如何在一张ggplot中绘制S& P 500和苏富比时间系列？

本文介绍了如何在一张ggplot中绘制S&amp; P 500和苏富比时间系列？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 29岁程序员，3月因学历无情被辞！ 我正在用quantmod软件包下载S& P 500时间系列和苏富比股票： 库（动物园） library（tseries） library（quantmod） library（ggplot2） env1 = new.env（） getSymbols（^ GSPC， env = env1，src =yahoo，from = as.Date（1988-06-01），to = as.Date（2013-05-29）） GSPC = env1 $ GSPC gspc.df = data.frame（date = time（GSPC），coredata（GSPC）） env2 = new.env（） getSymbols（BID，env = env2，src =yahoo，from = as.Date（1988-06-01），to = as.Date（2013-05-29）） BID = env2 $ BID sothebys.df = data.frame（date = time（BID），coredata（BID）） 我的目标是将调整后的价格合并或融合在一起，并将其与ggplot一起绘制。然而，我对df框架有问题： t = as.Date（0：9128，origin =1988-06 -01） y1 = gspc.df $ GSPC.Adjusted y2 = sothebys.df $ BID.Adjusted df = data.frame（t = t，values = c（y2，每个= 9129）） $ bg = ggplot（data = df，aes（x = t，y = values））+ geom_line（）+ facet_grid（type〜。，scales =free）+ scale_y_continuous（trans =log10）+ ylab（Log values）g 当我尝试执行df = data ...行时，行数。如何融化或合并数据，以便我可以将它们用于组合的ggplot？ 编辑 图表正常工作。在最后一步中，我将衰退条添加到图中。以下代码会产生衰退条，包括标准化次数seres： $ $ $ $ $ $ c $ recessions.df = read.table（textConnection（高峰，低谷 1857-06-01,1858-12-01 1860-10-01,1861-06-01 1865-04-01，1867-12-01 1869-06-01，1870-12-01 1873-10-01，1879-03-01 1882-03-01，1885-05-01 1887 -03-01，1888-04-01 1890-07-01，1891-05-01 1893-01-01，1894-06-01 1895-12-01， 1897-06-01 1899-06-01，1900-12-01 1902-09-01，1904-08-01 1907-05-01，1908-06-01 1910-01-01，1912-01-01 1913-01-01，1914-12-01 1918-08-01，1919-03-01 1920 -01-01,1921-07-01 1923-05-01,1924-07-01 1926-10-01,1927-11-01 1929-08-01， 1933-03-01 1937-05-01，1938-06-01 1945-02-01，1945-10-01 1948-11-01，1949-10-01 1953-07-01，1954-05-01 1957-08-01，1958-04-01 1960-04-01，1961-02-01 1969 -12-01,1970-11-01 1973-11 -01，1975-03-01 1980-01-01，1980-07-01 1981-07-01，1982-11-01 1990-07-01，1991- 03-01 2001-03-01，2001-11-01 2007-12-01，2009-06-01），sep ='，'， colClasses = c（ 'Date'，'Date'），header = TRUE） recessions.trim = subset（recessions.df，Peak> = min（gspc.df $ date）） g。 gspc = ggplot（data = df2）+ geom_line（aes（x = Date，y = GSPC，color =blue））+ geom_line（aes（x = Date，y = Sothebys，color =red））+ theme_bw （） g.gspc = g.gspc + geom_rect（data = recessions.trim，aes（xmin = Peak，xmax = Trough，ymin = -Inf，ymax = + Inf），fill ='pink'，alpha = 0.4） plot（g.gspc） 非常感谢您的协助/教学。我对编程和R相当陌生，感谢帮助我改进：） 顺便说一句，如果有人有进一步改进此解决方案的想法，请评论！ Thx 解决方案苏富比的数据比标准普尔的数据略少。如果您从S& P中删除没有出现在苏富比的日期，那么它可以正常工作。您在定义数据框方面做了一些奇怪的事情，所以我解决了这个问题。 library（zoo） library（tseries） library（quantmod） library（ggplot2） ＃import env1 = new.env（） getSymbols（^ GSPC ，env = env1，src =yahoo，from = as.Date（1988-06-01），to = as.Date（2013-05-29）） GSPC = env1 $ GSPC gspc.df = data.frame（date = time（GSPC），coredata（GSPC）） env2 = new.env（） getSymbols（BID， env = env2，src =yahoo，from = as.Date（1988-06-01），to = as.Date（2013-05-29）） BID = env2 $ BID sothebys.df = data.frame（date = time（BID），coredata（BID）） ＃查找哪些日期在GSPC中，但不在苏富比的 bad.dates< ; - sothebys.df $ date [-which（gspc.df $ date％in％sothebys.df $ date）] ＃从数据框中删除'坏日期'，这样两个股票都有代表性从每个日期观察＃ gspc.df< - gspc.df [-which（gspc.df $ date％in％bad.dates），] ＃v erify长度长度（gspc.df）==长度（sothebys.df） ＃建立数据框，其中包含日期和股票价格以用于图表 df =数据.frame（Date = gspc.df $ date， GSPC = gspc.df $ GSPC.Adjusted， Sothebys = sothebys.df $ BID.Adjusted） ＃plot价格随时间推移 ggplot（data = df，aes（x = Date））+ geom_line（aes（y = GSPC），color =blue）+ geom_line（aes（y = Sothebys） =红色） 您应该考虑只考虑日常价格变化，这是比较股票时常见的做法。指数和特定证券之间交易量的差异非常大，您无法通过查看您请求的图表学到很多东西。我偶尔会使用标准化函数。这对于这种情况并不完美（它把所有的事情都调整到0到1的范围内），但是我会留给你来正确地标准化你的数据。同时，下面的代码将给你一个他们如何比较的好主意： NormalizeVector< - function（x ）{ NormCalc （x - min（x，na.rm = TRUE））/（max（x，na.rm = TRUE） - min（x， na.rm = TRUE））} if（class（x）％in％c（integer，numeric））{ norm.val< - NormCalc（x ）} else norm.val< - x return（norm.val）} df2 = as.data.frame（lapply （df，NormalizeVector）） ＃随时间绘制标准化价格 ggplot（data = df2，aes（x = Date））+ geom_line（aes（y = GSPC），color =蓝色）+ geom_line（aes（y = Sothebys），color =red） I am downloading with the quantmod package the S&P 500 time series and the Sotheby's stock:library(zoo)library(tseries)library(quantmod)library(ggplot2)env1 = new.env()getSymbols("^GSPC", env = env1, src ="yahoo", from = as.Date("1988-06-01"),to = as.Date("2013-05-29"))GSPC = env1$GSPCgspc.df = data.frame(date=time(GSPC), coredata(GSPC))env2 = new.env()getSymbols("BID", env = env2, src ="yahoo", from = as.Date("1988-06-01"),to = as.Date("2013-05-29"))BID = env2$BIDsothebys.df = data.frame(date=time(BID), coredata(BID))My objective is to merge or melt the Adjusted Prices together and plot them with ggplot. However, I have problems with the df frame:t = as.Date(0:9128, origin="1988-06-01")y1 = gspc.df$GSPC.Adjustedy2 = sothebys.df$BID.Adjusteddf = data.frame(t=t, values=c(y2,y1), type=rep(c("Bytes", "Changes"), each=9129))g = ggplot(data=df, aes(x=t, y=values)) + geom_line() + facet_grid(type ~ ., scales="free") + scale_y_continuous(trans="log10") + ylab("Log values")gWhen I try to execute the df = data... line I get an error concerning the number of rows. How can I melt or merge the data, so that I can use them for the combined ggplot?EDITThe graph works fine. In the last step I included the recession bars into the graph. The following code produces the recession bars including the normalized times seres:recessions.df = read.table(textConnection( "Peak, Trough 1857-06-01, 1858-12-01 1860-10-01, 1861-06-01 1865-04-01, 1867-12-01 1869-06-01, 1870-12-01 1873-10-01, 1879-03-01 1882-03-01, 1885-05-01 1887-03-01, 1888-04-01 1890-07-01, 1891-05-01 1893-01-01, 1894-06-01 1895-12-01, 1897-06-01 1899-06-01, 1900-12-01 1902-09-01, 1904-08-01 1907-05-01, 1908-06-01 1910-01-01, 1912-01-01 1913-01-01, 1914-12-01 1918-08-01, 1919-03-01 1920-01-01, 1921-07-01 1923-05-01, 1924-07-01 1926-10-01, 1927-11-01 1929-08-01, 1933-03-01 1937-05-01, 1938-06-01 1945-02-01, 1945-10-01 1948-11-01, 1949-10-01 1953-07-01, 1954-05-01 1957-08-01, 1958-04-01 1960-04-01, 1961-02-01 1969-12-01, 1970-11-01 1973-11-01, 1975-03-01 1980-01-01, 1980-07-01 1981-07-01, 1982-11-01 1990-07-01, 1991-03-01 2001-03-01, 2001-11-01 2007-12-01, 2009-06-01"), sep=',',colClasses=c('Date', 'Date'), header=TRUE)recessions.trim = subset(recessions.df, Peak >= min(gspc.df$date))g.gspc = ggplot(data = df2) + geom_line(aes(x = Date, y = GSPC, colour = "blue")) + geom_line(aes(x = Date, y = Sothebys, colour = "red")) + theme_bw()g.gspc = g.gspc + geom_rect(data=recessions.trim, aes(xmin=Peak, xmax=Trough, ymin=-Inf, ymax=+Inf), fill='pink', alpha=0.4)plot(g.gspc)Thank you very much for your assistance / teaching. I am quite new to programming and R, thanks for helping me to improve :)By the way, if somebody has an idea to further improve this solution, please comment! Thx 解决方案 The data from Sotheby's has slightly fewer observations than the data from the S&P. If you remove the dates from S&P that do not appear in Sotheby's, then it works fine. You were doing some weird things in defining your dataframe as well, so I fixed that.library(zoo)library(tseries)library(quantmod)library(ggplot2)# importenv1 = new.env()getSymbols("^GSPC", env = env1, src ="yahoo", from = as.Date("1988-06-01"),to = as.Date("2013-05-29"))GSPC = env1$GSPCgspc.df = data.frame(date=time(GSPC), coredata(GSPC))env2 = new.env()getSymbols("BID", env = env2, src ="yahoo", from = as.Date("1988-06-01"),to = as.Date("2013-05-29"))BID = env2$BIDsothebys.df = data.frame(date=time(BID), coredata(BID))# find which dates are in GSPC but not in Sotheby'sbad.dates <- sothebys.df$date[-which(gspc.df$date %in% sothebys.df$date)]# remove the 'bad dates' from the dataframe so that both stocks have representative observations# from each dategspc.df <- gspc.df[-which(gspc.df$date %in% bad.dates),]# verify the lengthslength(gspc.df) == length(sothebys.df)# build the dataframe with dates and stock prices to be used in graphingdf = data.frame(Date = gspc.df$date, GSPC = gspc.df$GSPC.Adjusted, Sothebys = sothebys.df$BID.Adjusted)# plot prices over timeggplot(data = df, aes(x = Date)) + geom_line(aes(y = GSPC), colour = "blue") + geom_line(aes(y = Sothebys), colour = "red")You should definitely think about looking at just the day-to-day price changes, which is a common practice when comparing stocks. The difference in trading volume between an index and a particular security is so great that you can't learn much by looking at the chart you requested. I use the normalization function below occasionally. It isn't perfect for this situation (it scales everything to a range of 0 to 1), but I'll leave it up to you to standardize your data properly. In the mean time, the following code will give you a good idea of how they compare:NormalizeVector <- function(x) { NormCalc <- function(x) { (x - min(x, na.rm=TRUE))/(max(x,na.rm=TRUE) - min(x, na.rm=TRUE)) } if (class(x) %in% c("integer", "numeric")) { norm.val <- NormCalc(x) } else norm.val <- x return(norm.val)}df2 = as.data.frame(lapply(df, NormalizeVector))# plot normalized prices over timeggplot(data = df2, aes(x = Date)) + geom_line(aes(y = GSPC), colour = "blue") + geom_line(aes(y = Sothebys), colour = "red") 这篇关于如何在一张ggplot中绘制S&amp; P 500和苏富比时间系列？的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！ 上岸，阿里云！