Tutorial
Time Series 05: Plot Time Series with ggplot2 in R
Authors: Megan A. Jones, Marisa Guarinello, Courtney Soderberg, Leah A. Wasser
Last Updated: May 13, 2021
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 scales
package, how to add trend lines to a scatter plot and how to customize plot
labels, colors and overall plot appearance using ggthemes
.
Learning Objectives
After completing this tutorial, you will be able to:
- Create basic time series plots using
ggplot()
in R. - 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
-
lubridate:
install.packages("lubridate")
-
ggplot2:
install.packages("ggplot2")
-
scales:
install.packages("scales")
-
gridExtra:
install.packages("gridExtra")
-
ggthemes:
install.packages("ggthemes", dependencies = TRUE)
More on Packages in R – Adapted from Software Carpentry.
Download Data
NEON Teaching Data Subset: Meteorological Data for Harvard Forest
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.
An overview of setting the working directory in R can be found here.
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.
Additional Resources
- 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 lubridate
, ggplot2
, scales
and gridExtra
packages in
this tutorial.
Our data subset will be the daily meteorology data for 2009-2011 for the NEON
Harvard Forest field site
(NEON-DS-Met-Time-Series/HARV/FisherTower-Met/Met_HARV_Daily_2009_2011.csv
).
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
The 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 (geom_line
), etc).
The syntax begins with the base statement that includes the data_frame
(harMetDaily.09.11
) and associated x (date
) and y (airt
) variables to be
plotted:
ggplot(harMetDaily.09.11, aes(date, airt))
To successfully plot, the last piece that is needed is the geom
etry type. In
this case, we want to create a scatterplot so we can add + geom_point()
.
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 size=
, shape pch=
, and color=
in the geom_point
element.
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 +
symbol.
For example, we can add a title by using + ggtitle="TEXT",
and axis
labels using + 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 AirTempDaily
.
# 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 scale_x_date
. Let's
format the axis ticks so they read "month year" (%b %y
). To do this, we will
use the syntax:
scale_x_date(labels=date_format("%b %y")
Rather than re-coding the entire plot, we can add the scale_x_date
element
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
month).
From R HELP (
?date_breaks
):width
an 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
follows:
scale_x_date(limits=start.end) +
# 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
## [1] "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
ggplot() Themes
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
Using the 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 ggthemes
.
# 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:
Month-Year
. - Create a plot object called
PrecipDaily
. - 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
geom_point()
to geom_bar()
.
The default setting for a ggplot bar plot - geom_bar()
- is a histogram
designated by 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 scale_x_data()
element.
Bonus: Style your plot with a ggtheme
of choice.
## Warning: Removed 729 rows containing missing values (position_stack).
Color
We can change the bar fill color by within the
geom_bar(colour="blue")
element. We can also specify a separate fill and line
color using fill=
and 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 geom_line()
instead of bar
or point
.
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.
Trend Lines
We can add a trend line, which is a statistical transformation of our data to
represent general patterns, using stat_smooth()
. 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 (
method="lm"
).
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
the grid.arrange()
function from the gridExtra
package provides a more
efficient approach!
grid.arrange
requires 2 things:
- the names of the plots that you wish to render in the panel.
- the number of columns (
ncol
).
grid.arrange(plotOne, plotTwo, ncol=1)
Let's plot AirTempMonthly
and 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)
Plot AirTempMonthly
and 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:
- ggplot2 Cheatsheet from Zev Ross: ggplot2 Cheatsheet
- ggplot2 documentation index: ggplot2 Documentation