R/rob.summary.R
In MathiasHarrer/dmetar: Companion R Package For The Guide 'Doing Meta-Analysis in R'
Defines functions
rob.summary
Documented in
rob.summary
#' Create a RevMan-style risk of bias summary chart
#'
#' This function generates summary plots for study quality assessments using the
#' \href{https://bit.ly/2KGQtfG}{Cochrance Risk of Bias Tool}.
#' Summary plots follow the style of \href{https://bit.ly/30eJK29}{RevMan} Risk of Bias (RoB) summary charts.
#'
#' @usage rob.summary(data, name.high="High", name.unclear="Unclear",
#' name.low="Low", studies, name.missing, table = FALSE)
#'
#' @param data A \code{data.frame} containing a column for each risk of bias criterion, where
#' rows represent each individual studies. The risk of bias assessment for each criterion in each
#' study must be coded as a character string. Up to four codes can be used, referring to low risk of bias,
#' unclear risk of bias, high risk of bias, or missing information. The string used to specify the categories
#' must be specified in \code{name.high}, \code{name.unclear}, \code{name.low} and/or \code{name.missing},
#' unless defaults for those parameters are used.
#' @param name.high Character specifying how the "high risk of bias" category was coded in \code{data}
#' (e.g., \code{name.high = "high"}). Default is \code{"High"}.
#' @param name.unclear Character specifying how the "unclear risk of bias" category was coded in \code{data}
#' (e.g., \code{name.unclear = "unclear"}). Default is \code{"Unclear"}.
#' @param name.low Character specifying how the "low risk of bias" category was coded in \code{data}
#' (e.g., \code{name.low = "low"}). Default is \code{"Low"}.
#' @param name.missing Character specifying how missing information was coded in \code{data}
#' (e.g., \code{name.missing} = \code{"missing"}). Default is \code{"Missing"}. All ratings, including missing
#' information, must be coded as strings, so using \code{NA} in \code{data} to signify missing information
#' is not valid.
#' @param studies A vector of the same length as the number of rows in \code{data} specifying the study
#' labels for the risk of bias ratings. Only has to be specified when \code{table = TRUE}.
#' @param table Should an additional RevMan style risk of bias table be produced? If set to \code{TRUE},
#' \code{studies} must be specified. \code{FALSE} by default.
#'
#' @details The function automatically removes separators like "-" or "." from column names/risk of bias criteria. To produce
#' a "clean" plot, you may therefore separate words in the column names of the \code{data} data frame using these
#' symbols (e.g. \code{"Allocation_Concealment"} to return "Allocation Concealment").
#'
#' @references Harrer, M., Cuijpers, P., Furukawa, T.A, & Ebert, D. D. (2019).
#' \emph{Doing Meta-Analysis in R: A Hands-on Guide}. DOI: 10.5281/zenodo.2551803.
#' \href{https://bookdown.org/MathiasHarrer/Doing_Meta_Analysis_in_R/creating-a-revman-style-risk-of-bias-summary.html}{Chapter 10}
#'
#' @author Mathias Harrer & David Daniel Ebert
#'
#' @import ggplot2
#' @importFrom tidyr gather
#'
#' @export rob.summary
#'
#' @seealso
#' \code{\link{direct.evidence.plot}}
#'
#' @examples
#' # Example 1: No missing information, only produce summary plot
#' data = data.frame(
#' "study" = c("Higgins et al., 2011", "Borenstein et al., 2008", "Holm, 1971",
#' "Zajonc et al., 2005", "Cuijpers, 2014"),
#' "Allocation_concealment" = c("Low", "High", "High", "Unclear", "High"),
#' "Randomization" = c("Low", "High", "Unclear", "Low", "High"),
#' "Sequence_generation" = c("Low", "High", "Unclear", "Unclear", "High"),
#' "ITT.Analyses" = c("Low", "High", "Unclear", "Unclear", "Unclear"),
#' "Selective_outcome_reporting" = c("Low", "High", "High", "High", "Unclear")
#' )
#' rob.summary(data)
#'
#' # Example 2: Missing information, produce additional summary table
#' data2 = data.frame(
#' "study" = c("Higgins et al., 2011", "Borenstein et al., 2008", "Holm, 1971",
#' "Zajonc et al., 2005", "Cuijpers, 2014"),
#' "Allocation_concealment" = c("low", "high", "high", "uc", "high"),
#' "Randomization" = c("low", "high", "miss", "low", "high"),
#' "Sequence_generation" = c("low", "high", "uc", "uc", "high"),
#' "ITT.Analyses" = c("low", "high", "uc", "uc", "uc"),
#' "Selective_outcome_reporting" = c("low", "high", "high", "high", "uc")
#' )
#' rob.summary(data2, name.high = "high", name.unclear = "uc", name.low = "low",
#' name.missing = "miss", studies = data2$study, table = TRUE)
rob.summary = function(data,
name.high="High",
name.unclear="Unclear",
name.low="Low",
studies,
name.missing,
table = FALSE){
# Class Checks
if (class(data) != "data.frame"){
stop("'data' must be of class 'data.frame'.")
}
if (missing(name.missing)){
# Only select columns with RoB data
colnames.rob = character()
for (i in 1:ncol(data)){
vect = as.character(data[,i])
for (j in 1:length(data[,i])){
if (vect[j] %in% c(name.high, name.unclear, name.low)){
colnames.rob[i] = TRUE
} else {
colnames.rob[i] = FALSE
message(cat("Column '", colnames(data)[i],
"' removed from plot because it did not contain the specified RoB ratings (only). \n",
sep=""))
break
}
}
}
# Use mask: rob data
rob = data[ , as.logical(colnames.rob)]
# Relevel for plot
for (i in 1:ncol(rob)){
rob[,i] = as.character(rob[,i])
rob[rob[,i]==name.high,i] = "High"
rob[rob[,i]==name.unclear,i] = "Unclear"
rob[rob[,i]==name.low,i] = "Low"
}
# Make table
if (table == TRUE){
if (missing(studies)){
stop("'studies' has to be specified when 'table = TRUE'.")
}
if (length(as.vector(studies)) != nrow(data)){
stop("'studies' vector is not of equal length as the data.")
}
robby = rob
robby$study = studies
robby = gather(robby, condition, measurement, -study)
robby$condition = gsub("_"," ", robby$condition)
robby$condition = gsub("-"," ", robby$condition)
robby$condition = gsub("\\."," ", robby$condition)
robby[robby$measurement=="Low", "measurement"] = "+"
robby[robby$measurement=="Unclear", "measurement"] = "?"
robby[robby$measurement=="High", "measurement"] = "-"
rob.table = ggplot(data = robby, aes(y = condition, x = study)) +
geom_tile(color="black", fill="white", size = 0.8) +
geom_point(aes(color=as.factor(measurement)), size=20) +
geom_text(aes(label = measurement), size = 8) +
scale_x_discrete(position = "top") +
scale_color_manual(values = c("?" = "#E2DF07",
"-" = "#BF0000",
"+" = "#02C100")) +
theme_minimal() +
coord_equal() +
theme(axis.title.x = element_blank(),
axis.title.y = element_blank(),
axis.ticks.y = element_blank(),
axis.text.y = element_text(size = 15, color = "black"),
axis.text.x = element_text(size = 13, color = "black", angle = 90, hjust=0),
legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank())
}
# Make long format, clean the factors
rob.long = gather(rob, condition, measurement, factor_key = TRUE)
rob.long$condition = gsub("_"," ",rob.long$condition)
rob.long$condition = gsub("-"," ",rob.long$condition)
rob.long$condition = gsub("\\."," ",rob.long$condition)
rob.long$measurement = as.factor(rob.long$measurement)
rob.long$measurement = factor(rob.long$measurement, levels(rob.long$measurement)[c(1, 3, 2)])
# Make plot
rob.plot = ggplot(data = rob.long) +
geom_bar(mapping = aes(x = condition, fill = measurement), width = 0.7,
position = "fill", color = "black") +
coord_flip(ylim = c(0, 1)) +
guides(fill = guide_legend(reverse = TRUE)) +
scale_fill_manual("Risk of Bias",
labels = c(" High risk of bias ",
" Unclear risk of bias ",
" Low risk of bias "),
values = c(Unclear = "#E2DF07", High = "#BF0000", Low = "#02C100")) +
scale_y_continuous(labels = scales::percent) +
theme(axis.title.x = element_blank(),
axis.title.y = element_blank(),
axis.ticks.y = element_blank(),
axis.text.y = element_text(size = 18, color = "black"),
axis.line.x = element_line(colour = "black", size = 0.5, linetype = "solid"),
legend.position = "bottom",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
legend.background = element_rect(linetype = "solid", colour = "black"),
legend.title = element_blank(),
legend.key.size = unit(0.75, "cm"),
legend.text = element_text(size = 14))
plot(rob.plot)
if (table == TRUE){
plot(rob.table)
}
} else {
# Only select columns with RoB data
data = as.data.frame(data)
colnames.rob = character()
for (i in 1:ncol(data)){
vect = as.character(data[,i])
for (j in 1:length(data[,i])){
if (vect[j] %in% c(name.high, name.unclear, name.low, name.missing)){
colnames.rob[i] = TRUE
} else {
colnames.rob[i] = FALSE
message(cat("Column '", colnames(data)[i],
"' removed from plot because it did not contain the specified RoB ratings (only). \n",
sep=""))
break
}
}
}
# Use mask: rob data
rob = data[ , as.logical(colnames.rob)]
# Relevel for plot
for (i in 1:ncol(rob)){
rob[,i] = as.character(rob[,i])
rob[rob[,i]==name.high,i] = "High"
rob[rob[,i]==name.unclear,i] = "Unclear"
rob[rob[,i]==name.low,i] = "Low"
rob[rob[,i]==name.missing,i] = "Missing"
}
# Make Table
if (table == TRUE){
if (missing(studies)){
stop("'studies' has to be specified when 'table = TRUE'.")
}
if (length(as.vector(studies)) != nrow(data)){
stop("'studies' vector is not of equal length as the data.")
}
robby = rob
robby$study = studies
robby = gather(robby, condition, measurement, -study)
robby$condition = gsub("_"," ", robby$condition)
robby$condition = gsub("-"," ", robby$condition)
robby$condition = gsub("\\."," ", robby$condition)
robby[robby$measurement=="Low", "measurement"] = "+"
robby[robby$measurement=="Unclear", "measurement"] = "?"
robby[robby$measurement=="High", "measurement"] = "-"
robby[robby$measurement=="Missing", "measurement"] = " "
rob.table = ggplot(data = robby, aes(y = condition, x = study)) +
geom_tile(color="black", fill="white", size = 0.8) +
geom_point(aes(color=as.factor(measurement)), size=20) +
geom_text(aes(label = measurement), size = 8) +
scale_x_discrete(position = "top") +
scale_color_manual(values = c("?" = "#E2DF07",
"-" = "#BF0000",
"+" = "#02C100",
" " = "white")) +
theme_minimal() +
coord_equal() +
theme(axis.title.x = element_blank(),
axis.title.y = element_blank(),
axis.ticks.y = element_blank(),
axis.text.y = element_text(size = 15, color = "black"),
axis.text.x = element_text(size = 13, color = "black", angle = 90, hjust=0),
legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank())
}
# Make long format, clean the factors
rob.long = gather(rob, condition, measurement, factor_key = TRUE)
rob.long$condition = gsub("_"," ",rob.long$condition)
rob.long$condition = gsub("-"," ",rob.long$condition)
rob.long$condition = gsub("\\."," ",rob.long$condition)
rob.long$measurement = as.factor(rob.long$measurement)
rob.long$measurement = factor(rob.long$measurement, levels(rob.long$measurement)[c(3,1,4,2)])
rob.plot = ggplot(data = rob.long) +
geom_bar(mapping = aes(x = condition, fill = measurement), width = 0.7,
position = "fill", color = "black") +
coord_flip(ylim = c(0, 1)) +
guides(fill = guide_legend(reverse = TRUE)) +
scale_fill_manual("Risk of Bias",
labels = c(" Missing information ",
" High risk of bias ",
" Unclear risk of bias ",
" Low risk of bias "),
values = c(Unclear = "#E2DF07",
High = "#BF0000",
Low = "#02C100",
Missing = "white")) +
scale_y_continuous(labels = scales::percent) +
theme(axis.title.x = element_blank(),
axis.title.y = element_blank(),
axis.ticks.y = element_blank(),
axis.text.y = element_text(size = 18, color = "black"),
axis.line.x = element_line(colour = "black", size = 0.5, linetype = "solid"),
legend.position = "bottom",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
legend.background = element_rect(linetype = "solid", colour = "black"),
legend.title = element_blank(),
legend.key.size = unit(0.75, "cm"),
legend.text = element_text(size = 14))
plot(rob.plot)
if (table == TRUE){
plot(rob.table)
}
}
}
MathiasHarrer/dmetar documentation built on March 29, 2020, 7:46 a.m.
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Defines functions rob.summary
Documented in rob.summary
#' Create a RevMan-style risk of bias summary chart
#'
#' This function generates summary plots for study quality assessments using the
#' \href{https://bit.ly/2KGQtfG}{Cochrance Risk of Bias Tool}.
#' Summary plots follow the style of \href{https://bit.ly/30eJK29}{RevMan} Risk of Bias (RoB) summary charts.
#'
#' @usage rob.summary(data, name.high="High", name.unclear="Unclear",
#' name.low="Low", studies, name.missing, table = FALSE)
#'
#' @param data A \code{data.frame} containing a column for each risk of bias criterion, where
#' rows represent each individual studies. The risk of bias assessment for each criterion in each
#' study must be coded as a character string. Up to four codes can be used, referring to low risk of bias,
#' unclear risk of bias, high risk of bias, or missing information. The string used to specify the categories
#' must be specified in \code{name.high}, \code{name.unclear}, \code{name.low} and/or \code{name.missing},
#' unless defaults for those parameters are used.
#' @param name.high Character specifying how the "high risk of bias" category was coded in \code{data}
#' (e.g., \code{name.high = "high"}). Default is \code{"High"}.
#' @param name.unclear Character specifying how the "unclear risk of bias" category was coded in \code{data}
#' (e.g., \code{name.unclear = "unclear"}). Default is \code{"Unclear"}.
#' @param name.low Character specifying how the "low risk of bias" category was coded in \code{data}
#' (e.g., \code{name.low = "low"}). Default is \code{"Low"}.
#' @param name.missing Character specifying how missing information was coded in \code{data}
#' (e.g., \code{name.missing} = \code{"missing"}). Default is \code{"Missing"}. All ratings, including missing
#' information, must be coded as strings, so using \code{NA} in \code{data} to signify missing information
#' is not valid.
#' @param studies A vector of the same length as the number of rows in \code{data} specifying the study
#' labels for the risk of bias ratings. Only has to be specified when \code{table = TRUE}.
#' @param table Should an additional RevMan style risk of bias table be produced? If set to \code{TRUE},
#' \code{studies} must be specified. \code{FALSE} by default.
#'
#' @details The function automatically removes separators like "-" or "." from column names/risk of bias criteria. To produce
#' a "clean" plot, you may therefore separate words in the column names of the \code{data} data frame using these
#' symbols (e.g. \code{"Allocation_Concealment"} to return "Allocation Concealment").
#'
#' @references Harrer, M., Cuijpers, P., Furukawa, T.A, & Ebert, D. D. (2019).
#' \emph{Doing Meta-Analysis in R: A Hands-on Guide}. DOI: 10.5281/zenodo.2551803.
#' \href{https://bookdown.org/MathiasHarrer/Doing_Meta_Analysis_in_R/creating-a-revman-style-risk-of-bias-summary.html}{Chapter 10}
#'
#' @author Mathias Harrer & David Daniel Ebert
#'
#' @import ggplot2
#' @importFrom tidyr gather
#'
#' @export rob.summary
#'
#' @seealso
#' \code{\link{direct.evidence.plot}}
#'
#' @examples
#' # Example 1: No missing information, only produce summary plot
#' data = data.frame(
#' "study" = c("Higgins et al., 2011", "Borenstein et al., 2008", "Holm, 1971",
#' "Zajonc et al., 2005", "Cuijpers, 2014"),
#' "Allocation_concealment" = c("Low", "High", "High", "Unclear", "High"),
#' "Randomization" = c("Low", "High", "Unclear", "Low", "High"),
#' "Sequence_generation" = c("Low", "High", "Unclear", "Unclear", "High"),
#' "ITT.Analyses" = c("Low", "High", "Unclear", "Unclear", "Unclear"),
#' "Selective_outcome_reporting" = c("Low", "High", "High", "High", "Unclear")
#' )
#' rob.summary(data)
#'
#' # Example 2: Missing information, produce additional summary table
#' data2 = data.frame(
#' "study" = c("Higgins et al., 2011", "Borenstein et al., 2008", "Holm, 1971",
#' "Zajonc et al., 2005", "Cuijpers, 2014"),
#' "Allocation_concealment" = c("low", "high", "high", "uc", "high"),
#' "Randomization" = c("low", "high", "miss", "low", "high"),
#' "Sequence_generation" = c("low", "high", "uc", "uc", "high"),
#' "ITT.Analyses" = c("low", "high", "uc", "uc", "uc"),
#' "Selective_outcome_reporting" = c("low", "high", "high", "high", "uc")
#' )
#' rob.summary(data2, name.high = "high", name.unclear = "uc", name.low = "low",
#' name.missing = "miss", studies = data2$study, table = TRUE)
rob.summary = function(data,
name.high="High",
name.unclear="Unclear",
name.low="Low",
studies,
name.missing,
table = FALSE){
# Class Checks
if (class(data) != "data.frame"){
stop("'data' must be of class 'data.frame'.")
}
if (missing(name.missing)){
# Only select columns with RoB data
colnames.rob = character()
for (i in 1:ncol(data)){
vect = as.character(data[,i])
for (j in 1:length(data[,i])){
if (vect[j] %in% c(name.high, name.unclear, name.low)){
colnames.rob[i] = TRUE
} else {
colnames.rob[i] = FALSE
message(cat("Column '", colnames(data)[i],
"' removed from plot because it did not contain the specified RoB ratings (only). \n",
sep=""))
break
}
}
}
# Use mask: rob data
rob = data[ , as.logical(colnames.rob)]
# Relevel for plot
for (i in 1:ncol(rob)){
rob[,i] = as.character(rob[,i])
rob[rob[,i]==name.high,i] = "High"
rob[rob[,i]==name.unclear,i] = "Unclear"
rob[rob[,i]==name.low,i] = "Low"
}
# Make table
if (table == TRUE){
if (missing(studies)){
stop("'studies' has to be specified when 'table = TRUE'.")
}
if (length(as.vector(studies)) != nrow(data)){
stop("'studies' vector is not of equal length as the data.")
}
robby = rob
robby$study = studies
robby = gather(robby, condition, measurement, -study)
robby$condition = gsub("_"," ", robby$condition)
robby$condition = gsub("-"," ", robby$condition)
robby$condition = gsub("\\."," ", robby$condition)
robby[robby$measurement=="Low", "measurement"] = "+"
robby[robby$measurement=="Unclear", "measurement"] = "?"
robby[robby$measurement=="High", "measurement"] = "-"
rob.table = ggplot(data = robby, aes(y = condition, x = study)) +
geom_tile(color="black", fill="white", size = 0.8) +
geom_point(aes(color=as.factor(measurement)), size=20) +
geom_text(aes(label = measurement), size = 8) +
scale_x_discrete(position = "top") +
scale_color_manual(values = c("?" = "#E2DF07",
"-" = "#BF0000",
"+" = "#02C100")) +
theme_minimal() +
coord_equal() +
theme(axis.title.x = element_blank(),
axis.title.y = element_blank(),
axis.ticks.y = element_blank(),
axis.text.y = element_text(size = 15, color = "black"),
axis.text.x = element_text(size = 13, color = "black", angle = 90, hjust=0),
legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank())
}
# Make long format, clean the factors
rob.long = gather(rob, condition, measurement, factor_key = TRUE)
rob.long$condition = gsub("_"," ",rob.long$condition)
rob.long$condition = gsub("-"," ",rob.long$condition)
rob.long$condition = gsub("\\."," ",rob.long$condition)
rob.long$measurement = as.factor(rob.long$measurement)
rob.long$measurement = factor(rob.long$measurement, levels(rob.long$measurement)[c(1, 3, 2)])
# Make plot
rob.plot = ggplot(data = rob.long) +
geom_bar(mapping = aes(x = condition, fill = measurement), width = 0.7,
position = "fill", color = "black") +
coord_flip(ylim = c(0, 1)) +
guides(fill = guide_legend(reverse = TRUE)) +
scale_fill_manual("Risk of Bias",
labels = c(" High risk of bias ",
" Unclear risk of bias ",
" Low risk of bias "),
values = c(Unclear = "#E2DF07", High = "#BF0000", Low = "#02C100")) +
scale_y_continuous(labels = scales::percent) +
theme(axis.title.x = element_blank(),
axis.title.y = element_blank(),
axis.ticks.y = element_blank(),
axis.text.y = element_text(size = 18, color = "black"),
axis.line.x = element_line(colour = "black", size = 0.5, linetype = "solid"),
legend.position = "bottom",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
legend.background = element_rect(linetype = "solid", colour = "black"),
legend.title = element_blank(),
legend.key.size = unit(0.75, "cm"),
legend.text = element_text(size = 14))
plot(rob.plot)
if (table == TRUE){
plot(rob.table)
}
} else {
# Only select columns with RoB data
data = as.data.frame(data)
colnames.rob = character()
for (i in 1:ncol(data)){
vect = as.character(data[,i])
for (j in 1:length(data[,i])){
if (vect[j] %in% c(name.high, name.unclear, name.low, name.missing)){
colnames.rob[i] = TRUE
} else {
colnames.rob[i] = FALSE
message(cat("Column '", colnames(data)[i],
"' removed from plot because it did not contain the specified RoB ratings (only). \n",
sep=""))
break
}
}
}
# Use mask: rob data
rob = data[ , as.logical(colnames.rob)]
# Relevel for plot
for (i in 1:ncol(rob)){
rob[,i] = as.character(rob[,i])
rob[rob[,i]==name.high,i] = "High"
rob[rob[,i]==name.unclear,i] = "Unclear"
rob[rob[,i]==name.low,i] = "Low"
rob[rob[,i]==name.missing,i] = "Missing"
}
# Make Table
if (table == TRUE){
if (missing(studies)){
stop("'studies' has to be specified when 'table = TRUE'.")
}
if (length(as.vector(studies)) != nrow(data)){
stop("'studies' vector is not of equal length as the data.")
}
robby = rob
robby$study = studies
robby = gather(robby, condition, measurement, -study)
robby$condition = gsub("_"," ", robby$condition)
robby$condition = gsub("-"," ", robby$condition)
robby$condition = gsub("\\."," ", robby$condition)
robby[robby$measurement=="Low", "measurement"] = "+"
robby[robby$measurement=="Unclear", "measurement"] = "?"
robby[robby$measurement=="High", "measurement"] = "-"
robby[robby$measurement=="Missing", "measurement"] = " "
rob.table = ggplot(data = robby, aes(y = condition, x = study)) +
geom_tile(color="black", fill="white", size = 0.8) +
geom_point(aes(color=as.factor(measurement)), size=20) +
geom_text(aes(label = measurement), size = 8) +
scale_x_discrete(position = "top") +
scale_color_manual(values = c("?" = "#E2DF07",
"-" = "#BF0000",
"+" = "#02C100",
" " = "white")) +
theme_minimal() +
coord_equal() +
theme(axis.title.x = element_blank(),
axis.title.y = element_blank(),
axis.ticks.y = element_blank(),
axis.text.y = element_text(size = 15, color = "black"),
axis.text.x = element_text(size = 13, color = "black", angle = 90, hjust=0),
legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank())
}
# Make long format, clean the factors
rob.long = gather(rob, condition, measurement, factor_key = TRUE)
rob.long$condition = gsub("_"," ",rob.long$condition)
rob.long$condition = gsub("-"," ",rob.long$condition)
rob.long$condition = gsub("\\."," ",rob.long$condition)
rob.long$measurement = as.factor(rob.long$measurement)
rob.long$measurement = factor(rob.long$measurement, levels(rob.long$measurement)[c(3,1,4,2)])
rob.plot = ggplot(data = rob.long) +
geom_bar(mapping = aes(x = condition, fill = measurement), width = 0.7,
position = "fill", color = "black") +
coord_flip(ylim = c(0, 1)) +
guides(fill = guide_legend(reverse = TRUE)) +
scale_fill_manual("Risk of Bias",
labels = c(" Missing information ",
" High risk of bias ",
" Unclear risk of bias ",
" Low risk of bias "),
values = c(Unclear = "#E2DF07",
High = "#BF0000",
Low = "#02C100",
Missing = "white")) +
scale_y_continuous(labels = scales::percent) +
theme(axis.title.x = element_blank(),
axis.title.y = element_blank(),
axis.ticks.y = element_blank(),
axis.text.y = element_text(size = 18, color = "black"),
axis.line.x = element_line(colour = "black", size = 0.5, linetype = "solid"),
legend.position = "bottom",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
legend.background = element_rect(linetype = "solid", colour = "black"),
legend.title = element_blank(),
legend.key.size = unit(0.75, "cm"),
legend.text = element_text(size = 14))
plot(rob.plot)
if (table == TRUE){
plot(rob.table)
}
}
}
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