Novak Exercise 10 Answers

Exercise10_Answers.R

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+# Tutorial 12 
+## Exercise 10 
+
+# Set working directory
+setwd("~/Desktop/Fall-2021/Biocomputing/Biocomp_tutorial12/")
+
+# Load ggplot library
+library(ggplot2)
+
+
+
+# Question 1
+
+# Load marathon pace data text file 
+marathon <- read.table("Marathon_Pace.txt", header = FALSE, sep = "\t", stringsAsFactors = FALSE)
+colnames(marathon) <- c("mile","time")
+head(marathon)
+
+# Scatter plot of two variables: mile count vs time in minutes
+ggplot(data=marathon, aes(x=mile,y=time))+
+  geom_point(size=3)+
+  xlab("Mile Count")+
+  ylab("Time Spent Running (minutes)")+
+  theme_classic()+
+  geom_smooth(method="lm") # Include trend line 
+
+
+
+# Question 2
+
+# Load data from data.txt
+data <- read.table("data.txt", sep = ",", header=TRUE, stringsAsFactors = TRUE)
+head(data)
+
+# Bar plot of the means of the four populations
+ggplot(data=data, aes(x=region,y=observations,fill=region))+
+  stat_summary(fun='mean',geom='bar')+
+  xlab("Region")+
+  ylab("Mean of Observations")+
+  theme_classic()
+
+# Scatter plot of all observations 
+ggplot(data=data, aes(x=region,y=observations,color=region))+
+  geom_point()+
+  geom_jitter()+
+  xlab("Region")+
+  ylab("Observations")+
+  theme_classic()
+
+## Do the bar and scatter plots tell you different stories? Why? 
+
+# Yes, the scatter plots and bar plots tell very different stories. 
+# The bar plot only shows the mean of each population, which are very similar for all of the regions. 
+# This makes it look like the populations are very similar to each other in general. 
+# The scatter plot shows every observation, and gives a more accurate picture of what each population looks like. 
+# The East and West regions are more spread out, while the North region is concentrated in the middle and the South is concentrated on both ends. 
+# While the mean for each region is almost the same, the distributions are very different. 
+
+
+
+
+
+
+

Marathon_Pace.txt

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+1	8
+2	16
+3	24
+4	32
+5	40
+6	48
+7	56
+8	64
+9	72
+10	80
+11	88
+12	96
+13	104
+14	112
+15	120
+16	128
+17	136
+18	144
+19	152
+20	160
+21	168
+22	176
+23	184
+24	192
+25	200
+26	208
+26.2	209.45