Improving performance when working with geodata in R - r

Question

I wrote the following script to produce the raw data for plotting the above map. The problem is, for 550,000 data points, this takes about 2 hours to run on a relatively powerful machine. I'm new to R, however, and I'm wondering if there are any optimized functions I can take advantage of?
The basic idea is that, given a set of geospatial data, you split the set into 200 rows, and split each row into a bunch of squares. You then calculate the total of a value in each square in a row. The approach I've taken below is to take the "upper left" point of a square, calculate the latitude/longitude of edges of the square, and exclude all points not in those bounds, and then sum what remains. Is there a better way without using a solution like PostGIS?
all.data <- read.csv("FrederictonPropertyTaxDiffCleanedv3.csv", header=TRUE,
stringsAsFactors=FALSE)
all.data$X <- as.numeric(all.data$X)
all.data$Y <- as.numeric(all.data$Y)
startEnd <- function(lats, lngs) {
# Find the "upper left" (NW) and "bottom right" (SE) coordinates of a set of data.
#
# Args:
# lats: A list of latitude coordinates
# lngs: A list of longitude coordinates
#
# Returns:
# A list of values corresponding to the northwest-most and southeast-most coordinates
# Convert to real number and remove NA values
lats <- na.omit(as.numeric(lats))
lngs <- na.omit(as.numeric(lngs))
topLat <- max(lats)
topLng <- min(lngs)
botLat <- min(lats)
botLng <- max(lngs)
return(c(topLat, topLng, botLat, botLng))
}
startEndVals <- startEnd(all.data$Y, all.data$X)
startLat <- startEndVals[1]
endLat <- startEndVals[3]
startLng <- startEndVals[2]
endLng <- startEndVals[4]
num_intervals = 200.0
interval <- (startEndVals[1] - startEndVals[3]) / num_intervals
# testLng <- -66.6462379307115
# testLat <- 45.9581234392
# Prepare the data to be sent in
data <- all.data[,c("Y", "X", "levy2014_ha")]
sumInsideSquare <- function(pointLat, pointLng, interval, data) {
# Sum all the values that fall within a square on a map given a point,
# an interval of the map, and data that contains lat, lng and the values
# of interest
colnames(data) <- c("lat", "lng", "value")
# Data east of point
data <- data[data$lng > pointLng,]
# Data west of point + interval
data <- data[data$lng < pointLng + interval,]
# Data north of point + interval (down)
data <- data[data$lat > pointLat - interval,]
# Data south of point
data <- data[data$lat < pointLat, ]
# Clean remaining data
data <- na.omit(data)
return(sum(data$value))
}
# Debugging
# squareSumTemp <- sumInsideSquare(testLat, testLng, interval, data)
# Given a start longitude and an end longitude, calculate an array of values
# corresponding to the sums for that latitude
calcSumLat <- function(startLng, endLng, lat, interval, data) {
row <- c()
lng <- startLng
while (lng < endLng) {
row <- c(row, sumInsideSquare(lat, lng, interval, data))
lng <- lng + interval
}
return(row)
}
# Debugging
# rowTemp <- calcSumLat(startLng, endLng, testLat, interval, data)
# write.csv(rowTemp, file = "Temp.csv", row.names = FALSE)
# Get each line of data to plot
lat <- startLat
rowCount <- 1
all.sums <- list()
while (lat > endLat) {
col <- calcSumLat(startLng, endLng, lat, interval, data)
all.sums[[as.character(rowCount)]] <- col
lat <- lat - interval
rowCount <- rowCount + 1
}
# Convert to data frame
all.sums.frame <- data.frame(all.sums)
# Save to disk so I don't have to run it again
write.csv(all.sums.frame, file = "Levy2014Sums200.csv", row.names = FALSE)

Answer 1

Ended up finding a solution to this myself. The key to it was using the foreach package with the doParallel package so it could take advantage of all the cores on my computer. There is a great guide on it here: http://www.r-bloggers.com/a-brief-foray-into-parallel-processing-with-r/