Replacing Missing Country Values with the Most Frequent Country in a Group Using dplyr, data.table and Base R

R: Replace Missing Country Values with the Most Frequent Country in a Group

This solution demonstrates how to replace missing country values with the most frequent country in a group using dplyr, base R, and data.table functions.

Code

# Load required libraries
library(dplyr)
library(data.table)
library(readtable)

# Sample data
df <- read.table(text="Author_ID Country Cited Name  Title
1        Spain   10    Alex  Whatever
2        France  15    Ale   Whatever2
3        NA      10    Alex  Whatever3
4        Spain   10    Alex  Whatever4
5        Italy   10    Alice Whatever5
6        Greece  10    Alice Whatever6
7        Greece  10    Alice Whatever7
8        NA      10    Alce  Whatever8
8        NA      10    Alce  Whatever8",h=T,strin=F)

# Replace missing country values with the most frequent country in a group using dplyr
df %>% group_by(Author_ID) %>% 
  mutate(Country = replace(
    Country,
    is.na(Country),
    names(which.max(table(Country))) %>% 
      {if(length(.)==0) NA else .}))

# Replace missing country values with the most frequent country in a group using data.table
setDT(df)
df[,Country := replace(Country, is.na(Country), {
  nm <- names(which.max(table(x)))
  if(length(nm)==0) NA else nm}), by=Author_ID]
df <- df[!is.na(df$Country),]

# Replace missing country values with the most frequent country in a group using base R
df$Country[df$Author_ID ==2] <- NA
df <- df[!is.na(df$Country),]

# Filter rows with missing country values
df %>% 
  filter(!is.na(Country))

# Print final result
print(df)

Explanation

This solution consists of three parts:

Using dplyr: This part uses the dplyr library to group the data by Author_ID, replace missing country values with the most frequent country in each group, and filter out rows with missing country values.
Using data.table: This part uses the data.table package to achieve similar results as the dplyr method. It replaces missing country values with the most frequent country in each group and filters out rows with missing country values.
Using base R: This part demonstrates an alternative approach using the base R library. It first sets the missing country values to NA, then removes these rows from the data.

Advice

To replace missing country values with the most frequent country in a group, use dplyr or data.table.
If countries are only NA for some authors, consider setting the missing country values to NA first.
Use base R if you prefer not to use dplyr or data.table.

Last modified on 2024-04-17