Reordering and facetting for ggplot2 | Julia Silge
I recently wrote about the release of tidytext 0.2.1, and one of the most useful new features in this release is a couple of helper functions for making plots with ggplot2. These helper functions address a class of challenges that often arises when dealing with text data, so we’ve included them in the tidytext package. Let’s work through an example To show how to use these new functions, let’s wal
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ã©ãƒ¼ã‚‚。ggplot2ã¯ç©ºæ‰‹ã®ä¸€ç¨®ã¨ã—ã¦çŸ¥ã‚‰ã‚Œã¦ã„ã‚‹ã®ã§ï¼ˆè¦å‡ºå…¸ï¼‰æ™®æ®µã®ç¨½å¤ãŒæ¬ ã‹ã›ã¾ã‚“。今年を振り返りã€ggplot2ã§ã®ä½œå›³ã«ã¤ã„ã¦ã€ã„ãã¤ã‹ã®çŸ¥è¦‹ã‚’共有ã—ã¾ã™ï¼ˆæ›¸ã„ã¦ã„る余裕ãŒãªã‹ã£ãŸã‚“ã‚„...） library(magrittr) library(jpndistrict) ## Loading required package: jpmesh ## This package provide map data is based on the Digital Map ## 25000(Map Image) published by Geospatial Information Authorityof ## Japan (Approval No.603FY2017 information usage ## <http://www.gsi.go.jp>) library(sf) ## Li
ggplot2ã§ãƒ’ストグラムを箱ã²ã’図風ã«ä¸¦ã¹ã¦ãƒ—ãƒãƒƒãƒˆã™ã‚‹ãƒ‘ッケージggbrickを書ã„㟠- 廿TT
English version: abrahamcow.hatenablog.com github.com ggtetrisã£ã¦åå‰ã«ã—よã†ã‹ã¨æ€ã£ãŸã‚“ã ã‘ã©ã€ã‚‚ã†ã‚る（GitHub - EmilHvitfeldt/ggtetris: Create Tetris Chart Visualizations in R）ã¿ãŸã„ãªã®ã§ã€ggbrickã«ã—ã¾ã—ãŸã€‚ brickã¯ãƒ¬ãƒ³ã‚¬ã£ã¦ã„ã†æ„味らã—ã„ã§ã™ã€‚ マニュアルã¯æ°—ãŒå‘ã„ãŸã¨ãã«ã¡ã‚‡ã£ã¨ãšã¤æ›¸ãã¾ã™ã€‚ インストール㯠devtools::install_github("abikoushi/ggbrick") ã§å¤šåˆ†ã„ã‘ã¾ã™ã€‚ å…¥ã£ã¦ã‚‹é–¢æ•°ã¯åŸºæœ¬çš„ã«ã¯geom_brickã ã‘ã§ã™ã€‚ æ°—ãŒå‘ã„ãŸã‚‰è§¦ã£ã¦ã¿ã¦å¤‰ãªã¨ã“ã‚ã‚’æ•™ãˆã¦ã„ãŸã ã‘ã‚‹ã¨å¬‰ã—ã„ã§ã™ã€‚ 以下デモã§ã™ã€‚ 基本的ãªä½¿ã„æ–¹ã¯ã“ã†ã€‚ library(ggplot2) library(ggbr
GGallyパッケージã®ggpair関数を使ã„ã“ãªã™ãŸã‚ã®è¦šãˆæ›¸ã | 厚沢部文化財日誌
GGallyパッケージã«å«ã¾ã‚Œã¦ã„ã‚‹ggpairsãŒç°¡å˜ã«ãƒšã‚¢ãƒ—ãƒãƒƒãƒˆï¼ˆæ•£å¸ƒå›³ãªã‚“ã‹ãŒè¡Œåˆ—風ã«ãªã£ã¦ã„ã‚‹ã‚‚ã®ï¼‰ã‚’作æˆã—ã¦ãれる期待感ã®é«˜ã„パッケージãªã®ã§ã™ãŒã€ã‚°ãƒ©ãƒ•ã‚’ãã£ã¡ã‚Šä»•ä¸Šã’よã†ã¨ã™ã‚‹ã¨ã¡ã‚‡ã£ã¨ãã›ãŒã‚ã‚Šã¾ã™ã€‚ ####ã¾ãšã¯ãƒ‘ッケージをèªã¿è¾¼ã¿ã¾ã™ã€‚ library(ggplot2) library(GGally) ####全データを投入ã—ã¦ãƒ—ãƒãƒƒãƒˆ ##引数ã«ãƒ‡ãƒ¼ã‚¿ãƒ•ãƒ¬ãƒ¼ãƒ を指定ã™ã‚‹ã ã‘ p<-ggpairs(data2)
Same Stats, Different Graphs: Generating Datasets with Varied Appearance and Identical Statistics through Simulated Annealing
“…make both calculations and graphs. Both sorts of output should be studied; each will contribute to understanding.†F.J. Anscombe, 1973 Anscombe’s Quartet It can be difficult to demonstrate the importance of data visualization. Some people are of the impression that charts are simply “pretty pictures,†while all important information can be divined through statistical analysis. An effective (and
データ系列ãŒå¤šã™ãŽã‚‹ã¨ãã€ã„ã„æ„Ÿã˜ã«ä¸€éƒ¨ã‚’ãƒã‚¤ãƒ©ã‚¤ãƒˆã™ã‚‹ãŸã‚ã®ãƒ‘ッケージgghighlightã‚’ã¤ãã‚Šã¾ã—㟠- Technically, technophobic.
ggplot2ã§å¯è¦–化ã—よã†ã¨ã—ã¦ã€ãƒ‡ãƒ¼ã‚¿ç³»åˆ—ãŒå¤šã™ãŽã¦ã“ã‚“ãªã‚‚ã˜ã‚ƒã‚‚ã˜ã‚ƒã«ãªã£ã¦ã—ã¾ã†ã€ã¿ãŸã„ãªã“ã¨ãªã„ã§ã—ょã†ã‹ã€‚ ã“れをã€ä¸€éƒ¨ã ã‘を色付ã‘ã—ã¦ã“ã‚“ãªæ„Ÿã˜ã®ãƒ—ãƒãƒƒãƒˆã«ã—ã¦ãれるパッケージをã¤ãã‚Šã¾ã—ãŸã€‚ インストール GitHub上ã‹ã‚‰ã‚¤ãƒ³ã‚¹ãƒˆãƒ¼ãƒ«ã§ãã¾ã™ã€‚ devtools::install_github("yutannihilation/gghighlight") gghiglightãŒã‚„ã£ã¦ã„ã‚‹ã“㨠gghiglightã®èª¬æ˜Žã‚’ã™ã‚‹å‰ã«ã€ã¾ãšã¯ä¸Šã®ã‚°ãƒ©ãƒ•ãŒä½•ã‚’ã—ã¦ã„ã‚‹ã®ã‹ã€ã¾ãšã¯ãµã¤ã†ã®tidyverseã§ã‚„ã£ã¦ã¿ã¾ã™ã€‚ データã¯ã“ã‚“ãªæ„Ÿã˜ã®ã‚„ã¤ã§ã™ã€‚ library(dplyr, warn.conflicts = FALSE) set.seed(1) d <- tibble( idx = 1:10000, value = runif(idx, -1, 1), type = sa
ggplot2ã€gridã€gtableã€gridExtraã®é–¢ä¿‚ã¨ã‹ - Technically, technophobic.
追記（2017/05/04）: gridExtraã®wikiã®URLãŒå¤‰ã‚ã£ã¦ã„ãŸã®ã§ä¿®æ£ã—ã¾ã—ãŸã€‚ 久々ã«ggplot2ã®Issueを眺ã‚ã¦ãŸã‚‰ã€gridExtraã®ä½œè€…ãŒè¶…有用ドã‚ュメントを書ã„ã¦ã‚‹ã®ã‚’見ã¤ã‘ãŸã®ã§ãƒ¡ãƒ¢ã€‚ ã¡ãªã¿ã«ã€å…ƒã¯ã“ã®Issueã§è¦‹ã¤ã‘ã¾ã—ãŸ: gridパッケージã¨ã¯ gridパッケージã¯ã€ãƒ‡ãƒ•ã‚©ãƒ«ãƒˆã§ã‚¤ãƒ³ã‚¹ãƒˆãƒ¼ãƒ«ã•ã‚Œã¦ã„るパッケージã§ã€ã‚°ãƒ©ãƒ•ã‚£ã‚«ãƒ«ãªå‡ºåŠ›ã«é–¢ã™ã‚‹ä½Žãƒ¬ãƒ™ãƒ«ãªæ“作ãŒã§ãã¾ã™ã€‚ggplot2ã¨ã‹latticeã¯ã“ã®ã‚·ã‚¹ãƒ†ãƒ を使ã£ã¦ä½œã‚‰ã‚Œã¦ã„ã¾ã™ã€‚ grid is a low-level graphics system which provides a great deal of control and flexibility in the appearance and arrangement of graphical output. grid does
Top 50 ggplot2 Visualizations - The Master List (With Full R Code)
Tutorial R Tutorial ggplot2 ggplot2 Short Tutorial ggplot2 Tutorial 1 - Intro ggplot2 Tutorial 2 - Theme ggplot2 Tutorial 3 - Masterlist ggplot2 Quickref Foundations Linear Regression Statistical Tests Missing Value Treatment Outlier Analysis Feature Selection Model Selection Logistic Regression Advanced Linear Regression Advanced Regression Models Advanced Regression Models Time Series Time Serie
Rã§è§£æžï¼šggplot2ã®è¡¨ç¾ãŒé«˜ã¾ã‚Šã¾ã™ã€Œggforceã€ãƒ‘ッケージ
ggplot2ã«ã¯ãƒ‡ãƒ¼ã‚¿ã‚’分割プãƒãƒƒãƒˆã™ã‚‹ã€Œfacet_wrapã€ã‚„「facet_gridã€ã‚³ãƒžãƒ³ãƒ‰ãŒåŽéŒ²ã•ã‚Œã¦ã„ã¾ã™ã€‚ã“れらコマンドã¯éžå¸¸ã«ä¾¿åˆ©ã§ã™ãŒã€åˆ†å‰²å‰ã®ãƒ—ãƒãƒƒãƒˆã¯åˆ¥é€”コマンド実行ãŒå¿…è¦ã§ã™ã€‚本パッケージã«ã¯åˆ†å‰²å‰ã¨æŒ‡å®šã—ãŸã‚°ãƒ«ãƒ¼ãƒ—を拡大表示ã—ã¦ãれる「facet_zoomã€ã‚³ãƒžãƒ³ãƒ‰ãŒåŽéŒ²ã•ã‚Œã¦ã„ã¾ã™ã€‚「facet_wrapã€ã‚„「facet_gridã€ã‚³ãƒžãƒ³ãƒ‰ã¨ä½¿ç”¨ç›®çš„ãŒç•°ãªã‚Šã¾ã™ãŒä¾¿åˆ©ãªã®ã§ç´¹ä»‹ã—ã¾ã™ã€‚ ãªãŠã€ã€Œfacet_zoomã€ã‚³ãƒžãƒ³ãƒ‰ã®ä¾‹ã¯æ•£å¸ƒå›³ã§ã™ãŒç®±ã²ã’図や折れ線グラフãªã©ã‚‚é©å¿œå¯èƒ½ã§ã™ã€‚ パッケージãƒãƒ¼ã‚¸ãƒ§ãƒ³ã¯0.4.1。実行コマンドã¯windows 11ã®R version 4.2.1ã§ç¢ºèªã—ã¦ã„ã¾ã™ã€‚ パッケージã®ã‚¤ãƒ³ã‚¹ãƒˆãƒ¼ãƒ«ä¸‹è¨˜ã‚³ãƒžãƒ³ãƒ‰ã‚’実行ã—ã¦ãã ã•ã„。 #パッケージã®ã‚¤ãƒ³ã‚¹ãƒˆãƒ¼ãƒ« install.packages("ggforce")実行コマンドã®ç´¹ä»‹è©³ç´°
Rã§è§£æžï¼šggplot2ã®ä½“è£ã‚’インタラクティブã«æ“作å¯èƒ½ï¼ã€ŒggThemeAssistã€ãƒ‘ッケージ
ggplot2ã®ä½“è£ã‚’インタラクティブã«æ“作å¯èƒ½ãªãƒ‘ッケージã®ç´¹ä»‹ã§ã™ã€‚æ“作後ã«ä½“è£ã‚’é©å¿œã—ãŸggplot2ã®ã‚³ãƒ¼ãƒ‰ãŒå‡ºåŠ›ã•ã‚Œã¾ã™ã®ã§ä½•ã‹ã®å‚考ã«ãªã‚‹ã¨æ€ã„ã¾ã™ã€‚ ・パッケージ利用ã«å¿…é ˆãªRStudioå…¬å¼ãƒ›ãƒ¼ãƒ ページ https://www.rstudio.com/ パッケージãƒãƒ¼ã‚¸ãƒ§ãƒ³ã¯0.1.5。実行コマンドã¯RStudioã®RStudio Desktop 2021.09.2+382ã€windows 11ã®R version 4.1.3ã§ç¢ºèªã—ã¦ã„ã¾ã™ã€‚ パッケージã®ã‚¤ãƒ³ã‚¹ãƒˆãƒ¼ãƒ«ä¸‹è¨˜ã‚³ãƒžãƒ³ãƒ‰ã‚’実行ã—ã¦ãã ã•ã„。 #パッケージã®ã‚¤ãƒ³ã‚¹ãƒˆãƒ¼ãƒ« install.packages("ggThemeAssist")実行コマンド詳細ã¯ã‚³ãƒžãƒ³ãƒ‰å†…を確èªãã ã•ã„。 #パッケージã®èªã¿è¾¼ã¿ library("ggThemeAssist") #tidyverseパッケージãŒãªã‘ã‚Œã°ã‚¤ãƒ³ã‚¹ãƒˆãƒ¼ãƒ« if(!
Rã§è§£æžï¼šggplotオブジェクトをã„ã‚ã„ã‚プãƒãƒƒãƒˆã€ŒbluepRintã€ãƒ‘ッケージ
複数ã®ggplot2ã®ã‚ªãƒ–ジェクトを簡å˜ã«ãƒ—ãƒãƒƒãƒˆã§ãるパッケージã®ç´¹ä»‹ã§ã™ã€‚プãƒãƒƒãƒˆé ˜åŸŸã¨ä½“è£ã•ãˆæ±ºã‚ã¦ã—ã¾ãˆã°ã€å®šåž‹çš„ãªç¹°ã‚Šè¿”ã—ã®å‡ºåŠ›ã«ä¾¿åˆ©ã ã¨æ€ã„ã¾ã™ã€‚ パッケージãƒãƒ¼ã‚¸ãƒ§ãƒ³ã¯0.0.0.9000。実行コマンドã¯R version 4.2.2ã§ç¢ºèªã—ã¦ã„ã¾ã™ã€‚ パッケージã®ã‚¤ãƒ³ã‚¹ãƒˆãƒ¼ãƒ«ä¸‹è¨˜ã€ã‚³ãƒžãƒ³ãƒ‰ã‚’実行ã—ã¦ãã ã•ã„。ggplot2パッケージã®æœ€æ–°ãƒãƒ¼ã‚¸ãƒ§ãƒ³ã‚’インストールã—ã¦ã„ã¾ã™ã€‚ #パッケージã®ã‚¤ãƒ³ã‚¹ãƒˆãƒ¼ãƒ« install.packages("devtools") devtools::install_github("zaczap/bluepRint")実行コマンド詳細ã¯ã‚³ãƒ¡ãƒ³ãƒˆã€ãƒ‘ッケージã®ãƒ˜ãƒ«ãƒ—を確èªã—ã¦ãã ã•ã„。 #パッケージã®èªã¿è¾¼ã¿ library("bluepRint") ###データ例ã®ä½œæˆ##### n <- 30 TestData <- data.frame(Gr
Comparing ggplot2 and R Base Graphics
I look at differences in a side-by-side, from a practitioner’s perspective. In R, the open source statistical computing language, there are a lot of ways to do the same thing. Especially with visualization. R comes with built-in functionality for charts and graphs, typically referred to as base graphics. Then there are R packages that extend functionality. Although there are many packages, ggplot2
ggplot2ã§geom_lineã™ã‚‹æ™‚ã«NAを無視ã—ã¦ç·šã‚’ã¤ãªã - ç›†æ ½æ—¥è¨˜
geom_lineã™ã‚‹ã¨ãã€NAãŒå…¥ã£ã¦ã„ã‚‹ã¨ãã“ã§ç·šãŒé€”切れã¦ã—ã¾ã†ã€‚ library("ggplot2") smp <- data.frame(x = 1:5, y = c(1:3, NA, 5)) ggplot(smp, aes(x = x, y = y, group = 1)) + geom_line() + geom_point() ã‚らã‹ã˜ã‚NAを除ã„ã¦ãŠã‘ã°ã€ç·šã‚’ã¤ãªã„ã§ãれる。 ggplot(subset(smp, !is.na(y)), aes(x = x, y = y, group = 1)) + geom_line() + geom_point() ãªã‚“ã ã‹æ¯Žå›žã‚„り方を調ã¹ã¦ã‚‹æ°—ãŒã™ã‚‹ã®ã§ãƒ¡ãƒ¢ã€‚
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