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08-ialeUK25-analysisConcepts.Rmd
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08-ialeUK25-analysisConcepts.Rmd
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# Analysis by Concept
Bar charts to examine how contributions to conferences vary by methods
```{r}
#spec(cpdata)
conceptdata <- cpdata %>%
select_if(is.numeric) %>%
gather(key = Concept, value = count, `PPS of landscapes`:`Accuracy and uncertainty`) %>%
filter(count > 0) %>%
group_by(`Concept`) %>%
summarise_all(sum, na.rm=T)
```
## Total Conference Contributions
Quick observations:
- Connectivity & fragmentation, LUCC and spatial analysis & modelling most frequent (composing > 50%)
```{r}
#ggplot(authCounts, aes(x=Concept, y=count)) + geom_bar(stat="identity")
conceptdata %>%
select(Concept, count) %>%
mutate(prop = count/sum(count)) %>%
mutate(prop = round(prop,3)) %>%
kable() %>%
kable_styling() %>%
scroll_box(width = "100%")
ggplot(conceptdata, aes(x=Concept, y=count)) +
geom_bar(stat="identity") +
geom_text(aes(x=Concept, y=max(count), label = paste0(round(100*count / sum(count),1), "%"), vjust=-0.25)) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
```
## Author Affiliation
Quick observations:
- no obvious differences between concepts
```{r}
authCounts <- conceptdata %>%
select(Concept,Academic, Government,NGO,Business,Private) %>%
mutate(sum = rowSums(.[2:6])) %>% #calculate total for subsquent calcultation of proportion
gather(key = Type, value = count, -Concept, -sum) %>%
mutate(prop = count / sum) #calculate proportion
conceptdata %>%
select(Concept,Academic, Government,NGO,Business,Private) %>%
mutate(Total = rowSums(.[2:6])) %>% #calculate total
mutate_if(is.numeric, funs(prop = ./ Total)) %>%
mutate_at(vars(ends_with("prop")), round, 3) %>%
select(-Total_prop) %>%
kable() %>%
kable_styling() %>%
scroll_box(width = "100%")
ggplot(authCounts, aes(x=Concept, y=count, fill=Type)) +
geom_bar(stat="identity", colour="white") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
ggplot(authCounts, aes(x=Concept, y=prop, fill=Type)) +
geom_bar(stat="identity", colour="white") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
```
## Landscape Type
### Using all landscape types
Quick observations:
- Ecosystem services have greatest proportion of urban landscapes and lowest in lowland rural
- Connectivity & Fragmentation have greatest proportion of studies in Lowland rural landscapes
```{r}
lspCounts <- conceptdata %>%
select(Concept,`Upland rural`, `Lowland rural`, Urban, Riverscape, Seascape, `Undefined LspType`,Other) %>%
mutate(sum = rowSums(.[2:8])) %>% #calculate total for subsquent calcultation of proportion
gather(key = Type, value = count, -Concept, -sum) %>%
mutate(prop = count / sum) #calculate proportion
conceptdata %>%
select(Concept,`Upland rural`, `Lowland rural`, Urban, Riverscape, Seascape, `Undefined LspType`,Other) %>%
mutate(Total = rowSums(.[2:8])) %>% #calculate total
mutate_if(is.numeric, funs(prop = ./ Total)) %>%
mutate_at(vars(ends_with("prop")), round, 3) %>%
select(-Total_prop) %>%
kable() %>%
kable_styling() %>%
scroll_box(width = "100%")
ggplot(lspCounts, aes(x=Concept, y=count, fill=Type)) +
geom_bar(stat="identity", colour="white") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
ggplot(lspCounts, aes(x=Concept, y=prop, fill=Type)) +
geom_bar(stat="identity", colour="white") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
```
### Without 'Undefined LspType' and 'Other' landscape types
Quick observations:
- patterns seen above more obvious
```{r}
lspCounts <- conceptdata %>%
select(Concept,`Upland rural`, `Lowland rural`, Urban, Riverscape, Seascape) %>%
mutate(sum = rowSums(.[2:6])) %>% #calculate total for subsquent calcultation of proportion
gather(key = Type, value = count, -Concept, -sum) %>%
mutate(prop = count / sum) #calculate proportion
conceptdata %>%
select(Concept,`Upland rural`, `Lowland rural`, Urban, Riverscape, Seascape) %>%
mutate(Total = rowSums(.[2:6])) %>% #calculate total
mutate_if(is.numeric, funs(prop = ./ Total)) %>%
mutate_at(vars(ends_with("prop")), round, 3) %>%
select(-Total_prop) %>%
kable() %>%
kable_styling() %>%
scroll_box(width = "100%")
ggplot(lspCounts, aes(x=Concept, y=count, fill=Type)) +
geom_bar(stat="identity", colour="white") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
ggplot(lspCounts, aes(x=Concept, y=prop, fill=Type)) +
geom_bar(stat="identity", colour="white") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
```
## Species
Quick observations
- Ecosystem services have greatest proportion of humans (31%)
```{r}
speciesCounts <- conceptdata %>%
select(Concept, Mammals, Humans, Birds, Reptiles, Inverts, Plants, Amphibians, Fish, `Generic Habitat`,`Woodland Forests`) %>%
mutate(sum = rowSums(.[2:11])) %>% #calculate total for subsquent calcultation of proportion
gather(key = Type, value = count, -Concept, -sum) %>%
mutate(prop = count / sum) #calculate proportion
conceptdata %>%
select(Concept, Mammals, Humans, Birds, Reptiles, Inverts, Plants, Amphibians, Fish, `Generic Habitat`,`Woodland Forests`) %>%
mutate(Total = rowSums(.[2:11])) %>% #calculate total for subsquent calcultation of proportion
mutate_if(is.numeric, funs(prop = ./ Total)) %>%
mutate_at(vars(ends_with("prop")), round, 3) %>%
select(-Total_prop) %>%
kable() %>%
kable_styling() %>%
scroll_box(width = "100%")
ggplot(speciesCounts, aes(x=Concept, y=count, fill=Type)) +
geom_bar(stat="identity", colour="white") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
ggplot(speciesCounts, aes(x=Concept, y=prop, fill=Type)) +
geom_bar(stat="identity", colour="white") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
```
## Methods
Quick observations:
- Ecosystem services has smallest proportion of Empirical studies
```{r}
methodsCounts <- conceptdata %>%
select(Concept, Empirical, Theoretical, Qualitative, Quantitative, GIS, `Remote sensing`) %>%
mutate(sum = rowSums(.[2:7])) %>%
gather(key = Type, value = count, -Concept, -sum) %>%
mutate(prop = count / sum)
conceptdata %>%
select(Concept, Empirical, Theoretical, Qualitative, Quantitative, GIS, `Remote sensing`) %>%
mutate(Total = rowSums(.[2:7])) %>%
mutate_if(is.numeric, funs(prop = ./ Total)) %>%
mutate_at(vars(ends_with("prop")), round, 3) %>%
select(-Total_prop) %>%
kable() %>%
kable_styling() %>%
scroll_box(width = "100%")
ggplot(methodsCounts, aes(x=Concept, y=count, fill=Type)) +
geom_bar(stat="identity", colour="white") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
ggplot(methodsCounts, aes(x=Concept, y=prop, fill=Type)) +
geom_bar(stat="identity", colour="white") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
```
## Spatial Extent
Quick observations:
- Ecosystem services has the largest proportion of global studies and fewest mini and micro studies
- Connectivity & Fragmentation has largest proportion of local studies
```{r}
spatialCounts <- conceptdata %>%
select(Concept, Micro, Mini, Local, Regional, National, Continental, Global,`Undefined Extent`) %>%
mutate(sum = rowSums(.[2:9])) %>%
gather(key = Type, value = count, -Concept, -sum) %>%
mutate(prop = count / sum)
conceptdata %>%
select(Concept, Micro, Mini, Local, Regional, National, Continental, Global,`Undefined Extent`) %>%
mutate(Total = rowSums(.[2:9])) %>%
mutate_if(is.numeric, funs(prop = ./ Total)) %>%
mutate_at(vars(ends_with("prop")), round, 3) %>%
select(-Total_prop) %>%
kable() %>%
kable_styling() %>%
scroll_box(width = "100%")
ggplot(spatialCounts, aes(x=Concept, y=count, fill=Type)) +
geom_bar(stat="identity", colour="white") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
ggplot(spatialCounts, aes(x=Concept, y=prop, fill=Type)) +
geom_bar(stat="identity", colour="white") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
```
## Temporal Extent
### With undefined
Quick observations:
```{r echo=F}
temporalCounts <- conceptdata %>%
select(Concept, Hours, Days, Weeks, Months, Years, Decades, Centuries, Longer, `Undefined Temporal`
) %>%
mutate(sum = rowSums(.[2:10])) %>%
gather(key = Type, value = count, -Concept, -sum) %>%
mutate(prop = count / sum)
conceptdata %>%
select(Concept, Hours, Days, Weeks, Months, Years, Decades, Centuries, Longer, `Undefined Temporal`
) %>%
mutate(Total = rowSums(.[2:10])) %>%
mutate_if(is.numeric, funs(prop = ./ Total)) %>%
mutate_at(vars(ends_with("prop")), round, 3) %>%
select(-Total_prop) %>%
kable() %>%
kable_styling() %>%
scroll_box(width = "100%")
ggplot(temporalCounts, aes(x=Concept, y=count, fill=Type)) +
geom_bar(stat="identity", colour="white") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
ggplot(temporalCounts, aes(x=Concept, y=prop, fill=Type)) +
geom_bar(stat="identity", colour="white") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
```
### Without Undefined
- History and Legacy studies have greatest proportion of Centries and Longer studies
```{r echo=F}
temporalCounts <- conceptdata %>%
select(Concept, Hours, Days, Weeks, Months, Years, Decades, Centuries, Longer
) %>%
mutate(sum = rowSums(.[2:9])) %>%
gather(key = Type, value = count, -Concept, -sum) %>%
mutate(prop = count / sum)
conceptdata %>%
select(Concept, Hours, Days, Weeks, Months, Years, Decades, Centuries, Longer
) %>%
mutate(Total = rowSums(.[2:9])) %>%
mutate_if(is.numeric, funs(prop = ./ Total)) %>%
mutate_at(vars(ends_with("prop")), round, 3) %>%
select(-Total_prop) %>%
kable() %>%
kable_styling() %>%
scroll_box(width = "100%")
ggplot(temporalCounts, aes(x=Concept, y=count, fill=Type)) +
geom_bar(stat="identity", colour="white") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
ggplot(temporalCounts, aes(x=Concept, y=prop, fill=Type)) +
geom_bar(stat="identity", colour="white") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
```
## Other Concepts
Quick observations:
- Connectivity & Fragmentation and pattern-process-scale have greatest proportion of biodiversity studies
- History and Legacy have greatest proportion of cultural studies
```{r}
otherCounts <- conceptdata %>%
select(Concept, `Green Infrastructure`,`Planning and Architecture`,`Management and Conservation`,`Cultural Landscapes`,`Socio-economic Dimensions`,Biodiversity,`Landscape Assessment`,`Catchment Based Approach`,`Invasives Pests Diseases`
) %>%
mutate(sum = rowSums(.[2:10])) %>%
gather(key = Type, value = count, -Concept, -sum) %>%
mutate(prop = count / sum)
conceptdata %>%
select(Concept, `Green Infrastructure`,`Planning and Architecture`,`Management and Conservation`,`Cultural Landscapes`,`Socio-economic Dimensions`,Biodiversity,`Landscape Assessment`,`Catchment Based Approach`,`Invasives Pests Diseases`
) %>%
mutate(Total = rowSums(.[2:10])) %>%
mutate_if(is.numeric, funs(prop = ./ Total)) %>%
mutate_at(vars(ends_with("prop")), round, 3) %>%
select(-Total_prop) %>%
kable() %>%
kable_styling() %>%
scroll_box(width = "100%")
ggplot(otherCounts, aes(x=Concept, y=count, fill=Type)) +
geom_bar(stat="identity", colour="white") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
ggplot(otherCounts, aes(x=Concept, y=prop, fill=Type)) +
geom_bar(stat="identity", colour="white") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
```