install.packages(
c("rmarkdown","haven",
"DBI", "RMariaDB", "jsonlite"),
repos = "https://cloud.r-project.org"
)## package 'rmarkdown' successfully unpacked and MD5 sums checked
## package 'haven' successfully unpacked and MD5 sums checked## package 'DBI' successfully unpacked and MD5 sums checked
## package 'RMariaDB' successfully unpacked and MD5 sums checked## package 'jsonlite' successfully unpacked and MD5 sums checked##
## The downloaded binary packages are in
## C:\Users\MIFOTRA\AppData\Local\Temp\Rtmpa2SjaG\downloaded_packagesImport data from Statistical packages and from Database Management Systems.
R provides functions and packages that allow you to load data, analyze it, and create visualizations.
Statistical software often saves data in specific file formats.
First, install and load the haven package.
library(haven)
file_path <- "D:\\Document\\BIG-DATA\\R\\Assignment\\data\\"data <- read_dta(file.path(file_path, "survey.dta"))
View(data)
head(data)## # A tibble: 6 × 8
## student_id name age gender program year marks passed
## <dbl> <chr> <dbl> <chr> <chr> <dbl> <dbl> <dbl>
## 1 1001 Aline Mukamana 20 Female IT 2 78.5 1
## 2 1002 Jean Uwimana 22 Male Statistics 3 64 1
## 3 1003 Eric Niyonzima 19 Male Business 1 88 1
## 4 1004 Claudine Ingabire 21 Female IT 2 91.5 1
## 5 1005 Patrick Habimana 23 Male Economics 4 72 1
## 6 1006 Diane Uwamahoro 20 Female Statistics 2 84 1summary(data)## student_id name age gender program
## Min. :1001 Length :10 Min. :19.0 Length :10 Length :10
## 1st Qu.:1003 N.unique :10 1st Qu.:20.0 N.unique : 2 N.unique : 4
## Median :1006 N.blank : 0 Median :21.0 N.blank : 0 N.blank : 0
## Mean :1006 Min.nchar:12 Mean :21.1 Min.nchar: 4 Min.nchar: 2
## 3rd Qu.:1008 Max.nchar:19 3rd Qu.:22.0 Max.nchar: 6 Max.nchar:10
## Max. :1010 Max. :24.0
## year marks passed
## Min. :1.0 Min. :59.50 Min. :0.0
## 1st Qu.:2.0 1st Qu.:69.75 1st Qu.:1.0
## Median :2.0 Median :77.25 Median :1.0
## Mean :2.4 Mean :77.75 Mean :0.9
## 3rd Qu.:3.0 3rd Qu.:87.00 3rd Qu.:1.0
## Max. :4.0 Max. :95.00 Max. :1.0CSV files are common in statistical analysis.
data <- read.csv(file.path(file_path, "students.csv"))
View(data)Get Top 6 rows
head(data)## student_id name age gender program year marks passed
## 1 1001 Aline Mukamana 20 Female IT 2 78.5 True
## 2 1002 Jean Uwimana 22 Male Statistics 3 64.0 True
## 3 1003 Eric Niyonzima 19 Male Business 1 88.0 True
## 4 1004 Claudine Ingabire 21 Female IT 2 91.5 True
## 5 1005 Patrick Habimana 23 Male Economics 4 72.0 True
## 6 1006 Diane Uwamahoro 20 Female Statistics 2 84.0 TrueGet CSV Summary
summary(data)## student_id name age gender program
## Min. :1001 Length :10 Min. :19.0 Length :10 Length :10
## 1st Qu.:1003 N.unique :10 1st Qu.:20.0 N.unique : 2 N.unique : 4
## Median :1006 N.blank : 0 Median :21.0 N.blank : 0 N.blank : 0
## Mean :1006 Min.nchar:12 Mean :21.1 Min.nchar: 4 Min.nchar: 2
## 3rd Qu.:1008 Max.nchar:19 3rd Qu.:22.0 Max.nchar: 6 Max.nchar:10
## Max. :1010 Max. :24.0
## year marks passed
## Min. :1.0 Min. :59.50 Length :10
## 1st Qu.:2.0 1st Qu.:69.75 N.unique : 2
## Median :2.0 Median :77.25 N.blank : 0
## Mean :2.4 Mean :77.75 Min.nchar: 4
## 3rd Qu.:3.0 3rd Qu.:87.00 Max.nchar: 5
## Max. :4.0 Max. :95.00A DBMS stores structured data in tables. R can connect to a database, execute SQL queries, and retrieve the results into a data frame. Common database systems include: - MySQL - PostgreSQL - Microsoft SQL Server - Oracle Database - SQLite
Install the package:
library(DBI)
library(RMariaDB)
con <- dbConnect(
RMariaDB::MariaDB(),
dbname = "PayrollDB",
host = "localhost",
port = 3306,
user = "root",
password = "Password123!"
)
employees <- dbGetQuery(
con,
"SELECT e.EmployeeID, d.DepartmentName, p.PositionName, e.EmploymentDate, e.EmploymentDate, e.Status
FROM payrolldb.employees e
JOIN payrolldb.positions p on p.PositionID=e.PositionID
JOIN payrolldb.departments d on d.DepartmentID=e.DepartmentID
;"
)
View(employees)
dbDisconnect(con)head(employees)## EmployeeID DepartmentName PositionName EmploymentDate EmploymentDate Status
## 1 1 IT Finance offer 2026-10-21 2026-10-21 Active
## 2 3 CS Finance offer 2026-10-21 2026-10-21 Activelibrary(jsonlite)
users <- fromJSON(
"https://jsonplaceholder.typicode.com/users"
)
View(users)head(users)## id name username email
## 1 1 Leanne Graham Bret Sincere@april.biz
## 2 2 Ervin Howell Antonette Shanna@melissa.tv
## 3 3 Clementine Bauch Samantha Nathan@yesenia.net
## 4 4 Patricia Lebsack Karianne Julianne.OConner@kory.org
## 5 5 Chelsey Dietrich Kamren Lucio_Hettinger@annie.ca
## 6 6 Mrs. Dennis Schulist Leopoldo_Corkery Karley_Dach@jasper.info
## address.street address.suite address.city address.zipcode address.geo.lat
## 1 Kulas Light Apt. 556 Gwenborough 92998-3874 -37.3159
## 2 Victor Plains Suite 879 Wisokyburgh 90566-7771 -43.9509
## 3 Douglas Extension Suite 847 McKenziehaven 59590-4157 -68.6102
## 4 Hoeger Mall Apt. 692 South Elvis 53919-4257 29.4572
## 5 Skiles Walks Suite 351 Roscoeview 33263 -31.8129
## 6 Norberto Crossing Apt. 950 South Christy 23505-1337 -71.4197
## address.geo.lng phone website company.name
## 1 81.1496 1-770-736-8031 x56442 hildegard.org Romaguera-Crona
## 2 -34.4618 010-692-6593 x09125 anastasia.net Deckow-Crist
## 3 -47.0653 1-463-123-4447 ramiro.info Romaguera-Jacobson
## 4 -164.2990 493-170-9623 x156 kale.biz Robel-Corkery
## 5 62.5342 (254)954-1289 demarco.info Keebler LLC
## 6 71.7478 1-477-935-8478 x6430 ola.org Considine-Lockman
## company.catchPhrase company.bs
## 1 Multi-layered client-server neural-net harness real-time e-markets
## 2 Proactive didactic contingency synergize scalable supply-chains
## 3 Face to face bifurcated interface e-enable strategic applications
## 4 Multi-tiered zero tolerance productivity transition cutting-edge web services
## 5 User-centric fault-tolerant solution revolutionize end-to-end systems
## 6 Synchronised bottom-line interface e-enable innovative applications