Get data from IDEEA package embedded dataset
Arguments
- name
character, name of data table, one of: "coal", "oil", "gas", "biomass", "lignite", ...
- nreg
integer, number of region to return
- variable
character, regular expression for the name of variable(s) to return.
- sets
character, a regular expression to identify key-columns in the datasets, used for grouping. The default value (
IDEEA:::.ideea_sets_pattern) covers all sets in the embedded to the package data. If new datasets added with different names of sets, the default value has to be reviewed.- agg_fun
character, function to aggregate data by region, default "sum" (for volumes), "mean" is advised for costs variable.
- raw
logical, should the raw table be returned, FALSE by default
- offshore
logical, should the data for offshore areas be returned, FALSE by default
- islands
logical, should the data for remote islands be returned, FALSE by default
- as_DT
logical, results will be returned in
data.tableformat if TRUE (default)- drop_na
logical, should
NAvalues be dropped from the data- reg_tbl
data.frame with regional mapping (for custom weights or regions)
- weight
character name of column to use as disaggregating weights if the disaggregation of the data is required, for example the saved data is by 5 regions, but the requested data is for 32 regions. In general, it is not recommended to disaggregate due to the strong assumptions. But the algorithm is also used to convert data from 36 to 32 regions because of not exact match of regions' shapes (see Regions article). The default value is the area of the region (
"km^2") foragg_fun = sumand it is equal to1(no weights) foragg_fun = meanand all other functions.
Examples
get_ideea_data("coal", raw = T)
#> reg36 name36 mainland offshore reg1 name1
#> <char> <char> <lgcl> <lgcl> <char> <char>
#> 1: AP Andhra Pradesh TRUE FALSE IND India
#> 2: AR Arunachal Pradesh TRUE FALSE IND India
#> 3: AS Assam TRUE FALSE IND India
#> 4: BR Bihar TRUE FALSE IND India
#> 5: CH Chandigarh TRUE FALSE IND India
#> 6: CT Chhattisgarh TRUE FALSE IND India
#> 7: DD Daman and Diu TRUE FALSE IND India
#> 8: DL Delhi TRUE FALSE IND India
#> 9: DN Dadra and Nagar Haveli TRUE FALSE IND India
#> 10: GA Goa TRUE FALSE IND India
#> 11: GJ Gujarat TRUE FALSE IND India
#> 12: HP Himachal Pradesh TRUE FALSE IND India
#> 13: HR Haryana TRUE FALSE IND India
#> 14: JH Jharkhand TRUE FALSE IND India
#> 15: JK Jammu and Kashmir TRUE FALSE IND India
#> 16: KA Karnataka TRUE FALSE IND India
#> 17: KL Kerala TRUE FALSE IND India
#> 18: MH Maharashtra TRUE FALSE IND India
#> 19: ML Meghalaya TRUE FALSE IND India
#> 20: MN Manipur TRUE FALSE IND India
#> 21: MP Madhya Pradesh TRUE FALSE IND India
#> 22: MZ Mizoram TRUE FALSE IND India
#> 23: NL Nagaland TRUE FALSE IND India
#> 24: OR Odisha TRUE FALSE IND India
#> 25: PB Punjab TRUE FALSE IND India
#> 26: PY Puducherry TRUE FALSE IND India
#> 27: RJ Rajasthan TRUE FALSE IND India
#> 28: SK Sikkim TRUE FALSE IND India
#> 29: TG Telangana TRUE FALSE IND India
#> 30: TN Tamil Nadu TRUE FALSE IND India
#> 31: TR Tripura TRUE FALSE IND India
#> 32: UP Uttar Pradesh TRUE FALSE IND India
#> 33: UT Uttarakhand TRUE FALSE IND India
#> 34: WB West Bengal TRUE FALSE IND India
#> reg36 name36 mainland offshore reg1 name1
#> total_reserve_Mt production_2021 cost_USD_t_2020
#> <num> <num> <num>
#> 1: 4141.87 NA 50
#> 2: 90.23 NA 50
#> 3: 525.01 0.036 50
#> 4: 4437.18 NA 50
#> 5: NA NA NA
#> 6: 74191.76 158.409 50
#> 7: NA NA NA
#> 8: NA NA NA
#> 9: NA NA NA
#> 10: NA NA NA
#> 11: NA NA NA
#> 12: NA NA NA
#> 13: NA NA NA
#> 14: 86660.10 119.296 50
#> 15: NA NA NA
#> 16: NA NA NA
#> 17: NA NA NA
#> 18: 13220.71 47.435 50
#> 19: 576.48 NA 50
#> 20: NA NA NA
#> 21: 30916.73 132.531 50
#> 22: NA NA NA
#> 23: 478.31 NA 50
#> 24: 88104.60 154.150 50
#> 25: NA NA NA
#> 26: NA NA NA
#> 27: NA NA NA
#> 28: 101.23 NA 50
#> 29: 23034.20 52.603 50
#> 30: NA NA NA
#> 31: NA NA NA
#> 32: 1061.80 17.016 50
#> 33: NA NA NA
#> 34: 33871.25 34.596 50
#> total_reserve_Mt production_2021 cost_USD_t_2020
get_ideea_data("coal", nreg = 7, "reserve")
#> reg7 mainland offshore total_reserve_Mt
#> <char> <lgcl> <lgcl> <num>
#> 1: EAST TRUE FALSE 213174.36
#> 2: NORTH TRUE FALSE 1061.80
#> 3: NORTHEAST TRUE FALSE 1670.03
#> 4: SOUTH TRUE FALSE 27176.07
#> 5: WEST TRUE FALSE 118329.20
get_ideea_data("oil", nreg = 34, "reserve", islands = T)
#> reg34 offshore mainland oil_reserve_Mt_2021 oil_reserve_GWh_2021
#> <char> <lgcl> <lgcl> <num> <num>
#> 1: APY FALSE TRUE 7.33 85247.9
#> 2: AR FALSE TRUE 3.64 42333.2
#> 3: AS FALSE TRUE 153.05 1779971.5
#> 4: GJD FALSE TRUE 2.38 27679.4
#> 5: NL FALSE TRUE 34.77 404375.1
#> 6: RJ FALSE TRUE 9.08 105600.4
#> 7: TNY FALSE TRUE 0.07 814.1
#> 8: TR FALSE TRUE 219.27 2550110.1
get_ideea_data("coal", nreg = 7, "cost", agg_fun = mean)
#> reg7 mainland offshore cost_USD_t_2020
#> <char> <lgcl> <lgcl> <num>
#> 1: EAST TRUE FALSE 50
#> 2: NORTH TRUE FALSE 50
#> 3: NORTHEAST TRUE FALSE 50
#> 4: SOUTH TRUE FALSE 50
#> 5: WEST TRUE FALSE 50
get_ideea_data("merra_raw_2014", raw = T) |> head()
#> Key: <UTC, locid>
#> UTC locid W10M W50M SWGDN ALBEDO
#> <POSc> <int> <num> <num> <int> <num>
#> 1: 2014-01-01 00:30:00 109878 5.6 6.0 63 0.12
#> 2: 2014-01-01 00:30:00 109879 4.4 4.7 71 0.12
#> 3: 2014-01-01 00:30:00 110453 6.7 7.3 53 0.13
#> 4: 2014-01-01 00:30:00 110454 5.6 6.0 61 0.13
#> 5: 2014-01-01 00:30:00 110455 4.2 4.5 51 0.12
#> 6: 2014-01-01 00:30:00 111028 8.9 9.9 19 0.10