I’m settling into my new job at DWR, and one of my first tasks has been some analysis of monitoring data for the Delta Smelt Resiliency Strategy. We have long records of water quality data at a ton of stations, and we’ve been doing some time series analysis to quantify the effects of operating the salinity control gates in August on delta smelt habitat in Suisun Marsh. These data can be easily accessed from CDEC; here I use the R package CDECRetrieve to pull continuously-monitored data on salinity (OK, technically it’s specific conductivity) at a few stations scattered across the marsh. I produced hourly averages of the raw 15-minute data to minimize issues with missing data and applied a 25-hour moving average to filter out some of the noise in the salinity signal (including tides).

library(CDECRetrieve)
library(purrr)
library(dplyr)
library(zoo)

# CDEC station codes to retrieve data for
stations = c('BDL', 'BLL', 'CSE', 'FLT', 'GOD', 'GZL', 'HON', 'HUN',
'IBS', 'MAL', 'MRZ', 'MSL', 'NSL', 'PCT', 'RYC', 'TEA', 'VOL')

# get station locations
station.locations = map_dfr(stations, cdec_stations) %>%
transmute(location_id = toupper(station_id), name,
latitude, longitude)

# get station EC data
station.data = map_dfr(stations, cdec_query, sensor_num = 100L,
dur_code = "E", start_date = '2016-09-25',
end_date = '2016-11-05') %>%
group_by(
location_id,
datetime = as.POSIXct(round(datetime, 'hours'))
) %>%
summarize(mean = mean(parameter_value, na.rm = TRUE)) %>%
mutate(windowmean = rollmean(mean, 25, 'center')) %>%
ungroup()


I’m always interested in relationships between components of a system, and I often find it helpful to simultaneously look at both the spatial relationships between and temporal trends across the data. When data is collected at discrete locations, one of the most straightforward ways to do this is simply plot the time series graphs on a map. This lets your eye quickly identify similar patterns in the time series as well as spatial gradients in the system.

Many years ago there was a package called ggsubplot that could be used exactly for this sort of thing, but it wasn’t maintained and was eventually removed from CRAN. I’ve fantasized about trying to rebuild it myself, but never had the time or the motivation to learn about the inner workings of ggplot2. I was actually working on a completely unrelated problem—trying to understand the difference between how ggmap and ggspatial plot basemaps—when I stumbled across the ggplot2 function annotation_custom:

This is a special geom intended for use as static annotations that are the same in every panel. These annotations will not affect scales (i.e. the x and y axes will not grow to cover the range of the grob, and the grob will not be modified by any ggplot settings or mappings).

Woah. The exciting word here is “grob”—because ggplot objects can themselves be turned into grobs (see ggplotGrob. Could I use this to drop a plot on, say, a map made with ggplot?

The first step is to make all the plots. This is actually really easy to do with purrr, tidyr, and tibble support for list-columns.

library(tidyr)
library(ggplot2)

# make a separate ggplot for each station
station.plots = station.data %>%
nest(-location_id) %>%
mutate(plot = map2(data, location_id,
~ ggplot(.x) + ggtitle(.y) + theme_bw(base_size = 8) +
aes(x = datetime, y = windowmean) + geom_line() +
scale_x_datetime(NULL, breaks = as.POSIXct(c("2016-10-01",
"2016-10-15", "2016-10-31")), date_labels = "%b %d",
limits = as.POSIXct(c("2016-10-01", "2016-10-31"))) +
scale_y_continuous("Spec. Conductivity",
limits = c(4000, 30000))
)
)


I also need a spatial layer that defines where the plots will be placed. I use the sf package to convert the station locations to spatial points:

station.points = st_as_sf(station.locations, crs = 4326,
coords = c("longitude", "latitude")) %>%
st_transform(3857)


Next, I need to turn the plots into annotations. Again, this is easy with purrr. I extract the station coordinates from the spatial layer and join them to the plot data, and then generate a separate annotation layer for each plot. Note that I need to convert each plot to a grob via ggplotGrob and explicitly define the bounding box (xmin/xmax/ymin/ymax) of each annotation layer.

station.annotations = station.points %>%
bind_cols(as_tibble(st_coordinates(.))) %>%
st_drop_geometry() %>%
select(location_id, X, Y) %>%
left_join(station.plots, by = "location_id") %>%
mutate(annotation = pmap(list(X, Y, plot),
~ annotation_custom(ggplotGrob(..3),
xmin = ..1 - 2000, xmax = ..1 + 2000,
ymin = ..2 - 1000, ymax = ..2 + 1000))) %>%
pull(annotation)


Finally, I build the final ggplot map. I use ggspatial to obtain a basemap and simply add the list of annotations to the plot. I don’t actually want to plot a point at each station location, so I pass shape = NA to geom_sf to hide the points (although they would be covered up by the plot annotations anyway).

library(ggspatial)
ggplot(station.points) +
xlim(c(-13598000, -13563500)) +
annotation_map_tile(zoom = 13) +
geom_sf(shape = NA) +
station.annotations


Not bad! You do have to be careful with how you define your plots, because annotate_custom doesn’t try to make any adjustments to the plots when placing them on the map. My code above has a lot of formatting in my plot statement to reduce the base font size, set consistent axis limits, and reduce the number of breaks in the axes. There will inevitably be some iteration required to produce a map you are happy with, but it beats making the maps by hand in GIS software. Also note that you’re not restricted to just placing one kind of subplot at a time, so you could make a line plot here, a histogram there, etc. to create any combination of subplots you want.

It can’t be too hard to turn this process into an actual ggplot geometry (geom_subplot perhaps?) that takes care of some of the plot sizing and tweaking automatically, but I don’t have the motivation right now. Maybe someday I’ll take a stab at making ggsubplot2