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Whisker Odds (wodds)

sensible summary statistics for big data

The goal of wodds is to make the calculations of whisker odds (wodds) easy. Wodds follow the same rules as letter-values, but with a different naming system.

Installation

You can install the development version of wodds from GitHub with:

# install.packages("devtools")
devtools::install_github("alexhallam/wodds")

Example

This is a basic example which shows you how to solve a common problem:

options(digits=1)
library(wodds)
library(knitr)
set.seed(42)
a <- rnorm(n = 1e4, 0, 1)
df_wodds <- wodds::wodds(a)
df_wodds
#> # A tibble: 11 × 3
#>    lower_value wodd_name upper_value
#>          <dbl> <chr>           <dbl>
#>  1    -0.00625 M            -0.00625
#>  2    -0.694   F             0.663  
#>  3    -1.17    E             1.16   
#>  4    -1.57    S             1.52   
#>  5    -1.88    SM            1.87   
#>  6    -2.17    SF            2.15   
#>  7    -2.41    SE            2.41   
#>  8    -2.66    S2            2.64   
#>  9    -2.86    S2M           2.88   
#> 10    -3.01    S2F           3.22   
#> 11    -3.13    S2E           3.34

Outliers beyond the last wodd are marked with O<value> in ascending order. There should rarely be more than 7 outliers when using wodds.

df_wodds_and_outs <- wodds::wodds(a, include_outliers = TRUE)
df_wodds_and_outs
#> # A tibble: 17 × 3
#>    lower_value wodd_name upper_value
#>          <dbl> <chr>           <dbl>
#>  1    -0.00625 M            -0.00625
#>  2    -0.694   F             0.663  
#>  3    -1.17    E             1.16   
#>  4    -1.57    S             1.52   
#>  5    -1.88    SM            1.87   
#>  6    -2.17    SF            2.15   
#>  7    -2.41    SE            2.41   
#>  8    -2.66    S2            2.64   
#>  9    -2.86    S2M           2.88   
#> 10    -3.01    S2F           3.22   
#> 11    -3.13    S2E           3.34   
#> 12    -3.14    O1            3.34   
#> 13    -3.18    O2            3.47   
#> 14    -3.20    O3            3.50   
#> 15    -3.33    O4            3.58   
#> 16    -3.37    O5            4.33   
#> 17    -4.04    O6           NA

Though not necessary it is possible to include tail area if additional communication or teaching is needed. It is assumed that the wodd should be explanatory enough to not need to rely on tail_area.

df_wodds_and_outs <- wodds::wodds(a, include_tail_area  = TRUE)
df_wodds_and_outs
#> # A tibble: 11 × 4
#>    tail_area lower_value wodd_name upper_value
#>        <dbl>       <dbl> <chr>           <dbl>
#>  1         2    -0.00625 M            -0.00625
#>  2         4    -0.694   F             0.663  
#>  3         8    -1.17    E             1.16   
#>  4        16    -1.57    S             1.52   
#>  5        32    -1.88    SM            1.87   
#>  6        64    -2.17    SF            2.15   
#>  7       128    -2.41    SE            2.41   
#>  8       256    -2.66    S2            2.64   
#>  9       512    -2.86    S2M           2.88   
#> 10      1024    -3.01    S2F           3.22   
#> 11      2048    -3.13    S2E           3.34

An example with all options set to TRUE.

df_wodds_and_outs <- wodds::wodds(a, include_depth = TRUE, include_tail_area = TRUE, include_outliers = TRUE)
df_wodds_and_outs
#> # A tibble: 17 × 5
#>    depth tail_area lower_value wodd_name upper_value
#>    <int>     <dbl>       <dbl> <chr>           <dbl>
#>  1     1         2    -0.00625 M            -0.00625
#>  2     2         4    -0.694   F             0.663  
#>  3     3         8    -1.17    E             1.16   
#>  4     4        16    -1.57    S             1.52   
#>  5     5        32    -1.88    SM            1.87   
#>  6     6        64    -2.17    SF            2.15   
#>  7     7       128    -2.41    SE            2.41   
#>  8     8       256    -2.66    S2            2.64   
#>  9     9       512    -2.86    S2M           2.88   
#> 10    10      1024    -3.01    S2F           3.22   
#> 11    11      2048    -3.13    S2E           3.34   
#> 12    NA        NA    -3.14    O1            3.34   
#> 13    NA        NA    -3.18    O2            3.47   
#> 14    NA        NA    -3.20    O3            3.50   
#> 15    NA        NA    -3.33    O4            3.58   
#> 16    NA        NA    -3.37    O5            4.33   
#> 17    NA        NA    -4.04    O6           NA

A knitr::kable example for publication.

knitr::kable(df_wodds_and_outs, align = 'c',digits = 3)
depth tail_area lower_value wodd_name upper_value
1 2 -0.006 M -0.006
2 4 -0.694 F 0.663
3 8 -1.169 E 1.155
4 16 -1.569 S 1.524
5 32 -1.878 SM 1.866
6 64 -2.173 SF 2.150
7 128 -2.415 SE 2.409
8 256 -2.656 S2 2.637
9 512 -2.857 S2M 2.883
10 1024 -3.013 S2F 3.220
11 2048 -3.130 S2E 3.338
NA NA -3.139 O1 3.339
NA NA -3.181 O2 3.471
NA NA -3.200 O3 3.495
NA NA -3.331 O4 3.585
NA NA -3.372 O5 4.328
NA NA -4.043 O6 NA

Getting the depth

wodds::get_depth_from_n(n=15734L, alpha = 0.05)
#> [1] 11

Getting the sample size

wodds::get_n_from_depth(d = 11L)
#> [1] 15734

Whisker Odds and Letter-Values

Letter-Values are a fantastic tool! I think the naming could be improved. For this reason I introduce whisker odds (wodds) as an alternative naming system. My hypothesis is that with an alternative naming system the use of these descriptive statistics will be see more use. This is a rebranding of a what I think is a powerful modern statistical tool.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.