## ----opts, include = FALSE----------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

## ----install, eval = FALSE----------------------------------------------------
# install.packages("rKolada")

## ----setup--------------------------------------------------------------------
library("rKolada")

## ----datapoint_mock, eval = FALSE---------------------------------------------
# n00945 <- get_values(
#   kpi = "N00945",
#   municipality = c("0180", "1480", "1280"),
#   period = 2000:2020
# )
# 
# head(n00945)

## ----datapoint, echo = FALSE--------------------------------------------------
n00945 <- rKolada:::n00945
tibble::tibble(head(n00945))

## ----kpi_df_mock, eval = FALSE------------------------------------------------
# # Download all KPI metadata as a tibble (kpi_df)
# kpi_df <- get_kpi()
# 
# head(kpi_df, n = 10)

## ----kpi_df, echo = FALSE-----------------------------------------------------
kpi_df <- rKolada:::kpi_df
tibble::tibble(head(kpi_df, n = 10))

## ----kpi_filter---------------------------------------------------------------
# Search for KPIs with the term "skola" in their description or title
kpi_filter <- kpi_df |> kpi_search("skola", column = c("description", "title"))
tibble::tibble(head(kpi_filter))

## ----munic_g_mock, eval = FALSE-----------------------------------------------
# # Search for municipality groups containing the name "Arboga"
# munic_g <- get_municipality_groups()
# 
# arboga_groups <- munic_g |> municipality_grp_search("Arboga")
# arboga_groups

## ----munic_g, echo = FALSE----------------------------------------------------
munic_g <- rKolada:::munic_g
arboga_groups <- munic_g |> municipality_grp_search("Arboga")
arboga_groups

## ----describe_example, results='asis'-----------------------------------------
kpi_filter |> kpi_describe(max_n = 2, format = "md", heading_level = 4)

## ----keywords_example---------------------------------------------------------
# Add keywords to a KPI table
kpis_with_keywords <- kpi_filter |> kpi_bind_keywords(n = 4)

# count keywords
kpis_with_keywords |>
  tidyr::pivot_longer(dplyr::starts_with("keyword"), values_to = "keyword") |>
  dplyr::count(keyword, sort = TRUE)

## -----------------------------------------------------------------------------
# Top 10 rows of the table
kpi_filter |> dplyr::slice(1:10)

# Top 10 rows of the table, with non-distinct data removed
kpi_filter |> dplyr::slice(1:10) |> kpi_minimize()

## ----echo = FALSE-------------------------------------------------------------
kpi_filter <- rKolada:::kpi_filter
munic_grp_filter <- rKolada:::munic_grp_filter
arboga <- rKolada:::arboga
grp_data <- rKolada:::grp_data

## ----eval = FALSE-------------------------------------------------------------
# # Get KPIs describing Gross Regional Product of municipalities
# kpi_filter <- get_kpi() |>
#   kpi_search("bruttoregionprodukt") |>
#   kpi_search("K", column = "municipality_type")
# # Creates a table with two rows
# 
# # Get a suitable group of municipalities
# munic_grp_filter <- get_municipality_groups() |>
#   municipality_grp_search("Liknande kommuner socioekonomi, Arboga")
# # Creates a table with one group of 7 municipalities
# 
# # Also include Arboga itself
# arboga <- get_municipality() |> municipality_search("Arboga")
# 
# # Get data
# grp_data <- get_values(
#   kpi = kpi_extract_ids(kpi_filter),
#   municipality = c(
#     municipality_grp_extract_ids(munic_grp_filter),
#     municipality_extract_ids(arboga)
#   )
# )

## -----------------------------------------------------------------------------
# Visualize results
library("ggplot2")
ggplot(grp_data, aes(year, value, color = municipality)) +
  # One line per municipality; linetype adds distinction in B/W print
  geom_line(aes(linetype = municipality)) +
  # Separate panel per KPI; free y-scales since units differ
  facet_grid(kpi ~ ., scales = "free") +
  labs(
    title = "Gross Regional Product per capita 2012-2018",
    subtitle = "Swedish municipalities similar to Arboga",
    caption = values_legend(grp_data, kpi_filter)  # Auto-generated legend
  ) +
  # Colour-blind-friendly palette
  scale_color_viridis_d(option = "B") +
  scale_y_continuous(labels = scales::comma)