Matching data replacement in R - r

Question

I have a two datasets with a similar dimensions and a similar column names. The goal is to check if NA values exist in one of the datasets and replace with the corresponding values in the other dataset as shown in the example below.
I have tried running a for loop for to do solve the problem but that didn't work and failed miserably.
df is new data frame created with NA's
loop = for (a in 1:nrow(data1)) {
for (b in 1:ncol(data1)) {
for (c in 1:nrow(data2)) {
for (d in 1:ncol(data2)) {
for (x in 1:nrow(df)) {
for (y in 1:ncol(df)) {
df[x,y]<- ifelse(data1[a,b] != "NA", data1[a,b], data2[c,d])
return(df)`enter code here`
}
}
}
}
}
}
Example
# The first data frame
structure(list(age = c(23, 22, 21, 20), gender = c("M", "F",
NA, "F")), row.names = c(NA, -4L), class = c("tbl_df", "tbl",
"data.frame"))
# age gender
# 1 23 M
# 2 22 F
# 3 21 NA
# 4 20 F
# The second data frame
structure(list(age = c(23, 22, 21, 20), gender = c("M", "F",
"M", "F")), row.names = c(NA, -4L), class = c("tbl_df", "tbl",
"data.frame"))
# age gender
# 1 23 M
# 2 22 F
# 3 21 M
# 4 20 F
Desired output
Age Gender
23 M
22 F
21 M
20 F

Answer 1

You might try this:
df1 <- tibble(age = c(23,22,21,20),
gender = c("M", "F", NA, "F"))
# -------------------------------------------------------------------------
#> df1
# # A tibble: 4 x 2
# age gender
# <dbl> <chr>
# 1 23 M
# 2 22 F
# 3 21 NA
# 4 20 F
# -------------------------------------------------------------------------
df2 <- tibble(age = c(23,22,21,20),
gender = c("M", "F", "M", "F"))
# -------------------------------------------------------------------------
#> df2
# # A tibble: 4 x 2
# age gender
# <dbl> <chr>
# 1 23 M
# 2 22 F
# 3 21 M
# 4 20 F
# -------------------------------------------------------------------------
# get the na in df1 of gender var
df1.na <- is.na(df1$gender)
#> df1.na
# [1] FALSE FALSE TRUE FALSE
# -------------------------------------------------------------------------
# use the values in df2 to replace na in df1 (Note that this is index based)
df1$gender[df1.na] <- df2$gender[df1.na]
df1
# -------------------------------------------------------------------------
#> df1
# A tibble: 4 x 2
# age gender
# <dbl> <chr>
# 1 23 M
# 2 22 F
# 3 21 M
# 4 20 F
# -------------------------------------------------------------------------

Answer 2

This can be done using the natural_join function from the rqdatatable library. The function does require an index to merge on, so we will need to create one.
Creating a reproducible example will help other people help you. Here I've created two simple data frames that should cover most cases for your problem.
# Create example data
tbl1 <-
data.frame(
w = c(1, 2, 3, 4),
x = c(1, 2, 3, NA),
y = c(1, 2, 3, 4),
z = c(1, NA, NA, NA)
)
tbl2 <-
data.frame(
w = c(9, 9, 9, 9), # check value doesnt overwrite value,
x = c(1, 2, 3, 4), # check na gets filled in
y = c(1, 2, 3, NA), # check NA doesnt overwrite value
z = c(9, NA, NA, NA) # check NA in both stays NA
)
# Create join index
tbl1$indx <- 1:nrow(tbl1)
tbl2$indx <- 1:nrow(tbl2)
# Use natural_join
library("rqdatatable")
natural_join(tbl1, tbl2, by = "indx")