R/forest_plot.R
In MRCIEU/TwoSampleMR: Two Sample MR Functions and Interface to MR Base Database
Defines functions
mr_forest_plot_grouped
Documented in
mr_forest_plot_grouped
### Functions to generate grouped forest plots
### Version: 0.4
### Date: 20150914
### Authors: cl14022
### This script does:
### 1. Read in a set of meta-analysis data that arise from mr_singlesnp
### 2. Order sub-analyses alphabetically
### 2.1. Order the studies within each sub-analysis by effect size
### 3. Generate a list of spacings so that sub-analyses can have headers and be spaced apart for easy reading
### 4. Generate a properly-spaced forest plot (without a summary effect) for all studies
### 5. Generate a properly-spaced set of annotations for the left-hand side (lhs) of the figure
### 6. Generate a properly-spaced set of annotations for the right-hand side (rhs) of the figure
### 7. Combine the three sub-figures into a single figure
### 7.1.Generate a list of spacings for annotation columns and for the forest-plot column
### 8. Export results as a pdf/windows meta file and to screen
# library(ggplot2)
# library(plyr)
# library(gtable)
## 8.0.1. Unifiying all into a single function that calls each of the above methods
#' Grouped forest plot
#'
#' @param name (character) name of the delimited file containing all of the results on the first sheet (needs to have headers), or of the r object.
#' @param eff_Col (character) name of the column in the delimited file that contains the effect sizes.
#' @param exposure_Name (character) name of the column in the delimited file containing the *types* of studies.
#' @param outcome_Name (character) name of the column in the delimited file containing the names of each study.
#' @param outfile_Name (character) name to be used for output file (*.pdf) or (*.wmf).
#' @param forest_Title (character) the title to be used for a forest plot.
#' @param left_Col_Names (character vector) vector containing the names of the left-hand-side annotation columns in the delimited file.
#' @param left_Col_Titles (character vector) vector containing the titles for each left-hand-side annotation column.
#' @param right_Col_Names (character vector) vector containing the names of the right-hand-side annotation columns in the delimited file.
#' @param right_Col_Titles (character vector) vector containing the titles for each right-hand-side annotation column.
#' @param debug (logical) show warnings `TRUE`/`FALSE`?
#' @param log_ES (logical) perform natural log transform of effect sizes and confidence bounds `TRUE`/`FALSE`?
#' @param decrease (logical) sort the studies by decreasing effect sizes `TRUE`/`FALSE`?
#' @param se_Col (character) name of the column giving the standard error of the effect sizes.
#' @param returnRobj (logical) return the graph as an internal R object `TRUE`/`FALSE`?
#'
#' @keywords internal
#' @return grid object giving the forest plot (or plot as pdf)
mr_forest_plot_grouped <-
function(name, eff_Col = "b", exposure_Name="exposure", outcome_Name="outcome", forest_Title = '', outfile_Name = 'annot_FP.pdf', left_Col_Names=c("Exposure", "Outcome"), left_Col_Titles = NULL, right_Col_Names = c("p", "Outcome.n.case", "Outcome.n.control", "Outcome.sample.size"), right_Col_Titles =
NULL, debug = FALSE, log_ES = FALSE, decrease = TRUE, returnRobj = TRUE, se_Col = "se") {
# name = (character) name of the r object from mr_singlesnp()
# inRobj = (logical) is the data to be used an internal R object y/n?
# eff_Col = (character) name of the column in the delimited file that contains the effect sizes
# exposure_Name = (character) name of the column in the delimited file containing the exposure to use
# outcome_Name = (character) name of the column in the delimited file containing outcome to be analyzed
# outfile_Name = (character) name to be used for output file (*.pdf) or (*.wmf)
# forest_Title = (character) the title to be used for a forest plot
# left_Col_Names = (character vector) vector containing the names of the left-hand-side annotation columns in the delimited file
# left_Col_Titles = (character vector) vector containing the titles for each left-hand-side annotation column
# right_Col_Names = (character vector) vector containing the names of the right-hand-side annotation columns in the delimited file
# right_Col_Titles = (character vector) vector containing the titles for each right-hand-side annotation column
# debug = (logical) show warnings y/n?
# log_ES = (logical) perform natural log transform of effect sizes and confidence bounds y/n?
# decrease = (logical) sort the studies by decreasing effect sizes y/n?
# returnRobj = (logical) return the graph as an internal R object y/n?
name$lb <- name[,eff_Col] - stats::qnorm(p = 0.95) * name[,se_Col]
name$ub <- name[,eff_Col] + stats::qnorm(p = 0.95) * name[,se_Col]
data <- name
spacer <- function(exposure, eff_col, outcome, Data_Fm, decrease = TRUE) {
# exposure = (character), column name of the column containing the name of the particular exposure
# eff_col = (character), column name of the column giving the effect size of interest
# outcome = (character), column name giving study names/phenotype names
# Data_Fm = (name) name of the data frame containing the meta-analytic cata
# decrease = (logical) order the studies by decreasing effect size y/n?
N_study <- nrow(Data_Fm)
# Count the number of categories of studies (e.g. by type of illness )
N_sub <- length(unique(Data_Fm[,exposure]))
# The layout of rows is one per study, then, buffering room added for the subgroups
N_space <- N_study + 2 * N_sub
spacing_vec <- vector(mode = "numeric", length = N_space)
content_list <- list()
attr_list <- list()
row_list <- list()
idx <- 1
for (subgp in unique(Data_Fm[,exposure])) {
# loop over substudy ids to fill the 'spacing vector,' which determines placement of the rows in the forest plot
sg_idxs <- which(Data_Fm[,exposure] == subgp)
n_std <- length(sg_idxs)
spacing_vec[[idx]] <- idx
spacing_vec[[(idx + 1 + n_std)]] <- (idx + 1 + n_std)
std_sort <- order(as.numeric(Data_Fm[sg_idxs, eff_col]), decreasing = decrease)
spacing_vec[((idx + 1):(idx + n_std))] <- idx + 1:n_std
# In the same loop, fill a list with study names but also with headers to serve as the primary annotation of the forest plot
content_list[[idx]] <- subgp
content_list[[(idx + 1 + n_std)]] <- ""
content_list[((idx + 1):(idx + n_std))] <- Data_Fm[sg_idxs[std_sort], outcome]
# A list of attributes for the typeface so that headers are in bold face
attr_list[[idx]] <- "bold"
attr_list[[(idx + 1 + n_std)]] <- "plain"
attr_list[((idx + 1):(idx + n_std))] <- "plain"
# The most important list, contains mapping between spaces on the forest plot and rows in the meta-analytic data frame
row_list[[idx]] <- NA
row_list[[(idx + 1 + n_std)]] <- NA
row_list[((idx + 1):(idx + n_std))] <- sg_idxs[std_sort]
idx <- idx + 2 + n_std
}
# returns data frame giving spacings, primary annotation (content_list), typeface attributes for the primary annotation (attr_list), and mapping between rows in forest plot and rows in meta-analytic data frame
return(data.frame(spacing_vec = (1 + length(spacing_vec) - spacing_vec), content_list = unlist(content_list), row_list = unlist(row_list), attr_list = unlist(attr_list)))
}
space_Out <- function(data_Fm, space_Fm) {
# a function to expand the initial data frame and add the spacing vector in a proper way
# data_Fm = (name) data frame containing the meta-analytic data
# space_Fm = (name) data frame containing the spacing vector and the mapping between spaces in the forest plot and rows in the meta-analytic data
exp_data_Fm <- data.frame(space_Fm)
for (jj in colnames(data_Fm)) {
exp_data_Fm[,jj] <- data_Fm[space_Fm$row_list,jj]
}
# Returns a data frame containing spacing, primary annotations, typeface attributes for the primary annotation, and correctly-mapped rows in the meta-analytic data frame
return(exp_data_Fm)
}
### 4. Generate a properly-spaced forest plot (without a summary effect) for all studies
ggforest <- function(data_Fm, space_col, eff_col, lb_col = "lb", ub_col = "ub", title_text = '', log_ES = FALSE) {
# data_Fm = (name) spaced data frame (must be from output from space_Out()) containing meta-analytic data and appropriate annotation, spacing cols etc
# space_col = (character) column name giving the column listing spacing of effects in the forest plot
# eff_col = (character) column name of effect-size column
# lb_col = (character) column name of the confidence interval lower bound column
# ub_col = (character) column name of the confidence interval upper bound column
# title_text = (character) title text for main part of forest plot (will use column name if no text is supplied)
# log_ES = (logical) convert effect sizes to natural log scale y/n?
if (nchar(title_text) <= 1) {
title_text <- eff_col
}
## reassignment of column names to avoid ggplot scope problems
data_Fm$space_col <- data_Fm[,space_col]
data_Fm$lb_col <- data_Fm[,lb_col]
if (log_ES) {
data_Fm$lb_col <- log(as.numeric(data_Fm[,lb_col]))
}
data_Fm$ub_col <- data_Fm[, ub_col]
if (log_ES) {
data_Fm$ub_col <- log(as.numeric(data_Fm[,ub_col]))
}
data_Fm$eff_col <- data_Fm[, eff_col]
if (log_ES) {
data_Fm$eff_col <- log(as.numeric(data_Fm[,eff_col]))
}
# ggplot code to generate the forest plot using geom_segments and geom_points and to make a relatively minimal theme
raw_forest <- ggplot2::ggplot(data = data_Fm, ggplot2::aes(y = space_col, yend = space_col, x = as.numeric(lb_col), xend = as.numeric(ub_col))) + ggplot2::geom_segment() + ggplot2::geom_point(ggplot2::aes(y = space_col, x = as.numeric(eff_col), size = 4)) + ggplot2::theme_bw() + ggplot2::theme(axis.text.y = ggplot2::element_blank(), axis.ticks.y = ggplot2::element_blank(), axis.title = ggplot2::element_blank(), panel.grid = ggplot2::element_blank(), rect = ggplot2::element_blank(), title = ggplot2::element_text(size = 23), legend.position = 'none') + ggplot2::expand_limits(y = c(data_Fm[,space_col] - 1, data_Fm[,space_col] + 2)) + ggplot2::labs(title = title_text) # returns ggplot2 object with the (un-annotated) forest plot
return(raw_forest)
}
### 5. Generate a properly-spaced set of annotations for the left-hand side (lhs) of the figure
### and
### 6. Generate a properly-spaced set of annotations for the right-hand side (rhs) of the figure
## 5.1. Function to generate a single column of annotations
anot_col <- function(data_Fm, text_col, space_col, title_text = '') {
# data_Fm = (name) space_Out data frame
# text_col = (character) name of column containing annotations
# title_text = (character) title of annotation column, defaults to the column name if nothing is entered
data_Fm$space_col <- data_Fm[,space_col]
data_Fm$text_col <- data_Fm[,text_col]
# A hard rule to set the width of the annotation column, which sometimes truncates very wide columns (complex disease names, numbers with 16 digits, etc)
text_widths <- c(-1, max(10,0.5 * max(sapply(as.character(data_Fm[,text_col]), nchar))))
# GGplot rendering of the annotation column
lefttext <- ggplot2::ggplot(data = data_Fm, ggplot2::aes(y = space_col, x = 0, label = text_col, fontface = attr_list)) + ggplot2::geom_text(hjust = 0) + ggplot2::theme_bw() + ggplot2::theme(axis.text.y = ggplot2::element_blank(), axis.ticks.y = ggplot2::element_blank(), axis.text.x = ggplot2::element_text(colour = "white"), axis.ticks.x = ggplot2::element_line(colour = "white"), axis.title = ggplot2::element_blank(), rect = ggplot2::element_blank(), panel.grid = ggplot2::element_blank(), title = ggplot2::element_text(size = 23)) + ggplot2::expand_limits(x = text_widths, y = c(data_Fm[, space_col] - 1, data_Fm[, space_col] + 2)) + ggplot2::labs(title = title_text, size = 40) # returns two-item list with left_text, the GGplot annotations, and text_widths, the x-axis limits of the plot
return(list(left_text = lefttext, text_widths = text_widths))
}
## 5.2. Function to aggregate all of the left-hand (all of the right-hand) notation columns into a single subplot, which works by applying the anot_col function to each item in the list of annotation columns
anot_side <- function(data_Fm, space_col, col_names, title_list = '') {
# data_Fm = (name) space_Out data frame containing effect sizes, all annotations, and spacings
# space_col = (character) name of the column containing the spacing vector
# col_names = (character vector) vector of the column names for the columns to be the left-hand-side or right-hand-side annotations
# title_list = (character vector) vector of the titles to be given to each of the annotation columns, defaults to the column names
relative_widths <- vector(mode = "numeric", length = length(col_names))
output <- vector(mode = "list")
if (length(title_list) <= 1) {
title_list <- col_names
}
for (i in seq_along(col_names)) {
# loop to get the widths of each annotation column and to group the annotation objects together
col <- anot_col(data_Fm = data_Fm, text_col = col_names[i], space_col = space_col, title_text = title_list[[i]])
relative_widths[i] <- col$text_widths[2] - col$text_widths[1]
output[[col_names[i]]] <- ggplotGrob(col$left_text)
}
# calculate the widths of the annotation columns, relative to each other, and then multiply it by a third so that it fits onto one side of the plot
output$relative_widths <- relative_widths / (sum(relative_widths)) * 0.33
# returns an output object containing: each annotation plot, listed under its own name, and the relative_widths of the columns
return(output)
}
### 7. Combine the three sub-figures into a single figure
### 7.1.Generate a list of spacings for annotation columns and for
group_Plots <- function(forst_Pt, left_Hs, right_Hs) {
# forst_Pt = (name) the name of the forest plot object
# left_Hs = (name) the name of the aggregate annotation object for the left hand side of the plot
# right_Hs = (name) the name of the aggregate annotation object for the right hand side of the plot
# Aggregate all of the plots into a single grid object for plotting
grob_Bag <- vector(mode = 'list')
left_RW <- left_Hs$relative_widths
left_Grobs <- left_Hs
left_Grobs$relative_widths <- NULL
for (i in seq_along(left_Grobs)) {
grob_Bag[paste('l',names(left_Grobs)[i],sep = '')] <- left_Grobs[i]
}
grob_Bag$m_forest <- ggplotGrob(forst_Pt)
right_RW <- right_Hs$relative_widths
right_Grobs <- right_Hs
right_Grobs$relative_widths <- NULL
for (i in seq_along(right_Grobs)) {
grob_Bag[paste('r',names(right_Grobs)[i], sep = '')] <- right_Grobs[i]
}
width_vec <- c(left_RW,0.34,right_RW)
width_vec <- width_vec / sum(width_vec)
# convert the grid objects (now grouped) into a table of grid objects that can be plotted using grid.draw
grp_FP <- gtable::gtable_matrix(name = "groupplot", grobs = matrix(grob_Bag, nrow = 1), widths = unit(width_vec, "npc"), heights = unit(1,"npc"))
# return the grid object table, to be plotted
return(grp_FP)
}
## act on debug flag
if (debug == FALSE) {
options(warn = -1)
}
## read in data: beginning of the OVERALL FUNCTION
## order and structure effect sizes and CIs for forest plot
space1 <- spacer(exposure = exposure_Name, eff_col = eff_Col, outcome = outcome_Name, Data_Fm = data, decrease = decrease)
expand_data <- space_Out(data_Fm = data, space_Fm = space1)
## Make the forest plot
fo1 <- ggforest(data_Fm = expand_data, space_col = 'spacing_vec', eff_col = eff_Col, lb_col = "lb" ,ub_col = "ub", log_ES = log_ES, title_text = forest_Title)
## Construct left-hand-side annotations
left <- anot_side(data_Fm = expand_data, space_col = 'spacing_vec', col_names = left_Col_Names, title_list = left_Col_Titles)
## Construct right-hand-side annotations
right <- anot_side(data_Fm = expand_data, space_col = 'spacing_vec', col_names = right_Col_Names, title_list = right_Col_Titles)
## group all plots together
group <- group_Plots(forst_Pt = fo1, left_Hs = left, right_Hs = right)
## draw and export the annotated forest plot
grid.newpage()
grid.draw(group)
if (returnRobj == FALSE) {
grDevices::pdf(outfile_Name,width = 23.4, height = 16.5)
grid.draw(group)
grDevices::dev.off()
} else {
return(group)
}
## make sure not to leave warnings turned off
options(warn = 0)
}
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Defines functions mr_forest_plot_grouped
Documented in mr_forest_plot_grouped
### Functions to generate grouped forest plots
### Version: 0.4
### Date: 20150914
### Authors: cl14022
### This script does:
### 1. Read in a set of meta-analysis data that arise from mr_singlesnp
### 2. Order sub-analyses alphabetically
### 2.1. Order the studies within each sub-analysis by effect size
### 3. Generate a list of spacings so that sub-analyses can have headers and be spaced apart for easy reading
### 4. Generate a properly-spaced forest plot (without a summary effect) for all studies
### 5. Generate a properly-spaced set of annotations for the left-hand side (lhs) of the figure
### 6. Generate a properly-spaced set of annotations for the right-hand side (rhs) of the figure
### 7. Combine the three sub-figures into a single figure
### 7.1.Generate a list of spacings for annotation columns and for the forest-plot column
### 8. Export results as a pdf/windows meta file and to screen
# library(ggplot2)
# library(plyr)
# library(gtable)
## 8.0.1. Unifiying all into a single function that calls each of the above methods
#' Grouped forest plot
#'
#' @param name (character) name of the delimited file containing all of the results on the first sheet (needs to have headers), or of the r object.
#' @param eff_Col (character) name of the column in the delimited file that contains the effect sizes.
#' @param exposure_Name (character) name of the column in the delimited file containing the *types* of studies.
#' @param outcome_Name (character) name of the column in the delimited file containing the names of each study.
#' @param outfile_Name (character) name to be used for output file (*.pdf) or (*.wmf).
#' @param forest_Title (character) the title to be used for a forest plot.
#' @param left_Col_Names (character vector) vector containing the names of the left-hand-side annotation columns in the delimited file.
#' @param left_Col_Titles (character vector) vector containing the titles for each left-hand-side annotation column.
#' @param right_Col_Names (character vector) vector containing the names of the right-hand-side annotation columns in the delimited file.
#' @param right_Col_Titles (character vector) vector containing the titles for each right-hand-side annotation column.
#' @param debug (logical) show warnings `TRUE`/`FALSE`?
#' @param log_ES (logical) perform natural log transform of effect sizes and confidence bounds `TRUE`/`FALSE`?
#' @param decrease (logical) sort the studies by decreasing effect sizes `TRUE`/`FALSE`?
#' @param se_Col (character) name of the column giving the standard error of the effect sizes.
#' @param returnRobj (logical) return the graph as an internal R object `TRUE`/`FALSE`?
#'
#' @keywords internal
#' @return grid object giving the forest plot (or plot as pdf)
mr_forest_plot_grouped <-
function(name, eff_Col = "b", exposure_Name="exposure", outcome_Name="outcome", forest_Title = '', outfile_Name = 'annot_FP.pdf', left_Col_Names=c("Exposure", "Outcome"), left_Col_Titles = NULL, right_Col_Names = c("p", "Outcome.n.case", "Outcome.n.control", "Outcome.sample.size"), right_Col_Titles =
NULL, debug = FALSE, log_ES = FALSE, decrease = TRUE, returnRobj = TRUE, se_Col = "se") {
# name = (character) name of the r object from mr_singlesnp()
# inRobj = (logical) is the data to be used an internal R object y/n?
# eff_Col = (character) name of the column in the delimited file that contains the effect sizes
# exposure_Name = (character) name of the column in the delimited file containing the exposure to use
# outcome_Name = (character) name of the column in the delimited file containing outcome to be analyzed
# outfile_Name = (character) name to be used for output file (*.pdf) or (*.wmf)
# forest_Title = (character) the title to be used for a forest plot
# left_Col_Names = (character vector) vector containing the names of the left-hand-side annotation columns in the delimited file
# left_Col_Titles = (character vector) vector containing the titles for each left-hand-side annotation column
# right_Col_Names = (character vector) vector containing the names of the right-hand-side annotation columns in the delimited file
# right_Col_Titles = (character vector) vector containing the titles for each right-hand-side annotation column
# debug = (logical) show warnings y/n?
# log_ES = (logical) perform natural log transform of effect sizes and confidence bounds y/n?
# decrease = (logical) sort the studies by decreasing effect sizes y/n?
# returnRobj = (logical) return the graph as an internal R object y/n?
name$lb <- name[,eff_Col] - stats::qnorm(p = 0.95) * name[,se_Col]
name$ub <- name[,eff_Col] + stats::qnorm(p = 0.95) * name[,se_Col]
data <- name
spacer <- function(exposure, eff_col, outcome, Data_Fm, decrease = TRUE) {
# exposure = (character), column name of the column containing the name of the particular exposure
# eff_col = (character), column name of the column giving the effect size of interest
# outcome = (character), column name giving study names/phenotype names
# Data_Fm = (name) name of the data frame containing the meta-analytic cata
# decrease = (logical) order the studies by decreasing effect size y/n?
N_study <- nrow(Data_Fm)
# Count the number of categories of studies (e.g. by type of illness )
N_sub <- length(unique(Data_Fm[,exposure]))
# The layout of rows is one per study, then, buffering room added for the subgroups
N_space <- N_study + 2 * N_sub
spacing_vec <- vector(mode = "numeric", length = N_space)
content_list <- list()
attr_list <- list()
row_list <- list()
idx <- 1
for (subgp in unique(Data_Fm[,exposure])) {
# loop over substudy ids to fill the 'spacing vector,' which determines placement of the rows in the forest plot
sg_idxs <- which(Data_Fm[,exposure] == subgp)
n_std <- length(sg_idxs)
spacing_vec[[idx]] <- idx
spacing_vec[[(idx + 1 + n_std)]] <- (idx + 1 + n_std)
std_sort <- order(as.numeric(Data_Fm[sg_idxs, eff_col]), decreasing = decrease)
spacing_vec[((idx + 1):(idx + n_std))] <- idx + 1:n_std
# In the same loop, fill a list with study names but also with headers to serve as the primary annotation of the forest plot
content_list[[idx]] <- subgp
content_list[[(idx + 1 + n_std)]] <- ""
content_list[((idx + 1):(idx + n_std))] <- Data_Fm[sg_idxs[std_sort], outcome]
# A list of attributes for the typeface so that headers are in bold face
attr_list[[idx]] <- "bold"
attr_list[[(idx + 1 + n_std)]] <- "plain"
attr_list[((idx + 1):(idx + n_std))] <- "plain"
# The most important list, contains mapping between spaces on the forest plot and rows in the meta-analytic data frame
row_list[[idx]] <- NA
row_list[[(idx + 1 + n_std)]] <- NA
row_list[((idx + 1):(idx + n_std))] <- sg_idxs[std_sort]
idx <- idx + 2 + n_std
}
# returns data frame giving spacings, primary annotation (content_list), typeface attributes for the primary annotation (attr_list), and mapping between rows in forest plot and rows in meta-analytic data frame
return(data.frame(spacing_vec = (1 + length(spacing_vec) - spacing_vec), content_list = unlist(content_list), row_list = unlist(row_list), attr_list = unlist(attr_list)))
}
space_Out <- function(data_Fm, space_Fm) {
# a function to expand the initial data frame and add the spacing vector in a proper way
# data_Fm = (name) data frame containing the meta-analytic data
# space_Fm = (name) data frame containing the spacing vector and the mapping between spaces in the forest plot and rows in the meta-analytic data
exp_data_Fm <- data.frame(space_Fm)
for (jj in colnames(data_Fm)) {
exp_data_Fm[,jj] <- data_Fm[space_Fm$row_list,jj]
}
# Returns a data frame containing spacing, primary annotations, typeface attributes for the primary annotation, and correctly-mapped rows in the meta-analytic data frame
return(exp_data_Fm)
}
### 4. Generate a properly-spaced forest plot (without a summary effect) for all studies
ggforest <- function(data_Fm, space_col, eff_col, lb_col = "lb", ub_col = "ub", title_text = '', log_ES = FALSE) {
# data_Fm = (name) spaced data frame (must be from output from space_Out()) containing meta-analytic data and appropriate annotation, spacing cols etc
# space_col = (character) column name giving the column listing spacing of effects in the forest plot
# eff_col = (character) column name of effect-size column
# lb_col = (character) column name of the confidence interval lower bound column
# ub_col = (character) column name of the confidence interval upper bound column
# title_text = (character) title text for main part of forest plot (will use column name if no text is supplied)
# log_ES = (logical) convert effect sizes to natural log scale y/n?
if (nchar(title_text) <= 1) {
title_text <- eff_col
}
## reassignment of column names to avoid ggplot scope problems
data_Fm$space_col <- data_Fm[,space_col]
data_Fm$lb_col <- data_Fm[,lb_col]
if (log_ES) {
data_Fm$lb_col <- log(as.numeric(data_Fm[,lb_col]))
}
data_Fm$ub_col <- data_Fm[, ub_col]
if (log_ES) {
data_Fm$ub_col <- log(as.numeric(data_Fm[,ub_col]))
}
data_Fm$eff_col <- data_Fm[, eff_col]
if (log_ES) {
data_Fm$eff_col <- log(as.numeric(data_Fm[,eff_col]))
}
# ggplot code to generate the forest plot using geom_segments and geom_points and to make a relatively minimal theme
raw_forest <- ggplot2::ggplot(data = data_Fm, ggplot2::aes(y = space_col, yend = space_col, x = as.numeric(lb_col), xend = as.numeric(ub_col))) + ggplot2::geom_segment() + ggplot2::geom_point(ggplot2::aes(y = space_col, x = as.numeric(eff_col), size = 4)) + ggplot2::theme_bw() + ggplot2::theme(axis.text.y = ggplot2::element_blank(), axis.ticks.y = ggplot2::element_blank(), axis.title = ggplot2::element_blank(), panel.grid = ggplot2::element_blank(), rect = ggplot2::element_blank(), title = ggplot2::element_text(size = 23), legend.position = 'none') + ggplot2::expand_limits(y = c(data_Fm[,space_col] - 1, data_Fm[,space_col] + 2)) + ggplot2::labs(title = title_text) # returns ggplot2 object with the (un-annotated) forest plot
return(raw_forest)
}
### 5. Generate a properly-spaced set of annotations for the left-hand side (lhs) of the figure
### and
### 6. Generate a properly-spaced set of annotations for the right-hand side (rhs) of the figure
## 5.1. Function to generate a single column of annotations
anot_col <- function(data_Fm, text_col, space_col, title_text = '') {
# data_Fm = (name) space_Out data frame
# text_col = (character) name of column containing annotations
# title_text = (character) title of annotation column, defaults to the column name if nothing is entered
data_Fm$space_col <- data_Fm[,space_col]
data_Fm$text_col <- data_Fm[,text_col]
# A hard rule to set the width of the annotation column, which sometimes truncates very wide columns (complex disease names, numbers with 16 digits, etc)
text_widths <- c(-1, max(10,0.5 * max(sapply(as.character(data_Fm[,text_col]), nchar))))
# GGplot rendering of the annotation column
lefttext <- ggplot2::ggplot(data = data_Fm, ggplot2::aes(y = space_col, x = 0, label = text_col, fontface = attr_list)) + ggplot2::geom_text(hjust = 0) + ggplot2::theme_bw() + ggplot2::theme(axis.text.y = ggplot2::element_blank(), axis.ticks.y = ggplot2::element_blank(), axis.text.x = ggplot2::element_text(colour = "white"), axis.ticks.x = ggplot2::element_line(colour = "white"), axis.title = ggplot2::element_blank(), rect = ggplot2::element_blank(), panel.grid = ggplot2::element_blank(), title = ggplot2::element_text(size = 23)) + ggplot2::expand_limits(x = text_widths, y = c(data_Fm[, space_col] - 1, data_Fm[, space_col] + 2)) + ggplot2::labs(title = title_text, size = 40) # returns two-item list with left_text, the GGplot annotations, and text_widths, the x-axis limits of the plot
return(list(left_text = lefttext, text_widths = text_widths))
}
## 5.2. Function to aggregate all of the left-hand (all of the right-hand) notation columns into a single subplot, which works by applying the anot_col function to each item in the list of annotation columns
anot_side <- function(data_Fm, space_col, col_names, title_list = '') {
# data_Fm = (name) space_Out data frame containing effect sizes, all annotations, and spacings
# space_col = (character) name of the column containing the spacing vector
# col_names = (character vector) vector of the column names for the columns to be the left-hand-side or right-hand-side annotations
# title_list = (character vector) vector of the titles to be given to each of the annotation columns, defaults to the column names
relative_widths <- vector(mode = "numeric", length = length(col_names))
output <- vector(mode = "list")
if (length(title_list) <= 1) {
title_list <- col_names
}
for (i in seq_along(col_names)) {
# loop to get the widths of each annotation column and to group the annotation objects together
col <- anot_col(data_Fm = data_Fm, text_col = col_names[i], space_col = space_col, title_text = title_list[[i]])
relative_widths[i] <- col$text_widths[2] - col$text_widths[1]
output[[col_names[i]]] <- ggplotGrob(col$left_text)
}
# calculate the widths of the annotation columns, relative to each other, and then multiply it by a third so that it fits onto one side of the plot
output$relative_widths <- relative_widths / (sum(relative_widths)) * 0.33
# returns an output object containing: each annotation plot, listed under its own name, and the relative_widths of the columns
return(output)
}
### 7. Combine the three sub-figures into a single figure
### 7.1.Generate a list of spacings for annotation columns and for
group_Plots <- function(forst_Pt, left_Hs, right_Hs) {
# forst_Pt = (name) the name of the forest plot object
# left_Hs = (name) the name of the aggregate annotation object for the left hand side of the plot
# right_Hs = (name) the name of the aggregate annotation object for the right hand side of the plot
# Aggregate all of the plots into a single grid object for plotting
grob_Bag <- vector(mode = 'list')
left_RW <- left_Hs$relative_widths
left_Grobs <- left_Hs
left_Grobs$relative_widths <- NULL
for (i in seq_along(left_Grobs)) {
grob_Bag[paste('l',names(left_Grobs)[i],sep = '')] <- left_Grobs[i]
}
grob_Bag$m_forest <- ggplotGrob(forst_Pt)
right_RW <- right_Hs$relative_widths
right_Grobs <- right_Hs
right_Grobs$relative_widths <- NULL
for (i in seq_along(right_Grobs)) {
grob_Bag[paste('r',names(right_Grobs)[i], sep = '')] <- right_Grobs[i]
}
width_vec <- c(left_RW,0.34,right_RW)
width_vec <- width_vec / sum(width_vec)
# convert the grid objects (now grouped) into a table of grid objects that can be plotted using grid.draw
grp_FP <- gtable::gtable_matrix(name = "groupplot", grobs = matrix(grob_Bag, nrow = 1), widths = unit(width_vec, "npc"), heights = unit(1,"npc"))
# return the grid object table, to be plotted
return(grp_FP)
}
## act on debug flag
if (debug == FALSE) {
options(warn = -1)
}
## read in data: beginning of the OVERALL FUNCTION
## order and structure effect sizes and CIs for forest plot
space1 <- spacer(exposure = exposure_Name, eff_col = eff_Col, outcome = outcome_Name, Data_Fm = data, decrease = decrease)
expand_data <- space_Out(data_Fm = data, space_Fm = space1)
## Make the forest plot
fo1 <- ggforest(data_Fm = expand_data, space_col = 'spacing_vec', eff_col = eff_Col, lb_col = "lb" ,ub_col = "ub", log_ES = log_ES, title_text = forest_Title)
## Construct left-hand-side annotations
left <- anot_side(data_Fm = expand_data, space_col = 'spacing_vec', col_names = left_Col_Names, title_list = left_Col_Titles)
## Construct right-hand-side annotations
right <- anot_side(data_Fm = expand_data, space_col = 'spacing_vec', col_names = right_Col_Names, title_list = right_Col_Titles)
## group all plots together
group <- group_Plots(forst_Pt = fo1, left_Hs = left, right_Hs = right)
## draw and export the annotated forest plot
grid.newpage()
grid.draw(group)
if (returnRobj == FALSE) {
grDevices::pdf(outfile_Name,width = 23.4, height = 16.5)
grid.draw(group)
grDevices::dev.off()
} else {
return(group)
}
## make sure not to leave warnings turned off
options(warn = 0)
}
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