Answered: Create a tibble…

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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Create a tibble conttaiiscount_customer=1ning driver names of instance where free_wine=1,discount_customer=1 and the order contained more than 4 pizzas.(There can be repeated names). Assign the tibble to Q1
Create a Variable named ratio that is the ratio of bill to pizza , called ratio.What is the mean of that value (call the value mean_ratio) ?Assign this to Q2
For each day of the week , what is the variance in pizzas? Assign this to Q3

Transcribed Image Text:For this assignment, name your R file pizza.R • For all questions you should load tidyverse. You should not need to use any other libraries. o If the tidyverse package is not installed, you'll need to do a one-time installation from the Console Window in RStudio like this: install.packages ("tidyverse") You cannot attempt to install packages in code that you submit to Code Grade. Load tidyverse with: suppressPackageStartupMessages(library(tidyverse)) . Download the pizza.csv file from Brightspace and place it in the same folder/directory as your script file. Then in RStudio, set your Working Directory to your Source File location: • Session Build Debug Profile Tools Help New Session Interrupt R Terminate R... Restart R Set Working Directory Load Workspace... Save Workspace As... Clear Workspace... Quit Session... Ctrl+Shift+F10 Ctrl+Q Load the pizza.csv file like this: pizza <- read_csv('pizza.csv') Addins To Source File Location To Files Pane Location Choose Directory... Ctrl+Shift+H • Continue to use %>% for the pipe. CodeGrade does not support the new pipe. • Round all float/dbl values to two decimal places. o If your rounding does not work the way you expect, convert the tibble to a dataframe by using as.data.frame() • All statistics should be run with variables in the order I state o E.g., "Run a regression predicting mileage from mpg, make, and type" would be: lm (mileage ~ mpg + make + type...) • In each of these you must use at least two dplyr functions. You may use Google to look up how to do certain aspects.

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