10<\/a><\/sup>:<\/p>\n\n\n\nplot_100 <- Easter_100_summary %>%<\/span><\/em>\nggplot() +<\/span><\/em>\ngeom_bar(aes(x = interaction(Easter_100_day, Easter_100_month), y = perc ), stat = 'identity', colour = 'black', fill = 'blue') +<\/span><\/em>\nylab('Proportion of Easter Sunday on day [%]') +<\/span><\/em>\nggtitle(label = 'Easter between 2000 and 2100') +<\/span><\/em>\nscale_x_discrete('Date', labels = axis_label) +<\/span><\/em>\ngeom_text(label = 'March', x = 4, y = -0.1) +<\/span><\/em>\ngeom_text(label = 'April', x = 10, y = -0.1) +<\/span><\/em>\ntheme_bw() <\/span><\/em>\n\ndev.new()<\/span><\/em>\nplot_100<\/span><\/em><\/code><\/pre>\n\n\n\nThe plot clearly shows that the distribution of Easter dates is not uniform. The increase in proportion of Easters between 23rd March and 26th March as well as the decrease in proportion of Easters between 22nd April and 25th April are appreciated as they are at the beginning and end of the plot. However, the 31st March, 15th April and 20 April are more common Easter dates than other dates and 27th March, 2nd April, 7th April and 13th April are less common dates for Easter.<\/p>\n\n\n\n
1000 Years from beginning Gregorian Calendar<\/strong><\/em><\/p>\n\n\n\nPerhaps the unexpected distribution in dates is due to a relatively small number of dates? Similarly, it is easy to look from the beginning of the Gregorian calendar (1583) to the year 3000:<\/p>\n\n\n\n
# Easter from beginning Gregorian calendar to year 3000<\/span><\/em>\nEaster_3000 <- Easter(1583:3000) <\/span><\/em>\nEaster_3000_year <- as.numeric(strftime(Easter_3000, format = '%Y'))<\/span><\/em>\nEaster_3000_month <- as.numeric(strftime(Easter_3000, format = '%m'))<\/span><\/em>\nEaster_3000_day <- as.numeric(strftime(Easter_3000, format = '%d'))<\/span><\/em>\nEaster_3000_dates <- data.frame(Easter_3000_year, Easter_3000_month, Easter_3000_day)<\/span><\/em>\n\n#summarise the data and calculate frequencies:<\/span><\/em>\nEaster_3000_summary <- Easter_3000_dates %>%<\/span><\/em>\ndplyr::group_by(Easter_3000_month, Easter_3000_day) %>%<\/span><\/em>\ndplyr::tally() # function in dplyr to add up groups <\/span><\/em>\nEaster_3000_summary$perc = 100 * Easter_3000_summary$n \/ sum(Easter_3000_summary$n) # calculate percentage<\/span><\/em>\n\nplot_3000 <- Easter_3000_summary %>%<\/span><\/em>\nggplot() +<\/span><\/em>\ngeom_bar(aes(x = interaction(Easter_3000_day, Easter_3000_month), y = perc), stat = 'identity', colour = 'black', fill = 'red') +<\/span><\/em>\nylab('Proportion of Easter Sunday on day [%]') +<\/span><\/em>\nggtitle(label = 'Easter between 1583 and 3000') +<\/span><\/em>\nscale_x_discrete('Date', labels = axis_label) +<\/span><\/em>\ngeom_text(label = 'March', x = 4, y = -0.1) +<\/span><\/em>\ngeom_text(label = 'April', x = 12, y = -0.1) +<\/span><\/em>\ntheme_bw()<\/span><\/em>\n\ndev.new()<\/span><\/em>\nplot_3000<\/span><\/em>\n<\/code><\/pre>\n\n\n\nStill not a uniform distribution! Again, there are dips in the distribution, similarly to the previous plot. Perhaps we should look at even more dates?<\/p>\n\n\n\n
10,000 Years from beginning Gregorian Calendar<\/strong><\/em><\/p>\n\n\n\nSimilarly, look at 10,000 years; from the beginning of the Gregorian calendar to the year 10,000:<\/p>\n\n\n\n
# Easter from the beginning of the Gregorian calendar to the year 9999<\/em><\/span>\nEaster_10k <- Easter(1583:9999) <\/em><\/span>\nEaster_10k_year <- as.numeric(strftime(Easter_10k, format = '%Y'))<\/em><\/span>\nEaster_10k_month <- as.numeric(strftime(Easter_10k, format = '%m'))<\/em><\/span>\nEaster_10k_day <- as.numeric(strftime(Easter_10k, format = '%d'))<\/em><\/span>\nEaster_10k_dates <- data.frame(Easter_10k_year, Easter_10k_month, Easter_10k_day)<\/em><\/span>\n\n#summarise the data and calculate frequencies:<\/em><\/span>\nEaster_10k_summary <- Easter_10k_dates %>%<\/em><\/span>\ndplyr::group_by(Easter_10k_month, Easter_10k_day) %>%<\/em><\/span>\ndplyr::tally() # function in dplyr to add up groups <\/em><\/span>\nEaster_10k_summary$perc <- 100 * Easter_10k_summary$n \/ sum(Easter_10k_summary$n) # calculate percentage <\/em><\/span>\n\nplot_10k <- Easter_10k_summary %>%<\/em><\/span>\nggplot() +<\/em><\/span>\n
![\"\"](\"https:\/\/pcool.dyndns.org\/wp-content\/uploads\/2025\/06\/Easter_dates_1-1024x719.png\")
<\/figure>\n\n\n\n![\"\"](\"https:\/\/pcool.dyndns.org\/wp-content\/uploads\/2025\/06\/Easter_dates_2.png\")
<\/figure>\n\n\n\n