Understanding the Problem

The problem is to arrange rows in a data frame df6 such that if the values in the Reg column match with the values in the City column, they should appear first. If there’s no match, the rows should be arranged alphabetically based on the value of the City column.

Background

The provided code uses the dplyr library in R, which provides a grammar of data manipulation. The arrange_if function from the dplyr package is used to arrange the data frame by multiple columns. However, it seems like that’s not what we need here.

Instead, we’ll use a combination of functions such as mutate, arrange, and select to achieve the desired arrangement.

Solution

We can solve this problem using a combination of the following steps:

1. We create a new column called match.RegCity in the data frame df6. This column will contain TRUE if the values in the Reg column match with the values in the City column, and FALSE otherwise.
2. Then we use the arrange function to sort the rows by the Reg column first, then by the match.RegCity column in descending order (so that the matching rows come first), and finally by the City column.

Here’s the code:

library(dplyr)

df6 %>%  # Use pipes for readability
  mutate(match.RegCity = Reg == City) %>% # Create a new column 'match.RegCity'
  arrange(Reg,  # Arrange by 'Reg' first
           desc(match.RegCity), # Then by whether Reg == City (TRUE before FALSE)
           City) %>%
   select(-match.RegCity) # Finally, remove the 'match.RegCity' column

This will output:

Explanation

This code works as follows:

• mutate(match.RegCity = Reg == City): We create a new column called match.RegCity. This column contains TRUE if the values in the Reg column match with the values in the City column, and FALSE otherwise.
• arrange(Reg, desc(match.RegCity), City): We use the arrange function to sort the rows by the Reg column first. Then we arrange the rows based on whether there is a match between the values of the Reg column and the City column (in descending order). Finally, we sort the rows alphabetically based on the value of the City column.
• select(-match.RegCity): We remove the match.RegCity column from the data frame.

Example

Let’s take an example where we have a data frame called df6. The code above can be used to arrange rows in this data frame as follows:

library(dplyr)

# Create a sample data frame df6
df6 <- structure(list(Reg = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 
                                      1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("A", 
                                      "B"), class = "factor"), City = c("a", "a", "a", "A", "A", "A", 
                                      "b", "b", "b", "B", "B", "B", "c", "c", "c", "d", "d", "d", "e", 
                                      "e", "e"), Res = c("Total", "Rural", "Urban", "Total", "Rural", 
                                      "Urban", "Total", "Rural", "Urban", "Total", "Rural", "Urban", 
                                      "Total", "Rural", "Urban", "Total", "Rural", "Urban", "Total", 
                                      "Rural", "Urban"), Pop = c(109L, 55L, 54L, 204L, 101L, 103L, 
                                      95L, 46L, 49L, 325L, 166L, 159L, 119L, 53L, 66L, 108L, 61L, 47L, 
                                      98L, 52L, 46L), Pop1 = c(11L, 5L, 6L, 19L, 10L, 9L, 8L, 5L, 3L, 
                                      24L, 10L, 14L, 7L, 0L, 7L, 9L, 6L, 3L, 8L, 4L, 4L)), class = "data.frame", row.names = c(NA, 
                                      -21L), .Names = c("Reg", "City", "Res", "Pop", "Pop1"))

library(dplyr)

# Arrange rows in df6
arranged_df <- df6 %>%
  mutate(match.RegCity = Reg == City) %>% 
  arrange(Reg, desc(match.RegCity), City) %>% 
  select(-match.RegCity)

This code will output the arranged data frame arranged_df.

Last modified on 2023-12-02