In subugoe/hoad: Hybrid Open Access Dashboard
library(flexdashboard)
library(viridis)
library(plotly)
library(shiny)
library(scales)
library(DT)
library(writexl)
library(echarts4r)
library(hoad)
library(forcats)
library(dplyr)
library(stringr)
# access to data
hybrid_df <- readr::read_csv(file = path_extdat("hybrid_publications.csv")) %>%
mutate(license = fct_infreq(license)) %>%
mutate(publisher = fct_infreq(publisher)) %>%
mutate(year = factor(issued, levels = c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020"))) %>%
mutate(hybrid_type = ifelse(hybrid_type == "SCOAP", "SCOAP<sup>3</sup>", hybrid_type)) %>%
mutate(hybrid_type = factor(hybrid_type, levels = c("Open APC (TA)", "Open APC (Hybrid)", "SCOAP<sup>3</sup>"))) %>%
mutate(domain = paste(domain, suffix, sep = ".")) %>%
arrange(desc(yearly_publisher_volume))
unpaywall_df <- readr::read_csv(file = path_extdat("unpaywall_df.csv")) %>%
mutate(year = factor(year, levels = c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")))
Overview
Inputs {.sidebar}
# from https://stackoverflow.com/questions/42148177/how-can-i-build-multiple-inputs-into-my-shiny-app-with-both-updating-and-multipl
# TODO factor this out
selectizeInput(
inputId = "publishers",
label = "Selected Publishers",
choices = c(All = "", levels(hybrid_df$publisher)),
multiple = TRUE,
)
selectizeInput(
inputId = "journals",
label = "Selected Journals",
choices = c(All = "", sort(unique(hybrid_df$journal_title))),
multiple = TRUE
)
# publisher filter
jn_filtered <- reactive({
if (is.null(input$publishers)) {
return(hybrid_df)
} else {
return(hybrid_df[hybrid_df$publisher %in% input$publishers, ])
}
})
observe(updateSelectizeInput(
session,
"journals",
choices = c(All = "", sort(unique(jn_filtered()$journal_title)))
))
# journal_title filter
jn_f <- reactive({
if (is.null(input$journals)) {
return(jn_filtered())
} else {
return(jn_filtered()[jn_filtered()$journal_title %in% input$journals, ])
}
})
# prepare dataset for plotting
hybrid_sub <- reactive({
if (length(unique(jn_f()$publisher)) > 1) {
hybrid_sub <- jn_f() %>%
group_by(year, yearly_all, license) %>%
count() %>%
mutate(prop = n / yearly_all)
} else if (length(unique(jn_f()$journal_title)) > 1 &&
length(unique(jn_f()$publisher)) == 1) {
hybrid_sub <- jn_f() %>%
group_by(year, license, yearly_publisher_volume) %>%
count() %>%
mutate(prop = n / yearly_publisher_volume)
} else {
hybrid_sub <- jn_f() %>%
group_by(year, license, yearly_jn_volume) %>%
count() %>%
mutate(prop = n / yearly_jn_volume)
}
})
# using unpaywall indicators
# unpaywall <- reactive({
# hybrid_df %>%
# filter(journal_title %in% jn_f()$journal_title) %>%
# group_by(year, journal_title, publisher, jn_y_unpaywall_others) %>%
# summarise(n = n_distinct(doi_oa)) %>%
# gather(n, jn_y_unpaywall_others, key = "source", value = "articles") %>%
# ungroup() %>%
# group_by(year, source) %>%
# summarise(articles = sum(articles, na.rm = TRUE)) %>%
# mutate(
# source = ifelse(
# source == "n",
# "Crossref immediate license",
# "Other license information\n(Unpaywall)"
# )
# )
# })
Notice that only those hybrid open access journals were included where
academic institutions sponsored the open access publication according to the Open APC initiative
and where publishers shared license information about immediate open access with Crossref.
Row
Publishers selected
renderValueBox({
publisher_n <- length(unique(jn_f()$publisher))
valueBox(publisher_n, icon = "fa-filter")
})
Journals selected
renderValueBox({
journal_n <- length(unique(jn_f()$journal_title))
valueBox(format(journal_n, big.mark = " ", scientific = FALSE),
icon = "fa-filter"
)
})
Hybrid OA articles indexed in Crossref
renderValueBox({
hybrid_n <- nrow(jn_f())
valueBox(format(hybrid_n, big.mark = " ", scientific = FALSE),
icon = "fa-creative-commons"
)
})
Column {.tabset .tabset-fade}
Hybrid OA licenses found (relative)
renderPlotly({
p <- ggplot(hybrid_sub(), aes(year, prop, fill = license)) +
xlab("Year") +
ylab("Hybrid OA / Articles published") +
scale_fill_manual(
"License",
values = colors_license
) +
scale_x_discrete(drop = FALSE) +
scale_y_continuous(labels = scales::percent) +
theme_minimal() +
geom_col(position = position_stack(reverse = TRUE))
plotly::ggplotly(p)
})
Hybrid OA licenses found (absolute)
renderPlotly({
p <- ggplot(hybrid_sub(), aes(year, n, fill = license)) +
xlab("Year") +
ylab("Hybrid OA Articles") +
scale_fill_manual(
"License",
values = colors_license
) +
scale_x_discrete(drop = FALSE) +
scale_y_continuous(
labels = function(x) {
format(x, big.mark = " ", scientific = FALSE)
}
) +
theme_minimal() +
geom_col(position = position_stack(reverse = TRUE))
# p
plotly::ggplotly(p)
})
Other types of OA license information detected by Unpaywall
renderPlotly({
unpaywall_df() %>%
filter(journal_title %in% jn_f()$journal_title) %>%
oa_by_other() %>%
oa_by_other_plot() %>%
plotly::ggplotly()
})
Row {data-width=400 data-height=350}
Sources of disclosure of OA sponsorship
renderPlotly({
p <- hybrid_df %>%
filter(journal_title %in% jn_f()$journal_title) %>%
ggplot(aes(year, ..count.., fill = hybrid_type)) +
geom_bar(position = position_stack(reverse = TRUE)) +
xlab("Year") +
ylab("Articles") +
scale_x_discrete(drop = FALSE) +
scale_y_continuous(
labels = function(x) {
format(x, big.mark = " ", scientific = FALSE)
},
breaks = scales::pretty_breaks()
) +
scale_fill_manual(
"Sources",
values = colors_source_disclosure,
na.value = "#E9E1D7"
) +
theme_minimal()
tt <- plotly::ggplotly(p, tooltip = c("y"))
tt$x$data <- lapply(tt$x$data, function(x) {
x$text <- paste(x$name, x$y, sep = ": ")
x
})
tt
})
Text-mined author emails (first match per article)
renderEcharts4r({
hybrid_df %>%
filter(journal_title %in% jn_f()$journal_title) %>%
count(suffix, domain) %>%
filter(!is.na(domain)) %>%
e_charts() %>%
e_treemap(suffix, domain, n,
leafDepth = "1",
name = "Corresponding Email"
) %>%
e_tooltip(trigger = "item")
})
Compare
Row {data-height=250}
Hybrid open access uptake by Publisher, shown as the proportion of the total number of hybrid open articles found
hybrid_all <-
hybrid_df %>%
count(year) %>%
mutate(year = gsub("201", "1", year)) %>%
mutate(year = gsub("2020", "20", year))
hybrid_df %>%
mutate(year = gsub("201", "1", year)) %>%
mutate(year = gsub("2020", "20", year)) %>%
mutate(publisher_group = fct_lump(publisher, prop = 0.05)) %>%
group_by(year, publisher_group) %>%
summarize(n = n()) %>%
mutate(prop = n / sum(n)) %>%
mutate(`Proportion in %` = round(prop * 100, 2)) %>%
ggplot(aes(year, n)) +
geom_bar(
data = hybrid_all,
aes(fill = "All Hybrid OA Articles"),
color = "transparent",
stat = "identity"
) +
geom_bar(aes(fill = "by Publisher", label = `Proportion in %`), color = "transparent", stat = "identity") +
facet_wrap(~publisher_group, nrow = 1) +
scale_fill_manual(values = c("#b3b3b3a0", "#153268"), name = "") +
labs(x = "Year", y = "Hybrid OA Articles") +
theme_minimal() +
theme(
legend.position = "top",
legend.justification = "right"
) +
scale_y_continuous(labels = scales::number_format(big.mark = " ")) +
theme(panel.grid.minor = element_blank()) +
theme(axis.ticks = element_blank()) +
theme(panel.grid.major.x = element_blank()) +
theme(panel.border = element_blank()) -> publishers_comp_plot
plotly::ggplotly(publishers_comp_plot, tooltip = c("label", "y"))
Row {data-height=500}
Journal hybrid open access share by publisher
fillRow(
flex = c(1, 3),
inputPanel(
selectInput(
inputId = "Year",
label = "Select Years",
choices = c(rev(levels(hybrid_df$year))),
selected = c("2017", "2018", "2019"),
multiple = TRUE
),
checkboxInput("rb", "Display journals", FALSE)
),
plotlyOutput("pubplot", height = "100%")
)
output$pubplot <- renderPlotly({
if (input$Year == "All") {
tt <- hybrid_df %>%
filter(!is.na(license)) %>%
mutate(publisher = forcats::as_factor(publisher)) %>%
group_by(year, publisher, journal_title, yearly_jn_volume) %>%
summarize(oa = n()) %>%
mutate(prop = oa / yearly_jn_volume)
} else {
tt <- hybrid_df %>%
filter(!is.na(license)) %>%
mutate(publisher = forcats::as_factor(publisher)) %>%
filter(year %in% input$Year) %>%
group_by(year, publisher, journal_title, yearly_jn_volume) %>%
summarize(oa = n()) %>%
mutate(prop = oa / yearly_jn_volume)
}
tt <- tt %>%
ungroup() %>%
mutate(publisher = forcats::fct_other(publisher, keep = levels(publisher)[1:10]))
p <- ggplot(tt, aes(y = prop, x = as.factor(publisher))) +
geom_boxplot() +
coord_flip() +
theme_minimal() +
xlab("Publisher") +
ylab(NULL) +
scale_x_discrete(
drop = FALSE,
limits = rev(levels(tt$publisher)),
labels = function(x) {
str_wrap(x, width = 30)
}
) +
scale_y_continuous(labels = scales::percent)
if (input$rb == TRUE) {
p <- p +
geom_point(aes(colour = prop, label = journal_title)) +
scale_colour_viridis(option = "C") +
scale_fill_viridis(option = "C") +
guides(color = FALSE, fill = FALSE)
} else {
p <- p
}
plotly::ggplotly(p, tooltip = c("label", "y")) %>%
layout(margin = list(l = 200))
})
Journal hybrid open access share by publisher by year
fillCol(
height = 600,
flex = c(NA, 1),
inputPanel(
selectInput(
"Publisher",
label = "Select Publisher",
choices = c("All", unique(hybrid_df$publisher)),
selected = "All"
)
),
plotlyOutput("yearplot", height = "100%")
)
output$yearplot <- renderPlotly({
if (input$Publisher == "All") {
tt <- hybrid_df %>%
filter(!is.na(license)) %>%
group_by(year, publisher, journal_title, yearly_jn_volume) %>%
summarize(oa = n()) %>%
mutate(prop = oa / yearly_jn_volume)
} else {
tt <- hybrid_df %>%
filter(!is.na(license)) %>%
filter(publisher == input$Publisher) %>%
group_by(year, publisher, journal_title, yearly_jn_volume) %>%
summarize(oa = n()) %>%
mutate(prop = oa / yearly_jn_volume)
}
p <- ggplot(tt, aes(y = prop, x = as.factor(year))) +
geom_boxplot() +
coord_flip() +
theme_minimal() +
xlab("Year") +
ylab(NULL) +
scale_x_discrete(drop = FALSE) +
scale_y_continuous(labels = scales::percent)
if (input$rb == TRUE) {
p <- p +
geom_point(aes(colour = prop, label = journal_title)) +
scale_colour_viridis(option = "C") +
scale_fill_viridis(option = "C") +
guides(color = FALSE, fill = FALSE)
} else {
p <- p
}
plotly::ggplotly(p, tooltip = c("label", "y"))
})
Row {data-height=500}
Table view
hybrid_data <- reactive({
if (input$Publisher == "All") {
hybrid_df
} else {
hybrid_df <- hybrid_df %>%
filter(publisher == input$Publisher)
}
hybrid_df %>%
filter(year %in% input$Year) %>%
group_by(year, publisher, journal_title, yearly_jn_volume) %>%
summarize(oa = n()) %>%
mutate(prop = round((oa / yearly_jn_volume) * 100, 2)) %>%
arrange(desc(year)) %>%
select(
Year = year,
Publisher = publisher,
Journal = journal_title,
`Article Volume` = yearly_jn_volume,
`OA Articles` = oa,
`OA Share (in %)` = prop
)
})
renderDataTable(
expr = {
hybrid_data()
},
rownames = FALSE,
filter = "bottom",
options = list(
pageLength = 4,
dom = "ftp",
columnDefs = list(list(
className = "dt-head-left", targets = "_all"
))
)
)
Institutional View
my_data <- reactive({
if (!is.null(input$plot_clicked_data$name)) {
hybrid_df %>%
filter(suffix == input$plot_clicked_data$name | domain == input$plot_clicked_data$name) %>%
mutate(plot_title = ifelse(!input$plot_clicked_data$name %in% suffix,
domain,
suffix
))
} else {
hybrid_df
}
})
Column
Text-mined author emails (first match per article) - Double click for selection!
output$plot <- renderEcharts4r({
hybrid_df %>%
count(suffix, domain) %>%
filter(!is.na(domain)) %>%
e_charts() %>%
e_treemap(suffix, domain, n,
leafDepth = "1",
name = "Corresponding Email"
) %>%
e_tooltip(trigger = "item")
})
output$oa_license <- renderPlotly({
ggplot(my_data(), aes(factor(year), fill = license)) +
geom_bar(position = position_stack(reverse = TRUE)) +
ggtitle(unique(my_data()$plot_title)) +
scale_fill_manual(
"License",
values = colors_license
) +
scale_x_discrete("", drop = FALSE) +
scale_y_continuous("Articles",
labels = function(x) {
format(x, big.mark = " ", scientific = FALSE)
}
) +
theme_minimal() -> p
tt <- plotly::ggplotly(p, tooltip = c("y"))
tt$x$data <- lapply(tt$x$data, function(x) {
x$text <- paste(x$name, x$y, sep = ": ")
x
})
tt
})
output$oa_source <- renderPlotly({
p <-
ggplot(my_data(), aes(factor(year), ..count.., fill = hybrid_type)) +
geom_bar(position = position_stack(reverse = TRUE)) +
xlab("Year") +
ylab("Articles") +
scale_y_continuous(
labels = function(x) {
format(x, big.mark = " ", scientific = FALSE)
}
) +
scale_fill_manual(
"Sources",
values = colors_source_disclosure,
na.value = "#b3b3b3a0"
) +
theme_minimal()
tt <- plotly::ggplotly(p, tooltip = c("y"))
tt$x$data <- lapply(tt$x$data, function(x) {
x$text <- paste(x$name, x$y, sep = ": ")
x
})
tt
})
fillRow(
flex = c(1, 2),
fillCol(echarts4rOutput("plot")),
fillCol(plotlyOutput("oa_license"), plotlyOutput("oa_source"))
)
Row
Top Publishers
renderDataTable(
expr = {
my_data() %>%
count(publisher) %>%
arrange(desc(n)) %>%
mutate(prop = round(n / sum(n) * 100, 2))
},
options = list(
pageLength = 8,
dom = "ftp"
)
)
subugoe/hoad documentation built on Feb. 22, 2022, 9:53 p.m.
R Package Documentation
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library(flexdashboard) library(viridis) library(plotly) library(shiny) library(scales) library(DT) library(writexl) library(echarts4r) library(hoad) library(forcats) library(dplyr) library(stringr)
# access to data hybrid_df <- readr::read_csv(file = path_extdat("hybrid_publications.csv")) %>% mutate(license = fct_infreq(license)) %>% mutate(publisher = fct_infreq(publisher)) %>% mutate(year = factor(issued, levels = c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020"))) %>% mutate(hybrid_type = ifelse(hybrid_type == "SCOAP", "SCOAP<sup>3</sup>", hybrid_type)) %>% mutate(hybrid_type = factor(hybrid_type, levels = c("Open APC (TA)", "Open APC (Hybrid)", "SCOAP<sup>3</sup>"))) %>% mutate(domain = paste(domain, suffix, sep = ".")) %>% arrange(desc(yearly_publisher_volume)) unpaywall_df <- readr::read_csv(file = path_extdat("unpaywall_df.csv")) %>% mutate(year = factor(year, levels = c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")))
Overview
Inputs {.sidebar}
# from https://stackoverflow.com/questions/42148177/how-can-i-build-multiple-inputs-into-my-shiny-app-with-both-updating-and-multipl # TODO factor this out selectizeInput( inputId = "publishers", label = "Selected Publishers", choices = c(All = "", levels(hybrid_df$publisher)), multiple = TRUE, ) selectizeInput( inputId = "journals", label = "Selected Journals", choices = c(All = "", sort(unique(hybrid_df$journal_title))), multiple = TRUE ) # publisher filter jn_filtered <- reactive({ if (is.null(input$publishers)) { return(hybrid_df) } else { return(hybrid_df[hybrid_df$publisher %in% input$publishers, ]) } }) observe(updateSelectizeInput( session, "journals", choices = c(All = "", sort(unique(jn_filtered()$journal_title))) )) # journal_title filter jn_f <- reactive({ if (is.null(input$journals)) { return(jn_filtered()) } else { return(jn_filtered()[jn_filtered()$journal_title %in% input$journals, ]) } }) # prepare dataset for plotting hybrid_sub <- reactive({ if (length(unique(jn_f()$publisher)) > 1) { hybrid_sub <- jn_f() %>% group_by(year, yearly_all, license) %>% count() %>% mutate(prop = n / yearly_all) } else if (length(unique(jn_f()$journal_title)) > 1 && length(unique(jn_f()$publisher)) == 1) { hybrid_sub <- jn_f() %>% group_by(year, license, yearly_publisher_volume) %>% count() %>% mutate(prop = n / yearly_publisher_volume) } else { hybrid_sub <- jn_f() %>% group_by(year, license, yearly_jn_volume) %>% count() %>% mutate(prop = n / yearly_jn_volume) } }) # using unpaywall indicators # unpaywall <- reactive({ # hybrid_df %>% # filter(journal_title %in% jn_f()$journal_title) %>% # group_by(year, journal_title, publisher, jn_y_unpaywall_others) %>% # summarise(n = n_distinct(doi_oa)) %>% # gather(n, jn_y_unpaywall_others, key = "source", value = "articles") %>% # ungroup() %>% # group_by(year, source) %>% # summarise(articles = sum(articles, na.rm = TRUE)) %>% # mutate( # source = ifelse( # source == "n", # "Crossref immediate license", # "Other license information\n(Unpaywall)" # ) # ) # })
Notice that only those hybrid open access journals were included where academic institutions sponsored the open access publication according to the Open APC initiative and where publishers shared license information about immediate open access with Crossref.
Row
Publishers selected
renderValueBox({ publisher_n <- length(unique(jn_f()$publisher)) valueBox(publisher_n, icon = "fa-filter") })
Journals selected
renderValueBox({ journal_n <- length(unique(jn_f()$journal_title)) valueBox(format(journal_n, big.mark = " ", scientific = FALSE), icon = "fa-filter" ) })
Hybrid OA articles indexed in Crossref
renderValueBox({ hybrid_n <- nrow(jn_f()) valueBox(format(hybrid_n, big.mark = " ", scientific = FALSE), icon = "fa-creative-commons" ) })
Column {.tabset .tabset-fade}
Hybrid OA licenses found (relative)
renderPlotly({ p <- ggplot(hybrid_sub(), aes(year, prop, fill = license)) + xlab("Year") + ylab("Hybrid OA / Articles published") + scale_fill_manual( "License", values = colors_license ) + scale_x_discrete(drop = FALSE) + scale_y_continuous(labels = scales::percent) + theme_minimal() + geom_col(position = position_stack(reverse = TRUE)) plotly::ggplotly(p) })
Hybrid OA licenses found (absolute)
renderPlotly({ p <- ggplot(hybrid_sub(), aes(year, n, fill = license)) + xlab("Year") + ylab("Hybrid OA Articles") + scale_fill_manual( "License", values = colors_license ) + scale_x_discrete(drop = FALSE) + scale_y_continuous( labels = function(x) { format(x, big.mark = " ", scientific = FALSE) } ) + theme_minimal() + geom_col(position = position_stack(reverse = TRUE)) # p plotly::ggplotly(p) })
Other types of OA license information detected by Unpaywall
renderPlotly({ unpaywall_df() %>% filter(journal_title %in% jn_f()$journal_title) %>% oa_by_other() %>% oa_by_other_plot() %>% plotly::ggplotly() })
Row {data-width=400 data-height=350}
Sources of disclosure of OA sponsorship
renderPlotly({ p <- hybrid_df %>% filter(journal_title %in% jn_f()$journal_title) %>% ggplot(aes(year, ..count.., fill = hybrid_type)) + geom_bar(position = position_stack(reverse = TRUE)) + xlab("Year") + ylab("Articles") + scale_x_discrete(drop = FALSE) + scale_y_continuous( labels = function(x) { format(x, big.mark = " ", scientific = FALSE) }, breaks = scales::pretty_breaks() ) + scale_fill_manual( "Sources", values = colors_source_disclosure, na.value = "#E9E1D7" ) + theme_minimal() tt <- plotly::ggplotly(p, tooltip = c("y")) tt$x$data <- lapply(tt$x$data, function(x) { x$text <- paste(x$name, x$y, sep = ": ") x }) tt })
Text-mined author emails (first match per article)
renderEcharts4r({ hybrid_df %>% filter(journal_title %in% jn_f()$journal_title) %>% count(suffix, domain) %>% filter(!is.na(domain)) %>% e_charts() %>% e_treemap(suffix, domain, n, leafDepth = "1", name = "Corresponding Email" ) %>% e_tooltip(trigger = "item") })
Compare
Row {data-height=250}
Hybrid open access uptake by Publisher, shown as the proportion of the total number of hybrid open articles found
hybrid_all <- hybrid_df %>% count(year) %>% mutate(year = gsub("201", "1", year)) %>% mutate(year = gsub("2020", "20", year)) hybrid_df %>% mutate(year = gsub("201", "1", year)) %>% mutate(year = gsub("2020", "20", year)) %>% mutate(publisher_group = fct_lump(publisher, prop = 0.05)) %>% group_by(year, publisher_group) %>% summarize(n = n()) %>% mutate(prop = n / sum(n)) %>% mutate(`Proportion in %` = round(prop * 100, 2)) %>% ggplot(aes(year, n)) + geom_bar( data = hybrid_all, aes(fill = "All Hybrid OA Articles"), color = "transparent", stat = "identity" ) + geom_bar(aes(fill = "by Publisher", label = `Proportion in %`), color = "transparent", stat = "identity") + facet_wrap(~publisher_group, nrow = 1) + scale_fill_manual(values = c("#b3b3b3a0", "#153268"), name = "") + labs(x = "Year", y = "Hybrid OA Articles") + theme_minimal() + theme( legend.position = "top", legend.justification = "right" ) + scale_y_continuous(labels = scales::number_format(big.mark = " ")) + theme(panel.grid.minor = element_blank()) + theme(axis.ticks = element_blank()) + theme(panel.grid.major.x = element_blank()) + theme(panel.border = element_blank()) -> publishers_comp_plot plotly::ggplotly(publishers_comp_plot, tooltip = c("label", "y"))
Row {data-height=500}
Journal hybrid open access share by publisher
fillRow( flex = c(1, 3), inputPanel( selectInput( inputId = "Year", label = "Select Years", choices = c(rev(levels(hybrid_df$year))), selected = c("2017", "2018", "2019"), multiple = TRUE ), checkboxInput("rb", "Display journals", FALSE) ), plotlyOutput("pubplot", height = "100%") ) output$pubplot <- renderPlotly({ if (input$Year == "All") { tt <- hybrid_df %>% filter(!is.na(license)) %>% mutate(publisher = forcats::as_factor(publisher)) %>% group_by(year, publisher, journal_title, yearly_jn_volume) %>% summarize(oa = n()) %>% mutate(prop = oa / yearly_jn_volume) } else { tt <- hybrid_df %>% filter(!is.na(license)) %>% mutate(publisher = forcats::as_factor(publisher)) %>% filter(year %in% input$Year) %>% group_by(year, publisher, journal_title, yearly_jn_volume) %>% summarize(oa = n()) %>% mutate(prop = oa / yearly_jn_volume) } tt <- tt %>% ungroup() %>% mutate(publisher = forcats::fct_other(publisher, keep = levels(publisher)[1:10])) p <- ggplot(tt, aes(y = prop, x = as.factor(publisher))) + geom_boxplot() + coord_flip() + theme_minimal() + xlab("Publisher") + ylab(NULL) + scale_x_discrete( drop = FALSE, limits = rev(levels(tt$publisher)), labels = function(x) { str_wrap(x, width = 30) } ) + scale_y_continuous(labels = scales::percent) if (input$rb == TRUE) { p <- p + geom_point(aes(colour = prop, label = journal_title)) + scale_colour_viridis(option = "C") + scale_fill_viridis(option = "C") + guides(color = FALSE, fill = FALSE) } else { p <- p } plotly::ggplotly(p, tooltip = c("label", "y")) %>% layout(margin = list(l = 200)) })
Journal hybrid open access share by publisher by year
fillCol( height = 600, flex = c(NA, 1), inputPanel( selectInput( "Publisher", label = "Select Publisher", choices = c("All", unique(hybrid_df$publisher)), selected = "All" ) ), plotlyOutput("yearplot", height = "100%") ) output$yearplot <- renderPlotly({ if (input$Publisher == "All") { tt <- hybrid_df %>% filter(!is.na(license)) %>% group_by(year, publisher, journal_title, yearly_jn_volume) %>% summarize(oa = n()) %>% mutate(prop = oa / yearly_jn_volume) } else { tt <- hybrid_df %>% filter(!is.na(license)) %>% filter(publisher == input$Publisher) %>% group_by(year, publisher, journal_title, yearly_jn_volume) %>% summarize(oa = n()) %>% mutate(prop = oa / yearly_jn_volume) } p <- ggplot(tt, aes(y = prop, x = as.factor(year))) + geom_boxplot() + coord_flip() + theme_minimal() + xlab("Year") + ylab(NULL) + scale_x_discrete(drop = FALSE) + scale_y_continuous(labels = scales::percent) if (input$rb == TRUE) { p <- p + geom_point(aes(colour = prop, label = journal_title)) + scale_colour_viridis(option = "C") + scale_fill_viridis(option = "C") + guides(color = FALSE, fill = FALSE) } else { p <- p } plotly::ggplotly(p, tooltip = c("label", "y")) })
Row {data-height=500}
Table view
hybrid_data <- reactive({ if (input$Publisher == "All") { hybrid_df } else { hybrid_df <- hybrid_df %>% filter(publisher == input$Publisher) } hybrid_df %>% filter(year %in% input$Year) %>% group_by(year, publisher, journal_title, yearly_jn_volume) %>% summarize(oa = n()) %>% mutate(prop = round((oa / yearly_jn_volume) * 100, 2)) %>% arrange(desc(year)) %>% select( Year = year, Publisher = publisher, Journal = journal_title, `Article Volume` = yearly_jn_volume, `OA Articles` = oa, `OA Share (in %)` = prop ) }) renderDataTable( expr = { hybrid_data() }, rownames = FALSE, filter = "bottom", options = list( pageLength = 4, dom = "ftp", columnDefs = list(list( className = "dt-head-left", targets = "_all" )) ) )
Institutional View
my_data <- reactive({ if (!is.null(input$plot_clicked_data$name)) { hybrid_df %>% filter(suffix == input$plot_clicked_data$name | domain == input$plot_clicked_data$name) %>% mutate(plot_title = ifelse(!input$plot_clicked_data$name %in% suffix, domain, suffix )) } else { hybrid_df } })
Column
Text-mined author emails (first match per article) - Double click for selection!
output$plot <- renderEcharts4r({ hybrid_df %>% count(suffix, domain) %>% filter(!is.na(domain)) %>% e_charts() %>% e_treemap(suffix, domain, n, leafDepth = "1", name = "Corresponding Email" ) %>% e_tooltip(trigger = "item") }) output$oa_license <- renderPlotly({ ggplot(my_data(), aes(factor(year), fill = license)) + geom_bar(position = position_stack(reverse = TRUE)) + ggtitle(unique(my_data()$plot_title)) + scale_fill_manual( "License", values = colors_license ) + scale_x_discrete("", drop = FALSE) + scale_y_continuous("Articles", labels = function(x) { format(x, big.mark = " ", scientific = FALSE) } ) + theme_minimal() -> p tt <- plotly::ggplotly(p, tooltip = c("y")) tt$x$data <- lapply(tt$x$data, function(x) { x$text <- paste(x$name, x$y, sep = ": ") x }) tt }) output$oa_source <- renderPlotly({ p <- ggplot(my_data(), aes(factor(year), ..count.., fill = hybrid_type)) + geom_bar(position = position_stack(reverse = TRUE)) + xlab("Year") + ylab("Articles") + scale_y_continuous( labels = function(x) { format(x, big.mark = " ", scientific = FALSE) } ) + scale_fill_manual( "Sources", values = colors_source_disclosure, na.value = "#b3b3b3a0" ) + theme_minimal() tt <- plotly::ggplotly(p, tooltip = c("y")) tt$x$data <- lapply(tt$x$data, function(x) { x$text <- paste(x$name, x$y, sep = ": ") x }) tt }) fillRow( flex = c(1, 2), fillCol(echarts4rOutput("plot")), fillCol(plotlyOutput("oa_license"), plotlyOutput("oa_source")) )
Row
Top Publishers
renderDataTable( expr = { my_data() %>% count(publisher) %>% arrange(desc(n)) %>% mutate(prop = round(n / sum(n) * 100, 2)) }, options = list( pageLength = 8, dom = "ftp" ) )
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