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?

library(nycflights13)
glimpse(flights)
## Rows: 336,776
## Columns: 19
## $ year           <int> 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2…
## $ month          <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
## $ day            <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
## $ dep_time       <int> 517, 533, 542, 544, 554, 554, 555, 557, 557, 558, 558, …
## $ sched_dep_time <int> 515, 529, 540, 545, 600, 558, 600, 600, 600, 600, 600, …
## $ dep_delay      <dbl> 2, 4, 2, -1, -6, -4, -5, -3, -3, -2, -2, -2, -2, -2, -1…
## $ arr_time       <int> 830, 850, 923, 1004, 812, 740, 913, 709, 838, 753, 849,…
## $ sched_arr_time <int> 819, 830, 850, 1022, 837, 728, 854, 723, 846, 745, 851,…
## $ arr_delay      <dbl> 11, 20, 33, -18, -25, 12, 19, -14, -8, 8, -2, -3, 7, -1…
## $ carrier        <chr> "UA", "UA", "AA", "B6", "DL", "UA", "B6", "EV", "B6", "…
## $ flight         <int> 1545, 1714, 1141, 725, 461, 1696, 507, 5708, 79, 301, 4…
## $ tailnum        <chr> "N14228", "N24211", "N619AA", "N804JB", "N668DN", "N394…
## $ origin         <chr> "EWR", "LGA", "JFK", "JFK", "LGA", "EWR", "EWR", "LGA",…
## $ dest           <chr> "IAH", "IAH", "MIA", "BQN", "ATL", "ORD", "FLL", "IAD",…
## $ air_time       <dbl> 227, 227, 160, 183, 116, 150, 158, 53, 140, 138, 149, 1…
## $ distance       <dbl> 1400, 1416, 1089, 1576, 762, 719, 1065, 229, 944, 733, …
## $ hour           <dbl> 5, 5, 5, 5, 6, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 5, 6, 6, 6…
## $ minute         <dbl> 15, 29, 40, 45, 0, 58, 0, 0, 0, 0, 0, 0, 0, 0, 0, 59, 0…
## $ time_hour      <dttm> 2013-01-01 05:00:00, 2013-01-01 05:00:00, 2013-01-01 0…
cancelled_flights_by_month = flights %>%
  group_by(month) %>%
  summarize(num_cancelled = sum(is.na(dep_time)),
            ratio_cancelled = num_cancelled/n())
cancelled_flights_by_month[which.max(cancelled_flights_by_month$ratio_cancelled),]
## # A tibble: 1 × 3
##   month num_cancelled ratio_cancelled
##   <int>         <int>           <dbl>
## 1     2          1261          0.0505
# a. Month 2 i.e. February had the highest proportion of cancelled flights

cancelled_flights_by_month[which.min(cancelled_flights_by_month$ratio_cancelled),]
## # A tibble: 1 × 3
##   month num_cancelled ratio_cancelled
##   <int>         <int>           <dbl>
## 1    10           236         0.00817
# b. Month 10 i.e. October had the lowest proportion of cancelled flights

Question #2

Consider the following pipeline:

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

Here, aggregation seems to be done before the filtering. After summarize, the dataset will only be cyl and avg_mpg columns. Hence we will not be able to filter based on am.

What is the problem with this pipeline?

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)
# a. batting average (BA)
Teams = Teams %>%
  mutate(BA = H / AB)
summary(Teams$BA)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.1564  0.2494  0.2600  0.2607  0.2708  0.3498
# b. slugging percentage (SLG)
Teams = Teams %>%
  mutate(SLG = (H + 2 * X2B + 3 * X3B + 4 * HR) / AB)
summary(Teams$SLG)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.1659  0.4192  0.4596  0.4561  0.4950  0.6093

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(name, yearID, SLG) %>%
   arrange(desc(SLG)) %>%
  head(5)
##               name yearID       SLG
## 1   Houston Astros   2019 0.6092998
## 2  Minnesota Twins   2019 0.6071179
## 3   Boston Red Sox   2003 0.6033975
## 4 New York Yankees   2019 0.5996776
## 5   Atlanta Braves   2020 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.

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

c.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)
# a. Players who have accumulated at least 300 home runs (HR) and at least 300 stolen bases (SB)
Batting %>%
  group_by(playerID) %>%
  summarize(total_home_runs = sum(HR), total_stolen_bases = sum (SB)) %>%
  filter(total_home_runs >= 300 & total_stolen_bases >= 300) %>%
  inner_join(People, by = c("playerID" = "playerID")) %>%
  select(nameFirst, nameLast, total_home_runs, total_stolen_bases)
## # A tibble: 8 × 4
##   nameFirst nameLast  total_home_runs total_stolen_bases
##   <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
# b. Pitchers who have accumulated at least 300 wins (W) and at least 3,000 strikeouts (SO)
Pitching %>%
  group_by(playerID) %>%
  summarize(total_wins = sum(W), total_strikeouts = sum(SO)) %>%
  filter(total_wins >= 300 & total_strikeouts >= 3000) %>%
  inner_join(People, by =  c("playerID" = "playerID")) %>%
  select(nameFirst, nameLast, nameGiven, total_wins, total_strikeouts)
## # A tibble: 10 × 5
##    nameFirst nameLast nameGiven       total_wins total_strikeouts
##    <chr>     <chr>    <chr>                <int>            <int>
##  1 Steve     Carlton  Steven Norman          329             4136
##  2 Roger     Clemens  William Roger          354             4672
##  3 Randy     Johnson  Randall David          303             4875
##  4 Walter    Johnson  Walter Perry           417             3509
##  5 Greg      Maddux   Gregory Alan           355             3371
##  6 Phil      Niekro   Philip Henry           318             3342
##  7 Gaylord   Perry    Gaylord Jackson        314             3534
##  8 Nolan     Ryan     Lynn Nolan             324             5714
##  9 Tom       Seaver   George Thomas          311             3640
## 10 Don       Sutton   Donald Howard          324             3574
# c. name and year of every player who has hit at least 50 home runs in a single season. 
players = Batting %>%
  group_by(playerID, yearID) %>%
    summarize(total_home_runs = sum(HR), batting_avg = sum(H) / sum(AB)) %>%
    filter(total_home_runs >= 50) %>%
    inner_join(People, by = c("playerID" = "playerID")) %>%
    select(yearID, nameFirst, nameLast, total_home_runs, batting_avg) %>%
    arrange(batting_avg)
players
## # A tibble: 46 × 6
## # Groups:   playerID [30]
##    playerID  yearID nameFirst nameLast total_home_runs batting_avg
##    <chr>      <int> <chr>     <chr>              <int>       <dbl>
##  1 alonspe01   2019 Pete      Alonso                53       0.260
##  2 bautijo02   2010 Jose      Bautista              54       0.260
##  3 jonesan01   2005 Andruw    Jones                 51       0.263
##  4 marisro01   1961 Roger     Maris                 61       0.269
##  5 vaughgr01   1998 Greg      Vaughn                50       0.272
##  6 mcgwima01   1997 Mark      McGwire               58       0.274
##  7 fieldce01   1990 Cecil     Fielder               51       0.277
##  8 mcgwima01   1999 Mark      McGwire               65       0.278
##  9 stantmi03   2017 Giancarlo Stanton               59       0.281
## 10 judgeaa01   2017 Aaron     Judge                 52       0.284
## # … with 36 more rows
# c.1. Which player had the lowest batting average in that season?
players[which.min(players$batting_avg),]
## # A tibble: 1 × 6
## # Groups:   playerID [1]
##   playerID  yearID nameFirst nameLast total_home_runs batting_avg
##   <chr>      <int> <chr>     <chr>              <int>       <dbl>
## 1 alonspe01   2019 Pete      Alonso                53       0.260
# Pete Alonso had the lowest batting average that season.