Data: Women in science
Information on 10 women in science who changed the world
| name |
|---|
| Ada Lovelace |
| Marie Curie |
| Janaki Ammal |
| Chien-Shiung Wu |
| Katherine Johnson |
| Rosalind Franklin |
| Vera Rubin |
| Gladys West |
| Flossie Wong-Staal |
| Jennifer Doudna |
Source: Discover Magazine
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Inputs
professions## # A tibble: 10 × 2## name profession ## <chr> <chr> ## 1 Ada Lovelace Mathematician ## 2 Marie Curie Physicist and Chemist ## 3 Janaki Ammal Botanist ## 4 Chien-Shiung Wu Physicist ## 5 Katherine Johnson Mathematician ## 6 Rosalind Franklin Chemist ## 7 Vera Rubin Astronomer ## 8 Gladys West Mathematician ## 9 Flossie Wong-Staal Virologist and Molecular Biologist## 10 Jennifer Doudna Biochemistdates## # A tibble: 8 × 3## name birth_year death_year## <chr> <dbl> <dbl>## 1 Janaki Ammal 1897 1984## 2 Chien-Shiung Wu 1912 1997## 3 Katherine Johnson 1918 2020## 4 Rosalind Franklin 1920 1958## 5 Vera Rubin 1928 2016## 6 Gladys West 1930 NA## 7 Flossie Wong-Staal 1947 NA## 8 Jennifer Doudna 1964 NAworks## # A tibble: 9 × 2## name known_for ## <chr> <chr> ## 1 Ada Lovelace first computer algorithm ## 2 Marie Curie theory of radioactivity, discovery of elem…## 3 Janaki Ammal hybrid species, biodiversity protection ## 4 Chien-Shiung Wu confim and refine theory of radioactive bet…## 5 Katherine Johnson calculations of orbital mechanics critical …## 6 Vera Rubin existence of dark matter ## 7 Gladys West mathematical modeling of the shape of the E…## 8 Flossie Wong-Staal first scientist to clone HIV and create a m…## 9 Jennifer Doudna one of the primary developers of CRISPR, a …4 / 28
Desired output
## # A tibble: 10 × 5## name profession birth_year death_year known_for## <chr> <chr> <dbl> <dbl> <chr> ## 1 Ada Lovelace Mathematic… NA NA first co…## 2 Marie Curie Physicist … NA NA theory o…## 3 Janaki Ammal Botanist 1897 1984 hybrid s…## 4 Chien-Shiung Wu Physicist 1912 1997 confim a…## 5 Katherine Johnson Mathematic… 1918 2020 calculat…## 6 Rosalind Franklin Chemist 1920 1958 <NA> ## 7 Vera Rubin Astronomer 1928 2016 existenc…## 8 Gladys West Mathematic… 1930 NA mathemat…## 9 Flossie Wong-Staal Virologist… 1947 NA first sc…## 10 Jennifer Doudna Biochemist 1964 NA one of t…5 / 28
Inputs, reminder
names(professions)## [1] "name" "profession"names(dates)## [1] "name" "birth_year" "death_year"names(works)## [1] "name" "known_for"nrow(professions)## [1] 10nrow(dates)## [1] 8nrow(works)## [1] 96 / 28
Joining data frames
something_join(x, y)left_join(): all rows from xright_join(): all rows from yfull_join(): all rows from both x and ysemi_join(): all rows from x where there are matching values in y, keeping just columns from xinner_join(): all rows from x where there are matching values in y, return all combination of multiple matches in the case of multiple matchesanti_join(): return all rows from x where there are not matching values in y, never duplicate rows of x- ...
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Setup
For the next few slides...
x## # A tibble: 3 × 2## id value_x## <dbl> <chr> ## 1 1 x1 ## 2 2 x2 ## 3 3 x3y## # A tibble: 3 × 2## id value_y## <dbl> <chr> ## 1 1 y1 ## 2 2 y2 ## 3 4 y49 / 28
left_join()
left_join(x, y)## # A tibble: 3 × 3## id value_x value_y## <dbl> <chr> <chr> ## 1 1 x1 y1 ## 2 2 x2 y2 ## 3 3 x3 <NA>10 / 28
left_join()
professions %>% left_join(dates)## # A tibble: 10 × 4## name profession birth_year death_year## <chr> <chr> <dbl> <dbl>## 1 Ada Lovelace Mathematician NA NA## 2 Marie Curie Physicist and Chemist NA NA## 3 Janaki Ammal Botanist 1897 1984## 4 Chien-Shiung Wu Physicist 1912 1997## 5 Katherine Johnson Mathematician 1918 2020## 6 Rosalind Franklin Chemist 1920 1958## 7 Vera Rubin Astronomer 1928 2016## 8 Gladys West Mathematician 1930 NA## 9 Flossie Wong-Staal Virologist and Molec… 1947 NA## 10 Jennifer Doudna Biochemist 1964 NA11 / 28
right_join()
right_join(x, y)## # A tibble: 3 × 3## id value_x value_y## <dbl> <chr> <chr> ## 1 1 x1 y1 ## 2 2 x2 y2 ## 3 4 <NA> y412 / 28
right_join()
professions %>% right_join(dates)## # A tibble: 8 × 4## name profession birth_year death_year## <chr> <chr> <dbl> <dbl>## 1 Janaki Ammal Botanist 1897 1984## 2 Chien-Shiung Wu Physicist 1912 1997## 3 Katherine Johnson Mathematician 1918 2020## 4 Rosalind Franklin Chemist 1920 1958## 5 Vera Rubin Astronomer 1928 2016## 6 Gladys West Mathematician 1930 NA## 7 Flossie Wong-Staal Virologist and Molecu… 1947 NA## 8 Jennifer Doudna Biochemist 1964 NA13 / 28
full_join()
full_join(x, y)## # A tibble: 4 × 3## id value_x value_y## <dbl> <chr> <chr> ## 1 1 x1 y1 ## 2 2 x2 y2 ## 3 3 x3 <NA> ## 4 4 <NA> y414 / 28
full_join()
dates %>% full_join(works)## # A tibble: 10 × 4## name birth_year death_year known_for ## <chr> <dbl> <dbl> <chr> ## 1 Janaki Ammal 1897 1984 hybrid species, biod…## 2 Chien-Shiung Wu 1912 1997 confim and refine th…## 3 Katherine Johnson 1918 2020 calculations of orbi…## 4 Rosalind Franklin 1920 1958 <NA> ## 5 Vera Rubin 1928 2016 existence of dark ma…## 6 Gladys West 1930 NA mathematical modelin…## 7 Flossie Wong-Staal 1947 NA first scientist to c…## 8 Jennifer Doudna 1964 NA one of the primary d…## 9 Ada Lovelace NA NA first computer algor…## 10 Marie Curie NA NA theory of radioactiv…15 / 28
inner_join()
inner_join(x, y)## # A tibble: 2 × 3## id value_x value_y## <dbl> <chr> <chr> ## 1 1 x1 y1 ## 2 2 x2 y216 / 28
inner_join()
dates %>% inner_join(works)## # A tibble: 7 × 4## name birth_year death_year known_for ## <chr> <dbl> <dbl> <chr> ## 1 Janaki Ammal 1897 1984 hybrid species, biodi…## 2 Chien-Shiung Wu 1912 1997 confim and refine the…## 3 Katherine Johnson 1918 2020 calculations of orbit…## 4 Vera Rubin 1928 2016 existence of dark mat…## 5 Gladys West 1930 NA mathematical modeling…## 6 Flossie Wong-Staal 1947 NA first scientist to cl…## 7 Jennifer Doudna 1964 NA one of the primary de…17 / 28
semi_join()
semi_join(x, y)## # A tibble: 2 × 2## id value_x## <dbl> <chr> ## 1 1 x1 ## 2 2 x218 / 28
semi_join()
dates %>% semi_join(works)## # A tibble: 7 × 3## name birth_year death_year## <chr> <dbl> <dbl>## 1 Janaki Ammal 1897 1984## 2 Chien-Shiung Wu 1912 1997## 3 Katherine Johnson 1918 2020## 4 Vera Rubin 1928 2016## 5 Gladys West 1930 NA## 6 Flossie Wong-Staal 1947 NA## 7 Jennifer Doudna 1964 NA19 / 28
anti_join()
anti_join(x, y)## # A tibble: 1 × 2## id value_x## <dbl> <chr> ## 1 3 x320 / 28
anti_join()
dates %>% anti_join(works)## # A tibble: 1 × 3## name birth_year death_year## <chr> <dbl> <dbl>## 1 Rosalind Franklin 1920 195821 / 28
Putting it altogether
professions %>% left_join(dates) %>% left_join(works)## # A tibble: 10 × 5## name profession birth_year death_year known_for## <chr> <chr> <dbl> <dbl> <chr> ## 1 Ada Lovelace Mathematic… NA NA first co…## 2 Marie Curie Physicist … NA NA theory o…## 3 Janaki Ammal Botanist 1897 1984 hybrid s…## 4 Chien-Shiung Wu Physicist 1912 1997 confim a…## 5 Katherine Johnson Mathematic… 1918 2020 calculat…## 6 Rosalind Franklin Chemist 1920 1958 <NA> ## 7 Vera Rubin Astronomer 1928 2016 existenc…## 8 Gladys West Mathematic… 1930 NA mathemat…## 9 Flossie Wong-Staal Virologist… 1947 NA first sc…## 10 Jennifer Doudna Biochemist 1964 NA one of t…22 / 28
Student records
- Have:
- Enrolment: official university enrolment records
- Survey: Student provided info missing students who never filled it out and including students who filled it out but dropped the class
- Want: Survey info for all enrolled in class
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Student records
- Have:
- Enrolment: official university enrolment records
- Survey: Student provided info missing students who never filled it out and including students who filled it out but dropped the class
- Want: Survey info for all enrolled in class
enrolment## # A tibble: 3 × 2## id name ## <dbl> <chr> ## 1 1 Dave Friday ## 2 2 Hermine ## 3 3 Sura Selvarajahsurvey## # A tibble: 4 × 3## id name username ## <dbl> <chr> <chr> ## 1 2 Hermine bakealongwithhermine## 2 3 Sura surasbakes ## 3 4 Peter peter_bakes ## 4 5 Mark thebakingbuddha24 / 28
Student records
enrolment %>% left_join(survey, by = "id")## # A tibble: 3 × 4## id name.x name.y username ## <dbl> <chr> <chr> <chr> ## 1 1 Dave Friday <NA> <NA> ## 2 2 Hermine Hermine bakealongwithhermine## 3 3 Sura Selvarajah Sura surasbakesenrolment %>% anti_join(survey, by = "id")## # A tibble: 1 × 2## id name ## <dbl> <chr> ## 1 1 Dave Fridaysurvey %>% anti_join(enrolment, by = "id")## # A tibble: 2 × 3## id name username ## <dbl> <chr> <chr> ## 1 4 Peter peter_bakes ## 2 5 Mark thebakingbuddha25 / 28
Grocery sales
- Have:
- Purchases: One row per customer per item, listing purchases they made
- Prices: One row per item in the store, listing their prices
- Want: Total revenue
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Grocery sales
- Have:
- Purchases: One row per customer per item, listing purchases they made
- Prices: One row per item in the store, listing their prices
- Want: Total revenue
purchases## # A tibble: 5 × 2## customer_id item ## <dbl> <chr> ## 1 1 bread ## 2 1 milk ## 3 1 banana ## 4 2 milk ## 5 2 toilet paperprices## # A tibble: 5 × 2## item price## <chr> <dbl>## 1 avocado 0.5 ## 2 banana 0.15## 3 bread 1 ## 4 milk 0.8 ## 5 toilet paper 327 / 28
Grocery sales
purchases %>% left_join(prices)## # A tibble: 5 × 3## customer_id item price## <dbl> <chr> <dbl>## 1 1 bread 1 ## 2 1 milk 0.8 ## 3 1 banana 0.15## 4 2 milk 0.8 ## 5 2 toilet paper 3purchases %>% left_join(prices) %>% summarise(total_revenue = sum(price))## # A tibble: 1 × 1## total_revenue## <dbl>## 1 5.75purchases %>% left_join(prices)## # A tibble: 5 × 3## customer_id item price## <dbl> <chr> <dbl>## 1 1 bread 1 ## 2 1 milk 0.8 ## 3 1 banana 0.15## 4 2 milk 0.8 ## 5 2 toilet paper 3purchases %>% left_join(prices) %>% group_by(customer_id) %>% summarise(total_revenue = sum(price))## # A tibble: 2 × 2## customer_id total_revenue## <dbl> <dbl>## 1 1 1.95## 2 2 3.828 / 28