Pumps Data

This is a demonstration of basic point plotting from a CSV file and reading data from DBF files.

CSV Format Notes

Data are read from a spreadsheet format (CSV).

The latitude and longitude are not correct. The wrong translation of units was done.

Much of the code here sets up the use of sitemaps, the package for simple data point plotting.

## Load necessary libraries
library(ggplot2)
library(ggmap)
library(dplyr)
library(gt)

## Load sitemaps
## library(devtools)
## install_github("kimbridges/sitemaps") ## Do this just once
library(sitemaps)

## Use two functions from sitemaps to initialize parameters
column <- site_styles()
hide   <- site_google_hides()

column$margin = 2.0

## Register the Google Maps Key
googleKey <- Sys.getenv("GGMAP_GOOGLE_API_KEY")
register_google(key = googleKey, account_type = "standard")

## Establish a theme that improves the appearance of a map.
## This theme removes the axis labels and 
## puts a border around the map. No legend.
simple_black_box <- theme_void() +
              theme(panel.border = element_rect(
                color     = "black", 
                fill      = NA, 
                linewidth = 2),
                legend.position = "none")

## Load the data
data <- read.csv("Pump_Data_Table.csv")

## Rename the ID field to text
data <- data |>
  dplyr::rename(text = ID) |>
  dplyr::rename(lat = Latitude) |>
  dplyr::rename(lon = Longitude) 

gt(data) |>
  ## Title on the table
  tab_caption(caption = "CSV Input Example") |>
  ## Source Information
  tab_source_note(
    source_note = "Source: Pump_Data_Table.csv") |>
  tab_footnote(
    footnote  = "Likely a bad conversion to latitude, longitude",
    locations = cells_column_labels(columns=c(lon, lat)))

CSV Input Example

text	Name	Address	lon1	lat1
1	Oxford Market	Oxford Market	6.98502	57.11404
6	Little Marborough St.	Little Marblborough St.	6.98497	57.11042
2	Hanover Square	Hanover Sq	6.98239	57.11044
4	Berners St.	Berner's St.	6.98777	57.11340
5	Newman St.	Newman St.	6.98863	57.11366
7	Broad St.	Broad St.	6.98707	57.10973
3	Castle St. E.	Castle St. E.	6.98612	57.11405
12	Picadilly Circus	Tichbone St.	6.98894	57.10546
8	Bridle St.	Bridle St.	6.98764	57.10733
9	Rupert St.	Rupert St.	6.98928	57.10816
10	Warwick St.	Warwick St.	6.98600	57.10702
11	Dean St.	King St.	6.99074	57.10846
14	Vigo St.	Vigo St.	6.98498	57.10538
13	Marlborough Mews	Marlborough Mews	6.98500	57.11181
0	Oxford St. Smithy	365 Oxford St.	6.98497	57.11234
1 Likely a bad conversion to latitude, longitude
Source: Pump_Data_Table.csv

## Create the base map
basemap <- site_google_basemap(datatable = data)

## Plot the data points on the map
ggmap(basemap) +
  site_points(datatable = data) +
  site_labels(datatable = data) +
  theme_minimal()

DBF Format Notes

This appears to the a primary data source.

It’s always a good idea to look at the data.

## Install package "foreign" to read the dbf format data
library(foreign)
library(gt)

## Read the data
pumpsAll_data <- read.dbf("pumpsAll.dbf", as.is = FALSE)

## Get a data check
gt(pumpsAll_data)|>
  ## Title on the table
  tab_caption(caption = "DBF Input Example") |>
  ## Source Information
  tab_source_note(
    source_note = "Source: PumpAll.dbf") |>
  ## Positioning data in the cells
  cols_align(
    align = "left",
    columns = c(NAME, ADDRESS, Author)) |>
  tab_style(
    style = "vertical-align:top",
    locations = cells_body()) # Targets all data cells in the table body

DBF Input Example

Id	NAME	ADDRESS	Number_	Author	POINT_X	POINT_Y	cooper	percentage
1	Oxford Market	Oxford Market	10	Snow'55b	698501.8	5711404	no	NA
6	Little Marborough St.	Little Marblborough St.	2	cdc, Snow'55b, rW	698496.8	5711042	yes	4
2	Hanover Square	Hanover Sq	11	Snow'55b	698238.8	5711044	no	NA
4	Berners St.	Berner's St.	3	cdc, Snow'55b, W	698776.7	5711340	no	2
5	Newman St.	Newman St.	8	cdc, Snow55b, W	698862.9	5711366	no	NA
7	Broad St.	Broad St.	4	cdc, Snow'55b, W	698707.2	5710973	yes	17
3	Castle St. E.	Castle St. E.	14	Snow'55b	698612.5	5711405	no	NA
12	Picadilly Circus	Tichbone St.	12	cdc, Snow'55b, revW	698893.9	5710546	no	NA
8	Bridle St.	Bridle St.	5	cdc, Snow'55b, W	698763.9	5710733	yes	2
9	Rupert St.	Rupert St.	9	cdc, Snow'55b, W	698928.2	5710816	yes	4
10	Warwick St.	Warwick St.	6	cdc, Snow'55b, W	698599.7	5710702	no	NA
11	Dean St.	King St.	7	cdc, Snow'55b, W	699073.8	5710846	no	NA
14	Vigo St.	Vigo St.	1	cdc, Snow'55b	698498.0	5710538	no	NA
13	Marlborough Mews	Marlborough Mews	13	cdc, Snow'55b, W	698499.7	5711181	yes	NA
0	Oxford St. Smithy	365 Oxford St.	0	W	698496.8	5711234	yes	NA
Source: PumpAll.dbf

Coordinate conversion

The coordinates are in UTM values, so a primary task is converting them to latitude and longitude coordinates.

library(sf)
library(dplyr)
library(gt)

## Convert the data
new_data <- pumpsAll_data |>
  
  ## treat existing lon/lat as UTM coordinates
  st_as_sf(coords = c("POINT_X", "POINT_Y"), crs = 32630) |>
  
  ## transform to geographic coordinates
  st_transform(4326)  |>
  
  ## extract and name the lon/lat
  mutate(
    lon = st_coordinates(geometry)[, 1],
    lat = st_coordinates(geometry)[, 2]) |>
  dplyr::rename(text = Id) |>
  
  ## drop sf geometry to get a plain data frame
  st_drop_geometry()

## Data check
gt(new_data)|>
  ## Title on the table
  tab_header(
    title = "DBF Input",
    subtitle = "Coordinates converted to Latitude, Longitude")|>
  ## Source Information
  tab_source_note(
    source_note = "Source: PumpAll.dbf") |>
  ## Positioning data in the cells
  cols_align(
    align = "left",
    columns = c(NAME, ADDRESS, Author)) |>
  tab_style(
    style = "vertical-align:top",
    locations = cells_body()) |> # All data cells in the table body
  ## Format the geographic coordinates
  fmt_number(columns = c(lat,lon), decimals = 5) 

DBF Input
Coordinates converted to Latitude, Longitude
text	NAME	ADDRESS	Number_	Author	cooper	percentage	lon	lat
1	Oxford Market	Oxford Market	10	Snow'55b	no	NA	−0.13882	51.51883
6	Little Marborough St.	Little Marblborough St.	2	cdc, Snow'55b, rW	yes	4	−0.13910	51.51557
2	Hanover Square	Hanover Sq	11	Snow'55b	no	NA	−0.14281	51.51568
4	Berners St.	Berner's St.	3	cdc, Snow'55b, W	no	2	−0.13490	51.51815
5	Newman St.	Newman St.	8	cdc, Snow55b, W	no	NA	−0.13365	51.51835
7	Broad St.	Broad St.	4	cdc, Snow'55b, W	yes	17	−0.13611	51.51488
3	Castle St. E.	Castle St. E.	14	Snow'55b	no	NA	−0.13723	51.51880
12	Picadilly Circus	Tichbone St.	12	cdc, Snow'55b, revW	no	NA	−0.13366	51.51098
8	Bridle St.	Bridle St.	5	cdc, Snow'55b, W	yes	2	−0.13543	51.51271
9	Rupert St.	Rupert St.	9	cdc, Snow'55b, W	yes	4	−0.13302	51.51340
10	Warwick St.	Warwick St.	6	cdc, Snow'55b, W	no	NA	−0.13781	51.51249
11	Dean St.	King St.	7	cdc, Snow'55b, W	no	NA	−0.13090	51.51361
14	Vigo St.	Vigo St.	1	cdc, Snow'55b	no	NA	−0.13937	51.51105
13	Marlborough Mews	Marlborough Mews	13	cdc, Snow'55b, W	yes	NA	−0.13898	51.51682
0	Oxford St. Smithy	365 Oxford St.	0	W	yes	NA	−0.13899	51.51730
Source: PumpAll.dbf

########## MAPPING ##################

## Adjust the basemap coverage 
## Without this, the basemap shows too large an area
column$margin <- 0.4

## Adjust the size of the labels (pump Id)
new_data$label_text_size <- 3

## Create the base map
basemap <- site_google_basemap(datatable = new_data)

## Plot the data points on the map
ggmap(basemap) +
  site_points(datatable = new_data) +
  site_labels(datatable = new_data) +
  theme_minimal()