Getting Started With Dataframe .
Introduction
Dataframe are the mostly used data structure in R. Dataframe is a list where all components have name and are on the same line. Easiest way of understanding about dataframe is the visualization of spreadsheets. The first row is represented by header. The header is given by the list component name. Each column can store the different datatype which is called a variable and each row is an observation across multiple variables, since dataframe are like spreadsheet we can insert the data how we will like to. There are many possibilities to inserting data.
| Product | apple | Banana |
|---|---|---|
| price store A | 23 | 56 |
| price store B | 67 | 80 |
It is not dataframe because here price store is divided into two parts. If we rearrange the data by taking product is one variable and price is next variable and store is one variable then it become dataframe.
| Product | Price | Store |
|---|---|---|
| apple | 23 | A |
| apple | 67 | B |
| banana | 56 | A |
| banana | 80 | B |
Attributes of dataframe
- Length
- Dimension
- Name
- Class ## How to Create DataFrame
product <- c('apple','banana','orange','papaya','rice','wheat','pee','noodle')
catagory <- c( 'groceries','groceries','electronic','electronic','groceries','electronic','electronic','groceries')
price <- c(24,45,67,88,56,78,89,90)
quality <- c('high','low','high','low','high','low','high','low')
To create dataframe from above data we can do
shopping_data <- data.frame(product,catagory,price,quality,
budget = c(120,3000,600,500,45,67,89,90))
shopping_data
Output of the avove code is,dataframe.
To check wether it is dataframe or not we can use folowing code.
str(shopping_data)
Output of the above cde is,
'data.frame': 8 obs. of 5 variables:
$ product : chr "apple" "banana" "orange" "papaya" ...
$ catagory: chr "groceries" "groceries" "electronic" "electronic" ...
$ price : num 24 45 67 88 56 78 89 90
$ quality : chr "high" "low" "high" "low" ...
$ budget : num 120 3000 600 500 45 67 89 90
Check the attribute of dataframe.
names(shopping_data)
Check dimension of dataframe.
dim(shopping_data)
Check first six rows of dataframe
head(shopping_data)
Check last six rows of dataframe.
tail(shopping_data)
Take only two rows of dataframe.
head(shopping_data, n = 2)
Access specified column of database.
shopping_data$product
Output of the above code is,
'apple''banana''orange''papaya''rice''wheat''pee''noodle'
shopping_data[['product']]
Output of the above code is,
'apple''banana''orange''papaya''rice''wheat''pee''noodle'
Manipulating dataframe By manipulating data frame we khow how to select, add new row and how to sort and rank into dataframe. Dataframe are list where each elements are name vector of same length. Therefore we can select element as same as in list. we do by [[]] or $column. Dataframe are also two dimensional matricies which means we can index them as matrices by using square braces.[row,column].We fix data in one dimension they behave as list. Therefore dataframe can be index either as like list or as like matrices accoding to positions, rules, names.
List subsetting
#list subsetting
shopping_data[[2]]
shopping_data[['budget']]
shopping_data$price
shopping_data$price[1:3]
shopping_data[[3]][3]
shopping_data$price[3]
Output of the above code is,
'groceries''groceries''electronic''electronic''groceries''electronic''electronic''groceries'
120300060050045678990
2445678856788990
244567
67
67
Matrix subsetting
#Matrix subsetting
shopping_data[,1]
shopping_data[,"product"]
shopping_data[1,]
shopping_data[1,"price"]
Output will be
'apple''banana''orange''papaya''rice''wheat''pee''noodle'
'apple''banana''orange''papaya''rice''wheat''pee''noodle'
A data.frame: 1 × 5
1 apple groceries 24 high 120
24
Add new attribute into dataframe.
feedback<- c('good','outstanding','ordinary','nice','excilent','brillent','extra-ordinary','satisfactory')
shopping_data <- cbind(shopping_data,feedback)
shopping_data
Output will be
A data.frame: 8 × 6
apple groceries 24 high 120 good
banana groceries 45 low 3000 outstanding
orange electronic 67 high 600 ordinary
papaya electronic 88 low 500 nice
rice groceries 56 high 45 excilent
wheat electronic 78 low 67 brillent
pee electronic 89 high 89 extra-ordinary
noodle groceries 90 low 90 satisfactory
We can do the following operations to access the data from dataframe
shopping_data[c(1:3),1]
shopping_data[1]
shopping_data[[1]]
is.vector(shopping_data[1])
is.vector(shopping_data[[1]])
is.list(shopping_data[1])
is.list(shopping_data[1])
Output is,
'apple''banana''orange'
A data.frame: 8 × 1
apple
banana
orange
papaya
rice
wheat
pee
noodle
'apple''banana''orange''papaya''rice''wheat''pee''noodle'
FALSE
TRUE
TRUE
TRUE
Working with tidyverse
During data analysis we spend our most time in data cleaning and transforming the raw data. Tydyverse is an add on that let us perform operation such as cleaning data and creating powerful graph.
product <- c('apple','banana','orange','papaya','Rice','wheat','pee','noodle')
catagory <- c( 'groceries','groceries','electronic','electronic','groceries','electronic','electronic','groceries')
price <- c(24,45,67,88,56,78,89,90)
quality <- c('high','low','high','low','high','low','high','low')
shopping_data <- data.frame(product,catagory,price,quality,
budget = c(120,3000,600,500,45,67,89,90))
#arrange(desc(price))
shopping_data
Output is,
A data.frame: 8 × 5
apple groceries 24 high 120
banana groceries 45 low 3000
orange electronic 67 high 600
papaya electronic 88 low 500
Rice groceries 56 high 45
wheat electronic 78 low 67
pee electronic 89 high 89
noodle groceries 90 low 90
Select Function
Select function allow us to select specified data from dataframe.
# dplyr never change the original data
#install.packages("tidyverse")
#library(tidyverse)
library(dplyr)
product <- select(shopping_data,price,budget)
product
Output is,
A data.frame: 8 × 2
24 120
45 3000
67 600
88 500
56 45
78 67
89 89
90 90
Filter
Filter function work similar to the select. Using the pipe operator %>% we can write multiple operations at once without renaming the intermedating results.
filter(product,budget > 100)
Output is,
A data.frame: 4 × 2
24 120
45 3000
67 600
88 500
dataset2 <- shopping_data %>%
select(product,price)%>%
filter(price>45)%>%
group_by( product)%>%
summarize(avg = mean(price))
dataset2
Output is,
A tibble: 6 × 2
noodle 90
orange 67
papaya 88
pee 89
Rice 56
wheat 78
Arrange function
It sort our dataframe in acending order.arrange(price), to arrange dataframe in decending order we used arrange(desc(price))
arrange(product,price)
Output is,
A data.frame: 8 × 2
24 120
45 3000
56 45
67 600
78 67
88 500
89 89
90 90
Managing control statements:
- If statement:
If statement is the most common statement that execute code that only the condition place between bracket is true. Otherwise if statement ignore that particular piece of code. if(condition){ code to be executed} to overcome this abstacle we add extra element else # Paste function Paste converts its arguments ( via as.character) to character strings and concatenates them (separating them by the string given by sep ). If the arguments are vectors, they are concatenated term-by-term to give a character vector result.
product <- "tshirt"
price<- 110
if(price < 100){
print(paste('adding',product,'to cart'))
}else
{
print(paste('adding',product,'to wishlist'))
}
Output is,
[1] "adding tshirt to wishlist"
Control Statement in vectors
quantity <- c(1,1,2,3,4)
ifelse(quantity == 1,'Yes','No')
Output is,
'Yes''Yes''No''No''No'
price <- 100
if(price < 100){
print("price"< "budget")
}else if(price == 100){
print("the price is equal to budget")
}else{
print("The budget is less then price")
}
Output is,
[1] "the price is equal to budget"
price <- c(58,100,110)
if(price < 100){
print("price"< "budget")
}else if(price == 100){
print("the price is equal to budget")
}else{
print("The budget is less then price")
}
If the condition has the lenght grater than one then only the first input is tested. That means it check the first elements and then stop. This problem is resolved by using any function.
Any Function
if(any(price < 100)){
print('At least one price is under budget')
}
Output is,
[1] "At least one price is under budget"
All Function
if(all(price<100)){
print('all the price are under budget')
}else{
print('Not all prices satisfies the condition.')
}
Output is,
[1] "Not all prices satisfies the condition."
To combine the condition we can use && and || operator. single and and or are used to element wise vector. While double and or are used for vector compare on one(non vectorise form)
price <- 58
if(price> 50 && price < 100){
print('The price is between 50 and 100')
}else {
print("the price is not in between 50 and 100")
}
[1] "The price is between 50 and 100"
Switch Statement
We can add as many as if else statements however keeping more than four is difficult to keep track what is happing when the condition is true. The switch command work with the cases, each syntax contain value to be tested followed by the possible cases.
quantity <- c(1,3,4,5)
average_quantity <- function(quantity,type) {
switch(type,
arthematic = mean(quantity),
geometric = prod(quantity)^(1/length(quantity)))
}
average_quantity(quantity,"arthematic")
Output is,
3.25
x <- c(1,2,3,4,5)
sumfunction <- function(x,i){
switch(i,
s = sum(x)
)
}
sumfunction(x,"s")
Output is,
15
Loop
Loop is the sequence of instructions that are repeated untill a certain condition is reached.
- For loop It perform the same operations on all elements from input. Its syntax is
if(variable in sequence ){ Expression}between parenthesis there are three argument first argument is variable which can take any name then we have keyword in and last is sequence or vector of any kind.
For loop does not save output untill we print it.
cart <- c('apple','cookie','lemoan')
for(product in cart){
print(product)
}
Output is,
[1] "apple"
[1] "cookie"
[1] "lemoan"
While loop
While loop perform the operation as long as given conditions is true. Syntax is similary as for loop. To make loop stop there must be relation between condtion and expression other wise loop does not stop ever.
index <- 1
while(index <3 ) {
print(paste("The index value is",index))
index <- index + 1
}
Output is,
[1] "The index value is 1"
[1] "The index value is 2"
Repeat Loop
They repeat the same operation untill it hitting the stop key or by inserting special function to stop them. Repeat loop are important in algorithms optimization and maximization. As an syntax repeat expression
The next statement is used to discontinue one particular cycle and skip to the next.
x <- 1
repeat {
print(x)
x = x + 1
if( x==3){
break
}
}
Output is,
[1] 1
[1] 2
price <- c(123,456,78,900,987)
for(value in price){
if( value < 100){
next
}
discount <- value - value * 0.1
print(discount)
}
Output is,
[1] 110.7
[1] 410.4
[1] 810
[1] 888.3
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