Why I use ggplot2
David Robinson Director of Data Scientist at Heap, works in R. Email Twitter Github Stack Overflow Subscribe Recommended R Bloggers RStudio Blog R4Stats Simply Statistics Upfront If you’ve read my blog, taken one of my classes, or sat next to me on an airplane, you probably know I’m a big fan of Hadley Wickham’s ggplot2 package, especially compared to base R plotting. Not everyone agrees. Among th
ggplot2でロジスティック回帰の近似曲線を描く - データサイエンティスト上がりのDX参謀・起業家
バージョンアップで昔の指定方法ができなくなってた。 調べたら下記のようにやれば描ける。ggplot2は綺麗なんだけど仕様変更が多いから困る。 binomial_smooth <- function(...) { geom_smooth(method = "glm", method.args = list(family = "binomial"), ...) } ggplot(data, aes(x, y)) + geom_point() + binomial_smooth()
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ggplot2: きれいなグラフを簡単に合理的に
“The Grammer of Graphics” という体系に基づいて設計されたパッケージ。 単にいろんなグラフを「描ける」だけじゃなく「一貫性のある文法で合理的に描ける」。 Rのグラフ描画システムにはgraphicsとgridの2つが存在しており、 R標準のboxplot()やhist()などは前者の上に、 本項で扱うggplot2は後者の上に成り立っている。 使い方が全く異なるので、前者を知らずにいきなりggplot2から始めても大丈夫。 tidyverse に含まれているので、 install.packages("tidyverse") で一括インストール、 library(tidyverse) で一括ロード。 初学者向け講義資料2024 https://ggplot2.tidyverse.org https://r-graphics.org/ https://r4ds.hadle
グラフ描画ggplot2の辞書的まとめ20のコード – MrUnadon – Bayesian Statistical Modelings with R and Rstan
Rによる美しいグラフの作成に欠かせないパッケージ "ggplot2" ですが、 グラフ作成のたびにネット検索したり自分の以前のコードを掘り起こしたりしませんか？ author: Unadon (見習い飯炊き兵) 動作環境:Mac OS Sierra 10.12.1; R version３.３.１; rstan 2.10.1 まずはTwitterでこの記事をシェアする はじめに ggplot2のグラフ作成で迷った時、チートシートやマニュアルは役に立つけど援用しにくいんですよね。「何を言ってるのかわからない」っていうのが結構あります。 「軸ラベル」と「軸タイトル」ってどれがどれなん？など、そういう初歩的なところで引っかかったりして、 目的(こういう図にしたい！)と手段(コード)の間の知識を埋めるのが手間で「もうイヤ！」ってなる。 で、次すぐに忘れてる。 なので、頻繁に使用するggplot２の基
Map projections — coord_map
coord_map() projects a portion of the earth, which is approximately spherical, onto a flat 2D plane using any projection defined by the mapproj package. Map projections do not, in general, preserve straight lines, so this requires considerable computation. coord_quickmap() is a quick approximation that does preserve straight lines. It works best for smaller areas closer to the equator. Both coord_
Create Elegant Data Visualisations Using the Grammar of Graphics • ggplot2
Usage It’s hard to succinctly describe how ggplot2 works because it embodies a deep philosophy of visualisation. However, in most cases you start with ggplot(), supply a dataset and aesthetic mapping (with aes()). You then add on layers (like geom_point() or geom_histogram()), scales (like scale_colour_brewer()), faceting specifications (like facet_wrap()) and coordinate systems (like coord_flip()
Position scales for continuous data (x & y) — scale_continuous
scale_x_continuous() and scale_y_continuous() are the default scales for continuous x and y aesthetics. There are three variants that set the transform argument for commonly used transformations: scale_*_log10(), scale_*_sqrt() and scale_*_reverse(). scale_x_continuous( name = waiver(), breaks = waiver(), minor_breaks = waiver(), n.breaks = NULL, labels = waiver(), limits = NULL, expand = waiver()
Gradient colour scales — scale_colour_gradient
scale_*_gradient creates a two colour gradient (low-high), scale_*_gradient2 creates a diverging colour gradient (low-mid-high), scale_*_gradientn creates a n-colour gradient. For binned variants of these scales, see the color steps scales. scale_colour_gradient( name = waiver(), ..., low = "#132B43", high = "#56B1F7", space = "Lab", na.value = "grey50", guide = "colourbar", aesthetics = "colour"
stat_contour. ggplot2 0.9.3.1
Usage stat_contour(mapping = NULL, data = NULL, geom = "path", position = "identity", na.rm = FALSE, ...) Arguments na.rm If FALSE (the default), removes missing values with a warning. If TRUE silently removes missing values. mapping The aesthetic mapping, usually constructed with aes or aes_string. Only needs to be set at the layer level if you are overriding the plot defaults. data A layer speci
Rectangles — geom_raster
geom_rect() and geom_tile() do the same thing, but are parameterised differently: geom_rect() uses the locations of the four corners (xmin, xmax, ymin and ymax), while geom_tile() uses the center of the tile and its size (x, y, width, height). geom_raster() is a high performance special case for when all the tiles are the same size, and no pattern fills are applied. geom_raster( mapping = NULL, da
Compute empirical cumulative distribution — stat_ecdf
The empirical cumulative distribution function (ECDF) provides an alternative visualisation of distribution. Compared to other visualisations that rely on density (like geom_histogram()), the ECDF doesn't require any tuning parameters and handles both continuous and categorical variables. The downside is that it requires more training to accurately interpret, and the underlying visual tasks are so
Function reference
A layer combines data, aesthetic mapping, a geom (geometric object), a stat (statistical transformation), and a position adjustment. Typically, you will create layers using a geom_ function, overriding the default position and stat if needed. layer_geoms Layer geometry display geom_abline() geom_hline() geom_vline() Reference lines: horizontal, vertical, and diagonal geom_bar() geom_col() stat_cou
Legend guide — guide_legend
Legend type guide shows key (i.e., geoms) mapped onto values. Legend guides for various scales are integrated if possible. guide_legend( title = waiver(), theme = NULL, position = NULL, direction = NULL, override.aes = list(), nrow = NULL, ncol = NULL, reverse = FALSE, order = 0, ... ) Arguments title A character string or expression indicating a title of guide. If NULL, the title is not shown. By
A box and whiskers plot (in the style of Tukey) — geom_boxplot
The boxplot compactly displays the distribution of a continuous variable. It visualises five summary statistics (the median, two hinges and two whiskers), and all "outlying" points individually. geom_boxplot( mapping = NULL, data = NULL, stat = "boxplot", position = "dodge2", ..., outliers = TRUE, outlier.colour = NULL, outlier.color = NULL, outlier.fill = NULL, outlier.shape = 19, outlier.size =
Sequential, diverging and qualitative colour scales from ColorBrewer — scale_colour_brewer
The brewer scales provide sequential, diverging and qualitative colour schemes from ColorBrewer. These are particularly well suited to display discrete values on a map. See https://colorbrewer2.org for more information. scale_colour_brewer( name = waiver(), ..., type = "seq", palette = 1, direction = 1, aesthetics = "colour" ) scale_fill_brewer( name = waiver(), ..., type = "seq", palette = 1, dir
Line segments and curves — geom_segment
geom_segment() draws a straight line between points (x, y) and (xend, yend). geom_curve() draws a curved line. See the underlying drawing function grid::curveGrob() for the parameters that control the curve. geom_segment( mapping = NULL, data = NULL, stat = "identity", position = "identity", ..., arrow = NULL, arrow.fill = NULL, lineend = "butt", linejoin = "round", na.rm = FALSE, show.legend = NA
Compute function for each x value — stat_function • ggplot2
This stat makes it easy to superimpose a function on top of an existing plot. The function is called with a grid of evenly spaced values along the x axis, and the results are drawn (by default) with a line. stat_function( mapping = NULL, data = NULL, geom = "path", position = "identity", ..., fun, xlim = NULL, n = 101, args = list(), na.rm = FALSE, show.legend = NA, inherit.aes = TRUE ) Arguments
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