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…
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 ;-) |
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!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
#> ` ` `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] TRUEci2p(): compute the p-value related
with a provided confidence interval:ci2p(1.125, 0.634, 1.999, log_transform = TRUE)
#> [1] 0.367902pb_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_setsview_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_carsuse_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 functionimported_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.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()gdp(): A wrapper to relaxgdp(7)If you need some more features, please open an issue here.
If you encounter a bug, please file a reprex (minimal reproducible example) here.
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.
The {depigner}’s logo was lovely designed by Elisa Sovrano.