library(gapminder)
library(ggplot2)## Registered S3 methods overwritten by 'ggplot2':
## method from
## [.quosures rlang
## c.quosures rlang
## print.quosures rlang
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
Hints:
- this plot uses the following subset of the Gapminder data
- this plot uses facets to show the different years
- a linear trendline
- log10 x-axis
- altered transparency of the points
- renamed legend titles
- axis text is resized to 12
- axis title text is resized to 14
- facet titles are resized to 14
finalExampleData <- dplyr::filter(gapminder, year %in% c(1952, 2007))
pop_by_year <- select(gapminder, country, year, pop)
pop_by_year## # A tibble: 1,704 x 3
## country year pop
## <fct> <int> <int>
## 1 Afghanistan 1952 8425333
## 2 Afghanistan 1957 9240934
## 3 Afghanistan 1962 10267083
## 4 Afghanistan 1967 11537966
## 5 Afghanistan 1972 13079460
## 6 Afghanistan 1977 14880372
## 7 Afghanistan 1982 12881816
## 8 Afghanistan 1987 13867957
## 9 Afghanistan 1992 16317921
## 10 Afghanistan 1997 22227415
## # … with 1,694 more rows
Which country in which year has the highest population?
| Operator | Meaning |
|---|---|
| == | Equals |
| != | Not Equals |
| %in% | within vector |
| > | greater than |
| >= | greater than or equal |
| < | less than |
| =< | less than or equal |
filter(gapminder, country == "United States")## # A tibble: 12 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 United States Americas 1952 68.4 157553000 13990.
## 2 United States Americas 1957 69.5 171984000 14847.
## 3 United States Americas 1962 70.2 186538000 16173.
## 4 United States Americas 1967 70.8 198712000 19530.
## 5 United States Americas 1972 71.3 209896000 21806.
## 6 United States Americas 1977 73.4 220239000 24073.
## 7 United States Americas 1982 74.6 232187835 25010.
## 8 United States Americas 1987 75.0 242803533 29884.
## 9 United States Americas 1992 76.1 256894189 32004.
## 10 United States Americas 1997 76.8 272911760 35767.
## 11 United States Americas 2002 77.3 287675526 39097.
## 12 United States Americas 2007 78.2 301139947 42952.
filter(gapminder, year > 2000)## # A tibble: 284 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 2002 42.1 25268405 727.
## 2 Afghanistan Asia 2007 43.8 31889923 975.
## 3 Albania Europe 2002 75.7 3508512 4604.
## 4 Albania Europe 2007 76.4 3600523 5937.
## 5 Algeria Africa 2002 71.0 31287142 5288.
## 6 Algeria Africa 2007 72.3 33333216 6223.
## 7 Angola Africa 2002 41.0 10866106 2773.
## 8 Angola Africa 2007 42.7 12420476 4797.
## 9 Argentina Americas 2002 74.3 38331121 8798.
## 10 Argentina Americas 2007 75.3 40301927 12779.
## # … with 274 more rows
Multiple filters simultaneously (AND logic).
It is usually a good idea to have line breaks.
filter(gapminder,
year > 2000,
country == "United States")## # A tibble: 2 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 United States Americas 2002 77.3 287675526 39097.
## 2 United States Americas 2007 78.2 301139947 42952.
gdpPercap is adjusted for population (GDP/Population). We can recompute the original GDP by multiplying gdpPercap by population.
mutate(gapminder, GDP = pop*gdpPercap)## # A tibble: 1,704 x 7
## country continent year lifeExp pop gdpPercap GDP
## <fct> <fct> <int> <dbl> <int> <dbl> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779. 6567086330.
## 2 Afghanistan Asia 1957 30.3 9240934 821. 7585448670.
## 3 Afghanistan Asia 1962 32.0 10267083 853. 8758855797.
## 4 Afghanistan Asia 1967 34.0 11537966 836. 9648014150.
## 5 Afghanistan Asia 1972 36.1 13079460 740. 9678553274.
## 6 Afghanistan Asia 1977 38.4 14880372 786. 11697659231.
## 7 Afghanistan Asia 1982 39.9 12881816 978. 12598563401.
## 8 Afghanistan Asia 1987 40.8 13867957 852. 11820990309.
## 9 Afghanistan Asia 1992 41.7 16317921 649. 10595901589.
## 10 Afghanistan Asia 1997 41.8 22227415 635. 14121995875.
## # … with 1,694 more rows
Pipes send the output of 1 command to the 1st input of the next command.
gapminder %>%
filter(year > 2000)## # A tibble: 284 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 2002 42.1 25268405 727.
## 2 Afghanistan Asia 2007 43.8 31889923 975.
## 3 Albania Europe 2002 75.7 3508512 4604.
## 4 Albania Europe 2007 76.4 3600523 5937.
## 5 Algeria Africa 2002 71.0 31287142 5288.
## 6 Algeria Africa 2007 72.3 33333216 6223.
## 7 Angola Africa 2002 41.0 10866106 2773.
## 8 Angola Africa 2007 42.7 12420476 4797.
## 9 Argentina Americas 2002 74.3 38331121 8798.
## 10 Argentina Americas 2007 75.3 40301927 12779.
## # … with 274 more rows
We can string together complicated operations with pipes
gapminder %>%
mutate(GDP = gdpPercap * pop) %>%
filter(year > 2000) %>%
filter(continent == "Asia") %>%
select(country, GDP, year)## # A tibble: 66 x 3
## country GDP year
## <fct> <dbl> <int>
## 1 Afghanistan 1.84e10 2002
## 2 Afghanistan 3.11e10 2007
## 3 Bahrain 1.54e10 2002
## 4 Bahrain 2.11e10 2007
## 5 Bangladesh 1.54e11 2002
## 6 Bangladesh 2.09e11 2007
## 7 Cambodia 1.16e10 2002
## 8 Cambodia 2.42e10 2007
## 9 China 3.99e12 2002
## 10 China 6.54e12 2007
## # … with 56 more rows
gapminder %>%
arrange(year)## # A tibble: 1,704 x 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.
## # … with 1,694 more rows
desc() will sort in descending order.
arrange(pop_by_year, desc(year))## # A tibble: 1,704 x 3
## country year pop
## <fct> <int> <int>
## 1 Afghanistan 2007 31889923
## 2 Albania 2007 3600523
## 3 Algeria 2007 33333216
## 4 Angola 2007 12420476
## 5 Argentina 2007 40301927
## 6 Australia 2007 20434176
## 7 Austria 2007 8199783
## 8 Bahrain 2007 708573
## 9 Bangladesh 2007 150448339
## 10 Belgium 2007 10392226
## # … with 1,694 more rows
Summarize creates new data frames from functions.
gapminder %>%
summarize(mean(lifeExp))## # A tibble: 1 x 1
## `mean(lifeExp)`
## <dbl>
## 1 59.5
Columns can be created similar to mutate
gapminder %>%
summarize(mean_lifeExp = mean(lifeExp))## # A tibble: 1 x 1
## mean_lifeExp
## <dbl>
## 1 59.5
Group_by is silent
gapminder %>%
group_by(country)## # A tibble: 1,704 x 6
## # Groups: country [142]
## 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.
## # … with 1,694 more rows
summarize acts on groups
gapminder %>%
group_by(country) %>%
summarize(mean(lifeExp))## # A tibble: 142 x 2
## country `mean(lifeExp)`
## <fct> <dbl>
## 1 Afghanistan 37.5
## 2 Albania 68.4
## 3 Algeria 59.0
## 4 Angola 37.9
## 5 Argentina 69.1
## 6 Australia 74.7
## 7 Austria 73.1
## 8 Bahrain 65.6
## 9 Bangladesh 49.8
## 10 Belgium 73.6
## # … with 132 more rows
Can group by multiple columns & the summary operation will be applied to these groups.
gapminder %>%
group_by(continent, year) %>%
summarize(meanExp = mean(lifeExp)) ## # A tibble: 60 x 3
## # Groups: continent [5]
## continent year meanExp
## <fct> <int> <dbl>
## 1 Africa 1952 39.1
## 2 Africa 1957 41.3
## 3 Africa 1962 43.3
## 4 Africa 1967 45.3
## 5 Africa 1972 47.5
## 6 Africa 1977 49.6
## 7 Africa 1982 51.6
## 8 Africa 1987 53.3
## 9 Africa 1992 53.6
## 10 Africa 1997 53.6
## # … with 50 more rows
gapminder %>%
group_by()## # A tibble: 1,704 x 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.
## # … with 1,694 more rows
What is the mean life expectancy in each continent for the year 1967?
Retrieve country with highest GDP (hint: add a new column and name it GDP)
List continents with higest overall GDP from greatest to least
Make a new variable expOver70 that determines whether lifeExpectancy
is greater than 70 years old.
Which country/countries was the first to have a life expectancy over 70?
Make a bar plot showing the fraction of countries with life expectancy over 70 over time.
Bonus:
- Make the bar plot edges touch x axis
- Remove x axis ticks
- Resize x axis text
