manager <- 1:5
date <- c("10/24/08","10/28/08","10/1/08","10/12/08","5/1/09")
country <- c("US","US","UK","UK","UK")
gender <- c("M","F","F","M","F")
age <- c(32,45,25,39,99)
q1 <- c(5,3,3,3,2)
q2 <- c(4,5,5,3,2)
q3 <- c(5,2,5,4,1)
q4 <- c(5,5,5,NA,2)
q5 <- c(5,5,2,NA,1)
leadership <- data.frame(manager,date,country,gender,age,q1,q2,q3,q4,q5,
                         stringsAsFactors = F)
leadership$age[leadership$age == 99] <- NA
leadership <- within(leadership,{
  agecat = NA
  agecat[age > 75]   <- "Elder"
  agecat[age >= 55 & age <= 75] <- "Middle age"
  agecat[age < 55]   <- "Young"
})
leadership

new1 <- subset(leadership, country == "UK" & gender == "F",
               select = c(date, age, q4))
new1

new2 <- subset(leadership, country == "US" | gender == "M",
               select = 1:3)
new2

new3 <- subset(leadership, country == "US" | gender == "M",
               select = date:q2)
new3

a <- sample(1:20, 20)
b <- sample(letters[1:20],20)
c <- sample(LETTERS[1:20],20)
d <- data.frame(a,b,c)
d <- d[order(d$a),]
d
e <- subset(d, a > 5 & a < 9, select = c(a,b))
e