Attibute datasets previously downloaded from internet, now included w…

…ith package

DESCRIPTION

@@ -1,8 +1,8 @@
 Type: Package
 Package: geofi
 Title: Access Finnish Geospatial Data
-Version: 1.0.14
-Date: 2024-02-05
+Version: 1.0.15
+Date: 2024-03-13
 Authors@R: c(
     person("Markus", "Kainu", , "markus.kainu@kapsi.fi", role = c("aut", "cre"),
            comment = c(ORCID = "0000-0003-1376-503X")),

NEWS.md

@@ -1,5 +1,9 @@
 # geofi 1.0.15
 
++ Attibute datasets previously downloaded from internet, now included with package
+
+# geofi 1.0.14
+
 + 2024 regional classifications updated.
 + geofi_joining_attribute_data vignette fixed
 

R/data.R

@@ -1192,3 +1192,40 @@
 #' }
 #' @source \url{https://data.stat.fi/api/classifications/v2/classifications}
 "municipality_key_2013"
+
+
+#' Municipality level population data from Sotkanet
+#'
+#' This dataset contains population data at municipality level pulled from THL (Sotkanet) from 2000 to 2022
+#'
+#' @format A data frame with 7107 rows and 3 variables:
+#' \describe{
+#' \item{municipality_code}{municipality_code}
+#' \item{primary.value}{primary.value}
+#' \item{year}{year}
+#' }
+"sotkadata_population"
+
+#' Municipality level Swedish speaking population numbers from Sotkanet
+#'
+#' This dataset contains Swedish speaking population figures at municipality level pulled from THL (Sotkanet) from 2000 to 2022
+#'
+#' @format A data frame with 5761 rows and 3 variables:
+#' \describe{
+#' \item{municipality_code}{municipality_code}
+#' \item{indicator.title.fi}{indicator.title.fi}
+#' \item{primary.value}{primary.value}
+#' }
+"sotkadata_swedish_speaking_pop"
+
+#' Zipcode level population data from Statistics Finland
+#'
+#' This dataset contains population for each zipcode in Finland. Data is downloaded from Statistics Finland
+#'
+#' @format A data frame with 3027 rows and 2 variables:
+#' \describe{
+#' \item{posti_alue}{posti_alue}
+#' \item{X2022}{X2022}
+#' }
+"statfi_zipcode_population"
+

_pkgdown.yml

@@ -20,6 +20,7 @@ reference:
   contents:
   - municipality_central_localities
   - municipality_key
+  - municipality_key_2024
   - municipality_key_2023
   - municipality_key_2022
   - municipality_key_2021
@@ -57,6 +58,13 @@ reference:
   - grid_satakunta
   - grid_uusimaa
   - grid_varsinais_suomi
+- title: Attribute data
+  desc: |
+    geofi comes with a three statistical datasets that are used in vignettes
+  contents:
+  - sotkadata_population
+  - sotkadata_swedish_speaking_pop
+  - statfi_zipcode_population
 - title: Miscellaneous
   desc: |
     Support functions for API functions and spatial data transformations
@@ -65,6 +73,7 @@ reference:
   - check_api_access
   - wfs_api
   - to_sf
+
 url: https://ropengov.github.io/geofi/
 template:
   package: rogtemplate

cran-comments.md

@@ -1,16 +1,18 @@
 ## Test environments
-* local ubuntu 22.04 install, R 4.3.2
+* local ubuntu 22.04 install, R 4.3.3
 * win-builder (devel)
 * r-hub.io
 
 ## Submission note
 
-+ Failing vignette fixed
++ Attibute datasets previously downloaded from internet, now included with package
 
 ## R CMD check results
 
 0 errors | 0 warnings | 0 note
 
+
+
 ## Downstream dependencies
 
 There are currently no downstream dependencies for this package.

inst/extras/vignette_data.R

@@ -0,0 +1,45 @@
+# Create datasets to be used in Vignettes
+
+library(sotkanet)
+library(dplyr)
+sotkadata_population <- GetDataSotkanet(indicators = 127, years = 2000:2022) %>%
+  filter(region.category == "KUNTA") %>%
+  mutate(municipality_code = as.integer(region.code)) %>%
+  select(municipality_code,primary.value,year)
+
+save(sotkadata_population, file = "./data/sotkadata_population.rda",
+     compress = "bzip2")
+
+karttasovellus::document_data(dat = sotkadata_population,
+              neim = "sotkadata_population",
+              description = "This dataset contains population data at municipality level pulled from THL (Sotkanet) from 2000 to 2022")
+
+
+# ******************
+sotkadata_swedish_speaking_pop <- GetDataSotkanet(indicators = 2433, years = 2000:2022) %>%
+  filter(region.category == "KUNTA") %>%
+  mutate(municipality_code = as.integer(region.code)) %>%
+  select(municipality_code,indicator.title.fi,primary.value)
+
+save(sotkadata_swedish_speaking_pop, file = "./data/sotkadata_swedish_speaking_pop.rda",
+     compress = "bzip2")
+
+karttasovellus::document_data(dat = sotkadata_swedish_speaking_pop,
+                              neim = "sotkadata_swedish_speaking_pop",
+                              description = "This dataset contains Swedish speaking population figures at municipality level pulled from THL (Sotkanet) from 2000 to 2022")
+
+
+# ******************
+library(pxweb)
+px_data <- read.csv("https://pxdata.stat.fi:443/PxWeb/sq/43d3d0aa-636e-4a4b-bbe1-decae45fc2b4",
+                    header = TRUE, sep = ";", fileEncoding = "Latin1")
+px_data$posti_alue <- sub(" .+$", "", px_data$Postinumeroalue)
+statfi_zipcode_population <- px_data %>% select(posti_alue,X2022)
+
+save(statfi_zipcode_population, file = "./data/statfi_zipcode_population.rda",
+     compress = "bzip2")
+
+karttasovellus::document_data(dat = statfi_zipcode_population,
+                              neim = "statfi_zipcode_population",
+                              description = "This dataset contains population for each zipcode in Finland. Data is doanloaded from Statistics Finland")
+

vignettes/geofi_datasets.Rmd

@@ -44,7 +44,7 @@ check_namespaces <- function(pkgs){
   return(all(unlist(sapply(pkgs, requireNamespace,quietly = TRUE))))
 }
 apiacc <- geofi::check_api_access()
-pkginst <- check_namespaces(c("sotkanet","geofacet","ggplot2","dplyr"))
+pkginst <- check_namespaces(c("geofacet","ggplot2","dplyr"))
 apiacc_pkginst <- all(apiacc,pkginst)
 ```
 
@@ -189,14 +189,11 @@ as_tibble(d$results) %>%
   print(n = 100)
 ```
 
-Here is an example where population data at municipality level is pulled from THL from 2000 to 2022, then aggregated at the levels of regions (`maakunta`) and then plotted with ggplot2 using grid `geofi::grid_maakunta`.
+Here is an example where population data at municipality level is pulled from THL from 2000 to 2022, then aggregated at the levels of regions (`maakunta`) and then plotted with ggplot2 using grid `geofi::grid_maakunta`. Population data is provided as part of geofi package as `geofi::sotkadata_population`.
 
 ```{r geofacet, fig.height = 8, fig.width = 10, eval = apiacc_pkginst}
 # Let pull population data from THL
-library(sotkanet)
-sotkadata <- GetDataSotkanet(indicators = 127, years = 2000:2022) %>% 
-  filter(region.category == "KUNTA") %>% 
-  mutate(municipality_code = as.integer(region.code))
+sotkadata <- geofi::sotkadata_population
 
 # lets aggregate population data
 dat <- left_join(geofi::municipality_key_2023 %>% select(-year),

vignettes/geofi_joining_attribute_data.Rmd

@@ -45,7 +45,7 @@ check_namespaces <- function(pkgs){
   return(all(unlist(sapply(pkgs, requireNamespace,quietly = TRUE))))
 }
 apiacc <- geofi::check_api_access()
-pkginst <- check_namespaces(c("sotkanet","dplyr","tidyr","janitor","ggplot2"))
+pkginst <- check_namespaces(c("dplyr","tidyr","janitor","ggplot2"))
 apiacc_pkginst <- all(apiacc,pkginst)
 ```
 
@@ -56,17 +56,14 @@ Municipality data provided by `get_municipalities()`-function contains 77 indica
 
 ### Population data from Sotkanet
 
-In this first example we join municipality level indicators of *Swedish-speaking population at year end* from Sotkanet [population data](https://sotkanet.fi/sotkanet/en/haku?g=219), 
+In this first example we join municipality level indicators of *Swedish-speaking population at year end* from Sotkanet [population data](https://sotkanet.fi/sotkanet/en/haku?g=219), Dataset is provided as part of geofi package as `geofi::sotkadata_swedish_speaking_pop`.
 
 ```{r municipality_map, eval = apiacc_pkginst}
 library(geofi)
 muni <- get_municipalities(year = 2023)
 
-library(sotkanet)
 library(dplyr)
-sotkadata_swedish_speaking_pop <- GetDataSotkanet(indicators = 2433, years = 2000:2022) %>% 
-  filter(region.category == "KUNTA") %>% 
-  mutate(municipality_code = as.integer(region.code))
+sotkadata_swedish_speaking_pop <- geofi::sotkadata_swedish_speaking_pop
 ```
 
 This is not obvious to all, but have the municipality names in Finnish among other regional breakdowns which allows us to combine the data with spatial data using `municipality_name_fi`-variable. 
@@ -92,12 +89,10 @@ map_data %>%
 
 ## Zipcode level
 
-You can download data from [Paavo (Open data by postal code area)](https://pxdata.stat.fi/PXWeb/pxweb/en/Postinumeroalueittainen_avoin_tieto/) using [`pxweb`](https://ropengov.github.io/pxweb/)-package. In this example we will download preformatted population data in `csv` format directly from Statistics Finland and process it to match with spatial zipcode data.
+You can download data from [Paavo (Open data by postal code area)](https://pxdata.stat.fi/PXWeb/pxweb/en/Postinumeroalueittainen_avoin_tieto/) using [`pxweb`](https://ropengov.github.io/pxweb/)-package. In this example we use dataset that can be downloaded preformatted in `csv` format directly from Statistics Finland. Population data is provided as part of geofi package as `geofi::statfi_zipcode_population`.
 
 ```{r zipcode_with_statistics_finland, eval = apiacc_pkginst}
-px_data <- read.csv("https://pxdata.stat.fi:443/PxWeb/sq/43d3d0aa-636e-4a4b-bbe1-decae45fc2b4", 
-                    header = TRUE, sep = ";", fileEncoding = "Latin1")
-px_data$posti_alue <- sub(" .+$", "", px_data$Postinumeroalue)
+statfi_zipcode_population <- geofi::statfi_zipcode_population
 ```
 
 Before we can join the data, we must extract the numerical postal code from `postal_code_area`-variable.
@@ -106,7 +101,7 @@ Before we can join the data, we must extract the numerical postal code from `pos
 # Lets join with spatial data and plot the area of each zipcode
 zipcodes19 <- get_zipcodes(year = 2019) 
 zipcodes_map <- left_join(zipcodes19, 
-                          px_data)
+                          statfi_zipcode_population)
 ggplot(zipcodes_map) + 
   geom_sf(aes(fill = X2022), 
           color  = alpha("white", 1/3)) +