Tidyverse Fundamentals with R

This article will introduce R programming language and Tidyverse, an extremely powerful tool set. The tools dplyr and ggplot will help you understand the interconnected process of data manipulation, visualization, and how they work together. You'll learn how to sort and filter historical data in order to answer exploratory questions. The ggplot2 package can convert these data into informative line-and-bar plots, histograms and other plots. This introduction will provide a glimpse of exploratory data analysis and Tidyverse's power. This introduction is for those who are not familiar with R but are interested data analysis.

It can be overwhelming to deal with complex and confusing data sets. To organize this data, tidyr can be used to make it neater. The rows will contain the accessible values from column names. JSON files will convert to data frames. The missing values will not be lost again. These techniques can be used to analyze a wide range of data sets. This will reveal how many dogs were sent into space by the Soviet Union, and which bird is most popular in New Zealand. Any dataset can be transformed using the tidyr package in your tidyverse toolkit. This will allow you to easily complete the rest of your analysis in clean formats.

Hungarian physician Ignaz Semmelweis discovered handwashing in 1847. He discovered that childbed fever was caused by contamination of the hands. By requiring handwashing in his hospital, he saved hundreds of children's lives.

This course will show you how to use data to create models. Models attempt to capture the relationship between an outcome variable of interest and a series of explanatory/predictor variables. These models can be used for explanation (e.g. Does knowing the ages and teaching methods of professors help explain their teaching evaluation scores? Predictive purposes (e.g. Predictive purposes (e.g. These models can be built and interpreted with your tidyverse skills. This course will focus on linear regression. It is the easiest and most popular method of modeling. This type of thinking is used in many areas, including statistics, causal inference and machine learning.

As they say, a picture is worth a thousand words. Graphics are not enough to help you promote data analysis. You need to be unique and attractive. This course will teach you how to use the ggplot2 themes in order to create publication-quality graphics that are different from the many boilerplate plots. This course will teach you how to create unconventional plots that are very popular on social media, and how to optimize your ggplot2. This knowledge will allow you to combine it to create a professional-looking, professionally-looking report using RMarkdown.

Data scientists work often with non-numerical data, such as job titles and survey responses. Factors are a way R can represent them. This course will show you how to use tidyverse, forcats to aid you. You will also use tidyr, ggplot2, and dplyrstringr. Real-world datasets will be used, such as Kaggle State Data Science, fivethirtyeight flight dataset and ML Survey. You'll learn how to efficiently identify and manipulate factor variables, and visualize your data. You're ready to categorize!