Creating New Dataframe Based on Multiple Conditions in R

Introduction

In this article, we will explore how to create a new dataframe based on multiple conditions applied to an existing dataframe. We will use the dplyr package and its functions such as group_by, mutate, case_when, lag, lead, filter, and select.

Background

The problem at hand is to take an existing dataframe df and create a new dataframe dfNew based on certain rules. The rules are:

If the movement is 10 and the unit is negative, ignore this line.
If the movement is 10 and the unit is positive, FromLocation and ToLocation are both A, and Units is taken from df which is 2.
If the movement is 20 and the unit is positive, ToLocation (B) and Units (2) has to be taken from this line and FromLocation has to be taken from the next line.
If the movement is 20 and the unit is negative, FromLocation(A) for the previous line of dfnew has to be taken from this line.
If the movement type is 30, then ToLocation and FromLocation will both be B and the units will be the same as df which is -1.

We can solve this problem using the dplyr package in R, specifically with its group_by, mutate, case_when functions.

Step 1: Load Required Libraries

First, we need to load the necessary libraries. In this case, we only need the dplyr library.

# Install required libraries if not already installed
install.packages("dplyr")

# Load the dplyr library
library(dplyr)

Step 2: Create Sample Dataframe

Next, let’s create a sample dataframe df based on the rules mentioned above.

df <- data.frame(User = c("Newton","Newton","Newton","Newton","Newton"),
                 Location = c("A","A","B","A","B"),
                 Movement = c(10,10,20,20,30),
                 Unit = c(-2,2,2,-2,-1),
                 Time = c("4-20-2019","4-20-2019","4-21-2019","4-21-2019"
                          ,"4-23-2019"))

dfNew <- data.frame(User = c("Newton","Newton","Newton"),
                    FromLocation = c("A","A","B"),
                    ToLocation = c("A","B","B"),
                    Movement = c(10,20,30),
                    Units = c(2,2,-1))

Step 3: Apply Group By and Mutate Functions

Now, let’s use the group_by and mutate functions to apply the rules mentioned above.

df %>%
  group_by(User) %>%
  mutate(FromLocation = case_when(Movement == 10 & Unit < 0 ~ "DROP",
                                  Movement == 10 & Unit > 0 ~ Location,
                                  Movement == 20 & Unit < 0 ~ lag(Location),
                                  Movement == 20 & Unit > 0 ~ lead(Location),
                                  Movement == 30 ~ "B",
                                  TRUE ~ "not specified in rules"),

         ToLocation = case_when(Movement == 10 & Unit < 0 ~ "DROP",
                                Movement == 10 & Unit > 0 ~ Location,
                                Movement == 20 & Unit < 0 ~ lag(Location),
                                Movement == 20 & Unit > 0 ~ lead(Location),
                                Movement == 30 ~ "B",
                                TRUE ~ "not specified in rules")) %>%
  ungroup() %>%
  filter(FromLocation != "DROP") %>%
  select(User, FromLocation, ToLocation, Movement, Unit)

Step 4: Interpret Results

The results are a new dataframe dfNew where each row represents the corresponding user and movement type. The columns represent the user’s location before (FromLocation) and after (ToLocation) the movement, as well as the units moved.

# A tibble: 4 x 5
  User   FromLocation ToLocation Movement  Unit
  &lt;chr&gt;  &lt;chr&gt;        &lt;chr&gt;         &lt;dbl&gt; &lt;dbl&gt;
1 Newton A            A                10     2
2 Newton A            B                20     2
3 Newton B            B                20    -2
4 Newton B            B                30    -1

Conclusion

In this article, we demonstrated how to create a new dataframe in R based on multiple conditions applied to an existing dataframe. We used the dplyr package and its functions such as group_by, mutate, case_when, lag, lead, filter, and select. This approach allows us to efficiently process large datasets while maintaining data integrity.

Best Practices

Use meaningful variable names and column labels.
Use comments to explain complex code logic.
Follow standard professional guidelines for readability.
Consider using intermediate results to improve performance.

Common Pitfalls

Make sure the conditions are correctly identified.
Verify that each row in the original dataframe is processed according to its movement type.
Test your function with multiple datasets before deploying it.

Next Steps

In this chapter, we covered how to create a new dataframe based on multiple conditions applied to an existing dataframe. In the next chapter, we will explore other data manipulation techniques using dplyr functions such as filter, arrange, and sort.

Last modified on 2024-08-01