dacol provides utilities to add or modify columns in dataframe.
The utilities include:
mode
, confident_interval
, ceiling
cosine
, logistic
, zscore
euclidean
, pearson
,
cosine
, canberra
trim_outlier
, normalize_ptile
decile_band
, decile_ptile
, dc_rank_ptile
More info: https://ldanai.github.io/dacol/
You can install dacol from github with:
# install.packages("remotes")
remotes::install_github("ldanai/dacol")
This shows how to use dacol:
library(dacol)
library(dplyr)
max = 30
df = tibble(x1 = seq(-1.2*max, 1.2*max, length.out = 200),
x2 = seq(0, max, length.out = 200),
x3 = sample(200))
df
#> # A tibble: 200 x 3
#> x1 x2 x3
#> <dbl> <dbl> <int>
#> 1 -36 0 111
#> 2 -35.6 0.151 31
#> 3 -35.3 0.302 92
#> 4 -34.9 0.452 6
#> 5 -34.6 0.603 20
#> 6 -34.2 0.754 55
#> 7 -33.8 0.905 190
#> 8 -33.5 1.06 135
#> 9 -33.1 1.21 10
#> 10 -32.7 1.36 173
#> # ... with 190 more rows
df =
df %>%
mutate(
# Transformation
y_cosine = dc_cosine(x1, max),
y_logistic = dc_logistic(x2, max),
y_zcore = dc_zscore(x2),
# Distant between 2 vector columns
y_dist_canb = dc_dist_canberra(x2, x3),
y_dist_cos = dc_dist_cosine(x2, y_zcore),
y_dist_euc = dc_dist_euclidean(x2, y_zcore),
y_dist_pear = dc_dist_pearson(x2, y_zcore),
# Manage outliers
y_trim = dc_trim_outlier(x3, 0.01),
y_norm = dc_normalize_ptile(x3, 0.01),
# Stats measures
y_mode = dc_mode(x3),
y_ceil = dc_ceiling(x1, -1),
# Band segmentation
y_dec_band1 = dc_decile_band(x3),
y_dec_band2 = dc_decile_band(x3, c(seq(0, 0.9, 0.1))),
y_dec_ptile1 = dc_decile_ptile(x3),
y_dec_ptile2 = dc_decile_ptile(x3, c(seq(0, 0.9, 0.1))),
# Rank percentile
y_ranked1 = dc_rank_ptile(x3),
y_ranked2 = dc_rank_ptile(x3, c(seq(1, 100, 1)))
)
#> Warning in if (is.na(n)) n = max(dplyr::n_distinct(x), 10000): the condition has
#> length > 1 and only the first element will be used
df
#> # A tibble: 200 x 20
#> x1 x2 x3 y_cosine y_logistic y_zcore y_dist_canb y_dist_cos
#> <dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 -36 0 111 0 0 -1.72 0 0.498
#> 2 -35.6 0.151 31 0 0.0251 -1.70 0.00484 0.498
#> 3 -35.3 0.302 92 0 0.0502 -1.68 0.00327 0.498
#> 4 -34.9 0.452 6 0 0.0752 -1.67 0.0701 0.498
#> 5 -34.6 0.603 20 0 0.100 -1.65 0.0293 0.498
#> 6 -34.2 0.754 55 0 0.125 -1.63 0.0135 0.498
#> 7 -33.8 0.905 190 0 0.150 -1.62 0.00474 0.498
#> 8 -33.5 1.06 135 0 0.174 -1.60 0.00776 0.498
#> 9 -33.1 1.21 10 0 0.198 -1.58 0.108 0.498
#> 10 -32.7 1.36 173 0 0.222 -1.56 0.00778 0.498
#> # ... with 190 more rows, and 12 more variables: y_dist_euc <dbl>,
#> # y_dist_pear <dbl>, y_trim <dbl>, y_norm <dbl>, y_mode <int>, y_ceil <dbl>,
#> # y_dec_band1 <int>, y_dec_band2 <int>, y_dec_ptile1 <dbl>,
#> # y_dec_ptile2 <dbl>, y_ranked1 <dbl>, y_ranked2 <dbl>
Please note that the ‘dacol’ project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.