Tidy Data: Updated Data Processing With tidyr and dplyr in R

Learn to read CSV, text, and Excel data into RStudio with read.csv, read.table, and read_excel, handling headers and separators, and import datasets for inspection.

Learn to read data from online html tables into R using tidyr and dplyr workflows, extracting Olympics medal table data from Wikipedia for tidy processing.

Read data from HTML tables on Wikipedia pages by specifying the export details and reading the targeted tables, such as Olympic medal tables and World Heritage lists.

Connect to a database, list tables, and read the CO2 table into a data frame, then filter by concentration under 100 using select. Finally, disconnect from the database.

Read JSON data in R using a JSON package to import World Bank data and extract ID, ISO code, and other attributes with lapply and do.call(rbind).

Discover how the pipe operator sequences data preprocessing steps in R, chaining iris data operations like selecting columns, dropping variables, and viewing head results.

Inspect data with glimpse to view column types and counts, then use select to choose, drop, or rename columns by name, prefix, or suffix via the pipe.

Explore advanced data selection with dplyr in R, using select and matches to find pattern-based columns, select_if for type-based filtering, and rename operations to tidy column names.

Apply filter to the sleep dataset to retain mammals with body weight above a threshold and conservation status endangered, then combine criteria with and or logic.

Filter rows in tidy data workflows with dplyr in R, excluding carnivores, comparing sleep total to awake, and selecting records by conservation status such as endangered or vulnerable.

Learn to chain pipe operators to select specific columns and filter rows in a single flow, using dplyr verbs like select and filter on the data.

Create new variables in R with mutate and transmute on the Emslie sleep dataset, handling missing values via filter and computing a total sleep column S.

Group by conservation status and other factors to summarize qualitative attributes, then compute averages to reveal how sleep time varies across carnivore and herbivore categories.

Group olympic data by sport to compute mean age and height, explore height–weight correlations, and master arranging and per-group operations with do and summarise in r using tidyr and dplyr.

Learn to use dplyr for data summarizing on air quality data, selecting ozone and month, filtering, mutating, and summarizing with group_by to compute mean values by month.

Learn to use the dplyr pipe operator to select ozone and month from air quality data, then summarize by month with mean, min, max, and apply filters.

Introduce tidy worth and tidy worthy, the current system of packages for data manipulation, exploration, and visualization, and use the pipe operator with tables as a modern data-frame approach.

Rename columns in the iris data frame with the pipe operator and the rename function, including selective and conditional renaming, and convert names to lower or upper case.

revisit long and wide data formats using tidyr and dplyr, convert air quality data from wide to long with gather, summarize by month, and reshape back to wide with spread.

Join tables in NYC flight data using left_join by carrier to attach airline names, and by day to merge weather data; explore semi_join and anti_join to identify matches and mismatches.

Explore nesting in tidy data using tidyr and dplyr in R by grouping country data into a list column of tables, including life expectancy, population, GDP per capita, and continent.

Explore hypothesis testing, using sample data to evaluate population claims with null hypotheses and p-values. Interpret alpha levels, test statistics, power, and type I and II errors.

Explore conducting a t-test across cylinder categories, nest data with tidyverse tools, and use broom to present tidy results with p-values and confidence intervals.

Learn to tackle missing data in R by using complete cases, mean replacement, and advanced imputation with mice, including predictive mean matching, to prepare clean, analysis-ready data.

Remove missing values in R using dplyr and tidyr with tidyverse workflows, employing distinct, drop, and drop_na, guided by practical examples on the mammal sleep data.

Learn to perform data imputation with dplyr by replacing missing Sleep REM values with the column mean, using mutate and pipes on the mammal sleep data.

Explore data imputation with the tidy both package and tidy impute, replacing missing values in sleep total and sleep REM using mean and median approaches.

Visualize data to reveal patterns, trends, and correlations for exploratory analysis. Use charts like bar plots, histograms, pie charts, and scatter plots to present results.

Explore principles of data visualization, distinguish categorical and numerical data, and choose charts such as bar plots, pie charts, histograms, box plots, and scatter plots to reveal relationships.

Learn to create an intuitive CPI visualization in R by using dplyr and tidyr to gather and reshape data, then plot top and bottom 15 countries in 2016 with ggplot2.

Analyze Olympic data with tidyr and dplyr to compute country medal tallies by year, filter for top nations, and visualize trends in ggplot.

Learn to tidy Olympic data in R by computing and comparing medal tallies per country across summer and winter games, using group by, summarize, and filter in tidyr and dplyr.

analyze the gender distribution in the summer olympics dataset and track changes over time. group by year and sex, compare men and women, and visualize sport popularity and 2016 medals.

Explore the theory of linear regression for modeling a response variable Y from predictor X, including simple and multiple regression, least-squares line fitting, and interpreting slope, intercept, and R-squared.

apply ordinary least squares regression to relate life expectancy and gdp per capita using gapminder data, first for singapore, then across continents, with residual analysis and broom.

Master GitHub and GitHub Desktop to push your local projects to your data portfolio, create a repo, and manage collaboration workflows from terminal to web.

Explore time-based data grouping in R using tidyr and dplyr, computing daily averages and other temporal summaries such as week, month, and quarter, with airport and terminal as grouping attributes.

Weigh sql versus nosql to choose the right database, and apply a quick rule for sql data presented in the lecture.