R Programming for Data Analysis: Ultimate Guide

Learn to load and write data in csv or excel formats and set input and output folders. Explore R data objects: vectors, factors with levels, matrices, lists, and data frames.

Define file paths and set the working directory in R, then load data from single or multiple CSV and Excel files using read_csv and read_excel.

Learn to export data frames to csv and xls in R with file naming and optional row names, and explore data types, column renaming, and formatting.

This lab shows how to define a folder path and set up a directory, load subway delay data in csv and excel formats, and save results as csv or excel.

Learn to install and load R packages in RStudio by defining a package list, checking against already installed packages, and loading libraries for CSV and Excel data.

Define the input path and set the working directory with setwd in R programming, then verify with getwd; switch to output path as needed, preparing the workspace for loading data.

Learn to load csv and excel data into the R workspace, read single and multiple files, and combine them into a single data frame with proper headers and data types.

Learn to write data to csv and excel in R, including custom file names, paths, and headers, with multi-sheet workbooks and error avoidance tips.

Capture a snapshot date from weekly file names, insert it as a snapshot date column, and append data frames into one large frame to track changes over time.

Explore data types in R, check and rename columns, and perform data type conversion and formatting for dates and character values in real-world data analysis.

Check column names in a data object with names(data) and inspect specific variables. Rename columns by assigning new names with the equals operator, converting spaces to underscores.

Learn to format date objects in R using the format argument to extract year, month, and day, and to build year-month time intelligence columns for aggregation.

Learn to format characters in R with sprintf to add leading zeros, using six- or eight-digit width. Convert between character and numeric, apply the format, and preserve zeros.

Learn to check data types, rename columns, and format data in a fire incident dataset; add leading zeros to IDs for consistent character representation in databases.

Load the incident data from Excel into df_incident, check data types with summary.default and class, then convert date columns with as.Date (origin) and fix numeric/date issues.

Rename columns in df_incident using the names function, assign simpler labels such as fire_under_control_dt, then recheck names to confirm changes.

Learn to format dates in R using the format function to extract day, month, year, and year-month. Use subset and distinct to create a clean, duplicate-free data frame.

Learn to add leading zeros in R by extracting a substring, converting to numeric, formatting with sprintf('%012d'), and converting back to character to preserve 12-digit IDs.

Learn to clean data in R by handling duplicates, missing values, and data quality issues. Drop columns, remove duplicates, and apply simple imputation with mean, median, or mode.

Learn to recode and replace values in a data frame using the record and replace value functions, with conditional handling of unmatched values, negatives, and missing data.

Learn to rearrange a data frame by column names or indexes, and sort data by columns with ascending or descending order using square brackets and the order function in R.

Learn to subset data in R using the subset function to select columns, subset rows by lists, and apply conditions.

Learn to clean, sort, and subset data in R using the sample commercial properties pricing data; drop columns, remove duplicates, replace values, and subset by conditions.

Load toronto listing data from an Excel file into DF_listing, drop an unused column, back up to DF_listing_BCP, then check and remove duplicates to prepare for data cleaning.

Demonstrate recoding a variable in R using the unique function to list distinct property type values. Rename them to Building_commercial and Building Industrial, then verify updated values for data governance.

Learn to clean data in R by replacing missing values with minus one or the mean, using na checks and conditional replacements in data frames.

Learn to subset a data frame, select specific variables, and rearrange the column order by name or index to achieve your desired data structure.

Learn to sort a subsetted data frame in R using the order function, sorting first by district and then by id, with options for ascending and descending order.

Learn to subset data in R using the subset function, in-operator, and multiple conditions to extract Downtown Toronto records with square foot available greater than 80,000.

Explore joining data in R with dplyr, base, and SQL, covering inner, outer, anti, and cross joins and primary and foreign key concepts.

Learn to perform inner, left, and anti joins with the dplyr package in R by joining two tables, customer demographic and fact sales, on a common key with optional suffixes.

Use the SQL df package in R to perform inner, left, and anti joins on two data frames, using on keys, aliases, and optional where clauses.

Learn how to perform a temporal join by matching enrollment dates to an employee hierarchy using record effective and expiry dates, to identify the approving manager at the enrollment date.

Explore an advanced join problem using a subquery with max to identify the latest invoice owner per expense, employing self-join, group by invoice number, and max update date.

Learn to perform data joins in R using dplyr and SQL df packages, including inner, left, and anti joins, across tables with temporal joins and date-based primary key–foreign key relationships.

Learn to perform inner, left, anti, and outer joins with dplyr, load multiple data frames, and fix dates by converting integers with origin 1970.

Explore inner join, left outer join, and anti join between product and complaint data frames using the SQL package in R, selecting all columns and matching on product id.

Perform joins on multiple tables using dplyr and sql df approaches, combining complaint, prod, issue, and company data, deduplicating keys, and saving to an xls file.

Perform advanced temporal joins in r to link expense invoices with employee hierarchy over time, using methods and between the record effective date and expiry date to align enrollment dates.

Learn to use a subquery with max to find the latest invoice owner for expense invoices in R, by filtering update dates against enrollment dates and joining on invoice numbers.

Create a record id from branch, customer, and account numbers; compare January 31 2021 and May 31 2021 datasets using inner and anti-joins to track balance and status changes.

master data aggregation and feature engineering in r with summarize, group_by, and mutate to compute kpis. derive total price, total cost, and net revenue, and apply per-customer aggregation.

Learn to filter and slice data with dplyr, using filter, slice, and optional group by for aggregation. Apply and/or logic and max to select latest per customer by transaction date.

Learn to convert a long-format summary table to wide format in R using dcast, melt, and dplyr, with brand-level transaction counts, unique customers, and net paid for 2015–2016.

apply feature engineering with mutate to create new features from existing data, such as age from date of birth and age groups using if-else and break-based categorization.

Master data analysis in R by summarizing UK online retail data, computing KPIs, and performing feature engineering with dplyr to aggregate by customer and format summary tables.

Learn to summarize and aggregate data with the dplyr package, computing transaction counts, unique customer counts, total price, and total quantity.

Learn to filter data with dplyr using and and or conditions, create df_filter_one and df_retail_sales, apply stock_code, country, and quantity filters, and aggregate by customer ID.

Group customers by customer id and invoice date, aggregate total price with sum, then use slice max on invoice date to identify the latest purchase and amount spent.

Sort data with dplyr to arrange by total price in ascending order using df_order_asc, then in descending order.

Create a summary table by grouping by country and invoice year, calculating transaction count, distinct customer count, and total price, then convert between long and wide formats for year-over-year analysis.

Subset the retail data, compute duration from invoice date to today using mutate, create duration groups with cut, and recode stock codes with if-else for business meaning.

Learn time intelligence in r by manipulating dates with lubridate to compute time features—year, month, week, weekday—and intervals between dates for event sequence analysis and business day calculations.

Learn to calculate event sequences in R by arranging data by unique ID and date, then aggregating by ID and mutating to compute a per-customer transaction sequence.

Learn to calculate business days between dates using a business day package, excluding weekends and public holidays with a custom calendar, and apply a minus one correction for same-day cases.

Calculate the daily online transaction KPI as the percentage of online transactions over total transactions, extend to weekly and monthly frequencies using numerator and denominator, merge results, and clean values.

Learn time intelligence analytics by deriving time features from date manipulation and event sequence analysis, then compute business days and KPIs across daily, weekly, and monthly frequencies using Netflix data.

Learn to derive time-based features from date added in a Netflix titles dataset using R, extracting year, month, week, day, and weekday, and creating pre/post six-month date features.

Learn to calculate date intervals in R using the interval function, computing durations in days, months, and years between a start date and today's date with a sample data frame.

Compute event sequence analysis by director in R, preparing and aggregating data, deriving previous dates, calculating month gaps, and flagging sequences within a year to reveal director-led release patterns.

Calculate business days in R using the bizdays package with a custom Canadian calendar, loading holidays from an Excel file, and compute day counts between two dates.

Calculate the daily snapshot percentage of TV-14 titles by date added, using a numerator of distinct TV-14 titles and a denominator of all titles.

Aggregate the data by monthly frequency to create year-month snapshots from the date added, then calculate a kpi as numerator over denominator for each snapshot using summarize and mutate.

Explore exploratory data analysis with ggplot2, learning univariate and bivariate visualizations and how to tell a data story. Choose appropriate charts for comparison, trend, proportion, and distribution.

Explore univariate data with bar charts in ggplot, plotting counts by brand, creating simple and stacked bars, and faceting by product line.

Create a pie chart in R with ggplot by preparing a data frame, calculating category percentages, and placing inside labels with color and font customization.

Learn to create uni-variate line charts in ggplot with a date-based x axis and numerical y variable, including multiple lines by product line and optional themes.

Create a histogram to visualize the distribution of a continuous numerical variable using geom_histogram in a data frame. The example plots total revenue by date, highlighting a near-normal distribution.

Explore density plots to visualize distribution shape and compare multiple groups using geom_density, with a data frame and a numerical variable, optionally titled total revenue by date.

Explore bivariate data visualization with box whisker plots for a numerical variable across a categorical variable, using a data frame and geom_box_plot, revealing median, mean, Q1, Q3, and interquartile range.

Explore creating a scatter plot in R to examine the relationship between transactions and total revenue, using geom_point, color by brand, facets, and an optional line of best fit.

Learn to compute a correlation matrix and visualize it with a lower-triangular plot, using color scales and labels to reveal strong and weak variable relationships.

Explore cross tabulation in r to analyze relationships among multiple categorical variables, using ftable for more than two factor variables and counts.

Create data visualizations in R with ggplot for univariate and bivariate analysis, using bar, pie, line, histogram, density, box, and scatter plots plus a correlation matrix from insurance data.

Explore loading and examining Excel data set in R, convert date columns, and visualize with ggplot bar charts (sorted by count), plus stacked and facet charts by hearing loss severity.

Prepare a data frame for pie chart plotting, compute region-wise claim counts and percentages, calculate label positions, and visualize a polar pie chart with ggplot.

Learn to plot a univariate line chart of total pay per unit over service dates by aggregating data by brand name and service date and visualize variations by brand name.

Plot a histogram with ggplot to display the total pay per unit distribution, adjust bin counts for finer resolution, and compare with a density plot to reveal the right-skewed shape.

Learn to build cross tabulations in R with one, two, and three factor charts using x tabs. Master data prep, percentage interpretation, and crosstab limitations.

Learn to build bivariate numerical charts with ggplot by creating box whisker plots of paid amount, first overall, then by brand as a factor to compare medians and outliers.

This lecture prepares a data frame for a scatter plot. It computes paid per unit by age group and visualizes their relation with ggplot and a line of best fit.

Prepare a clean data frame by brand name, compute KPI metrics like clean count, service count, service unit, and total pay, then plot a correlation matrix.

Explore loading, cleaning, and joining openairbnb data in R to build a pricing strategy for toronto listings. Derive features, aggregate revenue, and visualize insights for better pricing and performance.

watch an r capstone project solution that loads listings, calendars, and reviews from excel, cleans data, formats dates, checks duplicates and missing values, and demonstrates subset and join prep.

Perform left outer joins with dplyr on listing, review, and calendar data; handle many-to-many issues and apply max-based subqueries to keep latest records for KPI summaries.

Explore capstone part c: create and subset data frames, derive time dimensions, compute listing sequences and durations, and build a Canadian holidays calendar for business-day KPI.

Learn to build data visualizations in R with ggplot, including bar charts, pie charts, histograms, and line charts. The capstone walkthrough covers data preparation and revenue insights by host.

Celebrate course completion by encouraging learners to practice Python for data analysis, explore Kaggle datasets such as Netflix or Steam, and prepare for future courses on Excel, Python, and SQL.