larmarange / jlutils

Some R functions

JLutils

Online documentation: https://larmarange.github.io/JLutils/

A collection of diverse R functions who don't fit (yet?) in another package. Please note that some functions could be removed from JLutils once incorporated in a more appropriate package.

Please note that this is a sandbox package. All functions have not been tested in details and some are very experimental.

Une collection de fonctions diverses R qui n'ont pas (encore ?) trouvé de place dans un autre package. Veuillez noter que certaines fonctions peuvent être retirées de JLutils une fois incorporées dans un package plus approprié.

Merci de noter qu'il s'agit d'un package "bac-à-sable". Toutes les fonctions n'ont pas été testées en profondeur et certaines sont très expérimentales.

How to install / Installation

Execute the following code within R:

Exécutez le code suivant dans R :

remotes::install_github("larmarange/JLutils")

jlutils's Issues

the function stat_fill_labels() does not work

stat_fill_labels broken

Need to be updated with the new version of ggplot2

Opérateur de concaténation

`%p%` <- function(x,y){paste0(x,y)}

Raccourcis de mise en forme

Créer des fonctions de raccourcis, round0 à round9, plus version avec virgule à la place d'un point, plus version avec séparateurs de milliers.

Ajouter aussi une fonction pourcent, version française de percent.

Update Number formatters

Once next version of scales on CRAN

wait release of GGally v2.0.0 for cleaning JLutils

remove deprecated functions and simply import those who have moved to GGally

Trouble during JLutils package installation

I try to install JLutils package using devtools::install_github("larmarange/JLutils"), but It failed. I work on R4.1.0. You can see below the errors :

install_github("larmarange/JLutils")
Downloading GitHub repo larmarange/JLutils@HEAD
✓ checking for file ‘/tmp/Rtmpj6wqtb/remotes464f41f80aa9/larmarange-JLutils-24ebeec/DESCRIPTION’ ...
─ preparing ‘JLutils’:
✓ checking DESCRIPTION meta-information ...
─ installing the package to process help pages
-----------------------------------
─ installing source package ‘JLutils’ ...
** using staged installation
** R
** byte-compile and prepare package for lazy loading
** help
*** installing help indices
*** copying figures
** building package indices
** testing if installed package can be loaded from temporary location
Error: le chargement du package ou de l'espace de noms a échoué pour ‘JLutils’ :
l'objet 'predictdf' est introuvable pendant le chargement de l'espace de noms 'JLutils'
Erreur : loading failed
Exécution arrêtée
ERROR: loading failed
─ removing ‘/tmp/RtmpjbbaM0/Rinst4d603f67d9ba/JLutils’
-----------------------------------
ERROR: package installation failed
Erreur : Failed to install 'JLutils' from GitHub:
System command 'R' failed, exit status: 1, stdout + stderr (last 10 lines):
E> ** building package indices
E> ** testing if installed package can be loaded from temporary location
E> Error: le chargement du package ou de l'espace de noms a échoué pour ‘JLutils’ :
E> l'objet 'predictdf' est introuvable pendant le chargement de l'espace de noms 'JLutils'
E> Erreur : loading failed
E> Exécution arrêtée
E> ERROR: loading failed
E> * removing ‘/tmp/RtmpjbbaM0/Rinst4d603f67d9ba/JLutils’
E> -----------------------------------
E> ERROR: package installation failed

confint.default

Would you please change the confint to confint.default?
confint sometimes gives a confidence interval that disagrees with the p-values.

Thank you.
Daniel

Error when using R Markdown for plots which use stat_fill_labels

I get the following error when I try to render plots which use stat_fill_labels function via an R markdown script.

Error in eval(_inherit, env, NULL) : object 'StatCount' not found
Calls: ... fetch_ggproto -> fetch_ggproto -> super -> eval -> eval
Execution halted

tidy_detailed() does not keep the order of factor levels

Super useful function My only problem is that unlike broom::tidy, the factor levels order is changed to the alphabetical order.

df <- data.frame(y = 1:3, xv= factor(c("a", "b", "c"), levels = c("a", "c", "b")))
lm(y ~ x, data = data) %>% tidy_detailed()

         term estimate std.error statistic p.value variable variable_label level
1 (Intercept)        1       NaN       NaN     NaN     <NA>           <NA>  <NA>
2          xb        1       NaN       NaN     NaN        x              x     b
3          xc        2       NaN       NaN     NaN        x              x     c
  level_detail   label
1         <NA>    <NA>
2      b vs. a b vs. a
3      c vs. a c vs. a