Problem Set #1 - dplyr basics

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?

Answer: I assume that a flight is cancelled if it has a missing value of the time of departure. As we can see, the proportion of missing values vary from 0.0164 in September 2013, to 0.0364 in December 2013. Therefore, the highest proportion of cancelled flights was observed in December 2013, and the lowest proportion - in September 2013.

library(nycflights13)
flights %>%
group_by(year, month) %>%
summarize(count_na = sum(is.na(dep_time))/(sum(is.na(dep_time))+sum(!is.na(dep_time))))

## # A tibble: 12 × 3
## # Groups:   year [1]
##     year month count_na
##    <int> <int>    <dbl>
##  1  2013     1  0.0193 
##  2  2013     2  0.0505 
##  3  2013     3  0.0299 
##  4  2013     4  0.0236 
##  5  2013     5  0.0196 
##  6  2013     6  0.0357 
##  7  2013     7  0.0319 
##  8  2013     8  0.0166 
##  9  2013     9  0.0164 
## 10  2013    10  0.00817
## 11  2013    11  0.00854
## 12  2013    12  0.0364

Question #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?

Answer: this code is not running, the error is caused because of the operations group_by and summarize() implement changes in the data frame structure: there are two columns left - avg_mpg and cyl. Therefore, there is no am column to filter. It would be better to write like following:

mtcars %>% filter(am == 1) %>% group_by(cyl) %>% summarize(avg_mpg = mean(mpg))

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)
myTeam<-Teams
myTeam<-mutate(myTeam, BA = H/AB)
myTeam<-mutate(myTeam, SLG=(4*HR+3*X3B+2*X2B+H)/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)
sortedTeam<-arrange(myTeam,desc(SLG)) %>%
  select(yearID, teamID, SLG)
head(sortedTeam,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

sortedSince1969 <- arrange(myTeam, desc(SLG)) %>%
    select(yearID,teamID,SLG) %>%
    filter(yearID >= 1969)
head(sortedSince1969, 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

Question #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(SBtotal = sum(SB), HRtotal = sum(HR)) %>%
    filter(SBtotal >= 300 & HRtotal >= 300) %>%
    inner_join(People, by = c('playerID' = 'playerID')) %>%
    select(nameFirst, nameLast, HRtotal, SBtotal)

## # A tibble: 8 × 4
##   nameFirst nameLast  HRtotal SBtotal
##   <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

Pitching %>%
    group_by(playerID) %>%
    summarize(Wtotal = sum(W), SOtotal = sum(SO)) %>%
    filter(Wtotal >= 300 & SOtotal >= 3000) %>%
    inner_join(People, by = c('playerID' = 'playerID')) %>%
    select(nameFirst, nameLast, Wtotal, SOtotal)

## # A tibble: 10 × 4
##    nameFirst nameLast Wtotal SOtotal
##    <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

Batting %>%
    group_by(yearID, playerID) %>%
    summarize(HRtotal = sum(HR), BA = sum(H)/sum(AB)) %>%
    filter(HRtotal >= 50) %>%
    inner_join(People, by = c('playerID' = 'playerID')) %>%
    select(nameFirst, nameLast, yearID, HRtotal, BA) %>%
    ungroup() %>%
    arrange(BA)

## # A tibble: 46 × 5
##    nameFirst nameLast yearID HRtotal    BA
##    <chr>     <chr>     <int>   <int> <dbl>
##  1 Pete      Alonso     2019      53 0.260
##  2 Jose      Bautista   2010      54 0.260
##  3 Andruw    Jones      2005      51 0.263
##  4 Roger     Maris      1961      61 0.269
##  5 Greg      Vaughn     1998      50 0.272
##  6 Mark      McGwire    1997      58 0.274
##  7 Cecil     Fielder    1990      51 0.277
##  8 Mark      McGwire    1999      65 0.278
##  9 Giancarlo Stanton    2017      59 0.281
## 10 Aaron     Judge      2017      52 0.284
## # … with 36 more rows

Problem Set #1 - `dplyr` basics

Data Wrangling with `dplyr`

Javad Mammadov

2023-01-23

Directions

Problem Set #1 - dplyr basics

Data Wrangling with dplyr

Javad Mammadov

2023-01-23

Directions

Problem Set #1 - `dplyr` basics

Data Wrangling with `dplyr`