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? b.What month had the lowest?
library(nycflights13)
flight_cancel <- flights %>%
group_by(month) %>%
summarize(cancelled = sum(is.na(dep_time)),
cancelled_proportion = cancelled/n()*100) %>%
arrange(cancelled_proportion)
flight_cancel## # A tibble: 12 × 3
## month cancelled cancelled_proportion
## <int> <int> <dbl>
## 1 10 236 0.817
## 2 11 233 0.854
## 3 9 452 1.64
## 4 8 486 1.66
## 5 1 521 1.93
## 6 5 563 1.96
## 7 4 668 2.36
## 8 3 861 2.99
## 9 7 940 3.19
## 10 6 1009 3.57
## 11 12 1025 3.64
## 12 2 1261 5.05# a. October had the highest proportion of cancelled flights
# b. June had the lowest proportion of cancelled flightsQuestion #2
Consider the following pipeline:
library(tidyverse)
mtcars %>%
group_by(cyl) %>%
summarize(avg_mpg = mean(mpg)) %>%
filter(am == 1)
#variable 'am' does not exist after the summarize function. So we can't filter 'am' after summarize. We should call filter function before summarize.
library(tidyverse)
mtcars %>%
group_by(cyl) %>%
filter(am == 1) %>%
summarize(avg_mpg = mean(mpg))What is the problem with this pipeline?
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) %>%
mutate(SLG = (H+2*X2B+3*X3B+4*HR)/AB)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 %>%
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 0.5964320Teams %>%
select(yearID,teamID, SLG) %>%
filter(yearID >= 1969) %>%
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)
Batting %>%
group_by(playerID) %>%
summarise(home_runs = sum(HR), stolen_bases = sum(SB)) %>%
filter(home_runs >= 300 & stolen_bases >= 300) %>%
left_join(People, by = c("playerID" = "playerID")) %>%
select(nameFirst, nameLast, home_runs, stolen_bases)## # A tibble: 8 × 4
## nameFirst nameLast home_runs stolen_bases
## <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) %>%
summarise(wins = sum(W), strikeouts = sum(SO)) %>%
filter(wins >= 300 & strikeouts >= 3000) %>%
left_join(People, by = c("playerID" = "playerID")) %>%
select(nameFirst, nameLast, wins, strikeouts)## # A tibble: 10 × 4
## nameFirst nameLast wins strikeouts
## <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) %>%
summarise(home_runs = sum(HR), BA = sum(H)/sum(AB)) %>%
filter(home_runs >= 50) %>%
left_join(People, by = c("playerID" = "playerID")) %>%
select(yearID,nameFirst, nameLast, home_runs, BA) %>%
arrange(desc(BA))## # A tibble: 46 × 6
## # Groups: playerID [30]
## playerID yearID nameFirst nameLast home_runs BA
## <chr> <int> <chr> <chr> <int> <dbl>
## 1 ruthba01 1921 Babe Ruth 59 0.378
## 2 ruthba01 1920 Babe Ruth 54 0.376
## 3 foxxji01 1932 Jimmie Foxx 58 0.364
## 4 ruthba01 1927 Babe Ruth 60 0.356
## 5 wilsoha01 1930 Hack Wilson 56 0.356
## 6 mantlmi01 1956 Mickey Mantle 52 0.353
## 7 foxxji01 1938 Jimmie Foxx 50 0.349
## 8 bondsba01 2001 Barry Bonds 73 0.328
## 9 sosasa01 2001 Sammy Sosa 64 0.328
## 10 gonzalu01 2001 Luis Gonzalez 57 0.325
## # … with 36 more rows