dplyr
basicsdplyrDuring 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, 2013 has highest flights cancelled with 1261 cancelled flight.
b.What month had the lowest?
October, 2013 has lowest cancelled flight with 236 number of cancelled flight.
library(nycflights13)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionmonth_cancelledFlights <- flights %>%
group_by(year,month) %>%
summarize(flights_cancelled = sum(is.na(dep_time)),
flights_cancelled_proportion = flights_cancelled/n()*100) %>%
arrange(flights_cancelled_proportion)## `summarise()` has grouped output by 'year'. You can override using the
## `.groups` argument.month_cancelledFlights## # A tibble: 12 × 4
## # Groups: year [1]
## year month flights_cancelled flights_cancelled_proportion
## <int> <int> <int> <dbl>
## 1 2013 10 236 0.817
## 2 2013 11 233 0.854
## 3 2013 9 452 1.64
## 4 2013 8 486 1.66
## 5 2013 1 521 1.93
## 6 2013 5 563 1.96
## 7 2013 4 668 2.36
## 8 2013 3 861 2.99
## 9 2013 7 940 3.19
## 10 2013 6 1009 3.57
## 11 2013 12 1025 3.64
## 12 2013 2 1261 5.05Question #2
Consider the following pipeline:
library(tidyverse)
mtcars %>%
filter(am == 1) %>%
group_by(cyl) %>%
summarize(avg_mpg = mean(mpg)) What is the problem with this pipeline?
The problem with above pipeline is that filter (am) is used before summarize funcation. If filter function is used after its sumarization then it becomes hard to filter.
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,
SLG = (H + 2 * X2B + 3 * X3B + 4 * HR) / AB)
head(Teams, 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 0.3960114summary(Teams)## yearID lgID teamID franchID divID
## Min. :1871 AA: 85 CHN : 146 ATL : 146 Length:2985
## 1st Qu.:1922 AL:1295 PHI : 139 CHC : 146 Class :character
## Median :1967 FL: 16 PIT : 135 CIN : 140 Mode :character
## Mean :1959 NA: 50 CIN : 132 PIT : 140
## 3rd Qu.:1997 NL:1519 SLN : 130 STL : 140
## Max. :2021 PL: 8 BOS : 121 PHI : 139
## UA: 12 (Other):2182 (Other):2134
## Rank G Ghome W
## Min. : 1.000 Min. : 6 Min. :24.00 Min. : 0.00
## 1st Qu.: 2.000 1st Qu.:154 1st Qu.:77.00 1st Qu.: 66.00
## Median : 4.000 Median :159 Median :81.00 Median : 77.00
## Mean : 4.039 Mean :150 Mean :78.05 Mean : 74.61
## 3rd Qu.: 6.000 3rd Qu.:162 3rd Qu.:81.00 3rd Qu.: 87.00
## Max. :13.000 Max. :165 Max. :84.00 Max. :116.00
## NA's :399
## L DivWin WCWin LgWin
## Min. : 4.00 Length:2985 Length:2985 Length:2985
## 1st Qu.: 65.00 Class :character Class :character Class :character
## Median : 76.00 Mode :character Mode :character Mode :character
## Mean : 74.61
## 3rd Qu.: 87.00
## Max. :134.00
##
## WSWin R AB H
## Length:2985 Min. : 24 Min. : 211 Min. : 33
## Class :character 1st Qu.: 614 1st Qu.:5135 1st Qu.:1299
## Mode :character Median : 691 Median :5402 Median :1390
## Mean : 681 Mean :5129 Mean :1339
## 3rd Qu.: 764 3rd Qu.:5519 3rd Qu.:1465
## Max. :1220 Max. :5781 Max. :1783
##
## X2B X3B HR BB
## Min. : 1.0 Min. : 0.00 Min. : 0.0 Min. : 1.0
## 1st Qu.:194.0 1st Qu.: 29.00 1st Qu.: 45.0 1st Qu.:425.8
## Median :234.0 Median : 40.00 Median :110.0 Median :494.0
## Mean :228.7 Mean : 45.67 Mean :105.9 Mean :473.6
## 3rd Qu.:272.0 3rd Qu.: 59.00 3rd Qu.:155.0 3rd Qu.:554.2
## Max. :376.0 Max. :150.00 Max. :307.0 Max. :835.0
## NA's :1
## SO SB CS HBP
## Min. : 3.0 Min. : 1.0 Min. : 3.00 Min. : 7.00
## 1st Qu.: 516.0 1st Qu.: 62.5 1st Qu.: 33.00 1st Qu.: 32.00
## Median : 761.0 Median : 93.0 Median : 44.00 Median : 43.00
## Mean : 762.1 Mean :109.4 Mean : 46.55 Mean : 45.82
## 3rd Qu.: 990.0 3rd Qu.:137.0 3rd Qu.: 56.00 3rd Qu.: 57.00
## Max. :1596.0 Max. :581.0 Max. :191.00 Max. :160.00
## NA's :16 NA's :126 NA's :832 NA's :1158
## SF RA ER ERA
## Min. : 7.00 Min. : 34 Min. : 23.0 Min. :1.220
## 1st Qu.:38.00 1st Qu.: 610 1st Qu.: 503.0 1st Qu.:3.370
## Median :44.00 Median : 689 Median : 594.0 Median :3.840
## Mean :44.11 Mean : 681 Mean : 573.4 Mean :3.841
## 3rd Qu.:50.00 3rd Qu.: 766 3rd Qu.: 671.0 3rd Qu.:4.330
## Max. :77.00 Max. :1252 Max. :1023.0 Max. :8.000
## NA's :1541
## CG SHO SV IPouts
## Min. : 0.00 Min. : 0.000 Min. : 0.00 Min. : 162
## 1st Qu.: 9.00 1st Qu.: 6.000 1st Qu.:10.00 1st Qu.:4080
## Median : 41.00 Median : 9.000 Median :25.00 Median :4252
## Mean : 47.55 Mean : 9.588 Mean :24.42 Mean :4013
## 3rd Qu.: 76.00 3rd Qu.:12.000 3rd Qu.:39.00 3rd Qu.:4341
## Max. :148.00 Max. :32.000 Max. :68.00 Max. :4518
##
## HA HRA BBA SOA
## Min. : 49 Min. : 0.0 Min. : 1.0 Min. : 0.0
## 1st Qu.:1287 1st Qu.: 51.0 1st Qu.:429.0 1st Qu.: 511.0
## Median :1389 Median :113.0 Median :495.0 Median : 762.0
## Mean :1339 Mean :105.9 Mean :473.7 Mean : 761.6
## 3rd Qu.:1468 3rd Qu.:153.0 3rd Qu.:554.0 3rd Qu.: 997.0
## Max. :1993 Max. :305.0 Max. :827.0 Max. :1687.0
##
## E DP FP name
## Min. : 20.0 Min. : 0.0 Min. :0.7610 Length:2985
## 1st Qu.:111.0 1st Qu.:116.0 1st Qu.:0.9660 Class :character
## Median :141.0 Median :140.0 Median :0.9770 Mode :character
## Mean :180.8 Mean :132.6 Mean :0.9664
## 3rd Qu.:207.0 3rd Qu.:157.0 3rd Qu.:0.9810
## Max. :639.0 Max. :217.0 Max. :0.9910
##
## park attendance BPF PPF
## Length:2985 Min. : 0 Min. : 60.0 Min. : 60.0
## Class :character 1st Qu.: 538461 1st Qu.: 97.0 1st Qu.: 97.0
## Mode :character Median :1190886 Median :100.0 Median :100.0
## Mean :1376599 Mean :100.2 Mean :100.2
## 3rd Qu.:2066598 3rd Qu.:103.0 3rd Qu.:103.0
## Max. :4483350 Max. :129.0 Max. :141.0
## NA's :279
## teamIDBR teamIDlahman45 teamIDretro BA
## Length:2985 Length:2985 Length:2985 Min. :0.1564
## Class :character Class :character Class :character 1st Qu.:0.2494
## Mode :character Mode :character Mode :character Median :0.2600
## Mean :0.2607
## 3rd Qu.:0.2708
## Max. :0.3498
##
## SLG
## Min. :0.1659
## 1st Qu.:0.4192
## Median :0.4596
## Mean :0.4561
## 3rd Qu.:0.4950
## Max. :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 %>%
mutate(SLG = (H + 2 * X2B + 3 * X3B + 4 * HR) / AB) %>%
arrange(desc(SLG)) %>%
head(5)## yearID lgID teamID franchID divID Rank G Ghome W L DivWin WCWin LgWin
## 1 2019 AL HOU HOU W 1 162 81 107 55 Y N Y
## 2 2019 AL MIN MIN C 1 162 81 101 61 Y N N
## 3 2003 AL BOS BOS E 2 162 81 95 67 N Y N
## 4 2019 AL NYA NYY E 1 162 81 103 59 Y N N
## 5 2020 NL ATL ATL E 1 60 30 35 25 Y N N
## WSWin R AB H X2B X3B HR BB SO SB CS HBP SF RA ER ERA CG SHO SV
## 1 N 920 5613 1538 323 28 288 645 1166 67 27 66 57 640 595 3.66 2 14 47
## 2 N 939 5732 1547 318 23 307 525 1334 28 21 81 41 754 680 4.18 1 10 50
## 3 N 961 5769 1667 371 40 238 620 943 88 35 53 64 809 729 4.48 5 6 36
## 4 N 943 5583 1493 290 17 306 569 1437 55 22 49 33 739 691 4.31 1 9 50
## 5 N 348 2074 556 130 3 103 239 573 23 4 23 7 288 257 4.41 0 0 13
## IPouts HA HRA BBA SOA E DP FP name park
## 1 4387 1205 230 448 1671 71 96 0.988 Houston Astros Minute Maid Park
## 2 4390 1456 198 452 1463 111 130 0.981 Minnesota Twins Target Field
## 3 4394 1503 153 488 1141 113 130 0.982 Boston Red Sox Fenway Park II
## 4 4329 1374 248 507 1534 102 135 0.982 New York Yankees Yankee Stadium III
## 5 1573 494 69 220 506 33 52 0.985 Atlanta Braves SunTrust Park
## attendance BPF PPF teamIDBR teamIDlahman45 teamIDretro BA SLG
## 1 2857367 103 100 HOU HOU HOU 0.2740068 0.6092998
## 2 2294152 100 99 MIN MIN MIN 0.2698883 0.6071179
## 3 2724165 105 103 BOS BOS BOS 0.2889582 0.6033975
## 4 3304404 98 96 NYY NYA NYA 0.2674190 0.5996776
## 5 0 107 106 ATL ATL ATL 0.2680810 0.5964320Teams %>%
filter(yearID >= 1969) %>%
mutate(SLG = (H + 2 * X2B + 3 * X3B + 4 * HR) / AB) %>%
arrange(desc(SLG)) %>%
head(5)## yearID lgID teamID franchID divID Rank G Ghome W L DivWin WCWin LgWin
## 1 2019 AL HOU HOU W 1 162 81 107 55 Y N Y
## 2 2019 AL MIN MIN C 1 162 81 101 61 Y N N
## 3 2003 AL BOS BOS E 2 162 81 95 67 N Y N
## 4 2019 AL NYA NYY E 1 162 81 103 59 Y N N
## 5 2020 NL ATL ATL E 1 60 30 35 25 Y N N
## WSWin R AB H X2B X3B HR BB SO SB CS HBP SF RA ER ERA CG SHO SV
## 1 N 920 5613 1538 323 28 288 645 1166 67 27 66 57 640 595 3.66 2 14 47
## 2 N 939 5732 1547 318 23 307 525 1334 28 21 81 41 754 680 4.18 1 10 50
## 3 N 961 5769 1667 371 40 238 620 943 88 35 53 64 809 729 4.48 5 6 36
## 4 N 943 5583 1493 290 17 306 569 1437 55 22 49 33 739 691 4.31 1 9 50
## 5 N 348 2074 556 130 3 103 239 573 23 4 23 7 288 257 4.41 0 0 13
## IPouts HA HRA BBA SOA E DP FP name park
## 1 4387 1205 230 448 1671 71 96 0.988 Houston Astros Minute Maid Park
## 2 4390 1456 198 452 1463 111 130 0.981 Minnesota Twins Target Field
## 3 4394 1503 153 488 1141 113 130 0.982 Boston Red Sox Fenway Park II
## 4 4329 1374 248 507 1534 102 135 0.982 New York Yankees Yankee Stadium III
## 5 1573 494 69 220 506 33 52 0.985 Atlanta Braves SunTrust Park
## attendance BPF PPF teamIDBR teamIDlahman45 teamIDretro BA SLG
## 1 2857367 103 100 HOU HOU HOU 0.2740068 0.6092998
## 2 2294152 100 99 MIN MIN MIN 0.2698883 0.6071179
## 3 2724165 105 103 BOS BOS BOS 0.2889582 0.6033975
## 4 3304404 98 96 NYY NYA NYA 0.2674190 0.5996776
## 5 0 107 106 ATL ATL ATL 0.2680810 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(HR=sum(HR), SB=sum(SB)) %>%
filter(HR >= 300 & SB >= 300) %>%
inner_join(People, by = c("playerID" = "playerID")) %>%
select(nameFirst, nameLast, HR, SB)## # A tibble: 8 × 4
## nameFirst nameLast HR SB
## <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 304Pitching %>%
group_by(playerID) %>%
summarize(W=sum(W), SO=sum(SO)) %>%
filter(W >= 300 & SO >= 3000) %>%
inner_join(People, by = c("playerID" = "playerID")) %>%
select(nameFirst, nameLast, W, SO)## # A tibble: 10 × 4
## nameFirst nameLast W SO
## <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 3574Batting %>%
group_by(playerID, yearID) %>%
summarize(HR = sum(HR), BA = sum(H)/sum(AB)) %>%
filter(HR >=50) %>%
left_join(People, by = c("playerID" = "playerID")) %>%
select(nameFirst, nameLast, nameGiven, HR, BA) %>%
arrange(BA)## `summarise()` has grouped output by 'playerID'. You can override using the
## `.groups` argument.
## Adding missing grouping variables: `playerID`## # A tibble: 46 × 6
## # Groups: playerID [30]
## playerID nameFirst nameLast nameGiven HR BA
## <chr> <chr> <chr> <chr> <int> <dbl>
## 1 alonspe01 Pete Alonso Peter Morgan 53 0.260
## 2 bautijo02 Jose Bautista Jose Antonio 54 0.260
## 3 jonesan01 Andruw Jones Andruw Rudolf 51 0.263
## 4 marisro01 Roger Maris Roger Eugene 61 0.269
## 5 vaughgr01 Greg Vaughn Gregory Lamont 50 0.272
## 6 mcgwima01 Mark McGwire Mark David 58 0.274
## 7 fieldce01 Cecil Fielder Cecil Grant 51 0.277
## 8 mcgwima01 Mark McGwire Mark David 65 0.278
## 9 stantmi03 Giancarlo Stanton Giancarlo Cruz-Michael 59 0.281
## 10 judgeaa01 Aaron Judge Aaron James 52 0.284
## # … with 36 more rows