The comtradR package provides an R interface for the United Nations Comtrade. comtradR is a fantastic CRAN package that provides country level shipping data for a variety of commodities.
Learn how to use the comtradR package by following this example below.
Extracting Data
Search for specific countries
# Search
ct_country_lookup("Ecuador", "reporter")
[1] "Ecuador"ct_country_lookup(c("USA", "China", "Canada", "South Africa", "France"), "partner")
[1] "Canada" "China"
[3] "China, Hong Kong SAR" "China, Macao SAR"
[5] "France" "South Africa"
[7] "USA" "USA (before 1981)" Search for specific commodities
# Gives all the codes related to "banana"
ct_commodity_lookup("banana")
$banana
[1] "0803 - Bananas, including plantains; fresh or dried"
[2] "080300 - Fruit, edible; bananas, (including plantains), fresh or dried"
[3] "080390 - Fruit, edible; bananas, other than plantains, fresh or dried" Search for specific data
# Search data on all monthly banana exports for 2018
BananaExport <- ct_search(reporters = "Ecuador",
partners = c("All"),
trade_direction = "exports",
commod_codes = "0803",
start_date = 2018,
end_date = 2018)
str(BananaExport)
'data.frame': 75 obs. of 35 variables:
$ classification : chr "H5" "H5" "H5" "H5" ...
$ year : int 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 ...
$ period : int 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 ...
$ period_desc : chr "2018" "2018" "2018" "2018" ...
$ aggregate_level : int 4 4 4 4 4 4 4 4 4 4 ...
$ is_leaf_code : int 0 0 0 0 0 0 0 0 0 0 ...
$ trade_flow_code : int 2 2 2 2 2 2 2 2 2 2 ...
$ trade_flow : chr "Export" "Export" "Export" "Export" ...
$ reporter_code : int 218 218 218 218 218 218 218 218 218 218 ...
$ reporter : chr "Ecuador" "Ecuador" "Ecuador" "Ecuador" ...
$ reporter_iso : chr "ECU" "ECU" "ECU" "ECU" ...
$ partner_code : int 0 8 12 31 32 36 44 48 51 56 ...
$ partner : chr "World" "Albania" "Algeria" "Azerbaijan" ...
$ partner_iso : chr "WLD" "ALB" "DZA" "AZE" ...
$ second_partner_code : logi NA NA NA NA NA NA ...
$ second_partner : chr NA NA NA NA ...
$ second_partner_iso : chr NA NA NA NA ...
$ customs_proc_code : chr NA NA NA NA ...
$ customs : chr NA NA NA NA ...
$ mode_of_transport_code: chr NA NA NA NA ...
$ mode_of_transport : chr NA NA NA NA ...
$ commodity_code : chr "0803" "0803" "0803" "0803" ...
$ commodity : chr "Bananas, including plantains; fresh or dried" "Bananas, including plantains; fresh or dried" "Bananas, including plantains; fresh or dried" "Bananas, including plantains; fresh or dried" ...
$ qty_unit_code : int 8 8 8 8 8 8 8 8 8 8 ...
$ qty_unit : chr "Weight in kilograms" "Weight in kilograms" "Weight in kilograms" "Weight in kilograms" ...
$ alt_qty_unit_code : logi NA NA NA NA NA NA ...
$ alt_qty_unit : chr NA NA NA NA ...
$ qty : num 7.00e+09 3.61e+07 6.68e+07 1.57e+07 2.69e+08 ...
$ alt_qty : logi NA NA NA NA NA NA ...
$ netweight_kg : num 6.78e+09 3.21e+07 5.99e+07 1.45e+07 2.63e+08 ...
$ gross_weight_kg : logi NA NA NA NA NA NA ...
$ trade_value_usd : num 3.22e+09 1.43e+07 3.01e+07 6.71e+06 1.16e+08 ...
$ cif_trade_value_usd : logi NA NA NA NA NA NA ...
$ fob_trade_value_usd : logi NA NA NA NA NA NA ...
$ flag : int 0 0 0 0 0 0 0 0 0 0 ...
- attr(*, "url")= chr "https://comtrade.un.org/api/get?max=50000&type=C&freq=A&px=HS&ps=2018&r=218&p=all&rg=2&cc=0803&fmt=json&head=H"
- attr(*, "time_stamp")= POSIXct[1:1], format: "2020-11-25 16:09:37"
- attr(*, "req_duration")= num 1.17Data Wrangling
# Select the data you need (in this case you only need the partner and qty columns)
quantitybanana <- dplyr::select(BananaExport, partner, qty)
# Rename the "partner" column and call it "NAME" to match the "world@data" data frame
names(quantitybanana)[names(quantitybanana) == "partner"] <- "NAME"
# Rename USA and call it "United States of America" to match the "world@data" data frame
quantitybanana$NAME <- gsub("USA", "United States of America", quantitybanana$NAME)
quantitybanana$NAME <- gsub("Russian Federation", "Russia", quantitybanana$NAME)
Visualizing Data
# Use "Left join" function to combine your two data frames ("quantitybanana" and "world@data")
world@data <- left_join(world@data, quantitybanana, by = "NAME")
# Create labels
world@data$NAME <- as.character(world@data$NAME)
labels <- sprintf("<strong>%s</strong><br/>%g", world@data$NAME, world@data$qty) %>% lapply(htmltools::HTML)
# Determinate the intervalls that will be shown in the map legend
bins <- c(0, 100, 1000, 5000, 10000, 50000, 100000, 250000, 500000, 1000000, 5000000, 10000000, Inf)
# Choose a color scheme for your map
colors <- colorBin("YlOrRd", domain = world@data$qty, bins = bins)
# Create custom icon to add to your map
bananaIcon <- makeIcon(
iconUrl = "banana.png",
iconWidth = 40, iconHeight = 40,
iconAnchorX = , iconAnchorY =
)
# Plot the data using leaflet
leaflet(world) %>%
addProviderTiles(providers$CartoDB.Positron) %>%
addMarkers(lng=-78.467834, lat=-0.180653, popup="Ecuador", icon = bananaIcon) %>%
addLegend(pal = colors, values = world@data$qty, opacity = 0.7, title = NULL, position = "bottomleft") %>%
addPolygons(fillColor = ~colors(world@data$qty),
weight = 2,
opacity = 1,
color = "white",
dashArray = 1,
fillOpacity = 0.5,
highlight = highlightOptions(weight = 2,
color = "black",
dashArray = 1,
fillOpacity = 0.5,
bringToFront = TRUE),
label = labels
)