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copied!<p>What I did in the end was print out the headers using libreoffice labeled as V1,V2, etc. then I just read in the file as</p> <pre><code> m1 <- read.csv('Sheet1.csv', header=FALSE, skip=1) </code></pre> <p>and then just did the analysis against m1$V10, m1$V23 etc...</p> <p>To get around the mess of multiple columns I used the following little function</p> <pre><code># function to merge columns into one with a space separator and then # remove multiple spaces mcols <- function(df, cols) { # e.g. mcols(df, c(14:18)) exp <- paste('df[,', cols, ']', sep='', collapse=',' ) # this creates something like... # "df[,14],df[,15],df[,16],df[,17],df[,18]" # now we just want to do a paste of this expression... nexp <- paste(" paste(", exp, ", sep=' ')") # so now nexp looks something like... # " paste( df[,14],df[,15],df[,16],df[,17],df[,18] , sep='')" # now we just need to parse this text... and eval() it... newcol <- eval(parse(text=nexp)) newcol <- gsub(' *', ' ', newcol) # replace duplicate spaces by a single one newcol <- gsub('^ *', '', newcol) # remove leading spaces gsub(' *$', '', newcol) # remove trailing spaces } # mcols(df, c(14:18)) </code></pre> <p>No doubt somebody will be able to clean this up!</p> <p>To tidy up Likert-like scales I used:</p> <pre><code># function to tidy c('Strongly Agree', 'Agree', 'Disagree', 'Strongly Disagree') tidylik4 <- function(x) { xlevels <- c('Strongly Disagree', 'Disagree', 'Agree', 'Strongly Agree') y <- ifelse(x == '', NA, x) ordered(y, levels=xlevels) } for (i in 44:52) { m2[,i] <- tidylik4(m2[,i]) } </code></pre> <p>Feel free to comment as no doubt this will come up again!</p>

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