1

I have a data frame that contains several columns with dates

col1<-seq( as.Date("2011-07-01"), by=20, len=10)
col2<-seq( as.Date("2011-09-01"), by=7, len=10)
col3<-seq( as.Date("2011-08-01"), by=1, len=10)
data.frame(col1,col2,col3)

The data frame looks like this:

         col1       col2       col3
1  2011-07-01 2011-09-01 2011-08-01
2  2011-07-21 2011-09-08 2011-08-02
3  2011-08-10 2011-09-15 2011-08-03
4  2011-08-30 2011-09-22 2011-08-04
5  2011-09-19 2011-09-29 2011-08-05
6  2011-10-09 2011-10-06 2011-08-06
7  2011-10-29 2011-10-13 2011-08-07
8  2011-11-18 2011-10-20 2011-08-08
9  2011-12-08 2011-10-27 2011-08-09
10 2011-12-28 2011-11-03 2011-08-10

I am trying to merge them into one column so that

A. Only the lowest (earliest) date remains per row and others get ignored

1  2011-07-01
2  2011-07-21
3  2011-08-03
4  2011-08-04
5  2011-08-05
6  2011-08-06
7  2011-08-07
8  2011-08-08
9  2011-08-09
10 2011-08-10

B. Only the highest (latest) date remains per row

1  2011-09-01
2  2011-09-08
3  2011-09-15
4  2011-09-22
5  2011-09-29
6  2011-10-09
7  2011-10-29
8  2011-11-18
9  2011-12-08
10 2011-12-28

The real dataset has NAs so if NA gets encountered it should be ignored unless all columns have a missing value for a particular row, in which case NA will be generated there as well.

Any thoughts?

igauravsehrawat
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Oposum
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2 Answers2

7

pmin and pmax are helpful here:

do.call(pmin, dat)
# [1] "2011-07-01" "2011-07-21" "2011-08-03" "2011-08-04" "2011-08-05"
# [6] "2011-08-06" "2011-08-07" "2011-08-08" "2011-08-09" "2011-08-10"
do.call(pmax, dat)
# [1] "2011-09-01" "2011-09-08" "2011-09-15" "2011-09-22" "2011-09-29"
# [6] "2011-10-09" "2011-10-29" "2011-11-18" "2011-12-08" "2011-12-28"

This also works for NA values, like:

do.call(pmin, c(dat, na.rm=TRUE) )

You can also select the specific columns you want to analyse like:

do.call(pmin, c(dat[c("col1","col2","col3")], na.rm=TRUE) )
thelatemail
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1

We can use max.col to find the index of the maximum values in each row, then cbind with row index and get the value per each row, convert to a data.frame

j1 <- sapply(df1, as.numeric)
df2 <- data.frame(Date = df1[cbind(1:nrow(df1),max.col(j1, 'first')  )])
df3 <- data.frame(Date = df1[cbind(1:nrow(df1), max.col(-1*j1, "first"))])
df2
#         Date
#1  2011-09-01
#2  2011-09-08
#3  2011-09-15
#4  2011-09-22
#5  2011-09-29
#6  2011-10-09
#7  2011-10-29
#8  2011-11-18
#9  2011-12-08
#10 2011-12-28

df3
#         Date
#1  2011-07-01
#2  2011-07-21
#3  2011-08-03
#4  2011-08-04
#5  2011-08-05
#6  2011-08-06
#7  2011-08-07
#8  2011-08-08
#9  2011-08-09
#10 2011-08-10

Or another option is

as.Date(apply(df1, 1, min, na.rm = TRUE))
as.Date(apply(df1, 1, max, na.rm = TRUE))

Or with tidyverse

library(tidyverse)
df1 %>%
      rowwise() %>%
      transmute(col1Max = max(col1, col2, col3), colMin = min(col1, col2, col3))
akrun
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