#Name: Tomin Tom
#Reg. No:21MIC0066
#1. Install and load the necessary packages tidyr, dplyr, ggplot
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(tidyr)
library(ggplot2)
#2. Load the gapminder dataset and look its structure
library(gapminder)
str(gapminder)
## tibble [1,704 × 6] (S3: tbl_df/tbl/data.frame)
## $ country : Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ continent: Factor w/ 5 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ year : int [1:1704] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## $ lifeExp : num [1:1704] 28.8 30.3 32 34 36.1 ...
## $ pop : int [1:1704] 8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
## $ gdpPercap: num [1:1704] 779 821 853 836 740 ...
#3. Extract the gapminder dataset where the continent is Asia
gapminder %>% filter(continent == "Asia")
## # A tibble: 396 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779.
## 2 Afghanistan Asia 1957 30.3 9240934 821.
## 3 Afghanistan Asia 1962 32.0 10267083 853.
## 4 Afghanistan Asia 1967 34.0 11537966 836.
## 5 Afghanistan Asia 1972 36.1 13079460 740.
## 6 Afghanistan Asia 1977 38.4 14880372 786.
## 7 Afghanistan Asia 1982 39.9 12881816 978.
## 8 Afghanistan Asia 1987 40.8 13867957 852.
## 9 Afghanistan Asia 1992 41.7 16317921 649.
## 10 Afghanistan Asia 1997 41.8 22227415 635.
## # ℹ 386 more rows
#4. Extract the gapminder dataset where the year is 1957
gapminder %>% filter(year == 1957)
## # A tibble: 142 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1957 30.3 9240934 821.
## 2 Albania Europe 1957 59.3 1476505 1942.
## 3 Algeria Africa 1957 45.7 10270856 3014.
## 4 Angola Africa 1957 32.0 4561361 3828.
## 5 Argentina Americas 1957 64.4 19610538 6857.
## 6 Australia Oceania 1957 70.3 9712569 10950.
## 7 Austria Europe 1957 67.5 6965860 8843.
## 8 Bahrain Asia 1957 53.8 138655 11636.
## 9 Bangladesh Asia 1957 39.3 51365468 662.
## 10 Belgium Europe 1957 69.2 8989111 9715.
## # ℹ 132 more rows
#5. Extract the gapminder dataset where year 2002 and country is China
gapminder %>% filter(year == 2002,country == "China")
## # A tibble: 1 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 China Asia 2002 72.0 1280400000 3119.
#6. Load and Sort lifeExp in ascending and descending order
lifeExpasc <- gapminder %>% arrange(lifeExp)
lifeExpdesc <- gapminder %>% arrange(desc(lifeExp))
lifeExpasc
## # A tibble: 1,704 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Rwanda Africa 1992 23.6 7290203 737.
## 2 Afghanistan Asia 1952 28.8 8425333 779.
## 3 Gambia Africa 1952 30 284320 485.
## 4 Angola Africa 1952 30.0 4232095 3521.
## 5 Sierra Leone Africa 1952 30.3 2143249 880.
## 6 Afghanistan Asia 1957 30.3 9240934 821.
## 7 Cambodia Asia 1977 31.2 6978607 525.
## 8 Mozambique Africa 1952 31.3 6446316 469.
## 9 Sierra Leone Africa 1957 31.6 2295678 1004.
## 10 Burkina Faso Africa 1952 32.0 4469979 543.
## # ℹ 1,694 more rows
lifeExpdesc
## # A tibble: 1,704 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Japan Asia 2007 82.6 127467972 31656.
## 2 Hong Kong, China Asia 2007 82.2 6980412 39725.
## 3 Japan Asia 2002 82 127065841 28605.
## 4 Iceland Europe 2007 81.8 301931 36181.
## 5 Switzerland Europe 2007 81.7 7554661 37506.
## 6 Hong Kong, China Asia 2002 81.5 6762476 30209.
## 7 Australia Oceania 2007 81.2 20434176 34435.
## 8 Spain Europe 2007 80.9 40448191 28821.
## 9 Sweden Europe 2007 80.9 9031088 33860.
## 10 Israel Asia 2007 80.7 6426679 25523.
## # ℹ 1,694 more rows
#7. Load and extract 1957 and sort pop in descending order
gapminder %>% filter(year == 1957) %>% arrange(desc(pop))
## # A tibble: 142 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 China Asia 1957 50.5 637408000 576.
## 2 India Asia 1957 40.2 409000000 590.
## 3 United States Americas 1957 69.5 171984000 14847.
## 4 Japan Asia 1957 65.5 91563009 4318.
## 5 Indonesia Asia 1957 39.9 90124000 859.
## 6 Germany Europe 1957 69.1 71019069 10188.
## 7 Brazil Americas 1957 53.3 65551171 2487.
## 8 United Kingdom Europe 1957 70.4 51430000 11283.
## 9 Bangladesh Asia 1957 39.3 51365468 662.
## 10 Italy Europe 1957 67.8 49182000 6249.
## # ℹ 132 more rows
#8. Create a new attribute as lifeExpMonths
gapminder %>% mutate(lifeExpMonths = lifeExp*12)
## # A tibble: 1,704 × 7
## country continent year lifeExp pop gdpPercap lifeExpMonths
## <fct> <fct> <int> <dbl> <int> <dbl> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779. 346.
## 2 Afghanistan Asia 1957 30.3 9240934 821. 364.
## 3 Afghanistan Asia 1962 32.0 10267083 853. 384.
## 4 Afghanistan Asia 1967 34.0 11537966 836. 408.
## 5 Afghanistan Asia 1972 36.1 13079460 740. 433.
## 6 Afghanistan Asia 1977 38.4 14880372 786. 461.
## 7 Afghanistan Asia 1982 39.9 12881816 978. 478.
## 8 Afghanistan Asia 1987 40.8 13867957 852. 490.
## 9 Afghanistan Asia 1992 41.7 16317921 649. 500.
## 10 Afghanistan Asia 1997 41.8 22227415 635. 501.
## # ℹ 1,694 more rows
#9. Append the existing attibute of lifeExp into months
gapminder %>% mutate(lifeExp = lifeExp*12)
## # A tibble: 1,704 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 346. 8425333 779.
## 2 Afghanistan Asia 1957 364. 9240934 821.
## 3 Afghanistan Asia 1962 384. 10267083 853.
## 4 Afghanistan Asia 1967 408. 11537966 836.
## 5 Afghanistan Asia 1972 433. 13079460 740.
## 6 Afghanistan Asia 1977 461. 14880372 786.
## 7 Afghanistan Asia 1982 478. 12881816 978.
## 8 Afghanistan Asia 1987 490. 13867957 852.
## 9 Afghanistan Asia 1992 500. 16317921 649.
## 10 Afghanistan Asia 1997 501. 22227415 635.
## # ℹ 1,694 more rows
#10. Extract Year 2007, lifeExpMonths and arrange in Desc
gapminder %>% filter(year == 2007) %>% mutate(lifeExpMonths = lifeExp*12) %>% arrange(desc(lifeExpMonths))
## # A tibble: 142 × 7
## country continent year lifeExp pop gdpPercap lifeExpMonths
## <fct> <fct> <int> <dbl> <int> <dbl> <dbl>
## 1 Japan Asia 2007 82.6 127467972 31656. 991.
## 2 Hong Kong, China Asia 2007 82.2 6980412 39725. 986.
## 3 Iceland Europe 2007 81.8 301931 36181. 981.
## 4 Switzerland Europe 2007 81.7 7554661 37506. 980.
## 5 Australia Oceania 2007 81.2 20434176 34435. 975.
## 6 Spain Europe 2007 80.9 40448191 28821. 971.
## 7 Sweden Europe 2007 80.9 9031088 33860. 971.
## 8 Israel Asia 2007 80.7 6426679 25523. 969.
## 9 France Europe 2007 80.7 61083916 30470. 968.
## 10 Canada Americas 2007 80.7 33390141 36319. 968.
## # ℹ 132 more rows
#11. Create gapminder_1952
gapminder_1952 <- gapminder %>% filter(year==1952)
gapminder_1952
## # A tibble: 142 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779.
## 2 Albania Europe 1952 55.2 1282697 1601.
## 3 Algeria Africa 1952 43.1 9279525 2449.
## 4 Angola Africa 1952 30.0 4232095 3521.
## 5 Argentina Americas 1952 62.5 17876956 5911.
## 6 Australia Oceania 1952 69.1 8691212 10040.
## 7 Austria Europe 1952 66.8 6927772 6137.
## 8 Bahrain Asia 1952 50.9 120447 9867.
## 9 Bangladesh Asia 1952 37.5 46886859 684.
## 10 Belgium Europe 1952 68 8730405 8343.
## # ℹ 132 more rows
#12. Using plot and qplot visualize gapminder_1952
plot(gapminder_1952$pop, gapminder_1952$lifeExp,
main = "21MIC0066 GDP per Capita vs Life Expectancy",
xlab = "GDP per Capita",
ylab = "Life Expectancy",log="xy")
qplot(data = gapminder_1952, x = gdpPercap, y = lifeExp,
main = "21MIC0066 GDP per capita vs Life Expectancy",
xlab = "GDP per Capita",
ylab = "Life Expectancy")+scale_x_log10()+scale_y_log10()
## Warning: `qplot()` was deprecated in ggplot2 3.4.0.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
#13. Using ggplot for the data gapminder_1952 Visualize boxplot pop Vs gdpPercap
ggplot(data=gapminder_1952,aes(x=pop,y=gdpPercap))+geom_boxplot()+
ggtitle("21MIC0066 Population vs GDP per capita") +
xlab("Population") +
ylab("Gdp per capita")
## Warning: Continuous x aesthetic
## ℹ did you forget `aes(group = ...)`?
#14. Visualize scatter plot for the data gapminder_1952, pop Vs gdpPercap and Scale both x and y axis.
ggplot(data=gapminder_1952,aes(x=pop,y=gdpPercap))+geom_point()+
ggtitle("21MIC0066 Population vs GDP per capita") +
xlab("Population") +
ylab("Gdp per capita")+scale_x_log10()+scale_y_log10()
#15. Visualize scatter plot for the data gapminder_1952, pop Vs lifeExp, differentiate color for continent and size for gdpPercap
ggplot(data=gapminder_1952,aes(x=pop,y=lifeExp,color=continent,size=gdpPercap))+geom_point()+
ggtitle("21MIC0066 Population vs Life Expectancy") +
xlab("Population") +
ylab("Life Expectancy")+scale_x_log10()+scale_y_log10()
#16. For gapminder_1952, pop Vs lifeExp, Scatter plot, Subgraph for continent
ggplot(data=gapminder_1952,aes(x=pop,y=lifeExp,color=continent))+geom_point()+facet_wrap(~continent)+
ggtitle("21MIC0066 Population vs Life Expectancy") +
xlab("Population") +
ylab("Life Expectancy")
#17. For gapminder, pop vs lifeExp, scatter plot, subgraph for year
ggplot(data=gapminder,aes(x=pop,y=lifeExp))+geom_point()+facet_wrap(~year)+
ggtitle("21MIC0066 Population vs Life Expectancy") +
xlab("Population") +
ylab("Life Expectancy")
#18. For gapminder, Summarize mean and median for lifeExp
#19. For gapminder 1957, median - lifeExp, max gdpPercap
#20. For gapminder group by Continent and year, summarize medain - lifeExp and max - gdpPercap