Beginner R Studio Course For Excel Users

Download the free R Studio from rstudio.com, install the core R language first, then install R Studio on Mac or Windows, and prepare for a UI tour.

Explore the essential R studio interface, focusing on the coding pane, run button, outlines and bookmarks, and the console for testing code before saving in a script.

Learn hello world in R Studio, using the console and script, and declare numeric, string, and date and time variables with the print function.

Create and manage an R project to organize multiple script files for modular work. Start a new project from the project menu, name the directory, and load scripts automatically.

Organize code in R Studio using comments and sections; create an R script, add hashtag comments, define sections with four dashes, collapse features for navigation via the bookmark bar.

Learn how to work with R packages by installing them via the packages tab or install.packages, loading with library, and updating to keep your RStudio projects current.

Learn to use the if statement as a conditional that triggers code blocks in R, with syntax for if, else, brackets, and curly braces, demonstrated on X <= Y.

Master the for statement in R to perform repetitive tasks by looping i from start to end (separated by a colon) and printing Hello world in the console.

Explore how to use paste and paste0 in R to combine strings. Compare paste's separator option with paste0's default no separator, and learn to insert separators to match results.

Learn to import data from external files in R Studio, using Excel and CSV formats, install and load readxl for xlsx, and view the imported data.

Learn data cleansing and wrangling in R Studio by converting strings to dates and numbers, trimming whitespace, and extracting text in the console.

Explore the data frame concept and the dplyr package, the grammar of data manipulation, and core functions like select, filter, rename, mutate, merge, group_by, and summarise with the pipe.

Learn to manipulate a data frame with dplyr using select, filter, mutate, rename, and combine data frames; chain operations with the pipe, use group_by and summarize for counts and averages.

Learn to visualize data in R using the plotly library and dplyr for data manipulation. Import CSV data, compute average vote percentages by state, and create interactive charts with plot_ly.

Import external datasets into r studio by inspecting excel and csv files for headers and entries, copy them to the working directory, and verify integrity with read_xlsx, read.csv, and view.

Explore imported data in R Studio, inspect its structure and contents with fdr and sdr functions, and preview columns using summary, names, head, and tail.

Learn to cleanse numeric data in R Studio by importing Brazilian real estate data, removing meter square notation, converting to numeric, handling missing values, and validating results.

Learn to cleanse string data and convert it into date and time formats in R, map date formats, create a new date column, and sort data chronologically for timeline analysis.

Cleanse and manipulate data in R using substring to extract numbers and trim to remove whitespace. Replace Campinas with Rio de Janeiro in the address column and validate the data.

Learn to manipulate a data frame by selecting columns, filtering rows, renaming columns, and mutating values, then chain these operations with pipes to run in one line of code.

Learn to merge two or more data frames into a single data frame using the urban function, the semion function, and the multi-function, with data imported from the Excel library.

Import data with Excel package, set working directory, and manipulate data frame with deployer package; aggregate by region and year to summarise stores and 2019–2020 sales with sanity checks.

Learn to visualize sales data in R Studio using Plotly by filtering to the United Kingdom, aggregating yearly sales, and creating interactive bar charts, scatter plots, and line charts.

Learn to build a histogram in R Studio with Plotly by importing sales data, converting to a data frame, and customizing the X axis and bar gaps to visualize trends.

Master how to add annotations and legends to charts in R, including hiding and rearranging the legend, changing its title and orientation, and annotating scatter plots with data-driven labels.

Export data frames from R Studio to Excel and formats by selecting columns, filtering for permanent positions, renaming the salary column to salary per annum, and saving charts as images.

Explore automating retail sales reporting with R Studio, replacing manual Excel tasks with reproducible charts: enriched UK data, monthly country sales, department totals, and top cities for January 2017.

Apply a case study to an R workflow by importing eight data files with readxl, creating a script, setting the working directory, and inspecting each data frame.

Learn practical data cleansing in R Studio by removing duplicates, filtering data frames, and preparing time series data with zoo for monthly sales analysis.

Merge six monthly sales data frames into a single sales_data frame and enrich it with store and department data to expand columns from eight to twelve.

Manipulate data frames in R to enrich cells, export charts and images, and create output while filtering, grouping, and summarizing for UK, Germany, and top 10 cities in January 2017.

Learn to export cleansed data from R Studio to Excel and create charts: monthly sales by country, Germany vs UK, total sales by department, and top cities by sales.

Explore next steps after mastering R Studio, by building data products like dashboards and interactive reports, and advance into data science with machine learning and recommendation systems.