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03_features.Rmd
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03_features.Rmd
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---
title: "03_features"
output: html_notebook
---
This notebook assembles and cleans features to be used in random forest runs.
```{r}
library(tidyverse)
library(tictoc)
library(reshape2)
library(ggplot2)
library(ggridges)
library(stringr)
library(tidytext)
library(measurements) # https://cran.r-project.org/web/packages/measurements/measurements.pdf
library(readxl)
library(colorspace)
library(Hmisc)
library(facetscales)
```
```{r}
SCEtypes = read.csv("circuitfiles/SCE_ICAallclasses.csv", header=T, na.strings="?")
SCE_ICAall = read.csv("circuitfiles/SCE_ICAall.csv", header=T, na.strings="?", colClasses=as.character(SCEtypes$x))
PGEtypes = read.csv("circuitfiles/PGE_ICAallclasses.csv", header=T, na.strings="?")
PGE_ICAall = read.csv("circuitfiles/PGE_ICAall.csv", header=T, na.strings="?", colClasses=as.character(PGEtypes$x))
PGEtypesall = read.csv("circuitfiles/PGE_allclasses.csv", header=T, na.strings="?")
PGE_all = read.csv("circuitfiles/PGE_all.csv", header=T, na.strings="?", colClasses=as.character(PGEtypesall$x))
```
--------------------------------------------------------------------------------------------
COUNTIES
--------------------------------------------------------------------------------------------
CHECK: do the counties line up?
```{r}
unique(select(SCE_ICAall, CountyId, county))
unique(select(PGE_ICAall, CountyId, county))
unique(select(PGE_all, CountyId, county))
```
Yes! And we can fix the NAs:
```{r}
SCE_ICAall$county[SCE_ICAall$CountyId==37] <- "Los Angeles"
SCE_ICAall$county[SCE_ICAall$CountyId==59] <- "Orange"
SCE_ICAall$county[SCE_ICAall$CountyId==83] <- "Santa Barbara"
SCE_ICAall$county[SCE_ICAall$CountyId==111] <- "Ventura"
PGE_ICAall$county[PGE_ICAall$CountyId==13] <- "Contra Costa"
PGE_ICAall$county[PGE_ICAall$CountyId==83] <- "Santa Barbara"
PGE_all$county[PGE_all$CountyId==13] <- "Contra Costa"
PGE_all$county[PGE_all$CountyId==83] <- "Santa Barbara"
unique(select(SCE_ICAall, CountyId, county))
unique(select(PGE_ICAall, CountyId, county))
unique(select(PGE_all, CountyId, county))
nrow(unique(select(PGE_ICAall, CountyId, county)))
```
--------------------------------------------------------------------------------------------
DENSITY
--------------------------------------------------------------------------------------------
Calculate household density
```{r}
SCE_ICAalldemo <- SCE_ICAall %>%
mutate(hhdensity_hhsqkm = tothh_Cpoly/(CpolyA*conv_unit(1,"m2","km2"))) %>%
mutate(hhdensity_log = log10(hhdensity_hhsqkm))
PGE_ICAalldemo <- PGE_ICAall %>%
mutate(hhdensity_hhsqkm = tothh_Cpoly/(CpolyA*conv_unit(1,"m2","km2"))) %>%
mutate(hhdensity_log = log10(hhdensity_hhsqkm))
PGE_alldemo <- PGE_all %>%
mutate(hhdensity_hhsqkm = tothh_Cpoly/(CpolyA*conv_unit(1,"m2","km2"))) %>%
mutate(hhdensity_log = log10(hhdensity_hhsqkm))
is.na(SCE_ICAalldemo) <- do.call(cbind,lapply(SCE_ICAalldemo, is.infinite))
is.na(PGE_ICAalldemo) <- do.call(cbind,lapply(PGE_ICAalldemo, is.infinite))
is.na(PGE_alldemo) <- do.call(cbind,lapply(PGE_alldemo, is.infinite))
```
--------------------------------------------------------------------------------------------
DEMOGRAPHICS
--------------------------------------------------------------------------------------------
Demographic info, created via CensusDataAggregation.Rmd
```{r}
demotypes = read.csv("../demographics/bgCAcensusCESclasses.csv", header=T, na.strings="?")
bgCAcensusCES = read.csv("../demographics/bgCAcensusCES.csv", header=T, na.strings="?", colClasses=as.character(demotypes$x))
```
```{r}
demofeatures = c("GEOID10", "GEOIDCT", "inc50kbelow_pct", "inc150kplus_pct",
"medhhinc", "racediversity", "black_pct", "asian_pct", "nlxwhite_pct", "latinx_pct",
"edavgyrs", "ownerocc_pct", "singleunit_pct", "unitsavg", "medyrbuilt",
"polexposure_pctl", "polenvt_pctl", "popsens_pctl", "lingisolation_pctl", "sb535disad")
SCE_ICAalldemo <- SCE_ICAalldemo %>% merge(bgCAcensusCES[, demofeatures], by="GEOID10", all.x=TRUE) %>% unique()
PGE_ICAalldemo <- PGE_ICAalldemo %>% merge(bgCAcensusCES[, demofeatures], by="GEOID10", all.x=TRUE) %>% unique()
PGE_alldemo <- PGE_alldemo %>% merge(bgCAcensusCES[, demofeatures], by="GEOID10", all.x=TRUE) %>% unique()
```
Subset to remove unrealistic population totals:
```{r}
SCE_ICAalldemo_real = subset(SCE_ICAalldemo, tothh_Cpoly >= 1)
PGE_ICAalldemo_real = subset(PGE_ICAalldemo, tothh_Cpoly >= 1)
PGE_alldemo_real = subset(PGE_alldemo, tothh_Cpoly >= 1)
```
```{r}
write.csv(PGE_ICAalldemo, file = "circuitfiles/PGE_ICAalldemo.csv", row.names = FALSE)
write.csv(PGE_ICAalldemo_real, file = "circuitfiles/PGE_ICAalldemoreal.csv", row.names = FALSE)
write.csv(PGE_alldemo, file = "circuitfiles/PGE_alldemo.csv", row.names = FALSE)
#write.csv(PGE_alldemo_real, file = "circuitfiles/PGE_alldemoreal.csv", row.names = FALSE)
write.csv(SCE_ICAalldemo, file = "circuitfiles/SCE_ICAalldemo.csv", row.names = FALSE)
write.csv(SCE_ICAalldemo_real, file = "circuitfiles/SCE_ICAalldemoreal.csv", row.names = FALSE)
PGEtypes <- sapply(PGE_ICAalldemo,class)
SCEtypes <- sapply(SCE_ICAalldemo,class)
write.csv(PGEtypes, file = "circuitfiles/PGE_ICAalldemoclasses.csv", row.names=FALSE)
write.csv(SCEtypes, file = "circuitfiles/SCE_ICAalldemoclasses.csv", row.names=FALSE)
```
Visualizations (Supplementary Figure S6)
```{r}
demof = c("medhhinc","inc50kbelow_pct","inc150kplus_pct","racediversity",
"nlxwhite_pct","black_pct","latinx_pct","asian_pct",
"ownerocc_pct","singleunit_pct","unitsavg","medyrbuilt",
"polexposure_pctl","polenvt_pctl","popsens_pctl","lingisolation_pctl",
"edavgyrs","tothh","hhdensity_hhsqkm" )
cols = c("GEOID10","CircuitName","IOU","tothh_Cpoly",demof)
demovizPGE <- PGE_ICAalldemo_real[,cols]
demovizPGEall <- PGE_alldemo_real[,cols] %>% mutate(IOU = "PGEall")
demovizSCE <- SCE_ICAalldemo_real[,cols]
perc = c(0.1,0.25,0.5,0.75,0.9)
demovizPGEq <- as.data.frame(sapply(demovizPGE[,demof], wtd.quantile, probs=perc, weight=demovizPGE$tothh_Cpoly, na.rm=TRUE))
demovizPGEq <- cbind(rownames(demovizPGEq), data.frame(demovizPGEq, row.names=NULL))
colnames(demovizPGEq)[1] <- "Percentile"
demovizPGEq <- mutate(demovizPGEq, IOU="PGE")
demovizPGEallq <- as.data.frame(sapply(demovizPGEall[,demof], wtd.quantile, probs=perc, weight=demovizPGEall$tothh_Cpoly, na.rm=TRUE))
demovizPGEallq <- cbind(rownames(demovizPGEallq), data.frame(demovizPGEallq, row.names=NULL))
colnames(demovizPGEallq)[1] <- "Percentile"
demovizPGEallq <- mutate(demovizPGEallq, IOU="PGEall")
demovizSCEq <- as.data.frame(sapply(demovizSCE[,demof], wtd.quantile, probs=perc, weight=demovizSCE$tothh_Cpoly, na.rm=TRUE))
demovizSCEq <- cbind(rownames(demovizSCEq), data.frame(demovizSCEq, row.names=NULL))
colnames(demovizSCEq)[1] <- "Percentile"
demovizSCEq <- mutate(demovizSCEq, IOU="SCE")
demovizq <- rbind(demovizPGEq, demovizPGEallq, demovizSCEq) %>% melt(id=c("Percentile","IOU")) %>%
mutate(IOU = factor(IOU, levels=c("PGE","PGEall","SCE")), variable = factor(variable, levels=demof)) %>%
mutate(value = ifelse(str_sub(variable,-4,-1)=="_pct",value*100,value))
demoviz <- rbind(demovizPGE, demovizPGEall, demovizSCE) %>% melt(id=c("GEOID10","CircuitName","IOU","tothh_Cpoly")) %>%
mutate(IOU = factor(IOU, levels=c("PGE","PGEall","SCE")), variable = factor(variable, levels=demof)) %>%
mutate(value = ifelse(str_sub(variable,-4,-1)=="_pct",value*100,value))
ggplot(demoviz, aes(y=value)) + theme_light() + facet_wrap(~variable, scales="free_y", ncol=4) +
stat_density(data=subset(demoviz, IOU=="PGE"), aes(x=-..scaled.., y=value, weight=tothh_Cpoly, fill=IOU), alpha=0.3, adjust=1/2) +
stat_density(data=subset(demoviz, IOU=="PGEall"), aes(x=-..scaled.., y=value, weight=tothh_Cpoly, fill=IOU), alpha=0.3, adjust=1/2) +
stat_density(data=subset(demoviz, IOU=="SCE"), aes(x=..scaled.., y=value, weight=tothh_Cpoly, fill=IOU), alpha=0.3, adjust=1/2) +
geom_segment(data=subset(demovizq, IOU=="PGE"), aes(x=-0.25, xend=0, y=value, yend=value, color=IOU), inherit.aes=FALSE) +
geom_segment(data=subset(demovizq, IOU=="PGEall"), aes(x=-0.25, xend=0, y=value, yend=value, color=IOU), inherit.aes=FALSE) +
geom_segment(data=subset(demovizq, IOU=="SCE"), aes(x=0, xend=0.25, y=value, yend=value, color=IOU), inherit.aes=FALSE) +
geom_vline(xintercept=0) + theme(legend.position="bottom") +
#scale_fill_manual(values = c("≥"="forestgreen", "<"="indianred3")) +
#scale_color_discrete_sequential(palette="Grays", nmax=7, order=3:7) +
scale_color_manual(values=c("PGE"="coral3", "PGEall"="forestgreen", "SCE"="dodgerblue4"),
labels=c("PG&E, ICA data available", "PG&E, full territory", "SCE")) +
scale_fill_discrete(labels=c("PG&E, ICA data available", "PG&E, full territory", "SCE")) +
scale_x_continuous(breaks=c(-1.0,-0.5,0.0,0.5,1.0), labels=c("1","0.5","0","0.5","1")) +
guides(fill = guide_legend(override.aes=list(alpha=0.3))) +
labs(x="Relative density of households", y="Demographic variable", fill="IOU")
ggsave("IOU_demo_wrap.png", path="figures/", width=12, height=8)
ggsave("IOU_demo_wrap.pdf", path="figures/", width=12, height=8)
rm(demovizSCE, demovizPGE, demovizPGEall, demovizSCEq, demovizPGEq, demovizPGEallq, demoviz, demovizq, demovizall, demovizmelt)
```