在R中对齐分类数据

Question

I have an excel data set in excel that I would like to load into R. The dataset has two variables "weight" and "height" in which each variable has its own date specified as to when it was recorded. The height variable has skipping/missing values, likewise in the weight variable if you go down in the data far enough. I am trying to create a consolidated data-set in which weight and height are combined and arranged by the date in the proper place and NA's are placed when a value isn't present. Are there any commands/functions that can help me do this? Thank you!

 obs     date   weight     date    height
  1   2010-10-04 52495  2010-10-04 11.6  
  2   2010-10-01 53000  2010-10-01 15.3
  3   2010-09-30 52916  2010-09-30 14.3
  4   2010-09-29 52785  2010-09-29 11.3
  5   2010-09-28 53348  2010-09-28 18.2
  6   2010-09-27 52885  2010-09-24 11.7
  7   2010-09-24 52174  2010-09-23 15.0
  8   2010-09-23 51461  2010-09-22 18.6
  9   2010-09-22 51286  2010-09-20 17.9
  10  2010-09-21 50968  
  11  2010-09-20 49250  

Answer 1

I'm assuming this question isn't about reading the data into R, but processing it after it has been read. Nevertheless, you can use the arguments check.names = FALSE and fill = TRUE when reading your data in to allow you to use Reduce to merge your data.

First, simulate reading the data in.

temp <- read.table(header = TRUE, 
text = "obs date weight date height
1   2010-10-04 52495  2010-10-04 11.6
2   2010-10-01 53000  2010-10-01 15.3
3   2010-09-30 52916  2010-09-30 14.3
4   2010-09-29 52785  2010-09-29 11.3
5   2010-09-28 53348  2010-09-28 18.2
6   2010-09-27 52885  2010-09-24 11.7
7   2010-09-24 52174  2010-09-23 15.0
8   2010-09-23 51461  2010-09-22 18.6
9   2010-09-22 51286  2010-09-20 17.9
10  2010-09-21 50968
11  2010-09-20 49250
", fill = TRUE, check.names = FALSE)

Second, use Reduce() and merge() .

Reduce(function(x, y) merge(x, y, all.x = TRUE), 
       list(temp[2:3], temp[4:5]))
#          date weight height
# 1  2010-09-20  49250   17.9
# 2  2010-09-21  50968     NA
# 3  2010-09-22  51286   18.6
# 4  2010-09-23  51461   15.0
# 5  2010-09-24  52174   11.7
# 6  2010-09-27  52885     NA
# 7  2010-09-28  53348   18.2
# 8  2010-09-29  52785   11.3
# 9  2010-09-30  52916   14.3
# 10 2010-10-01  53000   15.3
# 11 2010-10-04  52495   11.6

Answer 2

d <- read.table(header=FALSE, fill=TRUE, text="1   2010-10-04 52495  2010-10-04 11.6  
  2   2010-10-01 53000  2010-10-01 15.3
  3   2010-09-30 52916  2010-09-30 14.3
  4   2010-09-29 52785  2010-09-29 11.3
  5   2010-09-28 53348  2010-09-28 18.2
  6   2010-09-27 52885  2010-09-24 11.7
  7   2010-09-24 52174  2010-09-23 15.0
  8   2010-09-23 51461  2010-09-22 18.6
  9   2010-09-22 51286  2010-09-20 17.9
  10  2010-09-21 50968  
  11  2010-09-20 49250  ")

d1 <- d[2:3]
d2 <- d[!is.na(d[,5]),][4:5]

names(d1) <- c('Date', 'val1')
names(d2) <- c('Date', 'val2')
m <- merge(d1, d2, by='Date', all=TRUE)

> m

##          Date  val1 val2
## 1  2010-09-20 49250 17.9
## 2  2010-09-21 50968   NA
## 3  2010-09-22 51286 18.6
## 4  2010-09-23 51461 15.0
## 5  2010-09-24 52174 11.7
## 6  2010-09-27 52885   NA
## 7  2010-09-28 53348 18.2
## 8  2010-09-29 52785 11.3
## 9  2010-09-30 52916 14.3
## 10 2010-10-01 53000 15.3
## 11 2010-10-04 52495 11.6