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    An introduction to computer skills needed for the statistical sciences, using the software R. Covers reproducible research, data wrangling, visualisation, exploratory data analysis, resampling and simulation.

    Fundamental to modern statistical practice is proficiency in the use of specialised software packages. This paper introduces students to the world of statistical computing, which encompasses fundamental programming skills motivated by handling and manipulating data, and how this relates to exploratory data analysis, visualisation, model fitting, and numerical simulation. Focus is on implementation in the R language and associated mark-up tools. Many of the skills students learn are transportable to other statistics packages.

    About this paper

    Paper title Visualisation and Modelling in R
    Subject Statistics
    EFTS 0.15
    Points 18 points
    Teaching period Semester 2 (On campus)
    Domestic Tuition Fees ( NZD ) $981.75
    International Tuition Fees Tuition Fees for international students are elsewhere on this website.
    (BSNS 102 or BSNS 112 or STAT 110 or STAT 115) and 54 additional points
    STAT 380
    Schedule C
    Arts and Music, Science

    Teaching staff

    Dr Tilman Davies

    Dr Xun Xiao


    To be confirmed

    Graduate Attributes Emphasised
    Lifelong learning, Scholarship, Communication, Critical thinking, Information literacy, Research, Self-motivation.
    View more information about Otago's graduate attributes.
    Learning Outcomes

    Upon successful completion of the paper, the student will possess a range of skills used in and motivated by modern data analysis. They will be able to:

    • Use R programming syntax and control flow
    • Use R to read in, manipulate, tidy, subset, recode, and write out data sets
    • Create, interpret and customise common statistical plots
    • Use R to fit and interpret some common statistical models
    • Conduct simulation of data and execute numerically intensive operations
    • Write dynamic documents that include executable code


    Semester 2

    Teaching method
    This paper is taught On Campus
    Learning management system

    Computer Lab

    Stream Days Times Weeks
    Attend one stream from
    A1 Tuesday 13:00-13:50 29-35, 37-42
    A2 Tuesday 14:00-14:50 29-35, 37-42
    A3 Tuesday 15:00-15:50 29-35, 37-42
    AND one stream from
    B1 Thursday 13:00-13:50 29-35, 37-42
    B2 Thursday 14:00-14:50 29-35, 37-42
    B3 Thursday 15:00-15:50 29-35, 37-42


    Stream Days Times Weeks
    A1 Monday 13:00-13:50 29-35, 37-42
    Wednesday 13:00-13:50 29-35, 37-42
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