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The uploaded file is the one that I received from the expert.Based on the code Task B and C, I want to make the chart/graph.It would be better to get it such as the Bar graph or Scatter plot with some...

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The uploaded file is the one that I received from the expert.Based on the code Task B and C, I want to make the chart/graph.It would be better to get it such as the Bar graph or Scatter plot with some explanation.
group7hw1-5soxcvi4-wvys4vt1.r
Answered Same Day Aug 15, 2021

Solution

Naveen answered on Aug 15 2021
139 Votes
# Required packages are loading
li
ary(janitor)
li
ary(lu
idate)
li
ary(plyr)
li
ary(ggplot2)
li
ary(dplyr)
# Reading data file
data <- read.table("assignment1.txt", sep="\t", header = TRUE)
# Print top six records
head(data)
# Checking the dimension
dim(data)
## Task-A
# Removing the missing values
data_surveyed <- data[!is.na(data$satis_survey),]
# Printing the frequenct table contains counts and percentages of each survey
data_surveyed %>% tabyl(satis_survey, sort = TRUE)
# Changing the date format of survey_date column
data_surveyed = data_surveyed %>% mutate(survey_date = as.Date(survey_date,format = "%m/%d/%Y"))
# Print first six values in survey_date column
head(data_surveyed$survey_date)
# Inserting month column
data_surveyed[['Month']]<-month(data_surveyed$survey_date,label = T)
# Print first six values from Month column
head(data_surveyed$Month)
# Inserting counts column for our use
data_surveyed$counts<-1
# Printing all of the count of surveys by month
Part_A <- aggregate(data_surveyed["counts"], by = data_surveyed["Month"], FUN = sum)
Part_A
## Task-B
# Removing the missing values in each column
customers <- data_surveyed[!is.na(data_surveyed$total_investments),]
customers <- customers[!is.na(customers$cust_age),]
customers <- customers[!is.na(customers$cust_tenure),]
customers <- customers[!is.na(customers$tottrans),]
# Removing negative values in each column (Step by Step)
Customers <- customers[data_surveyed$total_investments>0,]
Customers <- Customers[Customers$cust_age>0,]
Customers <- Customers[Customers$cust_tenure>0,]
Customers <- Customers[Customers$tottrans>0,]
# Checking & removing NA values is there any produced after removing negative values
sum(is.na(Customers))
Customers = na.omit(Customers)
# print top six...
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