Ross Submission

Exercise10.R

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+## Exercise 10 - Analysis and plotting ##
+## by Nicholas Ross on 11/22/2021
+
+## Part 1- Plotting and comparing two variables from data set ##
+
+# load ggplot library
+library (ggplot2)
+
+#read in data from outside source
+stats = read.csv("NFL_Defense_stats_2021.csv", header = T, stringsAsFactors = F)
+
+# set variable for ggplot we want to run
+plot0 = ggplot(stats, aes(x = Rank, y = YDS.G))+
+
+        #give color to points that relate to rankings
+        geom_point(aes(color = Rank))+ 
+        scale_color_gradientn(colors = terrain.colors(100))+ # set the color gradient to use
+        xlab("Team Rank")+              # create x axis label
+        ylab("Yards per Game Allowed")+ # create y axis label
+        stat_smooth(method="lm")+       # implement trend line
+
+        #set background to black to display colors clearly
+        theme(panel.background = element_rect(fill = "black")) 
+
+
+## Part 2- Side-by-side comparisons using plots ##
+
+#if used in separate script, be sure load ggplot library with "library(ggplot2)"
+
+#read in data from outside source
+data = read.table("data.txt", header = T, sep = ",", stringsAsFactors = F)
+
+# set variable equal to ggplot we want to implement
+plot1 = ggplot(data, aes(x= region, y = observations, color = factor(region)))+
+        stat_summary(fun = mean, geom = "bar")+ # plot as a function of the mean using bar graph
+        xlab("Region")+                         # create x axis label
+        ylab("Average Population")+             # create y axis label
+        theme_classic()                         # set plot theme to classic
+
+## the code below can be used to find the mean population for each region to ensure plot is accurate
+## one could either replace each instance of "north" and "northMean" with a different
+## region or extend the if-else statements to include all regions
+
+# north = 0
+# south = 0
+# east = 0
+# west = 0
+# northMean = 0
+# southMean = 0
+# eastMean = 0
+# westMean = 0
+# 
+# for (i in 1:nrow(data)){
+#   if (data[i,1] == "north"){
+#     north [i] = data[i,2]
+#   }
+# }
+# northMean = mean(north)
+
+# create second variable for ggplot to compare side by side with plot1
+plot2 = ggplot(data, aes(x= region, y = observations))+
+        geom_jitter(aes(color = factor(region)))+ # create scatter plot using regions as the color factor
+        xlab("Region")+                           # create x axis label
+        ylab("Average Population")+               # create y axis label
+        theme_classic()                           # set plot theme to classic
+
+# display plot1 and plot2 in the same viewing window
+plot_grid(plot1,plot2) 
+
+## In analyzing both plots, one can see that plot1 displays the average population
+## per region. However, plot2 shows that plot1 doesn't tell the full story. The north
+## region shows that almost all populations in that region are close to the average. The south, however,
+## displays that none of the populations are equal to the average, but rather the populations are 
+## quite larger or relatively small. 

NFL_Defense_stats_2021.csv

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+Rank,TEAM,GP,PA ,PA/G,YDS/G,PYDS,PYDS/G,RYDS,RYDS/G,SCK,YDS,INT,INTTD,FF,FUMR,FUMTD,HUR,PDEF,
+12,Bills,9,127,14.1,274.1,"1,712",190.2,755,83.9,19,99,15,1,9,9,0,58,50,
+8,Patriots,11,165,15,313.2,"2,326",211.5,"1,119",101.7,28,200,18,3,6,3,0,50,57,
+20,Broncos,10,173,17.3,328.2,"2,181",218.1,"1,101",110.1,24,153,8,0,4,3,0,59,44,
+1,Cardinals,10,173,17.3,323,"2,064",206.4,"1,166",116.6,25,149,8,1,15,11,0,37,44,
+17,Saints,9,176,19.6,337.8,"2,384",264.9,656,72.9,20,143,11,2,6,2,0,26,46,
+3,Packers,10,178,17.8,309.9,"2,027",202.7,"1,072",107.2,24,183,11,0,5,5,0,41,40,
+13,Steelers,9,179,19.9,348.9,"2,033",225.9,"1,107",123,26,166,3,0,11,5,0,42,39,
+22,Panthers,10,181,18.1,280.7,"1,737",173.7,"1,070",107,27,206,8,0,9,4,0,37,39,
+5,Cowboys,9,183,20.3,354,"2,275",252.8,911,101.2,18,156,14,3,5,3,0,37,42,
+28,Seahawks,9,184,20.4,400.6,"2,478",275.3,"1,127",125.2,15,111,4,0,4,5,0,43,41,
+10,Bengals,9,187,20.8,361.2,"2,343",260.3,908,100.9,23,139,7,0,7,2,0,52,31,
+16,Vikings,9,199,22.1,369.1,"2,147",238.6,"1,175",130.6,29,198,8,1,6,4,0,54,36,
+7,Buccaneers,9,202,22.4,334,"2,288",254.2,718,79.8,22,127,10,2,7,5,0,43,43,
+6,Rams,10,203,20.3,346.9,"2,417",241.7,"1,052",105.2,29,216,12,0,7,2,0,49,37,
+4,Ravens,9,205,22.8,371.6,"2,550",283.3,794,88.2,19,137,5,0,5,2,0,38,40,
+18,49ers,9,206,22.9,331.4,"1,878",208.7,"1,105",122.8,20,151,4,2,10,3,0,31,31,
+26,Giants,9,210,23.3,372.4,"2,249",249.9,"1,103",122.6,19,120,9,1,7,5,0,30,45,
+30,Jaguars,9,214,23.8,366.4,"2,344",260.4,954,106,16,129,4,0,3,1,0,46,26,
+19,Raiders,9,214,23.8,360.1,"2,079",231,"1,162",129.1,21,109,4,0,9,7,0,38,36,
+14,Colts,10,216,21.6,363.6,"2,499",249.9,"1,137",113.7,22,135,10,0,11,11,1,22,38,
+11,Chargers,9,218,24.2,361.1,"1,854",206,"1,396",155.1,17,116,6,0,11,5,0,41,21,
+21,Eagles,10,221,22.1,350.6,"2,333",233.3,"1,173",117.3,18,137,6,0,8,3,3,40,21,
+27,Bears,9,222,24.7,348.9,"2,035",226.1,"1,105",122.8,25,192,4,1,6,5,1,26,31,
+9,Chiefs,10,225,22.5,373.2,"2,586",258.6,"1,146",114.6,14,106,8,1,8,4,0,56,29,
+2,Titans,10,230,23,366.5,"2,679",267.9,986,98.6,27,192,9,2,10,5,1,50,52,
+15,Browns,10,239,23.9,323.9,"2,292",229.2,947,94.7,29,194,5,1,6,3,0,34,46,
+23,Football Team,9,246,27.3,376.4,"2,514",279.3,874,97.1,19,97,6,0,7,5,0,39,32,
+32,Lions,9,246,27.3,379.8,"2,197",244.1,"1,221",135.7,14,116,5,0,6,5,0,33,33,
+25,Dolphins,10,250,25,383.1,"2,738",273.8,"1,093",109.3,21,170,7,1,12,8,1,36,55,
+24,Falcons,10,256,25.6,362.3,"2,390",239,"1,233",123.3,14,124,4,0,8,5,0,34,46,
+29,Texans,9,258,28.7,385.9,"2,241",249,"1,232",136.9,19,133,9,0,11,5,0,29,34,
+31,Jets,9,294,32.7,417.1,"2,549",283.2,"1,205",133.9,20,145,2,,,,,,,