基于Loc..quality分类经纬度地点，用ggplot2绘制差异化散点图

Got it, let's walk through how to create this plot exactly as you want it—differentiating your longitude/latitude points by Loc..quality using both unique colors and shapes.

Step 1: Prepare Your Data #

First, we'll turn your raw data into an R data frame. I've converted the numeric Loc..quality values to characters so ggplot treats all categories as discrete (no accidental continuous mapping):

# Create the data frame
location_data <- data.frame(
  Longitude = c(22.33496, 22.33924, 22.34901, 22.58998, 22.38683, 22.35649),
  Latitude = c(44.70298, 44.69002, 44.68629, 44.83182, 44.66142, 44.70147),
  Loc..quality = c("1", "0", "A", "B", "B", "A")
)

Step 2: Load ggplot2 and Create the Plot #

Next, we'll use ggplot2 to build the scatter plot. The key here is mapping both color and shape to the Loc..quality column in the aesthetic (aes)—this ensures each quality category gets a distinct color and shape, making them easy to tell apart.

# Load the ggplot2 package
library(ggplot2)

# Build the plot
ggplot(location_data, aes(x = Longitude, y = Latitude)) +
  # Map color and shape to Loc..quality, set point size for visibility
  geom_point(aes(color = Loc..quality, shape = Loc..quality), size = 3) +
  # Add clear labels and title
  labs(
    x = "Longitude",
    y = "Latitude",
    title = "Geographic Points Grouped by Location Quality",
    color = "Location Quality",
    shape = "Location Quality"
  ) +
  # Use a clean minimal theme, center the title
  theme_minimal() +
  theme(plot.title = element_text(hjust = 0.5))

What This Does #

• Dual Mapping: By linking both color and shape to Loc..quality, you get redundant visual cues—super helpful if someone viewing the plot is colorblind, or just needs extra clarity.
• Custom Labels: The labs() function makes the plot self-explanatory, with clear axis titles and a centered main title.
• Clean Theme: theme_minimal() keeps the focus on your data points without unnecessary clutter.

When you run this code, you'll get a plot where points labeled "1", "0", "A", and "B" each have their own unique color and shape, making it easy to distinguish the different location quality categories at a glance.

内容的提问来源于stack exchange，提问作者user9544701