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attempting mapping.R
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attempting mapping.R
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# i've downloaded some shapefiles
# and i'm willing to use them
# load packages
library(tidyverse)
library(readxl)
library(scales)
library(janitor)
library(ggrepel)
library(ggridges)
library(ggforce)
library(sf)
library(plotly)
# load everything that isn't a shapefile
# crf money: from the ace site
crf_money_round1 <-
read_excel("CRF investment data published 020421.xlsx",
2,
.name_repair = "universal") %>%
select(-Strand) %>%
mutate(source = "ACE grants round 1")
crf_money_capitalkickstart <-
read_excel("CRF investment data published 020421.xlsx",
3,
.name_repair = "universal") %>%
mutate(source = "ACE capital kickstart")
crf_money_round2 <-
read_excel("CRF investment data published 020421.xlsx",
4,
.name_repair = "universal") %>%
mutate(source = "ACE grants round 2")
# crf money: from dcms
# https://www.gov.uk/government/news/400-million-to-help-more-than-2700-arts-culture-heritage-organisations-and-independent-cinemas-survive-and-thrive
# sheet 1 seems to be the same, so steering clear for the moment
# bfi: weird missingness, trying to deal by replacing NAs with "per cinema"
bfi_money <-
read_excel("CRF_2-Awards_read-only.xlsx",
2,
skip = 1,
na = "-",
.name_repair = "universal") %>%
mutate(Award.... =
coalesce(Award....,
Award.per.Cinema....))
# nlhf: looks ok
nlhf_money <-
read_excel("CRF_2-Awards_read-only.xlsx", 3,
skip = 1,
.name_repair = "universal")
# repayable finance from dcms - seems fine
repayable_finance <-
read_excel("CRF_2-Awards_read-only.xlsx", 4,
skip = 1,
.name_repair = "universal") %>%
mutate(Local.authority = fct_recode(Local.authority,
"York" =
"City of York"))
# population data from 2019
# has updated local authority codes
# https://www.ons.gov.uk/peoplepopulationandcommunity/populationandmigration/populationestimates/datasets/populationestimatesforukenglandandwalesscotlandandnorthernireland
population <-
read_excel("ukmidyearestimates20192020ladcodes.xls",
6,
skip = 4,
.name_repair = "universal") %>%
select(Code:All.ages) %>%
rename(la_name =
Name,
population =
All.ages) %>%
filter(Geography1 != "Country" &
Geography1 != "Region" &
Geography1 != "Metropolitan County" &
Geography1 != "County"
) %>%
mutate(country = substr(Code,
1,
1)) %>%
filter(country == "E") %>%
select(-country)
# combine crf money
crf_money <-
bind_rows(crf_money_round1,
crf_money_round2,
crf_money_capitalkickstart)
# load shapefile
local_authorities <-
read_sf(dsn = "shapefiles/Local_Authority_Districts_(December_2020)_UK_BUC",
layer = "Local_Authority_Districts_(December_2020)_UK_BUC")
names(all_money_sources)
# ok, need a data frame with a column for all sources of money
all_money_sources <-
crf_money %>%
group_by(Local.Authority,
source) %>%
summarise(money =
sum(..Awarded)) %>%
pivot_wider(names_from = "source",
values_from = "money") %>%
full_join(bfi_money %>%
rename(Local.Authority =
Local.authority) %>%
group_by(Local.Authority) %>%
summarise(BFI =
sum(Award....))) %>%
full_join(nlhf_money %>%
rename(Local.Authority =
Local.authority) %>%
group_by(Local.Authority) %>%
summarise(NLHF =
sum(Award....))) %>%
pivot_longer(-Local.Authority) %>%
mutate(value = replace_na(value, 0)) %>%
rename(Source = name,
Money = value)
# combine with population data
all_money_population <-
all_money_sources %>%
full_join((population %>%
rename(Local.Authority =
la_name))) %>%
mutate(money_per_head =
round((Money /
population),
2))
# combine with shapefile, limit to england
money_population_geography <-
full_join(all_money_population,
(local_authorities %>%
rename(Code =
LAD20CD)) )%>%
mutate(country = substr(Code,
1,
1)) %>%
filter(country == "E") %>%
select(-country)
# draw simple plot
money_population_geography %>%
filter(Source == "BFI") %>%
ggplot() +
aes(geometry = geometry,
fill = money_per_head) +
geom_sf() +
theme_void() +
scale_fill_viridis_c(direction = -1,
option = "A") +
theme(legend.position = "none")
# ggplotly by source
bfi_plot_data <-
money_population_geography %>%
filter(Source == "BFI")
for_ggplotly_bfi <-
ggplot(bfi_plot_data) +
aes(geometry = geometry,
fill = Money,
text = paste(Local.Authority,
": \n£",
money_per_head,
"per head \n Total: £",
comma(Money,
accuracy = 2L))) +
geom_sf() +
theme_void() +
scale_fill_viridis_c(direction = -1,
option = "A") +
theme(legend.position = "none")
ggplotly(for_ggplotly_bfi,
tooltip = "text")
ace_capital_kickstart_plot_data <-
money_population_geography %>%
filter(Source == "ACE capital kickstart")
for_ggplotly_ace_capital_kickstart <-
ggplot(ace_capital_kickstart_plot_data) +
aes(geometry = geometry,
fill = Money,
text = paste(Local.Authority,
": \n£",
money_per_head,
"per head \n Total: £",
comma(Money,
accuracy = 2L))) +
geom_sf() +
theme_void() +
scale_fill_viridis_c(direction = -1,
option = "A") +
theme(legend.position = "none")
ggplotly(for_ggplotly_ace_capital_kickstart,
tooltip = "text")
ace_round1 <-
money_population_geography %>%
filter(Source == "ACE grants round 1")
for_ggplotly_ace_round1 <-
ggplot(ace_round1) +
aes(geometry = geometry,
fill = Money,
text = paste(Local.Authority,
": \n£",
money_per_head,
"per head \n Total: £",
comma(Money,
accuracy = 2L))) +
geom_sf() +
theme_void() +
scale_fill_viridis_c(direction = -1,
option = "A") +
theme(legend.position = "none")
ggplotly(for_ggplotly_ace_round1,
tooltip = "text")
ace_round2 <-
money_population_geography %>%
filter(Source == "ACE grants round 2")
for_ggplotly_ace_round2 <-
ggplot(ace_round2) +
aes(geometry = geometry,
fill = Money,
text = paste(Local.Authority,
": \n£",
money_per_head,
"per head \n Total: £",
comma(Money,
accuracy = 2L))) +
geom_sf() +
theme_void() +
scale_fill_viridis_c(direction = -1,
option = "A") +
theme(legend.position = "none")
ggplotly(for_ggplotly_ace_round2,
tooltip = "text")
nlhf <-
money_population_geography %>%
filter(Source == "NLHF")
for_ggplotly_nlhf <-
ggplot(nlhf) +
aes(geometry = geometry,
fill = Money,
text = paste(Local.Authority,
": \n£",
money_per_head,
"per head \n Total: £",
comma(Money,
accuracy = 2L))) +
geom_sf() +
theme_void() +
scale_fill_viridis_c(direction = -1,
option = "A") +
theme(legend.position = "none")
ggplotly(for_ggplotly_nlhf,
tooltip = "text")
table(money_population_geography$Source)