Commit 18afacbf authored by Ben Anderson's avatar Ben Anderson
Browse files

fixed typos

parent 50e3233c
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......@@ -72,12 +72,12 @@ setkey(pc_district_energy_dt, pcd_district)
nrow(pc_district_energy_dt)
pc_district <- pc_district_energy_dt[cats_DT] # keeps only postcode sectors where we have cat data
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
nrow(pc_district)
nrow(pc_district[!is.na(GOR10NM)])
# there are postcode sectors with no electricity meters - for now we'll remove them
# there are postcode districts with no electricity meters - for now we'll remove them
# pending further investigation
t <- pc_district[!is.na(GOR10NM), .(nPostcodeDistricts = .N,
......@@ -89,16 +89,19 @@ makeFlexTable(t, cap = "Regions covered")
# What do we find?
Well, in some places there seem to be a lot of estimated cats...
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]
head(pc_district[, .(PostcodeDistrict, EstimatedCatPopulation, mean_Cats, nPostcodes, nElecMeters)][order(-mean_Cats)])
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'")
```
SA63 is in south west [Wales](https://www.google.co.uk/maps/place/Clarbeston+Road+SA63/@51.8852685,-4.9147384,12z/data=!3m1!4b1!4m5!3m4!1s0x4868d5805b12efe5:0xca42ee4bc84a2f77!8m2!3d51.8900045!4d-4.8502065) while LL23 is on the edge of the [Snowdonia National Park](https://www.google.co.uk/maps/place/Bala+LL23/@52.8953768,-3.7752989,11z/data=!3m1!4b1!4m5!3m4!1s0x4865404ae1208f67:0x65a437b997c0dfb2!8m2!3d52.8825403!4d-3.6497989)....
Do these places have some largish catteries but few houses? 8,233 is a lot of estimated cats.
## More dwellings, more cats?
Is there a correlation between estimated total cats and the number of dwellings (electricity meters)?
......@@ -173,7 +176,7 @@ ggplot2::ggplot(pc_district[!is.na(cat_decile) & !is.na(GOR10NM)], aes(x = as.fa
facet_wrap(. ~ GOR10NM) +
labs(x = "Cat ownership deciles",
y = "Total domestic electricity & gas GWh",
caption = "Postcode sectors (Data: BEIS & Animal and Plant Health Agency, 2015)")
caption = "Postcode districts (Data: BEIS & Animal and Plant Health Agency, 2015)")
```
Well...
......
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