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https://campus.datacamp.com/courses/intermediate-r/chapter-4-the-apply-family?ex=3

CAPTIULO 4 EXERCICIO 3, USAR ENTAO BUSCA POR 4/3
FÁCIL DEMAIS NÉ



# Comparison of logicals
TRUE == FALSE

# Comparison of numerics
(-6 * 14) != (17 - 101)

# Comparison of character strings
"useR" == "user"

# Compare a logical with a numeric
1 == TRUE

1/3
# Comparison of numerics
(-6 *5 + 2) >= (-10+1)

# Comparison of character strings

"raining" <= "raining dogs"

# Comparison of logicals
TRUE > FALSE

1/4

# 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)

# Popular days

linkedin > 15

# Quiet days

linkedin <= 5

# LinkedIn more popular than Facebook

linkedin > facebook

1/5
# The social data has been created for you
linkedin <- c(16, 9, 13, 5, 2, 17, 14)
facebook <- c(17, 7, 5, 16, 8, 13, 14)
views <- matrix(c(linkedin, facebook), nrow = 2, byrow = TRUE)

# When does views equal 13?
views == 13

# When is views less than or equal to 14?

views <= 14


1/7
# The linkedin and last variable are already defined for you
linkedin <- c(16, 9, 13, 5, 2, 17, 14)
last <- tail(linkedin, 1)

# Is last under 5 or above 10?

last < 5 | last > 10

# Is last between 15 (exclusive) and 20 (inclusive)?

last > 15 & last <=20



1/8

# The social data (linkedin, facebook, views) has been created for you

# linkedin exceeds 10 but facebook below 10
(linkedin > 10) & (facebook < 10)

# When were one or both visited at least 12 times?
(linkedin >= 12) | (facebook >=12)


# When is views between 11 (exclusive) and 14 (inclusive)?

views > 11 & views <= 14

1/10
# li_df is pre-loaded in your workspace

# Select the second column, named day2, from li_df: second

second = li_df[,2]
# Build a logical vector, TRUE if value in second is extreme: extremes
 extremes <- (second > 25) | second<5

# Count the number of TRUEs in extremes
sum(extremes)

# Solve it with a one-liner

1/12
# Variables related to your last day of recordings
medium <- "LinkedIn"
num_views <- 14

# Examine the if statement for medium
if (medium == "LinkedIn") {
  print("Showing LinkedIn information")
}

# Write the if statement for num_views

if(num_views > 15)
{
  print("You're popular!")
}

1/13
# Variables related to your last day of recordings
medium <- "LinkedIn"
num_views <- 14

# Control structure for medium
if (medium == "LinkedIn") {
  print("Showing LinkedIn information")
}
else{
  print("Unknown medium")
}


# Control structure for num_views
if (num_views > 15) {
  print("You're popular!")
}
else{
  print("Try to be more visible!")
}

1/14
# Variables related to your last day of recordings
medium <- "LinkedIn"
num_views <- 14

# Control structure for medium
if (medium == "LinkedIn") {
  print("Showing LinkedIn information")
} else if (medium == "Facebook") {
  print("Showing Facebook information")
} else {
  print("Unknown medium")
}

# Control structure for num_views
if (num_views > 15) {
  print("You're popular!")
} else if (num_views <= 15 & num_views > 10) {
  print("Your number of views is average")
} else {
  print("Try to be more visible!")
}

1/16
# Variables related to your last day of recordings
li <- 15
fb <- 9

# Code the control-flow construct
if (li >= 15 & fb >=15) {
  sms <- 2 * (li + fb)
} else if(li < 10 & fb < 10) {
  sms <- 0.5 * (li + fb)
} else {
  sms <- li + fb
}

# Print the resulting sms to the console
sms

capitulo 3 funções

3/2

# Consult the documentation on the mean() function
?mean

# Inspect the arguments of the mean() function

args(mean)

3/3
# 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
avg_li
avg_fb

3/4
# 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 the mean of the sum
avg_sum <- mean(linkedin + facebook)

# Calculate the trimmed mean of the sum
avg_sum_trimmed <- mean(linkedin + facebook, trim = 0.2)

# Inspect both new variables
avg_sum
avg_sum_trimmed


3/5
# 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, na.rm = TRUE )

3/6
# 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)


3/ 9 functions
# Create a function pow_two()

pow_two <-function(a)
{
  a<- a*a
  return(a)
}


# Use the function
pow_two(12)

# Create a function sum_abs()
sum_abs <-function(a,b)
{
  return(abs(a)+abs(b) )
}


# Use the function
sum_abs(-2,3)

3/10
# Define the function hello()
hello <- function()
{
  print("Hi there!")
  return (TRUE)
}

# Call the function hello()

hello()

3/14
# 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])


3/15
# 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)


3/17
# Load the ggplot2 package
library(ggplot2)

# Retry the qplot() function
qplot(mtcars$wt, mtcars$hp)

# Check out the currently attached packages again

search()

4/2
# The vector pioneers has already been created for you
pioneers <- c("GAUSS:1777", "BAYES:1702", "PASCAL:1623", "PEARSON:1857")

# Split names from birth year
split_math <- strsplit(pioneers, split = ":")

# Convert to lowercase strings: split_low

split_low <- lapply(split_math,tolower)

# Take a look at the structure of split_low

str(split_low)

4/3
# Code from previous exercise:
pioneers <- c("GAUSS:1777", "BAYES:1702", "PASCAL:1623", "PEARSON:1857")
split <- strsplit(pioneers, split = ":")
split_low <- lapply(split, tolower)

# Write function select_first()
select_first <- function(x) {
  x[1]
}


# Apply select_first() over split_low: names
names <- lapply(split_low,select_first)

# Write function select_second()
select_second <- function(x)
{
  x[2]
}



# Apply select_second() over split_low: years
years <-lapply(split_low,select_second)

4/4
# Definition of split_low
pioneers <- c("GAUSS:1777", "BAYES:1702", "PASCAL:1623", "PEARSON:1857")
split <- strsplit(pioneers, split = ":")
split_low <- lapply(split, tolower)


names <- lapply(split_low, function(x){x[1]} )

years <- lapply(split_low, function(x){x[2]})

4/5
# Definition of split_low
pioneers <- c("GAUSS:1777", "BAYES:1702", "PASCAL:1623", "PEARSON:1857")
split <- strsplit(pioneers, split = ":")
split_low <- lapply(split, tolower)

# Generic select function
select_el <- function(x, index) {
  x[index]
}
# Use lapply() twice on split_low: names and years
names <- lapply(split_low,select_el,1)
years <- lapply(split_low,select_el,2)

4/9

# temp is already defined in the workspace

# Finish function definition of extremes_avg
extremes_avg <- function(x) {
  ( min(x) + max(x) ) / 2
}

# Apply extremes_avg() over temp using sapply()
sapply(temp,extremes_avg)

# Apply extremes_avg() over temp using lapply()
lapply(temp,extremes_avg)

4/10
# temp is already available in the workspace

# Create a function that returns min and max of a vector: extremes
extremes <- function(x) {
  c(min = min(x), max = max(x))
}

# Apply extremes() over temp with sapply()
sapply(temp,extremes)


# Apply extremes() over temp with lapply()
lapply(temp,extremes)



4/11
# temp is already prepared for you in the workspace

# Definition of below_zero()
below_zero <- function(x) {
  return(x[x < 0])
}

# Apply below_zero over temp using sapply(): freezing_s
freezing_s <- sapply(temp, below_zero)

# Apply below_zero over temp using lapply(): freezing_l
freezing_l <- lapply(temp,below_zero)

# Are freezing_s and freezing_l identical?

identical(freezing_l,freezing_s)

4/12
# temp is already available in the workspace

# Definition of print_info()
print_info <- function(x) {
  cat("The average temperature is", mean(x), "\n")
}

# Apply print_info() over temp using sapply()

sapply(temp,print_info)

# Apply print_info() over temp using lapply()
lapply(temp,print_info)


4/15
# temp is already available in the workspace

# Definition of basics()
basics <- function(x) {
  c(min = min(x), mean = mean(x), max = max(x))
}

# Apply basics() over temp using vapply()

vapply(temp,basics,numeric(3))

4/16
# temp is already available in the workspace

# Definition of the basics() function
basics <- function(x) {
  c(min = min(x), mean = mean(x), median = median(x), max = max(x))
}

# Fix the error:
vapply(temp, basics, numeric(4))


4/17
# temp is already defined in the workspace

# Convert to vapply() expression
vapply(temp, max,numeric(1))

# Convert to vapply() expression
vapply(temp, function(x, y) { mean(x) > y }, y = 5 , logical(1))

5/2
# The errors vector has already been defined for you
errors <- c(1.9, -2.6, 4.0, -9.5, -3.4, 7.3)

# Sum of absolute rounded values of errors
sum(round(abs(errors)))

5/3
# Don't edit these two lines
vec1 <- c(1.5, 2.5, 8.4, 3.7, 6.3)
vec2 <- rev(vec1)

# Fix the error
mean(c(abs(vec1), abs(vec2)))


5/4
# The linkedin and facebook lists have already been created for you
linkedin <- list(16, 9, 13, 5, 2, 17, 14)
facebook <- list(17, 7, 5, 16, 8, 13, 14)

# Convert linkedin and facebook to a vector: li_vec and fb_vec
li_vec <- unlist(linkedin)
fb_vec <- unlist(facebook)


# Append fb_vec to li_vec: social_vec
social_vec <- append(li_vec,fb_vec)

# Sort social_vec
sort(social_vec,decreasing = TRUE)

5/5
# Fix me
rep(seq(1, 7, by = 2), times = 7)

5/6
# Create first sequence: seq1

seq1 <- seq(from = 1,to = 500,3)
# Create second sequence: seq2
seq2 <- seq(from = 1200,to = 900,-7)

# Calculate total sum of the sequences

sum(c(seq1,seq2))

5/8
# The emails vector has already been defined for you
emails <- c("john.doe@ivyleague.edu", "education@world.gov", "dalai.lama@peace.org",
            "invalid.edu", "quant@bigdatacollege.edu", "cookie.monster@sesame.tv")

# Use grepl() to match for "edu"

grepl(pattern= "edu",x = emails)

# Use grep() to match for "edu", save result to hits

hits <- grep(pattern="edu",x=emails)

# Subset emails using hits
emails[hits]

5/10
# The emails vector has already been defined for you
emails <- c("john.doe@ivyleague.edu", "education@world.gov", "global@peace.org",
            "invalid.edu", "quant@bigdatacollege.edu", "cookie.monster@sesame.tv")

# Use sub() to convert the email domains to datacamp.edu

sub(pattern="@.*\\.edu$","@datacamp.edu",x = emails)


5/14
# Definition of character strings representing dates
str1 <- "May 23, '96"
str2 <- "2012-03-15"
str3 <- "30/January/2006"

# Convert the strings to dates: date1, date2, date3
date1 <- as.Date(str1, format = "%b %d, '%y")
date2 <- as.Date(str2, format = "%Y-%m-%d")
date3 <- as.Date(str3,format = "%d/%B/%Y")

# Convert dates to formatted strings
format(date1, "%A")
format(date2,"%d")
format(date3,"%b %Y")


5/16
# day1, day2, day3, day4 and day5 are already available in the workspace

# Difference between last and first pizza day
day5-day1

# Create vector pizza
pizza <- c(day1, day2, day3, day4, day5)

# Create differences between consecutive pizza days: day_diff
day_diff <- diff(pizza)

# Average period between two consecutive pizza days

mean(day_diff)

5/17
# login and logout are already defined in the workspace
# Calculate the difference between login and logout: time_online
time_online <- logout - login

# Inspect the variable time_online

time_online

# Calculate the total time online

sum(logout-login)

# Calculate the average time online
mean(logout-login)

5/18
# Convert astro to vector of Date objects: astro_dates

astro_dates<- as.Date(astro,format="%d-%b-%Y")

# Convert meteo to vector of Date objects: meteo_dates

meteo_dates<- as.Date(meteo,format="%B %d,%y")

# Calculate the maximum absolute difference between astro_dates and meteo_dates
max(abs(astro_dates-meteo_dates))