regions: update sp_id to be unique regardless of region_type, so easy to raster::extract values

see Global/NCEAS-Regions_v2014

Minor issue I am dealing with when trying to recreate the pressure layers from raw data. I first download the data from the original sources (thank you very much for providing links to those). Then, I changed the working directories in your code to my local machine. Subsequently, all calls using substr do not work because my working directory, which influences the full file path, is different. I have changed the code on my local machine to use gsub("\\.[a-z]{3}", "", sapply(strsplit(x, "-|_"), tail, 1)), which will work for some of the uses of substr to pull out the year.

thought this got taken care by Clip() in Global/NCEAS-Regions_v2014/model_create_regions.py, but maybe needs to be run again.

Mel will share error.

How to fix the identity etc?

Hi there!

Quick question - is there supposed to be a common location for the vgpm_func.R script referenced here (http://ohi-science.org/ohiprep_v2018/globalprep/prs_fish/v2018/npp.html#calculate_mean_annual_npp)?

Thanks!

Best,

J

Relevant to Global/WorldBank-WGI_v2013.

• R library WDI

Also see:

• GDP dataset on GitHub which has a process.py script and conforms to DataPackage format
• WorldBank API
• Python package wbdata
• R library rWBclimate for WorldBank Climate API

README.md from R library WDI...

World Bank Development indicators for R

The World Bank makes available a ton of great data from the World Development Indicators through its web API. The WDI package for R makes it easy to search and download data series from the WDI.

Installation

WDI is published on CRAN and so can be installed by simply typing this in the R console:

install.packages('WDI')

Searching for data

You can search for data by using keywords in WDIsearch. For instance, if you are looking for data on Gross Domestic Product:

WDIsearch('gdp')

Which produces this:

> WDIsearch('gdp')[1:10,]
      indicator              name                                                                      
 [1,] "BG.GSR.NFSV.GD.ZS"    "Trade in services (% of GDP)"                                            
 [2,] "BM.KLT.DINV.GD.ZS"    "Foreign direct investment, net outflows (% of GDP)"                      
 [3,] "BN.CAB.XOKA.GD.ZS"    "Current account balance (% of GDP)"                                      
 [4,] "BN.CUR.GDPM.ZS"       "Current account balance excluding net official capital grants (% of GDP)"
 [5,] "BN.GSR.FCTY.CD.ZS"    "Net income (% of GDP)"                                                   
 [6,] "BN.KLT.DINV.CD.ZS"    "Foreign direct investment (% of GDP)"                                    
 [7,] "BN.KLT.PRVT.GD.ZS"    "Private capital flows, total (% of GDP)"                                 
 [8,] "BN.TRF.CURR.CD.ZS"    "Net current transfers (% of GDP)"                                        
 [9,] "BNCABFUNDCD_"         "Current Account Balance, %GDP"                                           
[10,] "BX.KLT.DINV.WD.GD.ZS" "Foreign direct investment, net inflows (% of GDP)" 

WDIsearch uses grep and ignores cases, so you can also use regular expressions. For instance, if you are looking for GDP per capita in constant dollars:

WDIsearch('gdp.*capita.*constant')
     indicator           name                                                 
[1,] "GDPPCKD"           "GDP per Capita, constant US$, millions"             
[2,] "NY.GDP.PCAP.KD"    "GDP per capita (constant 2000 US$)"                 
[3,] "NY.GDP.PCAP.KN"    "GDP per capita (constant LCU)"                      
[4,] "NY.GDP.PCAP.PP.KD" "GDP per capita, PPP (constant 2005 international $)"

Download and use the data

Download a series you like for the countries you like:

dat = WDI(indicator='NY.GDP.PCAP.KD', country=c('MX','CA','US'), start=1960, end=2012)

Look at the data:

head(dat)
  iso2c country NY.GDP.PCAP.KD year
1    CA  Canada       9374.883 1960
2    CA  Canada       9479.824 1961
3    CA  Canada       9967.366 1962
4    CA  Canada      10290.362 1963
5    CA  Canada      10774.653 1964
6    CA  Canada      11283.606 1965

Plot the data:

library(ggplot2)
ggplot(dat, aes(year, NY.GDP.PCAP.KD, color=country)) + geom_line() + 
    xlab('Year') + ylab('GDP per capita')

Note: You can use country='all' to download data for all available countries. You can also feed a vector of indicator strings if you want to download multiple indicators at once.

Updating series list

To speed up search, WDI ships with a local list of all available WDI series as of 2012-06-18. This list will be updated semi-regularly, but you may still want to update it manually to get access to the very latest data series. To do so, use the cache function:

new_cache = WDIcache()
WDIsearch('gdp', cache=new_cache)

Bugs, suggestions, etc.

Thanks for using WDI! Please send all bug reports and suggestions through the github issue tracker or by email at [email protected]

@Gissues:{"order":50,"status":"inprogress"}

Incorporate R and Python packages for this simple data package format.

This limitation of 10 characters for shapefiles is uber lame, eg README.md explaining Antarctic specific set of shapefiles here:

\\neptune\data_edit\git-annex\Global\NCEAS-Regions_v2014\data

• antarctica_ccamlr_alleez_gcs.shp: CCAMLR regions before being clipped by EEZs
  • area_orig_ -> area_orig_km2
• antarctica_ccamlr_ohi2014_gcs.shp: CCAMLR regions after being clipped by EEZs with the following fields explained:
  • area_orig_ -> area_orig_km2
  • area_km2
  • area_pct_o -> area_pct_orig: area_km2 / area_orig_km2 * 100