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 proportion of cancelled flights = No of cancelled flights in that month/ Total flights in that month. cancelled flights: dep_time is NA, in other words, the plane never takes off.
b.What month had the lowest? #October
library(nycflights13)
flights[is.na(flights$dep_time),]## # A tibble: 8,255 × 19
## year month day dep_time sched_de…¹ dep_d…² arr_t…³ sched…⁴ arr_d…⁵ carrier
## <int> <int> <int> <int> <int> <dbl> <int> <int> <dbl> <chr>
## 1 2013 1 1 NA 1630 NA NA 1815 NA EV
## 2 2013 1 1 NA 1935 NA NA 2240 NA AA
## 3 2013 1 1 NA 1500 NA NA 1825 NA AA
## 4 2013 1 1 NA 600 NA NA 901 NA B6
## 5 2013 1 2 NA 1540 NA NA 1747 NA EV
## 6 2013 1 2 NA 1620 NA NA 1746 NA EV
## 7 2013 1 2 NA 1355 NA NA 1459 NA EV
## 8 2013 1 2 NA 1420 NA NA 1644 NA EV
## 9 2013 1 2 NA 1321 NA NA 1536 NA EV
## 10 2013 1 2 NA 1545 NA NA 1910 NA AA
## # … with 8,245 more rows, 9 more variables: flight <int>, tailnum <chr>,
## # origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
## # minute <dbl>, time_hour <dttm>, and abbreviated variable names
## # ¹sched_dep_time, ²dep_delay, ³arr_time, ⁴sched_arr_time, ⁵arr_delayQuestion #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? #After aggregating mtcars,“am” is not in the result. So you can’t filter on it. To fix it, you could filter on am first and then calculate avg mpg for each cyl.
#After Correction
library(tidyverse)
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)
#install.packages(dplyr)
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.
team %>%
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 0.5964320Question #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)
# R Replace Master (old) with People
# a:
Batting %>%
group_by(playerID) %>%
summarize(home_runs = sum(HR), stolen_bases = sum (SB)) %>%
filter(home_runs >= 300 & stolen_bases >= 300) %>%
inner_join(People, by = c("playerID" = "playerID")) %>%
select(nameFirst, nameLast, nameGiven, home_runs, stolen_bases)## # A tibble: 8 × 5
## nameFirst nameLast nameGiven home_runs stolen_bases
## <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