(in Polish) Basic Data Visualization in R

The course teaches the basics of 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. In this class we will learn

how to plot graphs: from simple graphs (e.g. bar graphs, boxplots,

scatterplots), to plotting stacked graphs, and paneled graphs. We will also

learn to customize the plots (e.g. colors, scales, labels), and to export

plots (e.g. into image files, pdfs). Every plot will be created by writing

code in R.

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).

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.

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