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 had the highest proportion of cancelled flights b.What month had the lowest? #October had the lowest proportion of cancelled flights

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…
flights_new <- flights %>%
  group_by(month) %>%
  summarize(cancelled_flights =  sum(is.na(arr_delay)),
            total_flights = n(),
            proportion_cancelled = cancelled_flights/total_flights) %>%
  arrange(desc(proportion_cancelled)) 

flights_new
## # A tibble: 12 × 4
##    month cancelled_flights total_flights proportion_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)

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

What is the problem with this pipeline? #Error caused by mask$eval_all_filter():! object ‘am’ not found. #To fix this, I moved the “filter” function before the “summarize” function.

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
Teams %>%
  filter(yearID >= 1969) %>%
  select(yearID, teamID, SLG) %>%
  arrange(desc(SLG)) %>%
  head(15)
##    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
## 6    2020    LAN 0.5910872
## 7    1997    SEA 0.5908443
## 8    1996    SEA 0.5906845
## 9    1994    CLE 0.5900050
## 10   2001    COL 0.5880492
## 11   2017    HOU 0.5854571
## 12   2009    NYA 0.5818021
## 13   2019    LAN 0.5814673
## 14   2004    BOS 0.5807692
## 15   1995    CLE 0.5799523

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)
#a.1.
Batting_5_A_1 <-
  Batting %>%
  group_by(playerID)%>%
  summarize(total_HR = sum(HR),
            total_SB = sum(SB)) %>%
  filter(total_HR >= 300 & total_SB >= 300) %>%
  left_join(People, by = c('playerID' = 'playerID')) %>%
  select(nameFirst, nameLast, total_HR, total_SB) %>%
   arrange(desc(total_HR))
Batting_5_A_1
## # A tibble: 8 × 4
##   nameFirst nameLast  total_HR total_SB
##   <chr>     <chr>        <int>    <int>
## 1 Barry     Bonds          762      514
## 2 Alex      Rodriguez      696      329
## 3 Willie    Mays           660      338
## 4 Andre     Dawson         438      314
## 5 Carlos    Beltran        435      312
## 6 Bobby     Bonds          332      461
## 7 Reggie    Sanders        305      304
## 8 Steve     Finley         304      320
#a.2.
Pitching_5_A_2 <-
  Pitching %>%
  group_by(playerID)%>%
  summarize(total_W = sum(W),
            total_SO = sum(SO)) %>%
  filter(total_W >= 300 & total_SO >= 3000) %>%
  left_join(People, by = c('playerID' = 'playerID')) %>%
  select(nameFirst, nameLast, total_W , total_SO) %>%
   arrange(desc(total_W))

Pitching_5_A_2
## # A tibble: 10 × 4
##    nameFirst nameLast total_W total_SO
##    <chr>     <chr>      <int>    <int>
##  1 Walter    Johnson      417     3509
##  2 Greg      Maddux       355     3371
##  3 Roger     Clemens      354     4672
##  4 Steve     Carlton      329     4136
##  5 Nolan     Ryan         324     5714
##  6 Don       Sutton       324     3574
##  7 Phil      Niekro       318     3342
##  8 Gaylord   Perry        314     3534
##  9 Tom       Seaver       311     3640
## 10 Randy     Johnson      303     4875
#b.1. 
Batting_5_B_1 <-
  Batting %>%
  group_by(playerID, yearID)%>%
  summarize(total_HR = sum(HR),
            batting_avg = sum(H)/sum(AB)) %>%
  filter(total_HR >= 50) %>%
  left_join(People, by = c('playerID' = 'playerID')) %>%
  select(yearID, nameFirst, nameLast, total_HR, batting_avg) %>%
   arrange(batting_avg)
Batting_5_B_1
## # A tibble: 46 × 6
## # Groups:   playerID [30]
##    playerID  yearID nameFirst nameLast total_HR 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
#b.2

Batting_5.B_2 <-
  Batting %>%
  group_by(playerID, yearID, HR)%>%
  summarize(batting_avg = sum(H)/sum(AB)) %>%
  filter(HR >= 50 & yearID == 2001) %>%
  left_join(People, by = c('playerID' = 'playerID')) %>%
  select(yearID, nameFirst, nameLast, HR, batting_avg) 
Batting_5.B_2
## # A tibble: 4 × 6
## # Groups:   playerID, yearID [4]
##   playerID  yearID nameFirst nameLast     HR batting_avg
##   <chr>      <int> <chr>     <chr>     <int>       <dbl>
## 1 bondsba01   2001 Barry     Bonds        73       0.328
## 2 gonzalu01   2001 Luis      Gonzalez     57       0.325
## 3 rodrial01   2001 Alex      Rodriguez    52       0.318
## 4 sosasa01    2001 Sammy     Sosa         64       0.328