mapreduceclass.r carolinaalvesdelimasalge Fri Nov 18 15:42:
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1 mapreduceclass.r carolinaalvesdelimasalge Fri Nov 18 15:42: # cr eat e a li st of 10 i nt eger s i nt s <- 1: 10 # equi val ent t o i nt s <- c( 1, 2, 3, 4, 5, 6, 7, 8, 9, 10) # comput e t he squar es (sappl y appli es a f uncti on over a list or vect or) result <- sappl y(i nt s,f uncti on( x) x^2) result ## [ 1] require(r mr 2) ## Loadi ng r equi r ed package: r mr 2 ## War ni ng: S3 met hods ' gor der. def ault', ' gor der.f act or', 'gor der. dat a.frame', ## ' gor der. matri x', ' gor der.raw' wer e decl ar ed i n NAMESPACE but not f ound ## Pl ease r evi ew your hadoop setti ngs. See hel p( hadoop.setti ngs) # Good i dea t o debug your mapreduce t ask i n l ocal mode wit h # a small dat aset t hen run it on a Hadoop cl ust er # l oad a li st of 10 i nt eger s i nt o a HDFS fil e hdf s.i nt s = t o. df s( 1: 10) # mapper f or t he key-val ue pai rs t o comput e squar es key <- v val ue <- key^2 # r un MapReduce out = mapr educe(i nput = hdf s.i nt s, map = mapper) # convert t o a dat a frame df 1 = as. dat a.frame(from. df s( out)) col names( df 1) = c(' n', ' n^2' ) #di spl ay t he results df 1 ## n n^2 ## ## ## ## ## ## ## ## ## ## li brary(readr)
2 #convert and round t emper at ur e t o an i nt eger t $t emper at ur e = round((t $t emper at ur e- 32)*5/ 9, 0) # t abul at e frequenci es t abl e(t $t emper at ur e) ## ## ## ## ## li brary(sql df) ## Loadi ng r equi r ed package: gsubf n ## Loadi ng r equi r ed package: pr ot o ## Loadi ng r equi r ed package: RSQLi t e ## Loadi ng r equi r ed package: DBI opti ons(sql df. dri ver =' SQLi t e' ) r mr. opti ons( backend = "l ocal ") #l ocal or hadoop # save t emper at ur e i n hdf s fil e hdf s.t emp <- t o. df s(t $t emper at ur e) # mapper f or conver si on t o C key <- round((v- 32)*5/ 9, 0) val ue <- 1 # r educer t o count frequenci es reducer <- f uncti on( k, v) { key <- k val ue = l engt h(v) out = mapr educe( i nput = hdf s.t emp, reduce = r educer) df 2 = as. dat a.frame(from. df s( out)) col names( df 2) = c('t emper at ur e', ' count' ) df 3 <-sql df(' SELECT * FROM df 2 ORDER BY count DESC;' ) ## Loadi ng r equi r ed package: tcltk pri nt(df 3, row. names = FALSE) # no row names ## t emper at ur e count ## ## ## 2 85 ## 22 80
3 ## ## 0 71 ## ## ## 1 66 ## 3 64 ## 4 62 ## ## ## 7 55 ## ## ## ## ## ## 5 52 ## 6 52 ## 9 52 ## 8 50 ## ## ## ## ## ## ## ## ## 27 9 ## -5 8 ## -6 1 ## -7 1 # Basi c st at s li brary(reshape) li brary(sql df) opti ons(sql df. dri ver =' SQLi t e' ) t <- read.t abl e( url, header =T, sep=',') st at s <- sql df(' SELECT year, max(t emper at ur e) as max, round( avg(t emper at ur e), 1) as mean, mi n(t emper at ur e) as mi n fromt GROUP BY year;' ) head( st at s) ## year max mean mi n ## ## ## ## ## ##
4 # convert t o hdf s fil e hdf s.t emp <- t o. df s( dat a.frame(t)) # mapper f or computi ng t emper at ur e measur es f or each year key <- v$year val ue <- v$t emper at ur e #r educer t o report st at s reducer <- f uncti on( k, v) { key <- k #year val ue <- c( max( v, na.r m=t), round( mean( v, na.r m=t), 1), mi n( v, na.r m=t)) #v i s li st of val ues f or a year out = mapr educe( i nput = hdf s.t emp, reduce = r educer) df 3 = as. dat a.frame(from. df s( out)) df 3$measur e <- c(' max',' mean',' mi n' ) # r eshape wit h year, max, mean, mi n i n one r ow st at s2 <- cast( df 3, key ~ measur e, val ue="val ") head( st at s2) ## key max mean mi n ## ## ## ## ## ## li brary(stri ngr) # r ead as a si ngl e char act er stri ng t <- readchar("htt p:// peopl e.t erry. uga. edu/r wat son/ dat a/ yogi quot es.t xt", nchar s=1e6) t 1 <- t ol ower(t[[1]]) # convert t o l ower case t 2 <- str_r epl ace_all(t 1,"[[: punct:]]","") # get ri d of punct uati on wor dli st <- str_split(t 2, "\\s") #split i nt o stri ngs wor dvect or <- unli st( wor dli st) # convert li st t o vect or t abl e( wor dvect or) ## wor dvect or ## a about ar ent come f ork half i if i n it ## ## li es me most never of road sai d t ake t ell t he ## ## t hey t hi ngs t o true you ## # r ead as a si ngl e char act er stri ng url <- "htt p:// peopl e.t erry. uga. edu/r wat son/ dat a/ yogi quot es.t xt" t <- readchar( url, nchar s=1e6) t ext. hdf s <- t o. df s(t)
5 mapper =f uncti on( k, v){ t 1 <- t ol ower(v) # convert t o l ower case t 2 <- str_r epl ace_all(t 1,"[[: punct:]]","") # get ri d of punct uati on wor dli st <- str_split(t 2, "\\s") #split i nt o wor ds wor dvect or <- unli st( wor dli st) # convert li st t o vect or keyval ( wor dvect or, 1) reducer = f uncti on(k, v) { keyval (k,l engt h(v)) out <- mapr educe (i nput = t ext. hdf s, reduce = r educer, combi ne=t) # convert out put t o a frame df 1 = as. dat a.frame(from. df s( out)) col names( df 1) = c(' wor d', ' count' ) #di spl ay t he results pri nt(df 1, row. names = FALSE) # no row names ## wor d count ## if 1 ## you 1 ## come 1 ## t o 1 ## a 1 ## f ork 1 ## i n 1 ## t he 3 ## road 1 ## t ake 1 ## it 1 ## i 2 ## never 1 ## sai d 2 ## most 1 ## of 1 ## t hi ngs 1 ## half 1 ## li es 1 ## t hey 1 ## t ell 1 ## about 1 ## me 1 ## ar ent 1 ## true 1
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