rnoaa (version 0.8.4)

isd: Get and parse NOAA ISD/ISH data

Description

Get and parse NOAA ISD/ISH data

Usage

isd(usaf, wban, year, overwrite = TRUE, cleanup = TRUE,
  additional = TRUE, parallel = FALSE, cores = getOption("cl.cores",
  2), progress = FALSE, force = FALSE, ...)

Arguments

usaf, wban

(character) USAF and WBAN code. Required

year

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

additional

(logical) include additional and remarks data sections in output. Default: TRUE. Passed on to isd_parse

parallel

(logical) do processing in parallel. Default: FALSE

cores

(integer) number of cores to use: Default: 2. We look in your option "cl.cores", but use default value if not found.

progress

(logical) print progress - ignored if parallel=TRUE. The default is FALSE because printing progress adds a small bit of time, so if processing time is important, then keep as FALSE

force

(logical) force download? Default: FALSE We use a cached version (an .rds compressed file) if it exists, but this will override that behavior.

...

Curl options passed on to HttpClient

Value

A tibble (data.frame).

Errors

Note that when you get an error similar to Error: download failed for ftp://ftp.ncdc.noaa.gov/pub/data/noaa/1955/011490-99999-1955.gz, the file does not exist on NOAA's ftp servers. If your internet is down, you'll get a different error.

File storage

We use rappdirs to store files, see user_cache_dir for how we determine the directory on your machine to save files to, and run rappdirs::user_cache_dir("rnoaa/isd") to get that directory.

Details

isd saves the full set of weather data for the queried site locally in the directory specified by the path argument. You can access the path for the cached file via attr(x, "source")

We use isdparser internally to parse ISD files. They are relatively complex to parse, so a separate package takes care of that.

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

Other isd: isd_read, isd_stations_search, isd_stations

Examples

# NOT RUN {
# Get station table
(stations <- isd_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="109711", wban=99999, year=1970))

# "additional" and "remarks" data sections included by default
# can toggle that parameter to not include those in output, saves time
(res1 <- isd(usaf="011490", wban="99999", year=1986, force = TRUE))
(res2 <- isd(usaf="011490", wban="99999", year=1986, force = TRUE,
  additional = FALSE))

# 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) )

# Plot data
## get data for multiple stations
res1 <- isd(usaf="011690", wban="99999", year=1993)
res2 <- isd(usaf="782680", wban="99999", year=2011)
res3 <- isd(usaf="008415", wban="99999", year=2016)
res4 <- isd(usaf="109711", wban=99999, year=1970)
## combine data
library(dplyr)
res_all <- bind_rows(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
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")

# print progress
(res <- isd(usaf="011690", wban="99999", year=1993, progress=TRUE))

# parallelize processing
(res <- isd(usaf="172007", wban="99999", year=2015, parallel=TRUE))
# }