Update population indicator

NEWS.md

@@ -1,10 +1,10 @@
 # tidyBdE (development version)
 
--   Update `bde_ind_unemployment_rate()` since the underlying identifier
-    changed.
--   `bde_ind_population()` under review, not providing results as of July 2024.
+-   Update `bde_ind_unemployment_rate()` and `bde_ind_population()` since the
+    underlying identifier changed.
 -   DOI changed to **CRAN** url:
     <https://doi.org/10.32614/CRAN.package.tidyBdE>.
+-   Native encoding when reading the `.csv` files changed to `"latin1"`.
 
 # tidyBdE 0.3.6
 

R/indicators.R

@@ -134,15 +134,13 @@ bde_ind_gdp_quarterly <- function(series_label = "GDP_quarterly_value", ...) {
 #'
 #' @export
 bde_ind_population <- function(series_label = "Population_Spain", ...) {
-  # seq_num <- 3078287
-  #
-  # econom_ind <- bde_series_load(seq_num, series_label = series_label, ...)
-  # econom_ind <- econom_ind[!is.na(econom_ind[[2]]), ]
-  #
-  # return(econom_ind)
-
-  message("Under review")
-  return(NULL)
+  # was 3078287
+  seq_num <- 4637737
+
+  econom_ind <- bde_series_load(seq_num, series_label = series_label, ...)
+  econom_ind <- econom_ind[!is.na(econom_ind[[2]]), ]
+
+  return(econom_ind)
 }
 
 #' @export

tests/testthat/test-indicators.R

@@ -27,6 +27,9 @@ test_that("Indicators", {
   n7 <- expect_silent(bde_ind_gdp_quarterly())
   expect_true(nrow(n7) > 10)
 
+  n8 <- expect_silent(bde_ind_population())
+  expect_true(nrow(n8) > 10)
+
   n9 <- expect_silent(bde_ind_ibex_daily())
   expect_true(nrow(n9) > 10)
 })

vignettes/articles/Main_Macroeconomic_Series.Rmd

@@ -42,7 +42,7 @@ br <- seq(nd, Sys.Date(), "6 months")
 
 ### Aggregated (last 4 quarters)
 
-```{r echo=FALSE}
+```{r gdp_agg, echo=FALSE, fig.alt="GDP of Spain - Aggregated last 4 quarters"}
 dataset <- bde_ind_gdp_quarterly(series_label = "data") %>%
   drop_na()
 
@@ -74,7 +74,7 @@ ggplot(dataset, aes(x = Date, y = data)) +
 
 ### Year-on-year variation
 
-```{r echo=FALSE}
+```{r gdpyoy, echo=FALSE, fig.alt="GDP of Spain - Year-on-year variation"}
 dataset <- bde_ind_gdp_var(series_label = "data") %>%
   filter(Date >= nd) %>%
   drop_na()
@@ -101,9 +101,52 @@ ggplot(dataset, aes(x = Date, y = data)) +
   theme_tidybde()
 ```
 
+### GDP per capita
+
+```{r gdppercap, echo=FALSE, fig.alt="GDP per Capita of Spain"}
+pop <- bde_ind_population(series_label = "pop")
+pib <- bde_ind_gdp_quarterly(series_label = "data")
+
+pib$LastY <- pib$data + lag(pib$data, 1) + lag(pib$data, 2) + lag(pib$data, 3)
+
+pib <- inner_join(pib, pop, by = "Date")
+
+pib <- pib %>% mutate(data = 1000 * LastY / pop)
+
+dataset <- pib %>%
+  select(Date, data) %>%
+  filter(Date >= nd) %>%
+  drop_na()
+
+
+l <- dataset[nrow(dataset), ]
+
+ggplot(dataset, aes(x = Date, y = data)) +
+  geom_line(color = col) +
+  geom_text(
+    data = l,
+    size = 3,
+    aes(label = paste(
+      x = prettyNum(data, big.mark = " "),
+      "€\n", format(Date, "%b-%Y")
+    ))
+  ) +
+  labs(
+    title = "GDP per capita of Spain",
+    subtitle = "€",
+    caption = "Source: BdE"
+  ) +
+  scale_y_continuous(labels = scales::label_number()) +
+  scale_x_date(
+    date_labels = "%b-%Y",
+    breaks = br
+  ) +
+  theme_tidybde()
+```
+
 ## Unemployment Rate
 
-```{r echo=FALSE}
+```{r unempl, echo=FALSE, fig.alt="Unemployment rate"}
 dataset <- bde_ind_unemployment_rate(series_label = "data") %>%
   filter(Date >= nd) %>%
   drop_na()
@@ -132,7 +175,7 @@ ggplot(dataset, aes(x = Date, y = data)) +
 
 ## Consumer Price Index
 
-```{r echo=FALSE}
+```{r cprix, echo=FALSE, fig.alt="Consumer Price Index"}
 dataset <- bde_ind_cpi_var(series_label = "data") %>%
   filter(Date >= nd) %>%
   drop_na()
@@ -161,7 +204,7 @@ ggplot(dataset, aes(x = Date, y = data)) +
 
 ## Monthly Euribor
 
-```{r echo=FALSE}
+```{r eur, echo=FALSE, fig.alt="Monthly Euribor"}
 dataset <- bde_ind_euribor_12m_monthly(series_label = "data") %>%
   filter(Date >= nd) %>%
   drop_na()
@@ -187,3 +230,36 @@ ggplot(dataset, aes(x = Date, y = data)) +
   ) +
   theme_tidybde()
 ```
+
+## Population
+
+```{r pop, echo=FALSE, fig.alt="Population in thousands"}
+dataset <- bde_ind_population(series_label = "data") %>%
+  filter(Date >= nd) %>%
+  drop_na()
+
+
+l <- dataset[nrow(dataset), ]
+
+ggplot(dataset, aes(x = Date, y = data)) +
+  geom_line(color = col) +
+  geom_text(
+    data = l,
+    size = 3,
+    aes(label = paste(
+      x = prettyNum(data, big.mark = " "),
+      "\n", format(Date, "%b-%Y")
+    ))
+  ) +
+  labs(
+    title = "Population of Spain",
+    subtitle = "thousands",
+    caption = "Source: BdE"
+  ) +
+  scale_x_date(
+    date_labels = "%b-%Y",
+    breaks = br
+  ) +
+  scale_y_continuous(labels = scales::label_number()) +
+  theme_tidybde()
+```

vignettes/articles/Series_Index.Rmd

@@ -49,8 +49,12 @@ series_clean <- series %>%
     Tipo_de_variable,
     Fecha_de_la_primera_observacion,
     Fecha_de_la_ultima_observacion,
-    Frecuencia_de_la_serie
-  )
+    Frecuencia_de_la_serie, Fuente
+  ) %>%
+  as_tibble()
+
+
+names(series_clean) <- gsub("_", " ", names(series_clean))
 
 
 library(reactable)
@@ -59,6 +63,7 @@ library(reactable)
 reactable(series_clean,
   filterable = TRUE,
   searchable = TRUE,
+  resizable = TRUE,
   showPageSizeOptions = TRUE,
   striped = TRUE,
   defaultColDef = colDef(