IEW12_slides.Rmd

---
title: "IEW12 Presentation Graphs"
author: "A H Sparks and E M Del Ponte"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{IEW12_slides}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 7,
  fig.height = 4.5
)
```

# Frontmatter

## Load libraries

```{r libraries, message=FALSE}
library(hrbrthemes)
library(ggthemes)
library(dplyr)
library(ggplot2)
library(gsheet)
library(magrittr)
library(tidyr)
```

## Set the theme to use for the graphs

```{r set_theme}
theme_set(theme_ipsum_rc())
```

# Import article evaluations

```{r import}
rrpp <- gsheet2tbl(
  "https://docs.google.com/spreadsheets/d/19gXobV4oPZeWZiQJAPNIrmqpfGQtpapXWcSxaXRw1-M/edit#gid=1699540381"
)
```

# Visualise journals

```{r }
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))
```


# Visualise evaluations

## Article classes

```{r}
rrpp %>%
  ggplot(aes(x = art_class,
             fill = molecular)) +
  geom_bar() +
  scale_fill_few() +
  labs(title = "Article Classification",
       x = "Classes",
       fill = "Molecular")
```

## Computational methods available

Are analysis scripts and any special software used available for inspection and
reuse to reproduce the work? Readily available (3) to not mentioned (1).

```{r message=FALSE, warning=FALSE}
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")
```

## Software availability

Is the software readily available? Open source (3, good) to proprietary and \$$
(1).

```{r message=FALSE, warning=FALSE}
rrpp %>%
  ggplot(aes(x = software_avail,
             fill = art_class)) +
  geom_bar() +
  scale_fill_few() +
  labs(title = "Software Availability",
       x = "Score",
       fill = "Article class")
```

## Data availability

Is the data readily available from a proper archiving repository, e.g. Zenodo
or Dataverse (3) to not mentioned (1).

```{r message=FALSE, warning=FALSE}
rrpp %>%
  ggplot(aes(x = data_avail,
             fill = art_class)) +
  geom_bar() +
  scale_fill_few() +
  labs(title = "Data Availability",
       x = "Score",
       fill = "Article class") 
```

## Software citations

Was the software used properly cited? All versions and packages cited (3) to
not described what was used (1).

```{r message=FALSE, warning=FALSE}
rrpp %>%
  ggplot(aes(x = software_cite,
             fill = art_class)) +
  geom_bar() +
  scale_fill_few() +
  labs(title = "Software cited",
       x = "Score",
       fill = "Article class") 
```

## Software used (cited)

What are the 10 most popular software packages used?

### Unnest the software that were used

```{r}
rrpp <-
  rrpp %>%
  unnest(software_used = strsplit(software_used, ", "))
```

### Graph the software that were used

```{r}
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))
```