19-19.2


3.Practice turning the following code snippets into functions. Think about what each function does. What would you call it? How many arguments does it need? Can you rewrite it to be more expressive or less duplicative?

#mean(is.na(x)) calculates the proportion of NAs in a vector
#I name it na_rate
na_rate <- function(x) {mean(is.na(x))}

na_rate(c(1,2,3,NA,4,5,NA))
## [1] 0.2857143
#x / sum(x, na.rm = TRUE) standardizes a vector so that it sums to one
#I’ll write a function named sum_to_one(), which is a function of a single argument, x, the vector to standardize, and an optional argument na.rm. The optional argument, na.rm, makes the function more expressive, since it can handle NA values in two ways (returning NA or dropping them)
sum_to_one <- function(x, na.rm=FALSE) {
  x/sum(x,na.rm=na.rm)
}

#no missing values
sum_to_one(1:5)
## [1] 0.06666667 0.13333333 0.20000000 0.26666667 0.33333333
#if any missing, return all missing
sum_to_one(c(1:5, NA))
## [1] NA NA NA NA NA NA
#drop missing values
sum_to_one(c(1:5, NA), na.rm=TRUE)
## [1] 0.06666667 0.13333333 0.20000000 0.26666667 0.33333333         NA
#sd(x, na.rm = TRUE) / mean(x, na.rm = TRUE)  calculates the coefficient of variation
coef_variation <- function(x, na.rm = FALSE) {
  sd(x, na.rm = na.rm) / mean(x, na.rm = na.rm)
}

coef_variation(1:5)
## [1] 0.5270463
coef_variation(c(1:5, NA))
## [1] NA
coef_variation(c(1:5, NA), na.rm=TRUE)
## [1] 0.5270463


5.Write both_na(), a function that takes two vectors of the same length and returns the number of positions that have an NA in both vectors. 

both_na <- function(x, y) {
  sum(is.na(x) & is.na(y))
}

both_na(
  c(NA, NA, 1, 2),
  c(NA, 1, NA, 2)
)
## [1] 1
both_na(
  c(NA, NA, 1, 2, NA, NA, 1),
  c(NA, 1, NA, 2, NA, NA, 1)
)
## [1] 3


19.3


1.Read the source code for each of the following three functions, puzzle out what they do, and then brainstorm better names.

f1 <- function(string, prefix) {
  substr(string, 1, nchar(prefix)) == prefix
}

f2 <- function(x) {
  if (length(x) <= 1) return(NULL)
  x[-length(x)]
}

f3 <- function(x, y) {
  rep(y, length.out = length(x))
}

f1(c("abc", "abcde", "ad"), "ab")
## [1]  TRUE  TRUE FALSE
f2(1:3)
## [1] 1 2
f2(2:5)
## [1] 2 3 4
f3(1:3, 4)
## [1] 4 4 4
  • f1 checks for each element in the vector nchar if it starts with string prefix. A better name would be has_prefix()
  • f2 drops the last element of the vector x. A better name would be drop_last()
  • f3 repeats y once for each element of x. A better name would be repeat().


19.3


1.What’s the difference between if and ifelse()? > Carefully read the help and construct three examples that illustrate the key differences.

  • f3 repeats y once for each element of x. A better name would be repeat().


2.Write a greeting function that says “good morning”, “good afternoon”, or “good evening”, depending on the time of day. (Hint: use a time argument that defaults to lubridate::now(). That will make it easier to test your function.).

greet <- function(time = lubridate::now()) {
  hr <- lubridate::hour(time)
  # I don't know what to do about times after midnight,
  # are they evening or morning?
  if (hr < 12) {
    print("good morning")
  } else if (hr < 17) {
    print("good afternoon")
  } else {
    print("good evening")
  }
}

greet()
## [1] "good evening"
greet(ymd_h("2017-01-08:05"))
## [1] "good morning"
greet(ymd_h("2017-01-08:13"))
## [1] "good afternoon"
greet(ymd_h("2017-01-08:20"))
## [1] "good evening"


4.How could you use cut() to simplify this set of nested if-else statements?

temp <- seq(-10, 50, by = 5)
cut(temp, c(-Inf, 0, 10, 20, 30, Inf),
  right = TRUE,
  labels = c("freezing", "cold", "cool", "warm", "hot")
)
##  [1] freezing freezing freezing cold     cold     cool     cool     warm    
##  [9] warm     hot      hot      hot      hot     
## Levels: freezing cold cool warm hot
#To have intervals open on the left (using <)
temp <- seq(-10, 50, by = 5)
cut(temp, c(-Inf, 0, 10, 20, 30, Inf),
  right = FALSE,
  labels = c("freezing", "cold", "cool", "warm", "hot")
)
##  [1] freezing freezing cold     cold     cool     cool     warm     warm    
##  [9] hot      hot      hot      hot      hot     
## Levels: freezing cold cool warm hot