A SQL primer for R userswith examples from PokemonNeal Fultz

UCLA Statistics

Goal of talkMake SQL look easyAnd present R equivalentsNot another 'customer db'

ParadigmsR: fundamental unit is the vectorRDBMS: fundamental unit is the table

PokemonBest selling video game of the 90s

sold in multiple versions(and major fad)

Turn based JRPGFeaturing hundreds(!) of characters to collect

Gotta catch em all!

Pokemon (2)

from http://www.giantbomb.com/pokemon-yellow-special-pikachu-edition/61-18673/

Pokemon (3)

from Pokemon for dummies

Pokemon (4)

from http://guides.ign.com/guides/818481/page_2.html

Data Model for PokemonPokemon

ID NumberNameType(s)Version

Type TableAttack TypeDefense TypeMultiplier

In R it's natural to represent this as a matrix.In SQL, it's natural to pivot it to tuples.

More concretelyid Name Type 1 Type 2 In Red In Blue

001 Bulbusaur Plant Poison T T002 Ivysaur Plant Poison T T003 Venusaur Plant Poison T T004 Charmander Fire T T005 Charmelion Fire T T006 Charzard Fire Flying T T

What's in Red only?select id, name from pokemon where red and not blue;

What's in Red only? (2)23;"Ekans"24;"Arbok"43;"Oddish"44;"Gloom"45;"Vileplume"56;"Mankey"57;"Primeape"58;"Growlithe"59;"Arcanine"

123;"Scyther"125;"Electabuzz"

What's in Red only? (R)pokemon[red & ! blue];

Consider Psyduck

select * from pokemon where name like 'Psyduck';

image from http://strategywiki.org/wiki/Pok%C3%A9mon_Gold_and_Silver/Ilex_Foresthttp://strategywiki.org/wiki/Pok%C3%A9mon_Gold_and_Silver/Ilex_Forest

Consider Psyduck (2)54;"Psyduck";"Water";"";t;t

Consider Psyduck (R)pokemon[grep('Psyduck', names)];

What types are least common?Select type1, Count(type1) as c from pokemon group by type1 order by c;

What types are least common? (2)"Ice";2"Ghost";3"Dragon";3...

What types are least common? (R)sort(table(type1));

Second Typesselect type1, type2, count(type2) as c from pokemon where type2 is not nullgroup by type1, type2 order by type2

Second Types (2)"Water";"Fighting";1"Normal";"Flying";8"Fire";"Flying";1

"Water";"Flying";1"Rock";"Flying";1

Second Types (R)table(type1, type2, exclude=type2==NULL);

Vs Gyarados?Select attackType, multiplier from pokemon, pokemonType where name like 'Gyarados' and defendType in (type1, type2)

Vs Gyarados (2)"Fighting";0.5"Ground";0"Rock";2"Bug";0.5"Fire";0.5"Water";0.5"Grass";0.5"Grass";2"Electric";2"Electric";2"Ice";2"Ice";0.5

Vs Gyarados (T)i <- grep("Gyarados", names);

multipliers <- types[, c(type1[i], type2[i])];multipliers[which(multipliers != 1)];

Vs Gyarados ContSelect attackType, round(exp(sum(ln(multiplier+.00000000000001))),3) from pokemon, pokemonType where name like 'Gyarados' and defendType in (type1, type2) group by AttackType

Vs Gyarados Cont (2)"Ground";0.000"Bug";0.500"Grass";1.000"Water";0.500"Ice";1.000"Rock";2.000"Fighting";0.500"Fire";0.500"Electric";4.00

Vs Gyarados Cont (R)i <- grep("Gyarados", names);multipliers <- types[, c(type1[i], type2[i])];apply(multipliers,2,prod);

Vs Gyarados FinalSelect o.name,

round(exp(sum(ln(multiplier+.00000000000001))),3) as mfrom pokemon p, pokemonType t, pokemon owhere p.name like 'Gyarados' and defendType in (p.type1, p.type2) and attackType in (o.type1, o.type2) group by o.nameorder by m desc;

Vs Gyarados Final (2)"Raichu";4.000"Electabuzz";4.000"Jolteon";4.000"Electrode";4.000"Zapados";4.000"Magneton";4.000"Pikachu";4.000"Magnemite";4.000"Voltorb";4.000"Aerodactyl";2.000"Bellsprout";1.000"Bulbasaur";1.000...

Vs Gyarados Final (R)i <- grep("Gyarados", names);multipliers <- types[, c(type1[i], type2[i])];totals <- apply(multipliers,2,prod);cbind(names, type1[totals] * type2[totals]);

Conclusions

See the pattern?SQL:

SELECT (cols) FROM (tables) WHERE (row condition)R:

Subsetting (Logical, index, multiple index)grep()table()apply()merge()See also: sqldf library

Questions/Comments

ResourcesPostgreSQL An open source RDBMSW3schools SQL tutorialWikipedia comparison pageBulbapedia Everything about pokemonPokemon for DummiesLog Parser A Win util for running SQL directly against files