CODEWITHSUNDEEP
rnamissing-data
mustafa
mustafa

Reputation: 223

Is there a way to replace NAs in R using horizontal order?

I have the following data frame:

df <-structure(list(time = c("12:00:00", "12:05:00", "12:10:00", "12:15:00", 
"12:20:00", "12:25:00", "12:30:00", "12:35:00", "12:40:00", "12:45:00", 
"12:50:00", "12:55:00", "13:00:00", "13:05:00", "13:10:00", "13:15:00", 
"13:20:00", "13:25:00"), speedA = c(60L, 75L, 65L, 45L, 12L, 
15L, 20L, 45L, 65L, 60L, 60L, 30L, 35L, 45L, 25L, 15L, 10L, 5L
), speedB = c(50L, 30L, NA, 40L, NA, NA, 18L, NA, NA, NA, 15L, 
10L, 25L, NA, NA, 12L, NA, NA), speedC = c(60L, 25L, NA, NA, 
30L, 15L, 50L, 60L, NA, 35L, 34L, NA, 15L, 64L, 10L, 7L, 60L, 
60L), speedD = c(NA, 10L, 60L, NA, 50L, 55L, 45L, 35L, NA, NA, 
45L, 60L, 35L, 34L, 36L, 39L, 48L, 47L)), class = "data.frame", row.names = c(NA, 
-18L))

I want to replace the NAs with values using interpolation between the horizontal values at the same row of each NA. The expected result:

df2<- structure(list(time = c("12:00:00", "12:05:00", "12:10:00", "12:15:00", 
"12:20:00", "12:25:00", "12:30:00", "12:35:00", "12:40:00", "12:45:00", 
"12:50:00", "12:55:00", "13:00:00", "13:05:00", "13:10:00", "13:15:00", 
"13:20:00", "13:25:00"), speedA = c(60L, 75L, 65L, 45L, 12L, 
15L, 20L, 45L, 65L, 60L, 60L, 30L, 35L, 45L, 25L, 15L, 10L, 5L
), speedB = c(50, 30, 63.33333, 40, 21, 15, 18, 52.5, 65, 47.5, 
15, 10, 25, 54.5, 17.5, 12, 35, 32.5), speedC = c(60, 25, 61.66667, 
40, 30, 15, 50, 60, 65, 35, 34, 35, 15, 64, 10, 7, 60, 60), speedD = c(60L, 
10L, 60L, 40L, 50L, 55L, 45L, 35L, 65L, 35L, 45L, 60L, 35L, 34L, 
36L, 39L, 48L, 47L)), class = "data.frame", row.names = c(NA, 
-18L))

Upvotes: 2

Views: 128

Answers (2)

Darren Tsai
Darren Tsai

Reputation: 35554

You can use zoo::na.approx() row-wise with c_across().

library(dplyr)
library(tidyr)
library(zoo)

df %>%
  rowwise() %>%
  mutate(speed = list(na.locf(na.approx(c_across(-time), na.rm = FALSE))), .keep = "unused") %>%
  unnest_wider(speed, names_sep = "")

# # A tibble: 18 x 5
#    time     speed1 speed2 speed3 speed4
#    <chr>     <dbl>  <dbl>  <dbl>  <dbl>
#  1 12:00:00     60   50     60       60
#  2 12:05:00     75   30     25       10
#  3 12:10:00     65   63.3   61.7     60
#  4 12:15:00     45   40     40       40
#  5 12:20:00     12   21     30       50
#  6 12:25:00     15   15     15       55
#  7 12:30:00     20   18     50       45
#  8 12:35:00     45   52.5   60       35
#  9 12:40:00     65   65     65       65
# 10 12:45:00     60   47.5   35       35
# 11 12:50:00     60   15     34       45
# 12 12:55:00     30   10     35       60
# 13 13:00:00     35   25     15       35
# 14 13:05:00     45   54.5   64       34
# 15 13:10:00     25   17.5   10       36
# 16 13:15:00     15   12      7       39
# 17 13:20:00     10   35     60       48
# 18 13:25:00      5   32.5   60       47

Upvotes: 0

Ronak Shah
Ronak Shah

Reputation: 388962

We can use zoo::na.approx to interpolate values. For values which we are not able to interpolate (NA values at the last) we use tidyr::fill to fill it.

library(dplyr)
library(tidyr)

df %>%
  pivot_longer(cols = -time) %>%
  group_by(time) %>%
  mutate(value = zoo::na.approx(value, na.rm = FALSE)) %>%
  fill(value) %>%
  pivot_wider()

#   time     speedA speedB speedC speedD
#   <chr>     <dbl>  <dbl>  <dbl>  <dbl>
# 1 12:00:00     60 50     60         60
# 2 12:05:00     75 30     25         10
# 3 12:10:00     65 63.333 61.667     60
# 4 12:15:00     45 40     40         40
# 5 12:20:00     12 21     30         50
# 6 12:25:00     15 15     15         55
# 7 12:30:00     20 18     50         45
# 8 12:35:00     45 52.5   60         35
# 9 12:40:00     65 65     65         65
#10 12:45:00     60 47.5   35         35
#11 12:50:00     60 15     34         45
#12 12:55:00     30 10     35         60
#13 13:00:00     35 25     15         35
#14 13:05:00     45 54.5   64         34
#15 13:10:00     25 17.5   10         36
#16 13:15:00     15 12      7         39
#17 13:20:00     10 35     60         48
#18 13:25:00      5 32.5   60         47

Upvotes: 1

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