From 65dd88846245c3603692c4ba3881ad59d42aeefe Mon Sep 17 00:00:00 2001 From: JAMES Date: Thu, 13 Oct 2022 21:46:25 +0100 Subject: [PATCH 1/2] added in new aps data for occupation --- Data/3_APSempOcc/nomis_2022_06_14_092401.xlsx | Bin 575146 -> 0 bytes Data/3_APSempOcc/nomis_2022_10_13_205752.xlsx | Bin 0 -> 159606 bytes .../4_APSempRate/nomis_2022_10_12_114302.xlsx | Bin 169276 -> 212109 bytes Data/AppData/C_EmpOcc_APS22.csv | 1 + Data/AppData/C_EmpRate_APS1822.csv | 1 - Data/AppData/C_EmpRate_APS1822_max_min.csv | 1 - Data/AppData/D_EmpOcc_APS22.csv | 442 ++++++++++++++++++ Data/AppData/D_EmpRate_APS1822.csv | 1 - ExtractLoadData.R | 2 +- R/Loading Core Indicators.R | 4 +- TransformData.R | 22 +- server.R | 12 +- 12 files changed, 463 insertions(+), 23 deletions(-) delete mode 100644 Data/3_APSempOcc/nomis_2022_06_14_092401.xlsx create mode 100644 Data/3_APSempOcc/nomis_2022_10_13_205752.xlsx create mode 100644 Data/AppData/C_EmpOcc_APS22.csv create mode 100644 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s>VN%g0kxq2L=6ZBJ3Ds-I2qvM`ro}d2hI-(2{rhA4tkJ59Bh*R0bRMnwg3PC diff --git a/Data/AppData/C_EmpOcc_APS22.csv b/Data/AppData/C_EmpOcc_APS22.csv new file mode 100644 index 00000000..6f7a40d2 --- /dev/null +++ b/Data/AppData/C_EmpOcc_APS22.csv @@ -0,0 +1 @@ +"year","area","geographic_level"," corporate managers and directors "," other managers and proprietors "," science research engineering and technology professionals "," health professionals "," teaching and other educational professionals "," business media and public service professionals "," science engineering and technology associate professionals "," health and social care associate professionals "," protective service occupations "," culture media and sports occupations "," business and public service associate professionals "," administrative occupations "," secretarial and related occupations "," skilled agricultural and related trades "," skilled metal electrical and electronic trades "," skilled construction and building trades "," textiles printing and other skilled trades "," caring personal service occupations "," leisure travel and related personal service occupations "," community and civil enforcement occupations "," sales occupations "," customer service occupations "," process plant and machine operatives "," transport and mobile machine drivers and operatives "," elementary trades and related occupations "," elementary administration and service occupations " diff --git a/Data/AppData/C_EmpRate_APS1822.csv b/Data/AppData/C_EmpRate_APS1822.csv index 3de9a197..0385d8a1 100644 --- a/Data/AppData/C_EmpRate_APS1822.csv +++ b/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 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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 diff --git a/Data/AppData/C_EmpRate_APS1822_max_min.csv b/Data/AppData/C_EmpRate_APS1822_max_min.csv index 9621d0e3..340ca42d 100644 --- a/Data/AppData/C_EmpRate_APS1822_max_min.csv +++ b/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 diff --git a/Data/AppData/D_EmpOcc_APS22.csv b/Data/AppData/D_EmpOcc_APS22.csv new file mode 100644 index 00000000..f5e41f67 --- /dev/null +++ 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"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" diff --git a/ExtractLoadData.R b/ExtractLoadData.R index e06ca4df..005f0c11 100644 --- a/ExtractLoadData.R +++ b/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 ------------ diff --git a/R/Loading Core Indicators.R b/R/Loading Core Indicators.R index 512d516f..298dd380 100644 --- a/R/Loading Core Indicators.R +++ b/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 diff --git a/TransformData.R b/TransformData.R index 8f72837f..7e8a3230 100644 --- a/TransformData.R +++ b/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)) @@ -78,23 +78,23 @@ 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( @@ -102,7 +102,7 @@ C_EmpOcc_APS1721 <- F_EmpOcc_APS1721 %>% 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) { diff --git a/server.R b/server.R index 8e328a44..4cc7105d 100644 --- a/server.R +++ b/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" | From 474e7fbdea32c255addfa91449ed84036b1e01af Mon Sep 17 00:00:00 2001 From: JAMES Date: Fri, 14 Oct 2022 10:30:38 +0100 Subject: [PATCH 2/2] fixed error with occ table cahnged some labels --- Data/AppData/C_EmpOcc_APS22.csv | 78 +++++++++++++++++++++++++++++++++ R/panels.R | 2 +- TransformData.R | 4 +- 3 files changed, 81 insertions(+), 3 deletions(-) diff --git a/Data/AppData/C_EmpOcc_APS22.csv b/Data/AppData/C_EmpOcc_APS22.csv index 6f7a40d2..a84e6e29 100644 --- a/Data/AppData/C_EmpOcc_APS22.csv +++ b/Data/AppData/C_EmpOcc_APS22.csv @@ -1 +1,79 @@ "year","area","geographic_level"," corporate managers and directors "," other managers and proprietors "," science research engineering and technology professionals "," health professionals "," teaching and other educational professionals "," business media and public service professionals "," science engineering and technology associate professionals "," health and social care associate professionals "," protective service occupations "," culture media and sports occupations "," business and public service associate professionals "," administrative occupations "," secretarial and related occupations "," skilled agricultural and related trades "," skilled metal electrical and electronic trades "," skilled construction and building trades "," textiles printing and other skilled trades "," caring personal service occupations "," leisure travel and related personal service occupations "," community and civil enforcement occupations "," sales occupations "," customer service occupations "," process plant and machine operatives "," transport and mobile machine drivers and operatives "," elementary trades and related occupations "," elementary administration and service occupations " +2022,"England","COUNTRY",2024800,905400,2262900,1237600,1424100,2266000,556900,768400,294700,596200,1940400,2348800,454900,244900,834300,754500,489600,1606300,511000,5946200,1328600,432300,610800,929200,511200,"2114300" +2022,"Black Country","LEP",24400,19000,20900,29200,26700,29400,9800,23500,3200,9700,41600,57300,10300,4800,20400,20100,7100,34800,11900,142900,22700,7500,32600,23400,13600,"43100" +2022,"Cheshire and Warrington","LEP",38300,12500,41300,16100,21400,35100,10200,12700,3800,6500,31900,40400,6000,4500,12700,7200,6900,31200,9700,87300,19200,5700,14000,11800,6400,"29700" +2022,"Cornwall and Isles of Scilly","LEP",10600,10800,13200,11100,12200,15600,3700,6400,2100,5900,11300,25000,7500,8900,12700,10700,4800,16000,8700,68100,21500,1300,5200,12200,5400,"22500" +2022,"Coventry and Warwickshire","LEP",20900,11100,47800,17900,29600,27800,13100,10300,5000,11900,37500,39800,6600,2400,19000,6700,9200,22200,7200,118800,17100,9800,10500,22100,10000,"49000" +2022,"Cumbria","LEP",11900,11300,16200,11600,10700,10700,6600,6000,4000,2500,11100,20100,5100,5500,9000,6200,3400,14200,3400,67200,23200,3500,8500,8500,6000,"17500" +2022,"Dorset","LEP",30900,8700,25400,19400,15200,25400,6300,8600,3700,8800,22800,28800,6000,4300,15800,13800,7500,22600,11800,72500,20500,3200,4400,11100,8000,"24900" +2022,"Gloucestershire","LEP",22900,11100,31800,13700,14200,21000,5300,8500,4800,10300,21800,24200,6800,6400,13700,10800,1900,23000,5800,63300,18700,1200,8200,5900,7500,"21600" +2022,"Greater Birmingham and Solihull","LEP",60400,27100,68900,53300,53200,68100,20700,30100,6100,15800,59400,83900,10000,6400,30200,20600,11800,40900,17900,230500,45400,18900,28600,36400,17600,"83400" +2022,"Greater Manchester","LEP",73100,39900,106200,71100,73000,97600,31400,49500,9600,23700,97800,118700,21500,5500,29900,33600,22700,74300,24500,315600,75600,20900,24300,49800,24000,"120000" +2022,"Heart of the South West","LEP",53100,31100,51800,44600,37200,47200,15200,23400,10300,11200,47400,58100,18000,14700,31200,32500,20100,58600,22800,205200,56100,10300,21900,32900,16800,"66500" +2022,"Lancashire","LEP",32800,24600,39300,31800,30900,41500,13500,25000,7800,7600,38100,64000,9300,5200,24600,21900,9600,46500,11300,174500,38600,15000,21300,23700,22000,"53500" +2022,"Leicester and Leicestershire","LEP",34200,11700,42900,16200,35100,28300,13000,13900,3400,8300,36600,40900,6400,4400,22300,16900,8200,38200,8400,129200,27000,8500,25000,13700,10400,"44600" +2022,"Liverpool City Region","LEP",39500,22600,49300,39100,39500,52600,13500,28400,11700,10900,52700,82600,13900,2700,25000,14500,14200,46100,13200,169300,30800,16100,17100,30800,14300,"58900" +2022,"North East","LEP",52400,26700,55400,43400,46600,52300,22000,27000,8100,12600,53300,85300,13500,9200,34800,28900,16100,56700,13600,239300,53500,20400,32400,30700,29600,"70100" +2022,"Stoke-on-Trent and Staffordshire","LEP",40600,21200,31200,26000,32000,32200,10000,12800,8600,9500,26800,50000,7200,5100,21900,16600,13900,32400,5800,159400,33800,10300,24000,20800,13100,"56700" +2022,"Swindon and Wiltshire","LEP",31000,13100,29000,12200,14300,24900,6500,10200,9800,7100,23200,31600,7100,4800,11400,12600,7100,24700,10700,72400,16800,2000,9300,10800,7200,"26000" +2022,"Tees Valley","LEP",12000,7100,12400,12100,13300,14200,8400,8900,3900,4100,15500,23900,3300,2900,13300,8500,4300,24000,7900,87000,15300,9300,11300,12100,7000,"31700" +2022,"Thames Valley Berkshire","LEP",44700,13200,60400,14200,23300,43200,12700,10300,5700,9400,38300,38200,6700,4900,12000,13400,8200,31000,6000,73500,14400,4300,7400,14500,5300,"27200" +2022,"The Marches","LEP",24400,13100,26100,15700,19200,17300,6900,6200,2200,5800,21700,23900,6800,10100,10500,8800,5900,19300,5100,77600,14900,3600,10800,11600,12200,"24300" +2022,"West of England","LEP",38700,23000,63800,29300,33300,68700,14000,22800,4000,12700,39400,50000,10700,6200,14500,17000,12400,32200,9700,120200,28100,9400,11500,18600,6100,"46200" +2022,"Worcestershire","LEP",26300,6300,18100,15700,15500,19000,3800,8800,4000,5700,17000,23600,4300,3300,7100,8000,4100,19600,1900,65200,10200,6500,8600,11300,6200,"21900" +2022,"Buckinghamshire","LEP",39200,9400,24800,11000,13000,23500,6600,3700,2200,7500,24200,21400,4000,1800,9400,7800,4700,14600,3100,45700,12100,4700,4800,6300,4500,"13200" +2022,"Coast to Capital","LEP",82000,24600,67600,34000,46600,83800,13300,18800,11500,16800,81800,57700,9100,9100,16500,13600,14300,45300,20700,139300,35600,10600,7800,19700,7400,"55100" +2022,"Derby, Derbyshire, Nottingham and Nottinghamshire","LEP",76500,31000,79300,55800,57600,64000,23000,29900,9800,12700,60100,93400,14500,8800,40700,30800,18300,74600,20800,257200,46500,19100,29300,42800,21800,"96600" +2022,"Enterprise M3","LEP",88200,29700,96700,27600,32000,73800,14400,19600,8400,7200,67900,78900,14300,8500,21500,10200,7300,35900,15900,113900,32800,7800,7600,15700,6500,"43400" +2022,"Hertfordshire","LEP",58900,21300,61400,33100,25800,65100,17300,13300,3400,15900,47100,64300,11300,4800,17400,13400,8800,23900,10900,94000,21900,4800,3800,21800,3600,"37400" +2022,"London","LEP",448800,150800,493800,221700,247700,677300,78100,106200,23700,199800,401600,366700,83500,10800,66900,110000,77200,221100,83400,736300,177700,67800,35800,121800,50300,"281200" +2022,"New Anglia","LEP",47000,35100,41800,30200,32200,47400,18200,24000,14100,11500,48100,52000,15300,10100,30900,23200,17600,58700,12700,209000,48800,17500,17600,35000,21800,"67700" +2022,"Oxfordshire","LEP",27700,12000,40500,21900,30300,31100,8700,6000,2500,8900,25400,26000,6700,3000,9300,7700,8600,14500,3700,55700,10300,1600,6500,6900,5800,"24500" +2022,"Sheffield City Region","LEP",33500,25300,45300,30900,33100,42800,9800,17000,8000,9000,45100,49200,12300,2400,29500,17900,10600,48400,7700,186500,38200,15900,24200,28000,11000,"68800" +2022,"Solent","LEP",36800,26300,47300,26800,32500,39900,14800,14600,9500,14600,36100,54600,8000,3600,19300,18000,12400,38400,9200,140200,32400,10900,13800,21100,10700,"50500" +2022,"South East","LEP",159400,72000,147400,71300,115600,143900,34700,61500,32500,32800,144300,194100,39800,21200,66700,71800,40000,135600,43000,430000,109600,28200,31800,63800,33200,"163300" +2022,"South East Midlands","LEP",56100,29100,70200,27000,37400,56000,23500,19500,16300,7500,64900,77300,9900,6600,29500,22100,12300,40400,14800,223000,43100,15700,26100,39800,20200,"77600" +2022,"York and North Yorkshire","LEP",30500,17000,33700,22200,24300,29100,8000,11200,6700,10000,26000,31200,7000,9800,12800,11800,9700,21500,9200,76700,19300,4700,6000,12400,8800,"25500" +2022,"Greater Cambridge and Greater Peterborough","LEP",32500,12500,52900,14800,22300,24200,9200,9400,4500,13500,24800,41900,8600,5500,15400,15800,7800,23700,7800,85300,20500,6800,9600,12100,9700,"26600" +2022,"Greater Lincolnshire","LEP",32300,14900,26900,19900,23800,20700,12100,19800,6400,10400,25700,39300,7900,7500,18900,15500,17700,35600,7500,152700,30000,6500,20100,25400,19100,"51100" +2022,"Hull and East Yorkshire","LEP",19300,7800,15300,12500,12800,13200,6300,11000,3800,2600,15500,22800,5200,3600,11100,10100,5100,19800,4800,77200,13200,5400,10500,10000,10100,"28000" +2022,"Leeds City Region","LEP",59900,35000,85700,51200,58000,78700,18800,36700,12300,22200,75200,93300,15300,9300,38100,35000,25100,57500,21200,273900,51600,23900,35300,45100,23800,"93900" +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,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA +2022,"Brighton and Hove, East Sussex, West Sussex","LSIP",74700,25300,65700,31800,48800,67100,11600,18100,5000,16200,82300,61000,6100,6900,15700,17800,9300,55700,17800,159300,41600,7800,2000,19500,6700,"60800" +2022,"Buckinghamshire ","LSIP",39200,9400,24800,11000,13000,23500,6600,3700,2200,7500,24200,21400,4000,1800,9400,7800,4700,14600,3100,45700,12100,4700,4800,6300,4500,"13200" +2022,"Cambridgeshire and Peterborough","LSIP",32400,11600,52900,13800,22400,24400,7000,7300,2600,11200,24700,41900,8200,4500,15400,14700,3600,22500,2700,85300,18400,3900,7500,11400,8500,"24900" +2022,"Cheshire and Warrington","LSIP",38300,12600,41200,16100,21500,35100,10200,12700,2200,6000,31900,40300,5200,3900,12700,7200,6800,31100,9700,87400,19300,5700,14000,11800,6400,"29700" +2022,"Cornwall and the Isles of Scilly","LSIP",10600,10800,13200,11100,12200,15600,3700,6400,2100,5900,11300,25000,7500,8900,12700,10700,4800,16000,8700,68100,21500,1300,5200,12200,5400,"22500" +2022,"Cumbria","LSIP",11700,11300,16200,11200,10100,10300,6600,5400,2800,1300,9900,20100,2500,4700,9100,4900,2000,14200,1800,67300,23200,1700,8200,7500,5500,"17500" +2022,"Derbyshire and Nottinghamshire","LSIP",76500,27600,79000,55000,56800,62300,20200,27500,7200,6500,57500,93600,8600,3600,36100,24900,13400,74000,14600,257100,45800,12800,26100,40400,12700,"94800" +2022,"Dorset","LSIP",70800,22700,70000,38000,25700,53500,12600,15900,7100,8800,56900,71200,6000,4800,28900,16300,9000,45300,21900,125500,33300,3200,6000,16300,8000,"44400" +2022,"Enterprise M3 LEP (including all of Surrey)","LSIP",74800,15200,67700,17500,34500,74500,6500,9200,0,5300,51000,53600,8900,7300,10900,0,2400,22900,1100,88400,24900,1600,1600,4600,0,"30400" +2022,"Essex, Southend-on-Sea and Thurrock","LSIP",63300,30500,72600,30200,42800,67400,12500,20400,11100,7200,58500,93100,15000,2900,31500,33300,13300,57700,16200,193600,44900,6800,7900,26500,11100,"77600" +2022,"Gloucestershire","LSIP",22900,11200,31800,13700,14200,21000,2100,6000,2400,9200,21800,24200,5600,5900,12800,10700,0,23100,4100,63200,18700,1100,7600,4700,4500,"21600" +2022,"Greater Lincolnshire","LSIP",30700,11200,26200,19000,20200,18200,8400,16500,2400,8300,24900,38400,6500,1000,15700,13500,14900,35600,4000,152700,28700,3100,14700,23600,15100,"51000" +2022,"Greater London","LSIP",447200,150900,491300,221700,244600,675900,59700,103900,13300,197700,400400,362300,75400,1300,63400,93200,61200,218800,74600,736300,176100,53300,20700,117200,35400,"280800" +2022,"Greater Manchester","LSIP",73200,40000,106200,71000,72900,97500,31200,49600,3800,23600,97700,118800,21500,3500,29800,33700,22600,74400,24200,315700,75600,18700,23200,49900,24100,"120000" +2022,"Heart of the South-West","LSIP",53200,28700,50100,43500,36500,46600,11900,20300,5100,7100,46500,58300,15400,13300,29500,30900,18300,57500,21000,205300,56100,6600,18500,33000,12900,"66200" +2022,"Hertfordshire","LSIP",58900,17600,61500,33100,25300,65000,11800,10200,1500,12500,44900,64200,5300,1400,11700,10800,4400,21200,6100,93900,19400,0,0,18500,0,"37400" +2022,"Hull and East Yorkshire","LSIP",19400,7800,15200,12500,12800,13300,6300,10900,3700,2700,15600,22800,5200,3600,11000,10100,5100,19700,4800,77200,13200,5400,10400,10000,10000,"28100" +2022,"Kent and Medway","LSIP",71800,30700,57500,28300,50000,58800,4000,19500,7800,10300,62700,80600,13000,1700,21200,22200,13100,53700,11600,189100,40900,10200,12400,23600,7000,"65200" +2022,"Lancashire","LSIP",32500,22800,37100,28800,28400,40000,9700,23100,1900,2700,36000,63200,5700,2900,24100,16400,5700,45500,6700,174500,37100,10800,15600,19600,20300,"53600" +2022,"Leicester and Leicestershire","LSIP",34300,11600,41700,12700,34300,28500,10100,12700,0,5100,35400,40200,4000,0,21000,14900,3700,38200,6200,129200,26000,7500,22900,11700,8400,"44700" +2022,"Liverpool City Region","LSIP",39500,22700,49400,39100,39600,52600,12200,28300,8100,10400,52700,82600,14000,2300,25100,14500,14300,46200,13100,169300,30800,15900,16300,30800,14300,"58900" +2022,"Norfolk and Suffolk ","LSIP",46300,34200,41600,29100,31700,46300,15500,22600,9600,7500,46700,51400,11500,6500,27000,16600,14000,58700,7900,209000,48800,12400,13900,33200,17000,"66100" +2022,"North East","LSIP",28500,16900,29200,20300,22000,27400,11200,15500,4100,6800,32400,52200,8700,3400,24900,18500,8500,34700,8900,149500,33600,14300,25500,16500,18600,"39300" +2022,"North of Tyne","LSIP",23900,9900,26300,23200,24700,24800,10800,11500,3700,5800,21100,33200,4800,4800,10000,10400,7100,22000,4700,89800,19800,6200,6800,14200,11200,"30800" +2022,"Oxfordshire","LSIP",27700,10900,40400,21900,30400,31100,8700,3300,0,6900,25500,26100,5300,3000,7500,5800,6400,14500,1700,55700,7900,0,5700,3500,4400,"24500" +2022,"Solent","LSIP",36800,26200,47200,26300,32500,38300,14100,13300,8100,14000,36100,54600,6600,3000,19000,16300,10300,38300,8900,140300,32300,10100,13400,21100,8600,"50600" +2022,"South East Midlands","LSIP",56000,29100,70400,26900,37200,56000,23400,19400,16500,6900,65000,77200,9700,5500,29600,22100,12000,40500,14800,222900,43100,14100,26200,38500,19100,"77600" +2022,"South Yorkshire","LSIP",33500,25300,45300,30900,33100,42800,9900,17000,7900,8700,45100,49200,12300,1800,29500,17800,10500,48600,7700,186500,38200,15900,24200,28000,11000,"68800" +2022,"Stoke-on-Trent and Staffordshire","LSIP",39600,20300,31300,25800,32000,32300,7500,9700,3800,5100,26700,50000,3200,2900,21800,13900,10600,31100,2400,159500,33800,7300,20200,20300,7500,"56700" +2022,"Swindon and Wiltshire","LSIP",31000,13100,28900,12300,14400,24900,6500,10300,9300,7100,23200,31500,7100,4800,11400,12600,7100,24600,10700,72400,16800,2000,9300,10900,7200,"26000" +2022,"Tees Valley","LSIP",12000,7100,12400,12100,13200,14300,8400,8800,3900,4000,15500,23900,2900,2600,13200,8500,3700,24000,7500,87000,15300,9300,11300,12000,7000,"31600" +2022,"Thames Valley Berkshire","LSIP",44700,13300,60300,14100,23300,43200,12800,10400,4400,8900,38300,38200,6400,4100,11900,13100,8200,31000,5700,73400,14400,3700,7200,14500,4900,"27200" +2022,"The Marches","LSIP",24300,13100,26100,15800,19200,17300,6800,6100,1300,5800,21800,23900,6800,10100,10500,8800,5900,19300,5000,77500,15000,3600,10800,11700,12200,"24300" +2022,"West Midlands and Warwickshire","LSIP",78800,42700,118500,81900,92100,104800,34500,56500,8600,29400,119900,155700,21200,8900,57500,35700,15500,86200,33800,403100,66500,27300,54700,71000,33200,"146100" +2022,"West of England and North Somerset","LSIP",38700,23100,63700,29200,33200,68700,14000,22800,3700,12700,39400,50000,10700,6200,14500,17000,12500,32200,9300,120100,28000,9400,11400,18300,6100,"46200" +2022,"West Yorkshire","LSIP",59800,34900,85700,51100,57900,78600,18700,36700,12400,22100,75300,93300,15200,9000,38100,34900,25100,57600,21200,273900,51500,24000,35300,45100,23700,"93900" +2022,"Worcestershire","LSIP",26400,5700,17600,15800,15100,19000,1400,7400,1800,4500,16400,23700,3100,1400,5600,5700,1100,19000,0,65100,8300,5200,8400,11000,3700,"21900" +2022,"York and North Yorkshire","LSIP",30500,15300,33700,21400,23600,28600,6200,9300,4900,6800,26100,31300,4200,8400,10400,10500,6800,20500,7900,76600,18300,3100,2900,12400,6500,"24500" diff --git a/R/panels.R b/R/panels.R index d3524044..e4690be9 100644 --- a/R/panels.R +++ b/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", diff --git a/TransformData.R b/TransformData.R index 7e8a3230..f7796eec 100644 --- a/TransformData.R +++ b/TransformData.R @@ -51,7 +51,7 @@ 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)) %>% - mutate(year=2022) %>% + 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 @@ -98,7 +98,7 @@ 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 )