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![- A/B
mean(x > 10)·
[1] 0.046
20/75](https://image.slidesharecdn.com/r-151022173623-lva1-app6892/85/R-20-320.jpg)
![-
suppressPackageStartupMessages(library(PerformanceAnalytics))
chart.Correlation(iris[-5], bg=iris$Species, pch=21)
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data(edhec)
chart.ACFplus(edhec[,1,drop=FALSE])
28/75](https://image.slidesharecdn.com/r-151022173623-lva1-app6892/85/R-28-320.jpg)
![--
[1] "TWII"
TWII.OPEN TWII.HIGH TWII.LOW TWII.CLOSE TWII.VOLUME TWII.ADJUSTED
7871.41 7937.26 7843.60 7920.80 5710600 7920.80
7954.96 7999.42 7917.30 7917.30 5951400 7917.30
7929.89 7955.90 7901.24 7934.51 5717400 7934.51
7940.20 7942.23 7821.71 7835.57 5181400 7835.57
7778.57 7797.57 7736.11 7736.71 4292400 7736.71
7778.38 7827.93 7778.38 7790.01 4516000 7790.01
library(quantmod)
getSymbols("^TWII")
head(TWII)
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YEARID NAME RANK W L R RA
1871 Boston Red Stockings 3 20 10 401 303
1871 Chicago White Stockings 2 19 9 302 241
1871 Cleveland Forest Citys 8 10 19 249 341
1871 Fort Wayne Kekiongas 7 7 12 137 243
1871 New York Mutuals 5 16 17 302 313
1871 Philadelphia Athletics 1 21 7 376 266
library(Lahman)
head(Teams[,c("yearID", "name", "Rank", "W", "L", "R", "RA")])
36/75](https://image.slidesharecdn.com/r-151022173623-lva1-app6892/85/R-36-320.jpg)
Uploaded byWush Wu
R 語言上手篇
This document appears to be notes from an R tutorial or course. It includes links to resources about statistics careers, mapping airline data with R, and R's growth. It also includes code examples for importing and exploring data, performing statistical tests, visualizing distributions, and more. Later sections discuss R packages, Shiny, APIs, the R community, installing R, using RStudio, and taking an online R course with swirl.
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R 語言上手篇
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- 2. R R R R R Rstudio R R swirl R · · · · · · · · 00:R 02: - - 2/75
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- 13. - shaprio.test(x) · Shapiro-Wilk normality test data:x W = 0.93385, p-value = 8.287e-05 13/75
- 14. - · plot(density(x1), xlim =range(c(x1, x2)), main = "Sample PDF") lines(density(x2), col = 2) legend("topright", c("x1", "x2"), lty = 1, col = 1:2) 14/75
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- 16. - ks.test(x1, x2)· Two-sample Kolmogorov-Smirnovtest data: x1 and x2 D = 0.2, p-value = 0.2719 alternative hypothesis: two-sided 16/75
- 17. - A/B A 1000010 B 5000 3 / · · · · 17/75
- 18. - A/B R · A B 1000010 - 3/5000 - · # A 3 / 5000 p <- 3/5000 # 1000 10000 A plot(density(x <- rbinom(1000, 10000, p))) 18/75
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- 20. - A/B mean(x >10)· [1] 0.046 20/75
- 21. - A/B · library(binom) binom.confint(c(10, 3),c(10000, 5000), methods = "exact") method x n mean lower upper 1 exact 10 10000 1e-03 0.0004796397 0.001838264 2 exact 3 5000 6e-04 0.0001237515 0.001752444 21/75
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- 26. - round(Ca <- cor(attitude),2) symnum(Ca) # simple graphic heatmap(Ca, symm = TRUE, margins = c(6,6)) 26/75
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- 32. ChihChengLiang: R Crawler2015· 32/75
- 33. R· Google Sheets RAPI Google Map Excel Minitab, S, SAS, SPSS, Stata, Systat and Weka... DBI - - - - - 33/75
- 34. -- [1] "TWII" TWII.OPEN TWII.HIGHTWII.LOW TWII.CLOSE TWII.VOLUME TWII.ADJUSTED 7871.41 7937.26 7843.60 7920.80 5710600 7920.80 7954.96 7999.42 7917.30 7917.30 5951400 7917.30 7929.89 7955.90 7901.24 7934.51 5717400 7934.51 7940.20 7942.23 7821.71 7835.57 5181400 7835.57 7778.57 7797.57 7736.11 7736.71 4292400 7736.71 7778.38 7827.93 7778.38 7790.01 4516000 7790.01 library(quantmod) getSymbols("^TWII") head(TWII) 34/75
- 35. - chartSeries(TWII, subset ="last 4 months", TA = c(addVo(), addBBands())) 35/75
- 36. - YEARID NAME RANKW L R RA 1871 Boston Red Stockings 3 20 10 401 303 1871 Chicago White Stockings 2 19 9 302 241 1871 Cleveland Forest Citys 8 10 19 249 341 1871 Fort Wayne Kekiongas 7 7 12 137 243 1871 New York Mutuals 5 16 17 302 313 1871 Philadelphia Athletics 1 21 7 376 266 library(Lahman) head(Teams[,c("yearID", "name", "Rank", "W", "L", "R", "RA")]) 36/75
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- 38. - PLAYERID YEARID WL ERA wangch01 2005 8 5 4.02 wangch01 2006 19 6 3.63 wangch01 2007 19 7 3.70 wangch01 2008 8 2 4.07 wangch01 2009 1 6 9.64 wangch01 2011 4 3 4.04 38/75
- 39. - suppressPackageStartupMessages({ library(jiebaR) # library(tm) # library(slam)# library(wordcloud) # library(topicmodels) # library(igraph) # }) 39/75
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- 46. R Community R Rstudio R RCommunity R swirl · Hadley Wickham R- · 46/75
- 47. Hadley R PACKAGE DOWNLOADS 1Rcpp 208987 2 ggplot2 182208 3 stringr 168142 4 stringi 167407 5 plyr 166594 6 digest 158068 7 magrittr 156989 8 scales 152784 9 reshape2 147723 10 RColorBrewer 140949 R Hadley· 47/75
- 48. R · Taiwan R UserGroup ptt R_Language R - - - 48/75
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- 51. R Windows: CRAN R3.2· 3.0.23.2 - - 51/75
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- 53. R Ubuntu: Ubuntu 14.04CRAN· http://cran.csie.ntu.edu.tw/bin/linux/ubuntu/README.htm- 53/75
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- 56. RStudio > "hello world" Enter 1+ 1 Enter 1 + Enter + Ctrl + C ESC > me Enter me tab · · · · · · · 56/75
- 57. RStudio R Script me Ctrl+ Enter me me tab · · · · 57/75
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- 63. swirl swirl windows· Taiwan RUser Group- # install.packages(c('swirl', 'curl'), repos = 'http://taiwanrusergroup.github.io/R') 63/75
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- 69. · R Rstudio( ) Taiwan R UserGroup DataScienceAndR - - - - 69/75
- 70. 1. RBasic-01-Introduction 2. RBasic-02-Data-Structure-Vectors 3.RBasic-03-Data-Structure-Object 4. RBasic-04-Factors 5. RBasic-05-Arrays-Matrices 6. RBasic-06-List-DataFrame 7. RBasic-07-Loading-Dataset · 70/75
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- 75. R vs Python ChoosingR or Python for data analysis? An infographic Pros and Cons of R vs Python Sci-kit learn Which is better for data analysis: R or Python? How to Choose Between Learning Python or R First · · · · 75/75