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1.14 Complete Code


1.14 Complete Code


1.      #Setting up working directory
2.      setwd("C:\\SAM\\R\\SA")
3.      getwd()

4.      #Loading data
5.      fin<-read.csv("Future-500.CSV", na.strings = c(""))

6.      #Exploring data
7.      head(fin,20)
8.      tail(fin, 10)
9.      str(fin)
10.  summary(fin)

11.  #Changing from Non-factor to factor
12.  fin$ID <- factor(fin$ID)
13.  summary(fin)
14.  str(fin)
15.  fin$Inception <- factor(fin$Inception)
16.  summary(fin)
17.  str(fin)
18.  fin$Profit <- factor(fin$Profit)
19.  str(fin)

20.  #SUB() and GSUB()
21.  fin$Expenses <- gsub(" Dollars", "", fin$Expenses)
22.  head(fin)
23.  fin$Expenses <- gsub(",", "", fin$Expenses)
24.  head(fin)
25.  fin$Revenue <- gsub("\\$", "", fin$Revenue)
26.  head(fin)
27.  fin$Revenue <- gsub(",", "", fin$Revenue)
28.  head(fin)
29.  str(fin)
30.  fin$Growth <- gsub("\\%", "", fin$Growth)
31.  str(fin)
32.  fin$Expenses <- as.numeric(fin$Expenses)
33.  fin$Revenue <- as.numeric(fin$Revenue)
34.  fin$Growth <- as.numeric(fin$Growth)
35.  fin$Profit <- as.numeric(as.character((fin$Profit)))

36.  head(fin,24)
37.  complete.cases(fin)
38.  fin[!complete.cases(fin),]

39.  #filtering using which() for non-missing data
40.  head(fin)
41.  fin[fin$Revenue==8567910,]
42.  which(fin$Revenue==8567910)
43.  fin[which(fin$Revenue==8567910),]
44.  fin[fin$Employees==45,]
45.  fin[which(fin$Employees==45),]
46.  head(fin,24)
47.  fin[fin$Expenses==NA,]

48.  a<-c(1,24,53,NA, 68, NA)
49.  is.na(a)
50.  fin[is.na(fin$Expenses),]

51.  #removing data with missing data
52.  fin_backup <- fin
53.  fin[!complete.cases(fin),]
54.  fin[is.na(fin$Industry),]
55.  fin <- fin[!is.na(fin$Industry),]
56.  fin
57.  fin[!complete.cases(fin),]

58.  #resetting Dataframe index
59.  rownames(fin) <- 1:nrow(fin)
60.  fin
61.  rownames(fin) <- NULL
62.  fin

63.  #Replacing missing data with factual analysis
64.  fin[!complete.cases(fin),]
65.  fin[is.na(fin$State),]
66.  fin[is.na(fin$State) & fin$City == "New York",]
67.  fin[is.na(fin$State) & fin$City == "New York","State"] <- "NY" 

68.  #Checking it with the row number
69.  fin[c(11,377),]
70.  fin[!complete.cases(fin),]
71.  fin[is.na(fin$State) & fin$City == "San Francisco",]
72.  fin[is.na(fin$State) & fin$City == "San Francisco","State"] <- "CA"

73.  #Checking it with the row number
74.  fin[c(82,265),]
75.  fin[!complete.cases(fin),]

76.  #Replacing missing data: Median Imputation method
77.  fin[!complete.cases(fin),]
78.  median(fin[,"Employees"])
79.  median(fin[,"Employees"], na.rm = TRUE)
80.  med_empl_retail <- median(fin[fin$Industry=="Retail","Employees"], na.rm = TRUE)
81.  med_empl_retail
82.  fin[is.na(fin$Employees) & fin$Industry == "Retail", "Employees"] <- med_empl_retail
83.  #check
84.  fin[3,]

85.  #Dealing with missing data in Financial Services
86.  med_empl_fs<-median(fin[fin$Industry=="Financial Services","Employees"],na.rm=TRUE)
87.  med_empl_fs
88.  fin[is.na(fin$Employees) & fin$Industry == "Financial Services", "Employees"]<- med_empl_fs
89.  #check
90.  fin[330,]

91.  #Dealing with missing data in Growth
92.  med_growth_construction <- median(fin[fin$Industry=="Construction","Growth"], na.rm = TRUE)
93.  med_growth_construction
94.  fin[is.na(fin$Growth) & fin$Industry == "Construction", "Growth"] <- med_growth_construction
95.  #check
96.  fin[8,]

97.  #Revenue
98.  med_rev_constr <- median(fin[fin$Industry=="Construction","Revenue"], na.rm = TRUE)
99.  med_rev_constr
100.                      fin[is.na(fin$Revenue) & fin$Industry == "Construction", "Revenue"] <- med_rev_constr
101.                      fin[!complete.cases(fin),]

102.                      #Expenses
103.                      #Be careful here, because we are performing operation only for certain ones
104.                      med_exp_constr <- median(fin[fin$Industry=="Construction","Expenses"], na.rm = TRUE)
105.                      med_exp_constr
106.                      fin[is.na(fin$Expenses) & fin$Industry == "Construction" & is.na(fin$Profit), "Expenses"] <- med_exp_constr
107.                      fin[!complete.cases(fin),]

108.                      #Replacing missing data : Deriving values
109.                      #Revenue = Expenses – Profit
110.                      #Expenses = Revenue – Profit
111.                      fin[is.na(fin$Profit),"Profit"] <- fin[is.na(fin$Profit),"Revenue"] - fin[is.na(fin$Profit),"Expenses"]
112.                      #check
113.                      fin[c(8,42),]

114.                      fin[!complete.cases(fin),]
115.                      fin[is.na(fin$Expenses),"Expenses"] <- fin[is.na(fin$Expenses),"Revenue"] - fin[is.na(fin$Expenses),"Profit"]
116.                      #check
117.                      fin[15,]
118.                      fin[!complete.cases(fin),]

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