Hey there! Let’s work through why your code isn’t updating the NA values and get it sorted out.

The Core Issue: Logical Operators (&& vs &) #

First off, the biggest problem here is using && instead of & in your subsetting condition.

In R:

• && is a short-circuit logical operator — it only checks the first element of your vectors. So if the first row of your data isn’t a female with a missing height, the entire condition returns FALSE, and no rows get selected for updating. That’s why your data table stayed unchanged!
• & is the element-wise logical operator — it checks every row individually, which is exactly what you need for vector-based operations in R.

The Data Type Error #

The error you saw (argument is not numeric or logical: returning NA) means your height column isn’t stored as a numeric type. It’s probably a character vector (maybe because there was non-numeric text in the column when you imported the data). Before calculating means, you’ll need to convert it to numeric:

# Check the current type of the height column
class(data$height)

# Convert to numeric (this will turn non-numeric values into NA, which is expected)
data$height <- as.numeric(data$height)

Correct Code to Fill NAs #

Now let’s rewrite your code with the right operator and fixed data type:

1. Calculate the mean height for each gender, excluding NA values:

female_mean <- mean(data$height[data$gender == "female"], na.rm = TRUE)
male_mean <- mean(data$height[data$gender == "male"], na.rm = TRUE)

2. Use & to target the correct rows and assign the means:

# Fill NAs for females
data$height[is.na(data$height) & data$gender == "female"] <- female_mean

# Fill NAs for males
data$height[is.na(data$height) & data$gender == "male"] <- male_mean

A Cleaner Alternative with dplyr #

If you’re open to using the dplyr package (super common for data manipulation in R), this approach is more readable and handles both genders in one go:

library(dplyr)

data <- data %>%
  group_by(gender) %>%
  mutate(height = ifelse(is.na(height), mean(height, na.rm = TRUE), height)) %>%
  ungroup()

This groups the data by gender, then replaces NA heights with the mean of that group’s heights.

Quick Check to Verify #

After running the code, you can confirm the NAs are gone with:

# Check how many NAs are left in height
sum(is.na(data$height))

内容的提问来源于stack exchange，提问作者LouC