A utility package to help you deal with pigne

Pigna
 * pìnna*
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)
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
#> Loading required package: lattice
#> Loading required package: survival
#> Loading required package: Formula
#> Loading required package: ggplot2
#> 
#> Attaching package: 'Hmisc'
#> The following objects are masked from 'package:base':
#> 
#>     format.pval, units
#> 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 3.37 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) %>%
  adjust_p()
#> Warning: Unknown or uninitialised column: `P-value`.
#> x   The object `x` does not have a P-value column.
#>     Have you select `test = TRUE` into `summary()`?
#> x `x` is returned without changes.
#> # A tibble: 4 x 5
#>   `&nbsp;`  `setosa \n(N=50)` `versicolor \n(… `virginica \n(N… `  Test\nStatis…
#>   <chr>     <chr>             <chr>            <chr>            <chr>           
#> 1 Sepal.Le… "     4.800/5.00… "     5.600/5.9… "     6.225/6.5… "F=136.85 d.f.=…
#> 2 Sepal.Wi… "     3.200/3.40… "     2.525/2.8… "     2.800/3.0… " F=54.69 d.f.=…
#> 3 Petal.Le… "     1.400/1.50… "     4.000/4.3… "     5.100/5.5… "F=515.64 d.f.=…
#> 4 Petal.Wi… "        0.2/0.2… "        1.2/1.… "        1.8/2.… "F=541.25 d.f.=…
  • 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
    ):
summary_interact(lrm_mod, age, abo) %>%
  pander()
#> New names:
#> * `Lower 0.95` -> Lower.0.95
#> * `Upper 0.95` -> Upper.0.95
#> New names:
#> * `Lower 0.95` -> Lower.0.95
#> * `Upper 0.95` -> Upper.0.95
#> New names:
#> * `Lower 0.95` -> Lower.0.95
#> * `Upper 0.95` -> Upper.0.95
#> New names:
#> * `Lower 0.95` -> Lower.0.95
#> * `Upper 0.95` -> Upper.0.95
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()
#> New names:
#> * `Lower 0.95` -> Lower.0.95
#> * `Upper 0.95` -> Upper.0.95
#> New names:
#> * `Lower 0.95` -> Lower.0.95
#> * `Upper 0.95` -> Upper.0.95
#> New names:
#> * `Lower 0.95` -> Lower.0.95
#> * `Upper 0.95` -> Upper.0.95
#> New names:
#> * `Lower 0.95` -> Lower.0.95
#> * `Upper 0.95` -> Upper.0.95
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

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"        "rlang"        "rms"         
#> [11] "rprojroot"    "stats"        "stringr"      "telegram.bot" "tibble"      
#> [16] "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

  1. 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”
     * pìnne*
    .