Fabio Votta 21.09.2018
# Install these packages if you don't have theme yet
# devtools::install_github("favstats/tidytemplate")
# install.packages("pacman")
pacman::p_load(tidyverse, readxl, sjmisc)
# Creates folders
# tidytemplate::data_dir()
# tidytemplate::images_dir()refugee_dat <- read_excel("data/refugee_dat.xls") %>%
drop_na(X__1) %>%
rename(cntry = X__1) %>%
select(-Religion, -X__2, -X__3, - Total) %>%
filter(!(str_detect(cntry, "Total|Data"))) %>%
gather(year, n, -cntry) %>%
mutate(year = str_replace(year, "CY ", "") %>% as.numeric)
refugee_dat %>% group_by(cntry) %>% tally() %>% arrange(desc(nn)) %>% .[1:10,] %>% .$cntry -> top10## Using `n` as weighting variable
year_dat <- tibble(year = c(2005, 2009, 2013, 2017), label = c("Bush II", "Obama I", "Obama II", "Trump I"))
refugee_total <- refugee_dat %>%
group_by(year) %>%
tally() %>%
ggplot(aes(year, nn)) +
geom_vline(data = year_dat, aes(xintercept = year), alpha = 0.15) +
geom_label(data = year_dat, aes(x = year, y = 100000, label = label),
angle = 0, color = "black") +
geom_line(linetype = "dashed") +
geom_point() +
theme_minimal() +
scale_color_manual("Country", values = qualitative) +
theme(plot.title = element_text(size = 13, face = "bold"),
plot.subtitle = element_text(size = 11, face = "bold"),
plot.caption = element_text(size = 10),
legend.position = "bottom") +
scale_x_continuous(breaks = 2002:2018, labels = 2002:2018) +
labs(x = "", y = "Number of Refugees\n",
title = "Accepted Refugees in the United States of America by Year (2002 - 2018)",
subtitle = "Total accepted Refugees in Timerange: 943.338\n",
caption = "Data: Department of State, Office of Admissions - Refugee Processing Center\n@FabioFavusMaxim; favstats.eu")## Using `n` as weighting variable
refugee_total
tidytemplate::ggsave_it(refugee_total, width = 10, height = 6)qualitative <- c('#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f','#ff7f00','#cab2d6','#6a3d9a')
gg_refugee_static_data <- refugee_dat %>%
filter(cntry %in% top10)
label_dat <- gg_refugee_static_data %>%
group_by(cntry) %>%
summarize(n = max(n)) %>%
select(-cntry) %>%
inner_join(gg_refugee_static_data)## Joining, by = "n"
gg_refugee_static <- gg_refugee_static_data %>%
ggplot(aes(year, n)) +
geom_vline(data = year_dat, aes(xintercept = year), alpha = 0.15) +
geom_label(data = year_dat, aes(x = year, y = 22000, label = label),
angle = 0, color = "black") +
geom_line(aes(linetype = cntry, color = cntry)) +
theme_minimal() +
ggrepel::geom_label_repel(data = label_dat, aes(label = cntry, color = cntry), show.legend = F) +
geom_point(data = label_dat, aes(color = cntry)) +
scale_color_manual("Country", values = qualitative) +
scale_linetype("Country") +
theme(plot.title = element_text(size = 13, face = "bold"),
plot.subtitle = element_text(size = 11, face = "bold"),
plot.caption = element_text(size = 10),
legend.key.width = unit(3, "line"),
legend.position = "bottom") +
scale_x_continuous(breaks = 2002:2018, labels = 2002:2018) +
labs(x = "", y = "Number of Refugees\n",
title = "Accepted Refugees in the United States of America by Year (2002 - 2018)", subtitle = "Top 10 Origin Countries\n",
caption = "Data: Department of State, Office of Admissions - Refugee Processing Center\n@FabioFavusMaxim; favstats.eu")
# geom_rect(aes(xmin = 2002, xmax = 2005, ymin = 22000, ymax = 22000),
# color = "black",
# alpha = 0.8,
# inherit.aes = FALSE)
gg_refugee_static
tidytemplate::ggsave_it(gg_refugee_static, width = 12, height = 8)library(gganimate)
gg_refugee <- refugee_dat %>%
filter(cntry %in% top10) %>%
ggplot(aes(year, n, color = cntry)) +
geom_vline(data = year_dat, aes(xintercept = year), alpha = 0.15) +
geom_label(data = year_dat, aes(x = year, y = 22000, label = label),
angle = 0, color = "black") +
geom_line() +
geom_segment(aes(xend = 2018, yend = n), alpha = 0.5) +
geom_point() +
geom_text(aes(x = 2019, label = cntry)) +
theme_minimal() +
scale_color_manual("Country", values = qualitative) +
theme(plot.title = element_text(size = 13, face = "bold"),
plot.subtitle = element_text(size = 12, face = "bold"),
plot.caption = element_text(size = 10),
legend.position = "bottom") +
scale_x_continuous(breaks = 2002:2018, labels = 2002:2018) +
labs(x = "", y = "Number of Refugees\n",
title = "Accepted Refugees in the United States of America by Year (2002 - 2018)", subtitle = "Top 10 Origin Countries\n",
caption = "Data: Department of State, Office of Admissions - Refugee Processing Center\n@FabioFavusMaxim; favstats.eu") +
guides(color = F, text = F) +
transition_reveal(cntry, year, keep_last = T)
gg_refugee %>% animate(
nframes = 500, fps = 15, width = 1000, height = 600, detail = 1
)
anim_save("images/gg_refugee.gif")
gg_refugee_static_region <- refugee_dat %>%
mutate(continent = countrycode::countrycode(cntry, "country.name", "continent")) %>%
mutate(region = countrycode::countrycode(cntry, "country.name", "region")) %>%
mutate(continent = case_when(
continent == "Asia" ~ region,
T ~ continent
)) %>%
mutate(continent = case_when(
cntry == "Tibet" ~ "Eastern Asia",
cntry == "Yemen (Sanaa)" ~ "Middle East",
cntry == "Yugoslavia" ~ "Europe",
str_detect(cntry, "Georgia|Armenia|Azerbaijan") ~ "Central Asia",
continent == "Western Asia" ~ "Middle East",
T ~ continent
)) %>%
# group_by(continent) %>% tally
# filter(continent == "Western Asia")
group_by(year, continent) %>%
tally() ## Warning in countrycode::countrycode(cntry, "country.name", "continent"): Some values were not matched unambiguously: Tibet, Yemen (Sanaa), Yugoslavia
## Warning in countrycode::countrycode(cntry, "country.name", "region"): Some values were not matched unambiguously: Tibet, Yemen (Sanaa), Yugoslavia
## Using `n` as weighting variable
# gg_refugee_static_region %>% filter(continent == "Southern Asia") %>% summarize_all(sum)
label_dat <- gg_refugee_static_region %>%
group_by(continent) %>%
summarize(nn = max(nn)) %>%
select(-continent) %>%
inner_join(gg_refugee_static_region) %>%
filter(!(year == 2018 & continent == "Eastern Asia")) %>%
filter(!(year == 2004 & continent == "Eastern Asia")) %>%
mutate(nn = ifelse(continent == "Southern Asia", 10385, nn)) %>%
mutate(year = ifelse(continent == "Southern Asia", 2015, year))## Joining, by = "nn"
gg_static_region <- gg_refugee_static_region %>%
ggplot(aes(year, nn)) +
geom_vline(data = year_dat, aes(xintercept = year), alpha = 0.15) +
geom_label(data = year_dat, aes(x = year, y = 41000, label = label),
angle = 0, color = "black") +
geom_line(aes(linetype = continent, color = continent)) +
theme_minimal() +
ggrepel::geom_label_repel(data = label_dat,
aes(label = continent, color = continent),
show.legend = F, seed = 13092018) +
geom_point(data = label_dat, aes(color = continent)) +
scale_color_manual("Region", values = qualitative) +
scale_linetype("Region") +
theme(plot.title = element_text(size = 13, face = "bold"),
# plot.subtitle = element_text(size = 11, face = "bold"),
plot.caption = element_text(size = 10),
legend.key.width = unit(3, "line"),
legend.position = "bottom") +
scale_x_continuous(breaks = 2002:2018, labels = 2002:2018,
minor_breaks = seq(2002, 2018, 1)) +
labs(x = "", y = "Number of Refugees\n",
title = "Refugees arriving in the United States of America by Year (2002 - 2018)",
caption = "Data: Department of State, Office of Admissions - Refugee Processing Center\nfavstats.eu; @favstats")
# geom_rect(aes(xmin = 2002, xmax = 2005, ymin = 22000, ymax = 22000),
# color = "black",
# alpha = 0.8,
# inherit.aes = FALSE)
gg_static_region
tidytemplate::ggsave_it(gg_static_region, width = 12, height = 8)gg_static_region_perc <- gg_refugee_static_region %>%
group_by(year) %>%
mutate(total = sum(nn)) %>%
mutate(perc = tidytemplate::get_percentage(nn, total))
label_dat <- gg_static_region_perc %>%
group_by(continent) %>%
summarize(perc = max(perc)) %>%
select(-continent) %>%
inner_join(gg_static_region_perc)## Joining, by = "perc"
gg_region_perc <- gg_static_region_perc %>%
ggplot(aes(year, perc)) +
geom_vline(data = year_dat, aes(xintercept = year), alpha = 0.15) +
geom_label(data = year_dat, aes(x = year, y = 60, label = label),
angle = 0, color = "black") +
geom_line(aes(linetype = continent, color = continent)) +
theme_minimal() +
ggrepel::geom_label_repel(data = label_dat,
aes(label = continent, color = continent),
show.legend = F, seed = 13092018) +
geom_point(data = label_dat, aes(color = continent)) +
scale_color_manual("Region", values = qualitative) +
scale_linetype("Region") +
theme(plot.title = element_text(size = 13, face = "bold"),
# plot.subtitle = element_text(size = 11, face = "bold"),
plot.caption = element_text(size = 10),
legend.key.width = unit(3, "line"),
legend.position = "bottom") +
scale_x_continuous(breaks = 2002:2018, labels = 2002:2018,
minor_breaks = seq(2002, 2018, 1)) +
labs(x = "", y = "Percentage of Refugees\n",
title = "Refugees arriving in the United States of America by Year (2002 - 2018)\n",
caption = "Data: Department of State, Office of Admissions - Refugee Processing Center\nfavstats.eu; @favstats")
gg_region_perc
tidytemplate::ggsave_it(gg_region_perc, width = 12, height = 8)library(gganimate)
gg_anim_region <- gg_refugee_static_region %>%
mutate(continent = ifelse(continent == "South-Eastern Asia", "S.E. Asia", continent)) %>%
ggplot(aes(year, nn, color = continent)) +
geom_vline(data = year_dat, aes(xintercept = year), alpha = 0.15) +
geom_label(data = year_dat, aes(x = year, y = 41000, label = label),
angle = 0, color = "black") +
geom_line() +
geom_segment(aes(xend = 2018, yend = nn), alpha = 0.5) +
geom_point() +
geom_text(aes(x = 2019, label = continent)) +
theme_minimal() +
scale_color_manual("Country", values = qualitative) +
theme(plot.title = element_text(size = 13, face = "bold"),
# plot.subtitle = element_text(size = 12, face = "bold"),
plot.caption = element_text(size = 10),
legend.position = "bottom") +
scale_x_continuous(breaks = 2002:2018, labels = 2002:2018,
minor_breaks = seq(2002, 2018, 1)) +
labs(x = "", y = "Number of Refugees\n",
title = "Refugees arriving in the United States of America by Year (2002 - 2018)\n",
caption = "Data: Department of State, Office of Admissions - Refugee Processing Center\nfavstats.eu; @favstats") +
guides(color = F, text = F) +
transition_reveal(continent, year, keep_last = T)
gg_anim_region %>% animate(
nframes = 500, fps = 15, width = 1000, height = 600, detail = 1
)
anim_save("images/gg_anim_region.gif")
relig_refugee <- read_excel("data/refugee_dat.xls") %>%
drop_na(Religion) %>%
rename(religion = Religion) %>%
select(-X__1, -X__2, -X__3, - Total) %>%
# filter(!(str_detect(cntry, "Total|Data"))) %>%
gather(year, n, -religion) %>%
mutate(year = str_replace(year, "CY ", "") %>% as.numeric) %>%
# group_by(religion, year) %>%
# mutate(n = sum(n)) %>%
mutate(religion_cat = case_when(
str_detect(religion, "Moslem|Ahmadiyya") ~ "Muslim",
str_detect(religion, "Christ|Baptist|Chald|Coptic|Greek|Jehovah|Lutheran|Mennonite|Orthodox|Pentecostalist|Protestant|Uniate|Adventist|Cath|Meth|Old Believer") ~ "Christian",
str_detect(religion, "Atheist|No Religion") ~ "Atheist/No Religion",
religion == "Hindu" ~ "Hindu",
T ~ "Other/Unknown"
)) %>%
# filter(religion == "Unknown") %>%
# .$n %>% sum%>%
group_by(religion_cat, year) %>%
summarize(n = sum(n)) label_dat <- relig_refugee %>%
filter(year == 2018)
gg_relig <- relig_refugee %>%
ggplot(aes(year, n)) +
geom_vline(data = year_dat, aes(xintercept = year), alpha = 0.15) +
geom_label(data = year_dat, aes(x = year, y = 48000, label = label),
angle = 0, color = "black") +
geom_line(aes(color = religion_cat), size = .8) +
theme_minimal() +
ggrepel::geom_text_repel(data = label_dat,
aes(label = religion_cat, color = religion_cat),
nudge_x = 2,
show.legend = F,
direction = "y", min.segment.length = 0.7) +
geom_point(data = label_dat, aes(color = religion_cat)) +
ggthemes::scale_color_gdocs("Religion") +
# scale_color_manual("Religion", values = qualitative) +
scale_linetype("Religion") +
theme(plot.title = element_text(size = 13, face = "bold"),
plot.subtitle = element_text(size = 11, face = "bold"),
plot.caption = element_text(size = 10),
legend.key.width = unit(3, "line"),
legend.position = "bottom") +
scale_x_continuous(breaks = 2002:2018, labels = 2002:2018,
limits = c(2002, 2021),
minor_breaks = seq(2002, 2018, 1)) +
labs(x = "", y = "Number of Refugees\n",
title = "Refugees arriving in the United States of America by Year (2002 - 2018)", subtitle = "Aggregated by Religion\n",
caption = "Data: Department of State, Office of Admissions - Refugee Processing Center\nfavstats.eu; @favstats") +
guides(color = F)
gg_relig
tidytemplate::ggsave_it(gg_relig, width = 10, height = 6)library(gganimate)
gg_religion <- relig_refugee %>%
ggplot(aes(year, n, color = religion_cat)) +
geom_vline(data = year_dat, aes(xintercept = year), alpha = 0.15) +
geom_label(data = year_dat, aes(x = year, y = 48000, label = label),
angle = 0, color = "black", size = 6) +
geom_line(size = 1) +
geom_segment(aes(xend = 2018, yend = n), alpha = 0.5) +
geom_point(size = 2) +
geom_text(aes(x = 2019, label = religion_cat),
size = 6, face = "bold", nudge_x = .75) +
theme_minimal() +
ggthemes::scale_color_gdocs("Religion") +
theme(plot.title = element_text(size = 18, face = "bold"),
plot.subtitle = element_text(size = 16, face = "bold"),
plot.caption = element_text(size = 14),
axis.title = element_text(size = 14, face = "bold"),
axis.text = element_text(size = 14),
legend.position = "bottom") +
scale_x_continuous(breaks = 2002:2018,
labels = c("'02", "'03", "'04",
"'05", "'06", "'07",
"'08", "'09", "'10",
"'11", "'12", "'13",
"'14", "'15", "'16",
"'17", "'18"),
limits = c(2002, 2020.5),
minor_breaks = seq(2002, 2018, 1)) +
labs(x = "", y = "Number of Refugees\n\n",
title = "Refugees arriving in the United States of America by Year (2002 - 2018)", subtitle = "Aggregated by Religion\n\n",
caption = "Data: Department of State, Office of Admissions - Refugee Processing Center\nfavstats.eu; @favstats") +
guides(color = F, text = F) +
transition_reveal(religion_cat, year, keep_last = T)
gg_religion %>% animate(
nframes = 500, fps = 15, width = 1000, height = 600, detail = 1
)
anim_save("images/gg_religion.gif")
load("data/world_map.Rdata")
refugee_map_total <- refugee_dat %>%
mutate(id = countrycode::countrycode(cntry, "country.name", "country.name")) %>%
mutate(id = ifelse(cntry == "Tibet", yes = "China", no = id)) %>%
group_by(id) %>%
summarize(n = sum(n, na.rm = T)) %>%
full_join(world_map) %>%
mutate(n = cut(n,
breaks = c(1, 100, 10000, 50000, 100000, 175000),
labels = c("< 100", "100 - 10.000",
"10.001 - 50.000", "50.001 - 100.000",
"100.001 - 175.000")))refugee_map_total %>%
ggplot() +
geom_map(map = world_map,
aes(x = long, y = lat, group = group, map_id = id),
color = "#7f7f7f", fill = "gray80", size = 0.15) +
geom_map(data = refugee_map_total,
map = world_map,
aes(map_id = id,
fill = n), size = 0.01) +
theme_void() +
# scale_fill_gradient(low = "red", high = "blue") +
coord_equal() +
viridis::scale_fill_viridis("Number of Refugees",
direction = -1,
option = "D",
discrete = T,
# begin = .2,
# end = .8,
na.value = "grey",
# limits = c(0, 1),
# breaks = c(0, .20, .40, .60, .8, 1),
labels = c("< 100", "100 - 10.000",
"10.000 - 50.000", "50.000 - 100.000",
"100.000 - 175.000", "No Refugees")) +
# facet_wrap(~year, ncol = 6) +
theme(
plot.title = element_text(size = 18, face = "bold", hjust = 0.5),
plot.caption = element_text(size = 14),
legend.justification = c(1, 0),
legend.position = c(0.2, 0.25),
legend.title = element_text(size = 10),
#axis.ticks.length = unit(3, "cm"),
legend.direction = "vertical") +
# guides(fill = guide_colorbar(barwidth = 0.7, barheight = 15,
# title.position = "bottom", title.hjust = 0.5,
# label.theme = element_text(colour = "black", size = 6, angle = 0))) +
labs(x = "", y = "",
title = "Refugees arriving in the United States of America by Nationality (2002 - 2018)",
caption = "Data: Department of State, Office of Admissions - Refugee Processing Center \nfavstats.eu; @favstats ") 
ggsave(filename = "images/refugee_total_map.png", height = 6, width = 12)
refuge_admissions75 <- 'https://static1.squarespace.com/static/580e4274e58c624696efadc6/t/5b8ff632aa4a999f85f99e8d/1536161330411/Graph+Refugee+Admissions+since+1975%289.5.18%29.pdf'
# Extract the table
out <- extract_tables(refuge_admissions75)
first_pages <- do.call(rbind, out[-length(out)]) %>% as_tibble()
correct_names <- first_pages[2,] %>% as.character()
final1 <- first_pages %>%
set_names(correct_names) %>%
.[-c(1:2),] %>%
.[-c(length(.)),]
last_page <- cbind(
out[[3]][,1],
out[[3]][,2],
out[[3]][,3],
out[[3]][,5],
out[[3]][,6],
out[[3]][,7],
out[[3]][,8],
out[[3]][,9],
out[[3]][,10],
out[[3]][,11]
) %>% as_tibble()
correct_lastpage <- last_page[2,] %>% as.character()
final2 <- last_page %>%
set_names(correct_lastpage) %>%
.[-c(1:2),]
admissions75 <- bind_rows(final1, final2) %>%
select(-Total) %>%
mutate_all(parse_number) %>%
na.omit() %>%
gather(region, n, -`Fiscal\rYear`) %>%
janitor::clean_names()year_lab <- paste0("'", stringi::stri_sub(1975:2018, -2 , -1))
year_dat <- tibble(fiscal_year = c(seq(1976, 2016, 4)),
label = c("Carter I", "Reagan I", "Reagan II",
"H.W. Bush I", "Clinton I", "Clinton II", "Bush I",
"Bush II", "Obama I", "Obama II", "Trump I"))
n_refugee_2018 <- admissions75 %>%
filter(fiscal_year == 2018) %>%
summarize(n = sum(n)) %>%
.$n
n_refugee_2002 <- admissions75 %>%
filter(fiscal_year == 2002) %>%
summarize(n = sum(n)) %>%
.$n
n_refugee_1992 <- admissions75 %>%
filter(fiscal_year == 1992) %>%
summarize(n = sum(n)) %>%
.$n
n_refugee_1980 <- admissions75 %>%
filter(fiscal_year == 1980) %>%
summarize(n = sum(n)) %>%
.$n
n_refugee_1975 <- admissions75 %>%
filter(fiscal_year == 1975) %>%
summarize(n = sum(n)) %>%
.$n
admissions75 %>%
summarize(n = sum(n)) %>%
.$n
admissions75 %>%
mutate(region = case_when(
region == "Former\rSoviet\rUnion" ~ "(Former) Soviet Union",
region == "Latin America\rCaribbean" ~ "Latin America/Caribbean",
region == "Near East\rSouth Asia" ~ "Near East/South Asia",
region == "PSI" ~ "Private Sector Initiative",
T ~ region
)) %>%
ggplot(aes(fiscal_year, n)) +
geom_vline(data = year_dat, aes(xintercept = fiscal_year), alpha = 0.35) +
geom_label(data = year_dat, aes(x = fiscal_year, y = 220000, label = label),
angle = 0, color = "black") +
geom_area(aes(fill = region), alpha = 0.9) +
geom_hline(yintercept = n_refugee_2018,
linetype = "dashed", color = "black", alpha = 0.85) +
annotate("label", x = 1978, y = 115000,
fill = "lightgrey", alpha = 0.85, label.size = NA,
label = "End of\n Vietnam War") +
annotate("label", x = 1984, y = 185000,
fill = "lightgrey", alpha = 0.85, label.size = NA,
label = "Refugee Act of 1980") +
annotate("label", x = 1997, y = 150000,
fill = "lightgrey", alpha = 0.85, label.size = NA,
label = "Fall of Soviet Union") +
annotate("label", x = 2000, y = 105000,
fill = "lightgrey", alpha = 0.85, label.size = NA,
label = "Drop after 9/11") +
annotate("label", x = 2015, y = 110000,
fill = "lightgrey", alpha = 0.85, label.size = NA,
label = "Number of Refugees in 2018\n lowest since 1977") +
theme_minimal() +
scale_y_continuous(labels = scales::comma) +
scale_fill_manual("Region", values = qualitative) +
geom_curve(aes(x = 1977, y = 125000, xend = 1975, yend = n_refugee_1975),
arrow = arrow(length = unit(0.03, "npc")), curvature = 0.2) +
geom_curve(aes(x = 1982, y = 190000, xend = 1980, yend = n_refugee_1980),
arrow = arrow(length = unit(0.03, "npc")), curvature = 0.2) +
geom_curve(aes(x = 1994, y = 150000, xend = 1992, yend = n_refugee_1992),
arrow = arrow(length = unit(0.03, "npc")), curvature = 0.2) +
geom_curve(aes(x = 2000, y = 100000, xend = 2002, yend = n_refugee_2002),
arrow = arrow(length = unit(0.03, "npc")), curvature = -0.3) +
geom_curve(aes(x = 2016, y = 100000, xend = 2018, yend = n_refugee_2018),
arrow = arrow(length = unit(0.03, "npc")), curvature = -0.2) +
theme(plot.title = element_text(size = 13, face = "bold"),
# plot.subtitle = element_text(size = 11, face = "bold"),
plot.caption = element_text(size = 10),
legend.key.width = unit(3, "line"),
legend.position = "bottom") +
scale_x_continuous(breaks = 1975:2018, labels = year_lab,
minor_breaks = seq(1975, 2018, 1)) +
labs(x = "", y = "Number of Refugees\n",
title = "Refugees arriving in the United States of America by Year (1975 - 2018)\n",
caption = "Data: Department of State, Office of Admissions - Refugee Processing Center\nTotal Number of Accepted Refugees since 1975: 3.340.709\nfavstats.eu; @favstats")
ggsave(filename = "images/refugee75.png", height = 7, width = 13)