R Data Pre-Processing & Data Management - Shape your Data!

Import data into R studio using standard csv formats with base and reader interfaces, adjust delimiters and encoding, and copy-paste snippets with the can function.

Explore how the fread function from data.table speeds importing big data in R, auto detects separators, returns a data.table, and supports clipboard paste and optional data.frame class.

Choose among data frame, data table, and data underscore frame to store and manipulate data; data table offers faster, lower-code operations, while data underscore frame enforces equal lengths.

Access the exercise database at Mianus Tutorials Dotcom, featuring blocks of 10 R questions on functions, data, frame handling, and graphs you can open and work on.

Learn how tidy data looks and how to clean a new data frame after import using tidyr to switch between wide and long formats and ensure one variable per column.

Convert a wide data set to long form in R using gather or melt, turning header columns into a single weight column with counts for each region.

Split a mixed biometrics column into hate and sex using the separate function after the third position. Rely on an equal-length pattern and fixed splitting points to simplify analysis.

Convert long form data to wide form by spreading the performance column into top and low counts using the spread function (TTR) or diecast, with optional fills for missing values.

Discover the data.table package as an advanced, faster alternative to data frames for pre-processing, manipulation, and querying in R, with an SQL-like i, j, by structure.

Learn to compare data.frame and data.table objects, set up test data, and use bracket-based querying to retrieve rows or columns in R.

Extract and rename column data in data.table using j and dot notation; compare list vs dot shorthand, count with .N, and filter with i or subset for data.frame equivalents.

Master the by parameter in data.table to group and summarize data, using i and j to sum column c for selected rows and count with .N.

Set keys in data.table to order and query data by a multi-column key. Transform row IDs into a sortable reference and learn to index and search by key in R.

Work with data.table on the diamonds dataset from ggplot2, converting to newdiam and solving exercises on price, color, cut, and data.table versus data.frame.

Explore data.table solutions in R for data pre-processing and data management, including counting diamonds with price greater than 10.000, grouping by color, and filtering by cut.

Develop skills in queries, filtering, and subsetting using the DataDot frame and data table systems for core data science tasks at entry-level roles.

Explore data frames through hands-on exercises on subsetting, filtering, and reformatting variables using iris and diamonds datasets, with guidance on data frame classes and base R functionality.

Explore hands-on data wrangling with iris and empty cars: subset and filter rows by species, compute sepal length–width differences, convert classes, and apply rep and round for clean data.

Explore data pre-processing in R with the diamonds data: filter by clarity and depth, count observations, convert to data frame or tibble, and use table-based extractions for targeted views.

Explore 10 practical data.table exercises in R to master fast data manipulation, subsetting, column operations, and key-based queries on the college data set.

Learn data.table techniques for pre-processing the college data set: convert to data table, subset, compute enrollment ratios, and reorder columns with set.

Master data.table techniques to add and remove columns, create a high-interest marker for APS over 10,000, and perform fast counts, subsetting, and ordering using keys and by.

Explore the deployer package and its five single table verbs—filter, arrange, select, mutate, and samurais—to manage tidy data frames with efficient storage and direct column references.

Learn how two-table mutating joins and filtering joins in R combine data frames by common keys, including inner, left, right, and full joins, with practical examples.

Explore filtering joins in R using inner_join, semi join, and anti join to control row matching and keep data from the first data frame, with by for mismatched column names.

Explore set operations with two data frames using union, intersect, and setdiff in the Deployer package, showing how identical structure enables inner, outer, left, and right joins on tidy data.

Explore how the deployer package translates R code to sql for Escorial database via translate_underscore. Compare numeric versus integer, object versus string, and boolean concepts as they convert to sql.

Learn to run sql-like queries inside R to filter the diamonds data frame, selecting carat > 3 and combining carat > 2.5 with color or x > 4.1.

Set up an on-machine SQLite database in R with the CircuLite package, load the diamonds dataset, and create and query the table with prices and colors.

Identify how outliers influence results, apply three-sigma and box-plot rules, and compare model-based and proximity-based multivariate detection methods for clear visualization.

Identify outliers in univariate data using simple methods like the esd interval and box plot statistics, then validate with the Dixon and graps test from the outliers package.

Detect outliers in multivariate data using sign one, sign two, and pick out methods that rely on PCA and covariance matrices, with practical R tools.

Learn to handle strings in R, define text data, and use base functions to lower, upper, and split, with regular expressions for text mining and data cleaning.

Explore the gsub and sub family of functions in R to perform pattern-based substitutions, ignore case, and manipulate strings, including removing numbers and spaces with regular expressions.

Master regular expressions syntax in R to match digits, spaces, words, and punctuation, using brackets, quantifiers, and replacements to clean text and remove nongraphic characters.

Explore the stringer package for string manipulation in r, using sdr_ functions to concatenate strings, count occurrences, locate positions, and perform replace and replace all operations.

Practice using regular expressions and a global substitute in R to clean strings, converting to lowercase, removing punctuation, spaces, and the euro sign, then extract the pattern is with str_extract_all.

Master time series management by standardizing date formats, handling time zones and units, and importing series from Excel, using the lubricant package in tidy reverse.

Import a monthly time series from Excel by cleaning headers and ensuring a data vector in R with scan, then create ts with start and frequency and plot to verify.

Compare base date-time tools in R, including POSIXct and POSIXlt and the Date class, with chron and lubridate to manage time zones, leap years, and seconds efficiently.

Install and activate the lubricate package to input dates in y, ymd, and dmy. Create time points with UTC or Central European Time, extract minute and day, and adjust hours.

Use lubridate's W Day function to identify the weekday and display the day name with label, then translate time points between zones and create intervals with interval and class interval.

Create a three-column data frame in R with date, time, and measurement using lubridate, explore multiple time input formats, and bind columns into a cohesive data frame.

Learn to perform time calculations with lubridate, handling minutes, seconds, and durations, and compare the years command with duration_years to understand leap year behavior.

Practice lubridate data handling by creating time points with year, month, day, and hour, adjusting minutes, and comparing Central European Time with London's Greenwich Mean Time to compute differences.

Explore the exercise database at Mianus Tutorials, organized in blocks of ten questions on topics like functions, data, frame handling, and graphs. Complete questions and outputs first to avoid cheating.