I am trying to use the function na_ma
from library(imputeTS)
; because I am dealing with missing values in a dataframe by replacing them with the average of the surrounding values.
Data example:
i1<-c(5,4,3,4,5)
i2<-c(2,NA,4,5,3)
i3<-c(NA,4,4,4,5)
i4<-c(3,5,5,NA,2)
data<-as.data.frame(cbind(i1,i2,i3,i4))
data
My code
data %>%
rowwise %>%
na_ma(as.numeric(x), k = 1, weighting = "simple")
The expected result:
i1 i2 i3 i4
1 5 2 2.5 3
2 4 4 4 5
3 3 4 4 5
4 4 5 4 4.5
5 5 3 5 2
The problem, I don't know how to apply na_ma(as.numeric(x), k = 1, weighting = "simple")
to each row of this dataframe.
Thank you!
CodePudding user response:
If you want to use tidyverse
to do this you may use pmap_df
.
library(dplyr)
library(purrr)
data %>%
pmap_df(~imputeTS::na_ma(c(...), k = 1, weighting = "simple"))
# i1 i2 i3 i4
# <dbl> <dbl> <dbl> <dbl>
#1 5 2 2.5 3
#2 4 4 4 5
#3 3 4 4 5
#4 4 5 4 4.5
#5 5 3 5 2
This can also be done in base R -
data[] <- t(apply(data, 1, imputeTS::na_ma, k = 1, weighting = "simple"))
CodePudding user response:
Are you really sure you want to do this? Normally we impute column-wise, with the mean of the columns.
cm <- colMeans(dat, na.rm=TRUE)
dat <- Map(\(x, y) ifelse(is.na(x), y, x), data, cm) |>
as.data.frame()
dat
# i1 i2 i3 i4
# 1 5 2.0 4.25 3.00
# 2 4 3.5 4.00 5.00
# 3 3 4.0 4.00 5.00
# 4 4 5.0 4.00 3.75
# 5 5 3.0 5.00 2.00
Actually it's better to use more sophisticated imputation techniques such as multiple imputation. Here a reading.
Data
dat <- structure(list(i1 = c(5, 4, 3, 4, 5), i2 = c(2, NA, 4, 5, 3),
i3 = c(NA, 4, 4, 4, 5), i4 = c(3, 5, 5, NA, 2)), class = "data.frame", row.names = c(NA,
-5L))