From 67c5c93321883a9683b00e43665c96281d2da984 Mon Sep 17 00:00:00 2001 From: Chris Bennett Date: Tue, 7 Nov 2023 08:20:57 +0000 Subject: [PATCH] working year code for 2021 and 2022 --- R/dashboard_panels.R | 20 ++- background_scripts/data_processing.R | 177 ++++++++++++++++++++++++++- data/all_student_numbers.rds | Bin 45759 -> 92113 bytes data/all_student_percentages.rds | Bin 113284 -> 186696 bytes data/grade_lookup.rds | Bin 3761 -> 7143 bytes data/qual_lookup.rds | Bin 6124 -> 10554 bytes global.R | 12 +- server.R | 42 ++++--- 8 files changed, 221 insertions(+), 30 deletions(-) diff --git a/R/dashboard_panels.R b/R/dashboard_panels.R index 6e22039..85154f0 100644 --- a/R/dashboard_panels.R +++ b/R/dashboard_panels.R @@ -130,11 +130,21 @@ dashboard_panel <- function() { class = "well", style = "min-height: 100%; height: 100%; overflow-y: visible", gov_row( + column( + width = 6, + selectizeInput( + inputId = "AcadYr_select", + label = "1. Select an academic year", + choices = list(AcadYr = sort(unique(qual_lookup$AcadYr))), + selected = max(qual_lookup$AcadYr) + ) + ), + column( width = 6, selectizeInput( inputId = "qual_select", - label = "1. Select a qualification", + label = "2. Select a qualification", choices = list(Qualifications = sort(unique(qual_lookup$Qual_Description))), selected = "GCE A level" ) @@ -144,7 +154,7 @@ dashboard_panel <- function() { width = 6, selectizeInput( inputId = "subj_select", - label = "2. Select a subject", + label = "3. Select a subject", choices = list(Subjects = sort(unique(qual_lookup$Subject))), selected = "Mathematics" ) @@ -154,7 +164,7 @@ dashboard_panel <- function() { width = 6, selectizeInput( inputId = "size_select", - label = "3. Select a size", + label = "4. Select a size", choices = list(Sizes = sort(qual_lookup$SIZE)) ) ), @@ -163,7 +173,7 @@ dashboard_panel <- function() { width = 6, selectizeInput( inputId = "grade_structure_select", - label = "4. Select a grade structure", + label = "5. Select a grade structure", choices = list(GradeStructures = sort(qual_lookup$gradeStructure)) ) ), @@ -171,7 +181,7 @@ dashboard_panel <- function() { column( width = 12, radioButtons(inputId="format", - label="5. Select format of data: ", + label="6. Select format of data: ", choices=c("Numbers data", "Percentage data") ), uiOutput("chart_band_appear") diff --git a/background_scripts/data_processing.R b/background_scripts/data_processing.R index 377d217..ed4727a 100644 --- a/background_scripts/data_processing.R +++ b/background_scripts/data_processing.R @@ -33,7 +33,7 @@ current_year <- "2022A" # establish connection to server con <- DBI::dbConnect(odbc::odbc(), driver = "SQL Server", - server = "3dcpri-pdb16\\acsqls") + server = "VMT1PR-DHSQL02") # Select data from SQL tables @@ -150,14 +150,189 @@ grades_ordered_lookup <- bind_rows(grades_char, grades_num) %>% +# ----------------------------------------------------------------------------------------------------------------------------- +# ---- add academic year to data ---- +# ----------------------------------------------------------------------------------------------------------------------------- + +student_numbers_bind <- student_numbers %>% + mutate(AcadYr = "2021/22") %>% + select(AcadYr, everything()) + +student_percentages_bind <- student_percentages %>% + mutate(AcadYr = "2021/22") %>% + select(AcadYr, everything()) + +qual_lookup_bind <- qual_lookup %>% + mutate(AcadYr = "2021/22") %>% + select(AcadYr, everything()) + +grades_ordered_lookup_bind <- grades_ordered_lookup %>% + mutate(AcadYr = "2021/22") %>% + select(AcadYr, everything()) # grade_list %>% filter(SUBLEVNO == 253, SUBJ == 20596, ASIZE == 1) + + + +# ----------------------------------------------------------------------------------------------------------------------------- +# ---- Things to change between runs ---- +# ----------------------------------------------------------------------------------------------------------------------------- + +tm_file = "[KS5_STATISTICS_RESTRICTED].[TM_2022].[TM_data_2021U]" + +current_year <- "2021U" + +# ----------------------------------------------------------------------------------------------------------------------------- +# ---- Reading in the data from SQL tables ---- +# ----------------------------------------------------------------------------------------------------------------------------- + + +# establish connection to server +con <- DBI::dbConnect(odbc::odbc(), driver = "SQL Server", + server = "VMT1PR-DHSQL02") + + +# 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() +# 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) + +student_numbers <- student_numbers %>% + mutate(AcadYr = "2020/21") %>% + select(AcadYr, everything()) + +student_percentages <- student_percentages %>% + mutate(AcadYr = "2020/21") %>% + select(AcadYr, everything()) + +qual_lookup <- qual_lookup %>% + mutate(AcadYr = "2020/21") %>% + select(AcadYr, everything()) + +grades_ordered <- grades_ordered_lookup %>% + mutate(AcadYr = "2020/21") %>% + select(AcadYr, everything()) + + + # ----------------------------------------------------------------------------------------------------------------------------- # ---- Saving Data ---- # ----------------------------------------------------------------------------------------------------------------------------- +student_numbers <- bind_rows(student_numbers, student_numbers_bind) +student_percentages <- bind_rows(student_percentages, student_percentages_bind) +qual_lookup <- bind_rows(qual_lookup, qual_lookup_bind) +grades_ordered_lookup <- bind_rows(grades_ordered_lookup, grades_ordered_lookup_bind) + saveRDS(student_numbers, "./data/all_student_numbers.rds") saveRDS(student_percentages, "./data/all_student_percentages.rds") diff --git a/data/all_student_numbers.rds b/data/all_student_numbers.rds index 14439cac89dcfddf55f6642325c1f6c51d93dd55..083044ce43ad49e585f6156b024d7fc24afa105f 100644 GIT binary patch literal 92113 zcmd?RXIPWlwl=&hfJze(QL2D|N>w5Bq9R2EZ1mo1=nx=O5m1mWMd@86bVz^@kS4uL 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zA#WQq|Ht|9HV+qSKQzwh+!o-)d_Q>G*q6V2hu=2#=4R9~1k31V{f%l`wk4l;jkzyJVZ(_WPT diff --git a/global.R b/global.R index e08b191..b3e7f53 100644 --- a/global.R +++ b/global.R @@ -55,7 +55,7 @@ tidy_code_function <- function() { # ----------------------------------------------------------------------------------------------------------------------------- # Returns a table from the Student Numbers CSV -number_select_function <- function(qual, subj, size, grade_structure) { +number_select_function <- function(AcadYr_sel, qual, subj, size, grade_structure) { filter_selection <- paste0(qual, subj, size, grade_structure) qual_grades <- filter( grade_lookup, @@ -66,7 +66,7 @@ number_select_function <- function(qual, subj, size, grade_structure) { grade_list <- qual_grades$GRADE table <- stud_numbers %>% - filter(QUAL_ID == filter_selection) %>% + filter(AcadYr == AcadYr_sel & QUAL_ID == filter_selection) %>% select(PRIOR_BAND, grade_list) return(table) @@ -77,7 +77,7 @@ number_select_function <- function(qual, subj, size, grade_structure) { # ----------------------------------------------------------------------------------------------------------------------------- # Returns a table from the Student Percentages CSV -percentage_select_function <- function(qual, subj, size, grade_structure) { +percentage_select_function <- function(AcadYr_sel, qual, subj, size, grade_structure) { filter_selection <- paste0(qual, subj, size, grade_structure) qual_grades <- filter(grade_lookup, SUBLEVNO == qual & SUBJ == subj & SIZE == size & gradeStructure == grade_structure) @@ -85,7 +85,7 @@ percentage_select_function <- function(qual, subj, size, grade_structure) { grade_list <- qual_grades$GRADE table <- stud_percentages %>% - filter(QUAL_ID == filter_selection) %>% + filter(AcadYr == AcadYr_sel & QUAL_ID == filter_selection) %>% select(PRIOR_BAND, grade_list) return(table) @@ -166,12 +166,12 @@ grade_boundaries <- c("<1", "1-<2", "2-<3", "3-<4", "4-<5", "5-<6", "6-<7", "7-< # Create a fixed table for example table user_selection_example <- qual_lookup %>% - filter(Qual_Description == "GCE A level" & Subject == "Mathematics" & ASIZE == 1 & gradeStructure == "*,A,B,C,D,E") %>% + filter(AcadYr == max(AcadYr) & Qual_Description == "GCE A level" & Subject == "Mathematics" & ASIZE == 1 & gradeStructure == "*,A,B,C,D,E") %>% distinct() example_data <- number_select_function( - user_selection_example$SUBLEVNO, user_selection_example$SUBJ, + user_selection_example$AcadYr, user_selection_example$SUBLEVNO, user_selection_example$SUBJ, user_selection_example$SIZE, user_selection_example$gradeStructure ) %>% rename("Prior Band" = PRIOR_BAND) %>% diff --git a/server.R b/server.R index 0db3cc0..f535348 100644 --- a/server.R +++ b/server.R @@ -49,7 +49,7 @@ server <- function(input, output, session) { # we need to identify which qualifications have only 1 subject option # use this output to update the qualification drop down box below single_subj <- qual_lookup %>% - group_by(SUBLEVNO) %>% + group_by(AcadYr, SUBLEVNO) %>% filter(n() == 1) # single_subj @@ -59,12 +59,12 @@ server <- function(input, output, session) { label = NULL, if (input$qual_select %in% single_subj$Qual_Description) { choices <- qual_lookup %>% - filter(Qual_Description == input$qual_select) %>% + filter(AcadYr == input$AcadYr_select & Qual_Description == input$qual_select) %>% select(Subject) %>% as.character() } else { choices <- qual_lookup %>% - filter(Qual_Description == input$qual_select) %>% + filter(AcadYr == input$AcadYr_select & Qual_Description == input$qual_select) %>% select(Subject) %>% arrange(Subject) } @@ -76,10 +76,10 @@ server <- function(input, output, session) { # we need to identify which subjects have multiple sizes # use this output to update the size select drop down box below multiple_sizes <- qual_lookup %>% - group_by(Qual_Description, SUBLEVNO, Subject, SUBJ) %>% + group_by(AcadYr, Qual_Description, SUBLEVNO, Subject, SUBJ) %>% count() %>% filter(n > 1) %>% - mutate(qual_subj_combined = paste0(Qual_Description, " - ", Subject)) + mutate(qual_subj_combined = paste0(AcadYr, " - ", Qual_Description, " - ", Subject)) # multiple_sizes @@ -88,9 +88,10 @@ server <- function(input, output, session) { updateSelectInput(session, inputId = "size_select", label = NULL, - if (paste0(input$qual_select, " - ", input$subj_select) %in% multiple_sizes$qual_subj_combined) { + if (paste0(input$AcadYr_select, " - ", input$qual_select, " - ", input$subj_select) %in% multiple_sizes$qual_subj_combined) { choices <- qual_lookup %>% filter( + AcadYr == input$AcadYr_select, Qual_Description == input$qual_select, Subject == input$subj_select ) %>% @@ -99,6 +100,7 @@ server <- function(input, output, session) { } else { choices <- qual_lookup %>% filter( + AcadYr == input$AcadYr_select, Qual_Description == input$qual_select, Subject == input$subj_select ) %>% @@ -114,10 +116,10 @@ server <- function(input, output, session) { # we need to identify which subject and sizes have multiple grade structures # use this output to update the grade select drop down box below multiple_gradestructures <- qual_lookup %>% - group_by(Qual_Description, SUBLEVNO, Subject, SUBJ, SIZE) %>% + group_by(AcadYr, Qual_Description, SUBLEVNO, Subject, SUBJ, SIZE) %>% count() %>% filter(n > 1) %>% - mutate(qual_subj_size_combined = paste0(Qual_Description, " - ", Subject, " - ", SIZE)) + mutate(qual_subj_size_combined = paste0(AcadYr, " - ", Qual_Description, " - ", Subject, " - ", SIZE)) # multiple_gradestructures @@ -126,9 +128,10 @@ server <- function(input, output, session) { updateSelectInput(session, inputId = "grade_structure_select", label = NULL, - if (paste0(input$qual_select, " - ", input$subj_select, " - ", input$size_select) %in% multiple_gradestructures$qual_subj_size_combined) { + if (paste0(input$AcadYr_select, " - ", input$qual_select, " - ", input$subj_select, " - ", input$size_select) %in% multiple_gradestructures$qual_subj_size_combined) { choices <- qual_lookup %>% filter( + AcadYr == input$AcadYr_select, Qual_Description == input$qual_select, Subject == input$subj_select, SIZE == input$size_select @@ -138,6 +141,7 @@ server <- function(input, output, session) { } else { choices <- qual_lookup %>% filter( + AcadYr == input$AcadYr_select, Qual_Description == input$qual_select, Subject == input$subj_select, SIZE == input$size_select @@ -164,17 +168,18 @@ server <- function(input, output, session) { lookup_characters <- qual_lookup %>% - mutate(across(c(SUBLEVNO, SUBJ, SIZE, ASIZE, GSIZE, gradeStructure), ~ as.character(.x))) + mutate(across(c(AcadYr, SUBLEVNO, SUBJ, SIZE, ASIZE, GSIZE, gradeStructure), ~ as.character(.x))) # use this output to update the prior band drop down box below prior_band_chart <- reactive({ req(input$qual_select) stud_percentages %>% - left_join(lookup_characters, by = c( + left_join(lookup_characters, by = c("AcadYr", "Qual_Description", "SUBLEVNO", "Subject", "SUBJ", "ASIZE", "GSIZE", "SIZE", "gradeStructure" )) %>% - subset(Qual_Description == input$qual_select & + subset(AcadYr == input$AcadYr_select & + Qual_Description == input$qual_select & Subject == input$subj_select & SIZE == input$size_select & gradeStructure == input$grade_structure_select) %>% @@ -197,7 +202,8 @@ server <- function(input, output, session) { ## Try and streamline the original code using reactive tables to prevent repetition lookup_selection <- reactive({ qual_lookup %>% - filter(Qual_Description == input$qual_select & + filter(AcadYr == input$AcadYr_select & + Qual_Description == input$qual_select & Subject == input$subj_select & SIZE == input$size_select & gradeStructure == input$grade_structure_select) %>% @@ -212,9 +218,9 @@ server <- function(input, output, session) { # Create a reactive table for numbers table ----------------------------------------------- # the function on the last line removes columns that are empty numbers_data <- reactive({ - req(c(lookup_selection()$SUBLEVNO, lookup_selection()$SUBJ, lookup_selection()$SIZE, lookup_selection()$gradeStructure)) + req(c(lookup_selection()$AcadYr, lookup_selection()$SUBLEVNO, lookup_selection()$SUBJ, lookup_selection()$SIZE, lookup_selection()$gradeStructure)) - number_select_function(lookup_selection()$SUBLEVNO, lookup_selection()$SUBJ, lookup_selection()$SIZE, lookup_selection()$gradeStructure) %>% + number_select_function(lookup_selection()$AcadYr, lookup_selection()$SUBLEVNO, lookup_selection()$SUBJ, lookup_selection()$SIZE, lookup_selection()$gradeStructure) %>% rename("Prior Band" = PRIOR_BAND) }) @@ -222,9 +228,9 @@ server <- function(input, output, session) { # Create a reactive table for percentage table ----------------------------------------------- percentage_data <- reactive({ - req(c(lookup_selection()$SUBLEVNO, lookup_selection()$SUBJ, lookup_selection()$SIZE, lookup_selection()$gradeStructure)) + req(c(lookup_selection()$AcadYr, lookup_selection()$SUBLEVNO, lookup_selection()$SUBJ, lookup_selection()$SIZE, lookup_selection()$gradeStructure)) - percentage_select_function(lookup_selection()$SUBLEVNO, lookup_selection()$SUBJ, lookup_selection()$SIZE, lookup_selection()$gradeStructure) %>% + percentage_select_function(lookup_selection()$AcadYr, lookup_selection()$SUBLEVNO, lookup_selection()$SUBJ, lookup_selection()$SIZE, lookup_selection()$gradeStructure) %>% mutate_all(list(~ str_replace(., "NA%", ""))) %>% rename("Prior Band" = PRIOR_BAND) }) @@ -307,7 +313,7 @@ server <- function(input, output, session) { percentage_chart_data <- reactive({ - percentage_select_function(lookup_selection()$SUBLEVNO, lookup_selection()$SUBJ, lookup_selection()$SIZE, lookup_selection()$gradeStructure) %>% + percentage_select_function(lookup_selection()$AcadYr, lookup_selection()$SUBLEVNO, lookup_selection()$SUBJ, lookup_selection()$SIZE, lookup_selection()$gradeStructure) %>% filter(PRIOR_BAND == input$chart_band) %>% # Now we have our selected row data it needs cleaning up because these values are characters # First we'll turn it into a list