Analysing the use of my shiny apps

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

Current data

Info	Value
First date in data	2024-02-07
Last date in data	2024-05-16
This analysis time/date	03:13 on 17/05/2024
Number of days	99
Number of sessions	810
Mean sessions per day	8.18

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.

Numbers of sessions per app

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

App	Sessions	First used	Last used	Days available	Sessions per day	Days used	% days used
RCI1	248	2024-02-07	2024-05-16	100	2.480	60	60.0%
CSC1	166	2024-02-07	2024-05-15	100	1.660	59	59.0%
ECDFplot	75	2024-02-07	2024-05-13	100	0.750	23	23.0%
CORE-OM_scoring	54	2024-04-16	2024-05-16	31	1.742	28	90.3%
COREpapers1	54	2024-05-11	2024-05-16	6	9.000	6	100.0%
Histogram_and_summary1	33	2024-03-25	2024-05-06	53	0.623	14	26.4%
CIproportion	22	2024-02-07	2024-05-05	100	0.220	16	16.0%
Cronbach1Feldt	22	2024-02-07	2024-05-16	100	0.220	17	17.0%
CIcorrelation	18	2024-02-07	2024-05-16	100	0.180	16	16.0%
CIdiff2proportions	15	2024-02-07	2024-05-06	100	0.150	8	8.0%
CImean	13	2024-02-07	2024-05-08	100	0.130	12	12.0%
Screening1	13	2024-02-07	2024-05-01	100	0.130	9	9.0%
plotCIPearson	11	2024-02-07	2024-05-16	100	0.110	8	8.0%
CISD	9	2024-02-07	2024-05-07	100	0.090	8	8.0%
Spearman-Brown	9	2024-05-03	2024-05-13	14	0.643	5	35.7%
g_from_d_and_n	9	2024-02-07	2024-05-02	100	0.090	7	7.0%
Gaussian1	8	2024-03-05	2024-05-15	73	0.110	7	9.6%
RCI2	8	2024-02-07	2024-05-16	100	0.080	5	5.0%
Create_univariate_data	7	2024-04-09	2024-05-14	38	0.184	6	15.8%
CISpearman	6	2024-02-07	2024-05-16	100	0.060	4	4.0%
Bonferroni1	5	2024-03-24	2024-05-13	54	0.093	5	9.3%
CSClookup2a	5	2024-02-07	2024-05-16	100	0.050	4	4.0%

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 currently (24.iii.24) most of the apps are shown as first being used on the same day (7.ii.2024) which was the first date I set up the app use logging (and I tested all the then existing apps that day so all appear on the day). The most used apps with come lower on the y axis within first use dates.

Sessions per day

Here’s the breakdown of sessions per day.

Like the map against time above this shows very clearly 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).

Breaking that down by app gives me this.

And facetting by app gives this.

Breakdown by day of the week

Weekday	n	percent
Mon	1,475	18%
Tue	2,614	31%
Wed	1,740	21%
Thu	657	8%
Fri	561	7%
Sat	320	4%
Sun	961	12%

Same sorted!

Weekday	n	percent
Tue	2,614	31%
Wed	1,740	21%
Mon	1,475	18%
Sun	961	12%
Thu	657	8%
Fri	561	7%
Sat	320	4%

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	4	0%
1	2	0%
2	4	0%
3	6	1%
4	4	0%
5	27	3%
6	63	8%
7	39	5%
8	81	10%
9	40	5%
10	26	3%
11	27	3%
12	52	6%
13	50	6%
14	110	14%
15	35	4%
16	30	4%
17	45	6%
18	26	3%
19	28	3%
20	53	7%
21	41	5%
22	12	1%
23	5	1%

Same sorted.

Hour	n	percent
14	110	14%
8	81	10%
6	63	8%
20	53	7%
12	52	6%
13	50	6%
17	45	6%
21	41	5%
9	40	5%
7	39	5%
15	35	4%
16	30	4%
19	28	3%
5	27	3%
11	27	3%
10	26	3%
18	26	3%
22	12	1%
3	6	1%
23	5	1%
0	4	0%
2	4	0%
4	4	0%
1	2	0%

Browsers

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

Browser	n
Firefox 125	131
Chrome 124	94
Firefox 124	69
Chrome 101	67
Chrome 122	67
Chrome 123	60
Firefox 123	44
Chrome 86	38
Firefox 122	34
Safari 17	31
Chrome 103	28
Chrome 100	27
Chrome 121	25
Chrome 104	21
Chrome 102	17
Chrome 119	13
Safari 16	10
Firefox 126	8
Chrome 112	4
Safari 15	3
Chrome 109	2
Chrome 114	2
Firefox 102	2
Opera 109	2
Chrome 110	1
Chrome 115	1
Chrome 117	1
Chrome 120	1
Chrome 125	1
Chrome 94	1
Firefox 115	1
Safari 604	1

I think the numbers are the version number of the browser when the browser reports that. This is more sensible isn’t it?

Browser	n
Chrome	471
Firefox	289
Safari	45
Opera	2

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 265, i.e. 32.7% 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.

So most, as you would expect given the nature of the apps, are sessions lasting only a few minutes.

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	3,471	60.1%
CSC1	970	16.8%
RCI1	729	12.6%
ECDFplot	136	2.4%
Histogram_and_summary1	129	2.2%
CORE-OM_scoring	128	2.2%
Spearman-Brown	111	1.9%
Cronbach1Feldt	26	0.4%
Create_univariate_data	19	0.3%
CImean	14	0.2%
CIproportion	11	0.2%
CIcorrelation	8	0.1%
CISpearman	7	0.1%
Screening1	5	0.1%
CISD	4	0.1%
plotCIPearson	4	0.1%
Gaussian1	3	0.1%
CIdiff2proportions	2	0.0%
RCI2	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	3,471	55	87
	date1	3,471	55	39
	date2	3,471	55	31
	embedded	3,471	55	9
	filterAssStructure	3,471	55	14
	filterCORElanguages	3,471	55	15
	filterCOREmeasures	3,471	55	12
	filterFormats	3,471	55	6
	filterGenderCats	3,471	55	7
	mainPlotDownload-filename	3,471	55	3
	mainPlotDownload-format	3,471	55	1
	or	3,471	55	4
	or2	3,471	55	3
	or3	3,471	55	4
	or4	3,471	55	3
	or5	3,471	55	2
	otherMeasure	3,471	55	26
	otherMeasures_cell_clicked	3,471	55	18
	otherMeasures_cells_selected	3,471	55	11
	otherMeasures_columns_selected	3,471	55	11
	otherMeasures_row_last_clicked	3,471	55	5
	otherMeasures_rows_all	3,471	55	59
	otherMeasures_rows_current	3,471	55	58
	otherMeasures_rows_selected	3,471	55	21
	otherMeasures_search	3,471	55	28
	otherMeasures_state	3,471	55	62
	paperLang	3,471	55	11
	papers2_cell_clicked	3,471	55	34
	papers2_cells_selected	3,471	55	12
	papers2_columns_selected	3,471	55	12
	papers2_row_last_clicked	3,471	55	7
	papers2_rows_all	3,471	55	84
	papers2_rows_current	3,471	55	84
	papers2_rows_selected	3,471	55	33
	papers2_search	3,471	55	41
	papers2_state	3,471	55	91
	papers_cell_clicked	3,471	55	74
	papers_cells_selected	3,471	55	53
	papers_columns_selected	3,471	55	53
	papers_row_last_clicked	3,471	55	14
	papers_rows_all	3,471	55	643
	papers_rows_current	3,471	55	645
	papers_rows_selected	3,471	55	107
	papers_search	3,471	55	59
	papers_state	3,471	55	647
	reqEmpCOREdata	3,471	55	15
	reqOA	3,471	55	12
	reqOpenData	3,471	55	5
	tabSelected	3,471	55	101
	therOrGen	3,471	55	25
	vecAssStructure	3,471	55	29
	vecCORElanguages	3,471	55	7
	vecFormats	3,471	55	5
	vecGenderCats	3,471	55	7
	vecWhichCOREused	3,471	55	22
CSC1
	SDHS	970	7	191
	SDNHS	970	7	194
	dp	970	7	21
	maxPoss	970	7	105
	meanHS	970	7	207
	meanNHS	970	7	218
	minPoss	970	7	34
RCI1
	SD	729	8	362
	ci	729	8	21
	compute	729	8	134
	dp	729	8	2
	generate	729	8	5
	max	729	8	2
	min	729	8	1
	rel	729	8	202
ECDFplot
	annotationSize	136	32	6
	fileHeight	136	32	6
	fileHeightQuantiles	136	32	2
	fileWidth	136	32	6
	fileWidthQuantiles	136	32	2
	inputType	136	32	7
	pastedData	136	32	5
	summary_cell_clicked	136	32	2
	summary_cells_selected	136	32	2
	summary_columns_selected	136	32	2
	summary_rows_all	136	32	4
	summary_rows_current	136	32	4
	summary_rows_selected	136	32	2
	summary_search	136	32	2
	summary_state	136	32	4
	tabSelected	136	32	26
	textSize	136	32	6
	textSizeQuantiles	136	32	2
	tibQuantiles_cell_clicked	136	32	2
	tibQuantiles_cells_selected	136	32	2
	tibQuantiles_columns_selected	136	32	2
	tibQuantiles_rows_all	136	32	4
	tibQuantiles_rows_current	136	32	4
	tibQuantiles_rows_selected	136	32	2
	tibQuantiles_search	136	32	2
	tibQuantiles_state	136	32	4
	title	136	32	6
	titleQuantiles	136	32	2
	xLab	136	32	6
	xLabQuantiles	136	32	2
	yLab	136	32	6
	yLabQuantiles	136	32	2
Histogram_and_summary1
	bins	129	24	6
	contents_cell_clicked	129	24	4
	contents_cells_selected	129	24	4
	contents_columns_selected	129	24	4
	contents_rows_all	129	24	8
	contents_rows_current	129	24	8
	contents_rows_selected	129	24	4
	contents_search	129	24	4
	contents_state	129	24	8
	dataType	129	24	6
	file1	129	24	6
	plotDownload-format	129	24	1
	summary_cell_clicked	129	24	3
	summary_cells_selected	129	24	3
	summary_columns_selected	129	24	3
	summary_rows_all	129	24	6
	summary_rows_current	129	24	6
	summary_rows_selected	129	24	3
	summary_search	129	24	3
	summary_state	129	24	6
	title	129	24	7
	var	129	24	9
	xLab	129	24	8
	yLab	129	24	9
CORE-OM_scoring
	compData_cell_clicked	128	27	4
	compData_cells_selected	128	27	4
	compData_columns_selected	128	27	4
	compData_rows_all	128	27	4
	compData_rows_current	128	27	10
	compData_rows_selected	128	27	4
	compData_search	128	27	4
	compData_state	128	27	16
	contents_cell_clicked	128	27	2
	contents_cells_selected	128	27	2
	contents_columns_selected	128	27	2
	contents_rows_all	128	27	4
	contents_rows_current	128	27	4
	contents_rows_selected	128	27	2
	contents_search	128	27	2
	contents_state	128	27	4
	dp	128	27	6
	file1	128	27	14
	summary_cell_clicked	128	27	1
	summary_cells_selected	128	27	1
	summary_columns_selected	128	27	1
	summary_rows_all	128	27	1
	summary_rows_current	128	27	1
	summary_rows_selected	128	27	1
	summary_search	128	27	1
	summary_state	128	27	1
	tabSelected	128	27	28
Spearman-Brown
	currK	111	13	9
	currRel	111	13	8
	maxK	111	13	6
	plotDownload-filename	111	13	1
	reliabilities_cell_clicked	111	13	3
	reliabilities_cells_selected	111	13	3
	reliabilities_columns_selected	111	13	3
	reliabilities_rows_all	111	13	20
	reliabilities_rows_current	111	13	22
	reliabilities_rows_selected	111	13	3
	reliabilities_search	111	13	3
	reliabilities_state	111	13	22
	step	111	13	8
Cronbach1Feldt
	alpha	26	4	17
	dp	26	4	1
	k	26	4	7
	n	26	4	1
Create_univariate_data
	charSeparator	19	10	4
	dataTable_cell_clicked	19	10	1
	dataTable_cells_selected	19	10	1
	dataTable_columns_selected	19	10	1
	dataTable_rows_all	19	10	2
	dataTable_rows_current	19	10	2
	dataTable_rows_selected	19	10	1
	dataTable_search	19	10	1
	dataTable_state	19	10	2
	generate	19	10	4
CImean
	SD	14	5	4
	SE	14	5	1
	dp	14	5	2
	mean	14	5	3
	n	14	5	4
CIproportion
	ci	11	4	1
	dp	11	4	2
	n	11	4	3
	x	11	4	5
CIcorrelation
	R	8	2	7
	n	8	2	1
CISpearman
	n	7	2	1
	rs	7	2	6
Screening1
	prev	5	1	5
CISD
	SD	4	4	1
	SDorVar	4	4	1
	ci	4	4	1
	n	4	4	1
plotCIPearson
	R	4	1	4
Gaussian1
	n	3	2	2
	nBins	3	2	1
CIdiff2proportions
	n1	2	1	2
RCI2
	compute	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 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