Skip to content

Perillo #1

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
Hperillo wants to merge 2 commits into
base: master
Choose a base branch
from
Open

Perillo #1

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
bb8e8c0
Answers to Ex 9
Nov 6, 2018
38f9d3d
Answers to exercise 9
Nov 7, 2018
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
26 changes: 26 additions & 0 deletions Exercise9Answers.py
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -0,0 +1,26 @@
#Answer to number 1
import pandas as pd
import numpy as np
from plotnine import *
cd ../
cd Desktop
pwd
wages=pd.read_csv("wages.csv")
a=ggplot(wages,aes(x="yearsSchool",y="wage"))
a+geom_point()+coord_cartesian()
a=ggplot(wages,aes(x="yearsSchool",y="wage"))+theme_classic()+geom_point()
a+xlab("Years of School")+ylab("Wage")+stat_smooth(method="lm")


#Anser to number 2 after converting data.txt into a csv manually
cd ../ICB_EX9
data=pd.read_table("data.txt", header=0, sep ",")
data.shape
! pip install scikit-misc
#Barplot of means of data
d=ggplot(data)+theme_classic()+xlab("Region")+ylab("Observations")
d+geom_bar(aes(x="region",y="observations"),stat="summary",fun_y=np.mean)
#Scatterplot of data
e=ggplot(data)+theme_classic()+coord_cartesian()+xlab("Region")+ylab("Observations")
e+geom_jitter(aes(x="region",y="observations"))
#Barplot and scatter tell similar stories since they show the general distribution and relationship of regions and observations.