For a few years now, it has become very to create interactive maps with R thanks to the package leaflet by the Rstudio team. Nevertheless, it only provides only a few functions to create basic shapes on a map, so the information that can be represented on a single map is limited: if you have some data associated to some points, you can only represent at most two variables by drawing circles and changing their radius and color according to data.
let’s have a look to a concrete example.
Data
The package provides a table that contains the production, consumption and exchanges of France from january 2010 to february 2017 and of 12 french regions from january 2013 to february 2017.
In addition to the total production, the table contains one column for each type of production. The table also contains the lattitude and longitude of the center of the regions.
library(leaflet.minicharts)
data("eco2mix")
head(eco2mix)
## area lng lat month total nuclear coal fuel
## 1 Auvergne-Rhône-Alpes 4.537338 45.51266 2015-03 11430 8230 6 40
## 2 Auvergne-Rhône-Alpes 4.537338 45.51266 2015-06 10056 7200 4 35
## 3 Auvergne-Rhône-Alpes 4.537338 45.51266 2013-04 10532 7410 0 31
## 4 Auvergne-Rhône-Alpes 4.537338 45.51266 2015-04 10103 7275 4 39
## 5 Auvergne-Rhône-Alpes 4.537338 45.51266 2014-08 9052 6357 0 25
## 6 Auvergne-Rhône-Alpes 4.537338 45.51266 2015-11 9258 7192 5 45
## gaz hydraulic wind solar bioenergy load balance export import balanceUK
## 1 200 2737 74 57 82 6326 4747 NA NA NA
## 2 5 2606 44 101 58 4848 4891 NA NA NA
## 3 3 2948 55 39 42 NA NA NA NA NA
## 4 86 2487 66 81 61 5283 4439 NA NA NA
## 5 -1 2487 38 76 68 4299 4447 NA NA NA
## 6 250 1563 75 40 86 5829 3141 NA NA NA
## balanceES balanceIT balanceCH balanceDEBE
## 1 NA NA NA NA
## 2 NA NA NA NA
## 3 NA NA NA NA
## 4 NA NA NA NA
## 5 NA NA NA NA
## 6 NA NA NA NA
The package also provides the contours of french regions.
data("regions")
class(regions)
## [1] "SpatialPolygons"
## attr(,"package")
## [1] "sp"
Renewable productions in 2016
Nowadays, France has an objective of 23% of renewable energies in the consumption of the country by 2020. Are the country close to its objective. Is the share of renewable energies similar in all regions?
To answer this question let us focus on the year 2016 We first prepare the required data with package dplyr:
library(dplyr)
prod2016 <- eco2mix %>%
mutate(
renewable = bioenergy + solar + wind + hydraulic,
non_renewable = total - bioenergy - solar - wind - hydraulic
) %>%
filter(grepl("2016", month) & area != "France") %>%
select(-month) %>%
group_by(area, lat, lng) %>%
summarise_all(sum) %>%
ungroup()
head(prod2016)
## # A tibble: 6 × 23
## area lat lng total nuclear coal fuel
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Auvergne-Rhône-Alpes 45.51266 4.537338 108517 75002 59 101
## 2 Bourgogne-Franche-Comté 47.23433 4.807411 2762 0 0 1
## 3 Bretagne 48.17299 -2.841621 3141 0 0 9
## 4 Centre-Val de Loire 47.48480 1.685109 78430 75733 0 0
## 5 Grand-Est 48.68889 5.613884 107755 82734 1650 41
## 6 Hauts-de-France 49.96995 2.771945 45593 31222 48 17
## # ... with 16 more variables: gaz <dbl>, hydraulic <dbl>, wind <dbl>,
## # solar <dbl>, bioenergy <dbl>, load <dbl>, balance <dbl>, export <lgl>,
## # import <lgl>, balanceUK <lgl>, balanceES <lgl>, balanceIT <lgl>,
## # balanceCH <lgl>, balanceDEBE <lgl>, renewable <dbl>,
## # non_renewable <dbl>
We also create a base map that will be used in all the following examples
library(leaflet)
tilesURL <- "http://server.arcgisonline.com/ArcGIS/rest/services/Canvas/World_Light_Gray_Base/MapServer/tile/{z}/{y}/{x}"
basemap <- leaflet(width = "100%", height = "400px") %>%
addTiles(tilesURL) %>%
addPolygons(data = regions, color = "brown", weight = 1, fillOpacity = 0)
basemap
We now add to the base map a pie chart for each region that represents the share of renewable energies. We also change the width of the pie charts so their area is proportional to the total production of the corresponding region. Finally we use addLegend a function of leaflet package to create a legend.
colors <- c("#4fc13c", "#cccccc")
basemap %>%
addMinicharts(
prod2016$lng, prod2016$lat,
type = "pie",
data = prod2016[, c("renewable", "non_renewable")],
colorPalette = colors,
width = 60 * sqrt(prod2016$total) / sqrt(max(prod2016$total))
) %>%
addLegend(
"topright",
colors = colors, opacity = 1,
labels = c("Renewable", "Non renewable")
)
We can see that the three south east regions exceed the target of 23%, but most regions are far from this objective. Globally, renewable energies represented only 19% percent of the production of 2016.
Now let’s represent the different types of renewable production using bar charts.
renewable2016 <- prod2016 %>% select(hydraulic, solar, wind)
colors <- c("#3093e5", "#fcba50", "#a0d9e8")
basemap %>%
addMinicharts(
prod2016$lng, prod2016$lat,
data = renewable2016,
colorPalette = colors,
width = 45, height = 45
) %>%
addLegend(
"topright",
colors = colors, opacity = 1,
labels = c("Hydraulic", "Solar", "Wind")
)
Hydraulic production is far more important than solar and wind. Without surprise, solar production is more important in south while wind production is more important in the north.
Representing a single variable
leaflet.minicharts has been designed to represent multiple variables at once, but you still may want to use it to represent a single variable. In the next example, we represent the total load of each french region in 2016. When data passed to addMinicharts contains a single column, it automatically represents it with circle which area is proportional to the corresponding value. In the example we also use the parameter showLabels to display rounded values of the variable inside the circles.
basemap %>%
addMinicharts(
prod2016$lng, prod2016$lat,
data = prod2016$load,
showLabels = TRUE,
width = 45
)
This is nice, isn’t it?
Updating existing mini charts
The second function of the package, updateMinicharts can be used inside a shiny application to update the data or the appearance of existing minicharts on a map.
To illustrate this, let create a simple application to visualize the evolution of the different renewable productions. First we prepare the data for our application by splitting it by month. But before splitting the data, we compute the maximal value of the columns we will represent. Indeed, the function addMinichart will only have a small proportion of the data and so it won’t be able to correctly set the maximal value data can take. This will create invalid scales and truncated charts.
appdata <- eco2mix %>% filter(area != "France")
maxValue <- max(appdata[, c("hydraulic", "solar", "wind")])
appdata <- split(appdata, appdata$month)
appdata is a list with one data.frame for each month in the data. We can now create our shiny application. The important thing to note is the usage of parameter layerId. This parameter is mandatory if one wants to use updateMinicharts and it must contain only unique values.
ui <- fluidPage(
sliderInput("monthId", "", 1, length(appdata), value = 1, step = 1, animate = TRUE),
tags$h4(textOutput("month")),
leafletOutput("map")
)
server <- function(input, output, session) {
# Display month
output$month <- renderText(names(appdata)[input$monthId])
# Initialize the map
output$map <- renderLeaflet({
data <- appdata[[1]]
basemap %>%
addMinicharts(
data$lng, data$lat,
data = data[, c("hydraulic", "solar", "wind")],
maxValues = maxValue,
colorPalette = colors,
width = 45, height = 45,
layerId = data$area
) %>%
addLegend(
"topright",
colors = colors, opacity = 1,
labels = c("Hydraulic", "Solar", "Wind")
)
})
# Update minicharts when the slider value changes
observeEvent(input$monthId ,{
data <- appdata[[input$monthId]]
leafletProxy("map", session) %>%
updateMinicharts(
layerId = data$area,
data = data[, c("hydraulic", "solar", "wind")]
)
})
}
shinyApp(ui, server)
Here is a gif of the result:
