Interact with the archive and prism data
You can view all the prism data you have downloaded with a simple command: prism_archive_ls(). This function gives a list of folder names, i.e., prism data (pd). All the functions in the prism package work off of one or more of these folder names (pd).
## Truncated to keep file list short
prism_archive_ls()
#> [1] "PRISM_ppt_30yr_normal_4kmM2_annual_bil"
#> [2] "PRISM_ppt_30yr_normal_800mM2_02_bil"
#> [3] "PRISM_ppt_stable_4kmD2_19810101_bil"
#> [4] "PRISM_ppt_stable_4kmD2_19820101_bil"
#> [5] "PRISM_ppt_stable_4kmD2_19830101_bil"
#> [6] "PRISM_ppt_stable_4kmD2_20120101_bil"
#> [7] "PRISM_ppt_stable_4kmM3_2000_bil"
#> [8] "PRISM_ppt_stable_4kmM3_2001_bil"
#> [9] "PRISM_ppt_stable_4kmM3_2002_bil"
#> [10] "PRISM_ppt_stable_4kmM3_2003_bil"
....While prism functions use this folder format, other files may need an absolute path (e.g. the raster package). The pd_to_file() function conveniently returns the absolute path. Alternatively, you may want to see what the normal name for the product is (not the file name), and we can get that with the pd_get_name() function.
## Truncated to keep file list short
pd_to_file(prism_archive_ls())
#> [1] "C:\\Users\\RAButler\\Documents\\prismtmp\\PRISM_ppt_30yr_normal_4kmM2_annual_bil\\PRISM_ppt_30yr_normal_4kmM2_annual_bil.bil"
#> [2] "C:\\Users\\RAButler\\Documents\\prismtmp\\PRISM_ppt_30yr_normal_800mM2_02_bil\\PRISM_ppt_30yr_normal_800mM2_02_bil.bil"
#> [3] "C:\\Users\\RAButler\\Documents\\prismtmp\\PRISM_ppt_stable_4kmD2_19810101_bil\\PRISM_ppt_stable_4kmD2_19810101_bil.bil"
#> [4] "C:\\Users\\RAButler\\Documents\\prismtmp\\PRISM_ppt_stable_4kmD2_19820101_bil\\PRISM_ppt_stable_4kmD2_19820101_bil.bil"
#> [5] "C:\\Users\\RAButler\\Documents\\prismtmp\\PRISM_ppt_stable_4kmD2_19830101_bil\\PRISM_ppt_stable_4kmD2_19830101_bil.bil"
....
pd_get_name(prism_archive_ls())
#> [1] "Annual 30-year normals - 4km resolution - Precipitation"
#> [2] "Feb 30-year normals - 800m resolution - Precipitation"
#> [3] "Jan 01 1981 - 4km resolution - Precipitation"
#> [4] "Jan 01 1982 - 4km resolution - Precipitation"
#> [5] "Jan 01 1983 - 4km resolution - Precipitation"
....Finally, prism_archive_subset() is a convenient way to search for specific parameters, time steps, days, months, years, or ranges of days, months, years.
# we know we have downloaded June 2013 daily data, so lets search for those
prism_archive_subset("tmean", "daily", mon = 6)
#> [1] "PRISM_tmean_stable_4kmD2_20130601_bil"
#> [2] "PRISM_tmean_stable_4kmD2_20130602_bil"
#> [3] "PRISM_tmean_stable_4kmD2_20130603_bil"
#> [4] "PRISM_tmean_stable_4kmD2_20130604_bil"
#> [5] "PRISM_tmean_stable_4kmD2_20130605_bil"
#> [6] "PRISM_tmean_stable_4kmD2_20130606_bil"
#> [7] "PRISM_tmean_stable_4kmD2_20130607_bil"
#> [8] "PRISM_tmean_stable_4kmD2_20130608_bil"
#> [9] "PRISM_tmean_stable_4kmD2_20130609_bil"
#> [10] "PRISM_tmean_stable_4kmD2_20130610_bil"
#> [11] "PRISM_tmean_stable_4kmD2_20130611_bil"
#> [12] "PRISM_tmean_stable_4kmD2_20130612_bil"
#> [13] "PRISM_tmean_stable_4kmD2_20130613_bil"
#> [14] "PRISM_tmean_stable_4kmD2_20130614_bil"
# or we can look for days between June 7 and June 10
prism_archive_subset(
"tmean", "daily", minDate = "2013-06-07", maxDate = "2013-06-10"
)
#> [1] "PRISM_tmean_stable_4kmD2_20130607_bil"
#> [2] "PRISM_tmean_stable_4kmD2_20130608_bil"
#> [3] "PRISM_tmean_stable_4kmD2_20130609_bil"
#> [4] "PRISM_tmean_stable_4kmD2_20130610_bil"Raster plots
You can easily make a quick plot of your data using the output of prism_archive_ls() or prism_archive_subset() with pd_image().
# Plot the January 30-year average temperatures
# grab only the first value, just in case multiple values are returned
jmean <- prism_archive_subset(
"tmean", "monthly normals", mon = 1, resolution = "4km"
)
pd_image(jmean)
It is easy to load the prism data with the raster package. This time we will look at January temperature anomalies. To do this we will examine the difference between January 2013 and the January 30-year normals. Conveniently, we already downloaded these data. We just need to grab them out of our archive.
library(raster)
#> Loading required package: sp
# knowing the name of the files you are after allows you to find them in the
# list of all files that exist
# jnorm_name <- "PRISM_tmean_30yr_normal_4kmM2_01_bil"
# j2013_name <- "PRISM_tmean_stable_4kmM3_201301_bil"
# but we will use prism_archive_subset() to find the files we need
jnorm <- prism_archive_subset(
"tmean", "monthly normals", mon = 1, resolution = "4km"
)
j2013 <- prism_archive_subset("tmean", "monthly", years = 2013, mon = 1)
# raster needs a full path, not the "short" prism data name
jnorm <- pd_to_file(jnorm)
j2013 <- pd_to_file(j2013)
## Now we'll load the rasters.
jnorm_rast <- raster(jnorm)
j2013_rast <- raster(j2013)
# Now we can do simple subtraction to get the anomaly by subtracting 2014
# from the 30 year normal map
anomCalc <- function(x, y) {
return(x - y)
}
anom_rast <- raster::overlay(j2013_rast,jnorm_rast,fun = anomCalc)
plot(anom_rast)
The plot shows that January 2013 was warmer than the average over the last 30 years. It also shows how easy it is to use the raster library to work with prism data. The package provides a simple framework to work with a large number of rasters that you can easily download and visualize or use with other data sets.
Single grid cell plot
You can also visualize a single point across multiple prism data files (slice) using pd_plot_slice(). This procedure will take a set of rasters, create a “raster::stack”, extract data at a point, and then create a ggplot2 object.
Let’s now make a plot of January temperatures in Boulder between 1982 and 2014. First we’ll grab all the data from the US (downloaded in the previous step), and then give our function a point to get data from. The point must be a vector in the form of longitude, latitude. Because pd_plot_slice() returns a gg object, it can be combined with other ggplot functions.
library(ggplot2)
# data already exist in the prism dl dir
boulder <- c(-105.2797, 40.0176)
# prism_archive_subset() will return prism data that matches the specified
# variable, time step, years, months, days, etc.
to_slice <- prism_archive_subset("tmean", "monthly", mon = 1)
p <- pd_plot_slice(to_slice, boulder)
# add a linear average and title
p +
stat_smooth(method="lm", se = FALSE) +
theme_bw() +
ggtitle("Average January temperature in Boulder, CO 1982-2014")
#> `geom_smooth()` using formula 'y ~ x'
leaflet map
Finally, the prism data are in a form that can be used with leaflet maps (with the help of the raster package). The leaflet package allows you to easily make JavaScript maps using the leaflet mapping framework using prism data. These can easily be hosted on websites like Rpubs or your own site. Here is a simple example of plotting the 30-year normal for annual temperature. If you run this code you will have an interactive map, instead of just the screen shot shown here.
library(leaflet)
library(raster)
library(prism)
# 30-year normal average temperature have already been downloaded for
norm <- prism_archive_subset(
"tmean", "annual normals", resolution = "4km"
)
rast <- raster(pd_to_file(norm))
# Create color palette and plot
pal <- colorNumeric(
c("#0000FF", "#FFFF00", "#FF0000"),
values(rast),
na.color = "transparent"
)
leaflet() %>%
addTiles(
urlTemplate = 'http://server.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer/tile/{z}/{y}/{x}'
) %>%
addRasterImage(rast, colors = pal, opacity=.65) %>%
addLegend(pal = pal, values = values(rast), title = "Deg C")[