264.TEMPLES OF INDIA DATASET ANALYSIS USING PROC PRINT | PROC CONTENTS | PROC SORT | PROC FREQ | PROC MEANS | PROC SQL | PROC TABULATE | PROC REPORT | PROC TRANSPOSE | PROC FORMAT | PROC RANK | PROC SGPLOT | MACROS

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166.MASTERING SAS DATA ANALYSIS | LEVERAGING PROC SQL | PROC MEANS | PROC TIMESERIES FOR COMPREHENSIVE INSIGHTS

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MASTERING SAS DATA ANALYSIS | LEVERAGING PROC SQL | PROC MEANS | PROC TIMESERIES FOR COMPREHENSIVE INSIGHTS 

/*Creating a unique dataset centered around Mobile App Usage Analytics*/

/*Dataset: Mobile App Usage Analytics*/

User_ID: Unique identifier for each user.

Session_ID: Unique identifier for each session.

Session_Start: Timestamp when the session began.

Session_End: Timestamp when the session ended.

Pages_Viewed: Number of pages/screens viewed during the session.

Actions_Performed: Number of actions (clicks, swipes, etc.) performed.

Device_Type: Type of device used (e.g., Android, iOS).

App_Version: Version of the app used during the session.

Location: Geographical location of the user during the session.

In_App_Purchase: Indicator if an in-app purchase was made (Yes/No).


/*Creating the Dataset*/

data app_usage;

    format Session_Start Session_End datetime20.;

    call streaminit(123); /* Initialize the random number stream with a seed */

    Session_ID = 0;

    do User_ID = 1 to 20;

        Session_ID + 1;

        Session_Start = datetime() - ceil(rand("Uniform")*30*24*60*60);

        Session_End = Session_Start + ceil(rand("Uniform")*3600);

        Pages_Viewed = ceil(rand("Uniform")*10);

        Actions_Performed = ceil(rand("Uniform")*20);

        Device_Type = CHOOSEC(ceil(rand("Uniform")*2), 'Android', 'iOS');

        App_Version = CHOOSEC(ceil(rand("Uniform")*3), '1.0', '1.1', '1.2');

        Location = CHOOSEC(ceil(rand("Uniform")*3), 'Hyderabad', 'Mumbai', 'Delhi');

        In_App_Purchase = CHOOSEC(ceil(rand("Uniform")*2), 'Yes', 'No');

        output;

    end;

run;

proc print;run;

Output:

Obs Session_Start Session_End Session_ID User_ID Pages_Viewed Actions_Performed Device_Type App_Version Location In_App_Purchase
1 27AUG2015:13:48:37 27AUG2015:13:50:46 1 1 1 8 Android 1.1 Mumbai Yes
2 12SEP2015:07:48:34 12SEP2015:07:53:22 2 2 10 5 Android 1.2 Delhi No
3 03SEP2015:20:06:10 03SEP2015:20:54:48 3 3 1 9 iOS 1.0 Hyderabad Yes
4 23AUG2015:16:49:58 23AUG2015:17:34:04 4 4 10 16 iOS 1.2 Mumbai No
5 31AUG2015:21:13:45 31AUG2015:22:13:12 5 5 7 6 iOS 1.2 Delhi No
6 28AUG2015:10:13:02 28AUG2015:11:10:43 6 6 8 11 iOS 1.0 Delhi No
7 16AUG2015:10:36:47 16AUG2015:10:57:54 7 7 8 19 iOS 1.2 Delhi Yes
8 23AUG2015:09:23:38 23AUG2015:10:13:46 8 8 9 2 Android 1.0 Delhi No
9 11SEP2015:01:34:04 11SEP2015:01:52:44 9 9 4 20 iOS 1.1 Delhi No
10 24AUG2015:21:52:02 24AUG2015:22:30:48 10 10 8 4 iOS 1.1 Hyderabad No
11 17AUG2015:18:03:04 17AUG2015:18:59:36 11 11 3 20 iOS 1.0 Delhi No
12 18AUG2015:16:13:49 18AUG2015:16:23:29 12 12 4 17 iOS 1.1 Mumbai Yes
13 10SEP2015:22:31:02 10SEP2015:22:44:26 13 13 10 8 iOS 1.1 Delhi Yes
14 03SEP2015:21:35:38 03SEP2015:22:11:16 14 14 2 14 iOS 1.1 Hyderabad Yes
15 19AUG2015:10:58:32 19AUG2015:11:09:17 15 15 3 6 Android 1.1 Delhi No
16 21AUG2015:17:02:57 21AUG2015:17:20:17 16 16 3 19 Android 1.1 Mumbai No
17 21AUG2015:11:27:40 21AUG2015:12:13:38 17 17 2 10 iOS 1.2 Delhi No
18 02SEP2015:01:09:16 02SEP2015:01:49:16 18 18 2 18 iOS 1.2 Hyderabad No
19 29AUG2015:16:29:06 29AUG2015:16:35:58 19 19 4 1 iOS 1.1 Mumbai No
20 05SEP2015:18:41:28 05SEP2015:19:09:00 20 20 7 4 iOS 1.1 Delhi Yes


/*Exploring the Dataset*/

proc contents data=app_usage;

run;

Output:

                                                                  The CONTENTS Procedure

Data Set Name WORK.APP_USAGE Observations 20
Member Type DATA Variables 10
Engine V9 Indexes 0
Created 14/09/2015 00:38:04 Observation Length 848
Last Modified 14/09/2015 00:38:04 Deleted Observations 0
Protection   Compressed NO
Data Set Type   Sorted NO
Label      
Data Representation WINDOWS_64    
Encoding wlatin1 Western (Windows)    


Engine/Host Dependent Information
Data Set Page Size 69632
Number of Data Set Pages 1
First Data Page 1
Max Obs per Page 82
Obs in First Data Page 20
Number of Data Set Repairs 0
ExtendObsCounter YES
Filename C:\Users\Lenovo\AppData\Local\Temp\SAS Temporary Files\_TD11280_DESKTOP-QFAA4KV_\app_usage.sas7bdat
Release Created 9.0401M2
Host Created X64_8HOME


Alphabetic List of Variables and Attributes
# Variable Type Len Format
6 Actions_Performed Num 8  
8 App_Version Char 200  
7 Device_Type Char 200  
10 In_App_Purchase Char 200  
9 Location Char 200  
5 Pages_Viewed Num 8  
2 Session_End Num 8 DATETIME20.
3 Session_ID Num 8  
1 Session_Start Num 8 DATETIME20.
4 User_ID Num 8  


proc print data=app_usage (obs=10);

run;

Output:

Obs Session_Start Session_End Session_ID User_ID Pages_Viewed Actions_Performed Device_Type App_Version Location In_App_Purchase
1 27AUG2015:13:48:37 27AUG2015:13:50:46 1 1 1 8 Android 1.1 Mumbai Yes
2 12SEP2015:07:48:34 12SEP2015:07:53:22 2 2 10 5 Android 1.2 Delhi No
3 03SEP2015:20:06:10 03SEP2015:20:54:48 3 3 1 9 iOS 1.0 Hyderabad Yes
4 23AUG2015:16:49:58 23AUG2015:17:34:04 4 4 10 16 iOS 1.2 Mumbai No
5 31AUG2015:21:13:45 31AUG2015:22:13:12 5 5 7 6 iOS 1.2 Delhi No
6 28AUG2015:10:13:02 28AUG2015:11:10:43 6 6 8 11 iOS 1.0 Delhi No
7 16AUG2015:10:36:47 16AUG2015:10:57:54 7 7 8 19 iOS 1.2 Delhi Yes
8 23AUG2015:09:23:38 23AUG2015:10:13:46 8 8 9 2 Android 1.0 Delhi No
9 11SEP2015:01:34:04 11SEP2015:01:52:44 9 9 4 20 iOS 1.1 Delhi No
10 24AUG2015:21:52:02 24AUG2015:22:30:48 10 10 8 4 iOS 1.1 Hyderabad No


/*Descriptive Statistics*/

data app_usage;

    set app_usage;

    Session_Duration = (Session_End - Session_Start)/60; /* Duration in minutes */

run;

proc print;run;

Output:

Obs Session_Start Session_End Session_ID User_ID Pages_Viewed Actions_Performed Device_Type App_Version Location In_App_Purchase Session_Duration
1 27AUG2015:13:48:37 27AUG2015:13:50:46 1 1 1 8 Android 1.1 Mumbai Yes 2.1500
2 12SEP2015:07:48:34 12SEP2015:07:53:22 2 2 10 5 Android 1.2 Delhi No 4.8000
3 03SEP2015:20:06:10 03SEP2015:20:54:48 3 3 1 9 iOS 1.0 Hyderabad Yes 48.6333
4 23AUG2015:16:49:58 23AUG2015:17:34:04 4 4 10 16 iOS 1.2 Mumbai No 44.1000
5 31AUG2015:21:13:45 31AUG2015:22:13:12 5 5 7 6 iOS 1.2 Delhi No 59.4500
6 28AUG2015:10:13:02 28AUG2015:11:10:43 6 6 8 11 iOS 1.0 Delhi No 57.6833
7 16AUG2015:10:36:47 16AUG2015:10:57:54 7 7 8 19 iOS 1.2 Delhi Yes 21.1167
8 23AUG2015:09:23:38 23AUG2015:10:13:46 8 8 9 2 Android 1.0 Delhi No 50.1333
9 11SEP2015:01:34:04 11SEP2015:01:52:44 9 9 4 20 iOS 1.1 Delhi No 18.6667
10 24AUG2015:21:52:02 24AUG2015:22:30:48 10 10 8 4 iOS 1.1 Hyderabad No 38.7667
11 17AUG2015:18:03:04 17AUG2015:18:59:36 11 11 3 20 iOS 1.0 Delhi No 56.5333
12 18AUG2015:16:13:49 18AUG2015:16:23:29 12 12 4 17 iOS 1.1 Mumbai Yes 9.6667
13 10SEP2015:22:31:02 10SEP2015:22:44:26 13 13 10 8 iOS 1.1 Delhi Yes 13.4000
14 03SEP2015:21:35:38 03SEP2015:22:11:16 14 14 2 14 iOS 1.1 Hyderabad Yes 35.6333
15 19AUG2015:10:58:32 19AUG2015:11:09:17 15 15 3 6 Android 1.1 Delhi No 10.7500
16 21AUG2015:17:02:57 21AUG2015:17:20:17 16 16 3 19 Android 1.1 Mumbai No 17.3333
17 21AUG2015:11:27:40 21AUG2015:12:13:38 17 17 2 10 iOS 1.2 Delhi No 45.9667
18 02SEP2015:01:09:16 02SEP2015:01:49:16 18 18 2 18 iOS 1.2 Hyderabad No 40.0000
19 29AUG2015:16:29:06 29AUG2015:16:35:58 19 19 4 1 iOS 1.1 Mumbai No 6.8667
20 05SEP2015:18:41:28 05SEP2015:19:09:00 20 20 7 4 iOS 1.1 Delhi Yes 27.5333

proc means data=app_usage mean std min max;

    var Session_Duration Pages_Viewed Actions_Performed;

run;

Output:

                                                                    The MEANS Procedure

Variable Mean Std Dev Minimum Maximum
Session_Duration
Pages_Viewed
Actions_Performed
30.4591667
5.3000000
10.8500000
19.3684817
3.2297955
6.4748501
2.1500000
1.0000000
1.0000000
59.4500000
10.0000000
20.0000000


/*Frequency Analysis*/

proc freq data=app_usage;

    tables Device_Type App_Version Location In_App_Purchase;

run;

Output:

                                                                 The FREQ Procedure

Device_Type Frequency Percent Cumulative
Frequency		 Cumulative
Percent
Android 5 25.00 5 25.00
iOS 15 75.00 20 100.00


App_Version Frequency Percent Cumulative
Frequency		 Cumulative
Percent
1.0 4 20.00 4 20.00
1.1 10 50.00 14 70.00
1.2 6 30.00 20 100.00


Location Frequency Percent Cumulative
Frequency		 Cumulative
Percent
Delhi 11 55.00 11 55.00
Hyderabad 4 20.00 15 75.00
Mumbai 5 25.00 20 100.00


In_App_Purchase Frequency Percent Cumulative
Frequency		 Cumulative
Percent
No 13 65.00 13 65.00
Yes 7 35.00 20 100.00


/*Cross-tabulation*/

proc freq data=app_usage;

    tables Device_Type*In_App_Purchase / chisq;

run;

Output:

                                                                              The FREQ Procedure

Frequency
Percent
Row Pct
Col Pct
Table of Device_Type by In_App_Purchase
Device_Type In_App_Purchase
No Yes Total
Android
4
20.00
80.00
30.77
1
5.00
20.00
14.29
5
25.00
 
 
iOS
9
45.00
60.00
69.23
6
30.00
40.00
85.71
15
75.00
 
 
Total
13
65.00
7
35.00
20
100.00



Statistics for Table of Device_Type by In_App_Purchase

Statistic DF Value Prob
Chi-Square 1 0.6593 0.4168
Likelihood Ratio Chi-Square 1 0.7035 0.4016
Continuity Adj. Chi-Square 1 0.0733 0.7866
Mantel-Haenszel Chi-Square 1 0.6264 0.4287
Phi Coefficient   0.1816  
Contingency Coefficient   0.1786  
Cramer's V   0.1816  
WARNING: 50% of the cells have expected counts less
than 5. Chi-Square may not be a valid test.


Fisher's Exact Test
Cell (1,1) Frequency (F) 4
Left-sided Pr <= F 0.9170
Right-sided Pr >= F 0.4058
   
Table Probability (P) 0.3228
Two-sided Pr <= P 0.6126



Sample Size = 20

/*Time Series Analysis*/

data session_trends;

    set app_usage;

    Session_Date = datepart(Session_Start);

    format Session_Date date9.;

run;

proc print;run;

Output:

Obs Session_Start Session_End Session_ID User_ID Pages_Viewed Actions_Performed Device_Type App_Version Location In_App_Purchase Session_Duration Session_Date
1 27AUG2015:13:48:37 27AUG2015:13:50:46 1 1 1 8 Android 1.1 Mumbai Yes 2.1500 27AUG2015
2 12SEP2015:07:48:34 12SEP2015:07:53:22 2 2 10 5 Android 1.2 Delhi No 4.8000 12SEP2015
3 03SEP2015:20:06:10 03SEP2015:20:54:48 3 3 1 9 iOS 1.0 Hyderabad Yes 48.6333 03SEP2015
4 23AUG2015:16:49:58 23AUG2015:17:34:04 4 4 10 16 iOS 1.2 Mumbai No 44.1000 23AUG2015
5 31AUG2015:21:13:45 31AUG2015:22:13:12 5 5 7 6 iOS 1.2 Delhi No 59.4500 31AUG2015
6 28AUG2015:10:13:02 28AUG2015:11:10:43 6 6 8 11 iOS 1.0 Delhi No 57.6833 28AUG2015
7 16AUG2015:10:36:47 16AUG2015:10:57:54 7 7 8 19 iOS 1.2 Delhi Yes 21.1167 16AUG2015
8 23AUG2015:09:23:38 23AUG2015:10:13:46 8 8 9 2 Android 1.0 Delhi No 50.1333 23AUG2015
9 11SEP2015:01:34:04 11SEP2015:01:52:44 9 9 4 20 iOS 1.1 Delhi No 18.6667 11SEP2015
10 24AUG2015:21:52:02 24AUG2015:22:30:48 10 10 8 4 iOS 1.1 Hyderabad No 38.7667 24AUG2015
11 17AUG2015:18:03:04 17AUG2015:18:59:36 11 11 3 20 iOS 1.0 Delhi No 56.5333 17AUG2015
12 18AUG2015:16:13:49 18AUG2015:16:23:29 12 12 4 17 iOS 1.1 Mumbai Yes 9.6667 18AUG2015
13 10SEP2015:22:31:02 10SEP2015:22:44:26 13 13 10 8 iOS 1.1 Delhi Yes 13.4000 10SEP2015
14 03SEP2015:21:35:38 03SEP2015:22:11:16 14 14 2 14 iOS 1.1 Hyderabad Yes 35.6333 03SEP2015
15 19AUG2015:10:58:32 19AUG2015:11:09:17 15 15 3 6 Android 1.1 Delhi No 10.7500 19AUG2015
16 21AUG2015:17:02:57 21AUG2015:17:20:17 16 16 3 19 Android 1.1 Mumbai No 17.3333 21AUG2015
17 21AUG2015:11:27:40 21AUG2015:12:13:38 17 17 2 10 iOS 1.2 Delhi No 45.9667 21AUG2015
18 02SEP2015:01:09:16 02SEP2015:01:49:16 18 18 2 18 iOS 1.2 Hyderabad No 40.0000 02SEP2015
19 29AUG2015:16:29:06 29AUG2015:16:35:58 19 19 4 1 iOS 1.1 Mumbai No 6.8667 29AUG2015
20 05SEP2015:18:41:28 05SEP2015:19:09:00 20 20 7 4 iOS 1.1 Delhi Yes 27.5333 05SEP2015


proc sql;

    create table session_daily as

    select Session_Date,

           count(Session_ID) as Num_Sessions

    from session_trends

    group by Session_Date

    order by Session_Date;

quit;


proc timeseries data=session_daily out=trend_analysis;

    id Session_Date interval=day;

    var Num_Sessions;

run;

proc print;run;

Output:
Obs Session_Date Num_Sessions
1 16AUG2015 1
2 17AUG2015 1
3 18AUG2015 1
4 19AUG2015 1
5 20AUG2015 .
6 21AUG2015 2
7 22AUG2015 .
8 23AUG2015 2
9 24AUG2015 1
10 25AUG2015 .
11 26AUG2015 .
12 27AUG2015 1
13 28AUG2015 1
14 29AUG2015 1
15 30AUG2015 .
16 31AUG2015 1
17 01SEP2015 .
18 02SEP2015 1
19 03SEP2015 2
20 04SEP2015 .
21 05SEP2015 1
22 06SEP2015 .
23 07SEP2015 .
24 08SEP2015 .
25 09SEP2015 .
26 10SEP2015 1
27 11SEP2015 1
28 12SEP2015 1

proc sgplot data=trend_analysis;

    series x=Session_Date y=Session_ID;

    xaxis label='Date';

    yaxis label='Number of Sessions';

run;

Log:

NOTE: PROCEDURE SGPLOT used (Total process time):

      real time           2.12 seconds

      cpu time            0.43 seconds


NOTE: The column format DATE9 is replaced by an auto-generated format on the axis.

NOTE: The column format DATE9 is replaced by an auto-generated format on the axis.

NOTE: Listing image output written to SGPlot1.png.

NOTE: There were 28 observations read from the data set WORK.TREND_ANALYSIS.


/*Using PROC SQL for Aggregation*/

proc sql;

    create table user_summary as

    select User_ID,

           count(Session_ID) as Total_Sessions,

           mean(Session_Duration) as Avg_Session_Duration

    from app_usage

    group by User_ID;

quit;

proc print;run;

Output:

Obs User_ID Total_Sessions Avg_Session_Duration
1 1 1 2.1500
2 2 1 4.8000
3 3 1 48.6333
4 4 1 44.1000
5 5 1 59.4500
6 6 1 57.6833
7 7 1 21.1167
8 8 1 50.1333
9 9 1 18.6667
10 10 1 38.7667
11 11 1 56.5333
12 12 1 9.6667
13 13 1 13.4000
14 14 1 35.6333
15 15 1 10.7500
16 16 1 17.3333
17 17 1 45.9667
18 18 1 40.0000
19 19 1 6.8667
20 20 1 27.5333


/*Creating Macro Variables*/

proc sql noprint outobs=5;

    select Location, count(*) as Session_Count

    into :top_loc1 - :top_loc5

    from app_usage

    group by Location

    order by Session_Count desc;

quit;


%put &top_loc1 

Log:Delhi

%put &top_loc2

Log:Mumbai

%put  &top_loc3 

Log:Hyderabad

%put &top_loc4 

%put &top_loc5;


/*Visualization*/

proc sgplot data=app_usage;

    vbar Device_Type / response=Session_Duration stat=mean;

    yaxis label='Average Session Duration (minutes)';

    xaxis label='Device Type';

run;

Log:

NOTE: PROCEDURE SGPLOT used (Total process time):

      real time           0.51 seconds

      cpu time            0.07 seconds


NOTE: Listing image output written to SGPlot3.png.

NOTE: There were 20 observations read from the data set WORK.APP_USAGE.


/*Handling Missing Values*/

proc means data=app_usage n nmiss;

    var Session_Duration Pages_Viewed Actions_Performed;

run;

Output:

                                                               The MEANS Procedure

Variable N N Miss
Session_Duration
Pages_Viewed
Actions_Performed
20
20
20
0
0
0

data app_usage_clean;

    set app_usage;

    if cmiss(of Session_Duration Pages_Viewed Actions_Performed) = 0;

run;

proc print;run;

Output:

Obs Session_Start Session_End Session_ID User_ID Pages_Viewed Actions_Performed Device_Type App_Version Location In_App_Purchase Session_Duration
1 27AUG2015:13:20:53 27AUG2015:13:23:02 1 1 1 8 Android 1.1 Mumbai Yes 2.1500
2 12SEP2015:07:20:50 12SEP2015:07:25:38 2 2 10 5 Android 1.2 Delhi No 4.8000
3 03SEP2015:19:38:26 03SEP2015:20:27:04 3 3 1 9 iOS 1.0 Hyderabad Yes 48.6333
4 23AUG2015:16:22:14 23AUG2015:17:06:20 4 4 10 16 iOS 1.2 Mumbai No 44.1000
5 31AUG2015:20:46:01 31AUG2015:21:45:28 5 5 7 6 iOS 1.2 Delhi No 59.4500
6 28AUG2015:09:45:18 28AUG2015:10:42:59 6 6 8 11 iOS 1.0 Delhi No 57.6833
7 16AUG2015:10:09:03 16AUG2015:10:30:10 7 7 8 19 iOS 1.2 Delhi Yes 21.1167
8 23AUG2015:08:55:54 23AUG2015:09:46:02 8 8 9 2 Android 1.0 Delhi No 50.1333
9 11SEP2015:01:06:20 11SEP2015:01:25:00 9 9 4 20 iOS 1.1 Delhi No 18.6667
10 24AUG2015:21:24:18 24AUG2015:22:03:04 10 10 8 4 iOS 1.1 Hyderabad No 38.7667
11 17AUG2015:17:35:20 17AUG2015:18:31:52 11 11 3 20 iOS 1.0 Delhi No 56.5333
12 18AUG2015:15:46:05 18AUG2015:15:55:45 12 12 4 17 iOS 1.1 Mumbai Yes 9.6667
13 10SEP2015:22:03:18 10SEP2015:22:16:42 13 13 10 8 iOS 1.1 Delhi Yes 13.4000
14 03SEP2015:21:07:54 03SEP2015:21:43:32 14 14 2 14 iOS 1.1 Hyderabad Yes 35.6333
15 19AUG2015:10:30:48 19AUG2015:10:41:33 15 15 3 6 Android 1.1 Delhi No 10.7500
16 21AUG2015:16:35:13 21AUG2015:16:52:33 16 16 3 19 Android 1.1 Mumbai No 17.3333
17 21AUG2015:10:59:56 21AUG2015:11:45:54 17 17 2 10 iOS 1.2 Delhi No 45.9667
18 02SEP2015:00:41:32 02SEP2015:01:21:32 18 18 2 18 iOS 1.2 Hyderabad No 40.0000
19 29AUG2015:16:01:22 29AUG2015:16:08:14 19 19 4 1 iOS 1.1 Mumbai No 6.8667
20 05SEP2015:18:13:44 05SEP2015:18:41:16 20 20 7 4 iOS 1.1 Delhi Yes 27.5333


/*Creating Derived Variables*/

data app_usage;

    set app_usage;

    length Session_Length $6;

    if Session_Duration < 5 then Session_Length = 'Short';

    else if Session_Duration < 15 then Session_Length = 'Medium';

    else Session_Length = 'Long';

run;

proc print;run;

Output:

Obs Session_Start Session_End Session_ID User_ID Pages_Viewed Actions_Performed Device_Type App_Version Location In_App_Purchase Session_Duration Session_Length
1 27AUG2015:13:20:53 27AUG2015:13:23:02 1 1 1 8 Android 1.1 Mumbai Yes 2.1500 Short
2 12SEP2015:07:20:50 12SEP2015:07:25:38 2 2 10 5 Android 1.2 Delhi No 4.8000 Short
3 03SEP2015:19:38:26 03SEP2015:20:27:04 3 3 1 9 iOS 1.0 Hyderabad Yes 48.6333 Long
4 23AUG2015:16:22:14 23AUG2015:17:06:20 4 4 10 16 iOS 1.2 Mumbai No 44.1000 Long
5 31AUG2015:20:46:01 31AUG2015:21:45:28 5 5 7 6 iOS 1.2 Delhi No 59.4500 Long
6 28AUG2015:09:45:18 28AUG2015:10:42:59 6 6 8 11 iOS 1.0 Delhi No 57.6833 Long
7 16AUG2015:10:09:03 16AUG2015:10:30:10 7 7 8 19 iOS 1.2 Delhi Yes 21.1167 Long
8 23AUG2015:08:55:54 23AUG2015:09:46:02 8 8 9 2 Android 1.0 Delhi No 50.1333 Long
9 11SEP2015:01:06:20 11SEP2015:01:25:00 9 9 4 20 iOS 1.1 Delhi No 18.6667 Long
10 24AUG2015:21:24:18 24AUG2015:22:03:04 10 10 8 4 iOS 1.1 Hyderabad No 38.7667 Long
11 17AUG2015:17:35:20 17AUG2015:18:31:52 11 11 3 20 iOS 1.0 Delhi No 56.5333 Long
12 18AUG2015:15:46:05 18AUG2015:15:55:45 12 12 4 17 iOS 1.1 Mumbai Yes 9.6667 Medium
13 10SEP2015:22:03:18 10SEP2015:22:16:42 13 13 10 8 iOS 1.1 Delhi Yes 13.4000 Medium
14 03SEP2015:21:07:54 03SEP2015:21:43:32 14 14 2 14 iOS 1.1 Hyderabad Yes 35.6333 Long
15 19AUG2015:10:30:48 19AUG2015:10:41:33 15 15 3 6 Android 1.1 Delhi No 10.7500 Medium
16 21AUG2015:16:35:13 21AUG2015:16:52:33 16 16 3 19 Android 1.1 Mumbai No 17.3333 Long
17 21AUG2015:10:59:56 21AUG2015:11:45:54 17 17 2 10 iOS 1.2 Delhi No 45.9667 Long
18 02SEP2015:00:41:32 02SEP2015:01:21:32 18 18 2 18 iOS 1.2 Hyderabad No 40.0000 Long
19 29AUG2015:16:01:22 29AUG2015:16:08:14 19 19 4 1 iOS 1.1 Mumbai No 6.8667 Medium
20 05SEP2015:18:13:44 05SEP2015:18:41:16 20 20 7 4 iOS 1.1 Delhi Yes 27.5333 Long

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