§2024-12-15

The Foolowin R script working with ```[ {"_id":{"$oid":"6715b7bff679da3f6e2a7f26"},"plateText":"BMH5561","inTime":"2024-03-31T21:41:05","outTime":"2024-03-31T23:42:37","carType":"car","inCameraSource":"","isExit":true,"isMonthlyCar":false,"isUserCreate":false,"outCameraSource":""} {"_id":{"$oid":"6715b7bff679da3f6e2a7f29"},"plateText":"RDT3105","inTime":"2024-03-31T21:36:40","outTime":"2024-03-31T23:37:08","carType":"car","inCameraSource":"","isExit":true,"isMonthlyCar":false,"isUserCreate":false,"outCameraSource":""} {"_id":{"$oid":"6715b7c0f679da3f6e2a8059"}, ....

# load licray
# jsonlite will help you read and parse your JSON data.
# dplyr provides functions to manipulate and summarize the data.
# lubridate helps you handle date and time.
library(jsonlite)
library(dplyr)
library(lubridate)

# it has to be an --jasonArray
data <- fromJSON("car_in_out_car-12-15-24-09-39.json", flatten = TRUE)

# Convert 'inTime' to Date (without time)
data$inDate <- as.Date(data$inTime)

# Summarize by day
summary_by_day <- data %>%
  group_by(inDate) %>%
  summarize(count = n()) %>%
  arrange(inDate)
  
# View the summary
# print(summary_by_day)
print(summary_by_day, n = nrow(summary_by_day))

what is


# Summarize by day
summary_by_day <- data %>%
  group_by(inDate) %>%
  summarize(count = n()) %>%
  arrange(inDate)

data %>%: The %>% operator is the "pipe" operator, which passes the data from the left side to the next operation on the right side. So the code is working on the data object.

group_by(inDate): This groups the data by the inDate column, which represents the date part of the inTime. So, instead of working with the entire dataset, it groups the data by each unique date in the inDate column.

summarize(count = n()): After grouping, the summarize function is used to create a new summary. The n() function counts the number of rows in each group. So for each date, count will store the number of records (i.e., the number of cars recorded on that date).

arrange(inDate): This orders the results by inDate in ascending order, meaning the summary will show the counts starting from the earliest date to the latest.

This is my partial json file of a parking lot cars in and out records mongoexport from momgoDB.

{"_id":{"$oid":"6715b7bff679da3f6e2a7f26"},"plateText":"BMH5561","inTime":"2024-03-31T21:41:05","outTime":"2024-03-31T23:42:37","carType":"car","inCameraSource":"","isExit":true,"isMonthlyCar":false,"isUserCreate":false,"outCameraSource":""}
{"_id":{"$oid":"6715b7bff679da3f6e2a7f29"},"plateText":"RDT3105","inTime":"2024-03-31T21:36:40","outTime":"2024-03-31T23:37:08","carType":"car","inCameraSource":"","isExit":true,"isMonthlyCar":false,"isUserCreate":false,"outCameraSource":""}
{"_id":{"$oid":"6715b7c0f679da3f6e2a8059"},"plateText":"BEW8761","inTime":"2024-03-31T19:37:27","outTime":"2024-03-31T21:54:59","carType":"car","inCameraSource":"","isExit":true,"isMonthlyCar":false,"isUserCreate":false,"outCameraSource":""}
{"_id":{"$oid":"6715b7c0f679da3f6e2a816a"},"plateText":"ASQ6012","inTime":"2024-03-31T18:40:00","outTime":"2024-03-31T21:39:26","carType":"car","inCameraSource":"","isExit":true,"isMonthlyCar":false,"isUserCreate":false,"outCameraSource":""}
{"_id":{"$oid":"6715b7c0f679da3f6e2a8219"},"plateText":"EBA5967","inTime":"2024-03-31T19:03:32","outTime":"2024-03-31T21:24:57","carType":"car","inCameraSource":"","isExit":true,"isMonthlyCar":false,"isUserCreate":false,"outCameraSource":""}
{"_id":{"$oid":"6715b7c1f679da3f6e2a823e"},"plateText":"AJT0915","inTime":"2024-03-31T19:35:16","outTime":"2024-03-31T21:19:13","carType":"car","inCameraSource":"","isExit":true,"isMonthlyCar":false,"isUserCreate":false,"outCameraSource":""}
{"_id":{"$oid":"6715b7c1f679da3f6e2a828a"},"plateText":"AJU1080","inTime":"2024-03-31T18:25:00","outTime":"2024-03-31T21:13:22","carType":"car","inCameraSource":"","isExit":true,"isMonthlyCar":false,"isUserCreate":false,"outCameraSource":""}
{"_id":{"$oid":"6715b7c1f679da3f6e2a830c"},"plateText":"9801ZG","inTime":"2024-03-31T19:09:04","outTime":"2024-03-31T21:01:01","carType":"car","inCameraSource":"","isExit":true,"isMonthlyCar":false,"isUserCreate":false,"outCameraSource":""}
{"_id":{"$oid":"6715b7c1f679da3f6e2a8331"},"plateText":"BRL9263","inTime":"2024-03-31T20:05:47","outTime":"2024-03-31T20:58:24","carType":"car","inCameraSource":"","isExit":true,"isMonthlyCar":false,"isUserCreate":false,"outCameraSource":""}
{"_id":{"$oid":"6715b7c1f679da3f6e2a843a"},"plateText":"3676U8","inTime":"2024-03-31T17:27:17","outTime":"2024-03-31T20:34:37","carType":"car","inCameraSource":"","isExit":true,"isMonthlyCar":false,"isUserCreate":false,"outCameraSource":""}
{"_id":{"$oid":"6715b7c1f679da3f6e2a8440"},"plateText":"BCM6155","inTime":"2024-03-31T18:09:59","outTime":"2024-03-31T20:33:02","carType":"car","inCameraSource":"","isExit":true,"isMonthlyCar":false,"isUserCreate":false,"outCameraSource":""}
{"_id":{"$oid":"6715b7c2f679da3f6e2a84ca"},"plateText":"BVL1986","inTime":"2024-03-31T18:17:24","outTime":"2024-03-31T20:21:45","carType":"car","inCameraSource":"","isExit":true,"isMonthlyCar":false,"isUserCreate":false,"outCameraSource":""}

How to do use R to summaries the count by day.

ChatGPT 4o mini

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Sign up You said: This is my partial json file of a parking lot cars in and out records mongoexport from momgoDB.

{"_id":{"$oid":"6715b7bff679da3f6e2a7f26"},"plateText":"BMH5561","inTime":"2024-03-31T21:41:05","outTime":"2024-03-31T23:42:37","carType":"car","inCameraSource":"","isExit":true,"isMonthlyCar":false,"isUserCreate":false,"outCameraSource":""} {"_id":{"$oid":"6715b7bff679da3f6e2a7f29"},"plateText":"RDT3105","inTime":"2024-03-31T21:36:40","outTime":"2024-03-31T23:37:08","carType":"car","inCameraSource":"","isExit":true,"isMonthlyCar":false,"isUserCreate":false,"outCameraSource":""} {"_id":{"$oid":"6715b7c0f679da3f6e2a8059"},"plateText":"BEW8761","inTime":"2024-03-31T19:37:27","outTime":"2024-03-31T21:54:59","carType":"car","inCameraSource":"","isExit":true,"isMonthlyCar":false,"isUserCreate":false,"outCameraSource":""} {"_id":{"$oid":"6715b7c0f679da3f6e2a816a"},"plateText":"ASQ6012","inTime":"2024-03-31T18:40:00","outTime":"2024-03-31T21:39:26","carType":"car","inCameraSource":"","isExit":true,"isMonthlyCar":false,"isUserCreate":false,"outCameraSource":""} {"_id":{"$oid":"6715b7c0f679da3f6e2a8219"},"plateText":"EBA5967","inTime":"2024-03-31T19:03:32","outTime":"2024-03-31T21:24:57","carType":"car","inCameraSource":"","isExit":true,"isMonthlyCar":false,"isUserCreate":false,"outCameraSource":""} {"_id":{"$oid":"6715b7c1f679da3f6e2a823e"},"plateText":"AJT0915","inTime":"2024-03-31T19:35:16","outTime":"2024-03-31T21:19:13","carType":"car","inCameraSource":"","isExit":true,"isMonthlyCar":false,"isUserCreate":false,"outCameraSource":""} {"_id":{"$oid":"6715b7c1f679da3f6e2a828a"},"plateText":"AJU1080","inTime":"2024-03-31T18:25:00","outTime":"2024-03-31T21:13:22","carType":"car","inCameraSource":"","isExit":true,"isMonthlyCar":false,"isUserCreate":false,"outCameraSource":""} {"_id":{"$oid":"6715b7c1f679da3f6e2a830c"},"plateText":"9801ZG","inTime":"2024-03-31T19:09:04","outTime":"2024-03-31T21:01:01","carType":"car","inCameraSource":"","isExit":true,"isMonthlyCar":false,"isUserCreate":false,"outCameraSource":""} {"_id":{"$oid":"6715b7c1f679da3f6e2a8331"},"plateText":"BRL9263","inTime":"2024-03-31T20:05:47","outTime":"2024-03-31T20:58:24","carType":"car","inCameraSource":"","isExit":true,"isMonthlyCar":false,"isUserCreate":false,"outCameraSource":""} {"_id":{"$oid":"6715b7c1f679da3f6e2a843a"},"plateText":"3676U8","inTime":"2024-03-31T17:27:17","outTime":"2024-03-31T20:34:37","carType":"car","inCameraSource":"","isExit":true,"isMonthlyCar":false,"isUserCreate":false,"outCameraSource":""} {"_id":{"$oid":"6715b7c1f679da3f6e2a8440"},"plateText":"BCM6155","inTime":"2024-03-31T18:09:59","outTime":"2024-03-31T20:33:02","carType":"car","inCameraSource":"","isExit":true,"isMonthlyCar":false,"isUserCreate":false,"outCameraSource":""} {"_id":{"$oid":"6715b7c2f679da3f6e2a84ca"},"plateText":"BVL1986","inTime":"2024-03-31T18:17:24","outTime":"2024-03-31T20:21:45","carType":"car","inCameraSource":"","isExit":true,"isMonthlyCar":false,"isUserCreate":false,"outCameraSource":""}

How to do use R to summaries the count by day. ChatGPT said: ChatGPT To summarize the count of cars by day using R, you'll need to:

Load your JSON data into R. Parse the inTime field into a date format. Group the data by the date part of the inTime. Count the number of records for each day. Here's how you can do it:

Step 1: Load the necessary libraries r Copy code library(jsonlite) library(dplyr) library(lubridate) jsonlite will help you read and parse your JSON data. dplyr provides functions to manipulate and summarize the data. lubridate helps you handle date and time. Step 2: Load and parse the JSON data If you have your JSON data saved as a file (e.g., parking_lot.json), you can read it using fromJSON() from the jsonlite package.

r Copy code

Read the JSON data from a file

data <- fromJSON("parking_lot.json", flatten = TRUE) If your data is in a string or directly available, you can use fromJSON() in a similar way.

Step 3: Convert inTime to a Date object Convert the inTime field to a date, extracting only the date part.

r Copy code

Convert 'inTime' to Date (without time)

data$inDate <- as.Date(data$inTime) Step 4: Summarize the count by day Now that you have the inDate field, you can group the data by day and count the occurrences of each day.

r Copy code

Summarize by day

summary_by_day <- data %>% group_by(inDate) %>% summarize(count = n()) %>% arrange(inDate)

View the summary

print(summary_by_day) This code will:

Group the data by inDate (which is the day part of inTime). Count how many records exist for each day (n()). Sort the results by date. Final Output The output summary_by_day will be a data frame showing the counts of cars entering the parking lot each day.

For example, it might look something like:

bash Copy code

A tibble: 3 × 2

inDate count 1 2024-03-31 12 This table tells you how many cars entered on March 31, 2024 (in this case, 12 cars).

You said:

data <- fromJSON("car_in_out_car-12-15-24-09:04.json", flatten = TRUE) Error in parse_con(txt, bigint_as_char) : parse error: trailing garbage :false,"outCameraSource":""} {"_id":{"$oid":"6715b7bff679da3 (right here) ------^ ChatGPT said: ChatGPT The error you encountered is due to the fact that your JSON file contains multiple JSON records without a proper array wrapper. JSON objects in the file are not enclosed in an array, which makes it invalid for fromJSON() to parse directly.

Here’s how you can handle this issue in R:

Solution 1: Add Square Brackets to Make It a Valid JSON Array Your file appears to contain multiple JSON objects that should be in an array. To fix this:

Manually add square brackets [] around the JSON objects. Separate each object with a comma. For example, your JSON should look like this:

json Copy code [ {"_id":{"$oid":"6715b7bff679da3f6e2a7f26"},"plateText":"BMH5561","inTime":"2024-03-31T21:41:05","outTime":"2024-03-31T23:42:37","carType":"car","inCameraSource":"","isExit":true,"isMonthlyCar":false,"isUserCreate":false,"outCameraSource":""}, {"_id":{"$oid":"6715b7bff679da3f6e2a7f29"},"plateText":"RDT3105","inTime":"2024-03-31T21:36:40","outTime":"2024-03-31T23:37:08","carType":"car","inCameraSource":"","isExit":true,"isMonthlyCar":false,"isUserCreate":false,"outCameraSource":""}, ... ] Solution 2: Parse It with jsonlite Using a Different Approach If manually editing the JSON file is not feasible, you can also try reading the file and manually adding the array brackets in R:

r Copy code library(jsonlite)

Read the raw JSON data from the file

raw_data <- readLines("car_in_out_car-12-15-24-09:04.json")

Join the lines into one large string and add the square brackets

raw_json <- paste0("[", paste(raw_data, collapse = ","), "]")

Now parse the JSON string

data <- fromJSON(raw_json, flatten = TRUE) This will:

Read the raw file line by line. Combine the lines into a single string. Add square brackets around the entire string to form a valid JSON array. Parse it using fromJSON(). After Parsing Once the JSON is correctly loaded into data, you can proceed with the steps to manipulate and summarize the data as described earlier (converting inTime to a Date and counting occurrences by day).

You said:

View the summary

print(summary_by_day)

A tibble: 351 × 2

inDate count 1 2023-12-31 32 2 2024-01-01 710 3 2024-01-02 178 4 2024-01-03 146 5 2024-01-04 130 6 2024-01-05 238 7 2024-01-06 715 8 2024-01-07 685 9 2024-01-08 176 10 2024-01-09 157

ℹ 341 more rows

ℹ Use print(n = ...) to see more rows how to print more ???

ChatGPT said: ChatGPT To print more rows from a tibble in R, you can use the print() function with the n argument, which specifies how many rows you want to display.

Here’s how you can print more rows:

r Copy code

Print more rows (for example, 50 rows)

print(summary_by_day, n = 50) This will display the first 50 rows of the summary_by_day tibble.

If you want to print all rows: You can set n to a larger number, or simply display all rows if you need:

r Copy code

Print all rows (be careful if you have a large dataset)

print(summary_by_day, n = Inf) Alternatively, if the dataset is too large and you only need to see a few records for inspection, you can also use the head() or tail() functions to preview the first or last few rows:

r Copy code

View the first 10 rows

head(summary_by_day, 10)

View the last 10 rows

tail(summary_by_day, 10) These functions are helpful when you're working with large datasets and don't want to print the entire data at once.

You said: How to run R in script ChatGPT said: ChatGPT Running R in a script is a common and efficient way to organize your R code and execute it in a batch-like manner. Here’s how you can run R code in a script:

Step 1: Create an R Script Create a new R script file: You can do this in any text editor, or preferably, an Integrated Development Environment (IDE) like RStudio.

In RStudio, you can create a new script by clicking on File → New File → R Script. Alternatively, you can create a file using any text editor and save it with a .R extension, e.g., my_script.R. Write your R code in the script: For example, you might include your data loading, manipulation, and summarization code.

Example my_script.R:

r Copy code

Load necessary libraries

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

Read the JSON data from a file

data <- fromJSON("car_in_out_car-12-15-24-09:04.json", flatten = TRUE)

Convert 'inTime' to Date (without time)

data$inDate <- as.Date(data$inTime)

Summarize by day

summary_by_day <- data %>% group_by(inDate) %>% summarize(count = n()) %>% arrange(inDate)

Print the summary

print(summary_by_day, n = 50) Step 2: Run the R Script There are multiple ways to execute an R script:

  1. Using RStudio: Open the .R script file in RStudio. To run the entire script, click on the "Run" button or press Ctrl + Shift + Enter. To run specific lines of the script, highlight the lines you want to run and press Ctrl + Enter (or click the "Run" button).
  2. Running from the R Console: If you're working in the R console (outside RStudio), you can run an R script by using the source() function. Make sure the script file is in your working directory or provide the full path to the file.

Example:

r Copy code source("my_script.R") This will execute the entire script in the current R session.

  1. Running from the Command Line (Terminal or Shell): If you prefer not to open an R session manually, you can run the R script directly from your terminal (command line).

On Windows:

Open the Command Prompt (press Win + R, type cmd, and hit Enter). Run the following command: sh Copy code Rscript my_script.R On Mac/Linux:

Open the Terminal. Run the same command: sh Copy code Rscript my_script.R Rscript is the command-line tool for running R scripts without launching an interactive R session.

Step 3: View Output When you run the script, any print() or output commands (like summary()) will be displayed in the R console or terminal. If you're using RStudio, the output will appear in the console window at the bottom. If you're using Rscript from the terminal, the output will be printed directly to the terminal. Step 4: Automating R Script Execution (Optional) If you want to schedule your R script to run at a certain time, you can use task schedulers like:

Windows: Task Scheduler Mac/Linux: cron jobs This allows you to run your script at specific intervals without manual intervention.

Let me know if you need further clarification on any of these steps!

# load licray
# jsonlite will help you read and parse your JSON data.
# dplyr provides functions to manipulate and summarize the data.
# lubridate helps you handle date and time.
library(jsonlite)
library(dplyr)
library(lubridate)

# it has to be an --jasonArray
data <- fromJSON("car_in_out_car-12-15-24-09:39.json", flatten = TRUE)

# Convert 'inTime' to Date (without time)
data$inDate <- as.Date(data$inTime)

# Summarize by day
summary_by_day <- data %>%
  group_by(inDate) %>%
  summarize(count = n()) %>%
  arrange(inDate)
  
# View the summary
# print(summary_by_day)
print(summary_by_day, n = nrow(summary_by_day))