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---
title: "Dataset two"
author: "Najko"
date: '2019-06-17'
output:
  html_document:
    keep_md: yes
---

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library(tidyverse)
library(rcrossref)
library(roadoi)

Load data

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my_query <-
  '(TITLE:"older adult" or TITLE:elderly) and (TITLE:falls or TITLE:trauma)'
europepmc::epmc_hits_trend(query = my_query,
                           period = 2008:2018,
                           data_src = 'med')
#> # A tibble: 11 x 3
#>     year all_hits query_hits
#>    <int>    <dbl>      <dbl>
#&gt; 1 2008 761217 72

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library(rcrossref)
cr_works(filter = c(
	issn = "1099-1255",
	from_pub_date = "2013-01-01",
	until_pub_date = "2019-12-31",
	type = "journal-article"
    ),
limit = 500)

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library(tidyverse)
library(jsonlite)
#> 
#> Attaching package: 'jsonlite'
#> The following object is masked from 'package:purrr':
#> 
#>     flatten
base_df <- jsonlite::stream_in(file("~/Downloads/base_dois.json"), verbose = FALSE)
#> Warning in readLines(con, n = pagesize, encoding = "UTF-8"): incomplete

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library(tidyverse)
library(rcrossref)
library(janitor)
library(crminer)
my_dois <- readxl::read_xlsx("dois_elsevier.xlsx") %>%
  clean_names()
# call Crossref
cr_df <- cr_works(my_dois$digital_object_identifier_doi, .progress = "text")
# get CC licensed articles
cc_df <- cr_df$data %>%

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    select
        pk_items,
        count,
        wos_b_2019.d_percentiles.c_max,
        wos_b_2019.classifications.classification,
        wos_b_2019.items.pubyear,
        wos_b_2019.items.doctype,
        wos_b_2019.items.pubtype,

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---
output: github_document
---

```{r, echo = FALSE}
knitr::opts_chunk$set(
  comment = "#>",
  collapse = TRUE,
  warning = FALSE,
  message = FALSE

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library(europepmc)
library(tidyverse)
library(cowplot)
#> 
#> ********************************************************
#> Note: As of version 1.0.0, cowplot does not change the
#>   default ggplot2 theme anymore. To recover the previous
#>   behavior, execute:
#>   theme_set(theme_cowplot())

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library(tidyverse)
my_df <- readr::read_csv("data/u4_13_17.csv") 
no_group <- my_df %>%
  filter(is.na(CA)) 
no_group %>%
  select(PY, UT, C1) %>%
  mutate(adresses = strsplit(C1, "; \\[")) -> tt
tt %>%
  unnest() %>%
  tidyr::separate(adresses, sep ="] ", c("authors", "address"), fill = "left") %>%