§2024-12-15
- car_average_stay_by_day.R
# 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
json_data <- fromJSON("car_in_out_car-12-15-24-09-39.json", flatten = TRUE)
# Convert inTime and outTime to date-time objects
json_data$inTime <- ymd_hms(json_data$inTime)
json_data$outTime <- ymd_hms(json_data$outTime)
# Warning message:
# 70 failed to parse.
# Calculate the stay time as the difference between outTime and inTime
json_data$stayTime <- as.numeric(difftime(json_data$outTime, json_data$inTime, units = "secs"))
# Extract the date from inTime
json_data$date <- as.Date(json_data$inTime)
# Group by date and calculate the average stay time
average_stay_by_day <- json_data %>%
group_by(date) %>%
summarise(average_stay_time = mean(stayTime, na.rm = TRUE))
# View the results
print(average_stay_by_day)
# A tibble: 352 × 2
# date average_stay_time
# <date> <dbl>
# 1 2023-12-31 16194.
# 2 2024-01-01 9583.
# 3 2024-01-02 8987.
# 4 2024-01-03 8133.
# 5 2024-01-04 8110.
# 6 2024-01-05 7727.
# ℹ 342 more rows
# ℹ Use `print(n = ...)` to see more rows
# Convert seconds to minutes
average_stay_by_day$average_stay_time_minutes <- average_stay_by_day$average_stay_time / 60
# View the results with stay time in minutes
print(average_stay_by_day)
# A tibble: 352 × 3
# date average_stay_time average_stay_time_minutes
# <date> <dbl> <dbl>
# 1 2023-12-31 16194. 270.
# 2 2024-01-01 9583. 160.
# 3 2024-01-02 8987. 150.
# 4 2024-01-03 8133. 136.
# ℹ 342 more rows
# ℹ Use `print(n = ...)` to see more rows
# Convert the stay time to hours (seconds divided by 3600)
average_stay_by_day$average_stay_time_hours <- average_stay_by_day$average_stay_time / 3600
# View the updated tibble with the average stay time in hours
print(average_stay_by_day)
# A tibble: 352 × 4
# date average_stay_time average_stay_time_minutes average_stay_time_ho…¹
# <date> <dbl> <dbl> <dbl>
# 1 2023-12-31 16194. 270. 4.50
# 2 2024-01-01 9583. 160. 2.66
# 3 2024-01-02 8987. 150. 2.50
# ℹ 342 more rows
# ℹ abbreviated name: ¹average_stay_time_hours
# ℹ Use `print(n = ...)` to see more rows
print(average_stay_by_day, n = nrow(average_stay_by_day))
# A tibble: 352 × 4
# date average_stay_time average_stay_time_minu…¹ average_stay_time_ho…²
# <date> <dbl> <dbl> <dbl>
# 1 2023-12-31 16194. 270. 4.50
#
This is a jason array that has the data exported using mongoexport of a parking lot.
[{"_id":{"$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":{"$oid":"6715b7c1f679da3f6e2a823e"},"plateText":"AJT0915","inTime":"2024-03-31T19:35:16","outTime":"2024-03-31T21:19:13","carType":"car","inCameraSource":"","isExit":true,"isMonthlyCar":false,"isUserCreate":false,"outCameraSource":""},
....
In there using inTime and outTime you should be able to figure out the stay time. How to use R to get average stay time by day.
ChatGPT 4o mini
ログイン
サインアップ あなた: This is a jason array that has the data exported using mongoexport of a parking lot.
[{"_id":{"$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":{"$oid":"6715b7c1f679da3f6e2a823e"},"plateText":"AJT0915","inTime":"2024-03-31T19:35:16","outTime":"2024-03-31T21:19:13","carType":"car","inCameraSource":"","isExit":true,"isMonthlyCar":false,"isUserCreate":false,"outCameraSource":""}, ....
In there using inTime and outTime you should be able to figure out the stay time. How to use R to get average stay time by day. ChatGPT: ChatGPT To calculate the average stay time by day in R, you'll need to do the following steps:
Load the JSON data: Import the data into R, which can be done using the jsonlite package. Convert inTime and outTime to Date objects: R can work with date-time objects to calculate the duration. Calculate the stay time: For each entry, subtract inTime from outTime to get the duration of stay. Aggregate the results by day: Group the data by the date of inTime and calculate the average stay time for each day. Here's a step-by-step implementation in R:
- Install and load necessary libraries First, make sure to install and load the necessary packages:
r コードをコピーする install.packages("jsonlite") install.packages("dplyr") install.packages("lubridate")
library(jsonlite) library(dplyr) library(lubridate) 2. Load and parse the JSON data Assuming you have your JSON data in a file called parking_data.json, load it into R:
r コードをコピーする
Load the JSON data
json_data <- fromJSON("parking_data.json")
View the structure of the data
str(json_data) 3. Convert inTime and outTime to date-time objects We will use the lubridate package to handle date-time conversion.
r コードをコピーする
Convert inTime and outTime to date-time objects
json_data$inTime <- ymd_hms(json_data$inTime) json_data$outTime <- ymd_hms(json_data$outTime) 4. Calculate the stay time Now, we calculate the stay duration for each parking session by subtracting inTime from outTime.
r コードをコピーする
Calculate the stay time as the difference between outTime and inTime
json_data$stayTime <- as.numeric(difftime(json_data$outTime, json_data$inTime, units = "secs")) 5. Extract the date from inTime We can now extract the date part of the inTime column to group by day.
r コードをコピーする
Extract the date from inTime
json_data$date <- as.Date(json_data$inTime) 6. Group by day and calculate average stay time Now, group the data by date and calculate the average stay time for each day.
r コードをコピーする
Group by date and calculate the average stay time
average_stay_by_day <- json_data %>% group_by(date) %>% summarise(average_stay_time = mean(stayTime, na.rm = TRUE))
View the results
print(average_stay_by_day) 7. Convert the average stay time to a more readable format The average_stay_time is in seconds, so you might want to convert it to minutes or hours:
r コードをコピーする
Convert seconds to minutes
average_stay_by_day$average_stay_time_minutes <- average_stay_by_day$average_stay_time / 60
View the results with stay time in minutes
print(average_stay_by_day) Example Output: The output will be a table showing the average stay time by day, in minutes (or any unit you choose):
r コードをコピーする
A tibble: 3 × 3
date average_stay_time average_stay_time_minutes
あなた:
library(jsonlite) data <- fromJSON("car_in_out_car-12-15-24-09:39.json", flatten = TRUE) Error: lexical error: invalid char in json text. car_in_out_car-12-15-24-09:39.j (right here) ------^ ChatGPT: ChatGPT The error you are encountering (lexical error: invalid char in json text) typically happens when R is trying to read a file and encounters an invalid character in the JSON string, often related to the way the file is named or formatted. In this case, the filename "car_in_out_car-12-15-24-09:39.json" might be problematic due to the : character in it.
Solutions: Rename the File: The colon (:) is not a valid character in some filesystems (such as Windows). It might cause issues when R tries to access the file. Try renaming the file to remove the colon or replace it with another character, such as an underscore (_).
Example:
Old file name: car_in_out_car-12-15-24-09:39.json New file name: car_in_out_car_12_15_24_09_39.json After renaming, try loading the file again:
r コードをコピーする data <- fromJSON("car_in_out_car_12_15_24_09_39.json", flatten = TRUE) Ensure Proper File Encoding: If the filename isn't the issue, make sure the JSON file is properly encoded (UTF-8 or similar). If you suspect there might be encoding issues, try opening the file in a text editor (e.g., Notepad++ or VSCode) and re-saving it with UTF-8 encoding.
Verify the JSON Format: Another possibility is that the content of the JSON file itself may be malformed. You can verify whether the file is a valid JSON by using online JSON validators like JSONLint.
Use File Path Carefully: If you're working in an environment where paths might be tricky (e.g., spaces or special characters), make sure the file path is correct. You could also try specifying the absolute path instead of the relative one.
For example, if you're on Windows, use double backslashes:
r コードをコピーする data <- fromJSON("C:\Users\YourUsername\Documents\car_in_out_car_12_15_24_09_39.json", flatten = TRUE) On Linux or macOS, you can use forward slashes:
r コードをコピーする data <- fromJSON("/home/yourusername/Documents/car_in_out_car_12_15_24_09_39.json", flatten = TRUE) Additional Diagnostic Step: If renaming the file and ensuring correct encoding doesn't solve the problem, try reading the file content directly to see if it's an issue with the file itself:
r コードをコピーする
Read the raw content of the file
file_content <- readLines("car_in_out_car_12_15_24_09_39.json") head(file_content) # Check the first few lines of the file This can help you debug the problem by showing if the file is getting loaded correctly and whether there is anything unusual in the content.
(R-4.4) alexlai@JetsonOrinNano:~/R-proj/car_in_out$ R
R version 4.4.2 (2024-10-31) -- "Pile of Leaves"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: aarch64-conda-linux-gnu
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
Natural language support but running in an English locale
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(jsonlite)
> data <- fromJSON("car_in_out_car-12-15-24-09-39.json", flatten = TRUE)
> library(dplyr)
library(lubridate)
Attaching package: ‘dplyr’
The following objects are masked from ‘package:stats’:
filter, lag
The following objects are masked from ‘package:base’:
intersect, setdiff, setequal, union
Attaching package: ‘lubridate’
The following objects are masked from ‘package:base’:
date, intersect, setdiff, union
> str(json_data)
Error: object 'json_data' not found
> str(data)
'data.frame': 142586 obs. of 10 variables:
$ plateText : chr "BMH5561" "RDT3105" "BEW8761" "ASQ6012" ...
$ inTime : chr "2024-03-31T21:41:05" "2024-03-31T21:36:40" "2024-03-31T19:37:27" "2024-03-31T18:40:00" ...
$ outTime : chr "2024-03-31T23:42:37" "2024-03-31T23:37:08" "2024-03-31T21:54:59" "2024-03-31T21:39:26" ...
$ carType : chr "car" "car" "car" "car" ...
$ inCameraSource : chr "" "" "" "" ...
$ isExit : logi TRUE TRUE TRUE TRUE TRUE TRUE ...
$ isMonthlyCar : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
$ isUserCreate : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
$ outCameraSource: chr "" "" "" "" ...
$ _id.$oid : chr "6715b7bff679da3f6e2a7f26" "6715b7bff679da3f6e2a7f29" "6715b7c0f679da3f6e2a8059" "6715b7c0f679da3f6e2a816a" ...
> jason_data <- fromJSON("car_in_out_car-12-15-24-09-39.json", flatten = TRUE)
> str(json_data)
Error: object 'json_data' not found
> str(jason_data)
'data.frame': 142586 obs. of 10 variables:
$ plateText : chr "BMH5561" "RDT3105" "BEW8761" "ASQ6012" ...
$ inTime : chr "2024-03-31T21:41:05" "2024-03-31T21:36:40" "2024-03-31T19:37:27" "2024-03-31T18:40:00" ...
$ outTime : chr "2024-03-31T23:42:37" "2024-03-31T23:37:08" "2024-03-31T21:54:59" "2024-03-31T21:39:26" ...
$ carType : chr "car" "car" "car" "car" ...
$ inCameraSource : chr "" "" "" "" ...
$ isExit : logi TRUE TRUE TRUE TRUE TRUE TRUE ...
$ isMonthlyCar : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
$ isUserCreate : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
$ outCameraSource: chr "" "" "" "" ...
$ _id.$oid : chr "6715b7bff679da3f6e2a7f26" "6715b7bff679da3f6e2a7f29" "6715b7c0f679da3f6e2a8059" "6715b7c0f679da3f6e2a816a" ...
> json_data <- fromJSON("car_in_out_car-12-15-24-09-39.json", flatten = TRUE)
> str(json_data)
'data.frame': 142586 obs. of 10 variables:
$ plateText : chr "BMH5561" "RDT3105" "BEW8761" "ASQ6012" ...
$ inTime : chr "2024-03-31T21:41:05" "2024-03-31T21:36:40" "2024-03-31T19:37:27" "2024-03-31T18:40:00" ...
$ outTime : chr "2024-03-31T23:42:37" "2024-03-31T23:37:08" "2024-03-31T21:54:59" "2024-03-31T21:39:26" ...
$ carType : chr "car" "car" "car" "car" ...
$ inCameraSource : chr "" "" "" "" ...
$ isExit : logi TRUE TRUE TRUE TRUE TRUE TRUE ...
$ isMonthlyCar : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
$ isUserCreate : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
$ outCameraSource: chr "" "" "" "" ...
$ _id.$oid : chr "6715b7bff679da3f6e2a7f26" "6715b7bff679da3f6e2a7f29" "6715b7c0f679da3f6e2a8059" "6715b7c0f679da3f6e2a816a" ...
> # Convert inTime and outTime to date-time objects
json_data$inTime <- ymd_hms(json_data$inTime)
json_data$outTime <- ymd_hms(json_data$outTime)
Warning message:
70 failed to parse.
> # Calculate the stay time as the difference between outTime and inTime
json_data$stayTime <- as.numeric(difftime(json_data$outTime, json_data$inTime, units = "secs"))
> # Extract the date from inTime
json_data$date <- as.Date(json_data$inTime)
> # Group by date and calculate the average stay time
average_stay_by_day <- json_data %>%
group_by(date) %>%
summarise(average_stay_time = mean(stayTime, na.rm = TRUE))
# View the results
print(average_stay_by_day)
# A tibble: 352 × 2
date average_stay_time
<date> <dbl>
1 2023-12-31 16194.
2 2024-01-01 9583.
3 2024-01-02 8987.
4 2024-01-03 8133.
5 2024-01-04 8110.
6 2024-01-05 7727.
7 2024-01-06 8993.
8 2024-01-07 9304.
9 2024-01-08 7260.
10 2024-01-09 8965.
# ℹ 342 more rows
# ℹ Use `print(n = ...)` to see more rows
> # Convert seconds to minutes
average_stay_by_day$average_stay_time_minutes <- average_stay_by_day$average_stay_time / 60
# View the results with stay time in minutes
print(average_stay_by_day)
# A tibble: 352 × 3
date average_stay_time average_stay_time_minutes
<date> <dbl> <dbl>
1 2023-12-31 16194. 270.
2 2024-01-01 9583. 160.
3 2024-01-02 8987. 150.
4 2024-01-03 8133. 136.
5 2024-01-04 8110. 135.
6 2024-01-05 7727. 129.
7 2024-01-06 8993. 150.
8 2024-01-07 9304. 155.
9 2024-01-08 7260. 121.
10 2024-01-09 8965. 149.
# ℹ 342 more rows
# ℹ Use `print(n = ...)` to see more rows
> # Convert the stay time to hours (seconds divided by 3600)
average_stay_by_day$average_stay_time_hours <- average_stay_by_day$average_stay_time / 3600
# View the updated tibble with the average stay time in hours
print(average_stay_by_day)
# A tibble: 352 × 4
date average_stay_time average_stay_time_minutes average_stay_time_ho…¹
<date> <dbl> <dbl> <dbl>
1 2023-12-31 16194. 270. 4.50
2 2024-01-01 9583. 160. 2.66
3 2024-01-02 8987. 150. 2.50
4 2024-01-03 8133. 136. 2.26
5 2024-01-04 8110. 135. 2.25
6 2024-01-05 7727. 129. 2.15
7 2024-01-06 8993. 150. 2.50
8 2024-01-07 9304. 155. 2.58
9 2024-01-08 7260. 121. 2.02
10 2024-01-09 8965. 149. 2.49
# ℹ 342 more rows
# ℹ abbreviated name: ¹average_stay_time_hours
# ℹ Use `print(n = ...)` to see more rows
> print(average_stay_by_day, n = nrow(average_stay_by_day))
# A tibble: 352 × 4
date average_stay_time average_stay_time_minu…¹ average_stay_time_ho…²
<date> <dbl> <dbl> <dbl>
1 2023-12-31 16194. 270. 4.50
2 2024-01-01 9583. 160. 2.66
3 2024-01-02 8987. 150. 2.50
4 2024-01-03 8133. 136. 2.26
5 2024-01-04 8110. 135. 2.25
6 2024-01-05 7727. 129. 2.15
7 2024-01-06 8993. 150. 2.50
8 2024-01-07 9304. 155. 2.58
9 2024-01-08 7260. 121. 2.02
10 2024-01-09 8965. 149. 2.49
11 2024-01-10 8068. 134. 2.24
12 2024-01-11 8729. 145. 2.42
13 2024-01-12 8553. 143. 2.38
14 2024-01-13 9070. 151. 2.52
15 2024-01-14 9369. 156. 2.60
16 2024-01-15 8253. 138. 2.29
17 2024-01-16 8917. 149. 2.48
18 2024-01-17 7463. 124. 2.07
19 2024-01-18 8468. 141. 2.35
20 2024-01-19 8260. 138. 2.29
21 2024-01-20 10055. 168. 2.79
22 2024-01-21 9501. 158. 2.64
23 2024-01-22 7981. 133. 2.22
24 2024-01-23 8543. 142. 2.37
25 2024-01-24 8362. 139. 2.32
26 2024-01-25 8314. 139. 2.31
27 2024-01-26 8475. 141. 2.35
28 2024-01-27 9867. 164. 2.74
29 2024-01-28 9888. 165. 2.75
30 2024-01-29 7903. 132. 2.20
31 2024-01-30 7995. 133. 2.22
32 2024-01-31 7182. 120. 1.99
33 2024-02-01 8276. 138. 2.30
34 2024-02-02 8652. 144. 2.40
35 2024-02-03 9661. 161. 2.68
36 2024-02-04 9880. 165. 2.74
37 2024-02-05 8026. 134. 2.23
38 2024-02-06 7691. 128. 2.14
39 2024-02-07 8352. 139. 2.32
40 2024-02-08 9203. 153. 2.56
41 2024-02-09 6532. 109. 1.81
42 2024-02-10 10343. 172. 2.87
43 2024-02-11 10080. 168. 2.80
44 2024-02-12 10221. 170. 2.84
45 2024-02-13 10250. 171. 2.85
46 2024-02-14 10212. 170. 2.84
47 2024-02-15 8508. 142. 2.36
48 2024-02-16 8064. 134. 2.24
49 2024-02-17 8711. 145. 2.42
50 2024-02-18 9636. 161. 2.68
51 2024-02-19 8468. 141. 2.35
52 2024-02-20 8129. 135. 2.26
53 2024-02-21 7595. 127. 2.11
54 2024-02-22 7207. 120. 2.00
55 2024-02-23 7610. 127. 2.11
56 2024-02-24 9477. 158. 2.63
57 2024-02-25 9524. 159. 2.65
58 2024-02-26 7251. 121. 2.01
59 2024-02-27 7857. 131. 2.18
60 2024-02-28 9443. 157. 2.62
61 2024-02-29 7213. 120. 2.00
62 2024-03-01 7948. 132. 2.21
63 2024-03-02 11223. 187. 3.12
64 2024-03-03 9271. 155. 2.58
65 2024-03-04 7897. 132. 2.19
66 2024-03-05 7930. 132. 2.20
67 2024-03-06 8225. 137. 2.28
68 2024-03-07 8318. 139. 2.31
69 2024-03-08 8076. 135. 2.24
70 2024-03-09 9871. 165. 2.74
71 2024-03-10 9300. 155. 2.58
72 2024-03-11 8003. 133. 2.22
73 2024-03-12 7389. 123. 2.05
74 2024-03-13 7075. 118. 1.97
75 2024-03-14 8645. 144. 2.40
76 2024-03-15 8478. 141. 2.36
77 2024-03-16 10314. 172. 2.86
78 2024-03-17 10678. 178. 2.97
79 2024-03-18 7643. 127. 2.12
80 2024-03-19 7889. 131. 2.19
81 2024-03-20 7801. 130. 2.17
82 2024-03-21 8267. 138. 2.30
83 2024-03-22 7863. 131. 2.18
84 2024-03-23 10059. 168. 2.79
85 2024-03-24 9349. 156. 2.60
86 2024-03-25 7717. 129. 2.14
87 2024-03-26 8056. 134. 2.24
88 2024-03-27 7441. 124. 2.07
89 2024-03-28 7442. 124. 2.07
90 2024-03-29 7620. 127. 2.12
91 2024-03-30 9769. 163. 2.71
92 2024-03-31 9296. 155. 2.58
93 2024-04-01 8265. 138. 2.30
94 2024-04-02 8353. 139. 2.32
95 2024-04-03 7639. 127. 2.12
96 2024-04-04 10159. 169. 2.82
97 2024-04-05 10473. 175. 2.91
98 2024-04-06 10357. 173. 2.88
99 2024-04-07 9645. 161. 2.68
100 2024-04-08 8879. 148. 2.47
101 2024-04-09 7905. 132. 2.20
102 2024-04-10 6986. 116. 1.94
103 2024-04-11 7709. 128. 2.14
104 2024-04-12 7418. 124. 2.06
105 2024-04-13 9251. 154. 2.57
106 2024-04-14 9582. 160. 2.66
107 2024-04-15 9983. 166. 2.77
108 2024-04-16 7513. 125. 2.09
109 2024-04-17 7699. 128. 2.14
110 2024-04-18 7757. 129. 2.15
111 2024-04-19 7496. 125. 2.08
112 2024-04-20 9844. 164. 2.73
113 2024-04-21 9697. 162. 2.69
114 2024-04-22 7155. 119. 1.99
115 2024-04-23 8156. 136. 2.27
116 2024-04-24 8441. 141. 2.34
117 2024-04-25 8012. 134. 2.23
118 2024-04-26 7943. 132. 2.21
119 2024-04-27 9967. 166. 2.77
120 2024-04-28 9920. 165. 2.76
121 2024-04-29 7302. 122. 2.03
122 2024-04-30 7554. 126. 2.10
123 2024-05-01 9276. 155. 2.58
124 2024-05-02 8254. 138. 2.29
125 2024-05-03 8194. 137. 2.28
126 2024-05-04 9866. 164. 2.74
127 2024-05-05 9740. 162. 2.71
128 2024-05-06 7490. 125. 2.08
129 2024-05-07 7527. 125. 2.09
130 2024-05-08 8160. 136. 2.27
131 2024-05-09 8651. 144. 2.40
132 2024-05-10 8135. 136. 2.26
133 2024-05-11 9982. 166. 2.77
134 2024-05-12 9751. 163. 2.71
135 2024-05-13 8037. 134. 2.23
136 2024-05-14 8794. 147. 2.44
137 2024-05-15 7281. 121. 2.02
138 2024-05-16 7560. 126. 2.10
139 2024-05-17 7833. 131. 2.18
140 2024-05-18 10235. 171. 2.84
141 2024-05-19 10284. 171. 2.86
142 2024-05-20 8029. 134. 2.23
143 2024-05-21 7857. 131. 2.18
144 2024-05-22 7690. 128. 2.14
145 2024-05-23 8713. 145. 2.42
146 2024-05-24 8195. 137. 2.28
147 2024-05-25 10035. 167. 2.79
148 2024-05-26 9222. 154. 2.56
149 2024-05-27 8119. 135. 2.26
150 2024-05-28 8192. 137. 2.28
151 2024-05-29 7289. 121. 2.02
152 2024-05-30 7485. 125. 2.08
153 2024-05-31 7816. 130. 2.17
154 2024-06-01 9427. 157. 2.62
155 2024-06-02 9460. 158. 2.63
156 2024-06-03 7721. 129. 2.14
157 2024-06-04 8315. 139. 2.31
158 2024-06-05 7770. 130. 2.16
159 2024-06-06 7458. 124. 2.07
160 2024-06-07 8482. 141. 2.36
161 2024-06-08 10176. 170. 2.83
162 2024-06-09 10103. 168. 2.81
163 2024-06-10 9299. 155. 2.58
164 2024-06-11 8349. 139. 2.32
165 2024-06-12 7924. 132. 2.20
166 2024-06-13 7955. 133. 2.21
167 2024-06-14 8433. 141. 2.34
168 2024-06-15 9751. 163. 2.71
169 2024-06-16 9441. 157. 2.62
170 2024-06-17 7814. 130. 2.17
171 2024-06-18 8128. 135. 2.26
172 2024-06-19 8532. 142. 2.37
173 2024-06-20 8214. 137. 2.28
174 2024-06-21 8001. 133. 2.22
175 2024-06-22 9537. 159. 2.65
176 2024-06-23 9490. 158. 2.64
177 2024-06-24 7786. 130. 2.16
178 2024-06-25 8416. 140. 2.34
179 2024-06-26 7962. 133. 2.21
180 2024-06-27 7893. 132. 2.19
181 2024-06-28 8252. 138. 2.29
182 2024-06-29 9986. 166. 2.77
183 2024-06-30 9675. 161. 2.69
184 2024-07-01 8837. 147. 2.45
185 2024-07-02 8772. 146. 2.44
186 2024-07-03 8331. 139. 2.31
187 2024-07-04 8314. 139. 2.31
188 2024-07-05 8430. 140. 2.34
189 2024-07-06 9667. 161. 2.69
190 2024-07-07 9787. 163. 2.72
191 2024-07-08 7900. 132. 2.19
192 2024-07-09 7827. 130. 2.17
193 2024-07-10 8414. 140. 2.34
194 2024-07-11 8393. 140. 2.33
195 2024-07-12 8821. 147. 2.45
196 2024-07-13 10040. 167. 2.79
197 2024-07-14 9974. 166. 2.77
198 2024-07-15 7847. 131. 2.18
199 2024-07-16 8439. 141. 2.34
200 2024-07-17 8623. 144. 2.40
201 2024-07-18 7818. 130. 2.17
202 2024-07-19 8257. 138. 2.29
203 2024-07-20 10021. 167. 2.78
204 2024-07-21 10091. 168. 2.80
205 2024-07-22 8149. 136. 2.26
206 2024-07-23 8446. 141. 2.35
207 2024-07-24 8239. 137. 2.29
208 2024-07-25 9159. 153. 2.54
209 2024-07-26 8708. 145. 2.42
210 2024-07-27 9986. 166. 2.77
211 2024-07-28 9882. 165. 2.74
212 2024-07-29 8465. 141. 2.35
213 2024-07-30 8245. 137. 2.29
214 2024-07-31 8044. 134. 2.23
215 2024-08-01 8664. 144. 2.41
216 2024-08-02 8308. 138. 2.31
217 2024-08-03 10075. 168. 2.80
218 2024-08-04 10059. 168. 2.79
219 2024-08-05 8107. 135. 2.25
220 2024-08-06 8077. 135. 2.24
221 2024-08-07 8081. 135. 2.24
222 2024-08-08 8301. 138. 2.31
223 2024-08-09 8396. 140. 2.33
224 2024-08-10 10059. 168. 2.79
225 2024-08-11 9614. 160. 2.67
226 2024-08-12 7588. 126. 2.11
227 2024-08-13 8355. 139. 2.32
228 2024-08-14 8220. 137. 2.28
229 2024-08-15 8585. 143. 2.38
230 2024-08-16 8207. 137. 2.28
231 2024-08-17 9670. 161. 2.69
232 2024-08-18 9126. 152. 2.54
233 2024-08-19 8403. 140. 2.33
234 2024-08-20 8376. 140. 2.33
235 2024-08-21 7893. 132. 2.19
236 2024-08-22 8130. 136. 2.26
237 2024-08-23 8526. 142. 2.37
238 2024-08-24 10195. 170. 2.83
239 2024-08-25 9529. 159. 2.65
240 2024-08-26 8070. 134. 2.24
241 2024-08-27 8250. 137. 2.29
242 2024-08-28 7812. 130. 2.17
243 2024-08-29 8778. 146. 2.44
244 2024-08-30 7459. 124. 2.07
245 2024-08-31 9782. 163. 2.72
246 2024-09-01 9214. 154. 2.56
247 2024-09-02 7906. 132. 2.20
248 2024-09-03 7177. 120. 1.99
249 2024-09-04 8038. 134. 2.23
250 2024-09-05 8036. 134. 2.23
251 2024-09-06 8203. 137. 2.28
252 2024-09-07 9749. 162. 2.71
253 2024-09-08 9676. 161. 2.69
254 2024-09-09 7670. 128. 2.13
255 2024-09-10 8440. 141. 2.34
256 2024-09-11 7816. 130. 2.17
257 2024-09-12 8913. 149. 2.48
258 2024-09-13 8790. 146. 2.44
259 2024-09-14 10679. 178. 2.97
260 2024-09-15 10519. 175. 2.92
261 2024-09-16 8969. 149. 2.49
262 2024-09-17 10457. 174. 2.90
263 2024-09-18 8149. 136. 2.26
264 2024-09-19 8510. 142. 2.36
265 2024-09-20 7959. 133. 2.21
266 2024-09-21 10538. 176. 2.93
267 2024-09-22 10144. 169. 2.82
268 2024-09-23 7364. 123. 2.05
269 2024-09-24 7627. 127. 2.12
270 2024-09-25 8575. 143. 2.38
271 2024-09-26 6984. 116. 1.94
272 2024-09-27 7738. 129. 2.15
273 2024-09-28 10189. 170. 2.83
274 2024-09-29 9903. 165. 2.75
275 2024-09-30 7548. 126. 2.10
276 2024-10-01 8161. 136. 2.27
277 2024-10-02 9018. 150. 2.51
278 2024-10-03 9047. 151. 2.51
279 2024-10-04 7938. 132. 2.21
280 2024-10-05 9819. 164. 2.73
281 2024-10-06 9503. 158. 2.64
282 2024-10-07 7462. 124. 2.07
283 2024-10-08 7725. 129. 2.15
284 2024-10-09 8612. 144. 2.39
285 2024-10-10 9921. 165. 2.76
286 2024-10-11 9132. 152. 2.54
287 2024-10-12 9861. 164. 2.74
288 2024-10-13 10017. 167. 2.78
289 2024-10-14 8103. 135. 2.25
290 2024-10-15 8007. 133. 2.22
291 2024-10-16 7946. 132. 2.21
292 2024-10-17 7994. 133. 2.22
293 2024-10-18 8261. 138. 2.29
294 2024-10-19 9693. 162. 2.69
295 2024-10-20 9617. 160. 2.67
296 2024-10-21 7485. 125. 2.08
297 2024-10-22 7747. 129. 2.15
298 2024-10-23 7641. 127. 2.12
299 2024-10-24 8010. 133. 2.22
300 2024-10-25 7827. 130. 2.17
301 2024-10-26 9942. 166. 2.76
302 2024-10-27 9448. 157. 2.62
303 2024-10-28 7224. 120. 2.01
304 2024-10-29 8141. 136. 2.26
305 2024-10-30 7524. 125. 2.09
306 2024-10-31 5688. 94.8 1.58
307 2024-11-01 7930. 132. 2.20
308 2024-11-02 9367. 156. 2.60
309 2024-11-03 9170. 153. 2.55
310 2024-11-04 7780. 130. 2.16
311 2024-11-05 7518. 125. 2.09
312 2024-11-06 6893. 115. 1.91
313 2024-11-07 8202. 137. 2.28
314 2024-11-08 8434. 141. 2.34
315 2024-11-09 9603. 160. 2.67
316 2024-11-10 9521. 159. 2.64
317 2024-11-11 7625. 127. 2.12
318 2024-11-12 7804. 130. 2.17
319 2024-11-13 7365. 123. 2.05
320 2024-11-14 8192. 137. 2.28
321 2024-11-15 7519. 125. 2.09
322 2024-11-16 10189. 170. 2.83
323 2024-11-17 9708. 162. 2.70
324 2024-11-18 7603. 127. 2.11
325 2024-11-19 8318. 139. 2.31
326 2024-11-20 9261. 154. 2.57
327 2024-11-21 7934. 132. 2.20
328 2024-11-22 8179. 136. 2.27
329 2024-11-23 10957. 183. 3.04
330 2024-11-24 9492. 158. 2.64
331 2024-11-25 8443. 141. 2.35
332 2024-11-26 8334. 139. 2.31
333 2024-11-27 8150. 136. 2.26
334 2024-11-28 8426. 140. 2.34
335 2024-11-29 8501. 142. 2.36
336 2024-11-30 9807. 163. 2.72
337 2024-12-01 9563. 159. 2.66
338 2024-12-02 7339. 122. 2.04
339 2024-12-03 8171. 136. 2.27
340 2024-12-04 7005. 117. 1.95
341 2024-12-05 8159. 136. 2.27
342 2024-12-06 8735. 146. 2.43
343 2024-12-07 9764. 163. 2.71
344 2024-12-08 10231. 171. 2.84
345 2024-12-09 8106. 135. 2.25
346 2024-12-10 7677. 128. 2.13
347 2024-12-11 8627. 144. 2.40
348 2024-12-12 8251. 138. 2.29
349 2024-12-13 8463. 141. 2.35
350 2024-12-14 10132. 169. 2.81
351 2024-12-15 3319 55.3 0.922
352 NA NaN NaN NaN
# ℹ abbreviated names: ¹average_stay_time_minutes, ²average_stay_time_hours
>