Replace a Value Na with the Value from Another Column in R

Replace a value NA with the value from another column in R

Perhaps the easiest to read/understand answer in R lexicon is to use ifelse. So borrowing Richard's dataframe we could do:

df <- structure(list(A = c(56L, NA, NA, 67L, NA),
                     B = c(75L, 45L, 77L, 41L, 65L),
                     Year = c(1921L, 1921L, 1922L, 1923L, 1923L)),.Names = c("A", 
                                                                                                                            "B", "Year"), class = "data.frame", row.names = c(NA, -5L))
df$A <- ifelse(is.na(df$A), df$B, df$A)

how to replace NA with the values from another column

You can use mutate to apply an ifelse across every column.

library(dplyr)

df.NA %>%
   mutate(across(everything(), ~ ifelse(is.na(.x), a, .x)))
#>        a      b      c      d
#> 1 value1 value1      9 value1
#> 2 value2      6 value2 value2
#> 3 value3      7 value3 value3
#> 4 value4 value4     10     12
#> 5 value5      8     11     13

R: Change NA in 1000s of columns to the value of another column

Turn the columns to characters and replace the NA values with the corresponding var_fill value.

dta1$var_fill <- as.character(dta1$var_fill)

dta1[vars] <- lapply(dta1[vars], function(x) {
                      x <- as.character(x)
                      x[is.na(x)] <- dta1$var_fill[is.na(x)]
                      x
                })

In dplyr, you can use coalesce.

library(dplyr)
dta1 <- dta1 %>% mutate(across(all_of(vars), ~coalesce(., var_fill)))

Replace NA values with value from another column in same dataframe

I would try with standard subsetting:

#subset the NAs of new price with the ones from price
df$NewPrice[is.na(df$NewPrice)] <- df$Price[is.na(df$NewPrice)]

Out:

df
#  Item   From Price Discount NewPrice
#1    A  Delhi   100      0.1      110
#2    A Mumbai   200      0.1      120
#3    A   Pune   150       NA      150
#4    A Nagpur   200      0.1      200

Manually replace missing value in a column based on another column

Does this work. Not sure if in your data you'd have NA for all values of geo == ny. Hence I've added & is.na(mark).

library(dplyr)
df %>% mutate(mark = case_when(geo == 'ny' & is.na(mark) ~ 'toyota', TRUE ~ mark))
# A tibble: 5 x 3
  geo   mark   value
  <chr> <chr>  <dbl>
1 texas nissan     2
2 texas nissan    78
3 ny    toyota    65
4 ny    toyota    15
5 ca    audi      22

Replacing NAs in a column with the values of other column

You can use coalesce:

library(dplyr)

df1 <- data.frame(Letters, Char, stringsAsFactors = F)

df1 %>%
  mutate(Char1 = coalesce(Char, Letters))

  Letters Char Char1
1       A    a     a
2       B    b     b
3       C <NA>     C
4       D    d     d
5       E <NA>     E

How to replace NA in a dataframe for a specific value using the results of another column and taking into account conditions of another column?

Some of them are not exact matches, so use str_detect

library(dplyr)
library(stringr)
df %>% 
   mutate(Weight = case_when(is.na(Weight) & 
     str_detect(Product, regex("Bread", ignore_case = TRUE)) ~ 2.5 * Units, 
     is.na(Weight) & Product == "Eggs"~ Units, TRUE ~ Weight))

-output

# A tibble: 5 × 4
  Product     Weight Units Price
  <chr>        <dbl> <dbl> <dbl>
1 Bread          5       2  1   
2 Oranges        1       6  3.5 
3 Eggs           1       1  0.5 
4 Bananas       NA       2  0.75
5 Whole Bread    2.5     1  1.5 

R Replacing NA values with the next value of another column value within groups

Here's is a possible dplyr solution. This is a combination of ifelse and lead, while the end product should be converted to as.POSIXct again as a result of lost information due to the use of ifelse

library(dplyr)
tmpdf %>%
  group_by(spaceNum) %>%
  mutate(time.OUT = as.POSIXct(ifelse(is.na(time.OUT), lead(time.IN), time.OUT), origin = "1970-01-01"))
# Source: local data frame [7 x 3]
# Groups: spaceNum
# 
#   spaceNum             time.IN            time.OUT
# 1        1 2015-09-04 16:30:00 2015-09-04 18:00:00
# 2        1 2015-09-04 19:50:00 2015-09-04 21:00:00
# 3        1 2015-09-04 21:00:00                <NA>
# 4        2 2015-09-05 12:00:00 2015-09-05 13:00:00
# 5        2 2015-09-05 13:00:00 2015-09-05 13:21:00
# 6        2 2015-09-05 16:00:00 2015-09-05 16:48:00
# 7        2 2015-09-05 17:00:00                <NA>

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