/
index.Rmd
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index.Rmd
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---
title: "Macro View"
output:
flexdashboard::flex_dashboard:
orientation: columns
vertical_layout: fill
css: inst/css/styles.css
source_code: NULL
logo: inst/imgs/logo.png
favicon: inst/imgs/favicon.png
social: menu
navbar:
- { icon: "fa-github", href: "https://github.com/schoulten/macroview", align: right }
- { icon: "fa-linkedin", href: "https://www.linkedin.com/in/schoulten/", align: right }
- { icon: "fa-at", href: "mailto:fernando@fortietwo.com", align: right }
---
<script>
$('.navbar-logo').wrap('<a href="https://fortietwo.com/" target=_blank>');
</script>
<!-- Icons by Freepik -->
```{r setup, include=FALSE}
# Set the default language of date in R
Sys.setlocale("LC_TIME", "English")
# Setup and load packages
knitr::opts_knit$set(progress = TRUE)
if (!require("pacman")) install.packages("pacman")
pacman::p_load(
"rmarkdown",
"highcharter",
"tidyverse",
"flexdashboard",
"htmlwidgets",
"gt",
"scales",
"colorspace",
"magrittr",
"htmltools",
"sparkline",
"rlist"
)
# Colors for the plots
colors <- c(
blue = "#282f6b",
red = "#b22200",
yellow = "#eace3f",
green = "#224f20",
purple = "#5f487c",
orange = "#b35c1e"
)
colors_extra <- c(
colors,
turquoise = "#419391",
green_two = "#839c56",
light_blue = "#3b89bc"
)
# Load funcitons
c("hc_plot_ft", "hc_theme_ft", "gt_sparkline", "utils") %>%
paste0("./R/", ., ".R") %>%
purrr::walk(~source(.x))
```
```{r data, include=FALSE}
# Load RDATA files
c("econ_activity", "fiscal", "inflation", "monetary", "labor") %>%
paste0("./inst/extdata/", ., ".Rdata") %>%
purrr::map( ~ load(.x, .GlobalEnv))
```
Overview {data-orientation=rows data-icon="fa-signal"}
=====================================
Row
-------------------------------------
### GDP growth (`r gdp_growth$date` - IBGE)
```{r section0_gdp_growth}
big_number_gdp <- gdp_growth %>%
pull(value) %>%
valueBox(
value = .,
icon = "fa-chart-line",
color = dplyr::case_when(
gdp_growth$value <= 0 ~ colors[2],
gdp_growth$value > 0 ~ colors[1])
)
big_number_gdp
```
### Inflation 12-months (`r format(ipca_yoy$date, "%b '%y")` - IBGE)
```{r section0_inflation_12m}
big_number_inflation <- ipca_yoy %>%
pull(value) %>%
valueBox(
value = .,
icon = "fa-usd",
color = dplyr::case_when(
ipca_yoy$value > inflation_target$value ~ colors[2],
ipca_yoy$value < 0 ~ colors[3],
ipca_yoy$value < inflation_target$value ~ colors[1],
ipca_yoy$value == inflation_target$value ~ colors[5])
)
big_number_inflation
```
### Unemployment rate (`r tail(unemployment$date, 1)` - IBGE)
```{r section0_unemployment_rate}
unemployment %>%
slice_tail() %>%
mutate(unemployment_rate = paste0(unemployment_rate, "%")) %>%
pull(unemployment_rate) %>%
valueBox(
value = .,
icon = "fa-users",
color = dplyr::case_when(
mean(unemployment$unemployment_rate) < tail(unemployment$unemployment_rate, 1) ~ colors[2],
mean(unemployment$unemployment_rate) > tail(unemployment$unemployment_rate, 1) ~ colors[4],
TRUE ~ colors[5]
)
)
```
### Primary Deficit (`r format(tail(primary_deficit$date, 1), "%b '%y")` - STN)
```{r section0_primary_deficit}
primary_deficit %>%
filter(date == max(date)) %>%
mutate(value = paste0(value, "%")) %>%
pull(value) %>%
valueBox(
value = .,
icon = "fa-area-chart",
color = dplyr::case_when(
tail(primary_deficit$value, 1) < 0 ~ colors[2],
tail(primary_deficit$value, 1) > 0 ~ colors[1],
tail(primary_deficit$value, 1) == 0 ~ colors[5])
)
```
### SELIC target rate (`r format(selic$date, "%b '%y")` - COPOM/BCB)
```{r section0_target_selic}
big_number_selic <- selic %>%
pull(value) %>%
valueBox(
value = .,
icon = "fa-percentage",
color = colors[5]
)
big_number_selic
```
Row {.tabset}
-------------------------------------
### GDP growth
```{r section0_gdp_trend}
gdp_trend <- gdp_growth_sector %>%
hchart("column", hcaes(x = date, y = value, group = sector), color = colors[1:4]) %>%
hc_plot_ft(
title = "GDP growth by sectors",
subtitle = "Accumulated growth rate in 4 quarters",
source = "IBGE",
range = FALSE,
navigator = FALSE
)
gdp_trend
```
### Inflation trend
```{r section0_ipca_trend}
inflation_trend <- hchart(
ipca_trend, "column", hcaes(x = date, y = `Month over Month (%)`),
color = colors[1], name = "Month over Month", showInLegend = TRUE
) %>%
hc_add_series(
ipca_trend, "line", hcaes(x = date, y = `Year over Year (%)`),
color = colors[2], name = "Year over Year", showInLegend = TRUE
) %>%
hc_plot_ft(
title = "Inflation trend (IPCA)",
subtitle = "Monthly and accumulated variation in 12 months (%)",
source = "IBGE",
navigator = FALSE
)
inflation_trend
```
### Unemployment rate
```{r section0_unemployment_trend}
unemployment_trend <- unemployment %>%
hchart(
"area", hcaes(x = date, y = unemployment_rate), color = colors[1],
name = "Unemployment rate", fillOpacity = 0.1) %>%
hc_plot_ft(
title = "Unemployment rate",
subtitle = "Quarterly data (PNADC-T)",
source = "IBGE",
range = FALSE,
navigator = FALSE
)
unemployment_trend
```
### Unemployment by states
```{r section0_unemployment_states}
unemployment_map <- hcmap(
map = "countries/br/br-all",
data = unemployment_states,
joinBy = "woe-name",
value = "unemployment_rate",
name = "Unemployment rate",
download_map_data = TRUE,
dataLabels = list(
enabled = TRUE,
format = "{point.name}"
),
mapNavigation = list(
enabled = TRUE,
buttonOptions = list(
verticalAlign = "bottom"
)
)
) %>%
hc_title(text = "Unemployment rate by states") %>%
hc_subtitle(text = paste0(
"Quarterly data, updated until ", unemployment_states$date[1], " (PNADC-T)"
)
) %>%
hc_credits(enabled = TRUE, text = "Source: IBGE", style = list(fontSize = "12px"))
unemployment_map
```
### Primary deficit trend
```{r section0_primary_deficit_trend}
primary_deficit_trend <- primary_deficit %>%
hchart(
"area", hcaes(x = date, y = value), color = colors[1],
name = "Primary Deficit", fillOpacity = 0.1
) %>%
hc_plot_ft(
title = "Central Government Primary Deficit",
subtitle = "Accumulated in 12 months (% GDP)",
source = "STN and IBGE",
navigator = FALSE
)
primary_deficit_trend
```
### Real interest rates
```{r section0_real_interest_rates}
real_interest_rate_trend <- real_interest_rate %>%
hchart(
"area", hcaes(x = date, y = value, group = variable),
color = colors[1:2], fillOpacity = 0.1
) %>%
hc_plot_ft(
title = "Real interest rates",
subtitle = "In % p.a.",
source = "B3, BCB and IBGE",
navigator = FALSE
)
real_interest_rate_trend
```
### Exchange rate
```{r section0_currency}
exchange_rate <- currencies %>%
gt() %>%
tab_header(
title = gt::html("<b>Exchange rate performance</b>")
) %>%
tab_source_note(
gt::html("Source: BCB")) %>%
tab_footnote(
footnote = footnote_currency,
locations = cells_column_labels(columns = vars(value))
) %>%
tab_footnote(
footnote = "MoM: monthly percentage change",
locations = cells_column_labels(columns = vars(mom))
) %>%
tab_footnote(
footnote = "YTD: percentage change in the year",
locations = cells_column_labels(columns = vars(ytd))
) %>%
tab_footnote(
footnote = "YoY: accumulated in 12 months percentage change against the same period of the previous year",
locations = cells_column_labels(columns = vars(yoy))
) %>%
tab_options(
data_row.padding = px(2)
) %>%
text_transform(
locations = cells_body(columns = vars(flag)),
fn = function(x){
map_chr(x, ~ gt::local_image(
filename = .x,
height = 25
))
}
) %>%
fmt_currency(
columns = vars(value),
currency = "BRL",
incl_space = TRUE
) %>%
fmt_percent(
columns = vars(mom, ytd, yoy),
scale_values = FALSE
) %>%
cols_align(
align = "center",
columns = vars(flag)
) %>%
cols_label(
flag = "",
currency = "Currency",
value = "CUR/BRL",
mom = "MoM",
ytd = "YTD",
yoy = "YoY",
spark = "Trend"
) %>%
tab_style(
style = cell_text(weight = "bold"),
locations = cells_column_labels(TRUE)
) %>%
data_color(
columns = 5:7,
colors = scales::col_numeric(
colorspace::diverge_hcl(n = 20, palette = "Blue-Red 3"),
reverse = TRUE,
domain = c(-max_abs(currencies[5:7]), max_abs(currencies[5:7]))
)
) %>%
gt_sparkline(spark, "spark") %>%
gt:::as.tags.gt_tbl() %>%
htmltools::attachDependencies(htmlwidgets::getDependency("sparkline"))
exchange_rate
```
### Ibovespa index
```{r section0_ibovespa}
ibovespa_candle <- highchart(type = "stock") %>%
hc_yAxis_multiples(create_yaxis(2, height = c(2, 1), turnopposite = TRUE)) %>%
hc_add_series(ibovespa, yAxis = 0, name = "Ibovespa") %>%
hc_add_series(
ibovespa$BVSP.Volume,
color = "gray",
yAxis = 1,
name = "Volume",
type = "column"
) %>%
hc_title(text = "Ibovespa index") %>%
hc_credits(enabled = TRUE, text = "Source: B3", style = list(fontSize = "12px")) %>%
hc_rangeSelector(selected = 1, inputEnabled = FALSE) %>%
hc_add_theme(theme_fortietwo) %>%
hc_plotOptions(
candlestick = list(
color = unname(colors[2]),
upColor = unname(colors[4])
)
)
ibovespa_candle
```
Economic activity {data-navmenu="Sections" data-icon="fa-chart-line"}
=====================================
Column {data-width=375}
-----------------------------------------------------------------------
### GDP growth in 4 quarters (`r gdp_growth$date` - IBGE)
```{r section1_gdp_growth}
big_number_gdp
```
### GDP at current prices in 4 quarters (`r gdp_cur_prices$date` - IBGE)
```{r section1_gdp_current_prices}
gdp_cur_prices %>%
pull(value) %>%
valueBox(
value = .,
icon = "fa-dollar-sign",
color = "#224f20"
)
```
### Cielo retail index (`r format(icva$date, "%B, %Y")` - ICVA)
```{r section1_icva}
icva %>%
pull(value) %>%
valueBox(
value = .,
icon = "fa-store",
color = ifelse(. < 0, "#b22200", "#282f6b")
)
```
### Vehicle production (`r format(vehicle$date, "%B, %Y")` - ANFAVEA)
```{r section1_vehicle_production}
vehicle %>%
pull(value) %>%
valueBox(
value = .,
icon = "fa-car",
color = "#5f487c"
)
```
### Installed Capacity Utilization Level (`r format(nuci$date, "%B, %Y")` - FGV)
```{r section1_nuci_fgv}
gauge(
value = nuci$value,
min = 0,
max = 100,
symbol = "%",
gaugeSectors(
success = c(85, 100),
warning = c(50, 85),
danger = c(0, 50),
colors = c("#224f20", "#b35c1e", "#b22200")
)
)
```
Column {data-width=575}
-----------------------------------------------------------------------
### GDP trend {.no-title}
```{r section1_gdp_trend}
gdp_trend
```
### IBC-Br trend {.no-title}
```{r section1_ibcbr_trend}
ibc_growth %>%
hchart("line", hcaes(x = date, y = yoy, group = variable), color = colors) %>%
hc_plot_ft(
title = "IBC-Br growth by region",
subtitle = "Accumulated growth rate in 12 months",
source = "BCB",
navigator = FALSE
)
```
Column {data-width=500 .tabset}
-----------------------------------------------------------------------
### GDP approaches
```{r section1_gdp_approaches}
gdp_measures %>%
gt(rowname_col = "sector") %>%
tab_source_note(footnote_gdp) %>%
tab_source_note("Source: IBGE") %>%
tab_footnote(
footnote = "Growth rate for the quarter versus the immediately preceding quarter",
locations = cells_column_labels(columns = vars(`Quarter over Quarter`))
) %>%
tab_footnote(
footnote = "Quarterly growth rate (compared to the same period of the previous year)",
locations = cells_column_labels(columns = vars(`Year over Year`))
) %>%
tab_footnote(
footnote = "Accumulated growth rate over the year (compared to the same period of the previous year)",
locations = cells_column_labels(columns = vars(`Year to Date`))
) %>%
tab_footnote(
footnote = "Growth rate accumulated in 4 quarters (compared to the same period of the previous year)",
locations = cells_column_labels(columns = vars(`Accumulated in 4 Quarters`))
) %>%
data_color(
columns = 2:5,
colors = scales::col_numeric(
colorspace::diverge_hcl(n = 20, palette = "Blue-Red 3"),
reverse = TRUE,
domain = c(-max_abs(gdp_measures[2:5]), max_abs(gdp_measures[2:5]))
)
) %>%
fmt_percent(columns = 2:5, scale_values = FALSE, decimals = 1) %>%
cols_width(2:5 ~ px(125)) %>%
tab_options(
column_labels.font.weight = "bold",
stub.font.weight = "bold",
footnotes.padding = 3,
data_row.padding = 6
)
```
### Retail (PMC)
```{r section1_pmc}
pmc %>%
hchart("line", hcaes(x = date, y = value, group = variable), color = colors[1:4]) %>%
hc_plot_ft(
title = "Retail trade index: PMC/IBGE",
subtitle = "Monthly percentage change (compared to the same month in the previous year)",
source = "IBGE"
)
```
### Services (PMS)
```{r section1_pms}
pms %>%
hchart("line", hcaes(x = date, y = value, group = variable), color = colors[1:2]) %>%
hc_plot_ft(
title = "Revenue and volume of services index: PMS/IBGE",
subtitle = "Monthly percentage change (compared to the same month in the previous year)",
source = "IBGE"
)
```
### Industry (PIM-PF)
```{r section1_pim}
pim %>%
hchart("line", hcaes(x = date, y = value, group = variable), color = colors[1:4]) %>%
hc_plot_ft(
title = "Industrial production: PIM-PF/IBGE",
subtitle = "Monthly percentage change (compared to the same month in the previous year)",
source = "IBGE"
)
```
Inflation {data-navmenu="Sections" data-icon="fa-hand-holding-usd"}
=====================================
Column {data-width=350}
-----------------------------------------------------------------------
### Inflation over 12 months (`r format(ipca_yoy$date, "%B, %Y")` - IPCA/IBGE)
```{r section2_inflation_12m}
big_number_inflation
```
### Inflation target for (`r inflation_target$date` - CMN/BCB)
```{r section2_inflation_target}
inflation_target %>%
mutate(value = paste0(value, "%")) %>%
pull(value) %>%
valueBox(
value = .,
icon = "fa-bullseye",
color = "#5f487c"
)
```
### Diffusion index (IPCA) {.no-title}
```{r section2_diffusion_ipca}
ipca_diffusion %>%
hchart(
"area", hcaes(x = date, y = value, group = variable),
color = colors[1], fillOpacity = 0.1) %>%
hc_plot_ft(
title = "Diffusion index - IPCA",
subtitle = "",
source = "BCB"
)
```
Column {data-width=550}
-----------------------------------------------------------------------
### Inflation trend {.no-title}
```{r section2_ipca_trend}
inflation_trend
```
### Inflation by metropolitan region {.no-title}
```{r section2_ipca_region}
ipca_region %>%
hchart("column", hcaes(x = date, y = value, group = region), color = colors) %>%
hc_plot_ft(
title = "Inflation by metropolitan region - IPCA",
subtitle = "Monthly percentage change",
source = "IBGE",
navigator = FALSE
) %>%
hc_rangeSelector(selected = 0)
```
Column {data-width=450 .tabset}
-----------------------------------------------------------------------
### CPI components
```{r section2_ipca_groups}
ipca_groups %>%
gt(rowname_col = "Group") %>%
tab_source_note(footnote_ipca_groups) %>%
tab_source_note("Source: IPCA/IBGE") %>%
tab_footnote(
footnote = "Month over Month: monthly percentage change",
locations = cells_column_labels(columns = vars(`MoM`))
) %>%
tab_footnote(
footnote = "Year to Date: percentage change accumulated in the year",
locations = cells_column_labels(columns = vars(`YTD`))
) %>%
data_color(
columns = 2:3,
colors = scales::col_numeric(
colorspace::diverge_hcl(n = 20, palette = "Blue-Red 3"),
reverse = TRUE,
domain = c(-max_abs(ipca_groups[2:3]), max_abs(ipca_groups[2:3]))
)
) %>%
fmt_percent(columns = 2:3, scale_values = FALSE, decimals = 1) %>%
cols_width(2:3 ~ px(130)) %>%
cols_width(1 ~ px(180)) %>%
tab_options(
column_labels.font.weight = "bold",
stub.font.weight = "bold",
footnotes.padding = 3,
data_row.padding = 6
)
```
### CPI cores
```{r section2_ipca_cores}
ipca_cores %>%
hchart("line", hcaes(x = date, y = value, group = variable), color = colors[1:5]) %>%
hc_plot_ft(
title = "CPI cores (IPCA)",
subtitle = "Monthly percentage change",
source = "BCB"
)
```
### IGP trend
```{r section2_igp}
igp %>%
hchart("line", hcaes(x = date, y = value, group = variable), color = colors[1:3]) %>%
hc_plot_ft(
title = "General Price Index (IGP)",
subtitle = "Monthly percentage change",
source = "FGV"
)
```
Labor market {data-navmenu="Sections" data-icon="fa-users"}
=====================================
Column {data-width=375}
-----------------------------------------------------------------------
### # Employed (`r big_numbers$date` - IBGE)
```{r section3_employed}
big_numbers %>%
pull(employed) %>%
valueBox(
value = .,
icon = "fa-users",
color = "#282f6b"
)
```
### # Unemployed (`r big_numbers$date` - IBGE)
```{r section3_unemployed}
big_numbers %>%
pull(unemployed) %>%
valueBox(
value = .,
icon = "fa-user-times",
color = "#b22200"
)
```
### Employment by category {.no-title}
```{r section3_employment_category}
highchart() %>%
hc_chart(type = "item") %>%
hc_title(text = "Employment by category") %>%
hc_subtitle(
text = paste0("In millions of employees, updated until ", employment_category$date[1])
) %>%
hc_credits(enabled = TRUE, text = "Source: IBGE", style = list(fontSize = "12px")) %>%
hc_legend(labelFormat = '{name} <span style="opacity: 0.4">{y}</span>') %>%
hc_series(
list(
name = "Employees",
keys = c("name", "y", "color"),
data = {
employment_category %>%
select(-date) %>%
mutate(color = colors) %>%
unname() %>%
transpose(.names = NULL)
},
dataLabels = list(
enabled = TRUE,
format = "{point.name} million"
),
center = c("50%", "88%"),
size = "50%"
)
)
```
Column {data-width=500}
-----------------------------------------------------------------------
### Unemployment rate {.no-title}
```{r section3_unemployment}
unemployment_trend
```
### Unemployment rate by gender and color {.no-title}
```{r section3_unemployment_gender_color}
unemployment_gender_color %>%
hchart("column", hcaes(x = color, y = unemployment_rate, group = gender), color = colors[1:2]) %>%
hc_plot_ft(
title = "Unemployment rate by gender and color",
subtitle = paste0(
"Quarterly data, updated until ", unemployment_gender_color$date[1], " (PNADC-T)"
),
source = "IBGE",
range = FALSE,
navigator = FALSE
)
```
Column {data-width=500 .tabset}
-----------------------------------------------------------------------
### Unemployment by states
```{r section3_unemployment_states}
unemployment_map
```
### Occupation
```{r section3_occupation_level}
occupation_level %>%
hchart(
"line", hcaes(x = date, y = value, group = gender), color = colors[1:3]) %>%
hc_plot_ft(
title = "Labor force occupation level",
subtitle = "Quarterly data (PNADC-T)",
source = "IBGE",
range = FALSE,
navigator = FALSE
)
```
### Participation
```{r section3_participation_rate}
participation_rate %>%
hchart(
"line", hcaes(x = date, y = value, group = gender), color = colors[1:3]) %>%
hc_plot_ft(
title = "Labor force participation rate",
subtitle = "Quarterly data (PNADC-T)",
source = "IBGE",
range = FALSE,
navigator = FALSE
)
```
### Income
```{r section3_income}
income %>%
hchart(
"line", hcaes(x = date, y = value, group = age), color = colors) %>%
hc_plot_ft(
title = "Average real income from work, by age group",
subtitle = "Quarterly data (PNADC-T)",
source = "IBGE",
range = FALSE,
navigator = FALSE
) %>%
hc_yAxis(labels = list(format = "R$ {value:,.0f}"))
```
### Gini
```{r section3_gini}
gini %>%
hchart(
"bar", hcaes(x = states, y = value), color = colors[1], name = "Gini") %>%
hc_plot_ft(
title = "Gini index by states",
subtitle = paste0(
"Quarterly data, updated until ", gini$date[1], " (PNADC-T)"
),
source = "IBGE",
range = FALSE,
navigator = FALSE
) %>%
hc_yAxis(labels = list(format = "{value}"))
```
Fiscal policy {data-navmenu="Sections" data-icon="fa-gavel"}
=====================================
Column {data-width=450}
-----------------------------------------------------------------------
### Central Government Primary Deficit {.no-title}
```{r section4_primary_deficit}
primary_deficit_trend
```
### Central Government Net Revenue and Total Expenditure {.no-title}
```{r section4_revenue_spending}
revenue_spending %>%
hchart("line", hcaes(x = date, y = value, group = variable), color = colors[1:2]) %>%
hc_plot_ft(
title = "Central Government Net Revenue and Total Expenditure",
subtitle = "Accumulated in 12 months and deflated by the IPCA index (% GDP)",
source = "STN and IBGE",
navigator = FALSE
)
```
Column {data-width=450}
-----------------------------------------------------------------------
### Detailed accounts of Central Government Income and Expenditure {.no-title}
```{r section4_revenue_spending_detail}
highchart() %>%
hc_add_series(
type = "treemap",
layoutAlgorithm = "squarified",
allowDrillToNode = TRUE,
levels = list(
list(level = 1,
dataLabels = list(enabled = TRUE, style = list(fontSize = "20px")),
borderWidth = 3
),
list(level = 2, dataLabels = list(enabled = TRUE), borderWidth = 2),
list(level = 3, dataLabels = list(enabled = TRUE), borderWidth = 1)
),
tooltip = list(valueDecimals = 2, valuePrefix = "R$ "),
data = list(
data = list(
# LEVEL I
list(
id = unique(revenue_spending_detail$group_1)[1] %>%
stringr::str_replace_all("[[:blank:]]", "_") %>% stringr::str_to_lower(),
name = unique(revenue_spending_detail$group_1)[1],
color = unname(colors[1])
),
list(
id = unique(revenue_spending_detail$group_1)[2] %>%
stringr::str_replace_all("[[:blank:]]", "_") %>% stringr::str_to_lower(),
name = unique(revenue_spending_detail$group_1)[2],
color = unname(colors[2])
),
list(
id = unique(revenue_spending_detail$group_1)[3] %>%
stringr::str_replace_all("[[:blank:]]", "_") %>% stringr::str_to_lower(),
name = unique(revenue_spending_detail$group_1)[3],
color = unname(colors[3])
)
)
) %>%
rlist::list.append(
# LEVEL II
{revenue_spending_detail %>%
select("name" = group_2, "parent" = group_1, value) %>%
mutate(parent = stringr::str_replace_all(parent, "[[:blank:]]", "_") %>%
stringr::str_to_lower()) %>%
group_by(name, parent) %>%
summarise(value = sum(value), .groups = "drop") %>%
mutate(id = stringr::str_replace_all(name, "[[:blank:]]", "_") %>%
stringr::str_to_lower()) %>%
transpose()
},
# LEVEL III
{revenue_spending_detail %>%
select("name" = group_3, "parent" = group_2, value) %>%
mutate(parent = stringr::str_replace_all(parent, "[[:blank:]]", "_") %>%
stringr::str_to_lower()) %>%
transpose()
}
) %>%
flatten()
) %>%
hc_plot_ft(
title = "Detailed Central Government Accounts",
subtitle = sprintf(
"Accumulated in 12 months and deflated by the IPCA index, R$ million (%s)",
revenue_spending_detail$date[1]
),
source = "STN and IBGE",
range = FALSE,
navigator = FALSE
)
```
### Single Account balance {.no-title}
```{r section4_single_account}
single_account %>%
hchart(
"area", hcaes(x = date, y = value, group = variable),
color = colors[4], fillOpacity = 0.1) %>%
hc_plot_ft(
title = "Single Account balance",
subtitle = "Deflated by the IPCA index, R$ million",
source = "BCB and IBGE",
navigator = FALSE
) %>%
hc_yAxis(labels = list(format = "R$ {value:,.0f}"))
```
Column {data-width=450 .tabset}
-----------------------------------------------------------------------
### Debt Stock
```{r section4_debt_stock}
debt_stock %>%
hchart(
"area", hcaes(x = date, y = value, group = variable),
color = colors[2], fillOpacity = 0.1) %>%
hc_plot_ft(
title = "Federal Public Debt Stock",
subtitle = "Deflated by the IPCA index, R$ billion",
source = "BCB and IBGE"
) %>%
hc_yAxis(labels = list(format = "R$ {value:,.0f}"))
```
### Government Securities Portfolio
```{r section4_gov_portfolio}
gov_portfolio %>%
hchart(
"pie", hcaes(y = value, name = variable, color = colors_extra[1:8]),
name = "Government Securities") %>%
hc_plot_ft(
title = "Details of the National Treasury Securities portfolio",
subtitle = sprintf("R$ billion (%s)", gov_portfolio$date_my[1]),
source = "BCB",