Given a data frame with certain rows having missing data in certain columns, though they belong to the same with similar A values, I want to prioritize rows with full records when there are multiple occurrences with the same value in column A to retain them.


```
# Create a sample dataframe
 df <- data.frame(
  A = c(1, 1, 2, 2, 3, 3, 4, 4),
  B = c("a", "a", "b", "b", "c", "c", "d", "e"),
  C = c("z", "z", "y", "y", "x", "x", "w", "v"),
  D = c(6, NA, 7, NA, 8, 8, NA, 10),
  E = c("f", NA, "g", NA, "h", "h", NA, "j")
)

#filter it this way
df_filtered <- df %>%
  group_by(A) %>%
  arrange(rowSums(is.na(.))) %>%
  slice(1) %>%
  ungroup()


```
The filtering process is as follows: 

*Group by column A.*

*For each group:
a. First, arrange by the number of NA values in descending order (so rows with fewer NA values come first).  **arrange(rowSums(is.na(.)))**
b. Then, slice to pick the first row.*

*Finally, ungroup.*

![Screenshot 2023-10-10 at 3.40.08 PM.png](https://files.peakd.com/file/peakd-hive/snippets/23tHbkj8J5pGkWZre8gBhk721vrFwYpaKtznDJaT6xuXnBqxRa7zpvimvqdh8LGnZ2ZrJ.png)


![snippets.png](https://files.peakd.com/file/peakd-hive/snippets/AKNMuVqPrNWuLjdyxwLdzm99obv1dcnZWufeJdBoWwecd9UBvGxERepophy4Epu.png)