data-processing

As you would expect from a DSL aimed at data analysis, R handles missing/incomplete data very well, for instance: Many R functions have an na.rm flag that when set to TRUE , remove the NAs: >>> v = mean( c(5, NA, 6, 12, NA, 87, 9, NA, 43, 67), na.rm=T) >>> v (5, 6, 12, 87, 9, 43, 67) But if you want to deal with NAs before the function call, you need to do something like this: to remove each 'NA' from a vector: vx = vx[!is.na(a)] to remove each 'NA' from a vector and replace it w/ a '0': ifelse(is.na(vx), 0, vx) to remove entire each row that contains 'NA' from a data frame: dfx = dfx[complete

Given a set of words, we need to find the anagram words and display each category alone using the best algorithm. input: man car kile arc none like output: man car arc kile like none The best solution I am developing now is based on an hashtable, but I am thinking about equation to convert anagram word into integer value. Example: man => 'm'+'a'+'n' but this will not give unique values. Any suggestion? See following code in C#: string line = Console.ReadLine(); string []words=line.Split(' '); int[] numbers = GetUniqueInts(words); for (int i = 0; i < words.Length; i++) { if (table.ContainsKey

This question already has an answer here: Only read selected columns 3 answers I have some very big delimited data files and I want to process only certain columns in R without taking the time and memory to create a data.frame for the whole file. The only options I know of are read.table which is very wasteful when I only want a couple of columns or scan which seems too low level for what I want. Is there a better option, either with pure R or perhaps calling out to some other shell script to do the column extraction and then using scan or read.table on it's output? (Which leads to the

Given a dataframe column which is a series of integers (age), I want to convert ranges of integers into ordinal variables. My current code doesn't work, how do I do this? df <- read.table("http://dl.dropbox.com/u/822467/df.csv", header = TRUE, sep = ",") df[(df >= 0) & (df <= 14)] <- "Age1" df[(df >= 15) & (df <= 44)] <- "Age2" df[(df >= 45) & (df <= 64)] <- "Age3" df[(df > 64)] <- "Age4" table(df) Use cut to do this in one step: dfc <- cut(df$x, breaks=c(0, 15, 45, 56, Inf)) str(dfc) Factor w/ 4 levels "(0,15]","(15,45]",..: 3 4 3 2 2 4 2 2 4 4 ... Once you are satisfied that the breaks are