rLadiesSpatial.qmd

---
title: "Dealing with Spatial Data using R"
author: "Alessia Calafiore"
format: 
  rladies-revealjs:
    footer: "[R-Ladies](https://rladies.org/) theme for [Quarto Presentations](https://quarto.org/docs/presentations/revealjs/index.html). Code available on [GitHub](https://github.com/beatrizmilz/quarto-rladies-theme)."
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embed-resources: true
---

## Overview

::: r-fit-text
-   Introducing R and Spatial Data

-   Mapping Gender Inequality in Scotland
:::

## R and Spatial Data

![](logos.jpg){.absolute top="50" left="50" height="400"}

## Simple Features for R (the sf package)

![](sf.gif){.absolute top="100" left="100" height="300"}

## 

From [Edzer Pebesma](https://r-spatial.github.io/sf/articles/sf1.html) vignette:

. . .

-   **sf** implements a widely used standard in spatial databases that describes how objects in the real world can be represented in computers;

. . .

-   A feature can be thought of as a thing, or an object in the real world, such as a building or a tree.

. . .

-   Features have a geometry describing where on Earth the feature is located, and they have attributes, which describe other properties.

## 

-   The most common simple features for spatial analysis are POINTS, LINESTRING, POLYGONS

. . .

Once installed sf can be loaded as any other R library

```{r echo=TRUE}
library(sf)
```

## Now let's load some spatial data

```{r echo = TRUE}
boundaries <- st_read("scottish_wards.gpkg")
```

```{r echo = TRUE}
class(boundaries)
```

## Coordinate Reference Systems

Coordinates correspond to specific location on the Earth's surface on the basis of a **coordinate reference system (CRS)**. A common way to identify a CRS is through its **EPSG code**.

```{r echo=TRUE}
st_crs(boundaries)[[1]] #EPSG = 27700
```

CRS can be transformed with sf.

```{r echo=TRUE}
boundaries_WGS84 <- st_transform(boundaries, 4326)
st_crs(boundaries_WGS84)[[1]]
```

## Geometry operations (some)

::: columns
::: {.column width="50%"}
**On a single object**

-   st_centroids()

-   st_buffer()

-   st_simplify()

-   st_union()
:::

::: {.column width="50%"}
**Between two objects**

-   st_intersection()

-   st_difference()

-   st_union()
:::
:::

## Spatial Join

Spatial joins are useful when you want to **join attributes** of one sf obj with an other object based on geometry operations.

```{r echo=TRUE}
pts <- st_sample(boundaries[1:2, ], 10) # creates sample points within boundaries
pts <- st_as_sf(pts) #transform it into an sf object 
names(pts)
names(boundaries[1:2, ])
```

As you can see there is no attribute we could use to join these two datasets.

## but can we do it spatially?

```{r}
plot(st_geometry(boundaries[1:2,]))
plot(pts, add=TRUE)
```

## Let's try then

```{r echo=TRUE}

pts_data<-st_join(pts, boundaries[1:2,])
head(pts_data)

```

# Mapping Gender Inequality in Scotland {background-color="#562457"}

## Gender Pay Gap and Gender Inequality

-   Broadly, the **Gender Pay Gap** is the **difference in pay between women and men**.

-   Globally, women on average, are paid **about 20 per cent less** than men (ILO).

-   Gender **inequality in job opportunities and career**.

## Measuring Gender Inequality in Scotland

***Socio-economic Classification (SeC - Scottish Census 2011)***

::: columns
::: {.column width="50%"}
**C1** - Higher managerial, administrative and professional occupations

**C2** - Lower managerial, administrative and professional occupations

**C3** - Intermediate occupations

**C4** - Small employers and own account workers
:::

::: {.column width="50%"}
**C5** - Lower supervisory and technical occupations

**C6** - Semi-routine occupations

**C7** - Routine occupations

**C8** - Never worked and long-term unemployed
:::
:::

## Let's load some data

```{r echo=TRUE, message=FALSE}
data <- read.csv("LC6121SC.csv")
names(data)
```

With these data let's create a simple *equality score* (E) to compare the likelihood of belonging to each of these SeC between females and males.

## A bit of data wrangling

```{r echo=TRUE, warning=FALSE}
library(tidyverse)

ward_data <- data %>%
  #filter out country level data
  filter(Name != "Scotland") %>%
  #reshape the data
  pivot_wider(id_cols = Name,
              names_from = Gender,
              values_from = starts_with("C")) %>%
  #compute the score
  mutate(
    C1_gap = ((C1_Females / C1_All) / (C1_Males / C1_All)) * 100,
    C2_gap = ((C2_Females / C2_All) / (C2_Males / C2_All)) * 100,
    C3_gap = ((C3_Females / C3_All) / (C3_Males / C3_All)) * 100,
    C4_gap = ((C4_Females / C4_All) / (C4_Males / C4_All)) * 100,
    C5_gap = ((C5_Females / C5_All) / (C5_Males / C5_All)) * 100,
    C6_gap = ((C6_Females / C6_All) / (C6_Males / C6_All)) * 100,
    C7_gap = ((C7_Females / C7_All) / (C7_Males / C7_All)) * 100,
    C8_gap = ((C8_Females / C8_All) / (C8_Males / C8_All)) * 100
) %>%
select(Name, contains("gap"))

```

## How to interpret the score (E)

-   If E = 100 females and males have the same likelihood to be in a SeC;

-   If E \> 100 it is more likely for females than males to be in a SeC;

-   if E \< 100 it is more likely for males than females to be in a SeC;

## Let's get some descriptive stats

```{r echo=TRUE}

ward_data %>%
  pivot_longer(cols = contains("gap"),
               names_to = "SeC",
               values_to = "Gap") %>%
  group_by(SeC) %>%
  summarise(
    Min = min(Gap),
    Max = max(Gap),
    Mean = mean(Gap),
    SD = sd(Gap)
  )
```

## Static maps with ggplot2

-   ggplot is a widely used R library, now over 10 years old

. . .

-   It is used to make any type of plot, including maps !!!

. . .

-   Browse the [R gallery](https://r-graph-gallery.com/) to find out more

. . .

# Let's make some maps !!! {background-color="#562457"}

## First we need to link the boundaries with the data

```{r echo=TRUE}
names(boundaries)
names(ward_data)
```

Let's join these two datasets

```{r echo=TRUE}
ward_data_geo <- boundaries %>%
  inner_join(ward_data, by = c("Name"="Name"))
names(ward_data_geo)
```

## Now we can make our first map

```{r echo=TRUE}

ggplot()+
  geom_sf(aes(fill = C1_gap),data = ward_data_geo)
```

## Let's make some changes

```{r, echo=TRUE, eval=FALSE}
#| code-line-numbers: "1-4"

ggplot()+
  geom_sf(aes(fill = C1_gap),
          data = ward_data_geo,
          colour = "NA") +
  #change color palette
  scale_fill_gradientn( 
            colors = c("#0002A1", "#332FD0", "#FB2576", "#3F0071")
                ) +
  #add a scale bar
  ggspatial::annotation_scale( 
    location = "bl",
    bar_cols = c("grey60", "white")
    ) +
  #change text 
  labs(fill = "Women every 100 Men\nin Higher Managerial Roles",
       title = "Gender Inequality in Scotland")+
  #customize theme
  theme_void() + 
  theme(
    text = element_text(family = "Futura-Medium",color = "#22211d"),
    legend.title = element_text(family = "Futura-Bold", size = 10),
    legend.text = element_text(family = "Futura-Medium", size = 10),
    plot.background = element_rect(fill = "#f5f5f2", color = NA),
    panel.background = element_rect(fill = "#f5f5f2", color = NA)
    )  

```

## 

```{r, echo=TRUE, eval=FALSE}
#| code-line-numbers: "5-13"

ggplot()+
  geom_sf(aes(fill = C1_gap),
          data = ward_data_geo,
          colour = "NA") +
  #change color palette
  scale_fill_gradientn( 
            colors = c("#0002A1", "#332FD0", "#FB2576", "#3F0071")
                ) +
  #add a scale bar
  ggspatial::annotation_scale(  
    location = "bl",
    bar_cols = c("grey60", "white")
    ) +
  #change text 
  labs(fill = "Women every 100 Men\nin Higher Managerial Roles",
       title = "Gender Inequality in Scotland")+
  #customize theme
  theme_void() + 
  theme(
    text = element_text(family = "Futura-Medium",color = "#22211d"),
    legend.title = element_text(family = "Futura-Bold", size = 10),
    legend.text = element_text(family = "Futura-Medium", size = 10),
    plot.background = element_rect(fill = "#f5f5f2", color = NA),
    panel.background = element_rect(fill = "#f5f5f2", color = NA)
    )  

```

## 

```{r, echo=TRUE, eval=FALSE}
#| code-line-numbers: "14-25"

ggplot()+
  geom_sf(aes(fill = C1_gap),
          data = ward_data_geo,
          colour = "NA") +
  #change color palette
  scale_fill_gradientn( 
            colors = c("#0002A1", "#332FD0", "#FB2576", "#3F0071")
                ) +
  #add a scale bar
  ggspatial::annotation_scale(  
    location = "bl",
    bar_cols = c("grey60", "white")
    ) +
  #change text 
  labs(fill = "Women every 100 Men\nin Higher Managerial Roles",
       title = "Gender Inequality in Scotland")+
  #customize theme
  theme_void() + 
  theme(
    text = element_text(family = "Futura-Medium",color = "#22211d"),
    legend.title = element_text(family = "Futura-Bold", size = 10),
    legend.text = element_text(family = "Futura-Medium", size = 10),
    plot.background = element_rect(fill = "#f5f5f2", color = NA),
    panel.background = element_rect(fill = "#f5f5f2", color = NA)
    )  

```

## and see the result

```{r echo=FALSE}

ggplot()+
  geom_sf(aes(fill = C1_gap),
          data = ward_data_geo,
          colour = "NA") +
  #change color palette
  scale_fill_gradientn( 
            colors = c("#0002A1", "#332FD0", "#FB2576", "#3F0071")
                ) +
  #change the background
  theme_void() + 
  #add a scale bar
  ggspatial::annotation_scale(  
    location = "bl",
    bar_cols = c("grey60", "white")
    ) +
  #change legend title
  labs(fill = "Women every 100 Men\nin Higher Managerial Roles",
       title = "Gender Inequality in Scotland")+
  #customize theme
  theme(
    text = element_text(family = "Futura-Medium",color = "#22211d"),
    legend.title = element_text(family = "Futura-Bold", size = 10),
    legend.text = element_text(family = "Futura-Medium", size = 10),
    plot.background = element_rect(fill = "#f5f5f2", color = NA),
    panel.background = element_rect(fill = "#f5f5f2", color = NA)
    )  

```

## Focus on Edinburgh

```{r}
#filter the data
ward_data_geo %>%
  filter(Council %in% c("City of Edinburgh")) %>%
  ggplot() +
  geom_sf(aes(fill = C1_gap),
          colour = "white") +
  scale_fill_gradientn(
            colors = c("#0002A1", "#332FD0", "#FB2576", "#3F0071")
            ) +
  theme_void() +
  ggspatial::annotation_scale(
    location = "br",
    bar_cols = c("grey60", "white")
    ) +
  labs(fill = "Women every 100 Men\nin Higher Managerial Roles",
       title = "Gender Inequality in Edinburgh")+
  #customize theme
  theme(
    text = element_text(family = "Futura-Medium",color = "#22211d"),
    legend.title = element_text(family = "Futura-Bold", size = 10),
    legend.text = element_text(family = "Futura-Medium", size = 10),
    plot.background = element_rect(fill = "#f5f5f2", color = NA),
    panel.background = element_rect(fill = "#f5f5f2", color = NA)
    )  
```

## Preparing data to add ward labels

```{r echo=TRUE}
#create polygon centroids to be used as location for the labels
label_poi <- ward_data_geo %>%
  filter(Council %in% c("City of Edinburgh")) %>%
  st_centroid()

#add long lat field
label_poi <- label_poi %>%
  mutate(LONG=as.numeric(st_coordinates(label_poi)[,1]),
         LAT=as.numeric(st_coordinates(label_poi)[,2]))

names(label_poi)

```

## Let's add the labels to the map

```{r, echo=TRUE, eval=FALSE}
#| code-line-numbers: "1-24"
#filter the data
ward_data_geo %>%
  filter(Council %in% c("City of Edinburgh")) %>%
  ggplot() +
  geom_sf(aes(fill = C1_gap),
          colour = "#f5f5f2") +
  scale_fill_gradientn(
            colors = c("#0002A1", "#332FD0", "#FB2576", "#3F0071")
                ) +
  theme_void() +
  ggspatial::annotation_scale(
    location = "br",
    bar_cols = c("grey60", "white")
    ) +
  labs(fill = "Women for every 100 Men\nin Higher Managerial Roles",
       title = "Gender Inequality in Edinburgh")+
  #customize theme
  theme(
    text = element_text(family = "Futura-Medium",color = "#22211d"),
    legend.title = element_text(family = "Futura-Bold", size = 10),
    legend.text = element_text(family = "Futura-Medium", size = 10),
    plot.background = element_rect(fill = "#f5f5f2", color = NA),
    panel.background = element_rect(fill = "#f5f5f2", color = NA)
    )  +
  #add labels
  geom_sf(data = label_poi, color="NA")+
  ggrepel::geom_text_repel(mapping = aes(x=LONG,
                                         y=LAT,
                                         label=Name), 
                           data = label_poi,
                           size=2,
                           color = "white",     # text color
                           bg.color = "grey30", # shadow color
                           bg.r = 0.15,
                           max.overlaps=Inf,
                           segment.color = "black")
```

## 

```{r, echo=TRUE, eval=FALSE}
#| code-line-numbers: "25-36"
#filter the data
ward_data_geo %>%
  filter(Council %in% c("City of Edinburgh")) %>%
  ggplot() +
  geom_sf(aes(fill = C1_gap),
          colour = "#f5f5f2") +
  scale_fill_gradientn(
            colors = c("#0002A1", "#332FD0", "#FB2576", "#3F0071")
                ) +
  theme_void() +
  ggspatial::annotation_scale(
    location = "br",
    bar_cols = c("grey60", "white")
    ) +
  labs(fill = "Women for every 100 Men\nin Higher Managerial Roles",
       title = "Gender Inequality in Edinburgh")+
  #customize theme
  theme(
    text = element_text(family = "Futura-Medium",color = "#22211d"),
    legend.title = element_text(family = "Futura-Bold", size = 10),
    legend.text = element_text(family = "Futura-Medium", size = 10),
    plot.background = element_rect(fill = "#f5f5f2", color = NA),
    panel.background = element_rect(fill = "#f5f5f2", color = NA)
    )  +
  #add labels
  geom_sf(data = label_poi, color="NA")+
  ggrepel::geom_text_repel(mapping = aes(x=LONG,
                                         y=LAT,
                                         label=Name), 
                           data = label_poi,
                           size=2,
                           color = "white",     # text color
                           bg.color = "grey30", # shadow color
                           bg.r = 0.15,
                           max.overlaps=Inf,
                           segment.color = "black")
```

## and see the result

```{r echo=FALSE}
ward_data_geo %>%
  filter(Council %in% c("City of Edinburgh")) %>%
  ggplot() +
  geom_sf(aes(fill = C1_gap),
          colour = "#f5f5f2") +
  scale_fill_gradientn(
            colors = c("#0002A1", "#332FD0", "#FB2576", "#3F0071")
                ) +
  theme_void() +
  ggspatial::annotation_scale(
    location = "br",
    bar_cols = c("grey60", "white")
    ) +
  labs(fill = "Number of Women for every\n10 Men in Managerial Roles")+
  theme(
    text = element_text(family = "Futura-Medium",color = "#22211d"),
    legend.title = element_text(family = "Futura-Bold", size = 10),
    legend.text = element_text(family = "Futura-Medium", size = 10),
    plot.background = element_rect(fill = "#f5f5f2", color = NA),
    panel.background = element_rect(fill = "#f5f5f2", color = NA)
    ) +
  geom_sf(data = label_poi, color="NA")+
  ggrepel::geom_text_repel(mapping = aes(x=LONG,
                                         y=LAT,
                                         label=Name), 
                           data = label_poi,
                           size=2.5,
                           color = "white",     # text color
                           bg.color = "grey30", # shadow color
                           bg.r = 0.15,
                           max.overlaps=Inf,
                           segment.color = "black")
```

# Let's add some interactivity {background-color="#562457"}

## Interactive maps with leaflet

Leaflet is JavaScript library for mobile-friendly interactive maps.

The R package makes it easy to integrate and control Leaflet maps in R.

After installed you can load it:

```{r, echo=TRUE}
library(leaflet)
```

## Some data prep

```{r, echo=TRUE}
#transform CRS to make it compatible with global map tiles prividers
ward_data_geo <- st_transform(ward_data_geo, 4326) %>%
  st_make_valid() %>%
  st_simplify()

#find centroid of Scotland to center the first view
scotland_ct <- ward_data_geo %>%
  st_make_valid() %>%
  st_union() %>%
  st_centroid()
```

## Basic example

```{r, echo=TRUE, eval=FALSE}
#| code-line-numbers: "1-2"
leaflet() %>%
  setView(scotland_ct[[1]][1], scotland_ct[[1]][2], zoom = 6) %>%
  # add a dark basemap
  addProviderTiles("CartoDB.DarkMatter") %>%
  # add the polygons
  addPolygons(
    data = ward_data_geo,
    color = "#E2E2E2",
    # set the opacity of the outline
    opacity = 1,
    # set the stroke width in pixels
    weight = 1,
    # set the fill opacity
    fillOpacity = 0.2
  ) 

```

## 

```{r, echo=TRUE, eval=FALSE}
#| code-line-numbers: "3-4"
leaflet() %>%
  setView(scotland_ct[[1]][1], scotland_ct[[1]][2], zoom = 6) %>%
  # add a dark basemap
  addProviderTiles("CartoDB.DarkMatter") %>%
  # add the polygons
  addPolygons(
    data = ward_data_geo,
    color = "#E2E2E2",
    # set the opacity of the outline
    opacity = 1,
    # set the stroke width in pixels
    weight = 1,
    # set the fill opacity
    fillOpacity = 0.2
  ) 

```

## 

```{r, echo=TRUE, eval=FALSE}
#| code-line-numbers: "5-15"
leaflet() %>%
  setView(scotland_ct[[1]][1], scotland_ct[[1]][2], zoom = 6) %>%
  # add a dark basemap
  addProviderTiles("CartoDB.DarkMatter") %>%
  # add the polygons
  addPolygons(
    data = ward_data_geo,
    color = "#E2E2E2",
    # set the opacity of the outline
    opacity = 1,
    # set the stroke width in pixels
    weight = 1,
    # set the fill opacity
    fillOpacity = 0.2
  ) 

```

## Let's see what we get

```{r}
#| code-line-numbers: "1-2"
leaflet() %>%
  setView(scotland_ct[[1]][1], scotland_ct[[1]][2], zoom = 6) %>%
  # add a dark basemap
  addProviderTiles("CartoDB.DarkMatter") %>%
  # add the polygons
  addPolygons(
    data = ward_data_geo,
    color = "#E2E2E2",
    # set the opacity of the outline
    opacity = 1,
    # set the stroke width in pixels
    weight = 1,
    # set the fill opacity
    fillOpacity = 0.2
  ) 

```

## Add fill and legend

```{r, echo=TRUE, eval=FALSE}
#| code-line-numbers: "1-2"
bins <- c(35, 45, 55, 65, 75, 85)
pal <- colorBin(c("#0002A1", "#332FD0", "#FB2576", "#3F0071"), domain = ward_data_geo$C1_gap, bins = bins)
leaflet(ward_data_geo) %>%
  # center the map view
  setView(scotland_ct[[1]][1], scotland_ct[[1]][2], zoom = 6) %>%
  # add a dark basemap
  addProviderTiles("CartoDB.DarkMatter") %>%
  # change polygon fill
  addPolygons(
    # set the opacity of the outline
    opacity = 0,
    # set the stroke width in pixels
    weight = 0.1,
    # set fill colors
    fillColor = ~pal(C1_gap),
    # set the fill opacity
    fillOpacity = 0.8
  ) %>%
  addLegend(pal = pal, 
            values = ~C1_gap, 
            opacity = 0.7,
            position = "bottomright",
            title = "Women every 100 Men</br>in Higher Managerial Roles")


```

## 

```{r, echo=TRUE, eval=FALSE}
#| code-line-numbers: "3-7"
bins <- c(35, 45, 55, 65, 75, 85)
pal <- colorBin(c("#0002A1", "#332FD0", "#FB2576", "#3F0071"), domain = ward_data_geo$C1_gap, bins = bins)
leaflet(ward_data_geo) %>%
  # center the map view
  setView(scotland_ct[[1]][1], scotland_ct[[1]][2], zoom = 6) %>%
  # add a dark basemap
  addProviderTiles("CartoDB.DarkMatter") %>%
  # change polygon fill
  addPolygons(
    # set the opacity of the outline
    opacity = 0,
    # set the stroke width in pixels
    weight = 0.1,
    # set fill colors
    fillColor = ~pal(C1_gap),
    # set the fill opacity
    fillOpacity = 0.8
  ) %>%
  addLegend(pal = pal, 
            values = ~C1_gap, 
            opacity = 0.7,
            position = "bottomright",
            title = "Women every 100 Men</br>in Higher Managerial Roles")


```

## 

```{r, echo=TRUE, eval=FALSE}
#| code-line-numbers: "8-18"
bins <- c(35, 45, 55, 65, 75, 85)
pal <- colorBin(c("#0002A1", "#332FD0", "#FB2576", "#3F0071"), domain = ward_data_geo$C1_gap, bins = bins)
leaflet(ward_data_geo) %>%
  # center the map view
  setView(scotland_ct[[1]][1], scotland_ct[[1]][2], zoom = 6) %>%
  # add a dark basemap
  addProviderTiles("CartoDB.DarkMatter") %>%
  # change polygon fill
  addPolygons(
    # set the opacity of the outline
    opacity = 0,
    # set the stroke width in pixels
    weight = 0.1,
    # set fill colors
    fillColor = ~pal(C1_gap),
    # set the fill opacity
    fillOpacity = 0.8
  ) %>%
  addLegend(pal = pal, 
            values = ~C1_gap, 
            opacity = 0.7,
            position = "bottomright",
            title = "Women every 100 Men</br>in Higher Managerial Roles")


```

## 

```{r, echo=TRUE, eval=FALSE}
#| code-line-numbers: "19-23"
bins <- c(35, 45, 55, 65, 75, 85)
pal <- colorBin(c("#0002A1", "#332FD0", "#FB2576", "#3F0071"), domain = ward_data_geo$C1_gap, bins = bins)
leaflet(ward_data_geo) %>%
  # center the map view
  setView(scotland_ct[[1]][1], scotland_ct[[1]][2], zoom = 6) %>%
  # add a dark basemap
  addProviderTiles("CartoDB.DarkMatter") %>%
  # change polygon fill
  addPolygons(
    # set the opacity of the outline
    opacity = 0,
    # set the stroke width in pixels
    weight = 0.1,
    # set fill colors
    fillColor = ~pal(C1_gap),
    # set the fill opacity
    fillOpacity = 0.8
  ) %>%
  addLegend(pal = pal, 
            values = ~C1_gap, 
            opacity = 0.7,
            position = "bottomright",
            title = "Women every 100 Men</br>in Higher Managerial Roles")


```

## 

```{r}
#| code-line-numbers: "19-23"
bins <- c(35, 45, 55, 65, 75, 85)
pal <- colorBin(c("#0002A1", "#332FD0", "#FB2576", "#3F0071"), domain = ward_data_geo$C1_gap, bins = bins)
leaflet(ward_data_geo) %>%
  # center the map view
  setView(scotland_ct[[1]][1], scotland_ct[[1]][2], zoom = 6) %>%
  # add a dark basemap
  addProviderTiles("CartoDB.DarkMatter") %>%
  # change polygon fill
  addPolygons(
    # set the opacity of the outline
    opacity = 0,
    # set the stroke width in pixels
    weight = 0.1,
    # set fill colors
    fillColor = ~pal(C1_gap),
    # set the fill opacity
    fillOpacity = 0.8
  ) %>%
  addLegend(pal = pal, 
            values = ~C1_gap, 
            opacity = 0.7,
            position = "bottomright",
            title = "Women every 100 Men</br>in Higher Managerial Roles")


```

## Add popup

```{r, echo=TRUE, eval=FALSE}
#| code-line-numbers: "18-22"

bins <- c(35, 45, 55, 65, 75, 85)
pal <- colorBin(c("#0002A1", "#332FD0", "#FB2576", "#3F0071"), domain = ward_data_geo$C1_gap, bins = bins)
leaflet(ward_data_geo) %>%
  # center the map view
  setView(scotland_ct[[1]][1], scotland_ct[[1]][2], zoom = 6) %>%
  # add a dark basemap
  addProviderTiles("CartoDB.DarkMatter") %>%
  # change polygon fill
  addPolygons(
    # set the opacity of the outline
    opacity = 0,
    # set the stroke width in pixels
    weight = 0.1,
    # set fill colors
    fillColor = ~pal(C1_gap),
    # set the fill opacity
    fillOpacity = 0.8,
    # add popup
    popup = paste('<strong>',"Gap: ",'</strong>', round(ward_data_geo$C1_gap), "<br>",
                  '<strong>',"Ward Name:",'</strong>', ward_data_geo$Name, "<br>",
                  '<strong>',"Council: ",'</strong>', ward_data_geo$Council, "<br>")
  ) %>%
  addLegend(pal = pal, 
            values = ~C1_gap, 
            opacity = 0.7,
            position = "bottomright",
            title = "Women every 100 Men</br>in Higher Managerial Roles")

```

## 

```{r}
bins <- c(35, 45, 55, 65, 75, 85)
pal <- colorBin(c("#0002A1", "#332FD0", "#FB2576", "#3F0071"), domain = ward_data_geo$C1_gap, bins = bins)
leaflet(ward_data_geo) %>%
  # center the map view
  setView(scotland_ct[[1]][1], scotland_ct[[1]][2], zoom = 6) %>%
  # add a dark basemap
  addProviderTiles("CartoDB.DarkMatter") %>%
  # change polygon fill
  addPolygons(
    # set the opacity of the outline
    opacity = 0,
    # set the stroke width in pixels
    weight = 0.1,
    # set fill colors
    fillColor = ~pal(C1_gap),
    # set the fill opacity
    fillOpacity = 0.8,
    # add popup
    popup = paste('<strong>',"Gap: ",'</strong>', round(ward_data_geo$C1_gap), "<br>",
                  '<strong>',"Ward Name:",'</strong>', ward_data_geo$Name, "<br>",
                  '<strong>',"Council: ",'</strong>', ward_data_geo$Council, "<br>")
  ) %>%
  addLegend(pal = pal, 
            values = ~C1_gap, 
            opacity = 0.7,
            position = "bottomright",
            title = "Women every 100 Men</br>in Higher Managerial Roles")

```

## Conclusions

-   These are **not research results**, but just some quick data exploration to showcase the **R magic** !

. . .

-   **More affluent** and urban areas seem to have **lower differences** between women and men.

. . .

-   It would be interesting to explore this more as it demonstrates the importance of **intersectional feminism** perspectives.

. . .

-   There are **many more** ways/libraries that can be used to make maps.

. . .

-   Hope you enjoyed the tutorial

## As always

![](smash.gif){.absolute top="100" left="100" height="300"}

## Contacts

acalafio\@ed.ac.uk

@alel_domi

@alel\@datasci.social