Commit c5350fd0 authored by Ben Anderson's avatar Ben Anderson
Browse files

Merge branch 'master' into 'master'

shifted some of the calcs to the makefile

See merge request !9
parents 3c9c9317 d7f69512
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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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