DataScience Classroomnotes 26/Dec/2021

Practical Exercises on Matrix

  • Construct a matrix with 3 rows that contain numbers from 1 to 9
basic_matrix <- matrix(1:9, byrow = FALSE, nrow=3)
print(basic_matrix)
  • Consider the following collection of movies
raise_of_skywalker <- c(515.202, 1072.848)
solo <- c(213.767, 393.151)
last_jedi <- c(620.181, 1055.135)

# create a new vector box_office combining the above there vectors
box_office <- c(raise_of_skywalker, solo, last_jedi)

# Construct a starwars matrix
star_wars_matrix <- matrix(box_office, byrow = TRUE, nrow=3)
print(star_wars_matrix)
  • Now add rownames and colnames to the star wars matrix
regions <- c("US", "Worldwide")
movies <- c("Raise of Skywalker", "Solo", "Last Jedi") 

colnames(star_wars_matrix) <- regions
rownames(star_wars_matrix) <- movies

print(star_wars_matrix)
  • Lets try to add the us collections + worldwide collections
rowSums(star_wars_matrix)
  • Now add this column to the star_wars_matrix
all_collections <- cbind(star_wars_matrix, rowSums(star_wars_matrix))
print(all_collections)

Preview

Factors

  • Lets have a gender vector
gender_vector <- c("Male", "Female", "Female", "Male", "Female", "Male")
  • Lets create a factor gender vector
factor_gender_vector <- factor(gender_vector)
print(factor_gender_vector)
  • Categorical Variable can be classified as nominal categorical variables and ordinal categories
# nominal
fruits_vector <- c("Apple", "Banana", "Mango")
factor_fruits_vector <- factor(fruits_vector)
print(factor_fruits_vector)

# ordinal
bp_vector <- c("High", "Low", "Normal", "High", "Low", "High")

factor_bp_vector <- factor(bp_vector, ordered=TRUE, levels = c("Low", "Normal", "High"))
print(bp_vector)
print(factor_bp_vector)
  • Adding levels post factor creation
levels(factor_fruits_vector)
levels(factor_bp_vector)

levels(factor_fruits_vector) <- c("Apple", "Banana", "Mango", "Orange")
print(factor_fruits_vector)
  • Now lets summarize
summary(fruits_vector)
summary(factor_fruits_vector)

summary(bp_vector)
summary(factor_bp_vector)
  • In R we have built-in datasets execute data()
    Preview

DataFrames

  • Lets take an existing dataframe mtcars
  • Now lets try the following
head(mtcars)
tail(mtcars)
str(mtcars)
summary(mtcars)
dim(mtcars)
nrow(mtcars)
ncol(mtcars)
  • Sort mtcars dataframe by mpg (miles per gallon) in decreasing
positions <- order(mtcars$mpg, decreasing = TRUE)
print(positions)

mtcars[positions, ]
  • sort rock by area

  • Install tidyverse package install.packages('tidyverse') Refer Here

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