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splot\nAn R package to ease data visualization.\n\nThe aim of this package is to make visualization an early part of the data analysis process by automating a few common plotting tasks.\n\nIn terms of design, it has three general principles:\n\n* **Flexibility**: splot prefers to try to make a reasonable plot from any input, rather than erroring out.\n* **Minimal specification**: You should be able to make most plots with just a formula input, and the same formula should generally be compatible with multiple plot types, regardless of variable types.\n* **Tweakability**: Though splot is focused on quick, automated plotting, you should be able to adjust any aspect of the display with additional arguments, if you find a plot you want to display elsewhere.\n\n## features\nBy entering a formula as the first argument in the `splot` function (e.g., `splot(y ~ x)`), you can make\n\n* Density distributions (overlaid on histograms when there is no `by` variable)\n* Scatter plots with prediction lines\n* Bar or line graphs with error bars\n\nFor each type, multiple `y` variables or data at levels of a `by` variable are shown in the same plot frame,\u003cbr /\u003e\nand data at levels of one or two `between` variables are shown in separate plot frames, organized in a grid.\n\n## resources\n* [Introduction](https://miserman.github.io/splot/articles/splot.html)\n* [Documentation](https://miserman.github.io/splot/reference/splot.html)  \n* [Style Guide](https://miserman.github.io/splot/articles/style.html)  \n* [Gallery](https://miserman.github.io/splot/articles/gallery.html)\n* Applied examples: [Exploring Data](https://miserman.github.io/splot/articles/explore.html) |\n[Refining a Result](https://miserman.github.io/splot/articles/refine.html)\n\n## installation\nDownload R from [r-project.org](https://www.r-project.org/).\n\nRelease ([version 0.5.4](https://CRAN.R-project.org/package=splot))\n```R\ninstall.packages(\"splot\")\n```\nDevelopment (version 0.5.5)\n```R\n# install.packages(\"remotes\")\nremotes::install_github(\"miserman/splot\")\n```\nThen load the package:\n```R\nlibrary(splot)\n```\n## examples\nMake some data: random group and x variables, and a y variable related to x:\n```R\ngroup = rep(c(\"group 1\", \"group 2\"), 50)\nx = rnorm(100)\ny = x * .5 + rnorm(100)\n```\nThe distribution of y:\n```R\nsplot(y)\n```\nA scatter plot between y and x:\n```R\nsplot(y ~ x)\n```\nSame data with a quadratic model:\n```R\nsplot(y ~ x + x^2 + x^3)\n```\nSame data separated by group:\n```R\nsplot(y ~ x * group)\n```\nCould also separate by median or standard deviations of x:\n```R\nsplot(y ~ x * x)\nsplot(y ~ x * x, split = \"sd\")\n```\nSummarize with a bar plot:\n```R\nsplot(y ~ x * group, type = \"bar\")\n```\nTwo-level y variable with a probability prediction line:\n```R\n# make some new data for this example:\n# a discrete y variable and related x variable:\ny_bin = rep(c(1, 5), 50)\nx_con = y_bin * .4 + rnorm(100)\n\n# lines = \"prob\" for a prediction line from a logistic model:\nsplot(y_bin ~ x_con, lines = \"prob\")\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmiserman%2Fsplot","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmiserman%2Fsplot","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmiserman%2Fsplot/lists"}