統計数理研究所TOP 統計数理研究所は高度な知識とスキルを持ったデータサイエンティストを育成するプログラムを積極的に推進しています。 統計科学の最先端理論・手法から基礎的なものまでを学べる多様な一般講座の他、系統的な講座編成により現代的な統計科学の姿を示すリーディングDAT講座を実施します。また、医療健康データ科学研究センターの講座も開講します。 リーディングDAT講座と一般講座は受付が始まったら統計数理研究所公開講座のX（旧Twitter)でお知らせいたします。 過去の公開講座は こちら からご覧いただけます。 リーディングDAT講座 ※2024年度の詳細は決定次第公開いたします。2023年度の情報はこちら 2017年度までの「統計学概論」は発展的にこの講座に吸収されました。 一般講座 日程等変更となる可能性があります。お申込前に必ず確認して下さい。 医療健康データ科学に関わる人材育成事
2021/9/10 追記: 改めて更新された話を統合して整理して書き直しました. 以降はこちらを参考にしてください: ill-identified.hatenablog.com 2021/1/15 追記: RStudio 1.4 がリリースされたのでなるべくアップデートしましょう 2020/12/06 追記: Japan.R で今回の話の要約+新情報を『Mac でも Windows でも, PNG でも PDF でもRのグラフに好きなフォントで日本語を表示したい (2020年最終版)/Display-CJK-Font-in-Any-Gpraphic-Device-and-Platform-2020 - Speaker Deck』として発表した. ハイライトは「近々出るRStudio 1.4 があれば fontregisterer はほぼいらなくなる」 2020/10/31 追記: geom
Details These functions can only be used with binary-mode connections. If con is a character string, the functions call file to obtain a binary-mode file connection which is opened for the duration of the function call. If the connection is open it is read/written from its current position. If it is not open, it is opened for the duration of the call in an appropriate mode (binary read or write) a
Description The value of the internal evaluation of a top-level R expression is always assigned to .Last.value (in package:base) before further processing (e.g., printing). Usage .Last.value Details The value of a top-level assignment is put in .Last.value, unlike S. Do not assign to .Last.value in the workspace, because this will always mask the object of the same name in package:base. See Also e
Value read.socket returns the string read as a length-one character vector. write.socket returns the number of bytes written. Author(s) Thomas Lumley See Also close.socket, make.socket Examples finger <- function(user, host = "localhost", port = 79, print = TRUE) { if (!is.character(user)) stop("user name must be a string") user <- paste(user,"\r\n") socket <- make.socket(host, port) on.exit(close
When we decided to rename part of the IPython project to Jupyter in 2014, we had many good reasons. Our goal was to make (Data)Science and Education better, by providing Free and Open-Source tools that can be used by everyone. The name “Jupyter” is a strong reference to Galileo, who detailed his discovery of the Moons of Jupiter in his astronomical notebooks. The name is also a play on the languag
Description When testing code, it is not sufficient to check that results are correct, but also that errors or warnings are signalled in appropriate situations. The functions described here provide a convenient facility for doing so. The three functions check that evaluating the supplied expression produces an error, a warning or one of a specified list of conditions, respectively. If the assertio
Description In R, the startup mechanism is as follows. Unless --no-environ was given on the command line, R searches for site and user files to process for setting environment variables. The name of the site file is the one pointed to by the environment variable R_ENVIRON; if this is unset, ‘R_HOME/etc/Renviron.site’ is used (if it exists, which it does not in a ‘factory-fresh’ installation). The
Incanter is a Clojure-based, R-like statistical computing and graphics environment for the JVM. At the core of Incanter are the Parallel Colt numerics library, a multithreaded version of Colt, and the JFreeChart charting library, as well as several other Java and Clojure libraries. The motivation for creating Incanter is to provide a JVM-based statistical computing and graphics platform with R-lik
Incanter is a Clojure-based, R-like platform for statistical computing and graphics. Incanter can be used as a standalone, interactive data analysis environment or embedded within other analytics systems as a modular suite of libraries. Features Charting & visualization functions Mathematical functions Statistical functions Matrix & linear algebra functions Data manipulation functions Interactive,
Description Density, distribution function, quantile function and random generation for the logistic distribution with parameters location and scale. Usage dlogis(x, location = 0, scale = 1, log = FALSE) plogis(q, location = 0, scale = 1, lower.tail = TRUE, log.p = FALSE) qlogis(p, location = 0, scale = 1, lower.tail = TRUE, log.p = FALSE) rlogis(n, location = 0, scale = 1) Arguments
logical. If TRUE (the default), warn if the length or dimensions of STATS do not match the specified dimensions of x. Set to FALSE for a small speed gain when you know that dimensions match. Details FUN is found by a call to match.fun. As in the default, binary operators can be supplied if quoted or backquoted. FUN should be a function of two arguments: it will be called with arguments x and an ar
Details If x has length 1, is numeric (in the sense of is.numeric) and x >= 1, sampling via sample takes place from 1:x. Note that this convenience feature may lead to undesired behaviour when x is of varying length in calls such as sample(x). See the examples. Otherwise x can be any R object for which length and subsetting by integers make sense: S3 or S4 methods for these operations will be disp
Exploratory Desktop provides a Simple and Easy-to-Use UI experience to access various data sources, clean and transform data, visualize and analyze data to gain deeper insights, communicate your discoveries with Notes, and monitor your business metrics with Dashboards. You can quickly extract data from various built-in data sources such as Redshift, BigQuery, PostgreSQL, MySQL, Oracle, SQL Server,
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()
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
2軸プロットが欲しくなるとき y軸が左右にあるいわゆる2軸プロットはExcelなんかでは簡単に作れるがggplot2では簡単には作れない。 つまりそもそもそんなもん作るなという話だが、欲しくなる場面はある。 例として気温(℃)と相対湿度(%)と飽差(Pa)をプロットする場合を挙げよう。飽差は気温と相対湿度から算出できる数値で、「乾きやすさ」の指標と考えてもらえればいい。 日常的な環境では、3つの変数のうち相対湿度が最も大きく変動するので、これらを1枚に収めると相対湿度の変動だけが目立ってしまう。したがって、相対湿度だけ第2軸に移してなんとかしたい、という動機が生ずる。 使用データ 上述の気温、相対湿度、飽差をプロットする例を想定し、次のように作成した。 ## function ---- svp <- function(t){ # 飽和水蒸気圧計算 Alduchov and Eskridge
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