merging data with SpatialPolygonsDataFrame

On Thu, 24 Jan 2008, Dylan Beaudette wrote:

Hi Roger. Thanks for contributing some answers to this.

I was recently working with a colleague on developing some sample
exercises for new students. Since joining new attribute data to a GIS
layer's table is a very common operation we included some samples on how
to do this within R. You have hinted at some possible ways to do it
above, but do you have a 'best practices' approach to doing this using
'sp' methods and objects?

For example:

# contains an attribute col named 'veg_code'
veg <- readOGR(something.shp)

# code meanings: indexed by 'veg_code'
codes <- read.dbf(table.dbf)

# what is the best way to join up the attributes in 'veg' with the rows
in 'codes' ?

Hi Dylan,

This is a different question, but I won't break it out of this thread yet.

You are doing look-up on codes to give labels to veg$veg_code, right?

veg$veg_code are integer indices to codes$V1 (say V1, I don't know what it
is). If length(unique(veg$veg_code)) == length(codes$V1), and
sort(unique(veg$veg_code)) is 1:length(codes$V1), you should think of the
factor as your friend:

veg$veg_code_factor <- factor(veg$veg_code, labels=as.character(codes$V1))

If not, you need another layer using perhaps order() or match() on the
matching substring of codes$V1 to find out which value of veg$veg_code
should have which label in as.character(codes$V1). Alternatively use the
levels= argument to factor().

Something like:

set.seed(1)
veg_code <- rpois(100, 4)
table(veg_code)
V1 <- paste("code", 0:10)
V1
levs <- 0:10
veg_code_factor <- factor(veg_code, levels=levs, labels=V1)
table(veg_code_factor, veg_code)

No merging or messing with veg itself is needed, apart from adding a
single extra factor column. The factor abstraction is a great strength of
the S language.

Have I misunderstood you?

Roger

As of now we are using merge to replace the dataframe slot of the
original file. We first re-order the results from merge to match the
original row ordering:


# an example file:
veg <- readOGR(dsn='ArcGISLabData/BrownsPond/', layer='vegevector')

# some example codes
veg_codes <- data.frame(code=1:4, meaning=c('code 1','code 2','code
3','code 4'))

# join the original data table with the veg codes table
combined <- merge(x=veg at data, y=veg_codes, by.x='CODE', by.y='code')


# overwrite the original data frame with the combined version
# note that the original order needs to be restored
# since the original data was sorted on 'ID', we can use that to restore
# the correct order in the 'combined' dataframe:
v at data <- combined[order(combined$ID),]


In summary, is there a safer or preferred way to do this?

thanks,

Dylan