1.  
2. install.packages("tibble")
3. library(tibble)
4.  
5. id<-c(101, 102, 103)
6. age<-c(31, 33, 36)
7. course<-c("c++" ,"r", "java")
8. stdata<- data.frame(id, cg,course)
9.  
10. #add columns
11. semester<-c(8,9,10)
12. stdata<-add_column(stdata, semester, .after =2)
13. stdata
14.  
15. #add column
16. stdata<- cbind(stdata, name= c("sojib", "md.", "shohidul"))
17.  
18. #delete ccolumn
19. stdata <- stdata[-c(5)]
20.  
21.  
22.  
23. #list
24.  
25. g<- "my first list"
26. h <- c(25, 26, 18, 39)
27. j <- matrix(1:10, nrow=5)
28. k <- c("one", " two", "three")
29. mylist <- list(title = g, ages =h, j, k)
30. mylist
31.  
32.  
33.  
34.  
35.  
36. #take input
37.  
38. var1 = readline(prompt = "hey give your name : ")
39. var2 = readline(prompt = "hey give your age : " )
40. var2 = as.integer(var2)
41. print(var1)
42. print(var2)
43.  
44. #take input with scan
45.  
46. x= scan()
47.  
48. print(x)
49.  
50.  
51.  
52.  
53.  
54.  
55. #edit function
56.  
57. mydata <- data.frame(age = numeric(0), gender= character(0), weight= numeric(0))
58. mydata <- edit(mydata)
59. mydata
60.  
61.  
62.  
63.  
64.  
65.  
66. #import excel(csv) file
67.  
68. #mydataframe<- read.csv(file, header =logical_value, sep="delimiter")
69.  
70. myexceldata <- read.csv("C:/datasci/employees.csv", header=TRUE , sep= ",")
71.  
72. myexceldata
73.  
74. #show attribute name
75. names(myexceldata)
76.  
77.  
78.  
79. myexdata<- cbind(myexceldata, gender= c())
80.  
81. #delete data
82. myexdata <- myexdata[-c(9)]
83. myexdata
84.  
85. myexdata<- cbind(myexceldata, gender= c("male", "female"))
86. myexdata
87.  
88. #level korte annotating datasets use korte hy (char to numeric)
89.  
90. myexdata$gender <- factor(myexdata$gender,levels=c("male", "female"), labels=c(1,2))
91.  
92.  
93. #show imported datatype
94. str(myexdata)
95.  
96.  
97. #show descriptive statistics value
98. summary(myexdata)
99.  
100. #standard deviation
101.  
102. s<- myexdata$SALARY
103. sd(s)
104.  
105. # standard deviation for multiple column
106.  
107.  
108. install.packages("dplyr")
109. library(dplyr)
110.  
111. myexdata%>% summarise_if(is.numeric, sd)
112.  
113. #add data in missing value
114. myexdata[] = lapply(myexdata, sub, pattern = " ", replacement = "10", fixed = TRUE)
115.  
116. myexdata
117.  
118. #count missing values
119. sum(is.na(myexdata$SALARY))
120.  
121. #for all attribute
122.  
123. colSums(is.na(myexdata))
124.  
125.  
126. #add data in missing value for specific column
127. which(is.na(myexdata&SALAR;))
128.  
129. myexdata
130.  
131.  
132. #remove missing values
133. myexdata<-na.omit()
134.  
135. myexdata
136.  
137.  
138.