Calculates Whisker Odds.
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.
