comtradr

In comtradr: Interface with the United Nations Comtrade API

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(comtradr)

Data availability

See here for an overview of available commodity classifications.

Package information

API wrapper for the UN Comtrade Database. UN Comtrade provides historical data on the weights and value of specific goods shipped between countries, more info can be found here. Full API documentation can be found here.

Install and load comtradr

Install the development version from GitHub:

install.packages("comtradr")

Load comtradr

library(comtradr)

Authentication 🔐

Do not be discouraged by the complicated access to the token - you can do it! 💪

As stated above, you need an API token, see the FAQ of Comtrade for details on how to obtain it:

➡️ https://uncomtrade.org/docs/api-subscription-keys/

You need to follow the detailed explanations, which include screenshots, in the Wiki of Comtrade to the letter. ☝️ I am not writing them out here, because they might be updated regularly. However, once you are signed up, select the comtrade - v1 product, which is the free API.

Storing the API key

If you are in an interactive session, you can call the following function to save your API token to the environment file for the current session.

library(comtradr)

set_primary_comtrade_key()

If you are not in an interactive session, you can register the token once in your session using the following base-r function.

Sys.setenv('COMTRADE_PRIMARY' = 'xxxxxxxxxxxxxxxxx')

If you would like to set the comtrade key permanently, we recommend editing the project .Renviron file, where you need to add a line with COMTRADE_PRIMARY = xxxx-your-key-xxxx.

ℹ️ Do not forget the line break after the last entry. This is the easiest by taking advantage of the great usethis package.

usethis::edit_r_environ(scope = 'project')

Making API calls

Lets say we want to get data on the total imports into the United States from Germany, France, Japan, and Mexico, for the last five years.

v_data_1 <- system.file("extdata", "vignette_data_1.rda", package = "comtradr")
if (!file.exists(v_data_1)) {
  stop("internal vignette data set '~/extdata/vignette_data_1.rda' not found",
       call. = FALSE)
}
load(v_data_1)

example_1 <- ct_get_data(
  reporter = 'USA',
  partner = c('DEU', 'FRA','JPN','MEX'),
  commodity_code = 'TOTAL',
  start_date = 2018,
  end_date = 2023,
  flow_direction = 'import'
)

API calls return a tidy data frame.

str(example_1)

Here are a few more examples to show the different parameter options:

By default, the return data is in yearly amounts. We can pass "monthly" to arg freq to return data in monthly amounts, however the API limits each "monthly" query to a single year.

# all monthly data for a single year (API max of 12 months per call).
q <- ct_search(reporters = "USA",
               partners = c("Germany", "France", "Japan", "Mexico"),
               flow_direction = "import",
               start_date = 2012,
               end_date = 2012,
               freq = "monthly")

# monthly data for specific span of months (API max of twelve months per call).
q <- ct_search(reporters = "USA",
               partners = c("Germany", "France", "Japan", "Mexico"),
               flow_direction = "import",
               start_date = "2012-03",
               end_date = "2012-07",
               freq = "monthly")

Countries passed to parameters reporters and partners must be spelled as they appear in the official ISO 3 character code convention.

Search trade related to specific commodities (say, tomatoes). We can query the Comtrade commodity reference table to see all of the different commodity descriptions available for tomatoes.

ct_commodity_lookup("tomato")

If we want to search for shipment data on all of the commodity descriptions listed, then we can simply adjust the parameters for ct_commodity_lookup so that it will return only the codes, which can then be passed along to ct_search.

tomato_codes <- ct_commodity_lookup("tomato",
                                    return_code = TRUE,
                                    return_char = TRUE)

q <- ct_get_data(
  reporter = 'USA',
  partner = c('DEU', 'FRA','JPN','MEX'),
  commodity_code = tomato_codes,
  start_date = "2012",
  end_date = "2013",
  flow_direction = 'import'
)

On the other hand, if we wanted to exclude juices and sauces from our search, we can pass a vector of the relevant codes to the API call.

q <- ct_get_data(
  reporter = 'USA',
  partner = c('DEU', 'FRA','JPN','MEX'),
  commodity_code  = c("0702", "070200", "2002", "200210", "200290"),
  start_date = "2012",
  end_date = "2013",
  flow_direction = 'import'
)

API search metadata

In addition to the trade data, each API return object contains metadata as attributes.

# The url of the API call.
attributes(q)$url
# The date-time of the API call.
attributes(q)$time

More on the lookup functions

Functions ct_commodity_lookup is able to take multiple search terms as input.

ct_commodity_lookup(c("tomato", "trout"), return_char = TRUE)

ct_commodity_lookup can return a vector (as seen above) or a named list, using parameter return_char

ct_commodity_lookup(c("tomato", "trout"), return_char = FALSE)

For ct_commodity_lookup, if any of the input search terms return zero results and parameter verbose is set to TRUE, a warning will be printed to console (set verbose to FALSE to turn off this feature).

ct_commodity_lookup(c("tomato", "sldfkjkfdsklsd"), verbose = TRUE)

API rate limits

The Comtrade API imposes rate limits on users. comtradr features automated throttling of API calls to ensure the user stays within the limits defined by Comtrade. Below is a breakdown of those limits, API docs on these details can be found here.

• Without user token: unlimited calls/day, up to 500 records per call (registration and API subscription key not required) -- this end-point is not implemented here.
• With valid user token: 500 calls/day, up to 100,000 records per call (free registration and API subscription key required).

The API also limits the amount of times it can be queried per minute, but we could not find documentation on this. Hence the function automatically responds to the parameters returned by each request to adjust to the changing wait times.

In addition to these rate limits, the API imposes some limits on parameter combinations.

• The arguments reporters, partners do not have an All value specified natively anymore, we have implemented it in R for convenience reasons on our side.
• For date range the start_date and end_date must not span more than twelve months or twelve years. There is no more parameter to specify All years.
• For arg commodity_codes, the maximum number of input values is dependent on the maximum length of the request. Hence, if specifying reporters or partners, this value might be shorter.

Package Data

comtradr ships with a few different package data objects, and functions for interacting with and using the package data.

Country/Commodity Reference Tables

As explained previously, making API calls with comtradr often requires the user to query the commodity reference table (this is done using functions ct_commodity_lookup). These reference tables are generated by the UN Comtrade, and are updated roughly once a year. Since they're updated infrequently, the tables are saved as cached data objects within the comtradr package, and are referenced by the package functions when needed.

The function features an update argument, that checks for updates, downloads the new tables if necessary and makes them available during the current R session. It will also print a message indicating whether updates were found, like so:

ct_commodity_lookup('tomato',update = T)

If any updates are found, the message will state which reference table(s) were updated.

Additionally, the Comtrade API features a number of different commodity reference tables, based on different trade data classification schemes (for more details, see this page from the API docs). comtradr ships with all available commodity reference tables. The user may return and access any of the available commodity tables by specifying arg commodity_type within function ct_get_ref_table (e.g., ct_get_ref_table(dataset_id = "S1") will return the commodity table that follows the "S1" scheme).

The dataset_id´s are listed in the help page of the function ct_get_ref_table(). They are as follows:

• Datasets that contain codes for the commodity_code argument. The name is the same as you would provide under commodity_classification.
• 'HS' This is probably the most common classification for goods.
• 'B4'
• 'B5'
• 'EB02'
• 'EB10'
• 'EB10S'
• 'EB'
• 'S1'
• 'S2'
• 'S3'
• 'S4'
• 'SS'
• Datasets that are related to other arguments, can be queried directly with the name of the argument in the ct_get_data()-function.
• 'reporter'
• 'partner'
• 'mode_of_transport'
• 'customs_code'

Furthermore, there is a dataset readily available, with the iso3c-codes for the respective partner and reporter countries country_codes$iso_3, but I would recommend using the ct_get_ref_table() function, as it allows to update to the latest values on the fly.

Visualize

Once the data is collected, we can use it to create some basic visualizations.

Plot 1: Plot total value (USD) of Chinese exports to Mexico, South Korea and the United States, by year.

v_data_2 <- system.file("extdata", "vignette_data_2.rda", package = "comtradr")
if (!file.exists(v_data_2)) {
  stop("internal vignette data set '~/extdata/vignette_data_2.rda' not found",
       call. = FALSE)
}
load(v_data_2)

# Comtrade api query.
example_2 <- ct_get_data(
  reporter = 'CHN',
  partner = c('KOR', 'USA','MEX'),
  commodity_code = 'TOTAL',
  start_date = 2012,
  end_date = 2023,
  flow_direction = 'export'
)

library(ggplot2)

# Apply polished col headers.
# Create plot.
ggplot(example_2, aes(period, primary_value/1000000000, color = partner_desc, 
                      group = partner_desc)) +
  geom_point(size = 2) +
  geom_line(size = 1) +
  scale_color_manual( values = c("darkgreen","red","grey30"),
                      name = "Destination\nCountry") +
  ylab('Export Value in billions') +
  xlab('Year') +
  labs(title = "Total Value (USD) of Chinese Exports", subtitle = 'by year') +
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  theme_minimal()

Plot 2: Plot the top eight destination countries/areas of Thai shrimp exports, by weight (KG), for 2007 - 2011.

v_data_3 <- system.file("extdata", "vignette_data_3.rda", package = "comtradr")
if (!file.exists(v_data_3)) {
  stop("internal vignette data set '~/extdata/vignette_data_3.rda' not found", 
       call. = FALSE)
}
load(v_data_3)

# First, collect commodity codes related to shrimp.
shrimp_codes <- ct_commodity_lookup("shrimp",
                                    return_code = TRUE,
                                    return_char = TRUE)

# Comtrade api query.
example_3 <- ct_get_data(reporter = "THA",
                partner = "all",
                trade_direction = "exports",
                start_date = 2007,
                end_date = 2011,
                commodity_code = shrimp_codes)

library(ggplot2)
library(dplyr)


# Create country specific "total weight per year" dataframe for plotting.
plotdf <- example_3 %>%
  group_by(partner_desc, period) %>%
  summarise(kg = as.numeric(sum(net_wgt, na.rm = TRUE))) 

# Get vector of the top 8 destination countries/areas by total weight shipped
# across all years, then subset plotdf to only include observations related
# to those countries/areas.
top8 <- plotdf |> 
  group_by(partner_desc) |> 
  summarise(kg = as.numeric(sum(kg, na.rm = TRUE))) |> 
  slice_max(n = 8, order_by = kg) |> 
  arrange(desc(kg)) |> 
  pull(partner_desc)
plotdf <- plotdf %>% filter(partner_desc %in% top8)

# Create plots (y-axis is NOT fixed across panels, this will allow us to ID
# trends over time within each country/area individually).
ggplot(plotdf,aes(period,kg/1000, group = partner_desc))+
  geom_line() + 
  geom_point() + 
  facet_wrap(.~partner_desc, nrow = 2, ncol = 4,scales = 'free_y')+
  labs(title = "Weight (KG in tons) of Thai Shrimp Exports", 
       subtitle ="by Destination Area, 2007 - 2011")+
  theme_minimal()+
  theme(axis.text.x = element_text(angle = 45,hjust = 1, vjust = 1))

Handling large amounts of Parameters

In the comtradr package, several function parameters can accept everything as a valid input. Using everything for these parameters has specific meanings and can be a powerful tool for querying data. Internally, these values are set to NULL and the parameter is omitted entirely in the request to the API, the API then by default returns all possible values. Here's a breakdown of how everything is handled for different parameters:

commodity_code

Setting commodity_code to everything will query all possible commodity values. This can be useful if you want to retrieve data for all commodities without specifying individual codes.

flow_direction

If flow_direction is set to everything, all possible values for trade flow directions are queried. This includes imports, exports, re-imports, re-exports and some more specified in ct_get_ref_table('flow_direction').

reporter and partner

Using everything for reporter or partner will query all possible values for reporter and partner countries, but also includes aggregates like World or some miscellaneous like ASEAN. Be careful when aggregating these values, so as to not count trade values multiple times in different aggregates. Alternatively, specifically for these values, you can also use all_countries, which allows you to query all countries which are not aggregates of some kind of grouped parameters like ASEAN. These values can usually be safely aggregated. This allows you to retrieve trade data for all countries without specifying individual ISO3 codes.

mode_of_transport, partner_2, and customs_code

Setting these parameters to everything will query all possible values related to the mode of transport, secondary partner, and customs procedures. This provides a comprehensive view of the data across different transportation modes and customs categories.

Example Usage

Here's an example of how you might use everything parameters to query comprehensive data:

# Querying all commodities and flow directions for USA and Germany from 
## 2010 to 2011
data <- ct_get_data(
  reporter = c('USA', 'DEU'),
  commodity_code = 'everything',
  flow_direction = 'everything',
  start_date = '2010',
  end_date = '2011'
)

Using everything parameters can lead to large datasets, as they often remove specific filters on the data. It's essential to be mindful of the size of the data being queried, especially when using multiple everything parameters simultaneously.

comtradr documentation built on April 4, 2025, 1:45 a.m.