Analysing the use of my shiny apps

This summarises the use of my shiny apps (at https://shiny.psyctc.org/). The analyses will evolve a bit through 2024 as, I hope, the level of use increases.

Current data

Info	Value
First date in data	2024-02-07
Last date in data	2025-01-21
This analysis time/date	03:13 on 21/01/2025
Number of days spanned	349
Total number of sessions	3313
Mean sessions per day	9.49

I am not using any way to separate different users and session is per app, so if someone used multiple apps during one visit to the server, each app used is counts as a separate session.

App uses per day

Here’s the plot of uses per day.

That shows one large burst of use after the apps were publicised through the Systemic Research Centre Email list (5.iii.24) and a smaller one after a posting to the IDANET list (9.iii.24). There are later bursts that I can’t directly ascribe to any publicity.

Sessions per week

More sensibly, here is the plot by week, actually plotting the sessions per day and counting from the launch from the launch on 7.ii.24. Where the last week is still an incomplete week that has been taken into account in the calculations. 95% CIs are Poisson model estimates.

Breaking that down by app gives me this.

And facetting by app gives this.

Sessions per Month

The first month was incomplete and the last month will usually be incomplete, that is taken into consideration in computing these session per day rates.

Numbers of sessions per app

Here’s the number of times each app has been used during that period.

App	Sessions	First used	Days available	Sessions per day	Days used	% days used
RCI1	1,364	2024-02-07	349	3.908	260	74%
CSC1	543	2024-02-07	349	1.556	201	58%
COREpapers1	206	2024-05-11	255	0.808	92	36%
Cronbach1Feldt	150	2024-02-07	349	0.430	96	28%
RCI2	149	2024-02-07	349	0.427	76	22%
CORE-OM_scoring	120	2024-04-16	280	0.429	73	26%
Gaussian1	100	2024-03-05	322	0.311	73	23%
CIcorrelation	99	2024-02-07	349	0.284	59	17%
ECDFplot	83	2024-02-07	349	0.238	31	9%
CSClookup2a	52	2024-02-07	349	0.149	29	8%
CImean	50	2024-02-07	349	0.143	41	12%
CIproportion	43	2024-02-07	349	0.123	35	10%
Spearman-Brown	42	2024-05-03	263	0.160	26	10%
Histogram_and_summary1	40	2024-03-25	302	0.132	21	7%
plotCIPearson	32	2024-02-07	349	0.092	25	7%
CISpearman	29	2024-02-07	349	0.083	25	7%
CIdiff2proportions	24	2024-02-07	349	0.069	15	4%
Create_univariate_data	23	2024-04-09	287	0.080	22	8%
Bonferroni1	21	2024-03-24	303	0.069	16	5%
g_from_d_and_n	21	2024-02-07	349	0.060	19	5%
CISD	20	2024-02-07	349	0.057	19	5%
Screening1	20	2024-02-07	349	0.057	15	4%
Attenuation2	19	2024-10-11	102	0.186	16	16%
Attenuation	17	2024-10-09	104	0.163	13	12%
Feldt2	17	2024-11-27	55	0.309	14	25%
random1	16	2024-11-19	63	0.254	14	22%
getCorrectedR	13	2024-10-13	100	0.130	9	9%

The columns of Sessions per day and of Percentage days used are rather misleading as different apps have been available for very different numbers of days. I won’t be able to get a less misleading forest plot of the mean usage per day per app until there has been far more usage than we have had so far so I will maybe add that later in the year.

However, I can get confidence intervals for proportions on what usage we already have so here’s a less misleading forest plot of proportion of the available days on which each app was used. The dotted reference line marks the overall usage as a proportion of days available across all the apps.

Here’s a map of usage per app against dates. The sizes of the points show how many times the app was used on that day. The y axis sorts by first date used and then by descending total number of times used.

That shows that many of the apps were first used on the same day (7.ii.2024) which was the day I set up this logging. I tested all the then existing apps that day so all appear on that day.

Breakdown by day of the week

Weekday	n	percent
Mon	5,024	18%
Tue	5,258	19%
Wed	5,017	18%
Thu	4,148	15%
Fri	4,078	14%
Sat	1,941	7%
Sun	2,771	10%

Same sorted!

Weekday	n	percent
Tue	5,258	19%
Mon	5,024	18%
Wed	5,017	18%
Thu	4,148	15%
Fri	4,078	14%
Sun	2,771	10%
Sat	1,941	7%

Time of day

I’ve broken this down by hour. The server is to some extent protected behind a proxy at my ISP which is good for forcing https access but it does mean that I don’t know where people come from so this is all UMT (i.e. old “GMT”: internet time). I think it also suggests, assuming that most accesses are during working hours, that most visitors/users are coming to the site from Europe or the Americas.

Hour	n	percent
0	27	1%
1	27	1%
2	25	1%
3	27	1%
4	95	3%
5	168	5%
6	218	7%
7	172	5%
8	259	8%
9	212	6%
10	175	5%
11	170	5%
12	209	6%
13	192	6%
14	247	7%
15	183	6%
16	145	4%
17	175	5%
18	122	4%
19	104	3%
20	139	4%
21	116	4%
22	63	2%
23	43	1%

Same sorted.

Hour	n	percent
8	259	8%
14	247	7%
6	218	7%
9	212	6%
12	209	6%
13	192	6%
15	183	6%
10	175	5%
17	175	5%
7	172	5%
11	170	5%
5	168	5%
16	145	4%
20	139	4%
18	122	4%
21	116	4%
19	104	3%
4	95	3%
22	63	2%
23	43	1%
0	27	1%
1	27	1%
3	27	1%
2	25	1%

Browsers

For what little it’s worth, here are the browser IDs picked up by shiny (in descending order of frequency).

The value of “ahrefs.com/robot/” is my translation of accesses that identify their browser as: “Netscape 5.0 (compatible; AhrefsBot/7.0; +http://ahrefs.com/robot/) -?”.

For reasons I don’t understand, my open source shiny does not seem to detect Microsoft Edge. I have used the apps with Edge (ugh) and it didn’t show up here. If you know why, or even how to detect Edge, do tell me (https://www.psyctc.org/psyctc/contact-me/)!

Browser	n
Chrome	2,115
Firefox	925
Safari	231
Other	22
Opera	6

The “Other” there refers to visits from browsers not identifying as one of Chrome, Firefox, Opera or Safari. These are usually or always crawlers, the breakdown of them was as follows.

Browser2	n
http://ahrefs.com/robot/	19
https://developers.facebook.com/docs/sharing/webmasters/crawler	3

I am a little bit interested in when these crawlers come and go so …

Browser2	firstSeen	lastSeen
http://ahrefs.com/robot/	2024-11-30	2025-01-16
https://developers.facebook.com/docs/sharing/webmasters/crawler	2025-01-08	2025-01-09

This shows the map against time, size shows number per day.

For what it’s worth, here are the numbers per day.

Other browser	date	nPerDay
http://ahrefs.com/robot/
	2024-11-30	1
	2024-12-01	1
	2024-12-13	2
	2024-12-14	5
	2024-12-18	1
	2024-12-24	1
	2024-12-31	1
	2025-01-01	2
	2025-01-05	1
	2025-01-11	1
	2025-01-15	2
	2025-01-16	1
https://developers.facebook.com/docs/sharing/webmasters/crawler
	2025-01-08	1
	2025-01-09	2

Browser versions

I can’t think it matters but here is the breakdown with the version numbers as well as the browser name.

Browser	n
Chrome 131	383
Chrome 130	312
Chrome 129	266
Chrome 128	194
Chrome 125	161
Chrome 126	145
Firefox 125	132
Chrome 127	125
Firefox 131	120
Safari 17	118
Chrome 124	111
Firefox 133	102
Firefox 130	89
Firefox 132	85
Chrome 122	76
Firefox 129	74
Firefox 128	70
Firefox 124	69
Chrome 101	67
Chrome 123	67
Safari 18	47
Firefox 123	44
Firefox 126	44
Chrome 86	38
Safari 16	36
Firefox 122	34
Firefox 127	34
Chrome 103	28
Chrome 100	27
Chrome 121	26
Chrome 104	21
Chrome 102	17
Chrome 119	16
Safari 604	16
Firefox 134	11
Safari 15	11
Firefox 119	9
Chrome 120	6
Firefox 115	6
Chrome 112	5
Chrome 106	4
Chrome 109	3
Safari 14	3
Chrome 107	2
Chrome 114	2
Chrome 116	2
Chrome 117	2
Chrome 132	2
Chrome 79	2
Firefox 102	2
Opera 109	2
Opera 113	2
Chrome 110	1
Chrome 111	1
Chrome 115	1
Chrome 4	1
Chrome 94	1
Opera 114	1
Opera 115	1

Durations of sessions

A bit more interesting is the durations of the sessions.
Some sessions don’t have a recorded termination time, currently that’s true for 817, i.e. 24.7% of the sessions. This could include occasional session still active at the time at which the copy of the database was pulled. However, I think most will be where someone leaves the session open. I have capped the sessions at one hour in the analyses below.

Here are the descriptive statistics.

name	nNA	nOK	min	lqrt	mean	uqrt	max
durMinsAll	817	2,496	0.0	1.0	76.0	39.0	2,861.0
durMinsCapped	817	2,496	0.0	1.0	19.9	39.0	60.0
durMinsCensored	1,333	1,980	0.0	1.0	9.4	16.0	60.0

durMinsAll includes all the sessions so far, durMinsCapped treats all sessions recorded as lasting 60 minutes as such, more realistically, durMinsCensored ignores those sessions assuming that they were abandoned sessions. (This shows a maximum duration of 60 minutes as session durations were measured to a fraction of a second so any duration of over 59’30" and less than 60’0" is rounded up to 60 minutes and counted as a genuine 60 minutes!).

Most of the sessions, as you would expect given the nature of the apps, are sessions lasting only a few minutes. If I use the censoring and ignore all the sessions that lasted more than an hour on the plausible assumption that they were abandoned sessions rather than someone continuing to try different parameters for any app for more than an hour then there have been 1980 such sessions so far. Of these 40 lasted under a minute. I guess it’s possible to launch an app and get useful output if only wanting the default parameters in under a minute but I think it would be rare so I think we can regard these as “just looking” sessions and they represent 2% of the 1980 uncensored sessions.

The number of sessions lasting a minute (rounding to the nearest minute) was 912, i.e. 46.1% of the uncensored sessions. I think these probably represent very quick but perhaps genuine uses of an app.

That leaves 1028 sessions lasting longer than a minute but less than an hour i.e. 51.9% of the uncensored sessions, I think these can be regarded as sessions in which someone entered parameters and perhaps played around with different parameters and perhaps noted or pulled down outputs.

For now (August 2024) I see those as pretty sensible breakdown proportions. I guess that as time goes by it may be interesting to break things down by months and by apps but for now the numbers don’t really merit that and the effects of different apps being added at different times mean that the two variables of app and month are structurally entwined.

Values input

Where it might be useful to me to know more about the usage I am logging input values for some apps. Here’s the breakdown of the numbers of sessions in which inputs were recorded.

app_name	n	percent
COREpapers1	6,562	38.4%
RCI1	5,479	32.1%
CSC1	2,712	15.9%
RCI2	489	2.9%
CImean	445	2.6%
CORE-OM_scoring	225	1.3%
Cronbach1Feldt	168	1.0%
CSClookup2a	156	0.9%
random1	146	0.9%
ECDFplot	139	0.8%
Spearman-Brown	136	0.8%
Histogram_and_summary1	135	0.8%
Create_univariate_data	101	0.6%
CIcorrelation	72	0.4%
CISpearman	43	0.3%
Gaussian1	27	0.2%
CIproportion	18	0.1%
Feldt2	12	0.1%
Screening1	7	0.0%
CISD	6	0.0%
plotCIPearson	4	0.0%
g_from_d_and_n	3	0.0%
Attenuation2	2	0.0%
CIdiff2proportions	2	0.0%

And here are the variables by app, nVisits is the total number of sessions with recorded inputs for that app, nVars is the number of variables that have been input for that app. Finally, nVals is the number of distinct values that have been input for that variable.

app_name	id	nVisits	nVars	nVals
COREpapers1
	authName	6,562	58	102
	clipbtn	6,562	58	1
	date1	6,562	58	45
	date2	6,562	58	31
	embedded	6,562	58	17
	filterAssStructure	6,562	58	18
	filterCORElanguages	6,562	58	15
	filterCOREmeasures	6,562	58	26
	filterFormats	6,562	58	17
	filterGenderCats	6,562	58	9
	mainPlotDownload-filename	6,562	58	3
	mainPlotDownload-format	6,562	58	1
	or	6,562	58	6
	or2	6,562	58	3
	or3	6,562	58	4
	or4	6,562	58	3
	or5	6,562	58	4
	otherMeasure	6,562	58	30
	otherMeasures_cell_clicked	6,562	58	19
	otherMeasures_cells_selected	6,562	58	12
	otherMeasures_columns_selected	6,562	58	12
	otherMeasures_row_last_clicked	6,562	58	5
	otherMeasures_rows_all	6,562	58	61
	otherMeasures_rows_current	6,562	58	60
	otherMeasures_rows_selected	6,562	58	22
	otherMeasures_search	6,562	58	29
	otherMeasures_state	6,562	58	64
	paperLang	6,562	58	23
	papers2_cell_clicked	6,562	58	41
	papers2_cells_selected	6,562	58	16
	papers2_columns_selected	6,562	58	16
	papers2_row_last_clicked	6,562	58	8
	papers2_rows_all	6,562	58	96
	papers2_rows_current	6,562	58	96
	papers2_rows_selected	6,562	58	38
	papers2_search	6,562	58	45
	papers2_state	6,562	58	103
	papers_cell_clicked	6,562	58	237
	papers_cells_selected	6,562	58	198
	papers_columns_selected	6,562	58	198
	papers_row_last_clicked	6,562	58	30
	papers_rows_all	6,562	58	1,277
	papers_rows_current	6,562	58	1,305
	papers_rows_selected	6,562	58	282
	papers_search	6,562	58	249
	papers_state	6,562	58	1,326
	reqEmpCOREdata	6,562	58	37
	reqOA	6,562	58	12
	reqOpenData	6,562	58	13
	reset_input	6,562	58	9
	shinyjs-resettable-side-panel	6,562	58	7
	tabSelected	6,562	58	117
	therOrGen	6,562	58	49
	vecAssStructure	6,562	58	29
	vecCORElanguages	6,562	58	7
	vecFormats	6,562	58	19
	vecGenderCats	6,562	58	8
	vecWhichCOREused	6,562	58	52
RCI1
	SD	5,479	8	2,180
	ci	5,479	8	225
	compute	5,479	8	1,374
	dp	5,479	8	76
	generate	5,479	8	5
	max	5,479	8	2
	min	5,479	8	1
	rel	5,479	8	1,616
CSC1
	SDHS	2,712	7	496
	SDNHS	2,712	7	511
	dp	2,712	7	31
	maxPoss	2,712	7	371
	meanHS	2,712	7	542
	meanNHS	2,712	7	631
	minPoss	2,712	7	130
RCI2
	SD	489	6	115
	ci	489	6	23
	compute	489	6	129
	dp	489	6	8
	n	489	6	94
	rel	489	6	120
CImean
	SD	445	5	196
	SE	445	5	1
	dp	445	5	2
	mean	445	5	192
	n	445	5	54
CORE-OM_scoring
	compData_cell_clicked	225	27	5
	compData_cells_selected	225	27	5
	compData_columns_selected	225	27	5
	compData_rows_all	225	27	6
	compData_rows_current	225	27	12
	compData_rows_selected	225	27	5
	compData_search	225	27	5
	compData_state	225	27	18
	contents_cell_clicked	225	27	2
	contents_cells_selected	225	27	2
	contents_columns_selected	225	27	2
	contents_rows_all	225	27	4
	contents_rows_current	225	27	4
	contents_rows_selected	225	27	2
	contents_search	225	27	2
	contents_state	225	27	4
	dp	225	27	15
	file1	225	27	17
	summary_cell_clicked	225	27	1
	summary_cells_selected	225	27	1
	summary_columns_selected	225	27	1
	summary_rows_all	225	27	1
	summary_rows_current	225	27	1
	summary_rows_selected	225	27	1
	summary_search	225	27	1
	summary_state	225	27	1
	tabSelected	225	27	102
Cronbach1Feldt
	alpha	168	5	91
	ci	168	5	2
	dp	168	5	4
	k	168	5	38
	n	168	5	33
CSClookup2a
	Age	156	5	2
	Gender	156	5	4
	Lookup	156	5	21
	Scoring	156	5	17
	YPscore	156	5	112
random1
	compute	146	11	12
	dataTable_cell_clicked	146	11	11
	dataTable_cells_selected	146	11	11
	dataTable_columns_selected	146	11	11
	dataTable_rows_all	146	11	24
	dataTable_rows_current	146	11	24
	dataTable_rows_selected	146	11	11
	dataTable_search	146	11	11
	dataTable_state	146	11	24
	valN	146	11	6
	valSeed	146	11	1
ECDFplot
	annotationSize	139	32	6
	fileHeight	139	32	6
	fileHeightQuantiles	139	32	2
	fileWidth	139	32	6
	fileWidthQuantiles	139	32	2
	inputType	139	32	10
	pastedData	139	32	5
	summary_cell_clicked	139	32	2
	summary_cells_selected	139	32	2
	summary_columns_selected	139	32	2
	summary_rows_all	139	32	4
	summary_rows_current	139	32	4
	summary_rows_selected	139	32	2
	summary_search	139	32	2
	summary_state	139	32	4
	tabSelected	139	32	26
	textSize	139	32	6
	textSizeQuantiles	139	32	2
	tibQuantiles_cell_clicked	139	32	2
	tibQuantiles_cells_selected	139	32	2
	tibQuantiles_columns_selected	139	32	2
	tibQuantiles_rows_all	139	32	4
	tibQuantiles_rows_current	139	32	4
	tibQuantiles_rows_selected	139	32	2
	tibQuantiles_search	139	32	2
	tibQuantiles_state	139	32	4
	title	139	32	6
	titleQuantiles	139	32	2
	xLab	139	32	6
	xLabQuantiles	139	32	2
	yLab	139	32	6
	yLabQuantiles	139	32	2
Spearman-Brown
	currK	136	13	9
	currRel	136	13	11
	maxK	136	13	6
	plotDownload-filename	136	13	1
	reliabilities_cell_clicked	136	13	5
	reliabilities_cells_selected	136	13	5
	reliabilities_columns_selected	136	13	5
	reliabilities_rows_all	136	13	24
	reliabilities_rows_current	136	13	26
	reliabilities_rows_selected	136	13	5
	reliabilities_search	136	13	5
	reliabilities_state	136	13	26
	step	136	13	8
Histogram_and_summary1
	bins	135	24	7
	contents_cell_clicked	135	24	4
	contents_cells_selected	135	24	4
	contents_columns_selected	135	24	4
	contents_rows_all	135	24	8
	contents_rows_current	135	24	8
	contents_rows_selected	135	24	4
	contents_search	135	24	4
	contents_state	135	24	8
	dataType	135	24	6
	file1	135	24	8
	plotDownload-format	135	24	1
	summary_cell_clicked	135	24	3
	summary_cells_selected	135	24	3
	summary_columns_selected	135	24	3
	summary_rows_all	135	24	6
	summary_rows_current	135	24	6
	summary_rows_selected	135	24	3
	summary_search	135	24	3
	summary_state	135	24	6
	title	135	24	8
	var	135	24	9
	xLab	135	24	9
	yLab	135	24	10
Create_univariate_data
	charSeparator	101	11	17
	dataTable_cell_clicked	101	11	6
	dataTable_cells_selected	101	11	6
	dataTable_columns_selected	101	11	6
	dataTable_rows_all	101	11	12
	dataTable_rows_current	101	11	12
	dataTable_rows_selected	101	11	6
	dataTable_search	101	11	6
	dataTable_state	101	11	12
	dist	101	11	2
	generate	101	11	16
CIcorrelation
	R	72	4	37
	ci	72	4	3
	dp	72	4	2
	n	72	4	30
CISpearman
	Gaussian	43	6	2
	ci	43	6	2
	dp	43	6	3
	method	43	6	6
	n	43	6	16
	rs	43	6	14
Gaussian1
	dp	27	4	2
	mean	27	4	20
	n	27	4	4
	nBins	27	4	1
CIproportion
	ci	18	4	1
	dp	18	4	2
	n	18	4	6
	x	18	4	9
Feldt2
	alpha1	12	5	1
	alpha2	12	5	3
	dp	12	5	3
	n1	12	5	3
	n2	12	5	2
Screening1
	prev	7	2	5
	spec	7	2	2
CISD
	SD	6	4	1
	SDorVar	6	4	3
	ci	6	4	1
	n	6	4	1
plotCIPearson
	R	4	1	4
g_from_d_and_n
	d	3	2	2
	n	3	2	1
Attenuation2
	unattR	2	1	2
CIdiff2proportions
	n1	2	1	2

So far nVars is a fixed number for each app as it’s going to be maximum number of input values the app requests from the user. Some apps, e.g. RCI1, have a variable “compute” that is just the button instructing the app to run which wasn’t present in early iterations of the app. Another change is that as I get more savvy about shiny some apps, perhaps existing ones, may develop a step-by-step interface so that the numbers of variables input for each use of the app may differ a bit depending on what the user has chosen to do.

Inputs for the RCI1 app

It becomes a bit messy to analyse the inputs as it has to be done (as far as I can currently see) individually by app. It was quite useful as I could see that it had, at least at some point, been possible to enter impossible zero values for reliability and SD. I have now filtered those values out.

Here’s a breakdown for RCI1. These counts only include values that the user entered manually so if the user just left the value at the default value that isn’t counted (however, if the user changes it and then back to the default value, that entry of the default value is counted). I guess I could fix that by filling in the default value where a variable doesn’t appear in the inputs for the session. I’m not sure that’s sufficiently interesting to be worth the faff.

I guess that the .7 entry for the CI was probably me checking the app worked even for that value but I can’t remember for sure. Otherwise it seems entirely sensible that the only other non-default value was .9. The spread of the reliability values is more interesting and looks sensible to me, similarly for the SD.

I guess I could make the app a more interesting information gathering tools if I invited users to input the scale/score being used (i.e. “CORE-OM total”, “BDI-II total”) and even perhaps also ask about dataset (e.g. “my last six months baseline values”, or “the Sheffield X study”) but I think the amount of post-processing that would be necessary to get anything even halfway clean out of that seems unlikely to make this worth the programming/cleaning hassle.

Version history

• Version 14. 15.i.2025 Cosmetic tweaks.
• Version 13. 14.i.2025 Corrected date order in the “Sessions per Month” section.
• Version 12. 12.i.2025 Small tweaks, only cosmetic really.
• Version 11. 5.i.2025 Tweaked browser detection to replace “Netscape 5.0 (compatible; AhrefsBot/7.0; +http://ahrefs.com/robot/) -?” with “ahrefs.com/robot/” for tidier tabulation!
• Version 10. 3.i.2025 Corrected monthly breakdown (wasn’t allowing for different years!)
• Version 9. 20.viii.2024 Tweaks to add histogram of browsers and descriptive statistics for durations of visits.
• Version 8. 2.vii.2024 Corrected mess with rate per day for latest week and first and last months.
• Version 7. 25.vi.2024 Order of sections made more logical, breakdowns by week and month added, polar plot for day in week.
• Version 6. 8.iv.2024 Fixed mess up arising from an error in Create_univariate_data telemetry.
• Version 5. 1.iv.2024 Tweaks only.
• Version 4. 24.iii.2024 Quite a lot of changes including better aesthetics, time map of use and first analyses of inputs.
• Version 3. 6.iii.2024
  
• Version 2. 16.ii.2024
  
• Version 1. Was probably 14.ii.2024