knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.width = 7, fig.height = 4.5 )
library(hrbrthemes) library(ggthemes) library(dplyr) library(ggplot2) library(gsheet) library(magrittr) library(tidyr)
theme_set(theme_ipsum_rc())
rrpp <- gsheet2tbl( "https://docs.google.com/spreadsheets/d/19gXobV4oPZeWZiQJAPNIrmqpfGQtpapXWcSxaXRw1-M/edit#gid=1699540381" )
rrpp %>% ggplot(aes(x = abbreviation, fill = art_class)) + geom_bar() + scale_fill_few() + labs( x = "Journal", title = "Random sample of 200 articles in 21 plant pathology journals", subtitle = "2012 to 2016", fill = "Article type" ) + theme(axis.text.x = element_text(angle = 45, hjust = 1))
rrpp %>% ggplot(aes(x = art_class, fill = molecular)) + geom_bar() + scale_fill_few() + labs(title = "Article Classification", x = "Classes", fill = "Molecular")
Are analysis scripts and any special software used available for inspection and reuse to reproduce the work? Readily available (3) to not mentioned (1).
rrpp %>% ggplot(aes(x = comp_mthds_avail, fill = art_class)) + geom_bar() + scale_fill_few() + labs(title = "Computational Methods Availability", x = "Score", fill = "Article class")
Is the software readily available? Open source (3, good) to proprietary and \$$ (1).
rrpp %>% ggplot(aes(x = software_avail, fill = art_class)) + geom_bar() + scale_fill_few() + labs(title = "Software Availability", x = "Score", fill = "Article class")
Is the data readily available from a proper archiving repository, e.g. Zenodo or Dataverse (3) to not mentioned (1).
rrpp %>% ggplot(aes(x = data_avail, fill = art_class)) + geom_bar() + scale_fill_few() + labs(title = "Data Availability", x = "Score", fill = "Article class")
Was the software used properly cited? All versions and packages cited (3) to not described what was used (1).
rrpp %>% ggplot(aes(x = software_cite, fill = art_class)) + geom_bar() + scale_fill_few() + labs(title = "Software cited", x = "Score", fill = "Article class")
What are the 10 most popular software packages used?
rrpp <- rrpp %>% unnest(software_used = strsplit(software_used, ", "))
tab <- table(rrpp$software_used) tab_s <- sort(tab) top10 <- tail(names(tab_s), 17) # checking the table, there are several ties top_software <- subset(rrpp, software_used %in% top10) top_software$software_used <- factor(top_software$software_used, levels = rev(top10)) top_software %>% ggplot(aes(x = software_used, fill = art_class)) + geom_bar() + scale_fill_few() + labs(title = "Top 10 Software Used", x = "Software", fill = "Molecular") + theme(axis.text.x = element_text(angle = 45, hjust = 1))
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