W4-Crosssref-API_exercise.Rmd

---
title: "API_lecture"
author: "Frédérique Bone"
date: "29/01/2021"
output: html_document
---

```{r setup, include=FALSE}
library(tidyverse)
library(httr)
library(rvest)
library(jsonlite)
```

## Get data from crossref

We are going to explore cross_ref which available through an API

Read documentation:
https://api.crossref.org/swagger-ui/index.html#/Works/get_works

Crossref has a specific R package to query the API, which we are going to use today to collect some data
Try the code chunck below, but replacing
(i) the query for the specific month and year assigned to you in class
(ii) increase the limit of the number of paper returned to 400
Running these queries take longer since we are waiting a response from a server
(iii) Check the first 10 rows in the dataframe
(iv) Add your email address to the polite and enter it at the end of query_full

```{r}
# Build the Crossref query with all the components
api_first <- "http://api.crossref.org/works?query="
query <- "artificial+neural+network"
filter <- "&filter="
funder<- "has-funder:true,"
from_date <- "from-pub-date:2015-01-01,"
to_date <- "until-pub-date:2015-01-31"
limit <- "&rows=20"
polite <- "&mailto=__"


# Put the query together and call it
query_full <- paste0(api_first, query,filter,funder, from_date,to_date, limit)
fetch <- GET(query_full)

# transform the call into a readable component
return <- rawToChar(fetch$content)
return_all <- jsonlite::fromJSON(return)
ANN_data <- as.data.frame(return_all$message$items)

__
```

(i) Check names of the columns available
```{r}

```


Create a new dataset from the crossref data collected to look at the top funders within those 400 first data point collected
(1) keep only the columns '`container-title`' (using ` `around the names), 'DOI', 'title', 'funder'
(2) Rename the DOI column to DOI_papers
(3) separate funders for each funder taking only one row (using 'unnest' of the purr package in tidyr)
(4) View the top 20 rows of the dataframe to see whether things have worked how you intended to

```{r}
ANN_funder <- __ %>%
  __ %>%
  __ %>%
  __(funder)

__

```


Clean the column names coming from the nested funder dataset:
(1) on top of the columns to keep before, keep 'name' and 'DOI_paper'
(2) rename 'name' to a more appropriate name (as this is specific to the funder)
(3) rename 'DOI' to a more appropriate name (as this is specific to the funder)

```{r}
ANN_funder <- ANN_funder %>%
  __ %>%
  __ %>%
  __
```

Create a summary of the funder data, to identify the top funder in your specific month
The resulting dataframe will be arranged in descending order
display the top three answers (these will be reported in class)
```{r}
Funder_summary <- ANN_funder %>%
  __(__)%>%
  __(count= __)%>%
  ungroup()%>%
  arrange(desc(count))

__
```