问题描述
我有以下数据框：
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,60L,30L,35L,25L,10L,5L
),speedB = c(50L,NA,40L,18L,NA),speedC = c(60L,50L,34L,64L,7L,60L),speedD = c(NA,55L,36L,39L,48L,47L)),class = "data.frame",row.names = c(NA,-18L))
我想通过在每个NA同一行的水平值之间进行插值来用值替换NA。 预期结果：
df2<- structure(list(time = c("12:00:00",speedB = c(50,30,63.33333,40,21,15,18,52.5,65,47.5,10,25,54.5,17.5,12,35,32.5),speedC = c(60,61.66667,50,60,34,64,7,60),speedD = c(60L,-18L))
解决方法
我们可以使用zoo::na.approx来插值。对于无法插值的值（最后是NA个值，我们使用tidyr::fill进行填充。
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
您可以将zoo::na.approx()与c_across()逐行使用。
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