(in Polish) Basic Data Visualization in R 2500-EN-F-229
The course teaches the basic data visualization in R, a programming
language used for data science. One of the main reasons data analysts
turn to R is for its strong graphic capabilities.
The course makes use of the core Tidyverse packages in R: tidyr, dplyr,
ggplot2. In the classes we will learn to how import and clean up the data
in R (tidyr), how to select and filter variables for data analysis (dplyr), and
how to visualize the data (ggplot2). We will start by learning how to plot
simple graphs (one group, one variable), and then steadily introduce
more complex plots (stacked and grouped, adding errorbars, fit lines,
plotting multiple groups on a single graph). We will also to customize the
plots (e.g. colors, scales, labels), learn to print multiple plots on one page
and to export plots (e.g. into image files, pdfs). For every task we will
write code in R.
PLEASE NOTE that this class (1) is not an R programming course (i.e. you
won’t learn conditional statements, loops, you won’t write your own
functions), (2) is not a statistics class (i.e. no hypothesis confirmation, you
won’t learn which statistical test fits your data best). The course focuses
specifically on basic data visualization in R. However, we will cover visual
representation of outcomes of statistical tests or analyses (performed in
R).
Learning outcomes
By the end of the course you will be able to:
- import various data files into R (excel, text, SPSS files);
- clean the data;
- subset the data;
- create a plot using base graphics;
- create plots using ggplot2: boxplots, dotplots, barplots, pie charts,
linegraphs, scatterplots;
- customize the plots (e.g. legends, colors, axes and text);
- stack the plots, combine the plots into a panel (multiple graphs on
the same page);
- export the plot in bitmap formats (jpeg, png, tiff), pdf, wmf.
Assessment criteria
To pass the course, you’ll need to send in four homework assignments
and send in (at the end of the course) a final-term assignment (graded).
For this, you’ll get a data file from the instructor, together with specific
instructions.
The assignment will require of you to:
- import a data file into R;
- clean up the data;
- create a specific plot using base graphics;
- create three specific plots using ggplot2;
- customize the graphs (according to specific instructions);
- combine the plots into a panel;
- export the plot in a specified format.
The final grade will compromise of the following:
- score for the final-term assignment (60% of the grade)
- score for passing the 4 homework assignments (together 40% of
the grade)
The grading scale:
5 93-100%
4.5 85-92%
4 77-84%
3.5 69-76%
3 61-68%
2 60% and less
Attendance rules
Up to 2 excused and 2 unexcused absences are allowed
Additional information
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: