This vignette explains the functions within this package. The idea is to show how this package simplifies obtaining data from (api.tradestatistics.io)[https://api.tradestatistics.io].
To improve the presentation of the tables I shall use tibble besides tradestatistics.
library(tradestatistics)
library(tibble)Provided that this package obtains data from an API, it is useful to know which tables can be accessed:
as_tibble(ots_tables)
#> # A tibble: 12 × 3
#> table description source
#> <chr> <chr> <chr>
#> 1 commodities Commodities metadata (HS codes, 6 digits long) Based…
#> 2 commodities_short Commodities metadata (HS codes, 4 digits long) Based…
#> 3 countries Countries metadata Based…
#> 4 countries_colors Countries colors Open …
#> 5 sections Sections metadata (HS codes) Based…
#> 6 sections_colors Sections colors (HS codes) Open …
#> 7 year_range Minimum and maximum years with available data Based…
#> 8 yc Commodity trade at aggregated level (Year and Commo… Based…
#> 9 yr Reporter trade at aggregated level (Year and Report… Based…
#> 10 yrc Reporter trade at commodity level (Year, Reporter a… Based…
#> 11 yrp Reporter-Partner trade at aggregated level (Year, R… Based…
#> 12 yrpc Reporter-Partner trade at commodity level (Year, Re… Based…You might notice the tables have a pattern. The letters indicate the presence of columns that account for the level of detail in the data:
y: year column.r: reporter columnp: partner columnc: commodity columnThe most aggregated table is yr which basically says how many dollars each country exports and imports for a given year.
The less aggregated table is yrpc which says how many dollars of each of the 1,242 commodities from the Harmonized System each country exports to other countries and imports from other countries.
For the complete detail you can check tradestatistics.io.
The Package Functions section explains that you don’t need to memorize all ISO codes. The functions within this package are designed to match strings (i.e. “United States” or “America”) to valid ISO codes (i.e. “USA”).
Just as a reference, the table with all valid ISO codes can be accessed by running this:
as_tibble(ots_countries)
#> # A tibble: 275 × 5
#> country_iso country_name_english country_fullname_eng…¹
#> <chr> <chr> <chr>
#> 1 abw Aruba Aruba
#> 2 afg Afghanistan Afghanistan
#> 3 ago Angola Angola
#> 4 aia Anguilla Anguilla
#> 5 alb Albania Albania
#> 6 all Alias for all valid ISO codes in the World <NA>
#> 7 and Andorra Andorra
#> 8 ant Neth. Antilles Neth. Antilles
#> 9 are United Arab Emirates United Arab Emirates
#> 10 arg Argentina Argentina
#> # ℹ 265 more rows
#> # ℹ abbreviated name: ¹country_fullname_english
#> # ℹ 2 more variables: continent_name_english <chr>, continent_id <int>The Package Functions section explains that you don’t need to memorize all HS codes. The functions within this package are designed to match strings (i.e. “apple”) to valid HS codes (i.e. “0808”).
as_tibble(ots_commodities)
#> # A tibble: 5,302 × 4
#> commodity_code commodity_code_short commodity_fullname_english section_code
#> <chr> <chr> <chr> <chr>
#> 1 010121 0101 Horses; live, pure-bred bre… 01
#> 2 010129 0101 Horses; live, other than pu… 01
#> 3 010130 0101 Asses; live 01
#> 4 010190 0101 Mules and hinnies; live 01
#> 5 010221 0102 Cattle; live, pure-bred bre… 01
#> 6 010229 0102 Cattle; live, other than pu… 01
#> 7 010231 0102 Buffalo; live, pure-bred br… 01
#> 8 010239 0102 Buffalo; live, other than p… 01
#> 9 010290 0102 Bovine animals; live, other… 01
#> 10 010310 0103 Swine; live, pure-bred bree… 01
#> # ℹ 5,292 more rowsThis table is provided to be used with ots_gdp_deflator_adjustment().
as_tibble(ots_gdp_deflator)
#> # A tibble: 8,010 × 4
#> year_from year_to country_iso gdp_deflator
#> <int> <int> <chr> <dbl>
#> 1 1986 1987 abw 1.04
#> 2 1987 1988 abw 1.03
#> 3 1988 1989 abw 1.04
#> 4 1989 1990 abw 1.06
#> 5 1990 1991 abw 1.06
#> 6 1991 1992 abw 1.04
#> 7 1992 1993 abw 1.05
#> 8 1993 1994 abw 1.06
#> 9 1994 1995 abw 1.03
#> 10 1995 1996 abw 1.03
#> # ℹ 8,000 more rowsThe end user can use this function to find an ISO code by providing a country name. This works by implementing partial search.
Basic examples:
# Single match with no replacement
as_tibble(ots_country_code("Chile"))
#> # A tibble: 1 × 5
#> country_iso country_name_english country_fullname_eng…¹ continent_name_english
#> <chr> <chr> <chr> <chr>
#> 1 chl Chile Chile Americas
#> # ℹ abbreviated name: ¹country_fullname_english
#> # ℹ 1 more variable: continent_id <int>
# Single match with replacement
as_tibble(ots_country_code("America"))
#> # A tibble: 1 × 5
#> country_iso country_name_english country_fullname_eng…¹ continent_name_english
#> <chr> <chr> <chr> <chr>
#> 1 usa USA USA, Puerto Rico and … Americas
#> # ℹ abbreviated name: ¹country_fullname_english
#> # ℹ 1 more variable: continent_id <int>
# Double match with no replacement
as_tibble(ots_country_code("Germany"))
#> # A tibble: 1 × 5
#> country_iso country_name_english country_fullname_eng…¹ continent_name_english
#> <chr> <chr> <chr> <chr>
#> 1 deu Germany Germany (former Feder… Europe
#> # ℹ abbreviated name: ¹country_fullname_english
#> # ℹ 1 more variable: continent_id <int>The function ots_country_code() is used by ots_create_tidy_data() in a way that you can pass parameters like ots_create_tidy_data(... reporters = "Chile" ...) and it will automatically replace your input for a valid ISO in case there is a match. This will be covered in detail in the Trade Data section.
The end user can find a code or a set of codes by looking for keywords for commodities or groups. The function ots_commodity_code() allows to search from the official commodities and groups in the Harmonized system:
as_tibble(ots_commodity_code(commodity = " Horse ", section = " ANIMAL "))
#> # A tibble: 8 × 5
#> section_code commodity_code commodity_code_short commodity_fullname_english
#> <chr> <chr> <chr> <chr>
#> 1 01 010121 0101 Horses; live, pure-bred bree…
#> 2 01 010129 0101 Horses; live, other than pur…
#> 3 01 020500 0205 Meat; of horses, asses, mule…
#> 4 01 020680 0206 Offal, edible; of sheep, goa…
#> 5 01 020690 0206 Offal, edible; of sheep, goa…
#> 6 01 030245 0302 Fish; fresh or chilled, jack…
#> 7 01 030355 0303 Fish; frozen, jack and horse…
#> 8 01 050290 0502 Animal products; badger hair…
#> # ℹ 1 more variable: section_fullname_english <chr>This function downloads data for a single year and needs (at least) some filter parameters according to the query type.
Here we cover aggregated tables to describe the usage.
If we want Chile-Argentina bilateral trade at community level in 2019:
yrpc <- ots_create_tidy_data(
years = 2019,
reporters = "chl",
partners = "arg",
table = "yrpc"
)
as_tibble(yrpc)We can pass two years or more, several reporters/partners, and filter by commodities with exact codes or code matching based on keywords:
# Note that here I'm passing Peru and not per which is the ISO code for Peru
# The same applies to Brazil
yrpc2 <- ots_create_tidy_data(
years = 2018:2019,
reporters = c("chl", "Peru", "bol"),
partners = c("arg", "Brazil"),
commodities = c("01", "food"),
table = "yrpc"
)The yrpc table returns some fields that deserve an explanation which can be seen at tradestatistics.io. This example is interesting because “01” return a set of commodities (all commodities starting with 01, which is the commodity group “Animals; live”), but “food” return all commodities with a matching description (“1601”, “1806”, “1904”, etc.). In addition, not all the requested commodities are exported from each reporter to each partner, therefore a warning is returned.
If we want Chile-Argentina bilateral trade at aggregated level in 2018 and 2019:
yrp <- ots_create_tidy_data(
years = 2018:2019,
reporters = c("chl", "per"),
partners = "arg",
table = "yrp"
)This table accepts different years, reporters and partners just like yrpc.
If we want Chilean trade at commodity level in 2019 with respect to commodity “010121” which means “Horses; live, pure-bred breeding animals”:
yrc <- ots_create_tidy_data(
years = 2019,
reporters = "chl",
commodities = "010121",
table = "yrc"
)This table accepts different years, reporters and commodity codes just like yrpc.
All the variables from this table are documented at tradestatistics.io.
If we want the aggregated trade of Chile, Argentina and Peru in 2018 and 2019:
yr <- ots_create_tidy_data(
years = 2018:2019,
reporters = c("chl", "arg", "per"),
table = "yr"
)This table accepts different years and reporters just like yrpc.
All the variables from this table are documented at tradestatistics.io.
If we want all commodities traded in 2019:
yc <- ots_create_tidy_data(
years = 2019,
table = "yc"
)If we want the traded values of the commodity “010121” which means “Horses; live, pure-bred breeding animals” in 2019:
yc2 <- ots_create_tidy_data(
years = 2019,
commodities = "010121",
table = "yc"
)This table accepts different years just like yrpc.
Taking the yr table from above, we can use ots_gdp_deflator_adjustment() to convert dollars from 2018 and 2019 to dollars of 2000:
inflation <- ots_gdp_deflator_adjustment(yr, reference_year = 2000)
as_tibble(inflation)