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

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

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

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

## ----datapoint_mock, eval = FALSE---------------------------------------------
#  n00945 <- get_values(
#    kpi = "N00945",
#    municipality = c("0180", "1480", "1280"),
#    period = 1970:2020
#  )
#  
#  n00945

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

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

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

## ----munic_g, echo = FALSE----------------------------------------------------
(munic_g <- rKolada:::munic_g)

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

## ----arboga_groups------------------------------------------------------------
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("BRP") %>%
#    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)) +
  geom_line(aes(linetype = municipality)) +
  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)
  ) +
  scale_color_viridis_d(option = "B") +
  scale_y_continuous(labels = scales::comma)