R package: Utility functions and addins for RStudio.
Helper tools I use in many projects.
By Ludvig R. Olsen, Cognitive Science, Aarhus University. Started in Feb. 2017
Contact at: r-pkgs@ludvigolsen.dk
Main functions:
- polynomializer
- %ni%
- insertPipe (Addin)
- scaler, scaler_, scaler_fit, scaler_fit_, scaler_transform, scaler_invert
- binarizer, binarizer_
- rename_col
- savage_dickey
- roll_previous
Development version:
install.packages("devtools")
devtools::install_github("LudvigOlsen/LRO.utilities")
- Install package.
- Add key command by going to:
- Tools > Addins > Browse Addins > Keyboard Shortcuts.
- Find Insert %>% and press its field under Shortcut.
- Press desired key command.
- Press Apply.
- Press Execute.
- Press chosen key command inside R Markdown document.
# Attach packages
library(LRO.utilities)
library(dplyr) # %>%
library(knitr) # kable()
library(ggplot2)Exponentiate vectors to make polynomials of degree 2 to degree.
E.g. create columns v^2, v^3, v^4...
# On vector
vect <- c(1,3,5,7,8)
polynomializer(vect, degree = 3) %>%
kable()| vect | vect_2 | vect_3 |
|---|---|---|
| 1 | 1 | 1 |
| 3 | 9 | 27 |
| 5 | 25 | 125 |
| 7 | 49 | 343 |
| 8 | 64 | 512 |
# On vectors in dataframe
data <- data.frame(vect = vect,
bect = vect*3,
dect = vect*5)
polynomializer(data,
cols = c('bect','dect'),
degree = 3) %>%
kable()| vect | bect | dect | bect_2 | bect_3 | dect_2 | dect_3 |
|---|---|---|---|---|---|---|
| 1 | 3 | 5 | 9 | 27 | 25 | 125 |
| 3 | 9 | 15 | 81 | 729 | 225 | 3375 |
| 5 | 15 | 25 | 225 | 3375 | 625 | 15625 |
| 7 | 21 | 35 | 441 | 9261 | 1225 | 42875 |
| 8 | 24 | 40 | 576 | 13824 | 1600 | 64000 |
"Not in"
c(1,3,5) %ni% c(2,3,6)
#> [1] TRUE FALSE TRUECenter and/or scale multiple columns of a dataframe.
scaler is designed to work with %>% pipelines.
scaler_fit returns fit_object with information used to transform data.
scaler_transform scales data based on the information in the fit_object.
scaler_invert inverts scaling based on the information in the fit_object.
scaler_ and scaler_fit_ are standard evalution versions.
# Scale and center 'vect' and 'bect'
# in dataframe from previous example
scaler(data, vect, bect) %>%
kable()| vect | bect | dect |
|---|---|---|
| -1.3270176 | -1.3270176 | 5 |
| -0.6285873 | -0.6285873 | 15 |
| 0.0698430 | 0.0698430 | 25 |
| 0.7682733 | 0.7682733 | 35 |
| 1.1174885 | 1.1174885 | 40 |
# Only scaling 'vect' - working in pipeline
data %>%
scaler(vect, center = F) %>%
kable()| vect | bect | dect |
|---|---|---|
| 0.3492151 | 3 | 5 |
| 1.0476454 | 9 | 15 |
| 1.7460757 | 15 | 25 |
| 2.4445060 | 21 | 35 |
| 2.7937212 | 24 | 40 |
# Only center 'bect' and 'dect'
# selecting with column index range
data %>%
scaler(2:3, scale = F) %>%
kable()| vect | bect | dect |
|---|---|---|
| 1 | -11.4 | -19 |
| 3 | -5.4 | -9 |
| 5 | 0.6 | 1 |
| 7 | 6.6 | 11 |
| 8 | 9.6 | 16 |
# Fit scaler
fitted_scaler <- data %>%
scaler_fit()
fitted_scaler %>% kable()| column | mean | sd | center | scale |
|---|---|---|---|---|
| vect | 4.8 | 2.863564 | TRUE | TRUE |
| bect | 14.4 | 8.590693 | TRUE | TRUE |
| dect | 24.0 | 14.317821 | TRUE | TRUE |
# Transform data
scaled_df <- data %>%
scaler_transform(fit_object = fitted_scaler)
scaled_df %>% kable()| vect | bect | dect |
|---|---|---|
| -1.3270176 | -1.3270176 | -1.3270176 |
| -0.6285873 | -0.6285873 | -0.6285873 |
| 0.0698430 | 0.0698430 | 0.0698430 |
| 0.7682733 | 0.7682733 | 0.7682733 |
| 1.1174885 | 1.1174885 | 1.1174885 |
# Invert scaling
scaled_df %>%
scaler_invert(fit_object = fitted_scaler) %>%
kable()| vect | bect | dect |
|---|---|---|
| 1 | 3 | 5 |
| 3 | 9 | 15 |
| 5 | 15 | 25 |
| 7 | 21 | 35 |
| 8 | 24 | 40 |
Binarize multiple columns of a dataframe based on a given threshold.
binarizer is designed to work with %>% pipelines.
binarizer_ is a standard evalution version.
scaled_df %>%
binarizer(thresh = 0)
#> # A tibble: 5 × 3
#> vect bect dect
#> <dbl> <dbl> <dbl>
#> 1 0 0 0
#> 2 0 0 0
#> 3 1 1 1
#> 4 1 1 1
#> 5 1 1 1Rename single column in dataframe. This is a bit like plyr::rename just only for 1 column at a time.
rename_col(data,
old_name = 'bect',
new_name = 'sect') %>%
kable()| vect | sect | dect |
|---|---|---|
| 1 | 3 | 5 |
| 3 | 9 | 15 |
| 5 | 15 | 25 |
| 7 | 21 | 35 |
| 8 | 24 | 40 |
Calculate Bayes factor from 2 distributions and plot the the two distributions.
Returns list with ggplot2 object, BF10, and BF01.
prior <- rnorm(1000, mean=0, sd=1)
posterior <- rnorm(1000, mean=2, sd=2)
s_d <- savage_dickey(posterior, prior, Q = 0, plot = TRUE)
s_d$BF10
#> [1] 0.274728
s_d$BF01
#> [1] 3.639963
s_d$post_prior_plot +
theme_bw()
Wrapper for zoo::rollapply for applying a function to rolling windows and getting the result of the previous window. Appends NAs at start only.
# Create dataframe
df <- data.frame('round' = c(1,2,3,4,5,6,7,8,9,10),
'score' = c(5,3,4,7,6,5,2,7,8,6))
# For each row we find the mean score of the previous 2 rounds
df$mean_prev_score = roll_previous(df$score, width = 2, FUN = mean)
df %>% kable()| round | score | mean_prev_score |
|---|---|---|
| 1 | 5 | NA |
| 2 | 3 | NA |
| 3 | 4 | 4.0 |
| 4 | 7 | 3.5 |
| 5 | 6 | 5.5 |
| 6 | 5 | 6.5 |
| 7 | 2 | 5.5 |
| 8 | 7 | 3.5 |
| 9 | 8 | 4.5 |
| 10 | 6 | 7.5 |