Tuija Sonkkila tts

## hkitrees_viz.R
library(tidyverse)
library(sf)

# Data from hri.fi
districts <- st_read("PKS_Kartta_Rajat_KML2011/PKS_suuralue.kml")

hki <- districts %>%
  filter(Name %in% c("Eteläinen", "Itäinen","Läntinen","Kaakkoinen",
                     "Keskinen","Pohjoinen","Koillinen")) %>%
  mutate(Name_en = case_when(

## energy.R
library(tidyverse)
library(readxl)
library(httr)

#-------------------------------------------------------------
# hri.fi
#
# Pääkaupunkiseudun kaukolämmön tuotannon energialähteet (GWh)
#-------------------------------------------------------------

## addMissingYears.vb
Public Sub addMissingYears()

    Dim readFrom As Long 'Row we read from
    Dim writeTo As Long 'Row we write to
    Dim allRows As Long 'Row count to loop over
    Dim thisC As Long
    Dim thisD As Long
    Dim sheetFrom As Worksheet 'Sheet where we read from
    Dim sheetTo As Worksheet 'Sheet where we write to


## pure2powerbi.R
# `dataset` holds the input data for this script
library(dplyr)

names(dataset) <- c("Type", "Unit", "Start", "End")

dataset$Unit <- iconv(dataset$Unit, from = "WINDOWS-1252", to = "UTF-8")

# We need to expand those rows where Start and End span more than one year,
# e.g. 2010-2012 into 2010, 2011, 2012
to_expand <- dataset %>%

## changes_metrics_small_multiples.R
library(dplyr)
library(tidyr)
library(ggplot2)
library(gridExtra)
library(grid)

data20162017 <- read.csv("toshiny_altm_2016_2017.csv", stringsAsFactors = FALSE)

data20162017 <- data20162017 %>%
  mutate(gap_cites = Citations2017 - Citations,

## draw_grid.R
# https://blog.dominodatalab.com/visualizing-homeownership-in-the-us-using-small-multiples-and-r/
opts <- theme(
  panel.background = element_rect(fill="white"),
  panel.border = element_rect(colour="black", fill=NA),
  axis.line = element_blank(),
  axis.ticks = element_blank(),
  panel.grid.major = element_blank(),
  panel.grid.minor = element_blank(),
  axis.text = element_text(colour="gray25", size=15, angle=90, hjust=1),
  axis.title = element_blank(),

## oadoidata_to_flare_json.R
library(purrr)
library(tidyr)
library(dplyr)
library(jsonlite)

# Data queried via oadoi.org API with the roadoi package. Title from local CRIS, joined with DOI.
# See https://users.aalto.fi/~sonkkila/oadoi.html

data <- read.csv(file = "repo_and_oadoi.csv", stringsAsFactors = F)
oa <- data[!is.na(data$free_fulltext_url), c("title", "evidence", "free_fulltext_url")]

## app.R
library(shiny)
library(highcharter)
library(dygraphs)
library(xts)
library(dplyr)

shinyApp(
  ui = fluidPage(
    fluidRow(
      column(12,

## springerLODconf.sq
PREFIX spr-p: <http://lod.springer.com/data/ontology/property/>
PREFIX spr-c: <http://lod.springer.com/data/ontology/class/>

SELECT ?volume ?title ?subtitle ?acronym ?isbn ?eisbn ?scopus ?sdate WHERE {
  ?volume spr-p:hasConference ?conf ;
          spr-p:isIndexedByScopus ?scopus ;
          spr-p:title ?title ;
          spr-p:subtitle ?subtitle ;
          spr-p:bookSeriesAcronym ?acronym ;
          spr-p:ISBN ?isbn ;

## query.R
# Query ACRIS on ORCID IDs. Based on these, fetch works and bio via ORCID Public API
#
# SQL query in Pure and save in CSV: SELECT firstname, lastname, orcid FROM PERSON WHERE orcid IS NOT NULL
# Change character set: iconv -f ISO-8859-1 -t UTF-8 orcidinpure.csv > orcidinpure_utf8.csv
#
# Tuija Sonkkila 7.7.2016

library(rorcid)