我有一個看起來像這樣的大資料框：
> dput(sb_data_omit_1950[sample(nrow(sb_data_omit_1950), 50),])
structure(list(lat = c("56", "61", "57", "59", "58", "56", "58",
"65", "59", "65", "63", "65", "56", "59", "59", "57", "59", "60",
"56", "57", "60", "65", "64", "63", "63", "59", "59", "65", "59",
"58", "63", "59", "64", "59", "58", "59", "63", "56", "58", "59",
"57", "55", "58", "64", "62", "60", "57", "58", "60", "66"),
long = c(18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18,
18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18,
18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18,
18, 18, 18, 18, 18, 18, 18, 18), date = c("2009-02-07", "1995-03-04",
"2007-01-28", "2010-03-28", "2010-04-01", "2018-02-22", "2017-03-24",
"2014-04-16", "1983-03-20", "2016-04-02", "2020-04-14", "2020-04-02",
"2005-03-25", "2003-03-22", "2016-04-02", "2006-03-19", "2009-04-05",
"2009-01-22", "2016-03-05", "2013-02-23", "2017-03-17", "2020-03-25",
"2021-03-27", "2008-04-08", "2018-04-10", "1984-04-04", "2005-01-29",
"2019-04-03", "1983-04-10", "2006-03-26", "2010-03-29", "2006-03-18",
"2014-05-06", "2010-01-23", "2006-03-26", "2014-02-25", "2008-04-16",
"2021-02-16", "2011-03-30", "2013-03-07", "1975-03-22", "2015-02-01",
"2013-03-21", "2011-04-07", "2021-04-06", "2021-02-02", "2000-03-19",
"1983-02-26", "2010-04-03", "2017-03-28"), julian_day = c(38,
63, 28, 87, 91, 53, 83, 106, 79, 93, 105, 93, 84, 81, 93,
78, 95, 22, 65, 54, 76, 85, 86, 99, 100, 95, 29, 93, 100,
85, 88, 77, 126, 23, 85, 56, 107, 47, 89, 66, 81, 32, 80,
97, 96, 33, 79, 57, 93, 87), year = c(2009L, 1995L, 2007L,
2010L, 2010L, 2018L, 2017L, 2014L, 1983L, 2016L, 2020L, 2020L,
2005L, 2003L, 2016L, 2006L, 2009L, 2009L, 2016L, 2013L, 2017L,
2020L, 2021L, 2008L, 2018L, 1984L, 2005L, 2019L, 1983L, 2006L,
2010L, 2006L, 2014L, 2010L, 2006L, 2014L, 2008L, 2021L, 2011L,
2013L, 1975L, 2015L, 2013L, 2011L, 2021L, 2021L, 2000L, 1983L,
2010L, 2017L), decade = c("2000-2009", "1990-1999", "2000-2009",
"2010-2019", "2010-2019", "2010-2019", "2010-2019", "2010-2019",
"1980-1989", "2010-2019", "2020-2029", "2020-2029", "2000-2009",
"2000-2009", "2010-2019", "2000-2009", "2000-2009", "2000-2009",
"2010-2019", "2010-2019", "2010-2019", "2020-2029", "2020-2029",
"2000-2009", "2010-2019", "1980-1989", "2000-2009", "2010-2019",
"1980-1989", "2000-2009", "2010-2019", "2000-2009", "2010-2019",
"2010-2019", "2000-2009", "2010-2019", "2000-2009", "2020-2029",
"2010-2019", "2010-2019", "1970-1979", "2010-2019", "2010-2019",
"2010-2019", "2020-2029", "2020-2029", "2000-2009", "1980-1989",
"2010-2019", "2010-2019"), time = c(15L, 14L, 15L, 16L, 16L,
16L, 16L, 16L, 13L, 16L, 17L, 17L, 15L, 15L, 16L, 15L, 15L,
15L, 16L, 16L, 16L, 17L, 17L, 15L, 16L, 13L, 15L, 16L, 13L,
15L, 16L, 15L, 16L, 16L, 15L, 16L, 15L, 17L, 16L, 16L, 12L,
16L, 16L, 16L, 17L, 17L, 15L, 13L, 16L, 16L), lat_grouped = c("1",
"2", "1", "1", "1", "1", "1", "3", "1", "3", "2", "3", "1",
"1", "1", "1", "1", "2", "1", "1", "2", "3", "2", "2", "2",
"1", "1", "3", "1", "1", "2", "1", "2", "1", "1", "1", "2",
"1", "1", "1", "1", "1", "1", "2", "2", "2", "1", "1", "2",
"3")), row.names = c(21286L, 5843L, 16479L, 24246L, 24483L,
40513L, 39121L, 33554L, 2704L, 37376L, 48602L, 48008L, 12473L,
9593L, 37380L, 14123L, 22712L, 21155L, 36663L, 29846L, 38722L,
47518L, 51286L, 20119L, 42528L, 3132L, 11764L, 44966L, 2874L,
14406L, 24290L, 14081L, 33634L, 23125L, 14393L, 31981L, 20790L,
50057L, 26126L, 30068L, 1381L, 34000L, 30253L, 26612L, 51918L,
49677L, 7640L, 2677L, 24745L, 39308L), class = "data.frame")
> head(df)
lat long date julian_day year decade time lat_grouped
24 59 18 1951-03-22 81 1951 1950-1959 10 1
25 59 18 1951-04-08 98 1951 1950-1959 10 1
26 55 18 1952-02-03 34 1952 1950-1959 10 1
27 59 18 1952-03-08 68 1952 1950-1959 10 1
28 59 18 1953-02-22 53 1953 1950-1959 10 1
29 63 18 1953-03-12 71 1953 1950-1959 10 2
從這些資料中，我想計算julian_day給定十年（兩個變數decade或time第一個變數的數字轉換）中每個儒略日（變數）的觀察量，并將其與其他十年的資料在同一張圖上進行對比.
到目前為止，我已經設法使用以下代碼繪制了幾十年來的觀察計數：
df %>%
ggplot(aes(x=julian_day))
geom_histogram(color="darkblue", fill="white", bins=152)
xlim(0, 155)
xlab("Day n°") ylab("Count")
我有直覺，我應該使用 agroup_by(time)但無法設法繪制一些選定的組。
輸出應該看起來像繪制在同一圖表上的多條高斯曲線。
任何人都可以幫忙嗎？非常感謝，如果缺少任何資訊，我可以編輯我的帖子:)
uj5u.com熱心網友回復：
我不是 100% 確定你的最終輸出應該是什么，但如果你想要一個圖上的所有直方圖：
df %>%
ggplot(aes(x=julian_day, fill = decade))
geom_histogram(bins=152)
xlim(0, 155)
xlab("Day n°") ylab("Count")
如果在同一個圖上分開直方圖：
df %>%
ggplot(aes(x=julian_day, fill = decade))
geom_histogram(bins=152)
xlim(0, 155)
xlab("Day n°") ylab("Count")
facet_wrap(~decade, scales = "free")
如果您只想選擇某些幾十年，您可以添加一個filter()引數。在這種情況下，最簡單的方法是按年份過濾，因為它是數字：
# first and last decade
keeps <- c(min(df$year), max(df$year))
# or any decade by referencing a year within that decade
# keeps <- c(2009, 1985)
df %>% filter(year %in% keeps) %>%
ggplot(aes(x=julian_day, fill = decade))
geom_histogram(bins=152)
xlim(0, 155)
xlab("Day n°") ylab("Count")
uj5u.com熱心網友回復：
也許你想要這樣的東西：
df %>%
count(decade, julian_day) %>%
ggplot(aes(x=julian_day, n))
geom_line(color="darkblue")
xlim(0, 155)
xlab("Day n°")
ylab("Count")
theme_bw()
輸出：
uj5u.com熱心網友回復：
更新：見評論：
我們可以結合geom_histogram應用計數統計和geom_density應用密度統計：
df %>%
count(decade, julian_day) %>%
ggplot(aes(x = julian_day, fill=decade))
stat_bin(bins = 30, aes(y = ..count..))
geom_density(aes(y = ..density..*(nrow(df1)*0.8)), fill="yellow", color="#e9ecef", alpha=0.3)
像這樣的東西？
library(tidyverse)
df %>%
count(decade, julian_day) %>%
ggplot(aes(x = julian_day, y=n, fill=decade))
geom_col(position= position_dodge())
scale_y_continuous(breaks = function(x) unique(floor(pretty(seq(0, (max(x) 1) * 1.1)))))
轉載請註明出處，本文鏈接：https://www.uj5u.com/yidong/482746.html