# First, set a file path where prism data will be stored
get_prism_monthlys(type = 'ppt', years = 2000:2016, mo = 1:12, keepZip = F)## You have not set a path to hold your prism files.## Please enter the full or relative path to download files to (hit enter to use default '~/prismtmp'):
##
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|======================================================================| 100%# Now, select the type of data (mean temperature and precipitation for us) and date range
get_prism_monthlys(type = 'tmean', years = 2000:2016, mo = 1:12, keepZip = F)##
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|======================================================================| 100%get_prism_monthlys(type = 'ppt', years = 2000:2016, mo = 1:12, keepZip = F)##
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|======================================================================| 100%'C:/Users/compu lab/Documents/prism'## [1] "C:/Users/compu lab/Documents/prism"# Grab the prism data and compile the files
climate_data <- ls_prism_data() %>%
prism_stack(.)
# Extract project coordinates from raster stack
climate_crs <- climate_data@crs@projargs# First, pull the metadata for all LTER sites using pplr_browse
if(!require(devtools, quietly = TRUE)) {
install.packages(devtools)
}
devtools::install_github('ropensci/popler')## Skipping install of 'popler' from a github remote, the SHA1 (65fd5a87) has not changed since last install.
## Use `force = TRUE` to force installationlter_sites <- popler::pplr_browse()## Warning: `tbl_df()` is deprecated as of dplyr 1.0.0.
## Please use `tibble::as_tibble()` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.# Select just the lat/long and site ID (3-letter code for site) columns and make a df
lter_sites <- lter_sites %>%
dplyr::select(lng_lter,lat_lter, lterid)## Adding missing grouping variables: `title`, `proj_metadata_key`, `datatype`, `structured_data`, `studytype`, `duration_years`, `community`, `studystartyr`, `studyendyr`, `structured_type_1`, `structured_type_2`, `structured_type_3`, `structured_type_4`, `treatment_type_1`, `treatment_type_2`, `treatment_type_3`lter_sites <- as.data.frame(lter_sites)
# Convert these locations to format that can be matched to Prism climate data
coordinates(lter_sites) <- c('lng_lter', 'lat_lter')
proj4string(lter_sites) <- CRS(climate_crs)# Extract the extracted from the raster stack for those sites
climate_lter <- data.frame(coordinates(lter_sites),
lter_sites$lterid,
extract(climate_data, lter_sites))## Warning in .doExtract(x, i, ..., drop = drop): some indices are invalid (NA
## returned)# Reshape data. Col 1:3 are lat, long, and site ID. Col 4:ncol are climate data
# Column headers include date and climate type info
climate_lter <- climate_lter %>%
gather(date, value, 4:ncol(climate_lter))
# The column header includes the date and data type, but also some other metadata that we don't need
# Here, I remove the extra info from the column header
climate_lter$date <- gsub('PRISM_', '', climate_lter$date) %>%
gsub('stable_4kmM3_', '', .) %>%
gsub('stable_4kmM2_', '', .) %>%
gsub('_bil', '', .)
# Split header into type (precipitation or temperature), year, and month
climate_lter <- separate(climate_lter, 'date',
into = c('type', 'YearMonth'),
sep = '_')
climate_lter <- separate(climate_lter, 'YearMonth',
into = c('year', 'month'),
sep = 4)
# Reshape data-- make a separate column for temperature and precipitation
climate_lter <- unique(climate_lter)
climate_lter <- climate_lter %>%
spread(type, value) %>%
rename(lng = lng_lter, lat = lat_lter, lterid = lter_sites.lterid)
# Make year and month numeric variables
climate_lter$year <- as.numeric(climate_lter$year)
# Order data by LTER site
climate_lter <- climate_lter[order(climate_lter$lterid),]head(climate_lter, 15) # view the first 15 rows of data## lng lat lterid year month ppt tmean
## 613 -122.26 44.21 AND 2000 01 474.277 1.786
## 614 -122.26 44.21 AND 2000 02 256.216 4.275
## 615 -122.26 44.21 AND 2000 03 171.267 5.025
## 616 -122.26 44.21 AND 2000 04 103.508 10.089
## 617 -122.26 44.21 AND 2000 05 142.102 11.818
## 618 -122.26 44.21 AND 2000 06 82.944 17.424
## 619 -122.26 44.21 AND 2000 07 8.006 18.935
## 620 -122.26 44.21 AND 2000 08 0.797 19.177
## 621 -122.26 44.21 AND 2000 09 42.048 16.160
## 622 -122.26 44.21 AND 2000 10 132.578 10.453
## 623 -122.26 44.21 AND 2000 11 127.390 3.226
## 624 -122.26 44.21 AND 2000 12 244.367 3.136
## 625 -122.26 44.21 AND 2001 01 110.946 2.879
## 626 -122.26 44.21 AND 2001 02 72.730 3.556
## 627 -122.26 44.21 AND 2001 03 171.775 6.829str(climate_lter) # view the structure of the data## 'data.frame': 5100 obs. of 7 variables:
## $ lng : num -122 -122 -122 -122 -122 ...
## $ lat : num 44.2 44.2 44.2 44.2 44.2 ...
## $ lterid: chr "AND" "AND" "AND" "AND" ...
## $ year : num 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 ...
## $ month : chr "01" "02" "03" "04" ...
## $ ppt : num 474 256 171 104 142 ...
## $ tmean : num 1.79 4.28 5.03 10.09 11.82 ...library(rworldmap)## ### Welcome to rworldmap ##### For a short introduction type : vignette('rworldmap') library(ggmap)## Google's Terms of Service: https://cloud.google.com/maps-platform/terms/.## Please cite ggmap if you use it! See citation("ggmap") for details.##
## Attaching package: 'ggmap'## The following object is masked from 'package:magrittr':
##
## inset