下面的代碼根據每個月的天數將每個月分別削減爲三分之一。
library(dplyr)
library(lubridate)
library(ggplot2)
# Fake data
df = data.frame(date=seq.Date(as.Date("2013-01-01"),
as.Date("2013-03-31"), by="day"))
set.seed(394)
df$value = rnorm(nrow(df), sqrt(1:nrow(df)), 2)
# Cut months into thirds
df = df %>%
# Create a new column to group by Year-Month
mutate(yr_mon = paste0(year(date) , "_", month(date, label=TRUE, abbr=TRUE))) %>%
group_by(yr_mon) %>%
# Cut each month into thirds
mutate(cutMonth = cut(day(date),
breaks=c(0, round(1/3*n()), round(2/3*n()), n()),
labels=c("1st third","2nd third","3rd third")),
# Add yr_mon to cutMonth so that we have a unique group label for
# each third of each month
cutMonth = paste0(yr_mon, "\n", cutMonth)) %>%
ungroup() %>%
# Turn cutMonth into a factor with correct date ordering
mutate(cutMonth = factor(cutMonth, levels=unique(cutMonth)))
這裏是結果：
# Show number of observations in each group
as.data.frame(table(df$cutMonth))
Var1 Freq
1 2013_Jan\n1st third 10
2 2013_Jan\n2nd third 11
3 2013_Jan\n3rd third 10
4 2013_Feb\n1st third 9
5 2013_Feb\n2nd third 10
6 2013_Feb\n3rd third 9
7 2013_Mar\n1st third 10
8 2013_Mar\n2nd third 11
9 2013_Mar\n3rd third 10
# Plot means by group (just to visualize the result of the date grouping operations)
ggplot(df, aes(cutMonth, value)) +
stat_summary(fun.y=mean, geom='point', size=4, colour="red") +
coord_cartesian(ylim=c(-0.2,10.2)) +
theme(axis.text.x = element_text(size=14))
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