scale_colour_gradient2. ggplot2 0.9.3.1
Usage scale_colour_gradient2(..., low = muted("red"), mid = "white", high = muted("blue"), midpoint = 0, space = "rgb", na.value = "grey50", guide = "colourbar") scale_fill_gradient2(..., low = muted("red"), mid = "white", high = muted("blue"), midpoint = 0, space = "rgb", na.value = "grey50", guide = "colourbar") scale_color_gradient2(..., low = muted("red"), mid = "white", high = muted("blue"),
Sequential grey colour scales — scale_colour_grey
scale_colour_grey( name = waiver(), ..., start = 0.2, end = 0.8, na.value = "red", aesthetics = "colour" ) scale_fill_grey( name = waiver(), ..., start = 0.2, end = 0.8, na.value = "red", aesthetics = "fill" ) Arguments name The name of the scale. Used as the axis or legend title. If waiver(), the default, the name of the scale is taken from the first mapping used for that aesthetic. If NULL, the
Summarise y values at unique/binned x — stat_summary_bin
stat_summary() operates on unique x or y; stat_summary_bin() operates on binned x or y. They are more flexible versions of stat_bin(): instead of just counting, they can compute any aggregate. stat_summary_bin( mapping = NULL, data = NULL, geom = "pointrange", position = "identity", ..., fun.data = NULL, fun = NULL, fun.max = NULL, fun.min = NULL, fun.args = list(), bins = 30, binwidth = NULL, bre
geom_errorbar. ggplot2 0.9.3.1
Usage geom_errorbar(mapping = NULL, data = NULL, stat = "identity", position = "identity", ...) Arguments 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 specific dataset - only needed if you want to override the plot defaults. stat The statistical transformation to use on the d
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()
Add components to a plot — +.gg
What can you add? You can add any of the following types of objects: An aes() object replaces the default aesthetics. A layer created by a geom_ or stat_ function adds a new layer. A scale overrides the existing scale. A theme() modifies the current theme. A coord overrides the current coordinate system. A facet specification overrides the current faceting. To replace the current default data fram
Connect observations — geom_path
geom_path() connects the observations in the order in which they appear in the data. geom_line() connects them in order of the variable on the x axis. geom_step() creates a stairstep plot, highlighting exactly when changes occur. The group aesthetic determines which cases are connected together. geom_path( mapping = NULL, data = NULL, stat = "identity", position = "identity", ..., lineend = "butt"
element_text. ggplot2 0.9.2.1
Usage element_text ( family = NULL , face = NULL , colour = NULL , size = NULL , hjust = NULL , vjust = NULL , angle = NULL , lineheight = NULL , color = NULL ) Arguments family font family face font face ("plain", "italic", "bold", "bold.italic") colour text colour size text size (in pts) hjust horizontal justification (in [0, 1]) vjust vertical justification (in [0, 1]) angle angle (in [0, 360])
Smoothed conditional means — geom_smooth
Aids the eye in seeing patterns in the presence of overplotting. geom_smooth() and stat_smooth() are effectively aliases: they both use the same arguments. Use stat_smooth() if you want to display the results with a non-standard geom. geom_smooth( mapping = NULL, data = NULL, stat = "smooth", position = "identity", ..., method = NULL, formula = NULL, se = TRUE, na.rm = FALSE, orientation = NA, sho
Hexagonal heatmap of 2d bin counts — geom_hex
Divides the plane into regular hexagons, counts the number of cases in each hexagon, and then (by default) maps the number of cases to the hexagon fill. Hexagon bins avoid the visual artefacts sometimes generated by the very regular alignment of geom_bin_2d(). geom_hex( mapping = NULL, data = NULL, stat = "binhex", position = "identity", ..., na.rm = FALSE, show.legend = NA, inherit.aes = TRUE ) s
Text — geom_label
Text geoms are useful for labeling plots. They can be used by themselves as scatterplots or in combination with other geoms, for example, for labeling points or for annotating the height of bars. geom_text() adds only text to the plot. geom_label() draws a rectangle behind the text, making it easier to read. geom_label( mapping = NULL, data = NULL, stat = "identity", position = "identity", ..., pa
Contours of a 2D density estimate — geom_density_2d
Perform a 2D kernel density estimation using MASS::kde2d() and display the results with contours. This can be useful for dealing with overplotting. This is a 2D version of geom_density(). geom_density_2d() draws contour lines, and geom_density_2d_filled() draws filled contour bands. geom_density_2d( mapping = NULL, data = NULL, stat = "density_2d", position = "identity", ..., contour_var = "densit
ggplot2 themes
The theming system in ggplot2 enables a user to control non-data elements of a ggplot object. It is composed of the following: theme elements , which refer to individual attributes of a graphic that are independent of the data, such as font size, axis ticks, appearance of grid lines or background color of a legend; theme element functions , which enables you to modify the settings of certain theme
Complete themes — ggtheme
These are complete themes which control all non-data display. Use theme() if you just need to tweak the display of an existing theme. theme_grey( base_size = 11, base_family = "", base_line_size = base_size/22, base_rect_size = base_size/22 ) theme_gray( base_size = 11, base_family = "", base_line_size = base_size/22, base_rect_size = base_size/22 ) theme_bw( base_size = 11, base_family = "", base
ggplot2で、複数の時系列を一枚にプロット - Qiita
> head(dat) X1 X2 X3 t 1 -0.50643974 1.047231 0.27683366 1 2 -0.09770514 3.045507 -0.35219898 2 3 -1.34254553 3.454165 0.02047701 3 4 -2.80358361 3.052366 -0.35403137 4 5 -3.84976651 2.441114 0.68818969 5 6 -4.64718281 1.449005 1.68518642 6
学会発表のためのggplot2の設定めも - Qiita
気がつけばもう何ヶ月も前の話ですが、学会発表のためにポスターに使う図の設定に戸惑ったのでめもです。なお作図ファイルの形式はpdfで、R Markdownによる自動作図です（png形式だとサイズを変更する際に粗くなってしまうので）。ここに貼り付けている画像はPDFをpng形式に変換したものです。 完成形はこんな感じ。 GitHubリポジトリにPDFファイルがありますので興味があればどうぞ。これからの作図はirisデータでやっていますが、データが違うだけでやった方法は同じです。 まず、通常のggplot2でのプロットを見てみましょう。このままでも良いのですが、ggplot2は各種の変更も簡単なので、好みの図となるようにあれこれ変えていきます。 library(ggplot2) p <- ggplot(data = iris, aes(x = Sepal.Length, y = Petal.Le
ggplot2に関する資料まとめ - Qiita
そろそろワンストップなまとめ記事があってもいいはずと思ったので、個人的なメモとしてまとめました。 2016/06/10追記: 資料1件追加 2016/03/15追記: ggplot2 v2向けに一部修正 ggplot2のイメージをつかもう まずはこのggplot2がどういうものなのかをつかみましょう。 ggplot2の完全な初心者向け ggplot2に初めて触れるという方は、まずこのスライドを一読されるのをオススメします: ggplot2によるグラフ化@HijiyamaR#2 ※Slideshareリンク ggplot2を理解するためには、その文法をイメージできるようになることが近道です。このスライドは従来のplotとggplot2との違いをわかりやすく解説してあります。まずはレイヤーの感覚をつかむためにも、提示してあるサンプルコードをひたすら打ちましょう。手を動かす、大事。 ggplot
ggplot2でplot()的な複数グラフ表示とその闇 - Qiita
概要 ggplot2でforループを使ったplot()的な複数プロットするときは、Rmisc::multiplotが使える。 このとき、aesのデータフレーム引数には必ず列名を用いる。 列要素を直接指定すると、全て同一プロットになる場合があります。なんでそうなるかは分からん。 解説 例えば通常のplot()関数を使って、１枚に複数のグラフをプロットするには par(mfrow = c(2,3)) for(i in 1:6){ data <- data.frame(1:10, rnorm(10)) plot(data, main = i) } とすればok。 ggplot2でも同じようにやりたい。 facet_grid,facet_wrapを使うのが普通だと思うけど、1枚に系列全て収まらない場合、tidyrやらのforループを入れることになって激しく面倒くさい。軸合わせもめんどいし。 もうい
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xlim. ggplot2 0.9.3
http://ggplot2.org/index.html