How to Add Missing Months to a Time Series DataFrame in R Using the tidyr Package

Adding Missing Months to a Time Series DataFrame in R

In this article, we’ll explore how to add missing months to a time series DataFrame in R. We’ll use the provided sample data to demonstrate the process and provide additional examples.

Introduction

R is a powerful programming language for statistical computing and graphics. One of its strengths is its ability to handle complex datasets, including time series data. However, sometimes we encounter datasets with missing values or incomplete data. In this article, we’ll show you how to add missing months to a time series DataFrame in R using the tidyr package.

Problem Description

The provided sample data has a column of 22 unique station identifiers with time series data for each month. However, some stations don’t have data for certain months. This results in plots that only show two months of data instead of all 12. Our goal is to add the missing months and zero values for the stations that are missing.

Solution

We’ll use the tidyr package to create a new DataFrame with complete month data. We’ll then merge this new DataFrame with the original dataset using the merge() function.

Step 1: Load necessary libraries

## Step 1: Load necessary libraries
library(dplyr)
library(tidyr)

Step 2: Create a new column for each unique month

We’ll create a new column in the original DataFrame to store the missing months. We’ll use dplyr’s mutate() function to add this column.

## Step 2: Create a new column for each unique month
data %>%
  mutate(months = map_lgl(.col, ~ all(.x %in% month) | any(.x %% 12 == 0)),
         months = ifelse(months, paste0("01", -which(!months)), ""))

Step 3: Add missing months to the original DataFrame

We’ll use tidyr’s fill() function to add the missing months to the original DataFrame.

## Step 3: Add missing months to the original DataFrame
data %>%
  fill(month = .after, fill = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12))

Step 4: Merge the new DataFrame with the original dataset

We’ll use dplyr’s merge() function to merge the new DataFrame with the original dataset.

## Step 4: Merge the new DataFrame with the original dataset
data %>%
  left_join(data, by = c("Station" = "Station"))

Example Use Case

Let’s create a sample time series DataFrame and demonstrate how to add missing months using the tidyr package.

## Create a sample time series DataFrame
set.seed(123)
df <- data.frame(
  Station = rep(c("A", "B", "C"), each = 4),
  month = rep(1:12, 3),
  Huc14 = rep(paste0("HUC", 1:3), 12),
  Total Number of Exceedance = runif(36, min = 0, max = 100)
)

## Print the original DataFrame
print(df)

## Add missing months to the original DataFrame
df %>% 
  mutate(months = map_lgl(.col, ~ all(.x %in% month) | any(.x %% 12 == 0)),
         months = ifelse(months, paste0("01", -which(!months)), ""))

## Merge the new DataFrame with the original dataset
df %>%
  left_join(data.frame(Station = c("A", "B", "C"), month = rep(1:12, 3), Huc14 = rep(paste0("HUC", 1:3), 12)))

Conclusion

In this article, we demonstrated how to add missing months to a time series DataFrame in R using the tidyr package. We created a new column for each unique month and used dplyr’s fill() function to add the missing months to the original DataFrame. Finally, we merged the new DataFrame with the original dataset using dplyr’s merge() function.

Additional Resources

Last modified on 2023-05-31