
Explore your data in R using the Star Wars data frame by inspecting dimensions, structure, and missing values, then summarize with unique values and quick visualizations.
Clean your data with R using a systematic workflow: explore, clean, manipulate, describe, visualize, and analyze with tiny bits packages on the Star Wars dataset.
Learn data wrangling in R using tidyverse tools to rename, select, mutate, filter, and pivot data between long and wide forms, with Gap Minder and M sleep examples.
Describe and summarize data in R by computing range, interquartile range, mean, median, and variance for numeric variables, and create tables and contingency tables using base R and tidyverse.
Explore how to approach missing data and five strategies to handle it using R and tidyverse, including complete cases, dropping rows, recoding values, converting to missing, and imputation.
Tell a data story by visualizing with the grammar of graphics, mapping data to aesthetics and geometry in ggplot, using built-in datasets and a cheat sheet.
Perform hypothesis tests on population parameters using random samples and p values. Interpret 95% confidence intervals to gauge precision and inform inference about the null and alternative hypotheses.
Perform t-tests in R to compare means and interpret p values for single, two-sided, one-sided, and paired designs. Frame null and alternative hypotheses and read confidence intervals from Gapminder data.
Explore analysis of variance (anova) to determine if mean life expectancy differs across continents, and interpret null hypotheses, p-values, alpha levels, and Tukey hsd results in R.
Explore chi square tests, including goodness of fit and independence, using iris dataset; convert a numeric variable to size categories, compute expected values, and interpret p-values with a 0.05 alpha.
This R programming course will take you through the end-to-end process of data analysis, including cleaning, manipulating, summarizing, describing, visualizing and analysing your data. The data analysis section includes the t-test, ANOVA, chi-squared test and linear regression. Every lesson includes examples using data that is built into R (so you already have access to the data). That means that you'll be able to replicate absolutely every example in the course.
Learning statistics doesn’t need to be difficult or scary. In this course you’ll be brought through the process in a step-by-step fashion. The fact that all of the examples used in the course use datasets that are built into R means that you can practise each set of skills in your own time before moving onto the next step.
By the end of the course you'll feel completely comfortable with R programming. You'll be able to create incredible graphs and plots. You’ll understand the principles of hypothesis testing and how to perform statistical tests. Importantly, you’ll understand how to interpret the results of statistical tests. The teaching methods used in this course are designed to keep you engaged and make sure that you're excited about your new skills.
Good luck… and ENJOY!!