The mrfable R package gives you quick, easy, and handy access to a large amount of foodrcrop data (area, production, and yield) for the federal States of India in the period 1967-2018. With mrfbale you can automatically download the data from https://eands.dacnet.nic.in/, and correct, clean, and aggregate them. This document provides a quick guide for using the package.
mrfable is based on the MADRaT engine (for more information run
vignettes("madrat"). To load mrfable simply type (the
typical output is shown under some commands as comment):
# load package
library(mrfable)
#> Loading required package: madrat
#> Loading required package: magclass
# set sub-national mapping
setConfig(extramappings = "mappingIndiaAPY.csv")
#> Global configuration update:
#> extramappings: -> mappingIndiaAPY.csvThe package is ready to use.
To compute the total foodcrop data for all India simply type (for e.g. area data):
for yield/production data use:
myDataY <- calcOutput("IndiaFoodcrop", subtype = "Yield")
myDataP <- calcOutput("IndiaFoodcrop", subtype = "Production")This will download, clean, sort, and aggregate the data automatically
(access to the individual steps will be shown further down). The object
mydata will contain your data. It is an object of the
magpie class, i.e. an array:
str(myData)
# Formal class 'magpie' [package "magclass"] with 1 slot
# ..@ .Data: num [1, 1:53, 1:40] 12240 12807 12052 12493 12913 ...
# .. ..- attr(*, "dimnames")=List of 3
# .. .. ..$ state : chr "IND"
# .. .. ..$ year : chr [1:53] "y1966" "y1967" "y1968" "y1969" ...
# .. .. ..$ crop.variable.unit.season: chr [1:40] "Bajra.Area.kHectares.total" "Barley.Area.kHectares.total" "Gram.Area.kHectares.total" "Jowar.Area.kHectares.total" ...The dimensions of the object are the spatial, temporal and data
dimension. To access any of the dimensions follow this scheme:
myData["spatial","temporal","data"], e.g.:
myData["IND", 2018, "Rice.Area.kHectares.total"]
# An object of class "magpie"
# , , crop.variable.unit.season = Rice.Area.kHectares.total
#
# year
# state y2018
# IND 44156.45
# ...This is not so spectacular, however. Let’s see how we can see more. (Note that you don’t have to type the unit each time, and you can choose more than one seasons/years):
myData["IND", 2000:2018, "Rice"][, , c("kharif", "rabi")]
# An object of class "magpie"
# , , crop.variable.unit.season = Rice.Area.kHectares.kharif
#
# year
# state y2000 y2001 y2002 y2003 y2004 y2005 y2006 y2007 y2008 y2009 y2010 y2011 y2012 y2013 y2014 y2015 y2016 y2017 y2018
# IND 22672.8 22255.4 20522 21699.7 21187 21790.9 22199.7 21978 22938.9 20979.07 22724.91 23113.69 22488.8 23029.17 39829 39656.45 39845.77 39349.27 39964.35
#
# , , crop.variable.unit.season = Rice.Area.kHectares.rabi
#
# year
# state y2000 y2001 y2002 y2003 y2004 y2005 y2006 y2007 y2008 y2009 y2010 y2011 y2012 y2013 y2014 y2015 y2016 y2017 y2018
# IND 1336.8 1502.1 812.2 975.7 982.8 1569.4 1468.5 1524.7 1688.9 1478.1 1939.762 1321.6 1352.174 2037.124 4281.56 3842.727 4147.582 4424.799 4192.091To compute the total foodcrop data for all India states simply type (for e.g. area data):
Again, to access the data you can filter them like this (a blank field means “show all data”):
myData[, 2000:2018, "Rice"][, , c("kharif", "rabi")]
# An object of class "magpie"
# , , crop.variable.unit.season = Rice.Area.kHectares.kharif
#
# year
# state y2000 y2001 y2002 y2003 y2004 y2005 y2006 y2007 y2008 y2009 y2010 y2011 y2012 y2013 y2014 y2015
# Andaman and Nicobar Islands 10.9 9.8 10.9 10.6 10.7 10.6 10.6 7.3 7.9 8.140 8.390 8.100 7.850 8.005 6.43 6.1000
# Andhra Pradesh 3004.0 2426.1 2109.3 2109.0 2215.0 2526.0 2641.0 2578.0 2803.0 2063.000 2922.000 2874.000 2487.000 2651.000 1635.00 1399.0000
# Arunachal Pradesh 118.6 119.5 124.6 119.2 121.6 122.3 122.3 124.0 126.8 121.468 121.570 123.500 126.085 131.990 127.20 128.3000
# Assam 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000 0.000 0.000 0.000 0.000 2078.94 2080.0050
# Bihar 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000 0.000 0.000 0.000 0.000 3181.11 3151.0120
# Chhattisgarh 3769.7 3810.1 3777.7 3829.0 3746.7 3747.2 3723.6 3752.4 3734.0 3670.700 3702.500 3773.800 3784.800 3802.100 3808.50 3816.0000
# Dadra and Nagar Haveli and Daman and Diu 15.6 15.3 15.3 15.3 15.2 15.4 15.4 15.6 15.4 14.545 12.800 12.320 15.850 15.260 15.38 17.5333
# Delhi 6.1 6.4 6.1 6.5 6.0 7.5 7.4 7.4 7.4 6.821 7.045 6.850 6.700 6.040 6.04 6.0350
# Goa 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000 0.000 0.000 0.000 0.000 27.84 27.7300
# Gujarat 583.6 667.9 468.9 651.4 661.7 666.0 693.0 726.0 722.0 658.000 728.000 752.000 672.000 757.000 755.00 741.0000
# Haryana 1054.0 1028.0 906.0 1015.0 1028.0 1052.0 1041.0 1075.0 1210.0 1205.000 1245.000 1235.000 1215.000 1228.000 1287.00 1354.0000
# Himachal Pradesh 81.5 80.6 83.3 81.3 81.0 79.4 79.2 78.6 77.7 76.696 77.064 77.230 76.900 74.360 72.47 73.6850
# Jammu & Kashmir 244.1 249.8 236.2 259.8 250.0 259.0 252.5 263.2 257.6 259.892 261.347 262.169 261.660 271.490 276.42 304.5000
# Jharkhand 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000 0.000 0.000 0.000 0.000 1502.20 1588.9790
# Karnataka 1120.5 1088.0 1030.5 944.3 1056.0 1076.0 1066.0 1051.0 1130.0 1102.000 1130.000 1118.000 1048.000 1035.000 1000.00 977.0000
# Kerala 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000 0.000 0.000 0.000 0.000 152.97 150.7663
# Madhya Pradesh 1707.6 1776.4 1681.3 1718.8 1622.5 1657.7 1661.3 1558.9 1682.3 1445.700 1602.900 1662.000 1882.600 1930.000 2153.00 2024.0000
# Maharashtra 1486.4 1487.2 1497.0 1500.0 1488.0 1474.0 1491.0 1535.0 1500.0 1450.000 1486.000 1516.000 1528.000 1568.000 1508.00 1471.0000
# ...To compute the total foodcrop data for all Zonal Councils of India states type (for e.g. area data):
And access the data same as above.
By use of typical functions (see
e.g. magclass::as.data.frame) you can transform the cleaned
data to other data types (e.g. data frames etc.) and/or write them in
files:
(see magclass::write.magpie for more data types).
You can also extract the whole cleaned dataset for use in projects (e.g. for use as input for a model) like this:
The location of the data on your machine will be reported in the
command output as outputfolder