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	2025-05-18
This analysis time/date	03:13 on 18/05/2025
Number of days spanned	466
Total number of sessions	4959
Mean sessions per day	10.64

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	2,189	2024-02-07	466	4.697	372	80%
CSC1	785	2024-02-07	466	1.685	276	59%
COREpapers1	284	2024-05-11	372	0.763	139	37%
RCI2	262	2024-02-07	466	0.562	137	29%
CORE-OM_scoring	190	2024-04-16	397	0.479	117	29%
Cronbach1Feldt	182	2024-02-07	466	0.391	124	27%
CIcorrelation	109	2024-02-07	466	0.234	66	14%
Gaussian1	106	2024-03-05	439	0.241	76	17%
ECDFplot	99	2024-02-07	466	0.212	41	9%
Spearman-Brown	72	2024-05-03	380	0.189	51	13%
CSClookup2a	66	2024-02-07	466	0.142	41	9%
CIproportion	61	2024-02-07	466	0.131	49	11%
CImean	56	2024-02-07	466	0.120	46	10%
CISpearman	50	2024-02-07	466	0.107	44	9%
random1	45	2024-11-19	180	0.250	39	22%
Histogram_and_summary1	42	2024-03-25	419	0.100	22	5%
plotCIPearson	40	2024-02-07	466	0.086	28	6%
Feldt2	39	2024-11-27	172	0.227	29	17%
Bonferroni1	35	2024-03-24	420	0.083	23	5%
CIdiff2proportions	34	2024-02-07	466	0.073	19	4%
Attenuation	33	2024-10-09	221	0.149	23	10%
Screening1	30	2024-02-07	466	0.064	24	5%
g_from_d_and_n	30	2024-02-07	466	0.064	28	6%
CISD	29	2024-02-07	466	0.062	26	6%
Attenuation2	26	2024-10-11	219	0.119	23	10%
Create_univariate_data	24	2024-04-09	404	0.059	23	6%
getCorrectedR	18	2024-10-13	217	0.083	14	6%
Hashing_IDs	12	2025-04-05	43	0.279	8	19%
useConvFiveNum	11	2025-04-07	41	0.268	10	24%

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	7,149	17%
Tue	7,825	18%
Wed	6,839	16%
Thu	6,118	14%
Fri	6,593	15%
Sat	3,844	9%
Sun	4,196	10%

Same sorted!

Weekday	n	percent
Tue	7,825	18%
Mon	7,149	17%
Wed	6,839	16%
Fri	6,593	15%
Thu	6,118	14%
Sun	4,196	10%
Sat	3,844	9%

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	51	1%
1	48	1%
2	49	1%
3	47	1%
4	136	3%
5	233	5%
6	317	6%
7	266	5%
8	328	7%
9	306	6%
10	292	6%
11	264	5%
12	301	6%
13	312	6%
14	361	7%
15	290	6%
16	236	5%
17	236	5%
18	178	4%
19	170	3%
20	207	4%
21	163	3%
22	98	2%
23	70	1%

Same sorted.

Hour	n	percent
14	361	7%
8	328	7%
6	317	6%
13	312	6%
9	306	6%
12	301	6%
10	292	6%
15	290	6%
7	266	5%
11	264	5%
16	236	5%
17	236	5%
5	233	5%
20	207	4%
18	178	4%
19	170	3%
21	163	3%
4	136	3%
22	98	2%
23	70	1%
0	51	1%
2	49	1%
1	48	1%
3	47	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	3,455
Firefox	1,013
Safari	400
Other	50
Opera	25

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/	44
https://developers.facebook.com/docs/sharing/webmasters/crawler	4
Netscape.0 (iPhone; CPU iPhone OS 18_1_1 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Mobile/15E148 [FBAN/FBIOS;FBAV/493.0.0.43.104;FBBV/693509315;FBDV/iPhone14,5;FBMD/iPhone;FBSN/iOS;FBSV/18.1.1;FBSS/3;FBCR/;FBID/phone;FBLC/sv_SE;FBOP/80] -?	2

Renaming “N”etscape.0 (iPhone; CPU iPhone OS 18_1_1 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Mobile/15E148 [FBAN/FBIOS;FBAV/493.0.0.43.104;FBBV/693509315;FBDV/iPhone14,5;FBMD/iPhone;FBSN/iOS;FBSV/18.1.1;FBSS/3;FBCR/;FBID/phone;FBLC/sv_SE;FBOP/80] -?" to “Netscape.0 (iPhone; CPU iPhone OS 18_1_1 like Mac OS X)” makes things more readable.

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

Browser2	firstSeen	lastSeen
http://ahrefs.com/robot/	2024-11-30	2025-05-17
https://developers.facebook.com/docs/sharing/webmasters/crawler	2025-01-08	2025-04-23
Netscape.0 (iPhone; CPU iPhone OS 18_1_1 like Mac OS X)	2025-02-10	2025-02-10

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
Netscape.0 (iPhone; CPU iPhone OS 18_1_1 like Mac OS X)
	2025-02-10	2
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
	2025-01-21	1
	2025-01-26	1
	2025-01-31	1
	2025-02-02	1
	2025-02-06	1
	2025-02-11	1
	2025-02-14	1
	2025-02-16	1
	2025-03-06	1
	2025-03-18	1
	2025-03-20	1
	2025-03-21	1
	2025-03-24	1
	2025-03-29	1
	2025-04-06	1
	2025-04-08	1
	2025-04-13	1
	2025-04-18	1
	2025-04-22	1
	2025-04-23	1
	2025-04-24	1
	2025-04-28	1
	2025-05-12	2
	2025-05-17	1
https://developers.facebook.com/docs/sharing/webmasters/crawler
	2025-01-08	1
	2025-01-09	2
	2025-04-23	1

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	454
Chrome 130	338
Chrome 134	323
Chrome 132	287
Chrome 129	271
Chrome 135	262
Chrome 128	200
Chrome 133	199
Chrome 125	194
Safari 18	168
Chrome 126	149
Safari 17	143
Firefox 125	132
Chrome 127	129
Firefox 131	120
Chrome 124	112
Firefox 133	102
Chrome 136	90
Firefox 130	89
Firefox 132	85
Chrome 122	82
Firefox 129	74
Firefox 128	71
Chrome 123	70
Firefox 124	69
Chrome 101	67
Safari 16	46
Firefox 123	44
Firefox 126	44
Chrome 86	38
Firefox 122	34
Firefox 127	34
Chrome 103	28
Chrome 100	27
Chrome 121	27
Firefox 134	27
Safari 604	26
Chrome 104	22
Chrome 120	20
Chrome 102	18
Firefox 135	18
Firefox 138	17
Chrome 119	16
Firefox 137	16
Firefox 136	14
Opera 117	11
Safari 15	11
Firefox 115	10
Firefox 119	9
Chrome 112	5
Opera 115	5
Safari 14	5
Chrome 106	4
Chrome 109	4
Chrome 79	4
Opera 118	4
Chrome 107	2
Chrome 114	2
Chrome 116	2
Chrome 117	2
Chrome 94	2
Firefox 102	2
Opera 109	2
Opera 113	2
Chrome 110	1
Chrome 111	1
Chrome 115	1
Chrome 4	1
Chrome 90	1
Firefox 109	1
Firefox 68	1
Opera 114	1
Safari 13	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 1198, i.e. 24.2% 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	1,198	3,761	0.0	1.0	64.7	34.0	2,861.0
durMinsCapped	1,198	3,761	0.0	1.0	18.7	34.0	60.0
durMinsCensored	1,913	3,046	0.0	1.0	9.0	14.0	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 3046 such sessions so far. Of these 58 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 1.9% of the 3046 uncensored sessions.

The number of sessions lasting a minute (rounding to the nearest minute) was 1432, i.e. 47% of the uncensored sessions. I think these probably represent very quick but perhaps genuine uses of an app.

That leaves 1556 sessions lasting longer than a minute but less than an hour i.e. 51.1% 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
RCI1	9,274	36.5%
COREpapers1	8,107	31.9%
CSC1	3,911	15.4%
RCI2	1,364	5.4%
random1	477	1.9%
CImean	445	1.8%
ECDFplot	381	1.5%
CORE-OM_scoring	285	1.1%
Cronbach1Feldt	199	0.8%
Histogram_and_summary1	191	0.8%
CSClookup2a	176	0.7%
Spearman-Brown	158	0.6%
CIcorrelation	115	0.5%
Create_univariate_data	113	0.4%
CISpearman	86	0.3%
CIproportion	58	0.2%
Gaussian1	27	0.1%
Feldt2	12	0.0%
Attenuation	11	0.0%
Hashing_IDs	8	0.0%
Screening1	8	0.0%
CISD	7	0.0%
plotCIPearson	4	0.0%
g_from_d_and_n	3	0.0%
useConvFiveNum	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
RCI1
	SD	9,274	8	3,662
	ci	9,274	8	358
	compute	9,274	8	2,388
	dp	9,274	8	139
	generate	9,274	8	5
	max	9,274	8	2
	min	9,274	8	1
	rel	9,274	8	2,719
COREpapers1
	authName	8,107	59	102
	clipbtn	8,107	59	7
	date1	8,107	59	48
	date2	8,107	59	36
	embedded	8,107	59	19
	filterAssStructure	8,107	59	18
	filterCORElanguages	8,107	59	16
	filterCOREmeasures	8,107	59	28
	filterFormats	8,107	59	17
	filterGenderCats	8,107	59	9
	mainPlotDownload-filename	8,107	59	3
	mainPlotDownload-format	8,107	59	1
	or	8,107	59	6
	or2	8,107	59	3
	or3	8,107	59	4
	or4	8,107	59	3
	or5	8,107	59	4
	otherMeasure	8,107	59	40
	otherMeasures_cell_clicked	8,107	59	24
	otherMeasures_cells_selected	8,107	59	17
	otherMeasures_columns_selected	8,107	59	17
	otherMeasures_row_last_clicked	8,107	59	5
	otherMeasures_rows_all	8,107	59	71
	otherMeasures_rows_current	8,107	59	70
	otherMeasures_rows_selected	8,107	59	27
	otherMeasures_search	8,107	59	34
	otherMeasures_state	8,107	59	74
	paperLang	8,107	59	23
	papers2_cell_clicked	8,107	59	47
	papers2_cells_selected	8,107	59	22
	papers2_columns_selected	8,107	59	22
	papers2_row_last_clicked	8,107	59	8
	papers2_rows_all	8,107	59	108
	papers2_rows_current	8,107	59	108
	papers2_rows_selected	8,107	59	44
	papers2_search	8,107	59	51
	papers2_state	8,107	59	115
	papers_cell_clicked	8,107	59	336
	papers_cells_selected	8,107	59	272
	papers_columns_selected	8,107	59	272
	papers_row_last_clicked	8,107	59	53
	papers_rows_all	8,107	59	1,557
	papers_rows_current	8,107	59	1,591
	papers_rows_selected	8,107	59	411
	papers_search	8,107	59	336
	papers_state	8,107	59	1,615
	reqEmpCOREdata	8,107	59	44
	reqOA	8,107	59	13
	reqOpenData	8,107	59	18
	reset_input	8,107	59	11
	shinyjs-resettable-side-panel	8,107	59	9
	tabSelected	8,107	59	117
	therOrGen	8,107	59	57
	titleWord	8,107	59	22
	vecAssStructure	8,107	59	29
	vecCORElanguages	8,107	59	8
	vecFormats	8,107	59	19
	vecGenderCats	8,107	59	8
	vecWhichCOREused	8,107	59	58
CSC1
	SDHS	3,911	7	694
	SDNHS	3,911	7	717
	dp	3,911	7	70
	maxPoss	3,911	7	497
	meanHS	3,911	7	773
	meanNHS	3,911	7	934
	minPoss	3,911	7	226
RCI2
	SD	1,364	6	436
	ci	1,364	6	39
	compute	1,364	6	330
	dp	1,364	6	10
	n	1,364	6	200
	rel	1,364	6	349
random1
	compute	477	12	40
	dataTable_cell_clicked	477	12	37
	dataTable_cells_selected	477	12	29
	dataTable_columns_selected	477	12	29
	dataTable_row_last_clicked	477	12	6
	dataTable_rows_all	477	12	76
	dataTable_rows_current	477	12	83
	dataTable_rows_selected	477	12	42
	dataTable_search	477	12	29
	dataTable_state	477	12	83
	valN	477	12	12
	valSeed	477	12	11
CImean
	SD	445	5	196
	SE	445	5	1
	dp	445	5	2
	mean	445	5	192
	n	445	5	54
ECDFplot
	annotationSize	381	38	10
	decChar	381	38	1
	file1	381	38	5
	fileHeight	381	38	10
	fileHeightQuantiles	381	38	4
	fileWidth	381	38	10
	fileWidthQuantiles	381	38	4
	inputType	381	38	20
	pastedData	381	38	30
	quantiles	381	38	19
	quoteChar	381	38	3
	sepChar	381	38	4
	summary_cell_clicked	381	38	6
	summary_cells_selected	381	38	6
	summary_columns_selected	381	38	6
	summary_rows_all	381	38	14
	summary_rows_current	381	38	14
	summary_rows_selected	381	38	6
	summary_search	381	38	6
	summary_state	381	38	14
	tabSelected	381	38	55
	textSize	381	38	10
	textSizeQuantiles	381	38	4
	tibQuantiles_cell_clicked	381	38	5
	tibQuantiles_cells_selected	381	38	5
	tibQuantiles_columns_selected	381	38	5
	tibQuantiles_rows_all	381	38	16
	tibQuantiles_rows_current	381	38	16
	tibQuantiles_rows_selected	381	38	5
	tibQuantiles_search	381	38	5
	tibQuantiles_state	381	38	16
	title	381	38	10
	titleQuantiles	381	38	4
	var	381	38	5
	xLab	381	38	10
	xLabQuantiles	381	38	4
	yLab	381	38	10
	yLabQuantiles	381	38	4
CORE-OM_scoring
	compData_cell_clicked	285	27	5
	compData_cells_selected	285	27	5
	compData_columns_selected	285	27	5
	compData_rows_all	285	27	6
	compData_rows_current	285	27	12
	compData_rows_selected	285	27	5
	compData_search	285	27	5
	compData_state	285	27	18
	contents_cell_clicked	285	27	2
	contents_cells_selected	285	27	2
	contents_columns_selected	285	27	2
	contents_rows_all	285	27	4
	contents_rows_current	285	27	4
	contents_rows_selected	285	27	2
	contents_search	285	27	2
	contents_state	285	27	4
	dp	285	27	23
	file1	285	27	18
	summary_cell_clicked	285	27	1
	summary_cells_selected	285	27	1
	summary_columns_selected	285	27	1
	summary_rows_all	285	27	1
	summary_rows_current	285	27	1
	summary_rows_selected	285	27	1
	summary_search	285	27	1
	summary_state	285	27	1
	tabSelected	285	27	153
Cronbach1Feldt
	alpha	199	6	106
	altAlpha	199	6	2
	ci	199	6	2
	dp	199	6	5
	k	199	6	43
	n	199	6	41
Histogram_and_summary1
	bins	191	25	9
	contents_cell_clicked	191	25	5
	contents_cells_selected	191	25	5
	contents_columns_selected	191	25	5
	contents_rows_all	191	25	10
	contents_rows_current	191	25	10
	contents_rows_selected	191	25	5
	contents_search	191	25	5
	contents_state	191	25	10
	dataType	191	25	6
	file1	191	25	10
	nDP	191	25	2
	plotDownload-format	191	25	1
	summary_cell_clicked	191	25	5
	summary_cells_selected	191	25	5
	summary_columns_selected	191	25	5
	summary_rows_all	191	25	12
	summary_rows_current	191	25	12
	summary_rows_selected	191	25	5
	summary_search	191	25	5
	summary_state	191	25	12
	title	191	25	10
	var	191	25	14
	xLab	191	25	11
	yLab	191	25	12
CSClookup2a
	Age	176	5	4
	Gender	176	5	6
	Lookup	176	5	29
	Scoring	176	5	21
	YPscore	176	5	116
Spearman-Brown
	currK	158	13	9
	currRel	158	13	11
	maxK	158	13	6
	plotDownload-filename	158	13	1
	reliabilities_cell_clicked	158	13	7
	reliabilities_cells_selected	158	13	7
	reliabilities_columns_selected	158	13	7
	reliabilities_rows_all	158	13	28
	reliabilities_rows_current	158	13	30
	reliabilities_rows_selected	158	13	7
	reliabilities_search	158	13	7
	reliabilities_state	158	13	30
	step	158	13	8
CIcorrelation
	R	115	4	60
	ci	115	4	3
	dp	115	4	2
	n	115	4	50
Create_univariate_data
	charSeparator	113	11	17
	dataTable_cell_clicked	113	11	7
	dataTable_cells_selected	113	11	7
	dataTable_columns_selected	113	11	7
	dataTable_rows_all	113	11	14
	dataTable_rows_current	113	11	14
	dataTable_rows_selected	113	11	7
	dataTable_search	113	11	7
	dataTable_state	113	11	14
	dist	113	11	2
	generate	113	11	17
CISpearman
	Gaussian	86	6	6
	ci	86	6	2
	dp	86	6	7
	method	86	6	10
	n	86	6	25
	rs	86	6	36
CIproportion
	ci	58	4	6
	dp	58	4	10
	n	58	4	18
	x	58	4	24
Gaussian1
	dp	27	4	2
	mean	27	4	20
	n	27	4	4
	nBins	27	4	1
Feldt2
	alpha1	12	5	1
	alpha2	12	5	3
	dp	12	5	3
	n1	12	5	3
	n2	12	5	2
Attenuation
	correlations_cell_clicked	11	8	1
	correlations_cells_selected	11	8	1
	correlations_columns_selected	11	8	1
	correlations_rows_all	11	8	2
	correlations_rows_current	11	8	2
	correlations_rows_selected	11	8	1
	correlations_search	11	8	1
	correlations_state	11	8	2
Hashing_IDs
	file1	8	2	3
	var	8	2	5
Screening1
	dp	8	3	1
	prev	8	3	5
	spec	8	3	2
CISD
	SD	7	4	1
	SDorVar	7	4	4
	ci	7	4	1
	n	7	4	1
plotCIPearson
	R	4	1	4
g_from_d_and_n
	d	3	2	2
	n	3	2	1
useConvFiveNum
	min	3	1	3
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 15. 15.ii.2025 Tweak to handle “Netscape.0 …” browser ID.
• 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