我有一个包含以下列的数据库：“Year”，“Month”，“T1”，......“T31”：
例如df_0是原始格式，我想在new_df中转换它（第二部分）
id0 <- c ("Year", "Month", "T_day1", "T_day2", "T_day3", "T_day4", "T_day5")
id1 <- c ("2010", "January", 10, 5, 2,3,3)
id2 <- c ("2010", "February", 20,36,5,8,1)
id3 <- c ("2010", "March", 12,23,23,5,25)
df_0 <- rbind (id1, id2, id3)
colnames (df_0)<- id0
head(df_0)
我想创建一个新的数据框，其中每个月和每年的T1 .... T31数据将加入一个包含所有日期的列，例如2010年1月1日到2012年1月4日：
date<-seq(as.Date("2010-01-01"), as.Date("2012-01-04"), by="days")
或根据其他三列（年，月，日）的值将值加入数据框的新列：
year <- lapply(strsplit(as.character(date), "\\-"), "[", 1)
month <- lapply(strsplit(as.character(date), "\\-"), "[", 2)
day <- lapply(strsplit(as.character(date), "\\-"), "[", 3)
df <- cbind (year, month, day)
我希望以这种方式获得包含信息的数据框：
Year <- rep(2010,15)
Month <- c(rep("January", 5), rep("February",5), rep("March",5))
Day<- rep(c(1,2,3,4,5))
Value <- c(10,5,2,3,3,20,36,5,8,1,12,23,23,5,25)
new_df <- cbind (Year, Month, Day, Value)
head(new_df)
提前致谢
答案 0 :(得分：2)
您正在寻找的是重塑您的数据。您可以使用的一个库是reshape2库。在这里，我们可以使用melt库中的reshape2函数：
melt(data.frame(df_0), id.vars=c("Year", "Month"))
根据您拥有的数据，输出将具有：
Year Month variable value
1 2010 January T_day1 10
2 2010 February T_day1 20
3 2010 March T_day1 12
4 2010 January T_day2 5
5 2010 February T_day2 36
6 2010 March T_day2 23
7 2010 January T_day3 2
8 2010 February T_day3 5
9 2010 March T_day3 23
10 2010 January T_day4 3
11 2010 February T_day4 8
12 2010 March T_day4 5
13 2010 January T_day5 3
14 2010 February T_day5 1
15 2010 March T_day5 25
然后，您可以根据格式化该列的方式将变量列更改为天数。
答案 1 :(得分：1)
首先，我生成了自己的测试数据。我使用了简化的date向量来简化演示：2010-01-01到2010-03-04。在我的df_0我为缩小日期向量中的每个日期生成了一个值，不包括上一个日期，并且还包括一个不在date向量中的其他日期：2010-03-05。后来为什么我会这样做会很清楚。
set.seed(1);
date <- seq(as.Date('2010-01-01'),as.Date('2010-03-04'),by='day');
df_0 <- reshape(setNames(as.data.frame(cbind(do.call(rbind,strsplit(strftime(c(date[-length(date)],as.Date('2010-03-05')),'%Y %B %d'),' ')),round(rnorm(length(date)),3))),c('Year','Month','Day','T_day')),dir='w',idvar=c('Year','Month'),timevar='Day');
attr(df_0,'reshapeWide') <- NULL;
df_0;
## Year Month T_day.01 T_day.02 T_day.03 T_day.04 T_day.05 T_day.06 T_day.07 T_day.08 T_day.09 T_day.10 T_day.11 T_day.12 T_day.13 T_day.14 T_day.15 T_day.16 T_day.17 T_day.18 T_day.19 T_day.20 T_day.21 T_day.22 T_day.23 T_day.24 T_day.25 T_day.26 T_day.27 T_day.28 T_day.29 T_day.30 T_day.31
## 1 2010 January -0.626 0.184 -0.836 1.595 0.33 -0.82 0.487 0.738 0.576 -0.305 1.512 0.39 -0.621 -2.215 1.125 -0.045 -0.016 0.944 0.821 0.594 0.919 0.782 0.075 -1.989 0.62 -0.056 -0.156 -1.471 -0.478 0.418 1.359
## 32 2010 February -0.103 0.388 -0.054 -1.377 -0.415 -0.394 -0.059 1.1 0.763 -0.165 -0.253 0.697 0.557 -0.689 -0.707 0.365 0.769 -0.112 0.881 0.398 -0.612 0.341 -1.129 1.433 1.98 -0.367 -1.044 0.57 <NA> <NA> <NA>
## 60 2010 March -0.135 2.402 -0.039 <NA> 0.69 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
解决方案的前半部分是从宽格式到长格式的重塑，只需调用reshape()即可完成。另外，我将其打包到na.omit()，以防止df_0中不可避免的NA单元格生成NA值：
df_1 <- na.omit(reshape(df_0,dir='l',idvar=c('Year','Month'),timevar='Day',varying=grep('^T_day\\.',names(df_0)),v.names='Value'));
rownames(df_1) <- NULL;
df_1[order(match(df_1$Month,month.name),df_1$Day),];
## Year Month Day Value
## 1 2010 January 1 -0.626
## 4 2010 January 2 0.184
## 7 2010 January 3 -0.836
## 10 2010 January 4 1.595
## 12 2010 January 5 0.33
## 15 2010 January 6 -0.82
## 17 2010 January 7 0.487
## 19 2010 January 8 0.738
## 21 2010 January 9 0.576
## 23 2010 January 10 -0.305
## 25 2010 January 11 1.512
## 27 2010 January 12 0.39
## 29 2010 January 13 -0.621
## 31 2010 January 14 -2.215
## 33 2010 January 15 1.125
## 35 2010 January 16 -0.045
## 37 2010 January 17 -0.016
## 39 2010 January 18 0.944
## 41 2010 January 19 0.821
## 43 2010 January 20 0.594
## 45 2010 January 21 0.919
## 47 2010 January 22 0.782
## 49 2010 January 23 0.075
## 51 2010 January 24 -1.989
## 53 2010 January 25 0.62
## 55 2010 January 26 -0.056
## 57 2010 January 27 -0.156
## 59 2010 January 28 -1.471
## 61 2010 January 29 -0.478
## 62 2010 January 30 0.418
## 63 2010 January 31 1.359
## 2 2010 February 1 -0.103
## 5 2010 February 2 0.388
## 8 2010 February 3 -0.054
## 11 2010 February 4 -1.377
## 13 2010 February 5 -0.415
## 16 2010 February 6 -0.394
## 18 2010 February 7 -0.059
## 20 2010 February 8 1.1
## 22 2010 February 9 0.763
## 24 2010 February 10 -0.165
## 26 2010 February 11 -0.253
## 28 2010 February 12 0.697
## 30 2010 February 13 0.557
## 32 2010 February 14 -0.689
## 34 2010 February 15 -0.707
## 36 2010 February 16 0.365
## 38 2010 February 17 0.769
## 40 2010 February 18 -0.112
## 42 2010 February 19 0.881
## 44 2010 February 20 0.398
## 46 2010 February 21 -0.612
## 48 2010 February 22 0.341
## 50 2010 February 23 -1.129
## 52 2010 February 24 1.433
## 54 2010 February 25 1.98
## 56 2010 February 26 -0.367
## 58 2010 February 27 -1.044
## 60 2010 February 28 0.57
## 3 2010 March 1 -0.135
## 6 2010 March 2 2.402
## 9 2010 March 3 -0.039
## 14 2010 March 5 0.69
解决方案的第二部分需要将上述长格式data.frame与您在结果data.frame中所需的确切日期合并。这需要相当数量的脚手架代码才能将日期向量转换为带有Year Month Day列的data.frame，但一旦完成，您只需使用all.x=T调用merge()即可保留每个日期在日期向量中是否存在于df_1中，并排除df_1中日期向量中不存在的任何日期：
df_2 <- merge(transform(setNames(as.data.frame(do.call(rbind,strsplit(strftime(date,'%Y %B %d'),' '))),c('Year','Month','Day')),Day=as.integer(Day)),df_1,all.x=T);
df_2[order(match(df_2$Month,month.name),df_2$Day),];
## Year Month Day Value
## 29 2010 January 1 -0.626
## 30 2010 January 2 0.184
## 31 2010 January 3 -0.836
## 32 2010 January 4 1.595
## 33 2010 January 5 0.33
## 34 2010 January 6 -0.82
## 35 2010 January 7 0.487
## 36 2010 January 8 0.738
## 37 2010 January 9 0.576
## 38 2010 January 10 -0.305
## 39 2010 January 11 1.512
## 40 2010 January 12 0.39
## 41 2010 January 13 -0.621
## 42 2010 January 14 -2.215
## 43 2010 January 15 1.125
## 44 2010 January 16 -0.045
## 45 2010 January 17 -0.016
## 46 2010 January 18 0.944
## 47 2010 January 19 0.821
## 48 2010 January 20 0.594
## 49 2010 January 21 0.919
## 50 2010 January 22 0.782
## 51 2010 January 23 0.075
## 52 2010 January 24 -1.989
## 53 2010 January 25 0.62
## 54 2010 January 26 -0.056
## 55 2010 January 27 -0.156
## 56 2010 January 28 -1.471
## 57 2010 January 29 -0.478
## 58 2010 January 30 0.418
## 59 2010 January 31 1.359
## 1 2010 February 1 -0.103
## 2 2010 February 2 0.388
## 3 2010 February 3 -0.054
## 4 2010 February 4 -1.377
## 5 2010 February 5 -0.415
## 6 2010 February 6 -0.394
## 7 2010 February 7 -0.059
## 8 2010 February 8 1.1
## 9 2010 February 9 0.763
## 10 2010 February 10 -0.165
## 11 2010 February 11 -0.253
## 12 2010 February 12 0.697
## 13 2010 February 13 0.557
## 14 2010 February 14 -0.689
## 15 2010 February 15 -0.707
## 16 2010 February 16 0.365
## 17 2010 February 17 0.769
## 18 2010 February 18 -0.112
## 19 2010 February 19 0.881
## 20 2010 February 20 0.398
## 21 2010 February 21 -0.612
## 22 2010 February 22 0.341
## 23 2010 February 23 -1.129
## 24 2010 February 24 1.433
## 25 2010 February 25 1.98
## 26 2010 February 26 -0.367
## 27 2010 February 27 -1.044
## 28 2010 February 28 0.57
## 60 2010 March 1 -0.135
## 61 2010 March 2 2.402
## 62 2010 March 3 -0.039
## 63 2010 March 4 <NA>
请注意2010-03-04是如何包含的，即使我没有在df_0中为其生成值，也排除了2010-03-05，即使我这样做了。