vignettes/blockbuster_vignette.R
In DFE-Capital/blockbuster: Deterioration model of the School Estate
## ----warning=FALSE, message=FALSE, error=FALSE---------------------------
# You could load tidyverse or you can load the individual packages, one-at-a-time.
library(dplyr) # need pipe %>%
library(ggplot2) # for visualisation
library(markovchain) # Recommended for markovchain S4 object handling (advanced).
library(blockbuster) # Required.
library(ggthemes) # Make pretty output with little effort.
## ------------------------------------------------------------------------
dplyr::glimpse(blockbuster_pds)
## ------------------------------------------------------------------------
# output demonstration
dplyr::glimpse( # this function makes the output nicer to read
blockbuster(blockbuster_tibble = blockbuster_pds[1:10, ], # ten rows for demo
forecast_horizon = 1) # We deteriorate through one year
[[2]]) # The initial data at timestep 0 is held in [[1]]
## ------------------------------------------------------------------------
formals(blockbuster)
## ------------------------------------------------------------------------
blockbuster_det_data[2, ]
## ------------------------------------------------------------------------
blockbuster_mc_list@markovchains[[2]]
# isS4(blockbuster_mc_list@markovchains[[1]]) # TRUE
## ------------------------------------------------------------------------
x <- blockbuster_pds[1, ]
dplyr::select(x, element, sub_element, const_type, grade, unit_area)
## ------------------------------------------------------------------------
# old method matched on strings of concated
mc1 <- det_what_tm(dplyr::mutate_(x,
concated = ~gsub(pattern = "[^[:alnum:] ]",
replacement = "",
paste(element, sub_element, const_type,
sep = ""))))
mc1
# new method tests for equality using elementid
mc2 <- det_what_tm(x)
identical(mc1, mc2) # they are the same, lookup no longer requires concated
## ------------------------------------------------------------------------
mc1@transitionMatrix[, ] # [i, j] i for row, j column
## ------------------------------------------------------------------------
my_wall <- dplyr::filter(blockbuster_pds,
buildingid == 4382, sub_element == "Walls - Structure",
const_type == "Brick / block")
my_wall_next_year <- blockbuster(my_wall, 1)
my_wall_next_year
my_wall_20year <- blockbuster(my_wall, 20)
## ------------------------------------------------------------------------
my_wall_next_year[[2]]
head(my_wall_next_year[[2]]$timestep, 1) # 1 timestep later than initial
## ------------------------------------------------------------------------
y <- blockbuster_pds[1:20, ] # select all rows associated with decision level of interest, perhaps one block or one LA?
my_block_10years <- blockbuster(y, 10)
dplyr::select(my_block_10years[[11]],
buildingid, elementid, grade, unit_area) %>%
dplyr::arrange(elementid, grade)
## ----fig.width=9, fig.height=6-------------------------------------------
p4 <- my_wall_20year %>%
purrr::map_df(
~ .x ,
.id = NULL
) %>%
dplyr::group_by(timestep, grade) %>%
dplyr::summarise(sum_area = sum(unit_area, na.rm = TRUE)) %>%
ggplot2::ggplot(aes(y = sum_area, x = grade)) +
ggplot2::geom_bar(stat = "identity") +
ggplot2::facet_wrap(
~timestep
)
p4 + ylab("Estimated area (m^2)") + xlab("Condition grade") +
govstyle::theme_gov()
## ----warning=FALSE, message=FALSE, error=FALSE, fig.width=9, fig.height=6----
my_wall_10 <- blockbuster(my_wall, 10)
# We are interested in the 10 th (+ 1) timestep
df <- my_wall_10[[11]]
# Use gov style for plot, install these if you don't have them
# devtools::install_github('mangothecat/visualTest') # required for govstyle
# devtools::install_github("UKGov-Data-Science/govstyle")
p <- ggplot2::ggplot(data = df, aes(x = grade, y = unit_area)) +
ggplot2::geom_bar(stat = "identity", fill = govstyle::gov_cols['turquoise']) +
xlab("Condition") +
ylab(expression(paste(
"Unit area (",m^2,
")", sep = ""))) +
govstyle::theme_gov()
p
## ------------------------------------------------------------------------
total_wall_area <- sum(df$unit_area)
dplyr::mutate(df, prop_area = unit_area / total_wall_area) %>%
dplyr::select(grade, unit_area, prop_area)
## ----fig.width=9, fig.height=6-------------------------------------------
# http://www.machinegurning.com/rstats/map_df/
#
# cool_cars <- list(mtcars, mtcars, mtcars) %>%
# map_df(
# ~ .x ,
# .id = "year"
# ) %>%
# group_by("year") %>%
# ggplot(aes(x = cyl, y = disp, group = cyl)) +
# geom_boxplot() +
# facet_wrap(
# ~year
# )
# cool_cars
p2 <- my_block_10years %>%
purrr::map_df(
~ .x ,
.id = NULL
) %>%
dplyr::group_by("timestep") %>%
ggplot2::ggplot(aes(x = grade, y = cost)) +
ggplot2::geom_boxplot(outlier.colour = "red") + # Set to NA to avoid double plot
ggplot2::facet_wrap(
~timestep
)
p2 + ylab("Cost of repairs (£)") + xlab("Condition grade") +
govstyle::theme_gov() + geom_point(position = position_jitter(width = 0.05),
alpha = 0.15)
## ----fig.width=9, fig.height=6-------------------------------------------
p3 <- my_block_10years %>%
purrr::map_df(
~ .x ,
.id = NULL
) %>%
dplyr::group_by(timestep, grade) %>%
dplyr::summarise(sum_cost = sum(cost, na.rm = TRUE)) %>%
ggplot2::ggplot(aes(x = grade, y = sum_cost)) +
ggplot2::geom_bar(stat = "identity") +
ggplot2::facet_wrap(
~timestep
)
p3 + ylab("Total cost of repairs (£)") + xlab("Condition grade") +
govstyle::theme_gov()
## ----fig.width=9, fig.height=6-------------------------------------------
p4 <- my_block_10years %>%
purrr::map_df(
~ .x ,
.id = NULL
) %>%
dplyr::group_by(timestep, grade) %>%
dplyr::summarise(sum_cost = sum(cost, na.rm = TRUE)) %>%
ggplot2::ggplot(aes(x = timestep, y = sum_cost, group = grade, colour = grade)) +
ggplot2::geom_line(size = 1.3)
p4 + ylab("Total cost of repairs (£)") + xlab("Years after PDS") +
govstyle::theme_gov() +
theme(legend.position = "bottom") +
# ggthemes::theme_hc() + ggthemes::scale_colour_hc() +
ggtitle("Cost of repairs through time for our example block")
## ------------------------------------------------------------------------
two_blocks_4_years_inflation <- blockbuster(dplyr::filter(blockbuster_pds,
buildingid == 4382 |
buildingid == 4472),
forecast_horizon = 4,
rebuild_cost_rate = c(1274, 1281, 1289, 1296)
)
two_blocks_4_years_inflation %>%
purrr::map_df(
~ .x ,
.id = NULL
) %>%
dplyr::group_by(buildingid, timestep, block_rebuild_cost) %>%
dplyr::tally()
## ----fig.width=9, fig.height=6-------------------------------------------
p5 <- my_block_10years %>%
purrr::map_df(
~ .x ,
.id = NULL
) %>%
dplyr::group_by(timestep, element) %>%
dplyr::summarise(sum_cost = sum(cost, na.rm = TRUE)) %>%
ggplot2::ggplot(aes(x = element, y = sum_cost)) +
ggplot2::geom_bar(stat = "identity") +
ggplot2::facet_wrap(
~timestep
)
p5 + ylab("Total cost of repairs (£)") + xlab("Building element") +
govstyle::theme_gov() + theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
theme(plot.margin = unit(c(20,10,10,10),"mm"))
## ----fig.width=9, fig.height=6-------------------------------------------
p6 <- my_block_10years[11] %>%
purrr::map_df(
~ .x ,
.id = NULL
) %>%
dplyr::group_by(timestep, grade, element_shrt = as.factor(abbreviate(element, 14))) %>%
dplyr::summarise(sum_unit_area = sum(unit_area, na.rm = TRUE),
sum_cost = sum(cost, na.rm = TRUE)) %>%
ggplot2::ggplot(aes(x = grade, y = sum_cost, colour = element_shrt, size = sum_unit_area)) +
ggplot2::geom_point(alpha = 0.7) +
ggplot2::facet_wrap(
~ timestep
)
p6 + ylab("Cost of repairs (£)") + xlab("Condition grade") +
theme_economist() +
scale_size_continuous(breaks = c(1000, 2000, 3000), range = c(3,10))
## ----fig.width=10, fig.height=12-----------------------------------------
# We filter our PDS sample for just three blocks to keep things simple
x <- dplyr::filter(blockbuster::blockbuster_pds, buildingid == 4382 | buildingid == 4472
| buildingid == 4487)
# Rebuild spending profile
y <- blockbuster(x, forecast_horizon = 8, rebuild_monies = c(0, 5e6, 0, 0, 0, 0, 0, 0), # five million
rebuild_cost_rate = c(1274, 1274 + 0, 1274 + 12.74, 1286.74, 1286.74,
1286.74, 1286.74, 1286.74)) # £/m^2
p7 <- y %>%
purrr::map_df(
~ .x ,
.id = NULL
) %>%
dplyr::group_by(timestep, element, grade) %>%
dplyr::summarise(sum_cost = sum(cost, na.rm = TRUE)) %>%
ggplot2::ggplot(aes(x = element, y = sum_cost, fill = grade)) +
ggplot2::geom_bar(stat = "identity") +
ggplot2::facet_wrap(
~timestep
) + scale_fill_discrete(drop = FALSE) + scale_x_discrete(drop = FALSE) # gives E a colour
p7 + ylab("Total cost of repairs (£)") + xlab("Building element") +
govstyle::theme_gov() + theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
theme(plot.margin = unit(c(20,10,10,10),"mm")) +
theme(legend.position = "top")
## ------------------------------------------------------------------------
# We filter our PDS sample for just three blocks to keep things simple
# using identical blocks to our rebuild example above.
x <- dplyr::filter(blockbuster::blockbuster_pds, buildingid == 4382 | buildingid == 4472
| buildingid == 4487)
# Repair spending profile of different strategies
drip_repairs <- blockbuster(x, forecast_horizon = 5, # sum(rep(3e4, 5))
repair_monies = rep(3e4, 5)) # £10k / block / year = £ 15e5
flood_repairs <- blockbuster(x, forecast_horizon = 5,
repair_monies = c(6e4, 6e4,
1e4, 1e4, 1e4))
# (20k, 20k, 5k, 5k, 5k)* 3 blocks = £ 10.5e5
## ------------------------------------------------------------------------
attributes(drip_repairs)
attributes(flood_repairs)
## ------------------------------------------------------------------------
drip <- dplyr::bind_rows(drip_repairs)
flood <- dplyr::bind_rows(flood_repairs)
## ------------------------------------------------------------------------
dripping <- drip %>% # extracat::visna(drip, sort = "b")
group_by(timestep) %>% # drip_missing <- drip %>% filter(!complete.cases(.))
# table(drip_missing$timestep) # the originals rows are not being given this variable
# maybe this is OK and makes sense, block can't be rebuilt at timestep 0, therefore NA cost
summarise(total_cost = sum(cost))
flooding <- flood %>%
group_by(timestep) %>%
summarise(total_cost = sum(cost))
# we can join them and create an ID variable
spending_profile_ts <- dplyr::bind_rows("drip" = dripping, "flood" = flooding,
.id = "spend_profiles")
spending_profile_ts
## ------------------------------------------------------------------------
p8 <- ggplot(spending_profile_ts, aes(timestep,
total_cost,
colour = spend_profiles,
group = spend_profiles)) +
geom_line(size = 2)
p8 <- p8 + ylab("Total cost of repairs (£)") + xlab("Years") +
govstyle::theme_gov() +
theme(plot.margin = unit(c(20,10,10,10),"mm")) +
theme(legend.position = "top")
## ----fig.width=10, fig.height=12-----------------------------------------
print(p8)
## ------------------------------------------------------------------------
# Repair spending profile of different strategies
# rebuild most expensive block in first year
rebuild_once <- blockbuster(x, forecast_horizon = 5,
rebuild_monies = c(2440984, rep(0, 4))) %>%
dplyr::bind_rows() %>%
group_by(timestep) %>%
summarise(total_cost = sum(cost, na.rm = TRUE))
# repair funding using same funds spread over five years
drip_fix <- blockbuster(x, forecast_horizon = 5,
repair_monies = rep(2440984/5, 5)) %>%
dplyr::bind_rows() %>%
group_by(timestep) %>%
summarise(total_cost = sum(cost, na.rm = TRUE))
# we can join them and create an ID variable
spending_profile_ts <- dplyr::bind_rows("Repairs only" = drip_fix,
"Rebuild only" = rebuild_once,
.id = "spend_profiles")
p9 <- ggplot(spending_profile_ts, aes(timestep,
total_cost,
colour = spend_profiles,
group = spend_profiles)) + geom_line(size = 2)
p9 <- p9 + ylab("Total cost of repairs (£)") + xlab("Years") +
govstyle::theme_gov() +
theme(plot.margin = unit(c(20,10,10,10),"mm")) +
theme(legend.position = "top")
## ----fig.width=10, fig.height=12-----------------------------------------
print(p9)
## ------------------------------------------------------------------------
sessionInfo()
DFE-Capital/blockbuster documentation built on May 26, 2019, 7:23 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
## ----warning=FALSE, message=FALSE, error=FALSE---------------------------
# You could load tidyverse or you can load the individual packages, one-at-a-time.
library(dplyr) # need pipe %>%
library(ggplot2) # for visualisation
library(markovchain) # Recommended for markovchain S4 object handling (advanced).
library(blockbuster) # Required.
library(ggthemes) # Make pretty output with little effort.
## ------------------------------------------------------------------------
dplyr::glimpse(blockbuster_pds)
## ------------------------------------------------------------------------
# output demonstration
dplyr::glimpse( # this function makes the output nicer to read
blockbuster(blockbuster_tibble = blockbuster_pds[1:10, ], # ten rows for demo
forecast_horizon = 1) # We deteriorate through one year
[[2]]) # The initial data at timestep 0 is held in [[1]]
## ------------------------------------------------------------------------
formals(blockbuster)
## ------------------------------------------------------------------------
blockbuster_det_data[2, ]
## ------------------------------------------------------------------------
blockbuster_mc_list@markovchains[[2]]
# isS4(blockbuster_mc_list@markovchains[[1]]) # TRUE
## ------------------------------------------------------------------------
x <- blockbuster_pds[1, ]
dplyr::select(x, element, sub_element, const_type, grade, unit_area)
## ------------------------------------------------------------------------
# old method matched on strings of concated
mc1 <- det_what_tm(dplyr::mutate_(x,
concated = ~gsub(pattern = "[^[:alnum:] ]",
replacement = "",
paste(element, sub_element, const_type,
sep = ""))))
mc1
# new method tests for equality using elementid
mc2 <- det_what_tm(x)
identical(mc1, mc2) # they are the same, lookup no longer requires concated
## ------------------------------------------------------------------------
mc1@transitionMatrix[, ] # [i, j] i for row, j column
## ------------------------------------------------------------------------
my_wall <- dplyr::filter(blockbuster_pds,
buildingid == 4382, sub_element == "Walls - Structure",
const_type == "Brick / block")
my_wall_next_year <- blockbuster(my_wall, 1)
my_wall_next_year
my_wall_20year <- blockbuster(my_wall, 20)
## ------------------------------------------------------------------------
my_wall_next_year[[2]]
head(my_wall_next_year[[2]]$timestep, 1) # 1 timestep later than initial
## ------------------------------------------------------------------------
y <- blockbuster_pds[1:20, ] # select all rows associated with decision level of interest, perhaps one block or one LA?
my_block_10years <- blockbuster(y, 10)
dplyr::select(my_block_10years[[11]],
buildingid, elementid, grade, unit_area) %>%
dplyr::arrange(elementid, grade)
## ----fig.width=9, fig.height=6-------------------------------------------
p4 <- my_wall_20year %>%
purrr::map_df(
~ .x ,
.id = NULL
) %>%
dplyr::group_by(timestep, grade) %>%
dplyr::summarise(sum_area = sum(unit_area, na.rm = TRUE)) %>%
ggplot2::ggplot(aes(y = sum_area, x = grade)) +
ggplot2::geom_bar(stat = "identity") +
ggplot2::facet_wrap(
~timestep
)
p4 + ylab("Estimated area (m^2)") + xlab("Condition grade") +
govstyle::theme_gov()
## ----warning=FALSE, message=FALSE, error=FALSE, fig.width=9, fig.height=6----
my_wall_10 <- blockbuster(my_wall, 10)
# We are interested in the 10 th (+ 1) timestep
df <- my_wall_10[[11]]
# Use gov style for plot, install these if you don't have them
# devtools::install_github('mangothecat/visualTest') # required for govstyle
# devtools::install_github("UKGov-Data-Science/govstyle")
p <- ggplot2::ggplot(data = df, aes(x = grade, y = unit_area)) +
ggplot2::geom_bar(stat = "identity", fill = govstyle::gov_cols['turquoise']) +
xlab("Condition") +
ylab(expression(paste(
"Unit area (",m^2,
")", sep = ""))) +
govstyle::theme_gov()
p
## ------------------------------------------------------------------------
total_wall_area <- sum(df$unit_area)
dplyr::mutate(df, prop_area = unit_area / total_wall_area) %>%
dplyr::select(grade, unit_area, prop_area)
## ----fig.width=9, fig.height=6-------------------------------------------
# http://www.machinegurning.com/rstats/map_df/
#
# cool_cars <- list(mtcars, mtcars, mtcars) %>%
# map_df(
# ~ .x ,
# .id = "year"
# ) %>%
# group_by("year") %>%
# ggplot(aes(x = cyl, y = disp, group = cyl)) +
# geom_boxplot() +
# facet_wrap(
# ~year
# )
# cool_cars
p2 <- my_block_10years %>%
purrr::map_df(
~ .x ,
.id = NULL
) %>%
dplyr::group_by("timestep") %>%
ggplot2::ggplot(aes(x = grade, y = cost)) +
ggplot2::geom_boxplot(outlier.colour = "red") + # Set to NA to avoid double plot
ggplot2::facet_wrap(
~timestep
)
p2 + ylab("Cost of repairs (£)") + xlab("Condition grade") +
govstyle::theme_gov() + geom_point(position = position_jitter(width = 0.05),
alpha = 0.15)
## ----fig.width=9, fig.height=6-------------------------------------------
p3 <- my_block_10years %>%
purrr::map_df(
~ .x ,
.id = NULL
) %>%
dplyr::group_by(timestep, grade) %>%
dplyr::summarise(sum_cost = sum(cost, na.rm = TRUE)) %>%
ggplot2::ggplot(aes(x = grade, y = sum_cost)) +
ggplot2::geom_bar(stat = "identity") +
ggplot2::facet_wrap(
~timestep
)
p3 + ylab("Total cost of repairs (£)") + xlab("Condition grade") +
govstyle::theme_gov()
## ----fig.width=9, fig.height=6-------------------------------------------
p4 <- my_block_10years %>%
purrr::map_df(
~ .x ,
.id = NULL
) %>%
dplyr::group_by(timestep, grade) %>%
dplyr::summarise(sum_cost = sum(cost, na.rm = TRUE)) %>%
ggplot2::ggplot(aes(x = timestep, y = sum_cost, group = grade, colour = grade)) +
ggplot2::geom_line(size = 1.3)
p4 + ylab("Total cost of repairs (£)") + xlab("Years after PDS") +
govstyle::theme_gov() +
theme(legend.position = "bottom") +
# ggthemes::theme_hc() + ggthemes::scale_colour_hc() +
ggtitle("Cost of repairs through time for our example block")
## ------------------------------------------------------------------------
two_blocks_4_years_inflation <- blockbuster(dplyr::filter(blockbuster_pds,
buildingid == 4382 |
buildingid == 4472),
forecast_horizon = 4,
rebuild_cost_rate = c(1274, 1281, 1289, 1296)
)
two_blocks_4_years_inflation %>%
purrr::map_df(
~ .x ,
.id = NULL
) %>%
dplyr::group_by(buildingid, timestep, block_rebuild_cost) %>%
dplyr::tally()
## ----fig.width=9, fig.height=6-------------------------------------------
p5 <- my_block_10years %>%
purrr::map_df(
~ .x ,
.id = NULL
) %>%
dplyr::group_by(timestep, element) %>%
dplyr::summarise(sum_cost = sum(cost, na.rm = TRUE)) %>%
ggplot2::ggplot(aes(x = element, y = sum_cost)) +
ggplot2::geom_bar(stat = "identity") +
ggplot2::facet_wrap(
~timestep
)
p5 + ylab("Total cost of repairs (£)") + xlab("Building element") +
govstyle::theme_gov() + theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
theme(plot.margin = unit(c(20,10,10,10),"mm"))
## ----fig.width=9, fig.height=6-------------------------------------------
p6 <- my_block_10years[11] %>%
purrr::map_df(
~ .x ,
.id = NULL
) %>%
dplyr::group_by(timestep, grade, element_shrt = as.factor(abbreviate(element, 14))) %>%
dplyr::summarise(sum_unit_area = sum(unit_area, na.rm = TRUE),
sum_cost = sum(cost, na.rm = TRUE)) %>%
ggplot2::ggplot(aes(x = grade, y = sum_cost, colour = element_shrt, size = sum_unit_area)) +
ggplot2::geom_point(alpha = 0.7) +
ggplot2::facet_wrap(
~ timestep
)
p6 + ylab("Cost of repairs (£)") + xlab("Condition grade") +
theme_economist() +
scale_size_continuous(breaks = c(1000, 2000, 3000), range = c(3,10))
## ----fig.width=10, fig.height=12-----------------------------------------
# We filter our PDS sample for just three blocks to keep things simple
x <- dplyr::filter(blockbuster::blockbuster_pds, buildingid == 4382 | buildingid == 4472
| buildingid == 4487)
# Rebuild spending profile
y <- blockbuster(x, forecast_horizon = 8, rebuild_monies = c(0, 5e6, 0, 0, 0, 0, 0, 0), # five million
rebuild_cost_rate = c(1274, 1274 + 0, 1274 + 12.74, 1286.74, 1286.74,
1286.74, 1286.74, 1286.74)) # £/m^2
p7 <- y %>%
purrr::map_df(
~ .x ,
.id = NULL
) %>%
dplyr::group_by(timestep, element, grade) %>%
dplyr::summarise(sum_cost = sum(cost, na.rm = TRUE)) %>%
ggplot2::ggplot(aes(x = element, y = sum_cost, fill = grade)) +
ggplot2::geom_bar(stat = "identity") +
ggplot2::facet_wrap(
~timestep
) + scale_fill_discrete(drop = FALSE) + scale_x_discrete(drop = FALSE) # gives E a colour
p7 + ylab("Total cost of repairs (£)") + xlab("Building element") +
govstyle::theme_gov() + theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
theme(plot.margin = unit(c(20,10,10,10),"mm")) +
theme(legend.position = "top")
## ------------------------------------------------------------------------
# We filter our PDS sample for just three blocks to keep things simple
# using identical blocks to our rebuild example above.
x <- dplyr::filter(blockbuster::blockbuster_pds, buildingid == 4382 | buildingid == 4472
| buildingid == 4487)
# Repair spending profile of different strategies
drip_repairs <- blockbuster(x, forecast_horizon = 5, # sum(rep(3e4, 5))
repair_monies = rep(3e4, 5)) # £10k / block / year = £ 15e5
flood_repairs <- blockbuster(x, forecast_horizon = 5,
repair_monies = c(6e4, 6e4,
1e4, 1e4, 1e4))
# (20k, 20k, 5k, 5k, 5k)* 3 blocks = £ 10.5e5
## ------------------------------------------------------------------------
attributes(drip_repairs)
attributes(flood_repairs)
## ------------------------------------------------------------------------
drip <- dplyr::bind_rows(drip_repairs)
flood <- dplyr::bind_rows(flood_repairs)
## ------------------------------------------------------------------------
dripping <- drip %>% # extracat::visna(drip, sort = "b")
group_by(timestep) %>% # drip_missing <- drip %>% filter(!complete.cases(.))
# table(drip_missing$timestep) # the originals rows are not being given this variable
# maybe this is OK and makes sense, block can't be rebuilt at timestep 0, therefore NA cost
summarise(total_cost = sum(cost))
flooding <- flood %>%
group_by(timestep) %>%
summarise(total_cost = sum(cost))
# we can join them and create an ID variable
spending_profile_ts <- dplyr::bind_rows("drip" = dripping, "flood" = flooding,
.id = "spend_profiles")
spending_profile_ts
## ------------------------------------------------------------------------
p8 <- ggplot(spending_profile_ts, aes(timestep,
total_cost,
colour = spend_profiles,
group = spend_profiles)) +
geom_line(size = 2)
p8 <- p8 + ylab("Total cost of repairs (£)") + xlab("Years") +
govstyle::theme_gov() +
theme(plot.margin = unit(c(20,10,10,10),"mm")) +
theme(legend.position = "top")
## ----fig.width=10, fig.height=12-----------------------------------------
print(p8)
## ------------------------------------------------------------------------
# Repair spending profile of different strategies
# rebuild most expensive block in first year
rebuild_once <- blockbuster(x, forecast_horizon = 5,
rebuild_monies = c(2440984, rep(0, 4))) %>%
dplyr::bind_rows() %>%
group_by(timestep) %>%
summarise(total_cost = sum(cost, na.rm = TRUE))
# repair funding using same funds spread over five years
drip_fix <- blockbuster(x, forecast_horizon = 5,
repair_monies = rep(2440984/5, 5)) %>%
dplyr::bind_rows() %>%
group_by(timestep) %>%
summarise(total_cost = sum(cost, na.rm = TRUE))
# we can join them and create an ID variable
spending_profile_ts <- dplyr::bind_rows("Repairs only" = drip_fix,
"Rebuild only" = rebuild_once,
.id = "spend_profiles")
p9 <- ggplot(spending_profile_ts, aes(timestep,
total_cost,
colour = spend_profiles,
group = spend_profiles)) + geom_line(size = 2)
p9 <- p9 + ylab("Total cost of repairs (£)") + xlab("Years") +
govstyle::theme_gov() +
theme(plot.margin = unit(c(20,10,10,10),"mm")) +
theme(legend.position = "top")
## ----fig.width=10, fig.height=12-----------------------------------------
print(p9)
## ------------------------------------------------------------------------
sessionInfo()
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