See the instructions for Python

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You already know how to visualise Macrobond’s wealth of macroeconomic and financial aggregate data using a variety of line, bar and other charts.

But did you know you can also use it to create geographic heat maps in just a few steps?

We take you through the process:

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Our World in Data, COVID-19, Vaccinations, People Fully Vaccinated Per Hundred

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Step 1: Define some functions to efficiently return the ISO 2 code for the region and return the last value of the series.

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library(MacrobondAPI)

library(ggplot2)

library(tidyverse)

library(rnaturalearth)

library(rnaturalearthdata)

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# Afunction to get the region of the series

GetRegion<- function(entity) getFirstMetadataValue(getMetadata(entity),"Region")

GetRegionNames<- function(entities) sapply(entities, GetRegion)

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# A function for getting the ISO2 for a country from the region code entity

metaInfoIso <- GetMetadataInformation("IsoCountryCode2")

GetRegionIso2 <- function(entity)

{

regionName <- getValuePresentationText(metaInfoIso, getFirstMetadataValue(getMetadata(entity), "Region"))

}

# A function for getting the last value using the metadata

GetLastValueFromMetadata <- function(entity)

{

getFirstMetadataValue(getMetadata(entity), "LastValue")

}

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‍Step 2: Find the same variable for several countries. Use the search functionality in the Macrobond API for a specific concept. Find this under “Time Series information” in the application or by using the “Get Concepts” function in R.

# Getting the concept of a series

series<-FetchOneTimeSeries("owidvacci_ar_pfvph")

concept<-getConcepts(series)

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# Searching for a concept in Macrobond and accessing the available data globally

query <- CreateSearchQuery()

setEntityTypeFilter(query, "TimeSeries")

addAttributeValueFilter(query, "RegionKey" , concept)

searchResult <- SearchEntities(query)

entities <- getEntities(searchResult)

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Step 3: Now create a data frame with the ISO 2 codes and the last value, which can be done through the functions defined earlier.

regionNames <- GetRegionNames(entities) #Getting the Region names from the search result

regionEntities <- FetchEntities(regionNames)#Getting the Region Entities from Region in order to translate it to ISO2

regionIso <- GetRegionsIso2(regionEntities) #Getting the ISO 2

entitiesAndRegions <- mapply(function (entity, iso) c(entity = entity, iso = iso), entities, regionIso) #Converting the ISO

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Step 4: Now it’s time to match your dataset with the map. Use rnaturalearth to get the coordinates for the countries on the map and join thatdataset with the Macrobond data.

# Inserting the country list for the map from rnaturalearth

mapdata <- ne_countries(scale = "medium", returnclass = "sf")

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# Remove entries for which we do not have matching iso codes

entitiesAndRegions <- entitiesAndRegions[unlist(sapply(entitiesAndRegions, function(x) !is.null(x$iso) && x$iso %in% mapdata$iso_a2))]

lastValues <- sapply(entitiesAndRegions, function(x) GetLastValueFromMetadata(x$entity))#Getting the last value from the meta data

isoCodes <- sapply(entitiesAndRegions, function(x) x$iso) #Getting the ISO2 codes

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# Creating a dataframe with region and last value

insert_data <- data.frame(iso_a2 = isoCodes, value = lastValues)

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# Joining the values with the map dataset

mapdata <- left_join(mapdata, insert_data, by="iso_a2")

mapdata <- subset(mapdata, select = c(name, value, geometry, iso_a2))

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Step 5: Now you’re all set to create your map through ggplot.

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 # Creating the map

map <- ggplot(data = mapdata, aes(text = paste(name, "<br>", "Fully vaccinated:", value,"%"))) +

geom_sf(aes(fill = value))+

ggtitle(GetConceptDescription(getConcepts(entities[[1]])))+

labs(title = GetConceptDescription(getConcepts(entities[[1]])), x = "Source: Macrobond")+

scale_fill_viridis_c(name = "%", option = "viridis", trans= "reverse")+

theme(axis.text.x = element_blank(),

axis.text.y = element_blank(),

axis.ticks = element_blank(),

axis.title.y=element_blank(),

rect = element_blank())

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Using Plotly

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If you would like to take this to the next level, you can also use Plotly to create the interactive map.

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# Creating a widget with plotly

library(plotly)

fig <- ggplotly(map, tooltip = c("text"))

fig

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‍Here’s the full code:

library(MacrobondAPI) #This sample requires Macrobond API 1.2-4 or later

library(ggplot2)

library(tidyverse)

library(rnaturalearth)

library(rnaturalearthdata)

library(plotly)

GetRegion <- function(entity) getFirstMetadataValue(getMetadata(entity), "Region")

GetRegionNames <- function(entities) sapply(entities, GetRegion)

GetLastValueFromMetadata <- function(entity) getFirstMetadataValue(getMetadata(entity), "LastValue")

GetRegionsIso2 <- function(regionEntities) sapply(regionEntities, function(entity) getFirstMetadataValue(getMetadata(entity), "IsoCountryCode2"))

FetchOneTimeSeries()

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# Searching for a concept in Macrobond and accessing the available data globally

query <- CreateSearchQuery()

setEntityTypeFilter(query, "TimeSeries")

addAttributeValueFilter(query, "RegionKey" , "owdc0008")

searchResult <- SearchEntities(query)

entities <- getEntities(searchResult)

regionNames <- GetRegionNames(entities) #Getting the Region names from the search result

regionEntities <- FetchEntities(regionNames)#Getting the Region Entities from Region in order to translate it to ISO2

regionIso <-GetRegionIso2(regionEntities) #Getting the ISO 2

entitiesAndRegions <- mapply(function (entity, iso) c(entity = entity, iso = iso), entities, regionIso) #Converting the ISO

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# Inserting the country list for the map from rnaturalearth

mapdata <- ne_countries(scale = "medium", returnclass = "sf")

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# Remove entries for which we do not have matching iso codes

entitiesAndRegions <- entitiesAndRegions[unlist(sapply(entitiesAndRegions, function(x) !is.null(x$iso) && x$iso %in% mapdata$iso_a2))]

lastValues <- sapply(entitiesAndRegions, function(x) GetLastValueFromMetadata(x$entity))#Getting the last value from the meta data

isoCodes <- sapply(entitiesAndRegions, function(x) x$iso) #Getting the ISO2 codes

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# Creating a dataframe with region and last value

insert_data <- data.frame(iso_a2 = isoCodes, value = lastValues)

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# Joining the values with the map dataset

mapdata <- left_join(mapdata, insert_data, by="iso_a2")

mapdata <- subset(mapdata, select = c(name, value, geometry, iso_a2))

library(viridis)

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# Creating the map

map <- ggplot(data = mapdata, aes(text = paste(name, "<br>", "Fully vaccinated:", value,"%"))) +

geom_sf(aes(fill = value))+

ggtitle(GetConceptDescription(getConcepts(entities[[1]])))+

labs(title = GetConceptDescription(getConcepts(entities[[1]])), x = "Source: Macrobond")+

scale_fill_viridis_c(name = "%", option = "viridis", trans= "reverse")+

theme(axis.text.x = element_blank(),

axis.text.y = element_blank(),

axis.ticks = element_blank(),

axis.title.y=element_blank(),

rect = element_blank())

map

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# Creating a widget

fig <- ggplotly(map, tooltip = c("text"))

fig

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