Summing Values Based on Date of Year in R

In this article, we will explore various ways to sum values based on the date of year in R. We will use a sample dataset and demonstrate different approaches using base R functions as well as popular packages such as dplyr, data.table, and zoo.

Introduction

R is a popular programming language for statistical computing and is widely used for data analysis. One common task in R is to sum values based on the date of year, which can be useful in various applications such as climate modeling, epidemiology, or finance. In this article, we will discuss different ways to achieve this task using base R functions.

Problem Statement

Suppose we have a dataset Precip15 with columns for precipitation, date of year (DOY), and date time in POSIXct format. We want to calculate the total precipitation for every day recorded. The data is presented as follows:

We want to transform the data into a format where we have the total precipitation for every day recorded.

Solution 1: Using aggregate()

The aggregate() function in R is used to group data by one or more variables and perform aggregation operations such as sum, mean, etc. In this case, we can use aggregate() to group the data by DOY and calculate the sum of Rain_cm.

aggregate(Rain_cm ~ DOY, DF, sum)
##   DOY Rain_cm
## 1 179     0.8
## 2 180     1.4

We can also use aggregate() to group the data by Date and calculate the sum of Rain_cm.

DF2 <- transform(DF, Date = as.Date(Date_Time))
aggregate(Rain_cm ~ Date, DF2, sum)
##         Date Rain_cm
## 1 2019-06-28     0.8
## 2 2019-06-29     1.4

Solution 2: Using rowsum()

The rowsum() function in R is used to calculate the sum of values for each row.

with(na.omit(DF), rowsum(Rain_cm, DOY))
##     [,1]
## 179  0.8
## 180  1.4

with(na.omit(DF2), rowsum(Rain_cm, Date))
##            [,1]
## 2019-06-28  0.8
## 2019-06-29  1.4

Solution 3: Using tapply()

The tapply() function in R is used to apply a function to each group of data.

with(DF, tapply(Rain_cm, DOY, sum, na.rm = TRUE))
## 179 180 
## 0.8 1.4 

with(DF2, tapply(Rain_cm, Date, sum, na.rm = TRUE))
## 2019-06-28 2019-06-29 
##        0.8        1.4

Solution 4: Using xtabs()

The xtabs() function in R is used to create a contingency table from two variables.

xtabs(Rain_cm ~ DOY, DF)
## DOY
## 179 180 
## 0.8 1.4 

xtabs(Rain_cm ~ Date, DF2)
## Date
## 2019-06-28 2019-06-29 
##        0.8        1.4 

Note

The data in reproducible form is assumed to be:

Lines <- "DOY     Rain     Rain_cm    Date_Time
179      6         0.6      2019-06-28 15:00:00
179      0         NA       2019-06-28 15:15:00
179      2         0.2      2019-06-28 16:45:00
180      0         NA       2019-06-29 10:00:00
180      10.2      1.2      2019-06-29 10:15:00
180      2         0.2      2019-06-29 13:00:00"

L <- readLines(textConnection(Lines))

DF <- read.csv(text = gsub("  +", ",", Lines))

Last modified on 2024-08-30