如何解决根据数据集中的位置在ggplot中子集特定日期
我已使用ggplot2
作了以下绘图。我想知道是否存在一种解决方案,可以根据它们在数据集中的位置自动xmin
和xmax
中的geom_rect
和xmin
子集,而不是手动填写日期。我希望始终将xmax
作为我数据集中的第6个最后日期观测值,并将scale_x_date
作为最后一个日期观测值。同样,在ggplot(psce_data,aes(Date,`PSCE Growth`)) +
geom_rect(aes(xmin = tail(date,1),xmax = as.Date('2020-12-01'),ymin = 0,ymax = 10),fill = "red",alpha = 0.2) +
geom_line(size = 1.2,col = '#75002B') +
scale_x_date(breaks = seq(as.Date('2018-01-01'),as.Date('2020-12-01'),by = '6 months'),date_labels = '%b-%Y') +
labs(y = 'Year-on-Year Growth (%)') +
scale_y_continuous(breaks = seq(0,10,by = 2)) +
theme_bw()
序列中,我想将其设置为自动提取数据集中的第一个和最后一个日期。
structure(list(Date = structure(c(17532,17563,17591,17622,17652,17683,17713,17744,17775,17805,17836,17866,17897,17928,17956,17987,18017,18048,18078,18109,18140,18170,18201,18231,18262,18293,18322,18353,18383,18414,18444,18475,18506,18536,18567,18597),class = "Date"),`PSCE Growth` = c(6.03806138152698,6.03809149124142,6.01532602228477,6.14343685180097,5.72725741494446,5.71673506872114,5.54860173966314,6.78058899222803,6.71018505753344,7.18229309747457,7.35166052339761,6.88491297221491,6.39557440155487,6.04095268041736,5.98793033021946,7.0149065226691,8.3391362577722,7.77825490464967,7.52521947220078,6.42564285250243,6.52665779068081,6.42119590515603,6.0627396381368,7.17023911171296,7.25116619687204,6.14998629821019,6.10667340304004,3.70834197649858,2.18774730704022,2.82551654988927,3.02881235983089,2.70973404541919,2.91094424831471,2.51635377656063,2.85799109044056,1.94656704508046)),row.names = c(NA,-36L),class = c("tbl_df","tbl","data.frame"))
这是我的数据集的可复制内容
import pytube as pt
def downloadVideo(url):
pt.YouTube(url).streams.first().download('/Downloads')
print("Done")
解决方法
您可以使用head(tail(Date,6),1)
:
ggplot(psce_data,aes(Date,`PSCE Growth`)) +
geom_rect(aes(xmin = head(tail(Date,1),xmax = tail(Date,ymin = 0,ymax = 10),fill = "red",alpha = 0.2) +
geom_line(size = 1.2,col = '#75002B') +
scale_x_date(breaks = seq(head(psce_data$Date,tail(psce_data$Date,by = '6 months'),date_labels = '%b-%Y') +
labs(y = 'Year-on-Year Growth (%)') +
scale_y_continuous(breaks = seq(0,10,by = 2)) +
theme_bw()
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