From 1f9d1ea87ff60916eca535a1600e6263f0b7f0a7 Mon Sep 17 00:00:00 2001 From: noahhaber Date: Tue, 12 Dec 2023 12:22:55 -0500 Subject: [PATCH 1/3] meta update --- _targets/meta/meta | 118 ++++++++++++++++++++++----------------------- 1 file changed, 59 insertions(+), 59 deletions(-) diff --git a/_targets/meta/meta b/_targets/meta/meta index 88dad3d..cf24e3b 100644 --- a/_targets/meta/meta +++ b/_targets/meta/meta @@ -15,9 +15,9 @@ create_repli_analytic|function|f732d32a5a166699||||||||||||||| export_orig|function|1d13cf2cfd45903d||||||||||||||| export_repli|function|48b6c3b2718590c9||||||||||||||| export_repro|function|0a7486339a7e6bca||||||||||||||| -finalized_claim4_file|stem|08b5c65587abf039|c8007a3e1323b441|ef46db3751d8e999|-750262725||t19674.8411045502s|b3654bf3682c79ca|86|rds|local|vector|||0|| -finalized_claim4_moddate|stem|b0555d1849304d18|2d0d81a000c050e0|6e76852deb7e5b69|1569643586||t19702.8638136269s|dff9495c51b724c1|77|rds|local|vector|||0.29|| -finalized_claim4_table|stem|7887fb5b44d07123|c7a936d0e47d0fc5|fe8ec385f98b9889|2060009599||t19674.841412857s|f1e3ad6a94194151|628374|rds|local|vector|||2.938|| +finalized_claim4_file|stem|08b5c65587abf039|c8007a3e1323b441|ef46db3751d8e999|-750262725||t19702.8678152924s|b3654bf3682c79ca|86|rds|local|vector|||0|| +finalized_claim4_moddate|stem|b0555d1849304d18|2d0d81a000c050e0|6e76852deb7e5b69|1569643586||t19703.7234116376s|dff9495c51b724c1|77|rds|local|vector|||0.554|| +finalized_claim4_table|stem|7887fb5b44d07123|c7a936d0e47d0fc5|fe8ec385f98b9889|2060009599||t19702.8680729932s|f1e3ad6a94194151|628374|rds|local|vector|||2.938|| generate_claim_ids|function|6819955c8beb1fd9||||||||||||||| generate_paper_ids|function|c3135e17a538d9a3||||||||||||||| generate_valid_rr_ids|function|414e92b343c9027c||||||||||||||| @@ -52,55 +52,55 @@ make_tilburg_orig_input_file|function|00cdb908e3f20429||||||||||||||| make_tilburg_rr_input|function|3afba25eb5c8dbfe||||||||||||||| merge_orig_input|function|29ac54b0ebd75ebf||||||||||||||| merge_repli_input|function|608aeac3c9e7b485||||||||||||||| -orig_dataset|stem|47335296505e2c59|ad827e718f77f533|f4f6efacf8f73e8a|-1495429541||t19702.8602950011s|a15a070785ae591e|104870|rds|local|vector|||0.512|| +orig_dataset|stem|47335296505e2c59|ad827e718f77f533|f4f6efacf8f73e8a|-1495429541||t19703.6645305504s|a15a070785ae591e|104870|rds|local|vector|||0.501|| orig_dataset_data_entry|stem|5a6e41ba10440444|2410fba7b8239d24|85de6c3fa4e321bb|-1836909783||t19621.6653282251s|ce0976375cb0dc52|87089|rds|local|vector|||0.351|| -orig_input_changelog|stem|8c679afa47f754de|74fc36a4af7e34f5|c5c9be44dc57c7f5|854218549||t19702.8601508545s|869dc918109f7fa2|9532|rds|local|vector|||1.483|| -orig_input_changelog_file|stem|6c95cc454144b201|d5c895b1deacd6c4|ef46db3751d8e999|-2101306176||t19674.8411041958s|f0d5811415b6b792|97|rds|local|vector|||0|| -orig_input_changelog_moddate|stem|2072fcb57bf4d05d|5c5d157bcf9bfcbf|34d9fe7d925779fc|1041070366||t19702.8638163485s|dff9495c51b724c1|77|rds|local|vector|||0.226|| +orig_input_changelog|stem|8c679afa47f754de|74fc36a4af7e34f5|c5c9be44dc57c7f5|854218549||t19702.8681043313s|869dc918109f7fa2|9532|rds|local|vector|||1.483|| +orig_input_changelog_file|stem|6c95cc454144b201|d5c895b1deacd6c4|ef46db3751d8e999|-2101306176||t19702.8678153636s|f0d5811415b6b792|97|rds|local|vector|||0|| +orig_input_changelog_moddate|stem|2072fcb57bf4d05d|5c5d157bcf9bfcbf|34d9fe7d925779fc|1041070366||t19703.7234149166s|dff9495c51b724c1|77|rds|local|vector|||0.273|| orig_rr_file|stem|678d64c2dc2f6eef|f4dea69694f8ec0d|af4986d355838f9b|-391603615|/Users/theresawisneskie/Documents/SCORE_Org/data/rr/orig_dataset.tsv|t19545.8751896821s|21552c36c78ea08a|346885|file|local|vector|||0.097|| -orig_statistics_dataset_p1|stem|8c7743089d4a7c11|aa939b0f558281aa|4dd7277b8c9e7570|1577914184||t19674.8415348554s|9274d6773c8e5ce3|93037|rds|local|vector|||0.834|One or more parsing issues, call problems on your data frame for details,e.g. dat vroom... problemsdat| +orig_statistics_dataset_p1|stem|8c7743089d4a7c11|aa939b0f558281aa|4dd7277b8c9e7570|1577914184||t19702.8682002707s|9274d6773c8e5ce3|93037|rds|local|vector|||0.834|One or more parsing issues, call problems on your data frame for details,e.g. dat vroom... problemsdat| orig_statistics_input|stem|dea84ce15745ea55|3854df6349665ec0|737711a03997e19c|766946636||t19599.7475975041s|31dbcdad4a9749ea|116370|rds|local|vector|||0.243|| orig_statistics_input_file|stem|6992fb119b69fe57|9e4ef12ebec638df|2f63b3e6aac469fd|-1553979786|/Users/theresawisneskie/Documents/SCORE/SCORE_papers/data/external_output/tilburg/orig_statistics_input.tsv|t19599.7475997756s|587083c3123a8856|400924|file|local|vector|||0.012|| orig_statistics_input_gsheet|stem|7137190230b3b9b4|d83b07f71154e7f2|ef46db3751d8e999|65061010||t19587.6728225445s|f0d5811415b6b792|97|rds|local|vector|||0.32|| orig_statistics_input_mod_date|stem|88ac7522b9d9beee|0c1c96b3fc42a6ad|4f89c0e0c8368fe1|773685720||t19600.5649332994s|dff9495c51b724c1|77|rds|local|vector|||0.844|| orig_statistics_input_tilburg|stem|cd9ae9a81649e348|bce9327c16d92420|64f8d6afc8dcaa5a|1121680062||t19599.7475279142s|d567f332af5e0242|120014|rds|local|vector|||2.623|| -orig_statistics_manual_data_entry|stem|10ce46ddb3b3165c|9614c9111222e219|db01eb91771a6e6b|654883457||t19674.8415533106s|b710176b916cf0e9|2771|rds|local|vector|||0.768|| -orig_statistics_manual_file|stem|b527092d6b502800|db94c9d617954695|ef46db3751d8e999|-589235286||t19674.8411042955s|b3654bf3682c79ca|86|rds|local|vector|||0|| -orig_statistics_manual_moddate|stem|b0555d1849304d18|bebcaff5ab1b3dbc|113def65aea2fd54|-1684405514||t19702.8638390827s|dff9495c51b724c1|77|rds|local|vector|||0.258|| -orig_statistics_output_p2|stem|87fb94191ee3e67f|10bfe5dbaffa65ce|8321a12ab0f9f451|-1548973816||t19702.8601714999s|e939cf0113bfa0a8|138980|rds|local|vector|||1.767|One or more parsing issues, call problems on your data frame for details,e.g. dat vroom... problemsdat| -orig_statistics_output_p2_mod_date|stem|9b83b4640342a250|85a85002001b69b6|758e05c4b910614c|1470382855||t19702.8638266977s|4bab66ba4bee4440|108|rds|local|vector|||0.472|| -orig_statistics_output_p2_osf|stem|bd33e183684407c4|4b01986bfdde8c16|ef46db3751d8e999|679538478||t19674.8411040518s|00c37fc0166b3b11|58|rds|local|vector|||0|| -orig_statistics_p1_file|stem|0111f57ad3809efc|896ea845b6c6df46|ef46db3751d8e999|380404372||t19674.8411045262s|b3654bf3682c79ca|86|rds|local|vector|||0|| -orig_statistics_p1_moddate|stem|b0555d1849304d18|1c325091d93a0529|87ec9fb6a2c67a4f|1821613396||t19702.8638320753s|dff9495c51b724c1|77|rds|local|vector|||0.187|| +orig_statistics_manual_data_entry|stem|10ce46ddb3b3165c|9614c9111222e219|db01eb91771a6e6b|654883457||t19702.8682264213s|b710176b916cf0e9|2771|rds|local|vector|||0.768|| +orig_statistics_manual_file|stem|b527092d6b502800|db94c9d617954695|ef46db3751d8e999|-589235286||t19702.8678152597s|b3654bf3682c79ca|86|rds|local|vector|||0|| +orig_statistics_manual_moddate|stem|b0555d1849304d18|bebcaff5ab1b3dbc|113def65aea2fd54|-1684405514||t19703.7234372531s|dff9495c51b724c1|77|rds|local|vector|||0.226|| +orig_statistics_output_p2|stem|87fb94191ee3e67f|10bfe5dbaffa65ce|8321a12ab0f9f451|-1548973816||t19703.6644389848s|e939cf0113bfa0a8|138980|rds|local|vector|||2.459|One or more parsing issues, call problems on your data frame for details,e.g. dat vroom... problemsdat| +orig_statistics_output_p2_mod_date|stem|9b83b4640342a250|85a85002001b69b6|758e05c4b910614c|1470382855||t19703.7234267123s|4bab66ba4bee4440|108|rds|local|vector|||0.523|| +orig_statistics_output_p2_osf|stem|bd33e183684407c4|4b01986bfdde8c16|ef46db3751d8e999|679538478||t19702.8678150685s|00c37fc0166b3b11|58|rds|local|vector|||0|| +orig_statistics_p1_file|stem|0111f57ad3809efc|896ea845b6c6df46|ef46db3751d8e999|380404372||t19702.8678150309s|b3654bf3682c79ca|86|rds|local|vector|||0|| +orig_statistics_p1_moddate|stem|b0555d1849304d18|1c325091d93a0529|87ec9fb6a2c67a4f|1821613396||t19703.723431811s|dff9495c51b724c1|77|rds|local|vector|||0.232|| orig_variables|stem|87cb5db81268fbb0|04c6598a17f17a11|630d84d321eb8fe9|1496817151||t19620.8650029206s|7a78efdfe3383505|85922|rds|local|vector|||0.032|| orig_variables_file|stem|a40476660b61e465|9def54d37c4f3211|4373217b14947adf|1841208288|/Users/theresawisneskie/Documents/SCORE_Org/data/rr/orig_variables.tsv|t19551.7748601636s|b799a26b40d8451f|365996|file|local|vector|||2.737|| -orig_vars_qa|stem|3608e829fbdf9a44|8583be5db3efe63c|0b269295c93ac5ab|-1698991417||t19702.8639027995s|cfc04b7e8e1bd12c|53617|rds|local|vector|||1.501|| -orig_vars_qa_gsheet|stem|9833933f52b9d70b|96429736e75ccccc|ef46db3751d8e999|-274810929||t19674.8411043853s|f0d5811415b6b792|97|rds|local|vector|||0.145|| -orig_vars_qa_mod_date|stem|7a035aff16ec4130|77ba80a476ab3159|8d942c7d465f3cec|-1676201819||t19702.8638192125s|dff9495c51b724c1|77|rds|local|vector|||0.242|| -original_inftest_dataset|stem|11c78ff81eb2cb28|6811fb35a7232fd7|415212331de3c0d2|-2015862532||t19674.8415124264s|6048de4c723205e6|2642|rds|local|vector|||0.673|| -original_inftest_file|stem|5956ae5ed3ff5cc8|7400299f5c394d4d|ef46db3751d8e999|-939075500||t19674.8411043333s|b3654bf3682c79ca|86|rds|local|vector|||0|| -original_inftest_moddate|stem|b0555d1849304d18|5eb243e54b0d731a|20f83a60de29329f|-1440710265||t19702.863851319s|dff9495c51b724c1|77|rds|local|vector|||0.211|| +orig_vars_qa|stem|3608e829fbdf9a44|8583be5db3efe63c|0b269295c93ac5ab|-1698991417||t19703.7235140709s|cfc04b7e8e1bd12c|53617|rds|local|vector|||2.225|| +orig_vars_qa_gsheet|stem|9833933f52b9d70b|96429736e75ccccc|ef46db3751d8e999|-274810929||t19702.8678152425s|f0d5811415b6b792|97|rds|local|vector|||0.145|| +orig_vars_qa_mod_date|stem|7a035aff16ec4130|77ba80a476ab3159|8d942c7d465f3cec|-1676201819||t19703.7234182007s|dff9495c51b724c1|77|rds|local|vector|||0.277|| +original_inftest_dataset|stem|11c78ff81eb2cb28|6811fb35a7232fd7|415212331de3c0d2|-2015862532||t19702.8682526636s|6048de4c723205e6|2642|rds|local|vector|||0.673|| +original_inftest_file|stem|5956ae5ed3ff5cc8|7400299f5c394d4d|ef46db3751d8e999|-939075500||t19702.8678153091s|b3654bf3682c79ca|86|rds|local|vector|||0|| +original_inftest_moddate|stem|b0555d1849304d18|5eb243e54b0d731a|20f83a60de29329f|-1440710265||t19703.7234493962s|dff9495c51b724c1|77|rds|local|vector|||0.218|| p2_id_key|stem|f050013e0f21a55f|ef5c6d9cc7630907|31d45ba360081ca3|-1288011618||t19549.6030189373s|5275404ce32c040e|14488|rds|local|vector|||0.038|| p2_id_key_file|stem|a727342764924ccb|9c4f1e6d75bbd0f1|4e3739ef91745af0|1754640234|/Users/theresawisneskie/Documents/SCORE/SCORE_papers/data/paper_level/p2_id_key.csv|t19397.8089282086s|09da6432e1f2331f|20631|file|local|vector|||0.001|| -p2_repli_vf|stem|f4a98a5ad960be2a|5b64ea07d9f13fbc|42f41c4f6711f485|1580136712||t19674.8413623456s|26d8fb4b526e39ee|3340|rds|local|vector|||0.701|| -p2_repli_vf_file|stem|9c78af257b304431|7865bbb416c1a2c1|ef46db3751d8e999|242621543||t19674.8411044652s|b3654bf3682c79ca|86|rds|local|vector|||0|| -p2_repli_vf_moddate|stem|b0555d1849304d18|13bd76f459bbdc1d|3db522f5b89a1824|714180891||t19702.8638534867s|dff9495c51b724c1|77|rds|local|vector|||0.184|| +p2_repli_vf|stem|f4a98a5ad960be2a|5b64ea07d9f13fbc|42f41c4f6711f485|1580136712||t19702.8681467821s|26d8fb4b526e39ee|3340|rds|local|vector|||0.701|| +p2_repli_vf_file|stem|9c78af257b304431|7865bbb416c1a2c1|ef46db3751d8e999|242621543||t19702.8678154382s|b3654bf3682c79ca|86|rds|local|vector|||0|| +p2_repli_vf_moddate|stem|b0555d1849304d18|13bd76f459bbdc1d|3db522f5b89a1824|714180891||t19703.7234516364s|dff9495c51b724c1|77|rds|local|vector|||0.189|| p2_repro_input_changelog|stem|5f96842df25069dc|dc2951b63aa83552|4f3adb6b99994ebe|1349257770||t19663.7600025376s|909c2ac50c1f63c3|1126|rds|local|vector|||1.067|| p2_repro_input_changelog_file|stem|8fea26f172c6060b|e2d3795cc5e0d824|ef46db3751d8e999|123532905||t19663.7598655165s|f0d5811415b6b792|97|rds|local|vector|||0|| p2_repro_input_changelog_moddate|stem|e2226f2bfbd6fb24|6e8c2ebf84883730|8068e54f63323784|286567377||t19663.7598822737s|dff9495c51b724c1|77|rds|local|vector|||0.222|| -p2_repro_vf|stem|1f8b8727d907f2b7|ce596b6126c589c0|ca2b29a27844c014|-2044233774||t19674.8412381791s|78116c1a4c40d04b|2851|rds|local|vector|||0.589|| -p2_repro_vf_file|stem|2241d6491312f4ab|76d85def17908ba4|ef46db3751d8e999|-2029585368||t19674.8411041262s|b3654bf3682c79ca|86|rds|local|vector|||0|| -p2_repro_vf_moddate|stem|b0555d1849304d18|0224388204cae393|3ec36c2f59ca3513|-1318523890||t19702.8638413815s|dff9495c51b724c1|77|rds|local|vector|||0.194|| +p2_repro_vf|stem|1f8b8727d907f2b7|ce596b6126c589c0|ca2b29a27844c014|-2044233774||t19702.8680466617s|78116c1a4c40d04b|2851|rds|local|vector|||0.589|| +p2_repro_vf_file|stem|2241d6491312f4ab|76d85def17908ba4|ef46db3751d8e999|-2029585368||t19702.8678152761s|b3654bf3682c79ca|86|rds|local|vector|||0|| +p2_repro_vf_moddate|stem|b0555d1849304d18|0224388204cae393|3ec36c2f59ca3513|-1318523890||t19703.723439359s|dff9495c51b724c1|77|rds|local|vector|||0.176|| process_raw_repli_vf|function|07494fa87a336cd6||||||||||||||| read_google_csv|function|f1f10b55fe1b6fce||||||||||||||| read_google_sheet|function|361769e0aad78a52||||||||||||||| read_google_tsv|function|9aaba7d64ca7b572||||||||||||||| repli_data_entry|stem|045a599605c86522|d4a1700b68f5352a|cdb5322553ecb135|-652185030||t19613.8653115365s|b14719f63f85659d|156770|rds|local|vector|||0.336|| -repli_export|stem|7ee66a7897bb2aed|9fbca4d6e07e6622|49b8452fc5df95c6|-411665577||t19702.8602888228s|f2ffa8ee7b4412f4|157449|rds|local|vector|||0.431|| -repli_input_changelog|stem|450ed9f9e16bc8d9|71412182b6b1ffea|2b37228831bac6c1|-463572932||t19702.860183601s|6f314cf4eb190907|7616|rds|local|vector|||1.04|| -repli_input_changelog_file|stem|1e8ab1cffb921f88|b3e1585768b4f21d|ef46db3751d8e999|584491267||t19674.8411042728s|f0d5811415b6b792|97|rds|local|vector|||0|| -repli_input_changelog_moddate|stem|1b140f4467aaca9c|6e8c2ebf84883730|8068e54f63323784|89727246||t19702.8638360417s|dff9495c51b724c1|77|rds|local|vector|||0.339|| -repli_outcomes|stem|bc40df21dcdee7fc|1bf85971762cd74f|a831a81c45eea480|-1550709889||t19702.8602960349s|9e8a29bdb5decb1e|173099|rds|local|vector|||0.071|There was 1 warning in mutate.ℹ In argument acrossfactors, as.factor.Caused by warning Using an external vector in selections was deprecated in tidyselect 1.1.0.ℹ Please use all_of or any_of instead. Was data selectfactors Now data selectall_offactorsSee httpstidyselect.rlib.orgreferencefaqexternalvector.html.| +repli_export|stem|7ee66a7897bb2aed|9fbca4d6e07e6622|49b8452fc5df95c6|-411665577||t19702.8682579172s|f2ffa8ee7b4412f4|157449|rds|local|vector|||0.431|| +repli_input_changelog|stem|450ed9f9e16bc8d9|71412182b6b1ffea|2b37228831bac6c1|-463572932||t19702.8681581595s|6f314cf4eb190907|7616|rds|local|vector|||1.04|| +repli_input_changelog_file|stem|1e8ab1cffb921f88|b3e1585768b4f21d|ef46db3751d8e999|584491267||t19702.8678154551s|f0d5811415b6b792|97|rds|local|vector|||0|| +repli_input_changelog_moddate|stem|1b140f4467aaca9c|6e8c2ebf84883730|8068e54f63323784|89727246||t19703.7234345426s|dff9495c51b724c1|77|rds|local|vector|||0.23|| +repli_outcomes|stem|bc40df21dcdee7fc|1bf85971762cd74f|a831a81c45eea480|-1550709889||t19703.6645316397s|9e8a29bdb5decb1e|173099|rds|local|vector|||0.071|There was 1 warning in mutate.ℹ In argument acrossfactors, as.factor.Caused by warning Using an external vector in selections was deprecated in tidyselect 1.1.0.ℹ Please use all_of or any_of instead. Was data selectfactors Now data selectall_offactorsSee httpstidyselect.rlib.orgreferencefaqexternalvector.html.| repli_primary|stem|2d364186e7502b1e|3f020df85e65b2ec|a3b960e1af7fc1d0|349140557||t19702.8602963309s|146fa8b5346d6cff|126755|rds|local|vector|||0.007|| repli_primary_file|stem|27f26ed895dfdfbb|0be1a6481bb48939|6bae89149a82eff2|1073006109||t19613.8653137577s|4f74cbc508fbc9d3|133|rds|local|vector|||0.11|| repli_secondary|stem|b230d9e8dffc6348|326d8567b27aa5b7|c1b88dbccc966dd4|1647155869||t19702.8602964266s|b2d4c6672ece891f|36020|rds|local|vector|||0.002|| @@ -112,49 +112,49 @@ replication_outcome_status|stem|3f17c7d167cbdad5|d0af1b6dd3b38195|dbe7e2d9cf978b replication_outcome_status_file|stem|4ae8db0fbc3cb371|ed435bbdef88871e|af4986d355838f9b|-996001090|/Users/theresawisneskie/Documents/SCORE_Org/data/rr/status/replication_outcome_status.csv|t19565.8340691159s|6f42eea7edcb0597|33097|file|local|vector|||0|| replication_outcome_status_loaded|stem|9af68188b2efbe0a|7b5f9e7adb3ce755|2029d98c9e8743fa|-281567976||t19566.7956739303s|c2a4caffc75de547|6335|rds|local|vector|||0.028|| replication_outcome_status_updated|stem|4ae8db0fbc3cb371|34eb71601fdc53df|54594e3cfb7c397e|1434854150|/Users/theresawisneskie/Documents/SCORE_Org/data/rr/status/replication_outcome_status.csv|t19565.8340691159s|6f42eea7edcb0597|33097|file|local|vector|||0.013|| -replication_qa|stem|a99659c9f5fa296e|28c1a9fc23b6c85c|1f4b01a3e2ead9dd|49819664||t19702.8639219297s|61544ba2d274cbb0|73464|rds|local|vector|||1.641|There was 1 warning in mutate.ℹ In argument acrossall_ofto_numeric, as.double.x.Caused by warning NAs introduced by coercion| +replication_qa|stem|a99659c9f5fa296e|28c1a9fc23b6c85c|1f4b01a3e2ead9dd|49819664||t19703.7235321798s|61544ba2d274cbb0|73464|rds|local|vector|||1.552|There was 1 warning in mutate.ℹ In argument acrossall_ofto_numeric, as.double.x.Caused by warning NAs introduced by coercion| replication_qa_file|stem|c40026a63e90a20e|851a6cd3ad4d93f5|af4986d355838f9b|-150818710|/Users/theresawisneskie/Documents/SCORE_Org/data/rr/replication_qa.tsv|t19397.8089301099s|22f0c50a699232be|246670|file|local|vector|||0.119|| -replication_qa_gsheet|stem|633840fb2a41ae9c|1a11df88e1762750|ef46db3751d8e999|524784443||t19674.841104231s|f0d5811415b6b792|97|rds|local|vector|||0|| +replication_qa_gsheet|stem|633840fb2a41ae9c|1a11df88e1762750|ef46db3751d8e999|524784443||t19702.8678152223s|f0d5811415b6b792|97|rds|local|vector|||0|| replication_qa_mod_date|stem|8ba52a628cf43ade|db8c419168bf73d4|61c9798914b8e8ed|-1954816758||t19615.725470641s|dff9495c51b724c1|77|rds|local|vector|||0.254|| repro_export|stem|a9276c6a7d7517e4|5e784d525f5aa3e6|7497db6e4beb8c21|-1658640441||t19663.7602710954s|5b2b86edc3065c4a|40005|rds|local|vector|||0.05|| -reproduction_qa|stem|a4c34b2b14be4943|609ddb796673a9d2|79c9ac01cce43979|-535869334||t19702.8638850228s|e77346534ddee027|41642|rds|local|vector|||2.214|| 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rr_attempt_status_file|stem|649256cb7de623f1|6a35068fb68aa51e|af4986d355838f9b|-2092996541|/Users/theresawisneskie/Documents/SCORE_Org/data/rr/status/rr_attempt_status.csv|t19397.808930134s|62be6b79cd38ef08|44153|file|local|vector|||0.117|| -rr_attempts_minted|stem|6052c42dd250c91f|1b041b138fc2b09c|bd5f0a009a63e524|-1347692668||t19674.8412819533s|a192a14775032a93|7205|rds|local|vector|||0.715|| -rr_attempts_minted_file|stem|ae893169619dd040|20ff57583a508b7e|ef46db3751d8e999|632770681||t19674.8411042538s|b3654bf3682c79ca|86|rds|local|vector|||0|| -rr_attempts_minted_moddate|stem|b0555d1849304d18|abd3e5d4cb15d99a|926ae77353facf3f|838030836||t19702.8638465519s|dff9495c51b724c1|77|rds|local|vector|||0.241|| +rr_attempts_minted|stem|6052c42dd250c91f|1b041b138fc2b09c|bd5f0a009a63e524|-1347692668||t19702.8680376154s|a192a14775032a93|7205|rds|local|vector|||0.715|| 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-rr_outcomes_dataset_p1_file|stem|38df086024aa936a|cfef50e626d048ff|ef46db3751d8e999|-1191766433||t19674.8411044484s|b3654bf3682c79ca|86|rds|local|vector|||0|| -rr_outcomes_dataset_p1_moddate|stem|b0555d1849304d18|42b9e3cec183fe0a|f8eaa8640d3721c2|-876425914||t19702.8638212003s|dff9495c51b724c1|77|rds|local|vector|||0.169|| -rr_projects|stem|08efd4e8b942d3df|e344f00d3940480c|78b93ce5d61fa046|-396283616||t19674.841330091s|66ee3a5822251b40|26845|rds|local|vector|||0.66|| -rr_projects_file|stem|3a74654718032a90|f3db8bccd707087e|ef46db3751d8e999|1335176940||t19674.8411043511s|b3654bf3682c79ca|86|rds|local|vector|||0|| -rr_projects_moddate|stem|b0555d1849304d18|4c1dd47e3948704f|d633010439db5bab|-1816630940||t19702.8638558217s|dff9495c51b724c1|77|rds|local|vector|||0.198|| +rr_outcomes_dataset_p1|stem|298094d73ae67f57|98d08a09dd6a20cf|6c9fd9ddf00583ce|557866776||t19702.8681172571s|3a50733422bc19b4|113515|rds|local|vector|||0.949|| 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rr_replication_outcomes_p2|stem|8b4e3a4182dd2a27|4ace702d8413a093|f426d4f3f5a82496|-166997433||t19570.612130227s|a7e32826ea933426|45940|rds|local|vector|||0.251|| -rr_reporting_checkin|stem|df01ade03192c3c0|c94c261b6a39c1a7|4c3d458525d7b560|-159602147||t19674.8412638012s|926e68571db54ad4|18709|rds|local|vector|||0.677|| -rr_reporting_checkin_file|stem|9302d8be5833a80b|1f62463992074b2d|ef46db3751d8e999|2052531205||t19674.8411041724s|b3654bf3682c79ca|86|rds|local|vector|||0|| -rr_reporting_checkin_moddate|stem|b0555d1849304d18|3a132ebf07988e7d|d331cc328694af1d|-1434497540||t19702.8638298607s|dff9495c51b724c1|77|rds|local|vector|||0.27|| +rr_reporting_checkin|stem|df01ade03192c3c0|c94c261b6a39c1a7|4c3d458525d7b560|-159602147||t19702.8680931084s|926e68571db54ad4|18709|rds|local|vector|||0.677|| +rr_reporting_checkin_file|stem|9302d8be5833a80b|1f62463992074b2d|ef46db3751d8e999|2052531205||t19702.8678153464s|b3654bf3682c79ca|86|rds|local|vector|||0|| 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rr_statistics_input_tilburg|stem|fcb99a1c30539da6|786a78570cca5463|649ce44edd675d9a|-2117780133||t19593.7156540616s|21ea163898f374d4|119790|rds|local|vector|||3.16|| -rr_statistics_output_p2|stem|6af69267c1a091a1|e27224de40a47a5a|1935511ad79f9a1b|1473213609||t19702.8602113278s|546b4b4fd9ecf17c|24903|rds|local|vector|||2.385|| -rr_statistics_output_p2_mod_date|stem|ba97c86785d7274b|c070acb820ed4655|abe1a46cbc2a4b27|1277869765||t19702.8638591671s|4bab66ba4bee4440|108|rds|local|vector|||0.285|| -rr_statistics_output_p2_osf|stem|94c0d5db24994642|449ef05e3f855762|ef46db3751d8e999|40585422||t19674.8411041515s|00c37fc0166b3b11|58|rds|local|vector|||0.001|| +rr_statistics_output_p2|stem|6af69267c1a091a1|e27224de40a47a5a|1935511ad79f9a1b|1473213609||t19702.8679912595s|546b4b4fd9ecf17c|24903|rds|local|vector|||2.385|| +rr_statistics_output_p2_mod_date|stem|ba97c86785d7274b|c070acb820ed4655|abe1a46cbc2a4b27|1277869765||t19703.7234600915s|4bab66ba4bee4440|108|rds|local|vector|||0.539|| 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+tagtable_covid_p1_file|stem|2c1def289ab881ed|6e25a7093500855c|ef46db3751d8e999|-640545787||t19702.867815328s|b3654bf3682c79ca|86|rds|local|vector|||0|| +tagtable_covid_p1_moddate|stem|b0555d1849304d18|2dbde51d2426fc25|d45ae9696f95897d|-996389966||t19703.7234415221s|dff9495c51b724c1|77|rds|local|vector|||0.182|| tagtable_p1|stem|2704f4c9702a3e31|434ed89b77f32783|4c6bde1b73e4009f|1857056087||t19549.7671955153s|1068772e20eda139|4402232|rds|local|vector|||0.296|| tagtable_p1_file|stem|49d1253dde9bd741|380d6a7857971970|4e3739ef91745af0|687716871|/Users/theresawisneskie/Documents/SCORE/SCORE_papers/data/claims/tagtable_p1.tsv|t19397.8089310373s|58ca8dff0689796d|12796579|file|local|vector|||0|| tagtable_p2_CES|stem|1ed69770db35c0bc|17cabf15e67ce621|ca553229fcba9988|-1337685309||t19549.7671979188s|b225d9cfb5ba95b4|741242|rds|local|vector|||0.086|| @@ -167,7 +167,7 @@ update_repli_attempts_staus|function|47ff8bc077efcc88||||||||||||||| update_repli_input|function|9e1432cc077993ae||||||||||||||| update_repli_outcomes_status|function|82b8035fc5144444||||||||||||||| valid_claims|stem|b73ee64e82307484|31503845aedce777|4f929f0cb9dbd9cb|-1047532097||t19564.7532019596s|e9eb8fcdc42fa72c|33756|rds|local|vector|||0.055|| -valid_ids|stem|e629014676cc0960|e591787d848101f2|00399ce59d67e2d2|1609612332||t19674.8414581629s|a9924331cd05df7a|226502|rds|local|vector|||0.027|| +valid_ids|stem|e629014676cc0960|e591787d848101f2|00399ce59d67e2d2|1609612332||t19702.8681886651s|a9924331cd05df7a|226502|rds|local|vector|||0.027|| valid_rr|stem|e7aa0a23363cfd68|89c45c6a3ef04dd3|3fc432f4037172bf|-942215095||t19555.8581479813s|34fe586450e60e2a|9302|rds|local|vector|||0.013|| write_hybrid|function|9e8963076cde222f||||||||||||||| write_repli_outcomes_status|function|a50b3907dbb546ad||||||||||||||| From 3fa62de21b7367523cf7a90f85fa0eaf4be0c5d5 Mon Sep 17 00:00:00 2001 From: noahhaber Date: Tue, 12 Dec 2023 12:25:24 -0500 Subject: [PATCH 2/3] final pull before moving everything to main --- _targets/meta/meta | 40 ++++++++++++++++++++-------------------- 1 file changed, 20 insertions(+), 20 deletions(-) diff --git a/_targets/meta/meta b/_targets/meta/meta index cf24e3b..4bc3f00 100644 --- a/_targets/meta/meta +++ b/_targets/meta/meta @@ -16,7 +16,7 @@ export_orig|function|1d13cf2cfd45903d||||||||||||||| export_repli|function|48b6c3b2718590c9||||||||||||||| export_repro|function|0a7486339a7e6bca||||||||||||||| finalized_claim4_file|stem|08b5c65587abf039|c8007a3e1323b441|ef46db3751d8e999|-750262725||t19702.8678152924s|b3654bf3682c79ca|86|rds|local|vector|||0|| -finalized_claim4_moddate|stem|b0555d1849304d18|2d0d81a000c050e0|6e76852deb7e5b69|1569643586||t19703.7234116376s|dff9495c51b724c1|77|rds|local|vector|||0.554|| +finalized_claim4_moddate|stem|b0555d1849304d18|2d0d81a000c050e0|6e76852deb7e5b69|1569643586||t19703.7249384837s|dff9495c51b724c1|77|rds|local|vector|||0.252|| finalized_claim4_table|stem|7887fb5b44d07123|c7a936d0e47d0fc5|fe8ec385f98b9889|2060009599||t19702.8680729932s|f1e3ad6a94194151|628374|rds|local|vector|||2.938|| generate_claim_ids|function|6819955c8beb1fd9||||||||||||||| generate_paper_ids|function|c3135e17a538d9a3||||||||||||||| @@ -56,7 +56,7 @@ orig_dataset|stem|47335296505e2c59|ad827e718f77f533|f4f6efacf8f73e8a|-1495429541 orig_dataset_data_entry|stem|5a6e41ba10440444|2410fba7b8239d24|85de6c3fa4e321bb|-1836909783||t19621.6653282251s|ce0976375cb0dc52|87089|rds|local|vector|||0.351|| orig_input_changelog|stem|8c679afa47f754de|74fc36a4af7e34f5|c5c9be44dc57c7f5|854218549||t19702.8681043313s|869dc918109f7fa2|9532|rds|local|vector|||1.483|| orig_input_changelog_file|stem|6c95cc454144b201|d5c895b1deacd6c4|ef46db3751d8e999|-2101306176||t19702.8678153636s|f0d5811415b6b792|97|rds|local|vector|||0|| -orig_input_changelog_moddate|stem|2072fcb57bf4d05d|5c5d157bcf9bfcbf|34d9fe7d925779fc|1041070366||t19703.7234149166s|dff9495c51b724c1|77|rds|local|vector|||0.273|| +orig_input_changelog_moddate|stem|2072fcb57bf4d05d|5c5d157bcf9bfcbf|34d9fe7d925779fc|1041070366||t19703.7249423386s|dff9495c51b724c1|77|rds|local|vector|||0.323|| orig_rr_file|stem|678d64c2dc2f6eef|f4dea69694f8ec0d|af4986d355838f9b|-391603615|/Users/theresawisneskie/Documents/SCORE_Org/data/rr/orig_dataset.tsv|t19545.8751896821s|21552c36c78ea08a|346885|file|local|vector|||0.097|| orig_statistics_dataset_p1|stem|8c7743089d4a7c11|aa939b0f558281aa|4dd7277b8c9e7570|1577914184||t19702.8682002707s|9274d6773c8e5ce3|93037|rds|local|vector|||0.834|One or more parsing issues, call problems on your data frame for details,e.g. dat vroom... problemsdat| orig_statistics_input|stem|dea84ce15745ea55|3854df6349665ec0|737711a03997e19c|766946636||t19599.7475975041s|31dbcdad4a9749ea|116370|rds|local|vector|||0.243|| @@ -66,31 +66,31 @@ orig_statistics_input_mod_date|stem|88ac7522b9d9beee|0c1c96b3fc42a6ad|4f89c0e0c8 orig_statistics_input_tilburg|stem|cd9ae9a81649e348|bce9327c16d92420|64f8d6afc8dcaa5a|1121680062||t19599.7475279142s|d567f332af5e0242|120014|rds|local|vector|||2.623|| orig_statistics_manual_data_entry|stem|10ce46ddb3b3165c|9614c9111222e219|db01eb91771a6e6b|654883457||t19702.8682264213s|b710176b916cf0e9|2771|rds|local|vector|||0.768|| orig_statistics_manual_file|stem|b527092d6b502800|db94c9d617954695|ef46db3751d8e999|-589235286||t19702.8678152597s|b3654bf3682c79ca|86|rds|local|vector|||0|| -orig_statistics_manual_moddate|stem|b0555d1849304d18|bebcaff5ab1b3dbc|113def65aea2fd54|-1684405514||t19703.7234372531s|dff9495c51b724c1|77|rds|local|vector|||0.226|| +orig_statistics_manual_moddate|stem|b0555d1849304d18|bebcaff5ab1b3dbc|113def65aea2fd54|-1684405514||t19703.7249649634s|dff9495c51b724c1|77|rds|local|vector|||0.374|| orig_statistics_output_p2|stem|87fb94191ee3e67f|10bfe5dbaffa65ce|8321a12ab0f9f451|-1548973816||t19703.6644389848s|e939cf0113bfa0a8|138980|rds|local|vector|||2.459|One or more parsing issues, call problems on your data frame for details,e.g. dat vroom... problemsdat| -orig_statistics_output_p2_mod_date|stem|9b83b4640342a250|85a85002001b69b6|758e05c4b910614c|1470382855||t19703.7234267123s|4bab66ba4bee4440|108|rds|local|vector|||0.523|| +orig_statistics_output_p2_mod_date|stem|9b83b4640342a250|85a85002001b69b6|758e05c4b910614c|1470382855||t19703.724952592s|4bab66ba4bee4440|108|rds|local|vector|||0.401|| orig_statistics_output_p2_osf|stem|bd33e183684407c4|4b01986bfdde8c16|ef46db3751d8e999|679538478||t19702.8678150685s|00c37fc0166b3b11|58|rds|local|vector|||0|| orig_statistics_p1_file|stem|0111f57ad3809efc|896ea845b6c6df46|ef46db3751d8e999|380404372||t19702.8678150309s|b3654bf3682c79ca|86|rds|local|vector|||0|| -orig_statistics_p1_moddate|stem|b0555d1849304d18|1c325091d93a0529|87ec9fb6a2c67a4f|1821613396||t19703.723431811s|dff9495c51b724c1|77|rds|local|vector|||0.232|| +orig_statistics_p1_moddate|stem|b0555d1849304d18|1c325091d93a0529|87ec9fb6a2c67a4f|1821613396||t19703.7249576324s|dff9495c51b724c1|77|rds|local|vector|||0.236|| orig_variables|stem|87cb5db81268fbb0|04c6598a17f17a11|630d84d321eb8fe9|1496817151||t19620.8650029206s|7a78efdfe3383505|85922|rds|local|vector|||0.032|| orig_variables_file|stem|a40476660b61e465|9def54d37c4f3211|4373217b14947adf|1841208288|/Users/theresawisneskie/Documents/SCORE_Org/data/rr/orig_variables.tsv|t19551.7748601636s|b799a26b40d8451f|365996|file|local|vector|||2.737|| -orig_vars_qa|stem|3608e829fbdf9a44|8583be5db3efe63c|0b269295c93ac5ab|-1698991417||t19703.7235140709s|cfc04b7e8e1bd12c|53617|rds|local|vector|||2.225|| +orig_vars_qa|stem|3608e829fbdf9a44|8583be5db3efe63c|0b269295c93ac5ab|-1698991417||t19703.7250289837s|cfc04b7e8e1bd12c|53617|rds|local|vector|||1.552|| orig_vars_qa_gsheet|stem|9833933f52b9d70b|96429736e75ccccc|ef46db3751d8e999|-274810929||t19702.8678152425s|f0d5811415b6b792|97|rds|local|vector|||0.145|| -orig_vars_qa_mod_date|stem|7a035aff16ec4130|77ba80a476ab3159|8d942c7d465f3cec|-1676201819||t19703.7234182007s|dff9495c51b724c1|77|rds|local|vector|||0.277|| +orig_vars_qa_mod_date|stem|7a035aff16ec4130|77ba80a476ab3159|8d942c7d465f3cec|-1676201819||t19703.724945511s|dff9495c51b724c1|77|rds|local|vector|||0.269|| original_inftest_dataset|stem|11c78ff81eb2cb28|6811fb35a7232fd7|415212331de3c0d2|-2015862532||t19702.8682526636s|6048de4c723205e6|2642|rds|local|vector|||0.673|| original_inftest_file|stem|5956ae5ed3ff5cc8|7400299f5c394d4d|ef46db3751d8e999|-939075500||t19702.8678153091s|b3654bf3682c79ca|86|rds|local|vector|||0|| -original_inftest_moddate|stem|b0555d1849304d18|5eb243e54b0d731a|20f83a60de29329f|-1440710265||t19703.7234493962s|dff9495c51b724c1|77|rds|local|vector|||0.218|| +original_inftest_moddate|stem|b0555d1849304d18|5eb243e54b0d731a|20f83a60de29329f|-1440710265||t19703.7249799171s|dff9495c51b724c1|77|rds|local|vector|||0.257|| p2_id_key|stem|f050013e0f21a55f|ef5c6d9cc7630907|31d45ba360081ca3|-1288011618||t19549.6030189373s|5275404ce32c040e|14488|rds|local|vector|||0.038|| p2_id_key_file|stem|a727342764924ccb|9c4f1e6d75bbd0f1|4e3739ef91745af0|1754640234|/Users/theresawisneskie/Documents/SCORE/SCORE_papers/data/paper_level/p2_id_key.csv|t19397.8089282086s|09da6432e1f2331f|20631|file|local|vector|||0.001|| p2_repli_vf|stem|f4a98a5ad960be2a|5b64ea07d9f13fbc|42f41c4f6711f485|1580136712||t19702.8681467821s|26d8fb4b526e39ee|3340|rds|local|vector|||0.701|| p2_repli_vf_file|stem|9c78af257b304431|7865bbb416c1a2c1|ef46db3751d8e999|242621543||t19702.8678154382s|b3654bf3682c79ca|86|rds|local|vector|||0|| -p2_repli_vf_moddate|stem|b0555d1849304d18|13bd76f459bbdc1d|3db522f5b89a1824|714180891||t19703.7234516364s|dff9495c51b724c1|77|rds|local|vector|||0.189|| +p2_repli_vf_moddate|stem|b0555d1849304d18|13bd76f459bbdc1d|3db522f5b89a1824|714180891||t19703.7249821359s|dff9495c51b724c1|77|rds|local|vector|||0.188|| p2_repro_input_changelog|stem|5f96842df25069dc|dc2951b63aa83552|4f3adb6b99994ebe|1349257770||t19663.7600025376s|909c2ac50c1f63c3|1126|rds|local|vector|||1.067|| p2_repro_input_changelog_file|stem|8fea26f172c6060b|e2d3795cc5e0d824|ef46db3751d8e999|123532905||t19663.7598655165s|f0d5811415b6b792|97|rds|local|vector|||0|| p2_repro_input_changelog_moddate|stem|e2226f2bfbd6fb24|6e8c2ebf84883730|8068e54f63323784|286567377||t19663.7598822737s|dff9495c51b724c1|77|rds|local|vector|||0.222|| p2_repro_vf|stem|1f8b8727d907f2b7|ce596b6126c589c0|ca2b29a27844c014|-2044233774||t19702.8680466617s|78116c1a4c40d04b|2851|rds|local|vector|||0.589|| p2_repro_vf_file|stem|2241d6491312f4ab|76d85def17908ba4|ef46db3751d8e999|-2029585368||t19702.8678152761s|b3654bf3682c79ca|86|rds|local|vector|||0|| -p2_repro_vf_moddate|stem|b0555d1849304d18|0224388204cae393|3ec36c2f59ca3513|-1318523890||t19703.723439359s|dff9495c51b724c1|77|rds|local|vector|||0.176|| +p2_repro_vf_moddate|stem|b0555d1849304d18|0224388204cae393|3ec36c2f59ca3513|-1318523890||t19703.7249671007s|dff9495c51b724c1|77|rds|local|vector|||0.178|| process_raw_repli_vf|function|07494fa87a336cd6||||||||||||||| read_google_csv|function|f1f10b55fe1b6fce||||||||||||||| read_google_sheet|function|361769e0aad78a52||||||||||||||| @@ -99,7 +99,7 @@ repli_data_entry|stem|045a599605c86522|d4a1700b68f5352a|cdb5322553ecb135|-652185 repli_export|stem|7ee66a7897bb2aed|9fbca4d6e07e6622|49b8452fc5df95c6|-411665577||t19702.8682579172s|f2ffa8ee7b4412f4|157449|rds|local|vector|||0.431|| repli_input_changelog|stem|450ed9f9e16bc8d9|71412182b6b1ffea|2b37228831bac6c1|-463572932||t19702.8681581595s|6f314cf4eb190907|7616|rds|local|vector|||1.04|| repli_input_changelog_file|stem|1e8ab1cffb921f88|b3e1585768b4f21d|ef46db3751d8e999|584491267||t19702.8678154551s|f0d5811415b6b792|97|rds|local|vector|||0|| -repli_input_changelog_moddate|stem|1b140f4467aaca9c|6e8c2ebf84883730|8068e54f63323784|89727246||t19703.7234345426s|dff9495c51b724c1|77|rds|local|vector|||0.23|| +repli_input_changelog_moddate|stem|1b140f4467aaca9c|6e8c2ebf84883730|8068e54f63323784|89727246||t19703.7249605953s|dff9495c51b724c1|77|rds|local|vector|||0.252|| repli_outcomes|stem|bc40df21dcdee7fc|1bf85971762cd74f|a831a81c45eea480|-1550709889||t19703.6645316397s|9e8a29bdb5decb1e|173099|rds|local|vector|||0.071|There was 1 warning in mutate.ℹ In argument acrossfactors, as.factor.Caused by warning Using an external vector in selections was deprecated in tidyselect 1.1.0.ℹ Please use all_of or any_of instead. Was data selectfactors Now data selectall_offactorsSee httpstidyselect.rlib.orgreferencefaqexternalvector.html.| repli_primary|stem|2d364186e7502b1e|3f020df85e65b2ec|a3b960e1af7fc1d0|349140557||t19702.8602963309s|146fa8b5346d6cff|126755|rds|local|vector|||0.007|| repli_primary_file|stem|27f26ed895dfdfbb|0be1a6481bb48939|6bae89149a82eff2|1073006109||t19613.8653137577s|4f74cbc508fbc9d3|133|rds|local|vector|||0.11|| @@ -112,49 +112,49 @@ replication_outcome_status|stem|3f17c7d167cbdad5|d0af1b6dd3b38195|dbe7e2d9cf978b replication_outcome_status_file|stem|4ae8db0fbc3cb371|ed435bbdef88871e|af4986d355838f9b|-996001090|/Users/theresawisneskie/Documents/SCORE_Org/data/rr/status/replication_outcome_status.csv|t19565.8340691159s|6f42eea7edcb0597|33097|file|local|vector|||0|| replication_outcome_status_loaded|stem|9af68188b2efbe0a|7b5f9e7adb3ce755|2029d98c9e8743fa|-281567976||t19566.7956739303s|c2a4caffc75de547|6335|rds|local|vector|||0.028|| replication_outcome_status_updated|stem|4ae8db0fbc3cb371|34eb71601fdc53df|54594e3cfb7c397e|1434854150|/Users/theresawisneskie/Documents/SCORE_Org/data/rr/status/replication_outcome_status.csv|t19565.8340691159s|6f42eea7edcb0597|33097|file|local|vector|||0.013|| -replication_qa|stem|a99659c9f5fa296e|28c1a9fc23b6c85c|1f4b01a3e2ead9dd|49819664||t19703.7235321798s|61544ba2d274cbb0|73464|rds|local|vector|||1.552|There was 1 warning in mutate.ℹ In argument acrossall_ofto_numeric, as.double.x.Caused by warning NAs introduced by coercion| +replication_qa|stem|a99659c9f5fa296e|28c1a9fc23b6c85c|1f4b01a3e2ead9dd|49819664||t19703.7250452923s|61544ba2d274cbb0|73464|rds|local|vector|||1.397|There was 1 warning in mutate.ℹ In argument acrossall_ofto_numeric, as.double.x.Caused by warning NAs introduced by coercion| replication_qa_file|stem|c40026a63e90a20e|851a6cd3ad4d93f5|af4986d355838f9b|-150818710|/Users/theresawisneskie/Documents/SCORE_Org/data/rr/replication_qa.tsv|t19397.8089301099s|22f0c50a699232be|246670|file|local|vector|||0.119|| replication_qa_gsheet|stem|633840fb2a41ae9c|1a11df88e1762750|ef46db3751d8e999|524784443||t19702.8678152223s|f0d5811415b6b792|97|rds|local|vector|||0|| replication_qa_mod_date|stem|8ba52a628cf43ade|db8c419168bf73d4|61c9798914b8e8ed|-1954816758||t19615.725470641s|dff9495c51b724c1|77|rds|local|vector|||0.254|| repro_export|stem|a9276c6a7d7517e4|5e784d525f5aa3e6|7497db6e4beb8c21|-1658640441||t19663.7602710954s|5b2b86edc3065c4a|40005|rds|local|vector|||0.05|| -reproduction_qa|stem|a4c34b2b14be4943|609ddb796673a9d2|79c9ac01cce43979|-535869334||t19703.7234878975s|e77346534ddee027|41642|rds|local|vector|||2.378|| +reproduction_qa|stem|a4c34b2b14be4943|609ddb796673a9d2|79c9ac01cce43979|-535869334||t19703.7250105549s|e77346534ddee027|41642|rds|local|vector|||1.851|| 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tagtable_p2_CES|stem|1ed69770db35c0bc|17cabf15e67ce621|ca553229fcba9988|-1337685309||t19549.7671979188s|b225d9cfb5ba95b4|741242|rds|local|vector|||0.086|| From f65c245c1925cae2f0019400d84327e3ce6e51b0 Mon Sep 17 00:00:00 2001 From: noahhaber Date: Tue, 12 Dec 2023 12:30:48 -0500 Subject: [PATCH 3/3] Revert "Merge pull request #3 from CenterForOpenScience/Analysis-updates" This reverts commit 17e37961ab36e91bdc474972d99594eb9b7859ea, reversing changes made to d25f86dae08088331894088d3c1faaf44ae5074a. --- .../Data exploration app/common functions.R | 142 ------ .../data exploration app.R | 435 ++++++------------ .../Data exploration app/orig_dataset.RData | Bin 104891 -> 0 bytes .../Data exploration app/repli_outcomes.RData | Bin 173127 -> 173149 bytes .../noahhaber-cos/data_exploration_app.dcf | 2 +- Analysis/code whiteboard.R | 354 ++++---------- renv/activate.R | 432 +++++------------ renv/settings.json | 19 - 8 files changed, 350 insertions(+), 1034 deletions(-) delete mode 100644 Analysis/Data exploration app/common functions.R delete mode 100644 Analysis/Data exploration app/orig_dataset.RData delete mode 100644 renv/settings.json diff --git a/Analysis/Data exploration app/common functions.R b/Analysis/Data exploration app/common functions.R deleted file mode 100644 index 7a34b66..0000000 --- a/Analysis/Data exploration app/common functions.R +++ /dev/null @@ -1,142 +0,0 @@ - -# Aesthetic functions and presets -{ - GeomSplitViolin <- ggproto("GeomSplitViolin", GeomViolin, - draw_group = function(self, data, ..., draw_quantiles = NULL) { - data <- transform(data, xminv = x - violinwidth * (x - xmin), xmaxv = x + violinwidth * (xmax - x)) - grp <- data[1, "group"] - newdata <- plyr::arrange(transform(data, x = if (grp %% 2 == 1) xminv else xmaxv), if (grp %% 2 == 1) y else -y) - newdata <- rbind(newdata[1, ], newdata, newdata[nrow(newdata), ], newdata[1, ]) - newdata[c(1, nrow(newdata) - 1, nrow(newdata)), "x"] <- round(newdata[1, "x"]) - - if (length(draw_quantiles) > 0 & !scales::zero_range(range(data$y))) { - stopifnot(all(draw_quantiles >= 0), all(draw_quantiles <= - 1)) - quantiles <- ggplot2:::create_quantile_segment_frame(data, draw_quantiles) - aesthetics <- data[rep(1, nrow(quantiles)), setdiff(names(data), c("x", "y")), drop = FALSE] - aesthetics$alpha <- rep(1, nrow(quantiles)) - both <- cbind(quantiles, aesthetics) - quantile_grob <- GeomPath$draw_panel(both, ...) - ggplot2:::ggname("geom_split_violin", grid::grobTree(GeomPolygon$draw_panel(newdata, ...), quantile_grob)) - } - else { - ggplot2:::ggname("geom_split_violin", GeomPolygon$draw_panel(newdata, ...)) - } - }) - - geom_split_violin <- function(mapping = NULL, data = NULL, stat = "ydensity", position = "identity", ..., - draw_quantiles = NULL, trim = TRUE, scale = "area", na.rm = FALSE, - show.legend = NA, inherit.aes = TRUE) { - layer(data = data, mapping = mapping, stat = stat, geom = GeomSplitViolin, - position = position, show.legend = show.legend, inherit.aes = inherit.aes, - params = list(trim = trim, scale = scale, draw_quantiles = draw_quantiles, na.rm = na.rm, ...)) - } - # Color palette - { - palette_weezer_blue <- c("#00a2e7","#dee5cd","#010c09","#083259","#b2915f","#d7b1b7","#00374b","#124e80", "#001212") - palette_weezer_pinkerton <- c("#d5bf98","#14140b","#70624b","#8a8d82","#304251","#465656","#945a2d","#708090") - palette_weezer_green <- c("#bece30","#020100","#4f6238","#cac986","#981f2c","#c13f33","#461005") - palette_weezer_maladroit <- c("#e0dcce","#575b61","#b69e44","#953d31","#e5b066","#343729","#3e3131") - palette_weezer_make_believe <- c("#000000","#EAECEB","#C2C2C2","#A0A0A0","#313131") - palette_weezer_red <- c("#ED1B34","#8A817C","#141311","#8B8D9C","#332E28") - palette_weezer_raditude <- c("#EC2221","#FBFFFB","#FDF600","#CEB181","#4E1110") - palette_weezer_everything <- c("#E8A662","#F4F5F1","#463D47","#7F3009","#35180E","F6F3CF") - palette_weezer_white <- c("#FDFDFD","#242424","#E3E3E3","#B6B6B6","#EEEDED") - palette_weezer_pacific_daydream <- c("#1E3555","#5C6455","#FBE4BC","#1D1F1E","#69797B","#F8E6CF","#F8E6CF") - palette_weezer_teal <- c("#1DBBBE","#D6A8CD","#F8F8F8","#182633","#90C5DF") - palette_weezer_black <- c("#2D2B2C","#060606","#E9E7E8","#0E0E0E") - palette_weezer_ok_human <- c("#B2A17A","#B3B470","#B1A78F","#D1BE8F","#726D5C","#B8B6A6","#5B4F3F") - palette_weezer_van_weezer <- c("#B2023E","#E933D3","#770265","#170032","#FDF8FF","#170032","#5329F9","#F3FED5") - - palette_score_charts <- c(palette_weezer_blue[1], - palette_weezer_red[1], - palette_weezer_green[1], - palette_weezer_teal[1], - - palette_weezer_pinkerton[1], - palette_weezer_van_weezer[3] - ) - } -} - -# Statistical functions -{ - bootstrap.clust <- function(data=NA,FUN=NA,clustervar=NA, - alpha=.05,tails="two-tailed",iters=200){ - # Set up cluster designations - if(anyNA(clustervar)){ data$cluster.id <- 1:nrow(data) - } else { data$cluster.id <- data[[clustervar]] } - cluster.set <- unique(data$cluster.id) - # Generate original target variable - point.estimate <- FUN(data) - # Create distribution of bootstrapped samples - estimates.bootstrapped <- replicate(iters,{ - # Generate sample of clusters to include - clust.list <- sample(cluster.set,length(cluster.set),replace = TRUE) - # Build dataset from cluster list - data.clust <- do.call(rbind,lapply(1:length(clust.list), function(x) { - data[data$cluster.id == clust.list[x],] - })) - # Run function on new data - tryCatch(FUN(data.clust),finally=NA) - }, - simplify=TRUE) - # Outcomes measures - if(is.matrix(estimates.bootstrapped)){ - n_estimates <- nrow(estimates.bootstrapped) - SE <- unlist(lapply(1:n_estimates,function(x) {sd(estimates.bootstrapped[x,],na.rm = TRUE)})) - if (tails == "two-tailed"){ - CI.lb <- unlist(lapply(1:n_estimates,function(x) {quantile(estimates.bootstrapped[x,], alpha/2,na.rm = TRUE)})) - CI.ub <- unlist(lapply(1:n_estimates,function(x) {quantile(estimates.bootstrapped[x,], 1-alpha/2,na.rm = TRUE)})) - } else if (tails == "one-tailed, upper"){ - CI.lb <- unlist(lapply(1:n_estimates,function(x) {NA})) - CI.ub <- unlist(lapply(1:n_estimates,function(x) {quantile(estimates.bootstrapped[x,], 1-alpha,na.rm = TRUE)})) - } else if (tails == "one-tailed, lower"){ - CI.lb <- unlist(lapply(1:n_estimates,function(x) {quantile(estimates.bootstrapped[x,], alpha,na.rm = TRUE)})) - CI.ub <- unlist(lapply(1:n_estimates,function(x) {NA})) - } - } else { - SE <- sd(estimates.bootstrapped,na.rm = TRUE) - if (tails == "two-tailed"){ - CI.lb <- quantile(estimates.bootstrapped, alpha/2,na.rm = TRUE) - CI.ub <- quantile(estimates.bootstrapped, 1-alpha/2,na.rm = TRUE) - } else if (tails == "one-tailed, upper"){ - CI.lb <- NA - CI.ub <- quantile(estimates.bootstrapped, 1-alpha,na.rm = TRUE) - } else if (tails == "one-tailed, lower"){ - CI.lb <- quantile(estimates.bootstrapped, alpha,na.rm = TRUE) - CI.ub <- NA - } - } - - # Outputs - return(list("point.estimate"=point.estimate,"SE"=SE, - "CI.lb"=CI.lb,"CI.ub"=CI.ub,"estimates.bootstrapped"=estimates.bootstrapped)) - } - - # Probability/Risk ratio - probability.ratio <- function(data=NA,exposure,outcome,weight=NA){ - # Generate dataset - if (is.na(data)){ - exposure <- as.numeric(exposure) - outcome <- as.numeric(outcome) - data <- data.frame(exposure,outcome) - if(anyNA(weight)){ - data$weight <- 1 - } else { - data$weight <- weight - } - } else { - data$expsoure <- as.numeric(data[[exposure]]) - data$outcome <- as.numeric(data[[exposure]]) - data$weight <- as.numeric(data[[weight]]) - } - - prob.exposed <- weighted.mean(data[data$exposure==1,]$outcome,data[data$exposure==1,]$weight,na.rm = TRUE) - prob.unexposed <- weighted.mean(data[data$exposure==0,]$outcome,data[data$exposure==0,]$weight,na.rm = TRUE) - probability.ratio <- prob.exposed/prob.unexposed - - return(probability.ratio) - } -} - diff --git a/Analysis/Data exploration app/data exploration app.R b/Analysis/Data exploration app/data exploration app.R index ee7ce14..a1898ee 100644 --- a/Analysis/Data exploration app/data exploration app.R +++ b/Analysis/Data exploration app/data exploration app.R @@ -1,4 +1,3 @@ - # Setup { # Libraries @@ -10,64 +9,77 @@ library(ggExtra) library(DT) library(tidyr) - library(pbapply) - } # Data loading { - if (file.exists("repli_outcomes.RData")) { - load(file="repli_outcomes.RData") - } else { - load(file="Analysis/Data exploration app/repli_outcomes.RData") - } - - if (file.exists("orig_dataset.RData")) { - load(file="orig_dataset.RData") - } else { - load(file="Analysis/Data exploration app/orig_dataset.RData") - } - - if (file.exists("common functions.R")) { - source("common functions.R") - } else { - source(file="Analysis/Data exploration app/common functions.R") - } - + load(file="repli_outcomes.RData") + # Temporary for data cleaning + repli_outcomes$generalizability <- ifelse(repli_outcomes$paper_id %in% c("Br0x", "EQxa", "J4W9", "plLK", "rjb", "zlm2"), + TRUE,FALSE) + repli_outcomes$claim_id_unique <- paste0(repli_outcomes$paper_id,"_",repli_outcomes$claim_id) + repli_outcomes$rr_type_internal <- NULL } # Data manipulation and other setup { # RR UI and selection options data { - select_repli_type_set <- c("new data","secondary data") - select_repli_type_labels <- c("New data","Secondary data") - select_repli_type_selected_default <- c("new data","secondary data") - - select_repli_version_of_record_set <- c(TRUE,FALSE) - select_repli_version_of_record_labels <- c("VoR","Not VoR") - select_repli_version_of_record_selected_default <- c(TRUE) + select_rr_type_set <- c("Direct Replication","Data Analytic Replication","Hybrid") + select_rr_type_labels <- c("Direct Replication","Data Analytic Replication","Hybrid") + select_rr_type_selected_default <- c("Direct Replication") - select_repli_is_generalizability_set <- c(FALSE,TRUE) - select_repli_is_generalizability_labels <- c("Standard","Generalizability study") - select_repli_is_generalizability_selected_default <- c(FALSE) + select_generalizability_set <- c(FALSE,TRUE) + select_generalizability_labels <- c("Standard","Generalizability study") + select_generalizability_selected_default <- c(FALSE) - select_is_manylabs_set <- c(FALSE,TRUE) - select_is_manylabs_labels <- c("Not ManyLabs","ManyLabs") - select_is_manylabs_selected_default <- c(FALSE) + select_rr_is_manylabs_set <- c("non_ml","ml_count","ml_instance_primary","ml_aggregation") + select_rr_is_manylabs_labels <- c("Not ManyLabs","Count","Primary","Aggregation") + select_rr_is_manylabs_selected_default <- c("non_ml") - select_power_for_effect_size_set <- c("50% for 100%","90% for 50%","90% for 75% lab power analysis","not performed") - select_power_for_effect_size_labels <- c("50% for 100%","90% for 50%","90% for 75% lab power analysis","Not performed") - select_power_for_effect_size_selected_default <- select_power_for_effect_size_set + select_rr_analytic_sample_stage_set <- c("stage 1","stage 2","threshold","no target","lab target") + select_rr_analytic_sample_stage_labels <- c("Stage 1","Stage 2","Threshold","No target","Lab target") + select_rr_analytic_sample_stage_selected_default <- c("stage 1") } } } +# Aesthetic functions +{ + GeomSplitViolin <- ggproto("GeomSplitViolin", GeomViolin, + draw_group = function(self, data, ..., draw_quantiles = NULL) { + data <- transform(data, xminv = x - violinwidth * (x - xmin), xmaxv = x + violinwidth * (xmax - x)) + grp <- data[1, "group"] + newdata <- plyr::arrange(transform(data, x = if (grp %% 2 == 1) xminv else xmaxv), if (grp %% 2 == 1) y else -y) + newdata <- rbind(newdata[1, ], newdata, newdata[nrow(newdata), ], newdata[1, ]) + newdata[c(1, nrow(newdata) - 1, nrow(newdata)), "x"] <- round(newdata[1, "x"]) + + if (length(draw_quantiles) > 0 & !scales::zero_range(range(data$y))) { + stopifnot(all(draw_quantiles >= 0), all(draw_quantiles <= + 1)) + quantiles <- ggplot2:::create_quantile_segment_frame(data, draw_quantiles) + aesthetics <- data[rep(1, nrow(quantiles)), setdiff(names(data), c("x", "y")), drop = FALSE] + aesthetics$alpha <- rep(1, nrow(quantiles)) + both <- cbind(quantiles, aesthetics) + quantile_grob <- GeomPath$draw_panel(both, ...) + ggplot2:::ggname("geom_split_violin", grid::grobTree(GeomPolygon$draw_panel(newdata, ...), quantile_grob)) + } + else { + ggplot2:::ggname("geom_split_violin", GeomPolygon$draw_panel(newdata, ...)) + } + }) + + geom_split_violin <- function(mapping = NULL, data = NULL, stat = "ydensity", position = "identity", ..., + draw_quantiles = NULL, trim = TRUE, scale = "area", na.rm = FALSE, + show.legend = NA, inherit.aes = TRUE) { + layer(data = data, mapping = mapping, stat = stat, geom = GeomSplitViolin, + position = position, show.legend = show.legend, inherit.aes = inherit.aes, + params = list(trim = trim, scale = scale, draw_quantiles = draw_quantiles, na.rm = na.rm, ...)) + } +} -ui <- { - -fluidPage(title = "SCORE data visualization playground", +ui <- fluidPage( tabsetPanel( tabPanel("Replications", page_sidebar( @@ -76,284 +88,113 @@ fluidPage(title = "SCORE data visualization playground", sidebar = sidebar( h3("Dataset selection"), checkboxGroupInput( - "select_repli_type_selected", "Replication type (repli_type)", - choiceNames = unique(select_repli_type_labels), - choiceValues = unique(select_repli_type_set), - selected = c(select_repli_type_selected_default) + "select_rr_type_selected", "Replication type (rr_type)", + choiceNames = unique(select_rr_type_labels), + choiceValues = unique(select_rr_type_set), + selected = c(select_rr_type_selected_default) ), checkboxGroupInput( - "select_repli_version_of_record_selected", "Version of Record (repli_version_of_record)", - choiceNames = unique(select_repli_version_of_record_labels), - choiceValues = unique(select_repli_version_of_record_set), - selected = c(select_repli_version_of_record_selected_default) + "select_generalizability_selected", "Generalizability (generalizability)", + choiceNames = unique(select_generalizability_labels), + choiceValues = unique(select_generalizability_set), + selected = c(select_generalizability_selected_default) ), checkboxGroupInput( - "select_repli_is_generalizability_selected", "Generalizability (repli_is_generalizability)", - choiceNames = unique(select_repli_is_generalizability_labels), - choiceValues = unique(select_repli_is_generalizability_set), - selected = c(select_repli_is_generalizability_selected_default) + "select_rr_is_manylabs_selected", "Many Labs (rr_is_manylabs)", + choiceNames = unique(select_rr_is_manylabs_labels), + choiceValues = unique(select_rr_is_manylabs_set), + selected = c(select_rr_is_manylabs_selected_default) ), checkboxGroupInput( - "select_is_manylabs_selected", "Many Labs (is_manylabs)", - choiceNames = unique(select_is_manylabs_labels), - choiceValues = unique(select_is_manylabs_set), - selected = c(select_is_manylabs_selected_default) - ), - checkboxGroupInput( - "select_power_for_effect_size_selected", "Replication type (power_for_effect_size)", - choiceNames = unique(select_power_for_effect_size_labels), - choiceValues = unique(select_power_for_effect_size_set), - selected = c(select_power_for_effect_size_selected_default) + "select_rr_analytic_sample_stage_selected", "Replication type (rr_analytic_sample_stage)", + choiceNames = unique(select_rr_analytic_sample_stage_labels), + choiceValues = unique(select_rr_analytic_sample_stage_set), + selected = c(select_rr_analytic_sample_stage_selected_default) ), ), navbarPage("", - tabPanel("Data properties", - htmlOutput("repli_data_text") - ), - tabPanel("Dataset", - DTOutput("repli_data_table") - ), - tabPanel("Key stats", - p("takes a bit to load..."), - htmlOutput("repli_success_text") - ), - tabPanel("Chart: repli vs original ES", - + tabPanel("Replication stats vs original", + # checkboxGroupInput( + # "rr_stat_outcomes_selected", "Outcome stats types", + # choiceNames = c("Pearson's R",unique(repli_outcomes$rr_effect_size_type_reported)), + # choiceValues = c("Pearson's R",unique(repli_outcomes$rr_effect_size_type_reported)), + # selected = c("Pearson's R") + # ), plotOutput("repli_outcomes_vs_orig"), - h4("Options:"), - fluidRow( - column(4, - #checkboxGroupInput( - radioButtons( - "rr_stat_outcomes_selected", "Effect size stats types", - choiceNames = c("Pearson's R",unique(as.character(repli_outcomes$repli_effect_size_type))), - choiceValues = c("Pearson's R",unique(as.character(repli_outcomes$repli_effect_size_type))), - selected = c("Pearson's R"), - inline=FALSE - ) - ), - column(1), - column(7, - p("Chart elements; add:"), - checkboxInput("repli_outcomes_vs_orig_smoothed_dist", - "Smoothed distributions",TRUE), - checkboxInput("repli_outcomes_vs_orig_points", - "Raw data points",TRUE), - checkboxInput("repli_outcomes_vs_orig_lines", - "Lines",TRUE), - checkboxInput("repli_outcomes_vs_orig_points_jitter", - "Jittered raw data points",FALSE), - checkboxInput("repli_outcomes_vs_orig_dotplot", - "Dot plot",FALSE), - - p("Chart extent limits"), - fluidRow( - column(6,numericInput("repli_outcomes_vs_orig_lb", - "Lower bound",0)), - column(6,numericInput("repli_outcomes_vs_orig_ub", - "Upper bound",1)) - ), - numericInput("repli_outcomes_vs_orig_null", - "Null value",0), - checkboxInput("repli_outcomes_vs_orig_abs", - "Take absolute value of effect size",FALSE) - - ) - ) - ) + ), + tabPanel("Tab 2"), + tabPanel("Tab 3") + ) ) ), tabPanel("Reproductions") ) ) -} server <- function(input, output, session) { - # Replication - # Data generation - df_repli_subsetted <- reactive({ - df <- repli_outcomes - - df <- df[df$repli_type %in% input$select_repli_type_selected,] - df <- df[df$repli_version_of_record %in% input$select_repli_version_of_record_selected,] - df <- df[df$repli_is_generalizability %in% input$select_repli_is_generalizability_selected,] - # note: change bottom ones to: - # df <- df[df$repli_is_manylabs %in% input$select_is_manylabs_selected,] - # df <- df[df$repli_power_for_effect_size %in% input$select_power_for_effect_size_selected,] - df <- df[df$is_manylabs %in% input$select_is_manylabs_selected,] - df <- df[df$power_for_effect_size %in% input$select_power_for_effect_size_selected,] + df.repli.subsetted <- reactive({ + df <- repli_outcomes + + df <- df[df$rr_type %in% input$select_rr_type_selected,] + df <- df[df$generalizability %in% input$select_generalizability_selected,] + df <- df[df$rr_is_manylabs %in% input$select_rr_is_manylabs_selected,] + df <- df[df$rr_analytic_sample_stage %in% input$select_rr_analytic_sample_stage_selected,] + + df - df - }) - # Objects / charts / figures - output$repli_outcomes_vs_orig <- renderPlot({ - df.chart <- df_repli_subsetted() - df.chart.orig <- orig_dataset - - # Merge in orig data - #df.chart <- merge(df.chart,df.chart.orig,by.x="claim_id",by.y="unique_claim_id",all.x=TRUE,all.y=FALSE) - df.chart <- merge(df.chart,df.chart.orig,by="claim_id",all.x=TRUE,all.y=FALSE) - - df.chart$orig_pearsons_r <- as.numeric(df.chart$original_pearsons_r_numeric) - df.chart$orig_effect_size_value <- as.numeric(df.chart$original_effect_size_value_reported) - - # Gather up new vs originals - # Pearsons - df.chart.pearsons <- df.chart[c("repli_pearsons_r_value","orig_pearsons_r")] - df.chart.pearsons$stat_type <- "Pearson's R" - colnames(df.chart.pearsons) <- c("Replication","Original","stat_type") - - # All others - df.chart.others <- df.chart[c("repli_effect_size_value","orig_effect_size_value","repli_effect_size_type")] - colnames(df.chart.others) <- c("Replication","Original","stat_type") - - # Combine and select - df.chart <- rbind(df.chart.pearsons,df.chart.others) - df.chart$pair <- as.character(1:nrow(df.chart)) - df.chart <- na.omit(df.chart) - df.chart <- df.chart %>% pivot_longer(!"stat_type" & !"pair", names_to = "comparison", values_to = "ES_value") - - df.chart$comparison <- factor(df.chart$comparison, - labels=c("Replication","Original"), - levels=c("Replication","Original")) - df.chart <- df.chart[df.chart$stat_type %in% input$rr_stat_outcomes_selected,] - - # Add options - if (input$repli_outcomes_vs_orig_abs==TRUE){ - df.chart$ES_value <- abs(df.chart$ES_value) - } - - # Chart generation - p <- ggplot(data=df.chart,aes(y=ES_value,x=reorder(comparison, desc(comparison)),fill=reorder(comparison, desc(comparison)))) + - theme_bw()+ - scale_fill_manual(values=palette_score_charts)+ - geom_hline(aes(yintercept =input$repli_outcomes_vs_orig_null),linetype=3,color="#454545")+ - theme( - legend.position = "bottom", - panel.grid = element_blank(), - axis.line = element_line(color="#393939"), - legend.title=element_blank(), - axis.title.x = element_blank(), - panel.border = element_blank() - )+ - scale_y_continuous(limits=c(input$repli_outcomes_vs_orig_lb,input$repli_outcomes_vs_orig_ub))+ - #scale_x_discrete(expand = c(4, 0))+ - theme(aspect.ratio = 1)+ - #xlab("Statistic type")+ - ylab("Effect size value") - - pd = position_dodge(width=0.4) - if (input$repli_outcomes_vs_orig_lines == TRUE){ - p <- p + geom_line(aes(group=pair),color="grey",show.legend=FALSE) - } - if (input$repli_outcomes_vs_orig_smoothed_dist == TRUE){ - p <- p + geom_split_violin(show.legend=FALSE) - } - - if (input$repli_outcomes_vs_orig_points_jitter == TRUE){ - p <- p + geom_point(position=position_jitterdodge(),size=1,show.legend=FALSE) - } - if (input$repli_outcomes_vs_orig_points == TRUE){ - p <- p + geom_point(position=pd,size=1,show.legend=FALSE) - } - if (input$repli_outcomes_vs_orig_dotplot == TRUE){ - p <- p+geom_dotplot(binaxis = "y", - stackdir = "center", - dotsize = 0.5,show.legend=FALSE) - } - - p - }) + }) - output$repli_data_table <- renderDT(df_repli_subsetted(), options = list(lengthChange = FALSE)) - - output$repli_data_text <- renderText({ - df <- df_repli_subsetted() - - text <- paste0("Replications (n): ",nrow(df)) - text <- paste0(text,"
","Papers (n): ",length(unique(df$paper_id))) - text <- paste0(text,"
","Claims (n): ",length(unique(df$claim_id))) - HTML(text) - }) - - output$repli_success_text <- renderText({ - df <- df_repli_subsetted() - - text <- "" - # Replication criteria - - mean.repli.success <- bootstrap.clust(data=df[c("paper_id","claim_id","repli_pattern_criteria_met")],FUN= - function(data) { - mean(data$repli_pattern_criteria_met,na.rm=TRUE) - }, - alpha=.05,tails="two-tailed") - - mean.repli.success.weighted <- bootstrap.clust(data=df[c("paper_id","claim_id","repli_pattern_criteria_met")],FUN= - function(data) { - data <- data %>% add_count(paper_id) - data$weight <- 1/data$n - weighted.mean(data$repli_pattern_criteria_met,data$weight,na.rm=TRUE) - }, - clustervar = "paper_id", alpha=.05,tails="two-tailed") - - text <- paste0(text,"Percent meeting replication criteria: ") - text <- paste0(text,"(n=",length(na.omit(df$repli_pattern_criteria_met)),")") - text <- paste0(text,"
") - text <- paste0(text,"Unweighted/unclustered: ",round(mean.repli.success$point.estimate,3)*100,"%") - text <- paste0(text," (95% CI: ",round(mean.repli.success$CI.lb,3)*100," - ", round(mean.repli.success$CI.ub,3)*100,"%)") - text <- paste0(text,"
") - - text <- paste0(text,"Clustered/weighted at the paper level: ",round(mean.repli.success.weighted$point.estimate,3)*100,"%") - text <- paste0(text," (95% CI: ",round(mean.repli.success.weighted$CI.lb,3)*100," - ", round(mean.repli.success.weighted$CI.ub,3)*100,"%)") - text <- paste0(text,"
") - text <- paste0(text,"
") - - mean.repli.success <- bootstrap.clust(data=df[c("paper_id","claim_id","repli_interpret_supported")],FUN= - function(data) { - mean(data$repli_interpret_supported=="yes",na.rm=TRUE) - }, - alpha=.05,tails="two-tailed",iters=100) - - mean.repli.success.weighted <- bootstrap.clust(data=df[c("paper_id","claim_id","repli_interpret_supported")],FUN= - function(data) { - data <- data %>% add_count(paper_id) - data$weight <- 1/data$n - weighted.mean(data$repli_interpret_supported=="yes",data$weight,na.rm=TRUE) - }, - clustervar = "paper_id", alpha=.05,tails="two-tailed") - - text <- paste0(text,"Percent interpretation supported (subjective assessment by lab): ") - text <- paste0(text,"(n=",length(na.omit(df$repli_interpret_supported)),")") - text <- paste0(text,"
") - text <- paste0(text,"Unweighted/unclustered: ",round(mean.repli.success$point.estimate,3)*100,"%") - text <- paste0(text," (95% CI: ",round(mean.repli.success$CI.lb,3)*100," - ", round(mean.repli.success$CI.ub,3)*100,"%)") - text <- paste0(text,"
") - - text <- paste0(text,"Clustered/weighted at the paper level: ",round(mean.repli.success.weighted$point.estimate,3)*100,"%") - text <- paste0(text," (95% CI: ",round(mean.repli.success.weighted$CI.lb,3)*100," - ", round(mean.repli.success.weighted$CI.ub,3)*100,"%)") - text <- paste0(text,"
") - text <- paste0(text,"
") - - rr.success.repli.type.weighted <- bootstrap.clust(data=df[c("paper_id","claim_id","repli_pattern_criteria_met","repli_type")],FUN= - function(data) { - data <- data %>% add_count(paper_id) - data$weight <- 1/data$n - probability.ratio(exposure = data$repli_type=="new data", - outcome = data$repli_pattern_criteria_met, - weight = data$weight) - }, - clustervar = "paper_id", alpha=.05,tails="two-tailed") - - text <- paste0(text,"Relative proportion replication success by data type: ",round(rr.success.repli.type.weighted$point.estimate,3)) - text <- paste0(text," (95% CI: ",round(rr.success.repli.type.weighted$CI.lb,3)," - ", round(rr.success.repli.type.weighted$CI.ub,3),")") - text <- paste0(text,"
") - text <- paste0(text,"Interpretation: Replication attempts using new data were ",round(rr.success.repli.type.weighted$point.estimate,3), - " times as likely to have replication criteria met compared with those replications using pre-existing/secondary data.") - text <- paste0(text,"
") + output$repli_outcomes_vs_orig <- renderPlot({ + df.chart <- df.repli.subsetted() + + # TEMPORARY FOR MADE UP DATA + df.chart$rr_pearsons_r_value_reference <- df.chart$rr_pearsons_r_value*1.1 + + # Gather up new vs originals + # Pearsons + df.chart.pearsons <- df.chart[c("rr_pearsons_r_value","rr_effect_size_value_reference")] + df.chart.pearsons$stat_type <- "Pearson's R" + colnames(df.chart.pearsons) <- c("Replication","Original","stat_type") + + # All others + df.chart.others <- df.chart[c("rr_statistic_value_reported","rr_effect_size_value_reference","rr_effect_size_type_reported")] + colnames(df.chart.others) <- c("Replication","Original","stat_type") + types_to_keep <- c("ser_method") + df.chart.others <- df.chart.others[df.chart.others$stat_type %in% types_to_keep,] + + # Combine + df.chart <- rbind(df.chart.pearsons,df.chart.others) + df.chart <- df.chart %>% pivot_longer(!"stat_type", names_to = "comparison", values_to = "ES_value") + df.chart <- na.omit(df.chart) + df.chart$stat_type <- factor(df.chart$stat_type, + labels=c("Pearson's R","SER"), + levels=c("Pearson's R","ser_method")) + df.chart$comparison <- factor(df.chart$comparison, + labels=c("Replication","Original"), + levels=c("Replication","Original")) + + ggplot(data=df.chart,aes(y=ES_value,x=stat_type,fill=comparison)) + + geom_split_violin()+ + #geom_histogram(fill="#2B2484",alpha=.4)+ + #geom_density(fill="#2B2484",alpha=.8)+ + #scale_x_continuous(expand=c(0,0))+ + #scale_y_continuous(expand=c(0,0))+ + theme_minimal()+ + theme( + legend.position = "bottom", + panel.grid = element_blank(), + axis.line = element_line(color="#393939") + )+ + xlab("Statistic type")+ + ylab("Effect size value") - HTML(text) - }) + }) + + output$data.table.temp = renderDT( + df.repli.subsetted(), options = list(lengthChange = FALSE) + ) + } shinyApp(ui, server) \ No newline at end of file diff --git a/Analysis/Data exploration app/orig_dataset.RData b/Analysis/Data exploration app/orig_dataset.RData 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app.R/shinyapps.io/noahhaber-cos/data_exploration_app.dcf index 4c8d868..3711367 100644 --- a/Analysis/Data exploration app/rsconnect/documents/data exploration app.R/shinyapps.io/noahhaber-cos/data_exploration_app.dcf +++ b/Analysis/Data exploration app/rsconnect/documents/data exploration app.R/shinyapps.io/noahhaber-cos/data_exploration_app.dcf @@ -5,7 +5,7 @@ account: noahhaber-cos server: shinyapps.io hostUrl: https://api.shinyapps.io/v1 appId: 10223971 -bundleId: 8022432 +bundleId: 7815897 url: https://noahhaber-cos.shinyapps.io/data_exploration_app/ version: 1 asMultiple: FALSE diff --git a/Analysis/code whiteboard.R b/Analysis/code whiteboard.R index 27ca160..868ecb8 100644 --- a/Analysis/code whiteboard.R +++ b/Analysis/code whiteboard.R @@ -11,37 +11,16 @@ library(ggsankey) library(plotly) library(viridis) - } # tar_make() # run this to pull new targets - -#tar_load("repli_export") -#tar_load("repli_primary") +tar_load("repli_export") +tar_load("repli_primary") tar_load("repli_outcomes") -#tar_load("orig_statistics_dataset_p1") - -# #tar_load("repli_export") -# #tar_load("repli_primary") -# tar_load("repli_outcomes") -# #tar_load("orig_statistics_dataset_p1") -# tar_load("orig_dataset") - - -# Update app data -if (FALSE){ - tar_load("repli_outcomes") - save(repli_outcomes,file="Analysis/Data exploration app/repli_outcomes.RData") - - tar_load("orig_dataset") - save(orig_dataset,file="Analysis/Data exploration app/orig_dataset.RData") - - tar_load("repro_export") - - -} +tar_load("orig_statistics_dataset_p1") +save(repli_outcomes,file="Analysis/Data exploration app/repli_outcomes.RData") # UI and selection options data { @@ -60,7 +39,7 @@ if (FALSE){ # Generate selection data functions { - df_repli_subsetted <- function(select_rr_type_selection_ui = c(TRUE,FALSE,FALSE), + generate_selected_repli_data <- function(select_rr_type_selection_ui = c(TRUE,FALSE,FALSE), select_generalizability_selection_ui = c(TRUE,FALSE), select_rr_is_manylabs_selection_ui = c(TRUE,FALSE,FALSE,FALSE), select_rr_analytic_sample_stage_selection_ui = c(TRUE,TRUE,FALSE,FALSE,FALSE)){ @@ -128,267 +107,110 @@ if (FALSE){ position = position, show.legend = show.legend, inherit.aes = inherit.aes, params = list(trim = trim, scale = scale, draw_quantiles = draw_quantiles, na.rm = na.rm, ...)) } - # Color palette - { - palette_weezer_blue <- c("#00a2e7","#dee5cd","#010c09","#083259","#b2915f","#d7b1b7","#00374b","#124e80", "#001212") - palette_weezer_pinkerton <- c("#d5bf98","#14140b","#70624b","#8a8d82","#304251","#465656","#945a2d","#708090") - palette_weezer_green <- c("#bece30","#020100","#4f6238","#cac986","#981f2c","#c13f33","#461005") - palette_weezer_maladroit <- c("#e0dcce","#575b61","#b69e44","#953d31","#e5b066","#343729","#3e3131") - palette_weezer_make_believe <- c("#000000","#EAECEB","#C2C2C2","#A0A0A0","#313131") - palette_weezer_red <- c("#ED1B34","#8A817C","#141311","#8B8D9C","#332E28") - palette_weezer_raditude <- c("#EC2221","#FBFFFB","#FDF600","#CEB181","#4E1110") - palette_weezer_everything <- c("#E8A662","#F4F5F1","#463D47","#7F3009","#35180E","F6F3CF") - palette_weezer_white <- c("#FDFDFD","#242424","#E3E3E3","#B6B6B6","#EEEDED") - palette_weezer_pacific_daydream <- c("#1E3555","#5C6455","#FBE4BC","#1D1F1E","#69797B","#F8E6CF","#F8E6CF") - palette_weezer_teal <- c("#1DBBBE","#D6A8CD","#F8F8F8","#182633","#90C5DF") - palette_weezer_black <- c("#2D2B2C","#060606","#E9E7E8","#0E0E0E") - palette_weezer_ok_human <- c("#B2A17A","#B3B470","#B1A78F","#D1BE8F","#726D5C","#B8B6A6","#5B4F3F") - palette_weezer_van_weezer <- c("#B2023E","#E933D3","#770265","#170032","#FDF8FF","#170032","#5329F9","#F3FED5") - - plaette_score_charts <- c(palette_weezer_blue[1], - palette_weezer_red[1], - palette_weezer_green[1], - palette_weezer_teal[1], - - palette_weezer_pinkerton[1], - palette_weezer_van_weezer[3] - ) - } } +# # Compare power Tillburg vs success rate +# { +# df.chart <- generate_selected_repli_data() +# +# } + # Main 3 parter { - tar_load("repli_outcomes") - df.chart <- repli_outcomes - #df.chart <- df_repli_subsetted() - - # Merge in orig data - tar_load("orig_dataset") - df.chart.orig <- orig_dataset[c("unique_claim_id", - "original_effect_size_value_reported", - "original_effect_size_type_reference", - "original_pearsons_r_numeric")] - df.chart <- merge(df.chart,df.chart.orig,by.x="claim_id",by.y="unique_claim_id",all.x=TRUE,all.y=FALSE) - - df.chart$orig_pearsons_r <- as.numeric(df.chart$original_pearsons_r_numeric) - df.chart$orig_effect_size_value <- as.numeric(df.chart$original_effect_size_value_reported) - - # Gather up new vs originals - # Pearsons - df.chart.pearsons <- df.chart[c("repli_pearsons_r_value","orig_pearsons_r")] - df.chart.pearsons$stat_type <- "Pearson's R" - colnames(df.chart.pearsons) <- c("Replication","Original","stat_type") - - # All others - df.chart.others <- df.chart[c("repli_effect_size_value","orig_effect_size_value","repli_effect_size_type")] - colnames(df.chart.others) <- c("Replication","Original","stat_type") - types_to_keep <- c("ser_method") - df.chart.others <- df.chart.others[df.chart.others$stat_type %in% types_to_keep,] - - # Combine - df.chart <- rbind(df.chart.pearsons,df.chart.others) - df.chart <- df.chart %>% pivot_longer(!"stat_type", names_to = "comparison", values_to = "ES_value") - df.chart <- na.omit(df.chart) - df.chart$stat_type <- factor(df.chart$stat_type, - labels=c("Pearson's R","SER"), - levels=c("Pearson's R","ser_method")) - df.chart$comparison <- factor(df.chart$comparison, - labels=c("Replication","Original"), - levels=c("Replication","Original")) - df.chart$jitter_bias <- ifelse(df.chart$comparison=="original",-.2,.2) - - ggplot(data=df.chart,aes(y=ES_value,x=stat_type,fill=reorder(comparison, desc(comparison)))) + + # Generate dataset + df.chart <- generate_selected_repli_data() + + # TEMPORARY FOR MADE UP DATA + df.chart$rr_pearsons_r_value_reference <- df.chart$rr_pearsons_r_value*1.1 + + # Gather up new vs originals + # Pearsons + df.chart.pearsons <- df.chart[c("rr_pearsons_r_value","rr_effect_size_value_reference")] + df.chart.pearsons$stat_type <- "Pearson's R" + colnames(df.chart.pearsons) <- c("Replication","Original","stat_type") + + # All others + df.chart.others <- df.chart[c("rr_statistic_value_reported","rr_effect_size_value_reference","rr_effect_size_type_reported")] + colnames(df.chart.others) <- c("Replication","Original","stat_type") + types_to_keep <- c("ser_method") + df.chart.others <- df.chart.others[df.chart.others$stat_type %in% types_to_keep,] + + # Combine + df.chart <- rbind(df.chart.pearsons,df.chart.others) + df.chart <- df.chart %>% pivot_longer(!"stat_type", names_to = "comparison", values_to = "ES_value") + df.chart <- na.omit(df.chart) + df.chart$stat_type <- factor(df.chart$stat_type, + labels=c("Pearson's R","SER"), + levels=c("Pearson's R","ser_method")) + df.chart$comparison <- factor(df.chart$comparison, + labels=c("Replication","Original"), + levels=c("Replication","Original")) + + ggplot(data=df.chart,aes(y=ES_value,x=stat_type,fill=comparison)) + geom_split_violin()+ - geom_point(position=position_jitterdodge(),size=.2)+ - theme_bw()+ - scale_fill_manual(values=plaette_score_charts)+ - geom_hline(aes(yintercept =0),linetype=3,color="#454545")+ + #geom_histogram(fill="#2B2484",alpha=.4)+ + #geom_density(fill="#2B2484",alpha=.8)+ + #scale_x_continuous(expand=c(0,0))+ + #scale_y_continuous(expand=c(0,0))+ + theme_minimal()+ theme( legend.position = "bottom", panel.grid = element_blank(), - axis.line = element_line(color="#393939"), - legend.title=element_blank() + axis.line = element_line(color="#393939") )+ - scale_y_continuous(expand=c(0,0),limits=c(-0,1))+ xlab("Statistic type")+ ylab("Effect size value") + #scale_fill_viridis()+ + #coord_flip() } - - -# Generate alluvial -{ - # Data generation - - tar_load("repli_outcomes") - tar_load("orig_dataset") - tar_load("repro_export") - - df.alluvial <- orig_dataset[c("paper_id","claim_id")] - df.alluvial$claim_id <- paste0(df.alluvial$paper_id,"_",df.alluvial$claim_id) - - repro.alluvial <- repro_export[c("paper_id","claim_id")] - repro.alluvial$claim_id <- paste0(repro.alluvial$paper_id,"_",repro.alluvial$claim_id) - repro.alluvial$paper_id <- NULL - repro.alluvial$reproduction <- TRUE - repro.alluvial$reproduction_outcome <- ifelse(repro_export$rr_repro_success_reported=="yes","Reproduced","Not reproduced") - - repli.alluvial <- repli_outcomes[c("claim_id")] - repli.alluvial$replication <- TRUE - repli.alluvial$replication_outcome <- ifelse(repli_outcomes$repli_pattern_criteria_met==TRUE,"Replicated","Not replicated") - - df.alluvial <- merge(df.alluvial,repro.alluvial,by = "claim_id",all.x=TRUE,all.y = FALSE) - df.alluvial <- merge(df.alluvial,repli.alluvial,by = "claim_id",all.x=TRUE,all.y = FALSE) - - - df.alluvial <- rbind(repro.alluvial,repli.alluvial) - - # df.repli.no.hier <- repli_outcomes[c("paper_id","claim_id","is_manylabs","power_for_effect_size")] - # df.repli.no.hier$claim_id <- paste0(df.repli.no.hier$paper_id,"_", df.repli.no.hier$claim_id) - - #df.repli.no.hier <- df.repli.no.hier[df.repli.no.hier$paper_id %in% unique(df.repli.no.hier$paper_id)[60:120],] - - df.alluvial <- df.alluvial %>% - ggsankey::make_long(paper_id,claim_id,type,outcome) - - ggplot(df.alluvial, aes(x = x, next_x = next_x, node = node, next_node = next_node, fill = factor(node), label = node)) + - ggsankey::geom_alluvial(flow.alpha = 0.75, show.legend = FALSE,space=1) + - #ggsankey::geom_sankey()+ - #geom_sankey_label(size = 3, color = "black", fill= "white", hjust = -0.5)+ - theme_bw() + - theme(legend.position = "none") + - theme(axis.title = element_blank() - , axis.text.x = element_blank() - , axis.ticks = element_blank() - , panel.grid = element_blank()) + - labs(fill = 'Nodes')+ - coord_flip() -} - -library(ggplot2) - -ID <- c(1,1,2,2,3,3,4,4,5,5) -group <- c(20,20, 50, 50,20, 20, 80, 80, 80, 80) -condition <- c("med", "placebo","med", "placebo","med", "placebo","med", "placebo","med", "placebo") -PropYes <- c(0.13, 0.15, 0.25, 0.13, 0.54, 0.34, 0.23, 0.45, 0.142, 0.344) -exampleData <- data.frame(ID, group, condition, PropYes) -exampleData <- within(exampleData, { - group <- as.factor(group) - condition <- as.factor(condition) -}) -pd = position_dodge(width=0) -ggplot(exampleData, aes(x=factor(condition), y=PropYes, - color=factor(group), group=factor(ID))) + - geom_point(position=pd) + geom_line(position=pd) -ggplot(df.chart, aes(x=comparison, y=ES_value, - group=factor(group))) + - geom_point(position=pd) + geom_line(position=pd) -ggplot(df.chart, aes(x=comparison, y=ES_value, - group=factor(group))) + - geom_point() + geom_line() - - - -# Startup and initialization -{ - rm(list=ls()) # yes I know this is bad, will get rid of later; just a convenience for now +# Play area +if(FALSE){ + # Main 3 + { + df.hierarchy <- repli_export %>% group_by(paper_id,claim_id,rr_analytic_sample_stage,rr_is_manylabs, .drop = FALSE) %>% count() + dup_paper_ids <- df.hierarchy$paper_id[df.hierarchy$n==2] + repli_export_dups <- repli_export[repli_export$paper_id %in% dup_paper_ids,] + + test <- repli_export %>% group_by(paper_id,claim_id,rr_is_manylabs, .drop = FALSE) %>% count() + + } - library(targets) - library(ggplot2) - library(ggalluvial) - library(tidyr) - library(dplyr) - library(devtools) - library(ggsankey) - library(plotly) - library(viridis) -} - -tar_load("repli_outcomes") - -bootstrap.clust <- function(data=NA,FUN=NA,clustervar=NA, - alpha=.05,tails="two-tailed",iters=100){ - # Set up cluster designations - if(anyNA(clustervar)){ data$cluster.id <- 1:nrow(data) - } else { data$cluster.id <- data[[clustervar]] } - cluster.set <- unique(data$cluster.id) - # Generate original target variable - point.estimate <- FUN(data) - # Create distribution of bootstrapped samples - estimates.bootstrapped <- replicate(iters,{ - # Generate sample of clusters to include - clust.list <- sample(cluster.set,length(cluster.set),replace = TRUE) - # Build dataset from cluster list - data.clust <- do.call(rbind,lapply(1:length(clust.list), function(x) { - data[data$cluster.id == clust.list[x],] - })) - # Run function on new data - tryCatch(FUN(data.clust),finally=NA) - }, - simplify=TRUE) - # Outcomes measures - if(is.matrix(estimates.bootstrapped)){ - n_estimates <- nrow(estimates.bootstrapped) - SE <- unlist(lapply(1:n_estimates,function(x) {sd(estimates.bootstrapped[x,],na.rm = TRUE)})) - if (tails == "two-tailed"){ - CI.lb <- unlist(lapply(1:n_estimates,function(x) {quantile(estimates.bootstrapped[x,], alpha/2,na.rm = TRUE)})) - CI.ub <- unlist(lapply(1:n_estimates,function(x) {quantile(estimates.bootstrapped[x,], 1-alpha/2,na.rm = TRUE)})) - } else if (tails == "one-tailed, upper"){ - CI.lb <- unlist(lapply(1:n_estimates,function(x) {NA})) - CI.ub <- unlist(lapply(1:n_estimates,function(x) {quantile(estimates.bootstrapped[x,], 1-alpha,na.rm = TRUE)})) - } else if (tails == "one-tailed, lower"){ - CI.lb <- unlist(lapply(1:n_estimates,function(x) {quantile(estimates.bootstrapped[x,], alpha,na.rm = TRUE)})) - CI.ub <- unlist(lapply(1:n_estimates,function(x) {NA})) - } - } else { - SE <- sd(estimates.bootstrapped,na.rm = TRUE) - if (tails == "two-tailed"){ - CI.lb <- quantile(estimates.bootstrapped, alpha/2,na.rm = TRUE) - CI.ub <- quantile(estimates.bootstrapped, 1-alpha/2,na.rm = TRUE) - } else if (tails == "one-tailed, upper"){ - CI.lb <- NA - CI.ub <- quantile(estimates.bootstrapped, 1-alpha,na.rm = TRUE) - } else if (tails == "one-tailed, lower"){ - CI.lb <- quantile(estimates.bootstrapped, alpha,na.rm = TRUE) - CI.ub <- NA - } - } + + + # Generate alluvial + { + df.repli.no.hier <- repli_export[c("paper_id","claim_id","rr_is_manylabs","rr_analytic_sample_stage","rr_id")] + df.repli.no.hier$claim_id <- paste0(df.repli.no.hier$paper_id,"_", df.repli.no.hier$claim_id) + + df.repli.no.hier <- df.repli.no.hier[df.repli.no.hier$paper_id %in% unique(df.repli.no.hier$paper_id)[60:120],] + + df <- df.repli.no.hier %>% + #make_long(paper_id, claim_id,rr_is_manylabs,rr_analytic_sample_stage) + make_long(paper_id, claim_id,rr_is_manylabs,rr_analytic_sample_stage) - # Outputs - return(list("point.estimate"=point.estimate,"SE"=SE, - "CI.lb"=CI.lb,"CI.ub"=CI.ub,"estimates.bootstrapped"=estimates.bootstrapped)) + + pl <- ggplot(df, aes(x = x, next_x = next_x, node = node, next_node = next_node, fill = factor(node), label = node)) + + geom_alluvial(flow.alpha = 0.75, show.legend = FALSE,space=1) + + #geom_sankey_label(size = 3, color = "black", fill= "white", hjust = -0.5)+ + theme_bw() + + theme(legend.position = "none") + + theme(axis.title = element_blank() + , axis.text.y = element_blank() + , axis.ticks = element_blank() + , panel.grid = element_blank()) + + labs(fill = 'Nodes') + pl <- pl + scale_fill_viridis_d(option = "inferno") + # pl <- pl + labs(title = "Sankey diagram using ggplot") + # pl <- pl + labs(subtitle = "using David Sjoberg's ggsankey package") + pl + } } -test <- bootstrap.clust(data=repli_outcomes[c("paper_id","claim_id","repli_pattern_criteria_met")],FUN=function(data) { - mean(data$repli_pattern_criteria_met,na.rm=TRUE) -},clustervar = "paper_id") -test$point.estimate - -test <- bootstrap.clust(data=repli_outcomes[c("paper_id","claim_id","repli_pattern_criteria_met")],FUN=function(data) { - mean(data$repli_pattern_criteria_met,na.rm=TRUE) -},clustervar = "paper_id") -test$point.estimate - -test <- bootstrap.clust(data=repli_outcomes[c("paper_id","claim_id","repli_pattern_criteria_met","repli_type")],FUN=function(data) { - reg <- lm("repli_pattern_criteria_met ~ repli_type",data=data) - reg$coefficients -},clustervar = "paper_id") -test$point.estimate - -test <- lm("repli_pattern_criteria_met ~ repli_type",data=repli_outcomes) - - - -repli_outcomes$new_data <- repli_outcomes$repli_type=="new data" -table(repli_outcomes$repli_pattern_criteria_met,repli_outcomes$new_data) - -rr <- (sum(repli_outcomes[repli_outcomes$repli_type=="new data",]$repli_pattern_criteria_met==TRUE)/sum(repli_outcomes$repli_type=="new data"))/ -(sum(repli_outcomes[repli_outcomes$repli_type=="secondary data",]$repli_pattern_criteria_met==TRUE)/sum(repli_outcomes$repli_type=="secondary data")) -test$coefficients diff --git a/renv/activate.R b/renv/activate.R index 31d28bc..019b5a6 100644 --- a/renv/activate.R +++ b/renv/activate.R @@ -3,26 +3,10 @@ local({ # the requested version of renv version <- "0.16.0" - attr(version, "sha") <- NULL # the project directory project <- getwd() - # use start-up diagnostics if enabled - diagnostics <- Sys.getenv("RENV_STARTUP_DIAGNOSTICS", unset = "FALSE") - if (diagnostics) { - start <- Sys.time() - profile <- tempfile("renv-startup-", fileext = ".Rprof") - utils::Rprof(profile) - on.exit({ - utils::Rprof(NULL) - elapsed <- signif(difftime(Sys.time(), start, units = "auto"), digits = 2L) - writeLines(sprintf("- renv took %s to run the autoloader.", format(elapsed))) - writeLines(sprintf("- Profile: %s", profile)) - print(utils::summaryRprof(profile)) - }, add = TRUE) - } - # figure out whether the autoloader is enabled enabled <- local({ @@ -76,75 +60,21 @@ local({ # load bootstrap tools `%||%` <- function(x, y) { - if (is.null(x)) y else x - } - - catf <- function(fmt, ..., appendLF = TRUE) { - - quiet <- getOption("renv.bootstrap.quiet", default = FALSE) - if (quiet) - return(invisible()) - - msg <- sprintf(fmt, ...) - cat(msg, file = stdout(), sep = if (appendLF) "\n" else "") - - invisible(msg) - - } - - header <- function(label, - ..., - prefix = "#", - suffix = "-", - n = min(getOption("width"), 78)) - { - label <- sprintf(label, ...) - n <- max(n - nchar(label) - nchar(prefix) - 2L, 8L) - if (n <= 0) - return(paste(prefix, label)) - - tail <- paste(rep.int(suffix, n), collapse = "") - paste0(prefix, " ", label, " ", tail) - - } - - startswith <- function(string, prefix) { - substring(string, 1, nchar(prefix)) == prefix + if (is.environment(x) || length(x)) x else y } bootstrap <- function(version, library) { - friendly <- renv_bootstrap_version_friendly(version) - section <- header(sprintf("Bootstrapping renv %s", friendly)) - catf(section) - # attempt to download renv - catf("- Downloading renv ... ", appendLF = FALSE) - withCallingHandlers( - tarball <- renv_bootstrap_download(version), - error = function(err) { - catf("FAILED") - stop("failed to download:\n", conditionMessage(err)) - } - ) - catf("OK") - on.exit(unlink(tarball), add = TRUE) + tarball <- tryCatch(renv_bootstrap_download(version), error = identity) + if (inherits(tarball, "error")) + stop("failed to download renv ", version) # now attempt to install - catf("- Installing renv ... ", appendLF = FALSE) - withCallingHandlers( - status <- renv_bootstrap_install(version, tarball, library), - error = function(err) { - catf("FAILED") - stop("failed to install:\n", conditionMessage(err)) - } - ) - catf("OK") - - # add empty line to break up bootstrapping from normal output - catf("") + status <- tryCatch(renv_bootstrap_install(version, tarball, library), error = identity) + if (inherits(status, "error")) + stop("failed to install renv ", version) - return(invisible()) } renv_bootstrap_tests_running <- function() { @@ -153,32 +83,28 @@ local({ renv_bootstrap_repos <- function() { - # get CRAN repository - cran <- getOption("renv.repos.cran", "https://cloud.r-project.org") - # check for repos override repos <- Sys.getenv("RENV_CONFIG_REPOS_OVERRIDE", unset = NA) - if (!is.na(repos)) { - - # check for RSPM; if set, use a fallback repository for renv - rspm <- Sys.getenv("RSPM", unset = NA) - if (identical(rspm, repos)) - repos <- c(RSPM = rspm, CRAN = cran) - + if (!is.na(repos)) return(repos) - } - # check for lockfile repositories repos <- tryCatch(renv_bootstrap_repos_lockfile(), error = identity) if (!inherits(repos, "error") && length(repos)) return(repos) + # if we're testing, re-use the test repositories + if (renv_bootstrap_tests_running()) + return(getOption("renv.tests.repos")) + # retrieve current repos repos <- getOption("repos") # ensure @CRAN@ entries are resolved - repos[repos == "@CRAN@"] <- cran + repos[repos == "@CRAN@"] <- getOption( + "renv.repos.cran", + "https://cloud.r-project.org" + ) # add in renv.bootstrap.repos if set default <- c(FALLBACK = "https://cloud.r-project.org") @@ -217,34 +143,33 @@ local({ renv_bootstrap_download <- function(version) { - sha <- attr(version, "sha", exact = TRUE) - - methods <- if (!is.null(sha)) { - - # attempting to bootstrap a development version of renv - c( - function() renv_bootstrap_download_tarball(sha), - function() renv_bootstrap_download_github(sha) + # if the renv version number has 4 components, assume it must + # be retrieved via github + nv <- numeric_version(version) + components <- unclass(nv)[[1]] + + # if this appears to be a development version of 'renv', we'll + # try to restore from github + dev <- length(components) == 4L + + # begin collecting different methods for finding renv + methods <- c( + renv_bootstrap_download_tarball, + if (dev) + renv_bootstrap_download_github + else c( + renv_bootstrap_download_cran_latest, + renv_bootstrap_download_cran_archive ) - - } else { - - # attempting to bootstrap a release version of renv - c( - function() renv_bootstrap_download_tarball(version), - function() renv_bootstrap_download_cran_latest(version), - function() renv_bootstrap_download_cran_archive(version) - ) - - } + ) for (method in methods) { - path <- tryCatch(method(), error = identity) + path <- tryCatch(method(version), error = identity) if (is.character(path) && file.exists(path)) return(path) } - stop("All download methods failed") + stop("failed to download renv ", version) } @@ -308,6 +233,8 @@ local({ type <- spec$type repos <- spec$repos + message("* Downloading renv ", version, " ... ", appendLF = FALSE) + baseurl <- utils::contrib.url(repos = repos, type = type) ext <- if (identical(type, "source")) ".tar.gz" @@ -324,10 +251,13 @@ local({ condition = identity ) - if (inherits(status, "condition")) + if (inherits(status, "condition")) { + message("FAILED") return(FALSE) + } # report success and return + message("OK (downloaded ", type, ")") destfile } @@ -384,6 +314,8 @@ local({ urls <- file.path(repos, "src/contrib/Archive/renv", name) destfile <- file.path(tempdir(), name) + message("* Downloading renv ", version, " ... ", appendLF = FALSE) + for (url in urls) { status <- tryCatch( @@ -391,11 +323,14 @@ local({ condition = identity ) - if (identical(status, 0L)) + if (identical(status, 0L)) { + message("OK") return(destfile) + } } + message("FAILED") return(FALSE) } @@ -409,7 +344,8 @@ local({ return() # allow directories - if (dir.exists(tarball)) { + info <- file.info(tarball, extra_cols = FALSE) + if (identical(info$isdir, TRUE)) { name <- sprintf("renv_%s.tar.gz", version) tarball <- file.path(tarball, name) } @@ -418,7 +354,7 @@ local({ if (!file.exists(tarball)) { # let the user know we weren't able to honour their request - fmt <- "- RENV_BOOTSTRAP_TARBALL is set (%s) but does not exist." + fmt <- "* RENV_BOOTSTRAP_TARBALL is set (%s) but does not exist." msg <- sprintf(fmt, tarball) warning(msg) @@ -427,7 +363,10 @@ local({ } - catf("- Using local tarball '%s'.", tarball) + fmt <- "* Bootstrapping with tarball at path '%s'." + msg <- sprintf(fmt, tarball) + message(msg) + tarball } @@ -454,6 +393,8 @@ local({ on.exit(do.call(base::options, saved), add = TRUE) } + message("* Downloading renv ", version, " from GitHub ... ", appendLF = FALSE) + url <- file.path("https://api.github.com/repos/rstudio/renv/tarball", version) name <- sprintf("renv_%s.tar.gz", version) destfile <- file.path(tempdir(), name) @@ -463,105 +404,26 @@ local({ condition = identity ) - if (!identical(status, 0L)) + if (!identical(status, 0L)) { + message("FAILED") return(FALSE) - - renv_bootstrap_download_augment(destfile) - - return(destfile) - - } - - # Add Sha to DESCRIPTION. This is stop gap until #890, after which we - # can use renv::install() to fully capture metadata. - renv_bootstrap_download_augment <- function(destfile) { - sha <- renv_bootstrap_git_extract_sha1_tar(destfile) - if (is.null(sha)) { - return() } - # Untar - tempdir <- tempfile("renv-github-") - on.exit(unlink(tempdir, recursive = TRUE), add = TRUE) - untar(destfile, exdir = tempdir) - pkgdir <- dir(tempdir, full.names = TRUE)[[1]] - - # Modify description - desc_path <- file.path(pkgdir, "DESCRIPTION") - desc_lines <- readLines(desc_path) - remotes_fields <- c( - "RemoteType: github", - "RemoteHost: api.github.com", - "RemoteRepo: renv", - "RemoteUsername: rstudio", - "RemotePkgRef: rstudio/renv", - paste("RemoteRef: ", sha), - paste("RemoteSha: ", sha) - ) - writeLines(c(desc_lines[desc_lines != ""], remotes_fields), con = desc_path) - - # Re-tar - local({ - old <- setwd(tempdir) - on.exit(setwd(old), add = TRUE) - - tar(destfile, compression = "gzip") - }) - invisible() - } + message("OK") + return(destfile) - # Extract the commit hash from a git archive. Git archives include the SHA1 - # hash as the comment field of the tarball pax extended header - # (see https://www.kernel.org/pub/software/scm/git/docs/git-archive.html) - # For GitHub archives this should be the first header after the default one - # (512 byte) header. - renv_bootstrap_git_extract_sha1_tar <- function(bundle) { - - # open the bundle for reading - # We use gzcon for everything because (from ?gzcon) - # > Reading from a connection which does not supply a 'gzip' magic - # > header is equivalent to reading from the original connection - conn <- gzcon(file(bundle, open = "rb", raw = TRUE)) - on.exit(close(conn)) - - # The default pax header is 512 bytes long and the first pax extended header - # with the comment should be 51 bytes long - # `52 comment=` (11 chars) + 40 byte SHA1 hash - len <- 0x200 + 0x33 - res <- rawToChar(readBin(conn, "raw", n = len)[0x201:len]) - - if (grepl("^52 comment=", res)) { - sub("52 comment=", "", res) - } else { - NULL - } } renv_bootstrap_install <- function(version, tarball, library) { # attempt to install it into project library + message("* Installing renv ", version, " ... ", appendLF = FALSE) dir.create(library, showWarnings = FALSE, recursive = TRUE) - output <- renv_bootstrap_install_impl(library, tarball) - - # check for successful install - status <- attr(output, "status") - if (is.null(status) || identical(status, 0L)) - return(status) - - # an error occurred; report it - header <- "installation of renv failed" - lines <- paste(rep.int("=", nchar(header)), collapse = "") - text <- paste(c(header, lines, output), collapse = "\n") - stop(text) - - } - - renv_bootstrap_install_impl <- function(library, tarball) { # invoke using system2 so we can capture and report output bin <- R.home("bin") exe <- if (Sys.info()[["sysname"]] == "Windows") "R.exe" else "R" - R <- file.path(bin, exe) + r <- file.path(bin, exe) args <- c( "--vanilla", "CMD", "INSTALL", "--no-multiarch", @@ -569,7 +431,19 @@ local({ shQuote(path.expand(tarball)) ) - system2(R, args, stdout = TRUE, stderr = TRUE) + output <- system2(r, args, stdout = TRUE, stderr = TRUE) + message("Done!") + + # check for successful install + status <- attr(output, "status") + if (is.numeric(status) && !identical(status, 0L)) { + header <- "Error installing renv:" + lines <- paste(rep.int("=", nchar(header)), collapse = "") + text <- c(header, lines, output) + writeLines(text, con = stderr()) + } + + status } @@ -779,60 +653,32 @@ local({ } - renv_bootstrap_validate_version <- function(version, description = NULL) { - - # resolve description file - # - # avoid passing lib.loc to `packageDescription()` below, since R will - # use the loaded version of the package by default anyhow. note that - # this function should only be called after 'renv' is loaded - # https://github.com/rstudio/renv/issues/1625 - description <- description %||% packageDescription("renv") + renv_bootstrap_validate_version <- function(version) { - # check whether requested version 'version' matches loaded version of renv - sha <- attr(version, "sha", exact = TRUE) - valid <- if (!is.null(sha)) - renv_bootstrap_validate_version_dev(sha, description) - else - renv_bootstrap_validate_version_release(version, description) - - if (valid) + loadedversion <- utils::packageDescription("renv", fields = "Version") + if (version == loadedversion) return(TRUE) - # the loaded version of renv doesn't match the requested version; - # give the user instructions on how to proceed - remote <- if (!is.null(description[["RemoteSha"]])) { - paste("rstudio/renv", description[["RemoteSha"]], sep = "@") - } else { - paste("renv", description[["Version"]], sep = "@") - } - - # display both loaded version + sha if available - friendly <- renv_bootstrap_version_friendly( - version = description[["Version"]], - sha = description[["RemoteSha"]] - ) + # assume four-component versions are from GitHub; three-component + # versions are from CRAN + components <- strsplit(loadedversion, "[.-]")[[1]] + remote <- if (length(components) == 4L) + paste("rstudio/renv", loadedversion, sep = "@") + else + paste("renv", loadedversion, sep = "@") fmt <- paste( "renv %1$s was loaded from project library, but this project is configured to use renv %2$s.", - "- Use `renv::record(\"%3$s\")` to record renv %1$s in the lockfile.", - "- Use `renv::restore(packages = \"renv\")` to install renv %2$s into the project library.", + "Use `renv::record(\"%3$s\")` to record renv %1$s in the lockfile.", + "Use `renv::restore(packages = \"renv\")` to install renv %2$s into the project library.", sep = "\n" ) - catf(fmt, friendly, renv_bootstrap_version_friendly(version), remote) - FALSE + msg <- sprintf(fmt, loadedversion, version, remote) + warning(msg, call. = FALSE) - } - - renv_bootstrap_validate_version_dev <- function(version, description) { - expected <- description[["RemoteSha"]] - is.character(expected) && startswith(expected, version) - } + FALSE - renv_bootstrap_validate_version_release <- function(version, description) { - expected <- description[["Version"]] - is.character(expected) && identical(expected, version) } renv_bootstrap_hash_text <- function(text) { @@ -854,12 +700,6 @@ local({ # warn if the version of renv loaded does not match renv_bootstrap_validate_version(version) - # execute renv load hooks, if any - hooks <- getHook("renv::autoload") - for (hook in hooks) - if (is.function(hook)) - tryCatch(hook(), error = warnify) - # load the project renv::load(project) @@ -999,66 +839,14 @@ local({ } - renv_bootstrap_version_friendly <- function(version, shafmt = NULL, sha = NULL) { - sha <- sha %||% attr(version, "sha", exact = TRUE) - parts <- c(version, sprintf(shafmt %||% " [sha: %s]", substring(sha, 1L, 7L))) - paste(parts, collapse = "") - } - - renv_bootstrap_exec <- function(project, libpath, version) { - if (!renv_bootstrap_load(project, libpath, version)) - renv_bootstrap_run(version, libpath) - } - - renv_bootstrap_run <- function(version, libpath) { - - # perform bootstrap - bootstrap(version, libpath) - - # exit early if we're just testing bootstrap - if (!is.na(Sys.getenv("RENV_BOOTSTRAP_INSTALL_ONLY", unset = NA))) - return(TRUE) - - # try again to load - if (requireNamespace("renv", lib.loc = libpath, quietly = TRUE)) { - return(renv::load(project = getwd())) - } - - # failed to download or load renv; warn the user - msg <- c( - "Failed to find an renv installation: the project will not be loaded.", - "Use `renv::activate()` to re-initialize the project." - ) - - warning(paste(msg, collapse = "\n"), call. = FALSE) - - } renv_json_read <- function(file = NULL, text = NULL) { - jlerr <- NULL - # if jsonlite is loaded, use that instead - if ("jsonlite" %in% loadedNamespaces()) { - - json <- catch(renv_json_read_jsonlite(file, text)) - if (!inherits(json, "error")) - return(json) - - jlerr <- json - - } - - # otherwise, fall back to the default JSON reader - json <- catch(renv_json_read_default(file, text)) - if (!inherits(json, "error")) - return(json) - - # report an error - if (!is.null(jlerr)) - stop(jlerr) + if ("jsonlite" %in% loadedNamespaces()) + renv_json_read_jsonlite(file, text) else - stop(json) + renv_json_read_default(file, text) } @@ -1172,9 +960,35 @@ local({ # construct full libpath libpath <- file.path(root, prefix) - # run bootstrap code - renv_bootstrap_exec(project, libpath, version) + # attempt to load + if (renv_bootstrap_load(project, libpath, version)) + return(TRUE) + + # load failed; inform user we're about to bootstrap + prefix <- paste("# Bootstrapping renv", version) + postfix <- paste(rep.int("-", 77L - nchar(prefix)), collapse = "") + header <- paste(prefix, postfix) + message(header) + + # perform bootstrap + bootstrap(version, libpath) + + # exit early if we're just testing bootstrap + if (!is.na(Sys.getenv("RENV_BOOTSTRAP_INSTALL_ONLY", unset = NA))) + return(TRUE) + + # try again to load + if (requireNamespace("renv", lib.loc = libpath, quietly = TRUE)) { + message("* Successfully installed and loaded renv ", version, ".") + return(renv::load()) + } + + # failed to download or load renv; warn the user + msg <- c( + "Failed to find an renv installation: the project will not be loaded.", + "Use `renv::activate()` to re-initialize the project." + ) - invisible() + warning(paste(msg, collapse = "\n"), call. = FALSE) }) diff --git a/renv/settings.json b/renv/settings.json deleted file mode 100644 index 2472d63..0000000 --- a/renv/settings.json +++ /dev/null @@ -1,19 +0,0 @@ -{ - "bioconductor.version": [], - "external.libraries": [], - "ignored.packages": [], - "package.dependency.fields": [ - "Imports", - "Depends", - "LinkingTo" - ], - "ppm.enabled": null, - "ppm.ignored.urls": [], - "r.version": [], - "snapshot.type": "implicit", - "use.cache": true, - "vcs.ignore.cellar": true, - "vcs.ignore.library": true, - "vcs.ignore.local": true, - "vcs.manage.ignores": true -}

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