Forest plots are commonly used in the medical research publications, especially in meta-analysis. And it can also be used to report the coefficients and confidence intervals (CIs) of the regression models.
There are lots of packages out there can be used to draw a forest plot. The most popular one is forestplot. Packages specialised for the meta-analysis, like meta, metafor and rmeta. Some other packages, like ggforestplot uses ggplot2 to draw a forest plot, it is not available on the CRAN yet.
The main differences of the forestploter from the other
packages are:
The layout of the forest plot is determined by the dataset provided. Please check out the other vignette if you want change the text or backgroud, add or insert text, add borders to cells, edit color of the CI in some cells.
The first step is to prepare a data.frame to be used as
a basic layout of the forest plot. Column names of the data will be
drawn as the header, and contents inside the data will be displayed in
the forest plot. One or multiple blank columns without any content
(blanks) should be provided to draw confidence interval. Width
of the box to draw the CI is determined by the width of this column.
Increase the number of space in the column to give more space to draw
CI.
First we need to get the data ready to plot.
library(grid)
library(forestploter)
# Read provided sample example data
dt <- read.csv(system.file("extdata", "example_data.csv", package = "forestploter"))
# Keep needed columns
dt <- dt[,1:6]
# indent the subgroup if there is a number in the placebo column
dt$Subgroup <- ifelse(is.na(dt$Placebo),
dt$Subgroup,
paste0(" ", dt$Subgroup))
# NA to blank or NA will be transformed to carachter.
dt$Treatment <- ifelse(is.na(dt$Treatment), "", dt$Treatment)
dt$Placebo <- ifelse(is.na(dt$Placebo), "", dt$Placebo)
dt$se <- (log(dt$hi) - log(dt$est))/1.96
# Add blank column for the forest plot to display CI.
# Adjust the column width with space.
dt$` ` <- paste(rep(" ", 20), collapse = " ")
# Create confidence interval column to display
dt$`HR (95% CI)` <- ifelse(is.na(dt$se), "",
sprintf("%.2f (%.2f to %.2f)",
dt$est, dt$low, dt$hi))
head(dt)
#> Subgroup Treatment Placebo est low hi se
#> 1 All Patients 781 780 1.869694 0.13245636 3.606932 0.3352463
#> 2 Sex NA NA NA NA
#> 3 Male 535 548 1.449472 0.06834426 2.830600 0.3414741
#> 4 Female 246 232 2.275120 0.50768005 4.042560 0.2932884
#> 5 Age NA NA NA NA
#> 6 <65 yr 297 333 1.509242 0.67029394 2.348190 0.2255292
#> HR (95% CI)
#> 1 1.87 (0.13 to 3.61)
#> 2
#> 3 1.45 (0.07 to 2.83)
#> 4 2.28 (0.51 to 4.04)
#> 5
#> 6 1.51 (0.67 to 2.35)The data we have above will be used as basic layout of the forest plot. The example below shows how to draw a simple forest plot. A footnote was added as a demonstration.
p <- forest(dt[,c(1:3, 8:9)],
est = dt$est,
lower = dt$low,
upper = dt$hi,
sizes = dt$se,
ci_column = 4,
ref_line = 1,
arrow_lab = c("Placebo Better", "Treatment Better"),
xlim = c(0, 4),
ticks_at = c(0.5, 1, 2, 3),
footnote = "This is the demo data. Please feel free to change\nanything you want.")
# Print plot
plot(p)Now we will use the same data above, and add a summary point. We also
want to change the graphical parameters for confidence interval and
other parts of the plot. Theme of the forest plot can be adjusted with
forest_theme function, check out the manual for more
details.
dt_tmp <- rbind(dt[-1, ], dt[1, ])
dt_tmp[nrow(dt_tmp), 1] <- "Overall"
dt_tmp <- dt_tmp[1:11, ]
# Define theme
tm <- forest_theme(base_size = 10,
# Confidence interval point shape, line type/color/width
ci_pch = 15,
ci_col = "#762a83",
ci_fill = "black",
ci_alpha = 0.8,
ci_lty = 1,
ci_lwd = 1.5,
ci_Theight = 0.2, # Set an T end at the end of CI
# Reference line width/type/color
refline_lwd = 1,
refline_lty = "dashed",
refline_col = "grey20",
# Vertical line width/type/color
vertline_lwd = 1,
vertline_lty = "dashed",
vertline_col = "grey20",
# Change summary color for filling and borders
summary_fill = "#4575b4",
summary_col = "#4575b4",
# Footnote font size/face/color
footnote_cex = 0.6,
footnote_fontface = "italic",
footnote_col = "blue")
pt <- forest(dt_tmp[,c(1:3, 8:9)],
est = dt_tmp$est,
lower = dt_tmp$low,
upper = dt_tmp$hi,
sizes = dt_tmp$se,
is_summary = c(rep(FALSE, nrow(dt_tmp)-1), TRUE),
ci_column = 4,
ref_line = 1,
arrow_lab = c("Placebo Better", "Treatment Better"),
xlim = c(0, 4),
ticks_at = c(0.5, 1, 2, 3),
footnote = "This is the demo data. Please feel free to change\nanything you want.",
theme = tm)
# Print plot
plot(pt)By default, all cells are left aligned. But it is possible to justify
any cells in the forest plot by setting parameters in
forest_theme. Set
core=list(fg_params=list(hjust=0, x=0)) to left align
content, and rowhead=list(fg_params=list(hjust=0.5, x=0.5)
to center header. Set hjust=1 and x=0.9 to
right align text. You can also change the justification of text
by with edit_plot, see details in another
vignette.
Same rule apply to change the background color by setting
core=list(bg_params=list(fill = c("#edf8e9", "#c7e9c0", "#a1d99b"))).
Change settings in core if you want to change graphical
parameters of contents, colhead for header. Change settings
in fg_params to modify the text, see parameters for
textGrob() in grid package. Change
bg_params to modify settings for background graphical
parameters, see gpar() in grid package. You
should pass parameters as a list. More details can be found here.
Provide a single value if you want cells have the same justification or vector for each cells. As you can notice that the second example justify text by row using the provided vector, and the vector will be recycled.
dt <- dt[1:4, ]
# Header center and content right
tm <- forest_theme(core=list(fg_params=list(hjust = 1, x = 0.9),
bg_params=list(fill = c("#edf8e9", "#c7e9c0", "#a1d99b"))),
colhead=list(fg_params=list(hjust=0.5, x=0.5)))
p <- forest(dt[,c(1:3, 8:9)],
est = dt$est,
lower = dt$low,
upper = dt$hi,
sizes = dt$se,
ci_column = 4,
title = "Header center content right",
theme = tm)
# Print plot
plot(p)
# Mixed justification
tm <- forest_theme(core=list(fg_params=list(hjust=c(1, 0, 0, 0.5),
x=c(0.9, 0.1, 0, 0.5)),
bg_params=list(fill = c("#f6eff7", "#d0d1e6", "#a6bddb", "#67a9cf"))),
colhead=list(fg_params=list(hjust=c(1, 0, 0, 0, 0.5),
x=c(0.9, 0.1, 0, 0, 0.5))))
p <- forest(dt[,c(1:3, 8:9)],
est = dt$est,
lower = dt$low,
upper = dt$hi,
sizes = dt$se,
ci_column = 4,
title = "Mixed justification",
theme = tm)
plot(p)Sometimes one may want to have multiple CI columns, each column may
represent different outcome. If this is the case, one only needs to
provide a vector of the position of the columns to be drawn in the data.
If the number of columns provided to draw the CI columns are same as
number of est, one CI will be drawn into each CI columns.
If the number of columns provided is less than number of
est, the extra est will be considered as a
group and will be drawn to the CI columns sequentially. In the latter
case, group number equals to number of est devided by
number of ci_column and multiple columns will be drawn into
one cell. As seen in the example below, the CI will be drawn in the
column 3 and 5. The first and second elements in est,
lower and upper will be drawn in column 3 and
column 5.
In a multiple groups example, two or more CI in one cell. The
solution is simple, provide all the values sequentially to
est, lower and upper. Which
means, the first n elements in the est,
lower and upper are considered as same group,
same for next n elements. The n is determined
by the number of ci_column. As it is shown in the example
below, est_gp1 and est_gp2 will be drawn in
column 3 and column 5, considered as group 1. The
est_gp3 and est_gp4 will be drawn in column 3
and column 5, considered as group 2.
This is an example of multiple CI columns and groups:
dt <- read.csv(system.file("extdata", "example_data.csv", package = "forestploter"))
# indent the subgroup if there is a number in the placebo column
dt$Subgroup <- ifelse(is.na(dt$Placebo),
dt$Subgroup,
paste0(" ", dt$Subgroup))
# NA to blank or NA will be transformed to carachter.
dt$n1 <- ifelse(is.na(dt$Treatment), "", dt$Treatment)
dt$n2 <- ifelse(is.na(dt$Placebo), "", dt$Placebo)
# Add two blank column for CI
dt$`CVD outcome` <- paste(rep(" ", 20), collapse = " ")
dt$`COPD outcome` <- paste(rep(" ", 20), collapse = " ")
# Set-up theme
tm <- forest_theme(base_size = 10,
refline_lty = "solid",
ci_pch = c(15, 18),
ci_col = c("#377eb8", "#4daf4a"),
footnote_col = "blue",
legend_name = "Group",
legend_value = c("Trt 1", "Trt 2"),
vertline_lty = c("dashed", "dotted"),
vertline_col = c("#d6604d", "#bababa"))
p <- forest(dt[,c(1, 19, 21, 20, 22)],
est = list(dt$est_gp1,
dt$est_gp2,
dt$est_gp3,
dt$est_gp4),
lower = list(dt$low_gp1,
dt$low_gp2,
dt$low_gp3,
dt$low_gp4),
upper = list(dt$hi_gp1,
dt$hi_gp2,
dt$hi_gp3,
dt$hi_gp4),
ci_column = c(3, 5),
ref_line = 1,
vert_line = c(0.5, 2),
nudge_y = 0.2,
theme = tm)
plot(p)If the desired forest plot is multiple column, some may want to have
different settings for different columns. For example, different CI
column has different xlim, x-axis ticks, x-axis labels, x_trans,
reference line, vertical line or arrow labels. This can be easily done
by providing a list or vector. Provide a list for xlim,
vert_line, arrow_lab and
ticks_at, atomic vector for xlab,
x_trans and ref_line. See the example
below.
dt$`HR (95% CI)` <- ifelse(is.na(dt$est_gp1), "",
sprintf("%.2f (%.2f to %.2f)",
dt$est_gp1, dt$low_gp1, dt$hi_gp1))
dt$`Beta (95% CI)` <- ifelse(is.na(dt$est_gp2), "",
sprintf("%.2f (%.2f to %.2f)",
dt$est_gp2, dt$low_gp2, dt$hi_gp2))
tm <- forest_theme(arrow_type = "closed",
arrow_label_just = "end")
p <- forest(dt[,c(1, 21, 23, 22, 24)],
est = list(dt$est_gp1,
dt$est_gp2),
lower = list(dt$low_gp1,
dt$low_gp2),
upper = list(dt$hi_gp1,
dt$hi_gp2),
ci_column = c(2, 4),
ref_line = c(1, 0),
vert_line = list(c(0.3, 1.4), c(0.6, 2)),
x_trans = c("log", "none"),
arrow_lab = list(c("L1", "R1"), c("L2", "R2")),
xlim = list(c(0, 3), c(-1, 3)),
ticks_at = list(c(0.1, 0.5, 1, 2.5), c(-1, 0, 2)),
xlab = c("OR", "Beta"),
nudge_y = 0.2,
theme = tm)
plot(p)One can use the base method or use ggsave function to
save plot. For the ggsave function, please don’t ignore the
plot parameter. The width and height should be tuned to get
a desired plot. You can also set autofit=TRUE in the
print or plot function to auto fit the plot,
but this may change not be compact as it should be.
# Base method
png('rplot.png', res = 300, width = 7.5, height = 7.5, units = "in")
p
dev.off()
# ggsave function
ggplot2::ggsave(filename = "rplot.png", plot = p,
dpi = 300,
width = 7.5, height = 7.5, units = "in")Or you can get the width and height of the forestplot with
get_wh, and use this width and height for saving.
# Get width and height
p_wh <- get_wh(plot = p, unit = "in")
png('rplot.png', res = 300, width = p_wh[1], height = p_wh[2], units = "in")
p
dev.off()
# Or get scale
get_scale <- function(plot,
width_wanted,
height_wanted,
unit = "in"){
h <- convertHeight(sum(plot$heights), unit, TRUE)
w <- convertWidth(sum(plot$widths), unit, TRUE)
max(c(w/width_wanted, h/height_wanted))
}
p_sc <- get_scale(plot = p, width_wanted = 6, height_wanted = 4, unit = "in")
ggplot2::ggsave(filename = "rplot.png",
plot = p,
dpi = 300,
width = 6,
height = 4,
units = "in",
scale = p_sc)