Midterm Project¶

Name : Francisca Idio¶

CDS 101¶

3/28/2017¶

# Set the location for R packages
.libPaths(new = "~/Rlibs")
# Load the Tidyverse packages
library(tidyverse)

Loading tidyverse: ggplot2
Loading tidyverse: tibble
Loading tidyverse: tidyr
Loading tidyverse: readr
Loading tidyverse: purrr
Conflicts with tidy packages ---------------------------------------------------
filter(): dplyr, stats
lag():    dplyr, stats

# Load the Public Passenger Vehicle Lisence dataset
energy <- read_csv("Energy Census and Economic Data US 2010-2014.csv")

Parsed with column specification:
cols(
  .default = col_double(),
  StateCodes = col_character(),
  State = col_character(),
  Region = col_integer(),
  Division = col_integer(),
  Coast = col_integer(),
  `Great Lakes` = col_integer(),
  TotalC2010 = col_integer(),
  TotalC2011 = col_integer(),
  TotalC2012 = col_integer(),
  TotalC2013 = col_integer(),
  TotalC2014 = col_integer(),
  TotalP2010 = col_integer(),
  TotalP2011 = col_integer(),
  TotalP2012 = col_integer(),
  TotalP2013 = col_integer(),
  TotalP2014 = col_integer(),
  BiomassC2010 = col_integer(),
  BiomassC2011 = col_integer(),
  BiomassC2012 = col_integer(),
  BiomassC2013 = col_integer()
  # ... with 77 more columns
)
See spec(...) for full column specifications.

print(energy)

# A tibble: 52 × 192
   StateCodes       State Region Division Coast `Great Lakes` TotalC2010
        <chr>       <chr>  <int>    <int> <int>         <int>      <int>
1          AL     Alabama      3        6     1             0    1931522
2          AK      Alaska      4        9     1             0     653221
3          AZ     Arizona      4        8     0             0    1383531
4          AR    Arkansas      3        7     0             0    1120632
5          CA  California      4        9     1             0    7760629
6          CO    Colorado      4        8     0             0    1513547
7          CT Connecticut      1        1     1             0     764970
8          DE    Delaware      3        5     1             0     250212
9          FL     Florida      3        5     1             0    4282673
10         GA     Georgia      3        5     1             0    3100144
# ... with 42 more rows, and 185 more variables: TotalC2011 <int>,
#   TotalC2012 <int>, TotalC2013 <int>, TotalC2014 <int>, TotalP2010 <int>,
#   TotalP2011 <int>, TotalP2012 <int>, TotalP2013 <int>, TotalP2014 <int>,
#   TotalE2010 <dbl>, TotalE2011 <dbl>, TotalE2012 <dbl>, TotalE2013 <dbl>,
#   TotalE2014 <dbl>, TotalPrice2010 <dbl>, TotalPrice2011 <dbl>,
#   TotalPrice2012 <dbl>, TotalPrice2013 <dbl>, TotalPrice2014 <dbl>,
#   `TotalC10-11` <dbl>, `TotalC11-12` <dbl>, `TotalC12-13` <dbl>,
#   `TotalC13-14` <dbl>, `TotalP10-11` <dbl>, `TotalP11-12` <dbl>,
#   `TotalP12-13` <dbl>, `TotalP13-14` <dbl>, `TotalE10-11` <dbl>,
#   `TotalE11-12` <dbl>, `TotalE12-13` <dbl>, `TotalE13-14` <dbl>,
#   `TotalPrice10-11` <dbl>, `TotalPrice11-12` <dbl>, `TotalPrice12-13` <dbl>,
#   `TotalPrice13-14` <dbl>, BiomassC2010 <int>, BiomassC2011 <int>,
#   BiomassC2012 <int>, BiomassC2013 <int>, BiomassC2014 <int>,
#   CoalC2010 <int>, CoalC2011 <int>, CoalC2012 <int>, CoalC2013 <int>,
#   CoalC2014 <int>, CoalP2010 <int>, CoalP2011 <int>, CoalP2012 <int>,
#   CoalP2013 <int>, CoalP2014 <int>, CoalE2010 <dbl>, CoalE2011 <dbl>,
#   CoalE2012 <dbl>, CoalE2013 <dbl>, CoalE2014 <dbl>, CoalPrice2010 <dbl>,
#   CoalPrice2011 <dbl>, CoalPrice2012 <dbl>, CoalPrice2013 <dbl>,
#   CoalPrice2014 <dbl>, ElecC2010 <int>, ElecC2011 <int>, ElecC2012 <int>,
#   ElecC2013 <int>, ElecC2014 <int>, ElecE2010 <dbl>, ElecE2011 <dbl>,
#   ElecE2012 <dbl>, ElecE2013 <dbl>, ElecE2014 <dbl>, ElecPrice2010 <dbl>,
#   ElecPrice2011 <dbl>, ElecPrice2012 <dbl>, ElecPrice2013 <dbl>,
#   ElecPrice2014 <dbl>, FossFuelC2010 <int>, FossFuelC2011 <int>,
#   FossFuelC2012 <int>, FossFuelC2013 <int>, FossFuelC2014 <int>,
#   GeoC2010 <int>, GeoC2011 <int>, GeoC2012 <int>, GeoC2013 <int>,
#   GeoC2014 <int>, GeoP2010 <int>, GeoP2011 <int>, GeoP2012 <int>,
#   GeoP2013 <int>, GeoP2014 <int>, HydroC2010 <int>, HydroC2011 <int>,
#   HydroC2012 <int>, HydroC2013 <int>, HydroC2014 <int>, HydroP2010 <int>,
#   HydroP2011 <int>, HydroP2012 <int>, HydroP2013 <int>, HydroP2014 <int>, ...

Dataset Cleaning¶

To clean this dataset, I used the gather function to take all the other columns that were not in the original shown dataset and made a column to show if there was a blank columns or NA in there accidentally in a column that is was not supposed to be in. Also since the values in the variable columns are mostly numerical except for the column of the state names, there were no mispelling of words in the columns. Since I will be mainly using the column of total consumption of energy throughout this poroject, I made them to be all in one column using the gather function to consolidate the data and bake it look easier to read and a bit cleaner. It didn't make a lot of sence to have the Total consumption spread out and for graphing and comparison purposes, having them it one column helped me to create more organized graphs. This dataset with the consolidated data is labeled "EN1"

energy2 <- gather(energy,TotalC2011: HydroP2014,
               key = "key", value = "numbers", 
               na.rm = TRUE)

print(energy2)

# A tibble: 5,200 × 94
   StateCodes       State Region Division Coast `Great Lakes` TotalC2010
*       <chr>       <chr>  <int>    <int> <int>         <int>      <int>
1          AL     Alabama      3        6     1             0    1931522
2          AK      Alaska      4        9     1             0     653221
3          AZ     Arizona      4        8     0             0    1383531
4          AR    Arkansas      3        7     0             0    1120632
5          CA  California      4        9     1             0    7760629
6          CO    Colorado      4        8     0             0    1513547
7          CT Connecticut      1        1     1             0     764970
8          DE    Delaware      3        5     1             0     250212
9          FL     Florida      3        5     1             0    4282673
10         GA     Georgia      3        5     1             0    3100144
# ... with 5,190 more rows, and 87 more variables: NatGasC2010 <int>,
#   NatGasC2011 <int>, NatGasC2012 <int>, NatGasC2013 <int>, NatGasC2014 <int>,
#   NatGasE2010 <dbl>, NatGasE2011 <dbl>, NatGasE2012 <dbl>, NatGasE2013 <dbl>,
#   NatGasE2014 <dbl>, NatGasPrice2010 <dbl>, NatGasPrice2011 <dbl>,
#   NatGasPrice2012 <dbl>, NatGasPrice2013 <dbl>, NatGasPrice2014 <dbl>,
#   LPGC2010 <int>, LPGC2011 <int>, LPGC2012 <int>, LPGC2013 <int>,
#   LPGC2014 <int>, LPGE2010 <dbl>, LPGE2011 <dbl>, LPGE2012 <dbl>,
#   LPGE2013 <dbl>, LPGE2014 <dbl>, LPGPrice2010 <dbl>, LPGPrice2011 <dbl>,
#   LPGPrice2012 <dbl>, LPGPrice2013 <dbl>, LPGPrice2014 <dbl>,
#   GDP2010Q1 <int>, GDP2010Q2 <int>, GDP2010Q3 <int>, GDP2010Q4 <int>,
#   GDP2010 <dbl>, GDP2011Q1 <int>, GDP2011Q2 <int>, GDP2011Q3 <int>,
#   GDP2011Q4 <int>, GDP2011 <dbl>, GDP2012Q1 <int>, GDP2012Q2 <int>,
#   GDP2012Q3 <int>, GDP2012Q4 <int>, GDP2012 <dbl>, GDP2013Q1 <int>,
#   GDP2013Q2 <int>, GDP2013Q3 <int>, GDP2013Q4 <int>, GDP2013 <dbl>,
#   GDP2014Q1 <int>, GDP2014Q2 <int>, GDP2014Q3 <int>, GDP2014Q4 <int>,
#   GDP2014 <dbl>, CENSUS2010POP <int>, POPESTIMATE2010 <int>,
#   POPESTIMATE2011 <int>, POPESTIMATE2012 <int>, POPESTIMATE2013 <int>,
#   POPESTIMATE2014 <int>, RBIRTH2011 <dbl>, RBIRTH2012 <dbl>,
#   RBIRTH2013 <dbl>, RBIRTH2014 <dbl>, RDEATH2011 <dbl>, RDEATH2012 <dbl>,
#   RDEATH2013 <dbl>, RDEATH2014 <dbl>, RNATURALINC2011 <dbl>,
#   RNATURALINC2012 <dbl>, RNATURALINC2013 <dbl>, RNATURALINC2014 <dbl>,
#   RINTERNATIONALMIG2011 <dbl>, RINTERNATIONALMIG2012 <dbl>,
#   RINTERNATIONALMIG2013 <dbl>, RINTERNATIONALMIG2014 <dbl>,
#   RDOMESTICMIG2011 <dbl>, RDOMESTICMIG2012 <dbl>, RDOMESTICMIG2013 <dbl>,
#   RDOMESTICMIG2014 <dbl>, RNETMIG2011 <dbl>, RNETMIG2012 <dbl>,
#   RNETMIG2013 <dbl>, RNETMIG2014 <dbl>, key <chr>, numbers <dbl>

EN1 <- gather(energy, TotalC2010:TotalC2014, key = "TotalCYear", value = "count")

EN1

Data Citation And Summary¶

Citation for Energy Consumption Dataset¶

File Name: United States Energy, Census, and GDP

Website of dataset: Kaggle.com

Link to dataset: https://www.kaggle.com/lislejoem/us_energy_census_gdp_10-14

I did not use any filters while downloading this data. The provider/ creator of this dataset got the information they put into ther own dataset from external sources such as the Bureau of Economic Analysis for the data on GDP/Census and the U.S. Department of Commerce and the U.S. Energy Information Administration for information on energy consumption¶

Summary¶

This dataset is about the U.S. Census, Gross Domestc Product, and Energy consumption of both renewable and non renewable resources. Some of the columns are labeled State/ StateCodes which is the state abbreviation, Region in which a number 1-4 is allocated to a state in a sspecific region ( i.e. 1 = Northeast, 2 = Midwest, 3 = South, 4 = West), TotalC(year) which means the total energy consumption in billion British Thermal Units (BTU) in a given year, the type of renewable/nonrenewable energy source with a "C" (consumption) at the end ("CoalC2012") to show the total consumption of that product. The rest of the columns either list the resources or tell about the human population of each state and the birth/death rate, etc. Also please take into account that the row with a lot of NA for the values is from the total of the entire United states (US). A lot of the column variables do not apply to this row, this is why there is NA present.

Data Analysis and Plots¶

Question 1¶

In the dataset, is there any region that has an extreme outlier when it comes to energy consumption in the year 2010?¶

To create this scatter plot I downloaded ggplot2 in the R package. I made the data used in the plot to be the energy2 dataset. After i made it into a scatterplot by using geom point and made the x-axis labeled regions and the y-axis labeled TotalC2010 and for the grapg to be color coded by the "StateCodes"

ggplot(data = energy2) + geom_point(mapping = aes(x =  Region, y = TotalC2010, color = StateCodes))

Warning message:
“Removed 100 rows containing missing values (geom_point).”

We see that the South and Midwestern Regions have outliers, which are Texas and California. These states are 2 of the biggest states in the U.S. and have some of the largest opulations, so it makes sence that their numbers would be outside of the frames of the other states. In other states in the 4 regions resented, we see that most states have an total of .03 billion British Themal Units

Question 2¶

In 2010 and 2014, which region paid the highest amount for energy consumption, were there any decreases in the total spending?¶

In this problem I wanted to make a graph comparing the prives from 2 years in the different divisions. Note: the prices are in billions and USD. I created a name for this new table which is energy3, and used the select function to choose the columns from the original plot I wanted to see in the graph.

energy3 <- select(EN1, Division, TotalPrice2010, TotalPrice2014)

energy3

ggplot(energy3) + geom_col(mapping = aes(x = Division, y =TotalPrice2010))

Warning message:
“Removed 5 rows containing missing values (position_stack).”

ggplot(energy3) + geom_col(mapping = aes(x = Division, y = TotalPrice2014))

Warning message:
“Removed 5 rows containing missing values (position_stack).”

In both 2010 and 2014, Division 5 has spent the most amount of money in both years, but in 2010, Division 5 actually spent a lower amount of money on energy in comparison to 2014

Question 3¶

For each state, what is the average consumption of energy from 2010 to 2014?¶

Mean_energy <- mutate(energy, mean_energy = (TotalC2010 + TotalC2011 + TotalC2012 + TotalC2013 + TotalC2014) / 5)

To add the mean column to the dataset, I assigned a name for the new column. Then I took the mutate function and added TotalC for all 5 years (2010 - 2014) and divided it by 5 since there were 5 columns being added together.

Mean_energy

Question 4¶

Compare 2 states, 1 that has a coast and 1 that has does not have a coast, do landlocked states use more or less energy than states that have a body of water next to them?¶

First I will assign a name to the subset of the table EN1 by naming it "Nonrenewable". Then I selected the groups of the columns from the original table to add to this subset table.

Nonrenewable <- group_by(EN1, StateCodes , Coast, TotalCYear, count )

by putting summerize in fromt of the new table name, the subset data can be printed to be viewd and analyzed

summarize(Nonrenewable)

Looking at both the land locked states and the states with with coastal fronts, it is fair to say that most of the time that states that have coasts use more energy than states that are land locked. For example Virginia, a coastal stste, in 2011 used a total of 2,380,922 btu of energy while Arizona used 1,114,409 btu of energy. But there are a few coastal states that use less energy than land locked states.

Question 5¶

Based on the Summarized report in the previous problem, what was New Yorks total consumption each year from 2010 to 2014? What line of best fit would show the trend of the graph accurately?¶

For this function, we are filtering out any row that has "NY" as a state in the Nonrenewable table and making it into its own table. I labeled this new table New_York.

library(dplyr)    
New_York <- filter(Nonrenewable, StateCodes == "NY" )

Afterwards I printed out the table for the filter by recalling the name "New_York"

New_York

Afterwards, I created a line plot by calling the package ggplot to print the graphing background. Then i set the data to equal the "New_York" table. I then call the plot to be a scatter plot by putting geom_point which is apart of the ggplot package. Lastly for the aesthetics of the graph, I made the x axis equal to the "TotalCYear" and the y axs equal to the "count" of each year.

ggplot(data = New_York) + 
  geom_point(mapping = aes(x = TotalCYear, y = count))

The total consumption in 2010 was 3,723,729 BTUs, the total consumption for 2011 was 3,611,091 BTUs, for 2012 it was 3,503,309 BTUs, 2013 3,626,150 BTUs and 2014 it was 3,742,892 BTUs. I think the line of best fit for ths data would be a quadratic line/ parabola since there is a decline until 2012 and then an inclineor even an absolute value line since there is a sharper dip towards the center of the graph.

StateCodes	State	Region	Division	Coast	Great Lakes	TotalP2010	TotalP2011	TotalP2012	TotalP2013	⋯	RDOMESTICMIG2011	RDOMESTICMIG2012	RDOMESTICMIG2013	RDOMESTICMIG2014	RNETMIG2011	RNETMIG2012	RNETMIG2013	RNETMIG2014	TotalCYear	count
AL	Alabama	3	6	1	0	1419613	1400108	1433370	1463647	⋯	-0.02044325	-0.1684135	0.3964159	0.4201015	1.01194084	1.0013329	1.56224745	1.57796256	TotalC2010	1931522
AK	Alaska	4	9	1	0	1738207	1641980	1563102	1513859	⋯	-1.17513721	-1.9495712	-3.7893131	-13.7544937	0.94818536	1.8353761	-0.58569541	-10.88473403	TotalC2010	653221
AZ	Arizona	4	8	0	0	580948	617956	598039	594994	⋯	1.34147180	-0.4208753	-0.5805623	-1.3130505	2.31780119	0.6219715	0.50947307	-0.22176714	TotalC2010	1383531
AR	Arkansas	3	7	0	0	1247709	1391190	1472778	1432074	⋯	1.36951366	5.1312819	3.9104760	6.2806359	3.33662764	7.1552124	6.05235310	8.41044101	TotalC2010	1120632
CA	California	4	9	1	0	2532205	2634789	2334863	2390424	⋯	-1.16207924	-1.1739507	-1.3412263	-0.8309823	2.76137748	2.7727695	2.86612681	3.34640648	TotalC2010	7760629
CO	Colorado	4	8	0	0	2561459	2750097	2921385	2838193	⋯	5.18339661	5.5536752	6.9775832	7.5871626	6.93315930	7.6608637	9.05178284	9.59789807	TotalC2010	1513547
CT	Connecticut	1	1	1	0	203188	195792	205073	207118	⋯	-3.38443506	-5.6114915	-4.7316382	-7.2862519	1.11689425	-1.0591659	0.02196365	-2.55530211	TotalC2010	764970
DE	Delaware	3	5	1	0	3575	3976	3530	3818	⋯	2.86684813	3.5983800	3.3971710	5.1481739	5.30328177	6.2212626	6.00612012	7.71366265	TotalC2010	250212
FL	Florida	3	5	1	0	510201	500907	442188	542570	⋯	5.54039345	5.1253203	4.9187834	7.0161227	11.35960582	10.7225728	10.70250086	12.70342279	TotalC2010	4282673
GA	Georgia	3	5	1	0	561796	549483	555238	581082	⋯	1.10581577	1.8522009	-0.5768876	2.2004666	3.37500669	4.4262675	1.93363794	4.67089008	TotalC2010	3100144
HI	Hawaii	4	9	1	0	16245	18378	20485	25465	⋯	-0.72861180	-2.5672885	-0.6631562	-3.6350806	4.25497620	4.3161049	5.76353447	2.43941415	TotalC2010	278046
ID	Idaho	4	8	0	0	130652	176717	154829	138799	⋯	0.05833087	-0.1635544	2.9865046	4.7386958	0.95294886	0.8485958	4.03935504	5.78263774	TotalC2010	516120
IL	Illinois	2	3	0	1	2090939	2189423	2436702	2519993	⋯	-5.42476227	-5.6904719	-5.2380977	-7.3691757	-3.18271398	-3.3823391	-2.73308256	-4.85062123	TotalC2010	3955091
IN	Indiana	2	3	0	1	987568	1062319	1046014	1106441	⋯	-1.30254263	-1.9541615	-0.2311505	-1.1921716	0.15591763	-0.5077296	1.35562499	0.39840311	TotalC2010	2863396
IA	Iowa	2	4	0	0	673102	689146	677875	730473	⋯	0.09157511	-1.3988317	0.1501230	-0.2613128	1.68465491	0.1895506	1.88253574	1.47044899	TotalC2010	1499729
KS	Kansas	2	4	0	0	812830	781704	793080	830704	⋯	-3.15696832	-1.7898060	-4.3695984	-4.7601461	-1.40654930	0.4746409	-2.26263009	-2.70939350	TotalC2010	1117631
KY	Kentucky	3	6	0	0	2781466	2840858	2391118	2138426	⋯	0.59702684	-1.2712625	-0.5421808	-0.8589545	1.77800694	0.1999200	0.85255141	0.49789857	TotalC2010	1978527
LA	Louisiana	3	7	1	0	3170742	3972994	3789540	3208044	⋯	0.45474712	-0.1679608	-0.4968579	-1.3115694	1.94023978	1.5255891	1.15507555	0.30908636	TotalC2010	4385758
ME	Maine	1	1	1	0	151940	154764	152331	155380	⋯	0.06176347	-0.4675286	-1.1026255	0.3994297	0.95130816	0.5345335	-0.05720105	1.43523880	TotalC2010	415065
MD	Maryland	3	5	1	0	250812	272882	245197	248554	⋯	0.06293021	-1.4320466	-1.5138764	-2.5673210	4.60404951	3.2782158	3.40085453	2.29271254	TotalC2010	1464503
MA	Massachusetts	1	1	1	0	108868	103058	110384	102521	⋯	-0.52366579	-1.6315826	-0.3254842	-2.4310476	4.78903744	3.6469429	5.26431452	3.11142579	TotalC2010	1416119
MI	Michigan	2	3	0	1	626639	680857	626107	654767	⋯	-4.37165740	-3.3942432	-2.9868108	-2.8956885	-2.54604112	-1.5226322	-0.95779330	-0.86681842	TotalC2010	2753536
MN	Minnesota	2	4	0	1	429062	425422	417674	405967	⋯	-0.62804606	-1.6574311	-0.4143418	-1.2309691	1.73345966	0.7482767	2.16927752	1.34310939	TotalC2010	1857095
MS	Mississippi	3	6	1	0	448713	441341	390246	410271	⋯	-1.96091120	-1.8894810	-1.6054616	-3.1344983	-1.33764198	-0.9667034	-0.82129781	-2.38277997	TotalC2010	1177620
MO	Missouri	2	4	0	0	198297	197714	203118	191759	⋯	-2.24542626	-2.2406441	-1.3487765	-1.3336080	-0.98795424	-0.8004437	0.10538352	0.09365317	TotalC2010	1910500
MT	Montana	4	8	0	0	1148154	1104165	1008736	1104932	⋯	3.43419996	3.5509860	5.2979490	4.4641915	4.04076780	4.4167635	6.09001761	5.21574555	TotalC2010	400855
NE	Nebraska	2	4	0	0	399822	390808	357156	370523	⋯	-0.64486314	-0.4916545	-0.4784591	-1.3603621	1.16935909	1.5144472	1.57768007	0.66871583	TotalC2010	860741
NV	Nevada	4	8	0	0	51846	53807	60252	70208	⋯	-2.82732878	5.2844160	4.7552264	8.3909457	-0.08078082	8.1453008	7.78763883	11.39453695	TotalC2010	645604
NH	New Hampshire	1	1	1	0	156829	131919	129929	166328	⋯	-1.63666494	-0.3985745	-1.9864496	0.8432006	-0.30288929	1.0456898	-0.45009045	2.37258670	TotalC2010	294473
NJ	New Jersey	1	2	1	0	376845	390931	393682	403225	⋯	-5.11033711	-5.5890376	-5.1205616	-6.2151265	0.45426226	-0.1989998	0.67867877	-0.43059603	TotalC2010	2395713
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋱	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
MN	Minnesota	2	4	0	1	429062	425422	417674	405967	⋯	-0.62804606	-1.65743112	-0.4143418	-1.2309691	1.73345966	0.7482767	2.16927752	1.34310939	TotalC2014	1912065
MS	Mississippi	3	6	1	0	448713	441341	390246	410271	⋯	-1.96091120	-1.88948096	-1.6054616	-3.1344983	-1.33764198	-0.9667034	-0.82129781	-2.38277997	TotalC2014	1155549
MO	Missouri	2	4	0	0	198297	197714	203118	191759	⋯	-2.24542626	-2.24064408	-1.3487765	-1.3336080	-0.98795424	-0.8004437	0.10538352	0.09365317	TotalC2014	1903839
MT	Montana	4	8	0	0	1148154	1104165	1008736	1104932	⋯	3.43419996	3.55098601	5.2979490	4.4641915	4.04076780	4.4167635	6.09001761	5.21574555	TotalC2014	403446
NE	Nebraska	2	4	0	0	399822	390808	357156	370523	⋯	-0.64486314	-0.49165445	-0.4784591	-1.3603621	1.16935909	1.5144472	1.57768007	0.66871583	TotalC2014	864347
NV	Nevada	4	8	0	0	51846	53807	60252	70208	⋯	-2.82732878	5.28441598	4.7552264	8.3909457	-0.08078082	8.1453008	7.78763883	11.39453695	TotalC2014	660256
NH	New Hampshire	1	1	1	0	156829	131919	129929	166328	⋯	-1.63666494	-0.39857453	-1.9864496	0.8432006	-0.30288929	1.0456898	-0.45009045	2.37258670	TotalC2014	310142
NJ	New Jersey	1	2	1	0	376845	390931	393682	403225	⋯	-5.11033711	-5.58903761	-5.1205616	-6.2151265	0.45426226	-0.1989998	0.67867877	-0.43059603	TotalC2014	2340188
NM	New Mexico	4	8	0	0	2255073	2260681	2315892	2367423	⋯	0.03089282	-3.42012889	-5.0629404	-6.7844755	1.09814337	-2.0783084	-3.77898635	-5.50369841	TotalC2014	679136
NY	New York	1	2	1	1	836703	879395	817908	872500	⋯	-4.32545483	-5.94680886	-5.4820494	-7.8049472	1.49434986	-0.3189971	0.56988277	-1.78094837	TotalC2014	3742892
NC	North Carolina	3	5	1	0	576162	572796	560887	606427	⋯	3.17214261	3.29953845	3.8869020	3.6636405	5.04794391	5.7729809	6.14754287	5.80461228	TotalC2014	2554776
ND	North Dakota	2	4	0	0	1253749	1518406	2135154	2634178	⋯	9.14542431	15.58963681	23.1894509	12.2651006	10.67971377	17.8045736	25.10448511	14.02819169	TotalC2014	640095
OH	Ohio	2	3	0	1	1032440	1067943	1067944	1149882	⋯	-3.18858790	-3.20406546	-1.9123029	-1.5749692	-1.81998332	-1.7622360	-0.39372214	-0.06103728	TotalC2014	3809648
OK	Oklahoma	3	7	0	0	2576077	2704301	2950525	3137443	⋯	1.67426274	2.35073293	3.5996562	1.1322997	3.00715292	3.9555020	5.10835669	2.60452203	TotalC2014	1679856
OR	Oregon	4	9	1	0	393763	513597	496254	458848	⋯	2.95766482	3.37611288	2.4896662	5.7404707	4.52890803	4.9325756	4.21784158	7.47805827	TotalC2014	987145
PA	Pennsylvania	1	2	0	1	3064745	3870671	4730336	5873742	⋯	-0.54676726	-1.35870300	-2.4839403	-2.4599014	1.57878163	0.7970514	-0.21118267	-0.18679231	TotalC2014	3902434
RI	Rhode Island	1	1	1	0	3399	3419	2822	2627	⋯	-5.83915808	-5.02599711	-4.8746647	-3.2126693	-2.05881151	-1.0319971	-0.76828438	0.85652211	TotalC2014	204459
SC	South Carolina	3	5	1	0	649978	661898	646738	690644	⋯	3.19120230	5.61300811	5.9543633	8.0408887	4.29289110	7.2294966	7.33368090	9.35153650	TotalC2014	1632085
SD	South Dakota	2	4	0	0	225387	248370	248616	236679	⋯	2.44945783	5.24394471	5.1975757	0.6616883	3.99058013	7.0598520	6.94875162	2.36771385	TotalC2014	391857
TN	Tennessee	3	6	0	0	505731	506701	468757	544916	⋯	2.45252515	4.28628133	2.0897983	3.7574480	3.69627104	5.7299274	3.50729121	5.16164300	TotalC2014	2194512
TX	Texas	3	7	1	0	11412134	12571450	14180982	15688922	⋯	4.57794456	5.63098177	4.4282840	5.7785074	7.67527476	8.7241630	7.62949715	8.94472107	TotalC2014	12899498
UT	Utah	4	8	0	0	1073063	1128821	1118599	1122868	⋯	-0.32882084	-0.03068503	1.9208122	-0.4225336	1.41976181	1.7599803	3.80966870	1.44722034	TotalC2014	797995
VT	Vermont	1	1	0	0	80143	80677	77979	84188	⋯	-0.75225076	-2.60420825	-1.0981705	-2.4716435	0.25554166	-1.5168595	0.05267388	-1.32437968	TotalC2014	139897
VA	Virginia	3	5	1	0	1096973	1085234	1045095	1023009	⋯	1.33171670	0.60391533	0.3041831	-2.4583298	4.97522119	5.0037998	4.49933481	1.64009160	TotalC2014	2430205
WA	Washington	4	9	1	0	907957	1101186	1108998	1003330	⋯	3.44647012	1.96217689	2.2507462	3.9989250	6.41638546	5.3702911	5.70595658	7.39508290	TotalC2014	2011941
WV	West Virginia	3	5	0	0	3699729	3820203	3720223	3809750	⋯	0.58611685	0.56206796	-1.2528613	-1.4843729	1.14365578	1.1715587	-0.62157876	-0.85584979	TotalC2014	752942
WI	Wisconsin	2	3	0	1	329441	312552	333635	310413	⋯	-1.09492384	-1.65388673	-1.3697434	-1.7270528	-0.05843103	-0.6192236	-0.24032423	-0.58797357	TotalC2014	1868867
WY	Wyoming	4	8	0	0	10536570	10353148	9611304	9233869	⋯	-0.36219433	9.65117376	4.5478211	-4.5777881	0.32332470	10.6105245	5.40635592	-3.74686476	TotalC2014	535612
DC	District of Columbia	3	5	0	0	95	271	277	323	⋯	11.33288241	10.00583847	9.7776663	1.7935725	16.80595478	15.5957903	15.64924997	7.54279039	TotalC2014	178929
US	United States	NA	NA	NA	NA	74593106	77778786	79032062	81604540	⋯	NA	NA	NA	NA	2.94196766	3.0519321	3.16821052	3.13508085	TotalC2014	98385210

Division	TotalPrice2010	TotalPrice2014
6	17.82	18.64
9	20.13	24.43
8	22.25	25.94
7	16.90	18.87
9	20.97	25.31
8	17.40	21.25
1	25.62	27.84
5	23.50	23.77
5	22.05	24.91
5	18.25	20.95
9	30.43	37.38
8	17.14	20.43
3	17.39	19.02
3	14.89	17.94
4	15.68	18.24
4	17.70	21.46
6	17.16	20.75
7	15.06	15.49
1	18.77	22.62
5	22.83	25.25
1	23.33	26.36
3	18.47	20.01
4	17.04	19.65
6	17.90	20.78
4	18.84	21.94
8	18.26	21.72
4	16.68	19.26
8	21.22	23.52
1	24.21	27.86
2	21.28	22.78
⋮	⋮	⋮
4	17.04	19.65
6	17.90	20.78
4	18.84	21.94
8	18.26	21.72
4	16.68	19.26
8	21.22	23.52
1	24.21	27.86
2	21.28	22.78
8	20.02	23.37
2	23.09	24.57
5	20.27	23.02
4	13.79	18.51
3	18.18	20.01
7	17.16	19.97
9	19.20	22.29
2	19.74	21.44
1	23.63	26.34
5	18.52	21.74
4	17.21	20.32
6	18.67	21.47
7	17.36	19.05
8	16.89	20.71
1	23.75	27.60
5	19.12	21.66
9	18.33	21.11
5	17.26	19.97
3	18.48	20.74
8	15.16	18.70
5	25.76	24.96
NA	18.92	21.33

StateCodes	State	Region	Division	Coast	Great Lakes	TotalC2010	TotalC2011	TotalC2012	TotalC2013	⋯	RINTERNATIONALMIG2014	RDOMESTICMIG2011	RDOMESTICMIG2012	RDOMESTICMIG2013	RDOMESTICMIG2014	RNETMIG2011	RNETMIG2012	RNETMIG2013	RNETMIG2014	mean_energy
AL	Alabama	3	6	1	0	1931522	1905207	1879716	1919365	⋯	1.1578610	-0.02044325	-0.16841354	0.3964159	0.4201015	1.01194084	1.0013329	1.56224745	1.57796256	1918806.2
AK	Alaska	4	9	1	0	653221	653637	649341	621107	⋯	2.8697597	-1.17513721	-1.94957118	-3.7893131	-13.7544937	0.94818536	1.8353761	-0.58569541	-10.88473403	636085.0
AZ	Arizona	4	8	0	0	1383531	1424944	1395839	1414383	⋯	1.0912833	1.34147180	-0.42087528	-0.5805623	-1.3130505	2.31780119	0.6219715	0.50947307	-0.22176714	1408257.4
AR	Arkansas	3	7	0	0	1120632	1122544	1067642	1096438	⋯	2.1298051	1.36951366	5.13128187	3.9104760	6.2806359	3.33662764	7.1552124	6.05235310	8.41044101	1104333.0
CA	California	4	9	1	0	7760629	7777115	7564063	7665241	⋯	4.1773888	-1.16207924	-1.17395070	-1.3412263	-0.8309823	2.76137748	2.7727695	2.86612681	3.34640648	7677426.0
CO	Colorado	4	8	0	0	1513547	1470445	1440781	1470844	⋯	2.0107355	5.18339661	5.55367522	6.9775832	7.5871626	6.93315930	7.6608637	9.05178284	9.59789807	1474558.8
CT	Connecticut	1	1	1	0	764970	739130	725019	754901	⋯	4.7309498	-3.38443506	-5.61149155	-4.7316382	-7.2862519	1.11689425	-1.0591659	0.02196365	-2.55530211	746807.8
DE	Delaware	3	5	1	0	250212	272568	273728	273716	⋯	2.5654887	2.86684813	3.59838002	3.3971710	5.1481739	5.30328177	6.2212626	6.00612012	7.71366265	268847.4
FL	Florida	3	5	1	0	4282673	4141711	4029903	4076406	⋯	5.6873001	5.54039345	5.12532032	4.9187834	7.0161227	11.35960582	10.7225728	10.70250086	12.70342279	4130474.6
GA	Georgia	3	5	1	0	3100144	2982837	2767491	2782782	⋯	2.4704235	1.10581577	1.85220088	-0.5768876	2.2004666	3.37500669	4.4262675	1.93363794	4.67089008	2896848.8
HI	Hawaii	4	9	1	0	278046	287113	280171	281329	⋯	6.0744948	-0.72861180	-2.56728847	-0.6631562	-3.6350806	4.25497620	4.3161049	5.76353447	2.43941415	281573.0
ID	Idaho	4	8	0	0	516120	516978	510869	526613	⋯	1.0439419	0.05833087	-0.16355442	2.9865046	4.7386958	0.95294886	0.8485958	4.03935504	5.78263774	518103.0
IL	Illinois	2	3	0	1	3955091	3937616	3820547	3991089	⋯	2.5185545	-5.42476227	-5.69047190	-5.2380977	-7.3691757	-3.18271398	-3.3823391	-2.73308256	-4.85062123	3949331.2
IN	Indiana	2	3	0	1	2863396	2847188	2770158	2894764	⋯	1.5905747	-1.30254263	-1.95416154	-0.2311505	-1.1921716	0.15591763	-0.5077296	1.35562499	0.39840311	2861427.2
IA	Iowa	2	4	0	0	1499729	1498973	1440053	1518870	⋯	1.7317618	0.09157511	-1.39883166	0.1501230	-0.2613128	1.68465491	0.1895506	1.88253574	1.47044899	1499905.0
KS	Kansas	2	4	0	0	1117631	1104843	1075435	1105160	⋯	2.0507526	-3.15696832	-1.78980603	-4.3695984	-4.7601461	-1.40654930	0.4746409	-2.26263009	-2.70939350	1107084.6
KY	Kentucky	3	6	0	0	1978527	1903208	1868483	1838898	⋯	1.3568530	0.59702684	-1.27126249	-0.5421808	-0.8589545	1.77800694	0.1999200	0.85255141	0.49789857	1871971.4
LA	Louisiana	3	7	1	0	4385758	4388867	4255161	4196622	⋯	1.6206558	0.45474712	-0.16796076	-0.4968579	-1.3115694	1.94023978	1.5255891	1.15507555	0.30908636	4301170.2
ME	Maine	1	1	1	0	415065	413893	399670	409785	⋯	1.0358091	0.06176347	-0.46752860	-1.1026255	0.3994297	0.95130816	0.5345335	-0.05720105	1.43523880	409788.6
MD	Maryland	3	5	1	0	1464503	1410012	1368600	1395961	⋯	4.8600336	0.06293021	-1.43204655	-1.5138764	-2.5673210	4.60404951	3.2782158	3.40085453	2.29271254	1407938.0
MA	Massachusetts	1	1	1	0	1416119	1397164	1363282	1428433	⋯	5.5424734	-0.52366579	-1.63158264	-0.3254842	-2.4310476	4.78903744	3.6469429	5.26431452	3.11142579	1408527.6
MI	Michigan	2	3	0	1	2753536	2785212	2687926	2832425	⋯	2.0288701	-4.37165740	-3.39424318	-2.9868108	-2.8956885	-2.54604112	-1.5226322	-0.95779330	-0.86681842	2788129.8
MN	Minnesota	2	4	0	1	1857095	1850749	1816866	1858816	⋯	2.5740785	-0.62804606	-1.65743112	-0.4143418	-1.2309691	1.73345966	0.7482767	2.16927752	1.34310939	1859118.2
MS	Mississippi	3	6	1	0	1177620	1155456	1143099	1138181	⋯	0.7517183	-1.96091120	-1.88948096	-1.6054616	-3.1344983	-1.33764198	-0.9667034	-0.82129781	-2.38277997	1153981.0
MO	Missouri	2	4	0	0	1910500	1856590	1781978	1847506	⋯	1.4272611	-2.24542626	-2.24064408	-1.3487765	-1.3336080	-0.98795424	-0.8004437	0.10538352	0.09365317	1860082.6
MT	Montana	4	8	0	0	400855	402355	395724	405934	⋯	0.7515540	3.43419996	3.55098601	5.2979490	4.4641915	4.04076780	4.4167635	6.09001761	5.21574555	401662.8
NE	Nebraska	2	4	0	0	860741	862675	852984	871815	⋯	2.0290779	-0.64486314	-0.49165445	-0.4784591	-1.3603621	1.16935909	1.5144472	1.57768007	0.66871583	862512.4
NV	Nevada	4	8	0	0	645604	632655	639190	659568	⋯	3.0035913	-2.82732878	5.28441598	4.7552264	8.3909457	-0.08078082	8.1453008	7.78763883	11.39453695	647454.6
NH	New Hampshire	1	1	1	0	294473	292979	284490	304538	⋯	1.5293861	-1.63666494	-0.39857453	-1.9864496	0.8432006	-0.30288929	1.0456898	-0.45009045	2.37258670	297324.4
NJ	New Jersey	1	2	1	0	2395713	2411816	2241207	2311685	⋯	5.7845304	-5.11033711	-5.58903761	-5.1205616	-6.2151265	0.45426226	-0.1989998	0.67867877	-0.43059603	2340121.8
NM	New Mexico	4	8	0	0	649962	668675	666540	670257	⋯	1.2807771	0.03089282	-3.42012889	-5.0629404	-6.7844755	1.09814337	-2.0783084	-3.77898635	-5.50369841	666914.0
NY	New York	1	2	1	1	3723729	3611091	3503309	3626150	⋯	6.0239988	-4.32545483	-5.94680886	-5.4820494	-7.8049472	1.49434986	-0.3189971	0.56988277	-1.78094837	3641434.2
NC	North Carolina	3	5	1	0	2685333	2558792	2481060	2530649	⋯	2.1409718	3.17214261	3.29953845	3.8869020	3.6636405	5.04794391	5.7729809	6.14754287	5.80461228	2562122.0
ND	North Dakota	2	4	0	0	476072	528508	552326	594439	⋯	1.7630911	9.14542431	15.58963681	23.1894509	12.2651006	10.67971377	17.8045736	25.10448511	14.02819169	558288.0
OH	Ohio	2	3	0	1	3824933	3792585	3655849	3739974	⋯	1.5139319	-3.18858790	-3.20406546	-1.9123029	-1.5749692	-1.81998332	-1.7622360	-0.39372214	-0.06103728	3764597.8
OK	Oklahoma	3	7	0	0	1579910	1585212	1561913	1620837	⋯	1.4722224	1.67426274	2.35073293	3.5996562	1.1322997	3.00715292	3.9555020	5.10835669	2.60452203	1605545.6
OR	Oregon	4	9	1	0	975067	1002476	975044	991867	⋯	1.7375876	2.95766482	3.37611288	2.4896662	5.7404707	4.52890803	4.9325756	4.21784158	7.47805827	986319.8
PA	Pennsylvania	1	2	0	1	3752280	3725014	3623997	3826959	⋯	2.2731090	-0.54676726	-1.35870300	-2.4839403	-2.4599014	1.57878163	0.7970514	-0.21118267	-0.18679231	3766136.8
RI	Rhode Island	1	1	1	0	195314	185731	183879	199165	⋯	4.0691914	-5.83915808	-5.02599711	-4.8746647	-3.2126693	-2.05881151	-1.0319971	-0.76828438	0.85652211	193709.6
SC	South Carolina	3	5	1	0	1643912	1601881	1558766	1590456	⋯	1.3106478	3.19120230	5.61300811	5.9543633	8.0408887	4.29289110	7.2294966	7.33368090	9.35153650	1605420.0
SD	South Dakota	2	4	0	0	378514	378470	375950	389619	⋯	1.7060255	2.44945783	5.24394471	5.1975757	0.6616883	3.99058013	7.0598520	6.94875162	2.36771385	382882.0
TN	Tennessee	3	6	0	0	2247273	2195401	2080953	2132508	⋯	1.4041950	2.45252515	4.28628133	2.0897983	3.7574480	3.69627104	5.7299274	3.50729121	5.16164300	2170129.4
TX	Texas	3	7	1	0	11687521	11906249	11931169	12660976	⋯	3.1662137	4.57794456	5.63098177	4.4282840	5.7785074	7.67527476	8.7241630	7.62949715	8.94472107	12217082.6
UT	Utah	4	8	0	0	756012	794058	790154	831668	⋯	1.8697539	-0.32882084	-0.03068503	1.9208122	-0.4225336	1.41976181	1.7599803	3.80966870	1.44722034	793977.4
VT	Vermont	1	1	0	0	153697	150475	130412	137527	⋯	1.1472638	-0.75225076	-2.60420825	-1.0981705	-2.4716435	0.25554166	-1.5168595	0.05267388	-1.32437968	142401.6
VA	Virginia	3	5	1	0	2483360	2380922	2343908	2414477	⋯	4.0984214	1.33171670	0.60391533	0.3041831	-2.4583298	4.97522119	5.0037998	4.49933481	1.64009160	2410574.4
WA	Washington	4	9	1	0	2031428	2059630	2037127	2036309	⋯	3.3961579	3.44647012	1.96217689	2.2507462	3.9989250	6.41638546	5.3702911	5.70595658	7.39508290	2035287.0
WV	West Virginia	3	5	0	0	738821	726341	720985	743612	⋯	0.6285231	0.58611685	0.56206796	-1.2528613	-1.4843729	1.14365578	1.1715587	-0.62157876	-0.85584979	736540.2
WI	Wisconsin	2	3	0	1	1791199	1778018	1721543	1813458	⋯	1.1390792	-1.09492384	-1.65388673	-1.3697434	-1.7270528	-0.05843103	-0.6192236	-0.24032423	-0.58797357	1794617.0
WY	Wyoming	4	8	0	0	540122	556548	550182	539146	⋯	0.8309234	-0.36219433	9.65117376	4.5478211	-4.5777881	0.32332470	10.6105245	5.40635592	-3.74686476	544322.0
DC	District of Columbia	3	5	0	0	190529	183806	172963	175560	⋯	5.7492179	11.33288241	10.00583847	9.7776663	1.7935725	16.80595478	15.5957903	15.64924997	7.54279039	180357.4
US	United States	NA	NA	NA	NA	97446021	96827465	94411432	97141368	⋯	3.1350809	NA	NA	NA	NA	2.94196766	3.0519321	3.16821052	3.13508085	96842299.2

StateCodes	State	Region	Division	Coast	Great Lakes	TotalP2010	TotalP2011	TotalP2012	TotalP2013	⋯	RDOMESTICMIG2011	RDOMESTICMIG2012	RDOMESTICMIG2013	RDOMESTICMIG2014	RNETMIG2011	RNETMIG2012	RNETMIG2013	RNETMIG2014	TotalCYear	count
NY	New York	1	2	1	1	836703	879395	817908	872500	⋯	-4.325455	-5.946809	-5.482049	-7.804947	1.49435	-0.3189971	0.5698828	-1.780948	TotalC2010	3723729
NY	New York	1	2	1	1	836703	879395	817908	872500	⋯	-4.325455	-5.946809	-5.482049	-7.804947	1.49435	-0.3189971	0.5698828	-1.780948	TotalC2011	3611091
NY	New York	1	2	1	1	836703	879395	817908	872500	⋯	-4.325455	-5.946809	-5.482049	-7.804947	1.49435	-0.3189971	0.5698828	-1.780948	TotalC2012	3503309
NY	New York	1	2	1	1	836703	879395	817908	872500	⋯	-4.325455	-5.946809	-5.482049	-7.804947	1.49435	-0.3189971	0.5698828	-1.780948	TotalC2013	3626150
NY	New York	1	2	1	1	836703	879395	817908	872500	⋯	-4.325455	-5.946809	-5.482049	-7.804947	1.49435	-0.3189971	0.5698828	-1.780948	TotalC2014	3742892