Meta-Analysis: Red Snapper in the Gulf of Mexico
Group 3
We used Web of Science to conduct a literature search:
searchTerms <- read_csv("search_terms.csv")
kable(searchTerms, align = 'cll')| search | terms | hits |
|---|---|---|
| 1 | ‘Red Snapper’ Distribution | 79 |
| 2 | ‘Gulf of Mexico’ ‘Red Snapper’ dynamic* | 29 |
Meta-analysis workflow
Reasons for paper eliminations
ggplot(paperReasons, aes(Reason, Papers)) + geom_bar(stat = "identity", fill="blue") + coord_flip() + theme_grey(base_size = 18)
A range of drivers were investigated:
data.simple <- data %>% group_by(Driver) %>% count()
ggplot(na.omit(data.simple), aes(Driver, n)) +
geom_bar(stat = "identity", fill = "blue") +
coord_flip()+ theme_grey(base_size = 18)
Three main study types were represented:
data.simple <- data %>% group_by(StudyType) %>% count()
ggplot(na.omit(data.simple), aes(StudyType, n)) +
geom_bar(stat = "identity", fill = "blue") +
theme_grey(base_size = 18) +
coord_flip()
#### Age classes and definitions varied:
data$Age_class[grep('juvenile',data$Age_class)] <- 'Juvenile'
data.simple <- data %>% group_by(Age_class) %>% count()
ggplot(na.omit(data.simple), aes(Age_class, n)) +
geom_bar(stat = "identity", fill = "blue") +
theme_grey(base_size = 18) +
coord_flip()
Effect sizes
m <- metagen(mean.EffectSize_d, error, studlab = Driver, data = treedata) #fit generic meta-analysis to an object
#viz (draw a standard forest plot or metaregression plot)
forest(m,leftcols = "Driver",rightcols = c("effect", "ci"), fontsize=12) #grid-based graphics so a bit of work to resize
