
A. Creating Vectors
In this section, you are expected to be able to shape data in vectors, perform basic mathematical operations, and also manipulate vectors.
Exercise 1
Create a vector A containing numeric values, starting from the last 2 digits of your student id up to 30.
Exercise 2
Create a vector B containing 12 character values; all names of your classmate including yourself.
Exercise 3
Create a vector C containing 12 numeric values, random number between 60 and 100.
B. Creating Matrices
In this section, you are expected to be able to shape data in Matrices, perform basic mathematical operations, and also manipulate Matrices.
Exercise 4
Create a matrices M1 order by \(rows \times columns \space (3 \times 4)\) containing 12 numeric values, random number between 60 and 100.
Exercise 5
Create a matrices M2 order by \(rows \times columns \space (3 \times 4)\) containing 12 numeric values, random number between 30 and 60. Find out the following tasks:
3 * M1, give your opinion about the result.
M1 + M2, give your opinion about the result.
M1 - M2, give your opinion about the result.
M1 * M2, give your opinion about the result.
M1 / M2, give your opinion about the result.
- determinan of
M1, give your opinion about the result.
- invers of
M1, give your opinion about the result.
Exercise 6
Create a matrix data that is contain the following vectors:
B that you has been created in the exercise 2. Name it as a ‘names’ variable
C that you has been created in the exercise 3. Name it as a ‘scores’ variable.
C. Lists
In this section, you are expected to be able to shape data by using the list() function, perform some basic manipulations.
Exercise 7
Please create a data set as the List variable by using the list() function, contain the following vectors:
- a variable
name, the values including your classmate and yourself
- a variable
age, the values including your classmate and yourself
- a variable
gender, the values including your classmate and yourself
D. Factors
In this section, you are expected to be able to shape data by using the factor() function, perform some basic manipulations.
Exercise 8
Please create a data set as the Factor variable as you have done at Exercise 7. Here, you add one more variable called marital_status by using the factor() function, as the following code:
marital_status <- factor(c("yes","no","yes","no", until 12 students))
E. Data Frames
In this section, you are expected to be able to shape data by using the data.frame() function, perform some basic manipulations.
Exercise 9
Please create a data set as the DF1 variable, contain the following vectors:
id, assume 1 up to 6
name the values according to your classmate and yourself
gender the values according to your classmate and yourself
age the values according to your classmate and yourself
marital_status the values according to your classmate and yourself
address_by_city the values according to your classmate and yourself
Please create a data set as the DF2 variable, contain the following vectors:
id, assume 7 up to 12
name the values according to your classmate and yourself
gender the values according to your classmate and yourself
age the values according to your classmate and yourself
marital_status the values according to your classmate and yourself
address_by_city the values according to your classmate and yourself
Exercise 10
In this final exercise, please consider the following tasks:
- Combine
DF1 and DF2, assign it as SB19 variable!
- Print the result of data frame
SB19!
- Print first 3 rows of the
SB19 dataset!
- How can you preview the
SB19 dataset like an Excel file on your Rstudio?
- Review the structure of the data frame
SB19!
- Check the dimension of the data.
- Please apply piping functions to the data frame
SB19, filter it by their gender accordingly! (as you have learn last week)
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