![]() ![]() ![]() append ( dict ( xref = 'paper', x = 0.05, y = y_trace, xanchor = 'right', yanchor = 'middle', text = label + ' %'. update_layout ( xaxis = dict ( showline = True, showgrid = False, showticklabels = True, linecolor = 'rgb(204, 204, 204)', linewidth = 2, ticks = 'outside', tickfont = dict ( family = 'Arial', size = 12, color = 'rgb(82, 82, 82)', ), ), yaxis = dict ( showgrid = False, zeroline = False, showline = False, showticklabels = False, ), autosize = False, margin = dict ( autoexpand = False, l = 100, r = 20, t = 110, ), showlegend = False, plot_bgcolor = 'white' ) annotations = # Adding labels for y_trace, label, color in zip ( y_data, labels, colors ): # labeling the left_side of the plot annotations. Scatter ( x =, x_data ], y =, y_data ], mode = 'markers', marker = dict ( color = colors, size = mode_size ) )) fig. Scatter ( x = x_data, y = y_data, mode = 'lines', name = labels, line = dict ( color = colors, width = line_size ), connectgaps = True, )) # endpoints fig. Import aph_objects as go import numpy as np title = 'Main Source for News' labels = colors = mode_size = line_size = x_data = np. update_layout ( title = 'Average High and Low Temperatures in New York', xaxis_title = 'Month', yaxis_title = 'Temperature (degrees F)' ) fig. Scatter ( x = month, y = low_2000, name = 'Low 2000', line = dict ( color = 'royalblue', width = 4, dash = 'dot' ))) # Edit the layout fig. ![]() Scatter ( x = month, y = high_2000, name = 'High 2000', line = dict ( color = 'firebrick', width = 4, dash = 'dot' ))) fig. Scatter ( x = month, y = low_2007, name = 'Low 2007', line = dict ( color = 'royalblue', width = 4, dash = 'dash' ))) fig. Scatter ( x = month, y = high_2007, name = 'High 2007', line = dict ( color = 'firebrick', width = 4, dash = 'dash' ) # dash options include 'dash', 'dot', and 'dashdot' )) fig. Scatter ( x = month, y = low_2014, name = 'Low 2014', line = dict ( color = 'royalblue', width = 4 ))) fig. Scatter ( x = month, y = high_2014, name = 'High 2014', line = dict ( color = 'firebrick', width = 4 ))) fig. Lines(data$var2, as.numeric(data$group), col = 2)Īxis(2, labels = as.character(data$group), at = as.Import aph_objects as go # Add data month = high_2000 = low_2000 = high_2007 = low_2007 = high_2014 = low_2014 = fig = go. Plot(data$var1, as.numeric(data$group), type = "l", Lines(as.numeric(data$group), data$var2, col = 2)Īxis(1, labels = as.character(data$group), at = as.numeric(data$group)) Plot(as.numeric(data$group), data$var1, type = "l", You can set the factor variable on the X-axis or on the Y-axis: par(mfrow = c(1, 2)) If you want to plot the data as a line graph in R you can transform the factor variable into numeric with the is.numeric function and create the plot. Consider the following sample data: # Dataĭata <- ame(group = as.factor(c("Group 1", "Group 2", "Group 3")), In addition to creating line charts with numerical data, it is also possible to create them with a categorical variable. Matplot(data, type = "l", main = "matplot function") You can plot all the columns at once with the function: # Plot all columns at once The matplot and matlines functionsĪ better approach when dealing with multiple variables inside a data frame or a matrix is the matplot function. Note that the lines function is not designed to create a plot by itself, but to add a new layer over a already created plot. ![]()
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