ECON 413
Factors, lists, functionals
Erol Taymaz
Department of Economics
Middle East Technical University
Factors
- Factors are categorical variables
aa <- c("small", "large", "medium")
aa
## [1] "small" "large" "medium"
bb <- sample(3, 10, replace = TRUE)
bb
## [1] 3 1 2 2 1 1 3 1 1 1
## [1] "medium" "small" "large" "large" "small" "small" "medium" "small"
## [9] "small" "small"
## [1] "character"
## [1] medium small large large small small medium small small small
## Levels: large medium small
## [1] 2 3 1 1 3 3 2 3 3 3
## [1] NA NA NA NA NA NA NA NA NA NA
aaof <- ordered(aaf, levels=c("small", "medium", "large"))
aaof
## [1] medium small large large small small medium small small small
## Levels: small < medium < large
## [1] 2 3 1 1 3 3 2 3 3 3
## [1] 2 1 3 3 1 1 2 1 1 1
# Converting characters to numeric values
aa <- factor(c("3", "11", "2", "23", "313", "2"))
aa
## [1] 3 11 2 23 313 2
## Levels: 11 2 23 3 313
aan <- as.numeric(aa)
aan
## [1] 4 1 2 3 5 2
aan <- as.numeric(as.character(aa))
aan
## [1] 3 11 2 23 313 2
Lists
- A list is a generic vector containing other objects (including other lists)
library(ggplot2)
a <- data.frame(x = rnorm(100))
b <- letters[1:5]
d <- c(TRUE, FALSE, TRUE, FALSE, FALSE)
g <- ggplot(a, aes(x=x)) + geom_density()
x <- list(a = a, b = b, d = d)
class(x)
## [1] "list"
## List of 3
## $ a:'data.frame': 100 obs. of 1 variable:
## ..$ x: num [1:100] -0.59 -0.904 1.303 -0.568 -1.883 ...
## $ b: chr [1:5] "a" "b" "c" "d" ...
## $ d: logi [1:5] TRUE FALSE TRUE FALSE FALSE
## [1] "list"
## [1] "data.frame"
## [1] "data.frame"
# x[[1]][1:5]
x[[1]][1:5,]
## [1] -0.5901170 -0.9038400 1.3033041 -0.5679117 -1.8828967
## [1] -0.5901170 -0.9038400 1.3033041 -0.5679117 -1.8828967
y <- list(a, g)
z <- list(y, x)
class(y)
## [1] "list"
## [1] 2
## [1] "list"
## [1] "data.frame"
## [1] "list"
## [1] "gg" "ggplot"
# Empty vectors
vlogical <- vector(mode = "logical", length = 3)
vlist <- vector(mode = "list", length = 7)
length(vlist)
## [1] 7
Functionals
A higher-order function is a function that takes a function as an input or returns a function as output.
descriptive <- function(x, f) {
f(x, na.rm=T)
}
set.seed(123)
aa <- rnorm(100)
descriptive(aa, mean)
## [1] 0.09040591
## [1] 2.187333
lapply function
- Apply a function on all elements of a list and return a list
myFun <- function(x, f, ...) {
out <- vector("list", length(x))
for (i in seq_along(x)) {
out[[i]] <- f(x[[i]], ...)
}
out
}
str(mtcars)
## 'data.frame': 32 obs. of 11 variables:
## $ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
## $ cyl : num 6 6 4 6 8 6 8 4 4 6 ...
## $ disp: num 160 160 108 258 360 ...
## $ hp : num 110 110 93 110 175 105 245 62 95 123 ...
## $ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
## $ wt : num 2.62 2.88 2.32 3.21 3.44 ...
## $ qsec: num 16.5 17 18.6 19.4 17 ...
## $ vs : num 0 0 1 1 0 1 0 1 1 1 ...
## $ am : num 1 1 1 0 0 0 0 0 0 0 ...
## $ gear: num 4 4 4 3 3 3 3 4 4 4 ...
## $ carb: num 4 4 1 1 2 1 4 2 2 4 ...
## [[1]]
## [1] 20.09062
##
## [[2]]
## [1] 6.1875
##
## [[3]]
## [1] 230.7219
##
## [[4]]
## [1] 146.6875
##
## [[5]]
## [1] 3.596563
##
## [[6]]
## [1] 3.21725
##
## [[7]]
## [1] 17.84875
##
## [[8]]
## [1] 0.4375
##
## [[9]]
## [1] 0.40625
##
## [[10]]
## [1] 3.6875
##
## [[11]]
## [1] 2.8125
unlist(myFun(mtcars, mean))
## [1] 20.090625 6.187500 230.721875 146.687500 3.596563 3.217250
## [7] 17.848750 0.437500 0.406250 3.687500 2.812500
unlist(myFun(mtcars, class))
## [1] "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## [8] "numeric" "numeric" "numeric" "numeric"
- lapply function does that!
unlist(lapply(mtcars, mean))
## mpg cyl disp hp drat wt qsec
## 20.090625 6.187500 230.721875 146.687500 3.596563 3.217250 17.848750
## vs am gear carb
## 0.437500 0.406250 3.687500 2.812500
unlist(lapply(mtcars, class))
## mpg cyl disp hp drat wt qsec vs
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## am gear carb
## "numeric" "numeric" "numeric"
- We can define the function within lapply
unlist(lapply(mtcars, function(x) sum((x -mean(x))^2)/length(x)))
## mpg cyl disp hp drat wt
## 3.518897e+01 3.089844e+00 1.488077e+04 4.553965e+03 2.769476e-01 9.274609e-01
## qsec vs am gear carb
## 3.093380e+00 2.460938e-01 2.412109e-01 5.273438e-01 2.527344e+00
unlist(lapply(c(1:11), function(x) sum(mtcars[,x])))
## [1] 642.900 198.000 7383.100 4694.000 115.090 102.952 571.160 14.000
## [9] 13.000 118.000 90.000
sapply function
- Same as lapply but returns a vector
## mpg cyl disp hp drat wt qsec
## 20.090625 6.187500 230.721875 146.687500 3.596563 3.217250 17.848750
## vs am gear carb
## 0.437500 0.406250 3.687500 2.812500
## mpg cyl disp hp drat wt qsec vs
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## am gear carb
## "numeric" "numeric" "numeric"
Map function
- Map function allows for multiple arguments
- What if we want to calculate weighted average
xs <- replicate(5, runif(10), simplify = FALSE)
ws <- replicate(5, c(1:10), simplify = FALSE)
# Unweighted means
unlist(lapply(xs, mean))
## [1] 0.4595448 0.4874797 0.5758510 0.4291318 0.6112719
# Weighted means
lapply(c(1:length(xs)), function(i) weighted.mean(xs[[i]], ws[[i]]))
## [[1]]
## [1] 0.3897107
##
## [[2]]
## [1] 0.5652652
##
## [[3]]
## [1] 0.6007108
##
## [[4]]
## [1] 0.4449518
##
## [[5]]
## [1] 0.6790272
# Use __Map__ function
Map(weighted.mean, xs, ws)
## [[1]]
## [1] 0.3897107
##
## [[2]]
## [1] 0.5652652
##
## [[3]]
## [1] 0.6007108
##
## [[4]]
## [1] 0.4449518
##
## [[5]]
## [1] 0.6790272