Use R to read the data from Tobias.

Setup

library(tidyverse)

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──

## ✓ ggplot2 3.3.3     ✓ purrr   0.3.4
## ✓ tibble  3.1.2     ✓ dplyr   1.0.6
## ✓ tidyr   1.1.3     ✓ stringr 1.4.0
## ✓ readr   1.4.0     ✓ forcats 0.5.1

## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

library(sf)

## Linking to GEOS 3.8.1, GDAL 3.2.1, PROJ 7.2.1

library(raster)

## Loading required package: sp

## 
## Attaching package: 'raster'

## The following object is masked from 'package:dplyr':
## 
##     select

## The following object is masked from 'package:tidyr':
## 
##     extract

Get the Shoreline

Read it and plot it with ggplot2.

sl=st_read("Shoreline.shp")

## Reading layer `Shoreline' from data source `/Users/haroldnelson/Dropbox/QGIS/Tobias/Shoreline.shp' using driver `ESRI Shapefile'
## Simple feature collection with 1 feature and 6 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -123.1086 ymin: 37.69325 xmax: -122.3277 ymax: 37.92975
## Geodetic CRS:  WGS84(DD)

sl %>% ggplot() + geom_sf()

Crop

It’s obvious that the data we want occupies only a very small portion of our screen area. There are probably small islands far to the west, essentially outliers. We need to focus on San Francisco.

Read the documentation for st_crop(). Then reduce sl to a reasonable size.

Note that west longitudes are negative.

Answer

sl = st_crop(sl,c(xmin = -122.6,xmax = 0,ymin = 0,ymax  = 37.9))

## although coordinates are longitude/latitude, st_intersection assumes that they are planar

## Warning: attribute variables are assumed to be spatially constant throughout all
## geometries

ggplot(sl) + geom_sf()

Now we have a reasonable view of San Francisco.