In djpr-data/djprlabourdash: DJSIR Jobs Dashboard

library(officedown)
library(flextable)
library(djprshiny)
library(djprlabourdash)
library(dplyr)
library(dbplyr)

knitr::opts_chunk$set(
  echo = FALSE,
  message = FALSE,
  error = FALSE,
  warning = FALSE,
  tab.topcaption = TRUE
  )

table_split <- function(tbl){
  title <- tbl$caption$value
  tbl$caption <- NULL

  return(list(tbl = tbl,
              title = title))
}

check_title <- function(table){
  if(inherits(table$title, "data.frame")){
    table$title <- table$title$txt
  }
  return(table)
}

ref_dates <- automate.labourforce.checks::reference_dates()

#dash_data <- get_dash_data()
Sys.setenv("R_DJPRLABOURDASH_TABLEDEST" = "briefing")

[Overview]{.ul}

Over the month of r ref_dates$reference_period (unless otherwise stated):

df <- get_labourforce_db(
  c("A84423354L",
    "A84423349V",
    "A84423357V",
    "pt_emp_vic",
    "A84423350C",
    "A84423355R",
    "A84426256L",
    "A85223450L",
    "A85223451R",
    "A84424691V")
)

df <- df %>%
  dplyr::filter(.data$series_id == "A84424691V") %>%  
  dplyr::arrange(date) |>
  dplyr::mutate(value = slider::slide_mean(.data$value, 
                                           before = 11L,
                                           complete = TRUE),
                series_id = "smoothed_youth_unemp") %>%
  dplyr::filter(!is.na(.data$value)) %>%
  dplyr::bind_rows(df)

summ_data <- ts_summarise(df)

# Function to return dot point text for a given series ID
summ_dot_point <- function(series_id) {
  prev <- get_summ(series_id, prev_value, summ_data) %>% 
    round2(1)
  curr <- get_summ(series_id, latest_value, summ_data) %>%
    round2(1)


  curr_date <- get_summ(series_id, latest_date, summ_data) %>%
    format("%B %Y")
  unit <- get_summ(series_id, unit, summ_data)

  if (unit != "Percent") {
    prev <- pretty_round(prev)
    curr <- pretty_round(curr)
  }

  raw_diff <- get_summ(series_id, d_period_abs, summ_data)
  diff_sign <- raw_diff %>%
    round2(1) %>%
    sign()

  diff <- dplyr::if_else(unit == "000",
                         pretty_round(abs(raw_diff)),
                         as.character(round2(abs(raw_diff), 1)))
  perc_diff <- get_summ(series_id, d_period_perc, summ_data) %>%
    abs() %>%
    round2(1)

  out <- dplyr::case_when(
    diff_sign == 0 ~ paste0(" remained at ", curr),
    diff_sign == -1 ~ paste0(" fell from ", prev, 
                            " to ", curr),
    diff_sign == 1 ~ paste0(" rose from ", prev,
                            " to ", curr)
  )

  if (unit == "Percent") {
    out <- paste0(out, " per cent")
  }

  out <- dplyr::case_when(
    diff_sign == 0 ~ out,
    diff_sign == -1 ~ paste0(out, ", a fall of ", diff),
    diff_sign == 1 ~ paste0(out, ", a rise of ", diff)
  )

    if (unit == "Percent") {
    out <- paste0(out, " percentage points")
    } else {
    out <- paste0(out, " workers")
  }

  out
}



dot_points <- c(
  glue::glue("-   Victoria's unemployment rate {summ_dot_point(\"A84423354L\")};"),
  glue::glue("-   The number of people employed in Victoria {summ_dot_point(\"A84423349V\")};")
)

ftemp_raw_diff <- get_summ("A84423357V", d_period_abs, summ_data)
ftemp_perc_diff <- get_summ("A84423357V", d_period_perc, summ_data) %>%
  round2(1)
ftemp_sign <- sign(ftemp_raw_diff)
ftemp_pretty_diff <- ftemp_raw_diff %>% abs() %>% pretty_round()
ftemp_text <- dplyr::case_when(
  ftemp_sign == 0 ~ " remained unchanged",
  ftemp_sign == -1 ~ paste0(" fell by ", ftemp_pretty_diff, " workers (",
                            ftemp_perc_diff, "%)"),
  ftemp_sign == 1 ~ paste0(" rose by ", ftemp_pretty_diff, " workers (",
                            ftemp_perc_diff, "%)")
)

ptemp_raw_diff <- get_summ("pt_emp_vic", d_period_abs, summ_data)
ptemp_perc_diff <- get_summ("pt_emp_vic", d_period_perc, summ_data) %>%
  round2(1)
ptemp_sign <- sign(ptemp_raw_diff)
ptemp_pretty_diff <- ptemp_raw_diff %>% abs() %>% pretty_round()
ptemp_text <- dplyr::case_when(
  ptemp_sign == 0 ~ " remained unchanged",
  ptemp_sign == -1 ~ paste0(" fell by ", ptemp_pretty_diff, " workers (",
                            ptemp_perc_diff, "%)"),
  ptemp_sign == 1 ~ paste0(" rose by ", ptemp_pretty_diff, " workers (",
                            ptemp_perc_diff, "%)")
)

dot_points <- c(
  dot_points,
  glue::glue("    *   Full-time employment {ftemp_text};"),
  glue::glue("    *   Part-time employment {ptemp_text};"),
  glue::glue("-   The number of unemployed people in Victoria {summ_dot_point(\"A84423350C\") %>% gsub(\"workers\", \"people\", .)};"),
  glue::glue("-   Victoria's participation rate {summ_dot_point(\"A84423355R\")};")
)

hours_change <- get_summ("A84426256L", d_period_perc, summ_data) %>%
  round2(1)
hours_sign <- sign(hours_change)
hours_text <- dplyr::case_when(
  hours_sign == 0 ~ " didn't change",
  hours_sign == -1 ~ paste0(" fell by ", abs(hours_change), "%"),
  hours_sign == 1 ~ paste0(" rose by ", abs(hours_change), "%")
)


dot_points <- c(
  dot_points,
  glue::glue("-   The total number of hours worked in Victoria {hours_text};"),
  glue::glue("-   Victoria's underemployment rate {summ_dot_point(\"A85223450L\")};"),
  glue::glue("-   The youth unemployment rate in Victoria ",
             summ_dot_point("smoothed_youth_unemp"),
             " (smoothed using a 12-month rolling average);"),
  glue::glue("-   The unsmoothed, original youth unemployment rate is ",
             get_summ("A84424691V", latest_value, summ_data) %>% round2(1),
             " percent, compared to ",
             get_summ("A84424691V", prev_value, summ_data) %>% round2(1),
             " percent a month earlier.")
)

cat(
  paste(dot_points, collapse = '\n')
)

# save to md for inclusion in email
writeLines(dot_points, here::here('inst','dotpoints.md'))

\newpage

abs_labour_force <- get_labourforce_db()

t1 <- table_overview(df = abs_labour_force) |>
  table_split()

t1 <- check_title(t1)

Table 1: `r t1$title`

r t1$tbl

\newpage

t2 <- table_gr_sex(df = abs_labour_force) |>
  table_split()

t2 <- check_title(t2)

Table 2: `r t2$title`

r t2$tbl

t3 <- table_ind_unemp_state(df = abs_labour_force) |>
  table_split()

t3 <- check_title(t3)

\newpage

Table 3: `r t3$title`

r t3$tbl

\newpage

[Youth]{.ul}

Youth labour market data provides insights into headline figures. Youth labour force data is volatile therefore DJSIR smooths the data by using 12-month averages. While this assists in observing underlying trends, it makes large month-to-month changes in underlying conditions less apparent.

t4 <- table_gr_youth_summary(df = abs_labour_force) |>
  table_split()

t4 <- check_title(t4)

Table 4: `r t4$title`

r t4$tbl

\newpage

t5 <- table_gr_youth_unemp_region(df = abs_labour_force) |>
  table_split()

t5 <- check_title(t5)

Table 5: `r t5$title`

r t5$tbl

\newpage

[Metropolitan Melbourne]{.ul}

t6 <- table_reg_metro_states_unemprate(df = abs_labour_force) |>
  table_split()

t6 <- check_title(t6)

Table 6: `r t6$title`

r t6$tbl

\newpage

t7 <- table_reg_metro_emp(df = abs_labour_force) |>
  table_split()

t7 <- check_title(t7)

Table 7: `r t7$title`

r t7$tbl

\newpage

t8 <- table_reg_metro_unemp(df = abs_labour_force) |>
  table_split()

t8 <- check_title(t8)

Table 8: `r t8$title`

r t8$tbl

\newpage

t9 <- table_reg_metro_unemprate(df = abs_labour_force) |>
  table_split()

t9 <- check_title(t9)

Table 9: `r t9$title`

r t9$tbl

\newpage

t10 <- table_reg_metro_partrate(df = abs_labour_force) |>
  table_split()

t10 <- check_title(t10)

Table 10: `r t10$title`

r t10$tbl

\newpage

[Regional Victoria]{.ul}

t11 <- table_reg_nonmetro_states_unemprate(df = abs_labour_force) |>
  table_split()

t11 <- check_title(t11)

Table 11: `r t11$title`

r t11$tbl

\newpage

t12 <- table_reg_nonmetro_emp(df = abs_labour_force) |>
  table_split()

t12 <- check_title(t12)

Table 12: `r t12$title`

r t12$tbl

\newpage

t13 <- table_reg_nonmetro_unemp(df = abs_labour_force) |>
  table_split()

t13 <- check_title(t13)

Table 13: `r t13$title`

r t13$tbl

\newpage

t14 <- table_reg_nonmetro_unemprate(df = abs_labour_force) |>
  table_split()

t14 <- check_title(t14)

Table 14: `r t14$title`

r t14$tbl

\newpage

t15 <- table_reg_nonmetro_partrate(df = abs_labour_force) |>
  table_split()

t15 <- check_title(t15)

Table 15: `r t15$title`

r t15$tbl

\newpage

[Industries]{.ul}

t16 <- table_industries_summary(df = abs_labour_force) |>
  table_split()

t16 <- check_title(t16)

Table 16: `r t16$title`

r t16$tbl

djpr-data/djprlabourdash documentation built on April 28, 2023, 6:16 p.m.

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djpr-data/djprlabourdash
DJSIR Jobs Dashboard

In djpr-data/djprlabourdash: DJSIR Jobs Dashboard

[Overview]{.ul}

Table 1: `r t1$title`

Table 2: `r t2$title`

Table 3: `r t3$title`

[Youth]{.ul}

Table 4: `r t4$title`

Table 5: `r t5$title`

[Metropolitan Melbourne]{.ul}

Table 6: `r t6$title`

Table 7: `r t7$title`

Table 8: `r t8$title`

Table 9: `r t9$title`

Table 10: `r t10$title`

[Regional Victoria]{.ul}

Table 11: `r t11$title`

Table 12: `r t12$title`

Table 13: `r t13$title`

Table 14: `r t14$title`

Table 15: `r t15$title`

[Industries]{.ul}

Table 16: `r t16$title`

R Package Documentation

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djpr-data/djprlabourdash DJSIR Jobs Dashboard

In djpr-data/djprlabourdash: DJSIR Jobs Dashboard

[Overview]{.ul}

Table 1: r t1$title

Table 2: r t2$title

Table 3: r t3$title

[Youth]{.ul}

Table 4: r t4$title

Table 5: r t5$title

[Metropolitan Melbourne]{.ul}

Table 6: r t6$title

Table 7: r t7$title

Table 8: r t8$title

Table 9: r t9$title

Table 10: r t10$title

[Regional Victoria]{.ul}

Table 11: r t11$title

Table 12: r t12$title

Table 13: r t13$title

Table 14: r t14$title

Table 15: r t15$title

[Industries]{.ul}

Table 16: r t16$title

R Package Documentation

Browse R Packages

We want your feedback!

djpr-data/djprlabourdash
DJSIR Jobs Dashboard

Table 1: `r t1$title`

Table 2: `r t2$title`

Table 3: `r t3$title`

Table 4: `r t4$title`

Table 5: `r t5$title`

Table 6: `r t6$title`

Table 7: `r t7$title`

Table 8: `r t8$title`

Table 9: `r t9$title`

Table 10: `r t10$title`

Table 11: `r t11$title`

Table 12: `r t12$title`

Table 13: `r t13$title`

Table 14: `r t14$title`

Table 15: `r t15$title`

Table 16: `r t16$title`