F23_6359_HW1_Avanti_Sethi
rtf
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School
University of Texas, Dallas *
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Course
6359
Subject
Industrial Engineering
Date
Jan 9, 2024
Type
rtf
Pages
4
Uploaded by MateToadPerson1001
#
Clear the environment
rm(list = ls())
# your info (NetID_LastName_FirstName); write to Console
name1 <- "MJS230000_Seelam_Monish";
name1
#
Assign dir1 to the folder on your computer where this excel file (HW1) is.
dir1 <- getwd()
dir1
#
set the working directory to dir1
setwd(dir1)
# Copy and paste this command into your R file. Keep it as a comment.
# setwd("C:/Users/kusum/OneDrive/Documents/TA - 6359")
#
load readxl library, if needs to be installed, once the package is installed, make it a
comment by adding #
library(readxl)
# read this excel file
(sheet = Pioneer). Do not include the file path here, only the file
name
table <- read_excel("HW1-6359-F23.xlsx", sheet="Pioneer")
# rename the Units column to Quantity
names(table) [names(table) == "Units"] <- "Quantity"
names(table)
# Create a new vector Sales which is Quantity x Price
table$Sales <- table$Quantity * table$Price
# Create a new vector Commission which is 15% of the total sales
table$Commission <- 0.15 * table$Sales
# add the new vectors to the excel file.
This will create two new columns.
#install.packages('openxlsx')
library(openxlsx)
write.xlsx(table, "HW1-6359-F23.xlsx", sheetName = "Pioneer", append = TRUE)
head(table)
# Create output file name using name1.
All your output will go to this file.
csvfile <- paste(name1,"_HW1.csv",sep="")
csvfile
# send the output to the csv file you just created
sink(csvfile)
# Use the cat function to write your name (First
Last).
This must be Row 1 of your CSV
file.
cat("NAME",
sep = ","
,
"Monish Seelam", "\n")
# Use cat function to write your netid.
This must be Row 2 of csv file.
cat("NETID" ,
sep = ","
, "MJS230000", "\n")
# write the length of the 1st column.
This must be Row 3 of csv file.
len1 <- length(table$`Date Sold`)
cat("LENGTH" , sep = "," , len1, "\n")
# Like above, calculate and print the following values along with the labels (as shown
above for length) to the cvs file
(in the order given below)
# Average Sales
avg_sales <- mean(table$Sales)
cat("AVERAGE SALES" , sep = "," ,avg_sales, "\n")
# Median Sales
med_sales <- median(table$Sales)
cat("MEDIAN SALES", sep = ",", med_sales, "\n")
#Total quantity
total_quantity <- sum(table$Quantity)
cat("TOTAL QUANTITY", sep = ",", total_quantity, "\n" )
#Total commission
total_commission <- sum(table$Commission)
cat("TOTAL COMMISSION", sep = ",", total_commission, "\n")
#Average commission
avg_commission <- mean(table$Commission)
cat("AVERAGE COMMISSION", sep = ",", avg_commission, "\n")
# How many invoices (records) have 30 or more units?
invoice_30_or_more <- sum(table$Quantity >= 30)
cat("NUMBER OF INVOICES (RECORDS) THAT HAVE 30 OR MORE UNITS", sep =
",", invoice_30_or_more, "\n")
# Total sales value of Skirts sold
library(dplyr)
skirts_data <- table[table$Product == "Skirt", ]
total_sales_value_skirts <- sum(skirts_data$Sales)
cat("TOTAL SALES VALUE OF SKIRTS SOLD", sep = ",", total_sales_value_skirts, "\n")
# Total quantity of T-shirts sold in Dallas
tshirts_dallas_data <- table[table$Product == "T-Shirt" & table$City == "Dallas", ]
total_quantity_tshirts_dallas <- sum(tshirts_dallas_data$Quantity)
cat("TOTAL QUANTITY OF T-SHIRTS SOLD IN DALLAS", sep = ",",
total_quantity_tshirts_dallas$TotalQuantity, "\n")
# create a divider line.
This must be Row 12 of csv file. Copy and Paste this Command
cat("--------------------------------", "\n")
cat("--------------------------------", "\n")
# For the Sales
data
(assume the data to be a sample)
# Find the following and write using cat function (as done above; see also the output)
# Mean
mean_sales <- mean(table$Sales)
cat("MEAN SALES", sep = ",", mean_sales, "\n")
# Median
median_sales <- median(table$Sales)
cat("MEDIAN SALES", sep = ",", median_sales, "\n")
# Standard deviation
std_dev_sales <- sd(table$Sales)
cat("STANDARD DEVIATION OF SALES", sep = ",", std_dev_sales, "\n")
# Skewness
(Package moments should be installed)
#install.packages('moments')
library(moments)
skewness_sales <- skewness(table$Sales, na.rm = TRUE)
cat("SKEWNESS OF SALES", sep = ",", skewness_sales, "\n")
# Lower cut-off using 2-sigma approach
sigma <- 2
lower_cutoff <- mean_sales - (sigma * std_dev_sales)
cat("LOWER CUTOFF (2 SIGMA APPROACH) FOR SALES", sep = ",", lower_cutoff,
"\n")
# Upper cut-off using 2-sigma approach
upper_cutoff <- mean_sales + (sigma * std_dev_sales)
cat("UPPER CUTOFF (2 SIGMA APPROACH) FOR SALES", sep = ",", upper_cutoff,
"\n")
# Plot a Histogram of the price
price_data <- as.numeric(sub("\\$", "", table$Price))
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hist(price_data, main = "Price Histogram", xlab = "Price", ylab = "Frequency", col =
"lightblue")
# Plot a BoxPlot of the Sales
sales_data <- as.numeric(sub("\\$", "", table$Sales))
boxplot(sales_data, main = "Sales Boxplot", ylab = "Sales", col = "lightblue")