Calculating Percentage Increase/Decrease of Time Series Data Table with Respect to First Row/Day

When working with time series data, it’s often necessary to calculate the percentage increase or decrease in values over time. This can be particularly useful for visualizing trends and patterns in data. In this article, we’ll explore how to calculate the percentage change in a time series table using R and the dplyr and data.table packages.

Introduction

Time series data is commonly used in various fields such as finance, economics, and weather forecasting. It typically consists of values measured at regular time intervals, which can be daily, monthly, or even hourly. When working with time series data, it’s essential to calculate the percentage change between consecutive periods to understand how values are evolving over time.

Calculating Percentage Increase/Decrease

The percentage increase/decrease between two consecutive periods in a time series table is calculated using the following formula:

Percentage Change = ((Current Value - Previous Value) / Previous Value) x 100

For example, if we have the following time series data with values for Power and Gas on each day from October 16 to October 23:

To calculate the percentage increase in Power and Gas from October 16 to October 23, we can use the following code:

library(dplyr)
set.seed(123)
dt.data <- data.table(date = seq(as.Date('2020-10-16'), by = '1 day', length.out = 8),
                      Power = rnorm(8, 30, 1), Gas = rnorm(8, 10, 1))
dt.data

Using dplyr to Calculate Percentage Increase/Decrease

With the dplyr package, we can calculate the percentage increase/decrease in Power and Gas using the across function.

library(dplyr)
dt.data %>% 
  mutate(across(Power:CO2, ~(. - first(.))/first(.)))

This will create a new column for each variable (Power, Gas, CO2) that contains the percentage increase/decrease from the first value in the dataset.

Using data.table to Calculate Percentage Increase/Decrease

Alternatively, we can use the data.table package to calculate the percentage increase/decrease. In this case, we need to specify the columns using the .SDcols argument.

library(data.table)
setDT(dt.data)
cols <- c('Power', 'Gas', 'CO2')
dt.data[, (cols) := lapply(.SD, function(x) (x - first(x))/first(x)), 
         .SDcols = cols]

This will also create a new column for each variable that contains the percentage increase/decrease from the first value in the dataset.

Visualizing the Results

To visualize the results, we can use a bar chart to display the daily deviation in percentage terms for each product. We’ll assume that the value on October 16 is set to 0% and then calculate the percentage change relative to this baseline.

# Create a new column with the baseline value (October 16) set to 0%
dt.data[, Power_Baseline := Power - Power[1],]
dt.data[, Gas_Baseline := Gas - Gas[1], ]

Then, we can create the bar chart using ggplot2.

library(ggplot2)
ggplot(dt.data, aes(x = date, y = Power)) + 
  geom_bar(stat = 'identity') +
  labs(title = "Power Deviation in Percentage", x = "Date")

Similarly, we can create the bar chart for Gas and CO2.

ggplot(dt.data, aes(x = date, y = Gas)) + 
  geom_bar(stat = 'identity') +
  labs(title = "Gas Deviation in Percentage", x = "Date")

ggplot(dt.data, aes(x = date, y = CO2)) + 
  geom_bar(stat = 'identity') +
  labs(title = "CO2 Deviation in Percentage", x = "Date")

By calculating the percentage increase/decrease in Power, Gas, and CO2 using R and dplyr or data.table, we can gain a deeper understanding of how values are changing over time. The bar charts will provide a visual representation of these changes, allowing us to identify patterns and trends in the data.

Conclusion

In this article, we’ve explored how to calculate the percentage increase/decrease in a time series table using R and dplyr or data.table. We’ve also discussed how to visualize the results using bar charts. By applying these techniques, you can gain insights into how values are changing over time and make more informed decisions based on your data analysis findings.

Last modified on 2024-12-03