From a8e0fae1b58a50c861b0f5be8975fe645bfbc6e4 Mon Sep 17 00:00:00 2001 From: Chris Bennett Date: Wed, 20 Nov 2024 10:45:08 +0000 Subject: [PATCH 1/5] Data updated to include 2024U --- .Rhistory | 164 +++++----- SQL_production/TM_2023A_data.sql | 6 +- SQL_production/TM_2024U_data.sql | 428 +++++++++++++++++++++++++++ background_scripts/data_processing.R | 9 +- data/all_student_numbers.rds | Bin 93358 -> 112328 bytes data/all_student_percentages.rds | Bin 184653 -> 212289 bytes data/grade_lookup.rds | Bin 7172 -> 8022 bytes data/qual_lookup.rds | Bin 6419 -> 7163 bytes 8 files changed, 518 insertions(+), 89 deletions(-) create mode 100644 SQL_production/TM_2024U_data.sql diff --git a/.Rhistory b/.Rhistory index c8ca8c4..f008642 100644 --- a/.Rhistory +++ b/.Rhistory @@ -1,85 +1,3 @@ -grade, -percentage) %>% -filter(percentage != "NA%") %>% -arrange(qualification_code, subject_code, size, grade_structure, prior_attainment_band, grade) -ancillary_data <- ancillary_data_numbers %>% -left_join(ancillary_data_percentages) -write_csv(ancillary_data, paste0(ancillary_save_path, 'tm_numbers_percentages_', current_year, '.csv')) -runApp() -# ----------------------------------------------------------------------------------------------------------------------------- -# ---- Load packages ---- -# ----------------------------------------------------------------------------------------------------------------------------- -library(odbc) -library(DBI) -library(dplyr) -library(dbplyr) -library(tidyr) -library(janitor) -library(readr) -rm(list=ls()) -# ----------------------------------------------------------------------------------------------------------------------------- -# ---- Things to change between runs ---- -# ----------------------------------------------------------------------------------------------------------------------------- -tm_file = "[KS5_STATISTICS_RESTRICTED].[TM_2022].[TM_data_2022A]" -ancillary_save_path <- "//lonnetapp01/DSGA2/!!Secure Data/SFR/2022/KS5/February_2023/DRY_RUN/06_ancillary/" -current_year <- "2022A" -# ----------------------------------------------------------------------------------------------------------------------------- -# ---- Reading in the data from SQL tables ---- -# ----------------------------------------------------------------------------------------------------------------------------- -# establish connection to server -con <- DBI::dbConnect(odbc::odbc(), driver = "SQL Server", -server = "3dcpri-pdb16\\acsqls") -# Select data from SQL tables -#tm_data_raw <- tbl(con, sql(paste("select * from", tm_file))) %>% collect() -# Error on 23/12/2022 with above line, related to underlying issue with ODBC, resolved by selecting for columns with 'long data' at the end. -# Long data referring to any columns with a negative 'type' in the following query -#dbColumnInfo(dbSendQuery(con, paste("select * from", tm_file))) -# Error seems to be resolved with the following - JB 23/12/2022 -tm_data_raw <- tbl(con, sql(paste("select all -[SUBLEVNO] -,[ASIZE] -,[GSIZE] -,[MAPPING] -,[Subject] -,[PRIOR_BAND] -,[SUBJ] -,[GRADE] -,[total_students] -,[Qual_Description] -,[Potential_Level] -,[gradeStructure] -FROM", tm_file))) %>% collect() -# Error on 23/12/2022 with above line, related to underlying issue with ODBC, resolved by selecting for columns with 'long data' at the end. -# Long data referring to any columns with a negative 'type' in the following query -dbColumnInfo(dbSendQuery(con, paste("select * from", tm_file))) -# ----------------------------------------------------------------------------------------------------------------------------- -# ---- Load packages ---- -# ----------------------------------------------------------------------------------------------------------------------------- -library(odbc) -library(DBI) -library(dplyr) -library(dbplyr) -library(tidyr) -library(janitor) -library(readr) -rm(list=ls()) -# ----------------------------------------------------------------------------------------------------------------------------- -# ---- Things to change between runs ---- -# ----------------------------------------------------------------------------------------------------------------------------- -tm_file = "[KS5_STATISTICS_RESTRICTED].[TM_2022].[TM_data_2022A]" -ancillary_save_path <- "//lonnetapp01/DSGA2/!!Secure Data/SFR/2022/KS5/February_2023/DRY_RUN/06_ancillary/" -current_year <- "2022A" -# ----------------------------------------------------------------------------------------------------------------------------- -# ---- Reading in the data from SQL tables ---- -# ----------------------------------------------------------------------------------------------------------------------------- -# establish connection to server -con <- DBI::dbConnect(odbc::odbc(), driver = "SQL Server", -server = "3dcpri-pdb16\\acsqls") -# Select data from SQL tables -tm_data_raw <- tbl(con, sql(paste("select * from", tm_file))) %>% collect() -# disconnect -DBI::dbDisconnect(con) -# ----------------------------------------------------------------------------------------------------------------------------- # ---- SORTING NAs ---- # ----------------------------------------------------------------------------------------------------------------------------- colSums(is.na(tm_data_raw)) %>% as.data.frame() @@ -510,3 +428,85 @@ shinytest2::record_test() renv::status() renv::snapshot() shinytest2::test_app() +renv::restore() +renv::status() +library(odbc) +library(DBI) +library(dplyr) +library(dbplyr) +library(tidyr) +library(janitor) +library(readr) +rm(list = ls()) +source("./background_scripts/data_processing_func.R") +ancillary_save_path <- "//lonnetapp01/DSGA2/!!Secure Data/SFR/2024/KS5/01_november_2024/06_ancillary/" +current_year <- "2024U" +# establish connection to server +con <- DBI::dbConnect(odbc::odbc(), driver = "SQL Server", server = "VMT1PR-DHSQL02") +# Select data from SQL tables - need to add the current year for unamended run and change from U to A for the amended run +tm_data_raw_2024 <- tbl(con, sql("select * from [KS5_STATISTICS_RESTRICTED].[TM_2024].[TM_data_2024U]")) %>% collect() +tm_data_raw_2023 <- tbl(con, sql("select * from [KS5_STATISTICS_RESTRICTED].[TM_2024].[TM_data_2023A]")) %>% collect() +tm_data_raw_2022 <- tbl(con, sql("select * from [KS5_STATISTICS_RESTRICTED].[TM_2024].[TM_data_2022A]")) %>% collect() +# disconnect +DBI::dbDisconnect(con) +processed_data_2024 <- TM_data_prod_func(tm_data_raw_2024, 2024) +processed_data_2023 <- TM_data_prod_func(tm_data_raw_2023, 2023) +processed_data_2022 <- TM_data_prod_func(tm_data_raw_2022, 2022) +current_year_data <- processed_data_2024 +ancillary_data_numbers <- current_year_data$student_numbers %>% +select(-ReportYr) %>% +pivot_longer(!c(Qual_Description, SUBLEVNO, Potential_Level, ASIZE, GSIZE, MAPPING, Subject, gradeStructure, PRIOR_BAND, SUBJ, SIZE, QUAL_ID, ROW_ID), +names_to = "grade", +values_to = "count" +) %>% +select( +qualification_name = Qual_Description, +qualification_code = SUBLEVNO, +subject_name = Subject, +subject_code = SUBJ, +size = SIZE, +grade_structure = gradeStructure, +prior_attainment_band = PRIOR_BAND, +grade, +count +) %>% +filter(!is.na(count)) %>% +arrange(qualification_code, subject_code, size, grade_structure, prior_attainment_band, grade, .locale = "en") +ancillary_data_percentages <- current_year_data$student_percentages %>% +select(-ReportYr) %>% +pivot_longer(!c(Qual_Description, SUBLEVNO, Potential_Level, ASIZE, GSIZE, MAPPING, Subject, gradeStructure, PRIOR_BAND, SUBJ, SIZE, QUAL_ID, ROW_ID), +names_to = "grade", +values_to = "percentage" +) %>% +select( +qualification_name = Qual_Description, +qualification_code = SUBLEVNO, +subject_name = Subject, +subject_code = SUBJ, +size = SIZE, +grade_structure = gradeStructure, +prior_attainment_band = PRIOR_BAND, +grade, +percentage +) %>% +filter(percentage != "NA%") %>% +arrange(qualification_code, subject_code, size, grade_structure, prior_attainment_band, grade, .locale = "en") +ancillary_data <- ancillary_data_numbers %>% +left_join(ancillary_data_percentages) +write_csv(ancillary_data, paste0(ancillary_save_path, "tm_numbers_percentages_", current_year, ".csv")) +student_numbers <- lapply(ls(pattern = "processed_data_", .GlobalEnv), function(x) { +bind_rows(mget(x, .GlobalEnv)[[1]]["student_numbers"]) +}) %>% bind_rows() +student_percentages <- lapply(ls(pattern = "processed_data_", .GlobalEnv), function(x) { +bind_rows(mget(x, .GlobalEnv)[[1]]["student_percentages"]) +}) %>% bind_rows() +qual_lookup <- lapply(ls(pattern = "processed_data_", .GlobalEnv), function(x) { +bind_rows(mget(x, .GlobalEnv)[[1]]["qual_lookup"]) +}) %>% bind_rows() +grades_ordered_lookup <- lapply(ls(pattern = "processed_data_", .GlobalEnv), function(x) { +bind_rows(mget(x, .GlobalEnv)[[1]]["grades_ordered_lookup"]) +}) %>% bind_rows() +saveRDS(student_numbers, "./data/all_student_numbers.rds") +saveRDS(student_percentages, "./data/all_student_percentages.rds") +saveRDS(qual_lookup, "./data/qual_lookup.rds") +saveRDS(grades_ordered_lookup, "./data/grade_lookup.rds") diff --git a/SQL_production/TM_2023A_data.sql b/SQL_production/TM_2023A_data.sql index 9734155..2bcfa44 100644 --- a/SQL_production/TM_2023A_data.sql +++ b/SQL_production/TM_2023A_data.sql @@ -41,7 +41,7 @@ select * into #CSCP_lookup from KS5_RESTRICTED.Internal.CSCP_subject_lookup If object_Id('tempDB..#indicator') is not null drop table #indicator select * into #indicator from KS5_RESTRICTED.[Outputs].[PupilIndicators_POST16_202223A] If object_Id('tempDB..#PRIOR') is not null drop table #PRIOR -select * into #PRIOR from KS5_STATISTICS_RESTRICTED.[EES_2023A].[PRIORS_KS4end2022] +select * into #PRIOR from KS5_STATISTICS_RESTRICTED.[EES_2024U].[PRIORS_KS4end2022] If object_Id('tempDB..#Allocations') is not null drop table #Allocations select PUPILID, END_KS into #Allocations from KS5_RESTRICTED.[Outputs].[PupilAllocations_POST16_202223A] @@ -402,7 +402,7 @@ where [Subject] is NULL -- should be zero ---drop table [KS5_STATISTICS_RESTRICTED].[TM_2022].[TM_data_2023A] +--drop table [KS5_STATISTICS_RESTRICTED].[TM_2024].[TM_data_2023A] select * @@ -423,4 +423,4 @@ from #TM_data -- and TABLE_NAME = 'TM_data_2022A' -- and then corrected using --- alter table [TM_2023].[TM_data_2023A] alter column gradeStructure varchar (200) not NULL; +-- alter table [KS5_STATISTICS_RESTRICTED].[TM_2024].[TM_data_2023A] alter column gradeStructure varchar (200) not NULL; diff --git a/SQL_production/TM_2024U_data.sql b/SQL_production/TM_2024U_data.sql new file mode 100644 index 0000000..5b86d92 --- /dev/null +++ b/SQL_production/TM_2024U_data.sql @@ -0,0 +1,428 @@ + +----------------------------------------------------------------------------- +----------------------------------------------------------------------------- +---- Data for TMs ---- +---- ---- +---- This code creates the data download tables and the front end data ---- +---- ---- +---- It has been updated for 2022 to include size in the breakdowns and ---- +---- exam cohort - this made a difference to VRQ3 sports studies where ---- +---- different gnumbers were classed as either applied general or tech ---- +---- level. The front end has also had an update on 2021 to include ---- +---- qualification type to disaggregate A and AS level and to give more ---- +---- detail to study skills. Also removed a lot of the unusual manual ---- +---- grade changes. ---- +---- ---- +----------------------------------------------------------------------------- +----------------------------------------------------------------------------- + +-- REMEBER to remove the VTQ filter on line 254 for 2024A + +-- declare the year and all the names of the base tables to be updated to appropriate tables +DECLARE @RPYEAR AS INTEGER = 2024 + +If object_Id('tempDB..#subj_tab') is not null drop table #subj_tab +select * into #subj_tab from QRD.dbo.Subje01_2024_09_25 +If object_Id('tempDB..#tab2') is not null drop table #tab2 +select * into #tab2 from QRD.dbo.Table2_2024_09_25 +If object_Id('tempDB..#tab3') is not null drop table #tab3 +select * into #tab3 from QRD.dbo.Table3_2024_09_25 +If object_Id('tempDB..#tab4') is not null drop table #tab4 +select * into #tab4 from QRD.dbo.Table4_2024_09_25 +If object_Id('tempDB..#subj') is not null drop table #subj + + +If object_Id('tempDB..#raw_inst') is not null drop table #raw_inst +select * into #raw_inst from KS5_RESTRICTED.[Outputs].[RAW_Inst_POST16_20241014_201235_TLEVEL_DISD] +If object_Id('tempDB..#raw_LA') is not null drop table #raw_LA +select * into #raw_LA from KS5_RESTRICTED.[Outputs].[RAW_LEA_POST16_20241014_201236_TLEVEL_DISD] +If object_Id('tempDB..#exam') is not null drop table #exam +select * into #exam from [KS5_RESTRICTED].[Outputs].[Exam_PT_POST16_20241014_190117_TLEVEL_DISD] +If object_Id('tempDB..#CSCP_lookup') is not null drop table #CSCP_lookup +select * into #CSCP_lookup from KS5_RESTRICTED.Internal.CSCP_subject_lookup +If object_Id('tempDB..#indicator') is not null drop table #indicator +select * into #indicator from KS5_RESTRICTED.[Outputs].[PupilIndicators_POST16_20241014_195753_TLEVEL_DISD] +If object_Id('tempDB..#PRIOR') is not null drop table #PRIOR +select * into #PRIOR from KS5_STATISTICS_RESTRICTED.[EES_2024U].[PRIORS_KS4end2023] +If object_Id('tempDB..#Allocations') is not null drop table #Allocations +select PUPILID, END_KS into #Allocations from KS5_RESTRICTED.[Outputs].[PupilAllocations_POST16_20241014_163831_TLEVEL_DISD] + +---------------------------------------------------------- +-- +--Most of this initial set up code is not important for TMs it's taken from the performance tables data download +-- +---------------------------------------------------------- + +--calculating the original PTQ INCLUDE ignoring COVID impacted to use to flag the COVID impacted results +--the spec for PTQ INCLUDE doesn't include removing # but it appears to be the case when QAing with RM - i think there is something somewhere about # quals +If object_Id('tempDB..#QRD_PTQINCLUDE') is not null drop table #QRD_PTQINCLUDE +select distinct a.QUID, case when a.syllabus_ref = '***' then '' else a.syllabus_ref end as syllabus_ref, a.Wolf_Included_1618 as Wolf_Included_1618_original, KS5_Academic, KS5_Subset, +case when syllabus_ref != 'KNO' and (App1618 = 1 and ((KS5_Equivalences = 1 and KS5_Academic = 1) or Wolf_Included_1618 in (1,2,3,5)) and QUID != '#') then 1 else 0 end PTQ_INCLUDE_original +into #QRD_PTQINCLUDE +from #subj_tab as a +left join #tab4 as b +on a.Qual_Type = b.Qual_Type + +--get the asize and gsize of the different qualications +If object_Id('tempDB..#QRD_SIZE') is not null drop table #QRD_SIZE +select a.QUID, +max(coalesce(b.GCSE_Equivalent, c.GCSE_Equivalent)) as GSIZE, +max(coalesce(b.AL_Equivalent, c.AL_Equivalent)) as ASIZE +into #QRD_SIZE +from #subj_tab as a +left join #tab2 as b +on a.Qual_Type = b.Qual_Type +left join #tab3 as c +on a.QUID = c.QUID +group by a.QUID + + +--Code from Ops to create the school type lookup +If object_Id('tempDB..#insti_data') is not null drop table #insti_data + select distinct r.LEALAB, URN, schname, TAB1618, +case when b.nftype = 20 then 'Sponsored Academy' +when b.nftype = 21 then 'Community School' +when b.nftype = 22 then 'Voluntary Aided School' +when b.nftype = 23 then 'Voluntary Controlled School' +when b.nftype = 24 then 'Foundation School' +when b.nftype = 25 then 'City Technology College' +when b.nftype = 26 then 'Community Special School' +when b.nftype = 27 then 'Foundation Special School' +when b.nftype = 28 then 'Non-Maintained Special School' +when b.nftype = 29 then 'Independent School Approved for SEN pupils' +when b.nftype = 30 then 'Independent School' +when b.nftype = 31 and b.FESITYPE=1 then 'Agriculture and Horticulture College' +when b.nftype = 31 and b.FESITYPE=2 then 'Art, Design and Performing Art College' +when b.nftype = 31 and b.FESITYPE=3 then 'General Further Education College' +when b.nftype = 31 and b.FESITYPE=4 then 'General Further Education College (Special)' +when b.nftype = 31 and b.FESITYPE=5 then 'Sixth Form College' +when b.nftype = 31 and b.FESITYPE=8 then 'Specialist Designated College' +when b.nftype = 31 and b.FESITYPE=9 then 'Tertiary College' +when b.nftype = 32 then 'Community hospital school' +when b.nftype = 33 then 'Foundation hospital school' +when b.nftype = 34 then 'Pupil Referral Unit' +when b.nftype = 35 then 'Sixth Form Centre / Consortia' +when b.nftype = 36 then 'Ministry of Defence Funded College' +when b.nftype = 38 then 'Special College' +when b.nftype = 41 then 'European Schools' +when b.nftype = 42 then 'Playing for Success Centres' +when b.nftype = 43 then 'Offshore Schools' +when b.nftype = 44 then 'Service Childrens Education' +when b.nftype = 45 then 'Higher Education Institutions' +when b.nftype = 46 then 'Welsh Establishment' +when b.nftype = 47 then 'LA Nursery School' +when b.nftype = 48 then 'Other Independent Special School' +when b.nftype = 49 then 'Early Years setting' +when b.nftype = 50 then 'Sponsored Special Academy' +when b.nftype = 51 then 'Converter Academy' +when b.nftype = 52 then 'Free School - Mainstream' +when b.nftype = 53 then 'Free School - Special' +when b.nftype = 54 then 'British Overseas School' +when b.nftype = 55 then 'Special Converter Academy' +when b.nftype = 56 then 'Alternative Provision' +when b.nftype = 57 then 'University Technical College' +when b.nftype = 58 then 'Studio School' +when b.nftype = 59 then 'Free School - 16-19' +when b.nftype = 60 then 'International Schools' +when b.nftype = 63 then 'Academy 16-19 Converter' +when b.nftype = 64 then 'Academy 16-19 Sponsor Led' +when b.nftype = 97 then 'Alternative provision' +when b.nftype = 98 then 'Legacy types' +when b.nftype = 99 then 'Secure Unit' +else b.NFTYPE end as [School or college type] +into #insti_data +from #raw_inst as b +left join #raw_LA as r +on b.LEA = r.LEA + + +/* Get data from exam file. Join on the front end friendly subject names - currently in an internal table on KS5_RESTRICTED +but ultimately we want this to end up in the QRD. Join on the calculated PTQ INCLUDE, ASIZE and GSIZE +Grade X has been removed for TMs +*/ +--DECLARE @RPYEAR AS INTEGER = 2022 +If object_Id('tempDB..#examprep') is not null drop table #examprep +select LAESTAB, URN, PUPILID, a.GNUMBER, a.BRDSUBNO, _QualificationTypeCode as QUAL_TYPE, SUBLEVNO, AMDEXAM, EXAMYEAR, d.ASIZE, d.GSIZE, MAPPING, +SEASON, GRADE, POINTS_1618, POTENTIAL_LEVEL, c.subject_user_engagement as [Subject], coalesce(PTQ_INCLUDE_original, 0) as PTQ_INCLUDE_original, PTQ_INCLUDE, +DISC_ALL, DISC_0_1, DISC_1_2, DISC_0_2, DISC_SINGLE, DISCS_ALL, DISCS_0_1, DISCS_1_2, DISCS_0_2, DISCS_SINGLE, DISCB_ALL, DISCB_0_1, DISCB_1_2, DISCB_0_2, DISCB_SINGLE, +DISC_ALL_FULL, DISC_0_1_FULL, DISC_1_2_FULL, DISC_0_2_FULL, DISC_SINGLE_FULL, _Wolf_Included_1618, EXAMNO +into #examprep +from #exam as a +left join #QRD_PTQINCLUDE as b +on a.GNUMBER = b.QUID and a.BRDSUBNO = b.Syllabus_Ref +left join #CSCP_lookup as c +on a.MAPPING = c.DISC_CODE +left join #QRD_SIZE as d +on a.GNUMBER = d.QUID +where GRADE != 'X' -- added to stop messing up AS level discounting + + +/* +select results for the appropriate provider based on COND and without discounting +make changes to the grade: + - including adding covid impact result based on ptq include - this can be removed once COVID impacted results are out of scope of the data + - setting pending and no result to no result but now seperating Fail out from no result so they are reported seperately + - making changes to IB where the cumulative points is given with a letter grade eg. 32D - we remove the letter grade. In the cases of a fail eg. 32F, the result is listed + as no result - using points to identify this + - sublevno 129 is an IB combined certificate - the grades that seem to exist are K and J where K is awarded and J is unawareded. This has been set as a pass for K and no result for J + in previous code and then later in the code the pass was set to no result. We are not sure why this was done and does not appear correct and so I have left as pass and fail. +*/ +--DECLARE @RPYEAR AS INTEGER = 2022 +If object_Id('tempDB..#examcut') is not null drop table #examcut +select b.*, Qual_Description, z.GPTSPE_1, z.PTSPE_1, +case when PTQ_INCLUDE = 0 and PTQ_INCLUDE_original = 1 then 'COVID result' -- this doesn't matter as I have added the PTQ_INCLUDE = 1 filter to the where statement so this is redundent +when GRADE IN ('Q','R','X') then 'No result' +when GRADE IN ('F','U','N') then 'Fail' +when SUBLEVNO = 130 and POINTS_1618 > 0 then LEFT(GRADE, 2) +when SUBLEVNO = 130 and POINTS_1618 = 0 then 'Fail' +when sublevno=129 then (case when grade='K' then 'Pass' when grade in ('J','F','Q','U','X') then 'Fail' end) +when grade = 'D*' then '*D' + when grade = 'PM' then 'MP' + when grade = 'MD' then 'DM' + when grade = 'D**' then '**D' + when grade = 'DD*' then '*DD' + when grade = 'MDD' then 'DDM' + when grade = 'MMD' then 'DMM' + when grade = 'PMM' then 'MMP' + when grade = 'PPM' then 'MPP' +else GRADE end GRADE_UPDATE, +case when _Wolf_Included_1618 IN (1,2,3) then case when PTSPE_1 <1 then '<1' +when PTSPE_1 >=1 and PTSPE_1 <2 then '1-<2' +when PTSPE_1 >=2 and PTSPE_1 <3 then '2-<3' +when PTSPE_1 >=3 and PTSPE_1 <4 then '3-<4' +when PTSPE_1 >=4 and PTSPE_1 <5 then '4-<5' +when PTSPE_1 >=5 and PTSPE_1 <6 then '5-<6' +when PTSPE_1 >=6 and PTSPE_1 <7 then '6-<7' +when PTSPE_1 >=6 and PTSPE_1 <7 then '6-<7' +when PTSPE_1 >=7 and PTSPE_1 <8 then '7-<8' +when PTSPE_1 >=8 and PTSPE_1 <9 then '8-<9' +when PTSPE_1 >=9 then '9>=' end +else case when GPTSPE_1 <1 then '<1' +when GPTSPE_1 >=1 and GPTSPE_1 <2 then '1-<2' +when GPTSPE_1 >=2 and GPTSPE_1 <3 then '2-<3' +when GPTSPE_1 >=3 and GPTSPE_1 <4 then '3-<4' +when GPTSPE_1 >=4 and GPTSPE_1 <5 then '4-<5' +when GPTSPE_1 >=5 and GPTSPE_1 <6 then '5-<6' +when GPTSPE_1 >=6 and GPTSPE_1 <7 then '6-<7' +when GPTSPE_1 >=6 and GPTSPE_1 <7 then '6-<7' +when GPTSPE_1 >=7 and GPTSPE_1 <8 then '7-<8' +when GPTSPE_1 >=8 and GPTSPE_1 <9 then '8-<9' +when GPTSPE_1 >=9 then '9>=' end end as PRIOR_BAND, +CASE WHEN (COND =1) THEN DISC_ALL -- not moved, discount over all years, otherwise + WHEN (COND =2) THEN DISC_0_1 -- else didn't move in RY and RY-1 and exam is in RY or RY-1 + WHEN (COND =3) THEN DISC_1_2-- else didn't move in RY-1 and RY-2 and exam is in RY-1 or RY-2 + WHEN (COND =4) THEN DISC_0_2-- else same inst in RY and RY-2 and exam is in RY or RY-2 + WHEN (COND =5) THEN DISC_SINGLE-- else exam is in RY + WHEN (COND =6) THEN DISC_SINGLE-- else exam is in RY-1 + WHEN (COND =7) THEN DISC_SINGLE-- else exam is in RY-2 + ELSE 1 END AS DISC, -- points discounting flag + CASE WHEN (COND =1) THEN DISCS_ALL + WHEN (COND =2) THEN DISCS_0_1 + WHEN (COND =3) THEN DISCS_1_2 + WHEN (COND =4) THEN DISCS_0_2 + WHEN (COND =5) THEN DISCS_SINGLE + WHEN (COND =6) THEN DISCS_SINGLE + WHEN (COND =7) THEN DISCS_SINGLE + ELSE 1 END AS DISCS,-- size discounting flag + CASE WHEN (COND =1) THEN DISCB_ALL + WHEN (COND =2) THEN DISCB_0_1 + WHEN (COND =3) THEN DISCB_1_2 + WHEN (COND =4) THEN DISCB_0_2 + WHEN (COND =5) THEN DISCB_SINGLE + WHEN (COND =6) THEN DISCB_SINGLE + WHEN (COND =7) THEN DISCB_SINGLE + ELSE 1 END AS DISCB,-- subset discounting flag, + CASE WHEN (COND =1) THEN DISC_ALL_FULL -- not moved, discount over all years, otherwise + WHEN (COND =2) THEN DISC_0_1_FULL -- else didn't move in RY and RY-1 and exam is in RY or RY-1 + WHEN (COND =3) THEN DISC_1_2_FULL -- else didn't move in RY-1 and RY-2 and exam is in RY-1 or RY-2 + WHEN (COND =4) THEN DISC_0_2_FULL -- else same inst in RY and RY-2 and exam is in RY or RY-2 + WHEN (COND =5) THEN DISC_SINGLE_FULL -- else exam is in RY + WHEN (COND =6) THEN DISC_SINGLE_FULL -- else exam is in RY-1 + WHEN (COND =7) THEN DISC_SINGLE_FULL -- else exam is in RY-2 + ELSE 1 END AS DISC_FULL +into #examcut +from #indicator as a +left join #PRIOR z +on a.PUPILID = z.pupilid and a.KS4_YEAR_CALC = z.KS4_YEAR_CALC +LEFT join #Allocations y +on a.PUPILID = y.PUPILID +left join #examprep as b +on a.PUPILID = b.PUPILID and a.URN = b.URN +left join #tab4 as c +on b.SUBLEVNO = c.Qual_Number +where [TRIGGER] = 1 and RECTYPE = 1 and COND IN (1,2,3,4,5,6,7) and NAT1618 = 1 and END_KS = 1 --?? + and PTQ_INCLUDE = 1 and _Wolf_Included_1618 NOT IN (1, 2, 3, 5) --- <<<------- needed in 2024U to remove VTQs, will needed removing for 2024A + AND (AMDEXAM NOT IN ('TO','CL', 'NR','D','W') OR AMDEXAM IS NULL) +and ((COND = 1 and ((EXAMYEAR IN (@RPYEAR, (@RPYEAR - 1), (@RPYEAR - 2)) and SEASON = 'S') or (EXAMYEAR IN ((@RPYEAR - 1), (@RPYEAR - 2), (@RPYEAR - 3)) and SEASON = 'W')) ) +OR (COND = 2 and ((EXAMYEAR IN (@RPYEAR, (@RPYEAR - 1)) and SEASON = 'S') or (EXAMYEAR IN ((@RPYEAR - 1), (@RPYEAR - 2)) and SEASON = 'W'))) +OR (COND = 3 and ((EXAMYEAR IN ((@RPYEAR - 1), (@RPYEAR - 2)) and SEASON = 'S') or (EXAMYEAR IN ((@RPYEAR - 2), (@RPYEAR - 3)) and SEASON = 'W')) ) +OR (COND = 4 and ((EXAMYEAR IN (@RPYEAR, (@RPYEAR - 2)) and SEASON = 'S') or (EXAMYEAR IN ((@RPYEAR - 1), (@RPYEAR - 3)) and SEASON = 'W')) ) +OR (COND = 5 and ((EXAMYEAR = @RPYEAR and SEASON = 'S') or (EXAMYEAR = (@RPYEAR - 1) and SEASON = 'W')) ) +OR (COND = 6 and ((EXAMYEAR = (@RPYEAR - 1) and SEASON = 'S') or (EXAMYEAR = (@RPYEAR - 2) and SEASON = 'W')) ) +OR (COND = 7 and ((EXAMYEAR = (@RPYEAR - 2) and SEASON = 'S') or (EXAMYEAR = (@RPYEAR - 3) and SEASON = 'W')) )) + + +--pull all the grades from the QRD tables 2 and 3 for the qualifications to be reported +If object_Id('tempDB..#grade_struc') is not null drop table #grade_struc +select a.*, b.Grade as grade2, c.Grade as grade3, b.points as points2, c.Points as points3, +coalesce(c.grade, b.grade) as QRD_grade, coalesce(c.points, b.points) as QRD_points/*, +coalesce(c.last_input_year, b.last_input_year) as last_input_year*/ +into #grade_struc +from ( +select distinct GNUMBER, QUAL_TYPE, SUBLEVNO +from #examcut) as a +left join #tab2 as b +on a.QUAL_TYPE = b.Qual_Type +left join #tab3 as c +on a.GNUMBER = c.QUID + +--altering some of the grades for no results and fails and the ordering of distinction, merit and pass +--eg MDD becomes DDM +--DECLARE @RPYEAR AS INTEGER = 2022 +If object_Id('tempDB..#grade_struc_update') is not null drop table #grade_struc_update +select distinct GNUMBER, QRD_points, QUAL_TYPE, +case when QRD_grade IN ('Q','R','X') then 'No result' +when QRD_grade IN ('F','U','N') then 'Fail' +when SUBLEVNO = 130 and QRD_points > 0 then LEFT(QRD_grade, 2) +when SUBLEVNO = 130 and QRD_points = 0 then 'Fail' +when sublevno=129 then (case when QRD_grade='K' then 'Pass' when QRD_grade in ('J','F','Q','U','X') then 'Fail' else QRD_grade end) + when QRD_grade = 'D*' then '*D' + when QRD_grade = 'PM' then 'MP' + when QRD_grade = 'MD' then 'DM' + when QRD_grade = 'D**' then '**D' + when QRD_grade = 'DD*' then '*DD' + when QRD_grade = 'MDD' then 'DDM' + when QRD_grade = 'MMD' then 'DMM' + when QRD_grade = 'PMM' then 'MMP' + when QRD_grade = 'PPM' then 'MPP' +else QRD_grade end as GRADE_update +into #grade_struc_update +from #grade_struc +--where last_input_year = @RPYEAR + +--concatenate the grades to make a grade structure but remove fail and no result in contrast to previous years +If object_Id('tempDB..#generic_gradeStruc') is not null drop table #generic_gradeStruc +Select Main.GNUMBER, Main.qual_type, + Left(Main.gradeStructure,Len(Main.gradeStructure)-1) As "gradeStructure" +into #generic_gradeStruc +From + ( + Select distinct ST2.GNUMBER, ST2.qual_type, + ( + Select ST1.GRADE_update + ',' AS [text()] + From #grade_struc_update ST1 + Where ST1.GNUMBER = ST2.GNUMBER and ST1.GRADE_update NOT IN ('No result', 'Fail') + ORDER BY ST1.GNUMBER, QRD_points desc, GRADE_update + For XML PATH ('') + ) gradeStructure + From #grade_struc_update ST2 + ) [Main] + +--create the exam cohort flag +If object_Id('tempDB..#all_dat') is not null drop table #all_dat +select a.*, b.gradeStructure, +case when KS5_Academic = 1 and KS5_Subset = 1 and SUBLEVNO != 101 then 'A level' +when KS5_Academic = 1 then 'Other academic' +when _Wolf_Included_1618 = 1 then 'Tech level' +when _Wolf_Included_1618 = 2 then 'Applied general' +when _Wolf_Included_1618 = 3 then 'Technical certificate' end as Qualification +into #all_dat +from #examcut as a +left join #generic_gradeStruc as b +on a.GNUMBER = b.GNUMBER +left join #QRD_PTQINCLUDE as y +on a.GNUMBER = y.QUID and a.BRDSUBNO = y.Syllabus_Ref + + +--national, qualification and subject +If object_Id('tempDB..#nat_subj') is not null drop table #nat_subj +select Qual_Description, a.SUBLEVNO, Potential_Level, ASIZE, GSIZE, a.MAPPING, a.MAPPING as SUBJ, [Subject], gradeStructure, PRIOR_BAND, GRADE_UPDATE as GRADE, --Qualification, +count(PUPILID) as total_students +into #nat_subj +from #all_dat as a +left join #insti_data as d +on a.URN = d.URN +where --TAB1618 = 1 + ((Qualification IN ('Other academic', 'Tech level', 'Applied general') and DISCS = 0) OR +(Qualification = 'A level' and (DISCS=0 OR (DISCS=1 AND DISC=1 AND DISCB=0))) OR +(Qualification = 'Technical certificate' and DISC_FULL = 0)) +group by Qual_Description, a.SUBLEVNO, Potential_Level, ASIZE, GSIZE, a.MAPPING, [Subject], gradeStructure, PRIOR_BAND, GRADE_UPDATE --Qualification, +order by Qual_Description, SUBLEVNO, ASIZE, GSIZE, [Subject], GRADE_UPDATE, PRIOR_BAND + +--create national data again but for AS so we can ignore usual discounting and implement AS only discounting +If object_Id('tempDB..#nat_subj_AS') is not null drop table #nat_subj_AS +select 'GCE AS level (All)' as Qual_Description, 699 as SUBLEVNO, Potential_Level, ASIZE, GSIZE, a.MAPPING, a.MAPPING as SUBJ, [Subject], gradeStructure, PRIOR_BAND, GRADE_UPDATE as GRADE, --Qualification, +count(PUPILID) as total_students +into #nat_subj_AS +from (select *, ROW_NUMBER() OVER (PARTITION BY LAESTAB, PUPILID, MAPPING, SUBLEVNO +ORDER BY POINTS_1618 DESC, EXAMYEAR DESC, SEASON DESC, EXAMNO) AS MAINEXAM +from #all_dat +where SUBLEVNO = 121) as a +left join #insti_data as d +on a.URN = d.URN +where MAINEXAM = 1 --TAB1618 = 1 +group by Qual_Description, a.SUBLEVNO, Potential_Level, ASIZE, GSIZE, a.MAPPING, [Subject], gradeStructure, PRIOR_BAND, GRADE_UPDATE --Qualification, +order by Qual_Description, SUBLEVNO, ASIZE, GSIZE, [Subject], GRADE_UPDATE, PRIOR_BAND + +-- bind the 2 tables together +If object_Id('tempDB..#bind_nat_dat') is not null drop table #bind_nat_dat +select * +into #bind_nat_dat +from +( +select Qual_Description, SUBLEVNO, Potential_Level, ASIZE, GSIZE, MAPPING, [Subject], gradeStructure, PRIOR_BAND, SUBJ, GRADE, total_students +from #nat_subj +where GRADE NOT IN ('COVID result', 'No result') +union +select Qual_Description, SUBLEVNO, Potential_Level, ASIZE, GSIZE, MAPPING, [Subject], gradeStructure, PRIOR_BAND, SUBJ, GRADE, total_students +from #nat_subj_AS +where GRADE NOT IN ('COVID result', 'No result')) as a + +--filter out quals where there are 15 or fewer entries and output final table +If object_Id('tempDB..#TM_data') is not null drop table #TM_data +select b.* +into #TM_data +from ( +select SUBLEVNO, Potential_Level, ASIZE, GSIZE, MAPPING, gradeStructure +from #bind_nat_dat +group by SUBLEVNO, Potential_Level, ASIZE, GSIZE, MAPPING, gradeStructure +having SUM(total_students) > 15) a +left join #bind_nat_dat b +on a.SUBLEVNO = b.SUBLEVNO and a.POTENTIAL_LEVEL = b.POTENTIAL_LEVEL +and a.ASIZE = b.ASIZE and a.GSIZE = b.GSIZE and a.MAPPING = b.MAPPING and a.gradeStructure = b.gradeStructure + + +select * from #TM_data +where PRIOR_BAND is NULL + +-- 11913 rows +-- up to 11914 rows with grade structure added in +-- 955 NULLS in prior band + +select * from #TM_data +where [Subject] is NULL +-- should be zero + + +--drop table [KS5_STATISTICS_RESTRICTED].[TM_2022].[TM_data_2023A] + + +select * +into [KS5_STATISTICS_RESTRICTED].[TM_2024].[TM_data_2024U] +from #TM_data + + +-- Note +-- JB, 05/01/2023 + +-- One of the columns had a character length that caused errors in R, +-- this was identified in SQL using + +-- use KS5_STATISTICS_RESTRICTED +-- select COLUMN_NAME, DATA_TYPE, CHARACTER_MAXIMUM_LENGTH +-- from INFORMATION_SCHEMA.COLUMNS +-- where TABLE_SCHEMA = 'TM_2022' +-- and TABLE_NAME = 'TM_data_2022A' + +-- and then corrected using +-- alter table [TM_2024].[TM_data_2024U] alter column gradeStructure varchar (200) not NULL; diff --git a/background_scripts/data_processing.R b/background_scripts/data_processing.R index 033dd34..65e5c6a 100644 --- a/background_scripts/data_processing.R +++ b/background_scripts/data_processing.R @@ -23,8 +23,8 @@ source("./background_scripts/data_processing_func.R") # ---- Things to change between runs ---- # ----------------------------------------------------------------------------------------------------------------------------- -ancillary_save_path <- "//lonnetapp01/DSGA2/!!Secure Data/SFR/2023/KS5/4 Dev/07_development/06_ancillary/" -current_year <- "2023A" +ancillary_save_path <- "//lonnetapp01/DSGA2/!!Secure Data/SFR/2024/KS5/01_november_2024/06_ancillary/" +current_year <- "2024U" # ----------------------------------------------------------------------------------------------------------------------------- # ---- Thing to add - Reading in the data from SQL tables and running function ---- @@ -35,7 +35,7 @@ con <- DBI::dbConnect(odbc::odbc(), driver = "SQL Server", server = "VMT1PR-DHSQ # Select data from SQL tables - need to add the current year for unamended run and change from U to A for the amended run - +tm_data_raw_2024 <- tbl(con, sql("select * from [KS5_STATISTICS_RESTRICTED].[TM_2024].[TM_data_2024U]")) %>% collect() tm_data_raw_2023 <- tbl(con, sql("select * from [KS5_STATISTICS_RESTRICTED].[TM_2024].[TM_data_2023A]")) %>% collect() tm_data_raw_2022 <- tbl(con, sql("select * from [KS5_STATISTICS_RESTRICTED].[TM_2024].[TM_data_2022A]")) %>% collect() @@ -46,6 +46,7 @@ DBI::dbDisconnect(con) # add current year to the function call to produce the processed data - only needs updating 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zA$*fEq@^b*m=!&^cJhV``xW!mx50mJ!KbyVG?d3B>yr{7W19KyV|k?M#vkbR@3k){ z(%|P!dEC>Lk)dcREZ2RqbGZA}qc7l7#qQ+U*K*IWf3AYt2e6%b_rV`JkBIk?AD2#a zsV{d8ebO@~y6oVZCA_qr)3ukML3%HO>+L@R|NZWF-~GM6Q@w$CHgejj6+81C@_y4WMXtL?E0RZkIel`FA From c47326484eb03c28cece76a3207519edf391ad99 Mon Sep 17 00:00:00 2001 From: Chris Bennett Date: Wed, 20 Nov 2024 14:11:19 +0000 Subject: [PATCH 2/5] Altered qualification drop down box to only show quals in each year and add warning box about VTQs --- .Rhistory | 2 +- R/dashboard_panels.R | 12 +++++++++--- server.R | 17 ++++++++++++++++- 3 files changed, 26 insertions(+), 5 deletions(-) diff --git a/.Rhistory b/.Rhistory index f008642..dbdb44b 100644 --- a/.Rhistory +++ b/.Rhistory @@ -1,4 +1,3 @@ -# ---- SORTING NAs ---- # ----------------------------------------------------------------------------------------------------------------------------- colSums(is.na(tm_data_raw)) %>% as.data.frame() # NAs in prior band which need removing @@ -510,3 +509,4 @@ saveRDS(student_numbers, "./data/all_student_numbers.rds") saveRDS(student_percentages, "./data/all_student_percentages.rds") saveRDS(qual_lookup, "./data/qual_lookup.rds") saveRDS(grades_ordered_lookup, "./data/grade_lookup.rds") +shiny::runApp() diff --git a/R/dashboard_panels.R b/R/dashboard_panels.R index 9c38331..477a754 100644 --- a/R/dashboard_panels.R +++ b/R/dashboard_panels.R @@ -1,6 +1,12 @@ homepage_panel <- function() { tabPanel( "Homepage", + modalDialog( + title = "Missing data", + "The 16 to 18 transition matrices does not include vocational and technical qualifications for 2024 provisional data due to a data collection issue. This will be resolved in revised publication.", + size = "l", + easyClose = FALSE + ), gov_main_layout( gov_row( column( @@ -144,7 +150,7 @@ dashboard_panel <- function() { selectizeInput( inputId = "qual_select", label = "2. Select a qualification", - choices = list(Qualifications = sort(unique(qual_lookup$Qual_Description))), + choices = unique(qual_lookup$Qual_Description), selected = "GCE A level" ) ), @@ -162,7 +168,7 @@ dashboard_panel <- function() { selectizeInput( inputId = "size_select", label = "4. Select a size", - choices = list(Sizes = sort(qual_lookup$SIZE)) + choices = list(Sizes = sort(unique(qual_lookup$SIZE))) ) ), column( @@ -170,7 +176,7 @@ dashboard_panel <- function() { selectizeInput( inputId = "grade_structure_select", label = "5. Select a grade structure", - choices = list(GradeStructures = sort(qual_lookup$gradeStructure)) + choices = list(GradeStructures = sort(unique(qual_lookup$gradeStructure))) ) ), column( diff --git a/server.R b/server.R index 6bfcf9b..81aa2a6 100644 --- a/server.R +++ b/server.R @@ -58,13 +58,28 @@ server <- function(input, output, session) { # to the qualification that a user selects in the qualification select drop down # qualifications with only one subject, and subjects with multiple sizes require special formatting + observe({ + updateSelectInput(session, + inputId = "qual_select", + label = NULL, + choices <- qual_lookup %>% + filter(ReportYr == input$ReportYr_select) %>% + pull(Qual_Description) %>% + unique(.) %>% + sort(.) + ) + }) + observe({ updateSelectInput(session, inputId = "subj_select", label = NULL, choices <- qual_lookup %>% - filter(ReportYr == input$ReportYr_select & Qual_Description == input$qual_select) %>% + filter( + ReportYr == input$ReportYr_select, + Qual_Description == input$qual_select + ) %>% pull(Subject) %>% sort(.) ) From 18f59c9f3bd54e2c4ecc2e1333f77a0ed5700894 Mon Sep 17 00:00:00 2001 From: Chris Bennett Date: Thu, 21 Nov 2024 14:08:10 +0000 Subject: [PATCH 3/5] Changes to data, use RECTYPE 5, remove END_KS, use all quals for prior --- .Rhistory | 180 +++++++++++++++---------------- SQL_production/TM_2022A_data.sql | 23 ++-- SQL_production/TM_2023A_data.sql | 19 +--- SQL_production/TM_2024U_data.sql | 25 ++--- data/all_student_numbers.rds | Bin 112328 -> 112927 bytes data/all_student_percentages.rds | Bin 212289 -> 213343 bytes data/grade_lookup.rds | Bin 8022 -> 8139 bytes data/qual_lookup.rds | Bin 7163 -> 7180 bytes 8 files changed, 107 insertions(+), 140 deletions(-) diff --git a/.Rhistory b/.Rhistory index dbdb44b..eb88cc9 100644 --- a/.Rhistory +++ b/.Rhistory @@ -1,93 +1,3 @@ -# ----------------------------------------------------------------------------------------------------------------------------- -colSums(is.na(tm_data_raw)) %>% as.data.frame() -# NAs in prior band which need removing -# NAs in subj because no subj code was assigned within the SQL production code -# SUBJ is assigned by joining on the QUAL_SUBJ_LOOKUP, -# but some subjects are filtered out of the L3VA process if they are not entered in 5 or more institutions -# for the TMs we don't mind this rule, so we've decided to leave them in but we will have to create new SUBJ codes -# to ensure I'm not overwriting any existing SUBJ codes, I'm generating 3 digit random numbers (SUBJ is usually 5 digits) -tm_data_subj_na <- tm_data_raw %>% -filter(!(is.na(PRIOR_BAND)), -is.na(SUBJ)) %>% -group_by(Subject) %>% -mutate(SUBJ = sample(100:900,1)) -tm_data <- tm_data_raw %>% -filter(!(is.na(PRIOR_BAND)), -!(is.na(SUBJ))) %>% -bind_rows(tm_data_subj_na) -colSums(is.na(tm_data)) %>% as.data.frame() -# ----------------------------------------------------------------------------------------------------------------------------- -# ---- NUMBERS & PERCENTAGES CALCULATED ---- -# ----------------------------------------------------------------------------------------------------------------------------- -student_numbers <- tm_data %>% -mutate(GRADE = case_when(GRADE == "Fail" & SUBLEVNO != 130 ~ "U", -TRUE ~ GRADE), -SIZE = case_when(ASIZE == 0 ~ GSIZE, -TRUE ~ ASIZE), -QUAL_ID = paste0(SUBLEVNO, SUBJ, SIZE, gradeStructure), -ROW_ID = paste0(SUBLEVNO, SUBJ, SIZE, PRIOR_BAND, gradeStructure)) %>% -mutate(across(c(everything(), -total_students), ~as.character(.))) %>% -arrange(ROW_ID) %>% -pivot_wider(names_from = GRADE, values_from = total_students) -student_percentages <- student_numbers %>% -janitor::adorn_percentages() %>% -mutate_if(is.numeric, function(x){round(x*100, 2)}) %>% -mutate_if(is.numeric, ~paste0(.x, "%")) -# ----------------------------------------------------------------------------------------------------------------------------- -# ---- LOOKUP & FULL GRADE OPTIONS CALCULATED ---- -# ----------------------------------------------------------------------------------------------------------------------------- -qual_lookup <- tm_data %>% -select(Qual_Description, SUBLEVNO, Subject, SUBJ, ASIZE, GSIZE, gradeStructure) %>% -distinct() %>% -mutate(SIZE = case_when(ASIZE == 0 ~ GSIZE, -TRUE ~ ASIZE)) -grade_lookup_u <- tm_data %>% -select(SUBLEVNO, SUBJ, ASIZE, GSIZE, gradeStructure) %>% -distinct() %>% -mutate(GRADE = case_when(SUBLEVNO == 130 ~ "Fail", -TRUE ~ "U")) -grade_lookup_sep <- tm_data %>% -select(SUBLEVNO, SUBJ, ASIZE, GSIZE, gradeStructure) %>% -distinct() %>% -mutate(GRADE = gradeStructure) %>% -separate_rows(. , GRADE, sep = ",") -grade_lookup <- bind_rows(grade_lookup_u, grade_lookup_sep) %>% -arrange(SUBLEVNO, SUBJ, ASIZE, GSIZE, gradeStructure, GRADE) %>% -mutate(SIZE = case_when(ASIZE == 0 ~ GSIZE, -TRUE ~ ASIZE)) -# would like to sort numeric and character grades differently so that all grades go from low - high -# for numeric this is ascending, for character this is descending -# will need to split again based on numeric and character and then re-combine again -grades_char <- grade_lookup %>% -mutate(char_grade_check = is.na(suppressWarnings(as.numeric(grade_lookup$GRADE)))) %>% # gives extra column - True is character, False is number -filter(char_grade_check == TRUE) %>% -arrange(desc(GRADE)) -grades_num <- grade_lookup %>% -mutate(char_grade_check = is.na(suppressWarnings(as.numeric(grade_lookup$GRADE)))) %>% # gives extra column - True is character, False is number -filter(char_grade_check == FALSE) %>% -arrange(GRADE) -grades_ordered_lookup <- bind_rows(grades_char, grades_num) %>% -arrange(SUBLEVNO) -# grade_list %>% filter(SUBLEVNO == 253, SUBJ == 20596, ASIZE == 1) -# ----------------------------------------------------------------------------------------------------------------------------- -# ---- Saving Data ---- -# ----------------------------------------------------------------------------------------------------------------------------- -saveRDS(student_numbers, "./data/all_student_numbers.rds") -saveRDS(student_percentages, "./data/all_student_percentages.rds") -saveRDS(qual_lookup, "./data/qual_lookup.rds") -saveRDS(grades_ordered_lookup, "./data/grade_lookup.rds") -# ----------------------------------------------------------------------------------------------------------------------------- -# ---- ANCILLARY DATA FOR EES ---- -# ----------------------------------------------------------------------------------------------------------------------------- -ancillary_data_numbers <- student_numbers %>% -pivot_longer(!c(Qual_Description, SUBLEVNO, Potential_Level, ASIZE, GSIZE, MAPPING, Subject, gradeStructure, PRIOR_BAND, SUBJ, SIZE, QUAL_ID, ROW_ID), -names_to = "grade", -values_to = "count") %>% -select(qualification_name = Qual_Description, -qualification_code = SUBLEVNO, -subject_name = Subject, -subject_code = SUBJ, -size = SIZE, grade_structure = gradeStructure, prior_attainment_band = PRIOR_BAND, grade, @@ -510,3 +420,93 @@ saveRDS(student_percentages, "./data/all_student_percentages.rds") saveRDS(qual_lookup, "./data/qual_lookup.rds") saveRDS(grades_ordered_lookup, "./data/grade_lookup.rds") shiny::runApp() +shiny::runApp() +library(odbc) +library(DBI) +library(dplyr) +library(dbplyr) +library(tidyr) +library(janitor) +library(readr) +rm(list = ls()) +# ----------------------------------------------------------------------------------------------------------------------------- +# ---- Source functions ---- +# ----------------------------------------------------------------------------------------------------------------------------- +source("./background_scripts/data_processing_func.R") +# ----------------------------------------------------------------------------------------------------------------------------- +# ---- Things to change between runs ---- +# ----------------------------------------------------------------------------------------------------------------------------- +ancillary_save_path <- "//lonnetapp01/DSGA2/!!Secure Data/SFR/2024/KS5/01_november_2024/06_ancillary/" +current_year <- "2024U" +# ----------------------------------------------------------------------------------------------------------------------------- +# ---- Thing to add - Reading in the data from SQL tables and running function ---- +# ----------------------------------------------------------------------------------------------------------------------------- +# establish connection to server +con <- DBI::dbConnect(odbc::odbc(), driver = "SQL Server", server = "VMT1PR-DHSQL02") +# Select data from SQL tables - need to add the current year for unamended run and change from U to A for the amended run +tm_data_raw_2024 <- tbl(con, sql("select * from [KS5_STATISTICS_RESTRICTED].[TM_2024].[TM_data_2024U]")) %>% collect() +tm_data_raw_2023 <- tbl(con, sql("select * from [KS5_STATISTICS_RESTRICTED].[TM_2024].[TM_data_2023A]")) %>% collect() +tm_data_raw_2022 <- tbl(con, sql("select * from [KS5_STATISTICS_RESTRICTED].[TM_2024].[TM_data_2022A]")) %>% collect() +# disconnect +DBI::dbDisconnect(con) +processed_data_2024 <- TM_data_prod_func(tm_data_raw_2024, 2024) +processed_data_2023 <- TM_data_prod_func(tm_data_raw_2023, 2023) +processed_data_2022 <- TM_data_prod_func(tm_data_raw_2022, 2022) +current_year_data <- processed_data_2024 +ancillary_data_numbers <- current_year_data$student_numbers %>% +select(-ReportYr) %>% +pivot_longer(!c(Qual_Description, SUBLEVNO, Potential_Level, ASIZE, GSIZE, MAPPING, Subject, gradeStructure, PRIOR_BAND, SUBJ, SIZE, QUAL_ID, ROW_ID), +names_to = "grade", +values_to = "count" +) %>% +select( +qualification_name = Qual_Description, +qualification_code = SUBLEVNO, +subject_name = Subject, +subject_code = SUBJ, +size = SIZE, +grade_structure = gradeStructure, +prior_attainment_band = PRIOR_BAND, +grade, +count +) %>% +filter(!is.na(count)) %>% +arrange(qualification_code, subject_code, size, grade_structure, prior_attainment_band, grade, .locale = "en") +ancillary_data_percentages <- current_year_data$student_percentages %>% +select(-ReportYr) %>% +pivot_longer(!c(Qual_Description, SUBLEVNO, Potential_Level, ASIZE, GSIZE, MAPPING, Subject, gradeStructure, PRIOR_BAND, SUBJ, SIZE, QUAL_ID, ROW_ID), +names_to = "grade", +values_to = "percentage" +) %>% +select( +qualification_name = Qual_Description, +qualification_code = SUBLEVNO, +subject_name = Subject, +subject_code = SUBJ, +size = SIZE, +grade_structure = gradeStructure, +prior_attainment_band = PRIOR_BAND, +grade, +percentage +) %>% +filter(percentage != "NA%") %>% +arrange(qualification_code, subject_code, size, grade_structure, prior_attainment_band, grade, .locale = "en") +ancillary_data <- ancillary_data_numbers %>% +left_join(ancillary_data_percentages) +write_csv(ancillary_data, paste0(ancillary_save_path, "tm_numbers_percentages_", current_year, ".csv")) +student_numbers <- lapply(ls(pattern = "processed_data_", .GlobalEnv), function(x) { +bind_rows(mget(x, .GlobalEnv)[[1]]["student_numbers"]) +}) %>% bind_rows() +student_percentages <- lapply(ls(pattern = "processed_data_", .GlobalEnv), function(x) { +bind_rows(mget(x, .GlobalEnv)[[1]]["student_percentages"]) +}) %>% bind_rows() +qual_lookup <- lapply(ls(pattern = "processed_data_", .GlobalEnv), function(x) { +bind_rows(mget(x, .GlobalEnv)[[1]]["qual_lookup"]) +}) %>% bind_rows() +grades_ordered_lookup <- lapply(ls(pattern = "processed_data_", .GlobalEnv), function(x) { +bind_rows(mget(x, .GlobalEnv)[[1]]["grades_ordered_lookup"]) +}) %>% bind_rows() +saveRDS(student_numbers, "./data/all_student_numbers.rds") +saveRDS(student_percentages, "./data/all_student_percentages.rds") +saveRDS(qual_lookup, "./data/qual_lookup.rds") +saveRDS(grades_ordered_lookup, "./data/grade_lookup.rds") diff --git a/SQL_production/TM_2022A_data.sql b/SQL_production/TM_2022A_data.sql index 37c00b0..3e2de76 100644 --- a/SQL_production/TM_2022A_data.sql +++ b/SQL_production/TM_2022A_data.sql @@ -184,7 +184,7 @@ when grade = 'D*' then '*D' when grade = 'PMM' then 'MMP' when grade = 'PPM' then 'MPP' else GRADE end GRADE_UPDATE, -case when _Wolf_Included_1618 IN (1,2,3) then case when PTSPE_1 <1 then '<1' +case when PTSPE_1 <1 then '<1' when PTSPE_1 >=1 and PTSPE_1 <2 then '1-<2' when PTSPE_1 >=2 and PTSPE_1 <3 then '2-<3' when PTSPE_1 >=3 and PTSPE_1 <4 then '3-<4' @@ -194,18 +194,7 @@ when PTSPE_1 >=6 and PTSPE_1 <7 then '6-<7' when PTSPE_1 >=6 and PTSPE_1 <7 then '6-<7' when PTSPE_1 >=7 and PTSPE_1 <8 then '7-<8' when PTSPE_1 >=8 and PTSPE_1 <9 then '8-<9' -when PTSPE_1 >=9 then '9>=' end -else case when GPTSPE_1 <1 then '<1' -when GPTSPE_1 >=1 and GPTSPE_1 <2 then '1-<2' -when GPTSPE_1 >=2 and GPTSPE_1 <3 then '2-<3' -when GPTSPE_1 >=3 and GPTSPE_1 <4 then '3-<4' -when GPTSPE_1 >=4 and GPTSPE_1 <5 then '4-<5' -when GPTSPE_1 >=5 and GPTSPE_1 <6 then '5-<6' -when GPTSPE_1 >=6 and GPTSPE_1 <7 then '6-<7' -when GPTSPE_1 >=6 and GPTSPE_1 <7 then '6-<7' -when GPTSPE_1 >=7 and GPTSPE_1 <8 then '7-<8' -when GPTSPE_1 >=8 and GPTSPE_1 <9 then '8-<9' -when GPTSPE_1 >=9 then '9>=' end end as PRIOR_BAND, +when PTSPE_1 >=9 then '9>=' end as PRIOR_BAND, CASE WHEN (COND =1) THEN DISC_ALL -- not moved, discount over all years, otherwise WHEN (COND =2) THEN DISC_0_1 -- else didn't move in RY and RY-1 and exam is in RY or RY-1 WHEN (COND =3) THEN DISC_1_2-- else didn't move in RY-1 and RY-2 and exam is in RY-1 or RY-2 @@ -245,10 +234,10 @@ on a.PUPILID = z.pupilid and a.KS4_YEAR_CALC = z.KS4_YEAR_CALC LEFT join #Allocations y on a.PUPILID = y.PUPILID left join #examprep as b -on a.PUPILID = b.PUPILID and a.URN = b.URN +on a.PUPILID = b.PUPILID left join #tab4 as c on b.SUBLEVNO = c.Qual_Number -where [TRIGGER] = 1 and RECTYPE = 1 and COND IN (1,2,3,4,5,6,7) and NAT1618 = 1 and END_KS = 1 --?? +where [TRIGGER] = 1 and RECTYPE = 5 and COND IN (1,2,3,4,5,6,7) --and NAT1618 = 1 and END_KS = 1 --?? and PTQ_INCLUDE = 1 --and PTQ_INCLUDE_original = 1 -- remove COVID impacted quals for 2022 TMs AND (AMDEXAM NOT IN ('TO','CL', 'NR','D','W') OR AMDEXAM IS NULL) and ((COND = 1 and ((EXAMYEAR IN (@RPYEAR, (@RPYEAR - 1), (@RPYEAR - 2)) and SEASON = 'S') or (EXAMYEAR IN ((@RPYEAR - 1), (@RPYEAR - 2), (@RPYEAR - 3)) and SEASON = 'W')) ) @@ -400,7 +389,7 @@ where PRIOR_BAND is NULL ---drop table [KS5_STATISTICS_RESTRICTED].[TM_2022].[TM_data_2022A] +--drop table [KS5_STATISTICS_RESTRICTED].[TM_2024].[TM_data_2022A] select * into [KS5_STATISTICS_RESTRICTED].[TM_2024].[TM_data_2022A] @@ -420,4 +409,4 @@ from #TM_data -- and TABLE_NAME = 'TM_data_2022A' -- and then corrected using --- alter table [TM_2022].[TM_data_2022A] alter column gradeStructure varchar (200) not NULL; +-- alter table [KS5_STATISTICS_RESTRICTED].[TM_2024].[TM_data_2022A] alter column gradeStructure varchar (200) not NULL; diff --git a/SQL_production/TM_2023A_data.sql b/SQL_production/TM_2023A_data.sql index 2bcfa44..d0ef9e6 100644 --- a/SQL_production/TM_2023A_data.sql +++ b/SQL_production/TM_2023A_data.sql @@ -184,7 +184,7 @@ when grade = 'D*' then '*D' when grade = 'PMM' then 'MMP' when grade = 'PPM' then 'MPP' else GRADE end GRADE_UPDATE, -case when _Wolf_Included_1618 IN (1,2,3) then case when PTSPE_1 <1 then '<1' +case when PTSPE_1 <1 then '<1' when PTSPE_1 >=1 and PTSPE_1 <2 then '1-<2' when PTSPE_1 >=2 and PTSPE_1 <3 then '2-<3' when PTSPE_1 >=3 and PTSPE_1 <4 then '3-<4' @@ -194,18 +194,7 @@ when PTSPE_1 >=6 and PTSPE_1 <7 then '6-<7' when PTSPE_1 >=6 and PTSPE_1 <7 then '6-<7' when PTSPE_1 >=7 and PTSPE_1 <8 then '7-<8' when PTSPE_1 >=8 and PTSPE_1 <9 then '8-<9' -when PTSPE_1 >=9 then '9>=' end -else case when GPTSPE_1 <1 then '<1' -when GPTSPE_1 >=1 and GPTSPE_1 <2 then '1-<2' -when GPTSPE_1 >=2 and GPTSPE_1 <3 then '2-<3' -when GPTSPE_1 >=3 and GPTSPE_1 <4 then '3-<4' -when GPTSPE_1 >=4 and GPTSPE_1 <5 then '4-<5' -when GPTSPE_1 >=5 and GPTSPE_1 <6 then '5-<6' -when GPTSPE_1 >=6 and GPTSPE_1 <7 then '6-<7' -when GPTSPE_1 >=6 and GPTSPE_1 <7 then '6-<7' -when GPTSPE_1 >=7 and GPTSPE_1 <8 then '7-<8' -when GPTSPE_1 >=8 and GPTSPE_1 <9 then '8-<9' -when GPTSPE_1 >=9 then '9>=' end end as PRIOR_BAND, +when PTSPE_1 >=9 then '9>=' end as PRIOR_BAND, CASE WHEN (COND =1) THEN DISC_ALL -- not moved, discount over all years, otherwise WHEN (COND =2) THEN DISC_0_1 -- else didn't move in RY and RY-1 and exam is in RY or RY-1 WHEN (COND =3) THEN DISC_1_2-- else didn't move in RY-1 and RY-2 and exam is in RY-1 or RY-2 @@ -245,10 +234,10 @@ on a.PUPILID = z.pupilid and a.KS4_YEAR_CALC = z.KS4_YEAR_CALC LEFT join #Allocations y on a.PUPILID = y.PUPILID left join #examprep as b -on a.PUPILID = b.PUPILID and a.URN = b.URN +on a.PUPILID = b.PUPILID left join #tab4 as c on b.SUBLEVNO = c.Qual_Number -where [TRIGGER] = 1 and RECTYPE = 1 and COND IN (1,2,3,4,5,6,7) and NAT1618 = 1 and END_KS = 1 --?? +where [TRIGGER] = 1 and RECTYPE = 5 and COND IN (1,2,3,4,5,6,7) --and NAT1618 = 1 and END_KS = 1 --?? and PTQ_INCLUDE = 1 --and PTQ_INCLUDE_original = 1 -- remove COVID impacted quals for 2022 TMs AND (AMDEXAM NOT IN ('TO','CL', 'NR','D','W') OR AMDEXAM IS NULL) and ((COND = 1 and ((EXAMYEAR IN (@RPYEAR, (@RPYEAR - 1), (@RPYEAR - 2)) and SEASON = 'S') or (EXAMYEAR IN ((@RPYEAR - 1), (@RPYEAR - 2), (@RPYEAR - 3)) and SEASON = 'W')) ) diff --git a/SQL_production/TM_2024U_data.sql b/SQL_production/TM_2024U_data.sql index 5b86d92..b930ad8 100644 --- a/SQL_production/TM_2024U_data.sql +++ b/SQL_production/TM_2024U_data.sql @@ -167,7 +167,7 @@ make changes to the grade: - sublevno 129 is an IB combined certificate - the grades that seem to exist are K and J where K is awarded and J is unawareded. This has been set as a pass for K and no result for J in previous code and then later in the code the pass was set to no result. We are not sure why this was done and does not appear correct and so I have left as pass and fail. */ ---DECLARE @RPYEAR AS INTEGER = 2022 +--DECLARE @RPYEAR AS INTEGER = 2024 If object_Id('tempDB..#examcut') is not null drop table #examcut select b.*, Qual_Description, z.GPTSPE_1, z.PTSPE_1, case when PTQ_INCLUDE = 0 and PTQ_INCLUDE_original = 1 then 'COVID result' -- this doesn't matter as I have added the PTQ_INCLUDE = 1 filter to the where statement so this is redundent @@ -186,7 +186,7 @@ when grade = 'D*' then '*D' when grade = 'PMM' then 'MMP' when grade = 'PPM' then 'MPP' else GRADE end GRADE_UPDATE, -case when _Wolf_Included_1618 IN (1,2,3) then case when PTSPE_1 <1 then '<1' +case when PTSPE_1 <1 then '<1' when PTSPE_1 >=1 and PTSPE_1 <2 then '1-<2' when PTSPE_1 >=2 and PTSPE_1 <3 then '2-<3' when PTSPE_1 >=3 and PTSPE_1 <4 then '3-<4' @@ -196,18 +196,7 @@ when PTSPE_1 >=6 and PTSPE_1 <7 then '6-<7' when PTSPE_1 >=6 and PTSPE_1 <7 then '6-<7' when PTSPE_1 >=7 and PTSPE_1 <8 then '7-<8' when PTSPE_1 >=8 and PTSPE_1 <9 then '8-<9' -when PTSPE_1 >=9 then '9>=' end -else case when GPTSPE_1 <1 then '<1' -when GPTSPE_1 >=1 and GPTSPE_1 <2 then '1-<2' -when GPTSPE_1 >=2 and GPTSPE_1 <3 then '2-<3' -when GPTSPE_1 >=3 and GPTSPE_1 <4 then '3-<4' -when GPTSPE_1 >=4 and GPTSPE_1 <5 then '4-<5' -when GPTSPE_1 >=5 and GPTSPE_1 <6 then '5-<6' -when GPTSPE_1 >=6 and GPTSPE_1 <7 then '6-<7' -when GPTSPE_1 >=6 and GPTSPE_1 <7 then '6-<7' -when GPTSPE_1 >=7 and GPTSPE_1 <8 then '7-<8' -when GPTSPE_1 >=8 and GPTSPE_1 <9 then '8-<9' -when GPTSPE_1 >=9 then '9>=' end end as PRIOR_BAND, +when PTSPE_1 >=9 then '9>=' end as PRIOR_BAND, CASE WHEN (COND =1) THEN DISC_ALL -- not moved, discount over all years, otherwise WHEN (COND =2) THEN DISC_0_1 -- else didn't move in RY and RY-1 and exam is in RY or RY-1 WHEN (COND =3) THEN DISC_1_2-- else didn't move in RY-1 and RY-2 and exam is in RY-1 or RY-2 @@ -247,10 +236,10 @@ on a.PUPILID = z.pupilid and a.KS4_YEAR_CALC = z.KS4_YEAR_CALC LEFT join #Allocations y on a.PUPILID = y.PUPILID left join #examprep as b -on a.PUPILID = b.PUPILID and a.URN = b.URN +on a.PUPILID = b.PUPILID left join #tab4 as c on b.SUBLEVNO = c.Qual_Number -where [TRIGGER] = 1 and RECTYPE = 1 and COND IN (1,2,3,4,5,6,7) and NAT1618 = 1 and END_KS = 1 --?? +where [TRIGGER] = 1 and RECTYPE = 5 and COND IN (1,2,3,4,5,6,7) --and NAT1618 = 1 --and END_KS = 1 --?? and PTQ_INCLUDE = 1 and _Wolf_Included_1618 NOT IN (1, 2, 3, 5) --- <<<------- needed in 2024U to remove VTQs, will needed removing for 2024A AND (AMDEXAM NOT IN ('TO','CL', 'NR','D','W') OR AMDEXAM IS NULL) and ((COND = 1 and ((EXAMYEAR IN (@RPYEAR, (@RPYEAR - 1), (@RPYEAR - 2)) and SEASON = 'S') or (EXAMYEAR IN ((@RPYEAR - 1), (@RPYEAR - 2), (@RPYEAR - 3)) and SEASON = 'W')) ) @@ -404,7 +393,7 @@ where [Subject] is NULL -- should be zero ---drop table [KS5_STATISTICS_RESTRICTED].[TM_2022].[TM_data_2023A] +--drop table [KS5_STATISTICS_RESTRICTED].[TM_2024].[TM_data_2024U] select * @@ -425,4 +414,4 @@ from #TM_data -- and TABLE_NAME = 'TM_data_2022A' -- and then corrected using --- alter table [TM_2024].[TM_data_2024U] alter column gradeStructure varchar (200) not NULL; +-- alter table [KS5_STATISTICS_RESTRICTED].[TM_2024].[TM_data_2024U] alter column gradeStructure varchar (200) not NULL; diff --git a/data/all_student_numbers.rds b/data/all_student_numbers.rds index 7531a0ea06cf8a684541f564c826b7b580889444..95290e467665679735b8c55d469ab052a3b242f4 100644 GIT binary patch literal 112927 zcmce-2UJtr);26iQ>55Hr71;JnkY4)hzf`ZSZSe1mlA5|5L5)zNI+0JgpLBC6FL!* zF1?1(YeFED&_m#hdd|7WbMN<-{~zOHWGrTqz4l&nu4g}a*4lIO!I%#H{9{<8Fu$|3 zMTs-0diLMzytf5;qMTjno7s6U>AG7-BJregBALepZ$F-!yU^N_o%;r#D<9d+AfI(5 zOQiF$)n>bTC-|L}1A9!<>;?8Si?ICTv@c#sew~1cv%M()dY-O_{_ss&2f;5fSzd;; zT$=~W0Zn6P3kQaQ9*aDy2gk=_J5GuWlj<>c|ukM z7YMDa;A?$ke^mm79vV{e+leopwlU=eE6Dro?g_RJa&pc2M1}GU~eFk*trbdXHCmQuHx8fc zdd?t~OhYx(ctW4|o-u7^ee&t%>FoPKS01Mv{beF`L*@EAbtH6>@cu6MgRgx^Ic9a~ 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adding a word in the warning message --- .Rhistory | 6 +++--- R/dashboard_panels.R | 2 +- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/.Rhistory b/.Rhistory index eb88cc9..aa1d9db 100644 --- a/.Rhistory +++ b/.Rhistory @@ -1,6 +1,3 @@ -grade_structure = gradeStructure, -prior_attainment_band = PRIOR_BAND, -grade, count) %>% filter(!is.na(count)) %>% arrange(qualification_code, subject_code, size, grade_structure, prior_attainment_band, grade) @@ -510,3 +507,6 @@ saveRDS(student_numbers, "./data/all_student_numbers.rds") saveRDS(student_percentages, "./data/all_student_percentages.rds") saveRDS(qual_lookup, "./data/qual_lookup.rds") saveRDS(grades_ordered_lookup, "./data/grade_lookup.rds") +shiny::runApp() +runApp() +runApp() diff --git a/R/dashboard_panels.R b/R/dashboard_panels.R index 477a754..878789e 100644 --- a/R/dashboard_panels.R +++ b/R/dashboard_panels.R @@ -3,7 +3,7 @@ homepage_panel <- function() { "Homepage", modalDialog( title = "Missing data", - "The 16 to 18 transition matrices does not include vocational and technical qualifications for 2024 provisional data due to a data collection issue. This will be resolved in revised publication.", + "The 16 to 18 transition matrices does not include vocational and technical qualifications for 2024 provisional data due to a data collection issue. This will be resolved in the revised publication.", size = "l", easyClose = FALSE ), From bde55e3cbe5b1baaebef4100682e234b8a38d5ab Mon Sep 17 00:00:00 2001 From: Chris Bennett Date: Thu, 28 Nov 2024 10:43:13 +0000 Subject: [PATCH 5/5] remove R history --- .Rhistory | 512 ------------------------------------------------------ 1 file changed, 512 deletions(-) delete mode 100644 .Rhistory diff --git a/.Rhistory b/.Rhistory deleted file mode 100644 index aa1d9db..0000000 --- a/.Rhistory +++ /dev/null @@ -1,512 +0,0 @@ -count) %>% -filter(!is.na(count)) %>% -arrange(qualification_code, subject_code, size, grade_structure, prior_attainment_band, grade) -ancillary_data_percentages <- student_percentages %>% -pivot_longer(!c(Qual_Description, SUBLEVNO, Potential_Level, ASIZE, GSIZE, MAPPING, Subject, gradeStructure, PRIOR_BAND, SUBJ, SIZE, QUAL_ID, ROW_ID), -names_to = "grade", -values_to = "percentage") %>% -select(qualification_name = Qual_Description, -qualification_code = SUBLEVNO, -subject_name = Subject, -subject_code = SUBJ, -size = SIZE, -grade_structure = gradeStructure, -prior_attainment_band = PRIOR_BAND, -grade, -percentage) %>% -filter(percentage != "NA%") %>% -arrange(qualification_code, subject_code, size, grade_structure, prior_attainment_band, grade) -ancillary_data <- ancillary_data_numbers %>% -left_join(ancillary_data_percentages) -write_csv(ancillary_data, paste0(ancillary_save_path, 'tm_numbers_percentages_', current_year, '.csv')) -shiny::runApp() -library(shinytest) -shinytest::testApp("../") -# Initialize a ShinyDriver object using the app (point to the right directory) -app <- ShinyDriver$new("../../", loadTimeout = 1e+05, ) -library(shinytest) -shinytest::testApp("../") -library(shinytest) -shinytest::testApp("./") -install.packages("shinytest") -library(shinytest) -shinytest::testApp("./") -shinytest::installDependencies() -library(shinytest) -shinytest::testApp("./") -shinytest::testApp("./") -library(shinytest) -shinytest::testApp("./") -library(shinytest) -shinytest::testApp("./") -library(shinytest) -shinytest::testApp("./") -rlang::last_error() -library(shinytest) -shinytest::testApp("./") -library(shinytest) -shinytest::testApp("./") -runApp() -runApp() -stud_numbers <- readRDS("data/all_student_numbers.rds") -stud_percentages <- readRDS("data/all_student_percentages.rds") -qual_lookup <- readRDS("data/qual_lookup.rds") -grade_lookup <- readRDS("data/grade_lookup.rds") -shiny::runApp() -View(user_selection_example) -View(grade_lookup) -View(grade_lookup) -View(qual_lookup) -View(stud_numbers) -rm(list=ls()) -stud_numbers <- readRDS("data/all_student_numbers.rds") -stud_percentages <- readRDS("data/all_student_percentages.rds") -qual_lookup <- readRDS("data/qual_lookup.rds") -grade_lookup <- readRDS("data/grade_lookup.rds") -View(grade_lookup) -View(qual_lookup) -View(stud_numbers) -View(stud_percentages) -# ----------------------------------------------------------------------------------------------------------------------------- -# ---- Load packages ---- -# ----------------------------------------------------------------------------------------------------------------------------- -library(odbc) -library(DBI) -library(dplyr) -library(dbplyr) -library(tidyr) -library(janitor) -library(readr) -rm(list=ls()) -# ----------------------------------------------------------------------------------------------------------------------------- -# ---- Source functions ---- -# ----------------------------------------------------------------------------------------------------------------------------- -source("./background_scripts/data_processing_func.R") -# ----------------------------------------------------------------------------------------------------------------------------- -# ---- Things to change between runs ---- -# ----------------------------------------------------------------------------------------------------------------------------- -ancillary_save_path <- "//lonnetapp01/DSGA2/!!Secure Data/SFR/2023/KS5/February_2024/06_ancillary/" -current_year <- "2023A" -# ----------------------------------------------------------------------------------------------------------------------------- -# ---- Thing to add - Reading in the data from SQL tables and running function ---- -# ----------------------------------------------------------------------------------------------------------------------------- -# establish connection to server -con <- DBI::dbConnect(odbc::odbc(), driver = "SQL Server", server = "VMT1PR-DHSQL02") -# Select data from SQL tables - need to add the current year for unamended run and change from U to A for the amended run -tm_data_raw_2023 <- tbl(con, sql("select * from [KS5_STATISTICS_RESTRICTED].[TM_2023].[TM_data_2023A]")) %>% collect() -tm_data_raw_2022 <- tbl(con, sql("select * from [KS5_STATISTICS_RESTRICTED].[TM_2022].[TM_data_2022A]")) %>% collect() -# disconnect -DBI::dbDisconnect(con) -processed_data_2023 <- TM_data_prod_func(tm_data_raw_2023, 2023) -processed_data_2022 <- TM_data_prod_func(tm_data_raw_2022, 2022) -# ----------------------------------------------------------------------------------------------------------------------------- -# ---- Load packages ---- -# ----------------------------------------------------------------------------------------------------------------------------- -library(odbc) -library(DBI) -library(dplyr) -library(dbplyr) -library(tidyr) -library(janitor) -library(readr) -rm(list=ls()) -# ----------------------------------------------------------------------------------------------------------------------------- -# ---- Source functions ---- -# ----------------------------------------------------------------------------------------------------------------------------- -source("./background_scripts/data_processing_func.R") -# ----------------------------------------------------------------------------------------------------------------------------- -# ---- Things to change between runs ---- -# ----------------------------------------------------------------------------------------------------------------------------- -ancillary_save_path <- "//lonnetapp01/DSGA2/!!Secure Data/SFR/2023/KS5/4 Dev/07_development/06_ancillary/" -current_year <- "2023A" -# establish connection to server -con <- DBI::dbConnect(odbc::odbc(), driver = "SQL Server", server = "VMT1PR-DHSQL02") -# Select data from SQL tables - need to add the current year for unamended run and change from U to A for the amended run -tm_data_raw_2023 <- tbl(con, sql("select * from [KS5_STATISTICS_RESTRICTED].test.[TM_data_2023A_subj_test]")) %>% collect() -tm_data_raw_2022 <- tbl(con, sql("select * from [KS5_STATISTICS_RESTRICTED].test.[TM_data_2022A_subj_test]")) %>% collect() -# disconnect -DBI::dbDisconnect(con) -processed_data_2023 <- TM_data_prod_func(tm_data_raw_2023, 2023) -processed_data_2023 <- TM_data_prod_func(tm_data_raw_2023, 2023) -sql_data <- tm_data_raw_2023 -ReportYear <- 2023 -colSums(is.na(sql_data)) %>% as.data.frame() -sql_data %>% -filter(!(is.na(PRIOR_BAND)), -is.na(SUBJ)) -tm_data_subj_na <- sql_data %>% -filter(!(is.na(PRIOR_BAND)), -is.na(SUBJ)) %>% -group_by(Subject) %>% -mutate(SUBJ = sample(100:900,1)) -tm_data <- sql_data %>% -filter(!(is.na(PRIOR_BAND)), -!(is.na(SUBJ))) %>% -bind_rows(tm_data_subj_na) -tm_data <- sql_data %>% -filter(!(is.na(PRIOR_BAND)), -!(is.na(SUBJ))) #%>% -colSums(is.na(tm_data)) %>% as.data.frame() -rm(list = ls()) -source("./background_scripts/data_processing_func.R") -# ----------------------------------------------------------------------------------------------------------------------------- -# ---- Things to change between runs ---- -# ----------------------------------------------------------------------------------------------------------------------------- -ancillary_save_path <- "//lonnetapp01/DSGA2/!!Secure Data/SFR/2023/KS5/4 Dev/07_development/06_ancillary/" -current_year <- "2023A" -# ----------------------------------------------------------------------------------------------------------------------------- -# ---- Thing to add - Reading in the data from SQL tables and running function ---- -# ----------------------------------------------------------------------------------------------------------------------------- -# establish connection to server -con <- DBI::dbConnect(odbc::odbc(), driver = "SQL Server", server = "VMT1PR-DHSQL02") -# Select data from SQL tables - need to add the current year for unamended run and change from U to A for the amended run -tm_data_raw_2023 <- tbl(con, sql("select * from [KS5_STATISTICS_RESTRICTED].test.[TM_data_2023A_subj_test]")) %>% collect() -tm_data_raw_2022 <- tbl(con, sql("select * from [KS5_STATISTICS_RESTRICTED].test.[TM_data_2022A_subj_test]")) %>% collect() -# disconnect -DBI::dbDisconnect(con) -# add current year to the function call to produce the processed data - only needs updating for unamended runs as -# the SQL update above deals with version -processed_data_2023 <- TM_data_prod_func(tm_data_raw_2023, 2023) -processed_data_2022 <- TM_data_prod_func(tm_data_raw_2022, 2022) -current_year_data <- processed_data_2023 -ancillary_data_numbers <- current_year_data$student_numbers %>% -select(-ReportYr) %>% -pivot_longer(!c(Qual_Description, SUBLEVNO, Potential_Level, ASIZE, GSIZE, MAPPING, Subject, gradeStructure, PRIOR_BAND, SUBJ, SIZE, QUAL_ID, ROW_ID), -names_to = "grade", -values_to = "count") %>% -select(qualification_name = Qual_Description, -qualification_code = SUBLEVNO, -subject_name = Subject, -subject_code = SUBJ, -size = SIZE, -grade_structure = gradeStructure, -prior_attainment_band = PRIOR_BAND, -grade, -count) %>% -filter(!is.na(count)) %>% -arrange(qualification_code, subject_code, size, grade_structure, prior_attainment_band, grade, .locale = "en") -ancillary_data_percentages <- current_year_data$student_percentages %>% -select(-ReportYr) %>% -pivot_longer(!c(Qual_Description, SUBLEVNO, Potential_Level, ASIZE, GSIZE, MAPPING, Subject, gradeStructure, PRIOR_BAND, SUBJ, SIZE, QUAL_ID, ROW_ID), -names_to = "grade", -values_to = "percentage") %>% -select(qualification_name = Qual_Description, -qualification_code = SUBLEVNO, -subject_name = Subject, -subject_code = SUBJ, -size = SIZE, -grade_structure = gradeStructure, -prior_attainment_band = PRIOR_BAND, -grade, -percentage) %>% -filter(percentage != "NA%") %>% -arrange(qualification_code, subject_code, size, grade_structure, prior_attainment_band, grade, .locale = "en") -ancillary_data <- ancillary_data_numbers %>% -left_join(ancillary_data_percentages) -write_csv(ancillary_data, paste0(ancillary_save_path, 'tm_numbers_percentages_', current_year, '.csv')) -student_numbers <- lapply(ls(pattern = "processed_data_", .GlobalEnv), function(x){ -bind_rows(mget(x, .GlobalEnv)[[1]]["student_numbers"])}) %>% bind_rows() -student_percentages <- lapply(ls(pattern = "processed_data_", .GlobalEnv), function(x){ -bind_rows(mget(x, .GlobalEnv)[[1]]["student_percentages"])}) %>% bind_rows() -qual_lookup <- lapply(ls(pattern = "processed_data_", .GlobalEnv), function(x){ -bind_rows(mget(x, .GlobalEnv)[[1]]["qual_lookup"])}) %>% bind_rows() -grades_ordered_lookup <- lapply(ls(pattern = "processed_data_", .GlobalEnv), function(x){ -bind_rows(mget(x, .GlobalEnv)[[1]]["grades_ordered_lookup"])}) %>% bind_rows() -saveRDS(student_numbers, "./data/all_student_numbers.rds") -saveRDS(student_percentages, "./data/all_student_percentages.rds") -saveRDS(qual_lookup, "./data/qual_lookup.rds") -saveRDS(grades_ordered_lookup, "./data/grade_lookup.rds") -shiny::runApp() -runApp() -# ----------------------------------------------------------------------------------------------------------------------------- -# ---- Load packages ---- -# ----------------------------------------------------------------------------------------------------------------------------- -library(odbc) -library(DBI) -library(dplyr) -library(dbplyr) -library(tidyr) -library(janitor) -library(readr) -rm(list=ls()) -# ----------------------------------------------------------------------------------------------------------------------------- -# ---- Source functions ---- -# ----------------------------------------------------------------------------------------------------------------------------- -source("./background_scripts/data_processing_func.R") -ancillary_save_path <- "//lonnetapp01/DSGA2/!!Secure Data/SFR/2023/KS5/4 Dev/07_development/06_ancillary/" -current_year <- "2023A" -# establish connection to server -con <- DBI::dbConnect(odbc::odbc(), driver = "SQL Server", server = "VMT1PR-DHSQL02") -tm_data_raw_2023 <- tbl(con, sql("select * from [KS5_STATISTICS_RESTRICTED].[TM_2024].[TM_data_2023A]")) %>% collect() -tm_data_raw_2022 <- tbl(con, sql("select * from [KS5_STATISTICS_RESTRICTED].[TM_2024].[TM_data_2022A]")) %>% collect() -tm_data_raw_2023 <- tbl(con, sql("select * from [KS5_STATISTICS_RESTRICTED].[TM_2024].[TM_data_2023A]")) %>% collect() -tm_data_raw_2022 <- tbl(con, sql("select * from [KS5_STATISTICS_RESTRICTED].[TM_2024].[TM_data_2022A]")) %>% collect() -# disconnect -DBI::dbDisconnect(con) -processed_data_2023 <- TM_data_prod_func(tm_data_raw_2023, 2023) -processed_data_2022 <- TM_data_prod_func(tm_data_raw_2022, 2022) -current_year_data <- processed_data_2023 -ancillary_data_numbers <- current_year_data$student_numbers %>% -select(-ReportYr) %>% -pivot_longer(!c(Qual_Description, SUBLEVNO, Potential_Level, ASIZE, GSIZE, MAPPING, Subject, gradeStructure, PRIOR_BAND, SUBJ, SIZE, QUAL_ID, ROW_ID), -names_to = "grade", -values_to = "count") %>% -select(qualification_name = Qual_Description, -qualification_code = SUBLEVNO, -subject_name = Subject, -subject_code = SUBJ, -size = SIZE, -grade_structure = gradeStructure, -prior_attainment_band = PRIOR_BAND, -grade, -count) %>% -filter(!is.na(count)) %>% -arrange(qualification_code, subject_code, size, grade_structure, prior_attainment_band, grade, .locale = "en") -ancillary_data_percentages <- current_year_data$student_percentages %>% -select(-ReportYr) %>% -pivot_longer(!c(Qual_Description, SUBLEVNO, Potential_Level, ASIZE, GSIZE, MAPPING, Subject, gradeStructure, PRIOR_BAND, SUBJ, SIZE, QUAL_ID, ROW_ID), -names_to = "grade", -values_to = "percentage") %>% -select(qualification_name = Qual_Description, -qualification_code = SUBLEVNO, -subject_name = Subject, -subject_code = SUBJ, -size = SIZE, -grade_structure = gradeStructure, -prior_attainment_band = PRIOR_BAND, -grade, -percentage) %>% -filter(percentage != "NA%") %>% -arrange(qualification_code, subject_code, size, grade_structure, prior_attainment_band, grade, .locale = "en") -ancillary_data <- ancillary_data_numbers %>% -left_join(ancillary_data_percentages) -write_csv(ancillary_data, paste0(ancillary_save_path, 'tm_numbers_percentages_', current_year, '.csv')) -student_numbers <- lapply(ls(pattern = "processed_data_", .GlobalEnv), function(x){ -bind_rows(mget(x, .GlobalEnv)[[1]]["student_numbers"])}) %>% bind_rows() -student_percentages <- lapply(ls(pattern = "processed_data_", .GlobalEnv), function(x){ -bind_rows(mget(x, .GlobalEnv)[[1]]["student_percentages"])}) %>% bind_rows() -qual_lookup <- lapply(ls(pattern = "processed_data_", .GlobalEnv), function(x){ -bind_rows(mget(x, .GlobalEnv)[[1]]["qual_lookup"])}) %>% bind_rows() -grades_ordered_lookup <- lapply(ls(pattern = "processed_data_", .GlobalEnv), function(x){ -bind_rows(mget(x, .GlobalEnv)[[1]]["grades_ordered_lookup"])}) %>% bind_rows() -saveRDS(student_numbers, "./data/all_student_numbers.rds") -saveRDS(student_percentages, "./data/all_student_percentages.rds") -saveRDS(qual_lookup, "./data/qual_lookup.rds") -saveRDS(grades_ordered_lookup, "./data/grade_lookup.rds") -shiny::runApp() -getwd() -shinytest2::migrate_from_shinytest("C:/Users/cbennett1/OneDrive - Department for Education/Documents/TM_shiny_app_github/ks5-transition-matrices") -shiny::runApp() -library(shinytest2) -shinytest2::record_test() -library(shinytest2) -record_test() -shinytest2::record_test() -renv::status() -renv::snapshot() -renv::status() -shiny::runApp() -testthat::snapshot_review('shinytest2/') -shinytest2::test_app() -shinytest2::record_test() -shinytest2::record_test() -testthat::snapshot_review('shinytest2/') -shinytest2::test_app() -runApp() -shinytest2::test_app() -runApp() -runApp() -runApp() -shiny::runApp() -shinytest2::test_app() -shinytest2::test_app() -shinytest2::use_shinytest2(".", setup = TRUE) -shinytest2::test_app() -shinytest2::record_test() -shinytest2::test_app() -shinytest2::test_app() -shinytest2::record_test() -shinytest2::record_test() -renv::status() -renv::snapshot() -renv::status() -shinytest2::record_test() -renv::status() -renv::snapshot() -shinytest2::test_app() -renv::restore() -renv::status() -library(odbc) -library(DBI) -library(dplyr) -library(dbplyr) -library(tidyr) -library(janitor) -library(readr) -rm(list = ls()) -source("./background_scripts/data_processing_func.R") -ancillary_save_path <- "//lonnetapp01/DSGA2/!!Secure Data/SFR/2024/KS5/01_november_2024/06_ancillary/" -current_year <- "2024U" -# establish connection to server -con <- DBI::dbConnect(odbc::odbc(), driver = "SQL Server", server = "VMT1PR-DHSQL02") -# Select data from SQL tables - need to add the current year for unamended run and change from U to A for the amended run -tm_data_raw_2024 <- tbl(con, sql("select * from [KS5_STATISTICS_RESTRICTED].[TM_2024].[TM_data_2024U]")) %>% collect() -tm_data_raw_2023 <- tbl(con, sql("select * from [KS5_STATISTICS_RESTRICTED].[TM_2024].[TM_data_2023A]")) %>% collect() -tm_data_raw_2022 <- tbl(con, sql("select * from [KS5_STATISTICS_RESTRICTED].[TM_2024].[TM_data_2022A]")) %>% collect() -# disconnect -DBI::dbDisconnect(con) -processed_data_2024 <- TM_data_prod_func(tm_data_raw_2024, 2024) -processed_data_2023 <- TM_data_prod_func(tm_data_raw_2023, 2023) -processed_data_2022 <- TM_data_prod_func(tm_data_raw_2022, 2022) -current_year_data <- processed_data_2024 -ancillary_data_numbers <- current_year_data$student_numbers %>% -select(-ReportYr) %>% -pivot_longer(!c(Qual_Description, SUBLEVNO, Potential_Level, ASIZE, GSIZE, MAPPING, Subject, gradeStructure, PRIOR_BAND, SUBJ, SIZE, QUAL_ID, ROW_ID), -names_to = "grade", -values_to = "count" -) %>% -select( -qualification_name = Qual_Description, -qualification_code = SUBLEVNO, -subject_name = Subject, -subject_code = SUBJ, -size = SIZE, -grade_structure = gradeStructure, -prior_attainment_band = PRIOR_BAND, -grade, -count -) %>% -filter(!is.na(count)) %>% -arrange(qualification_code, subject_code, size, grade_structure, prior_attainment_band, grade, .locale = "en") -ancillary_data_percentages <- current_year_data$student_percentages %>% -select(-ReportYr) %>% -pivot_longer(!c(Qual_Description, SUBLEVNO, Potential_Level, ASIZE, GSIZE, MAPPING, Subject, gradeStructure, PRIOR_BAND, SUBJ, SIZE, QUAL_ID, ROW_ID), -names_to = "grade", -values_to = "percentage" -) %>% -select( -qualification_name = Qual_Description, -qualification_code = SUBLEVNO, -subject_name = Subject, -subject_code = SUBJ, -size = SIZE, -grade_structure = gradeStructure, -prior_attainment_band = PRIOR_BAND, -grade, -percentage -) %>% -filter(percentage != "NA%") %>% -arrange(qualification_code, subject_code, size, grade_structure, prior_attainment_band, grade, .locale = "en") -ancillary_data <- ancillary_data_numbers %>% -left_join(ancillary_data_percentages) -write_csv(ancillary_data, paste0(ancillary_save_path, "tm_numbers_percentages_", current_year, ".csv")) -student_numbers <- lapply(ls(pattern = "processed_data_", .GlobalEnv), function(x) { -bind_rows(mget(x, .GlobalEnv)[[1]]["student_numbers"]) -}) %>% bind_rows() -student_percentages <- lapply(ls(pattern = "processed_data_", .GlobalEnv), function(x) { -bind_rows(mget(x, .GlobalEnv)[[1]]["student_percentages"]) -}) %>% bind_rows() -qual_lookup <- lapply(ls(pattern = "processed_data_", .GlobalEnv), function(x) { -bind_rows(mget(x, .GlobalEnv)[[1]]["qual_lookup"]) -}) %>% bind_rows() -grades_ordered_lookup <- lapply(ls(pattern = "processed_data_", .GlobalEnv), function(x) { -bind_rows(mget(x, .GlobalEnv)[[1]]["grades_ordered_lookup"]) -}) %>% bind_rows() -saveRDS(student_numbers, "./data/all_student_numbers.rds") -saveRDS(student_percentages, "./data/all_student_percentages.rds") -saveRDS(qual_lookup, "./data/qual_lookup.rds") -saveRDS(grades_ordered_lookup, "./data/grade_lookup.rds") -shiny::runApp() -shiny::runApp() -library(odbc) -library(DBI) -library(dplyr) -library(dbplyr) -library(tidyr) -library(janitor) -library(readr) -rm(list = ls()) -# ----------------------------------------------------------------------------------------------------------------------------- -# ---- Source functions ---- -# ----------------------------------------------------------------------------------------------------------------------------- -source("./background_scripts/data_processing_func.R") -# ----------------------------------------------------------------------------------------------------------------------------- -# ---- Things to change between runs ---- -# ----------------------------------------------------------------------------------------------------------------------------- -ancillary_save_path <- "//lonnetapp01/DSGA2/!!Secure Data/SFR/2024/KS5/01_november_2024/06_ancillary/" -current_year <- "2024U" -# ----------------------------------------------------------------------------------------------------------------------------- -# ---- Thing to add - Reading in the data from SQL tables and running function ---- -# ----------------------------------------------------------------------------------------------------------------------------- -# establish connection to server -con <- DBI::dbConnect(odbc::odbc(), driver = "SQL Server", server = "VMT1PR-DHSQL02") -# Select data from SQL tables - need to add the current year for unamended run and change from U to A for the amended run -tm_data_raw_2024 <- tbl(con, sql("select * from [KS5_STATISTICS_RESTRICTED].[TM_2024].[TM_data_2024U]")) %>% collect() -tm_data_raw_2023 <- tbl(con, sql("select * from [KS5_STATISTICS_RESTRICTED].[TM_2024].[TM_data_2023A]")) %>% collect() -tm_data_raw_2022 <- tbl(con, sql("select * from [KS5_STATISTICS_RESTRICTED].[TM_2024].[TM_data_2022A]")) %>% collect() -# disconnect -DBI::dbDisconnect(con) -processed_data_2024 <- TM_data_prod_func(tm_data_raw_2024, 2024) -processed_data_2023 <- TM_data_prod_func(tm_data_raw_2023, 2023) -processed_data_2022 <- TM_data_prod_func(tm_data_raw_2022, 2022) -current_year_data <- processed_data_2024 -ancillary_data_numbers <- current_year_data$student_numbers %>% -select(-ReportYr) %>% -pivot_longer(!c(Qual_Description, SUBLEVNO, Potential_Level, ASIZE, GSIZE, MAPPING, Subject, gradeStructure, PRIOR_BAND, SUBJ, SIZE, QUAL_ID, ROW_ID), -names_to = "grade", -values_to = "count" -) %>% -select( -qualification_name = Qual_Description, -qualification_code = SUBLEVNO, -subject_name = Subject, -subject_code = SUBJ, -size = SIZE, -grade_structure = gradeStructure, -prior_attainment_band = PRIOR_BAND, -grade, -count -) %>% -filter(!is.na(count)) %>% -arrange(qualification_code, subject_code, size, grade_structure, prior_attainment_band, grade, .locale = "en") -ancillary_data_percentages <- current_year_data$student_percentages %>% -select(-ReportYr) %>% -pivot_longer(!c(Qual_Description, SUBLEVNO, Potential_Level, ASIZE, GSIZE, MAPPING, Subject, gradeStructure, PRIOR_BAND, SUBJ, SIZE, QUAL_ID, ROW_ID), -names_to = "grade", -values_to = "percentage" -) %>% -select( -qualification_name = Qual_Description, -qualification_code = SUBLEVNO, -subject_name = Subject, -subject_code = SUBJ, -size = SIZE, -grade_structure = gradeStructure, -prior_attainment_band = PRIOR_BAND, -grade, -percentage -) %>% -filter(percentage != "NA%") %>% -arrange(qualification_code, subject_code, size, grade_structure, prior_attainment_band, grade, .locale = "en") -ancillary_data <- ancillary_data_numbers %>% -left_join(ancillary_data_percentages) -write_csv(ancillary_data, paste0(ancillary_save_path, "tm_numbers_percentages_", current_year, ".csv")) -student_numbers <- lapply(ls(pattern = "processed_data_", .GlobalEnv), function(x) { -bind_rows(mget(x, .GlobalEnv)[[1]]["student_numbers"]) -}) %>% bind_rows() -student_percentages <- lapply(ls(pattern = "processed_data_", .GlobalEnv), function(x) { -bind_rows(mget(x, .GlobalEnv)[[1]]["student_percentages"]) -}) %>% bind_rows() -qual_lookup <- lapply(ls(pattern = "processed_data_", .GlobalEnv), function(x) { -bind_rows(mget(x, .GlobalEnv)[[1]]["qual_lookup"]) -}) %>% bind_rows() -grades_ordered_lookup <- lapply(ls(pattern = "processed_data_", .GlobalEnv), function(x) { -bind_rows(mget(x, .GlobalEnv)[[1]]["grades_ordered_lookup"]) -}) %>% bind_rows() -saveRDS(student_numbers, "./data/all_student_numbers.rds") -saveRDS(student_percentages, "./data/all_student_percentages.rds") -saveRDS(qual_lookup, "./data/qual_lookup.rds") -saveRDS(grades_ordered_lookup, "./data/grade_lookup.rds") -shiny::runApp() -runApp() -runApp()