dplyr homework
Key Concepts
- dplyr verbs
- the pipe
%>%- the
tbl_df- variable creation
- multiple conditions
- properties of grouped data
- aggregation
- summary functions
- window functions
## Warning: package 'knitr' was built under R version 3.3.2Getting Started
We’re going to work with a different dataset for the homework here. It’s a cleaned-up excerpt from the Gapminder data. Download this data at bioconnector.org/data – it’s the gapminder.csv file. Download it, and save it in your project directory where you can access it easily from R.
Load the dplyr and readr packages, and read the gapminder data into R using the read_csv() function (n.b. read_csv() is not the same as read.csv()). Assign the data to an object called gm.
In your submitted homework assignment, I would prefer you use the read_csv() function to read the data directly from the web (see below). This way I can run your R code without worrying about whether I have the data/ directory or not.
library(dplyr)
library(readr)
# Preferably: read data from web
gm <- read_csv("http://bioconnector.org/data/gapminder.csv")
# Alternatively read from file:
# gm <- read_csv("data/gapminder.csv")
# Display the data
gm## # A tibble: 1,704 × 6
## country continent year lifeExp pop gdpPercap
## <chr> <chr> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 28.801 8425333 779.4453
## 2 Afghanistan Asia 1957 30.332 9240934 820.8530
## 3 Afghanistan Asia 1962 31.997 10267083 853.1007
## 4 Afghanistan Asia 1967 34.020 11537966 836.1971
## 5 Afghanistan Asia 1972 36.088 13079460 739.9811
## 6 Afghanistan Asia 1977 38.438 14880372 786.1134
## 7 Afghanistan Asia 1982 39.854 12881816 978.0114
## 8 Afghanistan Asia 1987 40.822 13867957 852.3959
## 9 Afghanistan Asia 1992 41.674 16317921 649.3414
## 10 Afghanistan Asia 1997 41.763 22227415 635.3414
## # ... with 1,694 more rowsProblem Set
Use dplyr functions to address the following questions:
- How many unique countries are represented per continent?
## # A tibble: 5 × 2
## continent n
## <chr> <int>
## 1 Africa 52
## 2 Americas 25
## 3 Asia 33
## 4 Europe 30
## 5 Oceania 2- Which European nation had the lowest GDP per capita in 1997?
## # A tibble: 1 × 6
## country continent year lifeExp pop gdpPercap
## <chr> <chr> <int> <dbl> <int> <dbl>
## 1 Albania Europe 1997 72.95 3428038 3193.055- According to the data available, what was the average life expectancy across each continent in the 1980s?
## # A tibble: 5 × 2
## continent mean.lifeExp
## <chr> <dbl>
## 1 Africa 52.46883
## 2 Americas 67.15978
## 3 Asia 63.73456
## 4 Europe 73.22428
## 5 Oceania 74.80500- What 5 countries have the highest total GDP over all years combined?
## # A tibble: 5 × 2
## country Total.GDP
## <chr> <dbl>
## 1 United States 7.676192e+13
## 2 Japan 2.543482e+13
## 3 China 2.039549e+13
## 4 Germany 1.949689e+13
## 5 United Kingdom 1.328937e+13- What countries and years had life expectancies of at least 80 years?
n.b. only output the columns of interest: country, life expectancy and year (in that order)
## # A tibble: 22 × 3
## country lifeExp year
## <chr> <dbl> <int>
## 1 Australia 80.370 2002
## 2 Australia 81.235 2007
## 3 Canada 80.653 2007
## 4 France 80.657 2007
## 5 Hong Kong, China 80.000 1997
## 6 Hong Kong, China 81.495 2002
## 7 Hong Kong, China 82.208 2007
## 8 Iceland 80.500 2002
## 9 Iceland 81.757 2007
## 10 Israel 80.745 2007
## # ... with 12 more rows- What 10 countries have the strongest correlation (in either direction) between life expectancy and per capita GDP?
## # A tibble: 10 × 2
## country r
## <chr> <dbl>
## 1 France 0.9962239
## 2 Austria 0.9929642
## 3 Belgium 0.9927496
## 4 Norway 0.9921416
## 5 Oman 0.9907526
## 6 United Kingdom 0.9898930
## 7 Italy 0.9897600
## 8 Israel 0.9884894
## 9 Denmark 0.9870896
## 10 Australia 0.9864457- Which combinations of continent (besides Asia) and year have the highest average population across all countries?
n.b. your output should include all results sorted by highest average population. With what you already know, this one may stump you. See this Q&A for how to ungroup before arrangeing.
## # A tibble: 48 × 3
## continent year mean.pop
## <chr> <int> <dbl>
## 1 Americas 2007 35954847
## 2 Americas 2002 33990910
## 3 Americas 1997 31876016
## 4 Americas 1992 29570964
## 5 Americas 1987 27310159
## 6 Americas 1982 25211637
## 7 Americas 1977 23122708
## 8 Americas 1972 21175368
## 9 Europe 2007 19536618
## 10 Europe 2002 19274129
## # ... with 38 more rows- Which three countries have had the most consistent population estimates (i.e. lowest standard deviation) across the years of available data?
## # A tibble: 3 × 2
## country sd.pop
## <chr> <dbl>
## 1 Sao Tome and Principe 45906.14
## 2 Iceland 48541.68
## 3 Montenegro 99737.94- Subset gm to only include observations from 1992 and store the results as gm1992. What kind of object is this?
## [1] "tbl_df" "tbl" "data.frame"- Bonus! Excluding records from 1952, which observations indicate that the population of a country has decreased from the previous year and the life expectancy has increased. See the vignette on window functions.
## # A tibble: 36 × 6
## country continent year lifeExp pop gdpPercap
## <chr> <chr> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1982 39.854 12881816 978.0114
## 2 Bosnia and Herzegovina Europe 1992 72.178 4256013 2546.7814
## 3 Bosnia and Herzegovina Europe 1997 73.244 3607000 4766.3559
## 4 Bulgaria Europe 2002 72.140 7661799 7696.7777
## 5 Bulgaria Europe 2007 73.005 7322858 10680.7928
## 6 Croatia Europe 1997 73.680 4444595 9875.6045
## 7 Czech Republic Europe 1997 74.010 10300707 16048.5142
## 8 Czech Republic Europe 2002 75.510 10256295 17596.2102
## 9 Czech Republic Europe 2007 76.486 10228744 22833.3085
## 10 Equatorial Guinea Africa 1977 42.024 192675 958.5668
## # ... with 26 more rows