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<!DOCTYPE html>
<html xmlns="http://www.w3.org/1999/xhtml" lang="" xml:lang="">
<head>
<title>static_map.utf8.md</title>
<meta charset="utf-8" />
<link href="libs/remark-css-0.0.1/default.css" rel="stylesheet" />
<link rel="stylesheet" href="coslide.css" type="text/css" />
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<body>
<textarea id="source">
class: title-slide, middle, right
.title-text[Mapping in R]
.title-sub[1: making static maps in R with ggplot2]
.title-spacer[]
.title-presenter[Matt Kerlogue]
.title-date[29 November 2019]
---
# The data we'll be using
.pull-left[
We'll be using the Cabinet Office's [Civil Service Statistics 2019](https://www.gov.uk/government/statistics/civil-service-statistics-2019). Specifically, the [machine readable](https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/836368/Statistical-tables-Civil-Service-Statistics-2019-machine-readable-headcounts-version-V2.csv/preview) csv.
```r
# download the civil service statistics data
civil_service_stats_2019 <- readr::read_csv(
"https://assets.publishing.service.gov.uk/
government/uploads/system/uploads/
attachment_data/file/836368/Statistical-
tables-Civil-Service-Statistics-2019-
machine-readable-headcounts-version-V2.csv",
col_types = paste0(rep("c", 12), collapse =""))
```
]
.pull-right[
]
---
# ONS Geoportal
.pull-left[
The ONS Geoportal (https://geoportal.statistics.gov.uk/), provides an extensive array of geographical information for mapping statistical outputs. Including:
* **boundaries** - the shapes of geographical areas
* **centroids** - point-based geographical data (including population weighted centroids)
* **lookups** - tables to convert from one geography to another
]
.pull-right[
]
---
---
.pull-left[
```r
t13_dt <- civil_service_stats_2019 %>%
filter(table == "t13",
category_2 == "All employees",
category_4 == "Total") %>%
mutate(value = as.numeric(value)) %>%
group_by(category_1) %>%
summarise(total = sum(value, na.rm = TRUE))
```
* Filter to table T13
* Filter to the total number of all employees (by region)
* Convert to numeric
* Group by region (`category_1`)
* Sum the totals
]
--
.pull-right[
```
## # A tibble: 6 x 12
## source table category_1 category_2 category_3 category_4 category_name_1
## <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 Still… t13 East Permanent Female Full-time css_region_name
## 2 Still… t13 East Permanent Female Part time css_region_name
## 3 Still… t13 East Permanent Female Unknown css_region_name
## 4 Still… t13 East Permanent Male Full time css_region_name
## 5 Still… t13 East Permanent Male Part time css_region_name
## 6 Still… t13 East Permanent Male Unknown css_region_name
## # … with 5 more variables: category_name_2 <chr>, category_name_3 <chr>,
## # category_name_4 <chr>, measure <chr>, value <chr>
```
```
## # A tibble: 15 x 2
## category_1 total
## <chr> <dbl>
## 1 All employees 445480
## 2 East 21420
## 3 East Midlands 19910
## 4 London 89110
## 5 North East 29400
## 6 North West 54900
## 7 Northern Ireland 3690
## 8 Not reported 3060
## 9 Overseas 4860
## 10 Scotland 43930
## 11 South East 38610
## 12 South West 40240
## 13 Wales 34110
## 14 West Midlands 28180
## 15 Yorkshire and The Humber 34090
```
]
---
.pull-left[
```r
t13_dt <- civil_service_stats_2019 %>%
filter(table == "t13",
category_2 == "All employees",
category_4 == "Total") %>%
mutate(value = as.numeric(value)) %>%
group_by(category_1) %>%
summarise(total = sum(value, na.rm = TRUE)) %>%
* filter(category_1 != "All employees",
* category_1 != "Not reported",
* category_1 != "Overseas") %>%
* rename(region = category_1)
```
* Filter out the line for all employees, those for which there is no location, and those based overseas
* Rename `category_1` to something useful
]
--
.pull-right[
```
## # A tibble: 12 x 2
## region total
## <chr> <dbl>
## 1 East 21420
## 2 East Midlands 19910
## 3 London 89110
## 4 North East 29400
## 5 North West 54900
## 6 Northern Ireland 3690
## 7 Scotland 43930
## 8 South East 38610
## 9 South West 40240
## 10 Wales 34110
## 11 West Midlands 28180
## 12 Yorkshire and The Humber 34090
```
]
---
# Back to geography..
.pull-left[
Region names are "ok"... but often different folk have different ways of naming and labelling geographic entities: "Yorkshire" / "Yorkshire and the Humber" / "Yorkshire and Humberside" / "Yorks and Humber".
Let's download the UK region boundaries and see what the region names are:
```r
sfdt <- st_read(
"https://opendata.arcgis.com/datasets/
01fd6b2d7600446d8af768005992f76a_4.geojson")
```
]
--
.pull-right[
```
## # A tibble: 12 x 5
## objectid nuts118cd nuts118nm long lat
## <int> <fct> <fct> <dbl> <dbl>
## 1 1 UKC North East (England) -1.73 55.3
## 2 2 UKD North West (England) -2.77 54.4
## 3 3 UKE Yorkshire and The Humber -1.29 53.9
## 4 4 UKF East Midlands (England) -0.850 52.8
## 5 7 UKI London -0.309 51.5
## 6 5 UKG West Midlands (England) -2.20 52.6
## 7 6 UKH East of England 0.504 52.2
## 8 8 UKJ South East (England) -0.993 51.5
## 9 9 UKK South West (England) -3.63 50.8
## 10 10 UKL Wales -3.99 52.1
## 11 11 UKM Scotland -3.97 56.2
## 12 12 UKN Northern Ireland -6.85 54.6
```
]
---
.pull-left[
```
## # A tibble: 12 x 1
## region
## <chr>
## 1 East
## 2 East Midlands
## 3 London
## 4 North East
## 5 North West
## 6 Northern Ireland
## 7 Scotland
## 8 South East
## 9 South West
## 10 Wales
## 11 West Midlands
## 12 Yorkshire and The Humber
```
]
.pull-right[
```
## # A tibble: 12 x 1
## nuts118nm
## <fct>
## 1 North East (England)
## 2 North West (England)
## 3 Yorkshire and The Humber
## 4 East Midlands (England)
## 5 London
## 6 West Midlands (England)
## 7 East of England
## 8 South East (England)
## 9 South West (England)
## 10 Wales
## 11 Scotland
## 12 Northern Ireland
```
]
---
.pull-right[
There are many packages that enable R to handle spatial data. `{sp}`, `{rgdal}`, `{rgeos}` are the most common, but there are over 97 packages containing 'geo' in their name and 272 packages include `{sp}` in their list of imports. We'll use a slightly newer package `{sf}`.
The `{sf}` package provides an R interface for the [simple features](https://en.wikipedia.org/wiki/Simple_Features) standard - a modern and increasingly common approach for handling and distributing geographical data. It is envisaged in due course that `{sf}` will replace `{sp}` as the central geospatial pacakge for R.
`{ggplot2}` has a built in function for ploting `{sf}` objects: `geom_sf`.
```r
library(sf)
ggplot(sfdt) +
geom_sf()
```
]
--
.pull-left[
<!-- -->
]
---
.pull-left[
```r
t13_dt <- t13_dt %>%
mutate(nuts118cd = case_when(
region == "East" ~ "UKH",
region == "East Midlands" ~ "UKF",
region == "London" ~ "UKI",
region == "North East" ~ "UKC",
region == "North West" ~ "UKD",
region == "Northern Ireland" ~ "UKN",
region == "Scotland" ~ "UKM",
region == "South East" ~ "UKJ",
region == "South West" ~ "UKK",
region == "Wales" ~ "UKL",
region == "West Midlands" ~ "UKG",
region == "Yorkshire and The Humber" ~ "UKE",
TRUE ~ NA_character_
))
```
* Rather than trying to match the different names/labels for the regions, it's better for use to use geographical codes, this makes sure both data.frames are talking about the same entities.
* Using `dplyr::case_when` we can add the column `nuts11cd` to our data with codes assigned using conditional statements evaluating the `region` column.
]
--
.pull-right[
```
## # A tibble: 12 x 3
## region total nuts118cd
## <chr> <dbl> <chr>
## 1 East 21420 UKH
## 2 East Midlands 19910 UKF
## 3 London 89110 UKI
## 4 North East 29400 UKC
## 5 North West 54900 UKD
## 6 Northern Ireland 3690 UKN
## 7 Scotland 43930 UKM
## 8 South East 38610 UKJ
## 9 South West 40240 UKK
## 10 Wales 34110 UKL
## 11 West Midlands 28180 UKG
## 12 Yorkshire and The Humber 34090 UKE
```
]
---
.pull-left[
```r
map_dt <- sfdt %>%
left_join(t13_dt, by = "nuts118cd")
```
* A key benefit of `{sf}` is that it easily integrates with the tidyverse methods. Here we have used `dplyr::left_join` to attach our data to the geographical data.
]
.pull-right[
```
## Observations: 12
## Variables: 12
## $ objectid <int> 1, 2, 3, 4, 7, 5, 6, 8, 9, 10, 11, 12
## $ nuts118cd <chr> "UKC", "UKD", "UKE", "UKF", "UKI", "UKG", "UKH", "UKJ"…
## $ nuts118nm <fct> North East (England), North West (England), Yorkshire …
## $ bng_e <int> 417313, 350015, 446903, 477660, 517516, 386294, 571074…
## $ bng_n <int> 600358, 506280, 448736, 322635, 178392, 295477, 263229…
## $ long <dbl> -1.728900, -2.772370, -1.287120, -0.849670, -0.308640,…
## $ lat <dbl> 55.29703, 54.44945, 53.93264, 52.79572, 51.49227, 52.5…
## $ st_areashape <dbl> 8609938893, 14182609113, 15432322860, 15658181358, 158…
## $ st_lengthshape <dbl> 657578.2, 1063052.7, 863264.1, 889656.3, 270855.1, 774…
## $ region <chr> "North East", "North West", "Yorkshire and The Humber"…
## $ total <dbl> 29400, 54900, 34090, 19910, 89110, 28180, 21420, 38610…
## $ geometry <MULTIPOLYGON [°]> MULTIPOLYGON (((-2.0344 55...., MULTIPOLY…
```
]
---
.pull-right[
```r
ggplot(map_dt) +
geom_sf(aes(fill = total))
```
* `geom_sf` allows us to plot `{sf}` objects in the same way as other data objects
* Here we have mapped the `fill` aesthetic of the regions to the `total` variable
]
--
.pull-left[
<!-- -->
]
---
.pull-right[
```r
theme_map <- function(...) {
theme_void() +
theme(
legend.position = "none",
...
)
}
```
* We can create a custom ggplot theme to remove the background features
]
.pull-left[
<!-- -->
]
---
.pull-right[
```r
ggplot(map_dt) +
* geom_sf(aes(fill = total), colour = "white", size = 0.5) +
* scale_fill_distiller(palette = "Oranges", direction = 1) +
* theme_map()
```
* `geom_sf` allows us to plot `{sf}` objects in the same way as other data objects
* Here we have mapped the `fill` aesthetic of the regions to the `total` variable
* We have also applied a colour scale to change the map from the default blue
]
.pull-left[
<!-- -->
]
---
.pull-right[
```r
ggplot(map_dt) +
geom_sf(aes(fill = total), colour = "white", size = 0.5) +
* geom_sf_text(aes(label = total)) +
scale_fill_distiller(palette = "Oranges", direction = 1) +
theme_map()
```
* `geom_sf_text` allows us to add text labels using the coordinates from the sf object... let's use the the total column (our headcounts)
]
.pull-left[
<!-- -->
]
---
.pull-right[
```r
ggplot(map_dt) +
geom_sf(aes(fill = total), colour = "white", size = 0.5) +
* geom_sf_text(aes(label = scales::comma(total))) +
scale_fill_distiller(palette = "Oranges", direction = 1) +
theme_map()
```
* we can use `scales::comma` to format the number with a thousands separator
]
.pull-left[
<!-- -->
]
---
.pull-right[
```r
ggplot(map_dt) +
geom_sf(aes(fill = total), colour = "white", size = 0.5) +
* geom_sf_text(aes(label = paste(region, scales::comma(total), sep = ": ")) +
scale_fill_distiller(palette = "Oranges", direction = 1) +
theme_map()
```
* let's also add the region name to the label using `base::paste`
]
.pull-left[
<!-- -->
]
---
.pull-right[
```r
ggplot(map_dt) +
geom_sf(aes(fill = total), colour = "white", size = 0.5) +
geom_sf_text(
* aes(label =
* str_wrap(paste(region, scales::comma(total), sep = ": "), 12)
* )) +
scale_fill_distiller(palette = "Oranges", direction = 1) +
theme_map()
```
* `stringr::str_wrap` provides a handy way of making strings wrap to a given length, in this case 12 characters
]
.pull-left[
<!-- -->
]
---
.pull-right[
```r
ggplot(map_dt) +
geom_sf(aes(fill = total), colour = "white", size = 0.5) +
geom_sf_text(
aes(label =
str_wrap(paste(region, scales::comma(total), sep = ": "), 12),
* nudge_x = if_else(map_dt$nuts118cd=="UKI",1.25,0),
* nudge_y = if_else(map_dt$nuts118cd=="UKJ",-0.25,0)) +
scale_fill_distiller(palette = "Oranges", direction = 1) +
theme_map()
```
* Finally, let's also adjust the x-position of London's label, and the y-position of the South East's label
* We could alternatively create adjustment columns in our data table and add `nudge_x = x_adjust` to sf_text's `aes` mapping
]
.pull-left[
<!-- -->
]
---
.pull-right[
```r
ggplot(map_dt) +
geom_sf(aes(fill = total), colour = "white", size = 0.5) +
* geom_sf_label(
aes(label =
str_wrap(paste(region, scales::comma(total), sep = ": "), 12),
nudge_x = if_else(map_dt$nuts118cd=="UKI",1.25,0),
nudge_y = if_else(map_dt$nuts118cd=="UKJ",-0.25,0)) +
scale_fill_distiller(palette = "Oranges", direction = 1) +
theme_map()
```
* Switching to `geom_sf_label` provides a label background and border for the text
]
.pull-left[
<!-- -->
]
---
.pull-right[
```r
ggplot(map_dt) +
geom_sf(aes(fill = total), colour = "white", size = 0.5) +
geom_sf_label(
aes(label =
str_wrap(paste(region, scales::comma(total), sep = ": "), 12),
nudge_x = if_else(map_dt$nuts118cd=="UKI",1.25,0),
nudge_y = if_else(map_dt$nuts118cd=="UKJ",-0.25,0),
* label.size = 0, alpha = 0.5) +
scale_fill_distiller(palette = "Oranges", direction = 1) +
theme_map()
```
* Finally, we can remove the border by setting `label.size` to 0, and adjust the background transparency via `alpha`
]
.pull-left[
<!-- -->
]
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