A utility package to help you deal with pigne

Pigna [pìn’n’a] is the Italian word for pine cone.¹ In jargon, it’s used to identify something (like a task…) boring, banal, annoying, painful, frustrating and maybe even with a not so beautiful or rewarding result, just like the obstinate act of trying to challenge yourself in extracting pine nuts from a pine cone, provided that at the end you will find at least one inside it…

Overview

This package aims to provide some useful functions to be used to solve small everyday problems of coding or analyzing data with R. The hope is to provide solutions to that kind of problems which would be normally solved using quick-and-dirty (ugly and maybe even wrong) patches.

Tools Category	Function(s)	Aim
Harrell’s verse	`tidy_summary()`	`pander`-ready data frame from `Hmisc::summary()`
	`paired_test_continuous`	Paired test for continuous variable into `Hmisc::summary`
	`paired_test_categorical`	Paired test for categorical variable into `Hmisc::summary`
	`adjust_p()`	Adjusts P-values for multiplicity of tests at `tidy_summary()`
	`summary_interact()`	data frame of OR for interaction from `rms::lrm()`
	`htypes()`	Will be your variables continuous or categorical in `Hmisc::describe()`?
Statistical	`ci2p()`	Get P-value form estimation and confidence interval
Programming	`pb_len()`	Quick set-up of a `progress::progress_bar()` progress bar
	`install_pkg_set()`	Politely install set of packages (topic-related sets at `?pkg_sets`)
	`view_in_excel()`	Open a data frame in Excel, even in the middle of a pipe chain, on interactive session only
Development	`use_ui()`	Activate usethis user interface into your own package
	`please_install()`	Politely ask the user to install a package
	`imported_from()`	List packages imported from a package (which has to be installed)
Telegram	`start_bot_for_chat()`	Quick start of a telegram.bot Telegram’s bot
	`send_to_telegram()`	Unified wrapper to send someRthing to a Telegram chat
	`errors_to_telegram()`	Divert all your error messages from the console to a Telegram chat
Why not?!	`gdp()`	Do you have TOO much pignas in your back?! … try this out ;-)

Installation

You can install the released version of depigner from CRAN with:

install.packages("depigner")

You can install the development version from GitHub calling:

# install.packages("devtools")
devtools::install_github("CorradoLanera/depigner")

Next, you can attach it to your session by:

library(depigner)
#> Welcome to depigner: we are here to un-stress you!

Provided Tools

Harrell’s Verse Tools

tidy_summary(): produces a data frame from the summary() functions provided by Hmisc [@R-Hmisc] and rms [@R-rms] packages ready to be pander::pander()ed [@R-pander].

Currently it is tested for method reverse only:

library(rms)
#> Loading required package: Hmisc
#> 
#> Attaching package: 'Hmisc'
#> The following objects are masked from 'package:base':
#> 
#>     format.pval, units
#> Loading required package: survival
#> Loading required package: lattice
#> Loading required package: ggplot2
#> Loading required package: SparseM
#> 
#> Attaching package: 'SparseM'
#> The following object is masked from 'package:base':
#> 
#>     backsolve
  options(datadist = 'dd')
library(survival)
library(pander)

dd <- datadist(iris)
my_summary <- summary(Species ~., data = iris, method = "reverse")
tidy_summary(my_summary) %>% 
  pander()

	setosa (N=50)	versicolor (N=50)	virginica (N=50)
Sepal.Length	4.800/5.000/5.200	5.600/5.900/6.300	6.225/6.500/6.900
Sepal.Width	3.200/3.400/3.675	2.525/2.800/3.000	2.800/3.000/3.175
Petal.Length	1.400/1.500/1.575	4.000/4.350/4.600	5.100/5.550/5.875
Petal.Width	0.2/0.2/0.3	1.2/1.3/1.5	1.8/2.0/2.3



dd <<- datadist(heart) # this to face a package build issue,
                       # use standard `<-` into analyses
surv <- Surv(heart$start, heart$stop, heart$event)
f    <- cph(surv ~ age + year + surgery, data = heart)
my_summary <- summary(f)
tidy_summary(my_summary) %>% 
  pander()

	Diff.	HR	Lower 95% CI	Upper 95% CI
age	10.69	1.336	1.009	1.767
year	3.374	0.6104	0.3831	0.9727
surgery	1	0.5286	0.2574	1.085

paired_test_*(): Paired test for categorical/continuous variables to be used in the summary() of the Hmisc [@R-Hmisc] package:

data(Arthritis)
# categorical -------------------------
## two groups
summary(Treatment ~ Sex,
    data    = Arthritis,
    method  = "reverse",
    test    = TRUE,
    catTest = paired_test_categorical
)
#> 
#> 
#> Descriptive Statistics by Treatment
#> 
#> +----------+--------------------+--------------------+------------------------------+
#> |          |Placebo             |Treated             |  Test                        |
#> |          |(N=43)              |(N=41)              |Statistic                     |
#> +----------+--------------------+--------------------+------------------------------+
#> |Sex : Male|           26%  (11)|           34%  (14)|Chi-square=5.92 d.f.=1 P=0.015|
#> +----------+--------------------+--------------------+------------------------------+
## more than two groups
summary(Improved ~ Sex,
    data    = Arthritis,
    method  = "reverse",
    test    = TRUE,
    catTest = paired_test_categorical
)
#> 
#> 
#> Descriptive Statistics by Improved
#> 
#> +----------+-----------------+-----------------+-----------------+------------------------+
#> |          |None             |Some             |Marked           |  Test                  |
#> |          |(N=42)           |(N=14)           |(N=28)           |Statistic               |
#> +----------+-----------------+-----------------+-----------------+------------------------+
#> |Sex : Male|        40%  (17)|        14%  ( 2)|        21%  ( 6)|chi2=1.71 d.f.=3 P=0.634|
#> +----------+-----------------+-----------------+-----------------+------------------------+

# continuous --------------------------
## two groups
summary(Species ~.,
    data    = iris[iris$Species != "setosa",],
    method  = "reverse",
    test    = TRUE,
    conTest = paired_test_continuous
)
#> 
#> 
#> Descriptive Statistics by Species
#> 
#> +------------+---------------------+---------------------+------------------------+
#> |            |versicolor           |virginica            |  Test                  |
#> |            |(N=50)               |(N=50)               |Statistic               |
#> +------------+---------------------+---------------------+------------------------+
#> |Sepal.Length|    5.600/5.900/6.300|    6.225/6.500/6.900| t=-5.28 d.f.=49 P<0.001|
#> +------------+---------------------+---------------------+------------------------+
#> |Sepal.Width |    2.525/2.800/3.000|    2.800/3.000/3.175| t=-3.08 d.f.=49 P=0.003|
#> +------------+---------------------+---------------------+------------------------+
#> |Petal.Length|    4.000/4.350/4.600|    5.100/5.550/5.875|t=-12.09 d.f.=49 P<0.001|
#> +------------+---------------------+---------------------+------------------------+
#> |Petal.Width |       1.2/1.3/1.5   |       1.8/2.0/2.3   |t=-14.69 d.f.=49 P<0.001|
#> +------------+---------------------+---------------------+------------------------+
## more than two groups
summary(Species ~.,
    data    = iris,
    method  = "reverse",
    test    = TRUE,
    conTest = paired_test_continuous
)
#> 
#> 
#> Descriptive Statistics by Species
#> 
#> +------------+--------------------+--------------------+--------------------+-----------------------+
#> |            |setosa              |versicolor          |virginica           |  Test                 |
#> |            |(N=50)              |(N=50)              |(N=50)              |Statistic              |
#> +------------+--------------------+--------------------+--------------------+-----------------------+
#> |Sepal.Length|   4.800/5.000/5.200|   5.600/5.900/6.300|   6.225/6.500/6.900| F=30.55 d.f.=2 P<0.001|
#> +------------+--------------------+--------------------+--------------------+-----------------------+
#> |Sepal.Width |   3.200/3.400/3.675|   2.525/2.800/3.000|   2.800/3.000/3.175| F=12.63 d.f.=2 P<0.001|
#> +------------+--------------------+--------------------+--------------------+-----------------------+
#> |Petal.Length|   1.400/1.500/1.575|   4.000/4.350/4.600|   5.100/5.550/5.875|F=322.89 d.f.=2 P<0.001|
#> +------------+--------------------+--------------------+--------------------+-----------------------+
#> |Petal.Width |      0.2/0.2/0.3   |      1.2/1.3/1.5   |      1.8/2.0/2.3   |F=234.21 d.f.=2 P<0.001|
#> +------------+--------------------+--------------------+--------------------+-----------------------+

adjust_p(): Adjust P-values of a tidy_summary objects:

my_summary <- summary(Species ~., data = iris,
  method = "reverse",
  test = TRUE
)

tidy_summary(my_summary, prtest = "P") %>%
  adjust_p()
#> ✔ P adjusted with BH method.
#> # A tibble: 4 × 5
#>   `&nbsp;`     `setosa \n(N=50)`   `versicolor \n(N=50)` `virginica \n(N=50)`
#>   <chr>        <chr>               <chr>                 <chr>               
#> 1 Sepal.Length "4.800/5.000/5.200" "5.600/5.900/6.300"   "6.225/6.500/6.900" 
#> 2 Sepal.Width  "3.200/3.400/3.675" "2.525/2.800/3.000"   "2.800/3.000/3.175" 
#> 3 Petal.Length "1.400/1.500/1.575" "4.000/4.350/4.600"   "5.100/5.550/5.875" 
#> 4 Petal.Width  "   0.2/0.2/0.3"    "   1.2/1.3/1.5"      "   1.8/2.0/2.3"    
#> # ℹ 1 more variable: `P-value` <chr>

summary_interact(): Produce a data frame of OR (with the corresponding CI95%) for the interactions between different combination of a continuous variable (for which it is possible to define the reference and the target values) and (every or a selection of levels of) a categorical one in a logistic model provided by lrm() (from the rms package [@R-rms]):

data("transplant", package = "survival")
censor_rows <- transplant[['event']] != 'censored' 
transplant <- droplevels(transplant[censor_rows, ])

dd <<- datadist(transplant) # this to face a package build issue,
                            # use standard `<-` into analyses

lrm_mod <- lrm(event ~ rcs(age, 3)*(sex + abo) + rcs(year, 3),
  data = transplant
)
summary_interact(lrm_mod, age, abo) %>%
  pander()

	Low	High	Diff.	Odds Ratio	Lower 95% CI	Upper 95% CI
age - A	43	58	15	1.002	0.557	1.802
age - B	43	58	15	1.817	0.74	4.463
age - AB	43	58	15	0.635	0.186	2.169
age - O	43	58	15	0.645	0.352	1.182


summary_interact(lrm_mod, age, abo, p = TRUE) %>%
  pander()

	Low	High	Diff.	Odds Ratio	Lower 95% CI	Upper 95% CI	P-value
age - A	43	58	15	1.002	0.557	1.802	0.498
age - B	43	58	15	1.817	0.74	4.463	0.137
age - AB	43	58	15	0.635	0.186	2.169	0.728
age - O	43	58	15	0.645	0.352	1.182	0.883

htypes() and friends: get/check types of variable with respect to the Hmisc ecosystem [@R-Hmisc].

htypes(mtcars)
#>    mpg    cyl   disp     hp   drat     wt   qsec     vs     am   gear   carb 
#>  "con" "none"  "con"  "con"  "con"  "con"  "con"  "cat"  "cat" "none" "none"

desc <- Hmisc::describe(mtcars)
htypes(desc)
#>    mpg    cyl   disp     hp   drat     wt   qsec     vs     am   gear   carb 
#>  "con" "none"  "con"  "con"  "con"  "con"  "con"  "cat"  "cat" "none" "none"
htype(desc[[1]])
#> [1] "con"
is_hcat(desc[[1]])
#> [1] FALSE
is_hcon(desc[[1]])
#> [1] TRUE

Statistical Tools

ci2p(): compute the p-value related with a provided confidence interval:

ci2p(1.125, 0.634,  1.999, log_transform = TRUE)
#> [1] 0.367902

Programming Tools

pb_len(): Progress bar of given length, wrapper from the progress [@R-progress] package:

pb <- pb_len(100)

for (i in 1:100) {
    Sys.sleep(0.1)
    tick(pb, paste("i = ", i))
}

install_pkg_set(): Simple and polite wrapper to install sets of packages. Moreover, depigner provides some sets already defined for common scenario in R (analyses, production, documenting, …). See them by call ?pgk_sets.

install_pkg_set() # this install the whole `?pkg_all`
install_pkg_set(pkg_stan)

?pkg_sets

view_in_excel(): A pipe-friendly function to view a data frame in Excel, optimal when used in the middle of a pipe-chain to see intermediate results. It works in interactive session only, so it is RMarkdown/Quarto friendly too!

four_cyl_cars <- mtcars %>%
  view_in_excel() %>%
  dplyr::filter(cyl == 4) %>%
  view_in_excel()

four_cyl_cars

Development Tools

use_ui(): Use usethis’ user interface [@R-usethis] in your package

# in the initial setup steps of the development of a package
use_ui()

lease_install(): This is a polite wrapper to install.packages() inspired (= w/ very minimal modification) by a function Hadley showed us during a course.

a_pkg_i_miss <- setdiff(available.packages(), installed.packages())[[1]]
please_install(a_pkg_i_miss)

imported_from(): If you would like to know which packages are imported by a package (eg to know which packages are required for its installation or either installed during it) you can use this function

imported_from("depigner")
#>  [1] "desc"         "dplyr"        "fs"           "ggplot2"      "Hmisc"       
#>  [6] "magrittr"     "progress"     "purrr"        "readr"        "rlang"       
#> [11] "rms"          "rprojroot"    "stats"        "stringr"      "telegram.bot"
#> [16] "tibble"       "tidyr"        "usethis"      "utils"

Telegram Tools

Wrappers to simple use of Telegram’s bots: wrappers from the telegram.bot package [@R-telegram.bot]:

# Set up a Telegram bot. read `?start_bot_for_chat`
start_bot_for_chat()

# Send something to telegram
send_to_telegram("hello world")

library(ggplot2)
gg <- ggplot(mtcars, aes(x = mpg, y = hp, colour = cyl)) +
    geom_point()
send_to_telegram(
  "following an `mtcars` coloured plot",
  parse_mode = "Markdown"
)
send_to_telegram(gg)

# Divert output errors to the telegram bot
errors_to_telegram()

Why Not?!

gdp(): A wrapper to relax

gdp(7)

Feature request

If you need some more features, please open an issue here.

Bug reports

If you encounter a bug, please file a reprex (minimal reproducible example) here.

Code of Conduct

Please note that the depigner project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

Acknowledgements

The depigner’s logo was lovely designed by Elisa Sovrano.

Reference

You can find all the possible meanings of pigna here, and you can listen how to pronounce it here. Note: the Italian plural for “pigna” is “pigne” [pìn’n’e].↩︎