Create buffer and count points in R

I asked this question previously but did not get a response, so I'll try and do a better job this time around!

I want to analyze spatial density of gas station points using R. I need to create a buffer (let's say 1,000m) around the gas stations and count the number of gas stations within the buffer. I'll then need to play around with buffer distances to see what's a reasonable buffer to see something interesting. I won't post the entire shape file because it's fairly messy, but this is what the data look like:

all <- readShapePoints("sbc_gas.shp") 
all.df <- as(all, "data.frame")
head(all)

OBJECTID Fuellocati               Name   Latitude      Longitude     
      1      34828     WORLD OIL #104    34.44190      -119.8304    
      2      48734  STOP AND SHOP GAS    34.41962      -119.6768    
      3      51276 EL RANCHERO MARKET    34.41911      -119.7162    
      4      52882  EDUCATED CAR WASH    34.44017      -119.7439    
      5      74038           CIRCLE K    34.63925      -120.4406    
      6     103685    7-ELEVEN #23855    34.40506      -119.5296

I was able to create a buffer around the points with the following code, but now how do I count the number of points within the buffer?

require(sp)
require(rdgal)
require(geosphere)

coordinates(all) <- c("Longitude", "Latitude")
pc <- spTransform(all, CRS( "+init=epsg:3347" ) ) 
distInMeters <- 1000
pc100km <- gBuffer(pc, width=100*distInMeters, byid=TRUE )
# Add data, and write to shapefile
pc100km <- SpatialPolygonsDataFrame(pc100km, data=pc100km@data )
writeOGR( pc100km, "pc100km", "pc100km", driver="ESRI Shapefile" )

plot(pc100km)

I'm open to other ways to go about this.

Came up with a (simple) solution for my own question using geosphere:

cbind(coordinates(all.df), X=rowSums(distm (coordinates(all.df)[,1:2], fun = distHaversine) / 1000 <= 10)) # number of points within distance 10 km
    Longitude Latitude  X
0   -119.8304 34.44190 25
1   -119.6768 34.41962 29
2   -119.7162 34.41911 34
3   -119.7439 34.44017 39
4   -120.4406 34.63925 13
5   -119.5296 34.40506  7
6   -120.4198 34.93860 26
7   -119.8221 34.43598 30