ggplot2 㧠axes を消ã™æ–¹æ³• - ã»ãã笑む
最近ã€æ¬¡ã®æœ¬ã‚’èªã‚“ã§æ—¥æœ¬åœ°å›³ã‚’æãã®ã«ãƒãƒžã£ã¦ã„る。 実践Data Scienceシリーズ Rã§ã¯ã˜ã‚る地ç†ç©ºé–“データã®çµ±è¨ˆè§£æžå…¥é–€ (KSæƒ…å ±ç§‘å¦å°‚門書) 作者:æ‘上 大輔講談社Amazon ggplot2 ã§ã‚·ãƒ³ãƒ—ルã«æ—¥æœ¬åœ°å›³ã‚’æãã¨ã“ã‚“ãªæ„Ÿã˜ã«ãªã‚‹ã€‚ library(tidyverse) library(sf) library(NipponMap) shp <- system.file("shapes/jpn.shp", package = "NipponMap") pref <- read_sf(shp) %>% filter(name != "Okinawa") ggplot(pref) + geom_sf() ã“ã®ã¨ãã€ãƒ‡ãƒ•ã‚©ãƒ«ãƒˆã§ã¯ x軸㨠y軸ã«çµŒåº¦ã¨ç·¯åº¦ãŒè¡¨ç¤ºã•ã‚Œã‚‹ã€‚ ã“ã‚Œã€ã„らãªããªã„ã§ã™ã‹ï¼Ÿ ã—ã‹ã‚‚ã“ã‚Œ (axes) を消ã™ã®ã¯ã‘ã£ã“ã†é¢å€’ãªã‚³ãƒ¼ãƒ‰ã‚’書ãå¿…è¦ãŒã‚る。
[書評]『データ分æžã®ãŸã‚ã®ãƒ‡ãƒ¼ã‚¿å¯è¦–化入門ã€ã¨æœ€è¿‘ã® R グラフィックスパッケージ事情 - ill-identified diary
概è¦Healy (2018) “Data Visualization: A Practical Introduction†ã®é‚¦è¨³ã€Žãƒ‡ãƒ¼ã‚¿åˆ†æžã®ãŸã‚ã®ãƒ‡ãƒ¼ã‚¿å¯è¦–化入門ã€ã‚’もらã£ãŸã®ã§, ã“ã®æœ¬ã®ãƒ¬ãƒ“ューã¨æœ¬æ›¸ã§ã¯ã‚ã¾ã‚Šå–り上ã’られã¦ã„ãªã„, R グラフィックスをã•ã‚‰ã«æ´»ç”¨ã™ã‚‹ãŸã‚ã®ãƒ’ントをæ示ã™ã‚‹. 2021/9/24 追記: 『Python ユーザã§ã‚‚『データå¯è¦–化入門ã€ã§ç·´ç¿’ã§ãるよã†ã«ãƒ‘ッケージを作ã£ãŸ + Plotnine ã¨ã®äº’æ›æ€§ã‚¬ã‚¤ãƒ‰ - ill-identified diaryã€ã«æ›¸ã„ãŸã‚ˆã†ã«, Python ã§ã‚‚ matplotlib ã®ã‚ˆã†ãªç…©é›‘ãªæ§‹æ–‡ã«æƒ‘ã‚ã•ã‚Œãšã«ã“ã®æœ¬ã§ç´¹ä»‹ã•ã‚Œã¦ã„るよã†ãªæ–¹æ³•ã‚’実ç¾ã™ã‚‹ãŸã‚ã®ãƒ‘ッケージ/用例を作ã£ã¦ã¿ãŸ. æ¦‚è¦ åˆã‚㫠本書ã®ç‰¹å¾´ ã©ã†ã„ã†æœ¬ãªã®ã‹ ã©ã†ã„ã†äººãŒèªã‚€ã¨ã‚ˆã„㋠邦訳ã«å¯¾ã™ã‚‹ç´°ã‹ã„ツッコミã©ã“゠本書をèªã‚“ã 人ã«ãŠã™ã™ã‚
![[書評]『データ分æžã®ãŸã‚ã®ãƒ‡ãƒ¼ã‚¿å¯è¦–化入門ã€ã¨æœ€è¿‘ã® R グラフィックスパッケージ事情 - ill-identified diary](https://cdn-ak-scissors.b.st-hatena.com/image/square/3722eb4485f92d6df39f0dd97780c8aa1cfbc9e2/height=288;version=1;width=512/https%3A%2F%2Fcdn.image.st-hatena.com%2Fimage%2Fscale%2F98ec770ffc7316473807ffe69c26e8a7e8e4bcd9%2Fbackend%3Dimagemagick%3Bversion%3D1%3Bwidth%3D1300%2Fhttps%253A%252F%252Fcdn-ak.f.st-hatena.com%252Fimages%252Ffotolife%252Fi%252Fill-identified%252F20210203%252F20210203222306.jpg)
メモ：dplyr・ggplot2 㧠{{ }} を使ㆠ- Technically, technophobic.
追記（2021/03/26）: 開発版ã§ã¯ã€Œfacet_*() ã®å ´åˆã€ã‚‚å‹•ãよã†ã«ãªã‚Šã¾ã—ãŸã€‚ ã“ã†ã„ã†ãƒ‡ãƒ¼ã‚¿ãŒæ‰‹å…ƒã«ã‚ã‚‹ã¨ã—ã¾ã™ã€‚ library(readr) library(dplyr, warn.conflicts = FALSE) library(ggplot2) library(lubridate, warn.conflicts = FALSE) d_raw <- read_csv( "https://stopcovid19.metro.tokyo.lg.jp/data/130001_tokyo_covid19_patients.csv", col_types = cols( No = col_integer(), 全国地方公共団体コード = col_integer(), 公表_年月日 = col_date(), 発症_年月日 = col_date(), 確定_年月日 = c
Plotting PCA (Principal Component Analysis)
This document explains PCA, clustering, LFDA and MDS related plotting using {ggplot2} and {ggfortify}. Plotting PCA (Principal Component Analysis) {ggfortify} let {ggplot2} know how to interpret PCA objects. After loading {ggfortify}, you can use ggplot2::autoplot function for stats::prcomp and stats::princomp objects. library(ggfortify) df <- iris[1:4] pca_res <- prcomp(df, scale. = TRUE) autoplo
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
ggplot2 ã§æ™‚系列プãƒãƒƒãƒˆã®ç«¯ç‚¹ã«ãƒ©ãƒ™ãƒ«ã‚’表示ã™ã‚‹ - æ ªå¼ä¼šç¤¾ãƒ›ã‚¯ã‚½ã‚¨ãƒ ã®ãƒ–ãƒã‚°
ã“ã†ã„ã†æ„Ÿã˜ã§æ™‚系列プãƒãƒƒãƒˆã®ç«¯ç‚¹ã«ãƒ©ãƒ™ãƒ«ã‚’表示ã—ãŸã„。 ã“ã®æ–¹ãŒæ™‚系列ã¨ãƒ©ãƒ™ãƒ«ã®å¯¾å¿œãŒã‚ã‹ã‚Šã‚„ã™ãã¦è‰¯ã„。 データ視覚化ã®ãƒ‡ã‚¶ã‚¤ãƒ³ #1｜Go Ando / THE GUILD｜note ggrepel パッケージを使ã†ã¨ç°¡å˜ã«ã§ãã‚‹ãŒã€ã„ãã¤ã‹æ³¨æ„点ãŒã‚ã‚‹ã®ã§ã“ã“ã§ç´¹ä»‹ã—ãŸã„。 ã¾ãšã¯ãƒ‡ãƒ¼ã‚¿ã‚’用æ„ã™ã‚‹ã€‚ library(tidyverse) set.seed(314) d <- map_dfr( c("setosa", "versicolor", "virginica"), ~ tibble( idx = 1:400, value = cumsum(runif(400, -1, 1)), type = . ) ) ggplot(d, aes(idx, value, colour = type)) + geom_line() 普通ã«ãƒ—ãƒãƒƒãƒˆã™ã‚‹ã¨ã“ã‚“ãªæ„Ÿã˜ã ãŒã€å³ç«¯ã«ãƒ©ãƒ™ãƒ«ã‚’ã¤ã‘ãŸã„。
ggplot2ベースã®å›³ã‹ã‚‰å‡¡ä¾‹ (legend) ã®ã¿å¾—る方法ã‚ã‚Œã“ã‚Œ - cucumber flesh
タイトルã«æ›¸ã„ãŸã‚ˆã†ã«ã€Œå‡¡ä¾‹ã ã‘ãŒæç”»ã—ãŸã„ã€ã“ã¨ãŒæ™‚々ã‚ã‚‹ã‹ã¨æ€ã„ã¾ã™ï¼ˆæ¢ã‚€ã‚’å¾—ãšã«å‡¡ä¾‹ã®ä½ç½®ãŒå›³ã‹ã‚‰åˆ†æ–ã•ã‚Œã¦ã—ã¾ã†ã¨ãã¨ã‹ï¼‰ã€‚ãã‚“ãªæ™‚ã«ã¯ã€ã“ã“ã§ç´¹ä»‹ã™ã‚‹æ–¹æ³•ã‚’使ã†ã¨è‰¯ã„ã§ã™ã€‚ã‚„ã‚Šæ–¹ã¯ã„ãã¤ã‹ã‚ã‚Šã¾ã™ã€‚ã“ã“ã§ã¯gtableパッケージを使ã†å ´åˆã®2例ã¨lemonパッケージを使ã†æ–¹æ³•ã®3通りを紹介ã—ã¾ã™ã€‚ âš ï¸ ggplot2ã®å›³ã®æç”»ã«ä½¿ã‚ã‚Œã¦ã„ã‚‹gridシステムやã“ã®è¨˜äº‹ã§æ‰±ã†gtableã¨ã®é–¢ä¿‚ã«ã¤ã„ã¦çŸ¥ã‚ŠãŸã„人ã¯ã€æ¯Žåº¦ãŠãªã˜ã¿ id:yutannihilation ã•ã‚“ã®ä¸‹è¨˜ã®è¨˜äº‹ã‚’èªã¿ã¾ã—ょã†ã€‚ notchained.hatenablog.com ã¾ãšã¯å‡¡ä¾‹ã‚’å«ã‚€å›³ã‚’作りã¾ã—ょã†ðŸŽ¨ library(ggplot2) p <- ggplot(mtcars, aes(wt, mpg)) + geom_point(aes(color = factor(cyl), shape = f
ggplot2, Ordering y axis
I have the below data.frame called df. My problem has to do with the order on the y-axis. I want that the names on the y-axis are ordered according to the variable depth. If I do this: ggplot(df,aes(x=factor(name),y=depth)) + geom_bar(stat='identity') + coord_flip() + labs(y='depth',x='species') I got graph1 below which is not ordered. Therefore I followed the instructions found here, I ordered th
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)
Alluvial Plots in ggplot2
Alluvial Plots in ggplot2 Jason Cory Brunson 2023-02-13 The {ggalluvial} package is a {ggplot2} extension for producing alluvial plots in a {tidyverse} framework. The design and functionality were originally inspired by the {alluvial} package and have benefitted from the feedback of many users. This vignette defines the essential components of alluvial plots as used in the naming schemes and docum
Plotly
Plotly ggplot2 Open Source Graphing Library With ggplotly() by Plotly, you can convert your ggplot2 figures into interactive ones powered by plotly.js, ready for embedding into Dash applications. ggplotly is free and open source and you can view the source, report issues or contribute on GitHub. Head over to the community forum to ask questions and get help.
{googleAnalyticsR} {ggplot2} 内訳をドリルダウンã—ã¦ã„ã棒グラフ - 廿TT
ggplot2 ã§å·¦ã‹ã‚‰å³ã«å†…訳をドリルダウンã—ã¦ã„ã棒グラフ（ツリーマップ？）を書ãã¾ã—ãŸã€‚ æ–°è¦è¨ªå•ã§è‡ªç„¶æ¤œç´¢çµŒç”±ã®ãƒ‡ã‚¹ã‚¯ãƒˆãƒƒãƒ—を利用ã—ã¦ã„る男性ã®25æ³ã€œ34æ³ã®CVãŒå¤šã„ã“ã¨ãŒã‚ã‹ã‚Šã¾ã™ã€‚ ã¤ã„ã§å†è¨ªå•ã§ã‚½ãƒ¼ã‚·ãƒ£ãƒ«çµŒç”±è¨ªå•ã®ãƒ¢ãƒã‚¤ãƒ«ã‚’利用ã—ã¦ã„る男性ã®25æ³ã€œ34æ³ã®CVãŒå¤šã„よã†ã§ã™ã€‚ R ã®ã‚³ãƒ¼ãƒ‰ã§ã™ã€‚ library(googleAnalyticsR) library(cowplot) library(tidyr) library(dplyr) library(plyr) ga_auth() account_list <- ga_account_list() ga_id <- account_list[3,'viewId'] CVfilter <- filter_clause_ga4(list(met_filter("goal3Completions", "GREATER_T
Rã§ãŠéŠã³ï¼šä½•ã‹ã«ä½¿ãˆã‚‹ã‹ã‚‚？å„都é“府県を四角ã§è¡¨ç¤ºï¼
å„都é“府県を四角ã§è¡¨ç¤ºã™ã‚‹æ—¥æœ¬åœ°å›³ãƒ‡ãƒ¼ã‚¿ã®ç´¹ä»‹ã§ã™ã€‚ggplot2パッケージãªã©ã§åˆ©ç”¨ãŒå¯èƒ½ã§ã™ã€‚ windows 11ã®R version 4.1.2ã§ç¢ºèªã—ã¦ã„ã¾ã™ã€‚ 日本地図データã®ä½œæˆä¸‹è¨˜ã‚³ãƒžãƒ³ãƒ‰ã‚’実行ã—ã¦ãã ã•ã„。作æ¥ãƒ•ã‚©ãƒ«ãƒ€ã«JpanMiniMap.csvã‚’ä¿å˜ã—ã¾ã™ã€‚ #tidyverseパッケージãŒãªã‘ã‚Œã°ã‚¤ãƒ³ã‚¹ãƒˆãƒ¼ãƒ« if(!require("tidyverse", quietly = TRUE)){ install.packages("tidyverse");require("tidyverse") } JpanMiniMap <- tibble( #都é“府県å Pref = c("北海é“", "é’森県", "岩手県", "宮城県", "ç¦å³¶çœŒ", "茨城県", "åƒè‘‰çœŒ", "秋田県", "山形県", "新潟県", "æ ƒæœ¨çœŒ", "埼玉県", "æ±äº¬éƒ½", "群馬県"
Using jet colormap in R and ggplot2
The jet colormap is widely used (at least in my research community). Here is an example to use jet colormap in ggplot2, but it is not limited to ggplot. All R function can use jet.colors() as topo.colors() or rainbow(). # default plot from Hadley's site. ggplot(melt(volcano), aes(x=X1, y=X2, fill=value)) + geom_tile() # define jet colormap jet.colors <- colorRampPalette(c("#00007F", "blue", "#007F
R graphics with ggplot2 workshop notes
Laptop users: You should have R installed; if not: Open a web browser and go to http://cran.r-project.org and download and install it Also helpful to install RStudio (download from http://rstudio.com) In R, type install.packages("tidyverse") to install a suite of usefull packages including ggplot2 Everyone: Download workshop materials: Download materials from http://tutorials.iq.harvard.edu/R/Rgra
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Make a timeline graph with dates in ggplot2
Adding labels to ends of ggplot lines of differing lengths
Mosaic plots with ggplot2
tick - r ggplot2 y axis
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