daily_fips: Pull average daily weather data by U.S. county.
In countyweather: Compiles Meterological Data for U.S. Counties

Description Usage Arguments Value Note Examples

Given a particular county FIPS code, this function returns data and meta-data for weather data, either for all available dates or for dates within a requested date range.

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daily_fips(fips, coverage = NULL, date_min = NULL, date_max = NULL,
  var = "all", average_data = TRUE, station_label = FALSE,
  verbose = TRUE)

`fips`	A string with the five-digit U.S. FIPS code of a county in numeric, character, or factor format.
`coverage`	A numeric value in the range of 0 to 1 that specifies the desired percentage coverage for the weather variable (i.e., what percent of each weather variable must be non-missing to include data from a monitor when calculating daily values averaged across monitors. The default is to include all monitors with any available data (i.e., `coverage = 0`).)
`date_min`	A string with the desired starting date in character, ISO format ("yyyy-mm-dd"). The dataframe returned will include only stations that have data for dates including and after the specified date.
`date_max`	A string with the desired ending date in character, ISO format ("yyyy-mm-dd"). The dataframe returned will include only stations that have data for dates up to and including the specified date.
`var`	A character vector specifying desired weather variables. For example, `var = c("tmin", "tmax", "prcp")` for maximum temperature, minimum temperature, and precipitation. The default is `"all"`, which includes all available weather variables at any weather station in the county. For a full list of all possible variable names, see NOAA's README file for the Daily Global Historical Climatology Network (GHCN-Daily) at http://www1.ncdc.noaa.gov/pub/data/ghcn/daily/readme.txt. Many of the weather variables are available for some, but not all, monitors, so your output from this function may not include all the variables specified using this argument. If you specify a variable here but it is not included in the output dataset, it means that it was not available in the time range for any monitor in the county.
`average_data`	TRUE / FALSE to indicate if you want the function to average daily weather data across multiple monitors. If you choose FALSE, the function will return a dataframe with separate entries for each monitor, while TRUE (the default) outputs a single estimate for each day in the dataset, giving the average value of the weather metric across all available monitors in the county that day.
`station_label`	TRUE / FALSE to indicate if you want your plot of weather station locations to include labels with station ids.
`verbose`	TRUE / FALSE to indicate if you want the function to print out the name of the county it's processing.

A list with three elements. The first element (daily_data) is a dataframe of daily weather data averaged across multiple stations, as well as columns ("var"_reporting) for each weather variable showing the number of stations contributing to the average for that variable on that day. The second element (station_metadata) is a dataframe of station metadata for stations included in the daily_data dataframe, as well as statistical information about these values. Columns include id, name, var, latitude, longitude, calc_coverage, standard_dev, min, max, and range. The third element (station_map) is a plot showing locations of all weather stations for a particular county satisfying the conditions present in daily_fips's arguments (coverage, date_min, date_max, and/or var).

Because this function uses the NOAA API to identify the weather monitors within a U.S. county, you will need to get an access token from NOAA to use this function. Visit NOAA's token request page (http://www.ncdc.noaa.gov/cdo-web/token) to request a token by email. You then need to set that API code in your R session (e.g., using options(noaakey = "your key"), replacing "your key" with the API key you've requested from NOAA). See the package vignette for more details.

## Not run: 
denver_ex <- daily_fips("08031", coverage = 0.90, date_min = "2010-01-01",
                 date_max = "2010-02-01", var = "prcp")

head(denver_ex$daily_data)
denver_ex$station_map

mobile_ex <- daily_fips("01097", date_min = "1997-07-13",
                        date_max = "1997-07-25", var = "prcp",
                        average_data = FALSE)
library(ggplot2)
ggplot(mobile_ex$daily_data, aes(x = date, y = prcp, color = id)) +
   geom_line()

## End(Not run)