diff --git a/docs/articles/vignette_getting_started.html b/docs/articles/vignette_getting_started.html index 2e1f1ace..7f9b40e3 100644 --- a/docs/articles/vignette_getting_started.html +++ b/docs/articles/vignette_getting_started.html @@ -112,7 +112,7 @@

Getting Started with gcamfaostat

-

2023-10-30

+

2023-10-31

Source: vignettes/vignette_getting_started.Rmd diff --git a/docs/articles/vignette_news.html b/docs/articles/vignette_news.html index 0f905424..08359aa7 100644 --- a/docs/articles/vignette_news.html +++ b/docs/articles/vignette_news.html @@ -112,7 +112,7 @@

News

-

2023-10-30

+

2023-10-31

Source: vignettes/vignette_news.Rmd diff --git a/docs/articles/vignette_preparing_data.html b/docs/articles/vignette_preparing_data.html index 42c6bebe..b5895679 100644 --- a/docs/articles/vignette_preparing_data.html +++ b/docs/articles/vignette_preparing_data.html @@ -113,7 +113,7 @@

Preparing data for gcamfaostat

-

2023-10-30

+

2023-10-31

Source: vignettes/vignette_preparing_data.Rmd @@ -237,26 +237,6 @@

2. Generate the me -TM - - -Trade: Detailed trade matrix - - -TRUE - - -TRUE - - -2022-02-14 - - -454MB - - - - OA @@ -455,6 +435,26 @@

2. Generate the me 72MB + + +TM + + +Trade: Detailed trade matrix + + +TRUE + + +TRUE + + +2022-02-14 + + +454MB + +

Note that if Exist_Prebuilt is TRUE for a dataset, it diff --git a/docs/articles/vignette_processing_flow.html b/docs/articles/vignette_processing_flow.html index 75bbb2ab..e8f83106 100644 --- a/docs/articles/vignette_processing_flow.html +++ b/docs/articles/vignette_processing_flow.html @@ -112,7 +112,7 @@

gcamfaostat data processing

-

2023-10-30

+

2023-10-31

Source: vignettes/vignette_processing_flow.Rmd diff --git a/docs/articles/vignette_use_cases.html b/docs/articles/vignette_use_cases.html index 6a14f51c..b51c1cb9 100644 --- a/docs/articles/vignette_use_cases.html +++ b/docs/articles/vignette_use_cases.html @@ -112,7 +112,7 @@

Use Cases

-

2023-10-30

+

2023-10-31

Source: vignettes/vignette_use_cases.Rmd @@ -241,6 +241,163 @@

Other helper functions
  • The output can be read into gcamdata directly.
    • +
    +

    +
    +

    Case study: wheat bilateral trade pattern changes +

    +
      +
    • The world wheat trade was affected by the Russian-Ukraine war in +2021 (see Figures below).
      • +
    • In the same time, Canada, another major wheat supplier was hit by +droughts and production plummeted.
      +
      • +
    • However, the total trade did not change in 2021 (197 Mt) compared to +2020 (198 Mt).
      • +
    • Mainly because Australia, USA, and other regions supplied more to +the world market.
      • +
    +

    +
    +devtools::load_all()
+
+# Download the latest TM (bilateral trade) from FAOSTAT
+# To the path specified in DIR_RAW_DATA_FAOSTAT (see constants.R)
+
+FF_download_FAOSTAT(DATASETCODE = "TM",
+                    DATA_FOLDER = file.path("..",DIR_RAW_DATA_FAOSTAT),
+                    OverWrite = TRUE)
+
+# We could use module_xfaostat_L101_RawDataPreProc6_TM to clean and process the data
+# But here we visualize the raw data directly for continence (TM data is huge)
+# and also because the updates will be included later  
+
+# Load raw data 
+# Date 10-31-2023
+FAOSTAT_load_raw_data(DATASETCODE = "TM", 
+                      DATA_FOLDER = file.path("..",DIR_RAW_DATA_FAOSTAT))
+
+
+TM %>%
+  filter(year %in% c(2020:2021),
+         item_code == 15, # Wheat
+         element %in% c("Export Quantity")) %>% 
+    select(source = reporter_countries,
+           area = partner_countries, item, 
+           element, year, value, unit) ->
+  TM_Wheat
+
+TM_Wheat %>% 
+  rm_accent("area", "source") %>% 
+  mutate(value = value / 1000000) %>%  #Mt
+  mutate(area = replace(area, area == "United States of America", "USA")) %>% 
+  mutate(area = replace(area, area == "United Arab Emirates", "UAE")) %>% 
+  mutate(area = replace(area, area == "Democratic Republic of the Congo", "DR Congo")) %>% 
+  mutate(area = gsub(" Federation| of Great Britain and Northern Ireland| Province of| \\(Bolivarian Republic of\\)|United Republic of | \\(Islamic Republic of\\)|\\(Kingdom of the\\)", "", area)) %>% 
+  mutate(source = replace(source, source == "United States of America", "USA")) %>% 
+  mutate(source = replace(source, source == "United Arab Emirates", "UAE")) %>% 
+  mutate(source = replace(source, source == "Democratic Republic of the Congo", "DR Congo")) %>% 
+  mutate(source = gsub(" Federation| of Great Britain and Northern Ireland| Province of| \\(Bolivarian Republic of\\)|United Republic of | \\(Islamic Republic of\\)|\\(Kingdom of the\\)", "", source)) %>% 
+  select(REG_ex = source, REG_im = area, flow = value, year) ->
+  TM_Wheat_1
+
+# focus on key players
+TM_Wheat_1 %>% filter(year == 2020) %>% top_n(35, flow) %>% 
+  select(REG_ex, REG_im) %>% unlist %>% 
+  unique() -> KeyPlayers
+
+TM_Wheat_1 %>% 
+  mutate(REG_ex = replace(REG_ex, !REG_ex %in% KeyPlayers, "ROW")) %>% 
+  mutate(REG_im = replace(REG_im, !REG_im %in% KeyPlayers, "ROW")) %>%
+  # aggregate small players 
+  group_by(REG_ex, REG_im, year) %>% 
+  summarise(flow = sum(flow), .groups = "drop") ->
+  dat_circular
+
+dat_circular %>% group_by(year) %>% summarise(flow = sum(flow))
+
+library(circlize)
+
+# A wrapper function
+chord_wrapper <- function(.DF,
+                          .FigTitle = NULL,
+                          .SaveDir = "../man/figures",
+                          .GRIDCOLOR = NULL,
+                          .ORDER = NULL,
+                          .SaveName,
+                          .SaveScaler = 1){
+  
+  # treemap save to a path
+  png(filename= file.path(.SaveDir, paste0(.SaveName,".png")), res = 600,
+      width= 7000 * .SaveScaler, height= 7000 * .SaveScaler )
+  
+  
+  chordDiagram(as.data.frame(.DF), 
+               transparency = 0.5, 
+               directional = 1,
+               direction.type = c("diffHeight", "arrows"), 
+               diffHeight = -uh(2, "mm")
+               ,link.arr.type = "big.arrow"
+               ,annotationTrack = c("grid")
+               ,grid.col = .GRIDCOLOR
+               ,order = .ORDER
+               ,preAllocateTracks = list(list(track.height = c(0.3))
+                                         ,list(track.height = c(0.035))
+               ))
+  
+  title(main = .FigTitle)
+  
+  circos.track(track.index = 3, panel.fun = function(x, y) {
+    circos.axis(h = 1, labels.cex = 0.8)   
+  }, bg.border = NA)
+  
+  circos.track(track.index = 1, panel.fun = function(x, y) {
+    xlim = get.cell.meta.data("xlim")
+    xplot = get.cell.meta.data("xplot")
+    ylim = get.cell.meta.data("ylim")
+    sector.name = get.cell.meta.data("sector.index")
+    
+    #make text label vertical when space is too small; cex to adjust font size
+    
+    if(abs(xplot[2] - xplot[1]) < 20 | abs(xplot[2] - xplot[1]) > 340) {
+      circos.text(mean(xlim), ylim[1], sector.name, facing = "clockwise",
+                  niceFacing = TRUE, adj = c(0, 0.5), col = "black", 
+                  cex = 1)
+    } else {
+      circos.text(mean(xlim), ylim[1], sector.name, facing = "inside", 
+                  niceFacing = TRUE, adj = c(0.5, 0), col= "black", 
+                  cex = 1)
+    }  }, bg.border = NA)
+  
+  dev.off() #dump
+  
+  circos.clear()
+}
+
+  
+# Get color
+GridColor <- rand_color(length(KeyPlayers) + 1) 
+
+# Plot 
+
+chord_wrapper(.DF = dat_circular %>% filter(year == 2021) %>% select(-year)%>% arrange(REG_ex, REG_im),
+              .GRIDCOLOR = GridColor,
+              .FigTitle = "Wheat trade, 2021",
+              .ORDER = c(KeyPlayers, "ROW"),
+              .SaveDir = "../man/figures",
+              .SaveName = "Fig_WheatTrade2021")
+
+chord_wrapper(.DF = dat_circular %>% filter(year == 2020) %>% select(-year) %>% arrange(REG_ex, REG_im),
+              .GRIDCOLOR = GridColor,
+              .FigTitle = "Wheat trade, 2020",
+              .ORDER = c(KeyPlayers, "ROW"),
+              .SaveDir = "../man/figures",
+              .SaveName = "Fig_WheatTrade2020")
+
    +
      +
    • Preview +
      • +
    diff --git a/docs/articles/vignette_visualization.html b/docs/articles/vignette_visualization.html index 318c6ac2..2309cfb6 100644 --- a/docs/articles/vignette_visualization.html +++ b/docs/articles/vignette_visualization.html @@ -112,7 +112,7 @@

    Visualization

    -

    2023-10-30

    +

    2023-10-31

    Source: vignettes/vignette_visualization.Rmd @@ -179,7 +179,16 @@

    Supply-utilization

    Food Calories

    -

    Per capita food availability is highly heterogeneous across regions and commodities

    +

    Per capita food availability is highly heterogeneous across regions and commodities +***

    +

    +
    +

    Trade flows +

    +
    +World bilateral trade flow in 2020. The world total trade was 198 million tonnes.
    World bilateral trade flow in 2020. The world +total trade was 198 million tonnes.
    +
    @@ -596,8 +605,10 @@

    5. Food calories

    -

    6. Trade [in process] +

    6. Trade

    +

    See Cast +Study for details.

    7. Primary equivalent [in process] diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml index 6d8bb942..e250c507 100644 --- a/docs/pkgdown.yml +++ b/docs/pkgdown.yml @@ -8,7 +8,7 @@ articles: vignette_processing_flow: vignette_processing_flow.html vignette_use_cases: vignette_use_cases.html vignette_visualization: vignette_visualization.html -last_built: 2023-10-30T15:41Z +last_built: 2023-10-31T19:12Z urls: reference: https://jgcri.github.io/gcamfaostat/reference article: https://jgcri.github.io/gcamfaostat/articles diff --git a/docs/reference/FAOSTAT_metadata.html b/docs/reference/FAOSTAT_metadata.html index 4522738c..c2a01831 100644 --- a/docs/reference/FAOSTAT_metadata.html +++ b/docs/reference/FAOSTAT_metadata.html @@ -126,7 +126,7 @@

    Examples

    #> } #> return(metadata) #> } -#> <bytecode: 0x000000002cd30f58> +#> <bytecode: 0x000000001401d380> #> <environment: namespace:gcamfaostat>

    diff --git a/docs/reference/figures/Fig_WheatTrade2020.png b/docs/reference/figures/Fig_WheatTrade2020.png new file mode 100644 index 00000000..f46697f0 Binary files /dev/null and b/docs/reference/figures/Fig_WheatTrade2020.png differ diff --git a/docs/reference/figures/Fig_WheatTrade2021.png b/docs/reference/figures/Fig_WheatTrade2021.png new file mode 100644 index 00000000..0aa78528 Binary files /dev/null and b/docs/reference/figures/Fig_WheatTrade2021.png differ diff --git a/inst/extdata/aglu/FAO/FAOSTAT/metadata_log/FAOSTAT_METADATA_2023-10-31.csv b/inst/extdata/aglu/FAO/FAOSTAT/metadata_log/FAOSTAT_METADATA_2023-10-31.csv new file mode 100644 index 00000000..6971f061 --- /dev/null +++ b/inst/extdata/aglu/FAO/FAOSTAT/metadata_log/FAOSTAT_METADATA_2023-10-31.csv @@ -0,0 +1,14 @@ +datasetcode,datasetname,topic,datasetdescription,contact,email,dateupdate,compressionformat,filetype,filesize,filerows,filelocation +CB,Food Balances: Commodity Balances (non-food) (2010-),"Most crop and livestock products, including processed products, under agricultural activity.","Food Balance Sheet presents a comprehensive picture of the pattern of a country's food supply during a specified reference period. The food balance sheet shows for each food item - i.e. each primary commodity and a number of processed commodities potentially available for human consumption - the sources of supply and its utilization. The total quantity of foodstuffs produced in a country added to the total quantity imported and adjusted to any change in stocks that may have occurred since the beginning of the reference period gives the supply available during that period. On the utilization side a distinction is made between the quantities exported, fed to livestock, used for seed, put to manufacture for food use and non-food uses, losses during storage and transportation, and food supplies available for human consumption. The per caput supply of each such food item available for human consumption is then obtained by dividing the respective quantity by the related data on the population actually partaking of it. Data on per caput food supplies are expressed in terms of quantity and - by applying appropriate food composition factors for all primary and processed products - also in terms of caloric value and protein and fat content.",Mr. Salar Tayyib,faostat@fao.org,2023-08-08T00:00:00,zip,csv,859KB,117560,https://fenixservices.fao.org/faostat/static/bulkdownloads/CommodityBalances_(non-food)_E_All_Data_(Normalized).zip +FBS,Food Balances: Food Balances (2010-),"Most crop and livestock products, including processed products, under agricultural activity.","Food Balance Sheet presents a comprehensive picture of the pattern of a country's food supply during a specified reference period. The food balance sheet shows for each food item - i.e. each primary commodity and a number of processed commodities potentially available for human consumption - the sources of supply and its utilization. The total quantity of foodstuffs produced in a country added to the total quantity imported and adjusted to any change in stocks that may have occurred since the beginning of the reference period gives the supply available during that period. On the utilization side a distinction is made between the quantities exported, fed to livestock, used for seed, put to manufacture for food use and non-food uses, losses during storage and transportation, and food supplies available for human consumption. The per caput supply of each such food item available for human consumption is then obtained by dividing the respective quantity by the related data on the population actually partaking of it. Data on per caput food supplies are expressed in terms of quantity and - by applying appropriate food composition factors for all primary and processed products - also in terms of caloric value and protein and fat content.",Mr. Salar Tayyib,faostat@fao.org,2023-09-04T00:00:00,zip,csv,55894KB,4320908,https://fenixservices.fao.org/faostat/static/bulkdownloads/FoodBalanceSheets_E_All_Data_(Normalized).zip +FBSH,"Food Balances: Food Balances (-2013, old methodology and population)","Most crop and livestock products, including processed products, under agricultural activity.","Food Balance Sheet presents a comprehensive picture of the pattern of a country's food supply during a specified reference period. The food balance sheet shows for each food item - i.e. each primary commodity and a number of processed commodities potentially available for human consumption - the sources of supply and its utilization. The total quantity of foodstuffs produced in a country added to the total quantity imported and adjusted to any change in stocks that may have occurred since the beginning of the reference period gives the supply available during that period. On the utilization side a distinction is made between the quantities exported, fed to livestock, used for seed, put to manufacture for food use and non-food uses, losses during storage and transportation, and food supplies available for human consumption. The per caput supply of each such food item available for human consumption is then obtained by dividing the respective quantity by the related data on the population actually partaking of it. Data on per caput food supplies are expressed in terms of quantity and - by applying appropriate food composition factors for all primary and processed products - also in terms of caloric value and protein and fat content.",Mr. Salar Tayyib,faostat@fao.org,2023-03-10T00:00:00,zip,csv,70695KB,11479903,https://fenixservices.fao.org/faostat/static/bulkdownloads/FoodBalanceSheetsHistoric_E_All_Data_(Normalized).zip +FO,Forestry: Forestry Production and Trade,"Forestry and logging, Manufacture of wood and wood products, Manufacture of pulp, paper and paper products, collection of paper for recycling.","The database contains data on the production and trade in roundwood and in primary wood and paper products for all countries and territories in the world.The main types of primary forest products included in this database are roundwood, sawnwood, wood-based panels, pulp, and paper and paperboard. These products are detailed further and defined in the Joint Forest Sector Questionnaire (JFSQ) (http://www.fao.org/forestry/statistics/80572/en/). The database contains details of the following topics: - Roundwood removals (production) by coniferous and non-coniferous wood, - production and trade in industrial Roundwood, sawnwood, wood-based panels, wood charcoal, pulp, paper paperboard, and other products. More detailed information on wood products, including definitions, can be found at http://www.fao.org/forestry/statistics/80572/en/",Ashley Steel (Ms),faostat@fao.org,2023-09-05T00:00:00,zip,csv,15739KB,2237225,https://fenixservices.fao.org/faostat/static/bulkdownloads/Forestry_E_All_Data_(Normalized).zip +OA,Population and Employment: Annual population,Demography,"The FAOSTAT Population module contains time series data on population, by sex and urban/rural. The series consist of both estimates and projections for different periods as available from the original sources, namely:1. Population data refers to the World Population Prospects: The 2019 Revision from the UN Population Division.2. Urban/rural population data refers to the World Urbanization Prospects: The 2018 Revision from the UN Population Division./",Piero Conforti,Piero.Conforti@fao.org,2022-10-24T00:00:00,zip,csv,1540KB,169142,https://fenixservices.fao.org/faostat/static/bulkdownloads/Population_E_All_Data_(Normalized).zip +PD,Prices: Deflators,"*GDP and GFCF: Total Economy Gross Domestic Product and Gross Fixed Capital Formation, respectively (ISIC Rev. 3, A-Q_01-99 and ISIC Rev. 4, A-U_01-99) as for the GDP deflator and the GFCF deflator;*AFF: - Agriculture, Forestry, and Fishing (ISIC Rev. 3, A-B_01-05 and ISIC Rev. 4, A_01-03) as for the agriculture value-added deflator;*MAN: - Manufacturing (ISIC Rev. 3, D_15-37 and ISIC Rev. 4, C_10-33) as for the manufacturing value-added deflator.","The FAOSTAT Deflators database provides the following selection of implicit price deflator series by country and at regional level: Gross Domestic Product (GDP) deflator, Gross Fixed Capital Formation (GFCF) deflator, Agriculture, Forestry, Fishery Value-Added (VA_AFF) deflator, and Manufacturing Valued-Added (VA_MAN) deflator.A deflator is a figure expressing the change in prices over a period of time for a product or a basket of products by comparing a reference period to a base period. It is obtained by dividing a current price value of a given aggregate by its real counterpart. When calculated from the major national accounting aggregates such as GDP or agriculture value added, implicit price deflators pertains to wider ranges of goods and services in the economy than that represented by any of the individual price indexes (such as CPIs, PPIs). Movements in an implicit price deflator reflect both changes in price and changes in the composition of the aggregate for which the deflator is calculated.In the FAOSTAT Deflators database, all series are derived from the United Nations Statistics Division (UNSD) National Accounts Analysis of Main Aggregates database (UNSD AMA). In particular, the implicit GDP deflator, the implicit GFCF deflator, the implicit value added deflator of Agriculture, Forestry, Fishery and the implicit value added deflator of Manufacturing are obtained by dividing the series in current prices by those in constant 2015 prices (base year).",Veronica Boero,macrostats@fao.org,2023-08-16T00:00:00,zip,csv,905KB,83524,https://fenixservices.fao.org/faostat/static/bulkdownloads/Deflators_E_All_Data_(Normalized).zip +PP,Prices: Producer Prices,Most crop and livestock products under agricultural activity,"This sub-domain contains data on Agriculture Producer Prices and Producer Price Index. Agriculture Producer Prices are prices received by farmers for primary crops, live animals and livestock primary products as collected at the point of initial sale (prices paid at the farm-gate). Annual data are provided from 1991, while mothly data from January 2010 for 180 country and 212 products.Producer Price Index is the index of agricultural producer prices that measures the average annual change over time in the selling prices received by farmers (prices at the farm-gate or at the first point of sale). The three categories of producer price index available in FAOSTAT comprise: single-item price index, commodity group index and the agriculture producer price index.",Veronica Boero/Michele Vollaro/Jean Marie Munyeshyaka,faostat@fao.org,2023-02-23T00:00:00,zip,csv,9911KB,1150071,https://fenixservices.fao.org/faostat/static/bulkdownloads/Prices_E_All_Data_(Normalized).zip +QCL,Production: Crops and livestock products,"Crops and live animals, primary and selected derived commodities.","Crop and livestock statistics are recorded for 278 products, covering the following categories: 1) CROPS PRIMARY: Cereals, Citrus Fruit, Fibre Crops, Fruit, Oil Crops, Oil Crops and Cakes in Oil Equivalent, Pulses, Roots and Tubers, Sugar Crops, Treenuts and Vegetables. Data are expressed in terms of area harvested, production quantity and yield. Cereals: Area and production data on cereals relate to crops harvested for dry grain only. Cereal crops harvested for hay or harvested green for food, feed or silage or used for grazing are therefore excluded. 2) CROPS PROCESSED: Beer of barley; Cotton lint; Cottonseed; Margarine, short; Molasses; Oil, coconut (copra); Oil, cottonseed; Oil, groundnut; Oil, linseed; Oil, maize; Oil, olive, virgin; Oil, palm; Oil, palm kernel; Oil, rapeseed; Oil, safflower; Oil, sesame; Oil, soybean; Oil, sunflower; Palm kernels; Sugar Raw Centrifugal; Wine. 3) LIVE ANIMALS: Animals live n.e.s.; Asses; Beehives; Buffaloes; Camelids, other; Camels; Cattle; Chickens; Ducks; Geese and guinea fowls; Goats; Horses; Mules; Pigeons, other birds; Pigs; Rabbits and hares; Rodents, other; Sheep; Turkeys. 4) LIVESTOCK PRIMARY: Beeswax; Eggs (various types); Hides buffalo, fresh; Hides, cattle, fresh; Honey, natural; Meat (ass, bird nes, buffalo, camel, cattle, chicken, duck, game, goat, goose and guinea fowl, horse, mule, Meat nes, meat other camelids, Meat other rodents, pig, rabbit, sheep, turkey); Milk (buffalo, camel, cow, goat, sheep); Offals, nes; Silk-worm cocoons, reelable; Skins (goat, sheep); Snails, not sea; Wool, greasy. 5) LIVESTOCK PROCESSED: Butter (of milk from sheep, goat, buffalo, cow); Cheese (of milk from goat, buffalo, sheep, cow milk); Cheese of skimmed cow milk; Cream fresh; Ghee (cow and buffalo milk); Lard; Milk (dry buttermilk, skimmed condensed, skimmed cow, skimmed dried, skimmed evaporated, whole condensed, whole dried, whole evaporated); Silk raw; Tallow; Whey (condensed and dry); Yoghurt",Mr. Salar Tayyib,faostat@fao.org,2023-03-22T00:00:00,zip,csv,30139KB,3761168,https://fenixservices.fao.org/faostat/static/bulkdownloads/Production_Crops_Livestock_E_All_Data_(Normalized).zip +RFN,"Land, Inputs and Sustainability: Fertilizers by Nutrient","Environment, Agricultural input statistics","The Fertilizers by Nutrient dataset contains information on the totals in nutrients for Production, Trade and Agriculture Use of inorganic (chemical or mineral) fertilizers, over the time series 1961-present. The data are provided for the three primary plant nutrients: nitrogen (N), phosphorus (expressed as P2O5) and potassium (expressed as K2O). Both straight and compound fertilizers are included. There is information on the methodology available at: http://fenixservices.fao.org/faostat/static/documents/RFN/RFN_EN_README.pdf",Francesco Nicola Tubiello,faostat@fao.org,2023-07-05T00:00:00,zip,csv,1796KB,220984,https://fenixservices.fao.org/faostat/static/bulkdownloads/Inputs_FertilizersNutrient_E_All_Data_(Normalized).zip +RL,"Land, Inputs and Sustainability: Land Use","Agriculture, Forestry and Fisheries (ISIC A)","The FAOSTAT Land Use domain contains data on forty-four categories of land use, irrigation and agricultural practices and five indicators relevant to monitor agriculture, forestry and fisheries activities at national, regional and global level. Data are available by country and year, with global coverage and annual updates.",Francesco Nicola Tubiello,faostat@fao.org,2023-07-10T00:00:00,zip,csv,2538KB,376295,https://fenixservices.fao.org/faostat/static/bulkdownloads/Inputs_LandUse_E_All_Data_(Normalized).zip +SCL,Food Balances: Supply Utilization Accounts (2010-),"Most crop and livestock products, including processed products, under agricultural activity.","Supply Utilization Accounts and Food Balance Sheet present a comprehensive picture of the pattern of a country's food supply during a specified reference period. The food balance sheet shows for each food item - i.e. each primary commodity and a number of processed commodities potentially available for human consumption - the sources of supply and its utilization. The total quantity of foodstuffs produced in a country added to the total quantity imported and adjusted to any change in stocks that may have occurred since the beginning of the reference period gives the supply available during that period. On the utilization side a distinction is made between the quantities exported, fed to livestock, used for seed, put to manufacture for food use and non-food uses, losses during storage and transportation, and food supplies available for human consumption. The per caput supply of each such food item available for human consumption is then obtained by dividing the respective quantity by the related data on the population actually partaking of it. Data on per caput food supplies are expressed in terms of quantity and - by applying appropriate food composition factors for all primary and processed products - also in terms of caloric value and protein and fat content.",Mr. Salar Tayyib,faostat@fao.org,2023-09-04T00:00:00,zip,csv,73725KB,9959324,https://fenixservices.fao.org/faostat/static/bulkdownloads/SUA_Crops_Livestock_E_All_Data_(Normalized).zip +TCL,Trade: Crops and livestock products,"The dataset contains all food and agricultural products imported and exported during the reference year by country. In addition to the individual country data, other item and country aggregates are disseminated. The processed trade data is essential for the compilation of Supply/Utilization Accounts (SUA) and Food Balance Sheets (FBS).","The food and agricultural trade dataset is collected, processed and disseminated by FAO according to the standard International Merchandise Trade Statistics (IMTS) Methodology. The data is mainly provided by UNSD, Eurostat, and other national authorities as needed. This source data is checked for outliers, trade partner data is used for non-reporting countries or missing cells, and data on food aid is added to take into account total cross-border trade flows. The trade database includes the following variables: export quantity, export value, import quantity, and import value. The trade database includes all food and agricultural products imported/exported annually by all the countries in the world.",Mr. Salar Tayyib,faostat@fao.org,2023-08-14T00:00:00,zip,csv,234036KB,15697216,https://fenixservices.fao.org/faostat/static/bulkdownloads/Trade_CropsLivestock_E_All_Data_(Normalized).zip +TM,Trade: Detailed trade matrix,"The dataset contains all food and agricultural products imported and exported during the reference year by country. In addition to the individual country data, other item and country aggregates are disseminated. The processed trade data is essential for the compilation of Supply/Utilization Accounts (SUA) and Food Balance Sheets (FBS).","The food and agricultural trade dataset is collected, processed and disseminated by FAO according to the standard International Merchandise Trade Statistics (IMTS) Methodology. The data is mainly provided by UNSD, Eurostat, and other national authorities as needed. This source data is checked for outliers, trade partner data is used for non-reporting countries or missing cells, and data on food aid is added to take into account total cross-border trade flows. The trade database includes the following variables: export quantity, export value, import quantity, and import value. The trade database includes all food and agricultural products imported/exported annually by all the countries in the world.",Mr. Salar Tayyib,faostat@fao.org,2022-02-14T00:00:00,zip,csv,465008KB,46807399,https://fenixservices.fao.org/faostat/static/bulkdownloads/Trade_DetailedTradeMatrix_E_All_Data_(Normalized).zip diff --git a/man/figures/Fig_WheatTrade2020.png b/man/figures/Fig_WheatTrade2020.png new file mode 100644 index 00000000..f46697f0 Binary files /dev/null and b/man/figures/Fig_WheatTrade2020.png differ diff --git a/man/figures/Fig_WheatTrade2021.png b/man/figures/Fig_WheatTrade2021.png new file mode 100644 index 00000000..0aa78528 Binary files /dev/null and b/man/figures/Fig_WheatTrade2021.png differ diff --git a/vignettes/vignette_use_cases.Rmd b/vignettes/vignette_use_cases.Rmd index ace42bc7..097b5cb5 100644 --- a/vignettes/vignette_use_cases.Rmd +++ b/vignettes/vignette_use_cases.Rmd @@ -13,8 +13,6 @@ knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) -library(devtools) -devtools::load_all() ``` @@ -90,3 +88,158 @@ Since the 1970s, the number of countries in the world has increased due to the d * A function to save data frames to csv files with desired header information. * The output can be read into gcamdata directly. +*** + +## Case study: wheat bilateral trade pattern changes + +* The world wheat trade was affected by the Russian-Ukraine war in 2021 (see Figures below). +* In the same time, Canada, another major wheat supplier was hit by droughts and production plummeted. +* However, the total trade did not change in 2021 (197 Mt) compared to 2020 (198 Mt). +* Mainly because Australia, USA, and other regions supplied more to the world market. + +![](../man/figures/Fig_WheatTrade2020.png){width=100%} +![](../man/figures/Fig_WheatTrade2021.png){width=100%} + +```{r, eval = FALSE} +devtools::load_all() + +# Download the latest TM (bilateral trade) from FAOSTAT +# To the path specified in DIR_RAW_DATA_FAOSTAT (see constants.R) + +FF_download_FAOSTAT(DATASETCODE = "TM", + DATA_FOLDER = file.path("..",DIR_RAW_DATA_FAOSTAT), + OverWrite = TRUE) + +# We could use module_xfaostat_L101_RawDataPreProc6_TM to clean and process the data +# But here we visualize the raw data directly for continence (TM data is huge) +# and also because the updates will be included later + +# Load raw data +# Date 10-31-2023 +FAOSTAT_load_raw_data(DATASETCODE = "TM", + DATA_FOLDER = file.path("..",DIR_RAW_DATA_FAOSTAT)) + + +TM %>% + filter(year %in% c(2020:2021), + item_code == 15, # Wheat + element %in% c("Export Quantity")) %>% + select(source = reporter_countries, + area = partner_countries, item, + element, year, value, unit) -> + TM_Wheat + +TM_Wheat %>% + rm_accent("area", "source") %>% + mutate(value = value / 1000000) %>% #Mt + mutate(area = replace(area, area == "United States of America", "USA")) %>% + mutate(area = replace(area, area == "United Arab Emirates", "UAE")) %>% + mutate(area = replace(area, area == "Democratic Republic of the Congo", "DR Congo")) %>% + mutate(area = gsub(" Federation| of Great Britain and Northern Ireland| Province of| \\(Bolivarian Republic of\\)|United Republic of | \\(Islamic Republic of\\)|\\(Kingdom of the\\)", "", area)) %>% + mutate(source = replace(source, source == "United States of America", "USA")) %>% + mutate(source = replace(source, source == "United Arab Emirates", "UAE")) %>% + mutate(source = replace(source, source == "Democratic Republic of the Congo", "DR Congo")) %>% + mutate(source = gsub(" Federation| of Great Britain and Northern Ireland| Province of| \\(Bolivarian Republic of\\)|United Republic of | \\(Islamic Republic of\\)|\\(Kingdom of the\\)", "", source)) %>% + select(REG_ex = source, REG_im = area, flow = value, year) -> + TM_Wheat_1 + +# focus on key players +TM_Wheat_1 %>% filter(year == 2020) %>% top_n(35, flow) %>% + select(REG_ex, REG_im) %>% unlist %>% + unique() -> KeyPlayers + +TM_Wheat_1 %>% + mutate(REG_ex = replace(REG_ex, !REG_ex %in% KeyPlayers, "ROW")) %>% + mutate(REG_im = replace(REG_im, !REG_im %in% KeyPlayers, "ROW")) %>% + # aggregate small players + group_by(REG_ex, REG_im, year) %>% + summarise(flow = sum(flow), .groups = "drop") -> + dat_circular + +dat_circular %>% group_by(year) %>% summarise(flow = sum(flow)) + +library(circlize) + +# A wrapper function +chord_wrapper <- function(.DF, + .FigTitle = NULL, + .SaveDir = "../man/figures", + .GRIDCOLOR = NULL, + .ORDER = NULL, + .SaveName, + .SaveScaler = 1){ + + # treemap save to a path + png(filename= file.path(.SaveDir, paste0(.SaveName,".png")), res = 600, + width= 7000 * .SaveScaler, height= 7000 * .SaveScaler ) + + + chordDiagram(as.data.frame(.DF), + transparency = 0.5, + directional = 1, + direction.type = c("diffHeight", "arrows"), + diffHeight = -uh(2, "mm") + ,link.arr.type = "big.arrow" + ,annotationTrack = c("grid") + ,grid.col = .GRIDCOLOR + ,order = .ORDER + ,preAllocateTracks = list(list(track.height = c(0.3)) + ,list(track.height = c(0.035)) + )) + + title(main = .FigTitle) + + circos.track(track.index = 3, panel.fun = function(x, y) { + circos.axis(h = 1, labels.cex = 0.8) + }, bg.border = NA) + + circos.track(track.index = 1, panel.fun = function(x, y) { + xlim = get.cell.meta.data("xlim") + xplot = get.cell.meta.data("xplot") + ylim = get.cell.meta.data("ylim") + sector.name = get.cell.meta.data("sector.index") + + #make text label vertical when space is too small; cex to adjust font size + + if(abs(xplot[2] - xplot[1]) < 20 | abs(xplot[2] - xplot[1]) > 340) { + circos.text(mean(xlim), ylim[1], sector.name, facing = "clockwise", + niceFacing = TRUE, adj = c(0, 0.5), col = "black", + cex = 1) + } else { + circos.text(mean(xlim), ylim[1], sector.name, facing = "inside", + niceFacing = TRUE, adj = c(0.5, 0), col= "black", + cex = 1) + } }, bg.border = NA) + + dev.off() #dump + + circos.clear() +} + + +# Get color +GridColor <- rand_color(length(KeyPlayers) + 1) + +# Plot + +chord_wrapper(.DF = dat_circular %>% filter(year == 2021) %>% select(-year)%>% arrange(REG_ex, REG_im), + .GRIDCOLOR = GridColor, + .FigTitle = "Wheat trade, 2021", + .ORDER = c(KeyPlayers, "ROW"), + .SaveDir = "../man/figures", + .SaveName = "Fig_WheatTrade2021") + +chord_wrapper(.DF = dat_circular %>% filter(year == 2020) %>% select(-year) %>% arrange(REG_ex, REG_im), + .GRIDCOLOR = GridColor, + .FigTitle = "Wheat trade, 2020", + .ORDER = c(KeyPlayers, "ROW"), + .SaveDir = "../man/figures", + .SaveName = "Fig_WheatTrade2020") + + +``` + +* Preview +![](../man/figures/Fig_WheatTrade2020.png){width=20%} +![](../man/figures/Fig_WheatTrade2021.png){width=20%} + diff --git a/vignettes/vignette_visualization.Rmd b/vignettes/vignette_visualization.Rmd index daa34bfd..289945a3 100644 --- a/vignettes/vignette_visualization.Rmd +++ b/vignettes/vignette_visualization.Rmd @@ -55,6 +55,11 @@ knitr::opts_chunk$set( ![](../man/figures/Fig_WorldFoodCalories.png){width=100%} ![](../man/figures/Fig_WorldFoodCaloriesReg.png){width=100%} ![Per capita food availability is highly heterogeneous across regions and commodities](../man/figures/Fig_WorldFoodCaloriesRegPcPerDay.png){width=100%} +*** + +## Trade flows + +![World bilateral trade flow in 2020. The world total trade was 198 million tonnes.](../man/figures/Fig_WheatTrade2020.png){width=100%} *** @@ -444,7 +449,9 @@ treemap_wrapper( ![](../man/figures/Fig_WorldFoodCaloriesReg.png){width=20%} ![](../man/figures/Fig_WorldFoodCaloriesRegPcPerDay.png){width=20%} -### 6. Trade [in process] +### 6. Trade + +See [Cast Study](https://jgcri.github.io/gcamfaostat/articles/vignette_use_cases.html#faostat-raw-data-and-processing-output-updates) for details. ### 7. Primary equivalent [in process]