November 2017
Beginner to intermediate
366 pages
7h 59m
English
Content preview from R Data Analysis Projects
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Feature generation
Let us begin with the feature generation R code:
library(RecordLinkage, quietly = TRUE)###### Quick look at our data #########data(RLdata500)str(RLdata500)head(RLdata500)#### Feature generation #############rec.pairs <- compare.dedup(RLdata500 ,blockfld = list(1, 5:7) ,strcmp = c(2,3,4) ,strcmpfun = levenshteinSim)summary(rec.pairs)matches <- rec.pairs$pairsmatches[c(1:3, 1203:1204), ]RLdata500[1,]RLdata500[174,]# String featuresrec.pairs.matches <- compare.dedup(RLdata500 ,blockfld = list(1, 5:7) ,strcmp = c(2,3,4) ,strcmpfun = levenshteinSim)# Not specifying the fields for string comparisionrec.pairs.matches <- compare.dedup(RLdata500 ,blockfld = list(1, 5:7) ,strcmp = TRUE ,strcmpfun = levenshteinSim)head(rec.pairs.matches$pairs) ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.