is.na() is used to deal with missing values in the dataset or data frame. We can use this method to check the NA field in a data frame and help to fill them. To deal with missing values, one's can fill empty fields by NA's using fill.NAs().
Note: We can also use
which()function to extract exact indexes ofNAvalues.
is.nan(x)
It takes the following value as an agument:
x: It can either be a vector or a data frame.
It returns the boolean or logical value either TRUE or FALSE.
Let’s discuss is.na() function with code examples:
# is.na in r examplex = c(1, 2, NA, 4, NA, 6, 7)# invoking is.na() to get NA's indexesprint(is.na(x))# show indexes of NA'sprint(which(is.na(x)))
We can also use is.na() to find the missing values in a dataframe and then replace them with new value.
Find missing values.
# Create a vector with missing valuesdf <- c(1, 2, NA, 4, NA, 6)# Filter out missing valuesnew_df <- df[!is.na(df)] # new data frame with no missing valuesprint(new_df)# Output: 1 2 4 6
Replace missing values with other values.
df <- c(1, 2, NA, 4, NA, 6)# Replace missing values with 0df_new<- ifelse(is.na(df), 0, df)print(df_new)# Output: 1 2 0 4 0 6
Count the number of missing values.
df <- c(1, 2, NA, 4, NA, 6)count <- sum(is.na(df))print(count)