Introduction to Data Science with R (REX)

This cour­se tea­ches the basics of using the R pro­gramming lan­guage for data ana­ly­sis. Empha­sis is pla­ced on new packa­ges (espe­ci­al­ly the packa­ge coll­ec­tion tidy­ver­se) that make working with R fas­ter and more con­ve­ni­ent.

The cour­se beg­ins with the instal­la­ti­on of R and RStu­dio (an IDE for R) and an intro­duc­tion to their use. The syn­tax of R is then sys­te­ma­ti­cal­ly explai­ned and the use of various packa­ges from the “Tidy­ver­se” is taught. In addi­ti­on, simp­le sta­tis­ti­cal methods and their appli­ca­ti­on in R are explai­ned. Final­ly, an out­look on the use of R for the crea­ti­on of dash­boards (shi­ny) or reports (mark­down) is given.

This cour­se is aimed at users who would like to use R in the con­text of data ana­ly­sis and have litt­le or no expe­ri­ence with R, or at tho­se who­se expe­ri­ence with R goes back fur­ther and who would like to get to know the new packa­ges and fea­tures of the last seven years.

Con­tents

• • Instal­ling R and RStu­dio as an IDE
  • Intro­duc­tion to the fea­tures and use of RStu­dio
  • Basics of the syn­tax of R (Base)
  • Pro­blem sol­ving using help func­tions and sear­ching the Inter­net
  • Intro­duc­tion to the Data Science Work Flow with the tidy­ver­se packa­ge:
    • Data manage­ment and data cle­an­sing with readr, tibb­le, tidyr
    • Data mani­pu­la­ti­on with dplyr
    • Gra­phi­cal data explo­ra­ti­on with ggplot2 (e.g. his­to­gram, line plot, scat­ter plot, box plot)
    • Adjus­t­ment of plots and mul­ti­ple plots with ggplot2
  • Simp­le ana­ly­ti­cal methods (line­ar regres­si­on, Student’s T-test, ANOVA)
  • Out­look: Dash­boar­ding (shi­ny) and report­ing (mark­down)

Type
R is a free pro­gramming lan­guage that is used and app­re­cia­ted in the fields of sta­tis­tics, gra­phics crea­ti­on and data mining.
Our R cour­ses are desi­gned to be inter­ac­ti­ve in order to give par­ti­ci­pan­ts the oppor­tu­ni­ty to try out the methods taught direct­ly. Each sec­tion also con­cludes with a series of exer­ci­s­es to rein­force what has been lear­ned. In our trai­ning cen­ter in Ham­burg, a trai­ning PC is available for each par­ti­ci­pant to sol­ve the exer­ci­s­es.
The cour­se pro­gress is ful­ly docu­men­ted so that no notes are requi­red and par­ti­ci­pan­ts can con­cen­tra­te on the acti­ve use of R.

Key­words
Argu­ments, aes­the­tics, sel­ec­tion of obser­va­tions, sel­ec­tion of varia­bles, bar chart, base, base plots, box­plot, data frames, data wrang­ling, rea­ding in data, export­ing data, data sets, date for­mats, dplyr, sca­lar values, unstack­ing, crea­ting varia­bles, facets, miss­ing values, floa­ting point num­bers, func­tions, ggplot2, his­to­gram, con­so­le, line­ar models, line plot, lubrida­te, objects, ope­ra­tors, packa­ges, pipe ope­ra­tor, qua­dra­tic models, R instal­la­ti­on, R script, readr, RStu­dio inter­face, scat­ter­plot, sort­ing rows, sepa­ra­ting colum­ns, mer­ging colum­ns, stack­ing, syn­tax, ter­mi­nal, tibb­le, tidyr, tidy­ver­se, vec­tors, working direc­to­ry, com­bi­ning mul­ti­ple values

Pre­re­qui­si­tes
No pre­vious know­ledge is requi­red for this trai­ning.