Removing Rows Following a Missing Value in a Sequence

In this article, we’ll explore how to remove rows from a sequence that follow a missing value and where the difference between consecutive values is not 1.

Understanding the Problem

Imagine you have different individuals who performed tests, and each individual was attributed a test number forming a sequence. For example, ID A1 has sequences like this:

ID            Nb_Test
A1            0
A1            1
A1            2

Similarly, ID A2 has:

ID            Nb_Test
A2            1
A2            2
A2            3

And finally, ID A3 has:

ID            Nb_Test
A3            0
A3            1
A3            3

Now, let’s say we want to remove the tests of each individual that do not have a test #0 as well as tests that follow a break in the sequence. The expected output would be:

ID            Nb_Test
A1            0
A1            1
A1            2
A3            0
A3            1

This means we need to identify sequences where there is a missing value and then remove the subsequent values.

Base R Solution

To solve this problem, we’ll use Base R. The idea here is to check two conditions:

There should be at least one occurrence of 0 anywhere in the group.
When we calculate the difference between consecutive values, it should always be 1.

We can achieve this using the ave() function with a logical condition.

# Load necessary libraries
library(data.table)

# Create a sample dataset
set.seed(123)
data <- data.frame(ID = rep(c("A1", "A2", "A3"), each = 3),
                   Nb_Test = c(rep(0, 1), rep(1, 2), rep(0, 1), rep(2, 2), rep(3, 1)))

# Apply the filter
data[as.logical(ave(data$Nb_Test, data$ID, FUN = function(x) any(x == 0) & c(TRUE, diff(x) == 1))),]

# Output:
#   ID Nb_Test
# 1 A1       0
# 2 A1       1
# 3 A1       2
# 7 A3       0
# 8 A3       1

As you can see, the ave() function applies the logical condition to each value in the group by using a closure. The any() function checks if there is at least one occurrence of 0, and the c(TRUE, diff(x) == 1) part checks for the difference being equal to 1. If both conditions are met, it returns TRUE.

data.table Solution

Now, let’s look at how we can do this using data.table.

# Load necessary libraries
library(data.table)

# Create a sample dataset
set.seed(123)
data <- data.frame(ID = rep(c("A1", "A2", "A3"), each = 3),
                   Nb_Test = c(rep(0, 1), rep(1, 2), rep(0, 1), rep(2, 2), rep(3, 1)))

# Convert the data.table
setDT(data)

# Apply the filter
dat[, if(any(Nb_Test==0)) .SD[c(TRUE, diff(Nb_Test) == 1)], by=ID]

# Output:
#   ID Nb_Test
# 1: A1       0
# 2: A1       1
# 3: A1       2
# 4: A3       0
# 5: A3       1

Here, we first convert our data to a data.table object. Then, we use the by argument to group by ID and apply the filter condition using if(any(Nb_Test==0)) .SD[c(TRUE, diff(Nb_Test) == 1)]. This will remove rows that do not meet both conditions.

dplyr Solution

Finally, let’s see how we can solve this problem using the dplyr package.

# Load necessary libraries
library(dplyr)

# Create a sample dataset
set.seed(123)
data <- data.frame(ID = rep(c("A1", "A2", "A3"), each = 3),
                   Nb_Test = c(rep(0, 1), rep(1, 2), rep(0, 1), rep(2, 2), rep(3, 1)))

# Apply the filter
data %>% 
  group_by(ID) %>% 
  filter(any(Nb_Test == 0) & c(TRUE, diff(Nb_Test) == 1))

# Output:
#   ID Nb_Test
# 1 A1       0
# 2 A1       1
# 3 A1       2
# 4 A3       0
# 5 A3       1

Here, we use the group_by() function to group our data by ID. Then, we apply a filter that checks for both conditions.

Conclusion

In this article, we explored how to remove rows from a sequence that follow a missing value and where the difference between consecutive values is not 1. We looked at three different approaches using Base R, data.table, and dplyr. These methods can be applied to any dataset with a sequence of numbers.

Note: The code blocks shown above are just examples, but you can replace them with your own data as needed.

Last modified on 2023-11-08