ImpactFactor.Rmd

---
title: "Impact Factor `r local({ load('data/workspace.RData'); sub('^X([0-9]{4})\\.JIF$', '\\1', grep('^X[0-9]{4}\\.JIF$', names(jcreco), value=TRUE)) })`"
author: "Marco Mello"
date: "`r format(Sys.time(), '%Y/%b/%d')`"
output:
  pdf_document: default
  html_document:
    df_print: paged
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)

knitr::knit_hooks$set(inline = function(x) {
prettyNum(x, big.mark=",")
})

# Load the workspace saved by jc.R. Run jc.R first to update the data.
load("data/workspace.RData")

library(ggplot2)
library(plyr)
library(RColorBrewer)
library(reshape2)
library(tidyverse)

# Detect the JIF column name dynamically (e.g. X2024.JIF)
jif_col <- grep("^X[0-9]{4}\\.JIF$", names(jcreco), value = TRUE)
year_label <- sub("^X([0-9]{4})\\.JIF$", "\\1", jif_col)
```

Impact factor analysis of journals classified in the categories "Ecology" (JCR) and "Biodiversity" (CAPES), using recently published data from last year, extracted from Journal Citation Reports ([JCR](https://jcr.clarivate.com)).

[Ecological Synthesis Lab](https://marcomellolab.wordpress.com) (SintECO).

See [README](https://github.com/marmello77/ImpactFactor/blob/master/README.md) for further info.

## Summary

1.  [Ecology](#ecology)

2.  [Biodiversity](#biodiversity)

## 1. Ecology {#ecology}

Let's examine the impact factor of the journals classified in the category "Ecology" on JCR.

```{r, echo=FALSE, include=FALSE}
str(jcreco)
```

```{r, echo=FALSE, include=FALSE}
jcreco.med <- summarise(jcreco,
                        IFmedian = median(.data[[jif_col]], na.rm = T))
eco.med = round(jcreco.med[1,1], digits = 2)

jcreco.qua1 <- summarise(jcreco,
                         IFqua1 = quantile(.data[[jif_col]], probs = 0.25, na.rm = T))
eco.qua1 = round(jcreco.qua1[1,1], digits = 2)

jcreco.qua2 <- summarise(jcreco,
                         IFqua2 = quantile(.data[[jif_col]], probs = 0.75, na.rm = T))
eco.qua2 = round(jcreco.qua2[1,1], digits = 2)

jcreco.min <- summarise(jcreco,
                        IFmin = min(.data[[jif_col]], na.rm = T))
eco.min = round(jcreco.min[1,1], digits = 2)

jcreco.max <- summarise(jcreco,
                        IFmax = max(.data[[jif_col]], na.rm = T))
eco.max = round(jcreco.max[1,1], digits = 2)
```

The median impact factor of Ecology journals is `r format(eco.med, scientific=FALSE)`. Fifty-percent of the journals vary between `r format(eco.qua1, scientific=FALSE)` and `r format(eco.qua2, scientific=FALSE)`. The minimum impact factor recorded is `r format(eco.min, scientific=FALSE)` and the maximum is `r format(eco.max, scientific=FALSE)`.

This is the distribution of impact factor for Ecology journals:

```{r, echo=FALSE, warning=FALSE, fig.height=7, fig.width=10, cache=FALSE, out.width='\\textwidth', fig.align='center'}
p1 <- ggplot(jcreco, aes(x=.data[[jif_col]])) +
  geom_density(color = F, fill = "red", alpha = 0.3, position = "identity") +
  xlab(paste("Impact factor", year_label, ": Ecology")) +
  ylab("Frequency") +
  theme(axis.text.x = element_text(colour="grey20",size=20,
                                   angle=0,hjust=.5,vjust=.5,face="plain"),
        axis.text.y = element_text(colour="grey20",size=20,
                                   angle=0,hjust=1,vjust=0,face="plain"),
        axis.title.x = element_text(colour="grey20",size=25,
                                    angle=0,hjust=.5,vjust=0,face="plain"),
        axis.title.y = element_text(colour="grey20",size=25,
                                    angle=90,hjust=.5,vjust=.5,face="plain")) +
  theme(plot.margin = unit(c(0.5,0.5,0.5,0.5),"cm")) +
  theme(axis.title.y=element_text(margin=margin(0,20,0,0)),
        axis.title.x=element_text(margin=margin(20,0,0,0))) +
  geom_vline(data=jcreco.med, aes(xintercept=eco.med),
             linetype="dashed", size=0.5, color = "red") +
  geom_text(aes(eco.med, 0.29, label = eco.med, angle = 0,
                hjust = -0.1, vjust = 0), size = 5, color = "red") +
  geom_vline(data=jcreco.qua1, aes(xintercept=eco.qua1),
             linetype="dashed", size=0.5, color = "red") +
  geom_text(aes(eco.qua1, 0.29, label = eco.qua1, angle = 0,
                hjust = -0.1, vjust = 0), size = 3, color = "red") +
  geom_vline(data=jcreco.qua1, aes(xintercept=eco.qua2),
             linetype="dashed", size=0.5, color = "red") +
  geom_text(aes(eco.qua2, 0.29, label = eco.qua2, angle = 0,
                hjust = -0.1, vjust = 0), size = 3, color = "red")

p1
```

## 2. Biodiversity {#biodiversity}

```{r, echo=FALSE, include=FALSE}
str(jcrbio)
```

The journals classified in the categories "Ecology", "Oceanography", "Plant Sciences", and "Zoology" on Journal Citation Reports (JCR) are pooled in the category "Biodiversity", used by the Brazilian Coordination for the Improvement of Higher Education Personnel (CAPES).

```{r, echo=FALSE, include=FALSE}
bio.med <- summarise(jcrbio,
                     IFmedian = median(.data[[jif_col]], na.rm = T))
bio.med = round(bio.med, digits = 2)

jcrbio.qua1 <- summarise(jcrbio,
                         IFqua1 = quantile(.data[[jif_col]], probs = 0.25, na.rm = T))
bio.qua1 = round(jcrbio.qua1[1,1], digits = 2)

jcrbio.qua2 <- summarise(jcrbio,
                         IFqua2 = quantile(.data[[jif_col]], probs = 0.75, na.rm = T))
bio.qua2 = round(jcrbio.qua2[1,1], digits = 2)

jcrbio.min <- summarise(jcrbio,
                        IFmin = min(.data[[jif_col]], na.rm = T))
bio.min = round(jcrbio.min[1,1], digits = 2)

jcrbio.max <- summarise(jcrbio,
                        IFmax = max(.data[[jif_col]], na.rm = T))
bio.max = round(jcrbio.max[1,1], digits = 2)

jcrbio.med <- ddply(jcrbio, "Category", summarise,
                    IFmedian = median(.data[[jif_col]], na.rm = T))
```

The median impact factor of Biodiversity journals is `r format(bio.med[1,1], scientific=FALSE)`. Fifty-percent of the journals vary between `r format(bio.qua1, scientific=FALSE)` and `r format(bio.qua2, scientific=FALSE)`. The minimum impact factor recorded is `r format(bio.min, scientific=FALSE)` and the maximum is `r format(bio.max, scientific=FALSE)`.

```{r, echo=FALSE, include=FALSE}
eco.med = round(jcrbio.med[1,2], digits = 2)
oce.med = round(jcrbio.med[2,2], digits = 2)
pla.med = round(jcrbio.med[3,2], digits = 2)
zoo.med = round(jcrbio.med[4,2], digits = 2)
```

These are the median impact factors by category:

1.  Ecology = `r format(eco.med, scientific=FALSE)`

2.  Oceanography = `r format(oce.med, scientific=FALSE)`

3.  Plant Sciences = `r format(pla.med, scientific=FALSE)`

4.  Zoology = `r format(zoo.med, scientific=FALSE)`

This is the distribution of impact factor for Biodiversity journals by category:

```{r, echo=FALSE, warning=FALSE, fig.height=7, cache=FALSE, fig.width=10, out.width='\\textwidth', fig.align='center'}
p2 = ggplot(jcrbio, aes(x=.data[[jif_col]], fill=Category)) +
  geom_density(alpha=.3, position="identity", color = F) +
  xlab(paste("Impact factor", year_label, ": CAPES's Biodiversity")) +
  ylab("Frequency") +
  theme(axis.text.x = element_text(colour="grey20",size=20,angle=0,
                                   hjust=.5,vjust=.5,face="plain"),
        axis.text.y = element_text(colour="grey20",size=20,angle=0,
                                   hjust=1,vjust=0,face="plain"),
        axis.title.x = element_text(colour="grey20",size=25,angle=0,
                                    hjust=.5,vjust=0,face="plain"),
        axis.title.y = element_text(colour="grey20",size=25,angle=90,
                                    hjust=.5,vjust=.5,face="plain")) +
  theme(plot.margin = unit(c(0.5,0.5,0.5,0.5),"cm")) +
  theme(axis.title.y=element_text(margin=margin(0,20,0,0)),
        axis.title.x=element_text(margin=margin(20,0,0,0))) +
  geom_vline(data=jcrbio.med, aes(xintercept=IFmedian, colour=Category),
             linetype="dashed", size=0.5) +
  geom_text(aes(eco.med, 0.60, label = eco.med, angle = 0,
                hjust = 0, vjust = 0), size = 3) +
  geom_text(aes(oce.med, 0.62, label = oce.med, angle = 0,
                hjust = 0, vjust = 0), size = 3) +
  geom_text(aes(pla.med, 0.64, label = pla.med, angle = 0,
                hjust = 0, vjust = 0), size = 3) +
  geom_text(aes(zoo.med, 0.66, label = zoo.med, angle = 0,
                hjust = 0, vjust = 0), size = 3)

p2
```