Commit d7f69512 by Ben Anderson

shifted some of the calcs to the makefile

parent 18afacbf
This diff is collapsed.
 ... ... @@ -74,6 +74,7 @@ nrow(pc_district_energy_dt) pc_district <- pc_district_energy_dt[cats_DT] # keeps only postcode districts where we have cat data # this may include areas where we have no energy data pc_district[, mean_Cats := EstimatedCatPopulation/nElecMeters] nrow(pc_district) nrow(pc_district[!is.na(GOR10NM)]) ... ... @@ -94,7 +95,7 @@ Well, in some places there seem to be a lot of estimated cats per household... (We calculated mean cats per household by dividing by the number of electricity meters - probably a reasonable proxy) ```{r maxCats} pc_district[, mean_Cats := EstimatedCatPopulation/nElecMeters] t <- head(pc_district[, .(PostcodeDistrict, EstimatedCatPopulation, mean_Cats, nPostcodes, nElecMeters)][order(-mean_Cats)],10) makeFlexTable(t, cap = "Top 10 postcode districts by number of cats per 'household'") ``` ... ... @@ -127,7 +128,7 @@ ggplot2::ggplot(pc_district[!is.na(GOR10NM)], Or mean gas use and mean cats? ```{r testMeanGas} pc_district[, mean_gas_kWh := total_gas_kWh/nGasMeters] ggplot2::ggplot(pc_district[!is.na(GOR10NM)], aes(x = mean_Cats, y = mean_gas_kWh, colour = GOR10NM)) + geom_smooth() + ... ... @@ -147,7 +148,7 @@ ggplot2::ggplot(pc_district[!is.na(GOR10NM)], aes(x = EstimatedCatPopulation, y Or mean elec use and mean cats? ```{r testMeanElec} pc_district[, mean_elec_kWh := total_elec_kWh/nElecMeters] ggplot2::ggplot(pc_district[!is.na(GOR10NM)], aes(x = mean_Cats, y = mean_elec_kWh, colour = GOR10NM)) + geom_smooth() + geom_point() ... ... @@ -158,7 +159,10 @@ ggplot2::ggplot(pc_district[!is.na(GOR10NM)], aes(x = mean_Cats, y = mean_elec_k Or total energy use and total cats? ```{r testTotalEnergy} pc_district[, total_gas_kWh := ifelse(is.na(total_gas_kWh), 0, total_gas_kWh)] pc_district[, total_energy_kWh := total_gas_kWh + total_elec_kWh] pc_district[, mean_energy_kWh := total_energy_kWh/nElecMeters] ggplot2::ggplot(pc_district[!is.na(GOR10NM)], aes(x = EstimatedCatPopulation, y = total_energy_kWh, colour = GOR10NM)) + geom_smooth() + ... ...
 ... ... @@ -49,6 +49,8 @@ setkey(pc_district_elec_dt, pcd_district) setkey(pc_district_gas_dt, pcd_district) pc_district_energy_dt <- pc_district_gas_dt[pc_district_elec_dt] pc_district_energy_dt[, mean_gas_kWh := total_gas_kWh/nGasMeters] pc_district_energy_dt[, mean_elec_kWh := total_elec_kWh/nElecMeters] # load one we prepared earlier using https://git.soton.ac.uk/SERG/mapping-with-r/-/blob/master/R/postcodeWrangling.R pc_district_region_dt <- data.table::fread(paste0(dp, "UK_postcodes/postcode_districts_2016.csv")) ... ...
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