Calculating R Values in Time Spans

Understanding the Problem

In this article, we’ll explore how to calculate probability values over time spans for a dataset of shared bicycles. The goal is to find the maximum number of bikes (MaxBikes) within a specific hour and then divide that by the total available docking capacity (Total Docks). This process involves data manipulation, grouping, and calculation.

Background

The problem revolves around handling large datasets with minute-level frequency. To efficiently solve this, we’ll break down the solution into manageable parts, focusing on data preprocessing, group-by operations, and cumulative sum calculations.

Data Structure

We’re dealing with a dataframe (analiseGiras) containing information about shared bicycles, including:

• Name: the bicycle’s name or identifier
• NrBikes: the number of bikes available at each time point
• Data: the date field for data consistency
• Time: the timestamp in format “YYYY-MM-DD HH:MM:SS”
• MaxBikes: the maximum number of bikes within a specific hour (x)
• TotalDocks: the total docking capacity
• Probability: the calculated probability value

Approach Overview

Our approach involves:

1. Data preprocessing and cleaning
2. Grouping by date, time, and dock identifier
3. Calculating cumulative sums for bike availability and total docks
4. Computing probability values

Step 1: Preprocessing and Cleaning

Before performing calculations, it’s essential to ensure data consistency and accuracy.

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

# Sample dataset (analiseGiras) is not provided; assume it's in the same format as the original example

Step 2: Grouping by Date, Time, and Dock Identifier

We need to group our data by date (Data), time (Time), and dock identifier (desigcomercial) to perform subsequent calculations.

# Group by date, time, and dock identifier
analiseGiras %>% 
  group_by(add_data, desigcomercial) %>% 
  mutate(
    Time = as_datetime(Time),
    Hour = hour(Time)
  )

Step 3: Calculating Cumulative Sums

We’ll calculate the cumulative sum of bike availability (NrBikes) and total docks within a specific hour (30 minutes in this case).

# Calculate cumulative sums for NrBikes and TotalDocks
analiseGiras %>% 
  group_by(add_data, desigcomercial) %>% 
  mutate(
    NrBikes_CumSum = cumsum(ifelse(NrBikes > 0, NrBikes, 0)),
    TotalDocks_CumSum = cumsum(ifelse(TotalDocks > 0, TotalDocks, 0))
  )

Step 4: Computing Probability Values

Finally, we can compute the probability values by dividing NrBikes_CumSum by TotalDocks_CumSum.

# Calculate probability values
analiseGiras %>% 
  group_by(add_data, desigcomercial) %>% 
  mutate(
    MaxBikes = max(NrBikes),
    Probability = NrBikes_CumSum / TotalDocks_CumSum,
    Time_Hour = hour(Time)
  )

Compiling the Results

We’ll compile our results into a single dataframe (analiseGirasNew) for easy access.

# Compile results into a single dataframe
df <- ldply(listofdfs, data.frame)

analiseGirasNew <- df

Example Use Case

Suppose we have the following dataset:

Running the code above would produce:

This shows the cumulative sums for bike availability and total docks, as well as the calculated probability values.

Conclusion

Calculating probability values in time spans is a useful technique for analyzing large datasets with minute-level frequency. By using data manipulation, grouping, and cumulative sum calculations, we can efficiently compute these values and gain insights into bike usage patterns.

Last modified on 2025-04-23