Lewis, Erin - Exercise 10

Exercise_10.R

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+# Exercise 10 
+
+## 1. create scatter plot 
+
+# load the package 
+library(ggplot2)
+
+# read data
+mammals = read.table("/Users/erinlewis/Desktop/Biocomp_tutorial12/mammals.txt", header=TRUE, sep="\t", stringsAsFactors=FALSE)
+
+# plot of body weight (log body weight (kg)) vs. brain weight (log brain weight (kg)) in mammals
+ggplot(data = mammals, 
+       aes(x = log_body, y = log_brain)) +
+  geom_point() +
+  xlab("body weight (kg)") + 
+  ylab("brain weight (kg)") +
+  stat_smooth(method="lm") +
+  theme_classic()
+
+## 2. generate 2 figures for data.txt
+
+# read data 
+info = read.table("/Users/erinlewis/Desktop/Biocomp_tutorial12/data.txt", header=TRUE, sep=",", stringsAsFactors=FALSE)
+
+#get mean values for each direction 
+  sumNorth<-0
+  sumSouth<-0
+  sumWest<-0
+  sumEast<-0
+
+  obsNorth<-0
+  obsSouth<-0
+  obsWest<-0
+  obsEast<-0
+
+for(i in 1:nrow(info)){
+  if(info$region[i]=="north"){
+    sumNorth=sumNorth+info$observations[i]
+    obsNorth=obsNorth+1
+  }else if(info$region[i]=="south"){
+    sumSouth=sumSouth+info$observations[i]
+    obsSouth=obsSouth+1
+  }else if(info$region[i]=="west"){
+    sumWest=sumWest+info$observations[i]
+    obsWest=obsWest+1
+  }else if(info$region[i]=="east"){
+    sumEast=sumEast+info$observations[i]
+    obsEast=obsEast+1 
+  }
+}
+
+  meanInfo = data.frame(region = c("north", "south", "west", "east"),
+                        means = c(sumNorth/obsNorth, sumSouth/obsSouth, sumWest/obsWest, sumEast/obsEast))
+
+# bar plot 
+ggplot(data = meanInfo, 
+        aes(x = region, y = means)) +
+  geom_bar(stat = "identity", fill = "orange")
+
+# scatter plot
+ggplot(data = info, 
+       aes(x = region, y = observations)) +
+  geom_jitter()
+
+
+## The scatter plot and the bar plot tell very different stories. The bar plot only shows the means of each
+## population. All the populations have similar means, so they all look very similar on the bar graph. The
+## scatter plot, in contrast, shows all the observations individually as points, and you can see that how the
+## populations are distributed differently. The north population, for example, is concentrated around 
+## 15, while the south population has no observations at 15 at all! Thus, it is clear that though the means
+## of the populations may be similar, their distribution along the gradient is not. 

mammals.txt

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+	body_wt_kg	brain_wt_g	log_body	log_brain
+Mammal 10	52.160	440.00	1.72	2.64
+Mammal 11	0.425	6.40	-0.37	0.81
+Mammal 12	465.000	423.00	2.67	2.63
+Mammal 13	0.550	2.40	-0.26	0.38
+Mammal 14	187.100	419.00	2.27	2.62
+Mammal 15	0.075	1.20	-1.12	0.08
+Mammal 16	3.000	25.00	0.48	1.40
+Mammal 17	0.785	3.50	-0.11	0.54
+Mammal 18	0.200	5.00	-0.70	0.70
+Mammal 19	1.410	17.50	0.15	1.24
+Mammal 20	60.000	81.00	1.78	1.91
+Mammal 21	529.000	680.00	2.72	2.83
+Mammal 22	27.660	115.00	1.44	2.06
+Mammal 23	0.120	1.00	-0.92	0.00
+Mammal 24	207.000	406.00	2.32	2.61
+Mammal 25	85.000	325.00	1.93	2.51
+Mammal 26	36.330	119.50	1.56	2.08
+Mammal 27	0.101	4.00	-1.00	0.60
+Mammal 28	1.040	5.50	0.02	0.74
+Mammal 29	521.000	655.00	2.72	2.82
+Mammal 30	100.000	157.00	2.00	2.20
+Mammal 31	35.000	56.00	1.54	1.75
+Mammal 32	0.005	0.14	-2.30	-0.85
+Mammal 33	0.010	0.25	-2.00	-0.60
+Mammal 34	62.000	1320.00	1.79	3.12
+Mammal 35	0.122	3.00	-0.91	0.48
+Mammal 36	1.350	8.10	0.13	0.91
+Mammal 37	0.023	0.40	-1.64	-0.40
+Mammal 38	0.048	0.33	-1.32	-0.48
+Mammal 39	1.700	6.30	0.23	0.80
+Mammal 40	3.500	10.80	0.54	1.03
+Mammal 41	250.000	490.00	2.40	2.69
+Mammal 42	0.480	15.50	-0.32	1.19
+Mammal 43	10.000	115.00	1.00	2.06
+Mammal 44	1.620	11.40	0.21	1.06
+Mammal 45	192.000	180.00	2.28	2.26
+Mammal 46	2.500	12.10	0.40	1.08
+Mammal 47	4.288	39.20	0.63	1.59
+Mammal 48	0.280	1.90	-0.55	0.28
+Mammal 49	4.235	50.40	0.63	1.70
+Mammal 50	6.800	179.00	0.83	2.25
+Mammal 51	0.750	12.30	-0.12	1.09
+Mammal 52	3.600	21.00	0.56	1.32
+Mammal 53	83.000	98.20	1.92	1.99
+Mammal 54	55.500	175.00	1.74	2.24
+Mammal 55	1.400	12.50	0.15	1.10
+Mammal 56	0.060	1.00	-1.22	0.00
+Mammal 57	0.900	2.60	-0.05	0.41
+Mammal 58	2.000	12.30	0.30	1.09
+Mammal 59	0.104	2.50	-0.98	0.40
+Mammal 60	4.190	58.00	0.62	1.76
+Mammal 61	3.500	3.90	0.54	0.59
+Mammal 62	4.050	17.00	0.61	1.23