Chen Exercise 10 Submission

Exercise 10 Script.R

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+## PROBLEM 1
+# read in data
+crime = read.table("crime.txt", header=TRUE, sep="\t", stringsAsFactors=FALSE)
+
+# load ggplot
+library(ggplot2)
+
+# use ggplot to produce scatterplot
+ggplot(crime, aes(x=Year, y=Crime.Rate)) +
+  geom_point() +
+  xlab("Year") +
+  ylab("Crime Rate (per 100,000 population)") +
+  ggtitle("Crime Rate Overtime") +
+  stat_smooth(method="loess") +
+  theme_classic() 
+
+
+## PROBLEM 2
+# read in data
+data = read.table("data.txt", header=TRUE, sep=",", stringsAsFactors = FALSE)
+
+# use ggplot to produce barplot
+ggplot(data, aes(x = region, y = observations)) +
+  stat_summary(fun = mean, geom = "bar") +
+  xlab("Region") +
+  ylab("Average Population") +
+  theme_classic()
+
+# produce scatterplot
+ggplot(data = data, aes(x = region, y = observations)) +
+  geom_point() +
+  geom_jitter() +
+  theme_classic()
+
+# the barplot and scatterplot are different because the scatterplot will show 
+# the distribution of the data, whereas the barplot just reports the average.
+# the barplot does not report very precisely. the scatterplot shows that for
+# south, the data is very split (bimodal distribution). The barplot does not
+# show this.

crime.txt

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+Year	Crime Rate												
+1965	26036												
+1966	27857												
+1967	28846												
+1968	33209												
+1969	38801												
+1970	38018												
+1971	41645												
+1972	44785												
+1973	49433												
+1974	52398												
+1975	61966												
+1976	62207												
+1977	58980												
+1978	60462												
+1979	62037												
+1980	56457												
+1981	65949												
+1982	62126												
+1983	60186												
+1984	61152												
+1985	58770												
+1986	62459												
+1987	53775												
+1988	49216												
+1989	47799												
+