Setup

# Load the necessary packages required to reproduce the report. For example:
library(kableExtra)
library(magrittr)
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following object is masked from 'package:kableExtra':
## 
##     group_rows
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(readr)

na<- c("HEMANTH RANGASWAMY")
no<- c(" s4069811")
pc<- c("100")
s<- data.frame(cbind(na,no,pc))
#colnames(s)<- c("HEMANTH RANGASWAMY", "s4069811", "100")
s %>% kbl(caption = "Individual Information") %>%
  kable_classic(full_width = F, html_font = "Cambria")
Individual Information
na no pc
HEMANTH RANGASWAMY s4069811 100

/* ## Explanation kableExtra: The kableExtra package improves table styling, adds features like striped tables and font size adjustments, works for both HTML and PDF formats, and includes a convenient pipe operator (%>%) for chaining operations. magrittr: The magrittr package allows you to chain operations by piping values forward, making code more readable and avoiding nested function calls. dplyr- This package provides a set of functions for data manipulation, transformation, and summarization. It’s particularly useful for working with data frames and tibbles. Some common functions include filter(), mutate(), select(), and group_by(). readr: This package is designed for efficient reading of flat files (such as CSV, TSV, and fixed-width files) into R. It provides functions like read_csv(), read_tsv(), and read_delim(). Source explanation : Kaggle is an online community platform for data scientists and machine learning enthusiasts. It allows collaboration, dataset sharing, GPU-integrated notebooks, and data science competitions. ## Read/Import Data #I am using readr library to read my dataset Filename: sample.csv File path: C:/Users/Hemanth Gowda/Downloads/

# Import the data, provide your R codes here.
Covid<- read.csv("C:/Users/Hemanth Gowda/Downloads/sample.csv")

Provide explanations here. read.csv: This is a function in R that reads data from a CSV (Comma-Separated Values) file. It takes the file path as an argument and returns a data frame containing the data from the CSV file. It reads the file from the file path and converts that file into a data frame named covid. ## Inspect and Understand

# Inspection of your data, provide R codes here.
View(Covid)
dim(Covid) 
## [1] 31822    16
names(Covid)
##  [1] "Date"                "Location"            "Location.Level"     
##  [4] "Total.Cases"         "Total.Deaths"        "Total.Recovered"    
##  [7] "New.Cases"           "New.Deaths"          "New.Recovered"      
## [10] "Province"            "Time.Zone"           "Population"         
## [13] "Longitude"           "Latitude"            "Case.Fatality.Rate" 
## [16] "Case.Recovered.Rate"
str(Covid)
## 'data.frame':    31822 obs. of  16 variables:
##  $ Date               : chr  "3/1/2020" "3/2/2020" "3/2/2020" "3/2/2020" ...
##  $ Location           : chr  "DKI Jakarta" "DKI Jakarta" "Indonesia" "Riau" ...
##  $ Location.Level     : chr  "Province" "Province" "Country" "Province" ...
##  $ Total.Cases        : int  39 41 2 1 43 2 1 1 45 2 ...
##  $ Total.Deaths       : int  20 20 0 0 20 0 1 0 20 0 ...
##  $ Total.Recovered    : int  75 75 0 1 75 0 60 1 75 0 ...
##  $ New.Cases          : int  2 2 2 1 2 0 1 0 2 0 ...
##  $ New.Deaths         : int  0 0 0 0 0 0 1 0 0 0 ...
##  $ New.Recovered      : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ Province           : chr  "DKI Jakarta" "DKI Jakarta" "" "Riau" ...
##  $ Time.Zone          : chr  "UTC+07:00" "UTC+07:00" "" "UTC+07:00" ...
##  $ Population         : int  10846145 10846145 265185520 6074100 10846145 265185520 45161325 6074100 10846145 265185520 ...
##  $ Longitude          : num  107 107 114 102 107 ...
##  $ Latitude           : num  -6.205 -6.205 -0.789 0.512 -6.205 ...
##  $ Case.Fatality.Rate : chr  "51.28%" "48.78%" "0.00%" "0.00%" ...
##  $ Case.Recovered.Rate: chr  "192.31%" "182.93%" "0.00%" "100.00%" ...
summary(Covid)
##      Date             Location         Location.Level      Total.Cases     
##  Length:31822       Length:31822       Length:31822       Min.   :      1  
##  Class :character   Class :character   Class :character   1st Qu.:   5223  
##  Mode  :character   Mode  :character   Mode  :character   Median :  23596  
##                                                           Mean   : 159450  
##                                                           3rd Qu.:  69928  
##                                                           Max.   :6405044  
##   Total.Deaths      Total.Recovered     New.Cases         New.Deaths      
##  Min.   :     0.0   Min.   :      0   Min.   :    0.0   Min.   :   0.000  
##  1st Qu.:   128.0   1st Qu.:   3914   1st Qu.:    3.0   1st Qu.:   0.000  
##  Median :   565.5   Median :  21028   Median :   27.0   Median :   0.000  
##  Mean   :  4564.8   Mean   : 149261   Mean   :  402.3   Mean   :   9.921  
##  3rd Qu.:  2189.0   3rd Qu.:  64142   3rd Qu.:  130.0   3rd Qu.:   3.000  
##  Max.   :157876.0   Max.   :6218708   Max.   :64718.0   Max.   :2069.000  
##  New.Recovered       Province          Time.Zone           Population       
##  Min.   :    0.0   Length:31822       Length:31822       Min.   :   648407  
##  1st Qu.:    2.0   Class :character   Class :character   1st Qu.:  1999539  
##  Median :   20.0   Mode  :character   Mode  :character   Median :  4216171  
##  Mean   :  390.4                                         Mean   : 15367656  
##  3rd Qu.:  123.0                                         3rd Qu.:  9095591  
##  Max.   :61361.0                                         Max.   :265185520  
##    Longitude         Latitude      Case.Fatality.Rate Case.Recovered.Rate
##  Min.   : 96.91   Min.   :-8.682   Length:31822       Length:31822       
##  1st Qu.:106.11   1st Qu.:-6.205   Class :character   Class :character   
##  Median :113.42   Median :-2.462   Mode  :character   Mode  :character   
##  Mean   :113.70   Mean   :-2.726                                         
##  3rd Qu.:121.20   3rd Qu.: 0.212                                         
##  Max.   :138.70   Max.   : 4.226
levels(factor(Covid$Longitude))
##  [1] "96.91052174" "99.05196442" "100.4650624" "101.8051092" "102.3384213"
##  [6] "102.7236404" "104.1694647" "105.0214366" "106.1090043" "106.5499324"
## [11] "106.8361183" "107.6037083" "108.261746"  "110.2011149" "110.4448783"
## [16] "111.1211776" "112.7329414" "113.4176536" "113.921327"  "115.1317136"
## [21] "115.4385783" "116.2188791" "116.4684405" "117.5086257" "119.3450194"
## [26] "120.1620559" "121.2010927" "121.592271"  "122.070311"  "122.3760581"
## [31] "124.5212396" "127.5391072" "129.576792"  "132.9762624" "138.69603"

Provide explanations here. The View() function in R opens an interactive data viewer within RStudio, allowing you to explore and understand your dataset visually. The dim() function in R is used to either retrieve or set the dimensions of an array, matrix, or data frame. The names() function in R returns the column names (variable names) of the data frame or data set stored in the variable. The str() function displays the internal structure of the R object Covid. The summary() function generates summary statistics for each numeric column in the dataset Covid. The levels() function Generates summary statistics for each numeric column in the dataset Covid. ## Subsetting 

# Subset your data and convert it to a matrix, provide R codes here.
covid_subset <- Covid[1:10]
covid_matrix <- as.matrix(covid_subset)
str(covid_matrix)
##  chr [1:31822, 1:10] "3/1/2020" "3/2/2020" "3/2/2020" "3/2/2020" "3/3/2020" ...
##  - attr(*, "dimnames")=List of 2
##   ..$ : NULL
##   ..$ : chr [1:10] "Date" "Location" "Location.Level" "Total.Cases" ...

Provide explanations here. 1.covid_subset <- Covid[1:10] Purpose: Creates a subset of the original Covid dataset containing the first 10 rows. Explanation: The code selects rows 1 to 10 from the Covid dataset and assigns them to the new data frame covid_subset 2.covid_matrix <- as.matrix(covid_subset) Purpose: Converts the covid_subset data frame into a matrix format. Explanation: The as.matrix() function transforms the data frame into a matrix, where each cell contains the corresponding value from the data frame. This can be useful for certain mathematical operations or compatibility with other functions that require matrix input. 3.str(covid_matrix) The str() function displays the internal structure of the R object covid_matrix. ## Create a new Data Frame 

# Create a new data frame, provide R codes here.
new_h <- data.frame(
Number_value = 1:3,
Character_value = factor(c("low", "medium", "high")))
str(new_h)
## 'data.frame':    3 obs. of  2 variables:
##  $ Number_value   : int  1 2 3
##  $ Character_value: Factor w/ 3 levels "high","low","medium": 2 3 1
levels(factor(new_h$"1"))
## character(0)
new_rcb <- c(5:7)
# Add vector to data frame using cbind()
new_h <- cbind(new_h, new_rcb)
View(new_h)
dim(new_h)
## [1] 3 3

Provide explanations here. Creating a Data Frame new_h - The data.frame() function generates a data frame named new_h. It consists of two columns: Number_value: A numeric column with values 1, 2, and 3. Character_value: A factor column with levels “low”, “medium”, and “high”. The str(new_h) command reveals the internal structure of the data frame. It displays column names, data types, and sample values. Modifying Levels of the Character_value Factor: The levels(factor(new_h$high)) command extracts the unique levels (categories) of the factor variable high within the data frame new_h. The resulting levels are: “low”, “medium”, and “high”. The cbind() function in R stands for column-bind. It allows you to combine vectors, matrices, or data frames by columns.