Visualizing Urban Decay: Detroit

Plan 6122 Term Project

library(flexdashboard)
library(tidyverse)
library(sf)
library(lubridate)
library(jsonlite)
library(plotly)
library(hablar)
library(RColorBrewer)
# devtools::install_github("hadley/emo")
library(emo)
library(tmap)
library(rsconnect)
library(ggplot2)

install.packages("tidycensus")

trying URL 'https://cran.rstudio.com/bin/macosx/contrib/4.2/tidycensus_1.3.2.tgz'
Content type 'application/x-gzip' length 3254143 bytes (3.1 MB)
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downloaded 3.1 MB

The downloaded binary packages are in
    /var/folders/3c/_nnr32td5vj918mbvndpd3fw0000gn/T//RtmpFKMIwn/downloaded_packages

library(tidycensus)
install.packages('gganimate')

trying URL 'https://cran.rstudio.com/bin/macosx/contrib/4.2/gganimate_1.0.8.tgz'
Content type 'application/x-gzip' length 1294329 bytes (1.2 MB)
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downloaded 1.2 MB

The downloaded binary packages are in
    /var/folders/3c/_nnr32td5vj918mbvndpd3fw0000gn/T//RtmpFKMIwn/downloaded_packages

library(gganimate)

Attaching package: ‘gganimate’

The following object is masked from ‘package:raster’:

    animate

#install.packages("transformr")
library(transformr)

Attaching package: ‘transformr’

The following object is masked from ‘package:sf’:

    st_normalize

In the following section, I manipulate and map population data provided by the American Community Survey. It is important to note that the data selected is for Wayne County, where the city of Detroit resides. The reasoning for this is twofold: 1. It was easier to download tract level data for the entire county. 2. I believe that mapping that illustrates changes in population of one specific region (in this case the city of Detroit) should include the surrounding area. Perhaps this mapping will find that as people move in or out of Detroit, the rest of Wayne County may have the opposite reaction (i.e., people moving from the city center to the suburbs, etc.)

# Downloading population data from TidyCensus for mapping. Population is one of they key factors looked into for this research project, as it marks the influx/exodus of people in Detroit. I limited my time period to 2010-2021, as I wanted to see changes in the Detroit area in the most recent decade.


pop21 <- get_acs(geography = "tract", variables = "B01003_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2021)
pop20 <- get_acs(geography = "tract", variables = "B01003_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2020)
pop19 <- get_acs(geography = "tract", variables = "B01003_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2019)
pop18 <- get_acs(geography = "tract", variables = "B01003_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2018)
pop17 <- get_acs(geography = "tract", variables = "B01003_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2017)
pop16 <- get_acs(geography = "tract", variables = "B01003_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2016)
pop15 <- get_acs(geography = "tract", variables = "B01003_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2015)
pop14 <- get_acs(geography = "tract", variables = "B01003_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2014)
pop13 <- get_acs(geography = "tract", variables = "B01003_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2013)
pop12 <- get_acs(geography = "tract", variables = "B01003_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2012)
pop11 <- get_acs(geography = "tract", variables = "B01003_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2011)
pop10 <- get_acs(geography = "tract", variables = "B01003_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2010)

# This data is pulled from the American Community Survey (ACS) and uses the 1-year estimate. This means that this data has a margin of error.

# Tidying my data
stastibble <- function(pop){as_tibble(pop)}
pop21_tibble <- stastibble(pop21)
pop20_tibble <- stastibble(pop20)
pop19_tibble <- stastibble(pop19)
pop18_tibble <- stastibble(pop18)
pop17_tibble <- stastibble(pop17)
pop16_tibble <- stastibble(pop16)
pop15_tibble <- stastibble(pop15)
pop14_tibble <- stastibble(pop14)
pop13_tibble <- stastibble(pop13)
pop12_tibble <- stastibble(pop12)
pop11_tibble <- stastibble(pop11)
pop10_tibble <- stastibble(pop10)

# Joining the data, making one dataframe with all of the years collected (2010 - 2021)

pop_joined <- left_join(pop21_tibble, pop20_tibble %>% dplyr::select(estimate, GEOID), by = 'GEOID')

pop_joined <- left_join(pop_joined, pop19_tibble %>% dplyr::select(estimate, GEOID), by = 'GEOID')

pop_joined <- left_join(pop_joined, pop18_tibble %>% dplyr::select(estimate, GEOID), by = 'GEOID')

pop_joined <- left_join(pop_joined, pop17_tibble %>% dplyr::select(estimate, GEOID), by = 'GEOID')

pop_joined <- left_join(pop_joined, pop16_tibble %>% dplyr::select(estimate, GEOID), by = 'GEOID')

pop_joined <- left_join(pop_joined, pop15_tibble %>% dplyr::select(estimate, GEOID), by = 'GEOID')

pop_joined <- left_join(pop_joined, pop14_tibble %>% dplyr::select(estimate, GEOID), by = 'GEOID')

pop_joined <- left_join(pop_joined, pop13_tibble %>% dplyr::select(estimate, GEOID), by = 'GEOID')

pop_joined <- left_join(pop_joined, pop12_tibble %>% dplyr::select(estimate, GEOID), by = 'GEOID')

pop_joined <- left_join(pop_joined, pop11_tibble %>% dplyr::select(estimate, GEOID), by = 'GEOID')

pop_joined <- left_join(pop_joined, pop10_tibble %>% dplyr::select(estimate, GEOID), by = 'GEOID')

# Transforming my data into an object with geographical/spatial information (an SF object)
pop_joined <- drop_na(pop_joined)
pop_joined_sf <- st_as_sf(pop_joined, sf_column_name = 'geometry', crs = 4326)

# Renaming columns

colnames(pop_joined_sf)[4] = "estimate21"
colnames(pop_joined_sf)[7] = "estimate20"
colnames(pop_joined_sf)[8] = "estimate19"
colnames(pop_joined_sf)[9] = "estimate18"
colnames(pop_joined_sf)[10] = "estimate17"
colnames(pop_joined_sf)[11] = "estimate16"
colnames(pop_joined_sf)[12] = "estimate15"
colnames(pop_joined_sf)[13] = "estimate14"
colnames(pop_joined_sf)[14] = "estimate13"
colnames(pop_joined_sf)[15] = "estimate12"
colnames(pop_joined_sf)[16] = "estimate11"
colnames(pop_joined_sf)[17] = "estimate10"

str(pop_joined_sf)

# Making a static map first to test out aesthetics

detroit_map_population <- ggplot() + geom_sf(data = pop_joined_sf, color = "black", aes(fill = estimate21)) + theme_light(base_size = 10) + scale_fill_gradient(low = "lightblue1", high = "indianred4")

detroit_map_population

# to utilize the gganimate package, you have to turn you data from wide format to long format
 pop_joined_Sf_long <- pivot_longer(data = pop_joined_sf, cols =c(estimate21, estimate20:estimate10))

# remapping my data that is now in long format
pop_joined_Sf_long %>% group_by(name)
detroit_map_pop_forgif <- ggplot() + geom_sf(data = pop_joined_Sf_long, color = "black", aes(fill = value)) + theme_light(base_size = 10) + scale_fill_gradient(low = "lightblue1", high = "indianred4")
detroit_map_pop_forgif

In the following section, I will map changes in vacancies in Detroit in the 2010s and early 2020s.

detroit_vacancies <- read_csv("detroitvac.characteristics.compiled - Sheet1 (2).csv")

Rows: 11 Columns: 9── Column specification ─────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
dbl (2): Year, For migrant workers
num (7): Total, For rent, Rented, not occupied, For sale only, Sold, not occupied, For seasonal, recr...
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

detroit_vacancies$`Rented, not occupied`<- as.integer(detroit_vacancies$`Rented, not occupied`)

detroit_line_graph_vacancies_1 <- ggplot() +  geom_point(data = detroit_vacancies, aes(x = Year, y= `Rented, not occupied`)) + transition_time(Year) + labs(title = "Rented, not Occupied Vacancies in Detroit by Year: {frame_time}")

detroit_line_graph_vacancies_1

detroit_line_graph_vacancies_2 <- ggplot() + geom_point(data = detroit_vacancies, aes(x = Year, y= `Sold, not occupied`)) + transition_time(Year) + labs(title = "Sold, not Occupied Vacancies in Detroit by Year: {frame_time}")

detroit_line_graph_vacancies_2

# Downloading vacancy data for Wayne County from the ACS at the Tract Level

vacancy_tract_21 <- get_acs(geography = "tract", variables = "B25004_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2021)
vacancy_tract_20 <- get_acs(geography = "tract", variables = "B25004_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2020)
vacancy_tract_19 <- get_acs(geography = "tract", variables = "B25004_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2019)
vacancy_tract_18 <- get_acs(geography = "tract", variables = "B25004_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2018)
vacancy_tract_17 <- get_acs(geography = "tract", variables = "B25004_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2017)
vacancy_tract_16 <- get_acs(geography = "tract", variables = "B25004_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2016)
vacancy_tract_15 <- get_acs(geography = "tract", variables = "B25004_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2015)
vacancy_tract_14 <- get_acs(geography = "tract", variables = "B25004_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2014)
vacancy_tract_13 <- get_acs(geography = "tract", variables = "B25004_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2013)
vacancy_tract_12 <- get_acs(geography = "tract", variables = "B25004_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2012)
vacancy_tract_11 <- get_acs(geography = "tract", variables = "B25004_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2011)
vacancy_tract_10 <- get_acs(geography = "tract", variables = "B25004_001",
                state = "MI", county = "Wayne", geometry = TRUE, year = 2010)

stastibble <- function(vacancy){as_tibble(vacancy)}
vacancy_tract_21_tibble <- stastibble(vacancy_tract_21)
vacancy_tract_20_tibble <- stastibble(vacancy_tract_20)
vacancy_tract_19_tibble <- stastibble(vacancy_tract_19)
vacancy_tract_18_tibble <- stastibble(vacancy_tract_18)
vacancy_tract_17_tibble <- stastibble(vacancy_tract_17)
vacancy_tract_16_tibble <- stastibble(vacancy_tract_16)
vacancy_tract_15_tibble <- stastibble(vacancy_tract_15)
vacancy_tract_14_tibble <- stastibble(vacancy_tract_14)
vacancy_tract_13_tibble <- stastibble(vacancy_tract_13)
vacancy_tract_12_tibble <- stastibble(vacancy_tract_12)
vacancy_tract_11_tibble <- stastibble(vacancy_tract_11)
vacancy_tract_10_tibble <- stastibble(vacancy_tract_10)

vacancy_joined <- left_join(vacancy_tract_21_tibble, vacancy_tract_20_tibble %>% dplyr::select(estimate, GEOID), by = 'GEOID')

vacancy_joined <- left_join(vacancy_joined, vacancy_tract_19_tibble %>% dplyr::select(estimate, GEOID), by = 'GEOID')

vacancy_joined <- left_join(vacancy_joined, vacancy_tract_18_tibble %>% dplyr::select(estimate, GEOID), by = 'GEOID')

vacancy_joined <- left_join(vacancy_joined, vacancy_tract_17_tibble %>% dplyr::select(estimate, GEOID), by = 'GEOID')

vacancy_joined <- left_join(vacancy_joined, vacancy_tract_16_tibble %>% dplyr::select(estimate, GEOID), by = 'GEOID')

vacancy_joined <- left_join(vacancy_joined, vacancy_tract_15_tibble %>% dplyr::select(estimate, GEOID), by = 'GEOID')

vacancy_joined <- left_join(vacancy_joined, vacancy_tract_14_tibble %>% dplyr::select(estimate, GEOID), by = 'GEOID')

vacancy_joined <- left_join(vacancy_joined, vacancy_tract_13_tibble %>% dplyr::select(estimate, GEOID), by = 'GEOID')

vacancy_joined <- left_join(vacancy_joined, vacancy_tract_12_tibble %>% dplyr::select(estimate, GEOID), by = 'GEOID')

vacancy_joined<- left_join(vacancy_joined, vacancy_tract_11_tibble %>% dplyr::select(estimate, GEOID), by = 'GEOID')

vacancy_joined <- left_join(vacancy_joined, vacancy_tract_10_tibble %>% dplyr::select(estimate, GEOID), by = 'GEOID')

colnames(vacancy_joined)[4] = "est2021"
colnames(vacancy_joined)[7] = "est2020"
colnames(vacancy_joined)[8] = "est2019"
colnames(vacancy_joined)[9] = "est2018"
colnames(vacancy_joined)[10] = "est2017"
colnames(vacancy_joined)[11] = "est2016"
colnames(vacancy_joined)[12] = "est2015"
colnames(vacancy_joined)[13] = "est2014"
colnames(vacancy_joined)[14] = "est2013"
colnames(vacancy_joined)[15] = "est2012"
colnames(vacancy_joined)[16] = "est2011"
colnames(vacancy_joined)[17] = "est2010"

vacancy_joined_sf <- st_as_sf(vacancy_joined, sf_column_name = 'geometry', crs = 4326)

Warning: st_crs<- : replacing crs does not reproject data; use st_transform for that

vacancy_joined_sf <- drop_na(vacancy_joined_sf)

vacancy_joined_Sf_long <- pivot_longer(data = vacancy_joined_sf, cols =c(est2021, est2020:est2010))

vacancy_joined_Sf_long$name[vacancy_joined_Sf_long$name == 'est2021'] <- '2021'
vacancy_joined_Sf_long$name[vacancy_joined_Sf_long$name == 'est2020'] <- '2020'
vacancy_joined_Sf_long$name[vacancy_joined_Sf_long$name == 'est2019'] <- '2019'
vacancy_joined_Sf_long$name[vacancy_joined_Sf_long$name == 'est2018'] <- '2018'
vacancy_joined_Sf_long$name[vacancy_joined_Sf_long$name == 'est2017'] <- '2017'
vacancy_joined_Sf_long$name[vacancy_joined_Sf_long$name == 'est2016'] <- '2016'
vacancy_joined_Sf_long$name[vacancy_joined_Sf_long$name == 'est2015'] <- '2015'
vacancy_joined_Sf_long$name[vacancy_joined_Sf_long$name == 'est2014'] <- '2014'
vacancy_joined_Sf_long$name[vacancy_joined_Sf_long$name == 'est2013'] <- '2013'
vacancy_joined_Sf_long$name[vacancy_joined_Sf_long$name == 'est2012'] <- '2012'
vacancy_joined_Sf_long$name[vacancy_joined_Sf_long$name == 'est2011'] <- '2011'
vacancy_joined_Sf_long$name[vacancy_joined_Sf_long$name == 'est2010'] <- '2010'

colnames(vacancy_joined_Sf_long)[6] <- 'year' 
vacancy_joined_Sf_long$year <- as.integer(vacancy_joined_Sf_long$year)

detroit_map_vacancy_gif <- ggplot() + geom_sf(data = vacancy_joined_Sf_long, color = "black", aes(fill = value)) + theme_light(base_size = 10) + scale_fill_gradient(low = "lightyellow1", high = "darkorchid4") + transition_time(year) + labs(title = "Vacancy by Census Tract by Year: {frame_time}")

detroit_map_vacancy_gif

Detroit_sidelots <- read_json("Side_Lots_Sold.geojson")
Detroit_propertysales <- read_csv("property_sales.csv")

Rows: 377839 Columns: 16── Column specification ──────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr  (10): parcel_number, address, grantor, grantee, liberpage, sale_terms, verified_by, sale_...
dbl   (4): object_id, sale_number, sale_price, sale_trans
lgl   (1): geom
date  (1): sale_date
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

detroit_property_sales_year <- format(as.Date(Detroit_propertysales$sale_date, format="%Y/%m/%d"),"%Y")
data.frame(detroit_property_sales_year)

detroit_propsales_year_1 <- cbind(detroit_property_sales_year, Detroit_propertysales)
detroit_propsales_year_1

Detroit_propertysales_2011 <- detroit_propsales_year_1 %>% filter(detroit_property_sales_year == 2011)
Detroit_propertysales_2012 <- detroit_propsales_year_1 %>% filter(detroit_property_sales_year == 2012)
Detroit_propertysales_2013 <- detroit_propsales_year_1 %>% filter(detroit_property_sales_year == 2013)
Detroit_propertysales_2014 <- detroit_propsales_year_1 %>% filter(detroit_property_sales_year == 2014)
Detroit_propertysales_2015 <- detroit_propsales_year_1 %>% filter(detroit_property_sales_year == 2015)
Detroit_propertysales_2016 <- detroit_propsales_year_1 %>% filter(detroit_property_sales_year == 2016)
Detroit_propertysales_2017 <- detroit_propsales_year_1 %>% filter(detroit_property_sales_year == 2017)
Detroit_propertysales_2018 <- detroit_propsales_year_1 %>% filter(detroit_property_sales_year == 2018)
Detroit_propertysales_2019 <- detroit_propsales_year_1 %>% filter(detroit_property_sales_year == 2019)
Detroit_propertysales_2020 <- detroit_propsales_year_1 %>% filter(detroit_property_sales_year == 2020)
Detroit_propertysales_2021 <- detroit_propsales_year_1 %>% filter(detroit_property_sales_year == 2021)
Detroit_propertysales_2022 <- detroit_propsales_year_1 %>% filter(detroit_property_sales_year == 2022)

sum_detroit_propsales_year_1 <- aggregate(x = detroit_propsales_year_1$sale_price,               
          by = list(detroit_propsales_year_1$detroit_property_sales_year),            
          FUN = sum) 
colnames(sum_detroit_propsales_year_1)[1] = 'year'
colnames(sum_detroit_propsales_year_1)[2] = 'price'

sum_detroit_propsales_year_1$year <- as.integer(sum_detroit_propsales_year_1$year)

detroit_line_graph_salesprices <- ggplot()+ geom_point(data = sum_detroit_propsales_year_1, aes(x = year, y= price)) + theme_bw() + transition_time(year) + labs(title = "Real Estate Prices in Detroit by Year: {frame_time}")

detroit_line_graph_salesprices

avg_detroit_propsales_year_1 <- aggregate(x = detroit_propsales_year_1$sale_price,               
          by = list(detroit_propsales_year_1$detroit_property_sales_year),            
          FUN = mean) 

colnames(avg_detroit_propsales_year_1)[1] = 'year'
colnames(avg_detroit_propsales_year_1)[2] = 'price'

avg_detroit_propsales_year_1$year <- as.integer(avg_detroit_propsales_year_1$year)

detroit_line_graph_salesprices_avg <- ggplot()+ geom_point(data = avg_detroit_propsales_year_1, aes(x = year, y= price)) + theme_bw() + transition_time(year) + labs(title = " Average Real Estate Prices in Detroit by Year: {frame_time}")

detroit_line_graph_salesprices_avg

detroitsales_401 <- detroit_propsales_year_1 %>% dplyr::filter(., property_class == 401)

detroitsales_402 <- detroit_propsales_year_1 %>% dplyr::filter(., property_class == 402)

sum_detroit_propsales_year_401 <- aggregate(x = detroitsales_401$sale_price,               
          by = list(detroitsales_401$detroit_property_sales_year),            
          FUN = sum) 
colnames(sum_detroit_propsales_year_401)[1] = 'year'
colnames(sum_detroit_propsales_year_401)[2] = 'price'

sum_detroit_propsales_year_401$year <- as.integer(sum_detroit_propsales_year_401$year)

detroit_line_graph_salesprices_401 <- ggplot()+ geom_point(data = sum_detroit_propsales_year_401, aes(x = year, y= price)) + theme_bw() + transition_time(year) + labs(title = "Real Estate Prices - Residential in Nature and Improved in Detroit by Year: {frame_time}")

detroit_line_graph_salesprices_401

avg_detroit_propsales_year_401 <- aggregate(x = detroitsales_401$sale_price,               
          by = list(detroitsales_401$detroit_property_sales_year),            
          FUN = mean) 
colnames(avg_detroit_propsales_year_401)[1] = 'year'
colnames(avg_detroit_propsales_year_401)[2] = 'price'

avg_detroit_propsales_year_401$year <- as.integer(avg_detroit_propsales_year_401$year)

detroit_line_graph_salesprices_401_avg <- ggplot()+ geom_point(data = avg_detroit_propsales_year_401, aes(x = year, y= price)) + theme_bw() + transition_time(year) + labs(title = " Average Real Estate Prices - Residential in Nature and Improved in Detroit by Year: {frame_time}")

detroit_line_graph_salesprices_401_avg

sum_detroit_propsales_year_402 <- aggregate(x = detroitsales_402$sale_price,               
          by = list(detroitsales_402$detroit_property_sales_year),            
          FUN = sum) 
colnames(sum_detroit_propsales_year_402)[1] = 'year'
colnames(sum_detroit_propsales_year_402)[2] = 'price'

sum_detroit_propsales_year_402$year <- as.integer(sum_detroit_propsales_year_402$year)

detroit_line_graph_salesprices_402 <- ggplot()+ geom_point(data = sum_detroit_propsales_year_402, aes(x = year, y= price), color = "royalblue", size = 4) + theme_bw() + transition_time(year) + labs(title = "Real Estate Prices - Residential and Vacant in Detroit by Year: {frame_time}")

detroit_line_graph_salesprices_402

avg_detroit_propsales_year_402 <- aggregate(x = detroitsales_402$sale_price,               
          by = list(detroitsales_402$detroit_property_sales_year),            
          FUN = mean) 
colnames(avg_detroit_propsales_year_402)[1] = 'year'
colnames(avg_detroit_propsales_year_402)[2] = 'price'

avg_detroit_propsales_year_402$year <- as.integer(avg_detroit_propsales_year_402$year)

detroit_line_graph_salesprices_402_avg <- ggplot()+ geom_point(data = avg_detroit_propsales_year_402, aes(x = year, y= price)) + theme_bw() + transition_time(year) + labs(title = " Average Real Estate Prices - Residential and Vacant in Detroit by Year: {frame_time}")

detroit_line_graph_salesprices_402_avg

residential_demolitions <- read_csv("Completed_Residential_Demolitions.csv")

Rows: 25604 Columns: 21── Column specification ──────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (13): address, parcel_id, contractor_name, funding_source, demolition_date, demo_rfp_group...
dbl  (8): X, Y, price, council_district, street_number, property_longitude, property_latitude,...
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

year_demo<- format(as.Date(residential_demolitions$demolition_date, format="%Y/%m/%d"),"%Y")
data.frame(residential_demolitions)

residential_demo_year <- cbind(year_demo, residential_demolitions)
residential_demo_year

residential_demo_year <- st_as_sf(residential_demo_year, coords = c("X", "Y"), crs = 4326)

residential_demo_year$year_demo <- as.integer(residential_demo_year$year_demo)

detroit_map_demo_gif <- ggplot() + geom_sf(data = residential_demo_year, color = "darkgreen", aes(fill = year_demo), size = .05, alpha = .5) + theme_light(base_size = 10) + scale_fill_gradient(low = "lightyellow1", high = "darkorchid4") 

detroit_map_demo_gif

residential_demo_year$year_demo <- as.integer(residential_demo_year$year_demo)

detroit_map_demo_gif <- ggplot() + geom_sf(data = residential_demo_year, color = "darkgreen", aes(fill = year_demo), size = .05, alpha = .5) + theme_light(base_size = 10) + scale_fill_gradient(low = "lightyellow1", high = "darkorchid4") + transition_time(year_demo) + labs(title = "Demolitions by Year: {frame_time}")

detroit_map_demo_gif

anim_save("detroit_map_demo_gif.gif", detroit_map_demo_gif)

anim_save("detroit_map_pop_gif.gif", detroit_map_pop_gif)

anim_save("detroit_line_graph_population.gif", detroit_line_graph_population)

anim_save("detroit_line_graph_vacancies_1.gif", detroit_line_graph_vacancies_1)

anim_save("detroit_line_graph_vacancies_2.gif", detroit_line_graph_vacancies_2)

anim_save("detroit_map_vacancy_gif.gif", detroit_map_vacancy_gif)

anim_save("detroit_line_graph_salesprices.gif", detroit_line_graph_salesprices)

anim_save("detroit_line_graph_salesprices_401.gif", detroit_line_graph_salesprices_401) 

anim_save("detroit_line_graph_salesprices_402.gif", detroit_line_graph_salesprices_402) 

anim_save("detroit_line_graph_salesprices_401_avg.gif", detroit_line_graph_salesprices_401_avg) 

anim_save("detroit_line_graph_salesprices_402_avg.gif", detroit_line_graph_salesprices_402_avg)

anim_save("detroit_line_graph_salesprices_avg.gif", detroit_line_graph_salesprices_avg)

install.packages("gifski")

Error in install.packages : Updating loaded packages

library(gifski)
library(rsconnect)