Merge branch 'yauemily:main' into main

R/outcome2_page.R

@@ -13,22 +13,86 @@ outcome2_tab <- function(){
  div(
  class = "input_box",
  style = "min-height:100%; height = 100%; overflow-y: visible",
- gov_row(
- column(
- width = 6,
- h2("Inputs go here")
+ layout_columns(
+ selectizeInput(
+ inputId = "select_geography_o2",
+ label = "Select a geographical level:",
+ choices = unique(workforce_data %>% pull('geographic_level')),
+ selected = NULL,
+ multiple = FALSE,
+ options = NULL
  ),
- column(
- width = 6,
- h2("Inputs go here")
- )
+ conditionalPanel(condition = "input.select_geography_o2 != 'National'",
+ selectizeInput(
+ inputId = "geographic_breakdown_o2",
+ label = "Select a breakdown: ",
+ choices = NULL,
+ selected = NULL,
+ multiple = FALSE,
+ options = NULL
+ )),
+ col_widths = c(4,8)
+ ),
+ layout_columns(
+ conditionalPanel(condition = "input.select_geography_o2 != 'National'",
+ column(
+ width = 3,
+ checkbox_Input(
+ inputId = "national_comparison_checkbox_o2",
+ cb_labels = "Compare with National",
+ checkboxIds = "Yes_national",
+ label = "",
+ hint_label = NULL,
+ small = TRUE
+ )
+ )),
+ conditionalPanel(
+ condition = "(input.select_geography_o2 == 'Local authority')",
+ column(
+ width = 3,
+ checkbox_Input(
+ inputId = "region_comparison_checkbox_o2",
+ cb_labels = "Compare with Region",
+ checkboxIds = "Yes_region",
+ label = "",
+ hint_label = NULL,
+ small = TRUE
+ )
+ )), col_widths = c(4,8)
  )
  )
  ),
+ # gov_row(
+ # div(
+ # class = "input_box",
+ # style = "min-height:100%; height = 100%; overflow-y: visible",
+ # gov_row(
+ # column(
+ # width = 6,
+ # h2("Inputs go here")
+ # ),
+ # column(
+ # width = 6,
+ # h2("Inputs go here")
+ # )
+ # )
+ # )
+ # ),
  br(),
  gov_row(
  br(),
- h2("Confirmation Sentence"),
+ p(htmlOutput("outcome2_choice_text1"),htmlOutput("outcome2_choice_text2")),
+ conditionalPanel(
+ condition = "(input.geographic_breakdown_o2 == 'Northamptonshire')",
+ p("To view 2021 and onwards data select ", strong("North Northamptonshire"),"or", strong("West Northamptonshire"),". Northamptonshire local authority was replaced with two new unitary authorities, North Northamptonshire and West Northamptonshire, in April 2021.") ),
+ conditionalPanel(
+ condition = "(input.geographic_breakdown_o2 == 'Poole')",
+ p("To view 2020 and onwards data select ", strong("Bournemouth, Christchurch and Poole"),". Bournemouth, Christchurch and Poole local authority was formed in April 2019.") ),
+ conditionalPanel(
+ condition = "(input.geographic_breakdown_o2 == 'Bournemouth')",
+ p("To view 2020 and onwards data select ", strong("Bournemouth, Christchurch and Poole"),". Bournemouth, Christchurch and Poole local authority was formed in April 2019.") ),
+ ),
+ gov_row(
  br(),
  div(
  tabsetPanel(
@@ -37,7 +101,138 @@ outcome2_tab <- function(){
  tabPanel(
  "Families engaging and receiving support from their family network",
  fluidRow(
- p("testing")
+ br()
+ ),
+ fluidRow(
+ column(
+ width = 6,
+ value_box(
+ title = "Percentage of children who cease being looked after due to moving into Special Guardianship Order (SGO)",
+ value = htmlOutput("SGO_headline_txt")
+ )
+ ),
+ column(
+ width = 6,
+ value_box(
+ title = "Percentage of children who cease being looked after due to moving into Residence order or Child Arrangement Order (CAO)",
+ value = htmlOutput("CAO_headline_txt")
+ )
+ ),
+ br(),
+ p("Unlocking family networks and kinship carers can be a key source of support where families are experiencing challenges. 
+ Moving children from care arrangements to a SGO or CAO shows that kinship care is being prioritised where children cannot safely live with their parents.")
+ ),
+
+ accordion(
+ accordion_panel(
+ "Percentage of children who cease being looked after due to moving into Special Guardianship Order (SGO)",
+ gov_row(
+ h2("Special Guardianship Order (SGO)"),
+ #p("Unlocking family networks and kinship carers can be a key source of support where families are experiencing challenges. 
+ #Moving children from care arrangements to a SGO or CAO shows that kinship care is being prioritised where children cannot safely live with their parents."),
+ br(),
+ plotlyOutput("SGO_time_series"),
+ br(),
+ details(
+ inputId = "tbl_sgo_ceased_cla",
+ label = "View chart as table",
+ help_text = (
+ dataTableOutput("table_sgo_ceased")
+ )
+ ),
+ details(
+ inputId = "sgo_info",
+ label = "Additional information:",
+ help_text = (
+ p("For more information on the data and definitions, please refer to the", a(href = "https://explore-education-statistics.service.gov.uk/find-statistics/children-looked-after-in-england-including-adoptions/data-guidance", "Children looked after in England including adoptions guidance."),
+ tags$br(),
+ "For more information on the methodology, please refer to the", a(href = "https://explore-education-statistics.service.gov.uk/methodology/children-looked-after-in-england-including-adoptions", "Children looked after in England including adoptions methodology.")))
+ )
+ ),
+ gov_row(
+ h2("Special Guardianship Order (SGO) by region"),
+ p("This is a static chart and will not react to geographical level and breakdown selected in the filters at the top.
+ 
+ The graph represents data from 2023."),
+ br(),
+ plotlyOutput("plot_sgo_ceased_reg"),
+ br(),
+ details(
+ inputId = "tbl_sgo_ceased_cla_reg",
+ label = "View chart as table",
+ help_text = (
+ dataTableOutput("table_sgo_ceased_reg")
+ )
+ )
+ ),
+ gov_row(
+ h2("Special Guardianship Order (SGO) by local authority"),
+ p("This chart is reactive to the Local Authority and Regional filters at the top and will not react to the National filter. The chart will display all Local Authorities overall or every Local Authority in the selected Region.
+
+The graph represents data from 2023."),
+ br(),
+ plotlyOutput("plot_SGO_la"),
+ br(),
+ details(
+ inputId = "tbl_sgo_ceased_la",
+ label = "View chart as table",
+ help_text = (
+ dataTableOutput("table_sgo_la")
+ )
+ )
+ )
+ ),
+ accordion_panel(
+ "Percentage of children who cease being looked after due to moving into Care Arrangement Order (CAO)",
+ gov_row(
+ h2("Residence order or Child Arrangement Order (CAO)"),
+ p("Unlocking family networks and kinship carers can be a key source of support where families are experiencing challenges. 
+ Moving children from care arrangements to a SGO or CAO shows that kinship care is being prioritised where children cannot safely live with their parents."),
+ br(),
+ plotlyOutput("CAO_time_series"),
+ br(),
+ details(
+ inputId = "tbl_cao_ceased_cla",
+ label = "View chart as table",
+ help_text = (
+ dataTableOutput("table_cao_ceased")
+ )
+ ),
+ ),
+ gov_row(
+ h2("Residence order or Child Arrangement Order (CAO) by region"),
+ p("This is a static chart and will not react to geographical level and breakdown selected in the filters at the top.
+ 
+ The graph represents data from 2023."),
+ br(),
+ plotlyOutput("plot_cao_ceased_reg"),
+ br(),
+ details(
+ inputId = "table_cao_ceased_reg",
+ label = "View chart as table",
+ help_text = (
+ dataTableOutput("table_cao_ceased_reg")
+ )
+ ),
+ ),
+ gov_row(
+ h2("Residence order or Child Arrangement Order (CAO) by local authority"),
+ p("This chart is reactive to the Local Authority and Regional filters at the top and will not react to the National filter. The chart will display all Local Authorities overall or every Local Authority in the selected Region.
+
+The graph represents data from 2023."),
+ br(),
+ plotlyOutput("plot_cao_la"),
+ br(),
+ details(
+ inputId = "table_cao_la",
+ label = "View chart as table",
+ help_text = (
+ dataTableOutput("table_cao_la")
+ )
+ ),
+ )
+ )
+ , open = FALSE
  )
  )
  )

R/read_data.R

@@ -538,3 +538,106 @@ read_cin_referral_data <- function(file = "data/c1_children_in_need_referrals_an
 
  return(cin_referral_data)
 }
+# Outcome 2 ----
+# read_outcome2 <- function(file = "data/la_children_who_ceased_during_the_year.csv"){
+# ceased_cla_data <- read.csv(file)
+# ceased_cla_data <- ceased_cla_data %>% mutate(geo_breakdown = case_when(
+# geographic_level == "National" ~ "National",#NA_character_,
+# geographic_level == "Regional" ~ region_name,
+# geographic_level == "Local authority" ~ la_name
+# )) %>%
+# mutate(number = case_when(
+# number == "z" ~ NA,
+# number == "x" ~ NA,
+# number == "c" ~ NA,
+# TRUE ~ as.numeric(number)
+# )) %>%
+# select("time_period", "geographic_level","geo_breakdown", "cla_group","characteristic", "number", "percentage")
+# }
+# read_outcome2 <- function(file = "data/la_children_who_ceased_during_the_year.csv"){
+# ceased_cla_data <- read.csv(file)
+# ceased_cla_data <- ceased_cla_data %>% mutate(geo_breakdown = case_when(
+# geographic_level == "National" ~ "National",#NA_character_,
+# geographic_level == "Regional" ~ region_name,
+# geographic_level == "Local authority" ~ la_name
+# )) %>%
+# mutate(number = case_when(
+# number == "z" ~ NA,
+# number == "x" ~ NA,
+# number == "c" ~ NA,
+# TRUE ~ as.numeric(number)
+# )) %>%
+# select("time_period", "geographic_level","geo_breakdown", "cla_group","characteristic", "number", "percentage")
+# 
+# totals <- ceased_cla_data %>% filter(characteristic == "Total" & cla_group == "Reason episode ceased") %>%
+# rename("Total" = "number") %>%
+# select(time_period, geographic_level, geo_breakdown, cla_group, Total)
+# 
+# 
+# test<- ceased_cla_data %>% filter(cla_group == "Reason episode ceased" & characteristic != "Total")
+# 
+# joined <- left_join(test, totals, by = c("time_period", "geographic_level","geo_breakdown", "cla_group"))
+# joined$perc <- round((joined$number/joined$Total)*100, digits = 1)
+# joined <- joined %>% mutate(perc = case_when(
+# percentage == "z" ~ "z",
+# percentage == "c" ~ "c",
+# percentage == "k" ~ "k",
+# percentage == "x" ~ "x",
+# TRUE ~ as.character(perc))) %>% mutate(`Percentage ceased %` = case_when(
+# percentage == "z" ~ NA,
+# percentage == "c" ~ NA,
+# percentage == "k" ~ NA,
+# percentage == "x" ~ NA,
+# TRUE ~ as.numeric(perc)
+# ))
+# 
+# return(joined)
+# }
+
+read_outcome2 <- function(file = "data/la_children_who_ceased_during_the_year.csv"){
+ ceased_cla_data <- read.csv(file)
+ old_dorset <- ceased_cla_data %>% filter(time_period <= 2019, new_la_code == "E10000009")
+ new_dorset <- ceased_cla_data %>% filter(time_period > 2019, new_la_code == "E06000059")
+ data_without_dorset <- ceased_cla_data %>% filter(la_name != "Dorset")
+
+ dorset_data <- bind_rows(new_dorset, old_dorset)
+ final_filtered_data <- bind_rows(data_without_dorset, dorset_data)
+
+ ceased_cla_data <- final_filtered_data %>% mutate(geo_breakdown = case_when(
+ geographic_level == "National" ~ "National",#NA_character_,
+ geographic_level == "Regional" ~ region_name,
+ geographic_level == "Local authority" ~ la_name
+ )) %>%
+ mutate(number = case_when(
+ number == "z" ~ NA,
+ number == "x" ~ NA,
+ number == "c" ~ NA,
+ TRUE ~ as.numeric(number)
+ )) %>%
+ filter("new_la_code" != "E10000009") %>%
+ select("time_period", "geographic_level","geo_breakdown", "cla_group","characteristic", "number", "percentage")
+
+ totals <- ceased_cla_data %>% filter(characteristic == "Total" & cla_group == "Reason episode ceased") %>%
+ rename("Total" = "number") %>%
+ select(time_period, geographic_level, geo_breakdown, cla_group, Total)
+
+
+ test<- ceased_cla_data %>% filter(cla_group == "Reason episode ceased" & characteristic != "Total")
+
+ joined <- left_join(test, totals, by = c("time_period", "geographic_level","geo_breakdown", "cla_group"))
+ joined$perc <- round((joined$number/joined$Total)*100, digits = 1)
+ joined <- joined %>% mutate(perc = case_when(
+ percentage == "z" ~ "z",
+ percentage == "c" ~ "c",
+ percentage == "k" ~ "k",
+ percentage == "x" ~ "x",
+ TRUE ~ as.character(perc))) %>% mutate(`Ceased (%)` = case_when(
+ percentage == "z" ~ NA,
+ percentage == "c" ~ NA,
+ percentage == "k" ~ NA,
+ percentage == "x" ~ NA,
+ TRUE ~ as.numeric(perc)
+ ))
+
+ return(joined)
+}

global.R

@@ -130,9 +130,13 @@ combined_cla_data <- merge_cla_dataframes()
 cin_rates <- read_cin_rate_data()
 cin_referrals <- read_cin_referral_data()
 
+
+#Read in outcome 2 data
+ceased_cla_data <- read_outcome2()
+
 #Dropdowns
-choice_breakdown_level <- workforce_data %>% select(geographic_level) %>% filter(geographic_level != "National")%>% distinct()
-choices_LA <- workforce_data %>% filter(geographic_level == "Local authority") %>% select()
+#choice_breakdown_level <- workforce_data %>% select(geographic_level) %>% filter(geographic_level != "National")%>% distinct()
+#choices_LA <- workforce_data %>% filter(geographic_level == "Local authority") %>% select()
 
 #choices_geographic_level <- dropdown_choices %>% select(geographic_level) %>% distinct()