Nplot Document
NPlot is a free charting library for.NET. It boasts an elegant, flexible API and includes controls for Windows.Forms, ASP.NET and a class for creating Bitmaps. A GTK# control is also available. NPlot has become my new main web charting squeeze because it gives me more control over the chart surface area, plotting behaviour, etc. Carlos Aguilar’s component is quick and simple and will still be useful for basic web charting, but NPlot is the more sophisticated alternative.
Tools for CNC Programmers
Thank you for trying NCPlot! You may download and try NCPlot free for 15 days. After this trial period has expired, the software will stop functioning and you must purchase a license key to continue using it. The minimum system requirements for NCPlot are: Pentium® 133 Mhz Processor or better Windows® 2000, XP, Vista, Windows® 7 or Windows® 8 (32 and 64 bit OS supported) 64MB RAM 800 x 600 screen resolution (1024 x 768 or higher is recommended) 256 Colors (24 or 32 bit color is recommended) For a printable version of the NCPlot help file, you can download the NCPlot Manual in PDF format. Disclaimer: We accept no liability for damage caused by the use or misuse of NCPlot. Of course NCPlot can help you verify G-Code, but it is only as good as the person interpreting the tool path display. If you have any kind of problem with NCPlot, please report it by email or post a message on theNCPlot Forum. |
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Generic X-Y Plotting
Generic function for plotting of R objects. For more details about the graphical parameter arguments, see par.
For simple scatter plots, plot.default will be used. However, there are plot methods for many R objects, including functions, data.frames, density objects, etc. Use methods(plot) and the documentation for these.
- Keywords
- hplot
Usage
Arguments
the coordinates of points in the plot. Alternatively, a single plotting structure, function or any R object with a plot method can be provided.
the y coordinates of points in the plot, optional if x is an appropriate structure.
Arguments to be passed to methods, such as graphical parameters (see par). Many methods will accept the following arguments:
typewhat type of plot should be drawn. Possible types are
'p'for points,'l'for lines,'b'for both,'c'for the lines part alone of'b','o'for both ‘overplotted’,'h'for ‘histogram’ like (or ‘high-density’) vertical lines,'s'for stair steps,'S'for other steps, see ‘Details’ below,'n'for no plotting.
types give a warning or an error; using, e.g., type = 'punkte' being equivalent to type = 'p' for S compatibility. Note that some methods, e.g.plot.factor, do not accept this.mainan overall title for the plot: see title.
suba sub title for the plot: see title.
xlaba title for the x axis: see title.
ylaba title for the y axis: see title.
aspthe (y/x) aspect ratio, see plot.window.
Details
The two step types differ in their x-y preference: Going from ((x1,y1)) to ((x2,y2)) with (x1 < x2), type = 's' moves first horizontal, then vertical, whereas type = 'S' moves the other way around.
See Also
plot.default, plot.formula and other methods; points, lines, par. For thousands of points, consider using smoothScatter() instead of plot().
For X-Y-Z plotting see contour, persp and image.
Aliases
Matplotlib Plot Documentation
- plot
Examples
library(graphics)# NOT RUN {require(stats) # for lowess, rpois, rnormplot(cars)lines(lowess(cars))plot(sin, -pi, 2*pi) # see ?plot.function## Discrete Distribution Plot:plot(table(rpois(100, 5)), type = 'h', col = 'red', lwd = 10, main = 'rpois(100, lambda = 5)')## Simple quantiles/ECDF, see ecdf() {library(stats)} for a better one:plot(x <- sort(rnorm(47)), type = 's', main = 'plot(x, type = 's')')points(x, cex = .5, col = 'dark red')# }Community examples
Plot Documentation Pandas
```r # Plot with multiple lines in different color: plot(sin,-pi, 4*pi, col = 'red') plot(cos,-pi, 4*pi, col = 'blue', add = TRUE) ```
```r ## Plot with multiple lines in different color: plot(sin,-pi, 4*pi, col = 'red') plot(cos,-pi, 4*pi, col = 'blue', add = TRUE) ```
plot(basedata1$iq, basedata$read_ab, main='Diagrama de Dispersión', xlab = 'read_ab', ylab = 'iq')
## Linear Regression ExamplePlot points and add linear regression model line:```rlinreg <- lm(dist ~ speed, cars)linreg_coeffs <- coef(linreg)lineq <- paste('distance = ', linreg_coeffs[2], ' * speed + ', linreg_coeffs[1])plot(cars, main = 'Car distance by speed', sub = lineq, xlab = 'speed', ylab = 'distance', pch = 19)abline(linreg, col = 'blue')```
Plot Document Download
Pass a numeric vector to the `x` and `y` arguments, and you get a scatter plot. The `main` argument provides a [`title()`](https://www.rdocumentation.org/packages/graphics/topics/title). ```{r} plot(1:100, (1:100) ^ 2, main = 'plot(1:100, (1:100) ^ 2)') ``` If you only pass a single argument, it is interpreted as the `y` argument, and the `x` argument is the sequence from 1 to the length of `y`. ```{r} plot((1:100) ^ 2, main = 'plot((1:100) ^ 2)') ``` `cex` ('character expansion') controls the size of points. `lwd` controls the line width. `pch` controls the shape of points - you get 25 symbols to choose from, as well as alphabetic characters. `col` controls the color of the points. When `pch` is `21:25`, the points also get a background color which is set using `bg`. [`points()`](https://www.rdocumentation.org/packages/graphics/topics/points) for more on how to change the appearance of points in a scatter plot. ```{r} plot( 1:25, cex = 3, lwd = 3, pch = 1:25, col = rainbow(25), bg = c(rep(NA, 20), terrain.colors(5)), main = 'plot(1:25, pch = 1:25, ...)' ) ``` If you specify `type = 'l'`, you get a line plot instead. See [`plot.default()`](https://www.rdocumentation.org/packages/graphics/topics/plot.default) for a demonstration of all the possible values for type. ```{r} plot( (1:100) ^ 2, type = 'l', main = 'plot((1:100) ^ 2, type = 'l')' ) ``` `lty` controls the line type. `col` and `lwd` work in the same way as with points. [`lines()`](https://www.rdocumentation.org/packages/graphics/topics/lines) for more on how to change the appearance of lines in a line plot. ```{r} plot( (1:100) ^ 2, type = 'l', lty = 'dashed', lwd = 3, col = 'chocolate', main = 'plot((1:100) ^ 2, type = 'l', lty = 'dashed', ...)' ) ``` It is best practise to keep your `x` and `y` variables together, rather than as separate variables. ```{r} with( cars, plot(speed, dist, main = 'with(cars, plot(speed, dist))') ) ``` The formula interface, similar to modeling functions like [`lm()`](https://www.rdocumentation.org/packages/stats/topics/lm), makes this convenient. See [`plot.formula()`](https://www.rdocumentation.org/packages/graphics/topics/plot.formula) for more information. ```{r} plot( dist ~ speed, data = cars, main = 'plot(dist ~ speed, data = cars)' ) ``` If you pass a two column data frame or matrix then the columns are treated as the x and y values. So in this case, you can simply do: ```{r} plot(cars, main = 'plot(cars)') ``` The [`lines()`](https://www.rdocumentation.org/packages/graphics/topics/lines), [`points()`](https://www.rdocumentation.org/packages/graphics/topics/points) and [`title()`](https://www.rdocumentation.org/packages/graphics/topics/title) functions add lines, points and titles respectively to an existing plot. ```{r} plot(cars) lines(lowess(cars)) title('plot(cars); lines(lowess(cars))') ``` If the `x` variable is categorical, `plot()` knows to draw a box plot instead of a scatter plot. See [`boxplot()`](https://www.rdocumentation.org/packages/graphics/topics/boxplot) for more information on drawing those. ```{r} with( sleep, plot(group, extra, main = 'with(sleep, plot(group, extra))') ) ``` Again, the formula interface can be useful here. ```{r} plot(extra ~ group, sleep, main = 'plot(extra ~ group, sleep)') ``` Axis limits can be set using `xlim` and `ylim`. ```{r} plot( (1:100) ^ 2, xlim = c(-100, 200), ylim = c(2500, 7500), main = 'plot((1:100) ^ 2, xlim = c(-100, 200), ylim = c(2500, 7500))' ) ``` You can set log-scale axes using the `log` argument. ```{r} plot( exp(1:10), 2 ^ (1:10), main = 'plot(exp(1:10), 2 ^ (1:10))' ) plot( exp(1:10), 2 ^ (1:10), log = 'x', main = 'plot(exp(1:10), 2 ^ (1:10), log = 'x')' ) plot( exp(1:10), 2 ^ (1:10), log = 'y', main = 'plot(exp(1:10), 2 ^ (1:10), log = 'y')' ) plot( exp(1:10), 2 ^ (1:10), log = 'xy', main = 'plot(exp(1:10), 2 ^ (1:10), log = 'xy')' ) ``` If you pass a table of counts for a vector, `plot()` draws a simple histogram-like plot. See [`hist()`](https://www.rdocumentation.org/packages/graphics/topics/hist) for a more comprehensive histogram function. ```{r} plot( table(rpois(100, 5)), main = 'plot(table(rpois(100, 5)))' ) ``` For multi-dimensional tables, you get a mosaic plot. See [`mosaicplot()`](https://www.rdocumentation.org/packages/graphics/topics/mosaicplot) for more information. ```{r} plot( table(X = rpois(100, 5), Y = rbinom(100, 10, 0.75)), main = 'plot(table(X = rpois(100, 5), Y = rbinom(100, 10, 0.75)))' ) ``` You can also pass functions to plot. See [`curve()`](https://www.rdocumentation.org/packages/graphics/topics/curve) for more examples. ```{r} plot( sin, from = -pi, to = 2 * pi, main = 'plot(sin, from = -pi, to = 2 * pi)' ) ``` Use the axis function to give fine control over how the axes are created. See [`axis()`](https://www.rdocumentation.org/packages/graphics/topics/axis) and [`Axis()`](https://www.rdocumentation.org/packages/graphics/topics/Axis) for more info. ```{r} plot( sin, from = -pi, to = 2 * pi, axes = FALSE, main = 'plot(sin, axes = FALSE, ...); axis(1, ...); axis(2)' ) axis( 1, # bottom axis pi * (-1:2), c(expression(-pi), 0, expression(pi), expression(2 * pi)) ) axis(2) # left axis ``` Further graphical parameters can be set using [`par()`](https://www.rdocumentation.org/packages/graphics/topics/par). See [`with_par()`](https://www.rdocumentation.org/packages/withr/topics/with_par) for the best way to use `par()`. ```{r} old_pars <- par(las = 1) # horizontal axis labels plot((1:100) ^ 2, main = 'par(las = 1); plot((1:100) ^ 2)') par(old_pars) # reset parameters ```