Luck Exercise10

Exercise10 Code.R

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+### Exercise 10 Code
+##Problem 1 - plot of scatter plot of heart rate vs. age with trend line
+
+# wrote "data3.txt" with 27 age and heart rate data points.
+# load text file 
+data <- read.table("data3.txt", header = TRUE, sep = "\t", stringsAsFactors = FALSE)
+
+# use ggplot to produce scatter plot of the two variable data with a trend line
+library(ggplot2)
+ggplot(data, aes(x=Age, y=Heartrate)) +
+  geom_point() +
+  xlab("age (months)") +
+  ylab("heart rate (bpm)") +
+  stat_smooth(method="lm") +
+  theme_classic()
+
+
+## Problem 2 - two figures that summarize "data.txt"
+
+# load data.txt
+data <- read.table("data.txt", header=TRUE, sep = ",", stringsAsFactors = FALSE)
+
+## bar plot of means of the four populations
+ggplot(data, aes(x=region, y=observations)) +
+  geom_bar(stat = "summary", fun.y="mean") +
+  theme_bw() +
+  xlab("region") +
+  ylab("mean observation") +
+  theme(axis.text.x = element_text(angle=65, vjust=0.6))
+
+## scatter plot of all observations
+ggplot(data, aes(x=region, y=observations)) +
+  geom_jitter(alpha=0.1) +
+  xlab("region") +
+  ylab("obersations") +
+  theme_classic()
+
+# Do the bar and scatter plots tell you different stories? - YES
+# While the bar plot shows that each population has a mean observation around
+# 15, the scatter plot shows that the distribution of observations differs
+# by region, with north oberservations clumped around 15, east and west observations
+# spread relatively evenly between 0 and 30, and south observations clumped into
+# two gorups around 5 and 25.