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--- r_programming_gl [2014/11/21 20:42]
glaroc [Data Table]
+++ r_programming_gl [2014/11/21 21:31] (current)
glaroc old revision restored (2014/11/21 15:54)
@@ Line 1: / Line 1: @@
+====== Knitr ======
+Knitr is a package that can be used to generate dynamic reports or web pages from R code. The code is evaluated at the moment the report is generated.
+Code can be easily written in RStudio use the Markdown language. View this page in Markdown language, and view the resulting web page.
 ====== Data Table ======
 Data table is a very useful package in R which allows to facilitate and to improve the efficiency of certain operations in R. Data tables are just like data frames. You can even create them from data frames.
@@ Line 8: / Line 14: @@
 library(data.table)
 </code>
-<code rsplus>
-library(reshape2)
-library(data.table)
-library(plyr)
-mydf=data.frame(a=rep(LETTERS,each=1e6),b=rnorm(26*1e6))
-mydt=data.table(mydf)
-setkey(mydt,a)
-mydt['F']
-# Returns all rows with column a (the key) equal to F
-mydt[,mean(b),by=a]
-# Gives the mean value of column b for each letter in column a.
-# Compare
-system.time(t1<-mydt[,mean(b),by=a])
-# 0.314 secs
-# With tapply()
-system.time(t2<-tapply(mydf$b,mydf$a,mean))
-# 7.239 secs
-# With reshape2
-meltdf=melt(mydf)
-system.time(t3<-dcast(meltdf,a~variable,mean))
-# 4.453 secs
-# With reshape plyr
-system.time(t4<-ddply(mydf,.(a),summarize,mean(b)))
-# 2.288 secs
-# With a FOR loop
-ti1<-proc.time()
-t5<-data.frame(letter=unique(mydf$a),mean=rep(0,26))
-for (i in t5$letter ){
-  t5[t5$letter==i,2]=mean(mydf[mydf$b==i,2])
-}
-eltime<-proc.time()-ti1
-</code>
-====== Data Table ======
-library(data.table)
 Generate very long data frame with one column with letters, and one column with random numbers
@@ Line 65: / Line 33: @@
 <file rsplus>
 mydt['F']
-</file
+</file>
 Gives the mean value of column b for each letter in column a.
@@ Line 77: / Line 45: @@
 </file>
-With tapply()
+**With tapply()**
 <file rsplus>
 system.time(t2<-tapply(mydf$b,mydf$a,mean))
 </file>
-With reshape2
+**With reshape2**
 <file rsplus>
 library(reshape2)
@@ Line 89: / Line 57: @@
 </file>
-With plyr
+**With plyr**
 <file rsplus>
 library(plyr)
@@ Line 95: / Line 63: @@
 </file>
-With sqldf. This package allows one to write Structured Query Language commands to perfom queries on a data frame.
+**With sqldf**. This package allows one to write Structured Query Language commands to perfom queries on a data frame.
 <file rsplus>
 library(sqldf)
@@ Line 101: / Line 69: @@
 </file>
-With a basic FOR loop
+**With a basic FOR loop**
 <file rsplus>
 ti1<-proc.time()
@@ Line 112: / Line 80: @@
 </file>
-### With a parallelized FOR loop
+**With a parallelized FOR loop**
 <file rsplus>
 library(foreach)
@@ Line 128: / Line 96: @@
 ====== RgoogleMaps! ======
-<file | rsplus>
+<file rsplus>
 library(RgoogleMaps)
-myhome=getGeoCode('McGill Biology Department');
+myhome=getGeoCode('Olympic stadium, Montreal');
 mymap<-GetMap(center=myhome, zoom=14)
 PlotOnStaticMap(mymap,lat=myhome['lat'],lon=myhome['lon'],cex=5,pch=10,lwd=3,col=c('red'));
@@ Line 136: / Line 104: @@
 ====== Taxize ======
-<file | rsplus>
+<file rsplus>
 library(taxize)
 spp<-tax_name(query=c("american beaver"),get="species")
@@ Line 145: / Line 113: @@
 ====== Spocc ======
-<file | rsplus>
+<file rsplus>
 library(spocc)
 occ_data <- occ(query = 'Acer nigrum', from = 'gbif')
@@ Line 152: / Line 120: @@
 Combine spocc and RgoogleMaps
-<file | rsplus>
+<file rsplus>
 occ_data <- occ(query = 'Puma concolor', from = 'gbif')
 occ_data_df=occ2df(occ_data)
@@ Line 162: / Line 130: @@
 ====== geonames ======
-<file | rsplus>
+<file rsplus>
 library(geonames)
 options(geonamesUsername="glaroc")
@@ Line 170: / Line 138: @@
 dc[,c('toponymName')]
 </file>