How to generate a frequency table in R with with cumulative frequency and relative frequency

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小鲜肉
小鲜肉 2020-12-12 21:23

I\'m new with R. I need to generate a simple Frequency Table (as in books) with cumulative frequency and relative frequency.

So I want to generate from some simple d

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  • 2020-12-12 21:28

    My suggestion is to check the agricolae package... check it out:

    library(agricolae)
    
    weight<-c( 68, 53, 69.5, 55, 71, 63, 76.5, 65.5, 69, 75, 76, 57, 70.5,
    + 71.5, 56, 81.5, 69, 59, 67.5, 61, 68, 59.5, 56.5, 73,
    + 61, 72.5, 71.5, 59.5, 74.5, 63)
    
    h1<- graph.freq(weight,col="yellow",frequency=1,las=2,xlab="h1")
    
    print(summary(h1),row.names=FALSE)
    
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  • 2020-12-12 21:30

    If you are looking for something pre-packaged, consider the freq() function from the descr package.

    library(descr)
    x = c(sample(10:20, 44, TRUE))
    freq(x, plot = FALSE)
    

    Or to get cumulative percents, use the ordered() function

    freq(ordered(x), plot = FALSE)
    

    To add a "cumulative frequencies" column:

    tab = as.data.frame(freq(ordered(x), plot = FALSE))
    CumFreq = cumsum(tab[-dim(tab)[1],]$Frequency)
    tab$CumFreq = c(CumFreq, NA)
    tab
    

    If your data has missing values, a valid percent column is added to the table.

    x = c(sample(10:20, 44, TRUE), NA, NA)
    freq(ordered(x), plot = FALSE)
    
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  • 2020-12-12 21:36

    You're close! There are a few functions that will make this easy for you, namely cumsum() and prop.table(). Here's how I'd probably put this together. I make some random data, but the point is the same:

    #Fake data
    x <- sample(10:20, 44, TRUE)
    #Your code
    factorx <- factor(cut(x, breaks=nclass.Sturges(x)))
    #Tabulate and turn into data.frame
    xout <- as.data.frame(table(factorx))
    #Add cumFreq and proportions
    xout <- transform(xout, cumFreq = cumsum(Freq), relative = prop.table(Freq))
    #-----
          factorx Freq cumFreq   relative
    1 (9.99,11.4]   11      11 0.25000000
    2 (11.4,12.9]    3      14 0.06818182
    3 (12.9,14.3]   11      25 0.25000000
    4 (14.3,15.7]    2      27 0.04545455
    5 (15.7,17.1]    6      33 0.13636364
    6 (17.1,18.6]    3      36 0.06818182
    7   (18.6,20]    8      44 0.18181818
    
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  • 2020-12-12 21:47

    The base functions table, cumsum and prop.table should get you there:

     cbind( Freq=table(x), Cumul=cumsum(table(x)), relative=prop.table(table(x)))
       Freq Cumul   relative
    10    2     2 0.04545455
    12    2     4 0.04545455
    15    1     5 0.02272727
    16   10    15 0.22727273
    17   16    31 0.36363636
    18    6    37 0.13636364
    19    4    41 0.09090909
    20    2    43 0.04545455
    22    1    44 0.02272727
    

    With cbind and naming of the columns to your liking this should be pretty easy for you in the future. The output from the table function is a matrix, so this result is also a matrix. If this were being done on something big it would be more efficient todo this:

    tbl <- table(x)
    cbind( Freq=tbl, Cumul=cumsum(tbl), relative=prop.table(tbl))
    
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  • 2020-12-12 21:53

    Yet another possibility:

     library(SciencesPo)
        x = c(sample(10:20, 50, TRUE))
        freq(x)
    
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