ryanburge/cces_nones_maps.R

## cces_nones_maps.R

cces <- cces %>%
  mutate(trad = frcode(evangelical == 1 ~ "Evangelical",
                       mainline    == 1 ~ "Mainline",
                       bprot       == 1 ~ "Black Protestant",
                       catholic    == 1 ~ "Catholic",
                       jewish      == 1 ~ "Jewish",
                       other       == 1 ~ "Other Faith",
                       none        == 1 ~ "No Religion",
                       TRUE ~ "Unclassified"))

graph <- cces %>%
  filter(year > 2006) %>%
  group_by(year) %>%
  ct(trad, wt = weight, show_na = TRUE)

colors <- c("Evangelical" = "#1F77B4", "Mainline" = "#FF7F0E", "Black Protestant" = "#E377C2", "Catholic" = "#D62728", "Jewish" = "#9467BD", "Other Faith" = "#8C564B", "No Religion" = "#2CA02C", "Unclassified" = "#FF9896")

graph %>%
  ggplot(., aes(x = year, y = pct, group = trad, color = trad)) +
  geom_point() +
  geom_line() +
  theme_gg("Abel") +
  scale_color_manual(values = colors) +
  scale_y_continuous(labels = percent) +
  scale_x_continuous(breaks = c(2008, 2010, 2012, 2014, 2016, 2018)) +
  annotate("text", x= 2013, y = .272, label = "No Religion", size = 4, family = "font") +
  annotate("text", x= 2013, y = .233, label = "Evangelical", size = 4, family = "font") +
  annotate("text", x= 2013, y = .19, label = "Catholic", size = 4, family = "font") +
  annotate("text", x= 2013, y = .141, label = "Mainline", size = 4, family = "font") +
  annotate("text", x= 2013, y = .095, label = "Other Faith", size = 4, family = "font") +
  annotate("text", x= 2013, y = .067, label = "Unclassified", size = 4, family = "font") +
  annotate("text", x= 2013, y = .038, label = "Black Protestant", size = 4, family = "font") +
  annotate("text", x= 2013, y = .015, label = "Jewish", size = 4, family = "font") +
  labs(x = "", y = "", title = "Changes in Religion over the Last Decade", caption = "@ryanburge\nData: CCES 2008-2018") +
  ggsave("E://growth_decline_lines.png", type = "cairo-png")


### Two Maps ####


com_fun <- function(df, var, name) {

  var <- enquo(var)

  st <- df %>%
    filter(year == 2008) %>%
    group_by(state) %>%
    mean_ci(!! var, wt = weight)

  st1 <- df %>%
    filter(year == 2018) %>%
    group_by(state) %>%
    mean_ci(!! var, wt = weight)

  bind_cols(st, st1) %>%
    mutate(change = mean1 - mean) %>%
    select(state, change) %>%
    mutate(group = name)
}

aaa1 <- cces %>% com_fun(evangelical, "Evangelical")
aaa2 <- cces %>% com_fun(mainline, "Mainline")
aaa3 <- cces %>% com_fun(bprot, "Black Protestant")
aaa4 <- cces %>% com_fun(catholic, "Catholic")
aaa5 <- cces %>% com_fun(jewish, "Jewish")
aaa6 <- cces %>% com_fun(other, "Other Faith")
aaa7 <- cces %>% com_fun(none, "No Religion")

graph <- bind_df("aaa")

gg <- graph %>%
  filter(change >= 0) %>%
  group_by(state) %>%
  top_n(1, change) %>%
  ungroup(state) %>%
  mutate(id = tolower(state))


library(maps)
us_states <- map_data("state")


mapp <- left_join(us_states, gg, by = c("region")) %>% as_tibble()

mapp %>%
  ggplot(., mapping = aes(x = long, y = lat, group = group.x, fill = group.y)) +
  geom_polygon(color = "gray90", size = 0.1) +
  coord_map(projection = "albers", lat0 = 39, lat1 = 45) +
  scale_fill_manual(values = colors) +
  theme_gg("Abel") +
  theme(axis.line = element_blank(),
        axis.text = element_blank(),
        axis.ticks = element_blank(),
        axis.title = element_blank(),
        panel.background = element_blank(),
        panel.border = element_blank(),
        panel.grid = element_blank(),
        panel.spacing = unit(0, "lines"),
        plot.background = element_blank(),
        legend.justification = c(0, 0),
        legend.position = "bottom",
        plot.title = element_text(size = 14)) +
  labs(title = "Which Religious Group Has Grown the Most Per State? (2008-2018)", caption = "@ryanburge\nData: CCES 2008 + 2018") +
  ggsave("E://biggest_change_map_growth.png", type = "cairo-png")


graph <- bind_df("aaa")

gg <- graph %>%
  filter(group != "Other Faith") %>%
  filter(change < 0) %>%
  mutate(absolute = abs(change)) %>%
  group_by(state) %>%
  top_n(1, absolute) %>%
  ungroup(state) %>%
  mutate(region = tolower(state))

library(maps)
us_states <- map_data("state")

mapp <- left_join(us_states, gg, by = c("region")) %>% as_tibble()

mapp %>%
  ggplot(., mapping = aes(x = long, y = lat, group = group.x, fill = group.y)) +
  geom_polygon(color = "gray90", size = 0.1) +
  coord_map(projection = "albers", lat0 = 39, lat1 = 45) +
  scale_fill_manual(values = colors) +
  theme_gg("Abel") +
  theme(axis.line = element_blank(),
        axis.text = element_blank(),
        axis.ticks = element_blank(),
        axis.title = element_blank(),
        panel.background = element_blank(),
        panel.border = element_blank(),
        panel.grid = element_blank(),
        panel.spacing = unit(0, "lines"),
        plot.background = element_blank(),
        legend.justification = c(0, 0),
        legend.position = "bottom",
        plot.title = element_text(size = 14)) +
  labs(title = "Which Religious Group Has Declined the Most Per State? (2008-2018)", caption = "@ryanburge\nData: CCES 2008 + 2018") +
  ggsave("E://biggest_change_map_decline.png", type = "cairo-png")


nn <- bind_df("aaa") %>%
  filter(group == "No Religion") %>%
  mutate(region = tolower(state))

mapp <- left_join(us_states, nn, by = c("region")) %>% as_tibble()


mapp %>%
  ggplot(., mapping = aes(x = long, y = lat, group = group.x, fill = change)) +
  geom_polygon(color = "gray90", size = 0.1) +
  coord_map(projection = "albers", lat0 = 39, lat1 = 45) +
  scale_fill_gradient(low = "azure3", high = "#2CA02C", labels = percent) +
  theme_gg("Abel") +
  theme(axis.line = element_blank(),
        axis.text = element_blank(),
        axis.ticks = element_blank(),
        axis.title = element_blank(),
        panel.background = element_blank(),
        panel.border = element_blank(),
        panel.grid = element_blank(),
        panel.spacing = unit(0, "lines"),
        plot.background = element_blank(),
        legend.justification = c(0, 0),
        legend.position = "right",
        plot.title = element_text(size = 14)) +
  labs(title = "The Change in Nones (2008-2018)", caption = "@ryanburge\nData: CCES 2008 + 2018") +
  ggsave("E://nones_change_map1.png", type = "cairo-png")


## Dumbbells #####
st08 <- cces %>%
  filter(year == 2008) %>%
  group_by(state) %>%
  mean_ci(none, wt = weight)

st18 <- cces %>%
  filter(year == 2018) %>%
  group_by(state) %>%
  mean_ci(none, wt = weight)

graph <- bind_cols(st08, st18) %>%
  rename(mean08 = mean) %>%
  rename(mean18 = mean1) %>%
  mutate(diff1 = mean18 - mean08) %>%
  mutate(diff = diff1 * 100) %>%
  mutate(diff = round(diff, 2)) %>%
  mutate(diff = paste0(diff, '%'))

graph %>%
  ggplot(., aes(x = mean08, xend = mean18, y = reorder(state, mean18))) +
  geom_dumbbell(colour_x = "#2F2D29", colour_xend = "#FE6F27",  size = .75, size_x =  2.75, size_xend = 2.75, shape = 21, stroke =2, fill = "white") +
  theme_gg("Abel") +
  theme(axis.text.y = element_text(size = 14)) +
  theme(axis.text.x = element_text(size = 18)) +
  theme(plot.title = element_text(size = 22)) +
  labs(x= "Percent Nones", y = "Religious Tradition", title = "Change in Nones from 2008 to 2018", caption = "Data: CCES 2008 and 2018", subtitle = "") +
  scale_x_continuous(labels = percent) +
  geom_text(data = filter(graph, state == "Oregon"), aes(x= mean08, y = state, label = "2008"), hjust = 1.35, family = "font", size = 4) +
  geom_text(data = filter(graph, state == "Oregon"), aes(x= mean18, y = state, label = "2018"), hjust = -.35, family = "font", size = 4) +
  geom_text(data = filter(graph, state == "Kentucky"), aes(x= mean08, y = state, label = "2008"), hjust = 1.35, family = "font", size = 4) +
  geom_text(data = filter(graph, state == "Kentucky"), aes(x= mean18, y = state, label = "2018"), hjust = -.35, family = "font", size = 4) +
  geom_rect(data=graph, aes(xmin=.48, xmax=.50, ymin=-Inf, ymax=Inf), fill="gray") +
  geom_text(data=graph, aes(label=diff, y=state, x=.49), fontface="bold", size=4, family="font") +
  # geom_text(data=filter(graph, state=="Oregon"), aes(x=.49 y=state, label=""), size=10, fontface="bold", family="font", vjust = -.65) +
  ggsave("E://dumbbell_nones.png", type = "cairo-png", height = 10, width = 10)

	cces <- cces %>%
	mutate(trad = frcode(evangelical == 1 ~ "Evangelical",
	mainline == 1 ~ "Mainline",
	bprot == 1 ~ "Black Protestant",
	catholic == 1 ~ "Catholic",
	jewish == 1 ~ "Jewish",
	other == 1 ~ "Other Faith",
	none == 1 ~ "No Religion",
	TRUE ~ "Unclassified"))

	graph <- cces %>%
	filter(year > 2006) %>%
	group_by(year) %>%
	ct(trad, wt = weight, show_na = TRUE)

	colors <- c("Evangelical" = "#1F77B4", "Mainline" = "#FF7F0E", "Black Protestant" = "#E377C2", "Catholic" = "#D62728", "Jewish" = "#9467BD", "Other Faith" = "#8C564B", "No Religion" = "#2CA02C", "Unclassified" = "#FF9896")

	graph %>%
	ggplot(., aes(x = year, y = pct, group = trad, color = trad)) +
	geom_point() +
	geom_line() +
	theme_gg("Abel") +
	scale_color_manual(values = colors) +
	scale_y_continuous(labels = percent) +
	scale_x_continuous(breaks = c(2008, 2010, 2012, 2014, 2016, 2018)) +
	annotate("text", x= 2013, y = .272, label = "No Religion", size = 4, family = "font") +
	annotate("text", x= 2013, y = .233, label = "Evangelical", size = 4, family = "font") +
	annotate("text", x= 2013, y = .19, label = "Catholic", size = 4, family = "font") +
	annotate("text", x= 2013, y = .141, label = "Mainline", size = 4, family = "font") +
	annotate("text", x= 2013, y = .095, label = "Other Faith", size = 4, family = "font") +
	annotate("text", x= 2013, y = .067, label = "Unclassified", size = 4, family = "font") +
	annotate("text", x= 2013, y = .038, label = "Black Protestant", size = 4, family = "font") +
	annotate("text", x= 2013, y = .015, label = "Jewish", size = 4, family = "font") +
	labs(x = "", y = "", title = "Changes in Religion over the Last Decade", caption = "@ryanburge\nData: CCES 2008-2018") +
	ggsave("E://growth_decline_lines.png", type = "cairo-png")




	### Two Maps ####


	com_fun <- function(df, var, name) {

	var <- enquo(var)

	st <- df %>%
	filter(year == 2008) %>%
	group_by(state) %>%
	mean_ci(!! var, wt = weight)

	st1 <- df %>%
	filter(year == 2018) %>%
	group_by(state) %>%
	mean_ci(!! var, wt = weight)

	bind_cols(st, st1) %>%
	mutate(change = mean1 - mean) %>%
	select(state, change) %>%
	mutate(group = name)
	}

	aaa1 <- cces %>% com_fun(evangelical, "Evangelical")
	aaa2 <- cces %>% com_fun(mainline, "Mainline")
	aaa3 <- cces %>% com_fun(bprot, "Black Protestant")
	aaa4 <- cces %>% com_fun(catholic, "Catholic")
	aaa5 <- cces %>% com_fun(jewish, "Jewish")
	aaa6 <- cces %>% com_fun(other, "Other Faith")
	aaa7 <- cces %>% com_fun(none, "No Religion")

	graph <- bind_df("aaa")

	gg <- graph %>%
	filter(change >= 0) %>%
	group_by(state) %>%
	top_n(1, change) %>%
	ungroup(state) %>%
	mutate(id = tolower(state))



	library(maps)
	us_states <- map_data("state")


	mapp <- left_join(us_states, gg, by = c("region")) %>% as_tibble()

	mapp %>%
	ggplot(., mapping = aes(x = long, y = lat, group = group.x, fill = group.y)) +
	geom_polygon(color = "gray90", size = 0.1) +
	coord_map(projection = "albers", lat0 = 39, lat1 = 45) +
	scale_fill_manual(values = colors) +
	theme_gg("Abel") +
	theme(axis.line = element_blank(),
	axis.text = element_blank(),
	axis.ticks = element_blank(),
	axis.title = element_blank(),
	panel.background = element_blank(),
	panel.border = element_blank(),
	panel.grid = element_blank(),
	panel.spacing = unit(0, "lines"),
	plot.background = element_blank(),
	legend.justification = c(0, 0),
	legend.position = "bottom",
	plot.title = element_text(size = 14)) +
	labs(title = "Which Religious Group Has Grown the Most Per State? (2008-2018)", caption = "@ryanburge\nData: CCES 2008 + 2018") +
	ggsave("E://biggest_change_map_growth.png", type = "cairo-png")


	graph <- bind_df("aaa")

	gg <- graph %>%
	filter(group != "Other Faith") %>%
	filter(change < 0) %>%
	mutate(absolute = abs(change)) %>%
	group_by(state) %>%
	top_n(1, absolute) %>%
	ungroup(state) %>%
	mutate(region = tolower(state))

	library(maps)
	us_states <- map_data("state")

	mapp <- left_join(us_states, gg, by = c("region")) %>% as_tibble()

	mapp %>%
	ggplot(., mapping = aes(x = long, y = lat, group = group.x, fill = group.y)) +
	geom_polygon(color = "gray90", size = 0.1) +
	coord_map(projection = "albers", lat0 = 39, lat1 = 45) +
	scale_fill_manual(values = colors) +
	theme_gg("Abel") +
	theme(axis.line = element_blank(),
	axis.text = element_blank(),
	axis.ticks = element_blank(),
	axis.title = element_blank(),
	panel.background = element_blank(),
	panel.border = element_blank(),
	panel.grid = element_blank(),
	panel.spacing = unit(0, "lines"),
	plot.background = element_blank(),
	legend.justification = c(0, 0),
	legend.position = "bottom",
	plot.title = element_text(size = 14)) +
	labs(title = "Which Religious Group Has Declined the Most Per State? (2008-2018)", caption = "@ryanburge\nData: CCES 2008 + 2018") +
	ggsave("E://biggest_change_map_decline.png", type = "cairo-png")



	nn <- bind_df("aaa") %>%
	filter(group == "No Religion") %>%
	mutate(region = tolower(state))

	mapp <- left_join(us_states, nn, by = c("region")) %>% as_tibble()


	mapp %>%
	ggplot(., mapping = aes(x = long, y = lat, group = group.x, fill = change)) +
	geom_polygon(color = "gray90", size = 0.1) +
	coord_map(projection = "albers", lat0 = 39, lat1 = 45) +
	scale_fill_gradient(low = "azure3", high = "#2CA02C", labels = percent) +
	theme_gg("Abel") +
	theme(axis.line = element_blank(),
	axis.text = element_blank(),
	axis.ticks = element_blank(),
	axis.title = element_blank(),
	panel.background = element_blank(),
	panel.border = element_blank(),
	panel.grid = element_blank(),
	panel.spacing = unit(0, "lines"),
	plot.background = element_blank(),
	legend.justification = c(0, 0),
	legend.position = "right",
	plot.title = element_text(size = 14)) +
	labs(title = "The Change in Nones (2008-2018)", caption = "@ryanburge\nData: CCES 2008 + 2018") +
	ggsave("E://nones_change_map1.png", type = "cairo-png")


	## Dumbbells #####
	st08 <- cces %>%
	filter(year == 2008) %>%
	group_by(state) %>%
	mean_ci(none, wt = weight)

	st18 <- cces %>%
	filter(year == 2018) %>%
	group_by(state) %>%
	mean_ci(none, wt = weight)

	graph <- bind_cols(st08, st18) %>%
	rename(mean08 = mean) %>%
	rename(mean18 = mean1) %>%
	mutate(diff1 = mean18 - mean08) %>%
	mutate(diff = diff1 * 100) %>%
	mutate(diff = round(diff, 2)) %>%
	mutate(diff = paste0(diff, '%'))

	graph %>%
	ggplot(., aes(x = mean08, xend = mean18, y = reorder(state, mean18))) +
	geom_dumbbell(colour_x = "#2F2D29", colour_xend = "#FE6F27", size = .75, size_x = 2.75, size_xend = 2.75, shape = 21, stroke =2, fill = "white") +
	theme_gg("Abel") +
	theme(axis.text.y = element_text(size = 14)) +
	theme(axis.text.x = element_text(size = 18)) +
	theme(plot.title = element_text(size = 22)) +
	labs(x= "Percent Nones", y = "Religious Tradition", title = "Change in Nones from 2008 to 2018", caption = "Data: CCES 2008 and 2018", subtitle = "") +
	scale_x_continuous(labels = percent) +
	geom_text(data = filter(graph, state == "Oregon"), aes(x= mean08, y = state, label = "2008"), hjust = 1.35, family = "font", size = 4) +
	geom_text(data = filter(graph, state == "Oregon"), aes(x= mean18, y = state, label = "2018"), hjust = -.35, family = "font", size = 4) +
	geom_text(data = filter(graph, state == "Kentucky"), aes(x= mean08, y = state, label = "2008"), hjust = 1.35, family = "font", size = 4) +
	geom_text(data = filter(graph, state == "Kentucky"), aes(x= mean18, y = state, label = "2018"), hjust = -.35, family = "font", size = 4) +
	geom_rect(data=graph, aes(xmin=.48, xmax=.50, ymin=-Inf, ymax=Inf), fill="gray") +
	geom_text(data=graph, aes(label=diff, y=state, x=.49), fontface="bold", size=4, family="font") +
	# geom_text(data=filter(graph, state=="Oregon"), aes(x=.49 y=state, label=""), size=10, fontface="bold", family="font", vjust = -.65) +
	ggsave("E://dumbbell_nones.png", type = "cairo-png", height = 10, width = 10)