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clusters_data_analysis.R
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clusters_data_analysis.R
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#Creative clusters data analysis
setwd("Desktop/Creative Clusters/")
source("Rcode/clusters_load_data.R")
source("Rcode/clusters_data_analysis_functions.R")
#Preparation
#Create df with all the TTWA data (also normalised)
#Rbind variables of interest (year, ttwa.name, industry, variables (employment, bcs,
# other performance variables we'll use later on))
#First all.cis
all.cis_ttwa_conc <- all.cis_ttwa %>%
filter(year!="2010") %>%
select(period.string,ttwa.name,business.count,employment,turnover,turn_pw,work_pb,
business_lq,turnover_lq,emp_lq) %>%
mutate(industry="All creative industries") %>%
select(period.string,industry,ttwa.name,business.count,turnover,employment,
turn_pw,work_pb,business_lq,turnover_lq,emp_lq) %>%
melt(id.vars=c("period.string","ttwa.name","industry"))
#Second ttwa subsector
subsector_ttwa_conc <- subsectors_ttwa %>%
select(year, ttwa.name,industry.short,
business.count, turnover,employment,turn_pw,work_pb,
business_lq,turnover_lq,emp_lq) %>%
mutate(industry.short=as.character(industry.short),
period.string=ifelse(year=="2007_10","first.period",
"second.period")) %>%
select(period.string,industry.short,ttwa.name,business.count,turnover,employment,
turn_pw,work_pb,business_lq,turnover_lq,emp_lq) %>%
rename(industry=industry.short) %>%
melt(id.vars=c("period.string","ttwa.name","industry"))
#all ttwas
total_activity_ttwa_conc <- ttwa_all_industries %>%
rename(ttwa.code=ttwa.2011.code..last.ons.revision.feb.2016.) %>%
merge(ttwa_names,by.x="ttwa.code",by.y="TTWA11CD",all.x=T) %>%
rename(ttwa.name=TTWA11NM) %>%
mutate(industry="All Industries",
period.string=ifelse(year<=2010,"first.period","second.period")) %>%
group_by(ttwa.name,period.string,industry) %>%
summarise(employment=mean(total.employment,na.rm=T),
business.count=mean(total.number.of.enterprises,na.rm=T),
turnover=mean(total.turnover,na.rm=T)) %>%
mutate(turn_pw = turnover/employment,
work_pb = employment/business.count) %>%
select(period.string,industry,ttwa.name,business.count,employment,turnover,
turn_pw,work_pb) %>%
melt(id.vars=c("period.string","ttwa.name","industry"))
#Bind
ttwa_merged <- rbind(all.cis_ttwa_conc,
subsector_ttwa_conc,
total_activity_ttwa_conc)
#Now we want to normalise by the "all industries" variables
ttwa_merged_no_lq <- ttwa_merged %>% filter(grepl("lq",variable)==FALSE) %>%
droplevels()
ttwa_merged_w_norm <- ldply(
split(ttwa_merged_no_lq,ttwa_merged_no_lq$variable),
function(x) {
#Cast to
x2 <-x %>%
dcast(period.string+ttwa.name~industry)
x2[,paste0(names(x2)[3:ncol(x2)],"_norm")] <- sapply(
x2[,3:ncol(x2)], function(x){
return(x/x2["All Industries"])
}, USE.NAMES = F)
x3 <- x2 %>% melt(id.vars=c("period.string","ttwa.name"))
x3
},.id="metric")
#Analysis
#Scatter plot
all.cis_ttwa_shares <- all.cis_ttwa %>%
select(year,ttwa.name,
business.count,
employment,
business_local.share,
emp_local.share,
region) %>%
filter(!is.na(business_local.share),
!is.na(emp_local.share),
year!=2010)
#Convert regions to aggregate regions
big_regions <- as.list(c("Midlands","East","London",
"North","North","Northern Ireland",
"Scotland","South","South","Wales",
"Midlands","North"))
names(big_regions) <- levels(as.factor(all.cis_ttwa$region))
#Prepare factors
my_palette4 <- c('#ffffcc','#ffeda0','#fed976','#feb24c','#fd8d3c','#fc4e2a','#e31a1c','#b10026')
#Change them
all.cis_ttwa_shares$big_region <- as.factor(unlist(big_regions[all.cis_ttwa_shares$region]))
all.cis_ttwa_shares$big_region <- factor(all.cis_ttwa_shares$big_region,
levels=c("Northern Ireland",
"Scotland","North","Wales",
"Midlands","East","London","South"))
#Create average scores for each variable.
mean_econ_imp_scores <- all.cis_ttwa_shares %>%
select(year,business_local.share,emp_local.share)
mean_econ_imp_scores2 <- ldply(split(mean_econ_imp_scores,
as.factor(mean_econ_imp_scores$year)),
function(x){
la <- colMeans(x[,-1],na.rm=T)
return(la)
},.id="year")
mean_econ_imp_scores2$year <- as.numeric(as.character(mean_econ_imp_scores2$year))
#Create labels
scatter_labels <- ldply(split(all.cis_ttwa_shares,
all.cis_ttwa_shares$year),
function(x){
x$ttwa.name <- as.character(x$ttwa.name)
top10_b_share <-
x %>% arrange(desc(business_local.share)) %>%
as.data.frame() %>%
extract(1:8,"ttwa.name")
top10_e_share <-
x %>% arrange(desc(emp_local.share)) %>%
as.data.frame() %>%
extract(1:8,"ttwa.name")
top10_emp <-
x %>% arrange(desc(employment)) %>%
as.data.frame() %>%
extract(1:8,"ttwa.name")
top_ttwas <- c(as.character(top10_emp),
as.character(top10_e_share),
as.character(top10_b_share)) %>%
unique() %>% as.factor()
x$ttwa_lab <- ifelse(x$ttwa.name %in% top_ttwas,
x$ttwa.name,"")
return(x)
},.id="year") %>% select(emp_local.share,business_local.share,
year,ttwa_lab) %>%
filter(ttwa_lab!="")
#Plot
econ_import_scatter <- ggplot(data=all.cis_ttwa_shares,
aes(y=business_local.share,
x=emp_local.share,size=employment,
fill=big_region))+
scale_fill_manual(values=rev(my_palette4),
limits=rev(levels(all.cis_ttwa_shares$big_region)))+
geom_point(alpha=0.9,pch=21,colour="darkgrey")+
geom_hline(data=mean_econ_imp_scores2,
aes(yintercept=business_local.share),colour="darkblue",
linetype=2)+
geom_vline(data=mean_econ_imp_scores2,
aes(xintercept=emp_local.share),
colour="darkblue",
linetype=2)+
geom_label_repel(data=scatter_labels,
aes(y=business_local.share,
x=emp_local.share,
label=ttwa_lab,fill=NULL,size=NULL),size=1.7,
label.padding=unit(0.15,"lines"),
force=2)+
scale_x_continuous(label=percent)+
scale_y_continuous(label=percent)+
labs(title="Creative industries as a share of local economies, 2010 and 2014 \n (dashed lines represent average for all areas for each variable and year)",
x="Creative industries as % of employment",
y="Creative industries as % of business",
fill="Region",
size="Total employment \n in creative industries")+
facet_grid(.~year)+
theme(axis.text=element_text(size=7),
panel.margin=unit(2,"lines"))
WriteChart(econ_import_scatter,"final-report-figures/",w=9,h=6)
#Consider other economic performance metrics
#Without normalising
performance_raw_df_not_norm <- ttwa_merged_w_norm %>%
filter(metric %in% c("turn_pw","work_pb"),
grepl("norm",variable)==FALSE,
grepl("All Industries",variable)==FALSE,
period.string=="second.period") %>% droplevels()
#The problem here is that turn per worker and worker per firm
#are in very different scales.
#Plot with log scale
#Get labels
ttwa_econ_perf_nn_labels <- ldply(split(performance_raw_df_not_norm,
list(performance_raw_df$metric,
performance_raw_df$variable)),
function(x){
#Extract my variable
my_var <- x$variable[[1]] %>% as.character()
x$ttwa.name <- as.character(x$ttwa.name)
#Extract my metric
my_metric <- x$metric[[1]] %>% as.character()
#Use it to find top locations
#in ttwa_merged_norm
valid_ttwas_df <- ttwa_merged_w_norm %>%
filter(period.string=="first.period",
metric=="employment",
variable==my_var,
!is.na(value))
valid_ttwas <- valid_ttwas_df$ttwa.name[
valid_ttwas_df$value > quantile(valid_ttwas_df$value,
na.rm=T)[[4]]]
top_5_areas <-
x %>% filter(ttwa.name %in% valid_ttwas) %>%
arrange(desc(value)) %>%
as.data.frame() %>% extract(1:5,"ttwa.name")
ttwa_lab <- data.frame(metric=my_metric,
variable=my_var,
tops=paste(c("Top Areas:",top_5_areas),
collapse="\n "))
return(ttwa_lab)
},.id=NULL)
#Some location variables to plot the labels
ttwa_econ_perf_nn_labels$location <-
ifelse(ttwa_econ_perf_nn_labels$metric=="turn_pw",
0,7)
ttwa_econ_perf_nn_labels$h_just <-
ifelse(ttwa_econ_perf_nn_labels$metric=="turn_pw",
0,1)
#Reorder variable
sectors_ranked_by_tpw_nn <- performance_raw_df_not_norm %>%
filter(metric=="turn_pw") %>%
group_by(variable) %>% summarise(median=median(value,na.rm=T)) %>%
arrange(desc(median)) %>% extract(,"variable") %>% as.data.frame()
#Reorder
performance_raw_df_not_norm$variable <- ReorderFactor(
performance_raw_df_not_norm$variable,sectors_ranked_by_tpw_nn$variable,z=T)
#Change metric labels
levels(performance_raw_df_not_norm$metric) <- c("Turnover per worker","Average business size")
levels(ttwa_econ_perf_nn_labels$metric) <- c("Turnover per worker","Average business size")
#Plot
econ_perf_plot_not_n <- ggplot(data=performance_raw_df_not_norm,
aes(x=variable,y=log(value)))+
geom_boxplot(fill="#ffeda0",outlier.size=0.2)+
geom_point(position=position_jitter(width=0.3), alpha=0.1,
colour="orange",size=1)+
geom_text(data=ttwa_econ_perf_nn_labels,
aes(label=tops,y=location,hjust=h_just),size=1.5)+
facet_grid(.~metric)+
labs(title="Creative sales per worker and average firm size \n in UK locations by sector",
x=NULL,y="Log(metric)")+
coord_flip()
WriteChart(econ_perf_plot_not_n,"final-report-figures/",w=9,h=6)
#Plot normalised
performance_raw_df <- ttwa_merged_w_norm %>%
filter(metric %in% c("turn_pw","work_pb"),
grepl("norm",variable)==TRUE,
grepl("All Industries",variable)==FALSE,
period.string=="second.period") %>% droplevels()
#Reorder factors (based on turnover per worker)
#Get ordered sectors
sectors_ranked_by_tpw <- performance_raw_df %>%
filter(metric=="turn_pw") %>%
group_by(variable) %>% summarise(median=median(value,na.rm=T)) %>%
arrange(desc(median)) %>% extract(,"variable") %>% as.data.frame()
#Reorder
performance_raw_df$variable <- ReorderFactor(
performance_raw_df$variable,sectors_ranked_by_tpw$variable,z=T)
#Tidy variable names
levels(performance_raw_df$variable) <- gsub("_norm","",levels(performance_raw_df$variable))
levels(performance_raw_df$metric) <- c("Turnover per worker","Average business size")
#Get labels
#We apply over a split variable and extract the top 5 areas for each sector/metric
#after focusing on areas on the top quartile of employment
ttwa_econ_perf_labels <- ldply(split(performance_raw_df,
list(performance_raw_df$metric,
performance_raw_df$variable)),
function(x){
#Extract my variable
my_var <- x$variable[[1]] %>% as.character()
x$ttwa.name <- as.character(x$ttwa.name)
#Extract my metric
my_metric <- x$metric[[1]] %>% as.character()
#Use it to find top locations
#in ttwa_merged_norm
valid_ttwas_df <- ttwa_merged_w_norm %>%
filter(period.string=="first.period",
metric=="employment",
variable==my_var,
!is.na(value))
valid_ttwas <- valid_ttwas_df$ttwa.name[
valid_ttwas_df$value > quantile(valid_ttwas_df$value,
na.rm=T)[[4]]]
top_5_areas <-
x %>% filter(ttwa.name %in% valid_ttwas) %>%
arrange(desc(value)) %>%
as.data.frame() %>% extract(1:5,"ttwa.name")
ttwa_lab <- data.frame(metric=my_metric,
variable=my_var,
tops=paste(c("Top Areas:",top_5_areas),
collapse="\n "))
return(ttwa_lab)
},.id=NULL)
#Plot
econ_perf_plot <- ggplot(data=performance_raw_df,
aes(x=variable,y=log(value)))+
geom_boxplot(fill="#ffeda0",outlier.size=0.2)+
geom_point(position=position_jitter(width=0.3), alpha=0.1,
colour="orange",size=1)+
geom_text(data=ttwa_econ_perf_labels,
aes(label=tops,y=3.5),size=1.5,hjust=1)+
facet_grid(.~metric)+
geom_hline(yintercept=0,colour="darkblue",linetype=2)+
labs(title="Creative sales per worker and average firm size \n in UK locations by sector, normalised by other industries",
x=NULL,y="Log(normalised score value)")+
coord_flip()
WriteChart(econ_perf_plot,"final-report-figures/",w=9,h=6)
#LOOK AT CHANGE BY SECTOR, ALSO NORMALISING BY "GENERAL GROWTH"
#How? Split by variable, metric and location, and return change
#where second period is divided by first period.
#Also need to consider the normalised variables. Once
#we have that, it's just a matter of plotting with the right facets.
ttwa_econ_change_df <- ldply(
split(ttwa_merged_w_norm,
list(ttwa_merged_w_norm$metric,ttwa_merged_w_norm$ttwa.name)),
function(x){
#Remove the normalised scores (irrelevant)
x2 <- x %>% filter(grepl("norm",variable)==FALSE)
#Cast to create a third "change" row;
#Convert period.string to character to facilitate this.
casted_df <- x2 %>% dcast(ttwa.name+period.string+metric~variable) %>%
mutate(period.string=as.character(period.string))
#Create change variable
casted_df[3,-c(1:3)] <- casted_df[2,-c(1:3)]/
casted_df[1,-c(1:3)]
#Rename variables
casted_df$period.string[3] <- "change"
casted_df$ttwa.name[3] <- casted_df$ttwa.name[2]
casted_df$metric[3] <- casted_df$metric[2]
#Normalise numerical variables by the "All industries variable"
norm_names <- paste0(names(casted_df[,-c(1:3)]),"_norm")
casted_df[,norm_names] <- sapply(
casted_df[,-c(1:3)],function(x){
n <- x/casted_df[,"All Industries"]
return(n)
})
return(casted_df)
},.id=NULL) %>% melt(id.vars=c("ttwa.name","period.string",
"metric")) %>%
mutate(is_normalised=ifelse(grepl("norm",variable)==TRUE,
"normalised","not.normalised"),
variable = gsub("_norm","",as.character(variable)))
#Plot.
#Focus only on the change variable, only on normalised varianles
ttwa_econ_change_to_plot <- ttwa_econ_change_df %>%
filter(period.string=="change",
variable!="All Industries",
value < 10,
value >0.05)
#is_normalised=="not.normalised")
#Reorder factors usign the mean of growth across four variables
industries_ranked_change <- ttwa_econ_change_to_plot %>%
filter(is_normalised=="not.normalised") %>%
group_by(variable) %>% summarise(mean=mean(value,na.rm=T)) %>%
arrange(desc(mean)) %>% as.data.frame() %>% extract(,"variable")
ttwa_econ_change_to_plot$variable <- ReorderFactor(ttwa_econ_change_to_plot$variable,
y=industries_ranked_change,
z=T)
ttwa_econ_change_to_plot$is_normalised <- factor(ttwa_econ_change_to_plot$is_normalised,
levels=c("not.normalised","normalised"))
levels(ttwa_econ_change_to_plot$is_normalised) <- c("Not normalised","Normalised")
#Rename variables
levels(ttwa_econ_change_to_plot$metric) <- c("Number of businesses",
"Turnover","Employment",
"Turnover per worker",
"Average firm size")
ttwa_econ_change_to_plot$metric <-
factor(ttwa_econ_change_to_plot$metric,
levels=rev(levels(ttwa_econ_change_to_plot$metric)))
#My palette for filling values.
change_palette <- c('#8dd3c7','#ffffb3','#bebada','#fb8072','#80b1d3')
econ_change_plot <- ggplot(ttwa_econ_change_to_plot,
aes(x=variable,y=log(value),fill=metric)) +
geom_boxplot(outlier.size=0.2)+
labs(title="Economic performance of creative industries and their subsectors, 2007-14",
fill="Measure",x=NULL,y="log(change between first and last period)")+
geom_hline(yintercept=0,colour="darkblue")+
scale_fill_manual(values=change_palette,
limits=rev(levels(ttwa_econ_change_to_plot$metric)))+
facet_grid(.~is_normalised)+
coord_flip()
WriteChart(econ_change_plot,"final-report-figures/",w=9,h=6)
#Change as a binary variable (grew / didn't grow)
ttwa_econ_change_to_plot_binary <-
ttwa_econ_change_to_plot %>%
mutate(grew=value>1) %>%
group_by(variable,metric,is_normalised) %>%
summarise(grew_tot=mean(grew))
econ_change_plot_binary <- ggplot(data=ttwa_econ_change_to_plot_binary,
aes(x=variable,y=grew_tot,fill=metric))+
geom_hline(yintercept=0.5,colour="darkblue",linetype=2)+
geom_bar(stat="identity",width=0.5,
position=position_dodge(width=0.8),colour="darkgrey",size=0.1)+
facet_grid(.~is_normalised)+coord_flip()+
scale_fill_manual(values=change_palette,
limits=rev(levels(ttwa_econ_change_to_plot_binary$metric)))+
scale_y_continuous(label=percent)+
labs(title="Economic performance of creative industries by sector, binary outcomes (2007,2014)",
y="% of areas that experienced growth in sector",x=NULL,fill="Metric")
WriteChart(econ_change_plot_binary,"final-report-figures/",w=9,h=6)
#Concentration
#What are the concentration levels in the creative industries in the UK?
#% jobs in top locations, how do they compare with total levels of employment.
#We use a combination of ttwa all cis,
#Plot levels of concentration in business and employment, by sector.
#Remove turn_pw and work_pb
concentration_raw_df <- ttwa_merged %>% filter(variable %in%
c("business.count",
"turnover",
"employment")) %>%
droplevels()
concentration_raw_df$variable))[[1]]
#Extract info
concentration_prop_df <- ldply(split(concentration_raw_df,
list(concentration_raw_df$period.string,
concentration_raw_df$industry,
concentration_raw_df$variable)),
function(x){
my_df <- x %>% arrange(desc(value)) %>%
mutate(prop=value/sum(value,na.rm=T),
prop2=cumsum(prop))
my_df$index = 1:nrow(my_df)
return(my_df)
},.id=NULL) %>% select(-ttwa.name) %>%
filter(variable!="turnover") %>% droplevels()
#Focus on the current period
concentration_prop_df_current <- concentration_prop_df %>%
filter(period.string=="second.period")
#Palette for plotting
my_palette <- c('#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f','#ff7f00','#cab2d6')
#Order of variables
ranking <- concentration_prop_df_current %>% filter(index==20) %>%
select(-value) %>%
group_by(industry) %>% summarize(mean=mean(prop2)) %>%
arrange(desc(mean))
concentration_prop_df_current$industry <-
ReorderFactor(concentration_prop_df_current$industry,
y=ranking$industry,z=F)
#Change levels
concentration_prop_df_current$variable <-
factor(concentration_prop_df_current$variable,
levels=c("employment","business.count"))
levels(concentration_prop_df_current$variable) <-
c("Employment","Business Count")
#Plot
conc_plot <- ggplot(data=concentration_prop_df_current,aes(x=index,
y=prop2,
group=industry,
colour=industry))+
geom_line(size=0.9,alpha=0.8)+
#geom_smooth() +
scale_y_continuous(label=percent)+
scale_color_manual(values=my_palette)+
labs(title="Distribution of creative employment and business, average 2011-2014",
y="% of economic activity covered",x="Areas (TTWAs) decreasing by size",
colour="industry")+
facet_grid(variable~.)
WriteChart(conc_plot,"final-report-figures/",w=9,h=6)
#Change in concentration
#We will use concentration_prop_df (with both periods) but
#focusing on the top 50 areas for each sector. We will
#order them by the % of activity covered by those top 50 in the 3 metrics
#we are considering
concentration_prop_df_top50 <- concentration_prop_df %>%
filter(index<=30)
#Then: get cumulative change at different positions in the ranking
concentration_change_cs <- ldply(split(concentration_prop_df_top50,
list(concentration_prop_df_top50$industry,
concentration_prop_df_top50$variable)),
function(x){
#For each position in the ranking,
#we look at the change in level of activity.
wide <- dcast(x,index+industry+variable~period.string,
value.var = "prop") %>%
mutate(change=second.period-first.period,
cum_change=cumsum(change))
return(wide)
},.id=NULL)
#Then: get ranking of industries for plotting?
#Do it by cumchange in level50
concentration_change_ranking <- concentration_change_cs %>%
filter(index==30) %>% select(industry,variable,index,cum_change) %>%
group_by(industry) %>%
summarise(mean=mean(cum_change)) %>%
arrange(desc(mean)) %>% as.data.frame() %>% extract(,"industry")
concentration_change_cs$industry <- as.factor(concentration_change_cs$industry)
#Reorder levels
concentration_change_cs$industry <- ReorderFactor(concentration_change_cs$industry,
concentration_change_ranking,
z=F)
#Add line breaks to labels
levels(concentration_change_cs$industry) <-
gsub(" ","\n",levels(concentration_change_cs$industry))
#Create palette
conc_change_palette <- c('#e41a1c','#377eb8')
change_conc <- ggplot(data=concentration_change_cs,
aes(x=index,y=cum_change,
group=variable,colour=variable))+
geom_line(size=1,alpha=0.8)+
scale_y_continuous(label=percent)+
scale_x_continuous(labels=seq(0,30,10),
breaks=seq(0,30,10))+
scale_colour_manual(values=conc_change_palette,
labels=c("Business count","Employment"))+
geom_hline(yintercept=0,colour="black",linetype=2)+
labs(title="Change in concentration in top 30 areas by creative subsector",
x="Top 50 areas in decreasing order of importance",
y="Cumulative change in economic activity represented by top areas",
fill="Metric of activity")+
facet_grid(.~industry)+
theme(legend.position="bottom",
panel.margin=unit(0.75,"lines"),
axis.text.x=element_text(size=8))
WriteChart(change_conc,"final-report-figures/",w=9,h=6)
#Then: change in concentration by region and industry.
#Need to merge this with a TTWA-Region lookup
ttwa_region_lu <- all.cis_ttwa %>% filter(year=="2014") %>%
select(ttwa.name,region)
#And get big region
ttwa_region_lu$big_region <- sapply(ttwa_region_lu$region,
function(x){
return(big_regions[[x]])
})
#Done
ttwa_econ_change_to_plot_region_binary <-
ttwa_econ_change_to_plot %>%
merge(ttwa_region_lu,by="ttwa.name") %>%
group_by(variable,metric,big_region,is_normalised) %>%
summarise(grew_mean=mean(value)-1) %>%
filter(metric %in% c("Employment","Number of businesses"),
is_normalised=="Normalised") %>% select(-is_normalised)
#levels(ttwa_econ_change_to_plot$is_normalised)
#Order regions by average scores.
region_change_rankings <- ttwa_econ_change_to_plot_region_binary %>%
group_by(big_region) %>%
#filter(metric=="Employment") %>%
summarise(mean=mean(grew_mean)) %>% arrange(desc(mean)) %>%
as.data.frame() %>% extract(,"big_region")
#Reorder region factors
ttwa_econ_change_to_plot_region_binary$big_region <-
ReorderFactor(ttwa_econ_change_to_plot_region_binary$big_region,
region_change_rankings,z=T)
#Palette
my_palette2 <- c('#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#cab2d6','#ff7f00')
#Plot
region_change_plot <- ggplot(data=ttwa_econ_change_to_plot_region_binary,
aes(x=big_region,y=grew_mean,fill=variable))+
geom_hline(yintercept=0,colour="darkgrey")+
geom_bar(stat="identity",position="dodge",width=0.8,
colour="darkgrey",size=0.1)+coord_flip()+
scale_fill_manual(values=rev(my_palette2),
limits=rev(levels(ttwa_econ_change_to_plot_region_binary$variable)))+
scale_y_continuous(label=percent)+
labs(title="Mean growth in areas (TTWAs) in region, normalised, 2010-2014",
y="Mean growth (%)",x=NULL,fill="Creative subsector")+
facet_grid(.~metric)
WriteChart(region_change_plot,"final-report-figures/",w=9,h=6)
#Change in % represented by every region.
#First create a data frame aggregating levels of activity over region
ttwa_region_concentration_change_df <-
ttwa_merged_w_norm %>%
merge(ttwa_region_lu,by="ttwa.name") %>%
filter(metric %in% c("business.count","employment"),
grepl("norm",variable)==FALSE) %>%
dcast(ttwa.name+big_region+period.string+variable~metric) %>%
group_by(big_region,period.string,variable) %>%
summarise(region_emp = sum(employment,na.rm=T),
region_business.count = sum(business.count,na.rm=T)) %>%
melt(id.vars=c("big_region","period.string","variable")) %>%
droplevels()
names(ttwa_region_concentration_change_df)[4] <- "metric"
ttwa_region_concentration_change_df2 <-
ldply(split(ttwa_region_concentration_change_df,
list(ttwa_region_concentration_change_df$variable,
ttwa_region_concentration_change_df$metric)),
function(x){
x2 <- x %>% dcast(big_region+variable+metric~period.string) %>%
mutate(period_1_perc=first.period/sum(first.period),
period_2_perc=second.period/sum(second.period),
change = period_2_perc - period_1_perc)
return(x2)
},.id=NULL) %>%
filter(variable!="All Industries")
#Need to reorder factors for visualisation
ttwa_region_concentration_change_df2$variable <-
ReorderFactor(ttwa_region_concentration_change_df2$variable,
levels(ttwa_econ_change_to_plot_region_binary$variable),
z=F)
#And reorder regions by concentration levels
regions_conc_change_ranked <-
ttwa_region_concentration_change_df2 %>%
select(big_region,variable,change) %>%
group_by(big_region) %>%
summarise(mean_change = mean(change,na.rm=T)) %>%
arrange(desc(mean_change)) %>% as.data.frame() %>%
extract(,"big_region")
ttwa_region_concentration_change_df2$big_region <-
ReorderFactor(ttwa_region_concentration_change_df2$big_region,
regions_conc_change_ranked,z=T)
#Change in variable names and palettes
#We use my_palette 2
levels(ttwa_region_concentration_change_df2$metric) <-
c("Employment","Business Count")
conc_change_plot_reg <- ggplot(data=ttwa_region_concentration_change_df2,
aes(x=big_region,y=change,fill=variable)) +
geom_hline(yintercept=0,colour="darkgrey")+
geom_bar(stat="identity",position="dodge",width=0.7)+
facet_grid(.~metric)+
scale_fill_manual(values=rev(my_palette2),
limits=rev(levels(ttwa_region_concentration_change_df2$variable))) +
scale_y_continuous(labels=percent)+
labs(title="Change in % of economic activity represented by areas in the region",
fill="Creative sector/subsector",y="Change in % of economic activity represented by area")+
coord_flip()
WriteChart(conc_change_plot_reg,"final-report-figures/",w=9,h=6)
#Interlude: How do we create a palette based on two colours?
#We created some functions to do it.
#Colours with RGB white,yellow,blue,green
colours <- list(c(224,224,224),
c(255,255,102),
c(40,40,255),
c(78,205,47))
palette_2_vars <- Get_RGB_values(colours,dims=20)
#Create a function to produce alphas
Get_Alphas <- function(lowest,dims) {
mat <- matrix(NA,dims,dims)
mat[1,] <- seq(lowest,1,length.out = dims)
mat[,1] <- seq(lowest,1,length.out = dims)
mat[,dims] <- seq(1,1,length.out=dims)
for (i in 2:(ncol(mat))) {
mat[i,] <- seq(mat[i,1],mat[i,dims],length.out=dims)
}
return(mat)
}
alpha_values <- Get_Alphas(lowest=0.4,dims=20)
#MAPPING
library(ggrepel)
library(maptools)
library(gridExtra)
library(grid)
#Import the shapefile as a df
ttwa_shape <- CreateTTWAmapping_df_2011() %>%
rename(ttwa.name=TTWA11NM)
#Also read the shapefile to get the centroids
uk_sh <-readOGR("metadata/Travel_to_Work_Areas_(UK)_2011_boundaries_(super_generalised_clipped)_V3/",
layer="TTWA_2011_UK_BSC_V3")
uk_cen <- cbind(ttwa.name=uk_sh@data$TTWA11NM,as.data.frame(coordinates(uk_sh))) %>%
rename(long=V1,lat=V2)
#We will merge the geo df with other dfs capturing TTWA activity/change
#-> these ones.
#1. Use all cis TTWA
#Select relevant variables and melt (to split by variables later)
all_cis_mapping_1 <- all.cis_ttwa %>%
filter(year!="2010") %>%
select(period.string,ttwa.name,
business.count,business_lq,
employment,emp_lq) %>%
melt(id.vars=c("ttwa.name","period.string")) %>% tbl_df()
#Split apply combine
all_cis_for_mapping <- ldply(split(all_cis_mapping_1,
list(all_cis_mapping_1$ttwa.name,
all_cis_mapping_1$variable)),
function(x){
x2 <- dcast(x,ttwa.name+variable~period.string) %>%
rename(metric=variable)
x2$change <- (x2$second.period/x2$first.period) -1
x3 <- melt(x2,id.vars=c("ttwa.name","metric"))
return(x3)
},.id=NULL) %>%
mutate(industry.short="All creative industries")
#Widen df to merge with shapefiles
#This is not really needed)
all_cis_for_mapping_wide <- dcast(all_cis_for_mapping,
ttwa.name~metric+variable)
#2. Use subsector ttwa.
#Get relevant variables
subsectors_ttwa_mapping_1 <- subsectors_ttwa %>%
select(year,industry.short,ttwa.name,
business.count,business_lq,
employment,emp_lq) %>%
mutate(period.string=ifelse(year=="2007_10","first.period","second.period")) %>%
select(-year) %>%
melt(id.vars=c("period.string","industry.short","ttwa.name")) %>%
rename(metric=variable)
#Split, apply combine (also including industry)
subsectors_ttwa_mapping <- ldply(split(subsectors_ttwa_mapping_1,
list(subsectors_ttwa_mapping_1$ttwa.name,
subsectors_ttwa_mapping_1$industry.short,
subsectors_ttwa_mapping_1$metric)),
function(x){
if(nrow(x)==2) {
x2 <- dcast(x,ttwa.name+industry.short+metric~period.string)
x2$change <- (x2$second.period/x2$first.period) -1
x3 <- melt(x2,id.vars=c("ttwa.name","industry.short","metric"))
return(x3)
}
},.id=NULL)
#3. Rbind all CI and subsector DFs
ttwa_ci_data_mapping <- rbind(subsectors_ttwa_mapping,all_cis_for_mapping)
#4. Split by metric (employment or business),
#create a ranking in top for both and use that to select
#the right colour and alphas in the palette_2_vars and
#values_alphas matrices
#First: create a variable we can use to split (LQ or employment)
ttwa_ci_data_mapping <- ttwa_ci_data_mapping %>%
mutate(var_type=ifelse(grepl("lq",metric)==TRUE,"lq","total"))
#colours_2 <- list(c(232,232,232),c(190,100,172),c(90,200,200),c(59,73,148))
#Extract plotting objects
plotting_objects <- Get_Sector_Mapping_Df(qs = 4)
#We then need to feed these to a mapping function (which
#subsets by sector)
#Output maps
my_maps <- lapply(unique(ttwa_ci_data_mapping$industry.short),
Make_Sector_Maps)
names(my_maps) <- unique(ttwa_ci_data_mapping$industry.short)
#Print maps (with viewports)
for (i in 1:length(my_maps)) {
#Finally: Matrix legend
legend_mat <- plotting_objects[[3]] %>%
melt()
leg_plot_cur <- ggplot(data=legend_mat,
aes(x=Var2,y=Var1,fill=value))+
geom_tile()+
scale_fill_identity(guide="none")+
labs(title="Current concentration \n colour legend",
x="Business concentration \n (quantile)",
y="Employment concentration \n (quantile)")+
theme(title=element_text(size=5),
axis.text=element_text(size=4))
leg_plot_ch <- ggplot(data=legend_mat,
aes(x=Var2,y=Var1,fill=value))+
geom_tile()+
scale_fill_identity(guide="none")+
labs(title="Change in concentration \n colour legend",
x="Business concentration \n increase (quantile)",
y="Employment concentration \n increase (quantile)")+
theme(title=element_text(size=5),
axis.text=element_text(size=4))
vp <- viewport(x=0.101,y=0.8,width=0.2,height=0.21)
vp2 <- viewport(x=0.601,y=0.8,width=0.2,height=0.21)
name <- names(my_maps)[i]
pdf(paste0("final-report-figures/map_",name,".pdf"),w=9,h=6)
print(my_maps[[i]])
print(leg_plot_cur,vp=vp)
print(leg_plot_ch,vp=vp2)
dev.off()
}
#Preliminary cluster analysis
#1st task: Identify similar sets of industries.
#Using locational correlation data (do industries tend to locate in the same places?)
#Using correlational data (do industries tend to employ people from the same
#occupations)
#Locational correlation.
#This is based on subsectors_ttwa
#Create a data.frame with columns for the variables we're interested in, and correlate.
#Produce correlation matrices
#Get DF with relevant data (industry, year, ttwa.name)
loc_correlation_tmp1 <- subsectors_ttwa %>% filter(
industry != "All creative industries") %>%
select(industry,year,ttwa.name,business_lq,emp_lq) %>%
mutate(period.string=ifelse(year=="2007_10","first.period","second.period")) %>%
select(-year) %>%
melt(id.vars=c("ttwa.name","industry","period.string"))
loc_correlation_tmp1$industry.short <- unlist(shorter.labels[loc_correlation_tmp1$industry])
loc_correlation_tmp1 <- loc_correlation_tmp1 %>% select(-industry)
#Extract correlation matrices.
#Pseudo-code: split by industry, calculate change in concentration,
#produce correlation matrix for concentration and change.
loc_correlation_matrices <- lapply(split(loc_correlation_tmp1,
loc_correlation_tmp1$variable),
function(x){
#Widen to get change in specialisation
x_wide <- x %>% select(-variable) %>%
dcast(ttwa.name+industry.short~period.string,
value.var="value") %>%
mutate(change=second.period-first.period)
#Run correlations
cor_mats <- lapply(list("second.period","change"),
function(x){
my_x <- x_wide %>%
select_("ttwa.name","industry.short",x) %>%
dcast(ttwa.name~industry.short)
my_mat <- cor(my_x[,-1],
use="pairwise.complete.obs")
return(my_mat)
})
names(cor_mats) <- list("second.period","change")
return(cor_mats)
})
my_colocation_matrices <- unlist(loc_correlation_matrices,recursive = F)
#Generate hierarchical clusters with occupational data
pdf("final-report-figures/dendrograms.pdf")
dendronames <- names(my_colocation_matrices)
for (i in 1:length(my_colocation_matrices)) {
d <- dist(my_colocation_matrices[[i]],method="euclidean")
fit <- hclust(d,method="ward")
plot(fit,main=paste("Cluster dendrogram - ",dendronames[i]))
}
dev.off()
#Colour branches based in our clusters
#install.packages("dendextend")
library(dendextend)
pdf("final-report-figures/dendrograms.pdf",w=9,h=6)
par(mfrow=c(1,2))
par(mar=c(5,5,5,5))
#Create two dendograms
ds <- lapply(as.list(c("business_lq.second.period","emp_lq.second.period")),
function(x){
my_c <- my_colocation_matrices[[x]]
colnames(my_c) <- sapply(colnames(my_c),function(x){
return(unlist(str_split(x," |,"))[[1]])
},USE.NAMES = F)
rownames(my_c) <- sapply(colnames(my_c),function(x){
return(unlist(str_split(x," |,"))[[1]])
},USE.NAMES = F)
d <- dist(my_c,method="euclidean")
fit <- hclust(d,method="ward")
p <- as.dendrogram(fit)
return(p)
})
d1 <- color_branches(ds[[1]],k=3,groupLabels = T)
d2 <- color_branches(ds[[2]],k=3,groupLabels = T)
plot(d1,lab.cex=0.8,nodePar=list(lab.cex=0.9),main="Business co-location dendrogram")
plot(d2,lab.cex=0.8,nodePar=list(lab.cex=0.9),main="Employment co-location dendrogram")
dev.off()
plot(ds[[1]],main="Business co-location dendrogram")
plot(ds[[2]],main="Employment co-location dendrogram")
dev.off()
#Tasks:
#Generate metrics for our key variables:
#business and employment lqs
#along the 6 clusters we have identified.
#Select top locations in each of these groups.
#Code: extract the sectors from employment lq and business lq, create a new
#dataframe with those sectors, and re-calculate employment, business count,
#turnover, and their lqs.
#First extract the labels
#Business counts
business.lq_clusters <- my_colocation_matrices$business_lq.second.period %>% dist(method="euclidean") %>%
hclust(method="ward") %>% cutree(h=1) %>% Make_keyed_frame()
business.lq_clusters$label <- NA
business.lq_clusters$label[business.lq_clusters$x==1] <- "bc_services"
business.lq_clusters$label[business.lq_clusters$x==2] <- "bc_architecture"
business.lq_clusters$label[business.lq_clusters$x==3] <- "bc_content"
#Employment
emp.lq_clusters <- my_colocation_matrices$emp_lq.second.period %>% dist(method="euclidean") %>%
hclust(method="ward") %>% cutree(h=1.4) %>% Make_keyed_frame()