Sys.setenv(
DB_USER = "root",
DB_PASSWORD = "Password123!",
DB_HOST = "localhost"
)R is a programming language and statistical computing environment used for data manipulation, statistical analysis, and visualization. One of the first steps in data analysis is importing data from different sources into R.
This assignment demonstrates how to import data from statistical software, flat files, relational database management systems (DBMS), and public APIs.
The objectives are to:
.dta)..csv)..sav)..sas7bdat).R packages extend the functionality of the R environment. The following packages are required for this assignment.
Package
haven: Imports data from Stata, SPSS, and SAS.
DBI: Provides a standard interface for connecting to
databases. RMariaDB: Connects R to MySQL-compatible and
MariaDB database servers. jsonlite | Converts JSON data
into R objects. rmarkdown | Generates reports in HTML and
PDF formats.
Install the packages once by running the following code in the R console. Package installation is not required every time the report is generated.
install.packages(
c("rmarkdown", "haven", "DBI", "RMariaDB", "jsonlite"),
repos = "https://cloud.r-project.org"
)## Installing packages into 'C:/Users/MIFOTRA/AppData/Local/R/win-library/4.6'
## (as 'lib' is unspecified)## package 'rmarkdown' successfully unpacked and MD5 sums checked
## package 'haven' successfully unpacked and MD5 sums checked## Warning: cannot remove prior installation of package 'haven'## Warning in file.copy(savedcopy, lib, recursive = TRUE): problem copying
## C:\Users\MIFOTRA\AppData\Local\R\win-library\4.6\00LOCK\haven\libs\x64\haven.dll
## to C:\Users\MIFOTRA\AppData\Local\R\win-library\4.6\haven\libs\x64\haven.dll:
## Permission denied## Warning: restored 'haven'## package 'DBI' successfully unpacked and MD5 sums checked
## package 'RMariaDB' successfully unpacked and MD5 sums checked## Warning: cannot remove prior installation of package 'RMariaDB'## Warning in file.copy(savedcopy, lib, recursive = TRUE): problem copying
## C:\Users\MIFOTRA\AppData\Local\R\win-library\4.6\00LOCK\RMariaDB\libs\x64\RMariaDB.dll
## to
## C:\Users\MIFOTRA\AppData\Local\R\win-library\4.6\RMariaDB\libs\x64\RMariaDB.dll:
## Permission denied## Warning: restored 'RMariaDB'## package 'jsonlite' successfully unpacked and MD5 sums checked## Warning: cannot remove prior installation of package 'jsonlite'## Warning in file.copy(savedcopy, lib, recursive = TRUE): problem copying
## C:\Users\MIFOTRA\AppData\Local\R\win-library\4.6\00LOCK\jsonlite\libs\x64\jsonlite.dll
## to
## C:\Users\MIFOTRA\AppData\Local\R\win-library\4.6\jsonlite\libs\x64\jsonlite.dll:
## Permission denied## Warning: restored 'jsonlite'##
## The downloaded binary packages are in
## C:\Users\MIFOTRA\AppData\Local\Temp\RtmpYBR5Kb\downloaded_packagesThe following setup chunk loads the packages needed for the assignment and configures the report.
The datasets used in this assignment are stored in a local directory on the computer.
file_path <- "D:/Document/BIG-DATA/R/Assignment/data/"
# Check whether the directory exists
dir.exists(file_path)## [1] TRUE# List the files available in the directory
list.files(file_path)## [1] "student_scores.sas7bdat" "students.csv"
## [3] "survey.dta"The file_path variable stores the directory containing
the local datasets. The dir.exists() function checks
whether the directory is available, while list.files()
displays its contents.
The following files are expected:
survey.dtastudents.csvstudent_scores.sas7bdatThe SPSS example in Section 4 is downloaded from a public GitHub URL.
Stata is statistical software commonly used in economics, social
sciences, public policy, and research. It stores datasets in files with
the .dta extension.
The haven package provides the read_dta()
function for importing Stata datasets into R.
stata_data <- read_dta(
file.path(file_path, "survey.dta")
)
head(stata_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 1# Display the first six rows
head(stata_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 1# Display the structure and variable types
str(stata_data)## tibble [10 × 8] (S3: tbl_df/tbl/data.frame)
## $ student_id: num [1:10] 1001 1002 1003 1004 1005 ...
## ..- attr(*, "format.stata")= chr "%12.0g"
## $ name : chr [1:10] "Aline Mukamana" "Jean Uwimana" "Eric Niyonzima" "Claudine Ingabire" ...
## ..- attr(*, "format.stata")= chr "%19s"
## $ age : num [1:10] 20 22 19 21 23 20 24 19 22 21
## ..- attr(*, "format.stata")= chr "%12.0g"
## $ gender : chr [1:10] "Female" "Male" "Male" "Female" ...
## ..- attr(*, "format.stata")= chr "%6s"
## $ program : chr [1:10] "IT" "Statistics" "Business" "IT" ...
## ..- attr(*, "format.stata")= chr "%10s"
## $ year : num [1:10] 2 3 1 2 4 2 4 1 3 2
## ..- attr(*, "format.stata")= chr "%12.0g"
## $ marks : num [1:10] 78.5 64 88 91.5 72 84 59.5 76 69 95
## ..- attr(*, "format.stata")= chr "%10.0g"
## $ passed : num [1:10] 1 1 1 1 1 1 0 1 1 1
## ..- attr(*, "format.stata")= chr "%8.0g"# Display the number of rows and columns
dim(stata_data)## [1] 10 8# Display the column names
names(stata_data)## [1] "student_id" "name" "age" "gender" "program"
## [6] "year" "marks" "passed"read_dta() reads the Stata file and returns an R data
frame or tibble.head() displays the first six rows by default.str() displays the dataset’s structure, including
variable names and data types.dim() returns the number of rows and columns.names() returns the variable names.Result: The Stata dataset is available in R for further processing and statistical analysis.
CSV stands for Comma-Separated Values. A CSV file stores tabular data as text, with records typically separated by line breaks and fields separated by commas.
CSV is widely used because it is supported by spreadsheet applications, databases, statistical software, and programming languages.
R provides the built-in read.csv() function, so an
additional package is not required.
csv_data <- read.csv(
file.path(file_path, "students.csv"),
header = TRUE,
stringsAsFactors = FALSE
)# Display the first six rows
head(csv_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 True# Examine variable types and structure
str(csv_data)## 'data.frame': 10 obs. of 8 variables:
## $ student_id: int 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010
## $ name : chr "Aline Mukamana" "Jean Uwimana" "Eric Niyonzima" "Claudine Ingabire" ...
## $ age : int 20 22 19 21 23 20 24 19 22 21
## $ gender : chr "Female" "Male" "Male" "Female" ...
## $ program : chr "IT" "Statistics" "Business" "IT" ...
## $ year : int 2 3 1 2 4 2 4 1 3 2
## $ marks : num 78.5 64 88 91.5 72 84 59.5 76 69 95
## $ passed : chr "True" "True" "True" "True" ...# Count rows and columns
dim(csv_data)## [1] 10 8# Display column names
names(csv_data)## [1] "student_id" "name" "age" "gender" "program"
## [6] "year" "marks" "passed"read.csv() reads the CSV file into R.header = TRUE indicates that the first row contains
column names.stringsAsFactors = FALSE prevents character columns
from automatically being converted into factors in R versions where this
setting applies.head(csv_data) displays the first six records.Result: The CSV file is imported into an R object
named csv_data.
SPSS (Statistical Package for the Social Sciences) is statistical software used for survey analysis, social research, and other forms of quantitative analysis.
SPSS datasets commonly use the .sav extension. These
files may contain variable labels, value labels, and other metadata.
The haven package provides the read_sav()
function for reading SPSS files.
The following example uses the Iris dataset provided in the
haven package’s public GitHub repository.
sav_url <- paste0(
"https://raw.githubusercontent.com/tidyverse/haven/",
"HEAD/inst/examples/iris.sav"
)
iris_spss <- read_sav(sav_url)
head(iris_spss)## # A tibble: 6 × 5
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## <dbl> <dbl> <dbl> <dbl> <dbl+lbl>
## 1 5.1 3.5 1.4 0.2 1 [setosa]
## 2 4.9 3 1.4 0.2 1 [setosa]
## 3 4.7 3.2 1.3 0.2 1 [setosa]
## 4 4.6 3.1 1.5 0.2 1 [setosa]
## 5 5 3.6 1.4 0.2 1 [setosa]
## 6 5.4 3.9 1.7 0.4 1 [setosa]# Display the first six rows
head(iris_spss)## # A tibble: 6 × 5
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## <dbl> <dbl> <dbl> <dbl> <dbl+lbl>
## 1 5.1 3.5 1.4 0.2 1 [setosa]
## 2 4.9 3 1.4 0.2 1 [setosa]
## 3 4.7 3.2 1.3 0.2 1 [setosa]
## 4 4.6 3.1 1.5 0.2 1 [setosa]
## 5 5 3.6 1.4 0.2 1 [setosa]
## 6 5.4 3.9 1.7 0.4 1 [setosa]# Examine the structure
str(iris_spss)## tibble [150 × 5] (S3: tbl_df/tbl/data.frame)
## $ Sepal.Length: num [1:150] 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
## ..- attr(*, "format.spss")= chr "F8.2"
## $ Sepal.Width : num [1:150] 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
## ..- attr(*, "format.spss")= chr "F8.2"
## $ Petal.Length: num [1:150] 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
## ..- attr(*, "format.spss")= chr "F8.2"
## $ Petal.Width : num [1:150] 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
## ..- attr(*, "format.spss")= chr "F8.2"
## $ Species : dbl+lbl [1:150] 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
## ..@ format.spss: chr "F8.0"
## ..@ labels : Named num [1:3] 1 2 3
## .. ..- attr(*, "names")= chr [1:3] "setosa" "versicolor" "virginica"# Count rows and columns
dim(iris_spss)## [1] 150 5# Display variable names
names(iris_spss)## [1] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width" "Species"sav_url stores the URL of the SPSS file.read_sav() downloads and reads the file into R.head() displays the first six observations.str() displays the variable types and structure.Result: The SPSS dataset is imported into an R
object named iris_spss.
Note: An internet connection is required to retrieve the file from GitHub.
SAS (Statistical Analysis System) is software used for statistical analysis, data management, and business intelligence.
SAS datasets can be stored in files with the .sas7bdat
extension. The haven package provides the
read_sas() function to import these files.
sas_data <- read_sas(
file.path(file_path, "student_scores.sas7bdat")
)
head(sas_data)## # A tibble: 6 × 7
## student_id gender course_code course_name semester score grade
## <chr> <chr> <chr> <chr> <chr> <dbl> <chr>
## 1 STU0001 F CSC201 Data Structures 2025-S2 77.4 B
## 2 STU0001 F ECO110 Principles of Economics 2025-S1 72.5 B
## 3 STU0001 F ENG120 Academic Writing 2025-S1 85.7 A
## 4 STU0001 F MTH102 Calculus I 2025-S2 72.8 B
## 5 STU0002 F BIO130 General Biology 2025-S2 58.5 D
## 6 STU0002 F ECO110 Principles of Economics 2025-S1 45.2 F# Display the first six rows
head(sas_data)## # A tibble: 6 × 7
## student_id gender course_code course_name semester score grade
## <chr> <chr> <chr> <chr> <chr> <dbl> <chr>
## 1 STU0001 F CSC201 Data Structures 2025-S2 77.4 B
## 2 STU0001 F ECO110 Principles of Economics 2025-S1 72.5 B
## 3 STU0001 F ENG120 Academic Writing 2025-S1 85.7 A
## 4 STU0001 F MTH102 Calculus I 2025-S2 72.8 B
## 5 STU0002 F BIO130 General Biology 2025-S2 58.5 D
## 6 STU0002 F ECO110 Principles of Economics 2025-S1 45.2 F# Examine variable types
str(sas_data)## tibble [800 × 7] (S3: tbl_df/tbl/data.frame)
## $ student_id : chr [1:800] "STU0001" "STU0001" "STU0001" "STU0001" ...
## ..- attr(*, "label")= chr "Student ID"
## $ gender : chr [1:800] "F" "F" "F" "F" ...
## ..- attr(*, "label")= chr "Gender"
## $ course_code: chr [1:800] "CSC201" "ECO110" "ENG120" "MTH102" ...
## ..- attr(*, "label")= chr "Course code"
## $ course_name: chr [1:800] "Data Structures" "Principles of Economics" "Academic Writing" "Calculus I" ...
## ..- attr(*, "label")= chr "Course name"
## $ semester : chr [1:800] "2025-S2" "2025-S1" "2025-S1" "2025-S2" ...
## ..- attr(*, "label")= chr "Semester"
## $ score : num [1:800] 77.4 72.5 85.7 72.8 58.5 45.2 71.5 41.4 53.4 71.5 ...
## ..- attr(*, "label")= chr "Final score (0-100)"
## $ grade : chr [1:800] "B" "B" "A" "B" ...
## ..- attr(*, "label")= chr "Letter grade"# Count rows and columns
dim(sas_data)## [1] 800 7# Display variable names
names(sas_data)## [1] "student_id" "gender" "course_code" "course_name" "semester"
## [6] "score" "grade"read_sas() reads the SAS dataset into R.file.path() constructs the file path using the data
directory and filename.head() displays the first six observations.str() provides information about the dataset’s
variables and structure.Result: The SAS dataset is imported into an R object
named sas_data.
A Database Management System (DBMS) is software used to store, organize, retrieve, and manage data.
Common database management systems include:
Unlike CSV and statistical data files, a database usually requires a connection before data can be retrieved.
R can connect to databases, execute SQL statements, and return query results as data frames.
The following packages are used for database connectivity:
DBI: Defines a common interface for database
operations.RMariaDB: Provides a database driver for
MySQL-compatible and MariaDB servers.library(DBI)
library(RMariaDB)The following example assumes that a MySQL-compatible database named
PayrollDB is running on the local computer.
For security, database credentials should be stored in environment variables rather than written directly into an assignment or report.
db_host <- Sys.getenv("DB_HOST", unset = "localhost")
db_name <- "PayrollDB"
db_user <- Sys.getenv("DB_USER")
db_password <- Sys.getenv("DB_PASSWORD")
if (db_user == "" || db_password == "") {
stop(
"Set the DB_USER and DB_PASSWORD environment variables ",
"before running this section."
)
}
con <- dbConnect(
RMariaDB::MariaDB(),
dbname = db_name,
host = db_host,
port = 3306,
user = db_user,
password = db_password
)
dbIsValid(con)## [1] TRUEThe dbConnect() function establishes the connection to
the database.
The dbIsValid() function checks whether the connection
is valid.
Before running this section, configure the DB_USER,
DB_PASSWORD, and, if necessary, DB_HOST
environment variables on your computer.
The following query retrieves employee information together
employees <- tryCatch(
{
dbGetQuery(
con,
"
SELECT
e.EmployeeID,
d.DepartmentName,
p.PositionName,
e.EmploymentDate,
e.Status
FROM Employees AS e
INNER JOIN Positions AS p
ON p.PositionID = e.PositionID
INNER JOIN Departments AS d
ON d.DepartmentID = e.DepartmentID
"
)
},
finally = {
dbDisconnect(con)
}
)
head(employees)## EmployeeID DepartmentName PositionName EmploymentDate Status
## 1 1 IT Finance offer 2026-10-21 Active
## 2 3 CS Finance offer 2026-10-21 ActiveThis example assumes that the Employees, Positions, and Departments tables are available in the PayrollDB database and have the specified columns.
# Display the first six records
head(employees)## EmployeeID DepartmentName PositionName EmploymentDate Status
## 1 1 IT Finance offer 2026-10-21 Active
## 2 3 CS Finance offer 2026-10-21 Active# Examine the data structure
str(employees)## 'data.frame': 2 obs. of 5 variables:
## $ EmployeeID : int 1 3
## $ DepartmentName: chr "IT" "CS"
## $ PositionName : chr "Finance offer" "Finance offer"
## $ EmploymentDate: Date, format: "2026-10-21" "2026-10-21"
## $ Status : chr "Active" "Active"# Count rows and columns
dim(employees)## [1] 2 5# Display column names
names(employees)## [1] "EmployeeID" "DepartmentName" "PositionName" "EmploymentDate"
## [5] "Status"An Application Programming Interface (API) allows one software application to communicate with another.
A REST API commonly exposes resources through HTTP endpoints. Data is often returned in JSON (JavaScript Object Notation) format.
JSON is a lightweight data-interchange format that represents objects, arrays, strings, numbers, Boolean values, and null values.
The jsonlite package provides functions for converting JSON responses into R objects.
This example uses JSONPlaceholder, a public demonstration API that provides sample user records.
library(jsonlite)
api_url <- "https://jsonplaceholder.typicode.com/users"
users <- fromJSON(api_url)
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# Display the first six 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# Examine the structure
str(users)## 'data.frame': 10 obs. of 8 variables:
## $ id : int 1 2 3 4 5 6 7 8 9 10
## $ name : chr "Leanne Graham" "Ervin Howell" "Clementine Bauch" "Patricia Lebsack" ...
## $ username: chr "Bret" "Antonette" "Samantha" "Karianne" ...
## $ email : chr "Sincere@april.biz" "Shanna@melissa.tv" "Nathan@yesenia.net" "Julianne.OConner@kory.org" ...
## $ address :'data.frame': 10 obs. of 5 variables:
## ..$ street : chr "Kulas Light" "Victor Plains" "Douglas Extension" "Hoeger Mall" ...
## ..$ suite : chr "Apt. 556" "Suite 879" "Suite 847" "Apt. 692" ...
## ..$ city : chr "Gwenborough" "Wisokyburgh" "McKenziehaven" "South Elvis" ...
## ..$ zipcode: chr "92998-3874" "90566-7771" "59590-4157" "53919-4257" ...
## ..$ geo :'data.frame': 10 obs. of 2 variables:
## .. ..$ lat: chr "-37.3159" "-43.9509" "-68.6102" "29.4572" ...
## .. ..$ lng: chr "81.1496" "-34.4618" "-47.0653" "-164.2990" ...
## $ phone : chr "1-770-736-8031 x56442" "010-692-6593 x09125" "1-463-123-4447" "493-170-9623 x156" ...
## $ website : chr "hildegard.org" "anastasia.net" "ramiro.info" "kale.biz" ...
## $ company :'data.frame': 10 obs. of 3 variables:
## ..$ name : chr "Romaguera-Crona" "Deckow-Crist" "Romaguera-Jacobson" "Robel-Corkery" ...
## ..$ catchPhrase: chr "Multi-layered client-server neural-net" "Proactive didactic contingency" "Face to face bifurcated interface" "Multi-tiered zero tolerance productivity" ...
## ..$ bs : chr "harness real-time e-markets" "synergize scalable supply-chains" "e-enable strategic applications" "transition cutting-edge web services" ...# Count the records and columns
dim(users)## [1] 10 8# Display column names
names(users)## [1] "id" "name" "username" "email" "address" "phone" "website"
## [8] "company"This assignment demonstrates how R imports data from different statistical packages, local files, relational databases, and public APIs.
The haven package supports Stata, SPSS, and SAS formats, while base R provides functions for importing CSV files. The DBI and RMariaDB packages enable database connectivity and SQL-based retrieval. The jsonlite package supports importing JSON data from REST APIs.
Understanding these methods is essential for data preparation, statistical analysis, business intelligence, and data analytics. Selecting the appropriate import method depends on the source of the data, its structure, the required metadata, and the way the data will be used in subsequent analysis.