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Description

A Utility Package to Help you Deal with "Pignas".

Pigna [_pìn'n'a_] is the Italian word for pine cone. In jargon, it is used to identify a task which is 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, in the end, you will find at least one inside it. Here you can find a backpack of functions to be used to solve small everyday problems of coding or analyzing (clinical) data, which would be normally solved using quick-and-dirty patches. You will be able to convert 'Hmisc' and 'rms' summary()es into data.frames ready to be rendered by 'pander' and 'knitr'. You can access easy-to-use wrappers to activate essential but useful progress bars (from 'progress') into your loops or functionals. Easy setup and control Telegram's bots (from 'telegram.bot') to send messages or to divert error messages to a Telegram's chat. You also have some utilities helping you in the development of packages, like the activation of the same user interface of 'usethis' into your package, or call polite functions to ask a user to install other packages. Finally, you find a set of thematic sets of packages you may use to set up new environments quickly, installing them in a single call.

depigner

A utility package to help you deal with pigne

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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 CategoryFunction(s)Aim
Harrell’s versetidy_summary()pander-ready data frame from Hmisc::summary()
paired_test_continuousPaired test for continuous variable into Hmisc::summary
paired_test_categoricalPaired 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()?
Statisticalci2p()Get P-value form estimation and confidence interval
Programmingpb_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
Developmentuse_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)
Telegramstart_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.Length4.800/5.000/5.2005.600/5.900/6.3006.225/6.500/6.900
Sepal.Width3.200/3.400/3.6752.525/2.800/3.0002.800/3.000/3.175
Petal.Length1.400/1.500/1.5754.000/4.350/4.6005.100/5.550/5.875
Petal.Width0.2/0.2/0.31.2/1.3/1.51.8/2.0/2.3


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.HRLower 95% CIUpper 95% CI
age10.691.3361.0091.767
year3.3740.61040.38310.9727
surgery10.52860.25741.085
  • 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()
LowHighDiff.Odds RatioLower 95% CIUpper 95% CI
age - A4358151.0020.5571.802
age - B4358151.8170.744.463
age - AB4358150.6350.1862.169
age - O4358150.6450.3521.182

summary_interact(lrm_mod, age, abo, p = TRUE) %>%
  pander()
LowHighDiff.Odds RatioLower 95% CIUpper 95% CIP-value
age - A4358151.0020.5571.8020.498
age - B4358151.8170.744.4630.137
age - AB4358150.6350.1862.1690.728
age - O4358150.6450.3521.1820.883
  • 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

[^1]: You can find all the possible meanings of pignahere, and you can listen how to pronounce it here. Note: the Italian plural for “pigna” is “pigne” [pìn’n’e].

Metadata

Version

0.9.1

License

Unknown

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