Line

Note

Hiplot website

This page is the tutorial for source code version of the Hiplot Line plugin. You can also use the Hiplot website to achieve no code ploting. For more information please see the following link:

https://hiplot.cn/basic/line?lang=en

The line chart is a statistical chart that USES a linear or logarithmic scale to draw data in a two - or three-dimensional view to show the data set or track the characteristics of the data over time.

Setup

• System Requirements: Cross-platform (Linux/MacOS/Windows)

• Programming language: R

• Dependent packages: ggplot2; ggthemes

# Install packages
if (!requireNamespace("ggplot2", quietly = TRUE)) {
  install.packages("ggplot2")
}
if (!requireNamespace("ggthemes", quietly = TRUE)) {
  install.packages("ggthemes")
}

# Load packages
library(ggplot2)
library(ggthemes)

Data Preparation

The loaded data are the horizontal axis values and their corresponding vertical axis values and groups.

# Load data
data <- read.delim("files/Hiplot/095-line-data.txt", header = T)

# Convert data structure
data[,3] <- factor(data[,3], levels = unique(data[,3]))

# View data
head(data)

  Value1 Value2  Group
1      1      1 treat1
2      2      4 treat1
3      3      9 treat1
4      4     16 treat1
5      5     25 treat1
6      6     36 treat1

Visualization

# Line
p <- ggplot(data, aes(x = Value1, y = Value2)) +
  geom_line(alpha = 1, aes(color = Group, linetype = Group)) +
  geom_point(aes(color = Group, shape = Group)) +
  ggtitle("Line Regression Plot") +
  scale_fill_manual(values = c("#e04d39","#5bbad6")) +
  theme_stata() +
  theme(text = element_text(family = "Arial"),
        plot.title = element_text(size = 12,hjust = 0.5),
        axis.title = element_text(size = 12),
        axis.text = element_text(size = 10),
        axis.text.x = element_text(angle = 0, hjust = 0.5,vjust = 1),
        legend.position = "right",
        legend.direction = "vertical",
        legend.title = element_text(size = 10),
        legend.text = element_text(size = 10))

p

Figure 1: Line

The diagram shows that value1 is positively correlated with Value2 in treatment plan 1, while Value1 is negatively correlated with Value2 in treatment plan 2.