CMSC 205 Homework 2
Casey Merkle
Administrative
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Question 1:
Use the mlb_teams.csv data set to create an informative data graphic that illustrates the relationship between winning percentage (WPct) and payroll in context. load libraries
library(ggplot2)
library(tidyverse)## Loading tidyverse: tibble
## Loading tidyverse: tidyr
## Loading tidyverse: readr
## Loading tidyverse: purrr
## Loading tidyverse: dplyr## Conflicts with tidy packages ----------------------------------------------## filter(): dplyr, stats
## lag(): dplyr, statsWinning percentage, payroll read in data
mlb<-read.csv("https://raw.githubusercontent.com/cmsc205/data/master/mlb_teams.csv")evaluate data
summary(mlb)## yearID teamID lgID W L
## Min. :2008 ARI : 7 AL:100 Min. : 51.00 Min. : 59.00
## 1st Qu.:2009 ATL : 7 NL:110 1st Qu.: 73.00 1st Qu.: 72.00
## Median :2011 BAL : 7 Median : 81.00 Median : 81.00
## Mean :2011 BOS : 7 Mean : 80.99 Mean : 80.99
## 3rd Qu.:2013 CHA : 7 3rd Qu.: 90.00 3rd Qu.: 89.00
## Max. :2014 CHN : 7 Max. :103.00 Max. :111.00
## (Other):168
## WPct attendance normAttend payroll
## Min. :0.3148 Min. :1335076 Min. :0.3106 Min. : 17890700
## 1st Qu.:0.4506 1st Qu.:1940441 1st Qu.:0.4514 1st Qu.: 67325266
## Median :0.5000 Median :2418204 Median :0.5625 Median : 85803966
## Mean :0.5000 Mean :2481715 Mean :0.5773 Mean : 94365324
## 3rd Qu.:0.5556 3rd Qu.:3041615 3rd Qu.:0.7076 3rd Qu.:114741109
## Max. :0.6358 Max. :4298655 Max. :1.0000 Max. :231978886
##
## metroPop name
## Min. : 1572245 Arizona Diamondbacks: 7
## 1st Qu.: 2785874 Atlanta Braves : 7
## Median : 4541584 Baltimore Orioles : 7
## Mean : 6014841 Boston Red Sox : 7
## 3rd Qu.: 6490180 Chicago Cubs : 7
## Max. :20092883 Chicago White Sox : 7
## (Other) :168head(mlb)## yearID teamID lgID W L WPct attendance normAttend payroll
## 1 2008 ARI NL 82 80 0.5061728 2509924 0.5838859 66202712
## 2 2008 ATL NL 72 90 0.4444444 2532834 0.5892155 102365683
## 3 2008 BAL AL 68 93 0.4223602 1950075 0.4536477 67196246
## 4 2008 BOS AL 95 67 0.5864198 3048250 0.7091172 133390035
## 5 2008 CHA AL 89 74 0.5460123 2500648 0.5817280 121189332
## 6 2008 CHN NL 97 64 0.6024845 3300200 0.7677285 118345833
## metroPop name
## 1 4489109 Arizona Diamondbacks
## 2 5614323 Atlanta Braves
## 3 2785874 Baltimore Orioles
## 4 4732161 Boston Red Sox
## 5 9554598 Chicago White Sox
## 6 9554598 Chicago Cubsplot
ggplot(mlb, aes(x = mlb$WPct*100, y = mlb$payroll/1000000)) +
geom_point() +
geom_smooth() +
labs(x = "Winning Percentage (%)", y = "Payroll (dollars in millions)") +
theme_minimal()## `geom_smooth()` using method = 'loess'
Question 2:
Using data from the nasaweather R package, use the path geometry (i.e. use a geom_path layer) to plot the path of each tropical storm in the storms data table. Use color to distinguish the storms from one another, and use faceting to plot each year in its own panel.
Hint: Don’t forget to install and load the nasaweather R package!
load in nasaweather
library(nasaweather)
library(RColorBrewer)
mydata<-stormsevaluate storms
head(storms)## # A tibble: 6 × 11
## name year month day hour lat long pressure wind
## <chr> <int> <int> <int> <int> <dbl> <dbl> <int> <int>
## 1 Allison 1995 6 3 0 17.4 -84.3 1005 30
## 2 Allison 1995 6 3 6 18.3 -84.9 1004 30
## 3 Allison 1995 6 3 12 19.3 -85.7 1003 35
## 4 Allison 1995 6 3 18 20.6 -85.8 1001 40
## 5 Allison 1995 6 4 0 22.0 -86.0 997 50
## 6 Allison 1995 6 4 6 23.3 -86.3 995 60
## # ... with 2 more variables: type <chr>, seasday <int>summary(storms)## name year month day
## Length:2747 Min. :1995 Min. : 6.000 Min. : 1.00
## Class :character 1st Qu.:1995 1st Qu.: 8.000 1st Qu.: 9.00
## Mode :character Median :1997 Median : 9.000 Median :18.00
## Mean :1997 Mean : 8.803 Mean :16.98
## 3rd Qu.:1999 3rd Qu.:10.000 3rd Qu.:25.00
## Max. :2000 Max. :12.000 Max. :31.00
## hour lat long pressure
## Min. : 0.000 Min. : 8.30 Min. :-107.30 Min. : 905.0
## 1st Qu.: 3.500 1st Qu.:17.25 1st Qu.: -77.60 1st Qu.: 980.0
## Median :12.000 Median :25.00 Median : -60.90 Median : 995.0
## Mean : 9.057 Mean :26.67 Mean : -60.87 Mean : 989.8
## 3rd Qu.:18.000 3rd Qu.:33.90 3rd Qu.: -45.80 3rd Qu.:1004.0
## Max. :18.000 Max. :70.70 Max. : 1.00 Max. :1019.0
## wind type seasday
## Min. : 15.00 Length:2747 Min. : 3.0
## 1st Qu.: 35.00 Class :character 1st Qu.: 84.0
## Median : 50.00 Mode :character Median :103.0
## Mean : 54.68 Mean :102.6
## 3rd Qu.: 70.00 3rd Qu.:125.0
## Max. :155.00 Max. :185.0distinguish storms by color
library(plyr)## -------------------------------------------------------------------------## You have loaded plyr after dplyr - this is likely to cause problems.
## If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
## library(plyr); library(dplyr)## -------------------------------------------------------------------------##
## Attaching package: 'plyr'## The following objects are masked from 'package:dplyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize## The following object is masked from 'package:purrr':
##
## compactnamecount<-count(storms, 'name')
namecount## name freq
## 1 Alberto 87
## 2 Alex 26
## 3 Allison 33
## 4 Ana 19
## 5 Arlene 26
## 6 Arthur 22
## 7 Barry 21
## 8 Bertha 50
## 9 Beryl 9
## 10 Bill 9
## 11 Bonnie 47
## 12 Bret 26
## 13 Cesar 17
## 14 Chantal 41
## 15 Charley 12
## 16 Chris 9
## 17 Cindy 51
## 18 Claudette 16
## 19 Danielle 18
## 20 Danny 45
## 21 Dean 21
## 22 Debby 20
## 23 Dennis 64
## 24 Dolly 24
## 25 Earl 33
## 26 Edouard 73
## 27 Emily 18
## 28 Erika 67
## 29 Erin 27
## 30 Ernesto 10
## 31 Fabian 17
## 32 Felix 69
## 33 Florence 29
## 34 Floyd 48
## 35 Fran 71
## 36 Frances 21
## 37 Gabrielle 10
## 38 Georges 64
## 39 Gert 49
## 40 Gordon 28
## 41 Grace 13
## 42 Gustav 29
## 43 Harvey 12
## 44 Helene 42
## 45 Hermine 14
## 46 Hortense 52
## 47 Humberto 41
## 48 Irene 30
## 49 Iris 65
## 50 Isaac 52
## 51 Isidore 33
## 52 Ivan 36
## 53 Jeanne 54
## 54 Jerry 22
## 55 Jose 32
## 56 Josephine 46
## 57 Joyce 28
## 58 Karen 31
## 59 Karl 26
## 60 Katrina 16
## 61 Keith 32
## 62 Kyle 6
## 63 Lenny 39
## 64 Leslie 26
## 65 Lili 59
## 66 Lisa 21
## 67 Luis 66
## 68 Marco 54
## 69 Marilyn 77
## 70 Michael 22
## 71 Mitch 76
## 72 Nadine 13
## 73 Nicole 35
## 74 Noel 45
## 75 Opal 37
## 76 Pablo 16
## 77 Roxanne 54
## 78 Sebastien 19
## 79 Tanya 29#79 row, 79 different names, only want tropical storms
namecount<-count(mydata, 'name')
namecount## name freq
## 1 Alberto 87
## 2 Alex 26
## 3 Allison 33
## 4 Ana 19
## 5 Arlene 26
## 6 Arthur 22
## 7 Barry 21
## 8 Bertha 50
## 9 Beryl 9
## 10 Bill 9
## 11 Bonnie 47
## 12 Bret 26
## 13 Cesar 17
## 14 Chantal 41
## 15 Charley 12
## 16 Chris 9
## 17 Cindy 51
## 18 Claudette 16
## 19 Danielle 18
## 20 Danny 45
## 21 Dean 21
## 22 Debby 20
## 23 Dennis 64
## 24 Dolly 24
## 25 Earl 33
## 26 Edouard 73
## 27 Emily 18
## 28 Erika 67
## 29 Erin 27
## 30 Ernesto 10
## 31 Fabian 17
## 32 Felix 69
## 33 Florence 29
## 34 Floyd 48
## 35 Fran 71
## 36 Frances 21
## 37 Gabrielle 10
## 38 Georges 64
## 39 Gert 49
## 40 Gordon 28
## 41 Grace 13
## 42 Gustav 29
## 43 Harvey 12
## 44 Helene 42
## 45 Hermine 14
## 46 Hortense 52
## 47 Humberto 41
## 48 Irene 30
## 49 Iris 65
## 50 Isaac 52
## 51 Isidore 33
## 52 Ivan 36
## 53 Jeanne 54
## 54 Jerry 22
## 55 Jose 32
## 56 Josephine 46
## 57 Joyce 28
## 58 Karen 31
## 59 Karl 26
## 60 Katrina 16
## 61 Keith 32
## 62 Kyle 6
## 63 Lenny 39
## 64 Leslie 26
## 65 Lili 59
## 66 Lisa 21
## 67 Luis 66
## 68 Marco 54
## 69 Marilyn 77
## 70 Michael 22
## 71 Mitch 76
## 72 Nadine 13
## 73 Nicole 35
## 74 Noel 45
## 75 Opal 37
## 76 Pablo 16
## 77 Roxanne 54
## 78 Sebastien 19
## 79 Tanya 29mypalette<-brewer.pal(12, "Paired")
Tropicalstorms<-filter(storms, type =="Tropical Storm")
ggplot(data = Tropicalstorms, aes(x = Tropicalstorms$long, y = Tropicalstorms$lat, group = Tropicalstorms$name, colour = Tropicalstorms$name)) +
geom_path(stat = 'identity') +
facet_wrap(~year) +
xlab("Longitude") + ylab("Latitude") +
ggtitle("Tropical Storm Paths")
Question 3:
Using the data set Top25CommonFemaleNames.csv, recreate the “Median Names for Females with the 25 Most Common Names” graphic from FiveThirtyEight (link to graphic; link to full article). load in dataset
library(viridis)## Loading required package: viridisLitelibrary(ggthemes)
femalenames<-read.csv("https://raw.githubusercontent.com/cmsc205/data/master/Top25CommonFemaleNames.csv")
summary(femalenames)## name n est_num_alive q1_age
## Amanda : 1 Min. : 72.0 Min. : 636663 Min. :12.00
## Ashley : 1 1st Qu.:110.0 1st Qu.: 698105 1st Qu.:23.00
## Barbara: 1 Median :111.0 Median : 781127 Median :30.00
## Carol : 1 Mean :106.2 Mean : 915910 Mean :36.84
## Deborah: 1 3rd Qu.:111.0 3rd Qu.:1010349 3rd Qu.:51.00
## Donna : 1 Max. :111.0 Max. :2209707 Max. :56.00
## (Other):19
## median_age q3_age
## Min. :19.00 Min. :27.00
## 1st Qu.:35.00 1st Qu.:42.00
## Median :43.00 Median :53.00
## Mean :45.28 Mean :52.96
## 3rd Qu.:58.00 3rd Qu.:66.00
## Max. :64.00 Max. :73.00
## arrange(femalenames, median_age)## name n est_num_alive q1_age median_age q3_age
## 1 Emily 111 689727.5 12 19 27
## 2 Ashley 72 804870.7 18 24 28
## 3 Jessica 111 1010348.9 21 27 32
## 4 Sarah 111 872465.8 19 28 36
## 5 Amanda 111 743784.5 23 28 33
## 6 Stephanie 111 698104.6 23 31 42
## 7 Melissa 111 715709.1 28 35 43
## 8 Jennifer 93 1406036.7 29 36 42
## 9 Elizabeth 111 1129408.9 23 38 57
## 10 Rebecca 111 660999.3 25 38 53
## 11 Michelle 93 764100.0 29 40 46
## 12 Kimberly 78 781127.1 30 41 48
## 13 Laura 111 636662.9 30 43 53
## 14 Lisa 104 909266.6 40 47 51
## 15 Karen 110 875021.1 47 54 61
## 16 Susan 111 969314.1 50 57 63
## 17 Donna 111 686294.0 51 57 65
## 18 Deborah 111 662363.1 51 57 61
## 19 Sandra 107 746714.8 49 58 66
## 20 Patricia 111 1238622.1 52 61 68
## 21 Nancy 111 768937.3 53 61 68
## 22 Mary 111 2209707.0 52 62 73
## 23 Linda 111 1232858.0 55 62 66
## 24 Barbara 111 1045870.2 56 64 72
## 25 Carol 111 639431.5 55 64 70ggplot(femalenames) +
geom_point(mapping= aes(x = name, y = median_age)) +
coord_flip()
ggplot(femalenames, mapping = aes(x = reorder(name, -median_age), y = median_age)) +
geom_point(color = "red", stat = 'identity', size = 1.5, show.legend = TRUE) +
geom_linerange(ymin = femalenames$q1_age, ymax = femalenames$q3_age, size = 3, color = "yellow", alpha = 0.2, show.legend = TRUE) +
scale_y_continuous(position = "top") +
coord_flip() +
labs(x= NULL, y = NULL, title = "Median Ages For Females With the 25 Most Common Names", subtitle="Among Americans estimated to be alive as of Jan. 1, 2014") +
geom_text(x = 16.1, y = 25.9, label = "25th") +
geom_text(x = 16.1, y = 50, label = "75th percentile")
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## - attr(*, "validate")= logi TRUElibrary(Hmisc)## Loading required package: lattice## Loading required package: survival## Loading required package: Formula##
## Attaching package: 'Hmisc'## The following objects are masked from 'package:plyr':
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## is.discrete, summarize## The following objects are masked from 'package:dplyr':
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## combine, src, summarize## The following objects are masked from 'package:base':
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## format.pval, round.POSIXt, trunc.POSIXt, units