R/format_request.R

@@ -24,7 +24,7 @@ json_request <- function(url) {
 #'
 #' @return dataframe response
 csv_request <- function(url) {
-  read.csv(url)
+  read.csv(url, encoding = "UTF-8")
 }
 
 # Request function based on expected response

README.md

@@ -11,11 +11,7 @@ This public library is made available for the internal FSO staff, the federal ad
 You can install the library with
 ```
 install.packages("remotes")
-remotes::install_gitlab("d6538/fso-metadata-r")
-```
-or
-```
-devtools::install_url("https://renkulab.io/gitlab/dscc/metadata-auto-r-library/-/archive/v003/metadata-auto-r-library-v003.tar.gz")
+remotes::install_gitlab("dscc/fso-metadata-r")
 ```
 
 then at the beginning of your R script, you will need to 
@@ -116,4 +112,6 @@ All the APIs made available in this library are also documented in Swagger UI sh
 
 Examples for each API are provided in the [R Markdown](https://renkulab.io/gitlab/dscc/metadata-auto-r-library/-/blob/master/example.Rmd).
 
-A documentation page is also available [here](https://d6538.gitlab.io/fso-metadata-r/).
+Practical [demo](https://renkulab.io/gitlab/dscc/metadata-auto-r-library/-/blob/master/demo.R).
+
+A documentation page is also available [here](https://dscc.gitlab.io/fso-metadata-r/).

demo.R

+install.packages("remotes")
+remotes::install_gitlab("dscc/fso-metadata-r")
 
-### Preparation ###
-devtools::install_url("https://renkulab.io/gitlab/dscc/metadata-auto-r-library/-/archive/v002/metadata-auto-r-library-v002.tar.gz")
 library(fso.metadata)
-library(ggplot2)
-library(tidyverse)
+
 
 ### 1. Get a codelist ###
 # All languages
-codelist <- get_codelist(identifier = "CL_NOGA_SECTION")
+codelist <- get_codelist(identifier = "CL_NOGA_DIVISION")
 head(codelist, 3)
 
+### 1. Get a codelist ###
 # In french
 codelist_fr <- get_codelist(identifier = "CL_NOGA_SECTION", language = "fr")
 head(codelist_fr, 3)
 
-
-### 2. Get a nomenclature of one level ###
-# All language: Level 1
-nomenclature <- get_nomenclature_one_level(
-  identifier = "HCL_CH_ISCO_19_PROF",
-  level_number = 2
-)
-head(nomenclature, 3)
-
-# French: Level 2
-nomenclature_fr <- get_nomenclature_one_level(
-  identifier = "HCL_CH_ISCO_19_PROF",
-  level_number = 1,
-  language = "fr"
-)
-head(nomenclature_fr, 5)
-
+# In german
+codelist_de <- get_codelist(identifier = "CL_NOGA_SECTION", language = "de")
+head(codelist_de, 3)
 
 
-### 3. Get a nomenclature of multiple levels ###
-# French
-multi_nomenclature_fr <- get_nomenclature_multiple_levels(
+### 2. Get a nomenclature of multiple levels ###
+# In italian
+multi_nomenclature_it <- get_nomenclature_multiple_levels(
   identifier = "HCL_CH_ISCO_19_PROF",
   level_from = 1,
   level_to = 6,
-  language = "fr"
+  language = "it"
 )
-head(multi_nomenclature_fr, 10)
-
+head(multi_nomenclature_it, 8)
 
 
-### 4. Concrete example from Mr. van Nieuwkoop with Noga Data
-system("git lfs pull data/pkagg.Rdata")
+### 3. Concrete example from Mr. van Nieuwkoop with Noga Data
+library(ggplot2)
+library(tidyverse)
 
-load("data/pkagg.Rdata")
-head(pk_agg$A88)
+# Load the production account data for the agriculture divisions
+load("data/pk_agr.Rdata")
+pk_agr <- rename(pk_agr, Component = Komponent, Year = Jahr)
+head(pk_agr)
 
-noga2 <- as_tibble(get_codelist(identifier = "CL_NOGA_DIVISION", language = "it"))
+# Load the descriptions of the NOGA divisions
+noga2 <- as_tibble(
+  get_codelist(identifier='CL_NOGA_DIVISION', language='fr')
+)
 names(noga2) <- c("id", "label", "name")
 head(noga2)
 
-# Get the completely disaggregated production accounts
-#  and join them with the noga2 descriptions
-a88 <- pk_agg$A88 %>%
-  left_join(noga2, by = c("Code" = "id"))
-head(a88)
-
-# Filter and prepare data
-a88_filtered <- a88 %>%
-  select(-Beschreibung, -name) %>%
+# Join the production account data with the noga2 descriptions
+pk <- pk_agr %>%
+  left_join(noga2, by = c("Code" = "id")) %>%
+  select(-name) %>%
   relocate(label, .after = Code) %>%
-  rename(Department = label, Year = Jahr, Component = Komponent) %>%
-  select(Code, Department, Component, Year, Nominal) %>%
-  filter(Nominal > 0) %>%
-  filter(!is.na(Department)) %>%
-  # keep
-  filter(Code %in% c("01", "02", "03")) # keep first 3 department
+  rename(Department = label)
+head(pk)
 
 # Plot the intermediate consumption (CI), the value added (VA), and the
 # production value (VP) for the section A (agriculture)
-ggplot(a88_filtered, aes(Year, Nominal, color = Component)) +
+pk %>%
+  select(Code, Department, Component, Year, Nominal) %>%
+  filter(Nominal > 0 & !is.na(Department)) %>%
+  ggplot(aes(Year, Nominal, color = Component)) +
   geom_line() +
   ylab("in Mio. CHF") +
-  facet_wrap(~Department, scales = "free")
+  facet_wrap(~Department,  scales = "free")
+
+
+
+
+plot_agriculture <- function(pk_agr, language) {
+  # Load the descriptions of the NOGA divisions
+  noga2 <- as_tibble(
+    get_codelist(
+      identifier='CL_NOGA_DIVISION', 
+      language=language,
+      environment='ABN') # for the demo, only available within network
+  )
+  names(noga2) <- c("id", "label", "name")
+  
+  # Join the production account data with the noga2 descriptions
+  pk <- pk_agr %>%
+    left_join(noga2, by = c("Code" = "id")) %>%
+    select(-name) %>%
+    relocate(label, .after = Code) %>%
+    rename(Department = label)
+  
+  # Plot the intermediate consumption (CI), the value added (VA), and the
+  # production value (VP) for the section A (agriculture)
+  pk %>%
+    select(Code, Department, Component, Year, Nominal) %>%
+    filter(Nominal > 0 & !is.na(Department)) %>%
+    ggplot(aes(Year, Nominal, color = Component)) +
+    geom_line() +
+    ylab("in Mio. CHF") +
+    facet_wrap(~Department,  scales = "free")
+}
+
+plot_agriculture(pk_agr, language='fr')
+plot_agriculture(pk_agr, language='de')
+plot_agriculture(pk_agr, language='it')
+plot_agriculture(pk_agr, language='en')

example.Rmd

@@ -14,7 +14,8 @@ knitr::opts_chunk$set(echo = TRUE)
 You can install the library with
 
 ```{r install}
-devtools::install_url("https://renkulab.io/gitlab/dscc/metadata-auto-r-library/-/archive/v002/metadata-auto-r-library-v002.tar.gz")
+install.packages("remotes")
+remotes::install_gitlab("dscc/fso-metadata-r")
 ```
 
 then at the beginning of your R script, you will need to 

public/pkgdown.yml

@@ -2,5 +2,5 @@ pandoc: 2.14.1
 pkgdown: 1.6.1
 pkgdown_sha: ~
 articles: {}
-last_built: 2022-03-07T16:49Z
+last_built: 2022-03-10T09:47Z