Update code

.Rbuildignore

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-^README\.Rmd$

NEWS.md

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-# News
-
-## 2018-10-25
-
-Project Started

R/00_download-data.R

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+library(dplyr)
+library(purrr)
+library(tidyr)
+library(stringr)
+library(readr)
+library(forcats)
+library(gganimate)
+
+DATA_DIR <- "data-raw"
+
+fs::dir_create(here::here(DATA_DIR))
+
+DOWNLOAD_DATA <- if (!exists(DOWNLOAD_DATA)) TRUE else DOWNLOAD_DATA
+
+download_file <- function(filename, url, basedir) {
+  filename <- gsub(" ", "_", filename)
+  filename <- gsub("/", "-", filename)
+  fs::dir_create(basedir)
+  download.file(url, destfile = fs::path(basedir, filename))
+}
+
+if (DOWNLOAD_DATA) {
+  ## ----download-popest-tables-1900-1980, eval=FALSE------------------------
+  x <- xml2::read_html("https://www2.census.gov/programs-surveys/popest/tables/1900-1980/national/asrh/")
+
+  xx <-
+    data_frame(
+      filename = rvest::html_nodes(x, "a") %>% rvest::html_text(),
+      url = rvest::html_nodes(x, "a") %>% rvest::html_attr("href")
+    ) %>%
+    filter(str_detect(url, "\\.csv")) %>%
+    mutate(url = str_c(
+      "https://www2.census.gov/programs-surveys/popest/tables/1900-1980/national/asrh/",
+      url
+    ))
+
+  pwalk(xx, download_file, basedir = here::here(DATA_DIR, "1900-1980"))
+
+  ## ----download-popest-tables-1980-1990, eval=FALSE------------------------
+  xml2::read_html("https://www.census.gov/data/datasets/time-series/demo/popest/1980s-national.html") %>%
+    rvest::html_nodes(".list.section .uscb-text-link") %>%
+    rvest::html_attrs() %>%
+    purrr::transpose() %>%
+    as_tibble() %>%
+    unnest() %>%
+    filter(str_detect(href, "rqi\\.zip")) %>%
+    select(filename = name, url = href) %>%
+    mutate(
+      url = paste0("https:", url),
+      filename = basename(url)
+    ) %>%
+    pwalk(download_file, basedir = here::here(DATA_DIR, "1980-1990"))
+
+  withr::with_dir(here::here(DATA_DIR, "1980-1990"), {
+    fs::dir_ls(regexp = "zip") %>%
+      purrr::walk(unzip)
+  })
+
+  ## ----download-popest-tables-1990-2000, eval=FALSE------------------------
+  xml2::read_html("https://www.census.gov/data/datasets/time-series/demo/popest/intercensal-1990-2000-national.html") %>%
+    rvest::html_nodes("#listArticlesContainer_list_0 .uscb-text-link") %>%
+    rvest::html_attrs() %>%
+    purrr::transpose() %>%
+    as_tibble() %>%
+    unnest() %>%
+    select(filename = name, url = href) %>%
+    mutate(url = paste0("https:", url),
+           filename = paste0(filename, ".csv")) %>%
+    pwalk(download_file, basedir = here::here(DATA_DIR, "1990-2000"))
+
+  ## ----download-popest-tables-2000-2010----
+  ## "2000-2010/us-est00int-01.xls"
+  download_file(
+    "us-est00int-alldata-5yr.csv",
+    "https://www2.census.gov/programs-surveys/popest/datasets/2000-2010/intercensal/national/us-est00int-alldata-5yr.csv",
+    here::here(DATA_DIR, "2000-2010")
+  )
+
+
+  ## ----download-popest-tables-2010----
+  ## "2010-2017/PEP_2017_PEPSYASEXN_with_ann.csv"
+  ## Download manually from https://factfinder.census.gov/bkmk/table/1.0/en/PEP/2017/PEPSYASEXN/0100000US
+  fs::dir_create(here::here(DATA_DIR, "2010-2017"))
+  if (!fs::file_exists(here::here(DATA_DIR, "2010-2017", "PEP_2017_PEPSYASEXN_with_ann.csv"))) {
+    rlang::abort(paste(
+      "Download the Annual Estimates of Resident Population by Single Year of Age from:\n",
+      "https://factfinder.census.gov/bkmk/table/1.0/en/PEP/2017/PEPSYASEXN/0100000US\n",
+      "and extract `PEP_2017_PEPSYASEXN_with_ann.csv` into:", here::here(DATA_DIR, "2010-2017")
+    ))
+  }
+
+  ## ----download-popest-projections
+  ## "np2017_d1.csv"
+  download_file(
+    "np2017_d1.csv",
+    "https://www2.census.gov/programs-surveys/popproj/datasets/2017/2017-popproj/np2017_d1.csv",
+    here::here(DATA_DIR)
+  )
+}

R/01_process-data.R

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+library(dplyr)
+library(purrr)
+library(tidyr)
+library(stringr)
+library(readr)
+library(readxl)
+library(fs)
+# library(here)
+library(lubridate)
+library(forcats)
+
+DATA_DIR <- "data-raw"
+
+pop <- list()
+
+import_pre1980 <- function(file) {
+  # cli::cat_bullet(file)
+  year <- str_extract(file, "\\d{4}\\.csv")
+  year <- as.integer(sub("\\.csv", "", year))
+  skip <- if (year >= 1960) 8 else 9
+  x <- read_csv(file, col_names = FALSE, skip = skip,
+                col_types = cols_only(
+                  X1 = col_guess(), X2 = col_guess(),
+                  X3 = col_guess(), X4 = col_guess()
+
+                ))
+  colnames(x) <- c("Age", "Total", "Total Male", "Total Female")
+  x <- x[1:max(which(x$Age %in% c("75+", "85+"))), ]
+  stopifnot(nrow(x) %in% c(76L, 86L))
+  x %>%
+    mutate(
+      Year = year,
+      Age = sub("\\+", "", Age),
+      Age = as.integer(Age)
+    ) %>%
+    select(Year, everything())
+}
+
+pop[["1900-1980"]] <- fs::dir_ls(
+  here::here(DATA_DIR, "1900-1980")
+) %>%
+  map_dfr(import_pre1980)
+
+import_1980_1990 <- function(file) {
+  read_fwf(
+    file,
+    col_positions = fwf_positions(
+      start = c(3, 5, 7, 12, 22, 32),
+      end = c(4, 6, 9, 20, 30, 40),
+      col_names = c("Month", "Year", "Age", "Total", "Total Male", "Total Female")
+    ),
+    skip = 0
+  ) %>%
+    filter(Age < 101, Month == 4, Year == min(Year)) %>%
+    mutate(Year = Year + 1900)
+}
+
+pop[["1980-1990"]] <- fs::dir_ls(
+  here::here(DATA_DIR, "1980-1990"),
+  regexp = "TXT"
+) %>%
+  map_dfr(import_1980_1990)
+
+import_1990_2000 <- function(file) {
+  x <- read_csv(file, col_names = FALSE, skip = 3)
+  colnames(x) <- c("Month", "Age", "Total", "Total Male", "Total Female")
+
+  x %>%
+    filter(
+      !is.na(Total),
+      !str_detect(Age, "All")
+    ) %>%
+    mutate(Month = lubridate::parse_date_time(Month, "mdy")) %>%
+    filter(
+      lubridate::month(Month) == 4
+    ) %>%
+    mutate(
+      Year = lubridate::year(Month),
+      Age = str_remove(Age, "\\+"),
+      Age = as.integer(Age)
+    ) %>%
+    select(Year, everything(), -Month)
+}
+
+pop[["1990-2000"]] <- fs::dir_ls(
+  here::here(DATA_DIR, "1990-2000"),
+  regexp = "\\d{4}\\.csv"
+) %>%
+  map_dfr(import_1990_2000)
+
+# pop[["2000-2010"]] <-  read_csv(
+#   here::here(DATA_DIR, "2000-2010/us-est00int-alldata.csv")
+# ) %>%
+#   select(Month = MONTH, Year = YEAR, Age = AGE,
+#          Total = TOT_POP, `Total Male` = TOT_MALE,
+#          `Total Female` = TOT_FEMALE) %>%
+#   filter(Year > 2000, Age <= 125)
+
+# pop[["2000-2010"]] <-  readxl::read_xls(
+#   here::here(DATA_DIR, "2000-2010/us-est00int-01.xls"),
+#   sheet = "Sheet1"
+# ) %>%
+#   mutate(age_label = Age, Age = str_extract(Age, "\\d{1,3}"), Age = as.integer(Age) - str_detect(age_label, "Under") * 5) %>%
+#   select(-age_label) %>%
+#   gather(Year, value, -1:-2) %>%
+#   spread(Group, value) %>%
+#   mutate(Year = as.integer(Year)) %>%
+#   filter(Year > 2000)
+
+pop[["2000-2010"]] <-
+  readr::read_csv(
+    here::here(DATA_DIR, "2000-2010", "us-est00int-alldata-5yr.csv")
+  ) %>%
+    select(
+      Year = year,
+      Age = AGEGRP,
+      Total = TOT_POP,
+      `Total Male` = TOT_MALE,
+      `Total Female` = TOT_FEMALE
+    ) %>%
+    filter(Age != 0, Year > 2000, Year < 2010) %>%
+    mutate(Age = (Age - 1) * 5)
+
+pop[["2010-2017"]] <- readr::read_csv(
+  here::here(DATA_DIR, "2010-2017/PEP_2017_PEPSYASEXN_with_ann.csv"),
+  skip = 1
+) %>%
+  select(-1:-4) %>%
+  gather(key, value, -Sex) %>%
+  filter(str_detect(key, "Population Estimate")) %>%
+  mutate(key = str_remove(key, "Pop.+\\) - "), key = str_remove(key, " Total - ")) %>%
+  filter(!str_detect(key, "Total|Median")) %>%
+  separate(key, c("Year", "Age"), sep = ";") %>%
+  mutate(
+    Year = as.integer(Year),
+    Age = sub("+", "", Age, fixed = TRUE),
+    Age = as.integer(Age),
+    Sex = recode(
+      Sex,
+      "Both Sexes" = "Total",
+      "Female" = "Total Female",
+      "Male" = "Total Male"
+    )
+  ) %>%
+  filter(Year < 2016) %>%
+  spread(Sex, value)
+
+# pop[["2018"]] <- readr::read_tsv(
+#   here::here(DATA_DIR, "national-population-projections_2014-2060.txt")
+# ) %>%
+#   select(-1) %>%
+#   filter(!is.na(Year)) %>%
+#   select(contains("Code"), contains("Pop")) %>%
+#   {colnames(.) <- sub(" Code", "", colnames(.)); .} %>%
+#   group_by(Year, Age) %>%
+#   mutate(Total = sum(`Projected Populations`)) %>%
+#   ungroup() %>%
+#   spread(Gender, `Projected Populations`) %>%
+#   mutate(
+#     Age = sub("+", "", Age, fixed = TRUE),
+#     Age = as.integer(Age)
+#   ) %>%
+#   filter(Year > 2017)
+
+pop[["2018"]] <-
+  readr::read_csv(
+    here::here(DATA_DIR, "np2017_d1.csv")
+  ) %>%
+  filter(ORIGIN == 0, RACE == 0) %>%
+  gather(Age, n, -SEX:-YEAR) %>%
+  mutate(SEX = c("Total", "Total Male", "Total Female")[SEX + 1]) %>%
+  spread(SEX, n) %>%
+  filter(Age != "TOTAL_POP") %>%
+  mutate(
+    Age = sub("POP_", "", Age, fixed = TRUE),
+    Age = as.integer(Age)
+  ) %>%
+  select(Year = YEAR, Age, starts_with("Total"))
+
+fs::dir_create(here::here("data"))
+saveRDS(pop, here::here("data", "pop_estimates_by_age.rds"))

R/02_population-animations.R

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+## ----setup, include=FALSE------------------------------------------------
+knitr::opts_chunk$set(echo=FALSE)
+
+## ----library-------------------------------------------------------------
+library(dplyr)
+library(purrr)
+library(tidyr)
+library(stringr)
+library(readr)
+library(forcats)
+library(gganimate)
+
+DATA_DIR <- "data-raw"
+ANIM_OUT <- "anim"
+fs::dir_create(here::here(ANIM_OUT))
+
+pop <- readRDS(here::here("data", "pop_estimates_by_age.rds"))
+
+## ------------------------------------------------------------------------
+ga <-
+  pop %>%
+  bind_rows() %>%
+  mutate(age_group_int = as.integer(Age) %/% 5) %>%
+  group_by(age_group_int) %>%
+  mutate(
+    # age_group = paste(min(Age), max(Age), sep = " - "),
+    age_group = paste(min(Age)),
+    age_group = ifelse(Age >= 100, "100+", age_group)
+  ) %>%
+  ungroup() %>%
+  arrange(age_group_int, Year) %>%
+  mutate(age_group = fct_inorder(age_group)) %>%
+  select(-age_group_int) %>%
+  group_by(Year, age_group) %>%
+  summarize(Total = sum(Total)) %>%
+  group_by(Year) %>%
+  mutate(GrandTotal  = sum(Total)) %>%
+  ungroup() %>%
+  mutate(Total = Total/GrandTotal) %>%
+  arrange(Year, age_group) %>%
+  # complete(Year, age_group, fill = list(Total = 0)) %>%
+  # filter(Year > 2000) %>%
+  {
+    ggplot(.) +
+      # aes(age_group, Total) +
+      # geom_vline(xintercept = c(1980, 1990), color = "grey50") +
+      # geom_line(aes(group = age_group)) +
+      # geom_point(size = 0.2) +
+      # geom_col(fill = "grey40") +
+      # facet_wrap(~ Year) +
+      # coord_flip() +
+      # scale_y_continuous(labels = function(x) {grkmisc::pretty_num(x, decimal_digits = 0)}) +
+      geom_segment(aes(yend = age_group, y = age_group, xend = Total), x = 0, size = 5) +
+      geom_segment(aes(yend = age_group, y = age_group, xend = Total),
+                   x = 0,
+                   size = 5,
+                   color = "grey40",
+                   alpha = 0.25,
+                   data = filter(., Year == min(Year)) %>% select(-Year)
+      ) +
+      # annotate("text", label = "{closest_state}", x = max(.$Total) * 0.8, y = 20, size = 12) +
+      theme_minimal(base_size = 16, base_family = "Fira Sans Condensed") +
+      theme(panel.grid.major.y = element_blank()) +
+      scale_x_continuous(labels = scales::percent, expand = c(0, 0, 0.05, 0)) +
+      # labs(x = "Percent of Population", y = NULL) +
+      theme(axis.title.x = element_text(hjust = 0.5, size = 30)) +
+      labs(x = "{closest_state}", y = NULL) +
+      gganimate::transition_time(Year) +
+      gganimate::transition_states(Year, 1, 0, wrap = FALSE) +
+      gganimate::ease_aes("linear") +
+      gganimate::enter_fade()
+  }
+
+grkmisc::logger(msg = "Writing MP4 population bar animation")
+anim_save(
+  here::here(ANIM_OUT, "pop-anim.mp4"),
+  animate(ga, width = 1040*2, height = 480*2, res = 144,
+          fps = 20, nframes = 161*2, renderer = av_renderer())
+)
+grkmisc::logger(msg = "Writing gif population bar animation")
+anim_save(
+  here::here(ANIM_OUT, "pop-anim.gif"),
+  animate(ga, width = 1040*2, height = 480*2, res = 144,
+          fps = 20, nframes = 161*2,
+          renderer = gganimate::gifski_renderer(loop = FALSE))
+)
+
+pop %>%
+  bind_rows() %>%
+  mutate(age_group_int = as.integer(Age) %/% 5) %>%
+  select(1:3, age_group_int) %>%
+  filter(age_group_int > 5) %>%
+  mutate(
+    old = age_group_int >= 13,
+    old = c("Adult", "Older Adult")[old + 1]
+  ) %>%
+  group_by(Year, old) %>%
+  summarize(n = sum(Total)) %>%
+  spread(old, n) %>%
+  {
+    ggplot(.) +
+      aes(x = Year) +
+      geom_segment(aes(xend = Year, y = Adult, yend = `Older Adult`)) +
+      geom_point(aes(y = Adult), color = grkmisc::moffitt_colors$blue) +
+      geom_point(aes(y = `Older Adult`), color = grkmisc::moffitt_colors$orange, size = 2)
+  } %>%
+  invisible()
+
+elder_dependency <-
+  pop %>%
+  bind_rows() %>%
+  mutate(age_group_int = as.integer(Age) %/% 5) %>%
+  select(1:3, age_group_int) %>%
+  mutate(
+    old = case_when(
+      age_group_int < 3 ~ "Children",
+      age_group_int >= 13 ~ "Older Adult",
+      TRUE ~ "Adult"
+    )
+  ) %>%
+  group_by(Year, old) %>%
+  summarize(n = sum(Total)) %>%
+  spread(old, n) %>%
+  mutate(
+    dependency_elder = `Older Adult` / Adult,
+    dependency_all = (`Older Adult` + Children) / Adult
+  )
+
+ga_elder_dep <-
+  ggplot(elder_dependency) +
+  aes(Year, dependency_elder) +
+  geom_point(color = "#00589a") +
+  geom_point(data = filter(elder_dependency, Year == 2018), size = 4, color = "#eb1455") +
+  labs(y = "Dependency Ratio", x = NULL) +
+  theme_minimal(base_size = 24, base_family = "Fira Sans Condensed") +
+  scale_y_continuous(labels = scales::percent) +
+  gganimate::transition_states(Year, 1, 0, FALSE) +
+  gganimate::shadow_mark()
+
+grkmisc::logger(msg = "Writing mp4 elder dependency animation")
+anim_save(
+  here::here(ANIM_OUT, "pop-dependency.mp4"),
+  animate(ga_elder_dep, width = 1040*2, height = 480*2, res = 144,
+          fps = 18, nframes = 161, renderer = av_renderer())
+)
+grkmisc::logger(msg = "Writing gif elder dependency animation")
+anim_save(
+  here::here(ANIM_OUT, "pop-dependency.gif"),
+  animate(ga_elder_dep, width = 1040*2, height = 480*2, res = 144,
+          fps = 18, nframes = 161,
+          renderer = gganimate::gifski_renderer(loop = FALSE))
+)
+
+elder_dep_together <-
+  elder_dependency %>%
+  ungroup() %>%
+  gather(timeseries, ratio, starts_with("depend")) %>%
+  select(-Adult:-`Older Adult`) %>%
+  mutate(timeseries = ifelse(timeseries == "dependency_elder", 0, 1))
+
+ga_dep_together <-
+  ggplot(elder_dep_together) +
+  aes(Year, ratio) +
+  geom_point(color = "#00589a") +
+  # geom_point(color = "#82c878") +
+  # geom_point(data = filter(., Year == 2018), size = 4, color = "#eb1455") +
+  labs(y = "Dependency Ratio", x = NULL) +
+  theme_minimal(base_size = 24, base_family = "Fira Sans Condensed") +
+  scale_y_continuous(labels = scales::percent) +
+  gganimate::transition_states(timeseries, 1, 0, FALSE) +
+  gganimate::view_follow(fixed_x = TRUE) +
+  gganimate::ease_aes("sine-in-out")+
+  gganimate::shadow_mark()
+
+grkmisc::logger(msg = "Writing mp4 all dependency animation")
+anim_save(
+  here::here(ANIM_OUT, "pop-dependency-both.mp4"),
+  animate(ga_dep_together, width = 1040*2, height = 480*2, res = 144,
+          fps = 18, nframes = 80,
+          renderer = av_renderer())
+)
+grkmisc::logger(msg = "Writing gif all dependency animation")
+anim_save(
+  here::here(ANIM_OUT, "pop-dependency-both.gif"),
+  animate(ga_dep_together, width = 1040*2, height = 480*2, res = 144,
+          fps = 18, nframes = 80,
+          renderer = gganimate::gifski_renderer(loop = FALSE))
+)

R/2018-10-21-population-animations.R

@@ -1,357 +0,0 @@
-## ----setup, include=FALSE------------------------------------------------
-knitr::opts_chunk$set(echo=FALSE)
-
-## ----library-------------------------------------------------------------
-library(dplyr)
-library(purrr)
-library(tidyr)
-library(stringr)
-library(readr)
-library(forcats)
-library(gganimate)
-
-DATA_DIR <- "data-raw"
-ANIM_OUT <- "anim"
-
-fs::dir_create(here::here(DATA_DIR))
-fs::dir_create(here::here(ANIM_OUT))
-
-DOWNLOAD_DATA <- TRUE
-
-
-download_file <- function(filename, url, basedir) {
-  filename <- gsub(" ", "_", filename)
-  filename <- gsub("/", "-", filename)
-  fs::dir_create(basedir)
-  download.file(url, destfile = fs::path(basedir, filename))
-}
-
-if (DOWNLOAD_DATA) {
-  ## ----download-popest-tables-1900-1980, eval=FALSE------------------------
-  x <- xml2::read_html("https://www2.census.gov/programs-surveys/popest/tables/1900-1980/national/asrh/")
-
-  xx <-
-    data_frame(
-      filename = rvest::html_nodes(x, "a") %>% rvest::html_text(),
-      url = rvest::html_nodes(x, "a") %>% rvest::html_attr("href")
-    ) %>%
-    filter(str_detect(url, "\\.csv")) %>%
-    mutate(url = str_c(
-      "https://www2.census.gov/programs-surveys/popest/tables/1900-1980/national/asrh/",
-      url
-    ))
-
-  pwalk(xx, download_file, basedir = here::here(DATA_DIR, "1900-1980"))
-
-  ## ----download-popest-tables-1980-1990, eval=FALSE------------------------
-  xml2::read_html("https://www.census.gov/data/datasets/time-series/demo/popest/1980s-national.html") %>%
-    rvest::html_nodes(".list.section .uscb-text-link") %>%
-    rvest::html_attrs() %>%
-    purrr::transpose() %>%
-    as_tibble() %>%
-    unnest() %>%
-    select(filename = name, url = href) %>%
-    mutate(url = paste0("https:", url)) %>%
-    pwalk(download_file, basedir = here::here(DATA_DIR, "1980-1990"))
-
-  ## ----download-popest-tables-1990-2000, eval=FALSE------------------------
-  xml2::read_html("https://www.census.gov/data/datasets/time-series/demo/popest/intercensal-1990-2000-national.html") %>%
-    rvest::html_nodes("#listArticlesContainer_list_0 .uscb-text-link") %>%
-    rvest::html_attrs() %>%
-    purrr::transpose() %>%
-    as_tibble() %>%
-    unnest() %>%
-    select(filename = name, url = href) %>%
-    mutate(url = paste0("https:", url),
-           filename = paste0(filename, ".csv")) %>%
-    pwalk(download_file, basedir = here::here(DATA_DIR, "1990-2000"))
-
-  ## ----download-popest-tables-2000-2010----
-  ## "2000-2010/us-est00int-01.xls"
-  download_file(
-    "us-est00int-alldata-5yr.csv",
-    "https://www2.census.gov/programs-surveys/popest/datasets/2000-2010/intercensal/national/us-est00int-alldata-5yr.csv",
-    here::here(DATA_DIR, "2000-2010")
-  )
-
-
-  ## ----download-popest-tables-2010----
-  ## "2010-2017/PEP_2017_PEPSYASEXN_with_ann.csv"
-  ## Download manually from https://factfinder.census.gov/bkmk/table/1.0/en/PEP/2017/PEPSYASEXN/0100000US
-
-  ## ----download-popest-projections
-  ## "np2017_d1.csv"
-  download_file(
-    "np2017_d1.csv",
-    "https://www2.census.gov/programs-surveys/popproj/datasets/2017/2017-popproj/np2017_d1.csv",
-    here::here(DATA_DIR)
-  )
-}
-
-## ----import-historical---------------------------------------------------
-pop <- list()
-
-import_pre1980 <- function(file) {
-  # cli::cat_bullet(file)
-  year <- str_extract(file, "\\d{4}\\.csv")
-  year <- as.integer(sub("\\.csv", "", year))
-  skip <- if (year >= 1960) 8 else 9
-  x <- read_csv(file, col_names = FALSE, skip = skip,
-                col_types = cols_only(
-                  X1 = col_guess(), X2 = col_guess(),
-                  X3 = col_guess(), X4 = col_guess()
-
-                ))
-  colnames(x) <- c("Age", "Total", "Total Male", "Total Female")
-  x <- x[1:max(which(x$Age %in% c("75+", "85+"))), ]
-  stopifnot(nrow(x) %in% c(76L, 86L))
-  x %>%
-    mutate(
-      Year = year,
-      Age = sub("\\+", "", Age),
-      Age = as.integer(Age)
-    ) %>%
-    select(Year, everything())
-}
-
-pop[["1900-1980"]] <- fs::dir_ls(
-  here::here(DATA_DIR, "1900-1980")
-  ) %>%
-  map_dfr(import_pre1980)
-
-import_1980_1990 <- function(file) {
-  read_fwf(
-    file,
-    col_positions = fwf_positions(
-      start = c(3, 5, 7, 12, 22, 32),
-      end = c(4, 6, 9, 20, 30, 40),
-      col_names = c("Month", "Year", "Age", "Total", "Total Male", "Total Female")
-    ),
-    skip = 0
-  ) %>%
-    filter(Age < 101, Month == 4, Year == min(Year)) %>%
-    mutate(Year = Year + 1900)
-}
-
-pop[["1980-1990"]] <- fs::dir_ls(
-  here::here(DATA_DIR, "1980-1990"),
-  regexp = "TXT"
-) %>%
-  map_dfr(import_1980_1990)
-
-import_1990_2000 <- function(file) {
-  x <- read_csv(file, col_names = FALSE, skip = 3)
-  colnames(x) <- c("Month", "Age", "Total", "Total Male", "Total Female")
-
-  x %>%
-    filter(
-      !is.na(Total),
-      !str_detect(Age, "All")
-    ) %>%
-    mutate(Month = lubridate::parse_date_time(Month, "mdy")) %>%
-    filter(
-      lubridate::month(Month) == 4
-    ) %>%
-    mutate(
-      Year = lubridate::year(Month),
-      Age = str_remove(Age, "\\+"),
-      Age = as.integer(Age)
-    ) %>%
-    select(Year, everything(), -Month)
-}
-
-pop[["1990-2000"]] <- fs::dir_ls(
-  here::here(DATA_DIR, "1990-2000"),
-  regexp = "\\d{4}\\.csv"
-) %>%
-  map_dfr(import_1990_2000)
-
-# pop[["2000-2010"]] <-  read_csv(
-#   here::here(DATA_DIR, "2000-2010/us-est00int-alldata.csv")
-# ) %>%
-#   select(Month = MONTH, Year = YEAR, Age = AGE,
-#          Total = TOT_POP, `Total Male` = TOT_MALE,
-#          `Total Female` = TOT_FEMALE) %>%
-#   filter(Year > 2000, Age <= 125)
-
-pop[["2000-2010"]] <-  readxl::read_xls(
-  here::here(DATA_DIR, "2000-2010/us-est00int-01.xls"),
-  sheet = "Sheet1"
-) %>%
-  mutate(age_label = Age, Age = str_extract(Age, "\\d{1,3}"), Age = as.integer(Age) - str_detect(age_label, "Under") * 5) %>%
-  select(-age_label) %>%
-  gather(Year, value, -1:-2) %>%
-  spread(Group, value) %>%
-  mutate(Year = as.integer(Year)) %>%
-  filter(Year > 2000)
-
-pop[["2010-2017"]] <- readr::read_csv(
-  here::here(DATA_DIR, "2010-2017/PEP_2017_PEPSYASEXN_with_ann.csv"),
-  skip = 1
-) %>%
-  select(-1:-4) %>%
-  gather(key, value, -Sex) %>%
-  filter(str_detect(key, "Population Estimate")) %>%
-  mutate(key = str_remove(key, "Pop.+\\) - "), key = str_remove(key, " Total - ")) %>%
-  filter(!str_detect(key, "Total|Median")) %>%
-  separate(key, c("Year", "Age"), sep = ";") %>%
-  mutate(
-    Year = as.integer(Year),
-    Age = sub("+", "", Age, fixed = TRUE),
-    Age = as.integer(Age),
-    Sex = recode(
-      Sex,
-      "Both Sexes" = "Total",
-      "Female" = "Total Female",
-      "Male" = "Total Male"
-    )
-  ) %>%
-  spread(Sex, value)
-
-# pop[["2018"]] <- readr::read_tsv(
-#   here::here(DATA_DIR, "national-population-projections_2014-2060.txt")
-# ) %>%
-#   select(-1) %>%
-#   filter(!is.na(Year)) %>%
-#   select(contains("Code"), contains("Pop")) %>%
-#   {colnames(.) <- sub(" Code", "", colnames(.)); .} %>%
-#   group_by(Year, Age) %>%
-#   mutate(Total = sum(`Projected Populations`)) %>%
-#   ungroup() %>%
-#   spread(Gender, `Projected Populations`) %>%
-#   mutate(
-#     Age = sub("+", "", Age, fixed = TRUE),
-#     Age = as.integer(Age)
-#   ) %>%
-#   filter(Year > 2017)
-
-pop[["2018"]] <-
-  readr::read_csv(
-    here::here(DATA_DIR, "np2017_d1.csv")
-  ) %>%
-    gather(Age, n, -SEX:-YEAR) %>%
-    mutate(SEX = c("Total", "Total Male", "Total Female")[SEX + 1]) %>%
-    spread(SEX, n) %>%
-    filter(Age != "TOTAL_POP") %>%
-    mutate(
-      Age = sub("POP_", "", Age, fixed = TRUE),
-      Age = as.integer(Age)
-    ) %>%
-    select(Year = YEAR, Age, starts_with("Total"))
-
-
-
-## ------------------------------------------------------------------------
-ga <-
-  pop %>%
-  bind_rows() %>%
-  mutate(age_group_int = as.integer(Age) %/% 5) %>%
-  group_by(age_group_int) %>%
-  mutate(
-    # age_group = paste(min(Age), max(Age), sep = " - "),
-    age_group = paste(min(Age)),
-    age_group = ifelse(Age >= 100, "100+", age_group)
-  ) %>%
-  ungroup() %>%
-  arrange(age_group_int, Year) %>%
-  mutate(age_group = fct_inorder(age_group)) %>%
-  select(-age_group_int) %>%
-  group_by(Year, age_group) %>%
-  summarize(Total = sum(Total)) %>%
-  group_by(Year) %>%
-  mutate(GrandTotal  = sum(Total)) %>%
-  ungroup() %>%
-  mutate(Total = Total/GrandTotal) %>%
-  arrange(Year, age_group) %>% View()
-  # complete(Year, age_group, fill = list(Total = 0)) %>%
-  # filter(Year > 2000) %>%
-  {
-    ggplot(.) +
-      # aes(age_group, Total) +
-      # geom_vline(xintercept = c(1980, 1990), color = "grey50") +
-      # geom_line(aes(group = age_group)) +
-      # geom_point(size = 0.2) +
-      # geom_col(fill = "grey40") +
-      # facet_wrap(~ Year) +
-      # coord_flip() +
-      # scale_y_continuous(labels = function(x) {grkmisc::pretty_num(x, decimal_digits = 0)}) +
-      geom_segment(aes(yend = age_group, y = age_group, xend = Total), x = 0, size = 5) +
-      geom_segment(aes(yend = age_group, y = age_group, xend = Total),
-                   x = 0,
-                   size = 5,
-                   color = "grey40",
-                   alpha = 0.25,
-                   data = filter(., Year == min(Year)) %>% select(-Year)
-      ) +
-      # annotate("text", label = "{closest_state}", x = max(.$Total) * 0.8, y = 20, size = 12) +
-      theme_minimal(base_size = 16, base_family = "Fira Sans Condensed") +
-      theme(panel.grid.major.y = element_blank()) +
-      scale_x_continuous(labels = scales::percent, expand = c(0, 0)) +
-      # labs(x = "Percent of Population", y = NULL) +
-      theme(axis.title.x = element_text(hjust = 0.5, size = 30)) +
-      labs(x = "{closest_state}", y = NULL) +
-      gganimate::transition_time(Year) +
-      gganimate::transition_states(Year, 1, 0, wrap = FALSE) +
-      gganimate::ease_aes("linear") +
-      gganimate::enter_fade()
-  }
-
-anim_save(
-  here::here(ANIM_OUT, "pop-anim.mp4"),
-  animate(ga, width = 1040*2, height = 480*2, res = 144,
-          fps = 20, nframes = 161*2,
-          renderer = av_renderer())
-)
-
-pop %>%
-  bind_rows() %>%
-  mutate(age_group_int = as.integer(Age) %/% 5) %>%
-  select(1:3, age_group_int) %>%
-  filter(age_group_int > 5) %>%
-  mutate(
-    old = age_group_int >= 13,
-    old = c("Adult", "Older Adult")[old + 1]
-  ) %>%
-  group_by(Year, old) %>%
-  summarize(n = sum(Total)) %>%
-  spread(old, n) %>%
-  {
-    ggplot(.) +
-      aes(x = Year) +
-      geom_segment(aes(xend = Year, y = Adult, yend = `Older Adult`)) +
-      geom_point(aes(y = Adult), color = grkmisc::moffitt_colors$blue) +
-      geom_point(aes(y = `Older Adult`), color = grkmisc::moffitt_colors$orange)
-  }
-
-ga2 <-
-  pop %>%
-  bind_rows() %>%
-  mutate(age_group_int = as.integer(Age) %/% 5) %>%
-  select(1:3, age_group_int) %>%
-  filter(age_group_int >= 3) %>%
-  mutate(
-    old = age_group_int >= 13,
-    old = c("Adult", "Older Adult")[old + 1]
-  ) %>%
-  group_by(Year, old) %>%
-  summarize(n = sum(Total)) %>%
-  spread(old, n) %>%
-  mutate(ratio = `Older Adult` / Adult) %>%
-  {
-    ggplot(.) +
-      aes(Year, ratio) +
-      geom_point() +
-      geom_point(data = filter(., Year == 2018), color = "red") +
-      labs(y = "Dependency Ratio", x = NULL) +
-      theme_minimal(base_size = 16, base_family = "Fira Sans Condensed") +
-      scale_y_continuous(labels = scales::percent) +
-      gganimate::transition_states(Year, 1, 0, FALSE) +
-      gganimate::shadow_mark()
-  }
-
-anim_save(
-  here::here(ANIM_OUT, "population-projections/pop-dependency.mp4"),
-  animate(ga2, width = 1040*2, height = 480*2, res = 144,
-          fps = 18, nframes = 161,
-          renderer = av_renderer())
-)