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README.md
In depigner: A Utility Package to Help you Deal with "Pignas"
depigner 
A utility package to help you deal with pigne
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|:----------------|:-------------------------------------------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------------------------------------:|
| Development | 
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| CRAN | 
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| CI | 
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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 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
#> ` ` `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
[^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].
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depigner documentation built on April 24, 2023, 5:08 p.m.
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depigner
A utility package to help you deal with pigne
| | | | |
|:----------------|:-------------------------------------------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------------------------------------:|
| Development | | | |
| CRAN | |
|
|
| CI |
| | |
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 thesummary()functions provided by{Hmisc}[@R-Hmisc] and{rms}[@R-rms] packages ready to bepander::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$event)
f <- cph(surv ~ age + year + surgery, data = heart)
my_summary <- summary(f)
tidy_summary(my_summary) %>%
pander()
| | Diff. | HR | Lower 95% CI | Upper 95% CI | |:-------:|:-----:|:------:|:------------:|:------------:| | age | 10.69 | 1.336 | 1.009 | 1.767 | | year | 3.374 | 0.6104 | 0.3831 | 0.9727 | | surgery | 1 | 0.5286 | 0.2574 | 1.085 |
paired_test_*(): Paired test for categorical/continuous variables to be used in thesummary()of the{Hmisc}[@R-Hmisc] package:
data(Arthritis)
# categorical -------------------------
## two groups
summary(Treatment ~ Sex,
data = Arthritis,
method = "reverse",
test = TRUE,
catTest = paired_test_categorical
)
#>
#>
#> Descriptive Statistics by Treatment
#>
#> +----------+--------------------+--------------------+------------------------------+
#> | |Placebo |Treated | Test |
#> | |(N=43) |(N=41) |Statistic |
#> +----------+--------------------+--------------------+------------------------------+
#> |Sex : Male| 26% (11)| 34% (14)|Chi-square=5.92 d.f.=1 P=0.015|
#> +----------+--------------------+--------------------+------------------------------+
## more than two groups
summary(Improved ~ Sex,
data = Arthritis,
method = "reverse",
test = TRUE,
catTest = paired_test_categorical
)
#>
#>
#> Descriptive Statistics by Improved
#>
#> +----------+-----------------+-----------------+-----------------+------------------------+
#> | |None |Some |Marked | Test |
#> | |(N=42) |(N=14) |(N=28) |Statistic |
#> +----------+-----------------+-----------------+-----------------+------------------------+
#> |Sex : Male| 40% (17)| 14% ( 2)| 21% ( 6)|chi2=1.71 d.f.=3 P=0.634|
#> +----------+-----------------+-----------------+-----------------+------------------------+
# continuous --------------------------
## two groups
summary(Species ~.,
data = iris[iris$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 atidy_summaryobjects:
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 bylrm()(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 toinstall.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].
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