1 Introduction | R for Data Science
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Once you have tidy data with the variables you need, there are two main engines of knowledge generation: visualisation and modelling
Once you’ve imported your data, it is a good idea to tidy it
Once you have tidy data, a common first step is to transform it.
Together, tidying and transforming are called wrangling
This tells you that tidyverse is loading the ggplot2, tibble, tidyr, readr, purrr, and dplyr packages. These are considered to be the core of the tidyverse because you’ll use them in almost every analysis.
Packages in the tidyverse change fairly frequently. You can see if updates are available, and optionally install them, by running tidyverse_update().
La ciencia de datos es una disciplina apasionante que le permite convertir datos sin procesar en comprensión, perspectiva y conocimiento
Ordenar sus datos significa almacenarlos en una forma consistente que coincida con la semántica del conjunto de datos con la forma en que se almacenan.
cuando sus datos están ordenados, cada columna es una variable y cada fila es una observación.
La transformación incluye reducir las observaciones de interés
ordenar y transformar se llaman disputas
a solid foundation in the most important tools.
import your data into R
storing it in a consistent form that matches the semantics of the dataset with the way it is stored
when your data is tidy, each column is a variable, and each row is an observation
tidying and transforming are called wrangling
narrowing in on observations of interest
creating new variables that are functions of existing variable
Visualisation is a fundamentally human activity.
Models are complementary tools to visualisation
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