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birth_rate_by_month.R
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# plot of birth rate by month
#
# data from UNData
# https://data.un.org/Default.aspx
# found at
# UNData Live births by month of birth
# https://data.un.org/Data.aspx?d=POP&f=tableCode:55
#
#
library(ggplot2)
library(dplyr)
library(gridExtra)
# read data here
#birthrate_file = "~/git/birth_rate_by_month/data/UNdata_Export_20210419_155726210.txt.gz"
birthrate_file = "~/git/birth_rate_by_month/data/UNdata_Export_20230630_084956534.txt.gz"
birthdata = read.table(birthrate_file, header=TRUE, sep=";", stringsAsFactors = FALSE )
summary(birthdata)
# months in order, to keep numerical order
months = c("January", "February", "March", "April", "May", "June",
"July", "August", "September", "October", "November", "December")
# adjusted number of days per month
d_per_month = (365/12) / c(31,28,31,30,31,30,31,31,30,31,30,31)
#
# big loop to make plots for all countries
country_list = unique(birthdata[["Country.or.Area"]])
#
i = 0
plot_list = list()
mo_yr_country_list = list()
for (country in country_list){
i = i+1
country_final_only = filter(birthdata, Reliability == "Final figure, complete",
Month %in% months,
Country.or.Area==country) %>%
select( Country.or.Area, Month, Year, Value ) %>%
arrange(Country.or.Area, Year, match(Month,months))
months_by_year_by_country = c(table(country_final_only[["Year"]],country_final_only[["Country.or.Area"]]))
months_by_year_by_country = months_by_year_by_country[months_by_year_by_country>0]
#mo_yr_country_list[[i]] = months_by_year_by_country
yearly_averages = country_final_only %>% group_by(Country.or.Area, Year) %>% summarise( yearly_mean = mean(Value) )
yearly_averages_c = rep(yearly_averages$yearly_mean, months_by_year_by_country )
d_per_month_c = d_per_month[match(country_final_only[["Month"]],months)]
number_of_years = length(yearly_averages$Year)
yearly_mean_range = round(range(yearly_averages$yearly_mean))
year_range = range(country_final_only$Year)
subtitle_text = paste("Data from UN Demographic Statistics Database, including",number_of_years,"years from",year_range[1],"to",year_range[2])
#subtitle_text = paste(number_of_years, "years from",year_range[1],"to",year_range[2])
caption_text = paste("Averages range from", yearly_mean_range[1] ,"to",yearly_mean_range[2])
cgg = ggplot(country_final_only, aes(x=match(Month,months),
y=Value/yearly_averages_c*d_per_month_c,
group=interaction(Country.or.Area,Year) ) ) +
theme(axis.text.y=element_text(size=13),
axis.title.y=element_text(size=16),
legend.position=c(1,0.8),
legend.justification = "right",
legend.title = element_text(size=16),
legend.key.size = unit(1, 'cm'),
plot.title = element_text(size=25)) +
coord_cartesian(xlim=c(1,12),ylim=c(0.75,1.25)) +
scale_x_continuous(breaks=c(1:12), labels=months, minor_breaks = NULL) +
scale_color_gradient(low="#00220b", high="#66e284") +
labs(x=NULL, y = "Births relative to monthly average of that year",
title=country, subtitle=subtitle_text,
caption=caption_text) +
geom_line(aes(colour=Year), alpha=0.3, size=3, lineend = "round")
country_w_underscores = gsub(" ","_",country)
if (yearly_mean_range[2] > 100) {
plot_list[[i]] = cgg
#outputfilename = paste0("~/git/birth_rate_by_month/countries/", country_w_underscores, ".UNdata_20210419.pdf")
outputfilename = paste0("~/git/birth_rate_by_month/countries/", country_w_underscores, ".UNdata_20230630.png")
#ggsave(outputfilename, cgg, device="pdf", width=8, height=6)
ggsave(outputfilename, cgg, device="png", width=8, height=6, dpi = 90)
}
} # end for loop
#pdf("~/git/birth_rate_by_month/all_countries_tiled.UNdata_20210419.pdf", paper="a4", width=8, height=10)
#for (i in seq(1, length(plot_list), 6)) {
# grid.arrange(grobs=plot_list[i:(i+5)], ncol=2)
#}
#dev.off()
################################################################################
# vector of countries by region, arbitrarily
is_M_europe = c("Germany", "Austria")
# begin analysis
#
# for Austria and Germany
ATDE_final_only = filter(birthdata, Reliability == "Final figure, complete",
Month %in% months,
Country.or.Area %in% is_M_europe) %>%
select( Country.or.Area, Month, Year, Value ) %>%
arrange(Country.or.Area, Year, match(Month,months))
#ATDE_final_only
#table(ATDE_final_only[["Year"]],ATDE_final_only[["Country.or.Area"]])
months_by_year_by_country = c(table(ATDE_final_only[["Year"]],ATDE_final_only[["Country.or.Area"]]))
months_by_year_by_country = months_by_year_by_country[months_by_year_by_country>0]
#table(table(ATDE_final_only[["Country.or.Area"]],ATDE_final_only[["Year"]]))
#sum(table(ATDE_final_only[["Country.or.Area"]],ATDE_final_only[["Year"]]))
#
#table(ATDE_final_only[["Year"]])
#sum(table(ATDE_final_only[["Year"]]))
#head(ATDE_final_only)
yearly_averages = ATDE_final_only %>% group_by(Country.or.Area, Year) %>% summarise( yearly_mean = mean(Value) )
yearly_averages_c = rep(yearly_averages$yearly_mean, months_by_year_by_country )
d_per_month_c = d_per_month[match(ATDE_final_only[["Month"]],months)]
atdegg = ggplot(ATDE_final_only, aes(x=match(Month,months),
y=Value/yearly_averages_c*d_per_month_c,
group=interaction(Country.or.Area,Year) ) ) +
theme(axis.text.y=element_text(size=13),
axis.title.y=element_text(size=16),
legend.position=c(0.25,0.15),
legend.title = element_text(size=14),
legend.key.size = unit(1, 'cm')) +
coord_cartesian(xlim=c(1,12),ylim=c(0.8,1.2)) +
scale_x_continuous(breaks=c(1:12), labels=months, minor_breaks = NULL) +
labs(caption="Data from UN Demographic Statistics Database, using data on AT and DE from 1973 to 2018",
x=NULL, y = "Births relative to monthly average of that year",
color = "Country") +
scale_color_manual(values=c("#b10026", "#fec44f")) +
geom_line(aes(colour=Country.or.Area), alpha=0.3, size=3, lineend = "round") +
guides(colour = guide_legend(override.aes = list(alpha = 1))) +
annotate( geom="text", x=10, y=1.18, label="Oktoberfest", fontface="bold" ) +
annotate( geom="text", x=7, y=1.19, label="9 months\nafter Oktoberfest", fontface="bold" ) +
annotate( geom="segment", x=10,y=1.15, xend=10, yend = 1.12, arrow = arrow(length = unit(3, "mm")) ) +
annotate( geom="segment", x=7,y=1.17, xend=7, yend = 1.14, arrow = arrow(length = unit(3, "mm")) )
atdegg
ggsave("~/git/birth_rate_by_month/images/UNdata_Export_20210419_AT_plus_DE.pdf", atdegg, device="pdf", width=8, height=6)
#
# for Sweden, Norway, and Finland
is_N_europe = c("Sweden", "Norway", "Finland")
SENOFI_final_only = filter(birthdata, Reliability == "Final figure, complete",
Month %in% months,
Country.or.Area %in% is_N_europe) %>%
select( Country.or.Area, Month, Year, Value ) %>%
arrange(Country.or.Area, Year, match(Month,months))
months_by_year_by_country = c(table(SENOFI_final_only[["Year"]],SENOFI_final_only[["Country.or.Area"]]))
months_by_year_by_country = months_by_year_by_country[months_by_year_by_country>0]
yearly_averages = SENOFI_final_only %>% group_by(Country.or.Area, Year) %>% summarise( yearly_mean = mean(Value) )
yearly_averages_c = rep(yearly_averages$yearly_mean, months_by_year_by_country )
d_per_month_c = rep(d_per_month, length(months_by_year_by_country) )
senofigg = ggplot(SENOFI_final_only, aes(x=match(Month,months),
y=Value/yearly_averages_c*d_per_month_c,
group=interaction(Country.or.Area,Year) ) ) +
theme(axis.text.y=element_text(size=13),
axis.title.y=element_text(size=16),
legend.position=c(0.25,0.17),
legend.title = element_text(size=14),
legend.key.size = unit(1, 'cm')) +
coord_cartesian(xlim=c(1,12),ylim=c(0.75,1.25)) +
scale_x_continuous(breaks=c(1:12), labels=months, minor_breaks = NULL) +
labs(x=NULL, y = "Births relative to monthly average of that year",
caption="Data from UN Demographic Statistics Database, using data for Norway, Sweden, and Finland from 1971 to 2018",
color = "Country") +
scale_color_manual(values=c("#3690c0", "#99000d", "#fec44f" )) +
geom_line(aes(colour=Country.or.Area), alpha=0.3, size=3, lineend = "round") +
guides(colour = guide_legend(override.aes = list(alpha = 1)))
senofigg
ggsave("~/git/birth_rate_by_month/images/UNdata_Export_20210419_SE_NO_FI.pdf", senofigg, device="pdf", width=8, height=6)
#summary(SENOFI_final_only)
# plot by year, rather than country
scandyeargg = ggplot(SENOFI_final_only, aes(x=match(Month,months),
y=Value/yearly_averages_c*d_per_month_c,
group=interaction(Country.or.Area,Year) ) ) +
theme(axis.text.y=element_text(size=13),
axis.title.y=element_text(size=16),
legend.position=c(1,0.8),
legend.justification = "right",
legend.title = element_text(size=16),
legend.key.size = unit(1, 'cm')) +
coord_cartesian(xlim=c(1,12),ylim=c(0.8,1.25)) +
scale_x_continuous(breaks=c(1:12), labels=months, minor_breaks = NULL) +
scale_color_gradient(low="#00220b", high="#66e284") +
labs(x=NULL, y = "Births relative to monthly average of that year",
title="Norway, Sweden, and Finland",
caption="Data from UN Demographic Statistics Database, using data for Norway, Sweden, and Finland from 1971 to 2018") +
geom_line(aes(colour=Year), alpha=0.3, size=3, lineend = "round")
scandyeargg
ggsave("~/git/birth_rate_by_month/images/UNdata_Export_20210419_scand_by_year.pdf", scandyeargg, device="pdf", width=8, height=6)
#
# for Mediterranean europe
#is_S_europe = c("Spain", "Italy", "Greece", "Croatia")
is_S_europe = c("Spain", "Italy" )
GRITSP_final_only = filter(birthdata, Reliability == "Final figure, complete",
Month %in% months,
Country.or.Area %in% is_S_europe) %>%
select( Country.or.Area, Month, Year, Value ) %>%
arrange(Country.or.Area, Year, match(Month,months))
months_by_year_by_country = c(table(GRITSP_final_only[["Year"]],GRITSP_final_only[["Country.or.Area"]]))
months_by_year_by_country = months_by_year_by_country[months_by_year_by_country>0]
yearly_averages = GRITSP_final_only %>% group_by(Country.or.Area, Year) %>% summarise( yearly_mean = mean(Value) )
yearly_averages_c = rep(yearly_averages$yearly_mean, months_by_year_by_country )
d_per_month_c = d_per_month[match(GRITSP_final_only[["Month"]],months)]
gritspgg = ggplot(GRITSP_final_only, aes(x=match(Month,months),
y=Value/yearly_averages_c*d_per_month_c,
group=interaction(Country.or.Area,Year) ) ) +
theme(axis.text.y=element_text(size=13),
axis.title.y=element_text(size=16),
legend.position=c(0.65,0.17),
legend.title = element_text(size=14),
legend.key.size = unit(1, 'cm')) +
coord_cartesian(xlim=c(1,12),ylim=c(0.75,1.25)) +
scale_x_continuous(breaks=c(1:12), labels=months, minor_breaks = NULL) +
labs(x=NULL, y = "Births relative to monthly average of that year",
caption="Data from UN Demographic Statistics Database, using data for Spain, Italy, Croatia and Greece from 1970 to 2018",
color = "Country") +
# scale_color_manual(values=c("#cb181d", "#08519c", "#006d2c", "#ffcf3d" )) +
scale_color_manual(values=c("#006d2c", "#ffcf3d" )) +
geom_line(aes(colour=Country.or.Area), alpha=0.3, size=3, lineend = "round") +
guides(colour = guide_legend(override.aes = list(alpha = 1)))
gritspgg
ggsave("~/git/birth_rate_by_month/images/UNdata_Export_20210419_medeurope.pdf", gritspgg, device="pdf", width=8, height=6)
################################################################################
################################################################################
# wood fire earth metal water, X4 and X5 of each decade are wood
# wood fire earth metal water
element_colors = rep(c("#12934d","#a72c01", "#bca626", "#8a78c1", "#2689bc"), each=2 )
# 1960 metal rat rat ox tiger rabbit dragon snake
zodiac_symbols = c("鼠", "牛", "虎", "兔", "龍", "蛇",
"馬", "羊", "猴", "雞", "狗", "豬" )
# horse goat monkey chicken dog pig
i = 0
plot_list = list()
mo_yr_country_list = list()
country_list = c( "China, Hong Kong SAR", "China, Macao SAR", "Japan", "Malaysia",
"Philippines", "Republic of Korea", "Singapore" )
country = "Japan"
print(country)
country_final_only = filter(birthdata, Reliability == "Final figure, complete",
Month %in% months,
Country.or.Area==country) %>%
select( Country.or.Area, Month, Year, Value ) %>%
mutate( mi = match(Month,months) ) %>%
arrange(Country.or.Area, Year, mi)
months_by_year_by_country = c(table(country_final_only[["Year"]],country_final_only[["Country.or.Area"]]))
months_by_year_by_country = months_by_year_by_country[months_by_year_by_country>0]
yearly_averages = country_final_only %>% group_by(Country.or.Area, Year) %>% summarise( yearly_mean = mean(Value) )
yearmonth_index = country_final_only$Year + (0.0833 * (match(country_final_only$Month, months )-1))
yearmonth_index
number_of_years = length(yearly_averages$Year)
yearly_mean_range = round(range(yearly_averages$yearly_mean))
year_range = range(country_final_only$Year)
subtitle_text = paste("Data from UN Demographic Statistics Database, including",number_of_years,"years from",year_range[1],"to",year_range[2])
caption_text = paste("Monthly range from", min(country_final_only$Value) ,"to", max(country_final_only$Value))
#zodiac_year = zodiac_symbols[(((unique(country_final_only$Year)-1960)%%12)+1)]
zodiac_year = zodiac_symbols[(((seq(year_range[1],year_range[2],1)-1960)%%12)+1)]
#zodiac_color = element_colors[(((unique(country_final_only$Year)-1964)%%10)+1)]
zodiac_color = element_colors[(((seq(year_range[1],year_range[2],1)-1964)%%10)+1)]
cgg = ggplot(country_final_only, aes(x=yearmonth_index,y=Value)) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
axis.text.y=element_text(size=13),
axis.title.y=element_text(size=16),
legend.position=c(1,0.8),
legend.justification = "right",
legend.title = element_text(size=16),
legend.key.size = unit(1, 'cm'),
plot.title = element_text(size=25)) +
coord_cartesian(xlim=year_range) +
scale_x_continuous(breaks=unique(country_final_only$Year), labels=unique(country_final_only$Year),
minor_breaks = NULL, expand = c(0.01,0)) +
#scale_y_continuous(limits = c(0,max(country_final_only$Value))) +
labs(x=NULL, y = "Births per month",
title=country, subtitle=subtitle_text,
caption=caption_text) +
geom_line(alpha=0.9, size=1, lineend = "round") +
annotate( geom="text", x=seq(year_range[1],year_range[2],1)+0.5, y=max(country_final_only$Value), label=zodiac_year, color=zodiac_color )
#annotate( geom="text", x=unique(country_final_only$Year)+0.5, y=max(country_final_only$Value), label=zodiac_year, color=zodiac_color )
country_w_underscores = gsub(",","",gsub(" ","_",country))
outputfilename = paste0("~/git/birth_rate_by_month/images/", country_w_underscores, ".timeline.UNdata_20230630.pdf")
print(outputfilename)
#outputfilename = paste0("~/git/birth_rate_by_month/countries/", country_w_underscores, ".timeline.UNdata_20230630.png")
#ggsave(outputfilename, cgg, device="pdf", width=12, height=6, family="Japan1")
#ggsave(outputfilename, cgg, device="png", width=8, height=6, dpi = 90)
cairo_pdf(filename = outputfilename, width=12, height=6, family="Arial Unicode MS")
cgg
dev.off()
#} # end for loop
################################################################################
################################################################################
# add data from
# https://pubmed.ncbi.nlm.nih.gov/2750875/
# attempt at circular plot
country = "Sweden"
print(country)
country_final_only = filter(birthdata, Reliability == "Final figure, complete",
Month %in% months,
Country.or.Area==country) %>%
select( Country.or.Area, Month, Year, Value ) %>%
mutate( mi = match(Month,months) ) %>%
arrange(Country.or.Area, Year, mi)
ggplot(country_final_only, aes(x=match(Month,months),
y=Value,
group=interaction(Country.or.Area,Year) ) ) +
theme(axis.text.y=element_text(size=13),
axis.title.y=element_text(size=16),
legend.position=c(1,0.8),
legend.justification = "right",
legend.title = element_text(size=16),
legend.key.size = unit(1, 'cm'),
plot.title = element_text(size=25)) +
coord_polar() +
xlim(0,12 ) +
scale_color_gradient(low="#00220b", high="#66e284") +
labs(x=NULL, y = "Births relative to monthly average of that year",
title=country, subtitle=subtitle_text,
caption=caption_text) +
geom_line(aes(x=mi%%12, colour=Year), alpha=0.3, size=3, lineend = "round")
#