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:
library(nycflights13)
flights_df = nycflights13::flights # NOTE: get `flights` dataframe
# Get columns
names(flights_df)## [1] "year" "month" "day" "dep_time"
## [5] "sched_dep_time" "dep_delay" "arr_time" "sched_arr_time"
## [9] "arr_delay" "carrier" "flight" "tailnum"
## [13] "origin" "dest" "air_time" "distance"
## [17] "hour" "minute" "time_hour"# Cancelled flights is defined as there is no dep_time
agg_flights_cancelled <- flights_df %>% # dataframe
group_by(month) %>% # aggregate/group by month
summarise(
flights_cancelled_per_month = sum(is.na(dep_time)), # NOTE: this is total records of dept_time is N.A.
flights_total_per_month = n(), # NOTE: this is total records per month
flights_cancelled_proportion = flights_cancelled_per_month / flights_total_per_month * 100, # NOTE: this is in percentage
) %>%
arrange(desc(flights_cancelled_proportion)) # NOTE: sort flights_cancelled_proportion in descending order
# NOTE: final aggregated output
print(agg_flights_cancelled)## # A tibble: 12 × 4
## month flights_cancelled_per_month flights_total_per_month flights_cancelled_…
## <int> <int> <int> <dbl>
## 1 2 1261 24951 5.05
## 2 12 1025 28135 3.64
## 3 6 1009 28243 3.57
## 4 7 940 29425 3.19
## 5 3 861 28834 2.99
## 6 4 668 28330 2.36
## 7 5 563 28796 1.96
## 8 1 521 27004 1.93
## 9 8 486 29327 1.66
## 10 9 452 27574 1.64
## 11 11 233 27268 0.854
## 12 10 236 28889 0.817a.What month had the highest proportion of cancelled flights?
Ans: month 2 (Feb) has the highest cancellation proportion (5.05 %). The first row of the aggregated dataframe
b.What month had the lowest?
Ans: month 10 (Oct) has the lowest (0.82 %). The last row of the dataframe
Question #2
Consider the following pipeline:
library(tidyverse)
test_agg <- mtcars %>%
group_by(cyl) %>%
summarize(avg_mpg = mean(mpg))
#filter(am == 1)
#
print(test_agg)What is the problem with this pipeline?
Ans: after summarizing, there is no am field anymore. See the
dataframe test_agg
We could filter mtcars by am first, then apply
summarise. See the dataframe test_agg_fixed
test_agg_fixed <- mtcars %>%
filter(am == 1) %>% # filter first
group_by(cyl) %>%
summarize(avg_mpg = mean(mpg))
#filter(am == 1) # comment out
print(test_agg_fixed)## # 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_df = Lahman::Teams # Get Teams
names(team_df)## [1] "yearID" "lgID" "teamID" "franchID"
## [5] "divID" "Rank" "G" "Ghome"
## [9] "W" "L" "DivWin" "WCWin"
## [13] "LgWin" "WSWin" "R" "AB"
## [17] "H" "X2B" "X3B" "HR"
## [21] "BB" "SO" "SB" "CS"
## [25] "HBP" "SF" "RA" "ER"
## [29] "ERA" "CG" "SHO" "SV"
## [33] "IPouts" "HA" "HRA" "BBA"
## [37] "SOA" "E" "DP" "FP"
## [41] "name" "park" "attendance" "BPF"
## [45] "PPF" "teamIDBR" "teamIDlahman45" "teamIDretro"# NOTE: create new field - BA
team_df$BA = team_df$H / team_df$AB # ans of a
# NOTE: create another field - SLG
# Reference: https://rdrr.io/cran/Lahman/man/BattingPost.html
# H: hits
# X2B: doubles
# X3B: triples
# HR: homerun
team_df$total_base = team_df$H * 1 + team_df$X2B * 2 + team_df$X3B * 3 + team_df$HR * 4
team_df$SLG = team_df$total_base / team_df$AB # ans of b
print(head(team_df))## 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 total_base SLG
## 1 BS1 0.3104956 689 0.5021866
## 2 CH1 0.2700669 530 0.4431438
## 3 CL1 0.2765599 546 0.4603710
## 4 FW1 0.2386059 248 0.3324397
## 5 NY2 0.2870370 556 0.3960114
## 6 PH1 0.3200625 659 0.5144418Question #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 5 in history
team_df %>%
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# since 1969
team_df %>%
filter(yearID>=1969) %>% # NOTE: filter by years
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.
library(Lahman)
batting_df = Lahman::Batting
names(batting_df)## [1] "playerID" "yearID" "stint" "teamID" "lgID" "G"
## [7] "AB" "R" "H" "X2B" "X3B" "HR"
## [13] "RBI" "SB" "CS" "BB" "SO" "IBB"
## [19] "HBP" "SH" "SF" "GIDP"people_df = Lahman::People
names(people_df)## [1] "playerID" "birthYear" "birthMonth" "birthDay" "birthCountry"
## [6] "birthState" "birthCity" "deathYear" "deathMonth" "deathDay"
## [11] "deathCountry" "deathState" "deathCity" "nameFirst" "nameLast"
## [16] "nameGiven" "weight" "height" "bats" "throws"
## [21] "debut" "finalGame" "retroID" "bbrefID" "deathDate"
## [26] "birthDate"batting_df %>%
group_by(playerID) %>% # aggregated by each playerID
summarise(
total_HR = sum(HR), # total of HR per player
total_SB = sum(SB), # total of SB per player
) %>%
filter(total_HR >= 300 & total_SB >= 300) %>%
inner_join(people_df, by = "playerID") %>% # inner join with people dataframe on playerID
select(nameFirst, nameLast, nameGiven, total_HR, total_SB) # pick the necessary field## # A tibble: 8 × 5
## nameFirst nameLast nameGiven total_HR total_SB
## <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 304pitching_df = Lahman::Pitching
names(pitching_df)## [1] "playerID" "yearID" "stint" "teamID" "lgID" "W"
## [7] "L" "G" "GS" "CG" "SHO" "SV"
## [13] "IPouts" "H" "ER" "HR" "BB" "SO"
## [19] "BAOpp" "ERA" "IBB" "WP" "HBP" "BK"
## [25] "BFP" "GF" "R" "SH" "SF" "GIDP"pitching_df %>%
group_by(playerID) %>%
summarise(
total_Win = sum(W),
total_SO = sum(SO),
) %>%
filter(total_Win >= 300 & total_SO >= 3000) %>%
inner_join(people_df, by = "playerID") %>%
select(nameFirst, nameLast, nameGiven, total_Win, total_SO)## # A tibble: 10 × 5
## nameFirst nameLast nameGiven total_Win total_SO
## <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 3574Ans: 2019, (Pete Alonso Peter Morgan)
# NOTE: season by year
batting_df %>%
group_by(playerID, yearID) %>%
summarise(
total_HR_per_year = sum(HR), # total Home Run per year
batting_avg_per_year = sum(H) / sum(AB), # batting average per year
) %>%
filter(total_HR_per_year >= 50) %>%
inner_join(people_df, by = "playerID") %>% # merge with people_df dataframe on playerID
select(nameFirst, nameLast, nameGiven, yearID, total_HR_per_year, batting_avg_per_year) %>%
arrange(batting_avg_per_year)## # A tibble: 46 × 7
## # Groups: playerID [30]
## playerID nameFirst nameLast nameGiven yearID total_HR_per_year
## <chr> <chr> <chr> <chr> <int> <int>
## 1 alonspe01 Pete Alonso Peter Morgan 2019 53
## 2 bautijo02 Jose Bautista Jose Antonio 2010 54
## 3 jonesan01 Andruw Jones Andruw Rudolf 2005 51
## 4 marisro01 Roger Maris Roger Eugene 1961 61
## 5 vaughgr01 Greg Vaughn Gregory Lamont 1998 50
## 6 mcgwima01 Mark McGwire Mark David 1997 58
## 7 fieldce01 Cecil Fielder Cecil Grant 1990 51
## 8 mcgwima01 Mark McGwire Mark David 1999 65
## 9 stantmi03 Giancarlo Stanton Giancarlo Cruz-Michael 2017 59
## 10 judgeaa01 Aaron Judge Aaron James 2017 52
## # … with 36 more rows, and 1 more variable: batting_avg_per_year <dbl>