library(plotcli)
plot_width = 80
plot_height = 40
# Create some example data
x <- seq(0, 2 * pi, length.out = 100)
y1 <- sin(x)
y2 <- cos(x)
data_1 = list(
x = x,
y = y1,
name = "y1 = sin(x)",
color = "blue",
type = "line",
braille = FALSE
)
data_2 = list(
x = x,
y = y2,
name = "y2 = cos(x)",
color = "red",
type = "line",
braille = FALSE
)
# Create a plotcli object with specified dimensions
plot <- plotcli$new(
plot_width = plot_width,
plot_height = plot_height,
x_label = "X-axis",
y_label = "Y-axis"
)
# Add data sets to the plot
plot$add_data(data_1)
plot$add_data(data_2)
# Print the plot
plot$print_plot()
#>
#>
#> ┌────────────────────────────────────────────────────────────────────────────────┐
#> 1.0 │****** ********** *****│
#> │ * ** ** * │
#> │ ** * * ** │
#> │ * ** * * │
#> │ * * * * │
#> │ ** * ** │
#> │ * * * * │
#> │ * * * * │
#> │ * * * * │
#> │ * * * * │
#> 0.5 │ * * * * │
#> │ * * * * │
#> │ * * * * │
#> │ * * * * │
#> │ * * * * │
#> │ * * * * │
#> │ * * * * │
#> │ * * * * │
#> │ * * * * │ y1 = sin(x)
#> Y-axis 0.0 │* * * * *│ y2 = cos(x)
#> │ * * * *│
#> │ * * * * │
#> │ * * * * │
#> │ * * * * │
#> │ * * * * │
#> │ * * * * │
#> │ * * * * │
#> │ * * * * │
#> │ * * * * │
#> -0.5 │ * * * * │
#> │ * * * * │
#> │ * * * * │
#> │ * * * * │
#> │ * * * │
#> │ * * * * │
#> │ * * * ** │
#> │ * * * * │
#> │ ** ** ** ** │
#> │ * * ** *** │
#> -1.0 │ ******* ****** │
#> └────────────────────────────────────────────────────────────────────────────────┘
#>
#> 0.0 1.6 3.1 4.7 6.3
#>
#> X-axis
#> plotcli can use braille characters for plotting scatter and line plots, which give a much higher resolution on supported terminals. Whether to use braille or ascii characters can be specified for each data set.
library(plotcli)
data(mtcars)
# Fit a linear model
lm_fit <- lm(wt ~ mpg, data = mtcars)
# Create a new dataset with the predicted values
predicted_data <- data.frame(mpg = mtcars$mpg, predicted_wt = predict(lm_fit, mtcars))
# we use braille characters for the regression line
data_1 = list(
x = predicted_data$mpg,
y = predicted_data$predicted_wt,
name = "Regression Line",
color = "red",
type = "line",
braille = TRUE
)
# and ascii characters for the raw data
data_2 = list(
x = mtcars$mpg,
y = mtcars$wt,
name = "Data Points",
color = "blue",
type = "scatter",
braille = FALSE
)
# Create a plotcli object
plot_width = 80
plot_height = 40
plot_obj <- plotcli$new(
plot_width,
plot_height,
x_label = "Miles per Gallon",
y_label = "Weight"
)
# Add raw data and regression line
plot_obj$add_data(data_1)
plot_obj$add_data(data_2)
# Print the plot
plot_obj$print_plot()
#>
#>
#> ┌────────────────────────────────────────────────────────────────────────────────┐
#> 5.4 │* * │
#> │* │
#> │ │
#> │ │
#> │ │
#> │ │
#> │ │
#> │⡢ │
#> │ ⠡⠡ │
#> │ ⠡⠡⠡ │
#> 4.4 │ ⠡⠡ │
#> │ ⠡⡂⡁ │
#> │ ⠁⡂⡂ * │
#> │ ⡓⡕⠢ │
#> │ * ⠖⠻⠵⠐ * │
#> │ *⠔⡈⡆⠲ * │
#> │ ⠒⠷⡙⠳ │
#> │ * ** ⠐⠶⡙⡇⠢ │
#> │ * ⠐⡀**⠥* * │ Regression Line
#> Weight 3.3 │ ⡃⡪⢩⡄ │ Data Points
#> │ ⠥⡩⡆ * │
#> │ * ⠠⡦⡆⠦ * * │
#> │ ⠠⡠⡦⡦ │
#> │ ⠐⠲⢅⡳⠣ │
#> │ ** ⠒⡢⢅⠥⠡ │
#> │ * ⠂⠒⠖⠥⠥ │
#> │ * ⠒⠒⠷⠥⠠ │
#> │ ⠒⠔⠢⠢ │
#> │ * ⠒⠒⠴⠢⠠ │
#> 2.3 │ * ⠤⠦⠦⠢ │
#> │ ⠠⠤⠦⠤ * │
#> │ * ⠠⠢⠤⠄⠂ │
#> │ ⠠⠢⠄⠂ │
#> │ * ⠠⠢⠤⠄⠂ │
#> │ ⠠⠢⠄⠄ *│
#> │ ⠠⡐⠴⠄⠂ │
#> │ *⠰⠲⠴⠤ │
#> │ * ⠐⠴⠴⠂ │
#> │ ⠐⠒⠒ │
#> 1.3 │ ⠒⠒│
#> └────────────────────────────────────────────────────────────────────────────────┘
#>
#> 10.4 16.3 22.1 28.0 33.9
#>
#> Miles per Gallon
#>