Urasky

Tutorial10_answers.R

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+#set working directory 
+setwd("~/Desktop/r-novice-inflammation/Biocomp_tutorial12/")
+
+################### number 1
+#read in data 
+data <- read.table(file="oat_smoothie_data - Sheet1.csv", header=TRUE, sep=",", stringsAsFactors = FALSE) 
+data
+
+#load ggplot
+library(ggplot2)
+
+#produce a scatter plot with a tend line 
+ggplot(data=data, aes(x=day, y=oatmeal))+ geom_point() + stat_smooth(method="lm", formula="y~x") + theme_classic()
+
+
+##################number 2
+#read in data 
+data <- read.table(file="data.txt", header=TRUE, sep=",", stringsAsFactors = FALSE)
+data
+
+#barplot of means of the four populations 
+ggplot(data=data, aes(x=region, y=observations)) + stat_summary(fun=mean, geom="bar") +theme_classic() 
+
+#scatter plot of all observations
+ggplot(data=data, aes(x=region, y=observations)) + geom_point() + geom_jitter() + theme_classic()
+
+#do the bar and scatter plots tell you different stories? 
+#answer= yes, because the bar plot doesn't show you any sort of distribution, just the overall mean. 
+#with the scatter plot, you can see that all of the observations in the regions are distributed differently depending on which region they are in.