function (content, topic, Name_label, subgroup, outcome_label, 
    outcome_type, displaynames, threshold_observations, threshold_value, 
    benchmark_value, benchmark_label, data_type, output_type, 
    x_min, x_max, theme) 
{
    `%notin%` <- Negate(`%in%`)
    if (is.na(benchmark_value) | benchmark_value == "NULL") {
        benchmark_value <- NA
    }
    benchmark_value <- as.numeric(benchmark_value)
    threshold_value <- as.numeric(threshold_value)
    threshold_observations <- as.numeric(threshold_observations)
    x_min <- as.numeric(x_min)
    x_max <- as.numeric(x_max)
    study_results <- ifelse(displaynames == "groups", FALSE, 
        TRUE)
    subgroup <- ifelse(displaynames == "groups", "YES", subgroup)
    if (is.data.frame(content)) {
        x <- content
    }
    else {
        first.row <- substr(content, 1, regexpr("\n", content))
        num.columns <- str_count(first.row, ",")
        temp <- content
        temp <- gsub("\r", " ", fixed = TRUE, temp)
        temp <- gsub("\n", " ", fixed = TRUE, temp)
        temp <- gsub("\t", " ", fixed = TRUE, temp)
        temp <- gsub(",", "\",\"", fixed = TRUE, temp)
        temp <- paste("\"", temp, "\"", sep = "")
        temp <- paste("Mymatrix <- matrix(c(", temp, "), ncol=", 
            num.columns, ", byrow=TRUE)")
        x <- eval(parse(file = "", n = NULL, text = temp))
    }
    first.row.header <- FALSE
    if (is.na(as.numeric(x[1, 5])) == TRUE) {
        first.row.header <- TRUE
    }
    if (first.row.header == TRUE) {
        x <- x[-c(1), ]
    }
    x <- x[!(is.na(as.numeric(x[, 5])) == TRUE), ]
    data <- data.frame(x)
    remove(x)
    column.names <- c("Name", "ID", "Group", "Outcomes", "Observations")
    colnames(data) <- column.names
    if (data_type == "m") {
        data$PosParenth1 <- regexpr("(", data$Outcomes, fixed = TRUE)
        data$PosParenth2 <- regexpr(")", data$Outcomes, fixed = TRUE)
        data$Mean <- as.numeric(substring(data$Outcomes, 1, data$PosParenth1 - 
            1))
        data$sd <- as.numeric(substring(data$Outcomes, data$PosParenth1 + 
            1, data$PosParenth2 - 1))
    }
    else {
        data$Outcomes <- ifelse(is.na(data$Outcomes), 0, data$Outcomes)
    }
    data$Name <- str_trim(as.character(data$Name))
    data$ID <- str_trim(as.character(data$ID))
    data$Observations <- as.numeric(as.numeric(gsub(",", "", 
        as.character(str_trim(data$Observations)))))
    size = nrow(data)
    size_population = sum(data$Observations)
    if (data_type == "m") {
        data$Mean <- as.numeric(as.numeric(gsub(",", "", as.character(str_trim(data$Mean)))))
        data$Outcome.value <- data$Mean
        data$sd <- as.numeric(data$sd)
        data$product <- (data$Mean * data$Observations)
        mean_population <- (sum(data$product))/size_population
        data <- data[order(data$Mean), ]
    }
    if (data_type == "p") {
        data$Outcomes <- as.numeric(as.numeric(gsub(",", "", 
            as.character(str_trim(data$Outcomes)))))
        data$Outcome.value <- data$Outcomes/data$Observations
        (proportion_population <- sum(data$Outcomes)/sum(data$Observations))
        variance <- sum(data$Observations) * (proportion_population * 
            (1 - proportion_population))
        (std.dev <- sqrt(variance))
        test <- 0.1
        (probability <- pbinom(test, size = size, prob = proportion_population, 
            lower.tail = TRUE, log = FALSE))
        (probability <- pnorm(test, mean = proportion_population, 
            sd = std.dev, log = FALSE))
        data <- data[order(data$Outcome.value), ]
    }
    if (data_type == "c") {
        data$Outcomes <- as.numeric(as.numeric(gsub(",", "", 
            as.character(str_trim(data$Outcomes)))))
        for (i in 1:nrow(data)) {
            data$ci.l[i] <- poisson.test(data$Outcomes[i])$conf.int[1]
            data$ci.u[i] <- poisson.test(data$Outcomes[i])$conf.int[2]
        }
        z_score <- qnorm(0.975)
        data$se <- (data$ci.u - data$ci.l)/(2 * z_score)
        data <- data[order(data$Outcomes), ]
    }
    if (data_type == "p") {
        if (subgroup == "YES") {
            meta1 <- metaprop(Outcomes, Observations, studlab = Name, 
                subgroup = Group, data = data, method = "GLMM", 
                hakn = TRUE, fixed = FALSE, title = "Rates")
        }
        else {
            meta1 <- metaprop(Outcomes, Observations, studlab = Name, 
                data = data, method = "GLMM", hakn = TRUE, fixed = FALSE, 
                title = "Rates")
        }
        (paste(meta1$lower, inv.logit(meta1$TE), meta1$upper))
    }
    if (data_type == "m") {
        if (subgroup == "YES") {
            meta1 <- metamean(Observations, Mean, sd, studlab = Name, 
                subgroup = Group, data = data, fixed = FALSE, 
                sm = "MLN", hakn = TRUE, title = "Means")
        }
        else {
            meta1 <- metamean(Observations, Mean, sd, studlab = Name, 
                data = data, fixed = FALSE, sm = "MLN", hakn = TRUE, 
                title = "Means")
        }
        (paste(meta1$lower, meta1$TE, meta1$upper))
    }
    if (data_type == "c") {
        if (subgroup == "YES") {
            meta1 <- metagen(Outcomes, se, studlab = Name, subgroup = Group, 
                data = data, fixed = FALSE, title = "Counts")
        }
        else {
            meta1 <- metagen(Outcomes, se, studlab = Name, data = data, 
                fixed = FALSE, title = "Counts")
        }
        (paste(meta1$lower, meta1$TE, meta1$upper))
    }
    summary(meta1)
    (TE = round(meta1$TE.random, 2))
    (TE_text = paste("Rate: ", TE, " (", round(meta1$lower.random, 
        2), " - ", round(meta1$upper.random, 2), ")", sep = ""))
    I2 = round(meta1$I2 * 100, 1)
    I2.L = round(meta1$lower.I2 * 100, 1)
    I2.U = round(meta1$upper.I2 * 100, 1)
    if (output_type == "d") {
        size <- nrow(data)
        n <- seq(0, size, by = 1)
        if (data_type == "m") {
            densities <- dnorm(n, mean = proportion_population, 
                sd = std.dev, log = FALSE)
        }
        if (data_type == "p") {
            densities <- dbinom(n, size = size, prob = proportion_population, 
                log = FALSE)
        }
        if (data_type == "c") {
            stop(paste("densities: for counts not implemented", 
                sep = ""))
        }
        adjust = 100/size
        adjust.axis = 100
        if (data_type == "m") {
            adjust.axis <- 1
        }
        plot(n * adjust, densities/adjust, type = "n", xlab = "", 
            ylab = "Probablity of result", main = paste("Distribution of ", 
                outcome_label, " by ", Name_label, sep = ""), 
            xlim = c(0, 100), ylim = c(0, 0.5))
        mtext(paste("Results: proportion with ", outcome_label, 
            sep = ""), side = 1, line = 3)
        x <- seq(0, size, by = 1)
        densities <- dbinom(x, size = size, prob = proportion_population, 
            log = FALSE)
        s <- spline(x * adjust, densities, xout = seq(0, 100, 
            by = 1))
        lines(s, col = "black")
        segments(benchmark_value * adjust.axis, 0, benchmark_value * 
            adjust.axis, s$y[1 + benchmark_value * adjust.axis], 
            col = "green")
        axis(1, at = benchmark_value * adjust.axis, labels = "B", 
            col.ticks = "green", col.axis = "green", col = "green", 
            font = 2, lwd = 2, padj = 1.2, las = 1)
        axis(1, at = proportion_population * adjust.axis, labels = "M", 
            col.ticks = "blue", col.axis = "blue", col = "blue", 
            lwd = 2, padj = 1.2, las = 1)
        data.threshold <- data[which(data$Observations >= threshold_observations), 
            ]
        (nrow(data.threshold))
        points(data.threshold$Outcome.value * adjust.axis, s$y[1 + 
            data.threshold$Outcome.value * adjust.axis], col = "black", 
            pch = 19, cex = 1)
        temp.list <- unique(data.threshold$Outcome.value, incomparables = FALSE)
        duplicates <- 0
        for (i in 1:length(temp.list)) {
            temp.data <- data.threshold[which(data.threshold$Outcome.value == 
                temp.list[i]), ]
            temp.value <- nrow(temp.data)
            if (temp.value > 1) {
                duplicates <- 1.5
                text(temp.list[i] * adjust.axis, s$y[1 + temp.data$Outcome.value * 
                  adjust.axis], temp.value, pos = 3, col = "black", 
                  font = 1)
            }
        }
        if (outcome_type != "NA") {
            deviants <- data.threshold[which(data.threshold$Observations >= 
                threshold_observations & data.threshold$Outcome.value < 
                threshold_value), ]
            if (outcome_type == "g") {
                deviants <- data.threshold[which(data.threshold$Observations >= 
                  threshold_observations & data.threshold$Outcome.value > 
                  threshold_value), ]
            }
            (nrow(deviants))
            (temp.list <- unique(deviants$Outcome.value, incomparables = FALSE))
            for (i in 1:length(temp.list)) {
                temp.data <- deviants[which(deviants$Outcome.value == 
                  temp.list[i]), ]
                temp.value <- nrow(temp.data)
                point.size <- 1 + 0.25 * (temp.value - 1)
                point.size <- 1
                points(temp.data$Outcome.value * adjust.axis, 
                  s$y[1 + temp.data$Outcome.value * adjust.axis], 
                  col = "green", pch = 19, cex = point.size)
                if (temp.value > 1) {
                  text(temp.list[i] * adjust.axis, s$y[1 + temp.data$Outcome.value * 
                    adjust.axis], temp.value, pos = 3, col = "green", 
                    font = 2)
                }
            }
            (temp.list <- unique(deviants$Name, incomparables = FALSE))
            (deviant.Outcomes <- sum(deviants$Outcomes))
            (deviant.Observations <- sum(deviants$Observations))
            (deviant.rate <- deviant.Outcomes/deviant.Observations)
        }
        textout0 <- paste("Summary:", sep = "")
        textout1 <- paste("Number of ", Name_label, " assessed: ", 
            size, " (observations: ", size_population, ")", sep = "")
        textout2 <- paste("Heterogeneity (I2): ", I2, "%", sep = "")
        textout3 <- paste("       ... among ", Name_label, " with ", 
            threshold_observations, " or more observations: ", 
            round(100 * min(data.threshold$Outcome.value), 0), 
            "% to ", round(100 * max(data.threshold$Outcome.value), 
                0), "%", sep = "")
        if (outcome_type != "NA") {
            textout4 <- paste("       ... among positive deviants: ", 
                round(100 * min(deviants$Outcome.value), 0), 
                "% to ", round(100 * max(deviants$Outcome.value), 
                  0), "%", sep = "")
            textout5 <- paste("Among local positive deviants:", 
                sep = "")
            textout6 <- paste("  ", round(100 * deviant.rate, 
                0), "% (", deviant.Outcomes, " of ", deviant.Observations, 
                " observations) have ", outcome_label, sep = "")
            if (data_type == "m") {
                textout6 <- paste("  ", 4.75, " is mean rating", 
                  sep = "")
            }
        }
        textout10 <- paste("Legend:", sep = "")
        textout11 <- paste("Points indicate ", Name_label, " with  ", 
            threshold_observations, " or more observations", 
            sep = "")
        textout12 <- paste("Numbers indicate number of ", Name_label, 
            " with that result (if > 1)", sep = "")
        textout13 <- paste("M: mean is ", round(proportion_population * 
            100, 0), "%", sep = "")
        if (data_type == "m") {
            textout13 <- paste("M: mean is ", 4, sep = "")
        }
        textout14 <- paste("B: ", benchmark_label, ": ", benchmark_value * 
            100, "%", sep = "")
        if (data_type == "m") {
            textout14 <- paste("B: ", benchmark_label, ": ", 
                benchmark_value, sep = "")
        }
        xpos <- par("usr")[1] + 2 * strwidth("A")
        text(xpos, par("usr")[4] - 2 * strheight("A"), textout0, 
            adj = c(0, 0), cex = 1, font = 2)
        text(xpos, par("usr")[4] - 3.5 * strheight("A"), textout2, 
            adj = c(0, 0), cex = 0.8)
        if (outcome_type != "NA") {
            text(xpos, par("usr")[4] - 5 * strheight("A"), textout5, 
                col = "black", font = 1, adj = c(0, 0), cex = 0.8)
            text(xpos, par("usr")[4] - 6.5 * strheight("A"), 
                textout6, col = "black", font = 1, adj = c(0, 
                  0), cex = 0.8)
        }
        xpos <- par("usr")[2] - 2 * strwidth("A")
        text(xpos, par("usr")[4] - 2 * strheight("A"), textout10, 
            adj = c(1, 0), cex = 1, font = 2)
        text(xpos, par("usr")[4] - 3.5 * strheight("A"), textout11, 
            adj = c(1, 0), cex = 0.8)
        if (duplicates == 1.5) {
            text(xpos, par("usr")[4] - 5 * strheight("A"), textout12, 
                adj = c(1, 0), cex = 0.8)
        }
        text(xpos, par("usr")[4] - (5 + duplicates) * strheight("A"), 
            textout13, adj = c(1, 0), cex = 0.8, col = "blue")
        if (benchmark_value != 0) {
            text(par("usr")[2] - 2 * strwidth("A"), par("usr")[4] - 
                (6.5 + duplicates) * strheight("A"), textout14, 
                adj = c(1, 0), font = 2, cex = 0.8, col = "green")
        }
    }
    if (output_type == "f") {
        left.deviants <- NULL
        right.deviants <- NULL
        for (i in 1:length(meta1$TE)) {
            if (data_type == "p") {
                if (meta1$upper[i] < inv.logit(meta1$TE.random)) {
                  left.deviants <- rbind(left.deviants, meta1$TE[i])
                }
                if (meta1$lower[i] > inv.logit(meta1$TE.random)) {
                  right.deviants <- rbind(right.deviants, meta1$TE[i])
                }
            }
            if (data_type %in% c("m", "c")) {
                if (meta1$upper[i] < (meta1$TE.random)) {
                  left.deviants <- rbind(left.deviants, meta1$TE[i])
                }
                if (meta1$lower[i] > (meta1$TE.random)) {
                  right.deviants <- rbind(right.deviants, meta1$TE[i])
                }
            }
        }
        (left.deviants)
        (right.deviants)
        for (i in 1:length(meta1$TE)) {
            if (displaynames == "none") {
                meta1$studlab[i] <- meta1$data$ID[i]
            }
            if (displaynames == "selected") {
                if (data_type == "p") {
                  if (outcome_type == "b" & meta1$upper[i] >= 
                    inv.logit(meta1$TE.random)) {
                    meta1$studlab[i] <- meta1$data$ID[i]
                  }
                  if (outcome_type == "g" & meta1$lower[i] <= 
                    inv.logit(meta1$TE.random)) {
                    meta1$studlab[i] <- meta1$data$ID[i]
                  }
                }
                if (data_type %in% c("m", "c")) {
                  if (outcome_type == "b" & meta1$upper[i] >= 
                    meta1$TE.random) {
                    meta1$studlab[i] <- meta1$data$ID[i]
                  }
                  if (outcome_type == "g" & meta1$lower[i] <= 
                    meta1$TE.random) {
                    meta1$studlab[i] <- meta1$data$ID[i]
                  }
                }
            }
        }
        for (i in 1:length(meta1$TE)) {
            if (data_type == "p") {
                if (meta1$upper[i] < (inv.logit(meta1$TE.random))) {
                  meta1$studlab[i] <- paste(meta1$studlab[i], 
                    "*", sep = "")
                }
                if (meta1$lower[i] > (inv.logit(meta1$TE.random))) {
                  meta1$studlab[i] <- paste(meta1$studlab[i], 
                    "*", sep = "")
                }
            }
            if (data_type %in% c("m", "c")) {
                if (meta1$upper[i] < meta1$TE.random) {
                  meta1$studlab[i] <- paste(meta1$studlab[i], 
                    "*", sep = "")
                }
                if (meta1$lower[i] >= meta1$TE.random) {
                  meta1$studlab[i] <- paste(meta1$studlab[i], 
                    "*", sep = "")
                }
            }
        }
        meta1$studlab
        if (data_type %in% c("p")) {
            xlim <- c(0, 1)
            leftcols = c("studlab", "event", "n")
            leftlabs = c(Name_label, outcome_label, "Observations")
        }
        else if (data_type %in% c("c")) {
            xlim <- c(0, x_max)
            leftcols = c("studlab", "TE")
            leftlabs = c(Name_label, outcome_label)
        }
        else {
            xlim <- c(x_min, x_max)
            leftcols = c("studlab", "n")
            leftlabs = c(Name_label, "Observations")
        }
        forest(meta1, leftcols = leftcols, leftlabs = leftlabs, 
            sortbar = meta1$TE, study.results = study_results, 
            bysort = TRUE, ref = benchmark_value, xlim = xlim, 
            print.I2.ci = TRUE, print.tau2 = FALSE, print.Q = FALSE, 
            print.pval.Q = FALSE, studlab = meta1$studlab)
        grid.text(topic, 0.5, 0.95, gp = gpar(cex = 1.4))
        if (!is.na(benchmark_value)) {
            grid.text("Notes:", 0.08, 0.08, hjust = 0, gp = gpar(cex = 1, 
                font = 2))
            Footer <- NULL
            Footer <- paste(Footer, "Goal is ", benchmark_label, 
                ": ", benchmark_value, " (solid vertical line)")
            grid.text(Footer, 0.08, 0.06, hjust = 0, gp = gpar(cex = 1, 
                font = 1))
        }
    }
}
<bytecode: 0x5590926182b8>
<environment: namespace:positivedeviance>