Editing Statistics and Statistical Programming (Fall 2020)/pset1
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== Programming Challenges == | == Programming Challenges == | ||
=== PC0. Get started=== | === PC0. Get started=== | ||
Open up RStudio, create a new file for this assignment (likely an R Markdown script), add relevant metadata (maybe your name, the date, and a title so that you/we know it is Problem Set 1 for this class?), and save it. | Open up RStudio, create a new file for this assignment (likely an R Markdown script), add relevant metadata (maybe your name, the date, and a title so that you/we know it is Problem Set 1 for this class?), and save it. | ||
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=== PC1. Access and describe a dataset provided in an R library === | === PC1. Access and describe a dataset provided in an R library === | ||
# Load the <code>openintro</code> R package and the <code> | # Load the <code>openintro</code> R package and the <code>counties</code> dataset so that they are available to you. Let's get to know this data! | ||
# Find out the class of the <code> | # Find out the class of the <code>counties</code> dataset object. | ||
# Find out how many rows and how many columns are in the <code> | # Find out how many rows and how many columns are in the <code>counties</code> dataset. | ||
# Find the names of all of the variables (columns) as well as the class of each of | # Find the names of all of the variables (columns) as well as the class of each of them. | ||
# Summarize at least one continuous or discrete numeric variable in the dataset. Calculate the length, range (minimum and maximum), mean, and standard deviation. | # Summarize at least one continuous or discrete numeric variable in the dataset. Calculate the length, range (minimum and maximum), mean, and standard deviation. | ||
# Plot a visual summary (maybe a boxplot or a histogram?) for the same numeric variable you used in PC1.4 above. | # Plot a visual summary (maybe a boxplot or a histogram?) for the same numeric variable you used in PC1.4 above. | ||
# Summarize at least one categorical variable in the dataset (e.g., if the variable takes values of TRUE/FALSE or NA, how many of each are value are there?). | # Summarize at least one categorical variable in the dataset (e.g., if the variable takes values of TRUE/FALSE or NA, how many of each are value are there?). | ||
=== PC2. Work with a dataset from the web === | === PC2. Work with a dataset from the web === | ||
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::<code>set.seed(<your.birthdate>)</code></br> | ::<code>set.seed(<your.birthdate>)</code></br> | ||
::<code>sample(x= c(1:20), size=1))</code> | ::<code>sample(x= c(1:20), size=1))</code> | ||
# Navigate to the [https://communitydata.science/~ads/teaching/2020/stats/data data repository for the course] and | # Navigate to the [https://communitydata.science/~ads/teaching/2020/stats/data data repository for the course] and download the RData file in the <code>week_02</code> subdirectory with your dataset number from PC2.1 (e.g., <code>group_<output>.Rdata</code> where <output> is replaced with the dataset number). | ||
# | # Find and load the .Rdata file for your dataset number into R. It should contain one variable. Find that variable! | ||
# Calculate summary statistics for your variable. Be sure to include the length, minimum, maximum, mean, and standard deviation. | # Calculate summary statistics for your variable. Be sure to include the length, minimum, maximum, mean, and standard deviation. | ||
# Create a visualization of your variable: at the very least, create a boxplot or a histogram. | # Create a visualization of your variable: at the very least, create a boxplot or a histogram. | ||
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===SQ2=== | ===SQ2=== | ||
Consider the results of PC2. | Consider the results of PC2.4 and PC2.5. Do the mean and standard deviation seem likely to provide good representations of the central tendency and spread of this variable? Why or why not? If not, what alternative measures could you use to characterize the central tendency and spread respectively? | ||
===SQ3=== | ===SQ3=== | ||
Briefly discuss any differences you observe between the untransformed/uncleaned version of the variable you summarized in PC2. | Briefly discuss any differences you observe between the untransformed/uncleaned version of the variable you summarized in PC2.4 and PC2.5 and the transformed/cleaned version you summarized in PC2.7. Which summary should you prefer and why? |