Pigna

Often, my colleagues and I have faced to the problem of using the data inside the output of the summary() function, and I have seen very many colorful patches to manage this issue.

{depigner}: depigner has a tidy_summary() pipable function that produces in output a lovely table already adjusted to be processed by {pander}!

• tidy_summary(): produces a data frame from the summary() functions provided by {Hmisc} (Harrell 2020) and {rms} (Harrell, Jr. 2020) packages ready to be pander::pander()ed (Daróczi and Tsegelskyi 2018).

Currently it is tested for method reverse only:

library(rms, quietly = TRUE)

## 
## Attaching package: 'Hmisc'

## The following objects are masked from 'package:base':
## 
##     format.pval, units

## 
## 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)
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()

Pigna

What if you have paired samples into the data you would like to summary()zed?

{depigner}: provide the required object to pass to the summary() function to perform a paired test, both for continuous and categorical data with two or more groups.¹

• paired_test_*(): Paired test for categorical/continuous variables to be used in the summary() of the {Hmisc} (Harrell 2020) 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|
## +------------+--------------------+--------------------+--------------------+-----------------------+

Pigna

How often were you asked to provide “all vs. all” checks and tests to find that unique randomly sampled P-value, which is less than 0.05 by a factor of \(10^{-15}\) order of magnitude? {depigner}: gives you a function to automatically adjust the resulting p-values for multiplicity into the summary() tables are also provided.

• 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() %>%
    pander()

## ✓ P adjusted with BH method.

Producing a lovely, concise, printable table when interaction kicks in into the model is the aim of the summary_interact() function.

• 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 (Harrell, Jr. 2020)):

summary_interact(lrm_mod, age, abo) %>%
  pander()

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

Pigna

Another super useful function provided by Hmisc is describe(), which has a lovely plot() method for its results. I had faced some issue when I needed to use those plots programmatically because their outputs are not that clear or easy to infer before to see them. I.e., suppose you want to know which variable will be plotted, and in which plot they will be plotted (or even if a plot will be produced). Hence, working with {Hmisc}, it is crucial to know which variables Harrell considers continuous or categorical. Among them, it is also mandatory to know which one he considers to have (good) enough information to have been plotted.

{depigner} solve those problems with its family of htypes() functions, which seems to have “reasonable” results… officially:

I only had time to glance at it; it looks reasonable. You can also just analyze the describe() source code :-)

— Frank Harrell (@f2harrell) June 5, 2020

• htypes() and friends: get/check types of variable with respect to the {Hmisc} ecosystem (Harrell 2020).

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

Pigna

Some people can and prefer to read more information into a (single) value than into two…

{depigner}: have a wrapper to provide an answer to them.

• ci2p(): compute the p-value related with a provided confidence interval (assuming every type o necessary assumptions):

ci2p(1.125, 0.634,  1.999, log_transform = TRUE)

## [1] 0.367902

Pigna

Progress bars provided by {progress} are great, complete, and powerful. They are also quite easy to set up, but for effortless usage (which comes quite often) an even more easy-to-use wrapper could be useful.²

{depigner}: helps you with this super simple wrapper, you have to know the length (i.e., number of steps) the progress should do overall, and that’s it!

• pb_len(): Progress bar of given length, wrapper from the {progress} (Csárdi and FitzJohn 2019) package:

pb <- pb_len(100)

for (i in 1:100) {
    Sys.sleep(0.1)
    tick(pb, paste("i = ", i))
}

Pigna

How often have you (re)installed the same set of packages on (your or other) computers? You need to remember which packages you or they need, you can forget one of them, and next… you have to repeat it for the next computer!

{depigner}: gives you a function and a set of predefined sets of packages which you can use to install all the packages you need in a single call. Moreover, it does it politely querying the use if they agree for the installation, and possibly for a general update.

• 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

Pigna

When developing a package, use the {usethis} interface would be fantastic. Anyway, importing {usethis} can be not enough: you still need to write the call for its functions explicitly, e.g., you could be face to the problem of writing something like

usethis::ui_stop('field {usethis::ui_field("foo")} must has value {usethis::ui_value(value)}, ...')

Which is not that nice, doesn’t it?

{depigner}: help you, in a single call in the {usethis} style to add to your NAMESPACE all the {usethis}’ UI permitting to you to go straight to something like

ui_stop('field {ui_field("foo")} must has value {ui_value(value)}, ...')

• use_ui(): Use {usethis}’ user interface (Wickham and Bryan 2020) in your package

# in the initial setup steps of the development of a package
use_ui()

Pigna

When you need to write code that others should run, it is not kind to force the installation or the update of packages in their environments.

{depigner}: thanks to a prompt given by He The Hadley during a fantastic workshop in NYC, gives you a function to politely ask others to install on their system the packages that you need.

• please_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)

Pigna

When you install or attach packages, often (quite ever), other packages (i.e., dependencies) were installed/attached too. Whose are they? Do we need to install both those packages explicitly or one of them it is sufficient because the other will be automatically installed as a consequence?

{depigner}: has a simple single function to provide you a quick answer to those questions.³

• 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"

Pigna

Have you ever run some long, very long, super long, extra long, computation… on a server? How many times did you need to come back to your PC, or to your ssh + screen session to check its state, to check the logs, to see if some errors evilly happened?

{depigner}: provide you some super easy to use wrappers on some functionalities of {telegram.bot} package which will permit you to be notified with custom messages, images, and even errors on your phone directly. You need only to define your bot (instruction in the help page), and you are ready to go!

• Wrappers to simple use of Telegram’s bots: wrappers from the {telegram.bot} package (Benedito 2019):

# 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()

Pigna

SSometimes piñatas are too many to be solved, and you just have to give in to them.

{depigner}: is near you.⁵

• gdp(): A wrapper to relax.

gdp(7)