How to create timberplots in R - r

Question

i am looking for a way to display estimates of a meta-analysis with lots of comparisons in a wide format instead of a forestplot. I came across a timberplot as displayed in this publication in figure 1:
https://www.researchgate.net/publication/283078594_Translational_failure_of_anti-inflammatory_compounds_for_myocardial_infarction_A_meta-Analysis_of_large_animal_models
So far, I was not able to find any r-code to create timberplots. Any hints would be highly appreciated.
As an example, here is a snippet of my current data:
structure(list(Author = c("Zuloaga 2014", "Kelly-Cobbs 2013",
"Kurita 2020", "Li (a) 2010", "Li (b) 2010", "Luo 2017", "Zhang 2016",
"Chen 2011", "Iwata 2015", "Guan 2011", "Mishiro 2014", "Zhang 2016",
"Rewell 2010", "Desilles 2017", "Cai 2018", "Yang 2015", "Augestad 2020",
"Kumas 2016", "Li 2004", "Pintana 2019", "Gao 2010", "Zhu 2016",
"Li 2013", "Chen 2019", "Iwata 2014"), Effect.size = c(35.200386286818,
-83.4784185709104, 36.1567339277335, -67.2836145890038, -66.2782956058588,
50.6942625098245, 2.16606498194945, 34.0909090909091, 34.6207954981455,
-75.7847533632287, 3.79249627522687, 33.8242513500245, 20.4,
53.381981476284, 55.8256496227997, 37.7068384829404, 35.7624831309042,
34.2436848134081, 44.0740740740741, 11.3382899628253, 78.1728075845723,
43.7891335083821, 32.0754716981132, 24.8822975517891, 56.9998933755769
), Standard.error = c(12.4780629739639, 35.8172017746254, 2.51216141038517,
45.4714925944508, 14.9052728665095, 15.9630454594002, 12.7738671567103,
7.27627754260179, 6.95739967875146, 6.46735654871385, 6.32805324709443,
4.51368516355712, 11.6488966431553, 12.4958199880194, 13.0017602415415,
12.1147303263766, 33.7832025707735, 21.5383168322688, 13.0893311456905,
21.8148377078391, 17.226146227274, 2.16584647411636, 6.82104394943358,
17.2913669783741, 4.81056206059614)), row.names = c(NA, 25L), class = "data.frame")
I ran a meta-analysis using the metagen() command from the meta package with the following code:
ma_results <- metagen(
`Effect.size`,
`Standard.error`,
sm = "NMD",
data = df,
studlab = Author,
random = TRUE,
method.tau = "REML",
prediction = TRUE
)
In the following metagen() object, the effect sizes are stored in ma_results$TE and the lower and upper bounds in ma_results$lower and ma_results$upper.
Following the suggestion of Alan Cameron (see below) my current code looks like:
ggplot(within(ma_results[order(ma_results$TE), ], id <- seq(nrow(25))), aes(id, TE)) +
geom_point(size = 0.5) +
geom_linerange(aes(ymin = lower, ymax = upper)) +
geom_hline(yintercept = TE.random, linetype = 2) +
theme_bw()
Here I get an error because of wrong number of dimensions within ma_results[order(ma_results$TE),].

Answer 1

It's fairly easy to create a plot like this using geom_linerange in ggplot. Here's an example with made up data. Whether you will be able to do this with your own data can't be known without a reproducible example:
library(ggplot2)
set.seed(1)
df <- data.frame(mean = runif(200), CI = runif(200))
ggplot(within(df[order(df$mean), ], id <- seq(nrow(df))), aes(id, mean)) +
geom_point(size = 0.5) +
geom_linerange(aes(ymin = mean - CI, ymax = mean + CI)) +
geom_hline(yintercept = mean(df$mean), linetype = 2) +
theme_bw()
EDIT
With the sample data, we can now do the following:
Make the papers a factor variable, with the ordering of the factor being from the lowest to highest effect size
Add upper and lower columns representing one standard error above and one standard error below the effect size. If you want this to be a 95% confidence interval instead, do effect size +/- 1.96 times the standard error.
First, we need to make sure every paper is uniquely identified. At the moment, your sample data contains two different papers with the same name (Zhang 2016), so we need to change one of them to mark it as unique:
df$Author[12] <- "Zhang (b) 2016"
Now let's get the papers arranged by effect size, and add our lower and upper bounds for each paper:
df$Author <- factor(df$Author, df$Author[order(df$Effect.size)])
df$lower <- df$Effect.size - df$Standard.error
df$upper <- df$Effect.size + df$Standard.error
The plot itself is then just:
ggplot(df, aes(Author, Effect.size)) +
geom_point() +
geom_linerange(aes(ymin = lower, ymax = upper)) +
geom_hline(yintercept = mean(df$Effect.size), linetype = 2) +
annotate(geom = 'text', x = 1, y = mean(df$Effect.size), vjust = -0.5,
label = paste('Mean =', round(mean(df$Effect.size), 1)), hjust = 0) +
theme_light() +
theme(axis.text.x = element_text(angle = 90, hjust = 1))