§2024-12-14

• Welcome to a Little Book of R for Time Series!

Here is a sample of car_in_out collections of cars in our parking lot in mongoDB

[
  {
    _id: ObjectId('675d2bcc118e03ee0709362d'),
    plateText: 'ALR9326',
    inTime: '2024-12-14T12:40:15',
    carType: 'car',
    inCameraSource: '003',
    outTime: '2024-12-14T15:09:59',
    isExit: false,
    isMonthlyCar: false,
    isUserCreate: false
  },
  {
    _id: ObjectId('675d2bac118e03ee0709359c'),
    plateText: 'MEE8000',
    inTime: '2024-12-14T13:30:29',
    carType: 'motor',
    inCameraSource: '001',
    outTime: '2024-12-14T15:09:59',
    isExit: false,
    isMonthlyCar: false,
    isUserCreate: false
  }
]
```

each car has a license plate in field 'plateText', in time is 'inTime`, out time is 'outTime", we sould like to analyze the average parked time of each day.  Can I use R time series to analyze it and how?

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    Time Series Data R

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You said:
give me a sample for time series data for R
ChatGPT said:
ChatGPT

Here's a sample time series dataset suitable for use in R. This dataset represents monthly sales figures over a 3-year period (from January 2021 to December 2023).
Sample Data

Month          Sales
2021-01        150
2021-02        180
2021-03        200
2021-04        210
2021-05        250
2021-06        300
2021-07        320
2021-08        280
2021-09        260
2021-10        310
2021-11        350
2021-12        400
2022-01        170
2022-02        190
2022-03        220
2022-04        230
2022-05        270
2022-06        320
2022-07        340
2022-08        300
2022-09        280
2022-10        330
2022-11        370
2022-12        420
2023-01        180
2023-02        200
2023-03        230
2023-04        240
2023-05        290
2023-06        340
2023-07        360
2023-08        310
2023-09        290
2023-10        340
2023-11        380
2023-12        450

How to Create This Time Series in R

You can create a time series object (ts) in R using this data:

# Create the sales vector
sales <- c(150, 180, 200, 210, 250, 300, 320, 280, 260, 310, 350, 400,
           170, 190, 220, 230, 270, 320, 340, 300, 280, 330, 370, 420,
           180, 200, 230, 240, 290, 340, 360, 310, 290, 340, 380, 450)

# Create a time series object starting from January 2021, frequency = 12 (monthly data)
sales_ts <- ts(sales, start = c(2021, 1), frequency = 12)

# Plot the time series
plot(sales_ts, main = "Monthly Sales Data (2021-2023)", ylab = "Sales", xlab = "Year")

Explanation

    sales: The sales data as a numeric vector.
    start = c(2021, 1): Specifies that the data starts in January 2021.
    frequency = 12: Indicates monthly data (12 periods per year).
    plot(): Plots the time series.

This provides a simple, clear example for analyzing and visualizing time series data in R.
You said:
Here is a sample of car_in_out collections of cars in our parking lot in mongoDB

`
[
  {
    _id: ObjectId('675d2bcc118e03ee0709362d'),
    plateText: 'ALR9326',
    inTime: '2024-12-14T12:40:15',
    carType: 'car',
    inCameraSource: '003',
    outTime: '2024-12-14T15:09:59',
    isExit: false,
    isMonthlyCar: false,
    isUserCreate: false
  },
  {
    _id: ObjectId('675d2bac118e03ee0709359c'),
    plateText: 'MEE8000',
    inTime: '2024-12-14T13:30:29',
    carType: 'motor',
    inCameraSource: '001',
    outTime: '2024-12-14T15:09:59',
    isExit: false,
    isMonthlyCar: false,
    isUserCreate: false
  }
]



each car has a license plate in field 'plateText', in time is 'inTime, out time is 'outTime", we sould like to analyze the average parked time of each day.  Can I use R time series to analyze it and how?
ChatGPT said:
ChatGPT

Yes, you can analyze the average parked time per day in R using time series techniques. To achieve this, you'll need to:

    Export the data from MongoDB to a format that R can read (e.g., CSV or JSON).
    Load and preprocess the data in R.
    Calculate the parked duration for each car entry.
    Aggregate the durations to compute the daily average parked time.
    Create a time series from the aggregated data and visualize/analyze it.

Here's a step-by-step guide:

1. Export Data from MongoDB

You can export the data to a JSON file using the following MongoDB command:

mongoexport --db your_database --collection car_in_out --out car_in_out.json

2. Load and Preprocess Data in R

Use the jsonlite and lubridate libraries in R to read and process the data.

library(jsonlite)
library(lubridate)
library(dplyr)

# Load data from JSON
data <- fromJSON("car_in_out.json")

# Convert 'inTime' and 'outTime' to POSIXct datetime objects
data$inTime <- as.POSIXct(data$inTime, format = "%Y-%m-%dT%H:%M:%S")
data$outTime <- as.POSIXct(data$outTime, format = "%Y-%m-%dT%H:%M:%S")

# Calculate parked duration in hours for each record
data$parkedDuration <- as.numeric(difftime(data$outTime, data$inTime, units = "hours"))

# Inspect the processed data
head(data)

3. Aggregate to Get Daily Average Parked Time

# Create a new date column based on 'inTime'
data$date <- as.Date(data$inTime)

# Group by date and calculate the average parked duration per day
daily_avg_parked <- data %>%
  group_by(date) %>%
  summarize(avgParkedTime = mean(parkedDuration, na.rm = TRUE))

# Inspect the aggregated data
print(daily_avg_parked)

4. Create a Time Series Object

Now, convert the aggregated data into a time series object.

# Create a time series object
daily_avg_ts <- ts(daily_avg_parked$avgParkedTime, start = c(year(min(daily_avg_parked$date)), month(min(daily_avg_parked$date))), frequency = 365)

# Plot the time series
plot(daily_avg_ts, main = "Daily Average Parked Time", ylab = "Average Parked Time (Hours)", xlab = "Date", col = "blue", type = "o")

5. Time Series Analysis and Visualization

You can analyze the time series further by performing decomposition, applying smoothing techniques, or forecasting future average parked times.
Example: Decomposing the Time Series

decomposed <- decompose(daily_avg_ts)
plot(decomposed)

Example: Simple Forecasting with Exponential Smoothing

library(forecast)

# Fit an exponential smoothing model
fit <- ets(daily_avg_ts)

# Forecast the next 7 days
forecasted <- forecast(fit, h = 7)

# Plot the forecast
plot(forecasted, main = "Forecasted Daily Average Parked Time")

Summary of Steps

    Export your MongoDB data.
    Load and preprocess it in R.
    Calculate parked durations and aggregate by day.
    Convert to a time series object and analyze or forecast.

This approach gives you the flexibility to explore trends and patterns in parking durations over time.
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