Data Quest R Basics

R Online Training By Data Quest

Basic Math

224 + 667

## [1] 891

35/7

## [1] 5

(22*5)+3

## [1] 113

Calculating scores for exam average

The following code chunk calculates exam average scores for seven subjects one by one

(92 + 87 + 85)/3 #Math

## [1] 88

(90 + 81 + 92)/3 #Chemistry

## [1] 87.66667

(84 + 95 + 79)/3 #Writing

## [1] 86

(95 + 86 + 79)/3 #Art

## [1] 86.66667

(77 + 85 + 90)/3 #History

## [1] 84

(92 + 90 + 91)/3 #Music

## [1] 91

(85 + 88 + 95)/3 #Physical_Education

## [1] 89.33333

Creating Variables

Creating varibles for storing average scores for the subjects

math <- (92 + 87 + 85)/3
math

## [1] 88

Chemistry <- (90 + 81 + 92)/3
Chemistry

## [1] 87.66667

Writing <- (84 + 95 + 79)/3
Writing

## [1] 86

Art <- (95 + 86 + 93)/3
Art

## [1] 91.33333

History <- (77 + 85 + 90)/3
History

## [1] 84

Music <- (92 + 90 + 91)/3
Music

## [1] 91

Physical_Education <- (85 + 88 + 95)/3
Physical_Education

## [1] 89.33333

Using variables for doing manipulations

average_math_and_chem <- (math + Chemistry)/2
average_math_and_chem

## [1] 87.83333

gpa <- (math + Chemistry + Writing + Art + History + Music + Physical_Education)/7
gpa

## [1] 88.19048

Vectors

Vectors -> Storage objects that can hold multiple values Storing values on vectors enables you to perfom operations on all of them at once c() is used to create a vector

math_chem <- c(math,Chemistry)
math_chem

## [1] 88.00000 87.66667

final_scores <- c(math, Chemistry, Writing, Art , History , Music , Physical_Education)
final_scores

## [1] 88.00000 87.66667 86.00000 91.33333 84.00000 91.00000 89.33333

`mean()` Fuction

The input to mean() is a vector, and the output is the average of the values contained in the vector.

mean (math_chem)

## [1] 87.83333

mean (final_scores)

## [1] 88.19048

Quick Useful R Functions

min(): Takes a vector as input, output is the smallest value in the vector.
max(): Takes a vector as input, output is the largest value in the vector.
length(): Takes a vector as input, output is the total number of values in the vector.
sum(): Takes a vector as input, output is the sum of all values in the vector.

min(math_chem)

## [1] 87.66667

min(final_scores)

## [1] 84

max(final_scores)

## [1] 91.33333

length(final_scores)

## [1] 7

sum(final_scores)

## [1] 617.3333

Working with vectors

Extracting a value

final_scores[2]

## [1] 87.66667

stem_grades <- final_scores[1:2]
stem_grades

## [1] 88.00000 87.66667

# non_stem_grades <- final_scores[c(3,4,5,6,7)]
non_stem_grades <- final_scores [3:7]
non_stem_grades

## [1] 86.00000 91.33333 84.00000 91.00000 89.33333

using the property to calcute mean for stem and non stem subjects

mean(stem_grades)

## [1] 87.83333

mean(non_stem_grades)

## [1] 88.33333

Naming vectors

Assigning names to elements of a vector can make indexing easier
Vectors may have attributes assigned to them
To assign names to vector elements, we can use the names() function.
How to use names() function

math_chemistry <- c(88.00000 , 87.66667)
math_chemistry

## [1] 88.00000 87.66667

class_names <- c('math', 'chemistry')
class_names

## [1] "math"      "chemistry"

names(math_chemistry) <- class_names
math_chemistry

##      math chemistry 
##  88.00000  87.66667

Use the class_names vector to assign names to elements of the final_scores vector.

class_names <- c('math','chemistry','writing','Art','history','music','physical_education')
class_names

## [1] "math"               "chemistry"          "writing"           
## [4] "Art"                "history"            "music"             
## [7] "physical_education"

final_scores <- c(88, 87.66667, 86, 91.33333, 84, 91, 89.33333)
final_scores

## [1] 88.00000 87.66667 86.00000 91.33333 84.00000 91.00000 89.33333

names(final_scores) <- class_names
final_scores

##               math          chemistry            writing 
##           88.00000           87.66667           86.00000 
##                Art            history              music 
##           91.33333           84.00000           91.00000 
## physical_education 
##           89.33333

Indexing

math_chemistry['chemistry']

## chemistry 
##  87.66667

math_chemistry[1]

## math 
##   88

math_chemistry[3]

## <NA> 
##   NA

As with indexing by position, we can index by name to return multiple elements using c():

liberal_arts <- final_scores [c('writing','history')]
liberal_arts

## writing history 
##      86      84

fine_arts <- final_scores [c('Art','music')]
fine_arts

##      Art    music 
## 91.33333 91.00000

mean(fine_arts)

## [1] 91.16666

mean(liberal_arts)

## [1] 85

Using Comparison Operators

math_comparison <- final_scores['math'] > final_scores
math_comparison

##               math          chemistry            writing 
##              FALSE               TRUE               TRUE 
##                Art            history              music 
##              FALSE               TRUE              FALSE 
## physical_education 
##              FALSE

typeof(math_comparison)

## [1] "logical"

gpa <- mean (final_scores)
gpa

## [1] 88.19048

above_average <- final_scores > gpa 
above_average

##               math          chemistry            writing 
##              FALSE              FALSE              FALSE 
##                Art            history              music 
##               TRUE              FALSE               TRUE 
## physical_education 
##               TRUE

Logical Indexing

By indexing above_average into final_scores, create a new vector…
…named best_grades that contains only class grades that are higher than your GPA.

best_grades <- final_scores[above_average]
best_grades

##                Art              music physical_education 
##           91.33333           91.00000           89.33333

Performing multiple Calculations

A very powerful feature of R: The ability to perform arithmetic operations on every element of multiple vectors at once.

Johhny Results - Subjects : math, chemistry, writing, art, history, music, physical_education

tests <- c(76, 89, 78, 88, 79, 93, 89)
homework <- c(85, 90, 88, 79, 88, 95, 74)
projects <- c(77, 93, 87, 90, 77, 82, 80)

math <- (76 + 85 + 77)/3
math

## [1] 79.33333

chemistry <- (89 + 90 +93)/3
chemistry

## [1] 90.66667

writing <- (78 + 88 + 87)/3
writing

## [1] 84.33333

art <- (88 + 79 + 90)/3
art

## [1] 85.66667

history <- (79 + 88 + 77)/3
history

## [1] 81.33333

music <- (93 + 95 + 82)/3
music

## [1] 90

physical_education <- (89 + 74 + 80)/3
physical_education

## [1] 81

Using Vector Arithmetic

-This is a simplified method of doing the above calculations.

johnny_scores <-(tests + homework + projects)/3
johnny_scores

## [1] 79.33333 90.66667 84.33333 85.66667 81.33333 90.00000 81.00000

mean(johnny_scores)

## [1] 84.61905

Self Lesson 01

Here i’m applying the knowledge of creating vectors and giving the vector elements names

scores <- c(76 , 81 ,92, 56)
scores_names <- c("Programming","Statistics","Pharmacy","Language")
names(scores) <- scores_names
scores

## Programming  Statistics    Pharmacy    Language 
##          76          81          92          56

Vector Recycling

Appending Elements To A Vector

Learning on how to work with incomplete results like the Kate results as given

tests <- c (76, 89, 78)
homework <- c (85 , 90 ,88, 79, 88, 95, 74)
projects <- c (77, 93, 87 ,77 , 82, 80)

kate_results <- tests + homework + projects
kate_results

## [1] 238 272 253 232 259 253 227

In the above calculation there is a problem as the tests vector is not of an equal length with the other vectors and hence R tends to repeat the numbers which does not reflect the truth.

Adding Missing Results To Kate Results

#Adding Missing Tests to Kate's results

tests <- c(tests,88,79,93,89)
tests

## [1] 76 89 78 88 79 93 89

length(tests)

## [1] 7

# deleting elements from a vector

tests_del <- tests[-c(8:43)]
tests_del

## [1] 76 89 78 88 79 93 89

tests

## [1] 76 89 78 88 79 93 89

Challenge

Kate has asked you to help her figure out which of her classes were her weakest so that she can improve

class_names <- c("math", "chemistry", "writing", "art", "history", "music", "physical_education")
kate_grades <- (tests + homework + projects)/3

## Warning in tests + homework + projects: longer object length is not a
## multiple of shorter object length

kate_grades

## [1] 79.33333 90.66667 84.33333 81.33333 83.00000 89.33333 80.00000

names (kate_grades) <- class_names
kate_grades

##               math          chemistry            writing 
##           79.33333           90.66667           84.33333 
##                art            history              music 
##           81.33333           83.00000           89.33333 
## physical_education 
##           80.00000

Data Quest R Basics

Castory Munishi

4/22/2019

R Online Training By Data Quest

Basic Math

Calculating scores for exam average

Creating Variables

Using variables for doing manipulations

Vectors

`mean()` Fuction

Quick Useful R Functions

Working with vectors

Naming vectors

Indexing

Using Comparison Operators

Logical Indexing

Performing multiple Calculations

Using Vector Arithmetic

Self Lesson 01

Vector Recycling

Appending Elements To A Vector

Adding Missing Results To Kate Results

Challenge

Data Quest R Basics

Castory Munishi

4/22/2019

R Online Training By Data Quest

Basic Math

Calculating scores for exam average

Creating Variables

Using variables for doing manipulations

Vectors

mean() Fuction

Quick Useful R Functions

Working with vectors

Naming vectors

Indexing

Using Comparison Operators

Logical Indexing

Performing multiple Calculations

Using Vector Arithmetic

Self Lesson 01

Vector Recycling

Appending Elements To A Vector

Adding Missing Results To Kate Results

Challenge

`mean()` Fuction