Lara Chalas

Exercise9hw.py

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+#!/usr/bin/env python2
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Nov  8 13:33:39 2018
+
+@author: saurylara
+"""
+
+import numpy 
+import pandas
+from plotnine import *
+
+#Import the SpouseAges text file
+SpouseAges = pandas.read_csv("/Users/saurylara/Desktop/SpouseAges.txt",sep="\t",header=0)
+
+#Create scatterplot of the two variables that includes a trendline
+a=ggplot(SpouseAges,aes(x="HusbandAge",y="WifeAge"))+theme_classic()+geom_point()
+print(a+xlab("HusbandAge (y)")+ylab("WifeAge (y)")+stat_smooth(method="lm"))
+
+#Import the data file
+data = pandas.read_csv("/Users/saurylara/Desktop/ICB_EX9/data.txt")
+
+#Generate a barplot with means of the four populations
+d=ggplot(data)+theme_classic()+xlab("region")+ylab("observations")
+d=d+geom_bar(aes(x="region",y="observations"),stat="summary",fun_y=numpy.mean)
+print(d)
+
+#Plot a scatter plot of all of the observations
+a=ggplot(data,aes(x="region",y="observations"))+theme_classic()+geom_jitter()
+print(a)
+
+# The bar and scatter plot do tell me different things because you do not get the full story just by looking at the bar graph with means.
+# When I first plotted the data using the bar graph, I thought that all of the observations were fairly similar for each region given the mean for all four was about 15.
+# When I plotted the scatter plot though, I was able to tell that the regions had very different data spreads.
+# The data points in the West region were fully spread out, in the East region fairly spread out, in the South region there were many high/low values, and in the North region all of the values were concentrated around 15.
+
+
+
+

SpouseAges.txt

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+Couple	HusbandAge	WifeAge
+1	25	22
+2	25	32
+3	51	50
+4	25	25
+5	38	33
+6	30	27
+7	60	45
+8	54	47
+9	31	30
+10	54	44
+11	23	23
+12	34	39
+13	25	24
+14	23	22
+15	19	16
+16	71	73
+17	26	27
+18	31	36
+19	26	24
+20	62	60
+21	29	26
+22	31	23
+23	29	28
+24	35	36