Problem Set #1 - dplyr
basics
Data Wrangling with dplyr
Rabah Douadi
2023-01-23
knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE)
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.4.0 ✔ purrr 1.0.1
## ✔ tibble 3.1.8 ✔ dplyr 1.0.10
## ✔ tidyr 1.2.1 ✔ stringr 1.5.0
## ✔ readr 2.1.3 ✔ forcats 0.5.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()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)
flights_p <- flights %>%
group_by(month) %>%
summarize(cancelled = sum(is.na(arr_delay)),
total = n(),
prop_cancelled = cancelled/total) %>%
arrange(desc(prop_cancelled))
flights_p %>% ggplot(aes(x = month, y = prop_cancelled)) + geom_point() +
labs(title = "% of Cancelled Flights Each Month", y = "% cancelled")
print(flights_p)## # 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#a) highest is in February (2)
#b) lowest is in October (10)Question #2
Consider the following pipeline:
mtcars %>%
group_by(cyl) %>%
summarize(avg_mpg = mean(mpg)) #%>%
filter(., am == 1)What is the problem with this pipeline?
Filter (am == 1) does not work since there is no column am. When doing summarize, only columns are cyl and avg_mpg.
Solution is to filter first, and then do summarize
#after correction
mtcars %>%
filter(am == 1) %>%
group_by(cyl) %>%
summarize(avg_mpg = mean(mpg))## # A tibble: 3 × 2
## cyl avg_mpg
## <dbl> <dbl>
## 1 4 28.1
## 2 6 20.6
## 3 8 15.4Question #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, 10)## 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
## 7 1871 NA RC1 ROK <NA> 9 25 NA 4 21 <NA> <NA> N
## 8 1871 NA TRO TRO <NA> 6 29 NA 13 15 <NA> <NA> N
## 9 1871 NA WS3 OLY <NA> 4 32 NA 15 15 <NA> <NA> N
## 10 1872 NA BL1 BLC <NA> 2 58 NA 35 19 <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
## 6 <NA> 376 1281 410 66 27 9 46 23 56 12 NA NA 266 137 4.95 27 0 0
## 7 <NA> 231 1036 274 44 25 3 38 30 53 10 NA NA 287 108 4.30 23 1 0
## 8 <NA> 351 1248 384 51 34 6 49 19 62 24 NA NA 362 153 5.51 28 0 0
## 9 <NA> 310 1353 375 54 26 6 48 13 48 13 NA NA 303 137 4.37 32 0 0
## 10 <NA> 617 2571 753 106 31 14 29 28 53 18 NA NA 434 166 2.90 48 1 1
## 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
## 7 678 315 3 34 16 220 14 0.821 Rockford Forest Citys
## 8 750 431 4 75 12 198 22 0.845 Troy Haymakers
## 9 846 371 4 45 13 218 20 0.850 Washington Olympics
## 10 1548 573 3 63 77 432 22 0.830 Baltimore Canaries
## 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
## 7 Agricultural Society Fair Grounds NA 97 99 ROK RC1
## 8 Haymakers' Grounds NA 101 100 TRO TRO
## 9 Olympics Grounds NA 94 98 OLY WS3
## 10 Newington Park NA 106 102 BAL BL1
## 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
## 7 RC1 0.2644788 0.4333977
## 8 TRO 0.3076923 0.4903846
## 9 WS3 0.2771619 0.4323725
## 10 BL1 0.2928821 0.4332944Question #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.
top <- arrange(team, desc(SLG)) %>%
select(yearID,teamID,SLG)
head(top, 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 0.5964320top_1969 <- arrange(team, desc(SLG)) %>%
select(yearID,teamID,SLG) %>%
filter(yearID >= 1969)
head(top_1969, 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?
#a.
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 305 304#b.
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 3574#c.
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