CODEWITHSUNDEEP
rdataframena
jc2525
jc2525

Reputation: 141

Row replacement without NA's in R

I have this dataframe

    # of int.   int.   not.int.  ID  group   odd      even
2      24      85.15    113.34   2   thc1    NA       486.66
3      33      134.94   158.17   3   thc2    465.06   NA
4      12      47.60    62.73    4   thc3    NA       537.27
1      50      218.41   372.16   1   veh     381.59   NA
5      44      176.81   268.92   5   veh     NA       331.08

How can I replace the numbers in 'int.' with the numbers in 'even' without adding the NA's and the same for 'odd' and 'not.int'? So it would look like this.

 # of int.   int.     not.int.  ID  group   odd      even
2      24    486.66   113.34    2   thc1    NA       486.66
3      33    134.94   465.06    3   thc2    465.06   NA
4      12    537.27   62.73     4   thc3    NA       537.27
1      50    218.41   381.59    1   veh     381.59   NA
5      44    331.08   268.92    5   veh     NA       331.08

Upvotes: 1

Views: 56

Answers (4)

moodymudskipper
moodymudskipper

Reputation: 47350

x <- df1[c("even","odd")]
df1[c("int.","not.int.")][!is.na(x)] <- x[!is.na(x)]

# #   of int.   int. not.int. ID group    odd   even
# 2        24 486.66   113.34  2  thc1     NA 486.66
# 3        33 134.94   465.06  3  thc2 465.06     NA
# 4        12 537.27    62.73  4  thc3     NA 537.27
# 1        50 218.41   381.59  1   veh 381.59     NA
# 5        44 331.08   268.92  5   veh     NA 331.08

Upvotes: 1

Thor6
Thor6

Reputation: 811

Using mutate and ifelse from the dplyr package :

library(dplyr)

df %>%
 mutate(int. = ifelse(is.na(even), int., even),
        not.int. = ifelse(is.na(odd), not.int., odd))

Upvotes: 2

www
www

Reputation: 39174

Here is an option in base R, with ifelse and is.na.

dat$int <- with(dat, ifelse(!is.na(even), even, int))
dat$not.int <- with(dat, ifelse(!is.na(odd), odd, not.int))

DATA

dat <- read.table(text = "    '# of int'   int   'not.int'  ID  group   odd      even
2      24      85.15    113.34   2   thc1    NA       486.66
3      33      134.94   158.17   3   thc2    465.06   NA
4      12      47.60    62.73    4   thc3    NA       537.27
1      50      218.41   372.16   1   veh     381.59   NA
5      44      176.81   268.92   5   veh     NA       331.08",
                  header = TRUE, stringsAsFactors = FALSE)

Upvotes: 1

akrun
akrun

Reputation: 887951

One option is to use Map in base R to get the corresponding columns and then do an assignment

df1[2:3] <- Map(function(x, y) {
              i1 <- !is.na(y)
              x[i1] <- y[i1]
               x}, df1[c('int.', 'not.int.')], df1[c('even', 'odd')])

df1
#  # of int.   int. not.int. ID group    odd   even
#2        24 486.66   113.34  2  thc1     NA 486.66
#3        33 134.94   465.06  3  thc2 465.06     NA
#4        12 537.27    62.73  4  thc3     NA 537.27
#1        50 218.41   381.59  1   veh 381.59     NA
#5        44 331.08   268.92  5   veh     NA 331.08

data

 df1 <- structure(list(`# of int.` = c(24L, 33L, 12L, 50L, 44L), int. = c(85.15, 
134.94, 47.6, 218.41, 176.81), not.int. = c(113.34, 158.17, 62.73, 
372.16, 268.92), ID = c(2L, 3L, 4L, 1L, 5L), group = c("thc1", 
"thc2", "thc3", "veh", "veh"), odd = c(NA, 465.06, NA, 381.59, 
NA), even = c(486.66, NA, 537.27, NA, 331.08)), .Names = c("# of int.", 
"int.", "not.int.", "ID", "group", "odd", "even"), 
class = "data.frame", row.names = c("2", 
"3", "4", "1", "5"))

Upvotes: 3

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