#PART I
####Calling packages we need
library(tidyverse)
## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
## v ggplot2 3.3.5 v purrr 0.3.4
## v tibble 3.1.4 v dplyr 1.0.7
## v tidyr 1.1.3 v stringr 1.4.0
## v readr 2.0.1 v forcats 0.5.1
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
library(dplyr)
library(ggplot2)
library(tidyr)
#### Import the package
#install.packages("Lahman")
library(Lahman)
#### Import the data for baseball players
data(People)
head(People)
## playerID birthYear birthMonth birthDay birthCountry birthState birthCity
## 1 aardsda01 1981 12 27 USA CO Denver
## 2 aaronha01 1934 2 5 USA AL Mobile
## 3 aaronto01 1939 8 5 USA AL Mobile
## 4 aasedo01 1954 9 8 USA CA Orange
## 5 abadan01 1972 8 25 USA FL Palm Beach
## 6 abadfe01 1985 12 17 D.R. La Romana La Romana
## deathYear deathMonth deathDay deathCountry deathState deathCity nameFirst
## 1 NA NA NA <NA> <NA> <NA> David
## 2 2021 1 22 USA GA Atlanta Hank
## 3 1984 8 16 USA GA Atlanta Tommie
## 4 NA NA NA <NA> <NA> <NA> Don
## 5 NA NA NA <NA> <NA> <NA> Andy
## 6 NA NA NA <NA> <NA> <NA> Fernando
## nameLast nameGiven weight height bats throws debut finalGame
## 1 Aardsma David Allan 215 75 R R 2004-04-06 2015-08-23
## 2 Aaron Henry Louis 180 72 R R 1954-04-13 1976-10-03
## 3 Aaron Tommie Lee 190 75 R R 1962-04-10 1971-09-26
## 4 Aase Donald William 190 75 R R 1977-07-26 1990-10-03
## 5 Abad Fausto Andres 184 73 L L 2001-09-10 2006-04-13
## 6 Abad Fernando Antonio 235 74 L L 2010-07-28 2019-09-28
## retroID bbrefID deathDate birthDate
## 1 aardd001 aardsda01 <NA> 1981-12-27
## 2 aaroh101 aaronha01 2021-01-22 1934-02-05
## 3 aarot101 aaronto01 1984-08-16 1939-08-05
## 4 aased001 aasedo01 <NA> 1954-09-08
## 5 abada001 abadan01 <NA> 1972-08-25
## 6 abadf001 abadfe01 <NA> 1985-12-17
#### Learn more about the data
#?People
#str(People)
##QUESTION 1
PeopleUSA <- People %>%
filter(birthCountry == "USA")
PeopleUSA %>%
group_by(birthState)%>%
summarize(n=n())%>%
arrange(n)%>%
print(n=51)
## # A tibble: 51 x 2
## birthState n
## <chr> <int>
## 1 AK 12
## 2 WY 16
## 3 ND 19
## 4 MT 26
## 5 ID 30
## 6 NM 31
## 7 VT 38
## 8 SD 39
## 9 UT 40
## 10 HI 46
## 11 NV 47
## 12 NH 56
## 13 DE 57
## 14 ME 79
## 15 RI 80
## 16 CO 98
## 17 DC 104
## 18 NE 115
## 19 AZ 121
## 20 WV 121
## 21 OR 137
## 22 AR 160
## 23 MN 172
## 24 SC 198
## 25 CT 209
## 26 MS 212
## 27 WA 212
## 28 KS 217
## 29 IA 223
## 30 WI 252
## 31 LA 263
## 32 OK 271
## 33 KY 290
## 34 VA 303
## 35 TN 322
## 36 MD 325
## 37 AL 344
## 38 IN 381
## 39 GA 392
## 40 NC 423
## 41 NJ 444
## 42 MI 446
## 43 FL 581
## 44 MO 619
## 45 MA 671
## 46 TX 962
## 47 OH 1062
## 48 IL 1084
## 49 NY 1246
## 50 PA 1451
## 51 CA 2348
#California has the most players. Oregon has 137 players.
##QUESTION 2
shortGuys <- People%>%
filter(height < 60)
head(shortGuys)
## playerID birthYear birthMonth birthDay birthCountry birthState birthCity
## 1 gaedeed01 1925 6 8 USA IL Chicago
## 2 healeto01 1853 NA NA USA RI Cranston
## deathYear deathMonth deathDay deathCountry deathState deathCity nameFirst
## 1 1961 6 18 USA IL Chicago Eddie
## 2 1891 2 6 USA ME Lewiston Tom
## nameLast nameGiven weight height bats throws debut finalGame retroID
## 1 Gaedel Edward Carl 65 43 R L 1951-08-19 1951-08-19 gaede101
## 2 Healey Thomas F. 155 55 <NA> R 1878-06-13 1878-09-09 healt101
## bbrefID deathDate birthDate
## 1 gaedeed01 1961-06-18 1925-06-08
## 2 healeto01 1891-02-06 <NA>
#there are 2 players shorter than 60 inches. Their names are Eddie Gaedel & Tom Healey.
## QUESTION 3
rightHand <- People %>%
filter(bats == "R")
leftHand <- People %>%
filter(bats == "L")
ambidextrous <- People %>%
filter(bats == "B")
12437/20093 #=.6189718
## [1] 0.6189718
5247/20093 #= .2611357
## [1] 0.2611357
1229/20093 #= .06116558
## [1] 0.06116558
#61.9% of players are right handed batters, 26.11% are left handed, and 6.12% are both.
## QUESTION 4
avgHtbyThrw <- People %>%
group_by(throws)
summarize(avgHtbyThrw, avgHt = mean(height, na.rm = TRUE))
## # A tibble: 4 x 2
## throws avgHt
## <fct> <dbl>
## 1 L 72.6
## 2 R 72.4
## 3 S 72
## 4 <NA> 69.3
#the average height for right handed throwers is 72.3621. For left handed throwers, it is 72.61. I got an average height for "S", I'm not sure what that is cant find help the average height for NA (players we either have no data on or who don't bat) is 69.2765. Therefore, left handed throwers are slightly taller on average
## QUESTION 5
byCountry <- People %>%
group_by(birthCountry)%>%
summarize(
count = n(),
height = mean(height, na.rm=TRUE)) %>%
arrange(height)
head(byCountry)
## # A tibble: 6 x 3
## birthCountry count height
## <chr> <int> <dbl>
## 1 Portugal 1 65
## 2 Denmark 1 67
## 3 Finland 1 69
## 4 Ireland 50 69.6
## 5 <NA> 61 69.6
## 6 Belize 1 70
#The country with the shortest players on average is Portugal at 65 in; the country with the tallest players on average is Indonesia at 78 in.
## QUESTION 6
playerBMI = People %>%
mutate(BMI = weight*703/height^2)
head(playerBMI)
## playerID birthYear birthMonth birthDay birthCountry birthState birthCity
## 1 aardsda01 1981 12 27 USA CO Denver
## 2 aaronha01 1934 2 5 USA AL Mobile
## 3 aaronto01 1939 8 5 USA AL Mobile
## 4 aasedo01 1954 9 8 USA CA Orange
## 5 abadan01 1972 8 25 USA FL Palm Beach
## 6 abadfe01 1985 12 17 D.R. La Romana La Romana
## deathYear deathMonth deathDay deathCountry deathState deathCity nameFirst
## 1 NA NA NA <NA> <NA> <NA> David
## 2 2021 1 22 USA GA Atlanta Hank
## 3 1984 8 16 USA GA Atlanta Tommie
## 4 NA NA NA <NA> <NA> <NA> Don
## 5 NA NA NA <NA> <NA> <NA> Andy
## 6 NA NA NA <NA> <NA> <NA> Fernando
## nameLast nameGiven weight height bats throws debut finalGame
## 1 Aardsma David Allan 215 75 R R 2004-04-06 2015-08-23
## 2 Aaron Henry Louis 180 72 R R 1954-04-13 1976-10-03
## 3 Aaron Tommie Lee 190 75 R R 1962-04-10 1971-09-26
## 4 Aase Donald William 190 75 R R 1977-07-26 1990-10-03
## 5 Abad Fausto Andres 184 73 L L 2001-09-10 2006-04-13
## 6 Abad Fernando Antonio 235 74 L L 2010-07-28 2019-09-28
## retroID bbrefID deathDate birthDate BMI
## 1 aardd001 aardsda01 <NA> 1981-12-27 26.87022
## 2 aaroh101 aaronha01 2021-01-22 1934-02-05 24.40972
## 3 aarot101 aaronto01 1984-08-16 1939-08-05 23.74578
## 4 aased001 aasedo01 <NA> 1954-09-08 23.74578
## 5 abada001 abadan01 <NA> 1972-08-25 24.27322
## 6 abadf001 abadfe01 <NA> 1985-12-17 30.16892
## QUESTION 7
obese <- filter(playerBMI, BMI > 30)%>%
arrange(desc(BMI))
head(obese)
## playerID birthYear birthMonth birthDay birthCountry birthState
## 1 kirkal01 1998 11 6 Mexico Baja California
## 2 colonba01 1973 5 24 D.R. Puerto Plata
## 3 sandopa01 1986 8 11 Venezuela Carabobo
## 4 fieldpr01 1984 5 9 USA CA
## 5 diazju03 1984 2 27 D.R. La Romana
## 6 moronre01 1993 1 6 D.R. Santiago
## birthCity deathYear deathMonth deathDay deathCountry deathState
## 1 Tijuana NA NA NA <NA> <NA>
## 2 Altamira NA NA NA <NA> <NA>
## 3 Puerto Cabello NA NA NA <NA> <NA>
## 4 Ontario NA NA NA <NA> <NA>
## 5 La Romana NA NA NA <NA> <NA>
## 6 Santiago NA NA NA <NA> <NA>
## deathCity nameFirst nameLast nameGiven weight height bats throws
## 1 <NA> Alejandro Kirk Alejandro 265 68 R R
## 2 <NA> Bartolo Colon Bartolo 285 71 R R
## 3 <NA> Pablo Sandoval Pablo Emilio 268 70 B R
## 4 <NA> Prince Fielder Prince Semien 275 71 L R
## 5 <NA> Jumbo Diaz Jose Rafael 315 76 R R
## 6 <NA> Reyes Moronta Reyes Armando 265 70 R R
## debut finalGame retroID bbrefID deathDate birthDate BMI
## 1 2020-09-12 2020-09-26 kirka001 kirkal01 <NA> 1998-11-06 40.28871
## 2 1997-04-04 2018-09-22 colob001 colonba01 <NA> 1973-05-24 39.74509
## 3 2008-08-14 2020-09-27 sandp001 sandopa01 <NA> 1986-08-11 38.44980
## 4 2005-06-13 2016-07-18 fielp001 fieldpr01 <NA> 1984-05-09 38.35053
## 5 2014-06-20 2017-07-16 diazj005 diazju03 <NA> 1984-02-27 38.33882
## 6 2017-09-05 2019-08-31 moror001 moronre01 <NA> 1993-01-06 38.01939
#there are 551 players that fall into the obese data set. Alejandro Kirk has the highest BMI, of 40.28871.
##QUESTION 8
#BMIbyPlace <- playerBMI %>%
#select(c("birthCountry", "birthState", "nameFirst", "nameLast", "BMI"))
#?select
#I have been trying to get this to run and trying a lot of different things, and I can't get it to run without error messages coming up.
## QUESTION 9
lifetime = People %>% mutate(lifeInYears = deathDate - birthDate)
deadPlayers <- lifetime%>%
filter(lifeInYears>0)%>%
arrange(desc(lifeInYears))
head(deadPlayers)
## playerID birthYear birthMonth birthDay birthCountry birthState
## 1 hoffch01 1891 5 8 USA NY
## 2 marreco01 1911 4 25 Cuba Villa Clara
## 3 wrighbo01 1891 12 13 USA IN
## 4 parkeac01 1912 5 17 USA VA
## 5 malinto01 1909 10 7 USA IL
## 6 swanska01 1900 12 17 USA IL
## birthCity deathYear deathMonth deathDay deathCountry deathState
## 1 Ossining 1998 9 17 USA FL
## 2 Sagua La Grande 2014 4 23 Cuba La Habana
## 3 Decatur County 1993 7 30 USA CA
## 4 Portsmouth 2013 11 6 USA VA
## 5 Collinsville 2011 2 8 USA CA
## 6 North Henderson 2002 4 3 USA IL
## deathCity nameFirst nameLast nameGiven weight height bats throws
## 1 Daytona Beach Red Hoff Chester Cornelius 162 69 L L
## 2 La Habana Connie Marrero Conrado Eugenio 158 65 R R
## 3 Carmichael Bob Wright Robert Cassius 175 73 R R
## 4 Portsmouth Ace Parker Clarence McKay 180 72 R R
## 5 Oxnard Tony Malinosky Anthony Francis 165 70 R R
## 6 Rock Island Karl Swanson Karl Edward 155 70 L R
## debut finalGame retroID bbrefID deathDate birthDate lifeInYears
## 1 1911-09-06 1915-10-02 hoffr102 hoffre01 1998-09-17 1891-05-08 39213 days
## 2 1950-04-21 1954-09-07 marrc101 marreco01 2014-04-23 1911-04-25 37619 days
## 3 1915-09-21 1915-09-24 wrigb101 wrighbo01 1993-07-30 1891-12-13 37119 days
## 4 1937-04-24 1938-09-04 parka102 parkeac01 2013-11-06 1912-05-17 37063 days
## 5 1937-04-26 1937-07-16 malit101 malinto01 2011-02-08 1909-10-07 37014 days
## 6 1928-08-12 1929-05-05 swank101 swanska01 2002-04-03 1900-12-17 36997 days
#the player who lived the longest is Red Hoff (given name is Chester Cornelius)
39213/365 # = 107.4329 years lived
## [1] 107.4329
## QUESTION 10
#as.Date(People$debut, "%Y-%m-%d")
#as.Date(People$finalGame, "%Y-%m-%d")
#When I'm running this, it appears to be working, but then when I look at the class of the columns, they are both still character. I've tried a lot of different combinations of things and just can't get it to actually change them.
#PART II
#install.packages("MASS")
library(MASS)
##
## Attaching package: 'MASS'
## The following object is masked from 'package:dplyr':
##
## select
data("oats")
#?oats
##DATA STRUCTURE
##QUESTION 1: what does each row of this dataset represent?
str(oats)
## 'data.frame': 72 obs. of 4 variables:
## $ B: Factor w/ 6 levels "I","II","III",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ V: Factor w/ 3 levels "Golden.rain",..: 3 3 3 3 1 1 1 1 2 2 ...
## $ N: Factor w/ 4 levels "0.0cwt","0.2cwt",..: 1 2 3 4 1 2 3 4 1 2 ...
## $ Y: int 111 130 157 174 117 114 161 141 105 140 ...
#Each row represents a plot that the farmer made, 72 rows for his 72 plots
## QUESTION 2: What do the columns of this dataset represent? Indicate whether each variable in the study is numerical or categorical. If numerical, identify as continuous or discrete. If categorical, identify as ordinal or nominal.
#1st column is "B" & represents which of the 6 blocks each plot is on.Variable "B" is categorical & nominal.
#2nd column is "V" & represents which of the 3 varieties of oats were planted. Variable "V" is categorical & nominal.
#3rd column is "N" & represents the nitrogen treatment is used for each plot. Variable is numerical & discrete.
#4th column is "Y" & represents yields in 1#/4lbs/subplot, w area 1/80 acre each. Variable is numerical & continuous.
## QUESTION 3: What are the response and explanatory variables in this study?
#Explanatory variables are oat variety & Nitrogen treatment in cwt. The response variable is yields in 1/4 lbs per sub-plot. The blocks variable is being used as a way to control where plots were located geographically on the farmer's land.
## QUESTION 4: Create a hypothesis about nitrogen fertilizer concentration levels.
#My hypothesis (which, due to my limited knowledge on this topic, is not very well supported) is that the "Victory" variety of oats, with the highest nitrogen treatment level will yield the most crop.
##GRAPHICS & EDA
##QUESTION 5: Plot 1- create a side by side boxplot that illustrates the yield distribution for EACH nitrogen fertilizer concentration level and allows for both visual comparison across and within treatments.
ggplot(oats, aes(x=N, y=Y))+
geom_boxplot()
##QUESTION 6: Look at plot 1. What are your observations from this plot? Does your hypothesis appear to be supported? Explain.
#Observations from plot -> looking at this box plot, we can see a general trend that the higher the nitrogen treatment level used, the more crop that was yielded from the corresponding field plot. This is true throughout the entire summary that is shown on the box plot, with the 0.6cwt N value having the highest minimum, Q1, median, Q3, and maximum values for Y.
#Hypothesis supported? -> This data does support my hypothesis from q4, since the box plot illustrates a general trend of higher values for N correlatingto higher values of Y. That being said, this doesn't address data collected onthe variety of oats being planted, so we cannot tell from this plot whether it is the nitrogen level, oat variety, or both that correlates to yield size.
##QUESTION 7:Plot 2- create a side by side box plot that illustrates the yield distribution for each oat variety treatment. Let's add some color! Fill the boxes with a different color for each variety.
ggplot(oats, aes(x=V, y=Y, color=V))+
geom_boxplot()
## QUESTION 8: Look at plot 2. What are your observations from this plot?Do any varieties stand out as the best producer? Explain.
#Observations from this plot -> this plot indicates that the Marvellous variety has the greatest median at about 112.5 1/4 lbs/sub-plot. Golden rain is next, with Victory having the lowest median. The minimum, Q3, and Maximum values for V are not very notably different from each other, but the Q1 values followa similar trend to the median with Marvellous having the greatest value, andVictory having the lowest value. This box plot also shows us that it appears the Victory oat variety data has an outlier point above the rest of the dataat about 175 1/4 lbs/sub-plot.
## QUESTION 9: Plot 3- add facets to your plot from part 6 to compare yields across nitrogen fertilizer concentration levels and the oat varieties.
ggplot(oats, aes(x=V, y=Y, color=V))+
geom_boxplot()+
facet_wrap(~N)
##QUESTION 10: Look at plot 3. What are your observations? Do the trends appear to be consistent?
#Observations; trends consistent? -> There are quite a few trends that remain consistent throughout the 3 different plots that are illustrated in Plot 3.Plot 3 illustrates the same trend noted in Plot 1, which is that is appears that as the nitrogen level increases, so does the value for crops yielded. Also, Plot 3 shows that even when grouped by nitrogen level, Victory variety has an outlier above the rest of the dataset. This indicates that whatever is causing this outlier, is something that happened throughout each N value. Across all four of the values for N level, Victory consistently has the lowest median yield value.
#New trends/observations -> When we separate the data out by Nitrogen level (like we do in Plot 3), it becomes clear to us that the Marvellous variety of oats does not always maintain the highest median value. At the value N=0.6cwt, Golden.rain has a higher median value for Y. In the plot for the value of N=0.4cwt, the box for the Golden.rain variety shows us that these data have a relatively wide range of values for Y; meanwhile, the range of values for the data in both the Marvellous and Victory varieties on this plot is quite narrow. I am not entirely sure why this might be, but it seems noteworthy. The Victory variety of oats has the lowest IQR value across all four N values.
#CONCLUSION
##QUESTION 11: What advice would you give the farmer exploring this data?
#In general, the trend seems to be that if you want to increase the oat yield, you should opt for fertilizers that have higher nitrogen content in them. According to the tests that we ran, the fertilizer/oat variety combo with the highest median value of yield is .6cwt nitrogen fertilizer/Golden.rain oat variety. That being said, the Marvellous oat variety produces high yields more consistently at both the .4cwt and .6cwt Nitrogen fertilizer levels. With the information gathered from this study, I would recommend using the Marvellous oat variety and the 0.6cwt Nitrogen Fertilizer level (though .4 would also do well). I would not recommend using the Victory oat Variety. I might suggest the farmer look at the correlating block numbers for each growing plot. Their knowledge of the ideal growing conditions as well as theconditions that occurred during the measured growing period might be able to help piece together some of these observations I was unsure about.