Commit d7f69512 authored by Ben Anderson's avatar Ben Anderson
Browse files

shifted some of the calcs to the makefile

parent 18afacbf
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......@@ -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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