Ejemplo de descarga de datos climaticos

DatosClimaAndalucia.R

# require(devtools)
# install_github('SevillaR/Andaclima')

require(Andaclima)
rm(list = ls())

stations <- getAndalusia_ACS()

metainfo <- getMetaData(provincia       = stations$province.code,
                        estacion        = stations$station.code ,
                        nombre_estacion = stations$station.name )

# save(stations, metainfo, file='010_listaEstaciones.RData')

load('010_listaEstaciones.RData')

#' Convierte cadena de caracter en grados, minutos y segundo 
#' en grados decimales 
Char2ll <- function(e) {    
    is.W <- gsub("^[1-90 º']+","",e) %in% c("W","N")
    e <- gsub(" [NSEW]$",'',e)
    ee <- strsplit(e, "[º' ]+")
    ee<-as.numeric((ee[[1]]))
    ee <- sum(ee * c(1,1/60,1/3600))
    if (!is.W) ee<- ee*-1
    return(ee)
}

Char2ll("30º 12' 15'' W")

## Coordenada UTM
metainfo$x <- as.numeric(metainfo$x)
metainfo$y <- as.numeric(metainfo$y)
## Coordenada Latitud y Longitud
metainfo$longitud <- sapply(metainfo$longitud, Char2ll ) 
metainfo$longitud <- sapply(metainfo$longitud, Char2ll ) 



start <- '15-07-2015'
end   <- '17-07-2015'

dclima <- data.frame()

i <- 1
for (i in 1:nrow(stations)) {
  cat(i, '/', nrow(stations), 
      ': Downloading data from station ', stations$station.code[i], 
      ' at province ', stations$province.code[i], '\n', sep ='')
  dclima <- rbind(dclima, readIFAPA(stations$province.code[i], 
                                stations$station.code[i], 
                                start =  start , end =end))
}
## Return data

# save(dclima, file='dclima')

load('dclima.RData')

### Une datos temperatura con metainfo
merge(dclima,metainfo, by=c("c_provincia","estacion")) -> dclima



###----
## Interpolación espacial ...
###

library(ggplot2)
library(gstat)
library(sp)
library(maptools)

subset(dclima, FECHA==as.Date('20150715', "%Y%m%d")) -> cdia

### Convierte en SpatialPoinsDataFrame
coordinates(cdia) = ~x + y
plot(cdia)
####--------------------

range(cdia$x)  #   123271.7 608847.0  21.86411 28.03960
range(cdia$y)  #   4019310 4263000    57.77708 59.50061

# Define the grid extent:
x.range <- c( 123271,  608847)  # min/max longitude of the interpolation area
y.range <- c(4019310, 4263000)  # min/max latitude of the interpolation area

# Create a data frame from all combinations of the supplied vectors or factors. 
# See the description of the return value for precise details of the way this is done. 
# Set spatial coordinates to create a Spatial object. Assign gridded structure:

grd <- expand.grid(x = seq(from = x.range[1], to = x.range[2], by = 10000),
                   y = seq(from = y.range[1],to = y.range[2], by =10000))  

# expand points to grid
class(grd)
coordinates(grd) <- ~x + y
gridded(grd) <- TRUE  # Transforma en un GRID

# Plot the weather station locations and interpolation grid:
plot(grd, cex = 1.5, col = "grey"  )
points(cdia, pch = 19, col = "red", cex = .4)


## Interpolate surface and fix the output:
idw <- idw(formula = TMed ~ 1, 
           locations = cdia, 
           newdata = grd)  # apply idw model for the data


idw.output = as.data.frame(idw)  # output is defined as a data table
class(idw.output)
names(idw.output)[1:3] <- c("long", "lat", "var1.pred")  # give names to the modelled variables

head(idw.output)

# Plot the results:
ggplot() + geom_tile(data = idw.output, aes(x = long, y = lat, fill = var1.pred)) +
      geom_point(data = metainfo, 
             aes(x = x, y = y), 
             shape = 19, colour = "red", size=.5)