Tast_1.Rmd

---
title: "Data Wrangling (Data Preprocessing)"
author: "Dhanesh Gaikwad"
subtitle: Practical assessment 1
date: ""
output:
  html_notebook: default
  pdf_document: default
  html_document:
    df_print: paged
---

```{r}


library(readr)
library(kableExtra)
library(magrittr)
library(dplyr)
library(knitr)
```

```{r}



students <- c("Dhanesh Gaikwad")
student_numbers <- c("4000700")
percentage_contributions <- c("100%")

s <- data.frame("Student name" = students, "Student number" = student_numbers, "Percentage of contribution" = percentage_contributions)


kbl(s, caption = "Group information") %>%
  kable_classic(full_width = F, html_font = "Cambria")
```

# **Data Description**

<https://www.kaggle.com/datasets/drahulsingh/best-selling-books>

-   This data set has been taken from www.kaggle.com (as link mentioned above).
-   Choose data set basically have list of the "best-selling books" and "book series" (that can be in any language).
-   Eventually "best-selling" term indicates the anticipated number of copies sold for individual book without considering the number of books that      are printed or currently owned(\$\$ Although text book and comics are excluded in this list).
-   Books are organised on the basis of "greatest sales". Majorly data set comprise of 6 "Columns" and 174 "Rows".

# **Attribute's Interpretation :** 

-   Book series - (The list of Book's title ) ["Datatype: string"] )
-   Author - (The list of writers name ["Datatype: string"])
-   Original Language - (Language in which book is English ("Datatype:string")
-   First Published - (Book's released dates ["Datatype : int"])
-   Approximate sale in million - ( Estimated trade of books ["Datatype : int"])
-   Genre -(Class of books [Datatype : string])

# **Read/Import Data**

-   We are reading data from the "best-selling-books.csv" CSV (Comma-Separated Values) file at the given directory.
-   CSV files are frequently used to hold tabular data with comma-delimited value separations.
-   To read CSV files in R and automatically convert the data into a structured format, use the' read_csv' function.
-   'data' is a data frame that has been loaded with the data from the CSV file.

```{r}

data <- read_csv("E:/RMIT/DW/Assignments/Assignment_1/best-selling-books.csv") 

```

# **Inspecting and Understanding**

-   *Displaying the first 6 rows of the data*

```{r}

head(data)          

```

-   *Displaying the last 6 rows of the data*

```{r}

tail(data)          

```

-   Summary function is used to summarize the data, including data types, missing values, and basic statistics

```{r}

summary(data)

```

-   The data structure is examined using the str() function, which displays the data types and the number of observations for each variable.

```{r}

str(data)

```

-   dim() function is used to check the dimension of the data frame

```{r}

dim(data)   

```

-   names() function shows the names of the variables in the data frame

```{r}

names(data)         

```

-   anyNA() function is used to check if there is any missing values in the data set

```{r}

anyNA(data)

```

# **Subsetting**

## Step 1:

-   In this step we are Subsetting the data frame into the variable named subset and we have to use [] brackets to subset a data frame. The first argument passed in the bracket gives the rows we want to include in our subset

```{r}

subset <- data[1:10,]   

```

## Step 2:

-   In this process we are converting subset data into a matrix.For converting the subset we have used as.maxtrix() function. This funtion is inbuild function so we dont need to import it

```{r}

martix1 <- as.matrix(subset)  

```

## Step 3:

-   In this step we are checking the structure of matrix, We have used srt() function which is an inbuild function. With this function we can inspect the structure of the object

```{r}

str(martix1)

```

# **New Data Frame**

-   A list of 10 names has been prepared as a new vector called "Names" by our team.
-   Each person on the list has had their gender determined using the factor function. To keep an order, the "ordered" argument is set to TRUE.
-   The "Collage" data frame's structure, including its columns and the associated data types, is displayed using the str() function.
-   This function displays the levels (categories) within the "Gender" factor column in the "Collage" data frame.
-   To indicate the ages of the people in the list, a new vector called "Age" has been constructed.
-   Using the cbind function, the "Age" vector is added as a new column to the already-existing "Collage" data frame.
-   Three columns---"Names," "Gender," and "Age"---are now present in the final "Collage" data frame, which is now visible.

```{r}


Names <- c("Dhanesh","Chirag","Yukta","Dhumal","Omkar","Vinay","Bhosale","Shivani","Shedge","Prasad")

Gender <- factor(c("Male","Male","Female","Male","Male","Female","Male","Female","Male","Male"),levels = c("Male","Female"), ordered = TRUE) 

Collage <- data.frame(Names, Gender) # We have combined the "Names" and "Gender" vectors to create a new data frame named "Collage" with two columns.

str(Collage)

levels(Collage$Gender)

Age <- c(23,54,24,26,25,23,22,20,19,29)

Collage <- cbind(Collage, Age)

Collage



```

-   In the beginning, we made two vectors, "Names" and "Gender," to represent names and genders of people.

-   A factor with specified levels ("Male" and "Female") was created from the "Gender" vector.

-   Once integrated, these vectors were placed in a data frame called "Collage." The str function was used to analyze the structure of "Collage".

-   The "Age" vector was then added to capture the ages of the people. Using the cbind function, this vector was added as a new column to the preexisting "Collage" data frame.

-   The end result is a thorough data frame with three columns: "Names," "Gender," and "Age," giving a detailed overview of the information gathered for each person.