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dplyr-grammar.Rmd 13.45 KiB
title: "Einführung in dplyr-Grammatik"
subtitle: "Daten bändigen & visualisieren"
author: "B. Philipp Kleer"
date: "11. Oktober 2021"
output: 
  slidy_presentation: 
    slide_level: 3
    footer: "Copyright: CC BY-SA 4.0, B. Philipp Kleer"
    css: styles/style-slides.css
    df_print: paged
    highlight: espresso
library("knitr")
library("rmarkdown")
library("tidyverse")

uni <- readRDS("../datasets/uni.rds")

opts_chunk$set(fig.path = "pics/s6-", # path for calculated figures
               fig.align = "center",  # alignment of figure (also possible right, left, default)
               fig.show = "hold", # how to show figures: hold -> direct at the end of code chunk; animate: all plots in an animation
               fig.width = 3,   # figure width
               fig.height = 4,  # figure height
               echo = TRUE,     # Code is printed
               eval = FALSE,    # Code is NOT evaluated
               warning = FALSE, # warnings are NOT displayed
               message = FALSE, # messages are NOT displayed
               size = "tiny",  # latex-size of code chunks
               background = "#E7E7E7", # background color of code chunks
               comment = "", # no hashtags before output
               options(width = 80),
               results = "markdown",
               rows.print = 15
)

htmltools::tagList(
  xaringanExtra::use_clipboard(
    button_text = "<i class=\"fa fa-clipboard\"></i>",
    success_text = "<i class=\"fa fa-check\" style=\"color: #90BE6D\"></i>",
    error_text = "<i class=\"fa fa-times-circle\" style=\"color: #F94144\"></i>"
  ),
  rmarkdown::html_dependency_font_awesome()
)

Starten wir!

Nun tauchen wir in die Welt von dplyr ein. Das Paket nutzt man oft, um Datenstrukturen zu erkunden oder Transformationen vorzunehmen. Dabei gibt es einen Grundstock an Vokabeln, die über piping miteinander verbunden werden.

Dazu installieren wir zuerst tidyverse:

install.packages("tidyverse")
library("tidyverse")

# alternativ: 
# install.packages("dplyr")
# library("dplyr")

Anschließend laden wir den Datensatz uni ins environment.

uni <- readRDS("../datasets/uni.rds") #oder eigener Pfad, wenn nicht in der Cloud

Wir verschaffen uns einen Überblick über den Datensatz:

uni