Analysing the use of my shiny apps
Chris Evans
2024-08-20
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 | 2026-01-28 |
This analysis time/date | 03:13 on 28/01/2026 |
Number of days spanned | 721 |
Total number of sessions | 8024 |
Mean sessions per day | 11.13 |
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 | 3,514 | 2024-02-07 | 721 | 4.874 | 595 | 83% |
CSC1 | 1,030 | 2024-02-07 | 721 | 1.429 | 375 | 52% |
COREpapers1 | 541 | 2024-05-11 | 627 | 0.863 | 237 | 38% |
RCI2 | 452 | 2024-02-07 | 721 | 0.627 | 245 | 34% |
CORE-OM_scoring | 444 | 2024-04-16 | 652 | 0.681 | 217 | 33% |
Cronbach1Feldt | 218 | 2024-02-07 | 721 | 0.302 | 156 | 22% |
YP-CORE_2_scores | 184 | 2025-06-16 | 226 | 0.814 | 48 | 21% |
CSClookup2a | 145 | 2024-02-07 | 721 | 0.201 | 72 | 10% |
Spearman-Brown | 133 | 2024-05-03 | 635 | 0.209 | 101 | 16% |
CIcorrelation | 130 | 2024-02-07 | 721 | 0.180 | 85 | 12% |
Gaussian1 | 119 | 2024-03-05 | 694 | 0.171 | 88 | 13% |
ECDFplot | 104 | 2024-02-07 | 721 | 0.144 | 45 | 6% |
CISpearman | 78 | 2024-02-07 | 721 | 0.108 | 66 | 9% |
CIproportion | 69 | 2024-02-07 | 721 | 0.096 | 56 | 8% |
g_from_d_and_n | 66 | 2024-02-07 | 721 | 0.092 | 59 | 8% |
Attenuation | 62 | 2024-10-09 | 476 | 0.130 | 41 | 9% |
CImean | 61 | 2024-02-07 | 721 | 0.085 | 51 | 7% |
get_Sval_from_Pval | 61 | 2025-09-02 | 148 | 0.412 | 57 | 39% |
random1 | 59 | 2024-11-19 | 435 | 0.136 | 52 | 12% |
Feldt2 | 54 | 2024-11-27 | 427 | 0.126 | 38 | 9% |
Histogram_and_summary1 | 52 | 2024-03-25 | 674 | 0.077 | 27 | 4% |
Attenuation2 | 49 | 2024-10-11 | 474 | 0.103 | 43 | 9% |
plotCIPearson | 44 | 2024-02-07 | 721 | 0.061 | 32 | 4% |
CISD | 43 | 2024-02-07 | 721 | 0.060 | 35 | 5% |
Mean i-i-corr from alpha | 42 | 2025-06-27 | 215 | 0.195 | 36 | 17% |
Screening1 | 42 | 2024-02-07 | 721 | 0.058 | 34 | 5% |
Bonferroni1 | 40 | 2024-03-24 | 675 | 0.059 | 28 | 4% |
getCorrectedR | 40 | 2024-10-13 | 472 | 0.085 | 34 | 7% |
useConvFiveNum | 37 | 2025-04-07 | 296 | 0.125 | 34 | 11% |
CIdiff2proportions | 35 | 2024-02-07 | 721 | 0.049 | 20 | 3% |
Create_univariate_data | 29 | 2024-04-09 | 659 | 0.044 | 28 | 4% |
Hashing_IDs | 27 | 2025-04-05 | 298 | 0.091 | 18 | 6% |
CORE-OM_scoring2 | 10 | 2025-10-09 | 111 | 0.090 | 5 | 4% |
Forest_plot_rates | 9 | 2026-01-08 | 20 | 0.450 | 4 | 20% |
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 | 12,500 | 19% |
Tue | 12,302 | 19% |
Wed | 9,740 | 15% |
Thu | 9,650 | 15% |
Fri | 9,585 | 15% |
Sat | 5,738 | 9% |
Sun | 6,147 | 9% |
Same sorted!
Weekday | n | percent |
|---|---|---|
Mon | 12,500 | 19% |
Tue | 12,302 | 19% |
Wed | 9,740 | 15% |
Thu | 9,650 | 15% |
Fri | 9,585 | 15% |
Sun | 6,147 | 9% |
Sat | 5,738 | 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 | 91 | 1% |
1 | 98 | 1% |
2 | 106 | 1% |
3 | 88 | 1% |
4 | 182 | 2% |
5 | 311 | 4% |
6 | 444 | 6% |
7 | 431 | 5% |
8 | 472 | 6% |
9 | 474 | 6% |
10 | 485 | 6% |
11 | 433 | 5% |
12 | 465 | 6% |
13 | 510 | 6% |
14 | 544 | 7% |
15 | 648 | 8% |
16 | 390 | 5% |
17 | 406 | 5% |
18 | 282 | 4% |
19 | 331 | 4% |
20 | 314 | 4% |
21 | 250 | 3% |
22 | 154 | 2% |
23 | 115 | 1% |
Same sorted.
Hour | n | percent |
|---|---|---|
15 | 648 | 8% |
14 | 544 | 7% |
13 | 510 | 6% |
10 | 485 | 6% |
9 | 474 | 6% |
8 | 472 | 6% |
12 | 465 | 6% |
6 | 444 | 6% |
11 | 433 | 5% |
7 | 431 | 5% |
17 | 406 | 5% |
16 | 390 | 5% |
19 | 331 | 4% |
20 | 314 | 4% |
5 | 311 | 4% |
18 | 282 | 4% |
21 | 250 | 3% |
4 | 182 | 2% |
22 | 154 | 2% |
23 | 115 | 1% |
2 | 106 | 1% |
1 | 98 | 1% |
0 | 91 | 1% |
3 | 88 | 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 | 5,854 |
Firefox | 1,195 |
Safari | 687 |
Other | 147 |
Opera | 59 |
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/ | 131 |
Netscape.0 (X11 | 4 |
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 |
Netscape.0 (iPhone; CPU iPhone OS 18_5 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Mobile/15E148 [LinkedInApp]/9.31.4037 -? | 2 |
Netscape.0 -? | 2 |
Netscape.0 (iPhone; CPU iPhone OS 18_3_2 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Mobile/15E148 [LinkedInApp]/9.31.3937 -? | 1 |
Netscape.0 (iPhone; CPU iPhone OS 18_6_1 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Mobile/15E148 [LinkedInApp]/9.31.4037 -? | 1 |
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 | 2026-01-26 |
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 |
Netscape.0 (iPhone; CPU iPhone OS 18_3_2 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Mobile/15E148 [LinkedInApp]/9.31.3937 -? | 2025-08-13 | 2025-08-13 |
Netscape.0 (iPhone; CPU iPhone OS 18_5 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Mobile/15E148 [LinkedInApp]/9.31.4037 -? | 2025-08-13 | 2025-08-17 |
Netscape.0 (iPhone; CPU iPhone OS 18_6_1 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Mobile/15E148 [LinkedInApp]/9.31.4037 -? | 2025-08-22 | 2025-08-22 |
Netscape.0 (X11 | 2025-12-04 | 2025-12-04 |
Netscape.0 -? | 2026-01-09 | 2026-01-13 |
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 (X11 | ||
2025-12-04 | 4 | |
Netscape.0 (iPhone; CPU iPhone OS 18_1_1 like Mac OS X) | ||
2025-02-10 | 2 | |
Netscape.0 (iPhone; CPU iPhone OS 18_3_2 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Mobile/15E148 [LinkedInApp]/9.31.3937 -? | ||
2025-08-13 | 1 | |
Netscape.0 (iPhone; CPU iPhone OS 18_5 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Mobile/15E148 [LinkedInApp]/9.31.4037 -? | ||
2025-08-13 | 1 | |
2025-08-17 | 1 | |
Netscape.0 (iPhone; CPU iPhone OS 18_6_1 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Mobile/15E148 [LinkedInApp]/9.31.4037 -? | ||
2025-08-22 | 1 | |
Netscape.0 -? | ||
2026-01-09 | 1 | |
2026-01-13 | 1 | |
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 | |
2025-05-25 | 2 | |
2025-05-26 | 2 | |
2025-06-19 | 1 | |
2025-06-25 | 1 | |
2025-06-26 | 3 | |
2025-06-27 | 2 | |
2025-07-02 | 1 | |
2025-07-05 | 1 | |
2025-07-10 | 1 | |
2025-07-15 | 1 | |
2025-07-17 | 1 | |
2025-07-25 | 1 | |
2025-07-26 | 3 | |
2025-07-28 | 2 | |
2025-07-30 | 1 | |
2025-08-02 | 1 | |
2025-08-04 | 1 | |
2025-08-08 | 2 | |
2025-08-13 | 1 | |
2025-08-15 | 1 | |
2025-08-18 | 1 | |
2025-08-29 | 2 | |
2025-08-31 | 2 | |
2025-09-05 | 1 | |
2025-09-11 | 1 | |
2025-09-16 | 1 | |
2025-09-20 | 1 | |
2025-09-25 | 1 | |
2025-09-29 | 1 | |
2025-10-02 | 4 | |
2025-10-03 | 1 | |
2025-10-23 | 1 | |
2025-10-28 | 2 | |
2025-11-04 | 8 | |
2025-11-05 | 2 | |
2025-11-09 | 1 | |
2025-11-14 | 1 | |
2025-11-18 | 1 | |
2025-11-24 | 1 | |
2025-11-25 | 2 | |
2025-11-29 | 2 | |
2025-12-04 | 1 | |
2025-12-08 | 2 | |
2025-12-09 | 2 | |
2025-12-13 | 1 | |
2025-12-17 | 1 | |
2025-12-22 | 1 | |
2025-12-26 | 1 | |
2025-12-29 | 1 | |
2025-12-30 | 1 | |
2025-12-31 | 1 | |
2026-01-04 | 1 | |
2026-01-08 | 1 | |
2026-01-09 | 2 | |
2026-01-12 | 1 | |
2026-01-13 | 1 | |
2026-01-17 | 1 | |
2026-01-22 | 1 | |
2026-01-26 | 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 | 527 |
Chrome 138 | 427 |
Chrome 136 | 410 |
Chrome 130 | 407 |
Chrome 140 | 397 |
Safari 18 | 355 |
Chrome 134 | 354 |
Chrome 132 | 341 |
Chrome 135 | 279 |
Chrome 129 | 271 |
Chrome 133 | 252 |
Chrome 125 | 221 |
Chrome 128 | 217 |
Chrome 141 | 206 |
Chrome 142 | 199 |
Chrome 137 | 181 |
Safari 17 | 173 |
Chrome 139 | 160 |
Chrome 126 | 149 |
Firefox 125 | 132 |
Chrome 127 | 129 |
Firefox 131 | 121 |
Chrome 124 | 113 |
Chrome 143 | 112 |
Firefox 133 | 102 |
Firefox 130 | 90 |
Firefox 132 | 85 |
Chrome 122 | 84 |
Firefox 128 | 80 |
Firefox 129 | 74 |
Chrome 123 | 70 |
Firefox 124 | 69 |
Chrome 101 | 67 |
Safari 16 | 51 |
Firefox 123 | 44 |
Firefox 126 | 44 |
Firefox 146 | 43 |
Safari 604 | 41 |
Chrome 86 | 38 |
Firefox 122 | 34 |
Firefox 127 | 34 |
Chrome 144 | 33 |
Firefox 142 | 33 |
Chrome 103 | 28 |
Firefox 138 | 28 |
Chrome 100 | 27 |
Chrome 121 | 27 |
Firefox 134 | 27 |
Safari 537 | 24 |
Chrome 104 | 22 |
Chrome 120 | 21 |
Safari 26 | 21 |
Opera 120 | 20 |
Chrome 102 | 19 |
Firefox 135 | 18 |
Firefox 140 | 17 |
Chrome 119 | 16 |
Firefox 137 | 16 |
Safari 15 | 16 |
Firefox 136 | 15 |
Firefox 141 | 14 |
Firefox 144 | 13 |
Firefox 145 | 13 |
Firefox 115 | 11 |
Opera 117 | 11 |
Firefox 143 | 10 |
Firefox 119 | 9 |
Chrome 79 | 8 |
Firefox 139 | 7 |
Firefox 147 | 7 |
Opera 123 | 6 |
Chrome 109 | 5 |
Chrome 112 | 5 |
Opera 115 | 5 |
Opera 122 | 5 |
Safari 14 | 5 |
Chrome 106 | 4 |
Chrome 4 | 4 |
Opera 118 | 4 |
Chrome 116 | 3 |
Chrome 107 | 2 |
Chrome 114 | 2 |
Chrome 117 | 2 |
Chrome 94 | 2 |
Chrome 98 | 2 |
Firefox 102 | 2 |
Opera 109 | 2 |
Opera 113 | 2 |
Opera 124 | 2 |
Chrome 110 | 1 |
Chrome 111 | 1 |
Chrome 115 | 1 |
Chrome 41 | 1 |
Chrome 52 | 1 |
Chrome 53 | 1 |
Chrome 54 | 1 |
Chrome 55 | 1 |
Chrome 58 | 1 |
Chrome 90 | 1 |
Chrome 99 | 1 |
Firefox 109 | 1 |
Firefox 59 | 1 |
Firefox 68 | 1 |
Opera 114 | 1 |
Opera 119 | 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 1972, i.e. 24.6% 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 | 1,972 | 6,051 | 0.0 | 1.0 | 53.0 | 30.0 | 9,564.0 |
durMinsCapped | 1,972 | 6,051 | 0.0 | 1.0 | 17.5 | 30.0 | 60.0 |
durMinsCensored | 3,006 | 5,017 | 0.0 | 1.0 | 8.7 | 12.0 | 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 5017 such sessions so far. Of these 135 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 2.7% of the 5017 uncensored sessions.
The number of sessions lasting a minute (rounding to the nearest minute) was 2429, i.e. 48.4% of the uncensored sessions. I think these probably represent very quick but perhaps genuine uses of an app.
That leaves 2453 sessions lasting longer than a minute but less than an hour i.e. 48.9% 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 |
|---|---|---|
RCI1 | 14,579 | 37.8% |
COREpapers1 | 12,017 | 31.1% |
CSC1 | 5,396 | 14.0% |
RCI2 | 1,718 | 4.5% |
CORE-OM_scoring | 894 | 2.3% |
random1 | 598 | 1.5% |
YP-CORE_2_scores | 506 | 1.3% |
CImean | 445 | 1.2% |
ECDFplot | 430 | 1.1% |
Cronbach1Feldt | 412 | 1.1% |
CSClookup2a | 257 | 0.7% |
CISpearman | 254 | 0.7% |
Histogram_and_summary1 | 234 | 0.6% |
Spearman-Brown | 231 | 0.6% |
CIcorrelation | 169 | 0.4% |
Create_univariate_data | 128 | 0.3% |
Forest_plot_rates | 70 | 0.2% |
CIproportion | 58 | 0.2% |
Attenuation | 35 | 0.1% |
Gaussian1 | 30 | 0.1% |
Mean i-i-corr from alpha | 30 | 0.1% |
Hashing_IDs | 19 | 0.0% |
Feldt2 | 17 | 0.0% |
g_from_d_and_n | 15 | 0.0% |
Attenuation2 | 10 | 0.0% |
CISD | 9 | 0.0% |
CORE-OM_scoring2 | 9 | 0.0% |
Screening1 | 8 | 0.0% |
getCorrectedR | 4 | 0.0% |
plotCIPearson | 4 | 0.0% |
get_Sval_from_Pval | 3 | 0.0% |
useConvFiveNum | 3 | 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 | 14,579 | 8 | 5,674 | |
ci | 14,579 | 8 | 470 | |
compute | 14,579 | 8 | 3,899 | |
dp | 14,579 | 8 | 234 | |
generate | 14,579 | 8 | 5 | |
max | 14,579 | 8 | 2 | |
min | 14,579 | 8 | 1 | |
rel | 14,579 | 8 | 4,294 | |
COREpapers1 | ||||
authName | 12,017 | 59 | 113 | |
clipbtn | 12,017 | 59 | 15 | |
date1 | 12,017 | 59 | 99 | |
date2 | 12,017 | 59 | 52 | |
embedded | 12,017 | 59 | 27 | |
filterAssStructure | 12,017 | 59 | 31 | |
filterCORElanguages | 12,017 | 59 | 24 | |
filterCOREmeasures | 12,017 | 59 | 38 | |
filterFormats | 12,017 | 59 | 21 | |
filterGenderCats | 12,017 | 59 | 13 | |
mainPlotDownload-filename | 12,017 | 59 | 3 | |
mainPlotDownload-format | 12,017 | 59 | 1 | |
or | 12,017 | 59 | 7 | |
or2 | 12,017 | 59 | 3 | |
or3 | 12,017 | 59 | 7 | |
or4 | 12,017 | 59 | 3 | |
or5 | 12,017 | 59 | 4 | |
otherMeasure | 12,017 | 59 | 40 | |
otherMeasures_cell_clicked | 12,017 | 59 | 29 | |
otherMeasures_cells_selected | 12,017 | 59 | 22 | |
otherMeasures_columns_selected | 12,017 | 59 | 22 | |
otherMeasures_row_last_clicked | 12,017 | 59 | 5 | |
otherMeasures_rows_all | 12,017 | 59 | 89 | |
otherMeasures_rows_current | 12,017 | 59 | 88 | |
otherMeasures_rows_selected | 12,017 | 59 | 32 | |
otherMeasures_search | 12,017 | 59 | 39 | |
otherMeasures_state | 12,017 | 59 | 92 | |
paperLang | 12,017 | 59 | 35 | |
papers2_cell_clicked | 12,017 | 59 | 51 | |
papers2_cells_selected | 12,017 | 59 | 26 | |
papers2_columns_selected | 12,017 | 59 | 26 | |
papers2_row_last_clicked | 12,017 | 59 | 8 | |
papers2_rows_all | 12,017 | 59 | 116 | |
papers2_rows_current | 12,017 | 59 | 116 | |
papers2_rows_selected | 12,017 | 59 | 48 | |
papers2_search | 12,017 | 59 | 55 | |
papers2_state | 12,017 | 59 | 123 | |
papers_cell_clicked | 12,017 | 59 | 592 | |
papers_cells_selected | 12,017 | 59 | 500 | |
papers_columns_selected | 12,017 | 59 | 500 | |
papers_row_last_clicked | 12,017 | 59 | 56 | |
papers_rows_all | 12,017 | 59 | 2,338 | |
papers_rows_current | 12,017 | 59 | 2,379 | |
papers_rows_selected | 12,017 | 59 | 643 | |
papers_search | 12,017 | 59 | 567 | |
papers_state | 12,017 | 59 | 2,402 | |
reqEmpCOREdata | 12,017 | 59 | 53 | |
reqOA | 12,017 | 59 | 15 | |
reqOpenData | 12,017 | 59 | 22 | |
reset_input | 12,017 | 59 | 17 | |
shinyjs-resettable-side-panel | 12,017 | 59 | 14 | |
tabSelected | 12,017 | 59 | 117 | |
therOrGen | 12,017 | 59 | 68 | |
titleWord | 12,017 | 59 | 60 | |
vecAssStructure | 12,017 | 59 | 33 | |
vecCORElanguages | 12,017 | 59 | 19 | |
vecFormats | 12,017 | 59 | 19 | |
vecGenderCats | 12,017 | 59 | 8 | |
vecWhichCOREused | 12,017 | 59 | 72 | |
CSC1 | ||||
SDHS | 5,396 | 7 | 936 | |
SDNHS | 5,396 | 7 | 979 | |
dp | 5,396 | 7 | 78 | |
maxPoss | 5,396 | 7 | 749 | |
meanHS | 5,396 | 7 | 1,072 | |
meanNHS | 5,396 | 7 | 1,269 | |
minPoss | 5,396 | 7 | 313 | |
RCI2 | ||||
SD | 1,718 | 6 | 559 | |
ci | 1,718 | 6 | 43 | |
compute | 1,718 | 6 | 425 | |
dp | 1,718 | 6 | 13 | |
n | 1,718 | 6 | 247 | |
rel | 1,718 | 6 | 431 | |
CORE-OM_scoring | ||||
Lookup | 894 | 36 | 10 | |
Scoring | 894 | 36 | 8 | |
compData_cell_clicked | 894 | 36 | 25 | |
compData_cells_selected | 894 | 36 | 25 | |
compData_columns_selected | 894 | 36 | 25 | |
compData_rows_all | 894 | 36 | 39 | |
compData_rows_current | 894 | 36 | 43 | |
compData_rows_selected | 894 | 36 | 25 | |
compData_search | 894 | 36 | 25 | |
compData_search_columns | 894 | 36 | 21 | |
compData_state | 894 | 36 | 63 | |
contents_cell_clicked | 894 | 36 | 2 | |
contents_cells_selected | 894 | 36 | 2 | |
contents_columns_selected | 894 | 36 | 2 | |
contents_rows_all | 894 | 36 | 4 | |
contents_rows_current | 894 | 36 | 4 | |
contents_rows_selected | 894 | 36 | 2 | |
contents_search | 894 | 36 | 2 | |
contents_state | 894 | 36 | 4 | |
dp | 894 | 36 | 29 | |
file1 | 894 | 36 | 51 | |
plotly_afterplot-A | 894 | 36 | 4 | |
plotly_brushed-A | 894 | 36 | 1 | |
plotly_brushing-A | 894 | 36 | 19 | |
plotly_hover-A | 894 | 36 | 114 | |
plotly_relayout-A | 894 | 36 | 18 | |
plotly_selected-A | 894 | 36 | 1 | |
summary_cell_clicked | 894 | 36 | 1 | |
summary_cells_selected | 894 | 36 | 1 | |
summary_columns_selected | 894 | 36 | 1 | |
summary_rows_all | 894 | 36 | 1 | |
summary_rows_current | 894 | 36 | 1 | |
summary_rows_selected | 894 | 36 | 1 | |
summary_search | 894 | 36 | 1 | |
summary_state | 894 | 36 | 1 | |
tabSelected | 894 | 36 | 318 | |
random1 | ||||
compute | 598 | 12 | 53 | |
dataTable_cell_clicked | 598 | 12 | 43 | |
dataTable_cells_selected | 598 | 12 | 35 | |
dataTable_columns_selected | 598 | 12 | 35 | |
dataTable_row_last_clicked | 598 | 12 | 6 | |
dataTable_rows_all | 598 | 12 | 94 | |
dataTable_rows_current | 598 | 12 | 101 | |
dataTable_rows_selected | 598 | 12 | 48 | |
dataTable_search | 598 | 12 | 35 | |
dataTable_state | 598 | 12 | 101 | |
valN | 598 | 12 | 20 | |
valSeed | 598 | 12 | 27 | |
YP-CORE_2_scores | ||||
Lookup | 506 | 34 | 9 | |
Scoring | 506 | 34 | 1 | |
file1 | 506 | 34 | 21 | |
plotly_afterplot-A | 506 | 34 | 34 | |
plotly_hover-A | 506 | 34 | 212 | |
plotly_relayout-A | 506 | 34 | 17 | |
searchableData_cell_clicked | 506 | 34 | 2 | |
searchableData_cells_selected | 506 | 34 | 2 | |
searchableData_columns_selected | 506 | 34 | 2 | |
searchableData_rows_all | 506 | 34 | 2 | |
searchableData_rows_current | 506 | 34 | 2 | |
searchableData_rows_selected | 506 | 34 | 2 | |
searchableData_search | 506 | 34 | 2 | |
searchableData_search_columns | 506 | 34 | 2 | |
searchableData_state | 506 | 34 | 2 | |
searchableErrorData_cell_clicked | 506 | 34 | 3 | |
searchableErrorData_cells_selected | 506 | 34 | 3 | |
searchableErrorData_columns_selected | 506 | 34 | 3 | |
searchableErrorData_rows_all | 506 | 34 | 3 | |
searchableErrorData_rows_current | 506 | 34 | 3 | |
searchableErrorData_rows_selected | 506 | 34 | 3 | |
searchableErrorData_search | 506 | 34 | 3 | |
searchableErrorData_search_columns | 506 | 34 | 3 | |
searchableErrorData_state | 506 | 34 | 3 | |
searchableMissingData_cell_clicked | 506 | 34 | 3 | |
searchableMissingData_cells_selected | 506 | 34 | 3 | |
searchableMissingData_columns_selected | 506 | 34 | 3 | |
searchableMissingData_rows_all | 506 | 34 | 3 | |
searchableMissingData_rows_current | 506 | 34 | 3 | |
searchableMissingData_rows_selected | 506 | 34 | 3 | |
searchableMissingData_search | 506 | 34 | 3 | |
searchableMissingData_search_columns | 506 | 34 | 3 | |
searchableMissingData_state | 506 | 34 | 3 | |
tabSelected | 506 | 34 | 140 | |
CImean | ||||
SD | 445 | 5 | 196 | |
SE | 445 | 5 | 1 | |
dp | 445 | 5 | 2 | |
mean | 445 | 5 | 192 | |
n | 445 | 5 | 54 | |
ECDFplot | ||||
annotationSize | 430 | 38 | 12 | |
decChar | 430 | 38 | 1 | |
file1 | 430 | 38 | 7 | |
fileHeight | 430 | 38 | 12 | |
fileHeightQuantiles | 430 | 38 | 4 | |
fileWidth | 430 | 38 | 12 | |
fileWidthQuantiles | 430 | 38 | 4 | |
inputType | 430 | 38 | 22 | |
pastedData | 430 | 38 | 30 | |
quantiles | 430 | 38 | 19 | |
quoteChar | 430 | 38 | 3 | |
sepChar | 430 | 38 | 4 | |
summary_cell_clicked | 430 | 38 | 8 | |
summary_cells_selected | 430 | 38 | 8 | |
summary_columns_selected | 430 | 38 | 8 | |
summary_rows_all | 430 | 38 | 18 | |
summary_rows_current | 430 | 38 | 18 | |
summary_rows_selected | 430 | 38 | 8 | |
summary_search | 430 | 38 | 8 | |
summary_state | 430 | 38 | 18 | |
tabSelected | 430 | 38 | 59 | |
textSize | 430 | 38 | 12 | |
textSizeQuantiles | 430 | 38 | 4 | |
tibQuantiles_cell_clicked | 430 | 38 | 5 | |
tibQuantiles_cells_selected | 430 | 38 | 5 | |
tibQuantiles_columns_selected | 430 | 38 | 5 | |
tibQuantiles_rows_all | 430 | 38 | 16 | |
tibQuantiles_rows_current | 430 | 38 | 16 | |
tibQuantiles_rows_selected | 430 | 38 | 5 | |
tibQuantiles_search | 430 | 38 | 5 | |
tibQuantiles_state | 430 | 38 | 16 | |
title | 430 | 38 | 12 | |
titleQuantiles | 430 | 38 | 4 | |
var | 430 | 38 | 10 | |
xLab | 430 | 38 | 12 | |
xLabQuantiles | 430 | 38 | 4 | |
yLab | 430 | 38 | 12 | |
yLabQuantiles | 430 | 38 | 4 | |
Cronbach1Feldt | ||||
alpha | 412 | 6 | 217 | |
altAlpha | 412 | 6 | 4 | |
ci | 412 | 6 | 2 | |
dp | 412 | 6 | 11 | |
k | 412 | 6 | 91 | |
n | 412 | 6 | 87 | |
CSClookup2a | ||||
Age | 257 | 7 | 11 | |
Gender | 257 | 7 | 6 | |
Lookup | 257 | 7 | 37 | |
Scoring | 257 | 7 | 28 | |
YPscore | 257 | 7 | 118 | |
YPscore1 | 257 | 7 | 39 | |
YPscore2 | 257 | 7 | 18 | |
CISpearman | ||||
Gaussian | 254 | 6 | 35 | |
ci | 254 | 6 | 2 | |
dp | 254 | 6 | 19 | |
method | 254 | 6 | 29 | |
n | 254 | 6 | 37 | |
rs | 254 | 6 | 132 | |
Histogram_and_summary1 | ||||
bins | 234 | 25 | 14 | |
contents_cell_clicked | 234 | 25 | 5 | |
contents_cells_selected | 234 | 25 | 5 | |
contents_columns_selected | 234 | 25 | 5 | |
contents_rows_all | 234 | 25 | 10 | |
contents_rows_current | 234 | 25 | 10 | |
contents_rows_selected | 234 | 25 | 5 | |
contents_search | 234 | 25 | 5 | |
contents_state | 234 | 25 | 10 | |
dataType | 234 | 25 | 6 | |
file1 | 234 | 25 | 17 | |
nDP | 234 | 25 | 2 | |
plotDownload-format | 234 | 25 | 1 | |
summary_cell_clicked | 234 | 25 | 5 | |
summary_cells_selected | 234 | 25 | 5 | |
summary_columns_selected | 234 | 25 | 5 | |
summary_rows_all | 234 | 25 | 12 | |
summary_rows_current | 234 | 25 | 12 | |
summary_rows_selected | 234 | 25 | 5 | |
summary_search | 234 | 25 | 5 | |
summary_state | 234 | 25 | 12 | |
title | 234 | 25 | 15 | |
var | 234 | 25 | 30 | |
xLab | 234 | 25 | 16 | |
yLab | 234 | 25 | 17 | |
Spearman-Brown | ||||
currK | 231 | 15 | 15 | |
currRel | 231 | 15 | 14 | |
dp | 231 | 15 | 4 | |
maxK | 231 | 15 | 7 | |
minK | 231 | 15 | 4 | |
plotDownload-filename | 231 | 15 | 1 | |
reliabilities_cell_clicked | 231 | 15 | 12 | |
reliabilities_cells_selected | 231 | 15 | 12 | |
reliabilities_columns_selected | 231 | 15 | 12 | |
reliabilities_rows_all | 231 | 15 | 38 | |
reliabilities_rows_current | 231 | 15 | 40 | |
reliabilities_rows_selected | 231 | 15 | 12 | |
reliabilities_search | 231 | 15 | 12 | |
reliabilities_state | 231 | 15 | 40 | |
step | 231 | 15 | 8 | |
CIcorrelation | ||||
R | 169 | 4 | 80 | |
ci | 169 | 4 | 3 | |
dp | 169 | 4 | 19 | |
n | 169 | 4 | 67 | |
Create_univariate_data | ||||
charSeparator | 128 | 11 | 19 | |
dataTable_cell_clicked | 128 | 11 | 8 | |
dataTable_cells_selected | 128 | 11 | 8 | |
dataTable_columns_selected | 128 | 11 | 8 | |
dataTable_rows_all | 128 | 11 | 16 | |
dataTable_rows_current | 128 | 11 | 16 | |
dataTable_rows_selected | 128 | 11 | 8 | |
dataTable_search | 128 | 11 | 8 | |
dataTable_state | 128 | 11 | 16 | |
dist | 128 | 11 | 2 | |
generate | 128 | 11 | 19 | |
Forest_plot_rates | ||||
file1 | 70 | 10 | 6 | |
rawData_cell_clicked | 70 | 10 | 5 | |
rawData_cells_selected | 70 | 10 | 5 | |
rawData_columns_selected | 70 | 10 | 5 | |
rawData_rows_all | 70 | 10 | 10 | |
rawData_rows_current | 70 | 10 | 10 | |
rawData_rows_selected | 70 | 10 | 5 | |
rawData_search | 70 | 10 | 5 | |
rawData_state | 70 | 10 | 10 | |
textPosn | 70 | 10 | 9 | |
CIproportion | ||||
ci | 58 | 4 | 6 | |
dp | 58 | 4 | 10 | |
n | 58 | 4 | 18 | |
x | 58 | 4 | 24 | |
Attenuation | ||||
correlations_cell_clicked | 35 | 12 | 2 | |
correlations_cells_selected | 35 | 12 | 2 | |
correlations_columns_selected | 35 | 12 | 2 | |
correlations_rows_all | 35 | 12 | 4 | |
correlations_rows_current | 35 | 12 | 4 | |
correlations_rows_selected | 35 | 12 | 2 | |
correlations_search | 35 | 12 | 2 | |
correlations_state | 35 | 12 | 4 | |
maxUnattR | 35 | 12 | 3 | |
minUnattR | 35 | 12 | 1 | |
rel2 | 35 | 12 | 2 | |
unattR | 35 | 12 | 7 | |
Gaussian1 | ||||
dp | 30 | 5 | 2 | |
mean | 30 | 5 | 22 | |
n | 30 | 5 | 4 | |
nBins | 30 | 5 | 1 | |
seed | 30 | 5 | 1 | |
Mean i-i-corr from alpha | ||||
alpha | 30 | 2 | 7 | |
k | 30 | 2 | 23 | |
Hashing_IDs | ||||
file1 | 19 | 3 | 4 | |
hashKey | 19 | 3 | 9 | |
var | 19 | 3 | 6 | |
Feldt2 | ||||
alpha1 | 17 | 6 | 1 | |
alpha2 | 17 | 6 | 3 | |
dp | 17 | 6 | 5 | |
k | 17 | 6 | 2 | |
n1 | 17 | 6 | 4 | |
n2 | 17 | 6 | 2 | |
g_from_d_and_n | ||||
d | 15 | 2 | 8 | |
n | 15 | 2 | 7 | |
Attenuation2 | ||||
rel2 | 10 | 2 | 5 | |
unattR | 10 | 2 | 5 | |
CISD | ||||
SD | 9 | 4 | 1 | |
SDorVar | 9 | 4 | 4 | |
ci | 9 | 4 | 1 | |
n | 9 | 4 | 3 | |
CORE-OM_scoring2 | ||||
Func | 9 | 5 | 1 | |
MeanClin | 9 | 5 | 5 | |
Prob | 9 | 5 | 1 | |
Risk | 9 | 5 | 1 | |
WB | 9 | 5 | 1 | |
Screening1 | ||||
dp | 8 | 3 | 1 | |
prev | 8 | 3 | 5 | |
spec | 8 | 3 | 2 | |
getCorrectedR | ||||
dp | 4 | 4 | 1 | |
obsR | 4 | 4 | 1 | |
rel1 | 4 | 4 | 1 | |
rel2 | 4 | 4 | 1 | |
plotCIPearson | ||||
R | 4 | 1 | 4 | |
get_Sval_from_Pval | ||||
pVal | 3 | 1 | 3 | |
useConvFiveNum | ||||
min | 3 | 1 | 3 | |
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