fix linting and add news

.github/workflows/pkgdown.yaml

@@ -2,7 +2,7 @@
 # Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help
 on:
   push:
-    branches: [main, develop, broken_links]
+    branches: [main, develop]
   release:
     types: [published]
   workflow_dispatch:

NEWS.md

@@ -1,5 +1,9 @@
 PatientLevelPrediction develop
 ======================
+- Fixed linting errors and R codestyle in docs to conform to HADES style
+- Remove links to pdf's, point to website instead.
+- Fix broken links in Readme and BuildingPredictiveModels vignette
+- Added an action to detect broken links in repo
 - Official maintainer updated to Egill Fridgeirsson
 
 

README.md

@@ -83,7 +83,7 @@ install Python 3.9 or higher using Anaconda (https://www.continuum.io/downloads)
 Getting Started
 ===============
 
-- To install the package please read the [Package Installation guide](https://github.com/OHDSI/PatientLevelPrediction/blob/main/inst/doc/InstallationGuide.pdf)
+- To install the package please read the [Package Installation guide](https://ohdsi.github.io/PatientLevelPrediction/articles/InstallationGuide.html)
 
 - Have a look at the video below for an extensive demo of the package.
 
@@ -93,24 +93,22 @@ alt="Video Vignette PLP Package" width="240" height="180" border="10" /></a>
 
 Please read the main vignette for the package:
 
-- [Building Single Patient-Level Predictive Models](https://github.com/OHDSI/PatientLevelPrediction/blob/main/inst/doc/BuildingPredictiveModels.pdf)
+- [Building Single Patient-Level Predictive Models](https://ohdsi.github.io/PatientLevelPrediction/articles/BuildingPredictiveModels.html)
 
 In addition we have created vignettes that describe advanced functionality in more detail:
 
-- [Building Multiple Patient-Level Predictive Models](https://github.com/OHDSI/PatientLevelPrediction/blob/main/inst/doc/BuildingMultiplePredictiveModels.pdf)
-- [Adding Custom Machine Learning Algorithms](https://github.com/OHDSI/PatientLevelPrediction/blob/main/inst/doc/AddingCustomModels.pdf)
+- [Building Multiple Patient-Level Predictive Models](https://ohdsi.github.io/PatientLevelPrediction/articles/BuildingMultiplePredictiveModels.html)
+- [Adding Custom Machine Learning Algorithms](https://ohdsi.github.io/PatientLevelPrediction/articles/AddingCustomModels.html)
 - [Building Deep Learning Models](https://github.com/OHDSI/DeepPatientLevelPrediction)
 - [Building Ensemble Models](https://github.com/OHDSI/EnsemblePatientLevelPrediction)
-- [Creating Learning Curves](https://github.com/OHDSI/PatientLevelPrediction/blob/main/inst/doc/CreatingLearningCurves.pdf)
+- [Creating Learning Curves](https://ohdsi.github.io/PatientLevelPrediction/articles/CreatingLearningCurves.html)
 
-Package manual: [PatientLevelPrediction.pdf](https://github.com/OHDSI/PatientLevelPrediction/blob/main/extras/PatientLevelPrediction.pdf)
+Package function reference: [Reference](https://ohdsi.github.io/PatientLevelPrediction/reference/index.html)
 
 User Documentation
 ==================
 Documentation can be found on the [package website](https://ohdsi.github.io/PatientLevelPrediction).
 
-PDF versions of the documentation are also available, as mentioned above.
-
 Support
 =======
 * Developer questions/comments/feedback: <a href="http://forums.ohdsi.org/c/developers">OHDSI Forum</a>
@@ -132,5 +130,5 @@ PatientLevelPrediction is being developed in R Studio.
 # Acknowledgements
 
 - The package is maintained by Egill Fridgeirsson and Jenna Reps and has been developed with major contributions from Peter Rijnbeek, Martijn Schuemie, Patrick Ryan, and Marc Suchard.
-- We like to thank the following persons for their contributions to the package: Seng Chan You, Ross Williams, Henrik John, Xiaoyong Pan, James Wiggins, Alex Rekkas
+- We like to thank the following persons for their contributions to the package: Seng Chan You, Ross Williams, Henrik John, Xiaoyong Pan, James Wiggins, Alexandros Rekkas
 - This project is supported in part through the National Science Foundation grant IIS 1251151.

vignettes/AddingCustomFeatureEngineering.Rmd

@@ -32,7 +32,7 @@ library(PatientLevelPrediction)
 
 # Introduction
 
-This vignette describes how you can add your own custom function for feature engineering in the Observational Health Data Sciences and Informatics (OHDSI) [`PatientLevelPrediction`](http://github.com/OHDSI/PatientLevelPrediction) package. This vignette assumes you have read and are comfortable with building single patient level prediction models as described in the [`BuildingPredictiveModels` vignette](https://github.com/OHDSI/PatientLevelPrediction/blob/main/inst/doc/BuildingPredictiveModels.pdf).
+This vignette describes how you can add your own custom function for feature engineering in the Observational Health Data Sciences and Informatics (OHDSI) [`PatientLevelPrediction`](http://github.com/OHDSI/PatientLevelPrediction) package. This vignette assumes you have read and are comfortable with building single patient level prediction models as described in the `vignette('BuildingPredictiveModels')`.
 
 **We invite you to share your new feature engineering functions with the OHDSI community through our [GitHub repository](http://github.com/OHDSI/PatientLevelPrediction).**
 
@@ -65,22 +65,18 @@ Let's consider the situation where we wish to create an age spline feature. To m
 Our age spline feature function will create a new feature using the `plpData$cohorts$ageYear` column. We will implement a restricted cubic spline that requires specifying the number of knots. Therefore, the inputs for this are: `knots` - an integer/double specifying the number of knots.
 
 ```{r, echo = TRUE, eval=FALSE}
-createAgeSpline <- function(
-                     knots = 5
-                     ){
-  
+createAgeSpline <- function(knots = 5) {
   # create list of inputs to implement function
   featureEngineeringSettings <- list(
     knots = knots
-    )
-  
+  )
+
   # specify the function that will implement the sampling
   attr(featureEngineeringSettings, "fun") <- "implementAgeSplines"
 
   # make sure the object returned is of class "sampleSettings"
   class(featureEngineeringSettings) <- "featureEngineeringSettings"
   return(featureEngineeringSettings)
-  
 }
 ```
 
@@ -93,65 +89,67 @@ All 'implement' functions must take as input the `trainData` and the `featureEng
 In our example, the `createAgeSpline()` will return a list with 'knots'. The `featureEngineeringSettings` therefore contains this.
 
 ```{r tidy=FALSE,eval=FALSE}
-implementAgeSplines <- function(trainData, featureEngineeringSettings, model=NULL) {
+implementAgeSplines <- function(trainData, featureEngineeringSettings, model = NULL) {
   # if there is a model, it means this function is called through applyFeatureengineering, meaning it   # should apply the model fitten on training data to the test data
   if (is.null(model)) {
     knots <- featureEngineeringSettings$knots
     ageData <- trainData$labels
     y <- ageData$outcomeCount
     X <- ageData$ageYear
     model <- mgcv::gam(
-      y ~ s(X, bs='cr', k=knots, m=2)
+      y ~ s(X, bs = "cr", k = knots, m = 2)
     )
     newData <- data.frame(
       rowId = ageData$rowId,
       covariateId = 2002,
       covariateValue = model$fitted.values
     )
-  }
-  else {
+  } else {
     ageData <- trainData$labels
     X <- trainData$labels$ageYear
     y <- ageData$outcomeCount
-    newData <- data.frame(y=y, X=X)
+    newData <- data.frame(y = y, X = X)
     yHat <- predict(model, newData)
     newData <- data.frame(
       rowId = trainData$labels$rowId,
       covariateId = 2002,
       covariateValue = yHat
     )
   }
-  
-  # remove existing age if in covariates 
-  trainData$covariateData$covariates <- trainData$covariateData$covariates |> 
-    dplyr::filter(!covariateId %in% c(1002))
-  
+
+  # remove existing age if in covariates
+  trainData$covariateData$covariates <- trainData$covariateData$covariates |>
+    dplyr::filter(!.data$covariateId %in% c(1002))
+
   # update covRef
-  Andromeda::appendToTable(trainData$covariateData$covariateRef, 
-                           data.frame(covariateId=2002,
-                                      covariateName='Cubic restricted age splines',
-                                      analysisId=2,
-                                      conceptId=2002))
-  
+  Andromeda::appendToTable(
+    trainData$covariateData$covariateRef,
+    data.frame(
+      covariateId = 2002,
+      covariateName = "Cubic restricted age splines",
+      analysisId = 2,
+      conceptId = 2002
+    )
+  )
+
   # update covariates
   Andromeda::appendToTable(trainData$covariateData$covariates, newData)
-  
+
   featureEngineering <- list(
-    funct = 'implementAgeSplines',
+    funct = "implementAgeSplines",
     settings = list(
       featureEngineeringSettings = featureEngineeringSettings,
       model = model
     )
   )
-  
-  attr(trainData$covariateData, 'metaData')$featureEngineering = listAppend(
-    attr(trainData$covariateData, 'metaData')$featureEngineering,
+
+  attr(trainData$covariateData, "metaData")$featureEngineering <- listAppend(
+    attr(trainData$covariateData, "metaData")$featureEngineering,
     featureEngineering
   )
-  
+
   return(trainData)
 }
-
 ```
 
 # Acknowledgments