Try R, a Statistical Data Analysis Tool

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3. What you want to do Algorithm 1: int main(int argc,char 2: int i=0; 3: char c; 4: while(i==0){ 5: c=getchar(); Program Do it! language environment
4. Agenda 1. Preparation (Installing Studio) 2. Invoking command and find the result 3. Importing data from Excel 4. Summarizing data 5. Visualizing data shape 6. Try a few statistical methods 4
5. 1. Preparation
6. Rͷ४උ ‣ R ✓ Engine of R ✓ Include minimum environment ‣ RStudio ✓ More convenience environment ✓ For your daily use ✓ It requires Engine of R 6 ʴ
7. RͷΠϯετʔϧ 7 https://www.r-project.org/about.html Select mirror site in Japanese CRAN page Download R for (Mac) OS X → R-3.4.3.pkg Download R for Windows → Install R for the first time ɹɹɹɹɹɹɹɹɹɹɹɹɹˠ Download R 3.4.3 for Windo
8. RStudio ͷΠϯετʔϧ 8 https://www.rstudio.com at the bottom RStudio 1.1.414 - Windows Vista/7/8/10 RStudio 1.1.414 - Mac OS X 10.6+ (64-bit)
9. Launch RStudio 9 Check version
10. 2. Invoking commands and Find the results
11. Input here
12. R is a calculator ‣ Try to input mathematical formula > 1+1 > 100/3 > 100/3*3 ‣ You can use parenthesis > (3+5)*7 > 3+5*7 # check the result 12
13. Save R commands to a File ‣ You can bind several files in "Project" ✓ Command file (script file) ✓ Data file ✓ Visualization ‣ File → New Project ɹɹ → New Directory → New Project 13
14. Save R commands to a File ‣ File→New File→R Script 14 Type and ctl-enter Results
15. R is a high level calculator ‣ Power > 2^16 ‣ Functions > sin(pi/2) # > exp(1) # > factorial(10) # > choose(5,2) # 15 sin ⇡ 2 10! 5C2 e1
16. Try graph > plot(c(5,5,4,3,3,4,1,1)) > x=c(5,4,3,3,1,4,1,1) # variable definition > plot(x) # simple! > plot(x,type="b") # What is this data? > plot(x,type="b",ylim=c(6,1)) > yr=2010:2017 > plot(yr,x,type="b",ylim=c(6,1)) 16
17. In R, variable (object) is vector > x # Just type name of variable > x+10 # Check the result > x+c(10,100) > x[1] # The first value of vector > x[c(1,3,5)] # the 1st, 3rd, 5th values 17
18. Close and re-open it ‣ Save script file ‣ Quit RStudio ‣ Check the script file in folder ‣ Re-open it by double-clicking the project file 18
19. 3. Importing data from Excel
20. Import Excel data with CSV ‣ Download "carp-e.xlsx" from Bb9 ‣ Open it with Excel and check it ‣ Save as "CSV (UTF-8)" → carp.csv ‣ Save "CSV" on Windows 20
21. "Data frame" > read.csv("carp.csv") # just print > c=read.csv("carp.csv") # save in "c" > c # print the content ‣ You can bind several vectors with name in "data frame" > c$height # vector with name "height" > c$height[3] # third value of the vector > mean(c$height) # average of "height" 21
22. "Data frame" (cont.) > c$BMI=c$weight/(c$weight/100)^2 #Calcurate Body Mass Index > c[c$BMI>30,] # Players whose BMI is lager than 30 22 BMI = Weight(kg) Height(m)2
23. Advance 1: You can read Excel file directly ‣ install.packages("readxl") ‣ library(readxl) ‣ carp=read_excel("carp.xlsx",1) ‣ dragons=read_excel("carp.xlsx",2) 23
24. 4. Summarizing data
25. At first, try summary() ✓ summary(c) ‣ Numerical data ✓ Min(Minimum value),1st Qu.(the 1st quarter), Median, Mean, 3rd Qu. (the 3rd quarter), Max(Maximum value) ‣ Categorical data ✓ Frequency 25
26. Summary of data group > # summary of players whose BMI is lager than 26 > summary(c[c$BMI>26,]) > # summary of height by position > tapply(c$height,c$position,summary) 26
27. 5. Visualizing data shape
28. On Mac, you need to set font to use Japanese in graphics > # ヒラギノ角ゴシックをW3使うように指定 > par(family = "HiraKakuProN-W3") 28
29. Histgram, scatter plot, box-wisker plot > hist(c$height) # histgram > barplot(table(c$position)) > plot(c$height,c$weight) # scatter plot > plot(c) # scatterplot matrix > plot(c[,c(4,5,6,9)]) > boxplot(c$height) # box-wicker plot > boxplot(c$height[c$position=="pitcher"],　 c$height[c$position!="pitcher"], names=c("pitcher","other"),ylab="height") 29
30. Advance 2: Package for psychology > install.packages("psych") > library(psych) > pairs.panels(c[,c(4,5,6,9)]) 30
31. How to draw? Google it! (1) 31
32. How to draw? Google it! (2) 32
33. 6. Try a few statistical methods
34. Linear regression > plot(c$weight ~ c$height) > result=lm(c$weight ~ c$height) > abline(result) > summary(result) 34
35. Regarding baseball player, is lefty ratio significantly large? Put the usual ratio to 0.1, try the binomial test. > summary(c$throwing) > binom.test(13,13+56,0.1) > cp=c[c$position=="pitcher",] > summary(cp$throwing) > binom.test(11,11+23,0.1) 35
36. The advantage of R ‣ Designed for statistic calculation ‣ Beautiful graphics ‣ Operations are recorded as script (=text), so it will be re-played easily ‣ New statistical methods are going to be implemented on R ‣ It's free! Open source! 36