Changing Months to Seasons

Introduction

As data analysts and scientists, we often work with datasets that contain temporal information, such as dates and times. However, when dealing with months instead of the actual date, it can be challenging to perform certain operations or analyses. In this article, we will explore how to convert a month into its corresponding season in R using both the dplyr library and base R.

Background

The concept of seasons is often used in climate science, agriculture, and ecology studies. Understanding the relationships between months and seasons can help us identify patterns, trends, and correlations that might not be immediately apparent from the data. In this article, we will focus on converting a month into its corresponding season using R.

Data Preparation

Before we dive into the code, let’s create a sample dataset to work with. We will use the data.frame function in base R to create a simple dataset containing dates, months, and water temperatures.

df1 <- structure(list(Date = c("2016-07-01", "2017-01-08", "2018-09-19",
                                "2019-10-24"), Month = c("July", "January", "September",
                                                        "October"), Temperature = c(13L, 5L, 11L,
                                                                 9L)), class = "data.frame",
               row.names = c(NA, -4L))

Using the dplyr Library

The dplyr library provides a powerful and flexible way to manipulate data in R. We can use it to create a named vector that maps months to seasons.

library(dplyr)

# Create a named vector that maps months to seasons
nm1 <- setNames(rep(c("Winter", "Spring", "Summer", "Fall"),
                    each = 3), month.name)

In this code, we use the rep function to create a repeating vector of season names, and then pass it to the setNames function along with the month.name vector. The result is a named vector nm1 that maps months to seasons.

To convert the Month column in our dataset to a new column called Season, we can use the mutate function from dplyr.

df1 %>% 
  mutate(Season = nm1[Month])

This code uses the pipe operator %> to chain together two operations: mutate and an anonymous function. The mutate function creates a new column called Season, and the anonymous function uses the nm1 vector to look up the corresponding season for each month in the Month column.

The resulting dataset will have a new column called Season, with the correct seasons assigned to each month.

#         Date     Month Temperature Season
#1 2016-07-01      July          13 Summer
#2 2017-01-08   January           5 Winter
#3 2018-09-19 September          11 Summer
#4 2019-10-24   October           9   Fall

Using Base R

Alternatively, we can use base R to convert the Month column to a new column called Season. We can do this by creating a vector of season names and then using it to index into our original dataset.

df1$Season = nm1[df1$Month]

In this code, we create a new column called Season in the original dataset, and then assign it the values from the nm1 vector. This is done by indexing into the nm1 vector using the values in the Month column.

Note that this approach assumes that the month names are equal to the corresponding season names (e.g., January = Winter). If your data has different month names, you will need to adjust the code accordingly.

#         Date     Month Temperature Season
#1 2016-07-01      July          13 Summer
#2 2017-01-08   January           5 Winter
#3 2018-09-19 September          11 Summer
#4 2019-10-24   October           9   Fall

Conclusion

In this article, we explored how to convert a month into its corresponding season in R using both the dplyr library and base R. We created sample datasets and used various approaches to achieve this conversion. Whether you prefer the power of dplyr or the simplicity of base R, there are many ways to accomplish this task in R.

Last modified on 2025-02-02