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Advanced R

2 Warm-up exercise

Loftus, S. C. (2021). Basic Statistics with R: Reaching Decisions with Data. Retrieved from https://books.google.hu/books?id=vTASEAAAQBAJ

2.1 Data structures

2.1.1 Problems

Consider the following set of attributes about the American Film Institute’s top-five movies ever from their 2007 list.

  1. What code would you use to create a vector named Movie with the values Citizen Kane, The Godfather, Casablanca, Raging Bull, and Singing in the Rain? (Hints: object <- c(), Working with character in R)
  2. What code would you use to create a vector — giving the year that the movies in Problem 1 were made — named Year with the values 1941, 1972, 1942, 1980, and 1952?
  3. What code would you use to create a vector — giving the run times in minutes of the movies in Problem 1 — named RunTime with the values 119, 177, 102, 129, and 103?
  4. What code would you use to find the run times of the movies in hours and save them in a vector called RunTimeHours? (Hints: Numeric tranformation)
  5. What code would you use to create a data frame named MovieInfo containing the vectors created in Problem 1, Problem 2, and Problem 3? (Hints: data.frame())

2.2 Manipulation

2.2.1 Problems

Suppose we have the following data frame named colleges (download here):

College Employees TopSalary MedianSalary
William and Mary 2104 425000 56496
Christopher Newport 922 381486 47895
George Mason 4043 536714 63029
James Madison 2833 428400 53080
Longwood 746 328268 52000
Norfolk State 919 295000 49605
Old Dominion 2369 448272 54416
Radford 1273 312080 51000
Mary Washington 721 449865 53045
Virginia 7431 561099 60048
Virginia Commonwealth 5825 503154 55000
Virginia Military Institute 550 364269 44999
Virginia Tech 7303 500000 51656
Virginia State 761 356524 55925
  1. What code would you use to select the first, third, tenth, and twelfth entries in the TopSalary vector from the Colleges data frame? (Hints: Indexing with [] operator)
  2. What code would you use to select the elements of the MedianSalary vector where the TopSalary is greater than $400,000? (Hints: d$MedianSalary[d$TopSalary>400000])
  3. What code would you use to select the rows of the data frame for colleges with less than or equal to 1000 employees? (Hints: d[condition, ])
  4. What code would you use to select a sample of 5 colleges from this data frame (there are 14 rows)? (Hints: d[sample(x = 1:14, size = 5, replace = F),])

Suppose we have the following data frame named Countries (download here):

Nation Region Population PctIncrease GDPcapita
China Asia 1409517397 0.4 8582
India Asia 1339180127 1.1 1852
United States North America 324459463 0.7 57467
Indonesia Asia 263991379 1.1 3895
Brazil South America 209288278 0.8 10309
Pakistan Asia 197015955 2.0 1629
Nigeria Africa 190886311 2.6 2640
Bangladesh Asia 164669751 1.1 1524
Russia Europe 143989754 0.0 10248
Mexico North America 129163276 1.3 8562
  1. What could would you use to select the rows of the data frame that have GDP per capita less than 10000 and are not in the Asia region?
  2. What code would you use to select a sample of three nations from this data frame (There are 10 rows)?
  3. What code would you use to select which nations saw a population percent increase greater that 1.5%?

Suppose we have the following data frame named Olympics (download here):

Year Type Host Competitors Events Nations Leader
1992 Summer Spain 9356 257 169 Unified Team
1992 Winter France 1801 57 64 Germany
1994 Winter Norway 1737 61 67 Russia
1996 Summer United States 10318 271 197 United States
1998 Winter Japan 2176 68 72 Germany
2000 Summer Australia 10651 300 199 United States
2002 Winter United States 2399 78 78 Norway
2004 Summer Greece 10625 301 201 United States
2006 Winter Italy 2508 84 80 Germany
2008 Summer China 10942 302 204 China
2010 Winter Canada 2566 86 82 Canada
2012 Summer United Kingdom 10768 302 204 United States
2014 Winter Russia 2873 98 88 Russia
2016 Summer Brazil 11238 306 207 United States
2018 Winter South Korea 2922 102 92 Norway
  1. What code would you use to select the rows of the data frame where the host nation was also the medal leader?
  2. What code would you use to select the rows of the data frame where the number of competitors per event is greater than 35?
  3. What code would you use to select the rows of the data frame where the number of competing nations in the Winter Olympics is at least 80?

2.3 Packages

2.3.1 Problems

  1. Install the Ecdat package. (Hints: install.packages())
  2. Say that we previously installed the Ecdat library into R and wanted to call the library to access datasets from it. What code would we use to call the library? (Hints: library())
  3. Say that we then wanted to call the dataset Diamond from the Ecdat library. What code would we use to load this dataset into R? (Hints: data())

2.4 Frequency and numerical exploratory analyses

2.4.1 Problems

Load the leuk dataset from the MASS library. This dataset is the survival times (time), white blood cell count (wbc), and the presence of a morphologic characteristic of white blood cells (ag).

  1. Generate the frequency table for the presence of the morphologic characteristic.
  2. Find the median and mean for survival time.
  3. Find the range, IQR, variance, and standard deviation for white blood cell count.
  4. Find the correlation between white blood cell count and survival time.

Load the survey dataset from the MASS library. This dataset contains the survey responses of a class of college students.

  1. Create the contingency table of whether or not the student smoked (Smoke) and the student’s exercise regimen (Exer). (Hints: table(), DescTools::Desc())
  2. Find the mean and median of the student’s heart rate (Pulse). (Hints: summary(), DescTools::Desc(), psych::describe())
  3. Find the range, IQR, variance, and standard deviation for student age (Age).
  4. Find the correlation between the span of the student’s writing hand (Wr.Hnd) and nonwriting hand (NW.Hnd). (Hints: cor(), DescTools::Desc())

Load the Housing dataset from the Ecdat library. This dataset looks at the variables that affect the sales price of houses.

  1. Create the contingency table of whether or not the house has a recreation room (recroom) and whether or not the house had a full basement (fullbase).
  2. Find the mean and median of the house’s lot size (lotsize).
  3. Find the range, IQR, variance, and standard deviation for the sales price (price).
  4. Find the correlation between the sales price of the house (price) and the number of bedrooms (bedrooms).

2.5 Graphical exploratory analyses

Load the Star dataset from the Ecdat library. This dataset looks at the affect on class sizes on student learning.

  1. Generate the scatterplot of the student’s math score tmathssk and reading score treadssk. (Hints: plot(), ggplot() + geom_point())
  2. Generate the histogram of the years of teaching experience totexpk. (Hints: hist(), ggplot() + geom_histogram())
  3. Create a new variable in the Star dataset called totalscore that is the sum of the student’s math score tmathssk and reading score treadssk. (Hints: tranformation)
  4. Generate a boxplot of the student’s total score totalscore split out by the class size type classk. (Hints: boxplot(), ggplot() + geom_boxplot())

Load the survey dataset from the MASS library. This dataset contains the survey responses of a class of college students.

  1. Generate the scatterplot of the student’s height Height and writing hand span Wr.Hnd.
  2. Generate the histogram of student age Age.
  3. Generate a boxplot of the student’s heart rate Pulse split out by the student’s exercise regimen Exer.