depigner

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.

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()

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()

• 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()

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

• 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 pigna here, and you can listen how to pronounce it here. Note: the Italian plural for “pigna” is “pigne” [pìn’n’e].