rnoaa (version 0.6.0)

isd: Get NOAA ISD/ISH data from NOAA FTP server.

Description

Get NOAA ISD/ISH data from NOAA FTP server.

Usage

isd(usaf, wban, year, path = "~/.rnoaa/isd", overwrite = TRUE, cleanup = TRUE, ...)
rbind.isd(...)

Arguments

usaf, wban

(character) USAF and WBAN code. Required

year

(numeric) One of the years from 1901 to the current year. Required.

path

(character) A path to store the files, a directory. Default: ~/.rnoaa/isd. Required.

overwrite

(logical) To overwrite the path to store files in or not, Default: TRUE

cleanup

(logical) If TRUE, remove compressed .gz file at end of function execution. Processing data takes up a lot of time, so we cache a cleaned version of the data. Cleaning up will save you on disk space. Default: TRUE

...

Curl options passed on to GET

Details

This function first looks for whether the data for your specific query has already been downloaded previously in the directory given by the path parameter. If not found, the data is requested form NOAA's FTP server. The first time a dataset is pulled down we must a) download the data, b) process the data, and c) save a compressed .rds file to disk. The next time the same data is requested, we only have to read back in the .rds file, and is quite fast. The benfit of writing to .rds files is that data is compressed, taking up less space on your disk, and data is read back in quickly, without changing any data classes in your data, whereas we'd have to jump through hoops to do that with reading in csv. The processing can take quite a long time since the data is quite messy and takes a bunch of regex to split apart text strings. We hope to speed this process up in the future. See examples below for different behavior.

References

ftp://ftp.ncdc.noaa.gov/pub/data/noaa/

See Also

isd_stations

Examples

## Not run: 
# # Get station table
# stations <- isd_stations()
# head(stations)
# 
# ## plot stations
# ### remove incomplete cases, those at 0,0
# df <- stations[complete.cases(stations$lat, stations$lon), ]
# df <- df[df$lat != 0, ]
# ### make plot
# library("leaflet")
# leaflet(data = df) %>%
#   addTiles() %>%
#   addCircles()
# 
# # Get data
# (res <- isd(usaf="011490", wban="99999", year=1986))
# (res <- isd(usaf="011690", wban="99999", year=1993))
# (res <- isd(usaf="172007", wban="99999", year=2015))
# (res <- isd(usaf="702700", wban="00489", year=2015))
# (res <- isd(usaf="109711", wban=99999, year=1970))
# 
# # The first time a dataset is requested takes longer
# system.time( isd(usaf="782680", wban="99999", year=2011) )
# system.time( isd(usaf="782680", wban="99999", year=2011) )
# 
# # Optionally pass in curl options
# res <- isd(usaf="011490", wban="99999", year=1986, config = verbose())
# 
# # Plot data
# ## get data for multiple stations
# res1 <- isd(usaf="011690", wban="99999", year=1993)
# res2 <- isd(usaf="172007", wban="99999", year=2015)
# res3 <- isd(usaf="702700", wban="00489", year=2015)
# res4 <- isd(usaf="109711", wban=99999, year=1970)
# ## combine data
# ### uses rbind.isd (all inputs of which must be of class isd)
# res_all <- rbind(res1, res2, res3, res4)
# # add date time
# library("lubridate")
# res_all$date_time <- ymd_hm(
#   sprintf("%s %s", as.character(res_all$date), res_all$time)
# )
# ## remove 999's
# library("dplyr")
# res_all <- res_all %>% filter(temperature < 900)
# ## plot
# library("ggplot2")
# ggplot(res_all, aes(date_time, temperature)) +
#   geom_line() +
#   facet_wrap(~usaf_station, scales = "free_x")
# ## End(Not run)