本文介绍了带有 R 的日历时间序列的处理方法,对大家解决问题具有一定的参考价值,需要的朋友们下面随着小编来一起学习吧!
问题描述
如何使用 ggplot2 制作像 this 这样的日历时间序列图表?我找不到任何东西,所以我继续写了.
How to make calendar time series charts like this with ggplot2? I couldn't find anything so I went ahead and wrote it up.
推荐答案
# Makes calendar time series plot
# The version rendered on the screen might look out of scale, the saved version should be better
CalendarTimeSeries <- function(
DateVector = 1,
ValueVector = c(1,2),
SaveToDisk = FALSE
) {
if ( length(DateVector) != length(ValueVector) ) {
stop('DateVector length different from ValueVector length')
}
require(ggplot2)
require(scales)
require(data.table)
# Pre-processing ============================================================
DateValue <- data.table(
ObsDate = DateVector,
IndexValue = ValueVector
)
DateValue[, Yr := as.integer(strftime(ObsDate, '%Y'))]
DateValue[, MthofYr := as.integer(strftime(ObsDate, '%m'))]
DateValue[, WkofYr := 1 + as.integer(strftime(ObsDate, '%W'))]
DateValue[, DayofWk := as.integer(strftime(ObsDate, '%w'))]
DateValue[DayofWk == 0L, DayofWk := 7L]
# Heatmap-ish layout to chalk out the blocks of colour on dates =============
p1 <- ggplot(
data = DateValue[,list(WkofYr, DayofWk)],
aes(
x = WkofYr,
y = DayofWk
)
) +
geom_tile(
data = DateValue,
aes(
fill = IndexValue
),
color = 'black'
) +
scale_fill_continuous(low = "green", high = "red") +
theme_bw()+
theme(
plot.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank()
) +
facet_grid(.~Yr, drop = TRUE, scales = 'free_x', space = 'free_x')
# adding borders for change of month ========================================
# vertical borders ( across weeks ) --------------------------------------
setkeyv(DateValue,c("Yr","DayofWk","WkofYr","MthofYr"))
DateValue[,MonthChange := c(0,diff(MthofYr))]
MonthChangeDatasetAcrossWks <- DateValue[MonthChange==1]
MonthChangeDatasetAcrossWks[,WkofYr := WkofYr - 0.5]
if ( nrow(MonthChangeDatasetAcrossWks) > 0 ) {
p1 <- p1 +
geom_tile(
data = MonthChangeDatasetAcrossWks,
color = 'black',
width = .2
)
}
# horizontal borders ( within a week ) -----------------------------------
setkeyv(DateValue,c("Yr","WkofYr","DayofWk","MthofYr"))
DateValue[,MonthChange := c(0,diff(MthofYr))]
MonthChangeDatasetWithinWk <- DateValue[MonthChange==1 & (! DayofWk %in% c(1))]
# MonthChangeDatasetWithinWk <- DateValue[MonthChange==1]
MonthChangeDatasetWithinWk[,DayofWk := DayofWk - 0.5]
if ( nrow(MonthChangeDatasetWithinWk) > 0 ) {
p1 <- p1 +
geom_tile(
data = MonthChangeDatasetWithinWk,
color = 'black',
width = 1,
height = .2
)
}
# adding axis labels and ordering Y axis Mon-Sun ============================
MonthLabels <- DateValue[,
list(meanWkofYr = mean(WkofYr)),
by = c('MthofYr')
]
MonthLabels[,MthofYr := month.abb[MthofYr]]
p1 <- p1 +
scale_x_continuous(
breaks = MonthLabels[,meanWkofYr],
labels = MonthLabels[, MthofYr],
expand = c(0, 0)
) +
scale_y_continuous(
trans = 'reverse',
breaks = c(1:7),
labels = c('Mon','Tue','Wed','Thu','Fri','Sat','Sun'),
expand = c(0, 0)
)
# saving to disk if asked for ===============================================
if ( SaveToDisk ) {
ScalingFactor = 10
ggsave(
p1,
file = 'CalendarTimeSeries.png',
height = ScalingFactor* 7,
width = ScalingFactor * 2.75 * nrow(unique(DateValue[,list(Yr, MthofYr)])),
units = 'mm'
)
}
p1
}
# some data
VectorofDates = seq(
as.Date("1/11/2013", "%d/%m/%Y"),
as.Date("31/12/2014", "%d/%m/%Y"),
"days"
)
VectorofValues = runif(length(VectorofDates))
# the plot
(ThePlot <- CalendarTimeSeries(VectorofDates, VectorofValues, TRUE))
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