Prepare data for this site: trademapper.aptivate.org/
library(knitr) opts_chunk$set(echo=TRUE) library(tradeflows) # Which package is this? library(dplyr)
# Dataset send by Ed in November 2015 meli <- read.csv("~/downloads/Swietenia_spp_2000_2012_all_trade.csv") names(meli)
# swd99 <- readRDS("data-raw/comtrade/swd99.rds") %>% # clean() swd99 <- readdbproduct(440799, "validated_flow_yearly") # Change quantity to an int for ease of export swd99 <- swd99 %>% mutate(quantity = round(quantity)) # Import flows imp <- swd99 %>% filter(flow == "Import") # Export flows change column names to importer and exporter accordingly # Merge import and export flows # distinguishing quantity reported by partner and reporter dtf <- imp # continue later # Change reporter and partner to iso 2 code # mapping UN country code to the ISO 2 country code # faocountry <- FAOcountryProfile %>% # select(iso2 = ISO2_WB_CODE, uncode = UN_CODE) # # add China and France # faocountry <- faocountry %>% # rbind(data_frame(iso2 = c("CN"), # uncode = c(156))) countryiso <- read.csv("data-raw/CountryCodeandNameISO2ISO3.csv") %>% select(iso2 = ISO2.digit.Alpha, uncode = Country.Code) dtf <- dtf %>% left_join(rename(countryiso,iso2reporter = iso2, reportercode = uncode), by="reportercode") %>% left_join(rename(countryiso,iso2partner = iso2, partnercode = uncode), by = "partnercode") # Rename columns according to the dataset above (is this necessary?) swd99flows <- dtf %>% select(Year = year, Family = productdescription, Importer = iso2reporter, Exporter = iso2partner, quantity = quantity, Term = productcode, Unit = unit) %>% mutate(App. = "II") # Not sure if this is necessary? write.csv(swd99flows,"/tmp/swd99flows.csv") # Filter Indonesia write.csv(filter(swd99flows, Exporter == "ID" & !is.na(Importer)), "/tmp/swd99ID.csv") # Filter Cameroon write.csv(filter(swd99flows, Exporter == "CM"& !is.na(Importer)), "/tmp/swd99CM.csv")
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