#install.packages("AER")
library("AER")Loading required package: carLoading required package: carDataLoading required package: lmtestLoading required package: zoo
Attaching package: 'zoo'The following objects are masked from 'package:base':
as.Date, as.Date.numericLoading required package: sandwichLoading required package: survivaldata()
#Listing datasets data("EuroEnergy")
str(EuroEnergy)'data.frame': 20 obs. of 2 variables:
$ gdp : int 45451 62049 2003 34540 28388 358675 38039 1331 11788 265863 ...
$ energy: int 30633 58894 1211 27049 26405 233907 20119 1124 11053 192453 ...#Store data set in global environment to be able to open up data in another page to view variables and observations.The variables in this data are energy consumption and GDP for the 20 European countries included in the data set for 1980. Each country has an entry of GDP and Energy, measured in million 1975 dollars and energy refers to aggregate energy consumption in million kilograms coal equivalency.
The datatype here is an example of a cross-sectional data set. This is due to the data taken in the time stamp of year 1980. No data is included throughout time. It is looking at individual countries for their GDP and Energy Consumption. A way to identify this is to notice it is taken at a specific time (1980) and the individual observations (countries) are unable to be ordered among themselves.
head(EuroEnergy) gdp energy
Austria 45451 30633
Belgium 62049 58894
Cyprus 2003 1211
Denmark 34540 27049
Finland 28388 26405
France 358675 233907tail(EuroEnergy) gdp energy
Spain 159602 88148
Sweden 59350 45132
Switzerland 42238 23234
Turkey 91946 32619
UK 279191 268056
WGermany 428888 352677#Displaying first 6 rows and last six
#Countries are row names currently and not a column#Creating "Country" as a labeled column for plots
?names
ncol(EuroEnergy)[1] 2EuroEnergyNew <- tibble::rownames_to_column(EuroEnergy, var = "Country")
ncol(EuroEnergyNew)[1] 3print(EuroEnergyNew) Country gdp energy
1 Austria 45451 30633
2 Belgium 62049 58894
3 Cyprus 2003 1211
4 Denmark 34540 27049
5 Finland 28388 26405
6 France 358675 233907
7 Greece 38039 20119
8 Iceland 1331 1124
9 Ireland 11788 11053
10 Italy 265863 192453
11 Malta 1251 456
12 Netherlands 82804 84416
13 Norway 27914 26086
14 Portugal 30642 12080
15 Spain 159602 88148
16 Sweden 59350 45132
17 Switzerland 42238 23234
18 Turkey 91946 32619
19 UK 279191 268056
20 WGermany 428888 352677#Creating Scatter Plot of Country vs Energy Consumption
library(ggplot2)
ggplot(EuroEnergyNew, aes(x = Country, y = energy)) +
geom_point() +
theme(axis.text.x = element_text(angle = 45)) +
labs(x = "Country", y = "Energy Consumption", title = "Energy Consumption by Country")
data()
data("TravelMode")
?TravelMode
str(TravelMode)'data.frame': 840 obs. of 9 variables:
$ individual: Factor w/ 210 levels "1","2","3","4",..: 1 1 1 1 2 2 2 2 3 3 ...
$ mode : Factor w/ 4 levels "air","train",..: 1 2 3 4 1 2 3 4 1 2 ...
$ choice : Factor w/ 2 levels "no","yes": 1 1 1 2 1 1 1 2 1 1 ...
$ wait : int 69 34 35 0 64 44 53 0 69 34 ...
$ vcost : int 59 31 25 10 58 31 25 11 115 98 ...
$ travel : int 100 372 417 180 68 354 399 255 125 892 ...
$ gcost : int 70 71 70 30 68 84 85 50 129 195 ...
$ income : int 35 35 35 35 30 30 30 30 40 40 ...
$ size : int 1 1 1 1 2 2 2 2 1 1 ...head(TravelMode) individual mode choice wait vcost travel gcost income size
1 1 air no 69 59 100 70 35 1
2 1 train no 34 31 372 71 35 1
3 1 bus no 35 25 417 70 35 1
4 1 car yes 0 10 180 30 35 1
5 2 air no 64 58 68 68 30 2
6 2 train no 44 31 354 84 30 2There are 9 variables in this data set, all related to individuals preferred choice of travel. The individual themselves (4 entries for each mode of transportation), the mode of transportation (being either air, train, car or bus), their choice (yes or no), the terminal wait time for each mode, the vehicle cost, the travel time in the vehicle, the income and the size of the party. The data set is an attempt to discover which mode of transportation is the preferred for the individuals.
Due to the data including entries for each individual asked and there being no time untt, I believe this data type is cross sectional data. Due to the four entries for each individual I initially thought it was pooled but there is no different sample at a different time it is the same sample, asked the questions for each and the “no”s are included in the data set as well as the “yes” chosen mode.
#Clean data to keep individuals "Yes" choice of mode and getting rid of the no entries
filtered_travel <- TravelMode[TravelMode$choice == "yes", ]
head(filtered_travel) individual mode choice wait vcost travel gcost income size
4 1 car yes 0 10 180 30 35 1
8 2 car yes 0 11 255 50 30 2
12 3 car yes 0 23 720 101 40 1
16 4 car yes 0 5 180 32 70 3
20 5 car yes 0 8 600 99 45 2
22 6 train yes 40 20 345 57 20 1#making sure only the yes values are kept
summary(filtered_travel$choice) no yes
0 210 summary(filtered_travel$mode) air train bus car
58 63 30 59 #Can see from the summary which mode is more popular amongst the sample# Base R plot of wait itme and income
plot(filtered_travel$wait,filtered_travel$travel, type = "p", xlab = "Wait Time", ylab = "Travel Time", main = "Wait Time vs Travel Time")
#No one is choosing any transportation that has a wait time greater than the travel time.library(ggplot2)
ggplot(filtered_travel, aes(x = mode, y = income)) +
geom_boxplot()+
theme_grey()+
labs(x = "Mode", y = "Income", title = "Preferred Mode of Travel vs Income")
#Created a boxplot here to see the average income for each mode.
#Air is highest which could be expected, train is lower than bus here although train has the range reaching the highest income.