fix the lsip map by aligning projections

also chaneg map comment text to say 36 not 38 leps

Data/AppData/I_DataTable.csv

@@ -1,7 +1,7 @@
 "Data​","Source​","Latest period (release date)​","Next period (release date)​"
-"Employment volumes","<a href='https://www.nomisweb.co.uk/datasets/apsnew'>Annual Population Survey</a>","Oct 2022 - Sep 2023 (16/01/24)","Jan 2023 - Dec 2023 (16/04/24)"
-"Employment by occupation","<a href='https://www.nomisweb.co.uk/datasets/apsnew'>Annual Population Survey</a>","Oct 2022 - Sep 2023 (16/01/24)","Jan 2023 - Dec 2023 (16/04/24)"
-"Employment by industry","<a href='https://www.nomisweb.co.uk/datasets/apsnew'>Annual Population Survey</a>","Oct 2022 - Sep 2023 (16/01/24)","Jan 2023 - Dec 2023 (16/04/24)"
+"Employment volumes","<a href='https://www.nomisweb.co.uk/datasets/apsnew'>Annual Population Survey</a>","Jul 2022 - Jun 2023 (24/10/23)","Oct 2022 - Sep 2023 (16/01/24)"
+"Employment by occupation","<a href='https://www.nomisweb.co.uk/datasets/apsnew'>Annual Population Survey</a>","Jul 2022 - Jun 2023 (24/10/23)","Oct 2022 - Sep 2023 (16/01/24)"
+"Employment by industry","<a href='https://www.nomisweb.co.uk/datasets/apsnew'>Annual Population Survey</a>","Jul 2022 - Jun 2023 (24/10/23)","Oct 2022 - Sep 2023 (16/01/24)"
 "Further education and skills achievements and participation by provision, level and age group","<a href='https://explore-education-statistics.service.gov.uk/data-catalogue/further-education-and-skills/2022-23'>Individualised Learner Record</a>","Aug 2022 – Jul 2023 (30/11/23)","Aug 2023 – Jul 2024 (Nov 24)"
 "Further education and skills achievements by sector subject area","<a href='https://explore-education-statistics.service.gov.uk/data-tables/permalink/93f9aa79-9a67-48d5-e9e2-08dc0dc60f26'>Individualised Learner Record</a>","Aug 2022 – Jul 2023 (30/11/23)","Aug 2023 – Jul 2024 (Nov 24)"
 "Highest qualification level by age and gender","<a href='https://www.nomisweb.co.uk/datasets/apsnew'>Annual Population Survey</a>","Jan 2021 - Dec 2021 (20/04/22)","TBC depending on ONS recoding the qualification framework."

Data/AppData/I_DataText.csv

@@ -1,51 +1,43 @@
 "metric","LatestPeriod","subheading","sourceText","dataText","caveatText","mapComment","timeTitle","timeComment","mapPop","breakdownTitle","breakdownComment","LaComment"
-"inemploymentRate","	
-Oct 2022-Sep 2023"," ","<a href='https://www.nomisweb.co.uk/datasets/apsnew'>Annual Population Survey</a>"," ","<ol>
+"inemploymentRate","Jul 2022 - Jun 2023 data"," ","<a href='https://www.nomisweb.co.uk/datasets/apsnew'>Annual Population Survey</a>"," ","<ol>
   <li>Figures are for 16-64 year olds.</li>
 <li>Each estimate from the Annual Population Survey carries a margin of error. These are available in the original data via NOMIS. Large margins of error are usually associated with groups with only a small number of respondents. Therefore, please take caution when interpreting data from small subgroups.</li>
 <li>Use caution when interpreting this data. A difference between subgroups does not necessarily imply any causality. There could be other contributing factors at work.</li>
 </ol>","The employment rate","are employment rates changing","The employment rate in ","Employment rate","employment rates","N/A","Employment rates are "
-"selfemployedRate","	
-Oct 2022-Sep 2023"," ","<a href='https://www.nomisweb.co.uk/datasets/apsnew'>Annual Population Survey</a>"," ","<ol>
+"selfemployedRate","Jul 2022 - Jun 2023 data"," ","<a href='https://www.nomisweb.co.uk/datasets/apsnew'>Annual Population Survey</a>"," ","<ol>
   <li>Figures are for 16-64 year olds.</li>
 <li>Each estimate from the Annual Population Survey carries a margin of error. These are available in the original data via NOMIS. Large margins of error are usually associated with groups with only a small number of respondents. Therefore, please take caution when interpreting data from small subgroups.</li>
 <li>Use caution when interpreting this data. A difference between subgroups does not necessarily imply any causality. There could be other contributing factors at work.</li>
 </ol>","The self-employment rate","are self-employment rates changing","The self-employment rate in ","Self-employment rate","self-employment rates","N/A","Self-employment rates are "
-"unemployedRate","	
-Oct 2022-Sep 2023"," ","<a href='https://www.nomisweb.co.uk/datasets/apsnew'>Annual Population Survey</a>"," ","<ol>
+"unemployedRate","Jul 2022 - Jun 2023 data"," ","<a href='https://www.nomisweb.co.uk/datasets/apsnew'>Annual Population Survey</a>"," ","<ol>
   <li>Figures are for 16-64 year olds.</li>
 <li>Each estimate from the Annual Population Survey carries a margin of error. These are available in the original data via NOMIS. Large margins of error are usually associated with groups with only a small number of respondents. Therefore, please take caution when interpreting data from small subgroups.</li>
 <li>Use caution when interpreting this data. A difference between subgroups does not necessarily imply any causality. There could be other contributing factors at work.</li>
 </ol>","The unemployment rate","are unemployment rates changing","The employment rate in ","Unemployment rate","unemployment rates","N/A","Unemployment rates are "
-"inactiveRate","	
-Oct 2022-Sep 2023"," ","<a href='https://www.nomisweb.co.uk/datasets/apsnew'>Annual Population Survey</a>"," ","<ol>
+"inactiveRate","Jul 2022 - Jun 2023 data"," ","<a href='https://www.nomisweb.co.uk/datasets/apsnew'>Annual Population Survey</a>"," ","<ol>
   <li>Figures are for 16-64 year olds.</li>
 <li>Each estimate from the Annual Population Survey carries a margin of error. These are available in the original data via NOMIS. Large margins of error are usually associated with groups with only a small number of respondents. Therefore, please take caution when interpreting data from small subgroups.</li>
 <li>Use caution when interpreting this data. A difference between subgroups does not necessarily imply any causality. There could be other contributing factors at work.</li>
 </ol>","The inactivity rate","are inactivity rates changing","The inactivity rate in ","Inactivity rate","inactivity rates","N/A","Inactivity rates are "
-"inemployment","	
-Oct 2022-Sep 2023"," ","<a href='https://www.nomisweb.co.uk/datasets/apsnew'>Annual Population Survey</a>"," ","<ol>
+"inemployment","Jul 2022 - Jun 2023 data"," ","<a href='https://www.nomisweb.co.uk/datasets/apsnew'>Annual Population Survey</a>"," ","<ol>
   <li>Employment volumes are for 16-64 year olds.</li>
 <li>Industry split volumes are for all ages. </li>
 <li>Each estimate from the Annual Population Survey carries a margin of error. These are available in the original data via NOMIS. Large margins of error are usually associated with groups with only a small number of respondents. Therefore, please take caution when interpreting data from small subgroups.</li>
 <li>Use caution when interpreting this data. A difference between subgroups does not necessarily imply any causality. There could be other contributing factors at work.</li>
 <li>Standard Occupational Classification 2020 (SOC2020).</li>
 <li>Industry groups are Standard Industrial Classification: SIC 2007.</li>
 </ol>","Employment","are employment volumes changing","Employment in","Employment","employment volumes","employment volume share","Employment is"
-"selfemployed","	
-Oct 2022-Sep 2023"," ","<a href='https://www.nomisweb.co.uk/datasets/apsnew'>Annual Population Survey</a>"," ","<ol>
+"selfemployed","Jul 2022 - Jun 2023 data"," ","<a href='https://www.nomisweb.co.uk/datasets/apsnew'>Annual Population Survey</a>"," ","<ol>
   <li>Figures are for 16-64 year olds.</li>
 <li>Each estimate from the Annual Population Survey carries a margin of error. These are available in the original data via NOMIS. Large margins of error are usually associated with groups with only a small number of respondents. Therefore, please take caution when interpreting data from small subgroups.</li>
 <li>Use caution when interpreting this data. A difference between subgroups does not necessarily imply any causality. There could be other contributing factors at work.</li>
 </ol>","Self-employment","are self-employment volumes changing","Self-employment in","Self-employment","self-employment volumes","self-employment volume","Self-employment is"
-"unemployed","	
-Oct 2022-Sep 2023"," ","<a href='https://www.nomisweb.co.uk/datasets/apsnew'>Annual Population Survey</a>"," ","<ol>
+"unemployed","Jul 2022 - Jun 2023 data"," ","<a href='https://www.nomisweb.co.uk/datasets/apsnew'>Annual Population Survey</a>"," ","<ol>
   <li>Figures are for 16-64 year olds.</li>
 <li>Each estimate from the Annual Population Survey carries a margin of error. These are available in the original data via NOMIS. Large margins of error are usually associated with groups with only a small number of respondents. Therefore, please take caution when interpreting data from small subgroups.</li>
 <li>Use caution when interpreting this data. A difference between subgroups does not necessarily imply any causality. There could be other contributing factors at work.</li>
 </ol>","Unemployment","are unemployment volumes changing","Unemployment in","Unemployment","unemployment volumes","unemployment volume","Unemployment is"
-"inactive","	
-Oct 2022-Sep 2023"," ","<a href='https://www.nomisweb.co.uk/datasets/apsnew'>Annual Population Survey</a>"," ","<ol>
+"inactive","Jul 2022 - Jun 2023 data"," ","<a href='https://www.nomisweb.co.uk/datasets/apsnew'>Annual Population Survey</a>"," ","<ol>
   <li>Figures are for 16-64 year olds.</li>
 <li>Each estimate from the Annual Population Survey carries a margin of error. These are available in the original data via NOMIS. Large margins of error are usually associated with groups with only a small number of respondents. Therefore, please take caution when interpreting data from small subgroups.</li>
 <li>Use caution when interpreting this data. A difference between subgroups does not necessarily imply any causality. There could be other contributing factors at work.</li>

TransformData.R

@@ -41,16 +41,19 @@ neatLA <- I_mapLA %>%
   left_join(C_mcalookup %>% mutate(MCA = paste0(CAUTH23NM, " MCA")) %>% select(LAD23CD, MCA), by = c("LAD23CD" = "LAD23CD")) %>%
   filter(is.na(LSIP) == FALSE) %>% # remove non England
   mutate(MCA = case_when(LEP == "The London Economic Action Partnership LEP" ~ "Greater London Authority MCA", TRUE ~ MCA)) %>% # add on gla as mca
-  rename(areaName = LAD23NM, areaCode = LAD23CD)
+  rename(areaName = LAD23NM, areaCode = LAD23CD) %>%
+  st_transform(4326) # transform to WG84 that leaflet can plot
 
 neatMCA <- I_mapMCA %>%
   mutate(geog = "MCA") %>% # add geog type
-  rename(areaCode = CAUTH22CD, areaName = CAUTH22NM) # consistent naming
+  rename(areaCode = CAUTH22CD, areaName = CAUTH22NM) %>% # consistent naming
+  st_transform(4326) # transform to WG84 that leaflet can plot
 
 neatLEP <- I_mapLEP %>%
   mutate(geog = "LEP") %>% # add geog type
   rename(areaCode = LEP22CD, areaName = LEP22NM) %>% # consistent naming
-  inner_join(distinct(F_LEP2020, LEP23CD1), by = c("areaCode" = "LEP23CD1")) # remove any areas that are no longer LEPs in 2023 (Black Country and Coventry)
+  inner_join(distinct(F_LEP2020, LEP23CD1), by = c("areaCode" = "LEP23CD1")) %>% # remove any areas that are no longer LEPs in 2023 (Black Country and Coventry)
+  st_transform(4326) # transform to WG84 that leaflet can plot
 
 addEngland <- data.frame(
   areaName = "England", areaCode = "x",
@@ -77,6 +80,7 @@ LSIPmap <- bind_cols(LSIPgeojson, F_LEP2020 %>%
 neatLSIP <- LSIPmap %>%
   rename(areaName = Area, geog = geographic_level) %>%
   mutate(areaCode = paste0("LSIP", row_number())) %>%
+  st_transform(4326) %>%
   mutate(
     LONG = map_dbl(geometry, ~ st_centroid(.x)[[1]]),
     LAT = map_dbl(geometry, ~ st_centroid(.x)[[2]])
@@ -1038,8 +1042,7 @@ C_Geog <- neatGeog %>%
       pivot_wider(names_from = metric, values_from = value)),
     by = c("geogConcat" = "geogConcat")
   ) %>%
-  rename(employmentProjection = employmentProjectionGrowth2023to2035) %>% # for the emp projections page we use two metrics on different charts. we give them the same name so the filters work
-  st_transform(4326) # transform to WG84 that leaflet can plot
+  rename(employmentProjection = employmentProjectionGrowth2023to2035) # for the emp projections page we use two metrics on different charts. we give them the same name so the filters work
 save(C_Geog, file = "Data\\AppData\\C_Geog.rdata")
 
 ## 4.2 C_time ----

server.R

@@ -927,7 +927,7 @@ server <- function(input, output, session) {
   groupCount <- reactive({
     validate(need(input$splashGeoType != "", ""))
     if (input$splashGeoType == "LEP") {
-      "38 LEPs."
+      "36 LEPs."
     } else {
       if (input$splashGeoType == "MCA") {
         "11 MCAs."