slides.Rmd

---
title: '`r paste("Climate emergency slide pack for ", filter(lookup, area_code == params$la)$area_name)`'
output: 
  powerpoint_presentation:
    reference_doc: template1.pptx
params:
  la: NA
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(error=TRUE, message=FALSE, warning=FALSE, echo=FALSE, fig.height=8, fig.width=12)

knitr::opts_knit$set(eval.after = "fig.cap")

library(tidyverse) ; library(sf) ; library(scales) ; library(sf) ; library(treemapify) ; library(ggsci) ; library(knitr)

#params2 <- data.frame(la = "E08000009",
#params2 <- data.frame(la = "E06000050",
#params2 <- data.frame(la =  "S12000034",
#params2 <- data.frame(la =  "N09000009",
#params2 <- data.frame(la =  "W06000001",
# stringsAsFactors = FALSE)
#la <- filter(la, area_code == params2$la)
#lsoa <- filter(lsoa, area_code == params2$la)
#id <- filter(lookup, area_code == params2$la)$area_name

la <- filter(la, area_code == params$la)
lsoa <- filter(lsoa, area_code == params$la)
id <- filter(lookup, area_code == params$la)$area_name


#la <- filter(la, area_code == "E08000009")
#id <- filter(lookup, area_code == la$area_code)$area_name
```

:::::::::::::: {.columns}
::: {.column}
`r paste0("These slides (hopefully!) include visualisations of open data relating to climate change for ", filter(lookup, area_code == params$la)$area_name, ". ")` A missing plot will generally indicate that there are no data available.
:::
::: {.column}
The visualisations are licensed under a [Creative Commons Attribution-ShareAlike 4.0 International License](http://creativecommons.org/licenses/by-sa/4.0/). You can find the data behind these visualisations in our [open data companion](http://www.trafforddatalab.io/climate_emergency/).
:::
::::::::::::::

#### Carbon dioxide emissions by sector
```{r}
co2_emissions_sector <- co2_emissions %>%
  filter(area_code == params$la, period == 2021, sector != "LULUCF") %>%
  mutate(percent = round(value/sum(value)*100,0))

co2_emissions_sector_test = nrow(co2_emissions_sector) != 0
```

```{r eval=co2_emissions_sector_test, dpi=300}
layout <- circleProgressiveLayout(co2_emissions_sector, sizecol = "percent")
vertices <- circleLayoutVertices(layout, npoints = 100)

ggplot(data = vertices) + 
  geom_polygon(aes(x, y, group = id, fill = factor(id)), color = "transparent", show.legend = FALSE) +
  geom_text(data = layout, aes(x, y), label = paste0(str_wrap(co2_emissions_sector$sector, width = 15), "\n", co2_emissions_sector$percent, "%"), 
            check_overlap = TRUE, color = "#000000") + 
  scale_fill_manual(values = c("#cc1333","#5F4690","#1D6996","#38A6A5","#a1cce6","#ecc30b","#1a68cc")) + 
  labs(title = "Carbon dioxide emissions by main sector",
       subtitle = paste0(id, ", 2021"),
       caption = "Excluding LULUCF\nSource: BEIS, DESNZ") +
  coord_equal() +
  theme_void() +
  theme(plot.title = element_text(size = 16, face = "bold", hjust = 0),
      plot.subtitle = element_text(hjust = 0, margin = margin(9, 0, 9, 0)),
      plot.caption = element_text(size = 12, color = "grey50", hjust = 1, margin = margin(t = 15)))
```

#### Carbon dioxide emissions from large industrial installations
```{r}
large_point_sources <- large_point_sources %>% 
  mutate(value = round(value,0)) %>% 
  filter(area_code == params$la) %>% 
  mutate(operator = str_wrap(operator, width = 15))

large_point_sources_test = nrow(large_point_sources) != 0
```

```{r eval=large_point_sources_test, dpi=300}
ggplot(large_point_sources, aes(lon, lat)) + 
  geom_sf(data = la, fill = NA, color = "#212121", size = 0.5) +
  geom_point(aes(size = value, alpha = value), fill = "#212121", shape = 21) +
  geom_text_repel(aes(x = lon, y = lat, label = operator), colour = "#757575", size = 3, fontface = "bold",
                  min.segment.length = 0, point.padding = NA, segment.color = "grey50", box.padding =	0.5,  max.overlaps = 100) +
  scale_alpha_continuous(guide = "legend", label = comma, breaks = pretty_breaks(5)) +
  scale_size_continuous(label = comma, range = c(2, 12), breaks = pretty_breaks(5)) +
  labs(x = NULL, y = NULL, size = "Tonnes", alpha = "Tonnes",
       title = expression(bold(paste(CO[2], " emissions from large industrial installations"))),
       subtitle = paste0(id, ", 2021"),
       caption = "Contains Ordnance Survey data © Crown copyright and database right 2019\nSource: NAEI") +
  coord_sf(datum = NA) +
  theme_x() +
  theme(plot.caption = element_text(size = 8, hjust = 0),
        legend.position = "right",
        legend.title = element_text(size = 10),
        legend.text = element_text(size = 8)) 
```

#### Industrial and commercial energy consumption
```{r}
industrial_commercial_energy_consumption <- industrial_commercial_energy_consumption %>% 
  filter(area_code == params$la) %>% 
  mutate(value = round(value,0)) %>% 
  arrange(value) %>% 
  mutate(group = factor(group, levels = group)) 

industrial_commercial_energy_consumption_test = nrow(industrial_commercial_energy_consumption) != 0
```


```{r eval=industrial_commercial_energy_consumption_test, dpi=300}
ggplot(industrial_commercial_energy_consumption, aes(x = value, y = group)) +
  geom_segment(aes(x = 0, xend = value, y = group, yend = group), 
               linetype = "dotted", colour = "#212121", size = 0.5) + 
  geom_point(colour = "#dd1c77", size = 15) +
  geom_text(aes(label = comma(value, accuracy = 1)), fontface = "bold", colour = "white") +
  labs(x = "Thousands of tonnes of oil equivalent (ktoe)", y = NULL, 
       title = "Industrial and commercial energy consumption",
       subtitle = paste0(id, ", 2021"),
       caption = "Source: DESNZ, BEIS",
       fill = NULL) +
  theme_x() +
  theme(aspect.ratio = 0.5,
        panel.grid.major = element_blank(),
        axis.text.x = element_blank(),
        axis.text.y = element_text(hjust = 0))
```

#### Road transport fuel consumption
```{r}
road_transport_fuel_consumption <- road_transport_fuel_consumption %>% 
  filter(area_code == params$la)

road_transport_fuel_consumption_test = nrow(road_transport_fuel_consumption) != 0
```

```{r eval=road_transport_fuel_consumption_test, dpi=300}
ggplot(road_transport_fuel_consumption, aes(x = period, y = value, colour = group, group = group)) +
  geom_line(size = 0.8) +
  geom_hline(yintercept = 0, size = 1, colour = "#212121") +
  scale_y_continuous(position = "right", labels = comma,
                     expand = c(0.005, 0.005)) +
  scale_color_futurama() +
  guides(colour = guide_legend(nrow = 1)) +
  labs(x = NULL, 
       y = "Thousand tonnes of oil equivalent (ktoe)l",
       title = "Fuel use by vehicle type",
       subtitle = paste0(id, ", 2005-2021"),
       caption = "Source: DESNZ, BEIS",
       colour = NULL) +
  theme_x() + 
  theme(axis.title.y = element_text(hjust = 0))
```

#### Domestic energy consumption
```{r}
domestic_energy_consumption <- domestic_energy_consumption %>% 
  filter(area_code == params$la) %>% 
  mutate(value = round(value,0)) %>% 
  arrange(value) %>% 
  mutate(group = factor(group, levels = group)) 

domestic_energy_consumption_test = nrow(domestic_energy_consumption) != 0
```

```{r eval=domestic_energy_consumption_test, dpi=300}
ggplot(domestic_energy_consumption, aes(x = value, y = group)) +
  geom_segment(aes(x = 0, xend = value, y = group, yend = group), 
               linetype = "dotted", colour = "#212121", size = 0.5) + 
  geom_point(colour = "#DDCC77", size = 15) +
  geom_text(aes(label = comma(value, accuracy = 1)), fontface = "bold", colour = "white") +
  labs(x = "Thousands of tonnes of oil equivalent (ktoe)", y = NULL, 
       title = "Domestic fuel use",
       subtitle = paste0(id, ", 2021"),
       caption = "Source: DESNZ, BEIS",
       fill = NULL) +
  theme_x() +
  theme(aspect.ratio = 0.5,
        panel.grid.major = element_blank(),
        axis.text.x = element_blank(),
        axis.text.y = element_text(hjust = 0))
```

#### Domestic property build period
```{r}
domestic_property_build_period <- domestic_property_build_period %>% 
  filter(area_code == params$la) %>% 
  mutate(period = as.factor(period),
         period = fct_relevel(period, "pre-1900")) 

domestic_property_build_period_test = nrow(domestic_property_build_period) != 0
```

```{r eval=domestic_property_build_period_test, dpi=300}
ggplot(domestic_property_build_period, aes(x = factor(period), y = value)) +
  geom_col(fill = "brown4") +
  geom_hline(yintercept = 0, size = 1, colour = "#212121") +
  scale_y_continuous(labels = comma) +
  labs(x = NULL, 
       y = "Dwellings",
       title = "Build period of domestic dwellings",
       subtitle = paste0(id, ", up to March 2023"),
       caption = "Source: Valuation Office Agency")  +
  theme_x()
```

#### EPC certificates A, B and C
```{r}
domestic_epcs_energy_efficiency <- domestic_epcs_energy_efficiency %>% 
  filter(area_code == params$la) 

domestic_epcs_energy_efficiency_test = nrow(domestic_epcs_energy_efficiency) != 0
```

```{r eval=domestic_epcs_energy_efficiency_test, results='asis', dpi=300}
cat(paste0(percent(pull(summarise(filter(mutate(domestic_epcs_energy_efficiency, percent = value/sum(value)), efficiency %in% c("A","B","C")), abc = sum(percent))), accuracy = 0.1), " of registered domestic Energy Performance Certificates in ", id, " have a rating of A, B or C, over a 10 year period to 2023/Q3."))
```


#### Fuel poverty
```{r}
fuel_poverty <- left_join(lsoa,
                          filter(fuel_poverty, area_code == params$la), by = "lsoa21cd")

fuel_poverty_test = nrow(fuel_poverty) != 0
```

```{r eval=fuel_poverty_test, dpi=300, fig.cap = "In 2021, 13.1% of all households in England (3.16 million households) were in fuel poverty."}
ggplot(fuel_poverty) + 
  geom_sf(aes(fill = value), color = "#FFFFFF", size = 0.5, alpha = 0.8) +
  scale_fill_viridis(discrete = F, 
                     label = function(x) paste0(x, "%"),
                     direction = -1,
                     guide = guide_colourbar(
                       direction = "vertical",
                       barwidth = unit(3, units = "mm"),
                       title.position = 'top',
                       title.vjust = 1)) +
  labs(x = NULL, y = NULL,
       title = "Proportion of households in fuel poverty by LSOA",
       subtitle = paste0(id, ", 2021"),
              caption = "Contains Ordnance Survey data © Crown copyright and database right 2023\nSource: BEIS",
       fill = NULL) +
  coord_sf(datum = NA) +
  theme_x() +
  theme(plot.title = element_text(size = 16),
        legend.position = "right",
        legend.text = element_text(size = 8))
```

#### Background N02 concentrations
```{r}
background_air_pollution <- background_air_pollution %>% 
  filter(area_code == params$la)

background_air_pollution_test = nrow(background_air_pollution) != 0
```

```{r eval=background_air_pollution_test, dpi=300, fig.cap="The World Health Organization and EU guideline annual mean level of NO2 is 40μg/m3."}
ggplot(data = background_air_pollution, 
       aes(fill = no22022, color = no22022)) +
  geom_sf(data = la, fill = NA, color = "#212121", size = 0.5) +
  geom_sf(color = NA, alpha = 0.8) +
  scale_fill_viridis_c(option = "magma", direction = -1,
                       guide = guide_colorbar(
                         barheight = unit(2, units = "mm"),
                         barwidth = unit(50, units = "mm"),
                         title.position = "left",
                         title.vjust = 1,
                         label.hjust = 0.5
                       )) +
  labs(title = expression(bold(paste("Modelled background ", NO[2], " concentrations"))),
       subtitle = paste0(id, ", 2022"),
       caption = "Contains Ordnance Survey data © Crown copyright and database right 2024\nSource: DEFRA",
       fill = "Annual mean (μg/m3)") +
  coord_sf(datum = NA) +
  theme_x() +
  theme(plot.caption = element_text(size = 8, hjust = 0),
        legend.title = element_text(size = 10),
        legend.text = element_text(size = 8)) 
```

#### Background PM10 concentrations
```{r eval=background_air_pollution_test, dpi=300, fig.cap="The World Health Organization guideline annual mean level of PM10 is 20μg/m3. The EU limit is 40 µg/m3."}
ggplot(data = background_air_pollution, 
       aes(fill = pm102022g, color = pm102022g)) +
  geom_sf(data = la, fill = NA, color = "#212121", size = 0.5) +
  geom_sf(color = NA, alpha = 0.8) +
  scale_fill_viridis_c(option = "magma", direction = -1,
                       guide = guide_colorbar(
                         barheight = unit(2, units = "mm"),
                         barwidth = unit(50, units = "mm"),
                         title.position = "left",
                         title.vjust = 1,
                         label.hjust = 0.5
                       )) +
  labs(title = expression(bold(paste("Modelled background ", PM[10], " concentrations"))),
       subtitle = paste0(id, ", 2022"),
       caption = "Contains Ordnance Survey data © Crown copyright and database right 2024\nSource: DEFRA",
       fill = "Annual mean (μg/m3)") +
  coord_sf(datum = NA) +
  theme_x() +
  theme(plot.caption = element_text(size = 8, hjust = 0),
        legend.title = element_text(size = 10),
        legend.text = element_text(size = 8)) 
```

#### Background PM2.5 concentrations
```{r eval=background_air_pollution_test, dpi=300, fig.cap="The World Health Organization guideline annual mean level of PM2.5 is 10μg/m3. The EU limit is 25μg/m3."}
ggplot(data = background_air_pollution, 
       aes(fill = pm252022g, color = pm252022g)) +
  geom_sf(data = la, fill = NA, color = "#212121", size = 0.5) +
  geom_sf(color = NA, alpha = 0.8) +
  scale_fill_viridis_c(option = "magma", direction = -1,
                       guide = guide_colorbar(
                         barheight = unit(2, units = "mm"),
                         barwidth = unit(50, units = "mm"),
                         title.position = "left",
                         title.vjust = 1,
                         label.hjust = 0.5
                       )) +
  labs(title = expression(bold(paste("Modelled background ", PM[2.5], " concentrations"))),
       subtitle = paste0(id, ", 2022"),
       caption = "Contains Ordnance Survey data © Crown copyright and database right 2024\nSource: DEFRA",
       fill = "Annual mean (μg/m3)") +
  coord_sf(datum = NA) +
  theme_x() +
  theme(plot.caption = element_text(size = 8, hjust = 0),
        legend.title = element_text(size = 10),
        legend.text = element_text(size = 8)) 
```

#### Renewable electricity generation
```{r}
renewable_electricity_generation <-  renewable_electricity_generation %>% 
  filter(area_code == params$la) %>%
  mutate(info = str_c(group, ": ",  comma(value)))


renewable_electricity_generation_test = nrow(renewable_electricity_generation) != 0
```

```{r eval=renewable_electricity_generation_test, dpi=300}
ggplot(renewable_electricity_generation %>% filter(value != 0), aes(area = value, fill = fct_reorder(group, value, .desc = TRUE), subgroup = group, label = info)) +
  geom_treemap(colour = "#212121") +
  geom_treemap_text(colour = "#FFFFFF", place = "bottomleft", reflow = TRUE, 
                    padding.x = grid::unit(1.5, "mm"),
                    padding.y = grid::unit(2, "mm"),
                    size = 14) +
  scale_fill_simpsons() +
  labs(x = NULL, y = NULL, 
       title = "Renewable electricity generation by source",
       subtitle = paste0(id, ", 2022"),
       caption = paste0(paste0(renewable_electricity_generation %>% filter(is.na(value)) %>% pull(group),collapse = ", "), " generation\n was suppressed due to disclosure control.\nSource: BEIS"),
       fill = "MWh") +
  theme_x() +
  theme(legend.position = "right") 

```

#### Onshore wind turbines
```{r}
onshore_wind <- onshore_wind %>% 
  filter(area_code == params$la)

onshore_wind_test = nrow(onshore_wind) != 0
```

```{r eval=onshore_wind_test, dpi=300, fig.cap= paste(nrow(onshore_wind), ' onshore wind turbines in ', filter(lookup, area_code == params$la)$area_name)}
ggplot(onshore_wind, aes(lon, lat)) + 
  geom_sf(data = la, fill = NA, color = "#212121", size = 0.5) +
  geom_point(color = "#756bb1", size = 3) +
  labs(x = NULL, y = NULL,
       title = "Onshore wind turbines",
       subtitle = paste0(distinct(onshore_wind, area_name), " 2023-10"),
       caption = "Contains Ordnance Survey data © Crown copyright and database right 2023\nSource: BEIS") +
  coord_sf(datum = NA) +
  theme_x() +
  theme(plot.caption = element_text(size = 8, hjust = 0),
        legend.position = "right",
        legend.title = element_text(size = 10),
        legend.text = element_text(size = 8)) 
```

#### Non-domestic Renewable Heat Incentive
```{r}
non_domestic_rhi <- non_domestic_rhi %>% 
  filter(area_code == params$la) 

non_domestic_rhi_test = nrow(non_domestic_rhi) != 0
```

```{r eval=non_domestic_rhi_test, results='asis', dpi=300}
cat(paste0("There have been ", pull(non_domestic_rhi, value), " non-domestic Renewable Heat Incentive applications accredited in ", id, " between November 2011 and March 2023."))
```

#### Domestic Renewable Heat Incentive
```{r}
domestic_rhi <- domestic_rhi %>% 
  filter(area_code == params$la) 

domestic_rhi_test = nrow(domestic_rhi) != 0
```

```{r eval=domestic_rhi_test, results='asis', dpi=300}
cat(paste0("A total of ", pull(domestic_rhi, value), " applications have been accredited for payment in ", id, " between April 2014 and March 2023."))
```

#### Recycling
```{r}
recycling <- recycling %>% 
  filter(area_code == params$la)

recycling_test = nrow(recycling) != 0
```

```{r eval=recycling_test, dpi=300}
ggplot(recycling, aes(x = period, y = value, group = 1)) +
  geom_line(colour = "#117733", size = 1) +
  geom_hline(yintercept = 0, size = 1, colour = "#212121") +
  scale_y_continuous(position = "right", label = function(x) paste0(x, "%")) +
  labs(x = NULL, y = NULL,
       title = "Reuse, recycling or composting of household waste",
       subtitle = paste0(id, ", 2015 - 2022"),
       caption = "Source: DEFRA") +
  theme_x()
```

#### Licensed ULEVs
```{r}
ulev <- ulev %>% 
  filter(area_code == params$la) %>% 
  spread(indicator, value) %>%
  drop_na() %>% 
  mutate(percent = `Ultra low emission vehicles`/`All licensed vehicles`)

ulev_test = nrow(ulev) != 0
```

```{r eval=ulev_test, dpi=300}
ggplot(ulev, aes(x = period, y = percent)) +
  geom_col(fill = "#ffa600") +
  geom_hline(yintercept = 0, size = 1, colour = "#333333") +
  scale_y_continuous(labels = percent_format(accuracy = 0.1)) +
  scale_x_continuous(breaks=seq(2011,2022, by = 1)) +
  labs(x = NULL, y = NULL, 
       title = "Licensed ULEVs as a proportion of all licensed vehicles",
       subtitle = paste0(id, ", 2011-2022"),
       caption = "Source: DfT and DVLA") +
  theme_x() +
  theme(axis.title.y = element_text(hjust = 0))
```

#### Licensed electric cars and vans
```{r}
ev <- ev %>% 
  filter(area_code == params$la) %>% 
  spread(indicator, value) %>%
  drop_na() %>% 
  mutate(percent = `Electric cars and vans`/`All cars and vans`)

ev_test = nrow(ev) != 0
```

```{r eval=ev_test, dpi=300}
ggplot(ev, aes(x = period, y = percent)) +
  geom_col(fill = "#332288") +
  geom_hline(yintercept = 0, size = 1, colour = "#333333") +
  scale_y_continuous(labels = percent_format(accuracy = 0.1)) +
  scale_x_continuous(breaks=seq(2011,2022, by = 1)) +
  labs(x = NULL, y = NULL, 
       title = "Licensed electric cars and vans as a proportion of all licensed cars and vans",
       subtitle = paste0(id, ", 2011-2022"),
       caption = "Source: DfT and DVLA") +
  theme_x() +
  theme(axis.title.y = element_text(hjust = 0))
```

#### Residents who cycle, 2022
```{r}
cycling <- cycling %>% 
  filter(area_code == params$la) 

cycling_test = nrow(cycling) != 0
```

```{r eval=cycling_test, dpi=300}
cycling %>% 
  mutate(frequency = paste("At least", tolower(frequency)),
         value = round(value,1)) %>% 
    select(`How often?` = frequency, `%` = value) %>% 
  kable(caption = "9.3% of adults in England cycle at least once a week. Source: Department for Transport")
```

#### Residents who walk, 2022
```{r}
walking <- walking %>% 
  filter(area_code == params$la) 

walking_test = nrow(walking) != 0
```

```{r eval=walking_test, dpi=300}
walking %>% 
  mutate(frequency = paste("At least", tolower(frequency)),
         value = round(value,1)) %>% 
    select(`How often?` = frequency, `%` = value) %>% 
  kable(caption = "69.1% of adults in England walk at least once a week. Source: Department for Transport")
```

#### Green belt
```{r}
green_belt <- green_belt %>% 
  filter(area_code == params$la) 

green_belt_test = nrow(green_belt) != 0
```

```{r eval=green_belt_test, results='asis', dpi=300}
cat(paste0("The extent of the designated Green Belt in ", id, " at 31 March 2023 was estimated at ", comma(pull(green_belt %>% filter(indicator == "Green Belt area"), value)), " hectares, or approximately ", round(pull(green_belt %>% filter(indicator == "Green Belt area"), value)/pull(green_belt %>% filter(indicator == "Total area"), value)*100, 1), "% of the land area of ", id, "."))
```

#### Further resources

- [Tyndall Centre: Carbon Budget Tool](https://carbonbudget.manchester.ac.uk/reports)
- [Friends of the Earth: Climate friendly communities](https://friendsoftheearth.uk/climate-friendly-communities)
- [Propensity to Cycle Tool](https://www.pct.bike)