R/viz_industries.R
In djpr-data/djprlabourdash: DJSIR Jobs Dashboard
Defines functions
viz_industries_emp_bysex_bar
viz_industries_emp_line
table_industries_employment
viz_industries_empchange_sincecovid_bar
Documented in
viz_industries_empchange_sincecovid_bar
#' Function to create the graphs for the 'Industries' subpage on the dashboard.
#' @param data the dataframe containing data to visualise
#' @examples
#' \dontrun{
#'
#' dash_data <- load_dash_data()
#'
#' # for 'viz_industries_empchange_sincecovid_bar':
#' data <- filter_dash_data(c(
#' "A84601680F",
#' "A84601683L",
#' "A84601686V",
#' "A84601665J",
#' "A84601704L",
#' "A84601707V",
#' "A84601710J",
#' "A84601638A",
#' "A84601653X",
#' "A84601689A",
#' "A84601656F",
#' "A84601713R",
#' "A84601668R",
#' "A84601695W",
#' "A84601698C",
#' "A84601650T",
#' "A84601671C",
#' "A84601641R",
#' "A84601716W",
#' "A84601662A"
#' )) %>%
#' dplyr::filter(date >= as.Date("2020-01-01"))
#' }
#' @import djprtheme
viz_industries_empchange_sincecovid_bar <- function(data = filter_dash_data(c(
"A84601680F",
"A84601683L",
"A84601686V",
"A84601665J",
"A84601704L",
"A84601707V",
"A84601710J",
"A84601638A",
"A84601653X",
"A84601689A",
"A84601656F",
"A84601713R",
"A84601668R",
"A84601695W",
"A84601698C",
"A84601650T",
"A84601671C",
"A84601641R",
"A84601716W",
"A84601662A"
),
df = dash_data
)) {
data <- data %>%
dplyr::group_by(.data$series) %>%
dplyr::mutate(value = 100 * ((.data$value / .data$value[date == as.Date("2020-02-01")]) - 1))
# reduce to only latest month (indexing already done above in data input into function) {
data <- data %>%
dplyr::group_by(.data$series) %>%
dplyr::filter(.data$date == max(.data$date)) %>%
dplyr::ungroup()
# add entry for data$industry for Victoria; employed total
data <- data %>%
dplyr::mutate(
industry = dplyr::if_else(.data$industry == "",
"Victoria, all industries",
.data$industry
)
)
lab_df <- data %>%
dplyr::select(.data$industry, .data$value) %>%
dplyr::mutate(
lab_y = dplyr::if_else(.data$value >= 0, .data$value + 0.1, .data$value - 0.75),
lab_hjust = dplyr::if_else(.data$value >= 0, 0, 1)
)
title <- paste0(
"Employment in ",
data$industry[data$value == max(data$value)],
dplyr::if_else(max(data$value) > 0, " grew", " shrank"),
" by ",
round2(max(data$value), 1),
" per cent between February 2020 and ",
format(max(data$date), "%B %Y"),
", while employment in ",
data$industry[data$value == min(data$value)],
dplyr::if_else(min(data$value) > 0, " grew", " shrank"),
" by ",
round2(abs(min(data$value)), 1),
" per cent"
)
# draw bar chart for all 19 industries plus Vic total
data %>%
ggplot(aes(
x = stats::reorder(.data$industry, .data$value),
y = .data$value
)) +
geom_col(
aes(fill = -.data$value)
) +
geom_text(
data = lab_df,
aes(
y = .data$lab_y,
hjust = .data$lab_hjust,
label = paste0(round2(.data$value, 1), "%")
),
colour = "black",
size = 11 / .pt
) +
geom_hline(
yintercept = 0
) +
coord_flip(clip = "off") +
scale_y_continuous(expand = expansion(mult = c(0.2, 0.15))) +
scale_fill_distiller(palette = "Blues") +
djprtheme::theme_djpr(flipped = TRUE) +
theme(
axis.title.x = element_blank(),
panel.grid = element_blank(),
axis.text.y = element_text(size = 12),
axis.text.x = element_blank()
) +
labs(
title = title,
subtitle = paste0(
"Growth in employment by industry between February 2020 and ",
format(max(data$date), "%B %Y")
),
caption = paste0(caption_lfs_det_q(), " Data not seasonally adjusted. ")
)
}
table_industries_employment <- function(data = filter_dash_data(c(
"A84601680F",
"A84601683L",
"A84601686V",
"A84601665J",
"A84601704L",
"A84601707V",
"A84601710J",
"A84601638A",
"A84601653X",
"A84601689A",
"A84601656F",
"A84601713R",
"A84601668R",
"A84601695W",
"A84601698C",
"A84601650T",
"A84601671C",
"A84601641R",
"A84601716W",
"A84601662A",
"A84601681J",
"A84601684R",
"A84601687W",
"A84601666K",
"A84601705R",
"A84601708W",
"A84601711K",
"A84601639C",
"A84601654A",
"A84601690K",
"A84601657J",
"A84601714T",
"A84601669T",
"A84601696X",
"A84601699F",
"A84601651V",
"A84601672F",
"A84601642T",
"A84601717X",
"A84601663C",
"A84601682K",
"A84601685T",
"A84601688X",
"A84601667L",
"A84601706T",
"A84601709X",
"A84601712L",
"A84601640L",
"A84601655C",
"A84601691L",
"A84601658K",
"A84601715V",
"A84601670A",
"A84601697A",
"A84601700C",
"A84601652W",
"A84601673J",
"A84601643V",
"A84601718A",
"A84601664F"
),
df = dash_data
),
chosen_industry = "Agriculture, Forestry and Fishing") {
latest_date <- format(max(data$date), "%b %Y")
# add entry for data$industry for "Victoria, all industries" where ""
df <- data %>%
dplyr::mutate(
industry = dplyr::if_else(.data$industry == "",
"Victoria, all industries",
.data$industry
)
)
# filter out the chosen industry and vic_total
df <- df %>%
group_by(.data$indicator) %>%
dplyr::filter(.data$industry %in%
c("Victoria, all industries", .env$chosen_industry))
table_df <- df %>%
dplyr::group_by(.data$industry, .data$indicator) %>%
dplyr::mutate(
d_quarter = 100 * ((.data$value / dplyr::lag(.data$value, 1)) - 1),
d_year = 100 * ((.data$value / dplyr::lag(.data$value, 4)) - 1)
) %>%
dplyr::filter(.data$date == max(.data$date)) %>%
dplyr::select(
.data$indicator, .data$value, .data$industry,
.data$d_quarter, .data$d_year
) %>%
dplyr::ungroup()
table_df <- table_df %>%
dplyr::mutate(across(
c(.data$value, .data$d_quarter, .data$d_year),
~ round2(.x, 1)
))
table_df <- table_df %>%
dplyr::mutate(across(
c(.data$value, .data$d_quarter, .data$d_year),
~ round2(.x, 1)
)) %>%
dplyr::mutate(
value = scales::comma(.data$value * 1000),
d_quarter = paste0(.data$d_quarter, "%"),
d_year = paste0(.data$d_year, "%")
)
table_df <- table_df %>%
dplyr::rename({{ latest_date }} := .data$value,
`Change over quarter` = .data$d_quarter,
`Change over year` = .data$d_year
)
table_df <- table_df %>%
tidyr::gather(
key = "series", value = "value",
-.data$indicator, -.data$industry
) %>%
tidyr::spread(key = .data$industry, value = .data$value)
table_df <- table_df %>%
dplyr::mutate(indic_order = dplyr::case_when(
indicator == "Employed total" ~ 1,
indicator == "Employed full-time" ~ 2,
indicator == "Employed part-time" ~ 3
)) %>%
mutate(series_order = dplyr::case_when(
series == "Change over quarter" ~ 2,
series == "Change over year" ~ 3,
TRUE ~ 1
)) %>%
dplyr::arrange(.data$indic_order, .data$series_order) %>%
dplyr::select(-ends_with("order"))
# Fix to ensure that Victoria, all industries is always the last column
table_df <- table_df %>%
dplyr::select(.data$indicator, .data$series, .env$chosen_industry, .data$`Victoria, all industries`)
out <- table_df %>%
dplyr::group_by(.data$indicator) %>%
dplyr::mutate(indicator = dplyr::if_else(
dplyr::row_number() != 1,
"",
.data$indicator
)) %>%
dplyr::rename(
` ` = .data$indicator,
` ` = .data$series
) %>%
flextable::flextable() %>%
flextable::bold(part = "header") %>%
flextable::border_remove() %>%
flextable::border(
part = "body",
j = 2:4,
i = 2:nrow(table_df),
border.top = flextable::fp_border_default(color = "grey90", width = 0.25)
) %>%
flextable::border(
part = "body",
i = c(1, 4, 7),
border.top = flextable::fp_border_default()
) %>%
flextable::border(
part = "body",
i = nrow(table_df),
border.bottom = flextable::fp_border_default()
) %>%
flextable::set_table_properties("autofit", width = 1) %>%
flextable::font(part = "body", fontname = "VIC-Regular") %>%
flextable::font(part = "header", fontname = "VIC-Regular") %>%
flextable::font(part = "footer", fontname = "VIC-Regular") %>%
flextable::fontsize(size = 9) %>%
flextable::fontsize(size = 9, part = "header")
table_caption <- caption_auto(
data = data,
notes = "Data not seasonally adjusted."
)
# Add caption
out <- out %>%
flextable::add_footer(` ` = table_caption) %>%
flextable::merge_at(
j = 1:flextable::ncol_keys(out),
part = "footer"
) %>%
# flextable::italic(part = "footer") %>%
flextable::font(fontname = "VIC-font") %>%
flextable::font(part = "body", fontname = "VIC-Regular") %>%
flextable::font(part = "header", fontname = "VIC-Regular") %>%
flextable::font(part = "footer", fontname = "VIC-Regular") %>%
flextable::fontsize(
size = 9 * 0.85,
part = "footer"
) %>%
flextable::color(
part = "footer",
color = "#343a40"
) %>%
flextable::line_spacing(
part = "footer",
space = 0.8
)
out
}
viz_industries_emp_line <- function(data = filter_dash_data(
c(
"A84601680F",
"A84601683L",
"A84601686V",
"A84601665J",
"A84601704L",
"A84601707V",
"A84601710J",
"A84601638A",
"A84601653X",
"A84601689A",
"A84601656F",
"A84601713R",
"A84601668R",
"A84601695W",
"A84601698C",
"A84601650T",
"A84601671C",
"A84601641R",
"A84601716W",
"A84601662A"
),
df = dash_data
),
chosen_industry = "Agriculture, Forestry and Fishing",
dates = as.Date(c("1910-01-01", "2030-01-01"))) {
df <- data %>%
dplyr::filter(date >= dates[1], date <= dates[2]) %>%
dplyr::mutate(
industry = dplyr::if_else(.data$industry == "",
"Victoria, all industries",
.data$industry
)
)
df <- df %>%
dplyr::filter(.data$industry %in% c("Victoria, all industries", .env$chosen_industry))
df <- df %>%
dplyr::group_by(.data$industry) %>%
dplyr::mutate(
value = 100 * ((.data$value / dplyr::lag(.data$value, 4)) - 1),
tooltip = paste0(
.data$industry, "\n",
format(.data$date, "%b %Y"), "\n",
round2(.data$value, 1), "%"
)
) %>%
dplyr::select(.data$date, .data$industry, .data$value, .data$tooltip) %>%
dplyr::ungroup() %>%
dplyr::filter(!is.na(.data$value))
colours <- c(
djprtheme::djpr_cobalt,
djprtheme::djpr_lima
)
names(colours) <- c(
"Victoria, all industries",
chosen_industry
)
df %>%
djpr_ts_linechart(
col_var = .data$industry,
label_num = paste0(round2(.data$value, 1), "%"),
y_labels = function(x) paste0(x, "%")
) +
scale_colour_manual(values = colours) +
labs(
subtitle = "Annual change in total employment",
caption = caption_lfs_det_q(),
title = paste0(
"Annual employment growth in ",
chosen_industry,
" compared to Victorian average"
)
)
}
viz_industries_emp_bysex_bar <- function(data = filter_dash_data(c(
"females_greater melbourne_accommodation and food services_employed full-time",
"females_greater melbourne_administrative and support services_employed full-time",
"females_greater melbourne_agriculture, forestry and fishing_employed full-time",
"females_greater melbourne_arts and recreation services_employed full-time",
"females_greater melbourne_construction_employed full-time",
"females_greater melbourne_education and training_employed full-time",
"females_greater melbourne_electricity, gas, water and waste services_employed full-time",
"females_greater melbourne_financial and insurance services_employed full-time",
"females_greater melbourne_health care and social assistance_employed full-time",
"females_greater melbourne_information media and telecommunications_employed full-time",
"females_greater melbourne_manufacturing_employed full-time",
"females_greater melbourne_mining_employed full-time",
"females_greater melbourne_other services_employed full-time",
"females_greater melbourne_professional, scientific and technical services_employed full-time",
"females_greater melbourne_public administration and safety_employed full-time",
"females_greater melbourne_rental, hiring and real estate services_employed full-time",
"females_greater melbourne_retail trade_employed full-time",
"females_greater melbourne_transport, postal and warehousing_employed full-time",
"females_greater melbourne_wholesale trade_employed full-time",
"males_greater melbourne_accommodation and food services_employed full-time",
"males_greater melbourne_administrative and support services_employed full-time",
"males_greater melbourne_agriculture, forestry and fishing_employed full-time",
"males_greater melbourne_arts and recreation services_employed full-time",
"males_greater melbourne_construction_employed full-time",
"males_greater melbourne_education and training_employed full-time",
"males_greater melbourne_electricity, gas, water and waste services_employed full-time",
"males_greater melbourne_financial and insurance services_employed full-time",
"males_greater melbourne_health care and social assistance_employed full-time",
"males_greater melbourne_information media and telecommunications_employed full-time",
"males_greater melbourne_manufacturing_employed full-time",
"males_greater melbourne_mining_employed full-time",
"males_greater melbourne_other services_employed full-time",
"males_greater melbourne_professional, scientific and technical services_employed full-time",
"males_greater melbourne_public administration and safety_employed full-time",
"males_greater melbourne_rental, hiring and real estate services_employed full-time",
"males_greater melbourne_retail trade_employed full-time",
"males_greater melbourne_transport, postal and warehousing_employed full-time",
"males_greater melbourne_wholesale trade_employed full-time",
"females_rest of vic._accommodation and food services_employed full-time",
"females_rest of vic._administrative and support services_employed full-time",
"females_rest of vic._agriculture, forestry and fishing_employed full-time",
"females_rest of vic._arts and recreation services_employed full-time",
"females_rest of vic._construction_employed full-time",
"females_rest of vic._education and training_employed full-time",
"females_rest of vic._electricity, gas, water and waste services_employed full-time",
"females_rest of vic._financial and insurance services_employed full-time",
"females_rest of vic._health care and social assistance_employed full-time",
"females_rest of vic._information media and telecommunications_employed full-time",
"females_rest of vic._manufacturing_employed full-time",
"females_rest of vic._mining_employed full-time",
"females_rest of vic._other services_employed full-time",
"females_rest of vic._professional, scientific and technical services_employed full-time",
"females_rest of vic._public administration and safety_employed full-time",
"females_rest of vic._rental, hiring and real estate services_employed full-time",
"females_rest of vic._retail trade_employed full-time",
"females_rest of vic._transport, postal and warehousing_employed full-time",
"females_rest of vic._wholesale trade_employed full-time",
"males_rest of vic._accommodation and food services_employed full-time",
"males_rest of vic._administrative and support services_employed full-time",
"males_rest of vic._agriculture, forestry and fishing_employed full-time",
"males_rest of vic._arts and recreation services_employed full-time",
"males_rest of vic._construction_employed full-time",
"males_rest of vic._education and training_employed full-time",
"males_rest of vic._electricity, gas, water and waste services_employed full-time",
"males_rest of vic._financial and insurance services_employed full-time",
"males_rest of vic._health care and social assistance_employed full-time",
"males_rest of vic._information media and telecommunications_employed full-time",
"males_rest of vic._manufacturing_employed full-time",
"males_rest of vic._mining_employed full-time",
"males_rest of vic._other services_employed full-time",
"males_rest of vic._professional, scientific and technical services_employed full-time",
"males_rest of vic._public administration and safety_employed full-time",
"males_rest of vic._rental, hiring and real estate services_employed full-time",
"males_rest of vic._retail trade_employed full-time",
"males_rest of vic._transport, postal and warehousing_employed full-time",
"males_rest of vic._wholesale trade_employed full-time",
"females_greater melbourne_accommodation and food services_employed part-time",
"females_greater melbourne_administrative and support services_employed part-time",
"females_greater melbourne_agriculture, forestry and fishing_employed part-time",
"females_greater melbourne_arts and recreation services_employed part-time",
"females_greater melbourne_construction_employed part-time",
"females_greater melbourne_education and training_employed part-time",
"females_greater melbourne_electricity, gas, water and waste services_employed part-time",
"females_greater melbourne_financial and insurance services_employed part-time",
"females_greater melbourne_health care and social assistance_employed part-time",
"females_greater melbourne_information media and telecommunications_employed part-time",
"females_greater melbourne_manufacturing_employed part-time",
"females_greater melbourne_mining_employed part-time",
"females_greater melbourne_other services_employed part-time",
"females_greater melbourne_professional, scientific and technical services_employed part-time",
"females_greater melbourne_public administration and safety_employed part-time",
"females_greater melbourne_rental, hiring and real estate services_employed part-time",
"females_greater melbourne_retail trade_employed part-time",
"females_greater melbourne_transport, postal and warehousing_employed part-time",
"females_greater melbourne_wholesale trade_employed part-time",
"males_greater melbourne_accommodation and food services_employed part-time",
"males_greater melbourne_administrative and support services_employed part-time",
"males_greater melbourne_agriculture, forestry and fishing_employed part-time",
"males_greater melbourne_arts and recreation services_employed part-time",
"males_greater melbourne_construction_employed part-time",
"males_greater melbourne_education and training_employed part-time",
"males_greater melbourne_electricity, gas, water and waste services_employed part-time",
"males_greater melbourne_financial and insurance services_employed part-time",
"males_greater melbourne_health care and social assistance_employed part-time",
"males_greater melbourne_information media and telecommunications_employed part-time",
"males_greater melbourne_manufacturing_employed part-time",
"males_greater melbourne_mining_employed part-time",
"males_greater melbourne_other services_employed part-time",
"males_greater melbourne_professional, scientific and technical services_employed part-time",
"males_greater melbourne_public administration and safety_employed part-time",
"males_greater melbourne_rental, hiring and real estate services_employed part-time",
"males_greater melbourne_retail trade_employed part-time",
"males_greater melbourne_transport, postal and warehousing_employed part-time",
"males_greater melbourne_wholesale trade_employed part-time",
"females_rest of vic._accommodation and food services_employed part-time",
"females_rest of vic._administrative and support services_employed part-time",
"females_rest of vic._agriculture, forestry and fishing_employed part-time",
"females_rest of vic._arts and recreation services_employed part-time",
"females_rest of vic._construction_employed part-time",
"females_rest of vic._education and training_employed part-time",
"females_rest of vic._electricity, gas, water and waste services_employed part-time",
"females_rest of vic._financial and insurance services_employed part-time",
"females_rest of vic._health care and social assistance_employed part-time",
"females_rest of vic._information media and telecommunications_employed part-time",
"females_rest of vic._manufacturing_employed part-time",
"females_rest of vic._mining_employed part-time",
"females_rest of vic._other services_employed part-time",
"females_rest of vic._professional, scientific and technical services_employed part-time",
"females_rest of vic._public administration and safety_employed part-time",
"females_rest of vic._rental, hiring and real estate services_employed part-time",
"females_rest of vic._retail trade_employed part-time",
"females_rest of vic._transport, postal and warehousing_employed part-time",
"females_rest of vic._wholesale trade_employed part-time",
"males_rest of vic._accommodation and food services_employed part-time",
"males_rest of vic._administrative and support services_employed part-time",
"males_rest of vic._agriculture, forestry and fishing_employed part-time",
"males_rest of vic._arts and recreation services_employed part-time",
"males_rest of vic._construction_employed part-time",
"males_rest of vic._education and training_employed part-time",
"males_rest of vic._electricity, gas, water and waste services_employed part-time",
"males_rest of vic._financial and insurance services_employed part-time",
"males_rest of vic._health care and social assistance_employed part-time",
"males_rest of vic._information media and telecommunications_employed part-time",
"males_rest of vic._manufacturing_employed part-time",
"males_rest of vic._mining_employed part-time",
"males_rest of vic._other services_employed part-time",
"males_rest of vic._professional, scientific and technical services_employed part-time",
"males_rest of vic._public administration and safety_employed part-time",
"males_rest of vic._rental, hiring and real estate services_employed part-time",
"males_rest of vic._retail trade_employed part-time",
"males_rest of vic._transport, postal and warehousing_employed part-time",
"males_rest of vic._wholesale trade_employed part-time"
), df = dash_data) %>%
dplyr::group_by(.data$series) %>%
dplyr::filter(.data$date == max(.data$date)) %>%
dplyr::ungroup(),
chosen_industry = "Agriculture, Forestry and Fishing") {
industry_total <- data %>%
dplyr::group_by(.data$sex, .data$date) %>%
dplyr::summarise(
value = sum(.data$value)
) %>%
dplyr::mutate(
industry = "Victoria, all industries"
) %>%
dplyr::filter(.data$date == max(.data$date)) # this is a quick fix for an issue where some series have a different max(date)
df <- data %>%
dplyr::filter(.data$industry == .env$chosen_industry) %>%
dplyr::select(
.data$date, .data$value, .data$series,
.data$indicator, .data$sex, .data$industry, .data$gcc_restofstate
)
df <- df %>%
dplyr::group_by(.data$sex, .data$industry, .data$date) %>%
dplyr::summarise(
value = sum(.data$value)
) %>%
dplyr::ungroup()
df <- df %>%
dplyr::bind_rows(industry_total)
df <- df %>%
dplyr::group_by(.data$industry) %>%
dplyr::mutate(perc = .data$value / sum(.data$value)) %>%
dplyr::ungroup()
df <- df %>%
dplyr::mutate(order = if_else(.data$industry == "Victoria, all industries", 2, 1))
label_df <- df %>%
dplyr::group_by(.data$industry) %>%
dplyr::arrange(desc(.data$sex)) %>%
dplyr::mutate(label_y = cumsum(.data$perc) - .data$perc + (.data$perc / 2))
legend_df <- label_df %>%
dplyr::filter(.data$industry != "Victoria, all industries")
chosen_industry_female_share <- round2(df$perc[df$sex == "Females" & df$industry != "Victoria, all industries"] * 100, 1)
vic_female_share <- round2(df$perc[df$sex == "Females" & df$industry == "Victoria, all industries"] * 100, 1)
title <- paste0(
"Women account for ",
chosen_industry_female_share,
" per cent of workers in the ",
chosen_industry,
" industry, which is ",
dplyr::case_when(
chosen_industry_female_share > vic_female_share ~
"higher than",
chosen_industry_female_share < vic_female_share ~
"lower than",
chosen_industry_female_share == vic_female_share ~
"the same as"
),
" the Victorian average"
)
df %>%
ggplot(aes(
x = stats::reorder(stringr::str_wrap(.data$industry, 15), -.data$order),
y = .data$value, fill = .data$sex
)) +
geom_col(
position = "fill",
alpha = 1,
col = djprtheme::djpr_cool_grey_11
) +
geom_text(
data = label_df,
aes(y = .data$label_y, label = paste0(round2(.data$perc * 100, 1), "%")),
size = 16 / .pt,
colour = "white"
) +
geom_text(
data = legend_df,
aes(y = .data$label_y, label = .data$sex, col = .data$sex),
nudge_x = 0.55,
size = 14 / .pt
) +
coord_flip() +
theme_djpr() +
djpr_fill_manual(2) +
djpr_colour_manual(2) +
theme(
axis.text.x = element_blank(),
axis.ticks = element_blank(),
axis.title = element_blank(),
panel.grid = element_blank(),
axis.line = element_blank()
) +
labs(
subtitle = paste0(
"Percentage share of men and women employed in industries in ",
format(max(df$date), "%B %Y"), "."
),
caption = paste0(caption_lfs_det_q(), " Data not seasonally adjusted."),
title = title
)
}
djpr-data/djprlabourdash documentation built on April 28, 2023, 6:16 p.m.
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Defines functions viz_industries_emp_bysex_bar viz_industries_emp_line table_industries_employment viz_industries_empchange_sincecovid_bar
Documented in viz_industries_empchange_sincecovid_bar
#' Function to create the graphs for the 'Industries' subpage on the dashboard.
#' @param data the dataframe containing data to visualise
#' @examples
#' \dontrun{
#'
#' dash_data <- load_dash_data()
#'
#' # for 'viz_industries_empchange_sincecovid_bar':
#' data <- filter_dash_data(c(
#' "A84601680F",
#' "A84601683L",
#' "A84601686V",
#' "A84601665J",
#' "A84601704L",
#' "A84601707V",
#' "A84601710J",
#' "A84601638A",
#' "A84601653X",
#' "A84601689A",
#' "A84601656F",
#' "A84601713R",
#' "A84601668R",
#' "A84601695W",
#' "A84601698C",
#' "A84601650T",
#' "A84601671C",
#' "A84601641R",
#' "A84601716W",
#' "A84601662A"
#' )) %>%
#' dplyr::filter(date >= as.Date("2020-01-01"))
#' }
#' @import djprtheme
viz_industries_empchange_sincecovid_bar <- function(data = filter_dash_data(c(
"A84601680F",
"A84601683L",
"A84601686V",
"A84601665J",
"A84601704L",
"A84601707V",
"A84601710J",
"A84601638A",
"A84601653X",
"A84601689A",
"A84601656F",
"A84601713R",
"A84601668R",
"A84601695W",
"A84601698C",
"A84601650T",
"A84601671C",
"A84601641R",
"A84601716W",
"A84601662A"
),
df = dash_data
)) {
data <- data %>%
dplyr::group_by(.data$series) %>%
dplyr::mutate(value = 100 * ((.data$value / .data$value[date == as.Date("2020-02-01")]) - 1))
# reduce to only latest month (indexing already done above in data input into function) {
data <- data %>%
dplyr::group_by(.data$series) %>%
dplyr::filter(.data$date == max(.data$date)) %>%
dplyr::ungroup()
# add entry for data$industry for Victoria; employed total
data <- data %>%
dplyr::mutate(
industry = dplyr::if_else(.data$industry == "",
"Victoria, all industries",
.data$industry
)
)
lab_df <- data %>%
dplyr::select(.data$industry, .data$value) %>%
dplyr::mutate(
lab_y = dplyr::if_else(.data$value >= 0, .data$value + 0.1, .data$value - 0.75),
lab_hjust = dplyr::if_else(.data$value >= 0, 0, 1)
)
title <- paste0(
"Employment in ",
data$industry[data$value == max(data$value)],
dplyr::if_else(max(data$value) > 0, " grew", " shrank"),
" by ",
round2(max(data$value), 1),
" per cent between February 2020 and ",
format(max(data$date), "%B %Y"),
", while employment in ",
data$industry[data$value == min(data$value)],
dplyr::if_else(min(data$value) > 0, " grew", " shrank"),
" by ",
round2(abs(min(data$value)), 1),
" per cent"
)
# draw bar chart for all 19 industries plus Vic total
data %>%
ggplot(aes(
x = stats::reorder(.data$industry, .data$value),
y = .data$value
)) +
geom_col(
aes(fill = -.data$value)
) +
geom_text(
data = lab_df,
aes(
y = .data$lab_y,
hjust = .data$lab_hjust,
label = paste0(round2(.data$value, 1), "%")
),
colour = "black",
size = 11 / .pt
) +
geom_hline(
yintercept = 0
) +
coord_flip(clip = "off") +
scale_y_continuous(expand = expansion(mult = c(0.2, 0.15))) +
scale_fill_distiller(palette = "Blues") +
djprtheme::theme_djpr(flipped = TRUE) +
theme(
axis.title.x = element_blank(),
panel.grid = element_blank(),
axis.text.y = element_text(size = 12),
axis.text.x = element_blank()
) +
labs(
title = title,
subtitle = paste0(
"Growth in employment by industry between February 2020 and ",
format(max(data$date), "%B %Y")
),
caption = paste0(caption_lfs_det_q(), " Data not seasonally adjusted. ")
)
}
table_industries_employment <- function(data = filter_dash_data(c(
"A84601680F",
"A84601683L",
"A84601686V",
"A84601665J",
"A84601704L",
"A84601707V",
"A84601710J",
"A84601638A",
"A84601653X",
"A84601689A",
"A84601656F",
"A84601713R",
"A84601668R",
"A84601695W",
"A84601698C",
"A84601650T",
"A84601671C",
"A84601641R",
"A84601716W",
"A84601662A",
"A84601681J",
"A84601684R",
"A84601687W",
"A84601666K",
"A84601705R",
"A84601708W",
"A84601711K",
"A84601639C",
"A84601654A",
"A84601690K",
"A84601657J",
"A84601714T",
"A84601669T",
"A84601696X",
"A84601699F",
"A84601651V",
"A84601672F",
"A84601642T",
"A84601717X",
"A84601663C",
"A84601682K",
"A84601685T",
"A84601688X",
"A84601667L",
"A84601706T",
"A84601709X",
"A84601712L",
"A84601640L",
"A84601655C",
"A84601691L",
"A84601658K",
"A84601715V",
"A84601670A",
"A84601697A",
"A84601700C",
"A84601652W",
"A84601673J",
"A84601643V",
"A84601718A",
"A84601664F"
),
df = dash_data
),
chosen_industry = "Agriculture, Forestry and Fishing") {
latest_date <- format(max(data$date), "%b %Y")
# add entry for data$industry for "Victoria, all industries" where ""
df <- data %>%
dplyr::mutate(
industry = dplyr::if_else(.data$industry == "",
"Victoria, all industries",
.data$industry
)
)
# filter out the chosen industry and vic_total
df <- df %>%
group_by(.data$indicator) %>%
dplyr::filter(.data$industry %in%
c("Victoria, all industries", .env$chosen_industry))
table_df <- df %>%
dplyr::group_by(.data$industry, .data$indicator) %>%
dplyr::mutate(
d_quarter = 100 * ((.data$value / dplyr::lag(.data$value, 1)) - 1),
d_year = 100 * ((.data$value / dplyr::lag(.data$value, 4)) - 1)
) %>%
dplyr::filter(.data$date == max(.data$date)) %>%
dplyr::select(
.data$indicator, .data$value, .data$industry,
.data$d_quarter, .data$d_year
) %>%
dplyr::ungroup()
table_df <- table_df %>%
dplyr::mutate(across(
c(.data$value, .data$d_quarter, .data$d_year),
~ round2(.x, 1)
))
table_df <- table_df %>%
dplyr::mutate(across(
c(.data$value, .data$d_quarter, .data$d_year),
~ round2(.x, 1)
)) %>%
dplyr::mutate(
value = scales::comma(.data$value * 1000),
d_quarter = paste0(.data$d_quarter, "%"),
d_year = paste0(.data$d_year, "%")
)
table_df <- table_df %>%
dplyr::rename({{ latest_date }} := .data$value,
`Change over quarter` = .data$d_quarter,
`Change over year` = .data$d_year
)
table_df <- table_df %>%
tidyr::gather(
key = "series", value = "value",
-.data$indicator, -.data$industry
) %>%
tidyr::spread(key = .data$industry, value = .data$value)
table_df <- table_df %>%
dplyr::mutate(indic_order = dplyr::case_when(
indicator == "Employed total" ~ 1,
indicator == "Employed full-time" ~ 2,
indicator == "Employed part-time" ~ 3
)) %>%
mutate(series_order = dplyr::case_when(
series == "Change over quarter" ~ 2,
series == "Change over year" ~ 3,
TRUE ~ 1
)) %>%
dplyr::arrange(.data$indic_order, .data$series_order) %>%
dplyr::select(-ends_with("order"))
# Fix to ensure that Victoria, all industries is always the last column
table_df <- table_df %>%
dplyr::select(.data$indicator, .data$series, .env$chosen_industry, .data$`Victoria, all industries`)
out <- table_df %>%
dplyr::group_by(.data$indicator) %>%
dplyr::mutate(indicator = dplyr::if_else(
dplyr::row_number() != 1,
"",
.data$indicator
)) %>%
dplyr::rename(
` ` = .data$indicator,
` ` = .data$series
) %>%
flextable::flextable() %>%
flextable::bold(part = "header") %>%
flextable::border_remove() %>%
flextable::border(
part = "body",
j = 2:4,
i = 2:nrow(table_df),
border.top = flextable::fp_border_default(color = "grey90", width = 0.25)
) %>%
flextable::border(
part = "body",
i = c(1, 4, 7),
border.top = flextable::fp_border_default()
) %>%
flextable::border(
part = "body",
i = nrow(table_df),
border.bottom = flextable::fp_border_default()
) %>%
flextable::set_table_properties("autofit", width = 1) %>%
flextable::font(part = "body", fontname = "VIC-Regular") %>%
flextable::font(part = "header", fontname = "VIC-Regular") %>%
flextable::font(part = "footer", fontname = "VIC-Regular") %>%
flextable::fontsize(size = 9) %>%
flextable::fontsize(size = 9, part = "header")
table_caption <- caption_auto(
data = data,
notes = "Data not seasonally adjusted."
)
# Add caption
out <- out %>%
flextable::add_footer(` ` = table_caption) %>%
flextable::merge_at(
j = 1:flextable::ncol_keys(out),
part = "footer"
) %>%
# flextable::italic(part = "footer") %>%
flextable::font(fontname = "VIC-font") %>%
flextable::font(part = "body", fontname = "VIC-Regular") %>%
flextable::font(part = "header", fontname = "VIC-Regular") %>%
flextable::font(part = "footer", fontname = "VIC-Regular") %>%
flextable::fontsize(
size = 9 * 0.85,
part = "footer"
) %>%
flextable::color(
part = "footer",
color = "#343a40"
) %>%
flextable::line_spacing(
part = "footer",
space = 0.8
)
out
}
viz_industries_emp_line <- function(data = filter_dash_data(
c(
"A84601680F",
"A84601683L",
"A84601686V",
"A84601665J",
"A84601704L",
"A84601707V",
"A84601710J",
"A84601638A",
"A84601653X",
"A84601689A",
"A84601656F",
"A84601713R",
"A84601668R",
"A84601695W",
"A84601698C",
"A84601650T",
"A84601671C",
"A84601641R",
"A84601716W",
"A84601662A"
),
df = dash_data
),
chosen_industry = "Agriculture, Forestry and Fishing",
dates = as.Date(c("1910-01-01", "2030-01-01"))) {
df <- data %>%
dplyr::filter(date >= dates[1], date <= dates[2]) %>%
dplyr::mutate(
industry = dplyr::if_else(.data$industry == "",
"Victoria, all industries",
.data$industry
)
)
df <- df %>%
dplyr::filter(.data$industry %in% c("Victoria, all industries", .env$chosen_industry))
df <- df %>%
dplyr::group_by(.data$industry) %>%
dplyr::mutate(
value = 100 * ((.data$value / dplyr::lag(.data$value, 4)) - 1),
tooltip = paste0(
.data$industry, "\n",
format(.data$date, "%b %Y"), "\n",
round2(.data$value, 1), "%"
)
) %>%
dplyr::select(.data$date, .data$industry, .data$value, .data$tooltip) %>%
dplyr::ungroup() %>%
dplyr::filter(!is.na(.data$value))
colours <- c(
djprtheme::djpr_cobalt,
djprtheme::djpr_lima
)
names(colours) <- c(
"Victoria, all industries",
chosen_industry
)
df %>%
djpr_ts_linechart(
col_var = .data$industry,
label_num = paste0(round2(.data$value, 1), "%"),
y_labels = function(x) paste0(x, "%")
) +
scale_colour_manual(values = colours) +
labs(
subtitle = "Annual change in total employment",
caption = caption_lfs_det_q(),
title = paste0(
"Annual employment growth in ",
chosen_industry,
" compared to Victorian average"
)
)
}
viz_industries_emp_bysex_bar <- function(data = filter_dash_data(c(
"females_greater melbourne_accommodation and food services_employed full-time",
"females_greater melbourne_administrative and support services_employed full-time",
"females_greater melbourne_agriculture, forestry and fishing_employed full-time",
"females_greater melbourne_arts and recreation services_employed full-time",
"females_greater melbourne_construction_employed full-time",
"females_greater melbourne_education and training_employed full-time",
"females_greater melbourne_electricity, gas, water and waste services_employed full-time",
"females_greater melbourne_financial and insurance services_employed full-time",
"females_greater melbourne_health care and social assistance_employed full-time",
"females_greater melbourne_information media and telecommunications_employed full-time",
"females_greater melbourne_manufacturing_employed full-time",
"females_greater melbourne_mining_employed full-time",
"females_greater melbourne_other services_employed full-time",
"females_greater melbourne_professional, scientific and technical services_employed full-time",
"females_greater melbourne_public administration and safety_employed full-time",
"females_greater melbourne_rental, hiring and real estate services_employed full-time",
"females_greater melbourne_retail trade_employed full-time",
"females_greater melbourne_transport, postal and warehousing_employed full-time",
"females_greater melbourne_wholesale trade_employed full-time",
"males_greater melbourne_accommodation and food services_employed full-time",
"males_greater melbourne_administrative and support services_employed full-time",
"males_greater melbourne_agriculture, forestry and fishing_employed full-time",
"males_greater melbourne_arts and recreation services_employed full-time",
"males_greater melbourne_construction_employed full-time",
"males_greater melbourne_education and training_employed full-time",
"males_greater melbourne_electricity, gas, water and waste services_employed full-time",
"males_greater melbourne_financial and insurance services_employed full-time",
"males_greater melbourne_health care and social assistance_employed full-time",
"males_greater melbourne_information media and telecommunications_employed full-time",
"males_greater melbourne_manufacturing_employed full-time",
"males_greater melbourne_mining_employed full-time",
"males_greater melbourne_other services_employed full-time",
"males_greater melbourne_professional, scientific and technical services_employed full-time",
"males_greater melbourne_public administration and safety_employed full-time",
"males_greater melbourne_rental, hiring and real estate services_employed full-time",
"males_greater melbourne_retail trade_employed full-time",
"males_greater melbourne_transport, postal and warehousing_employed full-time",
"males_greater melbourne_wholesale trade_employed full-time",
"females_rest of vic._accommodation and food services_employed full-time",
"females_rest of vic._administrative and support services_employed full-time",
"females_rest of vic._agriculture, forestry and fishing_employed full-time",
"females_rest of vic._arts and recreation services_employed full-time",
"females_rest of vic._construction_employed full-time",
"females_rest of vic._education and training_employed full-time",
"females_rest of vic._electricity, gas, water and waste services_employed full-time",
"females_rest of vic._financial and insurance services_employed full-time",
"females_rest of vic._health care and social assistance_employed full-time",
"females_rest of vic._information media and telecommunications_employed full-time",
"females_rest of vic._manufacturing_employed full-time",
"females_rest of vic._mining_employed full-time",
"females_rest of vic._other services_employed full-time",
"females_rest of vic._professional, scientific and technical services_employed full-time",
"females_rest of vic._public administration and safety_employed full-time",
"females_rest of vic._rental, hiring and real estate services_employed full-time",
"females_rest of vic._retail trade_employed full-time",
"females_rest of vic._transport, postal and warehousing_employed full-time",
"females_rest of vic._wholesale trade_employed full-time",
"males_rest of vic._accommodation and food services_employed full-time",
"males_rest of vic._administrative and support services_employed full-time",
"males_rest of vic._agriculture, forestry and fishing_employed full-time",
"males_rest of vic._arts and recreation services_employed full-time",
"males_rest of vic._construction_employed full-time",
"males_rest of vic._education and training_employed full-time",
"males_rest of vic._electricity, gas, water and waste services_employed full-time",
"males_rest of vic._financial and insurance services_employed full-time",
"males_rest of vic._health care and social assistance_employed full-time",
"males_rest of vic._information media and telecommunications_employed full-time",
"males_rest of vic._manufacturing_employed full-time",
"males_rest of vic._mining_employed full-time",
"males_rest of vic._other services_employed full-time",
"males_rest of vic._professional, scientific and technical services_employed full-time",
"males_rest of vic._public administration and safety_employed full-time",
"males_rest of vic._rental, hiring and real estate services_employed full-time",
"males_rest of vic._retail trade_employed full-time",
"males_rest of vic._transport, postal and warehousing_employed full-time",
"males_rest of vic._wholesale trade_employed full-time",
"females_greater melbourne_accommodation and food services_employed part-time",
"females_greater melbourne_administrative and support services_employed part-time",
"females_greater melbourne_agriculture, forestry and fishing_employed part-time",
"females_greater melbourne_arts and recreation services_employed part-time",
"females_greater melbourne_construction_employed part-time",
"females_greater melbourne_education and training_employed part-time",
"females_greater melbourne_electricity, gas, water and waste services_employed part-time",
"females_greater melbourne_financial and insurance services_employed part-time",
"females_greater melbourne_health care and social assistance_employed part-time",
"females_greater melbourne_information media and telecommunications_employed part-time",
"females_greater melbourne_manufacturing_employed part-time",
"females_greater melbourne_mining_employed part-time",
"females_greater melbourne_other services_employed part-time",
"females_greater melbourne_professional, scientific and technical services_employed part-time",
"females_greater melbourne_public administration and safety_employed part-time",
"females_greater melbourne_rental, hiring and real estate services_employed part-time",
"females_greater melbourne_retail trade_employed part-time",
"females_greater melbourne_transport, postal and warehousing_employed part-time",
"females_greater melbourne_wholesale trade_employed part-time",
"males_greater melbourne_accommodation and food services_employed part-time",
"males_greater melbourne_administrative and support services_employed part-time",
"males_greater melbourne_agriculture, forestry and fishing_employed part-time",
"males_greater melbourne_arts and recreation services_employed part-time",
"males_greater melbourne_construction_employed part-time",
"males_greater melbourne_education and training_employed part-time",
"males_greater melbourne_electricity, gas, water and waste services_employed part-time",
"males_greater melbourne_financial and insurance services_employed part-time",
"males_greater melbourne_health care and social assistance_employed part-time",
"males_greater melbourne_information media and telecommunications_employed part-time",
"males_greater melbourne_manufacturing_employed part-time",
"males_greater melbourne_mining_employed part-time",
"males_greater melbourne_other services_employed part-time",
"males_greater melbourne_professional, scientific and technical services_employed part-time",
"males_greater melbourne_public administration and safety_employed part-time",
"males_greater melbourne_rental, hiring and real estate services_employed part-time",
"males_greater melbourne_retail trade_employed part-time",
"males_greater melbourne_transport, postal and warehousing_employed part-time",
"males_greater melbourne_wholesale trade_employed part-time",
"females_rest of vic._accommodation and food services_employed part-time",
"females_rest of vic._administrative and support services_employed part-time",
"females_rest of vic._agriculture, forestry and fishing_employed part-time",
"females_rest of vic._arts and recreation services_employed part-time",
"females_rest of vic._construction_employed part-time",
"females_rest of vic._education and training_employed part-time",
"females_rest of vic._electricity, gas, water and waste services_employed part-time",
"females_rest of vic._financial and insurance services_employed part-time",
"females_rest of vic._health care and social assistance_employed part-time",
"females_rest of vic._information media and telecommunications_employed part-time",
"females_rest of vic._manufacturing_employed part-time",
"females_rest of vic._mining_employed part-time",
"females_rest of vic._other services_employed part-time",
"females_rest of vic._professional, scientific and technical services_employed part-time",
"females_rest of vic._public administration and safety_employed part-time",
"females_rest of vic._rental, hiring and real estate services_employed part-time",
"females_rest of vic._retail trade_employed part-time",
"females_rest of vic._transport, postal and warehousing_employed part-time",
"females_rest of vic._wholesale trade_employed part-time",
"males_rest of vic._accommodation and food services_employed part-time",
"males_rest of vic._administrative and support services_employed part-time",
"males_rest of vic._agriculture, forestry and fishing_employed part-time",
"males_rest of vic._arts and recreation services_employed part-time",
"males_rest of vic._construction_employed part-time",
"males_rest of vic._education and training_employed part-time",
"males_rest of vic._electricity, gas, water and waste services_employed part-time",
"males_rest of vic._financial and insurance services_employed part-time",
"males_rest of vic._health care and social assistance_employed part-time",
"males_rest of vic._information media and telecommunications_employed part-time",
"males_rest of vic._manufacturing_employed part-time",
"males_rest of vic._mining_employed part-time",
"males_rest of vic._other services_employed part-time",
"males_rest of vic._professional, scientific and technical services_employed part-time",
"males_rest of vic._public administration and safety_employed part-time",
"males_rest of vic._rental, hiring and real estate services_employed part-time",
"males_rest of vic._retail trade_employed part-time",
"males_rest of vic._transport, postal and warehousing_employed part-time",
"males_rest of vic._wholesale trade_employed part-time"
), df = dash_data) %>%
dplyr::group_by(.data$series) %>%
dplyr::filter(.data$date == max(.data$date)) %>%
dplyr::ungroup(),
chosen_industry = "Agriculture, Forestry and Fishing") {
industry_total <- data %>%
dplyr::group_by(.data$sex, .data$date) %>%
dplyr::summarise(
value = sum(.data$value)
) %>%
dplyr::mutate(
industry = "Victoria, all industries"
) %>%
dplyr::filter(.data$date == max(.data$date)) # this is a quick fix for an issue where some series have a different max(date)
df <- data %>%
dplyr::filter(.data$industry == .env$chosen_industry) %>%
dplyr::select(
.data$date, .data$value, .data$series,
.data$indicator, .data$sex, .data$industry, .data$gcc_restofstate
)
df <- df %>%
dplyr::group_by(.data$sex, .data$industry, .data$date) %>%
dplyr::summarise(
value = sum(.data$value)
) %>%
dplyr::ungroup()
df <- df %>%
dplyr::bind_rows(industry_total)
df <- df %>%
dplyr::group_by(.data$industry) %>%
dplyr::mutate(perc = .data$value / sum(.data$value)) %>%
dplyr::ungroup()
df <- df %>%
dplyr::mutate(order = if_else(.data$industry == "Victoria, all industries", 2, 1))
label_df <- df %>%
dplyr::group_by(.data$industry) %>%
dplyr::arrange(desc(.data$sex)) %>%
dplyr::mutate(label_y = cumsum(.data$perc) - .data$perc + (.data$perc / 2))
legend_df <- label_df %>%
dplyr::filter(.data$industry != "Victoria, all industries")
chosen_industry_female_share <- round2(df$perc[df$sex == "Females" & df$industry != "Victoria, all industries"] * 100, 1)
vic_female_share <- round2(df$perc[df$sex == "Females" & df$industry == "Victoria, all industries"] * 100, 1)
title <- paste0(
"Women account for ",
chosen_industry_female_share,
" per cent of workers in the ",
chosen_industry,
" industry, which is ",
dplyr::case_when(
chosen_industry_female_share > vic_female_share ~
"higher than",
chosen_industry_female_share < vic_female_share ~
"lower than",
chosen_industry_female_share == vic_female_share ~
"the same as"
),
" the Victorian average"
)
df %>%
ggplot(aes(
x = stats::reorder(stringr::str_wrap(.data$industry, 15), -.data$order),
y = .data$value, fill = .data$sex
)) +
geom_col(
position = "fill",
alpha = 1,
col = djprtheme::djpr_cool_grey_11
) +
geom_text(
data = label_df,
aes(y = .data$label_y, label = paste0(round2(.data$perc * 100, 1), "%")),
size = 16 / .pt,
colour = "white"
) +
geom_text(
data = legend_df,
aes(y = .data$label_y, label = .data$sex, col = .data$sex),
nudge_x = 0.55,
size = 14 / .pt
) +
coord_flip() +
theme_djpr() +
djpr_fill_manual(2) +
djpr_colour_manual(2) +
theme(
axis.text.x = element_blank(),
axis.ticks = element_blank(),
axis.title = element_blank(),
panel.grid = element_blank(),
axis.line = element_blank()
) +
labs(
subtitle = paste0(
"Percentage share of men and women employed in industries in ",
format(max(df$date), "%B %Y"), "."
),
caption = paste0(caption_lfs_det_q(), " Data not seasonally adjusted."),
title = title
)
}
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