r-data

name	r-data
description	R data manipulation, formats, and database packages. Use for data wrangling with dplyr/data.table, reading files (CSV, Excel, JSON, Arrow), and database connections (SQL, MongoDB, Redis).

R Data Skill

Sub-skills

Sub-skill	Description
r-data-manipulation	dplyr, data.table, tidyr
r-data-formats	CSV, Excel, JSON, Arrow, Parquet
r-data-database	SQL, MongoDB, Redis connections

Data manipulation, formats, and database management in R.

Data Manipulation

Package	Description
dplyr ★	Fast data frames manipulation and database query
data.table ★	Fast manipulation with concise syntax
tidyr	Tidy messy data with spread/gather
reshape2 ★	Flexible reshape and aggregate
broom ★	Convert models to tidy data frames
tidyverse	Meta-package for data science
lubridate	Date/time manipulation
stringr ★	Consistent string processing
stringi ★	ICU-based string processing
fuzzyjoin	Join on inexact matching
rlist	Non-tabular data manipulation
bigmemory	Shared memory matrices
snakecase	Case conversion
DataExplorer	Fast EDA with minimum code

Data Formats

Package	Description
arrow ★	Apache Arrow interface
fst ★	Lightning fast serialization
feather ★	Fast binary data frame storage
readr ★	Fast tabular data reading
readxl ★	Read Excel files
haven	Read SPSS, Stata, SAS
jsonlite	JSON parsing
vroom	Fast delimited file reading
rio	Swiss-Army Knife for Data I/O
qs	Quick serialization
writexl	Write Excel files
yaml	YAML conversion
readODS	Read OpenDocument Spreadsheets
RcppTOML	TOML file parsing

Database Management

Package	Description
DBI	Common database interface
odbc	ODBC connections (DBI interface)
RSQLite	SQLite interface
RPostgres	PostgreSQL (DBI-compliant)
RMariaDB	MariaDB/MySQL interface
RMySQL	MySQL interface
ROracle	Oracle database interface
RODBC	ODBC database access
RJDBC	JDBC interface
mongolite	MongoDB streaming client
rmongodb	MongoDB driver
redux	Redis client
elastic	Elasticsearch HTTP API
RNeo4j	Neo4j graph database
RCassandra	Apache Cassandra interface
RHive	Apache Hive integration
rpostgis	PostGIS spatial interface

Quick Examples

# dplyr pipeline
library(dplyr)
df %>%
  filter(x > 10) %>%
  select(a, b) %>%
  mutate(c = a + b) %>%
  group_by(category) %>%
  summarise(mean = mean(c))

# data.table
library(data.table)
dt <- as.data.table(df)
dt[x > 10, .(mean = mean(c)), by = category]

# Read various formats
readr::read_csv("data.csv")
readxl::read_excel("data.xlsx")
arrow::read_parquet("data.parquet")
jsonlite::fromJSON("data.json")

# Database query
library(DBI)
con <- dbConnect(RSQLite::SQLite(), "db.sqlite")
dbGetQuery(con, "SELECT * FROM table WHERE x > 10")
dbDisconnect(con)

Resources

• dplyr: https://dplyr.tidyverse.org/
• data.table: https://rdatatable.gitlab.io/data.table/
• arrow: https://arrow.apache.org/docs/r/
• DBI: https://dbi.r-dbi.org/