R Programming for Data Science for Absolute Beginners

Explore how a data frame acts as the data center in R, holding numbers, characters, and booleans, built from vectors and accessed with square brackets to select rows or columns.

Learn to build data frames in r by combining vectors into rows and columns. Use head, tail, str, and summary to inspect data types and structure.

Learn how to access elements in dataframes in R using row and column indices, names, and the dollar sign, with examples of selecting rows, columns, and single elements.

Add a department column to a data frame by assigning a vector and aligning names; rename as needed and verify equal rows when merging, using row and column counts.

Master uploading csv data into a dataframe in R, inspect and set data types (numeric, character, factors), apply simple operations, subset rows, and export the results to csv.

Explore how lists in R serve as a one-dimensional container that can hold vectors, matrices, data frames, and even other lists, and learn how to assemble these elements in RStudio.

Use lists as a one dimensional data structure to hold a data frame, a vector, and a matrix, with access via $ or [ ], and merging and conversion to vectors.

Explore constructs in R, including conditions, loops, and functions, as you work with vectors, factors, and data frames to model credit card eligibility using a bank marketing dataset.

Apply relational and logical operators in R to vector data, explore recycling rules, and build boolean conditions through a hands-on bank marketing case study.

Learn how to implement conditional statements in R, using if, else if, and else to execute code based on total marks and predefined conditions. Practice evaluating multiple conditions.

Explore a bank file in r by examining a telemarketing dataset of customers, their yes/no responses, and demographics, and learn to convert strings to factors and create focused subsets.

Learn to implement credit card issuance logic in R by reading bank customer attributes, evaluating marital status, salary, housing, and education, and looping through records to identify eligible customers.

Explore how loops drive actions in data analysis, using for loops to iterate over records, apply conditions, and print results, with examples on a data frame and handling non-numeric values.

Convert string columns to factors in R by using a loop to apply factor to selected columns (5, 6, 7, 8, 11) in a dataset, showcasing dynamic factor conversion.

Learn how R functions reduce workload, using built-ins like sum and mean, and create custom functions such as f_license_y_n with age as a parameter that returns yes or no.

Explore built-in R functions like min, median, and mean, using age and salary examples, and learn to handle any values by replacing them with mean or median.

Learn to write and call user-defined functions in R, with parameters and return values. Explore a license-by-age check, a percentage converter, and a credit-card eligibility loop.

Learn how the apply family of functions, especially sapply, simplifies data processing by replacing for loops and converting multiple columns to factors with a single line.

Demonstrate how to use scatterplots, histograms, line charts, and pies to reveal trends, distributions, and comparisons in data, emphasizing color, form, and positioning to convey messages quickly and reveal outliers.

Explore basic R base plots with the plot function on the iris dataset, creating scatterplots of sepal length and width and differentiating species by color to reveal relationships.

Explore base plots in R, including histograms and box plots to visualize frequency distribution, quartiles, median, interquartile range, and outliers, highlighting data skewness.

Learn to build ggplot visuals by selecting a data set, mapping x and y aesthetics, and using a geometry like geom_point for scatterplots, demonstrated with the diamond data set.

Install and load packages to enhance data visualization, then use a car dataset to create a ggplot scatterplot of mpg versus cylinder, converting cylinder to a factor for categorization.

Learn to create scatter plots with ggplot in R, map color, shape, and size to factors like cylinder, explore relationships between mpg and horsepower, and customize aesthetics.

Learn how to display actual values on a scatter plot by adding text labels for variables like displacement and horsepower, and explore when labeling clarifies or clutter the visualization.

Visualize large datasets with ggplot by creating a scatter plot of price versus carat in a diamond dataset, color by diamond color and clarity, and use alpha to reduce overplotting.

Learn to store a ggplot as a variable, add geometry with geom_point, and execute or reuse the plot within a session for flexible data visualization.

Explore plotting bar charts with ggplot in R, converting numeric to categorical with factors, and visualizing frequencies and proportions using fill, position, and auto/manual aesthetics.

Learn how to use jitters in ggplot to separate overlapping points in a scatterplot by adjusting the position parameter, improving readability without misrepresenting data.

Learn how to create dodge plots in R to compare automated and manual groups, using factors for grouping and side-by-side bar charts to display proportions.

Learn to visualize data distributions with ggplot histograms and frequency polygons, adjust bin widths and colors, and compare mpg and age groups across car data.

Explore time series plots of US economic data, plotting unemployment rate and saving rate over time with date on the x-axis, using line and step plots, color, and points.

Analyze supply-demand gap in ride-hailing, identify root causes of car non-availability during peak hours, and present actionable recommendations to the CEO using the CSC data file, time slots, and visualizations.

Learn to load and inspect a six-column Uber-style dataset in R, explore unique values and timestamps, and decide on initial data cleaning and column selection in RStudio.

Upload the data file and read the csv into R, then convert timestamps to a common date-time format using a library, and create time slots for request and drop times.

Create an R function to allocate time slots across 24 hours, then visualize status-by-trip data, distinguishing airport and city pickups with a customized plot.

Learn to create bar charts and time-slot visualisations in R to analyze demand and supply, identify peak hours, and compare airport versus city pickup patterns using median journey time.

Identify top investment opportunities for English-speaking countries using R programming skills, merge data sources, and analyze category, country, and investment type to reveal global trends.

Learn data preparation by cleaning and linking three excel sheets—company, mapping, and rounds—using permalink as the key, mapping categories, and filtering blanks for investment analysis.

Master data preparation by copying data across sheets and creating a category type column with an if formula. Map categories using a lookup on a mapping sheet and handle errors.

Copy funding and funding type to the company sheet, apply vlookup to create a funding round type lookup, remove unwanted columns, and verify results with a filter.

Learn practical data preparation in R programming for data science for absolute beginners: remove unwanted columns, copy values, and map country names to English-speaking three-letter country codes via lookup.

Identify and clean country records to map English-speaking countries, rename columns, remove unnecessary data, and apply lookup steps to assign funding type and amount, preparing the dataset for analysis.

Analyze country-wise data in Excel by identifying unique companies, removing duplicates, and counting distinct entries. Compare English speaking countries to find the top three and examine funding types.

Use pivot tables on the company dataset to compute average funding by venture, angel, seed, and private equity, and identify top english speaking countries and sector wise investments.

Perform sector-wise category-type analysis with a pivot table to identify top investment sectors across USA, GBR, and Canada, highlighting social finance analytics as the leading category.

Present data insights to executives by cleaning and mapping data, outlining the business problem and approach, and summarizing key findings with visuals like country and funding-type charts.