如何基于匹配R中的其他列的行值来填充列的值

Question

Data looks like below.

 time <- c('Nov 1st 2014, 17:36:50.000','Nov 1st 2014, 17:36:50.000',
          'Nov 1st 2014, 17:36:50.000','Nov 1st 2014, 17:36:50.000', 'Nov 1st 2014, 17:37:50.000','Nov 1st 2014, 17:37:50.000','Nov 1st 2014, 17:37:50.000')
A <- c('20.79','NA','NA','NA','21.8','NA','NA')  
B <- c('NA','97.017','94.321','85.014','NA','87.1','67.1')
C <- c('NA','C1','C2','C3','NA','C1','C2')
D <- c('L1','L1','L1','L1','L2','L2','L2')
C1 <- c('NA','NA','NA','NA','NA','NA','NA')
C2 <- c('NA','NA','NA','NA','NA','NA','NA')
C3 <- c('NA','NA','NA','NA','NA','NA','NA')
df <- data.frame(time,A,B,C,D,C1,C2,C3)

I need output in the below format.

#   time                           A     B  C    D        C1     C2      C3
# 1 Nov 1st 2014, 17:36:50.000  20.79    NA NA   L1       97.02  94.321  85.014
    Nov 1st 2014, 17:37:50.000  21.8     NA NA   L2       87.1   67.1   47.3

How do I get the data in the above format in just one row as columns "time" and "D" are same for all the rows?

Thanks in advance!

Answer 1

You can do this with dplyr::gather() to re-shape B into C1, C2, C3, and then dplyr::join() it with the other columns, assuming a unique date/time.

library(dplyr)
library(tidyr)

df %>%
  select(time, A, B, C, D) %>%
  filter(!is.na(A)) %>%
  left_join(
    df %>%
      select(time, C, B, D) %>%
      spread(C, B) %>%
      select(-`<NA>`),
    by = c("time", "D")
  )

#                         time     A  B    C  D     C1     C2     C3
# 1 Nov 1st 2014, 17:36:50.000 20.79 NA <NA> L1 97.017 94.321 85.014
# 2 Nov 1st 2014, 17:37:50.000 21.80 NA <NA> L2 87.100 67.100 47.300

data

df <- read.table(text = "time A B C D C1 C2 C3
1 'Nov 1st 2014, 17:36:50.000' 20.79 NA NA L1 NA NA NA
2 'Nov 1st 2014, 17:36:50.000' NA 97.017 C1 L1 NA NA NA
3 'Nov 1st 2014, 17:36:50.000' NA 94.321 C2 L1 NA NA NA
4 'Nov 1st 2014, 17:36:50.000' NA 85.014 C3 L1 NA NA NA
5 'Nov 1st 2014, 17:37:50.000' 21.8 NA NA L2 NA NA NA
6 'Nov 1st 2014, 17:37:50.000' NA 87.1 C1 L2 NA NA NA
7 'Nov 1st 2014, 17:37:50.000' NA 67.1 C2 L2 NA NA NA
8 'Nov 1st 2014, 17:37:50.000' NA 47.3 C3 L2 NA NA NA",
                 header = T,
                 stringsAsFactors = F)

Answer 2

Step-by-step approach

If I understand correctly, OP's dataset actually consists of two intermixed datasets:

df

  time ABCD C1 C2 C3 1 Nov 1st 2014, 17:36:50.000 20.79 NA NA L1 NA NA NA 2 Nov 1st 2014, 17:36:50.000 NA 97.017 C1 L1 NA NA NA 3 Nov 1st 2014, 17:36:50.000 NA 94.321 C2 L1 NA NA NA 4 Nov 1st 2014, 17:36:50.000 NA 85.014 C3 L1 NA NA NA 5 Nov 1st 2014, 17:37:50.000 21.8 NA NA L2 NA NA NA 6 Nov 1st 2014, 17:37:50.000 NA 87.1 C1 L2 NA NA NA 7 Nov 1st 2014, 17:37:50.000 NA 67.1 C2 L2 NA NA NA 

which need to be separated:

library(data.table)
df1 <- setDT(df)[A != "NA", .(time, A, D)]
df1

  time AD 1: Nov 1st 2014, 17:36:50.000 20.79 L1 2: Nov 1st 2014, 17:37:50.000 21.8 L2 

and

df2 <- df[A == "NA", .(time, B, C, D)]
df2

  time BCD 1: Nov 1st 2014, 17:36:50.000 97.017 C1 L1 2: Nov 1st 2014, 17:36:50.000 94.321 C2 L1 3: Nov 1st 2014, 17:36:50.000 85.014 C3 L1 4: Nov 1st 2014, 17:37:50.000 87.1 C1 L2 5: Nov 1st 2014, 17:37:50.000 67.1 C2 L2 

The key columns which identify unique subsets of rows are time and D . Columns C1 , C2 , and C3 are dropped as they will be created in the next step.

The second dataset is to be reshaped from long to wide format:

wide <- dcast(df2, time + D ~ C, value.var = "B")
wide

  time D C1 C2 C3 1: Nov 1st 2014, 17:36:50.000 L1 97.017 94.321 85.014 2: Nov 1st 2014, 17:37:50.000 L2 87.1 67.1 <NA> 

Now both partial results can be joined together:

df1[wide, on = .(time, D)]

  time AD C1 C2 C3 1: Nov 1st 2014, 17:36:50.000 20.79 L1 97.017 94.321 85.014 2: Nov 1st 2014, 17:37:50.000 21.8 L2 87.1 67.1 <NA> 

Note that columns B and C have been dropped from the result as they convey no information.

Compact code

This steps above can be combined into fewer statements:

library(data.table)
setDT(df)[, (paste0("C", 1:3)) := NULL]
df[A != "NA"][dcast(df[C != "NA"], time + D ~ C, value.var = "B"), on = .(time, D)]

  time ABCD C1 C2 C3 1: Nov 1st 2014, 17:36:50.000 20.79 NA NA L1 97.017 94.321 85.014 2: Nov 1st 2014, 17:37:50.000 21.8 NA NA L2 87.1 67.1 <NA> 

Data

as provided by the OP with NA values given as strings

time <- c('Nov 1st 2014, 17:36:50.000','Nov 1st 2014, 17:36:50.000',
          'Nov 1st 2014, 17:36:50.000','Nov 1st 2014, 17:36:50.000', 'Nov 1st 2014, 17:37:50.000','Nov 1st 2014, 17:37:50.000','Nov 1st 2014, 17:37:50.000')
A <- c('20.79','NA','NA','NA','21.8','NA','NA')  
B <- c('NA','97.017','94.321','85.014','NA','87.1','67.1')
C <- c('NA','C1','C2','C3','NA','C1','C2')
D <- c('L1','L1','L1','L1','L2','L2','L2')
C1 <- c('NA','NA','NA','NA','NA','NA','NA')
C2 <- c('NA','NA','NA','NA','NA','NA','NA')
C3 <- c('NA','NA','NA','NA','NA','NA','NA')
df <- data.frame(time,A,B,C,D,C1,C2,C3)