Directions

During ANLY 512 we will be studying the theory and practice of data visualization. We will be using R and the packages within R to assemble data and construct many different types of visualizations. Before we begin studying data visualizations we need to develop some data wrangling skills. We will use these skills to wrangle our data into a form that we can use for visualizations.

The objective of this assignment is to introduce you to R Studio, Rmarkdown, the tidyverse and more specifically the dplyr package.

Each question is worth 5 points.

To submit this homework you will create the document in Rstudio, using the knitr package (button included in Rstudio) and then submit the document to your Rpubs account. Once uploaded you will submit the link to that document on Canvas. Please make sure that this link is hyper linked and that I can see the visualization and the code required to create it.

Question #1

Use the nycflights13 package and the flights data frame to answer the following questions:

a.What month had the highest proportion of cancelled flights?
February

b.What month had the lowest?
October

library(nycflights13)

flightCancellationPropByMonth <- 
  flights %>%
  group_by(month) %>%
  summarise(
    cancelled = sum(is.na(dep_time)),
    cancelledProportion = (cancelled/n())*100,
    N = n()
  ) %>%
  arrange(cancelledProportion)

Question #2

Consider the following pipeline:

library(tidyverse)
#mtcars %>%
#  group_by(cyl) %>%
#  summarize(avg_mpg = mean(mpg)) %>%
#  filter(am == 1)

What is the problem with this pipeline?
The data should be filtered first and then summarized. If the data is summarized first, the columns/variables may not be present on which filter() is applied like we see in the above example. To fix this, apply filter( ) first followed by summarize( ).

library(tidyverse)
mtcars %>%
  filter(am == 1) %>%
  group_by(cyl) %>%
  summarize(avg_mpg = mean(mpg))
## # A tibble: 3 × 2
##     cyl avg_mpg
##   <dbl>   <dbl>
## 1     4    28.1
## 2     6    20.6
## 3     8    15.4

Question #3

Define two new variables in the Teams data frame in the pkg Lahman() package.

  1. batting average (BA). Batting average is the ratio of hits (H) to at-bats (AB)

  2. slugging percentage (SLG). Slugging percentage is total bases divided by at-bats (AB). To compute total bases, you get 1 for a single, 2 for a double, 3 for a triple, and 4 for a home run.

library(Lahman)
Teams <- 
  Teams %>%
  mutate(
    BA = H/AB,
    SLG = (H+2*X2B+3*X3B+4*HR)/AB
  )
head(select(Teams, BA, SLG))
##          BA       SLG
## 1 0.3104956 0.5021866
## 2 0.2700669 0.4431438
## 3 0.2765599 0.4603710
## 4 0.2386059 0.3324397
## 5 0.2870370 0.3960114
## 6 0.3200625 0.5144418

Question #4

Using the Teams data frame in the pkg Lahman() package. display the top-5 teams ranked in terms of slugging percentage (SLG) in Major League Baseball history. Repeat this using teams since 1969. Slugging percentage is total bases divided by at-bats.To compute total bases, you get 1 for a single, 2 for a double, 3 for a triple, and 4 for a home run.

Top 5 teams in terms of SLG every year since 1969:

library(Lahman)

Top5SLGbyYear <- 
  filter(Teams, yearID >= "1969") %>%
  group_by(yearID) %>%
  arrange(desc(SLG)) %>%
  slice(1:5) %>%
  select(yearID, name, SLG)
Top5SLGbyYear
## # A tibble: 265 × 3
## # Groups:   yearID [53]
##    yearID name                   SLG
##     <int> <chr>                <dbl>
##  1   1969 Boston Red Sox       0.500
##  2   1969 Cincinnati Reds      0.500
##  3   1969 Baltimore Orioles    0.493
##  4   1969 Minnesota Twins      0.486
##  5   1969 Pittsburgh Pirates   0.467
##  6   1970 Cincinnati Reds      0.524
##  7   1970 Boston Red Sox       0.515
##  8   1970 Chicago Cubs         0.497
##  9   1970 San Francisco Giants 0.491
## 10   1970 Pittsburgh Pirates   0.483
## # … with 255 more rows

Top 5 teams in terms of SLG (all time) since 1969:

Top5SLGsince1969 <- 
  filter(Teams, yearID >= "1969") %>%
  arrange(desc(SLG)) %>%
  slice(1:5) %>%
  select(yearID, name, SLG)
Top5SLGsince1969
##   yearID             name       SLG
## 1   2019   Houston Astros 0.6092998
## 2   2019  Minnesota Twins 0.6071179
## 3   2003   Boston Red Sox 0.6033975
## 4   2019 New York Yankees 0.5996776
## 5   2020   Atlanta Braves 0.5964320

Question #5

Use the Batting, Pitching, and People tables in the pkg Lahman() package to answer the following questions.

a.Name every player in baseball history who has accumulated at least 300 home runs (HR) and at least 300 stolen bases (SB). You can find the first and last name of the player in the Master data frame. Join this to your result along with the total home runs and total bases stolen for each of these elite players.

library(Lahman)

Batting %>%
  group_by(playerID) %>%
  summarise(totalHR = sum(HR), totalSB = sum(SB)) %>%
  filter(totalHR >= 300 & totalSB >= 300) %>%
  inner_join(People, by = c("playerID" = "playerID")) %>%
  select(nameFirst, nameLast, totalHR, totalSB)
## # A tibble: 8 × 4
##   nameFirst nameLast  totalHR totalSB
##   <chr>     <chr>       <int>   <int>
## 1 Carlos    Beltran       435     312
## 2 Barry     Bonds         762     514
## 3 Bobby     Bonds         332     461
## 4 Andre     Dawson        438     314
## 5 Steve     Finley        304     320
## 6 Willie    Mays          660     338
## 7 Alex      Rodriguez     696     329
## 8 Reggie    Sanders       305     304
  1. Similarly, name every pitcher in baseball history who has accumulated at least 300 wins (W) and at least 3,000 strikeouts (SO).
Pitching %>%
  group_by(playerID) %>%
  summarise(totalW = sum(W), totalSO = sum(SO)) %>%
  filter(totalW >= 300 & totalSO >= 3000) %>%
  inner_join(People, by = c("playerID" = "playerID")) %>%
  select(nameFirst, nameLast, totalW, totalSO)
## # A tibble: 10 × 4
##    nameFirst nameLast totalW totalSO
##    <chr>     <chr>     <int>   <int>
##  1 Steve     Carlton     329    4136
##  2 Roger     Clemens     354    4672
##  3 Randy     Johnson     303    4875
##  4 Walter    Johnson     417    3509
##  5 Greg      Maddux      355    3371
##  6 Phil      Niekro      318    3342
##  7 Gaylord   Perry       314    3534
##  8 Nolan     Ryan        324    5714
##  9 Tom       Seaver      311    3640
## 10 Don       Sutton      324    3574
  1. Identify the name and year of every player who has hit at least 50 home runs in a single season. Which player had the lowest batting average in that season?
    Pete Alonso (Peter Morgan) had the lowest batting average in 2019.
Batting %>%
  group_by(playerID, yearID) %>%
  summarize(homeRuns = sum(HR), battingAverage = sum(H)/sum(AB)) %>%
  filter(homeRuns >= 50) %>%
  inner_join(People, by = c("playerID" = "playerID")) %>%
  select(yearID, playerID, nameFirst, nameLast, nameGiven, homeRuns, battingAverage) %>%
  arrange(battingAverage)
## # A tibble: 46 × 7
## # Groups:   playerID [30]
##    yearID playerID  nameFirst nameLast nameGiven         homeRuns battingAverage
##     <int> <chr>     <chr>     <chr>    <chr>                <int>          <dbl>
##  1   2019 alonspe01 Pete      Alonso   Peter Morgan            53          0.260
##  2   2010 bautijo02 Jose      Bautista Jose Antonio            54          0.260
##  3   2005 jonesan01 Andruw    Jones    Andruw Rudolf           51          0.263
##  4   1961 marisro01 Roger     Maris    Roger Eugene            61          0.269
##  5   1998 vaughgr01 Greg      Vaughn   Gregory Lamont          50          0.272
##  6   1997 mcgwima01 Mark      McGwire  Mark David              58          0.274
##  7   1990 fieldce01 Cecil     Fielder  Cecil Grant             51          0.277
##  8   1999 mcgwima01 Mark      McGwire  Mark David              65          0.278
##  9   2017 stantmi03 Giancarlo Stanton  Giancarlo Cruz-M…       59          0.281
## 10   2017 judgeaa01 Aaron     Judge    Aaron James             52          0.284
## # … with 36 more rows