + theme_stata() AirTempDaily_strata
More on Themes
Hadley Wickham's documentation.
A list of themes loaded in the ggthemes library is found here.
Customize ggplot Themes
We can customize theme elements manually too. Let's customize the font size and style.
# format x axis with dates AirTempDaily_custom<-AirTempDaily_1y + # theme(plot.title) allows to format the Title separately from other text theme(plot.title = element_text(lineheight=.8, face="bold",size = 20)) + # theme(text) will format all text that isn't specifically formatted elsewhere theme(text = element_text(size=18)) AirTempDaily_custom
- Format the dates on the x-axis:
- Create a plot object called
- Be sure to add an appropriate title in addition to x and y axis labels.
- Increase the font size of the plot text and adjust the number of ticks on the x-axis.
Bar Plots with ggplot
We can use ggplot to create bar plots too. Let's create a bar plot of total
daily precipitation next. A bar plot might be a better way to represent a total
daily value. To create a bar plot, we change the
geom element from
The default setting for a ggplot bar plot -
geom_bar() - is a histogram
stat="bin". However, in this case, we want to plot actual
precipitation values. We can use
geom_bar(stat="identity") to force ggplot to
plot actual values.
# plot precip PrecipDailyBarA <- ggplot(harMetDaily.09.11, aes(date, prec)) + geom_bar(stat="identity", na.rm = TRUE) + ggtitle("Daily Precipitation\n Harvard Forest") + xlab("Date") + ylab("Precipitation (mm)") + scale_x_date(labels=date_format ("%b %y"), breaks=date_breaks("1 year")) + theme(plot.title = element_text(lineheight=.8, face="bold", size = 20)) + theme(text = element_text(size=18)) PrecipDailyBarA
Note that some of the bars in the resulting plot appear grey rather than black. This is because R will do it's best to adjust colors of bars that are closely spaced to improve readability. If we zoom into the plot, all of the bars are black.
- a descriptive title and axis labels,
- breaks every 4 months, and
- x-axis labels as only the full month (spelled out).
HINT: you will need to rebuild the precipitation plot as you will have to
specify a new
Bonus: Style your plot with a
ggtheme of choice.
## Warning: Removed 729 rows containing missing values (position_stack).
We can change the bar fill color by within the
geom_bar(colour="blue") element. We can also specify a separate fill and line
line=. Colors can be specified by name (e.g.,
"blue") or hexadecimal color codes (e.g, #FF9999).
There are many color cheatsheets out there to help with color selection!
# specifying color by name PrecipDailyBarB <- PrecipDailyBarA+ geom_bar(stat="identity", colour="darkblue") PrecipDailyBarB
Figures with Lines
We can create line plots too using
ggplot. To do this, we use
AirTempDaily_line <- ggplot(harMetDaily.09.11, aes(date, airt)) + geom_line(na.rm=TRUE) + ggtitle("Air Temperature Harvard Forest\n 2009-2011") + xlab("Date") + ylab("Air Temperature (C)") + scale_x_date(labels=date_format ("%b %y")) + theme(plot.title = element_text(lineheight=.8, face="bold", size = 20)) + theme(text = element_text(size=18)) AirTempDaily_line
Note that lines may not be the best way to represent air temperature data given lines suggest that the connecting points are directly related. It is important to consider what type of plot best represents the type of data that you are presenting.
We can add a trend line, which is a statistical transformation of our data to
represent general patterns, using
stat_smooth() requires a
statistical method as follows:
- For data with < 1000 observations: the default model is a loess model (a non-parametric regression model)
- For data with > 1,000 observations: the default model is a GAM (a general additive model)
- A specific model/method can also be specified: for example, a linear regression (
For this tutorial, we will use the default trend line model. The
gam method will
be used with given we have 1,095 measurements.
# adding on a trend lin using loess AirTempDaily_trend <- AirTempDaily + stat_smooth(colour="green") AirTempDaily_trend ## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Create a bar plot of total daily precipitation. Add a:
- Trend line for total daily precipitation.
- Make the bars purple (or your favorite color!).
- Make the trend line grey (or your other favorite color).
- Adjust the tick spacing and format the dates to appear as "Jan 2009".
- Render the title in italics.
## `geom_smooth()` using formula 'y ~ x'
Plot the monthly air temperature across 2009-2011 using the
harTemp.monthly.09.11 data_frame. Name your plot "AirTempMonthly". Be sure to
label axes and adjust the plot ticks as you see fit.
Display Multiple Figures in Same Panel
It is often useful to arrange plots in a panel rather than displaying them
individually. In base R, we use
par(mfrow=()) to accomplish this. However
grid.arrange() function from the
gridExtra package provides a more
grid.arrange requires 2 things:
- the names of the plots that you wish to render in the panel.
- the number of columns (
grid.arrange(plotOne, plotTwo, ncol=1)
AirTempDaily on top of each other. To do this,
we'll specify one column.
# note - be sure library(gridExtra) is loaded! # stack plots in one column grid.arrange(AirTempDaily, AirTempMonthly, ncol=1)
AirTempDaily next to each other rather than stacked
on top of each other.
Additional ggplot2 Resources
In this tutorial, we've covered the basics of
ggplot. There are many great
resources the cover refining
ggplot figures. A few are below:
Get Lesson Code
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