--3-Functions
#1-Introduction to Functions
​Intro to functions video​
#2-Function documentation
#Consult the documentation on the mean() function:
#?mean or help(mean).
#Inspect the arguments of the mean() function using
#the args() function.
#Consult the documentation on the mean() function
?mean
help(mean)
# Inspect the arguments of the mean() function
args(mean)#3-Use a function
#Calculate the average number of views for both linkedin
#and facebook and assign the result to avg_li and avg_fb,
#respectively.
#Experiment with different types of argument matching!
#Print out both avg_li and avg_fb.
# The linkedin and facebook vectors have already
#been created for you
linkedin <- c(16, 9, 13, 5, 2, 17, 14)
facebook <- c(17, 7, 5, 16, 8, 13, 14)
# Calculate average number of views
avg_li<-mean(linkedin)
avg_fb<-mean(facebook)
# Inspect avg_li and avg_fb
print(avg_li)
print(avg_fb)#4-Use a function (2)
#Calculate the mean of the element-wise sum of linkedin
#and facebook and store the result in a variable avg_sum.
#Calculate the mean once more, but this time set the
#trim argument equal to 0.2
#and assign the result to avg_sum_trimmed.
#Print out both avg_sum and avg_sum_trimmed;
#can you spot the difference?
linkedin <- c(16, 9, 13, 5, 2, 17, 14)
facebook <- c(17, 7, 5, 16, 8, 13, 14)
# Calculate the mean of the sum
avg_sum<-mean(linkedin+facebook)
# Calculate the trimmed mean of the sum
avg_sum_trimmed<-mean(linkedin+facebook,0.2)
# Inspect both new variables
print(avg_sum)
print(avg_sum_trimmed)#5-Use a function (3)
#Calculate the average number of LinkedIn profile views,
#without specifying any optional arguments.
#Simply print the result to the console.
#Calculate the average number of LinkedIn profile views,
#but this time tell R to strip missing values
#from the input vector.
# The linkedin and facebook vectors have
#already been created for you
linkedin <- c(16, 9, 13, 5, NA, 17, 14)
facebook <- c(17, NA, 5, 16, 8, 13, 14)
# Basic average of linkedin
mean(linkedin)
# Advanced average of linkedin
mean(linkedin,trim=0,na.rm=TRUE)#6-Functions inside functions
#Use abs() on linkedin - facebook to get the absolute
#differences between
#the daily Linkedin and Facebook profile views.
#Next, use this function call inside mean() to
#calculate the Mean Absolute Deviation.
#In the mean() call, make sure to specify na.rm to
#treat missing values correctly!
# The linkedin and facebook vectors have already
#been created for you
linkedin <- c(16, 9, 13, 5, NA, 17, 14)
facebook <- c(17, NA, 5, 16, 8, 13, 14)
# Calculate the mean absolute deviation
mean(abs(linkedin-facebook),na.rm=TRUE)#7-Required, or optional?
#Which of the following statements about
#the read.table() function are true?
#1-header, sep and quote are all optional arguments.
#2-row.names and fileEncoding don't have default values.
#3-read.table("myfile.txt", "-", TRUE) will throw an error.
#4-read.table("myfile.txt", sep = "-", header = TRUE)
#will throw an error.
#1 and 3: The right answer#8-Writing Functions
​Writing function video​
#9-Write your own function
#Create a function pow_two(): it takes one
#argument and returns
#that number squared (that number times itself).
#Call this newly defined function with 12 as input.
#Next, create a function sum_abs(), that takes
#two arguments
#and returns the sum of the absolute values of
#both arguments.
#Finally, call the function sum_abs() with
#arguments -2 and 3 afterwards.
# Create a function pow_two()
pow_two<-function(x){
x*x
}
# Use the function
pow_two(12)
# Create a function sum_abs()
sum_abs<-function(y,z){
abs(y)+abs(z)
}
# Use the function
sum_abs(-2,3)#10-Write your own function (2)
# Define the function hello()
hello<-function(){
print("Hi there!")
return(TRUE)
}
# Call the function hello()
hello()#11-Write your own function (3)
#Add an optional argument, named print_info,
#that is TRUE by default.
#Wrap an if construct around the print() function:
#this function should
#only be executed if print_info is TRUE.
#Feel free to experiment with the pow_two()
#function you've just coded.
# Finish the pow_two() function
pow_two <- function(x, print_info = TRUE) {
y <- x ^ 2
if (print_info) {
print(paste(x, "to the power two equals", y))
}
return(y)
}
# Some calls of the pow_two() function
pow_two(5)
pow_two(5, FALSE)
pow_two(5, TRUE)#12-Function scoping
#Which statement is correct about the following
#chunk of code?
#The function two_dice() is already available in the
#workspace.
two_dice <- function() {
possibilities <- 1:6
dice1 <- sample(possibilities, size = 1)
dice2 <- sample(possibilities, size = 1)
dice1 + dice2
}
two_dice()
#Executing two_dice() causes an error.
#Executing res <- two_dice() makes the contents of
#dice1 and dice2
#available outside the function.
#Whatever the way of calling the two_dice() function,
#R won't have access to dice1 and dice2 outside the
#function.->RIGHT#13-R passes arguments by value
#Can you tell which one of the following
#statements is false about
#the following piece of code?
increment <- function(x, inc = 1) {
x <- x + inc
x
}
count <- 5
a <- increment(count, 2)
b <- increment(count)
count <- increment(count, 2)
#a and b equal 7 and 6 respectively
#after executing this code block.
#After the first call of increment(), where a is defined,
#a equals 7 and count equals 5.
#In the end, count will equal 10.->FALSE
#In the last expression, the value of count
#was actually changed because of the explicit assignment.#14-R you functional?
#Finish the function definition for interpret(),
#that interprets the number of profile views on a single day:
#The function takes one argument, num_views.
#If num_views is greater than 15, the function prints out
#"You're popular!"
#to the console and returns num_views.
#Else, the function prints out "Try to be more visible!"
#and returns 0.
#Finally, call the interpret() function twice:
#on the first value of the linkedin
#vector and on the second element of the facebook vector.
linkedin <- c(16, 9, 13, 5, 2, 17, 14)
facebook <- c(17, 7, 5, 16, 8, 13, 14)
# The linkedin and facebook vectors
#have already been created for you
# Define the interpret function
interpret <- function(num_views) {
if (num_views > 15) {
print( "You're popular!")
return(num_views)
} else {
print("Try to be more visible!")
return(0)
}
}
# Call the interpret function twice
interpret(linkedin[1])
interpret(facebook[2])#15-R you functional? (2)
#Finish the template for the interpret_all() function:
#Make return_sum an optional argument, that is
#TRUE by default.
#Inside the for loop, iterate over all views:
#on every iteration, add the result of interpret(v)
#to count.
#Remember that interpret(v) returns v for popular days,
#and 0 otherwise.
#At the same time, interpret(v) will also do some printouts.
#Finish the if construct:
#If return_sum is TRUE, return count.
#Else, return NULL.
#Call this newly defined function on both
#linkedin and facebook.
# The linkedin and facebook vectors have
#already been created for you
linkedin <- c(16, 9, 13, 5, 2, 17, 14)
facebook <- c(17, 7, 5, 16, 8, 13, 14)
# The interpret() can be used inside interpret_all()
interpret <- function(num_views) {
if (num_views > 15) {
print("You're popular!")
return(num_views)
} else {
print("Try to be more visible!")
return(0)
}
}
# Define the interpret_all() function
# views: vector with data to interpret
# return_sum: return total number of views on popular days?
interpret_all <- function(views, return_sum=TRUE) {
count <- 0
for (v in views) {
count=count+interpret(v)
}
if (return_sum) {
return(count)
} else {
return(NULL)
}
}
# Call the interpret_all() function on both
#linkedin and facebook
interpret_all(linkedin)
interpret_all(facebook)#16-R Packages
​R packages video​
#17-Load an R Package
#To fix the error you saw in the console,
#load the ggplot2 package.
#Now, retry calling the qplot() function with
#the same arguments.
#Finally, check out the currently attached packages again.
datasets::mtcars
installed.packages("ggplot2")
# Load the ggplot2 package
library(ggplot2)
# Retry the qplot() function
qplot(mtcars$wt,mtcars$hp)
# Check out the currently attached packages again
search() #18-Different ways to load a package
install.packages("data.table") # install it
library(data.table) # load it
# Chunk 1
library(data.table)
require(rjson)
# Chunk 2
library("data.table")
require(rjson)
# Chunk 3
library(data.table)
require(rjson, character.only = TRUE)
# Chunk 4
library(c("data.table", "rjson"))
#The right answer: Chunk 1 and Chunk 3Last updated