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README.Rmd
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README.Rmd
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---
title: "Add more interactivity to interactive charts"
output: github_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(manipulateWidget)
```
[](http://cran.r-project.org/package=manipulateWidget)
[](http://cran.r-project.org/package=manipulateWidget)
[](https://travis-ci.org/rte-antares-rpackage/manipulateWidget)
[](https://ci.appveyor.com/project/rte-antares-rpackage/manipulatewidget/branch/master)
`manipulateWidget` lets you create in just a few lines of R code a nice user interface to modify the data or the graphical parameters of one or multiple interactive charts. It is useful to quickly explore visually some data or for package developers to generate user interfaces easy to maintain.
This R package is largely inspired by the `manipulate` package from Rstudio. It provides the function `manipulateWidget` that can be used to create in a very easy way a graphical interface that let the user modify the data or the parameters of an interactive chart. Technically, the function generates a Shiny gadget, but the user does not even have to know what is Shiny.
## Features
* Easily combine multiple interactive charts (`htmlwidgets`) in a single interactive chart with function `combineWidgets`.
* With only a few lines of code, create a complete user interface that lets a user change the settings of a chart: filter the input data, change the model, modify the chart type or anything else.
* Comparison mode: compare at a glance two set of parameters. For instance compare the same chart for two different countries or compare the results of several models or whatever.
* Export to HTML or to PNG with a single click.
## Why should you use it?
All functionalities of this package can be replicated with other packages like [shiny](https://shiny.rstudio.com/), [flexdashboard](http://rmarkdown.rstudio.com/flexdashboard/), [crosstalk](http://rstudio.github.io/crosstalk/) and others. So why another package?
`manipulateWidget` has three advantages:
* It is easy and fast to use. Only a few lines of `R` are necessary to create a user interface.
* Code can be included in any R script. No need to create a dedicated .R or .Rmd file.
* It works with all htmlwidgets. In contrast, `crosstalk` only supports a few of them.
`manipulateWidget` can be especially powerful for users who are exploring some data set and want to quickly build a graphical tool to see what is in their data. `manipulateWidget` has also some advanced features that can be used with almost no additional code and that could seduce some package developers: grouping inputs, conditional inputs and comparison mode.
## Installation
The package can be installed from CRAN:
```{r eval=FALSE}
install.packages("manipulateWidget")
```
You can also install the latest development version from github:
```{r eval=FALSE}
devtools::install_github("rte-antares-rpackage/manipulateWidget", ref="develop")
```
## Getting started
The hard part for the user is to write a code that generates an interactive chart. Once this is
done, he only has to describe what parameter of the code should be modified by what input control. For instance, consider the following code that identifies clusters in the iris data set and uses package `plotly` to generate an interactive scatter plot.
```{r plotevouse, message=FALSE, warning=FALSE, out.width=600, out.height=400}
library(manipulateWidget)
library(dplyr)
library(ggplot2)
library(plotly)
data("worldEnergyUse")
plotEvoUse <- function(Country, Period = c(1960,2014)) {
dataset <- worldEnergyUse %>%
filter(country == Country, year >= Period[1] & year <= Period[2])
ggplot(dataset, aes(year)) +
geom_line(aes(y = energy_used, color = "Total energy")) +
geom_line(aes(y = energy_fossil, color = "Fossil energy")) +
scale_color_manual(values = c("black", "red")) +
expand_limits(y = 0) +
ggtitle(paste("Evolution of energy\nconsumption in", Country)) +
xlab("") + ylab("Energy (kg of oil equivalent)") + labs(color = "") +
theme_bw() +
theme(plot.title = element_text(size=10)) +
theme(axis.title.y = element_text(size=9))
}
plotEvoUse("United States") %>% ggplotly()
```
We create a second function that represents the share of a given country in the world energy consumption and population. We create also create a custom tooltip.
```{r plotshareuse, message=FALSE, out.width=600, out.height=400}
tooltipText <- function(title, value) {
sprintf("%s: %s%%", title, round(value * 100, 1))
}
plotShareUse <- function(Country, Period = c(1960, 2014)) {
dataset <- worldEnergyUse %>%
filter(country == Country, year %in% Period)
ggplot(dataset) +
facet_grid(year ~ .) +
geom_bar(aes("Population", weight = prop_world_population,
text = tooltipText("Population", prop_world_population))) +
geom_bar(aes("Energy Use", weight = prop_world_energy_used,
text = tooltipText("Energy Use", prop_world_energy_used))) +
geom_bar(aes("Energy Fossil", weight = prop_world_energy_fossil,
text = tooltipText("Energy Fossil", prop_world_energy_fossil))) +
ggtitle("Share of world...") +
xlab("") + ylab("") +
scale_y_continuous(labels = scales::percent) +
theme_bw() +
theme(plot.title = element_text(size=10)) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
}
suppressWarnings(plotShareUse("Germany")) %>%
ggplotly(tooltip = "text")
```
We can combine two charts with the helper function `combineWidgets()`. We create a new function for clarity, but this is not a requirement.
```{r combinewidgets, message=FALSE, warning=FALSE, out.width=600, out.height=400}
combinedPlots <- function(Country, Period = c(1960, 2014)) {
combineWidgets(
plotEvoUse(Country, Period) %>% ggplotly() %>%
layout(
legend = list(orientation = "h", x = 0, y = 0, yanchor = "bottom")
),
plotShareUse(Country, Period) %>% ggplotly(tooltip = "text"),
ncol = 2, colsize = c(2, 1)
)
}
combinedPlots("Germany")
```
So we now have some R code that generates a nice interactive chart. Now we would like to create a user interface that lets a user choose the country and the period that he wants to visualize.
Here comes the magic of package `manipulateWidget`! With this package, you only have to write a few more lines of R code to achieve this result:
```{r eval = FALSE}
manipulateWidget(
combinedPlots(Period, Country),
Period = mwSlider(1960, 2014, c(1960, 2014)),
Country = mwSelect(sort(unique(worldEnergyUse$country)), "United States")
)
```
And voila!
For more information take a look at the [package vignette](https://cran.r-project.org/web/packages/manipulateWidget/vignettes/manipulateWidgets.html).
## License Information:
Copyright 2015-2020 RTE (France)
* RTE: http://www.rte-france.com
This Source Code is subject to the terms of the GNU General Public License, version 2 or any higher version. If a copy of the GPL-v2 was not distributed with this file, You can obtain one at https://www.gnu.org/licenses/old-licenses/gpl-2.0.en.html.