?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"

new_game <- matrix(data = "", ncol = 3, nrow = 3)

pieces <- c("rook", "knight", "bishop", "queen", "king", "bishop", "knight", "rook")
pawns <- rep("pawn", 8)
board <- rbind(rev(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)


(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")

ad_mouse_colony[[1]]
ad_mouse_colony[["room"]]
ad_mouse_colony$room

names(ad_mouse_colony)
ad_mouse_colony[c(1,4)]
ad_mouse_colony $bsl3 <- NULL

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

state_db <- data.frame(state.name, state.abb, state.area, state.center,
                       stringsAsFactors = FALSE)
state_db
summary(state_db)
head(state_db)

state_db$state.abb
state_db[[ "state.abb" ]]
state_db[[ 2 ]]
state_db[, 2]
state_db[ , 1:2]
state_db[41:50, 1:2]
state_db[c(50, 1), c("state.abb", "x", "y")]
state_db[order(state_db$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 
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))

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

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

num_iterations <- 100
my_means <- numeric(length = num_iterations)
for (i in 1:num_iterations) {
  x <- rnorm(10000)
  my_means[i] <- mean(x)
}
hist(my_means)