From 7cbee420ddc25a060541aa6da6c44551c0212d56 Mon Sep 17 00:00:00 2001 From: Rich Bielby Date: Thu, 5 Dec 2024 09:49:44 +0000 Subject: [PATCH] Updated tests --- R/Data_processing.R | 1393 +++++++++-------- .../_snaps/initial_load_test/001_.new.png | Bin 0 -> 121329 bytes .../Transition-matrices-dashboard-001.json | 32 +- ...Transition-matrices-dashboard-001_.new.png | Bin 0 -> 128414 bytes .../Transition-matrices-dashboard-001_.png | Bin 146096 -> 135951 bytes .../Transition-matrices-dashboard-002.json | 46 +- ...Transition-matrices-dashboard-002_.new.png | Bin 0 -> 144173 bytes .../Transition-matrices-dashboard-002_.png | Bin 142233 -> 144024 bytes .../Transition-matrices-dashboard-003.json | 58 +- ...Transition-matrices-dashboard-003_.new.png | Bin 0 -> 163811 bytes .../Transition-matrices-dashboard-003_.png | Bin 143732 -> 132328 bytes .../Transition-matrices-dashboard-004.json | 44 +- .../Transition-matrices-dashboard-004_.png | Bin 176400 -> 164287 bytes .../Transition-matrices-dashboard-005.json | 76 +- ...Transition-matrices-dashboard-005_.new.png | Bin 0 -> 139247 bytes .../Transition-matrices-dashboard-005_.png | Bin 160802 -> 149047 bytes .../Transition-matrices-dashboard-006.json | 86 +- ...Transition-matrices-dashboard-006_.new.png | Bin 0 -> 152822 bytes .../Transition-matrices-dashboard-006_.png | Bin 132136 -> 120976 bytes .../Transition-matrices-dashboard-007.json | 96 +- .../Transition-matrices-dashboard-007_.png | Bin 165384 -> 154434 bytes .../Transition-matrices-dashboard-008.json | 152 +- .../Transition-matrices-dashboard-008_.png | Bin 159445 -> 154218 bytes .../Transition-matrices-dashboard-009.json | 150 +- .../Transition-matrices-dashboard-009_.png | Bin 163064 -> 125644 bytes .../Transition-matrices-dashboard-010.json | 150 +- .../Transition-matrices-dashboard-010_.png | Bin 193925 -> 155668 bytes .../Transition-matrices-dashboard-011.json | 150 +- .../Transition-matrices-dashboard-011_.png | Bin 144651 -> 135575 bytes .../Transition-matrices-dashboard-012.json | 150 +- .../Transition-matrices-dashboard-012_.png | Bin 132246 -> 121975 bytes 31 files changed, 1198 insertions(+), 1385 deletions(-) create mode 100644 tests/testthat/_snaps/initial_load_test/001_.new.png create mode 100644 tests/testthat/_snaps/shinytest2/Transition-matrices-dashboard-001_.new.png create mode 100644 tests/testthat/_snaps/shinytest2/Transition-matrices-dashboard-002_.new.png create mode 100644 tests/testthat/_snaps/shinytest2/Transition-matrices-dashboard-003_.new.png create mode 100644 tests/testthat/_snaps/shinytest2/Transition-matrices-dashboard-005_.new.png create mode 100644 tests/testthat/_snaps/shinytest2/Transition-matrices-dashboard-006_.new.png diff --git a/R/Data_processing.R b/R/Data_processing.R index 36d59d0..fae513d 100644 --- a/R/Data_processing.R +++ b/R/Data_processing.R @@ -22,24 +22,24 @@ pull_latest_data <- function() { z <- z / 10^n z * posneg } -} -# Connect to the SQL server -SQL_con <- dbConnect(odbc(), - Driver = "SQL Server", - Server = "VMT1PR-DHSQL02", - Database = "KS4_RESTRICTED", # Enter the folder containing your SQL data - Trusted_Connection = "True" -) + + # Connect to the SQL server + SQL_con <- dbConnect(odbc(), + Driver = "SQL Server", + Server = "VMT1PR-DHSQL02", + Database = "KS4_RESTRICTED", # Enter the folder containing your SQL data + Trusted_Connection = "True" + ) -# STEP 1 -# Import the exam data -SQL_Statement <- "SELECT + # STEP 1 + # Import the exam data + SQL_Statement <- "SELECT exam.CANDNO ,WOLF_DISC_CODE ,exam.LAESTAB @@ -55,109 +55,109 @@ AND disc3B_ptq_ee = 0 AND sublevno in ('310', '391', '395', '450', '451', '760', '954') AND ENDKS = 1 AND NATRES = 1" -Exam_SQL_data <- dbGetQuery(SQL_con, SQL_Statement) - -# Import the exam data -# Exam_SQL_data <- tbl(SQL_con, ("KS4_exam_23_cohort_amended_v3"))%>% # Enter the name of your SQL database #update file name -# as.data.frame() %>% - -# Create subject groupings based on wolf codes from the exam file -# Exam_SQL_data <- SQL_data %>% -# filter( -# ptq_include == 1, -# disc3B_ptq_ee == 0, -# sublevno %in% c("310", "391", "395", "450", "451", "760", "954") -# ) %>% -# select( -# CANDNO, -# WOLF_DISC_CODE, -# LAESTAB, -# SUBLEVNO, -# GRADE, -# GNUMBER, -# COVID_IMPACTED_FLAG -# ) %>% - -Exam_SQL_data <- Exam_SQL_data %>% - mutate(subjects = case_when( - WOLF_DISC_CODE == "FK2B" ~ "English Language", - WOLF_DISC_CODE == "FC4" ~ "English Literature", - (WOLF_DISC_CODE == "RB1" | WOLF_DISC_CODE == "RB15" | WOLF_DISC_CODE == "RB1A" | WOLF_DISC_CODE == "RB1B" | WOLF_DISC_CODE == "RB31" | WOLF_DISC_CODE == "RB55" | - WOLF_DISC_CODE == "RB156" | WOLF_DISC_CODE == "RB71" | WOLF_DISC_CODE == "RB7B" | WOLF_DISC_CODE == "RB7E") & !(GNUMBER %in% c("10034912", "10060054", "10060091", "10060133", "10060170", "10060212", "1006025X", "10050395", "60310844", "60311770")) ~ "Mathematics", - WOLF_DISC_CODE == "RA1E" ~ "Combined Science", - WOLF_DISC_CODE == "RC1" ~ "Physics", - WOLF_DISC_CODE == "RD1" ~ "Chemistry", - WOLF_DISC_CODE == "RH3" ~ "Biology", - WOLF_DISC_CODE == "CK1" ~ "Computer Science", - WOLF_DISC_CODE == "RE1" | WOLF_DISC_CODE == "RF2" | WOLF_DISC_CODE == "RC52" | - WOLF_DISC_CODE == "QA3" ~ "Other Sciences", - WOLF_DISC_CODE == "VF1" ~ "Design and Technology", - WOLF_DISC_CODE == "XA5A" ~ "D & T: Textiles Technology", - WOLF_DISC_CODE == "VF2" | WOLF_DISC_CODE == "VF3" ~ "Other Design and Technology", - WOLF_DISC_CODE == "XA1" ~ "Engineering", - WOLF_DISC_CODE == "AA3" ~ "Business", - WOLF_DISC_CODE == "NH6" ~ "Food Preparation and Nutrition", - WOLF_DISC_CODE == "RF4" ~ "Geography", - WOLF_DISC_CODE == "DB" ~ "History", - WOLF_DISC_CODE == "DB21" ~ "Ancient History", - WOLF_DISC_CODE == "EB" ~ "Economics", - WOLF_DISC_CODE == "DE1" | WOLF_DISC_CODE == "EA" | WOLF_DISC_CODE == "EE31" | WOLF_DISC_CODE == "PK1" | WOLF_DISC_CODE == "EE2" ~ "Social Studies", - WOLF_DISC_CODE == "FKF" ~ "French", - WOLF_DISC_CODE == "FKG" ~ "German", - WOLF_DISC_CODE == "FKS" ~ "Spanish", - WOLF_DISC_CODE == "FKO" | WOLF_DISC_CODE == "FKQ" | WOLF_DISC_CODE == "F1H" | WOLF_DISC_CODE == "FKB" | WOLF_DISC_CODE == "F1P" | WOLF_DISC_CODE == "FKP" | WOLF_DISC_CODE == "FKI" | - WOLF_DISC_CODE == "FKJ" | WOLF_DISC_CODE == "FKM" | WOLF_DISC_CODE == "FKK" | WOLF_DISC_CODE == "FKU" | WOLF_DISC_CODE == "FKX" | WOLF_DISC_CODE == "FKC" | - WOLF_DISC_CODE == "FKR" | WOLF_DISC_CODE == "FKT" ~ "Other Modern Languages", - WOLF_DISC_CODE == "F1L" ~ "Latin", - WOLF_DISC_CODE == "DB2B" ~ "Classical Civilisation", - WOLF_DISC_CODE == "F1K" ~ "Classical Greek", - WOLF_DISC_CODE == "F1Z" ~ "Biblical Hebrew", - WOLF_DISC_CODE == "JA2" | WOLF_DISC_CODE == "KJ1" ~ "Art and Design", - WOLF_DISC_CODE == "KA2" & !(GNUMBER %in% c("60311150", "60311502", "60319434", "60320692", "60321052", "60322469")) ~ "Film Studies", - WOLF_DISC_CODE == "LB1" ~ "Dance", - WOLF_DISC_CODE == "LC11" ~ "Drama", - WOLF_DISC_CODE == "PA1" ~ "Health and Social Care", - WOLF_DISC_CODE == "KA2" & !(GNUMBER %in% c("10042799", "50022465", "50025995", "50030188", "60055029", "60308898", "60309702", "60309714", "60309726")) ~ "Media/Film/TV", - WOLF_DISC_CODE == "LF1" | WOLF_DISC_CODE == "LJ9" ~ "Music", - WOLF_DISC_CODE == "MA1" ~ "Physical Education", - WOLF_DISC_CODE == "DD1" ~ "Religious Studies", - WOLF_DISC_CODE == "RB71" & !(GNUMBER %in% c("60322615")) ~ "Statistics", - WOLF_DISC_CODE == "AK6" ~ "Accounting", - TRUE ~ NA_character_ - )) %>% - mutate(GRADE = ifelse(COVID_IMPACTED_FLAG == "1" & !(GRADE %in% c("Q")), "covid impacted", GRADE)) # %>% -# mutate(GRADE = case_when( -# COVID_IMPACTED_FLAG == "1" & !(GRADE %in% c("Q")) ~"covid_impacted", -# TRUE ~ NA_character_ -# )) %>% - -# as.data.frame() # need to make it a dataframe so you can do the join lower down in step 4. You can then view it too in your environment on the right - - - -# count the number of times each subject is in the data -Exam_SQL_data %>% - count(subjects) %>% - arrange(subjects) - -# count the number of pupils doing each subject and so removes duplicates, -# this does not match the SQL output -# Exam_SQL_data %>% select(CANDNO, subjects) %>% distinct() %>% count(subjects) %>% -# arrange(subjects) - -# count the number of times each subject is in the data -Exam_SQL_data %>% - group_by(subjects) %>% - summarise(n = n()) %>% - arrange(subjects) - - - - -# STEP 2 Define variables - -# Import the pupil data -SQL_Statement <- "SELECT + Exam_SQL_data <- dbGetQuery(SQL_con, SQL_Statement) + + # Import the exam data + # Exam_SQL_data <- tbl(SQL_con, ("KS4_exam_23_cohort_amended_v3"))%>% # Enter the name of your SQL database #update file name + # as.data.frame() %>% + + # Create subject groupings based on wolf codes from the exam file + # Exam_SQL_data <- SQL_data %>% + # filter( + # ptq_include == 1, + # disc3B_ptq_ee == 0, + # sublevno %in% c("310", "391", "395", "450", "451", "760", "954") + # ) %>% + # select( + # CANDNO, + # WOLF_DISC_CODE, + # LAESTAB, + # SUBLEVNO, + # GRADE, + # GNUMBER, + # COVID_IMPACTED_FLAG + # ) %>% + + Exam_SQL_data <- Exam_SQL_data %>% + mutate(subjects = case_when( + WOLF_DISC_CODE == "FK2B" ~ "English Language", + WOLF_DISC_CODE == "FC4" ~ "English Literature", + (WOLF_DISC_CODE == "RB1" | WOLF_DISC_CODE == "RB15" | WOLF_DISC_CODE == "RB1A" | WOLF_DISC_CODE == "RB1B" | WOLF_DISC_CODE == "RB31" | WOLF_DISC_CODE == "RB55" | + WOLF_DISC_CODE == "RB156" | WOLF_DISC_CODE == "RB71" | WOLF_DISC_CODE == "RB7B" | WOLF_DISC_CODE == "RB7E") & !(GNUMBER %in% c("10034912", "10060054", "10060091", "10060133", "10060170", "10060212", "1006025X", "10050395", "60310844", "60311770")) ~ "Mathematics", + WOLF_DISC_CODE == "RA1E" ~ "Combined Science", + WOLF_DISC_CODE == "RC1" ~ "Physics", + WOLF_DISC_CODE == "RD1" ~ "Chemistry", + WOLF_DISC_CODE == "RH3" ~ "Biology", + WOLF_DISC_CODE == "CK1" ~ "Computer Science", + WOLF_DISC_CODE == "RE1" | WOLF_DISC_CODE == "RF2" | WOLF_DISC_CODE == "RC52" | + WOLF_DISC_CODE == "QA3" ~ "Other Sciences", + WOLF_DISC_CODE == "VF1" ~ "Design and Technology", + WOLF_DISC_CODE == "XA5A" ~ "D & T: Textiles Technology", + WOLF_DISC_CODE == "VF2" | WOLF_DISC_CODE == "VF3" ~ "Other Design and Technology", + WOLF_DISC_CODE == "XA1" ~ "Engineering", + WOLF_DISC_CODE == "AA3" ~ "Business", + WOLF_DISC_CODE == "NH6" ~ "Food Preparation and Nutrition", + WOLF_DISC_CODE == "RF4" ~ "Geography", + WOLF_DISC_CODE == "DB" ~ "History", + WOLF_DISC_CODE == "DB21" ~ "Ancient History", + WOLF_DISC_CODE == "EB" ~ "Economics", + WOLF_DISC_CODE == "DE1" | WOLF_DISC_CODE == "EA" | WOLF_DISC_CODE == "EE31" | WOLF_DISC_CODE == "PK1" | WOLF_DISC_CODE == "EE2" ~ "Social Studies", + WOLF_DISC_CODE == "FKF" ~ "French", + WOLF_DISC_CODE == "FKG" ~ "German", + WOLF_DISC_CODE == "FKS" ~ "Spanish", + WOLF_DISC_CODE == "FKO" | WOLF_DISC_CODE == "FKQ" | WOLF_DISC_CODE == "F1H" | WOLF_DISC_CODE == "FKB" | WOLF_DISC_CODE == "F1P" | WOLF_DISC_CODE == "FKP" | WOLF_DISC_CODE == "FKI" | + WOLF_DISC_CODE == "FKJ" | WOLF_DISC_CODE == "FKM" | WOLF_DISC_CODE == "FKK" | WOLF_DISC_CODE == "FKU" | WOLF_DISC_CODE == "FKX" | WOLF_DISC_CODE == "FKC" | + WOLF_DISC_CODE == "FKR" | WOLF_DISC_CODE == "FKT" ~ "Other Modern Languages", + WOLF_DISC_CODE == "F1L" ~ "Latin", + WOLF_DISC_CODE == "DB2B" ~ "Classical Civilisation", + WOLF_DISC_CODE == "F1K" ~ "Classical Greek", + WOLF_DISC_CODE == "F1Z" ~ "Biblical Hebrew", + WOLF_DISC_CODE == "JA2" | WOLF_DISC_CODE == "KJ1" ~ "Art and Design", + WOLF_DISC_CODE == "KA2" & !(GNUMBER %in% c("60311150", "60311502", "60319434", "60320692", "60321052", "60322469")) ~ "Film Studies", + WOLF_DISC_CODE == "LB1" ~ "Dance", + WOLF_DISC_CODE == "LC11" ~ "Drama", + WOLF_DISC_CODE == "PA1" ~ "Health and Social Care", + WOLF_DISC_CODE == "KA2" & !(GNUMBER %in% c("10042799", "50022465", "50025995", "50030188", "60055029", "60308898", "60309702", "60309714", "60309726")) ~ "Media/Film/TV", + WOLF_DISC_CODE == "LF1" | WOLF_DISC_CODE == "LJ9" ~ "Music", + WOLF_DISC_CODE == "MA1" ~ "Physical Education", + WOLF_DISC_CODE == "DD1" ~ "Religious Studies", + WOLF_DISC_CODE == "RB71" & !(GNUMBER %in% c("60322615")) ~ "Statistics", + WOLF_DISC_CODE == "AK6" ~ "Accounting", + TRUE ~ NA_character_ + )) %>% + mutate(GRADE = ifelse(COVID_IMPACTED_FLAG == "1" & !(GRADE %in% c("Q")), "covid impacted", GRADE)) # %>% + # mutate(GRADE = case_when( + # COVID_IMPACTED_FLAG == "1" & !(GRADE %in% c("Q")) ~"covid_impacted", + # TRUE ~ NA_character_ + # )) %>% + + # as.data.frame() # need to make it a dataframe so you can do the join lower down in step 4. You can then view it too in your environment on the right + + + + # count the number of times each subject is in the data + Exam_SQL_data %>% + count(subjects) %>% + arrange(subjects) + + # count the number of pupils doing each subject and so removes duplicates, + # this does not match the SQL output + # Exam_SQL_data %>% select(CANDNO, subjects) %>% distinct() %>% count(subjects) %>% + # arrange(subjects) + + # count the number of times each subject is in the data + Exam_SQL_data %>% + group_by(subjects) %>% + summarise(n = n()) %>% + arrange(subjects) + + + + + # STEP 2 Define variables + + # Import the pupil data + SQL_Statement <- "SELECT CANDNO, URN, LAESTAB, NFTYPE, SEX, EAL, KS2EMSS, L2BASICS_94, L2BASICS_95, EBACC_94, EBACC_95, EBACC_E_PTQ_EE, ATT8, P8SCORE, disadvantage, sen_description, LA_name @@ -169,630 +169,631 @@ AND NFTYPE in (20,21,22,23,24,25,26,27,31,50,51,52,53,55,57,58)" -Pupil_SQL_data <- dbGetQuery(SQL_con, SQL_Statement) - -# Import the pupil data -# Pupil_SQL_data <- tbl(SQL_con, ("KS4_pupil_23_cohort_amended_v3")) %>% # Enter the name of your SQL database #update file name -# as.data.frame() -# -# Var_SQL_data <- Pupil_SQL_data %>% -# filter( -# endks == 1, -# natres == 1, -# (nftype %in% c("32", "33") | nftype %in% c("20", "21", "22", "23", "24", "25", "26", "27", "28", "31", "50", "51", "52", "53", "55", "57", "58") & -# norflage != 3) -# ) %>% # you can use norflage !=3 instead here :) -# select( -# CANDNO, URN, LAESTAB, NFTYPE, Sex, LANG1ST, KS2EMSS, L2BASICS_94, -# L2BASICS_95, EBACC_94, EBACC_95, EBACC_E_PTQ_EE, ATT8, P8SCORE, FSM6CLA1A, SENF -# ) %>% -# as.data.frame() %>% # Handy to cast it as a dataframe so you can view what's happening by clicking on the table in your environment on the right - -Pupil_SQL_data <- Pupil_SQL_data %>% - mutate(Disadvantage = case_when( - disadvantage == "Disadvantaged all other" ~ "Non Disadvantaged Pupils", - disadvantage == "Disadvantaged" ~ "Disadvantaged Pupils", - TRUE ~ NA_character_ - )) %>% - mutate(Sex = case_when( - SEX == "Girls" ~ "Female Pupils", - SEX == "Boys" ~ "Male Pupils", - TRUE ~ NA_character_ - )) %>% - mutate(SEN = case_when( - sen_description == "Any SEN" ~ "SEN Pupils", - sen_description == "No identified SEN" ~ "Non SEN Pupils", - TRUE ~ NA_character_ - )) %>% - mutate(EAL = case_when( - EAL == "Other than English" ~ "EAL Pupils", # - EAL == "English" ~ "Non EAL Pupils", - TRUE ~ NA_character_ - )) %>% - mutate(ks2em = case_when( - KS2EMSS >= 59 & KS2EMSS < 80 ~ "Less than 80", - KS2EMSS >= 80 & KS2EMSS <= 89.5 ~ "80 - 89.5", - KS2EMSS >= 90 & KS2EMSS <= 95.5 ~ "90 - 95.5", - KS2EMSS >= 96 & KS2EMSS <= 99.5 ~ "96 - 99.5", - KS2EMSS >= 100 & KS2EMSS <= 102 ~ "100 - 102", - KS2EMSS >= 102.5 & KS2EMSS <= 104.5 ~ "102.5 - 104.5", - KS2EMSS >= 105 & KS2EMSS <= 107 ~ "105 - 107", - KS2EMSS >= 107.5 & KS2EMSS <= 109.5 ~ "107.5 - 109.5", - KS2EMSS >= 110 & KS2EMSS <= 113 ~ "110 - 113", - KS2EMSS >= 113.5 & KS2EMSS <= 116.5 ~ "113.5 - 116.5", - KS2EMSS >= 117 & KS2EMSS <= 120 ~ "117 - 120", - TRUE ~ NA_character_ - )) %>% - mutate(ks2em_band = case_when( - KS2EMSS >= 59 & KS2EMSS < 80 ~ "01", # this section makes sure the data is ordered by KS2 groups in the final tidy data files - KS2EMSS >= 80 & KS2EMSS <= 89.5 ~ "02", - KS2EMSS >= 90 & KS2EMSS <= 95.5 ~ "03", - KS2EMSS >= 96 & KS2EMSS <= 99.5 ~ "04", - KS2EMSS >= 100 & KS2EMSS <= 102 ~ "05", - KS2EMSS >= 102.5 & KS2EMSS <= 104.5 ~ "06", - KS2EMSS >= 105 & KS2EMSS <= 107 ~ "07", - KS2EMSS >= 107.5 & KS2EMSS <= 109.5 ~ "08", - KS2EMSS >= 110 & KS2EMSS <= 113 ~ "09", - KS2EMSS >= 113.5 & KS2EMSS <= 116.5 ~ "10", - KS2EMSS >= 117 & KS2EMSS <= 120 ~ "11", - TRUE ~ NA_character_ - )) - - - -# # CHECKS -# Var_SQL_data %>% -# count(LANG1ST) %>% -# arrange(LANG1ST) -# -# Pupil_SQL_data %>% -# count(Disadvantage) %>% -# arrange(Disadvantage) -# -# Pupil_SQL_data %>% -# count(EAL) %>% -# arrange(EAL) -# -# Pupil_SQL_data %>% -# count(SEN) %>% -# arrange(SEN) -# -# Pupil_SQL_data %>% -# count(Sex) %>% -# arrange(Sex) -# -# Pupil_SQL_data %>% -# count(Sex, Disadvantage, EAL, SEN) %>% -# group_by(ks2em) -# -# Pupil_SQL_data %>% -# group_by(ks2em) %>% -# count(Sex, Disadvantage, EAL, SEN) - - - -# STEP 3 Join the exam and pupil data - -# Create a shorter version of the exam dataframe containing only the variables you need for the join - -Exam_short <- Exam_SQL_data %>% - select( - CANDNO, - LAESTAB, - WOLF_DISC_CODE, - SUBLEVNO, - GRADE, - subjects + Pupil_SQL_data <- dbGetQuery(SQL_con, SQL_Statement) + + # Import the pupil data + # Pupil_SQL_data <- tbl(SQL_con, ("KS4_pupil_23_cohort_amended_v3")) %>% # Enter the name of your SQL database #update file name + # as.data.frame() + # + # Var_SQL_data <- Pupil_SQL_data %>% + # filter( + # endks == 1, + # natres == 1, + # (nftype %in% c("32", "33") | nftype %in% c("20", "21", "22", "23", "24", "25", "26", "27", "28", "31", "50", "51", "52", "53", "55", "57", "58") & + # norflage != 3) + # ) %>% # you can use norflage !=3 instead here :) + # select( + # CANDNO, URN, LAESTAB, NFTYPE, Sex, LANG1ST, KS2EMSS, L2BASICS_94, + # L2BASICS_95, EBACC_94, EBACC_95, EBACC_E_PTQ_EE, ATT8, P8SCORE, FSM6CLA1A, SENF + # ) %>% + # as.data.frame() %>% # Handy to cast it as a dataframe so you can view what's happening by clicking on the table in your environment on the right + + Pupil_SQL_data <- Pupil_SQL_data %>% + mutate(Disadvantage = case_when( + disadvantage == "Disadvantaged all other" ~ "Non Disadvantaged Pupils", + disadvantage == "Disadvantaged" ~ "Disadvantaged Pupils", + TRUE ~ NA_character_ + )) %>% + mutate(Sex = case_when( + SEX == "Girls" ~ "Female Pupils", + SEX == "Boys" ~ "Male Pupils", + TRUE ~ NA_character_ + )) %>% + mutate(SEN = case_when( + sen_description == "Any SEN" ~ "SEN Pupils", + sen_description == "No identified SEN" ~ "Non SEN Pupils", + TRUE ~ NA_character_ + )) %>% + mutate(EAL = case_when( + EAL == "Other than English" ~ "EAL Pupils", # + EAL == "English" ~ "Non EAL Pupils", + TRUE ~ NA_character_ + )) %>% + mutate(ks2em = case_when( + KS2EMSS >= 59 & KS2EMSS < 80 ~ "Less than 80", + KS2EMSS >= 80 & KS2EMSS <= 89.5 ~ "80 - 89.5", + KS2EMSS >= 90 & KS2EMSS <= 95.5 ~ "90 - 95.5", + KS2EMSS >= 96 & KS2EMSS <= 99.5 ~ "96 - 99.5", + KS2EMSS >= 100 & KS2EMSS <= 102 ~ "100 - 102", + KS2EMSS >= 102.5 & KS2EMSS <= 104.5 ~ "102.5 - 104.5", + KS2EMSS >= 105 & KS2EMSS <= 107 ~ "105 - 107", + KS2EMSS >= 107.5 & KS2EMSS <= 109.5 ~ "107.5 - 109.5", + KS2EMSS >= 110 & KS2EMSS <= 113 ~ "110 - 113", + KS2EMSS >= 113.5 & KS2EMSS <= 116.5 ~ "113.5 - 116.5", + KS2EMSS >= 117 & KS2EMSS <= 120 ~ "117 - 120", + TRUE ~ NA_character_ + )) %>% + mutate(ks2em_band = case_when( + KS2EMSS >= 59 & KS2EMSS < 80 ~ "01", # this section makes sure the data is ordered by KS2 groups in the final tidy data files + KS2EMSS >= 80 & KS2EMSS <= 89.5 ~ "02", + KS2EMSS >= 90 & KS2EMSS <= 95.5 ~ "03", + KS2EMSS >= 96 & KS2EMSS <= 99.5 ~ "04", + KS2EMSS >= 100 & KS2EMSS <= 102 ~ "05", + KS2EMSS >= 102.5 & KS2EMSS <= 104.5 ~ "06", + KS2EMSS >= 105 & KS2EMSS <= 107 ~ "07", + KS2EMSS >= 107.5 & KS2EMSS <= 109.5 ~ "08", + KS2EMSS >= 110 & KS2EMSS <= 113 ~ "09", + KS2EMSS >= 113.5 & KS2EMSS <= 116.5 ~ "10", + KS2EMSS >= 117 & KS2EMSS <= 120 ~ "11", + TRUE ~ NA_character_ + )) + + + + # # CHECKS + # Var_SQL_data %>% + # count(LANG1ST) %>% + # arrange(LANG1ST) + # + # Pupil_SQL_data %>% + # count(Disadvantage) %>% + # arrange(Disadvantage) + # + # Pupil_SQL_data %>% + # count(EAL) %>% + # arrange(EAL) + # + # Pupil_SQL_data %>% + # count(SEN) %>% + # arrange(SEN) + # + # Pupil_SQL_data %>% + # count(Sex) %>% + # arrange(Sex) + # + # Pupil_SQL_data %>% + # count(Sex, Disadvantage, EAL, SEN) %>% + # group_by(ks2em) + # + # Pupil_SQL_data %>% + # group_by(ks2em) %>% + # count(Sex, Disadvantage, EAL, SEN) + + + + # STEP 3 Join the exam and pupil data + + # Create a shorter version of the exam dataframe containing only the variables you need for the join + + Exam_short <- Exam_SQL_data %>% + select( + CANDNO, + LAESTAB, + WOLF_DISC_CODE, + SUBLEVNO, + GRADE, + subjects + ) + + # Create a dataframe containing the subjects to be included in the TMs, you need to check with the Data Production Team to see if any subjects have changed + ks4_subjects <- c( + "Ancient History", "Art and Design", "Biblical Hebrew", "Biology", "Business", "Chemistry", "Classical Civilisation", "Classical Greek", # , "Combined Science", + "Computer Science", "Dance", "Design and Technology", "Drama", "Economics", "Engineering", "English Language", "English Literature", "Film Studies", + "Food Preparation and Nutrition", "French", "Geography", "German", "History", "Latin", "Mathematics", "Media/Film/TV", "Music", + "Other Modern Languages", "Other Sciences", "Physical Education", "Physics", "Religious Studies", "Social Studies", "Spanish", "Statistics" ) -# Create a dataframe containing the subjects to be included in the TMs, you need to check with the Data Production Team to see if any subjects have changed -ks4_subjects <- c( - "Ancient History", "Art and Design", "Biblical Hebrew", "Biology", "Business", "Chemistry", "Classical Civilisation", "Classical Greek", # , "Combined Science", - "Computer Science", "Dance", "Design and Technology", "Drama", "Economics", "Engineering", "English Language", "English Literature", "Film Studies", - "Food Preparation and Nutrition", "French", "Geography", "German", "History", "Latin", "Mathematics", "Media/Film/TV", "Music", - "Other Modern Languages", "Other Sciences", "Physical Education", "Physics", "Religious Studies", "Social Studies", "Spanish", "Statistics" -) + ## combined science has its own output below due to double award -## combined science has its own output below due to double award + ################################################################################################# + ################## 9-1 subjects tidy data ################# + ################################################################################################# -################################################################################################# -################## 9-1 subjects tidy data ################# -################################################################################################# + # STEP 4 Tidy Data for subjects graded 9-1 -# STEP 4 Tidy Data for subjects graded 9-1 + join_data <- Pupil_SQL_data %>% + left_join(Exam_short, + by = c("CANDNO", "LAESTAB") + ) %>% + filter(subjects %in% ks4_subjects) %>% + drop_na(ks2em) -join_data <- Pupil_SQL_data %>% - left_join(Exam_short, - by = c("CANDNO", "LAESTAB") - ) %>% - filter(subjects %in% ks4_subjects) %>% - drop_na(ks2em) + # a <- join_data %>% filter(GRADE == 'covid impacted') -# a <- join_data %>% filter(GRADE == 'covid impacted') + ##################################################################################### + func_counts_char <- function(data, char, char_name) { + data %>% + count(GRADE, subjects, ks2em_band, ks2em, {{ char }}) %>% + rename(characteristic_value = {{ char }}) %>% + mutate(characteristic_type = char_name) + } -##################################################################################### -func_counts_char <- function(data, char, char_name) { - data %>% - count(GRADE, subjects, ks2em_band, ks2em, {{ char }}) %>% - rename(characteristic_value = {{ char }}) %>% - mutate(characteristic_type = char_name) -} + func_counts_all <- function(data) { + data %>% + count(GRADE, subjects, ks2em_band, ks2em) %>% + mutate(characteristic_value = "All Pupils") %>% + mutate(characteristic_type = "All Pupils") + } -func_counts_all <- function(data) { - data %>% - count(GRADE, subjects, ks2em_band, ks2em) %>% - mutate(characteristic_value = "All Pupils") %>% - mutate(characteristic_type = "All Pupils") -} + # This section counts the number of grades in each breakdown and All Pupils + grade_counts_Sex <- func_counts_char(join_data, Sex, "Sex") + grade_counts_sen <- func_counts_char(join_data, SEN, "SEN") + grade_counts_eal <- func_counts_char(join_data, EAL, "EAL") + grade_counts_disadvantage <- func_counts_char(join_data, Disadvantage, "Disadvantage") + grade_counts_all <- func_counts_all(join_data) -# This section counts the number of grades in each breakdown and All Pupils -grade_counts_Sex <- func_counts_char(join_data, Sex, "Sex") -grade_counts_sen <- func_counts_char(join_data, SEN, "SEN") -grade_counts_eal <- func_counts_char(join_data, EAL, "EAL") -grade_counts_disadvantage <- func_counts_char(join_data, Disadvantage, "Disadvantage") -grade_counts_all <- func_counts_all(join_data) - - -# This combines the count dataframes -grade_counts_comb <- rbind( - grade_counts_Sex, grade_counts_sen, grade_counts_eal, - grade_counts_disadvantage, grade_counts_all -) - - -# creates the separate grade columns -grade_counts_spread <- grade_counts_comb %>% - pivot_wider(names_from = GRADE, values_from = n) %>% - select(-Q) %>% - rename("covid_impacted" = "covid impacted") - -# Calculates the percentages -grade_percentages_spread <- grade_counts_spread %>% - # select(-X) %>% # removes X from the % calculation - janitor::adorn_percentages() %>% - mutate_if(is.numeric, function(x) { - round2(x * 100, 1) - }) %>% - rename( - "perc_U" = "U", - "perc_1" = "1", - "perc_2" = "2", - "perc_3" = "3", - "perc_4" = "4", - "perc_5" = "5", - "perc_6" = "6", - "perc_7" = "7", - "perc_8" = "8", - "perc_9" = "9", - "perc_X" = "X", - "per_covid_impacted" = "covid_impacted" - ) -# Creates the final tidy data -tidy_data_subjects <- grade_counts_spread %>% - left_join(grade_percentages_spread, by = c( - "subjects", "ks2em", "ks2em_band", - "characteristic_type", # comment back for app use, - "characteristic_value" - )) %>% - mutate( - time_period = 202324, - time_identifier = "Academic year", - geographic_level = "National", - country_code = "E92000001", - country_name = "England", - version = "Provisional", - all_grades = rowSums(.[, c("U", "1", "2", "3", "4", "5", "6", "7", "8", "9", "X", "covid_impacted")], na.rm = TRUE) - ) %>% - arrange( - characteristic_type, # comment back for app use, - characteristic_value, subjects, ks2em_band - ) %>% - select(time_period, time_identifier, geographic_level, country_code, country_name, # LA_name, - version, - characteristic_type, # comment back for app use, - characteristic_value, subjects, - KS2_Prior = ks2em, - ## the grades below produce data files output when running app this need to be commented out and the section 2 commented back in. - # '1' = '1', '2'= '2', '3' = '3', '4' = '4', '5' = '5', '6' = '6', '7' = '7', '8' = '8', '9' = '9', 'U' = 'U', 'X' = 'X', 'all_grades', - #' %'='perc_1','%_2'='perc_2','%_3' ='perc_3', '%_4'='perc_4',' %_5'='perc_5', '%_6'='perc_6', '%_7'='perc_7', '%_8'='perc_8', - #' %_9'='perc_9','%_U'='perc_U' ,'%_X'='perc_X', 'covid_impacted' ) %>% ##comment out for app - - ### (section2 for app use) - "num_U" = "U", "num_1" = "1", "num_2" = "2", "num_3" = "3", "num_4" = "4", "num_5" = "5", "num_6" = "6", - "num_7" = "7", "num_8" = "8", "num_9" = "9", "num_X" = "X", "num_covid_impacted" = "covid_impacted", "all_grades", "perc_1", "perc_2", "perc_3", "perc_4", - "perc_5", "perc_6", "perc_7", "perc_8", "perc_9", "perc_U", "perc_X", "per_covid_impacted" - ) %>% ## comment back for app - mutate_all(~ replace(., is.na(.), 0)) + # This combines the count dataframes + grade_counts_comb <- rbind( + grade_counts_Sex, grade_counts_sen, grade_counts_eal, + grade_counts_disadvantage, grade_counts_all + ) -# copying data to an Excel file -# save_tidy_data_file = 'Y:/Pre-16 development/Routine products/Transition Matrices/TM Dev/8.TM_in_R/KS4_TM_Scaled_Scores/2021_Tidy_Data_Output_Scaled_Scores_Final.csv' -# save_tidy_data_file = 'C:/Users/SMANCHESTER.AD/OneDrive - Department for Education/Documents/R Projects/KS4_TM_Scaled_Scores/2021_Tidy_Data_Output_Scaled_Scores_Final.csv' -save_tidy_data_file_subjects <- "C:/Users/MPARMAR/OneDrive - Department for Education/Documents/2024_publication/R/TM/Transition-matrices-dashboard/data/2024_Tidy_Data_Output_91_Scaled_Scores_Final_v2.csv" # update year -write.table(tidy_data_subjects, save_tidy_data_file_subjects, row.names = FALSE, sep = ",") + # creates the separate grade columns + grade_counts_spread <- grade_counts_comb %>% + pivot_wider(names_from = GRADE, values_from = n) %>% + select(-Q) %>% + rename("covid_impacted" = "covid impacted") + + # Calculates the percentages + grade_percentages_spread <- grade_counts_spread %>% + # select(-X) %>% # removes X from the % calculation + janitor::adorn_percentages() %>% + mutate_if(is.numeric, function(x) { + round2(x * 100, 1) + }) %>% + rename( + "perc_U" = "U", + "perc_1" = "1", + "perc_2" = "2", + "perc_3" = "3", + "perc_4" = "4", + "perc_5" = "5", + "perc_6" = "6", + "perc_7" = "7", + "perc_8" = "8", + "perc_9" = "9", + "perc_X" = "X", + "per_covid_impacted" = "covid_impacted" + ) + + # Creates the final tidy data + tidy_data_subjects <- grade_counts_spread %>% + left_join(grade_percentages_spread, by = c( + "subjects", "ks2em", "ks2em_band", + "characteristic_type", # comment back for app use, + "characteristic_value" + )) %>% + mutate( + time_period = 202324, + time_identifier = "Academic year", + geographic_level = "National", + country_code = "E92000001", + country_name = "England", + version = "Provisional", + all_grades = rowSums(.[, c("U", "1", "2", "3", "4", "5", "6", "7", "8", "9", "X", "covid_impacted")], na.rm = TRUE) + ) %>% + arrange( + characteristic_type, # comment back for app use, + characteristic_value, subjects, ks2em_band + ) %>% + select(time_period, time_identifier, geographic_level, country_code, country_name, # LA_name, + version, + characteristic_type, # comment back for app use, + characteristic_value, subjects, + KS2_Prior = ks2em, + ## the grades below produce data files output when running app this need to be commented out and the section 2 commented back in. + # '1' = '1', '2'= '2', '3' = '3', '4' = '4', '5' = '5', '6' = '6', '7' = '7', '8' = '8', '9' = '9', 'U' = 'U', 'X' = 'X', 'all_grades', + #' %'='perc_1','%_2'='perc_2','%_3' ='perc_3', '%_4'='perc_4',' %_5'='perc_5', '%_6'='perc_6', '%_7'='perc_7', '%_8'='perc_8', + #' %_9'='perc_9','%_U'='perc_U' ,'%_X'='perc_X', 'covid_impacted' ) %>% ##comment out for app + + ### (section2 for app use) + "num_U" = "U", "num_1" = "1", "num_2" = "2", "num_3" = "3", "num_4" = "4", "num_5" = "5", "num_6" = "6", + "num_7" = "7", "num_8" = "8", "num_9" = "9", "num_X" = "X", "num_covid_impacted" = "covid_impacted", "all_grades", "perc_1", "perc_2", "perc_3", "perc_4", + "perc_5", "perc_6", "perc_7", "perc_8", "perc_9", "perc_U", "perc_X", "per_covid_impacted" + ) %>% ## comment back for app + mutate_all(~ replace(., is.na(.), 0)) + + + # copying data to an Excel file + # save_tidy_data_file = 'Y:/Pre-16 development/Routine products/Transition Matrices/TM Dev/8.TM_in_R/KS4_TM_Scaled_Scores/2021_Tidy_Data_Output_Scaled_Scores_Final.csv' + # save_tidy_data_file = 'C:/Users/SMANCHESTER.AD/OneDrive - Department for Education/Documents/R Projects/KS4_TM_Scaled_Scores/2021_Tidy_Data_Output_Scaled_Scores_Final.csv' + save_tidy_data_file_subjects <- "C:/Users/MPARMAR/OneDrive - Department for Education/Documents/2024_publication/R/TM/Transition-matrices-dashboard/data/2024_Tidy_Data_Output_91_Scaled_Scores_Final_v2.csv" # update year + write.table(tidy_data_subjects, save_tidy_data_file_subjects, row.names = FALSE, sep = ",") + + + + ################################################################################################# + ################## Combined Science tidy data ################# + ################################################################################################# + + # STEP 5 Tidy Data for Combined Science + + join_data_cs <- Pupil_SQL_data %>% + left_join(Exam_short, + by = c("CANDNO", "LAESTAB") + ) %>% + filter(subjects == "Combined Science") %>% + drop_na(ks2em) + + + func_counts_char <- function(data, char, char_name) { + data %>% + count(GRADE, subjects, ks2em_band, ks2em, {{ char }}) %>% + rename(characteristic_value = {{ char }}) %>% + mutate(characteristic_type = char_name) + } + func_counts_all <- function(data) { + data %>% + count(GRADE, subjects, ks2em_band, ks2em) %>% + mutate(characteristic_value = "All Pupils") %>% + mutate(characteristic_type = "All Pupils") + } + # a <- join_data_cs %>% filter(GRADE == 'covid impacted') -################################################################################################# -################## Combined Science tidy data ################# -################################################################################################# -# STEP 5 Tidy Data for Combined Science + grade_counts_Sex_cs <- func_counts_char(join_data_cs, Sex, "Sex") + grade_counts_sen_cs <- func_counts_char(join_data_cs, SEN, "SEN") + grade_counts_eal_cs <- func_counts_char(join_data_cs, EAL, "EAL") + grade_counts_disadvantage_cs <- func_counts_char(join_data_cs, Disadvantage, "Disadvantage") + grade_counts_all_cs <- func_counts_all(join_data_cs) -join_data_cs <- Pupil_SQL_data %>% - left_join(Exam_short, - by = c("CANDNO", "LAESTAB") + grade_counts_comb_cs <- rbind( + grade_counts_Sex_cs, grade_counts_sen_cs, grade_counts_eal_cs, + grade_counts_disadvantage_cs, grade_counts_all_cs ) %>% - filter(subjects == "Combined Science") %>% - drop_na(ks2em) + select(-subjects) + + + # creates the separate grade columns + grade_counts_spread_cs <- grade_counts_comb_cs %>% # creates the separate grade columns + pivot_wider(names_from = GRADE, values_from = n) # %>% + # rename("covid_impacted" = "covid impacted") #REMOVED + + # select(-Q) + + + + # calcs + grade_percentages_spread_cs <- grade_counts_spread_cs %>% + # select(-X) %>% # removes X from the % calculation, we are including X in the percentage calc + janitor::adorn_percentages() %>% + mutate_if(is.numeric, function(x) { + round2(x * 100, 1) + }) %>% + rename( + "perc_UU" = "U", + "perc_11" = "11", + "perc_21" = "21", + "perc_22" = "22", + "perc_32" = "32", + "perc_33" = "33", + "perc_43" = "43", + "perc_44" = "44", + "perc_54" = "54", + "perc_55" = "55", + "perc_65" = "65", + "perc_66" = "66", + "perc_76" = "76", + "perc_77" = "77", + "perc_87" = "87", + "perc_88" = "88", + "perc_98" = "98", + "perc_99" = "99", + "perc_XX" = "X" + # "per_covid_impacted_double" = "covid_impacted" + ) + + ## CS output + tidy_data_cs <- grade_counts_spread_cs %>% + left_join(grade_percentages_spread_cs, by = c( + "ks2em", "ks2em_band", + "characteristic_type", # comment back for app use, + "characteristic_value" + )) %>% + # ,"LA_name" + mutate( + time_period = "202324", + time_identifier = "Academic year", + geographic_level = "National", + country_code = "E92000001", + country_name = "England", + version = "Provisional", + all_grades = rowSums(.[, c("U", "11", "21", "22", "32", "33", "43", "44", "54", "55", "65", "66", "76", "77", "87", "88", "98", "99", "X")], na.rm = TRUE) + ) %>% + arrange( + characteristic_type, # comment back for app use, + characteristic_value, ks2em_band + ) %>% + select(time_period, time_identifier, geographic_level, country_code, country_name, version, + characteristic_type, # comment back for app use, + characteristic_value, + KS2_Prior = ks2em, + + ## the grades below produce data files output when running app this need to be commented out and the section 2 commented back in. + ## 'U' = 'U', + ##' 11' = '11', '22'= '22', '33' = '33', '44' = '44', '55' = '55', '66' = '66', + ## '77' = '77', '88' = '88', '99' = '99', 'X' = 'X', All_Grades, '%_U'='perc_U' ,'%_11'='perc_11','%_22'='perc_22','%_33' ='perc_33', '%_44'='perc_44', + ##' %_55'='perc_55', '%_66'='perc_66', '%_77'='perc_77', '%_88'='perc_88', '%_99'='perc_99') %>% ##comment out for app + + ### (section2 for app use) + + "num_11" = "11", "num_21" = "21", "num_22" = "22", "num_32" = "32", "num_33" = "33", "num_43" = "43", "num_44" = "44", "num_54" = "54", "num_55" = "55", "num_65" = "65", "num_66" = "66", "num_76" = "76", + "num_77" = "77", "num_87" = "87", "num_88" = "88", "num_98" = "98", "num_99" = "99", "num_UU" = "U", "num_XX" = "X", "all_grades", + "perc_UU", "perc_11", "perc_21", "perc_22", "perc_32", "perc_33", "perc_43", "perc_44", "perc_54", "perc_55", "perc_65", "perc_66", "perc_76", + "perc_77", "perc_87", "perc_88", "perc_98", "perc_99", "perc_XX" + ) %>% ## comment back for app + mutate_all(~ replace(., is.na(.), 0)) + + + # copying data to an Excel file + # save_tidy_data_file_cs = 'C:/Users/SMANCHESTER.AD/OneDrive - Department for Education/Documents/R Projects/KS4_TM_Scaled_Scores/2021_Tidy_Data_Output_Comb_Science_Scaled_Scores_Final.csv' + save_tidy_data_file_cs <- "C:/Users/MPARMAR/OneDrive - Department for Education/Documents/2024_publication/R/TM/Transition-matrices-dashboard/data/2024_Tidy_Data_Output_Comb_Science_Scaled_Scores_Final_v2.csv" # update year + write.table(tidy_data_cs, save_tidy_data_file_cs, row.names = FALSE, sep = ",") + + + + + ################################################################################################# + ################## Attainment tidy data ################# + ################################################################################################# + + # STEP 6 Tidy Data for Attainment + + join_data_attainment <- Pupil_SQL_data %>% # check ok + left_join(Exam_short, + by = c("CANDNO", "LAESTAB") + ) %>% + drop_na(ks2em) + + + ## function for calculating attainment columns for specified characteristic, + ## the slice makes sure only one entry per pupil is selected + func_attainment_char <- function(data, attainment, achieved, char, char_name) { + data %>% + filter({{ attainment }} == achieved) %>% + group_by(CANDNO) %>% + slice(1) %>% + ungroup() %>% + count({{ attainment }}, ks2em_band, ks2em, {{ char }}) %>% + rename(characteristic_value = {{ char }}) %>% + mutate(characteristic_type = char_name) + } -func_counts_char <- function(data, char, char_name) { - data %>% - count(GRADE, subjects, ks2em_band, ks2em, {{ char }}) %>% - rename(characteristic_value = {{ char }}) %>% - mutate(characteristic_type = char_name) -} + ## function for calculating all pupil attainment columns + func_attainment_allgen <- function(data, attainment) { + data %>% + group_by(CANDNO) %>% + slice(1) %>% + ungroup() %>% + count({{ attainment }}, ks2em_band, ks2em) %>% + mutate(characteristic_value = "All Pupils") %>% + mutate(characteristic_type = "All Pupils") + } -func_counts_all <- function(data) { - data %>% - count(GRADE, subjects, ks2em_band, ks2em) %>% - mutate(characteristic_value = "All Pupils") %>% - mutate(characteristic_type = "All Pupils") -} -# a <- join_data_cs %>% filter(GRADE == 'covid impacted') - - -grade_counts_Sex_cs <- func_counts_char(join_data_cs, Sex, "Sex") -grade_counts_sen_cs <- func_counts_char(join_data_cs, SEN, "SEN") -grade_counts_eal_cs <- func_counts_char(join_data_cs, EAL, "EAL") -grade_counts_disadvantage_cs <- func_counts_char(join_data_cs, Disadvantage, "Disadvantage") -grade_counts_all_cs <- func_counts_all(join_data_cs) - -grade_counts_comb_cs <- rbind( - grade_counts_Sex_cs, grade_counts_sen_cs, grade_counts_eal_cs, - grade_counts_disadvantage_cs, grade_counts_all_cs -) %>% - select(-subjects) - - -# creates the separate grade columns -grade_counts_spread_cs <- grade_counts_comb_cs %>% # creates the separate grade columns - pivot_wider(names_from = GRADE, values_from = n) # %>% -# rename("covid_impacted" = "covid impacted") #REMOVED - -# select(-Q) - - - -# calcs -grade_percentages_spread_cs <- grade_counts_spread_cs %>% - # select(-X) %>% # removes X from the % calculation, we are including X in the percentage calc - janitor::adorn_percentages() %>% - mutate_if(is.numeric, function(x) { - round2(x * 100, 1) - }) %>% - rename( - "perc_UU" = "U", - "perc_11" = "11", - "perc_21" = "21", - "perc_22" = "22", - "perc_32" = "32", - "perc_33" = "33", - "perc_43" = "43", - "perc_44" = "44", - "perc_54" = "54", - "perc_55" = "55", - "perc_65" = "65", - "perc_66" = "66", - "perc_76" = "76", - "perc_77" = "77", - "perc_87" = "87", - "perc_88" = "88", - "perc_98" = "98", - "perc_99" = "99", - "perc_XX" = "X" - # "per_covid_impacted_double" = "covid_impacted" - ) + # Calculating pupils entered for EBacc + EBACC_entered_Sex <- func_attainment_char(join_data_attainment, EBACC_E_PTQ_EE, 1, Sex, "Sex") + EBACC_notentered_Sex <- func_attainment_char(join_data_attainment, EBACC_E_PTQ_EE, 0, Sex, "Sex") + EBACC_entered_sen <- func_attainment_char(join_data_attainment, EBACC_E_PTQ_EE, 1, SEN, "SEN") + EBACC_notentered_sen <- func_attainment_char(join_data_attainment, EBACC_E_PTQ_EE, 0, SEN, "SEN") + EBACC_entered_eal <- func_attainment_char(join_data_attainment, EBACC_E_PTQ_EE, 1, EAL, "EAL") + EBACC_notentered_eal <- func_attainment_char(join_data_attainment, EBACC_E_PTQ_EE, 0, EAL, "EAL") + EBACC_entered_disadvantage <- func_attainment_char(join_data_attainment, EBACC_E_PTQ_EE, 1, Disadvantage, "Disadvantage") + EBACC_notentered_disadvantage <- func_attainment_char(join_data_attainment, EBACC_E_PTQ_EE, 0, Disadvantage, "Disadvantage") + EBACC_all_pupils <- func_attainment_allgen(join_data_attainment, EBACC_E_PTQ_EE) + -## CS output -tidy_data_cs <- grade_counts_spread_cs %>% - left_join(grade_percentages_spread_cs, by = c( - "ks2em", "ks2em_band", - "characteristic_type", # comment back for app use, - "characteristic_value" - )) %>% - # ,"LA_name" - mutate( - time_period = "202324", - time_identifier = "Academic year", - geographic_level = "National", - country_code = "E92000001", - country_name = "England", - version = "Provisional", - all_grades = rowSums(.[, c("U", "11", "21", "22", "32", "33", "43", "44", "54", "55", "65", "66", "76", "77", "87", "88", "98", "99", "X")], na.rm = TRUE) + EBACC_all <- rbind( + EBACC_entered_Sex, EBACC_notentered_Sex, EBACC_entered_sen, EBACC_notentered_sen, + EBACC_entered_eal, EBACC_notentered_eal, EBACC_entered_disadvantage, + EBACC_notentered_disadvantage, EBACC_all_pupils ) %>% - arrange( - characteristic_type, # comment back for app use, - characteristic_value, ks2em_band + pivot_wider(names_from = EBACC_E_PTQ_EE, values_from = n) %>% + rename("Entered_for_EBacc" = "1") %>% + rename("Not_Entered" = "0") + + # Percentages + EBACC_all_perc <- left_join(EBACC_all, + EBACC_all %>% janitor::adorn_percentages(), + by = c("ks2em_band", "ks2em", "characteristic_type", "characteristic_value") ) %>% - select(time_period, time_identifier, geographic_level, country_code, country_name, version, - characteristic_type, # comment back for app use, - characteristic_value, - KS2_Prior = ks2em, + rename("EBacc_all_Entered" = "Entered_for_EBacc.x") %>% + rename("EBacc_all_Not_Entered" = "Not_Entered.x") %>% + rename("EBacc_all_perc_Entered" = "Entered_for_EBacc.y") %>% + rename("EBacc_all_perc_Not_Entered" = "Not_Entered.y") - ## the grades below produce data files output when running app this need to be commented out and the section 2 commented back in. - ## 'U' = 'U', - ##' 11' = '11', '22'= '22', '33' = '33', '44' = '44', '55' = '55', '66' = '66', - ## '77' = '77', '88' = '88', '99' = '99', 'X' = 'X', All_Grades, '%_U'='perc_U' ,'%_11'='perc_11','%_22'='perc_22','%_33' ='perc_33', '%_44'='perc_44', - ##' %_55'='perc_55', '%_66'='perc_66', '%_77'='perc_77', '%_88'='perc_88', '%_99'='perc_99') %>% ##comment out for app - ### (section2 for app use) - "num_11" = "11", "num_21" = "21", "num_22" = "22", "num_32" = "32", "num_33" = "33", "num_43" = "43", "num_44" = "44", "num_54" = "54", "num_55" = "55", "num_65" = "65", "num_66" = "66", "num_76" = "76", - "num_77" = "77", "num_87" = "87", "num_88" = "88", "num_98" = "98", "num_99" = "99", "num_UU" = "U", "num_XX" = "X", "all_grades", - "perc_UU", "perc_11", "perc_21", "perc_22", "perc_32", "perc_33", "perc_43", "perc_44", "perc_54", "perc_55", "perc_65", "perc_66", "perc_76", - "perc_77", "perc_87", "perc_88", "perc_98", "perc_99", "perc_XX" - ) %>% ## comment back for app - mutate_all(~ replace(., is.na(.), 0)) + # Calculating 9-4 grade achievement in EBacc + EBACC94_achieved_Sex <- func_attainment_char(join_data_attainment, EBACC_94, 1, Sex, "Sex") + EBACC94_notachieved_Sex <- func_attainment_char(join_data_attainment, EBACC_94, 0, Sex, "Sex") + EBACC94_achieved_sen <- func_attainment_char(join_data_attainment, EBACC_94, 1, SEN, "SEN") + EBACC94_notachieved_sen <- func_attainment_char(join_data_attainment, EBACC_94, 0, SEN, "SEN") + EBACC94_achieved_eal <- func_attainment_char(join_data_attainment, EBACC_94, 1, EAL, "EAL") + EBACC94_notachieved_eal <- func_attainment_char(join_data_attainment, EBACC_94, 0, EAL, "EAL") + EBACC94_achieved_disadvantage <- func_attainment_char(join_data_attainment, EBACC_94, 1, Disadvantage, "Disadvantage") + EBACC94_notachieved_disadvantage <- func_attainment_char(join_data_attainment, EBACC_94, 0, Disadvantage, "Disadvantage") + EBACC94_all_pupils <- func_attainment_allgen(join_data_attainment, EBACC_94) -# copying data to an Excel file -# save_tidy_data_file_cs = 'C:/Users/SMANCHESTER.AD/OneDrive - Department for Education/Documents/R Projects/KS4_TM_Scaled_Scores/2021_Tidy_Data_Output_Comb_Science_Scaled_Scores_Final.csv' -save_tidy_data_file_cs <- "C:/Users/MPARMAR/OneDrive - Department for Education/Documents/2024_publication/R/TM/Transition-matrices-dashboard/data/2024_Tidy_Data_Output_Comb_Science_Scaled_Scores_Final_v2.csv" # update year -write.table(tidy_data_cs, save_tidy_data_file_cs, row.names = FALSE, sep = ",") + EBACC94_all <- rbind( + EBACC94_achieved_Sex, EBACC94_notachieved_Sex, EBACC94_achieved_sen, EBACC94_notachieved_sen, + EBACC94_achieved_eal, EBACC94_notachieved_eal, EBACC94_achieved_disadvantage, + EBACC94_notachieved_disadvantage, EBACC94_all_pupils + ) %>% + pivot_wider(names_from = EBACC_94, values_from = n) %>% + rename("Achieved_EBacc_9-4" = "1") %>% + rename("EBacc_9-4_Not_Achieved" = "0") + EBACC94_all_perc <- left_join(EBACC94_all, + EBACC94_all %>% janitor::adorn_percentages(), + by = c("ks2em_band", "ks2em", "characteristic_type", "characteristic_value") + ) %>% + rename("EBacc_9-4_Achieved" = "Achieved_EBacc_9-4.x") %>% + rename("EBacc_9-4_Not_Achieved" = "EBacc_9-4_Not_Achieved.x") %>% + rename("EBacc_9-4_perc_Achieved" = "Achieved_EBacc_9-4.y") %>% + rename("EBacc_9-4_perc_Not_Achieved" = "EBacc_9-4_Not_Achieved.y") -################################################################################################# -################## Attainment tidy data ################# -################################################################################################# -# STEP 6 Tidy Data for Attainment + # Calculating 9-5 grade achievement in EBacc + EBACC95_achieved_Sex <- func_attainment_char(join_data_attainment, EBACC_95, 1, Sex, "Sex") + EBACC95_notachieved_Sex <- func_attainment_char(join_data_attainment, EBACC_95, 0, Sex, "Sex") + EBACC95_achieved_sen <- func_attainment_char(join_data_attainment, EBACC_95, 1, SEN, "SEN") + EBACC95_notachieved_sen <- func_attainment_char(join_data_attainment, EBACC_95, 0, SEN, "SEN") + EBACC95_achieved_eal <- func_attainment_char(join_data_attainment, EBACC_95, 1, EAL, "EAL") + EBACC95_notachieved_eal <- func_attainment_char(join_data_attainment, EBACC_95, 0, EAL, "EAL") + EBACC95_achieved_disadvantage <- func_attainment_char(join_data_attainment, EBACC_95, 1, Disadvantage, "Disadvantage") + EBACC95_notachieved_disadvantage <- func_attainment_char(join_data_attainment, EBACC_95, 0, Disadvantage, "Disadvantage") + EBACC95_all_pupils <- func_attainment_allgen(join_data_attainment, EBACC_95) -join_data_attainment <- Pupil_SQL_data %>% # check ok - left_join(Exam_short, - by = c("CANDNO", "LAESTAB") + EBACC95_all <- rbind( + EBACC95_achieved_Sex, EBACC95_notachieved_Sex, EBACC95_achieved_sen, EBACC95_notachieved_sen, + EBACC95_achieved_eal, EBACC95_notachieved_eal, EBACC95_achieved_disadvantage, + EBACC95_notachieved_disadvantage, EBACC95_all_pupils ) %>% - drop_na(ks2em) - - -## function for calculating attainment columns for specified characteristic, -## the slice makes sure only one entry per pupil is selected -func_attainment_char <- function(data, attainment, achieved, char, char_name) { - data %>% - filter({{ attainment }} == achieved) %>% - group_by(CANDNO) %>% - slice(1) %>% - ungroup() %>% - count({{ attainment }}, ks2em_band, ks2em, {{ char }}) %>% - rename(characteristic_value = {{ char }}) %>% - mutate(characteristic_type = char_name) -} - + pivot_wider(names_from = EBACC_95, values_from = n) %>% + rename("Achieved_EBacc_9-5" = "1") %>% + rename("EBacc_9-5_Not_Achieved" = "0") -## function for calculating all pupil attainment columns -func_attainment_allgen <- function(data, attainment) { - data %>% - group_by(CANDNO) %>% - slice(1) %>% - ungroup() %>% - count({{ attainment }}, ks2em_band, ks2em) %>% - mutate(characteristic_value = "All Pupils") %>% - mutate(characteristic_type = "All Pupils") -} - - -# Calculating pupils entered for EBacc -EBACC_entered_Sex <- func_attainment_char(join_data_attainment, EBACC_E_PTQ_EE, 1, Sex, "Sex") -EBACC_notentered_Sex <- func_attainment_char(join_data_attainment, EBACC_E_PTQ_EE, 0, Sex, "Sex") -EBACC_entered_sen <- func_attainment_char(join_data_attainment, EBACC_E_PTQ_EE, 1, SEN, "SEN") -EBACC_notentered_sen <- func_attainment_char(join_data_attainment, EBACC_E_PTQ_EE, 0, SEN, "SEN") -EBACC_entered_eal <- func_attainment_char(join_data_attainment, EBACC_E_PTQ_EE, 1, EAL, "EAL") -EBACC_notentered_eal <- func_attainment_char(join_data_attainment, EBACC_E_PTQ_EE, 0, EAL, "EAL") -EBACC_entered_disadvantage <- func_attainment_char(join_data_attainment, EBACC_E_PTQ_EE, 1, Disadvantage, "Disadvantage") -EBACC_notentered_disadvantage <- func_attainment_char(join_data_attainment, EBACC_E_PTQ_EE, 0, Disadvantage, "Disadvantage") -EBACC_all_pupils <- func_attainment_allgen(join_data_attainment, EBACC_E_PTQ_EE) - - -EBACC_all <- rbind( - EBACC_entered_Sex, EBACC_notentered_Sex, EBACC_entered_sen, EBACC_notentered_sen, - EBACC_entered_eal, EBACC_notentered_eal, EBACC_entered_disadvantage, - EBACC_notentered_disadvantage, EBACC_all_pupils -) %>% - pivot_wider(names_from = EBACC_E_PTQ_EE, values_from = n) %>% - rename("Entered_for_EBacc" = "1") %>% - rename("Not_Entered" = "0") - -# Percentages -EBACC_all_perc <- left_join(EBACC_all, - EBACC_all %>% janitor::adorn_percentages(), - by = c("ks2em_band", "ks2em", "characteristic_type", "characteristic_value") -) %>% - rename("EBacc_all_Entered" = "Entered_for_EBacc.x") %>% - rename("EBacc_all_Not_Entered" = "Not_Entered.x") %>% - rename("EBacc_all_perc_Entered" = "Entered_for_EBacc.y") %>% - rename("EBacc_all_perc_Not_Entered" = "Not_Entered.y") - - - - -# Calculating 9-4 grade achievement in EBacc -EBACC94_achieved_Sex <- func_attainment_char(join_data_attainment, EBACC_94, 1, Sex, "Sex") -EBACC94_notachieved_Sex <- func_attainment_char(join_data_attainment, EBACC_94, 0, Sex, "Sex") -EBACC94_achieved_sen <- func_attainment_char(join_data_attainment, EBACC_94, 1, SEN, "SEN") -EBACC94_notachieved_sen <- func_attainment_char(join_data_attainment, EBACC_94, 0, SEN, "SEN") -EBACC94_achieved_eal <- func_attainment_char(join_data_attainment, EBACC_94, 1, EAL, "EAL") -EBACC94_notachieved_eal <- func_attainment_char(join_data_attainment, EBACC_94, 0, EAL, "EAL") -EBACC94_achieved_disadvantage <- func_attainment_char(join_data_attainment, EBACC_94, 1, Disadvantage, "Disadvantage") -EBACC94_notachieved_disadvantage <- func_attainment_char(join_data_attainment, EBACC_94, 0, Disadvantage, "Disadvantage") -EBACC94_all_pupils <- func_attainment_allgen(join_data_attainment, EBACC_94) - -EBACC94_all <- rbind( - EBACC94_achieved_Sex, EBACC94_notachieved_Sex, EBACC94_achieved_sen, EBACC94_notachieved_sen, - EBACC94_achieved_eal, EBACC94_notachieved_eal, EBACC94_achieved_disadvantage, - EBACC94_notachieved_disadvantage, EBACC94_all_pupils -) %>% - pivot_wider(names_from = EBACC_94, values_from = n) %>% - rename("Achieved_EBacc_9-4" = "1") %>% - rename("EBacc_9-4_Not_Achieved" = "0") - -EBACC94_all_perc <- left_join(EBACC94_all, - EBACC94_all %>% janitor::adorn_percentages(), - by = c("ks2em_band", "ks2em", "characteristic_type", "characteristic_value") -) %>% - rename("EBacc_9-4_Achieved" = "Achieved_EBacc_9-4.x") %>% - rename("EBacc_9-4_Not_Achieved" = "EBacc_9-4_Not_Achieved.x") %>% - rename("EBacc_9-4_perc_Achieved" = "Achieved_EBacc_9-4.y") %>% - rename("EBacc_9-4_perc_Not_Achieved" = "EBacc_9-4_Not_Achieved.y") - - - - -# Calculating 9-5 grade achievement in EBacc -EBACC95_achieved_Sex <- func_attainment_char(join_data_attainment, EBACC_95, 1, Sex, "Sex") -EBACC95_notachieved_Sex <- func_attainment_char(join_data_attainment, EBACC_95, 0, Sex, "Sex") -EBACC95_achieved_sen <- func_attainment_char(join_data_attainment, EBACC_95, 1, SEN, "SEN") -EBACC95_notachieved_sen <- func_attainment_char(join_data_attainment, EBACC_95, 0, SEN, "SEN") -EBACC95_achieved_eal <- func_attainment_char(join_data_attainment, EBACC_95, 1, EAL, "EAL") -EBACC95_notachieved_eal <- func_attainment_char(join_data_attainment, EBACC_95, 0, EAL, "EAL") -EBACC95_achieved_disadvantage <- func_attainment_char(join_data_attainment, EBACC_95, 1, Disadvantage, "Disadvantage") -EBACC95_notachieved_disadvantage <- func_attainment_char(join_data_attainment, EBACC_95, 0, Disadvantage, "Disadvantage") -EBACC95_all_pupils <- func_attainment_allgen(join_data_attainment, EBACC_95) - -EBACC95_all <- rbind( - EBACC95_achieved_Sex, EBACC95_notachieved_Sex, EBACC95_achieved_sen, EBACC95_notachieved_sen, - EBACC95_achieved_eal, EBACC95_notachieved_eal, EBACC95_achieved_disadvantage, - EBACC95_notachieved_disadvantage, EBACC95_all_pupils -) %>% - pivot_wider(names_from = EBACC_95, values_from = n) %>% - rename("Achieved_EBacc_9-5" = "1") %>% - rename("EBacc_9-5_Not_Achieved" = "0") - -EBACC95_all_perc <- left_join(EBACC95_all, - EBACC95_all %>% janitor::adorn_percentages(), - by = c("ks2em_band", "ks2em", "characteristic_type", "characteristic_value") -) %>% - rename("EBacc_9-5_Achieved" = "Achieved_EBacc_9-5.x") %>% - rename("EBacc_9-5_Not_Achieved" = "EBacc_9-5_Not_Achieved.x") %>% - rename("EBacc_9-5_perc_Achieved" = "Achieved_EBacc_9-5.y") %>% - rename("EBacc_9-5_perc_Not_Achieved" = "EBacc_9-5_Not_Achieved.y") - - -# Calculating 9-4 grade achievement in English and Maths -L2B94_achieved_Sex <- func_attainment_char(join_data_attainment, L2BASICS_94, 1, Sex, "Sex") -L2B94_notachieved_Sex <- func_attainment_char(join_data_attainment, L2BASICS_94, 0, Sex, "Sex") -L2B94_achieved_sen <- func_attainment_char(join_data_attainment, L2BASICS_94, 1, SEN, "SEN") -L2B94_notachieved_sen <- func_attainment_char(join_data_attainment, L2BASICS_94, 0, SEN, "SEN") -L2B94_achieved_eal <- func_attainment_char(join_data_attainment, L2BASICS_94, 1, EAL, "EAL") -L2B94_notachieved_eal <- func_attainment_char(join_data_attainment, L2BASICS_94, 0, EAL, "EAL") -L2B94_achieved_disadvantage <- func_attainment_char(join_data_attainment, L2BASICS_94, 1, Disadvantage, "Disadvantage") -L2B94_notachieved_disadvantage <- func_attainment_char(join_data_attainment, L2BASICS_94, 0, Disadvantage, "Disadvantage") -L2B94_all_pupils <- func_attainment_allgen(join_data_attainment, L2BASICS_94) - -L2B94_all <- rbind( - L2B94_achieved_Sex, L2B94_notachieved_Sex, L2B94_achieved_sen, L2B94_notachieved_sen, - L2B94_achieved_eal, L2B94_notachieved_eal, L2B94_achieved_disadvantage, - L2B94_notachieved_disadvantage, L2B94_all_pupils -) %>% - pivot_wider(names_from = L2BASICS_94, values_from = n) %>% - rename("Achieved_Basics_9-4" = "1") %>% - rename("Basics_9-4_Not_Achieved" = "0") - -L2B94_all_perc <- left_join(L2B94_all, - L2B94_all %>% janitor::adorn_percentages(), - by = c("ks2em_band", "ks2em", "characteristic_type", "characteristic_value") -) %>% - rename("Basics_9-4_Achieved" = "Achieved_Basics_9-4.x") %>% - rename("Basics_9-4_Not_Achieved" = "Basics_9-4_Not_Achieved.x") %>% - rename("Basics_9-4_perc_Achieved" = "Achieved_Basics_9-4.y") %>% - rename("Basics_9-4_perc_Not_Achieved" = "Basics_9-4_Not_Achieved.y") - - - -# Calculating 9-5 grade achievement in English and Maths -L2B95_achieved_Sex <- func_attainment_char(join_data_attainment, L2BASICS_95, 1, Sex, "Sex") -L2B95_notachieved_Sex <- func_attainment_char(join_data_attainment, L2BASICS_95, 0, Sex, "Sex") -L2B95_achieved_sen <- func_attainment_char(join_data_attainment, L2BASICS_95, 1, SEN, "SEN") -L2B95_notachieved_sen <- func_attainment_char(join_data_attainment, L2BASICS_95, 0, SEN, "SEN") -L2B95_achieved_eal <- func_attainment_char(join_data_attainment, L2BASICS_95, 1, EAL, "EAL") -L2B95_notachieved_eal <- func_attainment_char(join_data_attainment, L2BASICS_95, 0, EAL, "EAL") -L2B95_achieved_disadvantage <- func_attainment_char(join_data_attainment, L2BASICS_95, 1, Disadvantage, "Disadvantage") -L2B95_notachieved_disadvantage <- func_attainment_char(join_data_attainment, L2BASICS_95, 0, Disadvantage, "Disadvantage") -L2B95_all_pupils <- func_attainment_allgen(join_data_attainment, L2BASICS_95) - -L2B95_all <- rbind( - L2B95_achieved_Sex, L2B95_notachieved_Sex, L2B95_achieved_sen, L2B95_notachieved_sen, - L2B95_achieved_eal, L2B95_notachieved_eal, L2B95_achieved_disadvantage, - L2B95_notachieved_disadvantage, L2B95_all_pupils -) %>% - pivot_wider(names_from = L2BASICS_95, values_from = n) %>% - rename("Achieved_Basics_9-5" = "1") %>% - rename("Basics_9-5_Not_Achieved" = "0") - -L2B95_all_perc <- left_join(L2B95_all, - L2B95_all %>% janitor::adorn_percentages(), - by = c("ks2em_band", "ks2em", "characteristic_type", "characteristic_value") -) %>% - rename("Basics_9-5_Achieved" = "Achieved_Basics_9-5.x") %>% - rename("Basics_9-5_Not_Achieved" = "Basics_9-5_Not_Achieved.x") %>% - rename("Basics_9-5_perc_Achieved" = "Achieved_Basics_9-5.y") %>% - rename("Basics_9-5_perc_Not_Achieved" = "Basics_9-5_Not_Achieved.y") - - - -# create a single table to join all 5 headline measures together -attainment_TM <- EBACC_all_perc %>% - left_join(EBACC94_all_perc, by = c("ks2em_band", "ks2em", "characteristic_type", "characteristic_value")) %>% - left_join(EBACC95_all_perc, by = c("ks2em_band", "ks2em", "characteristic_type", "characteristic_value")) %>% - left_join(L2B94_all_perc, by = c("ks2em_band", "ks2em", "characteristic_type", "characteristic_value")) %>% - left_join(L2B95_all_perc, by = c("ks2em_band", "ks2em", "characteristic_type", "characteristic_value")) %>% - mutate_at(vars(contains("perc")), ~ (round2(. * 100, 1))) - - - -# create the final tidy data file from the attainment_TM table -attainment_tidy_data <- attainment_TM %>% - mutate( - time_period = "202324", - time_identifier = "Academic year", - geographic_level = "National", - country_code = "E92000001", - country_name = "England", - version = "Provisional" - ) %>% - arrange( - characteristic_type, # comment back for app use, - characteristic_value, ks2em_band + EBACC95_all_perc <- left_join(EBACC95_all, + EBACC95_all %>% janitor::adorn_percentages(), + by = c("ks2em_band", "ks2em", "characteristic_type", "characteristic_value") ) %>% - select(time_period, time_identifier, geographic_level, country_code, country_name, version, - characteristic_type, # comment back for app use, - - ## comment back in for data files - ## characteristic_value, KS2_Prior=ks2em, 'EBacc_all_Entered', - ## 'EBacc_all_Not_Entered','EBacc_all_%c_Entered', 'EBacc_all_%c_Not_Entered', 'EBacc_9-4_Achieved','EBacc_9-4_Not_Achieved', - ## 'EBacc_9-4_%c_Achieved','EBacc_9-4_%c_Not_Achieved','EBacc_9-5_Achieved','EBacc_9-5_Not_Achieved', - ## 'EBacc_9-5_%c_Achieved','EBacc_9-5_%c_Not_Achieved','Basics_9-4_Achieved','Basics_9-4_Not_Achieved', - ## 'Basics_9-4_%c_Achieved','Basics_9-4_%c_Not_Achieved', 'Basics_9-5_Achieved','Basics_9-5_Not_Achieved', - ## 'Basics_9-5_%c_Achieved', 'Basics_9-5_%c_Not_Achieved') %>% - - ## section (app use only) - characteristic_value, - KS2_Prior = ks2em, "EBacc_all_Entered", - "EBacc_all_Not_Entered", "EBacc_all_perc_Entered", "EBacc_all_perc_Not_Entered", "EBacc_9-4_Achieved", "EBacc_9-4_Not_Achieved", - "EBacc_9-4_perc_Achieved", "EBacc_9-4_perc_Not_Achieved", "EBacc_9-5_Achieved", "EBacc_9-5_Not_Achieved", - "EBacc_9-5_perc_Achieved", "EBacc_9-5_perc_Not_Achieved", "Basics_9-4_Achieved", "Basics_9-4_Not_Achieved", - "Basics_9-4_perc_Achieved", "Basics_9-4_perc_Not_Achieved", "Basics_9-5_Achieved", "Basics_9-5_Not_Achieved", - "Basics_9-5_perc_Achieved", "Basics_9-5_perc_Not_Achieved" + rename("EBacc_9-5_Achieved" = "Achieved_EBacc_9-5.x") %>% + rename("EBacc_9-5_Not_Achieved" = "EBacc_9-5_Not_Achieved.x") %>% + rename("EBacc_9-5_perc_Achieved" = "Achieved_EBacc_9-5.y") %>% + rename("EBacc_9-5_perc_Not_Achieved" = "EBacc_9-5_Not_Achieved.y") + + + # Calculating 9-4 grade achievement in English and Maths + L2B94_achieved_Sex <- func_attainment_char(join_data_attainment, L2BASICS_94, 1, Sex, "Sex") + L2B94_notachieved_Sex <- func_attainment_char(join_data_attainment, L2BASICS_94, 0, Sex, "Sex") + L2B94_achieved_sen <- func_attainment_char(join_data_attainment, L2BASICS_94, 1, SEN, "SEN") + L2B94_notachieved_sen <- func_attainment_char(join_data_attainment, L2BASICS_94, 0, SEN, "SEN") + L2B94_achieved_eal <- func_attainment_char(join_data_attainment, L2BASICS_94, 1, EAL, "EAL") + L2B94_notachieved_eal <- func_attainment_char(join_data_attainment, L2BASICS_94, 0, EAL, "EAL") + L2B94_achieved_disadvantage <- func_attainment_char(join_data_attainment, L2BASICS_94, 1, Disadvantage, "Disadvantage") + L2B94_notachieved_disadvantage <- func_attainment_char(join_data_attainment, L2BASICS_94, 0, Disadvantage, "Disadvantage") + L2B94_all_pupils <- func_attainment_allgen(join_data_attainment, L2BASICS_94) + + L2B94_all <- rbind( + L2B94_achieved_Sex, L2B94_notachieved_Sex, L2B94_achieved_sen, L2B94_notachieved_sen, + L2B94_achieved_eal, L2B94_notachieved_eal, L2B94_achieved_disadvantage, + L2B94_notachieved_disadvantage, L2B94_all_pupils ) %>% - mutate_all(~ replace(., is.na(.), 0)) + pivot_wider(names_from = L2BASICS_94, values_from = n) %>% + rename("Achieved_Basics_9-4" = "1") %>% + rename("Basics_9-4_Not_Achieved" = "0") + L2B94_all_perc <- left_join(L2B94_all, + L2B94_all %>% janitor::adorn_percentages(), + by = c("ks2em_band", "ks2em", "characteristic_type", "characteristic_value") + ) %>% + rename("Basics_9-4_Achieved" = "Achieved_Basics_9-4.x") %>% + rename("Basics_9-4_Not_Achieved" = "Basics_9-4_Not_Achieved.x") %>% + rename("Basics_9-4_perc_Achieved" = "Achieved_Basics_9-4.y") %>% + rename("Basics_9-4_perc_Not_Achieved" = "Basics_9-4_Not_Achieved.y") + + + + # Calculating 9-5 grade achievement in English and Maths + L2B95_achieved_Sex <- func_attainment_char(join_data_attainment, L2BASICS_95, 1, Sex, "Sex") + L2B95_notachieved_Sex <- func_attainment_char(join_data_attainment, L2BASICS_95, 0, Sex, "Sex") + L2B95_achieved_sen <- func_attainment_char(join_data_attainment, L2BASICS_95, 1, SEN, "SEN") + L2B95_notachieved_sen <- func_attainment_char(join_data_attainment, L2BASICS_95, 0, SEN, "SEN") + L2B95_achieved_eal <- func_attainment_char(join_data_attainment, L2BASICS_95, 1, EAL, "EAL") + L2B95_notachieved_eal <- func_attainment_char(join_data_attainment, L2BASICS_95, 0, EAL, "EAL") + L2B95_achieved_disadvantage <- func_attainment_char(join_data_attainment, L2BASICS_95, 1, Disadvantage, "Disadvantage") + L2B95_notachieved_disadvantage <- func_attainment_char(join_data_attainment, L2BASICS_95, 0, Disadvantage, "Disadvantage") + L2B95_all_pupils <- func_attainment_allgen(join_data_attainment, L2BASICS_95) + + L2B95_all <- rbind( + L2B95_achieved_Sex, L2B95_notachieved_Sex, L2B95_achieved_sen, L2B95_notachieved_sen, + L2B95_achieved_eal, L2B95_notachieved_eal, L2B95_achieved_disadvantage, + L2B95_notachieved_disadvantage, L2B95_all_pupils + ) %>% + pivot_wider(names_from = L2BASICS_95, values_from = n) %>% + rename("Achieved_Basics_9-5" = "1") %>% + rename("Basics_9-5_Not_Achieved" = "0") -# copying data to an Excel file -save_tidy_data_file_attainment <- "data/2024_Tidy_Data_Output_Attainment_Scaled_Scores_Final_v2.csv" # # update year -write.table(attainment_tidy_data, save_tidy_data_file_attainment, row.names = FALSE, sep = ",") + L2B95_all_perc <- left_join(L2B95_all, + L2B95_all %>% janitor::adorn_percentages(), + by = c("ks2em_band", "ks2em", "characteristic_type", "characteristic_value") + ) %>% + rename("Basics_9-5_Achieved" = "Achieved_Basics_9-5.x") %>% + rename("Basics_9-5_Not_Achieved" = "Basics_9-5_Not_Achieved.x") %>% + rename("Basics_9-5_perc_Achieved" = "Achieved_Basics_9-5.y") %>% + rename("Basics_9-5_perc_Not_Achieved" = "Basics_9-5_Not_Achieved.y") + + + + # create a single table to join all 5 headline measures together + attainment_TM <- EBACC_all_perc %>% + left_join(EBACC94_all_perc, by = c("ks2em_band", "ks2em", "characteristic_type", "characteristic_value")) %>% + left_join(EBACC95_all_perc, by = c("ks2em_band", "ks2em", "characteristic_type", "characteristic_value")) %>% + left_join(L2B94_all_perc, by = c("ks2em_band", "ks2em", "characteristic_type", "characteristic_value")) %>% + left_join(L2B95_all_perc, by = c("ks2em_band", "ks2em", "characteristic_type", "characteristic_value")) %>% + mutate_at(vars(contains("perc")), ~ (round2(. * 100, 1))) + + + + # create the final tidy data file from the attainment_TM table + attainment_tidy_data <- attainment_TM %>% + mutate( + time_period = "202324", + time_identifier = "Academic year", + geographic_level = "National", + country_code = "E92000001", + country_name = "England", + version = "Provisional" + ) %>% + arrange( + characteristic_type, # comment back for app use, + characteristic_value, ks2em_band + ) %>% + select(time_period, time_identifier, geographic_level, country_code, country_name, version, + characteristic_type, # comment back for app use, + + ## comment back in for data files + ## characteristic_value, KS2_Prior=ks2em, 'EBacc_all_Entered', + ## 'EBacc_all_Not_Entered','EBacc_all_%c_Entered', 'EBacc_all_%c_Not_Entered', 'EBacc_9-4_Achieved','EBacc_9-4_Not_Achieved', + ## 'EBacc_9-4_%c_Achieved','EBacc_9-4_%c_Not_Achieved','EBacc_9-5_Achieved','EBacc_9-5_Not_Achieved', + ## 'EBacc_9-5_%c_Achieved','EBacc_9-5_%c_Not_Achieved','Basics_9-4_Achieved','Basics_9-4_Not_Achieved', + ## 'Basics_9-4_%c_Achieved','Basics_9-4_%c_Not_Achieved', 'Basics_9-5_Achieved','Basics_9-5_Not_Achieved', + ## 'Basics_9-5_%c_Achieved', 'Basics_9-5_%c_Not_Achieved') %>% + + ## section (app use only) + characteristic_value, + KS2_Prior = ks2em, "EBacc_all_Entered", + "EBacc_all_Not_Entered", "EBacc_all_perc_Entered", "EBacc_all_perc_Not_Entered", "EBacc_9-4_Achieved", "EBacc_9-4_Not_Achieved", + "EBacc_9-4_perc_Achieved", "EBacc_9-4_perc_Not_Achieved", "EBacc_9-5_Achieved", "EBacc_9-5_Not_Achieved", + "EBacc_9-5_perc_Achieved", "EBacc_9-5_perc_Not_Achieved", "Basics_9-4_Achieved", "Basics_9-4_Not_Achieved", + "Basics_9-4_perc_Achieved", "Basics_9-4_perc_Not_Achieved", "Basics_9-5_Achieved", "Basics_9-5_Not_Achieved", + "Basics_9-5_perc_Achieved", "Basics_9-5_perc_Not_Achieved" + ) %>% + mutate_all(~ replace(., is.na(.), 0)) + + + # copying data to an Excel file + save_tidy_data_file_attainment <- "data/2024_Tidy_Data_Output_Attainment_Scaled_Scores_Final_v2.csv" # # update year + write.table(attainment_tidy_data, save_tidy_data_file_attainment, row.names = FALSE, sep = ",") +} diff --git a/tests/testthat/_snaps/initial_load_test/001_.new.png b/tests/testthat/_snaps/initial_load_test/001_.new.png new file 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a/tests/testthat/_snaps/shinytest2/Transition-matrices-dashboard-001.json b/tests/testthat/_snaps/shinytest2/Transition-matrices-dashboard-001.json index 6144263..d7405b4 100644 --- a/tests/testthat/_snaps/shinytest2/Transition-matrices-dashboard-001.json +++ b/tests/testthat/_snaps/shinytest2/Transition-matrices-dashboard-001.json @@ -60,31 +60,31 @@ 12 ], "y": [ - 6, - 1, + 0, + 2, + 0, + 0, 0, 0, 0, - 1, 0, 0, 0, 0, - 1, 0 ], "text": [ - "variable: U
value: 6
Characteristic: All Pupils", - "variable: 1
value: 1
Characteristic: All Pupils", + "variable: U
value: 0
Characteristic: All Pupils", + "variable: 1
value: 2
Characteristic: All Pupils", "variable: 2
value: 0
Characteristic: All Pupils", "variable: 3
value: 0
Characteristic: All Pupils", "variable: 4
value: 0
Characteristic: All Pupils", - "variable: 5
value: 1
Characteristic: All Pupils", + "variable: 5
value: 0
Characteristic: All Pupils", "variable: 6
value: 0
Characteristic: All Pupils", "variable: 7
value: 0
Characteristic: All Pupils", "variable: 8
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Characteristic: All Pupils", - "variable: % 5
value: 11.1
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Characteristic: All Pupils", - "variable: % 7
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Characteristic: All Pupils", - "variable: % 8
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Characteristic: All Pupils", - "variable: % 9
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Characteristic: All Pupils", - "variable: % U
value: 66.7
Characteristic: All Pupils", - "variable: % X
value: 11.1
Characteristic: All Pupils" + "variable: % 1
value: 100
Characteristic: All Pupils", + "variable: % 2
value: 0
Characteristic: All Pupils", + "variable: % 3
value: 0
Characteristic: All Pupils", + "variable: % 4
value: 0
Characteristic: All Pupils", + "variable: % 5
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Characteristic: All Pupils", + "variable: % 6
value: 0
Characteristic: All Pupils", + "variable: % 7
value: 0
Characteristic: All Pupils", + "variable: % 8
value: 0
Characteristic: All Pupils", + "variable: % 9
value: 0
Characteristic: All Pupils", + "variable: % U
value: 0
Characteristic: All Pupils", + "variable: % X
value: 0
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"text": [ - "variable: % 1
value: 27.4
Characteristic: All Pupils", - "variable: % 2
value: 22.9
Characteristic: All Pupils", - "variable: % 3
value: 10.3
Characteristic: All Pupils", - "variable: % 4
value: 8.6
Characteristic: All Pupils", - "variable: % 5
value: 6.3
Characteristic: All Pupils", - "variable: % 6
value: 6.3
Characteristic: All Pupils", - "variable: % 7
value: 6.3
Characteristic: All Pupils", - "variable: % 8
value: 1.7
Characteristic: All Pupils", - "variable: % 9
value: 1.1
Characteristic: All Pupils", - "variable: % U
value: 4.6
Characteristic: All Pupils", - "variable: % X
value: 4.6
Characteristic: All Pupils" + "variable: % 1
value: 25.3
Characteristic: All Pupils", + "variable: % 2
value: 14.8
Characteristic: All Pupils", + "variable: % 3
value: 14.3
Characteristic: All Pupils", + "variable: % 4
value: 8.8
Characteristic: All Pupils", + "variable: % 5
value: 7.1
Characteristic: All Pupils", + "variable: % 6
value: 4.9
Characteristic: All Pupils", + "variable: % 7
value: 7.7
Characteristic: All Pupils", + "variable: % 8
value: 7.7
Characteristic: All Pupils", + "variable: % 9
value: 1.6
Characteristic: All Pupils", + "variable: % U
value: 6.6
Characteristic: All Pupils", + "variable: % X
value: 1.1
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