diff --git a/docs/articles/vignette_getting_started.html b/docs/articles/vignette_getting_started.html index d8339072..972fc50a 100644 --- a/docs/articles/vignette_getting_started.html +++ b/docs/articles/vignette_getting_started.html @@ -112,7 +112,7 @@

Getting Started with gcamfaostat

-

2023-11-10

+

2024-02-12

Source: vignettes/vignette_getting_started.Rmd @@ -400,7 +400,7 @@

References IPCC. 2022. “Annex III: Scenarios and Modelling Methods.” In Climate Change 2022: Mitigation of Climate Change. -Cambridge, UK; New York, NY, USA: Cambridge University Press,. 10.1017/9781009157926.022. +Cambridge, UK; New York, NY, USA: Cambridge University Press,. https://doi.org/10.1017/9781009157926.022.

Lampe, Martin von, Dirk Willenbockel, Helal Ahammad, Elodie Blanc, @@ -439,14 +439,14 @@

References “The Critical Role of Conversion Cost and Comparative Advantage in Modeling Agricultural Land Use Change.” Climate Change Economics -11 (01): 2050004. +11 (01): 2050004. https://doi.org/10.1142/s2010007820500049.

Zhao, Xin, Katherine V Calvin, Marshall A Wise, and Gokul Iyer. 2021. “The Role of Global Agricultural Market Integration in Multiregional Economic Modeling: Using Hindcast Experiments to Validate an Armington Model.” Economic Analysis and Policy 72: -1–17. +1–17. https://doi.org/10.1016/j.eap.2021.07.007.
Zhao, Xin, and Marshall Wise. 2023. “Core Model Proposal# 360: diff --git a/docs/articles/vignette_news.html b/docs/articles/vignette_news.html index 94a6d3b3..f7c84dd0 100644 --- a/docs/articles/vignette_news.html +++ b/docs/articles/vignette_news.html @@ -112,7 +112,7 @@

News

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2024-02-11

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2024-02-12

Source: vignettes/vignette_news.Rmd diff --git a/docs/articles/vignette_preparing_data.html b/docs/articles/vignette_preparing_data.html index 8e9340c1..5aad41e0 100644 --- a/docs/articles/vignette_preparing_data.html +++ b/docs/articles/vignette_preparing_data.html @@ -113,7 +113,7 @@

Preparing data for gcamfaostat

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2023-11-10

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2024-02-12

Source: vignettes/vignette_preparing_data.Rmd @@ -277,7 +277,7 @@

2. Generate the me TRUE -2023-09-05 +2023-12-21 15MB @@ -297,10 +297,10 @@

2. Generate the me TRUE -2023-03-22 +2023-12-18 -29MB +32MB @@ -317,7 +317,7 @@

2. Generate the me TRUE -2023-08-16 +2023-11-07 1MB @@ -337,10 +337,10 @@

2. Generate the me TRUE -2023-08-14 +2023-12-13 -229MB +244MB @@ -417,7 +417,7 @@

2. Generate the me TRUE -2023-02-23 +2023-12-19 10MB @@ -457,10 +457,10 @@

2. Generate the me TRUE -2022-02-14 +2023-11-23 -454MB +446MB diff --git a/docs/articles/vignette_processing_flow.html b/docs/articles/vignette_processing_flow.html index 3d0f131b..e84435d4 100644 --- a/docs/articles/vignette_processing_flow.html +++ b/docs/articles/vignette_processing_flow.html @@ -112,7 +112,7 @@

gcamfaostat data processing

-

2023-11-10

+

2024-02-12

Source: vignettes/vignette_processing_flow.Rmd diff --git a/docs/articles/vignette_use_cases.html b/docs/articles/vignette_use_cases.html index a942e7e6..c6c38c32 100644 --- a/docs/articles/vignette_use_cases.html +++ b/docs/articles/vignette_use_cases.html @@ -112,7 +112,7 @@

Use Cases

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2024-02-11

+

2024-02-12

Source: vignettes/vignette_use_cases.Rmd @@ -284,43 +284,43 @@

Case study: wheat bila DATA_FOLDER = file.path("..",DIR_RAW_DATA_FAOSTAT)) -TM %>% +TM %>% filter(year %in% c(2020:2022), item_code == 15, # Wheat - element %in% c("Export Quantity")) %>% + 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)) %>% +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 == 2021) %>% top_n(35, flow) %>% - select(REG_ex, REG_im) %>% unlist %>% +TM_Wheat_1 %>% filter(year == 2021) %>% 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")) %>% +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) %>% + group_by(REG_ex, REG_im, year) %>% summarise(flow = sum(flow), .groups = "drop") -> dat_circular -dat_circular %>% group_by(year) %>% summarise(flow = sum(flow)) +dat_circular %>% group_by(year) %>% summarise(flow = sum(flow)) library(circlize) library(ggplot2) @@ -387,21 +387,21 @@

Case study: wheat bila # Plot -chord_wrapper(.DF = dat_circular %>% filter(year == 2022) %>% select(-year)%>% arrange(REG_ex, REG_im), +chord_wrapper(.DF = dat_circular %>% filter(year == 2022) %>% select(-year)%>% arrange(REG_ex, REG_im), .GRIDCOLOR = GridColor, .FigTitle = "Wheat trade, 2022", .ORDER = c(KeyPlayers, "ROW"), .SaveDir = "../man/figures", .SaveName = "Fig_WheatTrade2022") -chord_wrapper(.DF = dat_circular %>% filter(year == 2021) %>% select(-year)%>% arrange(REG_ex, REG_im), +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), +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"), @@ -410,21 +410,21 @@

Case study: wheat bila # Quickly adding time-series -TM_Wheat_1 %>% - group_by(REG_ex, year) %>% - summarize(value = sum(flow)) %>% ungroup() %>% - filter(year == 2021) %>%arrange(value) %>% top_n(12, value) %>% +TM_Wheat_1 %>% + group_by(REG_ex, year) %>% + summarize(value = sum(flow)) %>% ungroup() %>% + filter(year == 2021) %>%arrange(value) %>% top_n(12, value) %>% pull(REG_ex) -> KeyPlayers_exporter -TM_Wheat_1 %>% - group_by(REG_im, year) %>% - summarize(value = sum(flow)) %>% ungroup() %>% - filter(year == 2021) %>%arrange(value) %>% top_n(12, value) %>% +TM_Wheat_1 %>% + group_by(REG_im, year) %>% + summarize(value = sum(flow)) %>% ungroup() %>% + filter(year == 2021) %>%arrange(value) %>% top_n(12, value) %>% pull(REG_im) -> KeyPlayers_importer -TM_Wheat_1 %>% - group_by(year) %>% - summarize(value = sum(flow)) %>% ungroup() %>% +TM_Wheat_1 %>% + group_by(year) %>% + summarize(value = sum(flow)) %>% ungroup() %>% ggplot(aes(x = year, y = value)) + #geom_hline(yintercept = 0, size = 0.4, color = "grey") + geom_line(aes(x = year, y = value), color = "red", size = 1.2) + @@ -446,10 +446,10 @@

Case study: wheat bila ggsave(file.path("../man/figures", "Fig_WheatTradeWorldby2022.png"), p, width = 10, height = 8) -TM_Wheat_1 %>% - filter(REG_ex %in% KeyPlayers_exporter ) %>% - group_by(REG_ex, year) %>% - summarize(value = sum(flow)) %>% ungroup() %>% +TM_Wheat_1 %>% + filter(REG_ex %in% KeyPlayers_exporter ) %>% + group_by(REG_ex, year) %>% + summarize(value = sum(flow)) %>% ungroup() %>% ggplot(aes(x = year, y = value, group = REG_ex, color = REG_ex)) + facet_wrap(~REG_ex) + geom_hline(yintercept = 0, size = 0.4, color = "grey") + geom_line(aes(x = year, y = value, group = REG_ex, color = REG_ex), size = 1.2) + @@ -470,10 +470,10 @@

Case study: wheat bila ggsave(file.path("../man/figures", "Fig_WheatExportby2022.png"), p, width = 10, height = 8) -TM_Wheat_1 %>% - filter(REG_im %in% KeyPlayers_importer ) %>% - group_by(REG_im, year) %>% - summarize(value = sum(flow)) %>% ungroup() %>% +TM_Wheat_1 %>% + filter(REG_im %in% KeyPlayers_importer ) %>% + group_by(REG_im, year) %>% + summarize(value = sum(flow)) %>% ungroup() %>% ggplot(aes(x = year, y = value, group = REG_im, color = REG_im)) + facet_wrap(~REG_im) + geom_hline(yintercept = 0, size = 0.4, color = "grey") + geom_line(aes(x = year, y = value, group = REG_im, color = REG_im), size = 1.2) + diff --git a/docs/articles/vignette_visualization.html b/docs/articles/vignette_visualization.html index 7b4f91b2..bc3369d3 100644 --- a/docs/articles/vignette_visualization.html +++ b/docs/articles/vignette_visualization.html @@ -112,7 +112,7 @@

Visualization

-

2024-02-11

+

2024-02-12

Source: vignettes/vignette_visualization.Rmd diff --git a/docs/authors.html b/docs/authors.html index 7e866cad..74ca6a13 100644 --- a/docs/authors.html +++ b/docs/authors.html @@ -131,14 +131,14 @@

Citation

-

Zhao X, Chepeliev M, Patel P, Wise M, Calvin K, Narayan K, Vernon C (2023). +

Zhao X, Chepeliev M, Patel P, Wise M, Calvin K, Narayan K, Vernon C (2024). gcamfaostat: Prepare, process, and synthesize FAOSTAT data for global agroeconomic and multisector dynamic modeling. R package version 1.0.0, https://jgcri.github.io/gcamfaostat. 

@Manual{,
   title = {gcamfaostat: Prepare, process, and synthesize FAOSTAT data for global agroeconomic and multisector dynamic modeling},
   author = {Xin Zhao and Maksym Chepeliev and Pralit Patel and Marshall Wise and Kate Calvin and Kanishka Narayan and Chris Vernon},
-  year = {2023},
+  year = {2024},
   note = {R package version 1.0.0},
   url = {https://jgcri.github.io/gcamfaostat},
 }
diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml index 697c7a93..31506194 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: 2023-11-11T03:08Z +last_built: 2024-02-12T14:18Z 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 cd97ce14..30f90b13 100644 --- a/docs/reference/FAOSTAT_metadata.html +++ b/docs/reference/FAOSTAT_metadata.html @@ -126,7 +126,7 @@

Examples

#> } #> return(metadata) #> } -#> <bytecode: 0x000000002cfcd4c8> +#> <bytecode: 0x000000002d1d1ab0> #> <environment: namespace:gcamfaostat>
diff --git a/docs/reference/fast_left_join.html b/docs/reference/fast_left_join.html index 94b6b2e2..5aecded9 100644 --- a/docs/reference/fast_left_join.html +++ b/docs/reference/fast_left_join.html @@ -96,7 +96,7 @@

Fast left join for large tables

The dplyr join functions are a little on the slow side for very large tables. This version converts its inputs -data.table structures, and uses that package's +data.table structures, and uses that package's faster indexing capabilities to do a faster join.

diff --git a/docs/reference/figures/Fig_WheatExportby2022.png b/docs/reference/figures/Fig_WheatExportby2022.png new file mode 100644 index 00000000..eafb48db Binary files /dev/null and b/docs/reference/figures/Fig_WheatExportby2022.png differ diff --git a/docs/reference/figures/Fig_WheatImportby2022.png b/docs/reference/figures/Fig_WheatImportby2022.png new file mode 100644 index 00000000..3bac7f14 Binary files /dev/null and b/docs/reference/figures/Fig_WheatImportby2022.png differ diff --git a/docs/reference/figures/Fig_WheatTrade2020.png b/docs/reference/figures/Fig_WheatTrade2020.png index f46697f0..bb726f11 100644 Binary files a/docs/reference/figures/Fig_WheatTrade2020.png 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 index 0aa78528..b842a6a0 100644 Binary files a/docs/reference/figures/Fig_WheatTrade2021.png and b/docs/reference/figures/Fig_WheatTrade2021.png differ diff --git a/docs/reference/figures/Fig_WheatTrade2022.png b/docs/reference/figures/Fig_WheatTrade2022.png new file mode 100644 index 00000000..fab6c3c7 Binary files /dev/null and b/docs/reference/figures/Fig_WheatTrade2022.png differ diff --git a/docs/reference/figures/Fig_WheatTrade2022_2021.png b/docs/reference/figures/Fig_WheatTrade2022_2021.png new file mode 100644 index 00000000..a9b59e9a Binary files /dev/null and b/docs/reference/figures/Fig_WheatTrade2022_2021.png differ diff --git a/docs/reference/figures/Fig_WheatTradeWorldby2022.png b/docs/reference/figures/Fig_WheatTradeWorldby2022.png new file mode 100644 index 00000000..176438ed Binary files /dev/null and b/docs/reference/figures/Fig_WheatTradeWorldby2022.png differ diff --git a/inst/extdata/aglu/FAO/FAOSTAT/metadata_log/FAOSTAT_METADATA_2024-02-12.csv b/inst/extdata/aglu/FAO/FAOSTAT/metadata_log/FAOSTAT_METADATA_2024-02-12.csv new file mode 100644 index 00000000..a56248ae --- /dev/null +++ b/inst/extdata/aglu/FAO/FAOSTAT/metadata_log/FAOSTAT_METADATA_2024-02-12.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-),"Viale delle Terme di Caracalla, 00153 Rome, Italy",Food and Agriculture Organization of the United Nations (FAO),,,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-),"Viale delle Terme di Caracalla, 00153 Rome, Italy",Food and Agriculture Organization of the United Nations (FAO),,,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)","Viale delle Terme di Caracalla, 00153 Rome, Italy",Food and Agriculture Organization of the United Nations (FAO),,,2023-03-10T00:00:00,zip,csv,70769KB,11479903,https://fenixservices.fao.org/faostat/static/bulkdownloads/FoodBalanceSheetsHistoric_E_All_Data_(Normalized).zip +FO,Forestry: Forestry Production and Trade,"Viale delle Terme di Caracalla, 00153 Rome, Italy",Food and Agriculture Organization of the United Nations (FAO),,,2023-12-21T00:00:00,zip,csv,15798KB,2238265,https://fenixservices.fao.org/faostat/static/bulkdownloads/Forestry_E_All_Data_(Normalized).zip +OA,Population and Employment: Annual population,"Viale delle Terme di Caracalla, 00153 Rome, Italy",Food and Agriculture Organization of the United Nations,,,2022-10-24T00:00:00,zip,csv,1545KB,169142,https://fenixservices.fao.org/faostat/static/bulkdownloads/Population_E_All_Data_(Normalized).zip +PD,Prices: Deflators,FAO viale delle terme di Caracalla 1,Food and Agriculture Organization of the United Nations (FAO),,,2023-11-07T00:00:00,zip,csv,983KB,83486,https://fenixservices.fao.org/faostat/static/bulkdownloads/Deflators_E_All_Data_(Normalized).zip +PP,Prices: Producer Prices,"Viale delle Terme di Caracalla, 00153 Rome, Italy",Food and Agriculture Organization of the United Nations (FAO),,,2023-12-19T00:00:00,zip,csv,10345KB,1194830,https://fenixservices.fao.org/faostat/static/bulkdownloads/Prices_E_All_Data_(Normalized).zip +QCL,Production: Crops and livestock products,"Viale delle Terme di Caracalla, 00153 Rome, Italy",Food and Agriculture Organization of the United Nations (FAO),,,2023-12-18T00:00:00,zip,csv,32546KB,4127584,https://fenixservices.fao.org/faostat/static/bulkdownloads/Production_Crops_Livestock_E_All_Data_(Normalized).zip +RFN,"Land, Inputs and Sustainability: Fertilizers by Nutrient",,Food and Agriculture Organization of the United Nations (FAO),,,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","Viale delle Terme di Caracalla, 00153 Rome, Italy",Food and Agriculture Organization of the United Nations (FAO),,,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-),"Viale delle Terme di Caracalla, 00153 Rome, Italy",Food and Agriculture Organization of the United Nations (FAO),,,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,"Viale delle Terme di Caracalla, 00153 Rome, Italy",Food and Agriculture Organization of the United Nations (FAO),,,2023-12-13T00:00:00,zip,csv,250267KB,16047501,https://fenixservices.fao.org/faostat/static/bulkdownloads/Trade_CropsLivestock_E_All_Data_(Normalized).zip +TM,Trade: Detailed trade matrix,"Viale delle Terme di Caracalla, 00153 Rome, Italy",Food and Agriculture Organization of the United Nations (FAO),,,2023-11-23T00:00:00,zip,csv,456261KB,49159659,https://fenixservices.fao.org/faostat/static/bulkdownloads/Trade_DetailedTradeMatrix_E_All_Data_(Normalized).zip