Add ap semp occ jul jun2022

Data/AppData/C_EmpRate_APS1822.csv

@@ -194,7 +194,6 @@
 "2022","Greater Lincolnshire","LEP",657600,503000,488000,433800,53700,15000,154500,0.742092457420925,22
 "2022","Hull and East Yorkshire","LEP",359800,281500,270300,240500,28100,11200,78300,0.751250694830461,22
 "2022","Leeds City Region","LEP",1452400,1118700,1071500,946800,123800,47300,333700,0.73774442302396,22
-"2022","//www.ons.gov.uk/news/statementsandletters/occupationaldatainonssurveys.","AN ISSUE WITH THE COLLECTION OF OCCUPATIONAL DATA AFFECTED THE ACCURACY OF THE BREAKDOWNS OF SOME DETAILED OCCUPATIONS, AND DATA DERIVED FROM THEM. WE ARE URGING CAUTION IN THE INTERPRETATION OF THESE DETAILED DATA AS WE RESOLVE THE ISSUE. HTTPS",NA,NA,NA,NA,NA,NA,NA,NA,22
 "2018","Brighton and Hove, East Sussex, West Sussex","LSIP",1019300,803900,777900,631800,144600,20500,215600,0.763170803492593,18
 "2018","Buckinghamshire ","LSIP",320600,264300,259500,211000,47300,4800,56300,0.809419837804117,18
 "2018","Cambridgeshire and Peterborough","LSIP",525400,425900,413800,361400,52400,11100,99500,0.787590407308717,18

Data/AppData/C_EmpRate_APS1822_max_min.csv

@@ -1,5 +1,4 @@
 "area","minEmp","maxEmp"
-"//www.ons.gov.uk/news/statementsandletters/occupationaldatainonssurveys.",NA,NA
 "Black Country",491700,536500
 "Brighton and Hove, East Sussex, West Sussex",777900,815600
 "Buckinghamshire",251700,264500

Data/AppData/D_EmpRate_APS1822.csv

@@ -1739,7 +1739,6 @@
 "2022","Greater Lincolnshire","LEP","657600","503000","488000","433800","53700","15000","154500"
 "2022","Hull and East Yorkshire","LEP","359800","281500","270300","240500","28100","11200","78300"
 "2022","Leeds City Region","LEP","1452400","1118700","1071500","946800","123800","47300","333700"
-"2022","//www.ons.gov.uk/news/statementsandletters/occupationaldatainonssurveys.","AN ISSUE WITH THE COLLECTION OF OCCUPATIONAL DATA AFFECTED THE ACCURACY OF THE BREAKDOWNS OF SOME DETAILED OCCUPATIONS, AND DATA DERIVED FROM THEM. WE ARE URGING CAUTION IN THE INTERPRETATION OF THESE DETAILED DATA AS WE RESOLVE THE ISSUE. HTTPS",NA,NA,NA,NA,NA,NA,NA
 "2018","Brighton and Hove, East Sussex, West Sussex","LSIP","1019300","803900","777900","631800","144600","20500","215600"
 "2018","Buckinghamshire ","LSIP","320600","264300","259500","211000","47300","4800","56300"
 "2018","Cambridgeshire and Peterborough","LSIP","525400","425900","413800","361400","52400","11100","99500"

ExtractLoadData.R

@@ -29,7 +29,7 @@ I_missingLAD <- read.xlsx(xlsxFile = paste0("./Data/", folder, "/", list.files(p
 # Cell: T09a Employment by occupation (SOC2010) sub-major group and full-time/part-time; All people/ All people
 folder <- "3_APSempOcc"
 sheetNum <- 1
-I_EmpOcc_APS1721 <- read.xlsx(xlsxFile = paste0("./Data/", folder, "/", list.files(path = paste0("./Data/", folder))), sheet = sheetNum, skipEmptyRows = T)
+I_EmpOcc_APS22 <- read.xlsx(xlsxFile = paste0("./Data/", folder, "/", list.files(path = paste0("./Data/", folder))), sheet = sheetNum, skipEmptyRows = T)
 
 
 ### Employment level and rate ------------

R/Loading Core Indicators.R

@@ -6,9 +6,9 @@ C_LEP2020 <- read.csv(file = "./Data/AppData/C_LEP2020.csv", check.names = FALSE
 
 ## Employment by occupation ----
 # data for download
-D_EmpOcc_APS1721 <- read.csv(file = "./Data/AppData/D_EmpOcc_APS1721.csv", check.names = FALSE)
+D_EmpOcc_APS22 <- read.csv(file = "./Data/AppData/D_EmpOcc_APS22.csv", check.names = FALSE)
 # data for dashboard
-C_EmpOcc_APS1721 <- read.csv(file = "./Data/AppData/C_EmpOcc_APS1721.csv", check.names = FALSE)
+C_EmpOcc_APS22 <- read.csv(file = "./Data/AppData/C_EmpOcc_APS22.csv", check.names = FALSE)
 
 ## Employment level and rate ----
 # data for download

R/panels.R

@@ -175,7 +175,7 @@ panel_employment <- function() {
       ### Employment percentage by occupation data table ----
       column(
         width = 6,
-        h2("Employment share by occupation: Jan-Dec 2021"),
+        h2("Employment share by occupation: Jul-Jun 2022"),
         dataTableOutput("EmpOcc"),
         details(
           label = "Source: Annual Population Survey",

TransformData.R

@@ -51,7 +51,8 @@ format.EmpOcc.APS <- function(x) {
     mutate(check = ifelse(grepl(":", area), 1, 0)) %>% # remove anything but LEP and Country
     filter(check == 1) %>%
     filter(!grepl("nomisweb", area)) %>%
-    select(year = jan_2017_dec_2017, area, everything(), -check) %>% # reorder and remove
+    mutate(year = 2022) %>%
+    select(year, area, everything(), -check, -jul_2021_jun_2022) %>% # reorder and remove
     mutate(geographic_level = gsub(":.*", "", area)) %>% # Get geog type
     mutate(area = gsub(".*:", "", area)) %>% # Tidy up Area names
     mutate(area = case_when(
@@ -60,10 +61,9 @@ format.EmpOcc.APS <- function(x) {
       TRUE ~ area
     )) %>% # Rename so matches official name
     relocate(geographic_level, .after = area) %>%
-    mutate(year = as.numeric(substr(year, 5, 8))) %>%
     rename_with(
-      .fn = ~ str_replace_all(.x, c("t09a_" = "", "all_people" = "", "soc2010" = "", "_" = " ")),
-      .cols = starts_with("t09a_")
+      .fn = ~ str_replace_all(.x, c("t09b_" = "", "all_people" = "", "soc2020" = "", "_" = " ")),
+      .cols = starts_with("t09b_")
     ) %>%
     rename_with(~ gsub("[[:digit:]]+", "", .)) %>% # remove numbers from occupations since they don't match the ONS ones
     mutate(geographic_level = toupper(geographic_level))
@@ -78,31 +78,31 @@ format.EmpOcc.APS <- function(x) {
     rename(area = LSIP) %>%
     relocate(area, .before = geographic_level) %>%
     mutate_at(vars(-year, -area, -geographic_level), function(x) str_replace_all(x, c("!" = "", "\\*" = "", "~" = "", "-" = ""))) %>% # convert to blank to avoid error msg
-    mutate_at(c(4:28), as.numeric) %>% # Convert to numeric
+    mutate_at(c(4:29), as.numeric) %>% # Convert to numeric
     group_by(year, area, geographic_level) %>% # sum for each LSIP
     summarise(across(everything(), list(sum), na.rm = T)) %>%
     rename_with(~ gsub("_1", "", .)) %>% # remove numbers cretaed by the summarise function
-    mutate_at(c(4:28), as.character) # Convert to sring to bind
+    mutate_at(c(4:29), as.character) # Convert to sring to bind
 
   # join together
   bind_rows(reformat, addlsip)
 }
 # format data
-F_EmpOcc_APS1721 <- format.EmpOcc.APS(I_EmpOcc_APS1721)
+F_EmpOcc_APS22 <- format.EmpOcc.APS(I_EmpOcc_APS22)
 # create downloadable version with new suppression rules
-D_EmpOcc_APS1721 <- F_EmpOcc_APS1721 %>%
+D_EmpOcc_APS22 <- F_EmpOcc_APS22 %>%
   mutate_at(vars(-year, -area, -geographic_level), function(x) str_replace_all(x, c("!" = "c", "\\*" = "u", "~" = "low", "-" = "x")))
-write.csv(D_EmpOcc_APS1721, file = "Data\\AppData\\D_EmpOcc_APS1721.csv", row.names = FALSE)
+write.csv(D_EmpOcc_APS22, file = "Data\\AppData\\D_EmpOcc_APS22.csv", row.names = FALSE)
 # create version to use in dashboard
-C_EmpOcc_APS1721 <- F_EmpOcc_APS1721 %>%
+C_EmpOcc_APS22 <- F_EmpOcc_APS22 %>%
   mutate_at(vars(-year, -area, -geographic_level), function(x) str_replace_all(x, c("!" = "", "\\*" = "", "~" = "", "-" = ""))) %>% # convert to blank to avoid error msg
   mutate_at(c(4:28), as.numeric) %>% # Convert to numeric
   filter(
-    year == "2021",
+    year == "2022",
     geographic_level != "LADU" &
       geographic_level != "GOR" # cleans up for London and South East which is included as lep and gor
   )
-write.csv(C_EmpOcc_APS1721, file = "Data\\AppData\\C_EmpOcc_APS1721.csv", row.names = FALSE)
+write.csv(C_EmpOcc_APS22, file = "Data\\AppData\\C_EmpOcc_APS22.csv", row.names = FALSE)
 
 ## Employment level and rate ----
 format.EmpRate.APS <- function(x) {

server.R

@@ -49,7 +49,7 @@ server <- function(input, output, session) {
     },
     content = function(file) {
       write_xlsx(list(
-        "1a.Emp by occupation" = D_EmpOcc_APS1721
+        "1a.Emp by occupation" = D_EmpOcc_APS22
       ), path = file)
     }
   )
@@ -145,7 +145,7 @@ server <- function(input, output, session) {
   ### Downloads----
   # download all indicators
   list_of_datasets0 <- list(
-    "1a.Emp by occupation" = D_EmpOcc_APS1721,
+    "1a.Emp by occupation" = D_EmpOcc_APS22,
     "1b.Emp rate" = D_EmpRate_APS1822,
     "2.Vacancies" = C_Vacancy_ONS1722,
     "3a.FE achievements SSA" = D_Achieve_ILR21,
@@ -163,7 +163,7 @@ server <- function(input, output, session) {
   # Download current LEP indicators
   filtered_data0 <- reactive({
     list(
-      "1a.Emp by occupation" = filter(D_EmpOcc_APS1721, geographic_level == input$GeoType, area == input$lep1),
+      "1a.Emp by occupation" = filter(D_EmpOcc_APS22, geographic_level == input$GeoType, area == input$lep1),
       "1b.Emp rate" = filter(D_EmpRate_APS1822, geographic_level == input$GeoType, area == input$lep1),
       "2.Vacancies" = filter(C_Vacancy_ONS1722, geographic_level == input$GeoType, area == input$lep1),
       "3a.FE achievements SSA" = filter(D_Achieve_ILR21, geographic_level == input$GeoType, area == input$lep1),
@@ -693,7 +693,7 @@ server <- function(input, output, session) {
 
   ### Downloads----
   list_of_datasets1 <- list(
-    "1a.Emp by occupation" = D_EmpOcc_APS1721,
+    "1a.Emp by occupation" = D_EmpOcc_APS22,
     "1b.Emp rate" = D_EmpRate_APS1822
   )
   output$download_btn1a <- downloadHandler(
@@ -708,7 +708,7 @@ server <- function(input, output, session) {
   # Download current LEP indicators
   filtered_data1 <- reactive({
     list(
-      "1a.Emp by occupation" = filter(D_EmpOcc_APS1721, geographic_level == input$GeoType, area == input$lep1),
+      "1a.Emp by occupation" = filter(D_EmpOcc_APS22, geographic_level == input$GeoType, area == input$lep1),
       "1b.Emp rate" = filter(D_EmpRate_APS1822, geographic_level == input$GeoType, area == input$lep1)
     )
   })
@@ -850,7 +850,7 @@ server <- function(input, output, session) {
 
   ## Employment by occupation data table ----
   EmpOcc <- eventReactive(c(input$lep1, input$lep2), {
-    EmpOcc <- C_EmpOcc_APS1721 %>%
+    EmpOcc <- C_EmpOcc_APS22 %>%
       filter(
         geographic_level == input$GeoType | geographic_level == "COUNTRY",
         (area == "England" |