Splitting Lat and Lon observations into grid cells

grid_splitter.R

# R script for splitting grid space
# Variables:
# obs_lat = vector of observed lats
# obs_lon = vector of observed lons
# lats = vector of lists for the max and min values of each grid in lat-space
# lons = vector of lists for the max and min values of each grid in lon-space
# gird = matrix of indexes pointing to the lists in =lats and lons
# res = vector of indexes referring to indexes in grid

# Steps:
# 1. Create lists of the min and max values for each lon and lat (calls them lats and lons)
# 2. construct a grid using the expand.grid function that will find all possible combinations
#   for the lats and lons
# 3. Loop through each element in the grid and determine which observations are in which grid square
# 4. Produce a vector of each grid element index for each observation [RESULT]

# FUNCTIONS
genLonsandLats <- function(max_lat, min_lat, max_lon, min_lon, gsize=0.5) {
  # create list of list of list of grid cells
  # LATS!
  lats_values <- seq(from=min_lat, to=max_lat, by=gsize)
  lats_list <- list()
  for(i in 2:length(lats_values)) {
    lats_list[[i-1]] <- list(min=lats_values[i-1], max=lats_values[i])
  }
  # LONS!
  lons_values <- seq(from=min_lon, to=max_lon, by=gsize)
  lons_list <- list()
  for(i in 2:length(lons_values)) {
    lons_list[[i-1]] <- list(min=lons_values[i-1], max=lons_values[i])
  }
  latslons <- list("lats"=lats_list,
                   "lons"=lons_list)
  return(latslons)
}

# INPUT
# This is fake data for testing
obs_lons <- runif(min=0, max=1, 100)
obs_lats <- runif(min=0, max=1, 100)
# TODO Read in real data, make sure the two vectors are "paired", same length and identities

# CREATE GRID-SPACE
latslons <- genLonsandLats()
lats <- latslons[["lats"]]  # unpack
lons <- latslons[["lons"]]
grid <- expand.grid(1:length(lats),1:length(lons))
colnames(grid) <- c("lat", "lon")

# DESIGNATE
res <- rep(NA, length(obs_lons))
for (i in 1:nrow(grid)) {
  lon <- lons[[grid[i, "lon"]]]
  lat <- lats[[grid[i, "lat"]]]
#   cat("Corrds lon:\n")
#   cat("... min:", coords_lon$min, "\n")
#   cat("... max:", coords_lon$max, "\n")
#   cat("Corrds lat:\n")
#   cat("... min:", coords_lat$min, "\n")
#   cat("... max:", coords_lat$max, "\n")
  res_bool <- obs_lons >= lon$min & obs_lons < lon$max &
    obs_lats >= lat$min & obs_lats < lat$max
  res_is <- which(res_bool)
  res[res_is] <- i
}

# OUTPUT
# TODO

cat("Script complete!!! Have a nice day :-)\n")

# DEFUNCT
# constructObs <- function(lat, lat) {
#   # combine two separate vectors into a single list of $lon and $lat
#   obs <- list()
#   for(i in 1:length(lon)) {
#     obs[[i]] <- list(lon=lon[i], lat=lat[i])
#   }
#   obs
# }

useful_handling_of_tables.R

species <- c ("Sp1", "Sp1","Sp1","Sp2","Sp1","Sp2","Sp3","Sp1","Sp1","Sp1")
obs_vals <- runif(min=0, max=1, n=length(species))
table(species)
tapply(obs_vals, species, mean, na.rm=TRUE)  # remember to use rm.na if there are any missing values
tapply(obs_vals, species, sd, na.rm=TRUE)