dplyr
basicsdplyrQuestion #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)
flights_cancelled <- flights %>%
group_by(month) %>%
summarize(cancelled = sum(is.na(arr_delay)),
total = n(),
prop_cancelled = cancelled/total) %>%
arrange(desc(prop_cancelled))
flights_cancelled## # 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 0.00938February has highest cancelled flights October has low cancellations
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 issue with this pipeline is that the filter operation (am == 1) is placed last. This is problematic because the previous group_by and summarize operations have altered the structure of the data frame, leaving only the cyl and avg_mpg columns. As a result, there is no am column to be filtered on. To correct this, the filter operation should be placed before the group_by and summarize, like this: mtcars %>% filter(am == 1) %>% group_by(cyl) %>% summarize(avg_mpg = mean(mpg))
library(tidyverse)
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.
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)
Team <- Teams
Team <- mutate(Team, BA = H / AB)
Team <- mutate(Team, SLG = (H + 2 * X2B + 3 * X3B + 4 * HR) / AB)
head(Team, 5)## 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
## 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
## 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
## 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
## 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 0.3960114Question #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)
Top5 <- arrange(Team, desc(SLG)) %>%
select(yearID,teamID,SLG)
head(Top5, 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 0.5964320library(Lahman)
Top5_since1969 <- arrange(Team, desc(SLG)) %>%
select(yearID,teamID,SLG) %>%
filter(yearID >= 1969)
head(Top5_since1969, 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 0.5964320Question #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.
Similarly, name every pitcher in baseball history who has accumulated at least 300 wins (W) and at least 3,000 strikeouts (SO).
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) %>%
summarize(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 304library(Lahman)
Pitching %>%
group_by(playerID) %>%
summarize(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 3574library(Lahman)
Batting %>%
group_by(playerID, yearID) %>%
summarize(totalHR = sum(HR), BA = sum(H)/sum(AB)) %>%
filter(totalHR >= 50) %>%
inner_join(People, by = c('playerID' = 'playerID')) %>%
select(nameFirst, nameLast, yearID, totalHR, BA) %>%
ungroup() %>%
arrange(BA)## # A tibble: 46 × 6
## playerID nameFirst nameLast yearID totalHR BA
## <chr> <chr> <chr> <int> <int> <dbl>
## 1 alonspe01 Pete Alonso 2019 53 0.260
## 2 bautijo02 Jose Bautista 2010 54 0.260
## 3 jonesan01 Andruw Jones 2005 51 0.263
## 4 marisro01 Roger Maris 1961 61 0.269
## 5 vaughgr01 Greg Vaughn 1998 50 0.272
## 6 mcgwima01 Mark McGwire 1997 58 0.274
## 7 fieldce01 Cecil Fielder 1990 51 0.277
## 8 mcgwima01 Mark McGwire 1999 65 0.278
## 9 stantmi03 Giancarlo Stanton 2017 59 0.281
## 10 judgeaa01 Aaron Judge 2017 52 0.284
## # … with 36 more rows