---
title: "Introduction to forestploter"
author: "Alimu Dayimu"
date: "`r Sys.Date()`"
output: 
  rmarkdown::html_vignette:
    toc: yes
vignette: >
  %\VignetteIndexEntry{Introduction to forestploter}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  dpi=300,
  comment = "#>"
)
```

[Forest plots](https://en.wikipedia.org/wiki/Forest_plot) are commonly used in the medical research publications, especially in [meta-analysis](https://en.wikipedia.org/wiki/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](https://CRAN.R-project.org/package=forestplot). Packages specialised for the meta-analysis, like [meta](https://CRAN.R-project.org/package=meta), [metafor](https://CRAN.R-project.org/package=metafor) and [rmeta](https://CRAN.R-project.org/package=rmeta). Some other packages, like [ggforestplot](https://nightingalehealth.github.io/ggforestplot/index.html) uses [ggplot2](https://CRAN.R-project.org/package=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:

* Focus on the forest plot.
* Treat the forest plot as a table, elements are aligned in rows and columns. User has full control on what and how to display in the forest plot contents.
* Graphical parameters are controlled with a theme.
* Allowing post-hoc plot editing.
* CIs in multiple columns and by groups.

# Basic forest plot

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. 

## Simple forest plot

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.

```{r prepare-data}
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, increase number of space below 
# to have a larger area to draw the CI. 
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)
```

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.

```{r simple-plot, out.width="80%", fig.width  = 8, fig.height = 6}
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)

```

## Change theme

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.

```{r simple-plot-theme, out.width="80%", fig.width  = 7, fig.height = 3.3}
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)

```


## Text justification and background with theme
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](https://CRAN.R-project.org/package=gridExtra/vignettes/tableGrob.html).  

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.

```{r text-justification, out.width="80%", fig.width  = 7, fig.height = 2}
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)

```


# Multiple CI columns

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:

```{r multiple-group, out.width="80%", fig.width  = 8, fig.height = 6.5}
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)
```

# Different parameters for different CI columns

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.

```{r multiple-param, out.width="70%", fig.width  = 10, fig.height = 6.5}

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)
```

# Custom CIs
It is possible to pass custom CI drawing function to `forest`. The `fn_ci` accepts CI drawing function for normal confidence interval and `fn_summary` for summary CI. Other parameters for the those functions can be passed via `forest`. If you need to row values as `est` and `lower` to those functions, you need to define the name of the parameters you have passed via `index_args`. This is an advanced technique and not the purpose of this vignette to show how to create a function to draw CI, but you can find some tutorial [here](https://www.stat.auckland.ac.nz/~paul/RG3e/chapter8.html) if you are interested. Below is an example of the useage for a box plot CI with the built in `make_boxplot` function. 

```{r custom-ci, out.width="70%", fig.width  = 3, fig.height = 3}
# Function to calculate Box plot values
box_func <- function(x){
  iqr <- IQR(x)
  q3 <- quantile(x, probs = c(0.25, 0.5, 0.75), names = FALSE)
  c("min" = q3[1] - 1.5*iqr, "q1" = q3[1], "med" = q3[2],
    "q3" = q3[3], "max" = q3[3] + 1.5*iqr)
}
# Prepare data
val <- split(ToothGrowth$len, list(ToothGrowth$supp, ToothGrowth$dose))
val <- lapply(val, box_func)

dat <- do.call(rbind, val)
dat <- data.frame(Dose = row.names(dat),
                  dat, row.names = NULL)

dat$Box <- paste(rep(" ", 20), collapse = " ")

# Draw single group box plot
tm <- forest_theme(ci_Theight = 0.2)

p <- forest(dat[,c(1, 7)],
            est = dat$med,
            lower = dat$min,
            upper = dat$max,
            # sizes = sizes,
            fn_ci = make_boxplot,
            ci_column = 2,
            lowhinge = dat$q1, 
            uphinge = dat$q3,
            hinge_height = 0.2,
            # values of the lowhinge and uphinge will be used as row values
            index_args = c("lowhinge", "uphinge"), 
            gp_box = gpar(fill = "black", alpha = 0.4),
            theme = tm
)
p

```

# Saving plot
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. 

```{r eval=FALSE}
# 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. 

```{r eval=FALSE}
# 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)
```


# FAQs

**Q: The whisker/CI plot area is too narrow, pelase help!**

**A:** I have to admit that vignettes were not well written, but you should be able to get the idea of if you looked the vignette creafully and checked the examples. Increase the widths by having more blank space in the column that the CI to be drawn. Please checkout the first example on how to do this. 

**Q: Can I modify the width and height of each row and columns?**

**A:** Yes, althoug the content in of the data decide the heights and widths of the rows and columns, you can also modify these after you have finished plotting. See [this here for details](https://github.com/adayim/forestploter/issues/30#issuecomment-1459038988).

**Q: How should I use weight for sizes?**

**A:** The `forest` function will not transform the size thus it will be used as it is. If you want to weight the size on your own, checkout [here for some options](https://github.com/adayim/forestploter/issues/37#issuecomment-1450208581)


**Q: How can I plot grouped forestplot?**

**A:** You can leave few blank lines to indicate the group break. You can also use `arrangeGrob` from `gridExtra` package to combine two or more forestplot.


