# ggforestplot

**Repository Path**: joyeric_admin_admin/ggforestplot

## Basic Information

- **Project Name**: ggforestplot
- **Description**: NightingaleHealth/ggforestplot
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2022-12-13
- **Last Updated**: 2022-12-13

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README


<!-- README.md is generated from README.Rmd. Please edit that file -->

<br>

<div style="text-align:left">

<span><a href="https://nightingalehealth.github.io/ggforestplot/index.html">
<img src="man/figures/logo.png" width=110 alt="Logo"/> </a>

<h2>

<strong>ggforestplot</strong>

</h2>

<h4>

Visualizing Measures of Effect

</h4>

</span>

</div>

<br>

`ggforestplot` is an R package for plotting measures of effect and their
confidence intervals (e.g. linear associations or log and hazard ratios,
in a forestplot layout, a.k.a. blobbogram).

The main plotting function is `ggforestplot::forestplot()` which will
create a single-column forestplot of effects, given an input data frame.

The two vignettes [Using
ggforestplot](https://nightingalehealth.github.io/ggforestplot/articles/ggforestplot.html)
and [NMR data analysis
tutorial](https://nightingalehealth.github.io/ggforestplot/articles/nmr-data-analysis-tutorial.html)
provide an introduction to creating forestplot visualizations with
custom groupings and performing basic exploratory analysis (using demo
metabolic data of the [Nightingale Health NMR
platform](https://nightingalehealth.com/technology)).

## Installation

You can install `ggforestplot` from github as shown below (unless
already installed, you need to first install `devtools`):

``` r
# install.packages("devtools")
devtools::install_github("NightingaleHealth/ggforestplot")
```

If you want display package vignettes with `utils::vignette()`, install
`ggforestplot` with
`devtools::install_github("NightingaleHealth/ggforestplot",
build_vignettes = TRUE)`. However, installing with building the
vignettes takes little bit longer. (Note: If dependencies are not
installed automatically, try updating `devtools`.)

## Examples

Below we briefly showcase the usage of `ggforestplot` with publicly
available datasets, which are also included in the package (see [A. V.
Ahola-Olli et al.
(2019)](https://www.biorxiv.org/content/10.1101/513648v1)).

### Linear associations

Plot a vertical forestplot for linear associations of blood biomarkers
to insulin resistance (HOMA-IR), fasting glucose and Body Mass Index
(BMI).

``` r
# Load tidyverse and ggforestplot
# install.packages("tidyverse")
library(tidyverse)
library(ggforestplot)

# Get subset of example, linear associations, data frame
df_linear <-
  ggforestplot::df_linear_associations %>%
  dplyr::arrange(name) %>%
  dplyr::filter(dplyr::row_number() <= 30)

# Forestplot
forestplot(
  df = df_linear,
  estimate = beta,
  logodds = FALSE,
  colour = trait,
  title = "Associations to metabolic traits",
  xlab = "1-SD increment in cardiometabolic trait
  per 1-SD increment in biomarker concentration"
)
```

![](man/figures/README-unnamed-chunk-3-1.png)<!-- -->

### Odds ratios

Plot a vertical forestplot for odds ratios of blood biomarkers with risk
for future type 2 diabetes; visualize all 4 available cohorts and their
meta-analysis.

``` r
# Get subset of example, log odds ratios, data frame
df_logodds <-
  df_logodds_associations %>%
  dplyr::arrange(name) %>%
  dplyr::left_join(ggforestplot::df_NG_biomarker_metadata, by = "name") %>% 
  dplyr::filter(group == "Amino acids") %>%
  # Set the study variable to a factor to preserve order of appearance
  # Set class to factor to set order of display.
  dplyr::mutate(
    study = factor(
      study,
      levels = c("Meta-analysis", "NFBC-1997", "DILGOM", "FINRISK-1997", "YFS")
    )
  )

# Forestplot
forestplot(
  df = df_logodds,
  estimate = beta,
  logodds = TRUE,
  colour = study,
  shape = study,
  title = "Associations to type 2 diabetes",
  xlab = "Odds ratio for incident type 2 diabetes (95% CI)
  per 1−SD increment in metabolite concentration"
) +
  # You may also want to add a manual shape scale to mark meta-analysis with a
  # diamond shape
  ggplot2::scale_shape_manual(
    values = c(23L, 21L, 21L, 21L, 21L),
    labels = c("Meta-analysis", "NFBC-1997", "DILGOM", "FINRISK-1997", "YFS")
  )
```

![](man/figures/README-unnamed-chunk-4-1.png)<!-- -->