# inSilecoMisc 0.4.0 (part 2/2)

#### April 21, 2020

In the first part of this post I introduced several functions available in the package inSilecoMisc. In this post, I keep on introducing features of the package you might find useful! If you did not read the first part of this post and are interested in reproducing the examples below, simply install inSilecoMisc:

 1 2 3  # Run if package is not already installed install.packages("remotes") remotes::install_github("inSileco/inSilecoMisc") 

 1 2 3  library("inSilecoMisc") packageVersion("inSilecoMisc") #R> [1] '0.7.0.9000' 

and you’re good to go!

## scaleWithin()

I wrote scaleWithin() to handle color scales for a specific yet frequent situation. Let us say that I have 40 percentage values – meaning 0 to 100 – in a vector val

 1 2 3 4 5 6 7 8  val <- runif(40, 0, 100) val #R> [1] 86.392180 26.933686 39.130026 44.555717 46.096277 38.784155 23.139646 #R> [8] 54.421141 22.658730 82.744601 98.238363 54.314766 66.876253 48.648104 #R> [15] 76.447031 72.286251 41.122192 25.517201 40.532535 1.227411 44.382758 #R> [22] 12.442990 67.572657 9.281949 55.801186 27.684030 41.461390 58.911818 #R> [29] 81.594840 30.979049 57.597138 73.352640 38.201233 51.114658 71.874858 #R> [36] 7.328215 26.145596 64.981073 28.886274 45.177209 

and that I wish to create a color scale with 25 tones. I use showPalette() to show the color palette:

 1 2  pal <- colorRampPalette(c("#f9fa98", "#500127"))(25) graphicsutils::showPalette(pal) 

But the color scale should be used for the range [30%-70%], meaning that values below 30% should have the lowest values and values above 70%, the highest one. The caption should thus indicate $$\geqslant$$ 30% and $$\leqslant$$ 70%. Then the function scaleWithin() is very handy!

 1 2 3 4  scaleWithin(val, n = 25, mn = 30, mx = 70) #R> [1] 25 1 6 10 11 6 1 16 1 25 25 16 24 12 25 25 7 1 7 1 9 1 24 1 17 #R> [26] 1 8 19 25 1 18 25 6 14 25 1 1 22 1 10 graphicsutils::showPalette(pal[scaleWithin(val, n = 25, mn = 30, mx = 70)], add_codecolor = FALSE) 

Even though this function is pretty useful – at least I think it is! – I had a lot of trouble conveying why! So, in the last version of inSilecoMisc, I re-wrote the entire documentation and I hope that, together with this example, others will, as I so, find it useful.

## Messages

Daily, I use R packages and R functions to analyze data, create model, run simulations, and a number of other things! So I write scripts that combine functions from various packages to create pipelines that do the analyses I need. When running such scripts, I like having information reported on a clear and visual way, that is why I value packages such as progress, crayon and cli. In inSilecoMisc, inspired by messages reported by devtools when building a package, I created four simple message functions using crayon and cli packages to standardize messages in my scripts.

 1 2 3  # 1. msgInfo() indicates what the upcoming computation msgInfo("this is what's gonna happen next") #R> ℹ this is what's gonna happen next 
 1 2 3  # 2. msgWarning() reminds me something important that should not affect the run msgWarning("Got to be careful") #R> ⚠ Got to be careful 
 1 2 3  # 3. msgError() when something went wrong (and I anticipated that it could happen) msgError("Something wrong") #R> ✖ Something wrong 
 1 2 3  # 4. msgSuccess() when a step/ a computation has been successfully completed msgSuccess("All good") #R> ✔ All good 

These functions help me structure my scripts. Here is a contrived example:

  1 2 3 4 5 6 7 8 9 10 11 12 13  scr_min <- function() { # msgInfo() lets me know where I am in the script msgInfo("Average random values") set.seed(111) out <- mean(runif(100)) msgSuccess("Done!") # msgSuccess() indicates the successful completion of this part out } scr_min() #R> ℹ Average random values #R> ✔ Done! #R> [1] 0.4895239 

Another helpful aspect of these functions is that they all are based on message(). As such, if I want to execute a script quietly, all I need to do is to call suppressMessages() beforehand

 1 2 3  # quiet run suppressMessages(scr_min()) #R> [1] 0.4895239 

If you want to see an example of how I use these functions in a script for a scientific manuscript, check out the research compendium coocNotInteract.

## tblDown()

Last but not least, I’d like to introduce a function to quickly write table data frame (or a list of data frames) in documents of various formats. I created tblDown() for a colleague of mine that was looking for a quick way to export a table. In the package knitr, there is the very handy function kable() that quickly writes a data frame in various formats.

 1  knitr::kable(head(CO2)) 
Plant Type Treatment conc uptake
Qn1 Quebec nonchilled 95 16.0
Qn1 Quebec nonchilled 175 30.4
Qn1 Quebec nonchilled 250 34.8
Qn1 Quebec nonchilled 350 37.2
Qn1 Quebec nonchilled 500 35.3
Qn1 Quebec nonchilled 675 39.2

I wrote a function that calls kable() to write the data frame and then renders the table(s) in the desired format indicated by the extension of the output file (docx by default) using pandoc.

 1 2  # NB tblDown(head(CO2)) returns table.docx by default tblDown(head(CO2), output_file = "table.odt") 

As I mentioned above tblDown() handles lists of data frames and the user can also provide a set of captions for every table and even separate them with section headers (of level 1).

 1 2 3  tblDown(list(head(CO2), tail(CO2)), output_file = "tables.pdf", caption = c("This is the head of CO2", "This is the tail of CO2"), section = "Table") 

Check out the output file ➡️ ! Note that in the example above I only use one character string for section and tblDown() has appended an index; this is also the default behavior for caption: if there are less captions or sections titles than data frames, vectors of captions (and/or sections) are repeated and an index is appended.

If you are already writing your documents with R Markdown, you may not need this. Yet keep in mind that tblDown() quickly exports tables in various formats with only one line of command!

#### That’s all folks 🎉!

Session info
  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43  sessionInfo() #R> R version 4.2.2 (2022-10-31) #R> Platform: x86_64-pc-linux-gnu (64-bit) #R> Running under: Ubuntu 22.04.1 LTS #R> #R> Matrix products: default #R> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 #R> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so #R> #R> locale: #R> [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8 #R> [4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8 #R> [7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C #R> [10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C #R> #R> attached base packages: #R> [1] stats graphics grDevices utils datasets methods base #R> #R> other attached packages: #R> [1] inSilecoMisc_0.7.0.9000 inSilecoRef_0.0.1.9000 #R> #R> loaded via a namespace (and not attached): #R> [1] tidyselect_1.2.0 xfun_0.35 bslib_0.4.1 #R> [4] vctrs_0.5.1 generics_0.1.3 miniUI_0.1.1.1 #R> [7] htmltools_0.5.4 yaml_2.3.6 utf8_1.2.2 #R> [10] rlang_1.0.6 later_1.3.0 pillar_1.8.1 #R> [13] jquerylib_0.1.4 httpcode_0.3.0 glue_1.6.2 #R> [16] DBI_1.1.3 lifecycle_1.0.3 plyr_1.8.8 #R> [19] stringr_1.5.0 blogdown_1.15 htmlwidgets_1.5.4 #R> [22] evaluate_0.18 knitr_1.41 fastmap_1.1.0 #R> [25] httpuv_1.6.6 curl_4.3.3 fansi_1.0.3 #R> [28] highr_0.9 Rcpp_1.0.9 xtable_1.8-4 #R> [31] promises_1.2.0.1 backports_1.4.1 DT_0.26 #R> [34] cachem_1.0.6 jsonlite_1.8.4 rcrossref_1.2.0 #R> [37] mime_0.12 digest_0.6.30 stringi_1.7.8 #R> [40] bookdown_0.30 dplyr_1.0.10 shiny_1.7.3 #R> [43] bibtex_0.5.0 cli_3.4.1 tools_4.2.2 #R> [46] graphicsutils_1.6.0.9000 magrittr_2.0.3 sass_0.4.4 #R> [49] tibble_3.1.8 RefManageR_1.4.0 crul_1.3 #R> [52] crayon_1.5.2 pkgconfig_2.0.3 ellipsis_0.3.2 #R> [55] xml2_1.3.3 timechange_0.1.1 lubridate_1.9.0 #R> [58] assertthat_0.2.1 rmarkdown_2.18 httr_1.4.4 #R> [61] R6_2.5.1 compiler_4.2.2 

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