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Merge pull request #7 from leibniz-psychology/staging
Release 1.1.2
2 parents 3b80844 + 8d11af4 commit 1b39e3b

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Lines changed: 193 additions & 187 deletions

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‎CHANGELOG.md‎

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## Release 1.1.2
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### future
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## Release 1.1.2
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### 02.2026
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* fixed minor problem in createJSON function
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* fixed forest plot and cumul forest plot functions to load data session correctly
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## Release 1.1.1
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### 03.2025
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* Changed how the r functions ingest the data. Former, the data was loaded from the package.
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### 21.06.2021 / Online 21.06.2021
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Features:
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* Minimal viable product
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* All functions still need some optimization in the future
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* All functions still need some optimization in the future

‎R/createJsonFromData.R‎

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@@ -5,7 +5,7 @@ library(jsonlite)
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createJsonFromData <- function(d) {
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dat <- checkData(d)
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json_data <- toJSON(dat, pretty = TRUE, digits = NA)
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json_data <- jsonlite::toJSON(dat, pretty = TRUE, digits = NA)
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write(json_data, paste0(d, ".json"))
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‎R/cumulForestPlot.R‎

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#' @title cumulative Forest plot
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#' @description
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#' Using metafor rma.uni function to fit a meta-analytic multivariate/multilevel fixed- and random/mixed-effects model for the given dataset.
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#' The model then is used as input for the metaviz viz_forest function to print a forrest plot
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#' See Documentation of metafor and metaviz packages for details.
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#' @param yi
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#' A \code{string} of the variable which holds the vector of length k with the observed effect sizes or outcomes in the selected dataset (d)
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#' @param vi
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#' A \code{string} of the variable which holds the vector of length k with the corresponding sampling variances in the selected dataset (d)
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#' @param d
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#' A \code{string} representing the dataset name that should be used for fitting.
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#' @param effectName
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#' A \code{string} representing the effect name that should be printed as label. defaults to "Effect"
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#' @param measure
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#' A character string indicating underlying summary measure.
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#' @return
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#' creates a cumulative forest plot
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#' also creates a json file (imgHeight.json) that is used in a later api call to define the height of the plots
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#' @author Robert Studtrucker
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#' @export
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cumulforest <- function(yi,vi,measure,d,effectName="Effect") {
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#load needed dependencies
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requireNamespace("metafor")
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requireNamespace("ggplot2")
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requireNamespace("metaviz")
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requireNamespace("jsonlite")
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#load the in variable d defined dataset from the package
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dat <- checkData(d)
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checkParameter(dat,c(yi,vi))
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#order the loaded data depending on the r_year column
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dat <- dat[order(dat$r_year),]
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# depending on the given measure the input for rma.uni model is z transformed
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if(measure == "COR") {
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#fitting the rma.uni model based on z transformed data
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rma_model <- metafor::rma.uni(yi=metafor::transf.rtoz(dat[,yi],dat[,o_ni]), vi=metafor::transf.rtoz(dat[,vi],dat[,o_ni]),measure="ZCOR",slab=paste(dat$r_author, dat$r_year))
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tmp<-metafor::cumul(rma_model, order=order(dat$r_year))
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#creating a cumulative forest plot based on the fitted rma.uni model
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#fp <- metaviz::viz_forest(x = rma_model,
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# variant = "classic",
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# study_labels = rma_model$slab,
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# text_size =4,
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# xlab = effectName,
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# annotate_CI = TRUE,
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# x_trans_function = tanh,
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# type = "cumulative")
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fp<- metafor::forest(x=tmp,
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cex=0.75,
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xlab = "Correlation Coefficient",
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study_labels = tmp$slab,
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transf="ztor",
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efac=0.2
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)
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}else{
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# 1. Overall-Effekt und Cumulative forest ####
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#fitting the rma.uni model
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rma_model <- metafor::rma.uni(yi=dat[,yi],vi=dat[,vi],measure=measure,slab=paste(dat$r_author, dat$r_year))
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#tmp<-cumul(rma_model, order=order(dat$r_year))
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#creating a cumulative forest plot based on the fitted rma.uni model
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fp <- metaviz::viz_forest(x = rma_model,
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variant = "classic",
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study_labels = rma_model$slab,
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text_size =4,
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xlab = effectName,
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annotate_CI = TRUE,
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type = "cumulative")
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}
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# creating a json object with information about the height of the plot
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# this information is needed by the web service to define how big the requested image has to be.
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# If not specified standard height and wde is used wich may cause deformed plots when there are a lot of rows in the plot.
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height<-list("height" = length(rma_model$yi))
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jsonlite::write_json(height, "imgHeight.json")
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# print the cumul forest plot so the corresponding object can be retrieved by the web service
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print(fp)
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invisible();
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}
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#' @title cumulative Forest plot
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#' @description
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#' Using metafor rma.uni function to fit a meta-analytic multivariate/multilevel fixed- and random/mixed-effects model for the given dataset.
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#' The model then is used as input for the metaviz viz_forest function to print a forrest plot
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#' See Documentation of metafor and metaviz packages for details.
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#' @param yi
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#' A \code{string} of the variable which holds the vector of length k with the observed effect sizes or outcomes in the selected dataset (d)
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#' @param vi
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#' A \code{string} of the variable which holds the vector of length k with the corresponding sampling variances in the selected dataset (d)
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#' @param d
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#' A \code{string} representing the dataset name that should be used for fitting.
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#' @param effectName
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#' A \code{string} representing the effect name that should be printed as label. defaults to "Effect"
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#' @param measure
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#' A character string indicating underlying summary measure.
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#' @return
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#' creates a cumulative forest plot
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#' also creates a json file (imgHeight.json) that is used in a later api call to define the height of the plots
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#' @author Robert Studtrucker
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#' @export
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cumulforest <- function(yi,vi,measure,d,effectName="Effect") {
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#load needed dependencies
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requireNamespace("metafor")
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requireNamespace("ggplot2")
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requireNamespace("metaviz")
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requireNamespace("jsonlite")
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#load the in variable d defined dataset from the package
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#dat <- checkData(d)
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dat <- d
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checkParameter(dat,c(yi,vi))
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#order the loaded data depending on the r_year column
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dat <- dat[order(dat$r_year),]
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# depending on the given measure the input for rma.uni model is z transformed
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if(measure == "COR") {
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#fitting the rma.uni model based on z transformed data
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rma_model <- metafor::rma.uni(yi=metafor::transf.rtoz(dat[,yi],dat[,o_ni]), vi=metafor::transf.rtoz(dat[,vi],dat[,o_ni]),measure="ZCOR",slab=paste(dat$r_author, dat$r_year))
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tmp<-metafor::cumul(rma_model, order=order(dat$r_year))
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#creating a cumulative forest plot based on the fitted rma.uni model
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#fp <- metaviz::viz_forest(x = rma_model,
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# variant = "classic",
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# study_labels = rma_model$slab,
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# text_size =4,
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# xlab = effectName,
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# annotate_CI = TRUE,
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# x_trans_function = tanh,
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# type = "cumulative")
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fp<- metafor::forest(x=tmp,
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cex=0.75,
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xlab = "Correlation Coefficient",
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study_labels = tmp$slab,
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transf="ztor",
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efac=0.2
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)
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}else{
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# 1. Overall-Effekt und Cumulative forest ####
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#fitting the rma.uni model
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rma_model <- metafor::rma.uni(yi=dat[,yi],vi=dat[,vi],measure=measure,slab=paste(dat$r_author, dat$r_year))
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#tmp<-cumul(rma_model, order=order(dat$r_year))
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#creating a cumulative forest plot based on the fitted rma.uni model
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fp <- metaviz::viz_forest(x = rma_model,
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variant = "classic",
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study_labels = rma_model$slab,
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text_size =4,
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xlab = effectName,
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annotate_CI = TRUE,
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type = "cumulative")
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}
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# creating a json object with information about the height of the plot
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# this information is needed by the web service to define how big the requested image has to be.
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# If not specified standard height and wde is used wich may cause deformed plots when there are a lot of rows in the plot.
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height<-list("height" = length(rma_model$yi))
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jsonlite::write_json(height, "imgHeight.json")
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# print the cumul forest plot so the corresponding object can be retrieved by the web service
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print(fp)
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invisible();
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}

‎R/forestPlot.R‎

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#' @title Forest plot
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#' @description
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#' Using metafor package to create a forest plot and metaviz package to create a rain forest plot.
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#' @param yi
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#' A \code{string} of the variable which holds the vector of length k with the observed effect sizes or outcomes in the selected dataset (d)
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#' @param vi
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#' A \code{string} of the variable which holds the vector of length k with the corresponding sampling variances in the selected dataset (d)
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#' @param d
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#' A \code{string} representing the dataset name that should be used for fitting.
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#' @param effect
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#' A \code{string} representing the effect name that should be printed as label. defaults to "Effect"
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#' @param measure
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#' A character string indicating underlying summary measure.
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#' @return
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#' creates a forest and rainforest plot
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#' also creates a json file (imgHeight.json) that is used in a later api call to define the height of the plots
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#' @author Robert Studtrucker
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#' @export
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forest <- function(yi,vi,measure,d,effect="Effect") {
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requireNamespace("metafor")
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requireNamespace("ggplot2")
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requireNamespace("metaviz")
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requireNamespace("jsonlite")
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requireNamespace("psych")
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#load the in variable d defined dataset from the package
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dat <- checkData(d)
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if(measure == "COR") {
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rma_model <- metafor::rma.uni(yi=metafor::transf.rtoz(dat[,yi],dat[,o_ni]), vi=metafor::transf.rtoz(dat[,vi],dat[,o_ni]),measure="ZCOR",slab=paste(dat$r_author, dat$r_year))
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fp <- metaviz::viz_forest(x = rma_model,
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variant = "classic",
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study_labels = rma_model$slab,
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text_size =4,
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xlab = effect,
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annotate_CI = TRUE,
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method = "REML",
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x_trans_function = tanh,
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type = "standard")
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rainfp <-metaviz::viz_forest(x = rma_model,
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variant = "rain",
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study_labels = rma_model$slab,
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text_size =4,
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xlab = effect,
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annotate_CI = TRUE,
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method = "REML",
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x_trans_function = tanh,
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type = "standard")
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height<-list("height" = length(rma_model$yi))
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jsonlite::write_json(height, "imgHeight.json")
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print(fp)
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print(rainfp)
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invisible();
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}else{
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rma_model <- metafor::rma.uni(yi=dat[,yi],vi=dat[,vi],measure=measure,slab=paste(dat$r_author, dat$r_year))
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fp <- metaviz::viz_forest(x = rma_model,
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variant = "classic",
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study_labels = rma_model$slab,
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text_size =4,
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xlab = effect,
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annotate_CI = TRUE,
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type = "standard")
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rainfp <- metaviz::viz_forest(x = rma_model,
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variant = "rain",
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study_labels = rma_model$slab,
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text_size =4,
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xlab = effect,
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annotate_CI = TRUE,
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type = "standard")
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height<-list("height" = length(rma_model$yi))
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jsonlite::write_json(height, "imgHeight.json")
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print(fp)
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print(rainfp)
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invisible();
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}
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}
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#' @title Forest plot
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#' @description
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#' Using metafor package to create a forest plot and metaviz package to create a rain forest plot.
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#' @param yi
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#' A \code{string} of the variable which holds the vector of length k with the observed effect sizes or outcomes in the selected dataset (d)
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#' @param vi
7+
#' A \code{string} of the variable which holds the vector of length k with the corresponding sampling variances in the selected dataset (d)
8+
#' @param d
9+
#' A \code{string} representing the dataset name that should be used for fitting.
10+
#' @param effect
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#' A \code{string} representing the effect name that should be printed as label. defaults to "Effect"
12+
#' @param measure
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#' A character string indicating underlying summary measure.
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#' @return
15+
#' creates a forest and rainforest plot
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#' also creates a json file (imgHeight.json) that is used in a later api call to define the height of the plots
17+
#' @author Robert Studtrucker
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#' @export
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forest <- function(yi,vi,measure,d,effect="Effect") {
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requireNamespace("metafor")
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requireNamespace("ggplot2")
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requireNamespace("metaviz")
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requireNamespace("jsonlite")
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requireNamespace("psych")
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#load the in variable d defined dataset from the package
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#dat <- checkData(d)
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dat <- d
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if(measure == "COR") {
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32+
rma_model <- metafor::rma.uni(yi=metafor::transf.rtoz(dat[,yi],dat[,o_ni]), vi=metafor::transf.rtoz(dat[,vi],dat[,o_ni]),measure="ZCOR",slab=paste(dat$r_author, dat$r_year))
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fp <- metaviz::viz_forest(x = rma_model,
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variant = "classic",
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study_labels = rma_model$slab,
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text_size =4,
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xlab = effect,
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annotate_CI = TRUE,
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method = "REML",
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x_trans_function = tanh,
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type = "standard")
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rainfp <-metaviz::viz_forest(x = rma_model,
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variant = "rain",
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study_labels = rma_model$slab,
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text_size =4,
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xlab = effect,
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annotate_CI = TRUE,
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method = "REML",
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x_trans_function = tanh,
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type = "standard")
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height<-list("height" = length(rma_model$yi))
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jsonlite::write_json(height, "imgHeight.json")
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print(fp)
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print(rainfp)
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invisible();
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}else{
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rma_model <- metafor::rma.uni(yi=dat[,yi],vi=dat[,vi],measure=measure,slab=paste(dat$r_author, dat$r_year))
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fp <- metaviz::viz_forest(x = rma_model,
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variant = "classic",
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study_labels = rma_model$slab,
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text_size =4,
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xlab = effect,
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annotate_CI = TRUE,
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type = "standard")
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rainfp <- metaviz::viz_forest(x = rma_model,
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variant = "rain",
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study_labels = rma_model$slab,
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text_size =4,
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xlab = effect,
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annotate_CI = TRUE,
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type = "standard")
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height<-list("height" = length(rma_model$yi))
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jsonlite::write_json(height, "imgHeight.json")
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print(fp)
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print(rainfp)
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invisible();
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}
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}

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