问题描述
我有一个数据框 df ，其中包含x，y和month.year数据每个x，y点.
我正在尝试获取季节性总计.我需要计算季节性平均值，即
冬季平均值(12月，1月，2月)；春季平均值(3月，4月，5月)，夏季平均值(6月，7月，8月)和秋季平均值(9月，10月，11月).
I have dataframe df with x,y,and monthly.year data for each x,y point.
I am trying to get the seasonal aggregate.I need to calculate seasonal means i.e.
For winter mean of (December,January,February); for Spring mean of (March,April,May), for Summer mean of (June,July,August) and for autumn mean of (September,October,November).
数据看起来类似于:
set.seed(1)
df <- data.frame(x=1:3,y=1:3, matrix(rnorm(72),nrow=3) )
names(df)[3:26] <- paste(month.abb,rep(2009:2010,each=12),sep=".")
x y Jan.2009 Feb.2009 ... Dec.2010
1 1 1 -0.6264538 1.5952808 ... 2.1726117
2 2 2 0.1836433 0.3295078 ... 0.4755095
3 3 3 -0.8356286 -0.8204684 ... -0.7099464
除了融化数据并制作新数据框为
I could not think of going any further except melting the data and making new data frame as
ddt.m<-melt(df,id=c("x","y"))
我想要每年的x，y，mean之类的结果.请建议我我怎么能做到这一点.
I want result like x,y,mean of season of each year.Please suggest me how I may be able to do that.
推荐答案
以下是一种可能的方法:
Here is one possible approach:
...，然后使用colsplit将变量"分为星期一"和年份"列.
...and, use colsplit to split up the "variable" into the "Mon" and "Year" columns.
library(reshape2)
ddt.m <- melt(df, id = c("x", "y"))
ddt.m <- cbind(ddt.m, colsplit(ddt.m$variable, "\\.", c("Mon", "Year")))
使用factor和levels来获取您的季节
(我留在星期一"列中.糟糕！)
Use factor and levels to get your seasons
(which I've left in the "Mon" column. Oops.)
ddt.m$Mon <- factor(ddt.m$Mon)
levels(ddt.m$Mon) <- list(Winter = month.abb[c(12, 1, 2)],
Spring = month.abb[c(3:5)],
Summer = month.abb[c(6:8)],
Autumn = month.abb[c(9:11)])
head(ddt.m)
# x y variable value Mon Year
# 1 1214842 991964.4 Jan.2009 -1.332933 Winter 2009
# 2 1220442 991964.4 Jan.2009 -1.345808 Winter 2009
# 3 1226042 991964.4 Jan.2009 -1.314435 Winter 2009
# 4 1231642 991964.4 Jan.2009 -1.236600 Winter 2009
# 5 1237242 991964.4 Jan.2009 -1.261989 Winter 2009
# 6 1242842 991964.4 Jan.2009 -1.306614 Winter 2009
使用dcast汇总数据
Use dcast to aggregate the data
dfSeasonMean <- dcast(ddt.m, x + y ~ Mon + Year,
value.var="value", fun.aggregate=mean)
head(dfSeasonMean)
# x y Winter_2009 Winter_2010 Spring_2009 Spring_2010 Summer_2009
# 1 1214842 991964.4 -1.439480 -1.006512 -0.02509008 0.2823048 1.392440
# 2 1220442 964154.4 -1.457407 -1.039266 -0.04337596 0.2315217 1.422541
# 3 1220442 973424.4 -1.456991 -1.035115 -0.04117584 0.2423561 1.414473
# 4 1220442 982694.4 -1.456479 -1.029627 -0.03799926 0.2544062 1.405813
# 5 1220442 991964.4 -1.456234 -1.027081 -0.03815661 0.2610397 1.400743
# 6 1226042 945614.4 -1.463465 -1.031665 -0.04288670 0.2236609 1.434002
# Summer_2010 Autumn_2009 Autumn_2010
# 1 1.256840 0.06469363 -0.03823892
# 2 1.263593 0.04521096 -0.04485553
# 3 1.258328 0.04860321 -0.04477636
# 4 1.252779 0.05337575 -0.04729598
# 5 1.247251 0.05742809 -0.05152524
# 6 1.272742 0.04692731 -0.04915314
这篇关于每月数据的季节性汇总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！