countryExData: Example dataset for country level data (2008 Environmental...
In rworldmap: Mapping global data, vector and raster.
Description
Usage
Format
Details
Source
References
Examples
Description
A dataframe containing example country level data for 149 countries.
This is the 2008 Environmental Performance Index (EPI) downloaded from http://epi.yale.edu/.
Used here with permission, further details on the data can be found there.
The data are referenced by ISO 3 letter country codes and country names.
Usage
1
Format
A data frame with 149 observations on the following 80 variables.
ISO3V10a character vector
Countrya character vector
EPI_regionsa character vector
GEO_subregiona character vector
Population2005a numeric vector
GDP_capita.MRYAa numeric vector
landlocka numeric vector
landareaa numeric vector
densitya numeric vector
EPIa numeric vector
ENVHEALTHa numeric vector
ECOSYSTEMa numeric vector
ENVHEALTH.1a numeric vector
AIR_Ea numeric vector
WATER_Ea numeric vector
BIODIVERSITYa numeric vector
PRODUCTIVE_NATURAL_RESOURCESa numeric vector
CLIMATEa numeric vector
DALY_SCa numeric vector
WATER_Ha numeric vector
AIR_Ha numeric vector
AIR_E.1a numeric vector
WATER_E.1a numeric vector
BIODIVERSITY.1a numeric vector
FORESTa numeric vector
FISHa numeric vector
AGRICULTUREa numeric vector
CLIMATE.1a numeric vector
ACSAT_pta numeric vector
WATSUP_pta numeric vector
DALY_pta numeric vector
INDOOR_pta numeric vector
PM10_pta numeric vector
OZONE_H_pta numeric vector
SO2_pta numeric vector
OZONE_E_pta numeric vector
WATQI_pta numeric vector
WATSTR_pta numeric vector
WATQI_GEMS.station.dataa numeric vector
FORGRO_pta numeric vector
CRI_pta numeric vector
EFFCON_pta numeric vector
AZE_pta numeric vector
MPAEEZ_pta numeric vector
EEZTD_pta numeric vector
MTI_pta numeric vector
IRRSTR_pta numeric vector
AGINT_pta numeric vector
AGSUB_pta numeric vector
BURNED_pta numeric vector
PEST_pta numeric vector
GHGCAP_pta numeric vector
CO2IND_pta numeric vector
CO2KWH_pta numeric vector
ACSATa numeric vector
WATSUPa numeric vector
DALYa numeric vector
INDOORa numeric vector
PM10a numeric vector
OZONE_Ha numeric vector
SO2a numeric vector
OZONE_Ea numeric vector
WATQIa numeric vector
WATQI_GEMS.station.data.1a numeric vector
WATSTRa numeric vector
FORGROa numeric vector
CRIa numeric vector
EFFCONa numeric vector
AZEa numeric vector
MPAEEZa numeric vector
EEZTDa numeric vector
MTIa numeric vector
IRRSTRa numeric vector
AGINTa numeric vector
AGSUBa numeric vector
BURNEDa numeric vector
PESTa numeric vector
GHGCAPa numeric vector
CO2INDa numeric vector
CO2KWHa numeric vector
Details
2008 Environmental Performance Index (EPI) data downloaded from : http://epi.yale.edu/Downloads
Disclaimers
This 2008 Environmental Performance Index (EPI) tracks national environmental results on a quantitative basis,
measuring proximity to an established set of policy targets using the best data available. Data constraints
and limitations in methodology make this a work in progress. Further refinements will be undertaken over the
next few years.
Comments, suggestions, feedback, and referrals to better data sources are welcome at:
http://epi.yale.edu or epi@yale.edu.
Source
http://epi.yale.edu/Downloads
References
Esty, Daniel C., M.A. Levy, C.H. Kim, A. de Sherbinin, T. Srebotnjak, and V. Mara. 2008.
2008 Environmental Performance Index. New Haven: Yale Center for Environmental Law and Policy.
Examples
1
2
data(countryExData,envir=environment(),package="rworldmap")
str(countryExData)
Example output
Loading required package: sp
### Welcome to rworldmap ###
For a short introduction type : vignette('rworldmap')
'data.frame': 149 obs. of 80 variables:
$ ISO3V10 : chr "AGO" "ALB" "ARE" "ARG" ...
$ Country : chr "Angola" "Albania" "United Arab Emirates " "Argentina" ...
$ EPI_regions : chr "Sub-Saharan Africa" "Central and Eastern Europ" "Middle East and North Africa" "Latin America and Caribbe" ...
$ GEO_subregion : chr "Southern Africa" "Central Europe" "Arabian Peninsula" "South America" ...
$ Population2005 : num 15941 3130 4496 38747 3016 ...
$ GDP_capita.MRYA : num 2314 4955 22698 13652 5011 ...
$ landlock : int 0 0 0 0 1 0 1 1 1 0 ...
$ landarea : num 1251896 28346 74777 2736296 28273 ...
$ density : num 0.2 34.3 8.7 1.3 30.3 0.3 16.3 14.6 91 71.2 ...
$ EPI : num 39.5 84 64 81.8 77.8 79.8 89.4 72.2 54.7 78.4 ...
$ ENVHEALTH : num 8.9 89.3 89.8 91.1 88 99.3 98.1 76.4 37.6 98.8 ...
$ ECOSYSTEM : num 70.1 78.6 38.2 72.5 67.5 60.4 80.7 67.9 71.7 58 ...
$ ENVHEALTH.1 : num 8.9 89.3 89.8 91.1 88 99.3 98.1 76.4 37.6 98.8 ...
$ AIR_E : num 49.2 99.1 85.1 87.3 99.4 84.9 97 97.7 99.5 50.2 ...
$ WATER_E : num 61.6 96.5 27.1 74.9 28 62.5 79.9 48.5 62.8 52.3 ...
$ BIODIVERSITY : num 58.9 4 36.6 33.6 16 78.1 71.6 29 62.5 10 ...
$ PRODUCTIVE_NATURAL_RESOURCES: num 81.3 79.4 74.1 71.5 82.1 91.8 88.2 85.7 48 76.1 ...
$ CLIMATE : num 74.6 93.4 26.6 82.3 87.2 42.5 79.9 77.1 81.5 69.5 ...
$ DALY_SC : num 0 99.5 98.9 98 98.2 99.6 99.8 93 26.1 99.6 ...
$ WATER_H : num 19.8 91.3 98.8 91.3 83.3 100 100 53.6 44.7 100 ...
$ AIR_H : num 16 66.8 62.4 76.9 72.5 97.9 92.8 66.2 53.5 96 ...
$ AIR_E.1 : num 49.2 99.1 85.1 87.3 99.4 84.9 97 97.7 99.5 50.2 ...
$ WATER_E.1 : num 61.6 96.5 27.1 74.9 28 62.5 79.9 48.5 62.8 52.3 ...
$ BIODIVERSITY.1 : num 58.9 4 36.6 33.6 16 78.1 71.6 29 62.5 10 ...
$ FOREST : num 95.4 100 100 75.9 70.1 100 100 100 0 100 ...
$ FISH : num 87.3 62.5 50 58.8 NA 96.7 NA NA NA 47.4 ...
$ AGRICULTURE : num 61.3 75.6 72.3 79.9 94.2 78.7 76.4 71.4 95.9 80.8 ...
$ CLIMATE.1 : num 74.6 93.4 26.6 82.3 87.2 42.5 79.9 77.1 81.5 69.5 ...
$ ACSAT_pt : num 19.3 89.5 97.7 89.5 80.1 100 100 46.2 25.1 100 ...
$ WATSUP_pt : num 20.2 93.2 100 93.2 86.4 100 100 61 64.3 100 ...
$ DALY_pt : num 0 99.5 98.9 98 98.2 99.6 99.8 93 26.1 99.6 ...
$ INDOOR_pt : num 0 47.4 94.7 94.7 72.2 94.7 94.7 48.4 0 94.7 ...
$ PM10_pt : num 40 70.1 11.2 51.3 59 100 87.8 67 84.1 95.4 ...
$ OZONE_H_pt : num 0 99.1 100 92.4 100 100 99.2 100 99.4 99.7 ...
$ SO2_pt : num 98.4 98.5 70.2 98.8 98.8 69.9 94.4 95.4 99.3 0.6 ...
$ OZONE_E_pt : num 0 99.8 100 75.7 100 100 99.6 100 99.6 99.8 ...
$ WATQI_pt : num 29.4 93 0 76.4 31.7 75.3 59.8 31.7 25.6 59.6 ...
$ WATSTR_pt : num 98.3 90.3 100 100 100 73.4 100 100 63 100 ...
$ WATQI_GEMS.station.data : num NA 93 NA 76.4 NA 75.3 59.8 NA NA 59.6 ...
$ FORGRO_pt : num 95.4 100 100 75.9 70.1 100 100 100 0 100 ...
$ CRI_pt : num 99.7 5.5 100 39.8 37.7 86.1 80.1 46.2 84.1 9.6 ...
$ EFFCON_pt : num 95.7 1.6 2.3 33.9 10.4 79 63 11.9 40.9 11.5 ...
$ AZE_pt : num 0 NA NA 40 0 69.4 NA NA NA NA ...
$ MPAEEZ_pt : num 14 6 1 2 100 78 100 100 100 0 ...
$ EEZTD_pt : num 74.5 25.1 0 17.5 NA 93.5 NA NA NA 0 ...
$ MTI_pt : num 100 100 100 100 NA 100 NA NA NA 94.9 ...
$ IRRSTR_pt : num 97.5 100 51.8 74.6 97 50.7 100 82.9 100 100 ...
$ AGINT_pt : num 100 90.2 100 78.4 94.5 79.6 63.2 91.1 92 87.1 ...
$ AGSUB_pt : num 100 100 100 100 100 99.9 22.8 100 100 22.8 ...
$ BURNED_pt : num 0 78.9 96.1 55.7 79.5 63.3 96 78.4 87.7 98.6 ...
$ PEST_pt : num 9.1 9.1 13.6 90.9 100 100 100 4.5 100 95.5 ...
$ GHGCAP_pt : num 65.8 98.8 38.6 87.1 98 45.4 81.6 88.7 94 77.7 ...
$ CO2IND_pt : num 95 85 32.1 92.7 78.3 76.2 82.3 97.1 100 59.7 ...
$ CO2KWH_pt : num 63 96.3 9 67 85.1 5.9 75.7 45.6 50.5 71.1 ...
$ ACSAT : num 31 91 98 91 83 100 100 54 36 100 ...
$ WATSUP : num 53 96 100 96 92 100 100 77 79 100 ...
$ DALY : num 109 0.3 0.6 1.1 1 0.2 0.1 3.9 41 0.2 ...
$ INDOOR : num 95 50 5 5 26.4 5 5 49 95 5 ...
$ PM10 : num 91.4 55.5 125.6 77.9 68.7 ...
$ OZONE_H : num 4948.8 15.8 0 140.4 0 ...
$ SO2 : num 0.7 0.6 12.6 0.5 0.5 12.7 2.4 1.9 0.3 41.9 ...
$ OZONE_E : num 1.36e+09 6.81e+05 2.63e+01 9.96e+07 0.00 ...
$ WATQI : num 57.5 95.8 39.9 85.8 58.9 85.2 75.9 58.9 55.3 75.7 ...
$ WATQI_GEMS.station.data.1 : num NA 95.8 NA 85.8 NA 85.2 75.9 NA NA 75.7 ...
$ WATSTR : num 5.5 0 41.6 24.1 68.6 45.7 0 31.4 0 49.8 ...
$ FORGRO : num 1 1 1 0.9 0.9 1 1.1 1 0.6 1.1 ...
$ CRI : num 0.5 0 0.5 0.2 0.2 0.4 0.4 0.2 0.4 0 ...
$ EFFCON : num 9.6 0.2 0.2 3.4 1 7.9 6.3 1.2 4.1 1.2 ...
$ AZE : num 0 NA NA 40 0 69.4 NA NA NA NA ...
$ MPAEEZ : num 1.4 0.6 0.1 0.2 10 7.8 10 10 10 0 ...
$ EEZTD : num 0.255 0.749 1 0.825 NA ...
$ MTI : num 0.0016 0 0.0034 0.0044 NA 0.0014 NA NA NA -0.001 ...
$ IRRSTR : num 2.2 0 41 21.6 2.5 41.9 0 14.6 0 0 ...
$ AGINT : num 0 6.2 0 13.7 3.5 12.9 23.3 5.6 5.1 8.2 ...
$ AGSUB : num 0 0 0 0 0 0 36 0 0 36 ...
$ BURNED : num 15.3 2.9 0.5 6 2.8 5 0.5 2.9 1.7 0.2 ...
$ PEST : num 2 2 3 20 22 22 22 1 22 21 ...
$ GHGCAP : num 20 2.9 34.1 8.9 3.3 30.5 11.8 8.1 5.3 13.8 ...
$ CO2IND : num 1.2 1.9 5.5 1.4 2.3 2.5 2.1 1.1 0.8 3.6 ...
$ CO2KWH : num 343 34 844 306 138 873 225 505 459 268 ...
rworldmap documentation built on May 2, 2019, 4:50 p.m.
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Description Usage Format Details Source References Examples
Description
A dataframe containing example country level data for 149 countries. This is the 2008 Environmental Performance Index (EPI) downloaded from http://epi.yale.edu/. Used here with permission, further details on the data can be found there. The data are referenced by ISO 3 letter country codes and country names.
Usage
1 |
Format
A data frame with 149 observations on the following 80 variables.
ISO3V10a character vector
Countrya character vector
EPI_regionsa character vector
GEO_subregiona character vector
Population2005a numeric vector
GDP_capita.MRYAa numeric vector
landlocka numeric vector
landareaa numeric vector
densitya numeric vector
EPIa numeric vector
ENVHEALTHa numeric vector
ECOSYSTEMa numeric vector
ENVHEALTH.1a numeric vector
AIR_Ea numeric vector
WATER_Ea numeric vector
BIODIVERSITYa numeric vector
PRODUCTIVE_NATURAL_RESOURCESa numeric vector
CLIMATEa numeric vector
DALY_SCa numeric vector
WATER_Ha numeric vector
AIR_Ha numeric vector
AIR_E.1a numeric vector
WATER_E.1a numeric vector
BIODIVERSITY.1a numeric vector
FORESTa numeric vector
FISHa numeric vector
AGRICULTUREa numeric vector
CLIMATE.1a numeric vector
ACSAT_pta numeric vector
WATSUP_pta numeric vector
DALY_pta numeric vector
INDOOR_pta numeric vector
PM10_pta numeric vector
OZONE_H_pta numeric vector
SO2_pta numeric vector
OZONE_E_pta numeric vector
WATQI_pta numeric vector
WATSTR_pta numeric vector
WATQI_GEMS.station.dataa numeric vector
FORGRO_pta numeric vector
CRI_pta numeric vector
EFFCON_pta numeric vector
AZE_pta numeric vector
MPAEEZ_pta numeric vector
EEZTD_pta numeric vector
MTI_pta numeric vector
IRRSTR_pta numeric vector
AGINT_pta numeric vector
AGSUB_pta numeric vector
BURNED_pta numeric vector
PEST_pta numeric vector
GHGCAP_pta numeric vector
CO2IND_pta numeric vector
CO2KWH_pta numeric vector
ACSATa numeric vector
WATSUPa numeric vector
DALYa numeric vector
INDOORa numeric vector
PM10a numeric vector
OZONE_Ha numeric vector
SO2a numeric vector
OZONE_Ea numeric vector
WATQIa numeric vector
WATQI_GEMS.station.data.1a numeric vector
WATSTRa numeric vector
FORGROa numeric vector
CRIa numeric vector
EFFCONa numeric vector
AZEa numeric vector
MPAEEZa numeric vector
EEZTDa numeric vector
MTIa numeric vector
IRRSTRa numeric vector
AGINTa numeric vector
AGSUBa numeric vector
BURNEDa numeric vector
PESTa numeric vector
GHGCAPa numeric vector
CO2INDa numeric vector
CO2KWHa numeric vector
Details
2008 Environmental Performance Index (EPI) data downloaded from : http://epi.yale.edu/Downloads
Disclaimers This 2008 Environmental Performance Index (EPI) tracks national environmental results on a quantitative basis, measuring proximity to an established set of policy targets using the best data available. Data constraints and limitations in methodology make this a work in progress. Further refinements will be undertaken over the next few years. Comments, suggestions, feedback, and referrals to better data sources are welcome at: http://epi.yale.edu or epi@yale.edu.
Source
http://epi.yale.edu/Downloads
References
Esty, Daniel C., M.A. Levy, C.H. Kim, A. de Sherbinin, T. Srebotnjak, and V. Mara. 2008. 2008 Environmental Performance Index. New Haven: Yale Center for Environmental Law and Policy.
Examples
1 2 | data(countryExData,envir=environment(),package="rworldmap")
str(countryExData)
|
Example output
Loading required package: sp
### Welcome to rworldmap ###
For a short introduction type : vignette('rworldmap')
'data.frame': 149 obs. of 80 variables:
$ ISO3V10 : chr "AGO" "ALB" "ARE" "ARG" ...
$ Country : chr "Angola" "Albania" "United Arab Emirates " "Argentina" ...
$ EPI_regions : chr "Sub-Saharan Africa" "Central and Eastern Europ" "Middle East and North Africa" "Latin America and Caribbe" ...
$ GEO_subregion : chr "Southern Africa" "Central Europe" "Arabian Peninsula" "South America" ...
$ Population2005 : num 15941 3130 4496 38747 3016 ...
$ GDP_capita.MRYA : num 2314 4955 22698 13652 5011 ...
$ landlock : int 0 0 0 0 1 0 1 1 1 0 ...
$ landarea : num 1251896 28346 74777 2736296 28273 ...
$ density : num 0.2 34.3 8.7 1.3 30.3 0.3 16.3 14.6 91 71.2 ...
$ EPI : num 39.5 84 64 81.8 77.8 79.8 89.4 72.2 54.7 78.4 ...
$ ENVHEALTH : num 8.9 89.3 89.8 91.1 88 99.3 98.1 76.4 37.6 98.8 ...
$ ECOSYSTEM : num 70.1 78.6 38.2 72.5 67.5 60.4 80.7 67.9 71.7 58 ...
$ ENVHEALTH.1 : num 8.9 89.3 89.8 91.1 88 99.3 98.1 76.4 37.6 98.8 ...
$ AIR_E : num 49.2 99.1 85.1 87.3 99.4 84.9 97 97.7 99.5 50.2 ...
$ WATER_E : num 61.6 96.5 27.1 74.9 28 62.5 79.9 48.5 62.8 52.3 ...
$ BIODIVERSITY : num 58.9 4 36.6 33.6 16 78.1 71.6 29 62.5 10 ...
$ PRODUCTIVE_NATURAL_RESOURCES: num 81.3 79.4 74.1 71.5 82.1 91.8 88.2 85.7 48 76.1 ...
$ CLIMATE : num 74.6 93.4 26.6 82.3 87.2 42.5 79.9 77.1 81.5 69.5 ...
$ DALY_SC : num 0 99.5 98.9 98 98.2 99.6 99.8 93 26.1 99.6 ...
$ WATER_H : num 19.8 91.3 98.8 91.3 83.3 100 100 53.6 44.7 100 ...
$ AIR_H : num 16 66.8 62.4 76.9 72.5 97.9 92.8 66.2 53.5 96 ...
$ AIR_E.1 : num 49.2 99.1 85.1 87.3 99.4 84.9 97 97.7 99.5 50.2 ...
$ WATER_E.1 : num 61.6 96.5 27.1 74.9 28 62.5 79.9 48.5 62.8 52.3 ...
$ BIODIVERSITY.1 : num 58.9 4 36.6 33.6 16 78.1 71.6 29 62.5 10 ...
$ FOREST : num 95.4 100 100 75.9 70.1 100 100 100 0 100 ...
$ FISH : num 87.3 62.5 50 58.8 NA 96.7 NA NA NA 47.4 ...
$ AGRICULTURE : num 61.3 75.6 72.3 79.9 94.2 78.7 76.4 71.4 95.9 80.8 ...
$ CLIMATE.1 : num 74.6 93.4 26.6 82.3 87.2 42.5 79.9 77.1 81.5 69.5 ...
$ ACSAT_pt : num 19.3 89.5 97.7 89.5 80.1 100 100 46.2 25.1 100 ...
$ WATSUP_pt : num 20.2 93.2 100 93.2 86.4 100 100 61 64.3 100 ...
$ DALY_pt : num 0 99.5 98.9 98 98.2 99.6 99.8 93 26.1 99.6 ...
$ INDOOR_pt : num 0 47.4 94.7 94.7 72.2 94.7 94.7 48.4 0 94.7 ...
$ PM10_pt : num 40 70.1 11.2 51.3 59 100 87.8 67 84.1 95.4 ...
$ OZONE_H_pt : num 0 99.1 100 92.4 100 100 99.2 100 99.4 99.7 ...
$ SO2_pt : num 98.4 98.5 70.2 98.8 98.8 69.9 94.4 95.4 99.3 0.6 ...
$ OZONE_E_pt : num 0 99.8 100 75.7 100 100 99.6 100 99.6 99.8 ...
$ WATQI_pt : num 29.4 93 0 76.4 31.7 75.3 59.8 31.7 25.6 59.6 ...
$ WATSTR_pt : num 98.3 90.3 100 100 100 73.4 100 100 63 100 ...
$ WATQI_GEMS.station.data : num NA 93 NA 76.4 NA 75.3 59.8 NA NA 59.6 ...
$ FORGRO_pt : num 95.4 100 100 75.9 70.1 100 100 100 0 100 ...
$ CRI_pt : num 99.7 5.5 100 39.8 37.7 86.1 80.1 46.2 84.1 9.6 ...
$ EFFCON_pt : num 95.7 1.6 2.3 33.9 10.4 79 63 11.9 40.9 11.5 ...
$ AZE_pt : num 0 NA NA 40 0 69.4 NA NA NA NA ...
$ MPAEEZ_pt : num 14 6 1 2 100 78 100 100 100 0 ...
$ EEZTD_pt : num 74.5 25.1 0 17.5 NA 93.5 NA NA NA 0 ...
$ MTI_pt : num 100 100 100 100 NA 100 NA NA NA 94.9 ...
$ IRRSTR_pt : num 97.5 100 51.8 74.6 97 50.7 100 82.9 100 100 ...
$ AGINT_pt : num 100 90.2 100 78.4 94.5 79.6 63.2 91.1 92 87.1 ...
$ AGSUB_pt : num 100 100 100 100 100 99.9 22.8 100 100 22.8 ...
$ BURNED_pt : num 0 78.9 96.1 55.7 79.5 63.3 96 78.4 87.7 98.6 ...
$ PEST_pt : num 9.1 9.1 13.6 90.9 100 100 100 4.5 100 95.5 ...
$ GHGCAP_pt : num 65.8 98.8 38.6 87.1 98 45.4 81.6 88.7 94 77.7 ...
$ CO2IND_pt : num 95 85 32.1 92.7 78.3 76.2 82.3 97.1 100 59.7 ...
$ CO2KWH_pt : num 63 96.3 9 67 85.1 5.9 75.7 45.6 50.5 71.1 ...
$ ACSAT : num 31 91 98 91 83 100 100 54 36 100 ...
$ WATSUP : num 53 96 100 96 92 100 100 77 79 100 ...
$ DALY : num 109 0.3 0.6 1.1 1 0.2 0.1 3.9 41 0.2 ...
$ INDOOR : num 95 50 5 5 26.4 5 5 49 95 5 ...
$ PM10 : num 91.4 55.5 125.6 77.9 68.7 ...
$ OZONE_H : num 4948.8 15.8 0 140.4 0 ...
$ SO2 : num 0.7 0.6 12.6 0.5 0.5 12.7 2.4 1.9 0.3 41.9 ...
$ OZONE_E : num 1.36e+09 6.81e+05 2.63e+01 9.96e+07 0.00 ...
$ WATQI : num 57.5 95.8 39.9 85.8 58.9 85.2 75.9 58.9 55.3 75.7 ...
$ WATQI_GEMS.station.data.1 : num NA 95.8 NA 85.8 NA 85.2 75.9 NA NA 75.7 ...
$ WATSTR : num 5.5 0 41.6 24.1 68.6 45.7 0 31.4 0 49.8 ...
$ FORGRO : num 1 1 1 0.9 0.9 1 1.1 1 0.6 1.1 ...
$ CRI : num 0.5 0 0.5 0.2 0.2 0.4 0.4 0.2 0.4 0 ...
$ EFFCON : num 9.6 0.2 0.2 3.4 1 7.9 6.3 1.2 4.1 1.2 ...
$ AZE : num 0 NA NA 40 0 69.4 NA NA NA NA ...
$ MPAEEZ : num 1.4 0.6 0.1 0.2 10 7.8 10 10 10 0 ...
$ EEZTD : num 0.255 0.749 1 0.825 NA ...
$ MTI : num 0.0016 0 0.0034 0.0044 NA 0.0014 NA NA NA -0.001 ...
$ IRRSTR : num 2.2 0 41 21.6 2.5 41.9 0 14.6 0 0 ...
$ AGINT : num 0 6.2 0 13.7 3.5 12.9 23.3 5.6 5.1 8.2 ...
$ AGSUB : num 0 0 0 0 0 0 36 0 0 36 ...
$ BURNED : num 15.3 2.9 0.5 6 2.8 5 0.5 2.9 1.7 0.2 ...
$ PEST : num 2 2 3 20 22 22 22 1 22 21 ...
$ GHGCAP : num 20 2.9 34.1 8.9 3.3 30.5 11.8 8.1 5.3 13.8 ...
$ CO2IND : num 1.2 1.9 5.5 1.4 2.3 2.5 2.1 1.1 0.8 3.6 ...
$ CO2KWH : num 343 34 844 306 138 873 225 505 459 268 ...
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