Open Trade Statistics V6.0 is publicly available! | R-Bloggers

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I have added 2023 trade data and GDP information in addition to new suddenly that I think are easier to understand than the previous one: https://shiny.tradestatistics.io.

In 2017 I had to download tradedatasets and realized that gaining access to UN Comtrade in Latin America was particularly difficult because local universities there are no institutional access to.

I called this to colleagues from PUC Chile and decided to e -mail the United Nations to ask permission to get the data with an access of 48 hours so that I could download it and hang the data sets again. They agreed that I could share a derivative data set with cleaning/transforming steps, but the unprocessed data could not hang again, and I did. I cleaned the data set as much as possible and I used mirrored flows for consistency (ie on importer -based figures are more reliable).

Nine years later this project continues and it is worth receiving e -mails from Latin -America and other development regions that use this. For the record, I never formally studied it or IT. I learned SQL, Nginx and Rest APIs by reading and experimenting Stack Overflow to make this service.

You can download the data from the website in CSV/Excel -Layout or the R -package from Cran Install with:

install.packages("tradestatistics")

The package documentation includes several examples (https://docs.ropensci.org/tradestatistics/articles/basic-usage.html). Here is a simple example:

library(tradestatistics)
library(dplyr)
library(tidyr)
library(ggplot2)

# Bilateral aggregate trade between the United Kingdom, France and Germany 2020-2023
yr <- ots_create_tidy_data(
  years = 2020:2023,
  reporters = "GBR",
  partners = c("FRA", "DEU"),
  table = "yrp"
)

yr2 <- yr |>
  pivot_longer(
    cols = c("trade_value_usd_exp", "trade_value_usd_imp"),
    names_to = "trade_flow",
    values_to = "trade_value_usd"
  ) |>
  mutate(
    trade_flow = recode(trade_flow,
      "trade_value_usd_exp" = "Exports",
      "trade_value_usd_imp" = "Imports"
    )
  )

ggplot(yr2, aes(x = year, y = trade_value_usd / 1e9, fill = trade_flow)) +
  geom_col(position = "dodge") +
  facet_wrap(~partner_name, ncol = 2) +
  labs(
    title = "UK bilateral trade with France and Germany",
    subtitle = "2020-2023, in billion USD",
    x = "Year",
    y = "Trade value (billion USD)",
    fill = "Trade flow"
  ) +
  theme_minimal(base_size = 13) +
  theme(legend.position = "top") +
  tintin::scale_fill_tintin_d()

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