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# Selva Prabhakaranselva86

Created Dec 25, 2019
Mini challenge 13
View 3_13.R
 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
Created Jan 14, 2020
Practice exercise for master R course
View 4_5.R
 # 1. Direct assignment system.time ({ item_id_hypo <- numeric() for(i in 1:10000000){ item_id_hypo[i] <- i } item_id_hypo })
Created Jan 15, 2020
View 4_2_SetOperations_Challenge.R
 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
Created Jan 18, 2020
View 5_1_Lists.R
 # 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)
Last active Jan 21, 2020
View 6_challenges.R
 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)
Created Jan 25, 2020
View 6_2_Dataframes2.R
 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)
Created Jan 26, 2020
View 6_3_Dataframes.R
 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)
Created Feb 12, 2020
View error_handling_challenge.R
 # 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) }
Created Feb 13, 2020
View 7_1_Challenge_R_Course.R
 # 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)
Created Feb 13, 2020
View 7_1_Challenge_R_Course.R
 # 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)
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