due date : 07/12/2020 23:59
Part 1 - Basic Math
- Look at Figure - “Math Eq”
- Write the solution
![](data:image/png;base64,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)
# aritmetic operators
Part 2 - Assignment
- Create a new object with one value.
- hint : For example, use Part 1 for value, assign it in a new variable like
- Print your new object
#assignment arrow
#print()
Part 3 - Class
- Print your name as a character string.
- Print your age as a numeric type.
- Print your age as a character type.
- Try to print your name as a numeric type. (?!)
- hint : kidding
- Check classes for all.
- What is the class of TRUE and NA ?
# print()
# " ... "
# class()
Part 4 - Vector
- Create a new vector which has 4 elements with numeric class.
- Print your vector with sorting. (decreasing = TRUE)
- Add a new character element at your vector.
- Now you must have 5 elemets. Learn the length of your vector.
- Check the class of your vector. (Numeric or Character ?)
- Now create another new vector, but now use sequence function.
# combine them
# my_new_vector <-
# sort
# length()
# seq()
Part 5 - Matrice
- Create a new matrice with 4 rows and 5 colomns, using random variables.(random for the uniform distribution)
- Select the grid (or cell) located in 2nd row and 3rd coloms. (indexing)
- Change it with TRUE. (assigment)
- Check the dimension, structure, length and class of your matrice
- BONUS: Print values which is greater than or equal to 5 in your matrice.
# runif()
# matrix()
# indexing with []
# length()
# dim()
# str()
# class()
# which()
# >=
Part 6 - Array
- Create a new vector which has 4 elements, character.
- Create a new matrice with 2 rows and 4 colomns, numeric.
- Combine them, and create a new array with 3 rows, 4 columns and 2 layers. (first row must be vector, second and third rows must be matrix for each layer)
- hint : you can use repetations function
- Try to add +2 for each values of 2nd layer of array.
- Check the dimension, structure, length and class of your array.
# vector()
# matrix()
# array()
# length()
# dim()
# str()
# length()
# class()
Part 7 - Data Frame
- Create a new vector which has 4 elements, numerical.
- Create a new vector which has 4 elements, logical.
- Create a new matrice which has 4 rows and 2 columns, numerical.
- Create a data frame which has 4 rows and 4 columns with your numerical and logical vectors, and numreical matrice.
- Check the class and structure of your new data.
- Take the first column and assign it as a new variable. (It will look like a vector)
- hint : you can use $ symbol
- Plot this vector.
- BONUS: Find values which is lower than 20 and change them with NA. (now your vector has changed)
- Print and Plot this new vector.
# my_data <-
# class()
# my_data$
# plot()
# which()
# <20
Use the Ninova Message Board for questions or problems
Emir