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Updated re-referall tables to show supression symbols #12

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merged 1 commit into from
Mar 26, 2024
Merged

Updated re-referall tables to show supression symbols #12

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20 changes: 10 additions & 10 deletions R/plotting.R
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Original file line number Diff line number Diff line change
Expand Up @@ -1424,10 +1424,10 @@ plot_cin_rates_la <- function(selected_geo_breakdown = NULL, selected_geo_lvl =
plot_cin_referral_reg <- function() {
referral_reg_data <- cin_referrals %>%
filter(geographic_level == "Regional", time_period == max(time_period)) %>%
select(time_period, geo_breakdown, Re_referrals_percent) %>%
mutate(geo_breakdown = reorder(geo_breakdown, -Re_referrals_percent)) # Order by turnover rate
select(time_period, geo_breakdown, Re_referrals_percentage) %>%
mutate(geo_breakdown = reorder(geo_breakdown, -Re_referrals_percentage)) # Order by turnover rate

ggplot(referral_reg_data, aes(`geo_breakdown`, `Re_referrals_percent`, fill = factor(time_period))) +
ggplot(referral_reg_data, aes(`geo_breakdown`, `Re_referrals_percentage`, fill = factor(time_period))) +
geom_col(position = position_dodge()) +
ylab("Re-referrals (%)") +
xlab("Region") +
Expand Down Expand Up @@ -1463,17 +1463,17 @@ plot_cin_referral_la <- function(selected_geo_breakdown = NULL, selected_geo_lvl
if (selected_geo_lvl == "Local authority") {
LA_referral_data <- cin_referrals %>%
filter(geographic_level == "Local authority", time_period == max(time_period)) %>%
select(time_period, geo_breakdown, Re_referrals_percent) %>%
select(time_period, geo_breakdown, Re_referrals_percentage) %>%
mutate(
geo_breakdown = reorder(geo_breakdown, -Re_referrals_percent), # Order by vacancy rate
geo_breakdown = reorder(geo_breakdown, -Re_referrals_percentage), # Order by vacancy rate
is_selected = ifelse(geo_breakdown == selected_geo_breakdown, "Selected", "Not Selected")
)
} else if (selected_geo_lvl == "National") {
LA_referral_data <- cin_referrals %>%
filter(geographic_level == "Local authority", time_period == max(time_period)) %>%
select(time_period, geo_breakdown, Re_referrals_percent) %>%
select(time_period, geo_breakdown, Re_referrals_percentage) %>%
mutate(
geo_breakdown = reorder(geo_breakdown, -Re_referrals_percent), # Order by vacancy rate
geo_breakdown = reorder(geo_breakdown, -Re_referrals_percentage), # Order by vacancy rate
is_selected = "Not Selected"
)
} else if (selected_geo_lvl == "Regional") {
Expand All @@ -1492,15 +1492,15 @@ plot_cin_referral_la <- function(selected_geo_breakdown = NULL, selected_geo_lvl

LA_referral_data <- cin_referrals %>%
filter(geo_breakdown %in% location, time_period == max(time_period)) %>%
select(time_period, geo_breakdown, Re_referrals_percent) %>%
select(time_period, geo_breakdown, Re_referrals_percentage) %>%
mutate(
geo_breakdown = reorder(geo_breakdown, -Re_referrals_percent), # Order by vacancy rate
geo_breakdown = reorder(geo_breakdown, -Re_referrals_percentage),
is_selected = "Selected"
)
}


p <- ggplot(LA_referral_data, aes(`geo_breakdown`, `Re_referrals_percent`, fill = `is_selected`)) +
p <- ggplot(LA_referral_data, aes(`geo_breakdown`, `Re_referrals_percentage`, fill = `is_selected`)) +
geom_col(position = position_dodge()) +
ylab("Re-referrals (%)") +
xlab("") +
Expand Down
28 changes: 15 additions & 13 deletions R/read_data.R
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Original file line number Diff line number Diff line change
Expand Up @@ -547,40 +547,42 @@ read_cin_referral_data <- function(file = "data/c1_children_in_need_referrals_an
geographic_level == "Regional" ~ region_name,
geographic_level == "Local authority" ~ la_name
)) %>%
mutate(Referrals = case_when(
mutate(Referrals_num = case_when(
Referrals == "Z" ~ NA,
Referrals == "x" ~ NA,
Referrals == "c" ~ NA,
TRUE ~ as.numeric(Referrals)
)) %>%
mutate(Re_referrals = case_when(
mutate(Re_referrals_num = case_when(
Re_referrals == "Z" ~ NA,
Re_referrals == "x" ~ NA,
Re_referrals == "c" ~ NA,
TRUE ~ as.numeric(Re_referrals)
)) %>%
mutate(Re_referrals_percent = case_when(
mutate(Re_referrals_percentage = case_when(
Re_referrals_percent == "Z" ~ NA,
Re_referrals_percent == "x" ~ NA,
Re_referrals_percent == "c" ~ NA,
TRUE ~ as.numeric(Re_referrals_percent)
)) %>%
select(time_period, geographic_level, geo_breakdown, region_code, region_name, new_la_code, la_name, Referrals, Re_referrals, Re_referrals_percent) %>%
select(
time_period, geographic_level, geo_breakdown, region_code, region_name, new_la_code, la_name,
Referrals, Re_referrals, Re_referrals_percent, Referrals_num, Re_referrals_num, Re_referrals_percentage
) %>%
distinct()


# Calculate the number of referrals not including re-referrals
referrals <- cin_referral_data %>%
group_by(time_period, geographic_level, geo_breakdown, region_code, region_name, new_la_code, la_name) %>%
summarise(
referrals_not_including_re_referrals_perc = round((Referrals - Re_referrals) / Referrals * 100, 1),
referrals_not_including_re_referrals = Referrals - Re_referrals,
.groups = "drop"
)
# referrals <- cin_referral_data %>%
# group_by(time_period, geographic_level, geo_breakdown, region_code, region_name, new_la_code, la_name) %>%
# summarise(
# referrals_not_including_re_referrals_perc = round((Referrals - Re_referrals) / Referrals * 100, 1),
# referrals_not_including_re_referrals = Referrals - Re_referrals,
# )

# Join the referall back to the original dataframe
cin_referral_data <- merge(referrals, cin_referral_data) %>%
arrange(desc(time_period))
# cin_referral_data <- merge(referrals, cin_referral_data) %>%
# arrange(desc(time_period))


return(cin_referral_data)
Expand Down
4 changes: 2 additions & 2 deletions server.R
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Original file line number Diff line number Diff line change
Expand Up @@ -1535,7 +1535,7 @@ server <- function(input, output, session) {
stat <- "NA"
} else {
stat <- format(cin_referrals %>% filter(time_period == max(cin_referrals$time_period) & geo_breakdown %in% input$geographic_breakdown_o1)
%>% select(Re_referrals_percent), nsmall = 1)
%>% select(Re_referrals_percentage), nsmall = 1)
}

paste0(stat, "%", "<br>", "<p style='font-size:16px; font-weight:500;'>", "(", max(cin_referrals$time_period), ")", "</p>")
Expand Down Expand Up @@ -1671,7 +1671,7 @@ server <- function(input, output, session) {
}

ggplotly(
plotly_time_series_custom_scale(filtered_data, input$select_geography_o1, input$geographic_breakdown_o1, "Re_referrals_percent", "Re-referrals (%)", 100) %>%
plotly_time_series_custom_scale(filtered_data, input$select_geography_o1, input$geographic_breakdown_o1, "Re_referrals_percentage", "Re-referrals (%)", 100) %>%
config(displayModeBar = F),
height = 420
)
Expand Down
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