47 research outputs found
R for Data Science
No full textThis E-textbook, written by statisticians Garrett Grolemund and Hadly Wickham, teaches learners how to use R to transform data, change its structure, visualize it, and model the data. The material has been designed for those with a data science background and focuses on using R to put data science into practice. Within each part, students learn how to perform a variety of functions that assist with using R for data science, such as importing data, tidying data, R markdown, transforming data, vectors, and more. The E-book has thirty chapters split into the following parts:Part 1: Data ExplorationPart 2: Data WranglingPart 3: ProgrammingPart 4: Data ModelingPart 5: Communicating DataThis version of R for Data Science has been superseded by new edition, which is available to view separately
Dates and Times Made Easy with lubridate
No full textThis paper presents the lubridate package for R, which facilitates working with dates and times. Date-times create various technical problems for the data analyst. The paper highlights these problems and offers practical advice on how to solve them using lubridate. The paper also introduces a conceptual framework for arithmetic with date-times in R.
R for Data Science (2e)
No full textThis E-textbook, written by statisticians Hadley Wickham, Mine Çetinkaya-Rundel, and Garrett Grolemund, provides learners with the information needed to use R to put data science into practice. The material in the textbook has been designed for those with a data science background and focuses on using R to transform data, change its structure, visualize data, and model it. Within each part, students learn how to perform a variety of R functions relating to data science, such as importing data, tidying data, R markdown, transforming data, vectors, and more. The E-book has 29 chapters split into the following parts:Part 1: Whole GamePart 2: Data VisualizationPart 3: Transforming DataPart 4: Importing DataPart 5: ProgrammingPart 6: Communicating Dat
Tools and theory to improve data analysis
No full textThis thesis proposes a scientific model to explain the data analysis process. I argue that data analysis is primarily a procedure to build un- derstanding and as such, it dovetails with the cognitive processes of the human mind. Data analysis tasks closely resemble the cognitive process known as sensemaking. I demonstrate how data analysis is a sensemaking task adapted to use quantitative data. This identification highlights a uni- versal structure within data analysis activities and provides a foundation for a theory of data analysis. The model identifies two competing chal- lenges within data analysis: the need to make sense of information that we cannot know and the need to make sense of information that we can- not attend to. Classical statistics provides solutions to the first challenge, but has little to say about the second. However, managing attention is the primary obstacle when analyzing big data. I introduce three tools for managing attention during data analysis. Each tool is built upon a different method for managing attention. ggsubplot creates embedded plots, which transform data into a format that can be easily processed by the human mind. lubridate helps users automate sensemaking out- side of the mind by improving the way computers handle date-time data. Visual Inference Tools develop expertise in young statisticians that
can later be used to efficiently direct attention. The insights of this thesis are especially helpful for consultants, applied statisticians, and teachers of data analysis
Dates and Times Made Easy with lubridate
No full textThis paper presents the lubridate package for R, which facilitates working with dates and times. Date-times create various technical problems for the data analyst. The paper highlights these problems and oers practical advice on how to solve them using lubridate. The paper also introduces a conceptual framework for arithmetic with date-times in R
R for data science : import, tidy, transform, visualize, and model data
No full textxxiv, 492 p. ; 23 cm
