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ECON 216 Problem Set 5

Creation date：2024-05-02 14:10:20

Belonging category：经济Economics

Tag ECON 216

Problem Set 5

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ECON 216 Problem Set 5

Instructions
In this problem set you will investigate the change in annual average air temperatures over time at both the
global and country level using a time-series line plot and a choropleth. There are 40 total points in the
assignment.
You will need the files GlobalTemp.RData and WorldShape.RData and the packages tidyverse, sf, and
ggthemes.
The data in GlobalTemp.RData come from Berkeley Earth. There are two datasets in the file, glbl_av_temp
and cntry_av_temp. The first contains monthly average temperatures over the Earth’s surface since the
18th century while the second breaks this average down by country.
The shapefile for the project is contained in the file WorldShape.RData and is called world_shp.
When you turn in your problem set, please submit both an Rmd file and an html file labelled as before using
your CWRU ID: abc123_PS5.Rmd and abc123_PS5.html.
Part 1. (16 points) Global average temperatures began to rise following the massive increase in fossil
fuel consumption sparked by industrialization. In this part you will try to estimate the year in which global
average temperature began to rise.
You will use the dataset global_avg_temp to guess an inflection year for global average temperatures between
1750 and 2015.
We will call that inflection year INFLECT.
INFLECT is the latest year where you judge there has been no overall upward trend temperature in the
preceding years. Before INFLECT, temperatures will have trended neither up nor down, though there are
significant annual fluctuations.
A. Create a properly labeled time series line graph for annual average temperatures between 1750 and 2015.
(Remember a time series graph has time on the x-axis.) Note that the data is at the monthly level so you
will need to summarize first. Note that the data is spiky before the late 19th century because the underlying
measurements are sparse.
B. Guess the year for INFLECT using the following iterative method.
First, make an initial guess of a year, like 1880. Then add two smoothing lines to the plot from part
A by using geom_smooth, setting method=“lm”andse=FALSE. The first line will be limited to
years before your guess and the second line to the years after. Remember you can change
the data a geom uses like this:geom_any(filter(data, year<=1900))‘
For the first smoothed line, filter the data within the geom to include only the years between 1750 and your
guess of INFLECT. (See the final example in section 3.6 for a hint.) For the first
1
If the first smoothed line is flat and the second inclined up, your guess is pretty good. If both lines are
inclined up, you are probably too late in your guess. Adjust the year until the first smoothed line is flat and
the second inclined up. That year will be your guess of INFLECT.
Include a version of this plot with your final guess.
C. Redraw the plot from part A and place a red vertical line using geom_vline at your guess for INFLECT.
Part 2. (12 points) A. Create a country-level dataframe called cntry_av_temp_diff that has the
following four variables:
country, the name of the country. mean_temp_baseline, mean temperature between 1850 and your inflec-
tion point INFLECT, mean_temp_recent, mean temperature between 2000 and 2015. temp_change, the
difference between recent temperature and the baseline.
This new dataframe can be created in several ways. One way is via a pipeline starting with cntry_av_temp
and doing some mutating, grouping, summarizing, and pivoting, in that order.
B. Write a short paragraph that describes in words the steps that you took in part A to get from the raw
data to the finished data frame.
Part 3. (8 points) Create a world choropleth showing the change for each country in annual average
temperature between the baseline and recent periods. Exclude Antarctica from the choropleth.
A. Remove Antarctica from world_shp. Join cntry_av_temp_diff to world_shp.
B. Using the augmented world_shp, create a world choropleth of the change in temperatures between the
baseline and recent period. Use scale_fill_distiller using the palette Oranges for the fill colors.
Part 4. (4 points) Write a short paragraph that describes the patterns that you see on the finished
choropleth.

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LAW3016 CS371 STAT 334 FIN0008 ACC3004 STAT 371 COMP1921 MTH201 ACCT2000 UN3412 CIV6000 MATHS 3021 MATE7014 ECON1 CIBC6035 LAW121G COMM 321 CSC8204R TTM2033 CSCI 2600 MAE 115 LNG302 CENV6175W1 ECON30019 CPS2015 BST969 DDES9015 AMATH242 CS 1151 N1592 ACCT600 EG503G SMM519 EC318 Ecstatistics EEE 471 MARK2051 Topology COM00037H ACCT3000 MTHM501 EG501V COM00055H BUSN9085 GGR273 MATH 2B ITD102 MA214 ECON0060 ECE3114 ECMM447 Mechanics CCT361H5S BSAN3210 WELF2001 Fin3001 RESP5001 STA 137 Linguistics 101 CSCI368 CMPSC497 LUBS3655 OMBA 5355 BUSM2562 BFF5902 CYBR7001 CULT2005 ECON 7720 MTRX3760 FSE598 LUE1001 MGSC 7100 CS3240 COSC7500 ACCT3091 INFOGR TRAN 2080 DESIGN CIVE215001 CIVE2360 CPSC 8430 FNCE2003 Stoichiometry INFS6023 Quantitative Method Math 21D ECE5179 Design ECON8022 Chemical DMS 213 ADM1370 BFF5902 BFF2701 AGRI10051 AMN400 ECON7322 FINC5090 MATH1052 SciComp Elec4622 FINS5516 ENG 101 MMM143 CS431CA2 BSAN2201 EBSC7070 ES9v9-10 PLIN0009 DATA 8 MATH 101 BCPM0097 MAT 237 BISM2201 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SVM. (10 points) Consider an SVM classifier using RBF kernel, and finish the following python code to search for the optimum kernel parameter γ. You are required to calculate the RBF function and search yourself, instead of using functions in Scikit-Learn (sklearn). def parameter search(gamma list ,Xtr,ytr,Xts,yts ,...): """ Parameters gamma list: A list of RBF kernel parameter gamma to search for Xtr: Training data, shape (ntr,nrow,ncol) ytr: Training labels, shape (ntr,) Xts: Test data, shape (nts,nrow,ncol) yts: Test labels, shape (nts,) ... Add other parameters as needed Returns: gamma optimum: The gamma with minimum error on Xts, yts """ ... return gamma optimum

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