add looking at data lesson

R_Programming/Looking_at_Data/customTests.R

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+AUTO_DETECT_NEWVAR <- FALSE

R_Programming/Looking_at_Data/initLesson.R

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+# Path to data
+.datapath <- file.path(path.package('swirl'), 'Courses',
+                      'R_Programming', 'Looking_at_Data',
+                      'plant-data.txt')
+# Read in data
+plants <- read.csv(.datapath, strip.white=TRUE, na.strings="")
+
+# Remove annoying columns
+.cols2rm <- c('Accepted.Symbol', 'Synonym.Symbol')
+plants <- plants[, !(names(plants) %in% .cols2rm)]
+
+# Make names pretty
+names(plants) <- c('Scientific_Name', 'Duration', 'Active_Growth_Period',
+                   'Foliage_Color', 'pH_Min', 'pH_Max',
+                   'Precip_Min', 'Precip_Max',
+                   'Shade_Tolerance', 'Temp_Min_F')

R_Programming/Looking_at_Data/lesson.yaml

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+- Class: meta
+  Course: R Programming
+  Lesson: Looking at Data
+  Author: Nick Carchedi
+  Type: Standard
+  Organization: JHU Biostat
+  Version: 2.2.11
+
+- Class: text
+  Output: Whenever you're working with a new dataset, the first thing you should do is look at it! What is the format of the data? What are the dimensions? What are the variable names? How are the variables stored? Are there missing data? Are there any flaws in the data?
+
+- Class: text
+  Output: This lesson will teach you how to answer these questions and more using R's built-in functions. We'll be using a dataset constructed from the United States Department of Agriculture's PLANTS Database (http://plants.usda.gov/adv_search.html).
+
+- Class: cmd_question
+  Output: I've stored the data for you in a variable called plants. Type ls() to list the variables in your workspace, among which should be plants.
+  CorrectAnswer: ls()
+  AnswerTests: omnitest(correctExpr='ls()')
+  Hint: Use ls() to list the variables in your workspace.
+
+- Class: cmd_question
+  Output: Let's begin by checking the class of the plants variable with class(plants). This will give us a clue as to the overall structure of the data.
+  CorrectAnswer: class(plants)
+  AnswerTests: omnitest(correctExpr='class(plants)')
+  Hint: Use class(plants) to check the class of the plants variable.
+
+- Class: text
+  Output: It's very common for data to be stored in a data frame. It is the default class for data read into R using functions like read.csv() and read.table(), which you'll learn about in another lesson.
+
+- Class: cmd_question
+  Output: Since the dataset is stored in a data frame, we know it is rectangular. In other words, it has two dimensions (rows and columns) and fits neatly into a table or spreadsheet. Now use dim(plants) to see exactly how many rows and columns we're dealing with.
+  CorrectAnswer: dim(plants)
+  AnswerTests: omnitest(correctExpr='dim(plants)')
+  Hint: Use dim(plants) to see the exact dimensions of the plants dataset.
+
+- Class: text
+  Output: The first number you see (5166) is the number of rows (observations) and the second number (10) is the number of columns (variables). 
+
+- Class: cmd_question
+  Output: You can also use nrow(plants) to see only the number of rows. Try it out.
+  CorrectAnswer: nrow(plants)
+  AnswerTests: omnitest(correctExpr='nrow(plants)')
+  Hint: Use nrow(plants) to see only the number of rows.
+
+- Class: cmd_question
+  Output: ... And ncol(plants) to see only the number of columns.
+  CorrectAnswer: ncol(plants)
+  AnswerTests: omnitest(correctExpr='ncol(plants)')
+  Hint: Use ncol(plants) to see only the number of columns.
+
+- Class: cmd_question
+  Output: If you are curious as to how much space the dataset is occupying in memory, you can use object.size(plants).
+  CorrectAnswer: object.size(plants)
+  AnswerTests: omnitest(correctExpr='object.size(plants)')
+  Hint: object.size(plants) will tell you how much memory is occupyed by the plants data frame.
+
+- Class: cmd_question
+  Output: Now that we have a sense of the shape and size of the dataset, let's get a feel for what's inside. names(plants) will return a character vector of column (i.e. variable) names. Give it a shot.
+  CorrectAnswer: names(plants)
+  AnswerTests: omnitest(correctExpr='names(plants)')
+  Hint: names(plants) will return a character vector of column (i.e. variable) names.
+
+- Class: text
+  Output: We've applied fairly descriptive variable names to this dataset, but that won't always be the case. A logical next step is to peek at the actual data. However, our dataset contains over 5000 observations (rows), so it's impractical to view the whole thing all at once.
+
+- Class: cmd_question
+  Output: The head() function allows you to preview the top of the dataset. Give it a try with only one argument.
+  CorrectAnswer: head(plants)
+  AnswerTests: omnitest(correctExpr='head(plants)')
+  Hint: head(plants) will show you the very top of the dataset.
+
+- Class: text
+  Output: Take a minute to look through and understand the the output above. Each row is labeled with the observation number and each column with the variable name. Your screen is probably not wide enough to view all 10 columns side-by-side, in which case R displays as many columns as it can on each line before continuing on the next.
+
+- Class: cmd_question
+  Output: By default, head() shows you the first six rows of the data. You can alter this behavior by passing as a second argument the number of rows you'd like to view. Use head() to preview the first 10 rows of plants.
+  CorrectAnswer: head(plants, 10)
+  AnswerTests: omnitest(correctExpr='head(plants, 10)')
+  Hint: head(plants, 10) will show you the first 10 rows of the dataset.
+
+- Class: cmd_question
+  Output: The same applies for using tail() to preview the end of the dataset. Use tail() to view the last 15 rows.
+  CorrectAnswer: tail(plants, 15)
+  AnswerTests: omnitest(correctExpr='tail(plants, 15)')
+  Hint: tail(plants, 15) will show you the last 15 rows of the dataset.
+
+- Class: cmd_question
+  Output: After previewing the top and bottom of the data, you probably noticed lots of NAs, which are R's placeholders for missing values. Use summary(plants) to get a better feel for how each variable is distributed and how much of the dataset is missing.
+  CorrectAnswer: summary(plants)
+  AnswerTests: omnitest(correctExpr='summary(plants)')
+  Hint: Try summary(plants) to look at how each variable is distributed and how many missing values we are dealing with.
+
+- Class: text
+  Output: summary() provides different output for each variable, depending on its class. For numeric data such as Precip_Min, summary() displays the minimum, 1st quartile, median, mean, 3rd quartile, and maximum. These values help us understand how the data are distributed.
+
+- Class: text
+  Output: For categorical variables (called 'factor' variables in R), summary() displays the number of times each value (or 'level') occurs in the data. For example, each value of Scientific_Name only appears once, since it is unique to a specific plant. In contrast, the summary for Duration (also a factor variable) tells us that our dataset contains 3031 Perennial plants, 682 Annual plants, etc.
+
+- Class: cmd_question
+  Output: You can see that R truncated the summary for Active_Growth_Period by including by including a catch-all category called 'Other'. Since it is a categorical/factor variable, we can see how many times each value occurs in the data with table(plants$Active_Growth_Period).
+  CorrectAnswer: table(plants$Active_Growth_Period)
+  AnswerTests: omnitest(correctExpr='table(plants$Active_Growth_Period)')
+  Hint: table(plants$Active_Growth_Period) will display counts for each level of the factor variable.
+
+- Class: text
+  Output: Each of the functions we've introduced so far has its place in helping you to better understand the structure of your data. However, we've left the best for last....
+
+- Class: cmd_question
+  Output: Perhaps the most useful and concise function for understanding the *str*ucture of your data is str(). Give it a try now.
+  CorrectAnswer: str(plants)
+  AnswerTests: omnitest(correctExpr='str(plants)')
+  Hint: Use str(plants) to look at the structure of your data.
+
+- Class: text
+  Output: The beauty of str() is that it combines many of the features of the other functions you've already seen, all in a concise and readable format. At the very top, it tells us that the class of plants is 'data.frame' and that it has 5166 observations and 10 variables. It then gives us the name and class of each variable, as well as a preview of its contents.
+
+- Class: text
+  Output: str() is actually a very general function that you can use on most objects in R. Any time you want to understand the structure of something (a dataset, function, etc.), str() is a good place to start.
+
+- Class: text
+  Output: In this lesson, you learned how to get a feel for the structure of a new dataset using a collection of simple and useful functions. Taking the time to do this upfront can save you time and frustration later on in your analysis.