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?

b.What month had the lowest?

February has the highest proportion of cancelled flights. October, on the other hand, has the lowest proportion of cancelled flights.

library(nycflights13)
flightshigh <- flights %>% 
  group_by(month) %>%
  summarize(cancelled = sum(is.na(arr_delay)),
            total =n(),
            prop_cancelled = cancelled/total) %>%
  arrange(desc(prop_cancelled))

flightshigh
## # A tibble: 12 × 4
##    month cancelled total prop_cancelled
##    <int>     <int> <int>          <dbl>
##  1     2      1340 24951        0.0537 
##  2     6      1168 28243        0.0414 
##  3    12      1115 28135        0.0396 
##  4     7      1132 29425        0.0385 
##  5     3       932 28834        0.0323 
##  6     4       766 28330        0.0270 
##  7     5       668 28796        0.0232 
##  8     1       606 27004        0.0224 
##  9     9       564 27574        0.0205 
## 10     8       571 29327        0.0195 
## 11    11       297 27268        0.0109 
## 12    10       271 28889        0.00938

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 then summarized, since here it is summarized first, the variables might not be present and it is unclear how to filter “am” out. We can fix this by filter the dataframe first.

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

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) %>%
  mutate(SLG = (H+2*X2B+3*X3B+4*HR)/AB)

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.

library(Lahman)
Teams %>%
  select(yearID, teamID, SLG) %>%
  arrange(desc(SLG)) %>%
  head(5)
##   yearID teamID       SLG
## 1   2019    HOU 0.6092998
## 2   2019    MIN 0.6071179
## 3   2003    BOS 0.6033975
## 4   2019    NYA 0.5996776
## 5   2020    ATL 0.5964320

Top 15 teams in MLB since 1969 by SLG

Teams %>%
  filter(yearID >= 1969) %>%
  select(yearID, teamID, SLG) %>%
  arrange(desc(SLG)) %>%
  head(5)
##   yearID teamID       SLG
## 1   2019    HOU 0.6092998
## 2   2019    MIN 0.6071179
## 3   2003    BOS 0.6033975
## 4   2019    NYA 0.5996776
## 5   2020    ATL 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.

  1. Similarly, name every pitcher in baseball history who has accumulated at least 300 wins (W) and at least 3,000 strikeouts (SO).

  2. 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?

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
library(Lahman)

Pitching %>% 
  group_by(playerID) %>%
  summarise(totalW = sum(W), totalSO = sum(SO)) %>%
  filter(totalW >= 300 & totalSO >= 300) %>%
  inner_join(People, by = c("playerID" = "playerID")) %>%
  select(nameFirst, nameLast, totalW, totalSO)
## # A tibble: 24 × 4
##    nameFirst nameLast  totalW totalSO
##    <chr>     <chr>      <int>   <int>
##  1 Pete      Alexander    373    2198
##  2 Steve     Carlton      329    4136
##  3 John      Clarkson     328    1978
##  4 Roger     Clemens      354    4672
##  5 Pud       Galvin       365    1807
##  6 Tom       Glavine      305    2607
##  7 Lefty     Grove        300    2266
##  8 Randy     Johnson      303    4875
##  9 Walter    Johnson      417    3509
## 10 Tim       Keefe        342    2564
## # … with 14 more rows
library(Lahman)

Batting %>% 
  group_by(playerID, yearID) %>%
  summarise(totalHR = sum(HR), BattingAverage = sum(H)/sum(AB)) %>%
  filter(totalHR >= 50) %>%
  inner_join(People, by = c("playerID" = "playerID")) %>%
  select(yearID, playerID, nameFirst, nameLast, nameGiven, totalHR, BattingAverage) %>%
  arrange(BattingAverage)
## # A tibble: 46 × 7
## # Groups:   playerID [30]
##    yearID playerID  nameFirst nameLast nameGiven          totalHR 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-Mi…      59          0.281
## 10   2017 judgeaa01 Aaron     Judge    Aaron James             52          0.284
## # … with 36 more rows

Pete Alonso (Peter Morgan) is the player with the lowest Batting Average in 2019