Example of Time-range Aggregation¶

[1]:

# import openLA as la
import OpenLA as la

[2]:

course_info, event_stream = la.start_analysis(files_dir="dataset_sample", course_id="A")

Before conversion¶

[3]:

event_stream.df

[3]:

	userid	contentsid	operationname	pageno	marker	memo_length	devicecode	eventtime
0	A_U1	C1	PREV	10	NaN	0	tablet	2018-04-09 10:57:15
1	A_U1	C1	PREV	9	NaN	0	tablet	2018-04-09 11:00:59
2	A_U1	C1	PREV	8	NaN	0	tablet	2018-04-09 11:03:31
3	A_U1	C1	PREV	30	NaN	0	tablet	2018-04-10 10:14:12
4	A_U1	C1	PREV	29	NaN	0	tablet	2018-04-10 10:27:24
...	...	...	...	...	...	...	...	...
263279	A_U99	C8	NEXT	3	NaN	0	pc	2018-06-05 16:16:18
263280	A_U99	C8	ADD MARKER	4	difficult	0	pc	2018-06-05 16:18:34
263281	A_U99	C8	NEXT	4	NaN	0	pc	2018-06-05 16:19:24
263282	A_U99	C8	NEXT	5	NaN	0	pc	2018-06-05 16:20:45
263283	A_U99	C8	PREV	6	NaN	0	pc	2018-06-05 16:21:03

263284 rows × 8 columns

After conversion: Behavior in 1-minute intervals¶

[4]:

intervals = la.convert_into_time_range(course_info=course_info,
                                       event_stream=event_stream,
                                       interval_seconds=60,
                                       contents_id="C1",
                                       user_id=course_info.user_id()[:5],
                                       lecture_week=2,
                                       start_time='start_of_lecture',
                                       end_time='end_of_lecture',
                                       time_range_basis='minutes',
                                       count_operation=True,
                                       operation_name=["ADD MARKER", "ADD MEMO", "ADD BOOKMARK"])

[5]:

intervals.df

[5]:

	elapsed_minutes	start_of_range	end_of_range	userid	contentsid	pageno	ADD MARKER	ADD MEMO	ADD BOOKMARK
0	1.0	2018-04-17 14:50:00	2018-04-17 14:51:00	A_U1	C1	1	0	0	0
1	2.0	2018-04-17 14:51:00	2018-04-17 14:52:00	A_U1	C1	1	0	0	0
2	3.0	2018-04-17 14:52:00	2018-04-17 14:53:00	A_U1	C1	3	0	0	0
3	4.0	2018-04-17 14:53:00	2018-04-17 14:54:00	A_U1	C1	3	0	0	0
4	5.0	2018-04-17 14:54:00	2018-04-17 14:55:00	A_U1	C1	5	0	0	0
...	...	...	...	...	...	...	...	...	...
445	86.0	2018-04-17 16:15:00	2018-04-17 16:16:00	A_U102	C1	17	0	0	0
446	87.0	2018-04-17 16:16:00	2018-04-17 16:17:00	A_U102	C1	17	0	0	0
447	88.0	2018-04-17 16:17:00	2018-04-17 16:18:00	A_U102	C1	17	0	0	0
448	89.0	2018-04-17 16:18:00	2018-04-17 16:19:00	A_U102	C1	17	0	0	0
449	90.0	2018-04-17 16:19:00	2018-04-17 16:20:00	A_U102	C1	17	0	0	0

450 rows × 9 columns

Save the data to CSV file¶

[6]:

intervals.to_csv(save_file="data.csv")

Aggregate Information¶

[7]:

# Number of users in the data
intervals.num_users()

[7]:

[8]:

# User ids in the data
intervals.user_id()

[8]:

['A_U1', 'A_U10', 'A_U100', 'A_U101', 'A_U102']

[9]:

# Contents ids in the data
intervals.contents_id()

[9]:

['C1']

[10]:

# Operation names in the data
intervals.operation_name()

[10]:

['ADD MARKER', 'ADD MEMO', 'ADD BOOKMARK']

[11]:

# Number of selected operation logs
intervals.operation_count(operation_name="ADD MARKER",
                          user_id="A_U1",
                          contents_id="C1")

[11]:

[12]:

intervals.operation_count(operation_name=["ADD MARKER", "ADD BOOKMARK"],
                          user_id="A_U1",
                          contents_id="C1")

[12]:

{'ADD MARKER': 0, 'ADD BOOKMARK': 0}

Visualize information¶

The average operation count of all users in lecture 1

[13]:

la.visualize_operation_in_time_range(intervals,
                                     contents_id="C1",
                                     user_id=None,
                                     operation_name=None,
                                     calculate_type="average",
                                     operation_bar_colors=None,
                                     xlabel=None,
                                     ylabel=None,
                                     ax=None,
                                     figsize=(8, 4),
                                     save_file=None)

[13]:

<matplotlib.axes._subplots.AxesSubplot at 0x2c6b8da1390>

../_images/jupyter_examples_example_timerange_19_1.png

The pages users read in each time in lecture 1

[15]:

la.visualize_pages_in_time_range(intervals,
                                 contents_id=course_info.contents_id()[0],
                                 user_id=None,
                                 xlabel=None,
                                 ylabel=None, ax=None,
                                 figsize=None,
                                 show_legend=False,
                                 save_file=None)

[15]:

<matplotlib.axes._subplots.AxesSubplot at 0x2c6b953a940>

../_images/jupyter_examples_example_timerange_21_1.png

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