Section 4 Accesing the data

4.1 Before downloading datasets

If you are going to download data, you have to read the Code of Conduct first.

4.2 API

The advantage of the API over https download is that you can filter what to obtain and also access some additional tables.

To obtain exactly the same data as with compressed files, please refer to 4.2.5.

If you use R you’ll need jsonlite and dplyr packages.

library(jsonlite)

These packages are also useful:

library(dplyr)
library(stringr)

4.2.1 Available tables

as_tibble(fromJSON("https://api.tradestatistics.io/tables"))
## # A tibble: 12 x 3
##    table      description                        source                    
##    <chr>      <chr>                              <chr>                     
##  1 countries  Countries metadata                 UN Comtrade               
##  2 products   Product metadata                   UN Comtrade               
##  3 reporters  Reporting countries                UN Comtrade               
##  4 communiti… Product communities                Center for International …
##  5 product_s… Product short names                The Observatory of Econom…
##  6 country_r… Ranking of countries               Open Trade Statistics     
##  7 product_r… Ranking of products                Open Trade Statistics     
##  8 yrpc       Bilateral trade at product level … Open Trade Statistics     
##  9 yrp        Reporter trade at aggregated leve… Open Trade Statistics     
## 10 yrc        Reporter trade at aggregated leve… Open Trade Statistics     
## 11 yr         Reporter trade at aggregated leve… Open Trade Statistics     
## 12 yc         Product trade at aggregated level… Open Trade Statistics

4.2.2 Metadata

## Countries (no filter)
rda_countries <- "countries.rda"

if (!file.exists(rda_countries)) {
  countries <- as_tibble(fromJSON(
    "https://api.tradestatistics.io/countries"
  ))

  save(countries, file = rda_countries, compress = "xz")

  countries
} else {
  load(rda_countries)

  countries
}
## # A tibble: 249 x 6
##    country_iso country_name_en… country_fullnam… continent_id continent
##    <chr>       <chr>            <chr>                   <int> <chr>    
##  1 afg         Afghanistan      Afghanistan                 1 Asia     
##  2 alb         Albania          Albania                     2 Europe   
##  3 dza         Algeria          Algeria                     3 Africa   
##  4 asm         American Samoa   American Samoa              4 Oceania  
##  5 and         Andorra          Andorra                     2 Europe   
##  6 ago         Angola           Angola                      3 Africa   
##  7 aia         Anguilla         Anguilla                    5 Americas 
##  8 atg         Antigua and Bar… Antigua and Bar…            5 Americas 
##  9 arg         Argentina        Argentina                   5 Americas 
## 10 arm         Armenia          Armenia                     1 Asia     
## # … with 239 more rows, and 1 more variable: eu28_member <int>
## Products (no filter)
rda_products <- "products.rda"

if (!file.exists(rda_products)) {
  products <- as_tibble(fromJSON(
    "https://api.tradestatistics.io/products"
  ))

  save(products, file = rda_products, compress = "xz")

  products
} else {
  load(rda_products)

  products
}
## # A tibble: 1,320 x 4
##    product_code product_fullname_english          group_code group_name    
##    <chr>        <chr>                             <chr>      <chr>         
##  1 0101         Horses, asses, mules and hinnies… 01         Animals; live 
##  2 0102         Bovine animals; live              01         Animals; live 
##  3 0103         Swine; live                       01         Animals; live 
##  4 0104         Sheep and goats; live             01         Animals; live 
##  5 0105         Poultry; live, fowls of the spec… 01         Animals; live 
##  6 0106         Animals, n.e.c. in chapter 01; l… 01         Animals; live 
##  7 0201         Meat of bovine animals; fresh or… 02         Meat and edib…
##  8 0202         Meat of bovine animals; frozen    02         Meat and edib…
##  9 0203         Meat of swine; fresh, chilled or… 02         Meat and edib…
## 10 0204         Meat of sheep or goats; fresh, c… 02         Meat and edib…
## # … with 1,310 more rows

Please notice that these tables include some aliases.

countries includes some meta-codes, c-xx where xx must the first two letters of a continent and all, this is:

Alias Meaning
c-af Alias for all valid ISO codes in Africa
c-am Alias for all valid ISO codes in the Americas
c-as Alias for all valid ISO codes in Asia
c-eu Alias for all valid ISO codes in Europe
c-oc Alias for all valid ISO codes in Oceania
all Alias for all valid ISO codes in the World

products also includes some meta-codes, xx for the first two digits of a code and those digits are the product group and all, this is:

Alias Meaning
01 Alias for all codes in the group Animals; live
02 Alias for all codes in the group Meat and edible meat offal
03 Alias for all codes in the group Fish and crustaceans, molluscs and other aquatic invertebrates
04 Alias for all codes in the group Dairy produce; birds’ eggs; natural honey; edible products of animal origin, not elsewhere specified or included
05 Alias for all codes in the group Animal originated products; not elsewhere specified or included
06 Alias for all codes in the group Trees and other plants, live; bulbs, roots and the like; cut flowers and ornamental foliage
07 Alias for all codes in the group Vegetables and certain roots and tubers; edible
08 Alias for all codes in the group Fruit and nuts, edible; peel of citrus fruit or melons
09 Alias for all codes in the group Coffee, tea, mate and spices
10 Alias for all codes in the group Cereals
11 Alias for all codes in the group Products of the milling industry; malt, starches, inulin, wheat gluten
12 Alias for all codes in the group Oil seeds and oleaginous fruits; miscellaneous grains, seeds and fruit, industrial or medicinal plants; straw and fodder
13 Alias for all codes in the group Lac; gums, resins and other vegetable saps and extracts
14 Alias for all codes in the group Vegetable plaiting materials; vegetable products not elsewhere specified or included
15 Alias for all codes in the group Animal or vegetable fats and oils and their cleavage products; prepared animal fats; animal or vegetable waxes
16 Alias for all codes in the group Meat, fish or crustaceans, molluscs or other aquatic invertebrates; preparations thereof
17 Alias for all codes in the group Sugars and sugar confectionery
18 Alias for all codes in the group Cocoa and cocoa preparations
19 Alias for all codes in the group Preparations of cereals, flour, starch or milk; pastrycooks’ products
20 Alias for all codes in the group Preparations of vegetables, fruit, nuts or other parts of plants
21 Alias for all codes in the group Miscellaneous edible preparations
22 Alias for all codes in the group Beverages, spirits and vinegar
23 Alias for all codes in the group Food industries, residues and wastes thereof; prepared animal fodder
24 Alias for all codes in the group Tobacco and manufactured tobacco substitutes
25 Alias for all codes in the group Salt; sulphur; earths, stone; plastering materials, lime and cement
26 Alias for all codes in the group Ores, slag and ash
27 Alias for all codes in the group Mineral fuels, mineral oils and products of their distillation; bituminous substances; mineral waxes
28 Alias for all codes in the group Inorganic chemicals; organic and inorganic compounds of precious metals; of rare earth metals, of radio-active elements and of isotopes
29 Alias for all codes in the group Organic chemicals
30 Alias for all codes in the group Pharmaceutical products
31 Alias for all codes in the group Fertilizers
32 Alias for all codes in the group Tanning or dyeing extracts; tannins and their derivatives; dyes, pigments and other colouring matter; paints, varnishes; putty, other mastics; inks
33 Alias for all codes in the group Essential oils and resinoids; perfumery, cosmetic or toilet preparations
34 Alias for all codes in the group Soap, organic surface-active agents; washing, lubricating, polishing or scouring preparations; artificial or prepared waxes, candles and similar articles, modelling pastes, dental waxes and dental preparations with a basis of plaster
35 Alias for all codes in the group Albuminoidal substances; modified starches; glues; enzymes
36 Alias for all codes in the group Explosives; pyrotechnic products; matches; pyrophoric alloys; certain combustible preparations
37 Alias for all codes in the group Photographic or cinematographic goods
38 Alias for all codes in the group Chemical products n.e.c.
39 Alias for all codes in the group Plastics and articles thereof
40 Alias for all codes in the group Rubber and articles thereof
41 Alias for all codes in the group Raw hides and skins (other than furskins) and leather
42 Alias for all codes in the group Articles of leather; saddlery and harness; travel goods, handbags and similar containers; articles of animal gut (other than silk-worm gut)
43 Alias for all codes in the group Furskins and artificial fur; manufactures thereof
44 Alias for all codes in the group Wood and articles of wood; wood charcoal
45 Alias for all codes in the group Cork and articles of cork
46 Alias for all codes in the group Manufactures of straw, esparto or other plaiting materials; basketware and wickerwork
47 Alias for all codes in the group Pulp of wood or other fibrous cellulosic material; recovered (waste and scrap) paper or paperboard
48 Alias for all codes in the group Paper and paperboard; articles of paper pulp, of paper or paperboard
49 Alias for all codes in the group Printed books, newspapers, pictures and other products of the printing industry; manuscripts, typescripts and plans
50 Alias for all codes in the group Silk
51 Alias for all codes in the group Wool, fine or coarse animal hair; horsehair yarn and woven fabric
52 Alias for all codes in the group Cotton
53 Alias for all codes in the group Vegetable textile fibres; paper yarn and woven fabrics of paper yarn
54 Alias for all codes in the group Man-made filaments; strip and the like of man-made textile materials
55 Alias for all codes in the group Man-made staple fibres
56 Alias for all codes in the group Wadding, felt and nonwovens, special yarns; twine, cordage, ropes and cables and articles thereof
57 Alias for all codes in the group Carpets and other textile floor coverings
58 Alias for all codes in the group Fabrics; special woven fabrics, tufted textile fabrics, lace, tapestries, trimmings, embroidery
59 Alias for all codes in the group Textile fabrics; impregnated, coated, covered or laminated; textile articles of a kind suitable for industrial use
60 Alias for all codes in the group Fabrics; knitted or crocheted
61 Alias for all codes in the group Apparel and clothing accessories; knitted or crocheted
62 Alias for all codes in the group Apparel and clothing accessories; not knitted or crocheted
63 Alias for all codes in the group Textiles, made up articles; sets; worn clothing and worn textile articles; rags
64 Alias for all codes in the group Footwear; gaiters and the like; parts of such articles
65 Alias for all codes in the group Headgear and parts thereof
66 Alias for all codes in the group Umbrellas, sun umbrellas, walking-sticks, seat sticks, whips, riding crops; and parts thereof
67 Alias for all codes in the group Feathers and down, prepared; and articles made of feather or of down; artificial flowers; articles of human hair
68 Alias for all codes in the group Stone, plaster, cement, asbestos, mica or similar materials; articles thereof
69 Alias for all codes in the group Ceramic products
70 Alias for all codes in the group Glass and glassware
71 Alias for all codes in the group Natural, cultured pearls; precious, semi-precious stones; precious metals, metals clad with precious metal, and articles thereof; imitation jewellery; coin
72 Alias for all codes in the group Iron and steel
73 Alias for all codes in the group Iron or steel articles
74 Alias for all codes in the group Copper and articles thereof
75 Alias for all codes in the group Nickel and articles thereof
76 Alias for all codes in the group Aluminium and articles thereof
78 Alias for all codes in the group Lead and articles thereof
79 Alias for all codes in the group Zinc and articles thereof
80 Alias for all codes in the group Tin; articles thereof
81 Alias for all codes in the group Metals; n.e.c., cermets and articles thereof
82 Alias for all codes in the group Tools, implements, cutlery, spoons and forks, of base metal; parts thereof, of base metal
83 Alias for all codes in the group Metal; miscellaneous products of base metal
84 Alias for all codes in the group Nuclear reactors, boilers, machinery and mechanical appliances; parts thereof
85 Alias for all codes in the group Electrical machinery and equipment and parts thereof; sound recorders and reproducers; television image and sound recorders and reproducers, parts and accessories of such articles
86 Alias for all codes in the group Railway, tramway locomotives, rolling-stock and parts thereof; railway or tramway track fixtures and fittings and parts thereof; mechanical (including electro-mechanical) traffic signalling equipment of all kinds
87 Alias for all codes in the group Vehicles; other than railway or tramway rolling stock, and parts and accessories thereof
88 Alias for all codes in the group Aircraft, spacecraft and parts thereof
89 Alias for all codes in the group Ships, boats and floating structures
90 Alias for all codes in the group Optical, photographic, cinematographic, measuring, checking, medical or surgical instruments and apparatus; parts and accessories
91 Alias for all codes in the group Clocks and watches and parts thereof
92 Alias for all codes in the group Musical instruments; parts and accessories of such articles
93 Alias for all codes in the group Arms and ammunition; parts and accessories thereof
94 Alias for all codes in the group Furniture; bedding, mattresses, mattress supports, cushions and similar stuffed furnishings; lamps and lighting fittings, n.e.c.; illuminated signs, illuminated name-plates and the like; prefabricated buildings
95 Alias for all codes in the group Toys, games and sports requisites; parts and accessories thereof
96 Alias for all codes in the group Miscellaneous manufactured articles
97 Alias for all codes in the group Works of art; collectors’ pieces and antiques
99 Alias for all codes in the group Commodities not specified according to kind
all Alias for all codes

4.2.3 API parameters

The tables provided withing our API contain at least one of these fields:

  • Year (y)
  • Reporter ISO (r)
  • Partner ISO (p)
  • Product Code (c)

The most detailed table is yrpc that contains all bilateral flows at product level.

With respect to y you can pass any integer contained in \([1962,2018]\).

Both r and p accept any valid ISO code or alias contained in the countries table. For example, both chl (valid ISO code) and c-am (continent Americas, an alias) are valid API filtering parameters.

c takes any valid product code or alias from the products. For example, both 0101 (valid HS product code) and 01 (valid HS group code) are valid API filtering parameters.

By default the API takes c = "all" by default.

You can always skip c, but y, r and p are requiered to return data.

4.2.4 Available reporters

The only applicable filter is by year.

# Available reporters (filter by year)
as_tibble(fromJSON(
  "https://api.tradestatistics.io/reporters?y=2018"
))
## # A tibble: 226 x 1
##    reporter_iso
##    <chr>       
##  1 zwe         
##  2 zmb         
##  3 zaf         
##  4 yem         
##  5 wsm         
##  6 wlf         
##  7 vut         
##  8 vnm         
##  9 vgb         
## 10 ven         
## # … with 216 more rows

4.2.5 YRPC (Year, Reporter, Partner and Product Code)

The applicable filters here are year, reporter, partner and product code.

# Year - Reporter - Partner - Product Code

yrpc_1 <- as_tibble(fromJSON(
  "https://api.tradestatistics.io/yrpc?y=1962&r=usa&p=mex&c=8703"
))

yrpc_1
## # A tibble: 1 x 6
##    year reporter_iso partner_iso product_code export_value_usd
##   <int> <chr>        <chr>       <chr>                   <int>
## 1  1962 usa          mex         8703                 72334238
## # … with 1 more variable: import_value_usd <int>

Columns definition:

  • reporter_iso: Official ISO-3 code for the reporter (e.g. the country that reports X dollars in exports/imports from/to country Y)
  • partner_iso: Official ISO-3 code for the partner
  • product_code: Official Harmonized System rev. 2007 (HS07) product code (e.g. according to the table in the API, 8703 stands for “Motor cars and other motor vehicles; principally designed for the transport of persons (other than those of heading no. 8702), including station wagons and racing cars”)
  • export_value_usd: Exports measured in nominal United States Dollars (USD)
  • import_value_usd: Imports measured in nominal United States Dollars (USD)

4.2.6 YRPC-GA (Year - Reporter - Partner - Product Code, Group Code Aggregated)

The applicable filters here are year, reporter, partner and group code. Here the group code is just an aggregation over product code.

# Year - Reporter - Partner - Product Code, Group Code Aggregated

yrpc_ga <- as_tibble(fromJSON(
  "https://api.tradestatistics.io/yrpc-ga?y=1962&r=usa&p=mex&g=87"
))

yrpc_ga
## # A tibble: 1 x 6
##    year reporter_iso partner_iso group_code export_value_usd
##   <int> <chr>        <chr>       <chr>                 <int>
## 1  1962 usa          mex         87                136983479
## # … with 1 more variable: import_value_usd <int>

Columns definition:

  • group_code: Official Harmonized System rev. 2007 (HS07) group code (e.g. according to the table in the API, 87 stands for “Vehicles; other than railway or tramway rolling stock, and parts and accessories thereof”)

4.2.7 YRPC-CA (Year - Reporter - Partner - Product Code, Community Code Aggregated)

The applicable filters here are year, reporter, partner and community code. Here the community code is just an aggregation over both product code and group code.

# Year - Reporter - Partner - Product Code, Community Code Aggregated

yrpc_ca <- as_tibble(fromJSON(
  "https://api.tradestatistics.io/yrpc-ca?y=1962&r=usa&p=mex&o=17"
))

yrpc_ca
## # A tibble: 1 x 6
##    year reporter_iso partner_iso community_code export_value_usd
##   <int> <chr>        <chr>       <chr>                     <int>
## 1  1962 usa          mex         17                    196732494
## # … with 1 more variable: import_value_usd <int>

Columns definition:

  • community_code: Unofficial Harvard CID community code (e.g. according to the table in the API, 17 stands for “Transportation”)

4.2.8 YRPC-GCA (Year - Reporter - Partner - Product Code, Group Code and Community Code Aggregated)

The applicable filters here are year, reporter, partner and community code. Here the community code is just an aggregation over both product code and group code.

# Year - Reporter - Partner - Product Code, Community Code Aggregated

yrpc_gca <- as_tibble(fromJSON(
  "https://api.tradestatistics.io/yrpc-gca?y=1962&r=usa&p=mex&o=17"
))

yrpc_gca
## # A tibble: 4 x 7
##    year reporter_iso partner_iso group_code community_code export_value_usd
##   <int> <chr>        <chr>       <chr>      <chr>                     <int>
## 1  1962 usa          mex         86         17                     26210502
## 2  1962 usa          mex         87         17                    136983479
## 3  1962 usa          mex         88         17                     28529998
## 4  1962 usa          mex         89         17                      5008515
## # … with 1 more variable: import_value_usd <int>

4.2.9 YRC (Year, Reporter and Product Code)

The only applicable filter is by year, reporter, product code and (optionally) product code length.

# Year - Reporter - Product Code

yrc <- as_tibble(fromJSON(
  "https://api.tradestatistics.io/yrc?y=1962&r=chl"
))

yrc
## # A tibble: 912 x 7
##     year reporter_iso product_code export_value_usd import_value_usd
##    <int> <chr>        <chr>                   <int>            <int>
##  1  1962 chl          0101                   289076            50833
##  2  1962 chl          0102                    17685         22167651
##  3  1962 chl          0103                        0            32399
##  4  1962 chl          0104                    12870           199519
##  5  1962 chl          0105                        0           132064
##  6  1962 chl          0106                    20259            10548
##  7  1962 chl          0201                    13075          3520194
##  8  1962 chl          0203                        0           275764
##  9  1962 chl          0204                   387625            78528
## 10  1962 chl          0206                    38106                0
## # … with 902 more rows, and 2 more variables: export_rca <dbl>,
## #   import_rca <dbl>

Columns definition:

4.2.10 YRP (Year, Reporter and Partner)

The only applicable filter is by year, reporter and partner.

# Year - Reporter - Partner
yrp <- as_tibble(fromJSON(
  "https://api.tradestatistics.io/yrp?y=2018&r=chl&p=arg"
))

yrp
## # A tibble: 1 x 5
##    year reporter_iso partner_iso export_value_usd import_value_usd
##   <int> <chr>        <chr>                  <int>            <dbl>
## 1  2018 chl          arg                837640220       3768079208

4.2.11 YC (Year and Product Code)

The only applicable filter is by year, product and (optionally) product code length.

# Year - Product Code
yc <- as_tibble(fromJSON(
  "https://api.tradestatistics.io/yc?y=2018&c=0101"
))

yc
## # A tibble: 1 x 14
##    year product_code export_value_usd import_value_usd pci_fitness_met…
##   <int> <chr>                   <dbl>            <dbl>            <dbl>
## 1  2018 0101               4073362162       4073362162            0.293
## # … with 9 more variables: pci_rank_fitness_method <int>,
## #   pci_reflections_method <dbl>, pci_rank_reflections_method <int>,
## #   pci_eigenvalues_method <dbl>, pci_rank_eigenvalues_method <int>,
## #   top_exporter_iso <chr>, top_exporter_trade_value_usd <int>,
## #   top_importer_iso <chr>, top_importer_trade_value_usd <int>

Columns definition:

  • pci_fitness_method: Product Complexity Index (PCI) computed by using the Fitness Method.
  • pci_reflections_method: Product Complexity Index (PCI) computed by using the Reflections Method.
  • pci_eigenvalues_method: Product Complexity Index (PCI) computed by using the Eigenvalues Method.
  • pci_rank_*_method: The rank of a product given its PCI (e.g. the highest PCI obtains the #1)

4.2.12 YR (Year and Reporter)

The only applicable filter is by year and reporter.

## Year - Reporter
yr <- as_tibble(fromJSON(
  "https://api.tradestatistics.io/yr?y=2018&r=chl"
))

yr
## # A tibble: 1 x 14
##    year reporter_iso export_value_usd import_value_usd cci_fitness_met…
##   <int> <chr>                   <dbl>            <dbl>            <dbl>
## 1  2018 chl               82917211883      83743442685            0.420
## # … with 9 more variables: cci_rank_fitness_method <int>,
## #   cci_reflections_method <dbl>, cci_rank_reflections_method <int>,
## #   cci_eigenvalues_method <dbl>, cci_rank_eigenvalues_method <int>,
## #   top_export_product_code <chr>, top_export_trade_value_usd <dbl>,
## #   top_import_product_code <chr>, top_import_trade_value_usd <dbl>

Columns definition:

  • cci_fitness_method: Country Complexity Index (CCI) computed by using the Fitness Method.
  • cci_reflections_method: Country Complexity Index (CCI) computed by using the Reflections Method.
  • cci_eigenvalues_method: Country Complexity Index (CCI) computed by using the Eigenvalues Method.
  • cci_rank_*_method: The rank of a product given its CCI (e.g. the highest CCI obtains the #1)

4.2.13 Other group/community aggregated tables

As you might notice in api.tradestatistics.io/tables, there are more tables:

  • yrc-ga
  • yrc-ca
  • yrc-gca
  • yr-short
  • yr-ga
  • yr-ca

These tables follow the same parameters as the examples above.

4.2.14 Country rankings

The only applicable filter is by year.

# Country rankings
country_rankings <- as_tibble(fromJSON(
  "https://api.tradestatistics.io/country_rankings?y=2018"
))

4.2.15 Product rankings

The only applicable filter is by year.

# Product rankings
product_rankings <- as_tibble(fromJSON(
  "https://api.tradestatistics.io/product_rankings?y=2018"
))

4.3 R Package

To ease API using, we provide an R Package. This package is a part of ROpenSci and its documentation is available on a separate pkgdown site.

Here’s what the package does:

R package flow

Figure 4.1: R package flow

4.4 Dashboard (beta)

To ease API using, we provide a Shiny Dashboard that is still under improvements.

4.5 RDS datasets

Please check the md5sums to verify data integrity after downloading.

Year Last updated File size (MB) MD5 sum
1962 2020-02-11 2.63 0710271b2ad39456b1eb535f5d5e9f56
1963 2020-02-11 3.17 152bddc2916d86090da498f92945de74
1964 2020-02-11 3.47 d528349e5505c7763d776b21a1f5f15a
1965 2020-02-11 3.92 db6b5cad4c2c17b7d0b8e1cc6a68d028
1966 2020-02-11 4.17 2dfcf4fa79e8fe3951d4502ca3e7ccf1
1967 2020-02-11 4.33 289e46b050a6a552e8dc18ac9ba4d568
1968 2020-02-11 4.51 55a02f559401d8d59438855d02fad980
1969 2020-02-11 4.77 747810a983548041bf7b255f9c1aabed
1970 2020-02-11 5.29 4e1b4f1d117c20202d514334ef4c4d91
1971 2020-02-11 5.47 ae058dbef525921c1ffe6fca579f019a
1972 2020-02-11 5.75 7d742e516e06a764c464b578a817b121
1973 2020-02-11 6.21 a561dbf7598452dd79b996e6c19ed053
1974 2020-02-11 6.69 6b139d5e9399cf98f776cdb81b713ad5
1975 2020-02-11 6.80 487683a9f033afac988c9f00c966e410
1976 2020-02-11 6.78 48b632a07a3d097400811b0d80ceb566
1977 2020-02-11 7.16 1048fb22908b42c5dc3ebda762286e6a
1978 2020-02-11 7.55 2dcac6cebcb28839c2c7317863746d29
1979 2020-02-11 7.97 bc15d15ac627685e1eea7f4d53642f0d
1980 2020-02-11 8.35 f7af0c5caa489f0f772459eebcf6f46f
1981 2020-02-11 8.42 5a09b82d76cced2043db08bd6077dbf0
1982 2020-02-11 8.32 ee43f099b2263e5048928ec9792dcb20
1983 2020-02-11 8.39 1cc3c5839d91c9ae6186751c09b23e84
1984 2020-02-11 8.37 56577fbc39804dcccf37546c8718fe7b
1985 2020-02-11 8.61 ff48557e8caeef78ca5add4100dd9744
1986 2020-02-11 8.99 74795130828662098ba6c4e002dad054
1987 2020-02-11 9.29 5804b112b6eef00d5ba806a5d305f524
1988 2020-02-11 9.70 115e004f3685c8656cd30905ab8dfefd
1989 2020-02-11 10.19 b29fadadd15d9d0385c96a965c1575bf
1990 2020-02-11 10.62 533b8912d031d899304613b4e7af141c
1991 2020-02-11 10.62 dac5c5dee613a2d793d99614f0e9477b
1992 2020-02-11 10.89 fc7590a189a9b69819fc2583d0e6da3c
1993 2020-02-11 11.55 e23704d41b26e8674034297e54b5a0e7
1994 2020-02-11 12.79 cca7adb027006e6da12afb78f3aa2786
1995 2020-02-11 13.66 48d28b20bd88a58f56d56ac3ef7951d7
1996 2020-02-11 13.13 83f7d600d240546b198fccbb5e118e73
1997 2020-02-11 14.25 5492caac7f67796d7da8cbbe269016d7
1998 2020-02-11 14.79 011c64f6687369e4a663fc094a9a8847
1999 2020-02-11 15.54 799cc174df9e4c95f1bdd05e6e148593
2000 2020-02-11 18.25 ee77cab73284d85dbae53138804bbc51
2001 2020-02-11 18.92 ab25b48877b6d3dd9167f9872823fdf4
2002 2020-02-11 17.90 889147aa362e5aa566a0c796b008c86a
2003 2020-02-11 19.15 f21d81db973122c26863e2def9f287a8
2004 2020-02-11 20.55 8c3dccd426b43556a03e6b3dc37d7674
2005 2020-02-11 21.65 c7263a3b189eb397f8795714c844e4ae
2006 2020-02-11 22.70 0db819dfb53195b4ab4a81ba93c4512b
2007 2020-02-11 21.45 b1e904f2e057cd65be7e4cbaf92fb7d5
2008 2020-02-11 23.21 01d5456efecc2048f1e454997cc5b70b
2009 2020-02-11 23.74 08b37c4359023841a53c46dfec83d5cb
2010 2020-02-11 24.55 ed76c148725cd7bc3014309bf7d87542
2011 2020-02-11 25.32 be3aabb2d2c1730ab143d0d3a35201e3
2012 2020-02-11 25.90 1c791e5a4aef24203939ae8b231ab086
2013 2020-02-11 26.41 7abbc88a788f2fffbe10a6403ed9ee5e
2014 2020-02-11 26.67 760eae713d043fd65623c9b2e6ab1d33
2015 2020-02-11 26.92 81555b1726508ee7676419cf379ad265
2016 2020-02-11 26.70 b316c512d846d763183b3ee964dc9987
2017 2020-02-11 27.47 270270ed38577e600c97f6c140f81573
2018 2020-02-11 26.85 30186ff0d29e785cf5db3c914793b5e0