weatherData is a collection of functions that will fetch weather (Temperature, Pressure, Humidity etc.) data from the Web for you as clean data frame.
Example showing How to Get Detailed Wind Data
For this example, we are interested in Wind data for the town of Barrow in Alaska. We need to find the airport code, then we have to find out which columns are relevant, and then we can fetch the data we need.
library(weatherData)
First, get the station code
getStationCode("Barrow")
[[1]]
Station State airportCode
18 Barrow AK PABR
[[2]]
[1] "USA AK BARROW PABR BRW 70026 71 17N 156 48W 7 X T X A 5 US"
[2] "USA AK BARROW ARM-NSA 70027 71 19N 156 37W 7 X 8 US"
[3] "USA GA WINDER/BARROW KWDR WDR 33 59N 083 40W 287 X 7 US"
[4] "AUSTRALIA BARROW ISLAND YBWX 94304 20 53S 115 24E 6 X 8 AU"
Either the 4-letter code PABR or the airport code BRW would work.
Which columns relate to Wind?
> showAvailableColumns("PABR", "2014-01-01", opt_detailed=T)
columnNumber columnName
1 1 TimeAKST
2 2 TemperatureF
3 3 Dew_PointF
4 4 Humidity
5 5 Sea_Level_PressureIn
6 6 VisibilityMPH
7 7 Wind_Direction
8 8 Wind_SpeedMPH
9 9 Gust_SpeedMPH
10 10 PrecipitationIn
11 11 Events
12 12 Conditions
13 13 WindDirDegrees
14 14 DateUTC
So the columns we are interested in are 7, 8 and 13, because they relate to Wind. Please note that the column numbers are very different depending on whether or not opt_detailed is TRUE or FALSE.
When fetching the data, we have to set opt_custom_columns=TRUE and custom_columns=c(7,8,13).
We are now ready to actually fetch the data.
Fetch the Data
>getWeatherForDate("BRW", start_date="2014-01-01",
opt_detailed=T,
opt_custom_columns=T, custom_columns=c(7,8,13))
And this gets us:
Checking Data Availability For BRW
Found Records for 2014-01-01
Data is Available
Will be fetching these Columns:
[1] "Time" "Wind_Direction" "Wind_SpeedMPH"
[4] "WindDirDegrees"
Begin getting Daily Data for BRW
BRW 1 2014-01-01 : Fetching 27 Rows with 4 Column(s)
Time Wind_Direction Wind_SpeedMPH WindDirDegrees
1 2014-01-01 00:53:00 ENE 27.6 70
2 2014-01-01 01:53:00 ENE 24.2 70
3 2014-01-01 02:53:00 ENE 26.5 70
4 2014-01-01 03:53:00 ENE 31.1 70
5 2014-01-01 04:53:00 ENE 28.8 70
6 2014-01-01 05:53:00 ENE 31.1 70
...
...
We can save these values in a data frame and start the actual analysis.