Generic function that allows you to tabulate continuous and categorical data (quantitative or qualitative) in frequency distribution. Depending on the nature of the data, they can be grouped into class ranges or not.
Arguments
- data
R object (data structure vector) of class leem. Use
new_leem()function.- ...
further arguments passed to or from other methods.
Value
The result of tabfreq() is a list. This list has two elements: table and statistics. The first is the data frequency table, and the second represents some useful statistics for methods of leem class.
Examples
# Example 1
library(leem)
x <- rbinom(36, 10, 0.6)
x <- new_leem(x, variable = "discrete")
tabfreq(x)
#>
#> Table of frequency
#> Type of variable: discrete
#>
#> Groups Fi Fr Fac1 Fac2 Fp Fac1p Fac2p
#> 1 2 4 0.11 4 36 11 11.11 100.00
#> 2 3 2 0.06 6 32 6 16.67 88.89
#> 3 4 6 0.17 12 30 17 33.33 83.33
#> 4 5 7 0.19 19 24 19 52.78 66.67
#> 5 6 6 0.17 25 17 17 69.44 47.22
#> 6 7 3 0.08 28 11 8 77.78 30.56
#> 7 8 5 0.14 33 8 14 91.67 22.22
#> 8 9 3 0.08 36 3 8 100.00 8.33
#> ==============================================
#> Groups: Discretized grouping
#> Fi: Absolute frequency
#> Fr: Relative frequency
#> Fac1: Cumulative frequency (below)
#> Fac2: Cumulative frequency (above)
#> Fp: Percentage frequency
#> Fac1p: Cumulative percentage frequency (below)
#> Fac2p: Cumulative percentage frequency (above)
#>
# Example 2 (Pipe operator)
rnorm(36, 100, 4) |>
new_leem(variable = "continuous") |> tabfreq()
#>
#> Table of frequency
#> Type of variable: continuous
#>
#> Classes Fi PM Fr Fac1 Fac2 Fp Fac1p Fac2p
#> 1 92.8 |--- 96.18 4 94.49 0.11 4 36 11 11.11 100.00
#> 2 96.18 |--- 99.56 11 97.87 0.31 15 32 31 41.67 88.89
#> 3 99.56 |--- 102.94 12 101.25 0.33 27 21 33 75.00 58.33
#> 4 102.94 |--- 106.32 5 104.63 0.14 32 9 14 88.89 25.00
#> 5 106.32 |--- 109.7 3 108.01 0.08 35 4 8 97.22 11.11
#> 6 109.7 |--- 113.08 1 111.39 0.03 36 1 3 100.00 2.78
#>
#> ==============================================
#> Classes: Grouping of classes
#> Fi: Absolute frequency
#> PM: Midpoint
#> Fr: Relative frequency
#> Fac1: Cumulative frequency (below)
#> Fac2: Cumulative frequency (above)
#> Fp: Percentage frequency
#> Fac1p: Cumulative percentage frequency (below)
#> Fac2p: Cumulative percentage frequency (above)
#>
# Example 3
x <- rbinom(36, 10, 0.6)
# Constructor (object of leem class)
x <- new_leem(x, variable = "discrete")
tab <- tabfreq(x)
# Details
tab$table
#> Groups Fi Fr Fac1 Fac2 Fp Fac1p Fac2p
#> 1 4 4 0.11 4 36 11 11.11 100.00
#> 2 5 9 0.25 13 32 25 36.11 88.89
#> 3 6 8 0.22 21 23 22 58.33 63.89
#> 4 7 7 0.19 28 15 19 77.78 41.67
#> 5 8 7 0.19 35 8 19 97.22 22.22
#> 6 9 1 0.03 36 1 3 100.00 2.78
tab$statistics
#> $ngroups
#> [1] 6
#>
#> $minv
#> [1] 4
#>
#> $maxv
#> [1] 9
#>
#> $raw_data
#> [1] 6 5 7 6 8 6 6 4 5 7 6 8 6 8 7 7 5 9 5 8 5 8 6 8 4 4 5 6 5 4 7 5 7 8 7 5
#>
# Example 3 - ordered categories ("d","a", "b", "c")
w <- rep(letters[1:4], 1:4)
w |> new_leem(variable = "discrete") |> tabfreq(ordered = c("d","a", "b", "c"))
#>
#> Table of frequency
#> Type of variable: discrete
#>
#> Groups Fi Fr Fac1 Fac2 Fp Fac1p Fac2p
#> 1 d 4 0.4 4 10 40 40 100
#> 2 a 1 0.1 5 6 10 50 60
#> 3 b 2 0.2 7 5 20 70 50
#> 4 c 3 0.3 10 3 30 100 30
#> ==============================================
#> Groups: Discretized grouping
#> Fi: Absolute frequency
#> Fr: Relative frequency
#> Fac1: Cumulative frequency (below)
#> Fac2: Cumulative frequency (above)
#> Fp: Percentage frequency
#> Fac1p: Cumulative percentage frequency (below)
#> Fac2p: Cumulative percentage frequency (above)
#>