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)
flight2 <- flights %>%
  group_by(month) %>%
  summarise(cancelled = sum(is.na(arr_delay)),
            total = n(),
            prop_cancelled = cancelled/total) %>%
  arrange(desc(prop_cancelled))
flight2

## # 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? “filter” Line should be placed before “summarize” Line, so R can filter the given condition before summarizing the data.

Fixing the pipline:

mtcars %>%
  group_by(cyl) %>%
  filter(am == 1) %>%
  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.

Batting Average (BA). Batting average is the ratio of hits (H) to at-bats (AB)
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)
head(Teams)

##   yearID lgID teamID franchID divID Rank  G Ghome  W  L DivWin WCWin LgWin
## 1   1871   NA    BS1      BNA  <NA>    3 31    NA 20 10   <NA>  <NA>     N
## 2   1871   NA    CH1      CNA  <NA>    2 28    NA 19  9   <NA>  <NA>     N
## 3   1871   NA    CL1      CFC  <NA>    8 29    NA 10 19   <NA>  <NA>     N
## 4   1871   NA    FW1      KEK  <NA>    7 19    NA  7 12   <NA>  <NA>     N
## 5   1871   NA    NY2      NNA  <NA>    5 33    NA 16 17   <NA>  <NA>     N
## 6   1871   NA    PH1      PNA  <NA>    1 28    NA 21  7   <NA>  <NA>     Y
##   WSWin   R   AB   H X2B X3B HR BB SO SB CS HBP SF  RA  ER  ERA CG SHO SV
## 1  <NA> 401 1372 426  70  37  3 60 19 73 16  NA NA 303 109 3.55 22   1  3
## 2  <NA> 302 1196 323  52  21 10 60 22 69 21  NA NA 241  77 2.76 25   0  1
## 3  <NA> 249 1186 328  35  40  7 26 25 18  8  NA NA 341 116 4.11 23   0  0
## 4  <NA> 137  746 178  19   8  2 33  9 16  4  NA NA 243  97 5.17 19   1  0
## 5  <NA> 302 1404 403  43  21  1 33 15 46 15  NA NA 313 121 3.72 32   1  0
## 6  <NA> 376 1281 410  66  27  9 46 23 56 12  NA NA 266 137 4.95 27   0  0
##   IPouts  HA HRA BBA SOA   E DP    FP                    name
## 1    828 367   2  42  23 243 24 0.834    Boston Red Stockings
## 2    753 308   6  28  22 229 16 0.829 Chicago White Stockings
## 3    762 346  13  53  34 234 15 0.818  Cleveland Forest Citys
## 4    507 261   5  21  17 163  8 0.803    Fort Wayne Kekiongas
## 5    879 373   7  42  22 235 14 0.840        New York Mutuals
## 6    747 329   3  53  16 194 13 0.845  Philadelphia Athletics
##                           park attendance BPF PPF teamIDBR teamIDlahman45
## 1          South End Grounds I         NA 103  98      BOS            BS1
## 2      Union Base-Ball Grounds         NA 104 102      CHI            CH1
## 3 National Association Grounds         NA  96 100      CLE            CL1
## 4               Hamilton Field         NA 101 107      KEK            FW1
## 5     Union Grounds (Brooklyn)         NA  90  88      NYU            NY2
## 6     Jefferson Street Grounds         NA 102  98      ATH            PH1
##   teamIDretro        BA       SLG
## 1         BS1 0.3104956 0.5021866
## 2         CH1 0.2700669 0.4431438
## 3         CL1 0.2765599 0.4603710
## 4         FW1 0.2386059 0.3324397
## 5         NY2 0.2870370 0.3960114
## 6         PH1 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.

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

Repeat this using teams since 1969. Same result

library(Lahman)
Teams %>%
  select(yearID,teamID, SLG) %>%
  filter(yearID>=1969) %>%
  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.

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) %>%
  summarize(TotalHR = sum(HR),TotalSB = sum(SB)) %>%
  right_join(People, by = c("playerID" = "playerID")) %>%
  filter(TotalHR >= 300 & TotalSB >= 300) %>%
  select(nameFirst, nameLast, nameGiven, TotalHR, TotalSB)

## # A tibble: 8 × 5
##   nameFirst nameLast  nameGiven          TotalHR TotalSB
##   <chr>     <chr>     <chr>                <int>   <int>
## 1 Carlos    Beltran   Carlos Ivan            435     312
## 2 Barry     Bonds     Barry Lamar            762     514
## 3 Bobby     Bonds     Bobby Lee              332     461
## 4 Andre     Dawson    Andre Nolan            438     314
## 5 Steve     Finley    Steven Allen           304     320
## 6 Willie    Mays      Willie Howard          660     338
## 7 Alex      Rodriguez Alexander Enmanuel     696     329
## 8 Reggie    Sanders   Reginald Laverne       305     304

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

library(Lahman)
Pitching %>%
  group_by(playerID) %>%
  summarize(TotalWin = sum(W),TotalSO = sum(SO)) %>%
  right_join(People, by = c("playerID" = "playerID")) %>%
  filter(TotalWin >= 300 & TotalSO >= 3000) %>%
  select(nameFirst, nameLast, nameGiven, TotalWin, TotalSO)

## # A tibble: 10 × 5
##    nameFirst nameLast nameGiven       TotalWin TotalSO
##    <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

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, yearID) %>%
  summarize(TotalHR = sum(HR), BA = sum(H)/sum(AB)) %>%
  right_join(People, by = c("playerID" = "playerID")) %>%
  filter(TotalHR >= 50) %>%
  select(nameFirst, nameLast, nameGiven, yearID, TotalHR, BA) %>%
  arrange(BA)

## # A tibble: 46 × 7
## # Groups:   playerID [30]
##    playerID  nameFirst nameLast nameGiven              yearID TotalHR    BA
##    <chr>     <chr>     <chr>    <chr>                   <int>   <int> <dbl>
##  1 alonspe01 Pete      Alonso   Peter Morgan             2019      53 0.260
##  2 bautijo02 Jose      Bautista Jose Antonio             2010      54 0.260
##  3 jonesan01 Andruw    Jones    Andruw Rudolf            2005      51 0.263
##  4 marisro01 Roger     Maris    Roger Eugene             1961      61 0.269
##  5 vaughgr01 Greg      Vaughn   Gregory Lamont           1998      50 0.272
##  6 mcgwima01 Mark      McGwire  Mark David               1997      58 0.274
##  7 fieldce01 Cecil     Fielder  Cecil Grant              1990      51 0.277
##  8 mcgwima01 Mark      McGwire  Mark David               1999      65 0.278
##  9 stantmi03 Giancarlo Stanton  Giancarlo Cruz-Michael   2017      59 0.281
## 10 judgeaa01 Aaron     Judge    Aaron James              2017      52 0.284
## # … with 36 more rows

Problem Set #1 - `dplyr` basics

Janvich Vanichpipat

2022-09-12

Directions

Question #1

Question #2

Question #3

Question #4

Question #5

Problem Set #1 - dplyr basics

Janvich Vanichpipat

2022-09-12

Directions

Question #1

Question #2

Question #3

Question #4

Question #5

Problem Set #1 - `dplyr` basics