R:ddply重复年度累计数据 [英] R: ddply repeats yearly cumulative data
问题描述
与此问题相关此处 ，但为了清晰起见，我决定再问一个问题，因为新"问题与原始问题并不直接相关.简而言之，我正在使用ddply来累计三年中每个年份的值.我的代码从第一年开始获取数据，并在列的第二年和第三年重复.我的猜测是每个1年的块都被复制到整个专栏中，但是我不明白为什么.
问如何在指定列的右行中获得每年的累计总和值?
我经常使用ddply和cumsum组合，所以突然遇到问题很烦人.
require(lubridate)
require(plyr)
require(xts)
require(reshape)
require(reshape2)
set.seed(12345)
# create dummy time series data
monthsback <- 24
startdate <- as.Date(paste(year(now()),month(now()),"1",sep = "-")) - months(monthsback)
mydf <- data.frame(mydate = seq(as.Date(startdate), by = "month", length.out = monthsback),
myvalue1 = runif(monthsback, min = 600, max = 800),
myvalue2 = runif(monthsback, min = 1900, max = 2400),
myvalue3 = runif(monthsback, min = 50, max = 80),
myvalue4 = runif(monthsback, min = 200, max = 300))
mydf$year <- as.numeric(format(as.Date(mydf$mydate), format="%Y"))
mydf$month <- as.numeric(format(as.Date(mydf$mydate), format="%m"))
# Select columns to process
newcolnames <- c('myvalue1','myvalue4','myvalue2')
# melt n' cast
mydf.m <- mydf[,c('mydate','year',newcolnames)]
mydf.m <- melt(mydf.m, measure.vars = newcolnames)
mydf.m <- ddply(mydf.m, c("year", "variable"), transform, newcol = cumsum(value))
mydf.m <- dcast(mydate ~ variable, data = mydf.m, value.var = "newcol")
colnames(mydf.m) <- c('mydate',paste(newcolnames, "_cum", sep = ""))
mydf <- merge(mydf, mydf.m, by = 'mydate', all = FALSE)
mydf
我并没有真正关注您的for循环，但是您使事情变得过于复杂了吗?您不能直接使用transform和ddply吗?
#Make sure it's ordered properly
mydf <- mydf[order(mydf$year, mydf$month),]
#Use ddply to calculate the cumsum by year:
ddply(mydf, "year", transform,
cumsum1 = cumsum(myvalue1),
cumsum2 = cumsum(myvalue2))
#----------
mydate myvalue1 myvalue2 year month cumsum1 cumsum2
1 2010-05-01 744.1808 264.4543 2010 5 744.1808 264.4543
2 2010-06-01 775.1546 238.9828 2010 6 1519.3354 503.4371
3 2010-07-01 752.1965 269.8544 2010 7 2271.5319 773.2915
....
9 2011-01-01 745.5411 218.7712 2011 1 745.5411 218.7712
10 2011-02-01 797.9474 268.1834 2011 2 1543.4884 486.9546
11 2011-03-01 606.9071 237.0104 2011 3 2150.3955 723.9650
...
21 2012-01-01 690.7456 225.9681 2012 1 690.7456 225.9681
22 2012-02-01 665.3505 232.1225 2012 2 1356.0961 458.0906
23 2012-03-01 793.0831 206.0195 2012 3 2149.1792 664.1101
EDIT -未经测试，因为我在这台计算机上没有R，但这是我的初衷:
require(reshape2)
mydf.m <- melt(mydf, measure.vars = newcolnames)
mydf.m <- ddply(mydf.m, c("year", "variable"), transform, newcol = cumsum(value))
dcast(mydate + year + month ~ variable, data = mydf.m, value.var = "newcol")
Related to this question here, but I decided to ask another question for the sake of clarity as the 'new' question is not directly related to the original. Briefly, I am using ddply to cumulatively sum a value for each of three years. My code takes data from the first year and repeats in in the second and third-year rows of the column. My guess is that each 1-year chunk is being copied to the whole of the column, but I don't understand why.
Q. How can I get a cumulatively summed value for each year, in the right rows of the designated column?
[Edit: the for loop - or something similar - is important, as ultimately I want to automagically calculate new columns based on a list of column names, rather than calculating each new column by hand. The loop iterates over the list of column names.]
I use the ddply and cumsum combination frequently so it is rather vexing to suddenly be having problems with it.
[Edit: this code has been updated to the solution I settled on, which is based on @Chase's answer below]
require(lubridate)
require(plyr)
require(xts)
require(reshape)
require(reshape2)
set.seed(12345)
# create dummy time series data
monthsback <- 24
startdate <- as.Date(paste(year(now()),month(now()),"1",sep = "-")) - months(monthsback)
mydf <- data.frame(mydate = seq(as.Date(startdate), by = "month", length.out = monthsback),
myvalue1 = runif(monthsback, min = 600, max = 800),
myvalue2 = runif(monthsback, min = 1900, max = 2400),
myvalue3 = runif(monthsback, min = 50, max = 80),
myvalue4 = runif(monthsback, min = 200, max = 300))
mydf$year <- as.numeric(format(as.Date(mydf$mydate), format="%Y"))
mydf$month <- as.numeric(format(as.Date(mydf$mydate), format="%m"))
# Select columns to process
newcolnames <- c('myvalue1','myvalue4','myvalue2')
# melt n' cast
mydf.m <- mydf[,c('mydate','year',newcolnames)]
mydf.m <- melt(mydf.m, measure.vars = newcolnames)
mydf.m <- ddply(mydf.m, c("year", "variable"), transform, newcol = cumsum(value))
mydf.m <- dcast(mydate ~ variable, data = mydf.m, value.var = "newcol")
colnames(mydf.m) <- c('mydate',paste(newcolnames, "_cum", sep = ""))
mydf <- merge(mydf, mydf.m, by = 'mydate', all = FALSE)
mydf
I don't really follow your for loop there, but are you overcomplicating things? Can't you just directly use transform and ddply?
#Make sure it's ordered properly
mydf <- mydf[order(mydf$year, mydf$month),]
#Use ddply to calculate the cumsum by year:
ddply(mydf, "year", transform,
cumsum1 = cumsum(myvalue1),
cumsum2 = cumsum(myvalue2))
#----------
mydate myvalue1 myvalue2 year month cumsum1 cumsum2
1 2010-05-01 744.1808 264.4543 2010 5 744.1808 264.4543
2 2010-06-01 775.1546 238.9828 2010 6 1519.3354 503.4371
3 2010-07-01 752.1965 269.8544 2010 7 2271.5319 773.2915
....
9 2011-01-01 745.5411 218.7712 2011 1 745.5411 218.7712
10 2011-02-01 797.9474 268.1834 2011 2 1543.4884 486.9546
11 2011-03-01 606.9071 237.0104 2011 3 2150.3955 723.9650
...
21 2012-01-01 690.7456 225.9681 2012 1 690.7456 225.9681
22 2012-02-01 665.3505 232.1225 2012 2 1356.0961 458.0906
23 2012-03-01 793.0831 206.0195 2012 3 2149.1792 664.1101
EDIT - this is untested as I don't have R on this machine, but this is what I had in mind:
require(reshape2)
mydf.m <- melt(mydf, measure.vars = newcolnames)
mydf.m <- ddply(mydf.m, c("year", "variable"), transform, newcol = cumsum(value))
dcast(mydate + year + month ~ variable, data = mydf.m, value.var = "newcol")
这篇关于R:ddply重复年度累计数据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IT屋！
