12 Use data_2002. Use ggplot. Plot gdpPercap vs lifeExp. 13 Use data_2002. Use ggplot. Plot gdpPercap...
12 Use data_2002. Use ggplot. Plot gdpPercap vs lifeExp.
13 Use data_2002. Use ggplot. Plot gdpPercap vs lifeExp by continent (color)
14 Use data_2002. Use ggplot. Plot gdpPercap vs lifeExp by continent and pop (color and size)
15 Get data for Europe in 2002. Call it data_Europe
Looking for these problems in R command code answers.
1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007
142 142 142 142 142 142 142 142 142 142 142 142
> table(gapminder$country)
Afghanistan Albania Algeria
12 12 12
Angola Argentina Australia
12 12 12
Austria Bahrain Bangladesh
12 12 12
Belgium Benin Bolivia
12 12 12
Bosnia and Herzegovina Botswana Brazil
12 12 12
Bulgaria Burkina Faso Burundi
12 12 12
Cambodia Cameroon Canada
12 12 12
Central African Republic Chad Chile
12 12 12
China Colombia Comoros
12 12 12
Congo, Dem. Rep. Congo, Rep. Costa Rica
12 12 12
Cote d'Ivoire Croatia Cuba
12 12 12
Czech Republic Denmark Djibouti
12 12 12
Dominican Republic Ecuador Egypt
12 12 12
El Salvador Equatorial Guinea Eritrea
12 12 12
Ethiopia Finland France
12 12 12
Gabon Gambia Germany
12 12 12
Ghana Greece Guatemala
12 12 12
Guinea Guinea-Bissau Haiti
12 12 12
Honduras Hong Kong, China Hungary
12 12 12
Iceland India Indonesia
12 12 12
Iran Iraq Ireland
12 12 12
Israel Italy Jamaica
12 12 12
Japan Jordan Kenya
12 12 12
Korea, Dem. Rep. Korea, Rep. Kuwait
12 12 12
Lebanon Lesotho Liberia
12 12 12
Libya Madagascar Malawi
12 12 12
Malaysia Mali Mauritania
12 12 12
Mauritius Mexico Mongolia
12 12 12
Montenegro Morocco Mozambique
12 12 12
Myanmar Namibia Nepal
12 12 12
Netherlands New Zealand Nicaragua
12 12 12
Niger Nigeria Norway
12 12 12
Oman Pakistan Panama
12 12 12
Paraguay Peru Philippines
12 12 12
Poland Portugal Puerto Rico
12 12 12
Reunion Romania Rwanda
12 12 12
Sao Tome and Principe Saudi Arabia Senegal
12 12 12
Serbia Sierra Leone Singapore
12 12 12
Slovak Republic Slovenia Somalia
12 12 12
South Africa Spain Sri Lanka
12 12 12
Sudan Swaziland Sweden
12 12 12
Switzerland Syria Taiwan
12 12 12
Tanzania Thailand Togo
12 12 12
Trinidad and Tobago Tunisia Turkey
12 12 12
Uganda United Kingdom United States
12 12 12
Uruguay Venezuela Vietnam
12 12 12
West Bank and Gaza Yemen, Rep. Zambia
12 12 12 Homework Answers
data_2002 = gapminder %>% filter(year==2002)
#12
library(ggplot2)
ggplot(data_2002, aes(x = gdpPercap, y= lifeExp)) +
geom_point()
#13
ggplot(data_2002, aes(x = gdpPercap, y = lifeExp, color =
continent)) + geom_point()
#14
ggplot(data_2002, aes(x = gdpPercap, y = lifeExp, color =
continent, size = pop)) + geom_point()
#15
data_Europe = gapminder %>% filter(year == 2002, continent ==
"Europe")
Please rate
Add Answer to:
12 Use data_2002. Use ggplot. Plot gdpPercap vs lifeExp.
13 Use data_2002. Use ggplot. Plot gdpPercap...
22 Use data_Americas. Plot year vs gdpPercap. Scale gdpPercap by log10. Color the data by country....
22 Use data_Americas. Plot year vs gdpPercap. Scale gdpPercap by log10. Color the data by country. 23 Use data_Americas. Plot year vs gdpPercap. Scale gdpPercap by log10. Color the data by country and size by pop. Looking for the answers in R command codes. 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007 142 142 142 142 142 142 142 142 142 142 142 142 > table(gapminder$country) Afghanistan Albania Algeria 12 12 12 Angola Argentina Australia 12...
Will reward thumbs up 100% if works. thank you Pickling with Python code and Pandas code Do both ...
Will reward thumbs up 100% if works. thank you Pickling with Python code and Pandas code Do both pickling assignment in one Jupyter Notebook file. Python Pickle steps: Download the CSV file. Load into a Pandas DataFrame. Make the column ‘country’ the index. Print the header. Using Python code, pickle the DataFrame and name the file: PythonPickle. Load back the PythonPickle data into the DataFrame. Print the header. (Note both printed headers should match.) Pandas Pickle steps: Download the CSV...
8 AutoSave D The Home Data Review View Help Power Pivot Formulas Insert Draw Page Layout...
8 AutoSave D The Home Data Review View Help Power Pivot Formulas Insert Draw Page Layout ES - per capita GDP 1 Country Name 2 Central African Republic 3 Myanmar 4 Congo, Dem. Rep. 5 South Sudan 6 Madagascar 7 Burundi 8 Ethiopia 9 Guinea 10 Malawi 11 Niger 12 Gambia, The 13 Bangladesh 14 Guinea-Bissau 15 Lao PDR 16 Benin 17 Pakistan 18 Chad 19 Nepal 20 Mozambique 21 uberia 22 Kenya 23 Senegal 24 Burkina Faso 25 Mauritania...
DATA: # happy2.py import csv def main(): happy_dict = make_happy_dict() print_sorted_dictionary(happy_dict) def make_happy_dict(): filename = "happiness.csv"...
DATA:
# happy2.py
import csv
def main():
happy_dict = make_happy_dict()
print_sorted_dictionary(happy_dict)
def make_happy_dict():
filename = "happiness.csv"
happy_dict={}
with open(filename, 'r') as infile:
csv_happy = csv.reader(infile)
infile.readline()
for line in csv_happy:
happy_dict[line[0]] = line[2]
return happy_dict
def lookup_happiness_by_country(happy_dict):
return
def print_sorted_dictionary(D):
if type(D) != type({}):
print("Dictionary not found")
return
print("Contents of dictionary sorted by key.")
print("Key","Value")
for key in sorted(D.keys()):
print(key, D[key])
main()
"happines.csv"
Country,Year of Estimate,Happiness Index
Afghanistan,2018,2.694303274
Albania,2018,5.004402637
Algeria,2018,5.043086052
Angola,2014,3.794837952
Argentina,2018,5.792796612
Armenia,2018,5.062448502
Australia,2018,7.17699337
Austria,2018,7.396001816
Azerbaijan,2018,5.167995453
Bahrain,2017,6.227320671
Bangladesh,2018,4.499217033...
8 AutoSave D The Home Data Review View Help Power Pivot Formulas Insert Draw Page Layout ES - per capita GDP 1 Country Name 2 Central African Republic 3 Myanmar 4 Congo, Dem. Rep. 5 South Sudan 6 Madagascar 7 Burundi 8 Ethiopia 9 Guinea 10 Malawi 11 Niger 12 Gambia, The 13 Bangladesh 14 Guinea-Bissau 15 Lao PDR 16 Benin 17 Pakistan 18 Chad 19 Nepal 20 Mozambique 21 uberia 22 Kenya 23 Senegal 24 Burkina Faso 25 Mauritania...
DATA:
# happy2.py
import csv
def main():
happy_dict = make_happy_dict()
print_sorted_dictionary(happy_dict)
def make_happy_dict():
filename = "happiness.csv"
happy_dict={}
with open(filename, 'r') as infile:
csv_happy = csv.reader(infile)
infile.readline()
for line in csv_happy:
happy_dict[line[0]] = line[2]
return happy_dict
def lookup_happiness_by_country(happy_dict):
return
def print_sorted_dictionary(D):
if type(D) != type({}):
print("Dictionary not found")
return
print("Contents of dictionary sorted by key.")
print("Key","Value")
for key in sorted(D.keys()):
print(key, D[key])
main()
"happines.csv"
Country,Year of Estimate,Happiness Index
Afghanistan,2018,2.694303274
Albania,2018,5.004402637
Algeria,2018,5.043086052
Angola,2014,3.794837952
Argentina,2018,5.792796612
Armenia,2018,5.062448502
Australia,2018,7.17699337
Austria,2018,7.396001816
Azerbaijan,2018,5.167995453
Bahrain,2017,6.227320671
Bangladesh,2018,4.499217033...
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