Reordering Dataframe by Rank in R

In this article, we will explore how to reorder a dataframe based on the rank of values in one or more columns. We will use several approaches, including reshape and pivot techniques.

Introduction

Reordering a dataframe can be useful in various data analysis tasks, such as sorting data by frequency, ranking values, or reorganizing categories. In this article, we will focus on how to reorder a dataframe based on the rank of values in one or more columns.

Problem Statement

Suppose we have a dataframe df1 with 5 rows and 11 columns:

We want to reorder the dataframe df1 based on the rank of values in columns V1, R1, and R2.

Approach 1: Using Dplyr

One approach to reorder a dataframe is by using the dplyr library. We can use the arrange() function to sort the dataframe.

library(dplyr)

df1 %>% 
  arrange(R1, R2) %>% 
  mutate(sorted_V = row_number())

The above code will reorder the dataframe based on the rank of values in columns R1 and R2. We also create a new column sorted_V to keep track of the rank.

Approach 2: Using Base R

Another approach is by using base R. We can use the order() function to sort the dataframe.

df1[order(df1$R1, df1$R2), ]

The above code will reorder the dataframe based on the rank of values in columns R1 and R2.

Approach 3: Using Pivot

We can also use pivot techniques to reorder a dataframe. One approach is by using the pivot_longer() function from the tidyr library.

library(tidyr)

df1 %>% 
  pivot_longer(cols = c("V1", "R1", "R2"), names_to = "column", value_to = "value") %>% 
  arrange(value)

The above code will reorder the dataframe based on the rank of values in columns V1, R1, and R2.

Approach 4: Removing Zero or NA Elements

If we want to remove the ‘0’ or NA elements in column R, we can use the filter() function.

df1 %>% 
  pivot_longer(cols = c("V1", "R1", "R2"), names_to = "column", value_to = "value") %>% 
  filter(value != 0) %>% 
  arrange(value)

The above code will remove the ‘0’ or NA elements in column R.

Conclusion

In this article, we have explored how to reorder a dataframe based on the rank of values in one or more columns. We used several approaches, including reshape and pivot techniques. We also discussed how to remove zero or NA elements in column R.

Example Use Case

Suppose we are working with a dataset that contains stock prices for different companies over time. We want to reorder the dataframe based on the rank of values in the “Price” column.

df_stock_prices <- structure(list(
  Company = c("Company A", "Company B", "Company C"),
  Price = c(100, 50, 200)
), class = "data.frame", row.names = c(1, 2, 3))

library(dplyr)

df_stock_prices %>% 
  arrange(desc(Price)) %>% 
  mutate(rank = row_number())

The above code will reorder the dataframe based on the rank of values in the “Price” column. We also create a new column “rank” to keep track of the rank.

References

• Dplyr: Tidy Data Manipulation
• tidyr: A Grammar of Data Manipulation
• R Base: Order Function

Last modified on 2023-08-05