selva86

set.seed(100)
clicks_28            <- round(runif (28,3000,4000))
sales_28             <- seq(from=100, to=150, length = 28)
visitors_28          <- runif (28, 1000, 1100) 
discount_28          <- rep(c(1,1,1,0.5),7) 
mrp                  <- 5              # maximum retail price
daily_max_revenue    <- sales_28 * mrp # daily maximum revenue
daily_actual_revenue <- daily_max_revenue * discount_28  # revenue with discount
total_revenue        <- sum(daily_actual_revenue)  # total revenue

selva86

# 1. Direct assignment
system.time ({
  item_id_hypo <- numeric()
  for(i in 1:10000000){
    item_id_hypo[i] <- i
  }
  item_id_hypo
})

selva86

m1 <- c(7, 4, 4, 14, 8, 14, 8, 1, 4, 1, 13, 5, 12, 13, 11, 5, 15, 1, 7, 4, 8, 4)
m2 <- c(17, 18, 7, 6, 20, 9, 20, 14, 5, 12, 15, 20, 8, 14, 14, 15, 12, 7, 20, 8, 8, 13, 8)
m1
m2

# Find items that are not common between m1 and m2

selva86

# 1. From the list m below, get the number of page likes on the 10th day of the month

reviews <- c("spongy burgers", "hot and good","crispier than expected", 
             "hard to chew", "too large to chew", "takes time", "filling", 
             "unhealthy but delicious" )
set.seed(100)
pages <- 1:100
page_likes <- round(runif(30,1000,8000),0)
m <- list(reviews,pages,page_likes)

selva86

country    <- c("France", "Germany", "Greece", "Italy", "Portugal", "Spain") # Countries
gdp_growth <- c(1.3, 0.3, 1.9, 0.3, NA, 2)    # GDP growth
mkt_type   <- factor(c("High", "High", "Low", "Medium", "Low", "Medium"))  # Categories
df <- data.frame(country = country, 
                 gdp_grwth = gdp_growth, 
                 market_typ = mkt_type, 
                 stringsAsFactors = F)

selva86

country    <- c("France", "Germany", "Greece", 
                "Italy", "Portugal", "Spain", 'Spain')    # Countries
gdp_growth <- c(1.3, 0.3, 1.9, 0.3, NA, 2, 0) # GDP growth
mkt_type   <- factor(c("High", "High", "Low", "Medium", "Low", "Medium", 'Low'))  # Categories
df <- data.frame(country = country, 
                 gdp_grwth = gdp_growth, 
                 market_typ = mkt_type, 
                 stringsAsFactors = F)

df <- rbind(df, df, df, df)

selva86

country    <- c("France", "Germany", "Greece", "Italy", "Portugal", "Spain")   # Countries
gdp_growth <- c(1.3, 0.3, 1.9, 0.3, NA, 2)   # GDP growth
mkt_type   <- factor(c("High", "High", "Low", "Medium", "Low", "Medium"))  # Categories
df         <- data.frame(country = country, 
                         gdp_growth = gdp_growth, 
                         market_typ = mkt_type, 
                         stringsAsFactors = F)

selva86

# Add error handling to `largest_hypotenuse()` so it ignores incorrect cases and captures largest 
# hypotenuse for eligible cases.


hypotenuse <- function(side1, side2){
  side1 <- as.numeric(side1); side2 <- as.numeric(side2) 
  sqrt(side1^2 + side2^2)  
} 

selva86

# Make a scatterplot with following variables and which looks like this.
# Top half: Points above 150 on Y axis is 'green3' else 'red'.



# Input
set.seed(42)
x <- runif(100, min = 10,  max=20)
y <- runif(100, min = 100, max= 200)

selva86

# Make a scatterplot with following variables and which looks like this.
# Top half: Points above 150 on Y axis is 'green3' else 'red'.



# Input
set.seed(42)
x <- runif(100, min = 10,  max=20)
y <- runif(100, min = 100, max= 200)