I have a process to create a df for a single weather station over a singular month period. However, I have about 25 stations that I would like to aquire precipitation data for over a 5 year period.
I have the station ids in a df, that looks like the table below (but with 23 more stations.
stationid County
GHCND:USW00093721 ANNEARUNDEL
GHCND:USC00182308 BALTIMORE
The weather dataset is aquired through the following code
library("rnoaa")
ANNEARUNDEL_2006 <- ncdc(datasetid='GHCND', stationid = "GHCND:USC00182060", datatypeid='PRCP', startdate = '2006-07-01', enddate = '2006-08-01', limit=400, token = "API KEY")
ANNEARUNDEL_2006 <- ANNEARUNDEL_2006$data
I familiar with very basic for loops that work for one process. Is there a way to set this up the loop would create a new df using the county name and year over the span of 2006 to 2011 for all 25 stations? Is a loop the best way to accomplish this?
I like loops for things like this because they are easier to read and write. You could do it like this with two loops:
my_df <- read.table(text = "stationid County
GHCND:USW00093721 ANNEARUNDEL
GHCND:USC00182308 BALTIMORE",
header = T)
library(rnoaa)
results <- list() # list as storage variable for the loop results
i <- 1 # indexing variable
for(sid in unique(my_df$stationid)) { # each station in your stationid dataframe
for(year in 2006:2011) { # each year you care about
data <- ncdc(datasetid='GHCND', stationid = sid,
datatypeid='PRCP', startdate = paste0(year, '-01-01'),
enddate = paste0(year, '-12-31'), limit=400, token = "API KEY")$data # subset the returned list right away here with $data
# add info from each loop iteration
data$county <- my_df[my_df$stationid == sid,]$County
data$year <- year
results[[i]] <- data # store it
i <- i + 1 # rinse and repeat
}
}
one_big_df <- do.call(rbind, results) # stack all of the data frames together rowwise
Of course, you could always adjust a for loop to using lapply or it's friends. If speed became an issue you might want to consider it.