?USPersonalExpenditure
USPersonalExpenditure
rownames(USPersonalExpenditure)
colnames(USPersonalExpenditure)
dim(USPersonalExpenditure)
USPersonalExpenditure[1, 3]
USPersonalExpenditure["Food and Tobacco", "1950"]
USPersonalExpenditure[1, "1950"]
USPersonalExpenditure[1, c(5, 3, 1)]
USPersonalExpenditure["Food and Tobacco", c("1960", "1950", "1940")]
USPersonalExpenditure[1, ]
USPersonalExpenditure["Food and Tobacco", ]
USPersonalExpenditure["Food and Tobacco", , drop = FALSE]
USPersonalExpenditure[ , c("1940","1950")]
USPersonalExpenditure[1:3, c("1940","1950")]
sum(USPersonalExpenditure[ , "1940"])
USPersonalExpenditure[1]
USPersonalExpenditure[2]
USPersonalExpenditure[7]
length(USPersonalExpenditure)
sum(USPersonalExpenditure)

game1 <- matrix(c("X","","O","","X","O","","",""), ncol = 3)
game2 <- matrix(c("X","","O","","X","O","","",""), ncol = 3, byrow = TRUE)
game1[3, 3] <- "X" # I win!!!!!
new.game <- matrix(data = "", ncol = 3, nrow = 3)

pieces <- c("rook", "knight", "bishop", "queen", "king", "bishop", "knight", "rook")
pawns <- rep("pawn", 8)
board <- rbind(pieces, pawns, matrix("", nrow = 4, ncol = 8), pawns, pieces)
rownames(board) <- 8:1
colnames(board) <- letters[1:8]

USPersonalExpenditure["Personal Care", "1955"] <- "Unknown"
USPersonalExpenditure
rm(USPersonalExpenditure) # revert to built-in dataset

(edu.spend <- unname(USPersonalExpenditure["Private Education", ]))
(edu.yr <- seq(from = 0, to = 20, by = 5))
plot(edu.yr, edu.spend)
my.model <- lm(edu.spend ~ edu.yr)
my.model
summary(my.model)
abline(my.model)
plot(my.model)
str(my.model)

ad.mouse.colony <- list("9.1",  FALSE)
ad.mouse.colony <- list(room = "9.1",  bsl3 = FALSE)
ad.mouse.colony$conditions <- list(bedding = "straw", light_hrs = 12)
ad.mouse.colony$count <- c(male = 10, female = 0)
ad.mouse.colony[["variants"]] <- c("APP695swe", "PS1-dE9")
names(ad.mouse.colony)

ad.mouse.colony[[3]]
ad.mouse.colony[[3]]$bedding
ad.mouse.colony[3]$bedding
ad.mouse.colony$bsl3 <- NULL

x <- c(0, 1, 1, 2, 3, 5, 8, 13, 21)
attr(x, "description") <- "Fibonacci sequence"
attr(x, "description")
attributes(x)
str(attributes(x))

state.db <- data.frame(state.name, state.abb, state.area, state.center,
                       stringsAsFactors = FALSE)
state.db
state.db$state.abb
state.db[[ "state.abb" ]]
state.db[[ 2 ]]
state.db[ , 1:2]
state.db[41:50, 1:2]
state.db[c(50, 1), c("state.abb", "x", "y")]
state.db[order(state.db$state.area)[1:5], ]
state.db[order(state.db$state.area), ][1:5, ]
rownames(state.db) <- state.abb
state.db[c("NY", "NJ", "CT", "RI"), c("x", "y")]
names(state.db) <- c("name", "abb", "area", "long", "lat")

state.db$division <- state.division # Remember, this is a factor
state.db$z.size <- (state.db$area - mean(state.db$area))/sd(state.db$area)
state.db[ , "z.size", drop = FALSE]
state.db[state.db$area < median(state.db$area), "name"]
state.db[state.db$area < median(state.db$area), "name", drop = FALSE]
coastal <- state.db[ state.db$division %in%
  c("New England", "Middle Atlantic", "South Atlantic", "Pacific"), ]

subset(state.db, area < median(area), select = name)
coastal <- subset(state.db, division %in%
  c("New England", "Middle Atlantic", "South Atlantic", "Pacific") )
subset(state.db, select = c(name, abb))
subset(state.db, select = -c(long, lat))

plot(area ~ division, data = state.db)
plot(log(area) ~ division, data = state.db)
plot(lat ~ long, data = state.db)
text(lat ~ long, data = state.db, rownames(state.db))

summary(state.db)
head(state.db)

ablation <- read.csv("ablation.csv", header = TRUE, stringsAsFactors = TRUE)
names(ablation)[names(ablation) == "SCORE"] <- "Score"

write.table(ablation,
            file = "ablation.txt",
            quote = FALSE, col.names = NA,
            sep = "\t")

write.table(ablation,
            file = "ablation.txt",
            quote = FALSE, row.names = FALSE,
            sep = "\t")

mySummary <- function(x) {
  mean <- mean(x)
  sd <- sd(x)
  list(mean = mean, sd = sd)
}
mySummary(rnorm(100))

raiseNumber <- function(x, power = 1) {
  x ^ power
}
raiseNumber(10)
raiseNumber(10, 3)