Exam 1 study guide

Author

Katie Schuler

Additional study materials

The first exam will test the following learning objectives, divided into the following topic areas. For each topic area, you should be able to do the list that follows. You can think of this as a studying checklist!

  1. R Basics: general
    • Assign an object to a valid variable name, list all variables in the environment and remove them
    • Use packages and differentiate between installing and loading
    • Get help with a function or package from R
    • Read an error message or warning message and interpret
    • Return information about an object, including its structure, data type, and length.
  2. R Basics: vectors, operations, and subsetting
    • Distinguish between an atomic vector and a list
    • Create atomic vectors and determine their data types
    • Differentiate between implicit and explicit coercion and coerce an object to another type
    • Use arithmetic, comparison, and logical operators on vectors
    • Explain how more complex data structures are built from atomic vectors and create them (dataframe and matrix)
    • Distinguish between NA and NULL
  3. Data visualization: basics
    • Describe how to create a plot with ggplot2 including the 3 basic requirements
    • Distinguish between mapping and setting aesthetics
    • Describe how ggplot2 maps categorical variables to aesthetics and interpret the 3 common warnings people encounter in this process
    • Interpret ggplot() calls with explicit or implicit arguments for data and mapping
    • Recognize the geoms we discussed in class and select which to use for a given situation
    • Differentiate between globally and locally defined mappings and recognize them in given plot (or code)
  4. Data visualization: layers
    • Use the position argument to modify the position of the geoms in geom_bar() or geom_point()
    • Describe stat="identity" and describe the default transformations for geom_bar(), geom_histogram(), and geom_smooth()
    • Set the smoothing method for geom_smooth() and the bins or bindwidth for geom_histogram()
    • Facet a plot with facet_wrap() and facet_grid()
    • Modify axis, legend, and plot labels with labs()
    • Apply a given theme to a plot and adjust the base font size or family.
    • Describe scales and recognize the outcome of adding a scale layer
  5. Data importing
    • Load the tidyverse, recognize the included packages, and critique code for redundant loading
    • Construct a tidy dataset and critique whether a given dataset is tidy
    • Use the map function from the purr package
    • Create a tibble and distinguish between a tibble and a data frame
    • Use readr to read delimited files and determine whether readr can read files of a given type
    • Use col_types to add a column specifications and explain how readr guesses without it
    • Solve the 3 most common importing problems we discussed in class
  6. Data wrangling
    • Describe the common structure of dplyr functions (aka verbs)
    • Combine dplyr functions with the pipe operator to solve complex problems
    • Manipulate rows with filter(), arrange(), and distinct()
    • Maniuplate columns with mutate(), select(), and rename()
    • Group and summarise data with group_by() and summarise()
    • Evaulate dplyr functions that include the common arguments we covered in class
  7. Sampling distribution
    • Explore a dataset with an appropriate figure (histogram, boxplot, scatterplot) and summary statistics appropriate for the distribution.
    • Recognize uniform and Gaussian probability distributions in a plot or equation and use R’s functions d*(), p*(), and r*() to work with these distributions
    • Explain the difference between the parameter and the paramter estimate
    • Construct the sampling distribution of a paramater estimate with infer and quantify the spread of the distribution with a confidence interval.
    • Understand the difference between constructing a confidence interval the standard error method vs. the percentile method.
  8. Hypothesis testing
    • Given a set of data, implement the 3-step hypothesis testing framework nonparametrically: (1) Pose a null hypothesis, (2) quantify how likely a given pattern of results is under the null, and (3) determine whether to reject the null (conceptually and with the infer framework).
    • Given a theoretical distriubiton (e.g. t), implement the 3-step hypothesis testing framework parametrically.
    • Given an observed correlation, determine whether a correlation is positive, negative, or no correlation.