An R package to generate highly customizable forest plots.
It relies on the R graphics package and generates an overall layout that is then populated with the desired elements (e.g. a text column or a specific plot).
Usage
forplot contains two main functions:
genfobjgenerates a list of class fobj based on a data frame (dat) and a layoutplotfobjproduces the plot from the fobj
The fobj can be modified using a set of helper functions or by directly changing the list. The individual elements are from the R graphics package and all options available there can be used.
Example data
The forplot package includes three data sets:
- forplotdata: summary data with a 10 continuous variables
- forplotdata_prop: summary data with 10 binary variables
- forplotdata_bp: data with actual observation of forplotdata in a long format
data(forplotdata)
forplotdata
#> vlabel n1 n2 n3 n4 beta_format beta
#> 1 out1 100 5.1 (0.9) 100 5.5 (1.0) -0.35 (-0.61 to -0.09) -0.35330456
#> 2 out2 100 5.0 (1.0) 100 5.6 (1.0) -0.52 (-0.80 to -0.24) -0.52192832
#> 3 out3 100 5.0 (1.2) 100 5.5 (1.0) -0.49 (-0.79 to -0.20) -0.49461488
#> 4 out4 100 4.8 (1.1) 100 5.5 (1.1) -0.70 (-1.00 to -0.40) -0.69983471
#> 5 out5 100 5.0 (1.1) 100 5.5 (1.1) -0.49 (-0.79 to -0.20) -0.49398774
#> 6 out6 100 5.0 (1.0) 100 5.3 (1.0) -0.36 (-0.64 to -0.08) -0.35838850
#> 7 out7 100 5.2 (1.1) 100 5.3 (0.9) -0.08 (-0.36 to 0.19) -0.08429217
#> 8 out8 100 5.1 (1.0) 100 5.5 (1.1) -0.40 (-0.70 to -0.11) -0.40311974
#> 9 out9 100 4.9 (1.0) 100 5.4 (1.2) -0.51 (-0.81 to -0.20) -0.50709366
#> 10 out10 100 5.1 (1.1) 100 5.4 (1.1) -0.29 (-0.58 to 0.01) -0.28700316
#> beta_lci beta_uci p1
#> 1 -0.6122615 -0.094347655 0.008
#> 2 -0.8044964 -0.239360217 0.000
#> 3 -0.7938491 -0.195380711 0.001
#> 4 -1.0035691 -0.396100319 0.000
#> 5 -0.7928756 -0.195099871 0.001
#> 6 -0.6395674 -0.077209590 0.013
#> 7 -0.3567376 0.188153237 0.542
#> 8 -0.6955442 -0.110695309 0.007
#> 9 -0.8110643 -0.203123036 0.001
#> 10 -0.5822050 0.008198722 0.057
data(forplotdata_prop)
forplotdata_prop
#> vlabel n1 n2 n3 n4 prop1 prop2 beta_format beta
#> 1 out1 100 48 (48%) 100 40 (40%) 0.48 0.40 8.0% (-6.7 to 22.7%) 0.08
#> 2 out2 100 38 (38%) 100 37 (37%) 0.38 0.37 1.0% (-13.4 to 15.4%) 0.01
#> 3 out3 100 47 (47%) 100 47 (47%) 0.47 0.47 0.0% (-13.8 to 13.8%) 0.00
#> 4 out4 100 54 (54%) 100 41 (41%) 0.54 0.41 13.0% (-1.7 to 27.7%) 0.13
#> 5 out5 100 45 (45%) 100 40 (40%) 0.45 0.40 5.0% (-9.7 to 19.7%) 0.05
#> 6 out6 100 51 (51%) 100 38 (38%) 0.51 0.38 13.0% (-1.7 to 27.7%) 0.13
#> 7 out7 100 47 (47%) 100 36 (36%) 0.47 0.36 11.0% (-3.6 to 25.6%) 0.11
#> 8 out8 100 44 (44%) 100 46 (46%) 0.44 0.46 -2.0% (-16.8 to 12.8%) -0.02
#> 9 out9 100 53 (53%) 100 33 (33%) 0.53 0.33 20.0% (5.6 to 34.4%) 0.20
#> 10 out10 100 54 (54%) 100 38 (38%) 0.54 0.38 16.0% (1.4 to 30.6%) 0.16
#> beta_lci beta_uci p1
#> 1 -0.06714147 0.2271415 0.319
#> 2 -0.13418240 0.1541824 1.000
#> 3 -0.13834069 0.1383407 1.000
#> 4 -0.01723947 0.2772395 0.089
#> 5 -0.09684704 0.1968470 0.567
#> 6 -0.01656604 0.2765660 0.088
#> 7 -0.03571955 0.2557195 0.151
#> 8 -0.16786783 0.1278678 0.887
#> 9 0.05560305 0.3443969 0.007
#> 10 0.01364509 0.3063549 0.033
data(forplotdata_bp)
head(forplotdata_bp)
#> value variable arm
#> 1 4.373546 var1 1
#> 2 5.183643 var1 1
#> 3 4.164371 var1 1
#> 4 6.595281 var1 1
#> 5 5.329508 var1 1
#> 6 4.179532 var1 1forplotdata and forplotdata_prop have a character variable with the variable names (vlabel), further character variables n[1-4] with the number of observations and descriptives for each arm (either mean (sd) or n(%)), the formatted treatment effect (beta_format) and the p-value (p1), and three numeric variables to draw the forest (beta, beta_lci and beta_uci).
In forplotdata_prop there are two further numeric variables with the proportion in each arm (prop1, prop2) to draw a strip chart.
forplotdata_bp has three variables with the actual observations of forplotdata with
- value: the variable value (numerical),
- variable: the variable name (factor), and
- arm: the treatment arm (factor).
Generating the fobj with genfobj
The required input for genfobj are a layout and
a data frame (dat).
layout must be a character vector with elements t (text), f (forest), s[1-9] (strip chart), or b (boxplot).
For each t element, dat must contain a single variable, for each f element three variables (point estimate, lower confidence limit, upper confidence limit, in that order), and for each s[1-9] the number of variables indicated in [].
The order of the variables in dat must correspond to the layout.
A b element does not need a column in dat but the specifcation of obs, a data frame in a long format with columns value (the outcome value), variable (the outcome variable), and arm (the treatment arm)
Let’s generate a forest plot with the forplotdata including the label, the descriptives, the formatted effect, the p-value, and a forest for the beta:
The produced fobj is a list of length 5 with class fobj. It includes those elements:
- dat and obs: the data (with obs = NULL in this example)
- setup: overall composition of the plot, layout, lmatrix, lheights, lwidths, y.at, and ylim
- items: a list of length from layout with the options for each item
- header: a list with the options for the header
class(fobj)
#> [1] "fobj" "list"
names(fobj)
#> [1] "dat" "obs" "setup" "items" "header"
fobj$setup
#> $layout
#> [1] "t" "t" "t" "t" "t" "t" "f" "t"
#>
#> $lmatrix
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
#> [1,] 10 10 10 10 10 10 10 10
#> [2,] 1 2 3 4 5 6 7 8
#> [3,] 9 9 9 9 9 9 9 9
#>
#> $lwidths
#> [1] 1 1 1 1 1 1 1 1
#>
#> $lheights
#> [1] 0.1 1.0 0.1
#>
#> $y.at
#> [1] 10 9 8 7 6 5 4 3 2 1
#>
#> $ylim
#> [1] 0.5 10.5
length(fobj$items)
#> [1] 8By default, text items include these options:
names(fobj$item[[1]])
#> [1] "type" "vname" "plot" "text"
fobj$item[[1]]
#> $type
#> [1] "t"
#>
#> $vname
#> [1] "vlabel"
#>
#> $plot
#> $plot$x
#> [1] 0
#>
#> $plot$type
#> [1] "n"
#>
#> $plot$xlim
#> [1] 0 1
#>
#> $plot$ylim
#> [1] 0.5 10.5
#>
#> $plot$yaxt
#> [1] "n"
#>
#> $plot$ylab
#> [1] ""
#>
#> $plot$xlab
#> [1] ""
#>
#> $plot$axes
#> [1] FALSE
#>
#> $plot$xaxs
#> [1] "i"
#>
#> $plot$yaxs
#> [1] "i"
#>
#>
#> $text
#> $text$x
#> [1] 0.5
#>
#> $text$y
#> [1] 10 9 8 7 6 5 4 3 2 1
#>
#> $text$labels
#> [1] "out1" "out2" "out3" "out4" "out5" "out6" "out7" "out8" "out9"
#> [10] "out10"
#>
#> $text$adj
#> [1] 0.5 0.5type is the item type, vname is the variable in dat the item corresponds to, plot and text are used for plotting with the R graphics package.
The f items are more complex, including options for axis, points and arrows of the fores plots.
names(fobj$items[[which(fobj$setup$layout=="f")]])
#> [1] "type" "vname" "plot" "axis" "points" "arrows"
fobj$items[[which(fobj$setup$layout=="f")]]$type
#> [1] "f"
fobj$items[[which(fobj$setup$layout=="f")]]$vname
#> [1] "beta"For the variable name, the first of the three variables used to specify the forest is used. Options for axis, point and arrows can be changed using helper functions (or directly in the list). Examples or shown below.
Plot the fobj
The fobj can be plotted using
plotfobj
plotfobj(fobj)
Column widths can be given as options in genfobj or
directly changed in the fobj:
Horizontal gridlines and stripes can be added with helper functions gridlines and stripes, which generate further elements in the fobj:
fobj<-gridlines(fobj)
fobj<-stripes(fobj)
names(fobj)
#> [1] "dat" "obs" "setup" "items" "header" "gridlines"
#> [7] "stripes"
plotfobj(fobj)
Modify the items
All items, gridlines and stripes can be modified using helper functions or by changing the fobj directly.
With helper function t_options, t items can be modified using all options from R graphics::text(). That can be done for a specific t item by using the number or column name of the item, or for all t items (by keeping item=NULL):
fobj<-t_options(fobj = fobj, item = c("vlabel"), cex = 1.1, font = 2, col = "red", x=0.2, adj=0)
plotfobj(fobj)
For f items there are several helper function to modify the options for the different elements: f_axis, f_points, f_arrows. Also here, all options from graphics::axis(), graphics::points() and graphics::arrows() can be used. As we do only have one forest item, we do not have to specify the item.
fobj<-f_axis(fobj = fobj, xlim = c(-1.1,0.2), at = c(-1,-0.6,-0.2,0.2),
tck = -0.03, mgp = c(2,0.5,0))
fobj<-f_points(fobj = fobj, pch = 16, cex = 1.5)
plotfobj(fobj)
With f_refline, reference line(s) can be added and with f_direction a label for the direction below the axis. Note that the footer height has to be increased to fit the direction label.
fobj<-f_refline(fobj, v = 0)
fobj<-f_direction(fobj, text = "A better B better", line = 1.6)
fobj$setup$lheights[3]<-0.15
plotfobj(fobj)
To remove an added item, it can be set to NULL (or a new fobj could be generated):
Piping
The modifying elements can be piped. For example to reproduce the fobj from above:
genfobj(dat = forplotdata,
layout = c("t","t","t","t","t","t","f","t"),
lwidth = c(0.3,0.4,0.6,0.4,0.6,1,1,0.5),
lheight = c(0.1,1,0.15)) |>
gridlines() |>
stripes() |>
t_options(item = c("vlabel"), cex = 1.1, font = 2, col = "red", x=0.2, adj=0) |>
f_axis(xlim=c(-1.1,0.2), at = c(-1,-0.6,-0.2,0.2), tck = -0.03, mgp = c(2,0.5,0)) |>
f_points(pch = 16, cex = 1.5) |>
f_refline(v = 0) |>
f_direction(text = "A better B better", line = 1.6) |>
plotfobj()
Arrowheads
When using xlim via f_axis, x-axis limits that are narrower then the confidence limits can be set (even though that is usually not recommended). The cap at the end of the confidence interval is then not plotted (as it is outside of the plotting region). However, directional arrowheads can help to make the distinction more prominent. Arrowheads can be modified via function f_arrows but it is not completely straightforward because arrows have to be drawn twice to have different heads on both sides (e.g. with code 1 and 2 and angles 30 and 90). The helper function f_cutarrows does that automatically:
# Modify data to generate some very wide limits:
dat<-forplotdata
dat$beta_lci[1]<-c(-3)
dat$beta_uci[2]<-c(3)
dat[3,c("beta_lci","beta_uci")]<-c(-3,3)
dat[4,c("beta","beta_lci")]<-c(-2,-3)
dat$beta_format<-paste0(sprintf("%2.2f",dat$beta),
" (",sprintf("%2.2f",dat$beta_lci)," to ",
sprintf("%2.2f",dat$beta_uci),")")
# Generate and plot fobj:
genfobj(layout = c("t","t","t","t","t","t","f","t"),
dat = dat, lwidths = c(0.8,0.4,0.6,0.4,0.6,1,1,0.5)) |>
f_axis(xlim=c(-1.5,1)) |>
f_cutarrows() |>
plotfobj()
The header
By default a header with the columns names is used and stored in the header element of fobj, which is a list of length 1 with these elements.
fobj <- genfobj(dat = forplotdata,
layout = c("t","t","t","t","t","t","f","t"),
lwidths = c(1.5,1,2,1,2,3,3,1)) |>
gridlines() |>
stripes()
fobj$header
#> [[1]]
#> [[1]]$hlayout
#> [1] 1 2 3 4 5 6 7 8
#>
#> [[1]]$text
#> [[1]]$text$x
#> NULL
#>
#> [[1]]$text$y
#> [1] 0.5
#>
#> [[1]]$text$labels
#> [1] "vlabel" "n1" "n2" "n3" "n4"
#> [6] "beta_format" "beta" "p1"
#>
#> [[1]]$text$adj
#> [1] 0.5 0.5The header helper function can be used with all options from graphics::text() As an extra element, the hlayout can be used to merge columns, i.e. to print a label over more than one column. And more than one header row can be specified using headernr, leading to a list of length > 1.
Let’s first use different names, also including a line separator. Note that an empty character has to be included to leave column 1 and 8 empty. And the y is also modified to place the label higher.
fobj |>
header(labels = c("","Arm A\nN","Arm A\nmean (sd)","Arm B\nN","Arm B\nmean (sd)",
"Mean difference\n(95% CI)","","P-value"),
y = 0.6) |>
plotfobj()
We can merge the label for the effect over the format and forest columns using the layout option where 6 is included twice.
fobj |>
header(hlayout = c(1,2,3,4,5,6,6,7),
labels = c("","Arm A\nN","Arm A\nmean (sd)","Arm B\nN","Arm B\nmean (sd)",
"Mean difference (95% CI)","P-value")) |>
plotfobj()
In order to merge two arm labels, we would need two header rows using option headernr, leading to a header list with length 2. As before we can use further graphics::text() options.
fobj |>
header(headernr = 1, hlayout = c(1,2,2,3,3,4,4,4),
labels = c("", "Arm A", "Arm B", ""), y = 0.8) |>
header(headernr = 2, hlayout = c(1,2,3,4,5,6,6,7),
labels = c("", "N", "Mean (SD)", "N", "Mean (SD)",
"Mean difference (95% CI)", "P value"), y = 0.3) |>
plotfobj()
Boxplots
For a more in-depth presentation of the data in each group, a boxplot can be added. However, it depends on the input of the actual observations as a data frame obs in a long format with the numerical outcome value (value), the variable name (variable, as a factor) and the treatment arm (arm, as a factor).
Note that the names of variable do not have to be the same as in the summary data (dat) but the order has to be kept (i.e. the first level of variable must correspond the first row in dat).
Boxplot layout can be controlled via helper functions b_boxplot and b_axis using all options available for graphics::boxplot() and graphics::axis().
fobj<-genfobj(dat = forplotdata, obs = forplotdata_bp,
layout = c("t","t","t","t","t","b","t","f","t"),
lwidths = c(0.6,0.4,0.6,0.4,0.6,1,1,1,0.5))
plotfobj(fobj)
Adding header gridlines and stripes:
fobj |>
gridlines() |>
stripes(fobj) |>
header(hlayout = c(1,2,2,3,3,4,4,4,4), headernr = 1,
labels=c("","Arm A","Arm B",""),
col = c(1,"red","blue",1,1),
y=0.9) |>
header(hlayout = c(1,2,3,4,5,6,7,7,8), headernr = 2,
labels=c("","N","Mean (sd)","N","Mean (sd)","","Mean difference (95% CI)","P-value"),
col=1, y=0.3) |>
plotfobj()
Density plots
Density plots can be specified using layout item d and depend on the same data with the observations as the boxplots.
genfobj(dat = forplotdata, obs = forplotdata_bp,
layout = c("t","t","t","t","t","b","d","t","f","t"),
lwidths = c(0.3,0.4,0.6,0.4,0.6,1,1,1,1,0.5)) |>
b_axis(xlim=c(0,9.5)) |>
gridlines() |>
stripes() |>
header(hlayout = c(1,2,2,3,3,4,4,4,4), headernr = 1,
labels=c("","Arm A","Arm B",""),
col = c(1,"red","blue",1,1),
y=0.9) |>
header(hlayout = c(1,2,3,4,5,6,7,7,8), headernr = 2,
labels=c("","N","Mean (sd)","N","Mean (sd)","","Mean difference (95% CI)","P-value"),
col=1, y=0.3) |>
plotfobj()
#> Warning in cbind(ind = fobj$header[[hi]]$hlayout, cwidth): number of rows of
#> result is not a multiple of vector length (arg 1)
#> Warning in cbind(ind = fobj$header[[hi]]$hlayout, cwidth): number of rows of
#> result is not a multiple of vector length (arg 1)
Options can be change via d_axis and d_lines using all options available for graphics::axis() and graphics::lines().
Note that the lines element of the d items is a nested list with variable and arm. Using d_lines, line options can be accessed all at once, over all variables for one arm, for all arms and one variable, or for a specific variable-arm combination via linenr. linenr is a vector of length two where the first element specifies the variable and the second the arm.
fobj<-genfobj(dat = forplotdata, obs = forplotdata_bp,
layout = c("t","t","t","t","t","d","t","f","t"),
lwidths = c(0.3,0.4,0.6,0.4,0.6,1,1,1,0.5))
#all lines:
fobj<-d_lines(fobj=fobj, lw=1.5)
plotfobj(fobj)
Different density curves could be added by using x and y options in d_lines (or the lines list in the fobj). Note that the y-position has to be shifted by y.at for each variable.
Strip charts for proportions
For binary outcomes and in particular for serious adverse event reporting a graphical representation of the proportion in both arms has been recommended.
A strip chart for the proportions can be added via s[1-9], where the number would indicate the number of points in the strip chart and the number of columns in the dat (usually two if there are two treatment arms). The “s” items then contains several points elements.
For example:
fobj<-genfobj(dat = forplotdata_prop,
layout = c("t","t","t","t","t","s2","t","f","t"),
lwidths = c(0.3,0.4,0.6,0.4,0.6,1.0,1.2,1,0.5))
plotfobj(fobj)
Options can be modified via s_axis, s_hline and s_points using all options available for using all options available for graphics::axis(), graphics::abline() and graphics::points().
Note that for points, each sub-item has can be specified separately using pointnr (e.g. to specify colors).
Left and right borders can be added via s_borders.
fobj<-s_axis(fobj=fobj, xlim = c(0,1),
at = seq(0,1,by=0.25), labels = seq(0,100,by=25)) |>
s_points(pch = 16, cex=1.5) |>
s_points(pointnr = 1, col = "red") |>
s_points(pointnr = 2, col = "blue") |>
s_borders() |>
f_axis(at=seq(-0.1,0.4,by=0.1),labels=seq(-10,40,by=10)) |>
gridlines()
plotfobj(fobj)
Combine fobjs
A list of fobj can be combined using
combinefobj resulting in an object of class
cfobj, which includes an overall setup, an
overall header and the list of the individual
fobj.
The layout is adapted automatically. If there are different number of columns, they are distributed using a grid with the least common multiple. If the lwdiths vary, the average is taken.
#prepare first fobj
fobj1<-genfobj(dat = forplotdata, obs = forplotdata_bp,
layout = c("t","t","t","t","t","b","t","f","t"),
lwidths = c(0.3,0.4,0.6,0.4,0.6,1,1,1,0.5),
lheight=c(0.12,1,0.15)) |>
gridlines() |>
stripes() |>
header(hlayout = c(1), headernr = 1,
labels=c("Continuous variables"),
col = 1, y = 0.9, font = 2, cex=1.2) |>
header(hlayout = c(1,2,2,3,3,4,4,4,4), headernr = 2,
labels=c("","Arm A","Arm B",""),
col = c(1,"red","blue",1,1), y = 0.7, font = 1, cex = 1) |>
header(hlayout = c(1,2,3,4,5,6,7,7,8), headernr = 3,
labels=c("","N","Mean (sd)","N","Mean (sd)","","Mean difference (95% CI)","P-value"),
col = 1, y = 0.3, font = 1, cex = 1)
#prepare second fobj
fobj2<-genfobj(dat = forplotdata_prop,
layout = c("t","t","t","t","t","s2","t","f","t"),
lwidths = c(0.3,0.4,0.6,0.4,0.6,1,1,1,0.5),
lheight=c(0.12,1,0.15)) |>
s_axis(xlim = c(0,1),
at = seq(0,1,by=0.25), labels = seq(0,100,by=25)) |>
s_borders() |>
s_points(pointnr = 1, col = "red") |>
s_points(pointnr = 2, col = "blue") |>
f_axis(at=seq(-0.1,0.4,by=0.1),labels=seq(-10,40,by=10)) |>
gridlines() |>
stripes() |>
header(hlayout = c(1), headernr = 1,
labels=c("Binary variables"),
col = 1, y = 0.9, font = 2, cex=1.2) |>
header(hlayout = c(1,2,2,3,3,4,4,4,4), headernr = 2,
labels=c("","Arm A","Arm B",""),
col = c(1,"red","blue",1,1), y = 0.7, font = 1, cex = 1) |>
header(hlayout = c(1,2,3,4,5,6,7,7,8), headernr = 3,
labels=c("","N","n (%)","N","n (%)","Proportion (%)","Risk difference (95% CI)","P-value"),
col = 1, y = 0.3, font = 1, cex = 1)
#combine:
cfobj<-combinefobj(list(fobj1, fobj2))
names(cfobj)
#> [1] "setup" "header" "fobjs"
cfobj$setup
#> $lmatrix
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
#> [1,] 1 1 1 1 1 1 1 1 1
#> [2,] 12 12 12 12 12 12 12 12 12
#> [3,] 2 3 4 5 6 7 8 9 10
#> [4,] 11 11 11 11 11 11 11 11 11
#> [5,] 23 23 23 23 23 23 23 23 23
#> [6,] 13 14 15 16 17 18 19 20 21
#> [7,] 22 22 22 22 22 22 22 22 22
#> [8,] 24 24 24 24 24 24 24 24 24
#>
#> $lwidths
#> [1] 0.05172414 0.06896552 0.10344828 0.06896552 0.10344828 0.17241379 0.17241379
#> [8] 0.17241379 0.08620690
#>
#> $lheights
#> [1] 0.1200 0.1200 1.0000 0.1500 0.1200 1.0000 0.1500 0.0254
#>
#> $iheadfoot
#> [1] TRUE TRUE
#top header and footer are not used
cfobj$setup$lheights[c(1,length(cfobj$setup$lheights))]<-c(0.01,0.01)The cfobj can be plotted using the same plot function:
plotfobj(fobj = cfobj)
Combining plots can also be used to get different scaling for the plots of the individual variables. Assume we would like to present a number of differently scaled continuous variables with boxplots and densities. Note that we removed the headers of the indiviual fobj but kept the footer (to have space for the axes) by setting ikeepheadfoot to c(FALSE, TRUE).
set.seed(1)
fobj<-vector(10,mode="list")
for (i in 1:length(fobj)) {
#modify the data
obs<-forplotdata_bp[forplotdata_bp$variable==paste0("var",i),]
obs$value<-obs$value + runif(1,-10,10)
obs$variable<-factor(obs$variable)
dat<-cbind(forplotdata[i,1:5],"")
dat$n2<-paste0(sprintf("%1.1f",mean(obs$value[obs$arm==1]))," (",
sprintf("%1.1f",sd(obs$value[obs$arm==1])),")")
dat$n4<-paste0(sprintf("%1.1f",mean(obs$value[obs$arm==2]))," (",
sprintf("%1.1f",sd(obs$value[obs$arm==1])),")")
#generate individual fplots
fobji<-genfobj(dat = dat, obs = obs,
layout = c("t","t","t","t","t","b","t","d"),
lwidths = c(0.3,0.4,0.6,0.4,0.6,1,0.1,1),
lheights = c(0.2,1,0.25))
fobji<-b_axis(fobji, cex.axis=0.8, mgp=c(0,0.3,0), tck=-0.1)
fobji<-d_axis(fobji, cex.axis=0.8, mgp=c(0,0.3,0), tck=-0.1)
#remove individual headers
#fobji$header<-NULL
#gridlines on first and last fobj
if (i==1) {
fobji<-gridlines(fobji, h=fobji$setup$ylim[2])
}
if (i==length(fobj)) {
fobji<-gridlines(fobji, h=fobji$setup$ylim[1])
}
#collect
fobj[[i]]<-fobji
}
cfobj<-combinefobj(fobj)
#combine and add overall header
cfobj<-combinefobj(fobj, keepiheadfoot = c(FALSE, TRUE)) |>
header(hlayout = c(1,2,2,3,3,4,5,6), headernr = 1,
labels=c("","Arm A","Arm B","Boxplot","","Density"),
col = c(1,"red","blue",1,1), y = 0.9) |>
header(hlayout = c(1,2,3,4,5,6,7,8), headernr = 2,
labels=c("","N","mean (sd)","N","mean (sd)","","",""),
col = 1, y = 0.35)
#adapt overall header and footer
cfobj$setup$lheights[1]<-1
cfobj$setup$lheights[length(cfobj$setup$lheights)]<-0.5
#combine and plot the list of fobj
plotfobj(cfobj)
Inserting subtitles
Subtitles can be inserted using the combinefobj
functionality with an fobj that does only have one
column. The function insert_subtitle helps with the
preparation:
fobj<-genfobj(dat = forplotdata,
layout = c("t","t","t","t","t","t","f","t"),
lwidths = c(0.3,0.4,0.6,0.4,0.6,1,1,0.5))
cfobj<-insert_subtitle(fobj,
atrows=c(1, 3, 5),
subtitle=c("A first long title is added here",
"A second long title is added here",
"A third long title is added here")
)
plotfobj(cfobj)
The resulting cfobj contains 6 individual fobj (for each subtitle and the parts in between), which can be modified individually. We can e.g. modify subtitles or remove the axes in the first and second part of the plot.
Note that there are no individual headers and footers for this 6
fobj, which is the case if they are combined directly
via combinefobj (unless option keepiheadfoot is
set to FALSE).
The overall header can be accessed via the header
function in the same way as for an individual fobj.
#change text and background color, add gridlines
cfobj$fobjs[[1]]<-t_options(cfobj$fobjs[[1]], col = "red")
for (i in c(1,3,5)) {
cfobj$fobjs[[i]]<-cfobj$fobjs[[i]] |>
stripes() |>
gridlines()
}
#only keep bottom axis
cfobj$fobjs[[2]]$items[[7]]$axis<-NULL
cfobj$fobjs[[4]]$items[[7]]$axis<-NULL
#add gridline at the bottom
cfobj$fobjs[[6]]<-gridlines(cfobj$fobjs[[6]])
#overall header
cfobj<-cfobj |>
header(hlayout = c(1,2,2,3,3,4,4,4), headernr = 1,
labels=c("","Arm A","Arm B",""),
y=0.9) |>
header(hlayout = c(1,2,3,4,5,6,6,7), headernr = 2,
labels=c("","N","Mean (sd)","N","Mean (sd)","Mean difference (95% CI)","P-value"),y=0.3)
#adapt height of header and footer
cfobj$setup$lheights[1]<-0.15
cfobj$setup$lheights[length(cfobj$setup$lheights)]<-0.15
plotfobj(cfobj)