Time Series 06: Create Plots with Multiple Panels, Grouped by Time Using ggplot Facets

This tutorial covers how to plot subsetted time series data using the facets() function (e.g., plot by season) and to plot time series data with NDVI.

Plot Data Subsets Using Facets

In this tutorial we will learn how to create a panel of individual plots - known as facets in ggplot2. Each plot represents a particular data_frame time-series subset, for example a year or a season.

Load the Data

We will use the daily micro-meteorology data for 2009-2011 from the Harvard Forest. If you do not have this data loaded into an R data_frame, please load them and convert date-time columns to a date-time class now.

# Remember it is good coding technique to add additional libraries 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
library(grid)   # for arranging plots
library(dplyr)  # for subsetting by season

# 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)

ggplot2 Facets

Facets allow us to plot subsets of data in one cleanly organized panel. We use facet_grid() to create a plot of a particular variable subsetted by a particular group.

Let's plot air temperature as we did previously. We will name the ggplot object AirTempDaily.

AirTempDaily <- ggplot(harMetDaily.09.11, aes(date, airt)) +
           geom_point() +
           ggtitle("Daily Air Temperature\n NEON Harvard Forest\n 2009-2011") +
           xlab("Date") + ylab("Temperature (C)") +
           scale_x_date(labels=date_format ("%m-%y"))+
           theme(plot.title = element_text(lineheight=.8, face="bold",
                 size = 20)) +
           theme(text = element_text(size=18))

AirTempDaily

This plot tells us a lot about the annual increase and decrease of temperature at the NEON Harvard Forest field site. However, what if we want to plot each year's worth of data individually?

We can use the facet() element in ggplot to create facets or a panel of plots that are grouped by a particular category or time period. To create a plot for each year, we will first need a year column in our data to use as a subset factor. We created a year column using the year function in the lubridate package in the Subset and Manipulate Time Series Data with dplyr tutorial.

# add year column to daily values
harMetDaily.09.11$year <- year(harMetDaily.09.11$date)

# view year column head and tail
head(harMetDaily.09.11$year)

## [1] 2009 2009 2009 2009 2009 2009

tail(harMetDaily.09.11$year)

## [1] 2011 2011 2011 2011 2011 2011

Facet by Year

Once we have a column that can be used to group or subset our data, we can create a faceted plot - plotting each year's worth of data in an individual, labelled panel.

# run this code to plot the same plot as before but with one plot per season
AirTempDaily + facet_grid(. ~ year)

## Error: At least one layer must contain all faceting variables: `year`.
## * Plot is missing `year`
## * Layer 1 is missing `year`

Oops - what happened? The plot did not render because we added the year column after creating the ggplot object AirTempDaily. Let's rerun the plotting code to ensure our newly added column is recognized.

AirTempDaily <- ggplot(harMetDaily.09.11, aes(date, airt)) +
           geom_point() +
           ggtitle("Daily Air Temperature\n NEON Harvard Forest") +
            xlab("Date") + ylab("Temperature (C)") +
            scale_x_date(labels=date_format ("%m-%y"))+
           theme(plot.title = element_text(lineheight=.8, face="bold",
                 size = 20)) +
           theme(text = element_text(size=18))

# facet plot by year
AirTempDaily + facet_grid(. ~ year)

The faceted plot is interesting, however the x-axis on each plot is formatted as: month-day-year starting in 2009 and ending in 2011. This means that the data for 2009 is on the left end of the x-axis and the data for 2011 is on the right end of the x-axis of the 2011 plot.

Our plots would be easier to visually compare if the days were formatted in Julian or year days rather than date. We have Julian days stored in our data_frame (harMetDaily.09.11) as jd.

AirTempDaily_jd <- ggplot(harMetDaily.09.11, aes(jd, airt)) +
           geom_point() +
           ggtitle("Air Temperature\n NEON Harvard Forest Field Site") +
           xlab("Julian Day") + ylab("Temperature (C)") +
           theme(plot.title = element_text(lineheight=.8, face="bold",
                 size = 20)) +
           theme(text = element_text(size=18))

# create faceted panel
AirTempDaily_jd + facet_grid(. ~ year)

Using Julian day, our plots are easier to visually compare. Arranging our plots this way, side by side, allows us to quickly scan for differences along the y-axis. Notice any differences in min vs max air temperature across the three years?

Arrange Facets

We can rearrange the facets in different ways, too.

# move labels to the RIGHT and stack all plots
AirTempDaily_jd + facet_grid(year ~ .)

If we use facet_wrap we can specify the number of columns.

# display in two columns
AirTempDaily_jd + facet_wrap(~year, ncol = 2)

Graph Two Variables on One Plot

Next, let's explore the relationship between two variables - air temperature and soil temperature. We might expect soil temperature to fluctuate with changes in air temperature over time.

We will use ggplot() to plot airt and s10t (soil temperature 10 cm below the ground).

airSoilTemp_Plot <- ggplot(harMetDaily.09.11, aes(airt, s10t)) +
           geom_point() +
           ggtitle("Air vs. Soil Temperature\n NEON Harvard Forest Field Site\n 2009-2011") +
           xlab("Air Temperature (C)") + ylab("Soil Temperature (C)") +
           theme(plot.title = element_text(lineheight=.8, face="bold",
                 size = 20)) +
           theme(text = element_text(size=18))

airSoilTemp_Plot

The plot above suggests a relationship between the air and soil temperature as we might expect. However, it clumps all three years worth of data into one plot.

Let's create a stacked faceted plot of air vs. soil temperature grouped by year.

Lucky for us, we can do this quickly with one line of code while reusing the plot we created above.

# create faceted panel
airSoilTemp_Plot + facet_grid(year ~ .)

Have a close look at the data. Are there any noticeable min/max temperature differences between the three years?

Faceted Plots & Categorical Groups

In the challenge above, we grouped our data by month - specified by a numeric value between 1 (January) and 12 (December). However, what if we wanted to organize our plots using a categorical (character) group such as month name? Let's do that next.

If we want to group our data by month name, we first need to create a month name column in our data_frame. We can create this column using the following syntax:

format(harMetDaily.09.11$date,"%B"),

which tells R to extract the month name (%B) from the date field.

# add text month name column
harMetDaily.09.11$month_name <- format(harMetDaily.09.11$date,"%B")

# view head and tail
head(harMetDaily.09.11$month_name)

## [1] "January" "January" "January" "January" "January" "January"

tail(harMetDaily.09.11$month_name)

## [1] "December" "December" "December" "December" "December" "December"

# recreate plot
airSoilTemp_Plot <- ggplot(harMetDaily.09.11, aes(airt, s10t)) +
           geom_point() +
           ggtitle("Air vs. Soil Temperature \n NEON Harvard Forest Field Site\n 2009-2011") +
            xlab("Air Temperature (C)") + ylab("Soil Temperature (C)") +
           theme(plot.title = element_text(lineheight=.8, face="bold",
                 size = 20)) +
           theme(text = element_text(size=18))

# create faceted panel
airSoilTemp_Plot + facet_wrap(~month_name, nc=3)

Great! We've created a nice set of plots by month. However, how are the plots ordered? It looks like R is ordering things alphabetically, yet we know that months are ordinal not character strings. To account for order, we can reassign the month_name field to a factor. This will allow us to specify an order to each factor "level" (each month is a level).

The syntax for this operation is

1. Turn field into a factor: factor(fieldName) .
2. Designate the levels using a list c(level1, level2, level3).

In our case, each level will be a month.

# order the factors
harMetDaily.09.11$month_name = factor(harMetDaily.09.11$month_name, 
                                      levels=c('January','February','March',
                                               'April','May','June','July',
                                               'August','September','October',
                                               'November','December'))

Once we have specified the factor column and its associated levels, we can plot again. Remember, that because we have modified a column in our data_frame, we need to rerun our ggplot code.

# recreate plot
airSoilTemp_Plot <- ggplot(harMetDaily.09.11, aes(airt, s10t)) +
           geom_point() +
           ggtitle("Air vs. Soil Temperature \n NEON Harvard Forest Field Site\n 2009-2011") +
            xlab("Air Temperature (C)") + ylab("Soil Temperature (C)") +
           theme(plot.title = element_text(lineheight=.8, face="bold",
                 size = 20)) +
           theme(text = element_text(size=18))

# create faceted panel
airSoilTemp_Plot + facet_wrap(~month_name, nc=3)

Subset by Season - Advanced Topic

Sometimes we want to group data by custom time periods. For example, we might want to group by season. However, the definition of various seasons may vary by region which means we need to manually define each time period.

In the next coding section, we will add a season column to our data using a manually defined query. Our field site is Harvard Forest (Massachusetts), located in the northeastern portion of the United States. Based on the climate of this region, we can divide the year into 4 seasons as follows:

• Winter: December - February
• Spring: March - May
• Summer: June - August
• Fall: September - November

In order to subset the data by season we will use the dplyr package. We can use the numeric month column that we added to our data earlier in this tutorial.

# add month to data_frame - note we already performed this step above.
harMetDaily.09.11$month  <- month(harMetDaily.09.11$date)

# view head and tail of column
head(harMetDaily.09.11$month)

## [1] 1 1 1 1 1 1

tail(harMetDaily.09.11$month)

## [1] 12 12 12 12 12 12

We can use mutate() and a set of ifelse statements to create a new categorical variable called season by grouping three months together.

Within dplyr %in% is short-hand for "contained within". So the syntax

ifelse(month %in% c(12, 1, 2), "Winter",

can be read as "if the month column value is 12 or 1 or 2, then assign the value "Winter"".

Our ifelse statement ends with

ifelse(month %in% c(9, 10, 11), "Fall", "Error")

which we can translate this as "if the month column value is 9 or 10 or 11, then assign the value "Winter"."

The last portion , "Error" tells R that if a month column value does not fall within any of the criteria laid out in previous ifelse statements, assign the column the value of "Error".

harMetDaily.09.11 <- harMetDaily.09.11 %>% 
  mutate(season = 
           ifelse(month %in% c(12, 1, 2), "Winter",
           ifelse(month %in% c(3, 4, 5), "Spring",
           ifelse(month %in% c(6, 7, 8), "Summer",
           ifelse(month %in% c(9, 10, 11), "Fall", "Error")))))


# check to see if this worked
head(harMetDaily.09.11$month)

## [1] 1 1 1 1 1 1

head(harMetDaily.09.11$season)

## [1] "Winter" "Winter" "Winter" "Winter" "Winter" "Winter"

tail(harMetDaily.09.11$month)

## [1] 12 12 12 12 12 12

tail(harMetDaily.09.11$season)

## [1] "Winter" "Winter" "Winter" "Winter" "Winter" "Winter"

Now that we have a season column, we can plot our data by season!

# recreate plot
airSoilTemp_Plot <- ggplot(harMetDaily.09.11, aes(airt, s10t)) +
           geom_point() +
           ggtitle("Air vs. Soil Temperature\n 2009-2011\n NEON Harvard Forest Field Site") +
            xlab("Air Temperature (C)") + ylab("Soil Temperature (C)") +
           theme(plot.title = element_text(lineheight=.8, face="bold",
                 size = 20)) +
           theme(text = element_text(size=18))

# run this code to plot the same plot as before but with one plot per season
airSoilTemp_Plot + facet_grid(. ~ season)

Note, that once again, we re-ran our ggplot code to make sure our new column is recognized by R. We can experiment with various facet layouts next.

# for a landscape orientation of the plots we change the order of arguments in
# facet_grid():
airSoilTemp_Plot + facet_grid(season ~ .)

Once again, R is arranging the plots in an alphabetical order not an order relevant to the data.

Work with Year-Month Data: base R and zoo Package

Some data will have month formatted in Year-Month (e.g. met_monthly_HARV$date).

(Note: You will load this file in the Challenge below)

## [1] "2001-03" "2001-04" "2001-05" "2001-06" "2001-07" "2001-08"

For many analyses, we might want to summarize this data into a yearly total. Base R does NOT have a distinct year-month date class. Instead to work with a year-month field using base R, we need to convert to a Date class, which necessitates adding an associated day value. The syntax would be:

as.Date(paste(met_monthly_HARV$date,"-01", sep=""))

The syntax above creates a Date column from the met_montly_HARV$date column. We then add the arbitrary date - the first ("-01"). The final bit of code (sep="") designates the character string used to separate the month, day, and year portions of the returned string (in our case nothing, as we have included the hyphen with our inserted date value).

Alternatively, to work directly with a year-month data we could use the zoo package and its included year-month date class - as.yearmon. With zoo the syntax would be:

as.Date(as.yearmon(met_monthly_HARV$date))

Do you prefer to use base R or zoo to convert these data to a date/time class?