From fe54baeeec817ee61321c171340cd7c9ea68b00f Mon Sep 17 00:00:00 2001
From: Ben Anderson <dataknut@icloud.com>
Date: Thu, 23 Apr 2020 22:47:57 +0100
Subject: [PATCH] updated IoW analysis to help Ellis estimate low income
off-gas owner-occupiers
---
isleOfWight/beisIoWData.Rmd | 299 +-
isleOfWight/beisIoWData.html | 3584 ++++++++++++++++-
.../figure-html/report20pc-1.png | Bin 0 -> 83682 bytes
3 files changed, 3843 insertions(+), 40 deletions(-)
create mode 100644 isleOfWight/beisIoWData_files/figure-html/report20pc-1.png
diff --git a/isleOfWight/beisIoWData.Rmd b/isleOfWight/beisIoWData.Rmd
index 768b6e8..599a0d8 100644
--- a/isleOfWight/beisIoWData.Rmd
+++ b/isleOfWight/beisIoWData.Rmd
@@ -67,8 +67,8 @@ Developing a model of residential electricity demand for IoW.
## Load spine of OAs and filter to IoW
```{r loadOA}
-oaSpine <- paste0(censusLutsPath, "/OA11_LSOA11_MSOA11_LAD11_EW_LUv2.csv")
-dtOA <- data.table::fread(oaSpine)
+oaSpineF <- paste0(censusLutsPath, "/OA11_LSOA11_MSOA11_LAD11_EW_LUv2.csv")
+dtOA <- data.table::fread(oaSpineF)
head(dtOA)
dtOA_IoW <- dtOA[LAD11NM %like% "Wight"]
@@ -197,9 +197,302 @@ nrow(dtMsoaSpineIoW[elecRatio > 0.90])
nrow(dtMsoaSpineIoW[elecRatio > 0.80])
```
+# Ellis' question...
+
+1) join income deprivation score to LSOA shapefiles. If you could produce a map that would be great.
+2) Count dwellings in LSOAs with have EPC < C (D-G) and main fuel type not mains gas. If you could map all EPCs and then the EPCs that match the criteria.
+3) Join tenure to LSOA shapefiles. Multiply count by LSOA level census figure for % of private dwellings.
+4) Multiply by 1/0.687 to correct for dwellings without an EPC.
+
+```{r loadData}
+# this will break here if you don't have the data obvs
+dPath <- "~/Dropbox/IoW_Fuel_Poverty/Fuel Poverty/"
+
+censusDT <- data.table::fread(paste0(dPath, "Data_TENURE_UNIT.csv"))
+censusDT[, pcOwned := `Owned (%)`]
+censusDT[, lsoaCode := `LSOA CODE`]
+setkey(censusDT, lsoaCode)
+
+epcDT <- data.table::fread(paste0(dPath, "DomesticEPCFuelTypes.csv"))
+
+imdDT <- data.table::fread(paste0(dPath, "IoW_Deprivation_2019.csv"))
+imdDT[, imdScore := `Index of Multiple Deprivation (IMD) Score`]
+imdDT[, lsoaCode := `LSOA code (2011)`]
+
+# postcodes
+pcPath <- "~/Data/UK_NSPL/NSPL_FEB_2020_UK/Data/"
+postcodesDT <- data.table::fread(paste0(pcPath, "NSPL_FEB_2020_UK.csv.gz"))
+
+# we only need postcode & LSOA but keep OA in case
+# we also only need the ones that are current
+# and in the IoW. But helpfully these are only codes not names
+# E06000044 Portsmouth
+# E06000045 Southampton
+# E06000046 Isle of Wight
+# in case you ever need to know
+
+pcDT <- postcodesDT[is.na(doterm) &
+ laua == "E06000046",
+ .(pcd, pcd2, pcds, lat, long, oa11, lsoa11, laua)]
+
+
+```
+
+Steps:
+
+```{r postcodeMatch}
+
+
+nrow(pcDT)
+setkey(pcDT, pcds)
+#head(pcDT)
+# so we're going to try to match epc postcode to pcds
+# who thinks this will go well?
+epcDT[, pcds := POSTCODE]
+setkey(epcDT, pcds)
+#head(epcDT)
+nrow(epcDT)
+dt <- pcDT[epcDT]
+nrow(dt)
+# shit. worked
+
+```
+
+ * start with the EPC file for the Isle of Wight. This gives us `r nrow(epcDT)` addresses.
+ * match the postcode in the EPC file to postcode in the National Statistics Postcode Lookup File. We get a complete match so now we have OA and LSOA codes attached to each EPC row.
+
+
+Next we code the EPC records:
+
+ * we want EPC < C so = D, E, F, G
+ * we want main fuel type to be not mains gas
+
+
+```{r recoding}
+
+# flag the ones with
+# EPC < C : D, E, F, G and H
+dt[, lowEPC := ifelse(CURRENT_ENERGY_RATING == "D" |
+ CURRENT_ENERGY_RATING == "E" |
+ CURRENT_ENERGY_RATING == "F" |
+ CURRENT_ENERGY_RATING == "G" |
+ CURRENT_ENERGY_RATING == "H",
+ "lowEPC", "okEPC")
+ ]
+# main fuel type not mains gas
+#table(dt$FuelType)
+dt[, offGas := ifelse(FuelType != "Mains Gas", "notGas", "haveGas")]
+
+```
+
+Table \@ref(tab:epcTable) shows this cross-classification.
+
+```{r epcTable}
+t <- with(epcDT, table(CURRENT_ENERGY_RATING, FuelType, useNA = "always"))
+kableExtra::kable(t, caption = "IoW EPC records by fuel type and current energy rating") %>%
+ kable_styling()
+```
+
+Table \@ref(tab:dtTable) shows cassification checks and the result.
+
+```{r dtTable}
+# check
+addmargins(with(dt, table(CURRENT_ENERGY_RATING,lowEPC)))
+addmargins(with(dt, table(FuelType,offGas)))
+
+t <- with(dt, table(lowEPC, offGas, useNA = "always"))
+kableExtra::kable(addmargins(t), caption = "IoW EPC records by fuel type and current energy rating") %>%
+ kable_styling()
+
+dt[, lowEPCoffGas := ifelse(lowEPC == "lowEPC" &
+ offGas == "notGas",
+ "Yes", "No")]
+#table(dt$lowEPCoffGas)
+
+```
+
+Now:
+
+ * we add up the number of households that do not have gas and have EPCs D-H per LSOA.
+
+We need to do this so that we can count the ones in LSOAs with high income deprivation. There is no other way to do this because income is not a variable on the EPC and LSOA level income distributions are not available.
+
+```{r lsoaSum}
+
+hhCountByLSOADT <- dt[, .(nHHs = .N),
+ keyby = .(lsoa11, lowEPCoffGas)]
+hhCountByLSOADT[, lsoaCode := lsoa11]
+
+# probably helpful not to have long form
+dtNo <- hhCountByLSOADT[lowEPCoffGas == "No"]
+dtNo[, notSelectedEPCAddresses := nHHs]
+setkey(dtNo, lsoaCode)
+dtYes <- hhCountByLSOADT[lowEPCoffGas == "Yes"]
+dtYes[, selectedEPCAddresses := nHHs]
+setkey(dtYes, lsoaCode)
+tempLSOADT <- dtNo[, .(lsoaCode,notSelectedEPCAddresses)][dtYes[,.(lsoaCode,selectedEPCAddresses)]]
+```
+
+That gives us `r sum(tempLSOADT$selectedEPCAddresses)` households who fit this coding (see also Table \@ref(tab:dtTable)) spread across `r uniqueN(tempLSOADT$lsoaCode)` LSOAs.
+
+Next:
+
+ * we match this data to Census and Index of Multiple Deprivation data.
+
+```{r linkIMD}
+
+
+lsoaDT <- tempLSOADT[censusDT]
+# test n LSOAs
+uniqueN(lsoaDT$lsoaCode)
+lsoaDT[, censusLSOA := "Yes"]
+
+imdDT[, lsoaCode := `LSOA code (2011)`]
+imdDT[, imdLSOA := "Yes"]
+
+setkey(imdDT, lsoaCode)
+
+lsoaDT <- imdDT[lsoaDT]
+
+# test n LSOAs
+with(lsoaDT,
+ table(imdLSOA,
+ censusLSOA,
+ useNA = "always"
+ )
+)
+# we lost 3 with IMD - Ellis knows
+
+```
+
+Note that 3 LSOAs are missing from the IMD data.
+
+Next:
+
+ * we calculate the income deprivation quintile and decile so we can select the LSOAs which are more income deprived on this measure. Note that this will _miss_ households who are poor but live in areas which have lower income deprivation. We have no way of finding these households...
+
+
+```{r getIMDDec}
+# summary(lsoaDT$`Income Score (rate)`)
+quins<- quantile(lsoaDT$`Income Score (rate)`, probs = seq(0, 1, 0.2), na.rm = TRUE)
+cut <- quins[[5]]
+paste0("IMD income deprivation score 20% cut = ", cut)
+lsoaDT[, deprived20pc := ifelse(`Income Score (rate)` > cut,
+ "highDep", "notHighDep")] # set cut
+
+decs<- quantile(lsoaDT$`Income Score (rate)`, probs = seq(0, 1, 0.1) ,na.rm = TRUE)
+cut <- decs[[10]]
+paste0("IMD income deprivation score 10% cut = ", cut)
+
+lsoaDT[, deprived10pc := ifelse(`Income Score (rate)` > cut,
+ "highDep", "notHighDep")] # set cut
+
+lsoaDT[, nHHsEPC := selectedEPCAddresses + notSelectedEPCAddresses]
+lsoaDT[, nHHsCensus := `Tenure : Owned - Unit : Households` +
+ `Tenure : Social rented - Unit : Households` +
+ `Tenure : Private rented - Unit : Households`]
+
+lsoaDT[, hhDiff := nHHsCensus-nHHsEPC]
+lsoaDT[, hhDiffpc := 100*(hhDiff/nHHsCensus)]
+```
+
+```{r report20pc"}
+t <- lsoaDT[, .(nAddressesEPC = sum(nHHsEPC),
+ nHHsCensus = sum(nHHsCensus),
+ nLSOAs = .N), keyby = .(deprived20pc)]
+
+kableExtra::kable(t, digits = 2,
+ caption = "Summary of EPC addresses and all census households by LSOA deprivation type (20% most income deprived)") %>%
+ kable_styling()
+
+epcMissingFactor <- 1/0.687
+```
+
+Table \@ref(tab:report20pc) shows the number of addresses aggregated from the EPC data compared to the number of households from the 2011 Census tenure variable. As we can see there are `r uniqueN(lsoaDT[deprived20pc == "highDep"])` LSOAs classed as highly income deprived using this threshold.
+
+The histogram shows the distribution of the % difference between Census household count and the number of EPCs across all LSOAs. As we can see in some places there are more EPCs than households (-ve values). However overall we believe that EPCs exist for ~ `r 100*(1/epcMissingFactor)` addresses so we use this as our final uprating factor.
+
+```{r depr10pc}
+t <- lsoaDT[, .(nAddressesEPC = sum(nHHsEPC),
+ nHHsCensus = sum(nHHsCensus),
+ nLSOAs = .N), keyby = .(deprived10pc)]
+
+kableExtra::kable(t, digits = 2,
+ caption = "Summary of EPC households and all census households by LSOA deprivation type (10% most income deprived)") %>%
+ kable_styling()
+
+```
+
+Table \@ref(tab:depr10pc) shows the same results but for the 10% most income deprived LSOAs. As we can see there are `r uniqueN(lsoaDT[deprived10pc == "highDep"])` LSOAs classed as highly income deprived using this threshold.
+
+Next we assume that the EPC addresses of interest are as likely to be social/private rented as owner-occupied and multiply the EPC address counts by the Census-derived household ownership tenure rate for each LSOA. Without knowing the tensure status of the EPC we cannot do anything else.
+
+```{r select20pc}
+lsoaDT[, nHHsSelected := selectedEPCAddresses*`Owned (%)`]
+
+t <- lsoaDT[, .(nHHsSelected = sum(nHHsSelected),
+ nHHsCensus = sum(nHHsCensus),
+ nLSOAs = .N), keyby = .(deprived20pc)]
+
+kableExtra::kable(t, digits = 2,
+ caption = "Summary of EPC households and all census households by LSOA deprivation type (20% most income deprived)") %>%
+ kable_styling()
+res20pc <- round(t[deprived20pc == "highDep", nHHsSelected])
+```
+
+```{r select10pc}
+
+t <- lsoaDT[, .(nHHsSelected = sum(nHHsSelected),
+ nHHsCensus = sum(nHHsCensus),
+ nLSOAs = .N), keyby = .(deprived10pc)]
+
+kableExtra::kable(t, digits = 2,
+ caption = "Summary of EPC households and all census households by LSOA deprivation type (10% most income deprived)") %>%
+ kable_styling()
+
+res10pc <- round(t[deprived10pc == "highDep", nHHsSelected])
+
+epcMissingPC <- 100*(1-(1/epcMissingFactor))
+```
+
+> In summary using the EPC data we have estimated the number of addresses in low income areas which are owned and do not have gas to be:
+
+ * 10% most deprived: `r res10pc` or, if we uprate to allow for the `r epcMissingPC` % missing EPCs, `r round(res10pc * epcMissingFactor)`;
+ * 20% most deprived: `r res20pc` or, if we uprate to allow for the `r epcMissingPC` % missing EPCs, `r round(res20pc * epcMissingFactor)`;
+
+Table \@ref(tab:lsoaTable) shows the LSOA level results.
+
+```{r lsoaTable}
+
+t<- lsoaDT[, .(lsoaCode, nAddressesEPC = nHHsEPC, notSelectedEPCAddresses, selectedEPCAddresses, pcOwned=pcOwned*100, deprived20pc, deprived10pc,nHHsSelected)]
+setkey(t, deprived20pc, deprived10pc)
+
+kableExtra::kable(t, caption = "Final results at LSOA level",
+ digits = 2) %>%
+ kable_styling()
+```
+
+
+```{r resultsChecks}
+
+totSelectedHH <- sum(lsoaDT[!is.na(lsoaCode)]$selectedEPCAddresses)
+
+meanOwnership <- mean(lsoaDT$pcOwned)
+
+res10 <- totSelectedHH * meanOwnership * 0.1
+res20 <- totSelectedHH * meanOwnership * 0.2
+```
+
+As a rough check, if we multiply the total number of EPC addresses which fit the criteria by the mean ownership rate we get:
+
+ * 10% of households = `r round(res10)`
+ * 20% of households = `r round(res20)`
+
+Note that this is only a rough guide since it assumes that LSOAs have exactly the same number of addresses/households in them.
+
# Annexes
-## Beis data
+## BEIS data
### LSOA
Domestic elec
diff --git a/isleOfWight/beisIoWData.html b/isleOfWight/beisIoWData.html
index fd9b607..d2d0f3c 100644
--- a/isleOfWight/beisIoWData.html
+++ b/isleOfWight/beisIoWData.html
@@ -269,7 +269,7 @@ div.tocify {
<h1 class="title toc-ignore">Exploring Isle of Wight Data</h1>
<h3 class="subtitle">Code and notes</h3>
<h4 class="author">Ben Anderson (<a href="mailto:b.anderson@soton.ac.uk" class="email">b.anderson@soton.ac.uk</a>), <a href="http://www.energy.soton.ac.uk/">SERG, Energy & Climate Change</a>, University of Southampton</h4>
-<h4 class="date">Last run at: 2020-03-02 17:57:31</h4>
+<h4 class="date">Last run at: 2020-04-23 22:45:36</h4>
</div>
@@ -282,8 +282,8 @@ div.tocify {
<h1><span class="header-section-number">2</span> Data prep</h1>
<div id="load-spine-of-oas-and-filter-to-iow" class="section level2">
<h2><span class="header-section-number">2.1</span> Load spine of OAs and filter to IoW</h2>
-<pre class="r"><code>oaSpine <- paste0(censusLutsPath, "/OA11_LSOA11_MSOA11_LAD11_EW_LUv2.csv")
-dtOA <- data.table::fread(oaSpine)
+<pre class="r"><code>oaSpineF <- paste0(censusLutsPath, "/OA11_LSOA11_MSOA11_LAD11_EW_LUv2.csv")
+dtOA <- data.table::fread(oaSpineF)
head(dtOA)</code></pre>
<pre><code>## OA11CD LSOA11CD LSO11ANM MSOA11CD MSOA11NM
## 1: E00000001 E01000001 City of London 001A E02000001 City of London 001
@@ -419,13 +419,319 @@ dtMsoaSpine <- dtMsoaSpine[dtMsoaNDElec]
dtMsoaSpine[, elecRatio := kwhDElec/(kwhDElec + kwhNDElec)]
dtMsoaSpine[, gasRatio := kwhDGas/(kwhNDGas + kwhDGas)]
-dtMsoaSpineIoW <- dtMsoaSpine[MSOAName %like% "Wight"]
+t <- dtMsoaSpine[order(-elecRatio)]
+kableExtra::kable(head(t[, .(MSOAName, MSOACode,
+ elecRatio, kwhDElec, kwhNDElec,
+ gasRatio, kwhDGas, kwhNDGas)],10),
+ caption = "All MSOAs") %>%
+ kable_styling()</code></pre>
+<table class="table" style="margin-left: auto; margin-right: auto;">
+<caption>
+<span id="tab:linkBeisMSOA">Table 2.2: </span>All MSOAs
+</caption>
+<thead>
+<tr>
+<th style="text-align:left;">
+MSOAName
+</th>
+<th style="text-align:left;">
+MSOACode
+</th>
+<th style="text-align:right;">
+elecRatio
+</th>
+<th style="text-align:right;">
+kwhDElec
+</th>
+<th style="text-align:right;">
+kwhNDElec
+</th>
+<th style="text-align:right;">
+gasRatio
+</th>
+<th style="text-align:right;">
+kwhDGas
+</th>
+<th style="text-align:right;">
+kwhNDGas
+</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td style="text-align:left;">
+Wirral 009
+</td>
+<td style="text-align:left;">
+E02001475
+</td>
+<td style="text-align:right;">
+1.2425646
+</td>
+<td style="text-align:right;">
+9040195
+</td>
+<td style="text-align:right;">
+-1764762
+</td>
+<td style="text-align:right;">
+0.5176534
+</td>
+<td style="text-align:right;">
+26213042
+</td>
+<td style="text-align:right;">
+24425168
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+Crosshouse and Lindsayfield
+</td>
+<td style="text-align:left;">
+S02002429
+</td>
+<td style="text-align:right;">
+0.9933112
+</td>
+<td style="text-align:right;">
+7123734
+</td>
+<td style="text-align:right;">
+47970
+</td>
+<td style="text-align:right;">
+0.8973401
+</td>
+<td style="text-align:right;">
+29485432
+</td>
+<td style="text-align:right;">
+3373272
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+Westwood South
+</td>
+<td style="text-align:left;">
+S02002432
+</td>
+<td style="text-align:right;">
+0.9891581
+</td>
+<td style="text-align:right;">
+7093307
+</td>
+<td style="text-align:right;">
+77748
+</td>
+<td style="text-align:right;">
+0.8192189
+</td>
+<td style="text-align:right;">
+24070604
+</td>
+<td style="text-align:right;">
+5311780
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+Cardiff 002
+</td>
+<td style="text-align:left;">
+W02000368
+</td>
+<td style="text-align:right;">
+0.9854291
+</td>
+<td style="text-align:right;">
+8242575
+</td>
+<td style="text-align:right;">
+121878
+</td>
+<td style="text-align:right;">
+0.9277238
+</td>
+<td style="text-align:right;">
+28068763
+</td>
+<td style="text-align:right;">
+2186754
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+North Tyneside 021
+</td>
+<td style="text-align:left;">
+E02001758
+</td>
+<td style="text-align:right;">
+0.9800142
+</td>
+<td style="text-align:right;">
+7163177
+</td>
+<td style="text-align:right;">
+146081
+</td>
+<td style="text-align:right;">
+0.8085944
+</td>
+<td style="text-align:right;">
+29185677
+</td>
+<td style="text-align:right;">
+6908656
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+King’s Lynn and West Norfolk 010
+</td>
+<td style="text-align:left;">
+E02005560
+</td>
+<td style="text-align:right;">
+0.9785961
+</td>
+<td style="text-align:right;">
+14676294
+</td>
+<td style="text-align:right;">
+321000
+</td>
+<td style="text-align:right;">
+0.4602298
+</td>
+<td style="text-align:right;">
+32183327
+</td>
+<td style="text-align:right;">
+37745487
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+Dudley 043
+</td>
+<td style="text-align:left;">
+E02002042
+</td>
+<td style="text-align:right;">
+0.9762961
+</td>
+<td style="text-align:right;">
+12582203
+</td>
+<td style="text-align:right;">
+305489
+</td>
+<td style="text-align:right;">
+0.9619305
+</td>
+<td style="text-align:right;">
+50649892
+</td>
+<td style="text-align:right;">
+2004526
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+Carluke East
+</td>
+<td style="text-align:left;">
+S02002361
+</td>
+<td style="text-align:right;">
+0.9761045
+</td>
+<td style="text-align:right;">
+6745578
+</td>
+<td style="text-align:right;">
+165135
+</td>
+<td style="text-align:right;">
+0.8267440
+</td>
+<td style="text-align:right;">
+24471367
+</td>
+<td style="text-align:right;">
+5128325
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+Torbay 012
+</td>
+<td style="text-align:left;">
+E02003165
+</td>
+<td style="text-align:right;">
+0.9756266
+</td>
+<td style="text-align:right;">
+10660238
+</td>
+<td style="text-align:right;">
+266317
+</td>
+<td style="text-align:right;">
+0.9447697
+</td>
+<td style="text-align:right;">
+33065834
+</td>
+<td style="text-align:right;">
+1932997
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+Dunfermline Garvock Hill
+</td>
+<td style="text-align:left;">
+S02001749
+</td>
+<td style="text-align:right;">
+0.9751429
+</td>
+<td style="text-align:right;">
+4702029
+</td>
+<td style="text-align:right;">
+119858
+</td>
+<td style="text-align:right;">
+0.9541834
+</td>
+<td style="text-align:right;">
+24819128
+</td>
+<td style="text-align:right;">
+1191728
+</td>
+</tr>
+</tbody>
+</table>
+<pre class="r"><code>dtMsoaSpineIoW <- dtMsoaSpine[MSOAName %like% "Wight"]
+
t <- dtMsoaSpineIoW[order(-elecRatio)]
kableExtra::kable(head(t[, .(MSOAName, MSOACode,
elecRatio, kwhDElec, kwhNDElec,
- gasRatio, kwhDGas, kwhNDGas)],10)) %>%
+ gasRatio, kwhDGas, kwhNDGas)],10),
+ caption = "IoW MSOAs") %>%
kable_styling()</code></pre>
<table class="table" style="margin-left: auto; margin-right: auto;">
+<caption>
+<span id="tab:linkBeisMSOA">Table 2.2: </span>IoW MSOAs
+</caption>
<thead>
<tr>
<th style="text-align:left;">
@@ -717,21 +1023,3225 @@ E02003590
</tr>
</tbody>
</table>
+<pre class="r"><code># how many > X%?
+# All MSOAs > 95%
+nrow(dtMsoaSpine[elecRatio > 0.95])</code></pre>
+<pre><code>## [1] 80</code></pre>
+<pre class="r"><code># Solent sample region?
+
+# IoW MSOAs > 90%
+nrow(dtMsoaSpineIoW[elecRatio > 0.90])</code></pre>
+<pre><code>## [1] 0</code></pre>
+<pre class="r"><code># IoW MSOAs > 80%
+nrow(dtMsoaSpineIoW[elecRatio > 0.80])</code></pre>
+<pre><code>## [1] 6</code></pre>
+</div>
</div>
+<div id="ellis-question" class="section level1">
+<h1><span class="header-section-number">3</span> Ellis’ question…</h1>
+<ol style="list-style-type: decimal">
+<li>join income deprivation score to LSOA shapefiles. If you could produce a map that would be great.</li>
+<li>Count dwellings in LSOAs with have EPC < C (D-G) and main fuel type not mains gas. If you could map all EPCs and then the EPCs that match the criteria.</li>
+<li>Join tenure to LSOA shapefiles. Multiply count by LSOA level census figure for % of private dwellings.</li>
+<li>Multiply by 1/0.687 to correct for dwellings without an EPC.</li>
+</ol>
+<pre class="r"><code># this will break here if you don't have the data obvs
+dPath <- "~/Dropbox/IoW_Fuel_Poverty/Fuel Poverty/"
+
+censusDT <- data.table::fread(paste0(dPath, "Data_TENURE_UNIT.csv"))
+censusDT[, pcOwned := `Owned (%)`]
+censusDT[, lsoaCode := `LSOA CODE`]
+setkey(censusDT, lsoaCode)
+
+epcDT <- data.table::fread(paste0(dPath, "DomesticEPCFuelTypes.csv"))
+
+imdDT <- data.table::fread(paste0(dPath, "IoW_Deprivation_2019.csv"))
+imdDT[, imdScore := `Index of Multiple Deprivation (IMD) Score`]
+imdDT[, lsoaCode := `LSOA code (2011)`]
+
+# postcodes
+pcPath <- "~/Data/UK_NSPL/NSPL_FEB_2020_UK/Data/"
+postcodesDT <- data.table::fread(paste0(pcPath, "NSPL_FEB_2020_UK.csv.gz"))
+
+# we only need postcode & LSOA but keep OA in case
+# we also only need the ones that are current
+# and in the IoW. But helpfully these are only codes not names
+# E06000044 Portsmouth
+# E06000045 Southampton
+# E06000046 Isle of Wight
+# in case you ever need to know
+
+pcDT <- postcodesDT[is.na(doterm) &
+ laua == "E06000046",
+ .(pcd, pcd2, pcds, lat, long, oa11, lsoa11, laua)]</code></pre>
+<p>Steps:</p>
+<pre class="r"><code>nrow(pcDT)</code></pre>
+<pre><code>## [1] 5091</code></pre>
+<pre class="r"><code>setkey(pcDT, pcds)
+#head(pcDT)
+# so we're going to try to match epc postcode to pcds
+# who thinks this will go well?
+epcDT[, pcds := POSTCODE]
+setkey(epcDT, pcds)
+#head(epcDT)
+nrow(epcDT)</code></pre>
+<pre><code>## [1] 48968</code></pre>
+<pre class="r"><code>dt <- pcDT[epcDT]
+nrow(dt)</code></pre>
+<pre><code>## [1] 48968</code></pre>
+<pre class="r"><code># shit. worked</code></pre>
+<ul>
+<li>start with the EPC file for the Isle of Wight. This gives us 48968 addresses.</li>
+<li>match the postcode in the EPC file to postcode in the National Statistics Postcode Lookup File. We get a complete match so now we have OA and LSOA codes attached to each EPC row.</li>
+</ul>
+<p>Next we code the EPC records:</p>
+<ul>
+<li>we want EPC < C so = D, E, F, G</li>
+<li>we want main fuel type to be not mains gas</li>
+</ul>
+<pre class="r"><code># flag the ones with
+# EPC < C : D, E, F, G and H
+dt[, lowEPC := ifelse(CURRENT_ENERGY_RATING == "D" |
+ CURRENT_ENERGY_RATING == "E" |
+ CURRENT_ENERGY_RATING == "F" |
+ CURRENT_ENERGY_RATING == "G" |
+ CURRENT_ENERGY_RATING == "H",
+ "lowEPC", "okEPC")
+ ]
+# main fuel type not mains gas
+#table(dt$FuelType)
+dt[, offGas := ifelse(FuelType != "Mains Gas", "notGas", "haveGas")]</code></pre>
+<p>Table <a href="#tab:epcTable">3.1</a> shows this cross-classification.</p>
+<pre class="r"><code>t <- with(epcDT, table(CURRENT_ENERGY_RATING, FuelType, useNA = "always"))
+kableExtra::kable(t, caption = "IoW EPC records by fuel type and current energy rating") %>%
+ kable_styling()</code></pre>
+<table class="table" style="margin-left: auto; margin-right: auto;">
+<caption>
+<span id="tab:epcTable">Table 3.1: </span>IoW EPC records by fuel type and current energy rating
+</caption>
+<thead>
+<tr>
+<th style="text-align:left;">
+</th>
+<th style="text-align:right;">
+Biomass
+</th>
+<th style="text-align:right;">
+Coal
+</th>
+<th style="text-align:right;">
+Electric
+</th>
+<th style="text-align:right;">
+LPG
+</th>
+<th style="text-align:right;">
+Mains Gas
+</th>
+<th style="text-align:right;">
+Oil
+</th>
+<th style="text-align:right;">
+NA
+</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td style="text-align:left;">
+A
+</td>
+<td style="text-align:right;">
+5
+</td>
+<td style="text-align:right;">
+0
+</td>
+<td style="text-align:right;">
+13
+</td>
+<td style="text-align:right;">
+1
+</td>
+<td style="text-align:right;">
+21
+</td>
+<td style="text-align:right;">
+1
+</td>
+<td style="text-align:right;">
+0
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+B
+</td>
+<td style="text-align:right;">
+507
+</td>
+<td style="text-align:right;">
+0
+</td>
+<td style="text-align:right;">
+533
+</td>
+<td style="text-align:right;">
+2
+</td>
+<td style="text-align:right;">
+3741
+</td>
+<td style="text-align:right;">
+17
+</td>
+<td style="text-align:right;">
+0
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+C
+</td>
+<td style="text-align:right;">
+133
+</td>
+<td style="text-align:right;">
+0
+</td>
+<td style="text-align:right;">
+1902
+</td>
+<td style="text-align:right;">
+70
+</td>
+<td style="text-align:right;">
+10703
+</td>
+<td style="text-align:right;">
+139
+</td>
+<td style="text-align:right;">
+0
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+D
+</td>
+<td style="text-align:right;">
+47
+</td>
+<td style="text-align:right;">
+7
+</td>
+<td style="text-align:right;">
+2090
+</td>
+<td style="text-align:right;">
+162
+</td>
+<td style="text-align:right;">
+14652
+</td>
+<td style="text-align:right;">
+397
+</td>
+<td style="text-align:right;">
+0
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+E
+</td>
+<td style="text-align:right;">
+64
+</td>
+<td style="text-align:right;">
+17
+</td>
+<td style="text-align:right;">
+2250
+</td>
+<td style="text-align:right;">
+159
+</td>
+<td style="text-align:right;">
+6400
+</td>
+<td style="text-align:right;">
+440
+</td>
+<td style="text-align:right;">
+0
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+F
+</td>
+<td style="text-align:right;">
+96
+</td>
+<td style="text-align:right;">
+27
+</td>
+<td style="text-align:right;">
+1658
+</td>
+<td style="text-align:right;">
+162
+</td>
+<td style="text-align:right;">
+1135
+</td>
+<td style="text-align:right;">
+236
+</td>
+<td style="text-align:right;">
+0
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+G
+</td>
+<td style="text-align:right;">
+67
+</td>
+<td style="text-align:right;">
+21
+</td>
+<td style="text-align:right;">
+928
+</td>
+<td style="text-align:right;">
+71
+</td>
+<td style="text-align:right;">
+65
+</td>
+<td style="text-align:right;">
+29
+</td>
+<td style="text-align:right;">
+0
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+NA
+</td>
+<td style="text-align:right;">
+0
+</td>
+<td style="text-align:right;">
+0
+</td>
+<td style="text-align:right;">
+0
+</td>
+<td style="text-align:right;">
+0
+</td>
+<td style="text-align:right;">
+0
+</td>
+<td style="text-align:right;">
+0
+</td>
+<td style="text-align:right;">
+0
+</td>
+</tr>
+</tbody>
+</table>
+<p>Table <a href="#tab:dtTable">3.2</a> shows cassification checks and the result.</p>
+<pre class="r"><code># check
+addmargins(with(dt, table(CURRENT_ENERGY_RATING,lowEPC)))</code></pre>
+<pre><code>## lowEPC
+## CURRENT_ENERGY_RATING lowEPC okEPC Sum
+## A 0 41 41
+## B 0 4800 4800
+## C 0 12947 12947
+## D 17355 0 17355
+## E 9330 0 9330
+## F 3314 0 3314
+## G 1181 0 1181
+## Sum 31180 17788 48968</code></pre>
+<pre class="r"><code>addmargins(with(dt, table(FuelType,offGas)))</code></pre>
+<pre><code>## offGas
+## FuelType haveGas notGas Sum
+## Biomass 0 919 919
+## Coal 0 72 72
+## Electric 0 9374 9374
+## LPG 0 627 627
+## Mains Gas 36717 0 36717
+## Oil 0 1259 1259
+## Sum 36717 12251 48968</code></pre>
+<pre class="r"><code>t <- with(dt, table(lowEPC, offGas, useNA = "always"))
+kableExtra::kable(addmargins(t), caption = "IoW EPC records by fuel type and current energy rating") %>%
+ kable_styling()</code></pre>
+<table class="table" style="margin-left: auto; margin-right: auto;">
+<caption>
+<span id="tab:dtTable">Table 3.2: </span>IoW EPC records by fuel type and current energy rating
+</caption>
+<thead>
+<tr>
+<th style="text-align:left;">
+</th>
+<th style="text-align:right;">
+haveGas
+</th>
+<th style="text-align:right;">
+notGas
+</th>
+<th style="text-align:right;">
+NA
+</th>
+<th style="text-align:right;">
+Sum
+</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td style="text-align:left;">
+lowEPC
+</td>
+<td style="text-align:right;">
+22252
+</td>
+<td style="text-align:right;">
+8928
+</td>
+<td style="text-align:right;">
+0
+</td>
+<td style="text-align:right;">
+31180
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+okEPC
+</td>
+<td style="text-align:right;">
+14465
+</td>
+<td style="text-align:right;">
+3323
+</td>
+<td style="text-align:right;">
+0
+</td>
+<td style="text-align:right;">
+17788
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+NA
+</td>
+<td style="text-align:right;">
+0
+</td>
+<td style="text-align:right;">
+0
+</td>
+<td style="text-align:right;">
+0
+</td>
+<td style="text-align:right;">
+0
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+Sum
+</td>
+<td style="text-align:right;">
+36717
+</td>
+<td style="text-align:right;">
+12251
+</td>
+<td style="text-align:right;">
+0
+</td>
+<td style="text-align:right;">
+48968
+</td>
+</tr>
+</tbody>
+</table>
+<pre class="r"><code>dt[, lowEPCoffGas := ifelse(lowEPC == "lowEPC" &
+ offGas == "notGas",
+ "Yes", "No")]
+#table(dt$lowEPCoffGas)</code></pre>
+<p>Now:</p>
+<ul>
+<li>we add up the number of households that do not have gas and have EPCs D-H per LSOA.</li>
+</ul>
+<p>We need to do this so that we can count the ones in LSOAs with high income deprivation. There is no other way to do this because income is not a variable on the EPC and LSOA level income distributions are not available.</p>
+<pre class="r"><code>hhCountByLSOADT <- dt[, .(nHHs = .N),
+ keyby = .(lsoa11, lowEPCoffGas)]
+hhCountByLSOADT[, lsoaCode := lsoa11]
+
+# probably helpful not to have long form
+dtNo <- hhCountByLSOADT[lowEPCoffGas == "No"]
+dtNo[, notSelectedEPCAddresses := nHHs]
+setkey(dtNo, lsoaCode)
+dtYes <- hhCountByLSOADT[lowEPCoffGas == "Yes"]
+dtYes[, selectedEPCAddresses := nHHs]
+setkey(dtYes, lsoaCode)
+tempLSOADT <- dtNo[, .(lsoaCode,notSelectedEPCAddresses)][dtYes[,.(lsoaCode,selectedEPCAddresses)]]</code></pre>
+<p>That gives us 8928 households who fit this coding (see also Table <a href="#tab:dtTable">3.2</a>) spread across 90 LSOAs.</p>
+<p>Next:</p>
+<ul>
+<li>we match this data to Census and Index of Multiple Deprivation data.</li>
+</ul>
+<pre class="r"><code>lsoaDT <- tempLSOADT[censusDT]
+# test n LSOAs
+uniqueN(lsoaDT$lsoaCode)</code></pre>
+<pre><code>## [1] 89</code></pre>
+<pre class="r"><code>lsoaDT[, censusLSOA := "Yes"]
+
+imdDT[, lsoaCode := `LSOA code (2011)`]
+imdDT[, imdLSOA := "Yes"]
+
+setkey(imdDT, lsoaCode)
+
+lsoaDT <- imdDT[lsoaDT]
+
+# test n LSOAs
+with(lsoaDT,
+ table(imdLSOA,
+ censusLSOA,
+ useNA = "always"
+ )
+)</code></pre>
+<pre><code>## censusLSOA
+## imdLSOA Yes <NA>
+## Yes 86 0
+## <NA> 3 0</code></pre>
+<pre class="r"><code># we lost 3 with IMD - Ellis knows</code></pre>
+<p>Note that 3 LSOAs are missing from the IMD data.</p>
+<p>Next:</p>
+<ul>
+<li>we calculate the income deprivation quintile and decile so we can select the LSOAs which are more income deprived on this measure. Note that this will <em>miss</em> households who are poor but live in areas which have lower income deprivation. We have no way of finding these households…</li>
+</ul>
+<pre class="r"><code># summary(lsoaDT$`Income Score (rate)`)
+quins<- quantile(lsoaDT$`Income Score (rate)`, probs = seq(0, 1, 0.2), na.rm = TRUE)
+cut <- quins[[5]]
+paste0("IMD income deprivation score 20% cut = ", cut)</code></pre>
+<pre><code>## [1] "IMD income deprivation score 20% cut = 0.18"</code></pre>
+<pre class="r"><code>lsoaDT[, deprived20pc := ifelse(`Income Score (rate)` > cut,
+ "highDep", "notHighDep")] # set cut
+
+decs<- quantile(lsoaDT$`Income Score (rate)`, probs = seq(0, 1, 0.1) ,na.rm = TRUE)
+cut <- decs[[10]]
+paste0("IMD income deprivation score 10% cut = ", cut)</code></pre>
+<pre><code>## [1] "IMD income deprivation score 10% cut = 0.223"</code></pre>
+<pre class="r"><code>lsoaDT[, deprived10pc := ifelse(`Income Score (rate)` > cut,
+ "highDep", "notHighDep")] # set cut
+
+lsoaDT[, nHHsEPC := selectedEPCAddresses + notSelectedEPCAddresses]
+lsoaDT[, nHHsCensus := `Tenure : Owned - Unit : Households` +
+ `Tenure : Social rented - Unit : Households` +
+ `Tenure : Private rented - Unit : Households`]
+
+lsoaDT[, hhDiff := nHHsCensus-nHHsEPC]
+lsoaDT[, hhDiffpc := 100*(hhDiff/nHHsCensus)]</code></pre>
+<pre class="r"><code>t <- lsoaDT[, .(nAddressesEPC = sum(nHHsEPC),
+ nHHsCensus = sum(nHHsCensus),
+ nLSOAs = .N), keyby = .(deprived20pc)]
+
+kableExtra::kable(t, digits = 2,
+ caption = "Summary of EPC addresses and all census households by LSOA deprivation type (20% most income deprived)") %>%
+ kable_styling()</code></pre>
+<table class="table" style="margin-left: auto; margin-right: auto;">
+<caption>
+(#tab:report20pc")Summary of EPC addresses and all census households by LSOA deprivation type (20% most income deprived)
+</caption>
+<thead>
+<tr>
+<th style="text-align:left;">
+deprived20pc
+</th>
+<th style="text-align:right;">
+nAddressesEPC
+</th>
+<th style="text-align:right;">
+nHHsCensus
+</th>
+<th style="text-align:right;">
+nLSOAs
+</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td style="text-align:left;">
+NA
+</td>
+<td style="text-align:right;">
+2085
+</td>
+<td style="text-align:right;">
+2288
+</td>
+<td style="text-align:right;">
+3
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+highDep
+</td>
+<td style="text-align:right;">
+10293
+</td>
+<td style="text-align:right;">
+11475
+</td>
+<td style="text-align:right;">
+17
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+notHighDep
+</td>
+<td style="text-align:right;">
+36467
+</td>
+<td style="text-align:right;">
+46216
+</td>
+<td style="text-align:right;">
+69
+</td>
+</tr>
+</tbody>
+</table>
+<pre class="r"><code>epcMissingFactor <- 1/0.687</code></pre>
+<p>Table <a href="#tab:report20pc"><strong>??</strong></a> shows the number of addresses aggregated from the EPC data compared to the number of households from the 2011 Census tenure variable. As we can see there are 17 LSOAs classed as highly income deprived using this threshold.</p>
+<p>The histogram shows the distribution of the % difference between Census household count and the number of EPCs across all LSOAs. As we can see in some places there are more EPCs than households (-ve values). However overall we believe that EPCs exist for ~ 68.7 addresses so we use this as our final uprating factor.</p>
+<pre class="r"><code>t <- lsoaDT[, .(nAddressesEPC = sum(nHHsEPC),
+ nHHsCensus = sum(nHHsCensus),
+ nLSOAs = .N), keyby = .(deprived10pc)]
+
+kableExtra::kable(t, digits = 2,
+ caption = "Summary of EPC households and all census households by LSOA deprivation type (10% most income deprived)") %>%
+ kable_styling()</code></pre>
+<table class="table" style="margin-left: auto; margin-right: auto;">
+<caption>
+<span id="tab:depr10pc">Table 3.3: </span>Summary of EPC households and all census households by LSOA deprivation type (10% most income deprived)
+</caption>
+<thead>
+<tr>
+<th style="text-align:left;">
+deprived10pc
+</th>
+<th style="text-align:right;">
+nAddressesEPC
+</th>
+<th style="text-align:right;">
+nHHsCensus
+</th>
+<th style="text-align:right;">
+nLSOAs
+</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td style="text-align:left;">
+NA
+</td>
+<td style="text-align:right;">
+2085
+</td>
+<td style="text-align:right;">
+2288
+</td>
+<td style="text-align:right;">
+3
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+highDep
+</td>
+<td style="text-align:right;">
+5891
+</td>
+<td style="text-align:right;">
+6066
+</td>
+<td style="text-align:right;">
+9
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+notHighDep
+</td>
+<td style="text-align:right;">
+40869
+</td>
+<td style="text-align:right;">
+51625
+</td>
+<td style="text-align:right;">
+77
+</td>
+</tr>
+</tbody>
+</table>
+<p>Table <a href="#tab:depr10pc">3.3</a> shows the same results but for the 10% most income deprived LSOAs. As we can see there are 9 LSOAs classed as highly income deprived using this threshold.</p>
+<p>Next we assume that the EPC addresses of interest are as likely to be social/private rented as owner-occupied and multiply the EPC address counts by the Census-derived household ownership tenure rate for each LSOA. Without knowing the tensure status of the EPC we cannot do anything else.</p>
+<pre class="r"><code>lsoaDT[, nHHsSelected := selectedEPCAddresses*`Owned (%)`]
+
+t <- lsoaDT[, .(nHHsSelected = sum(nHHsSelected),
+ nHHsCensus = sum(nHHsCensus),
+ nLSOAs = .N), keyby = .(deprived20pc)]
+
+kableExtra::kable(t, digits = 2,
+ caption = "Summary of EPC households and all census households by LSOA deprivation type (20% most income deprived)") %>%
+ kable_styling()</code></pre>
+<table class="table" style="margin-left: auto; margin-right: auto;">
+<caption>
+<span id="tab:select20pc">Table 3.4: </span>Summary of EPC households and all census households by LSOA deprivation type (20% most income deprived)
+</caption>
+<thead>
+<tr>
+<th style="text-align:left;">
+deprived20pc
+</th>
+<th style="text-align:right;">
+nHHsSelected
+</th>
+<th style="text-align:right;">
+nHHsCensus
+</th>
+<th style="text-align:right;">
+nLSOAs
+</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td style="text-align:left;">
+NA
+</td>
+<td style="text-align:right;">
+158.58
+</td>
+<td style="text-align:right;">
+2288
+</td>
+<td style="text-align:right;">
+3
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+highDep
+</td>
+<td style="text-align:right;">
+821.68
+</td>
+<td style="text-align:right;">
+11475
+</td>
+<td style="text-align:right;">
+17
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+notHighDep
+</td>
+<td style="text-align:right;">
+5297.50
+</td>
+<td style="text-align:right;">
+46216
+</td>
+<td style="text-align:right;">
+69
+</td>
+</tr>
+</tbody>
+</table>
+<pre class="r"><code>res20pc <- round(t[deprived20pc == "highDep", nHHsSelected])</code></pre>
+<pre class="r"><code>t <- lsoaDT[, .(nHHsSelected = sum(nHHsSelected),
+ nHHsCensus = sum(nHHsCensus),
+ nLSOAs = .N), keyby = .(deprived10pc)]
+
+kableExtra::kable(t, digits = 2,
+ caption = "Summary of EPC households and all census households by LSOA deprivation type (10% most income deprived)") %>%
+ kable_styling()</code></pre>
+<table class="table" style="margin-left: auto; margin-right: auto;">
+<caption>
+<span id="tab:select10pc">Table 3.5: </span>Summary of EPC households and all census households by LSOA deprivation type (10% most income deprived)
+</caption>
+<thead>
+<tr>
+<th style="text-align:left;">
+deprived10pc
+</th>
+<th style="text-align:right;">
+nHHsSelected
+</th>
+<th style="text-align:right;">
+nHHsCensus
+</th>
+<th style="text-align:right;">
+nLSOAs
+</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td style="text-align:left;">
+NA
+</td>
+<td style="text-align:right;">
+158.58
+</td>
+<td style="text-align:right;">
+2288
+</td>
+<td style="text-align:right;">
+3
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+highDep
+</td>
+<td style="text-align:right;">
+496.50
+</td>
+<td style="text-align:right;">
+6066
+</td>
+<td style="text-align:right;">
+9
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+notHighDep
+</td>
+<td style="text-align:right;">
+5622.68
+</td>
+<td style="text-align:right;">
+51625
+</td>
+<td style="text-align:right;">
+77
+</td>
+</tr>
+</tbody>
+</table>
+<pre class="r"><code>res10pc <- round(t[deprived10pc == "highDep", nHHsSelected])
+
+epcMissingPC <- 100*(1-(1/epcMissingFactor))</code></pre>
+<blockquote>
+<p>In summary using the EPC data we have estimated the number of addresses in low income areas which are owned and do not have gas to be:</p>
+</blockquote>
+<ul>
+<li>10% most deprived: 496 or, if we uprate to allow for the 31.3 % missing EPCs, 722;</li>
+<li>20% most deprived: 822 or, if we uprate to allow for the 31.3 % missing EPCs, 1197;</li>
+</ul>
+<p>Table <a href="#tab:lsoaTable">3.6</a> shows the LSOA level results.</p>
+<pre class="r"><code>t<- lsoaDT[, .(lsoaCode, nAddressesEPC = nHHsEPC, notSelectedEPCAddresses, selectedEPCAddresses, pcOwned=pcOwned*100, deprived20pc, deprived10pc,nHHsSelected)]
+setkey(t, deprived20pc, deprived10pc)
+
+kableExtra::kable(t, caption = "Final results at LSOA level",
+ digits = 2) %>%
+ kable_styling()</code></pre>
+<table class="table" style="margin-left: auto; margin-right: auto;">
+<caption>
+<span id="tab:lsoaTable">Table 3.6: </span>Final results at LSOA level
+</caption>
+<thead>
+<tr>
+<th style="text-align:left;">
+lsoaCode
+</th>
+<th style="text-align:right;">
+nAddressesEPC
+</th>
+<th style="text-align:right;">
+notSelectedEPCAddresses
+</th>
+<th style="text-align:right;">
+selectedEPCAddresses
+</th>
+<th style="text-align:right;">
+pcOwned
+</th>
+<th style="text-align:left;">
+deprived20pc
+</th>
+<th style="text-align:left;">
+deprived10pc
+</th>
+<th style="text-align:right;">
+nHHsSelected
+</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td style="text-align:left;">
+E01033229
+</td>
+<td style="text-align:right;">
+615
+</td>
+<td style="text-align:right;">
+576
+</td>
+<td style="text-align:right;">
+39
+</td>
+<td style="text-align:right;">
+83.49
+</td>
+<td style="text-align:left;">
+NA
+</td>
+<td style="text-align:left;">
+NA
+</td>
+<td style="text-align:right;">
+32.56
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+E01033237
+</td>
+<td style="text-align:right;">
+566
+</td>
+<td style="text-align:right;">
+531
+</td>
+<td style="text-align:right;">
+35
+</td>
+<td style="text-align:right;">
+39.25
+</td>
+<td style="text-align:left;">
+NA
+</td>
+<td style="text-align:left;">
+NA
+</td>
+<td style="text-align:right;">
+13.74
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+E01033239
+</td>
+<td style="text-align:right;">
+904
+</td>
+<td style="text-align:right;">
+778
+</td>
+<td style="text-align:right;">
+126
+</td>
+<td style="text-align:right;">
+89.11
+</td>
+<td style="text-align:left;">
+NA
+</td>
+<td style="text-align:left;">
+NA
+</td>
+<td style="text-align:right;">
+112.28
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+E01017322
+</td>
+<td style="text-align:right;">
+558
+</td>
+<td style="text-align:right;">
+473
+</td>
+<td style="text-align:right;">
+85
+</td>
+<td style="text-align:right;">
+45.39
+</td>
+<td style="text-align:left;">
+highDep
+</td>
+<td style="text-align:left;">
+highDep
+</td>
+<td style="text-align:right;">
+38.58
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+E01017328
+</td>
+<td style="text-align:right;">
+597
+</td>
+<td style="text-align:right;">
+458
+</td>
+<td style="text-align:right;">
+139
+</td>
+<td style="text-align:right;">
+48.03
+</td>
+<td style="text-align:left;">
+highDep
+</td>
+<td style="text-align:left;">
+highDep
+</td>
+<td style="text-align:right;">
+66.76
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+E01017331
+</td>
+<td style="text-align:right;">
+589
+</td>
+<td style="text-align:right;">
+531
+</td>
+<td style="text-align:right;">
+58
+</td>
+<td style="text-align:right;">
+57.29
+</td>
+<td style="text-align:left;">
+highDep
+</td>
+<td style="text-align:left;">
+highDep
+</td>
+<td style="text-align:right;">
+33.23
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+E01017332
+</td>
+<td style="text-align:right;">
+710
+</td>
+<td style="text-align:right;">
+688
+</td>
+<td style="text-align:right;">
+22
+</td>
+<td style="text-align:right;">
+47.62
+</td>
+<td style="text-align:left;">
+highDep
+</td>
+<td style="text-align:left;">
+highDep
+</td>
+<td style="text-align:right;">
+10.48
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+E01017336
+</td>
+<td style="text-align:right;">
+905
+</td>
+<td style="text-align:right;">
+622
+</td>
+<td style="text-align:right;">
+283
+</td>
+<td style="text-align:right;">
+38.65
+</td>
+<td style="text-align:left;">
+highDep
+</td>
+<td style="text-align:left;">
+highDep
+</td>
+<td style="text-align:right;">
+109.37
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+E01017343
+</td>
+<td style="text-align:right;">
+437
+</td>
+<td style="text-align:right;">
+348
+</td>
+<td style="text-align:right;">
+89
+</td>
+<td style="text-align:right;">
+58.38
+</td>
+<td style="text-align:left;">
+highDep
+</td>
+<td style="text-align:left;">
+highDep
+</td>
+<td style="text-align:right;">
+51.96
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+E01017345
+</td>
+<td style="text-align:right;">
+753
+</td>
+<td style="text-align:right;">
+636
+</td>
+<td style="text-align:right;">
+117
+</td>
+<td style="text-align:right;">
+38.45
+</td>
+<td style="text-align:left;">
+highDep
+</td>
+<td style="text-align:left;">
+highDep
+</td>
+<td style="text-align:right;">
+44.99
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+E01017363
+</td>
+<td style="text-align:right;">
+887
+</td>
+<td style="text-align:right;">
+667
+</td>
+<td style="text-align:right;">
+220
+</td>
+<td style="text-align:right;">
+50.25
+</td>
+<td style="text-align:left;">
+highDep
+</td>
+<td style="text-align:left;">
+highDep
+</td>
+<td style="text-align:right;">
+110.55
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+E01017366
+</td>
+<td style="text-align:right;">
+455
+</td>
+<td style="text-align:right;">
+406
+</td>
+<td style="text-align:right;">
+49
+</td>
+<td style="text-align:right;">
+62.40
+</td>
+<td style="text-align:left;">
+highDep
+</td>
+<td style="text-align:left;">
+highDep
+</td>
+<td style="text-align:right;">
+30.58
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+E01017295
+</td>
+<td style="text-align:right;">
+356
+</td>
+<td style="text-align:right;">
+315
+</td>
+<td style="text-align:right;">
+41
+</td>
+<td style="text-align:right;">
+63.43
+</td>
+<td style="text-align:left;">
+highDep
+</td>
+<td style="text-align:left;">
+notHighDep
+</td>
+<td style="text-align:right;">
+26.01
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+E01017318
+</td>
+<td style="text-align:right;">
+320
+</td>
+<td style="text-align:right;">
+306
+</td>
+<td style="text-align:right;">
+14
+</td>
+<td style="text-align:right;">
+59.26
+</td>
+<td style="text-align:left;">
+highDep
+</td>
+<td style="text-align:left;">
+notHighDep
+</td>
+<td style="text-align:right;">
+8.30
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+E01017319
+</td>
+<td style="text-align:right;">
+533
+</td>
+<td style="text-align:right;">
+421
+</td>
+<td style="text-align:right;">
+112
+</td>
+<td style="text-align:right;">
+68.64
+</td>
+<td style="text-align:left;">
+highDep
+</td>
+<td style="text-align:left;">
+notHighDep
+</td>
+<td style="text-align:right;">
+76.87
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+E01017326
+</td>
+<td style="text-align:right;">
+815
+</td>
+<td style="text-align:right;">
+636
+</td>
+<td style="text-align:right;">
+179
+</td>
+<td style="text-align:right;">
+39.21
+</td>
+<td style="text-align:left;">
+highDep
+</td>
+<td style="text-align:left;">
+notHighDep
+</td>
+<td style="text-align:right;">
+70.18
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+E01017340
+</td>
+<td style="text-align:right;">
+736
+</td>
+<td style="text-align:right;">
+645
+</td>
+<td style="text-align:right;">
+91
+</td>
+<td style="text-align:right;">
+57.81
+</td>
+<td style="text-align:left;">
+highDep
+</td>
+<td style="text-align:left;">
+notHighDep
+</td>
+<td style="text-align:right;">
+52.61
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+E01017341
+</td>
+<td style="text-align:right;">
+411
+</td>
+<td style="text-align:right;">
+384
+</td>
+<td style="text-align:right;">
+27
+</td>
+<td style="text-align:right;">
+62.83
+</td>
+<td style="text-align:left;">
+highDep
+</td>
+<td style="text-align:left;">
+notHighDep
+</td>
+<td style="text-align:right;">
+16.96
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+E01017350
+</td>
+<td style="text-align:right;">
+559
+</td>
+<td style="text-align:right;">
+536
+</td>
+<td style="text-align:right;">
+23
+</td>
+<td style="text-align:right;">
+63.22
+</td>
+<td style="text-align:left;">
+highDep
+</td>
+<td style="text-align:left;">
+notHighDep
+</td>
+<td style="text-align:right;">
+14.54
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+E01017356
+</td>
+<td style="text-align:right;">
+672
+</td>
+<td style="text-align:right;">
+565
+</td>
+<td style="text-align:right;">
+107
+</td>
+<td style="text-align:right;">
+55.80
+</td>
+<td style="text-align:left;">
+highDep
+</td>
+<td style="text-align:left;">
+notHighDep
+</td>
+<td style="text-align:right;">
+59.71
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+E01017282
+</td>
+<td style="text-align:right;">
+458
+</td>
+<td style="text-align:right;">
+430
+</td>
+<td style="text-align:right;">
+28
+</td>
+<td style="text-align:right;">
+73.38
+</td>
+<td style="text-align:left;">
+notHighDep
+</td>
+<td style="text-align:left;">
+notHighDep
+</td>
+<td style="text-align:right;">
+20.55
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+E01017283
+</td>
+<td style="text-align:right;">
+506
+</td>
+<td style="text-align:right;">
+397
+</td>
+<td style="text-align:right;">
+109
+</td>
+<td style="text-align:right;">
+81.46
+</td>
+<td style="text-align:left;">
+notHighDep
+</td>
+<td style="text-align:left;">
+notHighDep
+</td>
+<td style="text-align:right;">
+88.79
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+E01017284
+</td>
+<td style="text-align:right;">
+714
+</td>
+<td style="text-align:right;">
+660
+</td>
+<td style="text-align:right;">
+54
+</td>
+<td style="text-align:right;">
+85.55
+</td>
+<td style="text-align:left;">
+notHighDep
+</td>
+<td style="text-align:left;">
+notHighDep
+</td>
+<td style="text-align:right;">
+46.20
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+E01017285
+</td>
+<td style="text-align:right;">
+664
+</td>
+<td style="text-align:right;">
+577
+</td>
+<td style="text-align:right;">
+87
+</td>
+<td style="text-align:right;">
+80.49
+</td>
+<td style="text-align:left;">
+notHighDep
+</td>
+<td style="text-align:left;">
+notHighDep
+</td>
+<td style="text-align:right;">
+70.03
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+E01017286
+</td>
+<td style="text-align:right;">
+457
+</td>
+<td style="text-align:right;">
+414
+</td>
+<td style="text-align:right;">
+43
+</td>
+<td style="text-align:right;">
+84.43
+</td>
+<td style="text-align:left;">
+notHighDep
+</td>
+<td style="text-align:left;">
+notHighDep
+</td>
+<td style="text-align:right;">
+36.30
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+E01017287
+</td>
+<td style="text-align:right;">
+439
+</td>
+<td style="text-align:right;">
+423
+</td>
+<td style="text-align:right;">
+16
+</td>
+<td style="text-align:right;">
+90.08
+</td>
+<td style="text-align:left;">
+notHighDep
+</td>
+<td style="text-align:left;">
+notHighDep
+</td>
+<td style="text-align:right;">
+14.41
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+E01017288
+</td>
+<td style="text-align:right;">
+489
+</td>
+<td style="text-align:right;">
+432
+</td>
+<td style="text-align:right;">
+57
+</td>
+<td style="text-align:right;">
+79.41
+</td>
+<td style="text-align:left;">
+notHighDep
+</td>
+<td style="text-align:left;">
+notHighDep
+</td>
+<td style="text-align:right;">
+45.26
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+E01017289
+</td>
+<td style="text-align:right;">
+650
+</td>
+<td style="text-align:right;">
+571
+</td>
+<td style="text-align:right;">
+79
+</td>
+<td style="text-align:right;">
+75.99
+</td>
+<td style="text-align:left;">
+notHighDep
+</td>
+<td style="text-align:left;">
+notHighDep
+</td>
+<td style="text-align:right;">
+60.03
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+E01017290
+</td>
+<td style="text-align:right;">
+366
+</td>
+<td style="text-align:right;">
+34
+</td>
+<td style="text-align:right;">
+332
+</td>
+<td style="text-align:right;">
+83.36
+</td>
+<td style="text-align:left;">
+notHighDep
+</td>
+<td style="text-align:left;">
+notHighDep
+</td>
+<td style="text-align:right;">
+276.76
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+E01017291
+</td>
+<td style="text-align:right;">
+412
+</td>
+<td style="text-align:right;">
+69
+</td>
+<td style="text-align:right;">
+343
+</td>
+<td style="text-align:right;">
+87.57
+</td>
+<td style="text-align:left;">
+notHighDep
+</td>
+<td style="text-align:left;">
+notHighDep
+</td>
+<td style="text-align:right;">
+300.36
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+E01017292
+</td>
+<td style="text-align:right;">
+549
+</td>
+<td style="text-align:right;">
+534
+</td>
+<td style="text-align:right;">
+15
+</td>
+<td style="text-align:right;">
+76.04
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+</td>
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+</td>
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+notHighDep
+</td>
+<td style="text-align:right;">
+30.10
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+E01017369
+</td>
+<td style="text-align:right;">
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+</td>
+<td style="text-align:right;">
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+</td>
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+</td>
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+</td>
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+notHighDep
+</td>
+<td style="text-align:left;">
+notHighDep
+</td>
+<td style="text-align:right;">
+111.74
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+E01017370
+</td>
+<td style="text-align:right;">
+486
+</td>
+<td style="text-align:right;">
+418
+</td>
+<td style="text-align:right;">
+68
+</td>
+<td style="text-align:right;">
+77.06
+</td>
+<td style="text-align:left;">
+notHighDep
+</td>
+<td style="text-align:left;">
+notHighDep
+</td>
+<td style="text-align:right;">
+52.40
+</td>
+</tr>
+</tbody>
+</table>
+<pre class="r"><code>totSelectedHH <- sum(lsoaDT[!is.na(lsoaCode)]$selectedEPCAddresses)
+
+meanOwnership <- mean(lsoaDT$pcOwned)
+
+res10 <- totSelectedHH * meanOwnership * 0.1
+res20 <- totSelectedHH * meanOwnership * 0.2</code></pre>
+<p>As a rough check, if we multiply the total number of EPC addresses which fit the criteria by the mean ownership rate we get:</p>
+<ul>
+<li>10% of households = 635</li>
+<li>20% of households = 1269</li>
+</ul>
+<p>Note that this is only a rough guide since it assumes that LSOAs have exactly the same number of addresses/households in them.</p>
</div>
<div id="annexes" class="section level1">
-<h1><span class="header-section-number">3</span> Annexes</h1>
+<h1><span class="header-section-number">4</span> Annexes</h1>
<div id="beis-data" class="section level2">
-<h2><span class="header-section-number">3.1</span> Beis data</h2>
+<h2><span class="header-section-number">4.1</span> BEIS data</h2>
<div id="lsoa" class="section level3">
-<h3><span class="header-section-number">3.1.1</span> LSOA</h3>
+<h3><span class="header-section-number">4.1.1</span> LSOA</h3>
<p>Domestic elec</p>
<pre class="r"><code># Elec
skimr::skim(dtLsoaElec)</code></pre>
<table style='width: auto;'
class='table table-condensed'>
<caption>
-<span id="tab:skimLSOAElec">Table 3.1: </span>Data summary
+<span id="tab:skimLSOAElec">Table 4.1: </span>Data summary
</caption>
<thead>
<tr>
@@ -1330,7 +4840,7 @@ skimr::skim(dtLsoaGas)</code></pre>
<table style='width: auto;'
class='table table-condensed'>
<caption>
-<span id="tab:skimLSOAGas">Table 3.2: </span>Data summary
+<span id="tab:skimLSOAGas">Table 4.2: </span>Data summary
</caption>
<thead>
<tr>
@@ -1925,14 +5435,14 @@ kwhDMedianGas
</table>
</div>
<div id="msoa" class="section level3">
-<h3><span class="header-section-number">3.1.2</span> MSOA</h3>
+<h3><span class="header-section-number">4.1.2</span> MSOA</h3>
<p>Domestic elec</p>
<pre class="r"><code># Elec
skimr::skim(dtMsoaDElec)</code></pre>
<table style='width: auto;'
class='table table-condensed'>
<caption>
-<span id="tab:skimMSOADElec">Table 3.3: </span>Data summary
+<span id="tab:skimMSOADElec">Table 4.3: </span>Data summary
</caption>
<thead>
<tr>
@@ -2479,7 +5989,7 @@ skimr::skim(dtMsoaDGas)</code></pre>
<table style='width: auto;'
class='table table-condensed'>
<caption>
-<span id="tab:skimMSOADGas">Table 3.4: </span>Data summary
+<span id="tab:skimMSOADGas">Table 4.4: </span>Data summary
</caption>
<thead>
<tr>
@@ -3026,7 +6536,7 @@ skimr::skim(dtMsoaNDElec)</code></pre>
<table style='width: auto;'
class='table table-condensed'>
<caption>
-<span id="tab:skimMSOANDElec">Table 3.5: </span>Data summary
+<span id="tab:skimMSOANDElec">Table 4.5: </span>Data summary
</caption>
<thead>
<tr>
@@ -3555,7 +7065,7 @@ skimr::skim(dtMsoaNDGas)</code></pre>
<table style='width: auto;'
class='table table-condensed'>
<caption>
-<span id="tab:skimMSOANDGas">Table 3.6: </span>Data summary
+<span id="tab:skimMSOANDGas">Table 4.6: </span>Data summary
</caption>
<thead>
<tr>
@@ -4100,13 +7610,13 @@ kwhNDMedianGas
</div>
</div>
<div id="runtime" class="section level1">
-<h1><span class="header-section-number">4</span> Runtime</h1>
-<p>Analysis completed in 4.44 seconds ( 0.07 minutes) using <a href="https://cran.r-project.org/package=knitr">knitr</a> in <a href="http://www.rstudio.com">RStudio</a> with R version 3.6.2 (2019-12-12) running on x86_64-apple-darwin15.6.0.</p>
+<h1><span class="header-section-number">5</span> Runtime</h1>
+<p>Analysis completed in 49.09 seconds ( 0.82 minutes) using <a href="https://cran.r-project.org/package=knitr">knitr</a> in <a href="http://www.rstudio.com">RStudio</a> with R version 3.6.3 (2020-02-29) running on x86_64-apple-darwin15.6.0.</p>
</div>
<div id="r-environment" class="section level1">
-<h1><span class="header-section-number">5</span> R environment</h1>
+<h1><span class="header-section-number">6</span> R environment</h1>
<div id="r-packages-used" class="section level2">
-<h2><span class="header-section-number">5.1</span> R packages used</h2>
+<h2><span class="header-section-number">6.1</span> R packages used</h2>
<ul>
<li>base R <span class="citation">(R Core Team 2016)</span></li>
<li>bookdown <span class="citation">(Xie 2016a)</span></li>
@@ -4121,10 +7631,10 @@ kwhNDMedianGas
</ul>
</div>
<div id="session-info" class="section level2">
-<h2><span class="header-section-number">5.2</span> Session info</h2>
-<pre><code>## R version 3.6.2 (2019-12-12)
+<h2><span class="header-section-number">6.2</span> Session info</h2>
+<pre><code>## R version 3.6.3 (2020-02-29)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
-## Running under: macOS High Sierra 10.13.6
+## Running under: macOS Catalina 10.15.4
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
@@ -4137,23 +7647,23 @@ kwhNDMedianGas
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
-## [1] XML_3.98-1.20 skimr_2.0.2 kableExtra_1.1.0 ggplot2_3.2.1
-## [5] hms_0.5.2 lubridate_1.7.4 data.table_1.12.8
+## [1] XML_3.99-0.3 skimr_2.1 kableExtra_1.1.0 ggplot2_3.3.0
+## [5] hms_0.5.3 lubridate_1.7.4 data.table_1.12.8
##
## loaded via a namespace (and not attached):
-## [1] Rcpp_1.0.3 highr_0.8 pillar_1.4.2 compiler_3.6.2
-## [5] base64enc_0.1-3 tools_3.6.2 bit_1.1-14 zeallot_0.1.0
-## [9] digest_0.6.23 jsonlite_1.6 viridisLite_0.3.0 evaluate_0.14
-## [13] tibble_2.1.3 lifecycle_0.1.0 gtable_0.3.0 pkgconfig_2.0.3
-## [17] rlang_0.4.2 rstudioapi_0.10 yaml_2.2.0 xfun_0.11
-## [21] repr_1.0.1 xml2_1.2.2 httr_1.4.1 withr_2.1.2
-## [25] stringr_1.4.0 dplyr_0.8.3 knitr_1.26 vctrs_0.2.0
-## [29] bit64_0.9-7 webshot_0.5.2 grid_3.6.2 tidyselect_0.2.5
-## [33] glue_1.3.1 R6_2.4.1 rmarkdown_2.0 bookdown_0.16
-## [37] tidyr_1.0.0 readr_1.3.1 purrr_0.3.3 magrittr_1.5
-## [41] backports_1.1.5 scales_1.1.0 htmltools_0.4.0 rvest_0.3.5
-## [45] assertthat_0.2.1 colorspace_1.4-1 stringi_1.4.3 lazyeval_0.2.2
-## [49] munsell_0.5.0 crayon_1.3.4</code></pre>
+## [1] Rcpp_1.0.4 highr_0.8 compiler_3.6.3 pillar_1.4.3
+## [5] R.methodsS3_1.8.0 R.utils_2.9.2 base64enc_0.1-3 tools_3.6.3
+## [9] bit_1.1-15.2 digest_0.6.25 jsonlite_1.6.1 viridisLite_0.3.0
+## [13] evaluate_0.14 lifecycle_0.2.0 tibble_2.1.3 gtable_0.3.0
+## [17] pkgconfig_2.0.3 rlang_0.4.5 rstudioapi_0.11 yaml_2.2.1
+## [21] xfun_0.12 repr_1.1.0 xml2_1.2.5 httr_1.4.1
+## [25] withr_2.1.2 stringr_1.4.0 dplyr_0.8.5 knitr_1.28
+## [29] vctrs_0.2.4 bit64_0.9-7 webshot_0.5.2 grid_3.6.3
+## [33] tidyselect_1.0.0 glue_1.3.2 R6_2.4.1 rmarkdown_2.1
+## [37] bookdown_0.18 tidyr_1.0.2 purrr_0.3.3 readr_1.3.1
+## [41] magrittr_1.5 scales_1.1.0 htmltools_0.4.0 rvest_0.3.5
+## [45] assertthat_0.2.1 colorspace_1.4-1 stringi_1.4.6 munsell_0.5.0
+## [49] crayon_1.3.4 R.oo_1.23.0</code></pre>
</div>
</div>
<div id="references" class="section level1 unnumbered">
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--
GitLab