source(here::here("scripts/load-libraries.R"))
theme_set(theme_light() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
plot.title = element_text(size = rel(2))))
my_caption_rbf = "Daniel Moul. Source: Reserve Bank of Fiji"
my_caption_fbos = "Daniel Moul. Source: Fiji Bureau of Statistics via Reserve Bank of Fiji"The Reserve Bank of Fiji provides the data used in this chapter.
trade <- read_xlsx(here("data/raw/rbf/8.4-Direction-of-Trade-by-Major-Trading-Partner.xlsx"),
sheet = "Sheet1",
skip = 5) |>
clean_names() |>
remove_empty(which = c("rows", "cols")) |>
filter(row_number() > 1) |>
fill(country, .direction = "down") |>
mutate(country = case_match(
country,
"China, Peoples Republic" ~ "China",
"China, Taiwan" ~ "Taiwan",
"Germany, Federal" ~ "Germany",
"Others & Other Asian Countries2/" ~ "Others",
.default = country
),
across(year:trade_deficit_surplus, as.numeric),
domestic_imports = imports - re_exports,
domestic_imports = if_else(domestic_imports >= 0,
domestic_imports,
NA_real_)
)
trade_year_min <- min(trade$year)
trade_year_max <- max(trade$year)
dta_for_plot_all <- trade |>
filter(country != "TOTAL",
country != "Germany", # omit, since not enough trade to be interesting
!year %in% c(2020, 2021, 2022)) |>
pivot_longer(domestic_exports:domestic_imports,
names_to = "metric",
values_to = "value")dta_for_plot <- dta_for_plot_all |>
filter(metric %in% c("domestic_exports", "re_exports") )
dta_for_plot_labels <- dta_for_plot |>
filter(year == 2023) |>
group_by(metric) |>
slice_max(order_by = abs(value),
n = 5) |>
ungroup() |>
select(country, metric) |>
mutate(country_label = country)
p1 <- dta_for_plot |>
left_join(
dta_for_plot_labels,
by = c("country", "metric")
) |>
ggplot(aes(year, value, color = country, group = country)) +
geom_line(
linewidth = 0.5,
na.rm = TRUE,
show.legend = FALSE
) +
geom_text_repel(aes(x = 2024,
y = if_else(year == 2023,
value,
NA_real_),
label = country_label),
size = 3, hjust = 0,
direction = "y",
na.rm = TRUE,
show.legend = FALSE) +
scale_y_continuous(expand = expansion(mult = c(0.005, 0.02))) +
expand_limits(x = 2030) +
facet_wrap(~ metric) +
labs(
x = NULL,
y = "FJD ($M)"
)
p2 <- dta_for_plot |>
left_join(
dta_for_plot_labels,
by = c("country", "metric")
) |>
ggplot(aes(year, value, color = country, group = country)) +
geom_smooth(
linewidth = 0.5,
se = FALSE, method = 'loess', formula = 'y ~ x',
na.rm = TRUE,
show.legend = FALSE
) +
geom_text_repel(aes(x = 2024,
y = if_else(year == 2023,
value,
NA_real_),
label = country_label),
size = 3, hjust = 0,
direction = "y",
na.rm = TRUE,
show.legend = FALSE) +
scale_y_continuous(expand = expansion(mult = c(0.005, 0.02))) +
expand_limits(x = 2030) +
facet_wrap(~ metric) +
coord_cartesian(ylim = c(0, NA)) +
labs(
subtitle = "Smoothed lines",
x = NULL,
y = "FJD ($M)"
)
p1 / p2 +
plot_annotation(
title = "Fiji exports",
subtitle = glue("Top five countries in 2023 labeled for each metric.",
" {trade_year_min} to {trade_year_max}, excluding 2020-2022."),
caption = my_caption_fbos
)
dta_for_plot <- dta_for_plot_all |>
filter(metric %in% c("domestic_exports", "re_exports") ) |>
mutate(country_facet = fct_reorder(country, -value, sum))
p1 <- dta_for_plot |>
left_join(
dta_for_plot_labels,
by = c("country", "metric")
) |>
ggplot(aes(year, value, color = metric, group = metric)) +
geom_line(
linewidth = 0.5,
na.rm = TRUE,
show.legend = TRUE
) +
scale_y_continuous(expand = expansion(mult = c(0.005, 0.02))) +
scale_color_viridis_d(end = 0.9, direction = 1) +
guides(color = guide_legend(override.aes = list(linewidth = 3))) +
facet_wrap(~ country_facet) +
theme(legend.position = "top") +
labs(
x = NULL,
y = "FJD ($M)",
color = NULL
)
p2 <- dta_for_plot |>
ggplot(aes(year, value, color = metric, group = metric)) +
geom_smooth(
linewidth = 0.5,
se = FALSE, method = 'loess', formula = 'y ~ x',
na.rm = TRUE,
show.legend = FALSE
) +
guides(color = guide_legend(override.aes = list(linewidth = 3))) +
scale_y_continuous(expand = expansion(mult = c(0.005, 0.02))) +
scale_color_viridis_d(end = 0.9, direction = 1) +
facet_wrap(~ country_facet) +
coord_cartesian(ylim = c(0, NA)) +
labs(
subtitle = "Smoothed lines",
x = NULL,
y = "FJD ($M)",
color = NULL
)
p1 / p2 +
plot_annotation(
title = "Fiji exports faceted by country",
subtitle = glue("{trade_year_min} to {trade_year_max}, excluding 2020-2022.",
" Facets ordered by total exports over time period."),
caption = my_caption_fbos
)
dta_for_plot <- dta_for_plot_all |>
filter(metric %in% c("domestic_exports", "re_exports") ) |>
mutate(country_facet = fct_reorder(country, -value, sum))
p1 <- dta_for_plot |>
left_join(
dta_for_plot_labels,
by = c("country", "metric")
) |>
ggplot(aes(year, value, color = metric, group = metric)) +
geom_line(
linewidth = 0.5,
na.rm = TRUE,
show.legend = TRUE
) +
scale_y_continuous(expand = expansion(mult = c(0.005, 0.02))) +
scale_color_viridis_d(end = 0.9, direction = 1) +
guides(color = guide_legend(override.aes = list(linewidth = 3))) +
facet_wrap(~ country_facet, scales = "free_y") +
theme(legend.position = "top") +
labs(
x = NULL,
y = "FJD ($M)",
color = NULL
)
p2 <- dta_for_plot |>
ggplot(aes(year, value, color = metric, group = metric)) +
geom_smooth(
linewidth = 0.5,
se = FALSE, method = 'loess', formula = 'y ~ x',
na.rm = TRUE,
show.legend = FALSE
) +
guides(color = guide_legend(override.aes = list(linewidth = 3))) +
scale_y_continuous(expand = expansion(mult = c(0.005, 0.02))) +
scale_color_viridis_d(end = 0.9, direction = 1) +
facet_wrap(~ country_facet, scales = "free_y") +
coord_cartesian(ylim = c(0, NA)) +
labs(
subtitle = "Smoothed lines",
x = NULL,
y = "FJD ($M)",
color = NULL
)
p1 / p2 +
plot_annotation(
title = "Fiji exports faceted by country (Y axis scale varies)",
subtitle = glue("{trade_year_min} to {trade_year_max}, excluding 2020-2022.",
" Facets ordered by total exports over time period."),
caption = my_caption_fbos
)
dta_for_plot <- dta_for_plot_all |>
filter(metric %in% c("domestic_imports", "imports") ) |>
mutate(country_facet = fct_reorder(country, -value, sum))
p1 <- dta_for_plot |>
left_join(
dta_for_plot_labels,
by = c("country", "metric")
) |>
ggplot(aes(year, value, color = metric, group = metric)) +
geom_line(
linewidth = 0.5,
na.rm = TRUE,
show.legend = TRUE
) +
scale_y_continuous(expand = expansion(mult = c(0.005, 0.02))) +
scale_color_viridis_d(end = 0.9, direction = -1) +
guides(color = guide_legend(override.aes = list(linewidth = 3))) +
facet_wrap(~ country_facet) +
theme(legend.position = "top") +
labs(
x = NULL,
y = "FJD ($M)",
color = NULL
)
p2 <- dta_for_plot |>
ggplot(aes(year, value, color = metric, group = metric)) +
geom_smooth(
linewidth = 0.5,
se = FALSE, method = 'loess', formula = 'y ~ x',
na.rm = TRUE,
show.legend = FALSE
) +
guides(color = guide_legend(override.aes = list(linewidth = 3))) +
scale_y_continuous(expand = expansion(mult = c(0.005, 0.02))) +
scale_color_viridis_d(end = 0.9, direction = -1) +
facet_wrap(~ country_facet) +
coord_cartesian(ylim = c(0, NA)) +
labs(
subtitle = "Smoothed lines",
x = NULL,
y = "FJD ($M)",
color = NULL
)
p1 / p2 +
plot_annotation(
title = "Fiji imports faceted by country",
subtitle = glue("{trade_year_min} to {trade_year_max}, excluding 2020-2022.",
"\nFacets ordered by total imports over time period."),
caption = my_caption_fbos
)
dta_for_plot <- dta_for_plot_all |>
filter(metric %in% c("domestic_imports", "imports") ) |>
mutate(country_facet = fct_reorder(country, -value, sum))
p1 <- dta_for_plot |>
left_join(
dta_for_plot_labels,
by = c("country", "metric")
) |>
ggplot(aes(year, value, color = metric, group = metric)) +
geom_line(
linewidth = 0.5,
na.rm = TRUE,
show.legend = TRUE
) +
scale_y_continuous(expand = expansion(mult = c(0.005, 0.02))) +
scale_color_viridis_d(end = 0.9, direction = -1) +
guides(color = guide_legend(override.aes = list(linewidth = 3))) +
expand_limits(y = 0) +
facet_wrap(~ country_facet, scales = "free_y") +
theme(legend.position = "top") +
labs(
x = NULL,
y = "FJD ($M)",
color = NULL
)
p2 <- dta_for_plot |>
ggplot(aes(year, value, color = metric, group = metric)) +
geom_smooth(
linewidth = 0.5,
se = FALSE, method = 'loess', formula = 'y ~ x',
na.rm = TRUE,
show.legend = FALSE
) +
guides(color = guide_legend(override.aes = list(linewidth = 3))) +
scale_y_continuous(expand = expansion(mult = c(0.005, 0.02))) +
scale_color_viridis_d(end = 0.9, direction = -1) +
expand_limits(y = 0) +
facet_wrap(~ country_facet, scales = "free_y") +
coord_cartesian(ylim = c(0, NA)) +
labs(
subtitle = "Smoothed lines",
x = NULL,
y = "FJD ($M)",
color = NULL
)
p1 / p2 +
plot_annotation(
title = "Fiji imports faceted by country (Y axis scale varies)",
subtitle = glue("{trade_year_min} to {trade_year_max}, excluding 2020-2022.",
" Facets ordered by total imports over time period."),
caption = my_caption_fbos
)
dta_for_plot <- dta_for_plot_all |>
filter(metric %in% c("trade_deficit_surplus") )
dta_for_plot_labels <- dta_for_plot |>
filter(year == 2023) |>
group_by(metric) |>
slice_max(order_by = abs(value),
n = 10) |>
ungroup() |>
select(country, metric) |>
mutate(country_label = country)
p1 <- dta_for_plot |>
left_join(
dta_for_plot_labels,
by = c("country", "metric")
) |>
ggplot(aes(year, value, color = country, group = country)) +
geom_hline(yintercept = 0, lty = 2, linewidth = 0.15, alpha = 0.5) +
geom_line(
linewidth = 0.5,
na.rm = TRUE,
show.legend = FALSE
) +
geom_text_repel(aes(x = 2024,
y = if_else(year == 2023,
value,
NA_real_),
label = country_label),
size = 3, hjust = 0,
direction = "y",
na.rm = TRUE,
show.legend = FALSE) +
scale_y_continuous(expand = expansion(mult = c(0.005, 0.02))) +
expand_limits(x = 2030) +
facet_wrap(~ metric) +
labs(
x = NULL,
y = "FJD ($M)"
)
p2 <- dta_for_plot |>
left_join(
dta_for_plot_labels,
by = c("country", "metric")
) |>
ggplot(aes(year, value, color = country, group = country)) +
geom_hline(yintercept = 0, lty = 2, linewidth = 0.15, alpha = 0.5) +
geom_smooth(
linewidth = 0.5,
se = FALSE, method = 'loess', formula = 'y ~ x',
na.rm = TRUE,
show.legend = FALSE
) +
geom_text_repel(aes(x = 2024,
y = if_else(year == 2023,
value,
NA_real_),
label = country_label),
size = 3, hjust = 0,
direction = "y",
na.rm = TRUE,
show.legend = FALSE) +
scale_y_continuous(expand = expansion(mult = c(0.005, 0.02))) +
expand_limits(x = 2030) +
facet_wrap(~ metric) +
labs(
subtitle = "Smoothed lines",
x = NULL,
y = "FJD ($M)"
)
p1 / p2 +
plot_annotation(
title = "Fiji blance of trade",
subtitle = glue("Top ten countries with which Fiji had largest surplus or deficit in 2023 labeled.",
" {trade_year_min} to {trade_year_max}, excluding 2020-2022."),
caption = my_caption_fbos
)
dta_for_plot <- dta_for_plot_all |>
filter(metric %in% c("trade_deficit_surplus") ) |>
mutate(country_facet = fct_reorder(country, -value, sum))
p1 <- dta_for_plot |>
# left_join(
# dta_for_plot_labels,
# by = c("country", "metric")
# ) |>
ggplot(aes(year, value, color = metric, group = metric)) +
geom_hline(yintercept = 0, lty = 2, linewidth = 0.15, alpha = 0.5) +
geom_line(
linewidth = 0.5,
na.rm = TRUE,
show.legend = FALSE
) +
scale_y_continuous(expand = expansion(mult = c(0.005, 0.02))) +
guides(color = guide_legend(override.aes = list(linewidth = 3))) +
facet_wrap(~ country_facet) +
theme(legend.position = "top") +
labs(
x = NULL,
y = "FJD ($M)"
)
p2 <- dta_for_plot |>
ggplot(aes(year, value, color = metric, group = metric)) +
geom_hline(yintercept = 0, lty = 2, linewidth = 0.15, alpha = 0.5) +
geom_smooth(
linewidth = 0.5,
se = FALSE, method = 'loess', formula = 'y ~ x',
na.rm = TRUE,
show.legend = FALSE
) +
guides(color = guide_legend(override.aes = list(linewidth = 3))) +
scale_y_continuous(expand = expansion(mult = c(0.005, 0.02))) +
facet_wrap(~ country_facet) +
labs(
subtitle = "Smoothed lines",
x = NULL,
y = "FJD ($M)"
)
p1 / p2 +
plot_annotation(
title = "Fiji balance of trade faceted by country",
subtitle = glue("{trade_year_min} to {trade_year_max}, excluding 2020-2022.",
" Facets ordered by sum of balance over time period."),
caption = my_caption_fbos
)
dta_for_plot <- dta_for_plot_all |>
filter(metric %in% c("trade_deficit_surplus") ) |>
mutate(country_facet = fct_reorder(country, -value, sum))
p1 <- dta_for_plot |>
left_join(
dta_for_plot_labels,
by = c("country", "metric")
) |>
ggplot(aes(year, value, color = metric, group = metric)) +
geom_hline(yintercept = 0, lty = 2, linewidth = 0.15, alpha = 0.5) +
geom_line(
linewidth = 0.5,
na.rm = TRUE,
show.legend = FALSE
) +
scale_y_continuous(expand = expansion(mult = c(0.005, 0.02))) +
guides(color = guide_legend(override.aes = list(linewidth = 3))) +
facet_wrap(~ country_facet, scales = "free_y") +
theme(legend.position = "top") +
labs(
x = NULL,
y = "FJD ($M)"
)
p2 <- dta_for_plot |>
ggplot(aes(year, value, color = metric, group = metric)) +
geom_hline(yintercept = 0, lty = 2, linewidth = 0.15, alpha = 0.5) +
geom_smooth(
linewidth = 0.5,
se = FALSE, method = 'loess', formula = 'y ~ x',
na.rm = TRUE,
show.legend = FALSE
) +
guides(color = guide_legend(override.aes = list(linewidth = 3))) +
scale_y_continuous(expand = expansion(mult = c(0.005, 0.02))) +
facet_wrap(~ country_facet, scales = "free_y") +
# coord_cartesian(ylim = c(0, NA)) +
labs(
subtitle = "Smoothed lines",
x = NULL,
y = "FJD ($M)"
)
p1 / p2 +
plot_annotation(
title = "Fiji balance of trade faceted by country",
subtitle = glue("{trade_year_min} to {trade_year_max}, excluding 2020-2022.",
" Facets ordered by sum of balance over time period."),
caption = my_caption_fbos
)
Daniel’s additional notes