Counting NAs between First and Last Occurred Numbers

Overview

In this article, we will explore a common problem in data analysis: counting the number of missing values (NAs) between the first and last occurrence of numbers in each column of a dataframe. We will use R as our programming language and discuss various approaches to solve this problem.

Understanding NA Behavior

Before diving into the solution, let’s understand how R handles missing values. In R, NA is not just a placeholder value; it has its own set of properties and behavior. When creating a dataframe with missing values using the tribble() function, we can see that each column has its own NA strategy.

# Load necessary libraries
library(rlang)
library(dplyr)

# Create a sample dataframe with missing values
df <- tribble(
  ~x, ~y, ~z,
  7,   NA, 4,
  8,   2,  NA,
  NA,  NA, NA,
  NA,  4,  6)

In the above example, column x has its first occurrence of a number at row 1 and its last occurrence at row 3. Similarly, for columns y and z, their first and last occurrences are at rows 2 and 4 respectively.

Counting NAs between First and Last Occurred Numbers

Our goal is to create a new dataframe with the following structure:

We will achieve this by creating functions f1() and f2() that return the first and last indices of non-missing values in each column, respectively.

# Define function f1()
f1 <- function(x) {
  i1 <- which(!is.na(x))
  head(i1, 1):tail(i1, 1)
}

# Define function f2()
f2 <- function(x) {
  i1 <- which(!is.na(x))
  head(i1, 1):length(x)
}

Now we can use the stack() and sapply() functions to count the number of NAs between first and last occurrences of numbers in each column.

# Apply f1() and f2() to each column
f1_values <- sapply(df, f1)
f2_values <- sapply(df, f2)

# Stack the values horizontally
stacked_f1 <- stack(sapply(f1_values, function(x) sum(is.na(x))))
stacked_f2 <- stack(sapply(f2_values, function(x) sum(is.na(x))))

# Merge the two dataframes based on 'ind'
merged_df <- merge(stacked_f1, stacked_f2, by = 'ind')

The final merged_df dataframe has the desired structure:

This solution uses R’s built-in functions and data structures to efficiently count the number of NAs between first and last occurrences of numbers in each column. However, this approach has its limitations, such as requiring manual indexing and merging.

Alternative Approaches

There are other ways to solve this problem using alternative approaches:

1. Using dplyr: We can use the rowwise() function from the dplyr package to create a dataframe with the desired structure.

Load necessary libraries

library(dplyr)

Create a sample dataframe with missing values

df <- tribble( ~x, ~y, ~z, 7, NA, 4, 8, 2, NA, NA, NA, NA, NA, 4, 6) )

Create a new dataframe with the desired structure

desired_df <- df %>% rowwise() %>% summarise( na_count_between_1st_last_num = sum(is.na(x)), na_count_between_1st_num_last_row = sum(is.na(lead(x))) )

2.  **Using `base R` with `cumsum()` and `which()`**: We can use the `cumsum()` function to create a cumulative sum of non-missing values in each column, and then use `which()` to find the indices where this sum changes.

    ```markdown
# Load necessary libraries
library(base)

# Create a sample dataframe with missing values
df <- tribble(
  ~x, ~y, ~z,
  7,   NA, 4,
  8,   2,  NA,
  NA,  NA, NA,
  NA,  4,  6)
)

# Create a new dataframe with the desired structure
desired_df <- df %>%
  rowwise() %>%
  summarise(
    na_count_between_1st_last_num = sum(is.na(x)),
    na_count_between_1st_num_last_row = length(which(cumsum(!is.na(x)) != cumsum(!is.na(lead(x))))))

Conclusion

Counting the number of missing values between first and last occurrences of numbers in each column is a common problem in data analysis. This article has provided several solutions to this problem using R, including manual indexing, dplyr, and alternative approaches with base R. We have also discussed how to efficiently count NAs between first and last occurrences of numbers in each column using built-in functions and data structures.

Last modified on 2025-03-14