RosettaCodeData/Task/Text-processing-1/Julia/text-processing-1.julia

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using DataFrames
function mungdata(filename)
lines = readlines(filename)
numlines = length(lines)
dates = Array{DateTime, 1}(numlines)
means = zeros(Float64, numlines)
numvalid = zeros(Int, numlines)
invalidlength = zeros(Int, numlines)
invalidpos = zeros(Int, numlines)
datamatrix = Array{Float64,2}(numlines, 24)
datamatrix .= NaN
totalsum = 0.0
totalgood = 0
for (linenum,line) in enumerate(lines)
data = split(line)
validcount = badlength = 0
validsum = 0.0
for i in 2:2:length(data)-1
if parse(Int, data[i+1]) >= 0
validsum += (datamatrix[linenum, Int(i/2)] = parse(Float64, data[i]))
validcount += 1
badlength = 0
else
badlength += 1
if badlength > invalidlength[linenum]
invalidlength[linenum] = badlength
invalidpos[linenum] = Int(i/2) - invalidlength[linenum] + 1
end
end
end
dates[linenum] = DateTime(data[1], "y-m-d")
means[linenum] = validsum / validcount
numvalid[linenum] = validcount
totalsum += validsum
totalgood += validcount
end
dt = DataFrame(Date = dates, Mean = means, ValidValues = numvalid,
MaximumGap = invalidlength, GapPosition = invalidpos)
for i in 1:size(datamatrix)[2]
dt[Symbol("$(i-1):00")] = datamatrix[:,i]
end
dt, totalsum/totalgood
end
datafilename = "data.txt" # this is taken from the example listed on the task, since the actual text file is not available
df, dmean = mungdata(datafilename)
println(df)
println("The overall mean is $dmean")
maxbadline = indmax(df[:MaximumGap])
maxbadval = df[:MaximumGap][maxbadline]
maxbadtime = df[:GapPosition][maxbadline] - 1
maxbaddate = replace("$(df[:Date][maxbadline])", r"T.+$", "")
println("The largest run of bad values is $(maxbadval), on $(maxbaddate) beginning at $(maxbadtime):00 hours.")