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# ----------------------------- | |
# SUDOKU | |
# ----------------------------- | |
# Solve a Sudoku problem in seconds | |
# create a text file under c:\ drive called "hard sudoku problem.txt" with following text (without # symbol) and then run the code below | |
#85---24-- | |
#72------9 | |
#--4------ | |
#---1-7--2 |
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foreach i in A B C D { | |
forval n=1990/2000 { | |
local m = 'n'-1 | |
# create new columns from existing ones on-the-fly | |
generate pop'i''n' = pop'i''m' * (1 + trend'n') | |
} | |
} |
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# 1) Stata command: | |
use growth_and_uptake_assumptions.dta | |
# Corresponding R command: | |
load(file="my_data.RData") | |
attach(my_data) | |
# ------------------------------------------------ | |
# 2) Stata command: |
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Player Club Pos Price Pts | |
A Carroll Newcastle STR 2.0 84 | |
A Cole Chelsea DEF 7.0 92 | |
C Tevez Man City STR 7.0 104 | |
D Berbatov Man Utd STR 6.0 95 | |
F Malouda Chelsea MID 5.5 106 | |
G Bale Tottenham DEF 5.0 122 | |
J Hart Man City GK 4.0 86 | |
P Zabaleta Man City DEF 3.0 71 | |
S Nasri Arsenal MID 4.0 121 |
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# Load lpsolve library | |
# install.packages('lpSolve') | |
library('lpSolve') | |
# Read player data (name, position, price, points) | |
players <- read.csv("http://spreadsheets.google.com/pub?key=0AvZWqry9LOMddDY3OVNLeDVNWTEtTEczbkRtUmx5SFE&hl=en&single=true&gid=0&output=csv", sep=",", header=TRUE) | |
# OBJECTIVE FUNCTION: Points of all players | |
f.obj <- players$Pts |
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# DOT PLOT | |
x <- c(1:100) | |
y <- x*x | |
plot(x, y) | |
# LINE PLOT | |
x <- c(1:100) | |
y <- x*x | |
plot(x, y, type="l") |
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# LOOPS IN R | |
# For | |
i <- 1 | |
for(i in 1:10){ | |
print(paste("value of iterator: ", i)) | |
} | |
# While | |
i <- 1 |
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# DATA FRAME OPERATIONS IN R | |
# Create data frame | |
# A dataset is ~ table (list of vectors) | |
id <- c(1,2,3) | |
name <- c("John", "Kirk", "AJ") | |
age <- c(21,27,18) | |
employees <- data.frame(ID=id, Name=name, Age=age) | |
employees |
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CURRICULUM FOR INTRODUCTION TO R (DRAFT) | |
Day 1: | |
a) Install R | |
b) Use R as a calculator | |
Day 2: | |
a) Getting help | |
b) Use variables in R | |
c) Understand atomic data types in R |
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# LOCAL HELP | |
# R offers great help articles with the install | |
# Just type ? and the function if you know the function name | |
?sqrt # Pay special attention to function definition and take a look at the examples | |
# Type ?? and your search keyword to search a keyword in R help documentation | |
??maximum |
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