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Data Wrangling - Dates and Strings - Tidy Data - Statistics Assessment Answer
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This table is organized as observations of driver performance over 4 races. The number 4 seems arbitrary. What if a driver doesn’t participate in all races? What if the length of the season is different from year to year? This may be a reasonable way to view the data, and perhaps to enter the data, but it is not a great form for analysis.
Todo: Tidy and plot race performance as a multi-line plot by driver.
Problem 2. Consider the table below
This table is organized to be thin. Although it could have been spread out over multiple columns, it may have been stored this way for convenience when printing.
Todo: Tidy and show a bar chart comparing the two types of cars in terms of average top 10 finishes.
Problem 3. Consider the table below
This table has similar issues as table 1. It has some missing values. An NA for a race start might imply that the driver did not participate, while an NA for a Time may imply that the driver did not finish the race (e.g. a crash).
Todo: Tidy and show a scatter plot of race starting positions by times in seconds, colored by driver. Hint: gather all columns together to start, then mutate the key into two variables (measurement_type and race), then spread according to the measurement_type. You’ll also need to mutate the time from hms format to just s (use lubridate).
Problem 4. Regular expressions.
Using the “words” dataset, built into stringr, practice writing regular expressions to achieve the following
Data Wrangling Statistics Assessment Answer
Assignment Task: Assignment 3: Data Wrangling, Dates, and Strings Dealing with “raw” data is one of the hardest parts of modern data science. Rarely does data come in a form that can be directed analyzed by a pre-built tool (e.g. a linear modeling procedure). Mapping data to the tools is known as “wrangling”, and, by most accounts is over 80% of the time spent doing real data science. One objective you can have is to transform all of your data into so-called “tidy” data, which suggests that the goal is to organize data into a table of values, where each row is an observation and each column is a variable. When data is organized in this way, common tools can be built to facilitate its analysis. Here, we focus on common data wrangling problems, with a particular visualization as our goal.
This table is organized as observations of driver performance over 4 races. The number 4 seems arbitrary. What if a driver doesn’t participate in all races? What if the length of the season is different from year to year? This may be a reasonable way to view the data, and perhaps to enter the data, but it is not a great form for analysis.
Todo: Tidy and plot race performance as a multi-line plot by driver.
Problem 2. Consider the table below
This table is organized to be thin. Although it could have been spread out over multiple columns, it may have been stored this way for convenience when printing.
Todo: Tidy and show a bar chart comparing the two types of cars in terms of average top 10 finishes.
Problem 3. Consider the table below
This table has similar issues as table 1. It has some missing values. An NA for a race start might imply that the driver did not participate, while an NA for a Time may imply that the driver did not finish the race (e.g. a crash).
Todo: Tidy and show a scatter plot of race starting positions by times in seconds, colored by driver. Hint: gather all columns together to start, then mutate the key into two variables (measurement_type and race), then spread according to the measurement_type. You’ll also need to mutate the time from hms format to just s (use lubridate).
Problem 4. Regular expressions.
Using the “words” dataset, built into stringr, practice writing regular expressions to achieve the following
- a) Get the subset of words that start with “bu”, e.g. budget
- b) Get the subset of words that end with “nd”, e.g. ground
- c) Get the subset of words that have the letter “a” and the letter “b” in them
- d) Get the subset of words that have 5 or more vowels, e.g. associate
- e) Get the subset of words that start and end with the same 2 letters, e.g. church
- f) Get the subset of words that end in repeated consonants, e.g. small
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