library(gePoints)
library(readr)
markers <- read_csv(
"text, lat, lon, symbol
Pushpin marker, 35.0, -110.0, pushpin
Paddle marker, 35.0, -109.5, paddle"
)
create_kml(markers, "symbol_types.kml")2 Marker Styles
gePoints supports two marker types (pushpins and paddles) in up to nine colors. This chapter shows how to control marker appearance through the color, symbol, and symbol_scale columns.
2.1 Symbol types
The symbol column accepts two values:
"pushpin"— the default; a pin icon that appears to stick into the terrain"paddle"— a circular marker on a stick, useful when you want a cleaner look or need to distinguish categories
2.2 Available colors
Both pushpins and paddles support the following colors:
| Color | Pushpin | Paddle |
|---|---|---|
| red | yes | yes |
| blue | yes | yes |
| green | yes | yes |
| yellow | yes | yes |
| purple | yes | yes |
| white | yes | yes |
| pink | yes | yes |
| lightblue | yes | yes |
| orange | — | yes |
Orange is available only for paddle markers. If you specify orange with a pushpin, the marker defaults to red.
color_demo <- read_csv(
"text, lat, lon, color, symbol
red, 35.0, -110.0, red, pushpin
blue, 35.1, -110.0, blue, pushpin
green, 35.2, -110.0, green, pushpin
yellow, 35.3, -110.0, yellow, pushpin
purple, 35.4, -110.0, purple, pushpin
white, 35.5, -110.0, white, pushpin
pink, 35.6, -110.0, pink, pushpin
lightblue, 35.7, -110.0, lightblue, pushpin"
)
create_kml(color_demo, "pushpin_colors.kml")2.3 Symbol scaling
The symbol_scale column controls marker size. The default is 1.2. Values between 0.5 and 2.0 cover most practical needs. Smaller values work well for dense point clouds; larger values make isolated markers easier to spot at wide zoom levels.
scales <- read_csv(
"text, lat, lon, symbol_scale
Small (0.5), 35.0, -110.0, 0.5
Default (1.2), 35.2, -110.0, 1.2
Large (2.0), 35.4, -110.0, 2.0"
)
create_kml(scales, "symbol_scales.kml")2.4 Mixing styles in one file
Every row in the data frame can have its own styling. This is the mechanism for encoding categorical variables visually — for example, color by site type, symbol by data source:
mixed <- read_csv(
"text, lat, lon, color, symbol
Weather station, 35.0, -110.0, blue, pushpin
Stream gauge, 35.1, -110.0, green, paddle
Weather station, 35.3, -110.5, blue, pushpin
Stream gauge, 35.4, -110.5, green, paddle"
)
create_kml(mixed, "mixed_styles.kml")In Google Earth, the visual distinction between blue pushpins (weather stations) and green paddles (stream gauges) is immediate. This is a simple form of thematic mapping — no GIS software required.