class: center, middle, inverse, title-slide .title[ # Introduction to Data Science ] .subtitle[ ## Week 3: Basic Functions and Data Manipulations ] .author[ ### Ugur Aytun ] .institute[ ### METU, Department of Economics | ECON 413 ] --- --- # Before starting -- - Last week we have learned the basic operations in R. -- - This week we will learn some basic functions and begin to data manipulations and see some sources in Economics. --- # Utilities -- - install.packages, update.packages, library, require -- - setwd, getwd -- - ?, ??, rm, ls.str, object.size ``` r econ <- c(2, 5, 5) # create a vector week_3 <- c("basic functions", "week 3") # create a vector with strings object.size(econ) ``` ``` ## 80 bytes ``` ``` r ls.str() ``` ``` ## econ : num [1:3] 2 5 5 ## week_3 : chr [1:2] "basic functions" "week 3" ``` ``` r rm(econ) # remove the object econ ``` --- # Basic math -- - +, -, *, /, ^, %%, %/% -- - abs, sqrt, log, exp, sin, cos, tan, asin, acos, atan -- - ceiling, floor, round, trunc, signif --- # Basic functions -- - &, |, ! -- - ==, !=, >, <, >=, <= -- - str, class, summary, describe, head, tail --- # Common functions -- - dim, ncol, nrow, length, nchar, names -- - subset, unique -- - ordered --- # Vector and Data object functions -- - vector, matrix, array, list, numeric, character -- - data.table, cbind, rbind -- - is.numeric, as.integer, as.data.frame, setDT --- # String functions -- - paste, strsplit, substr --- # Reading and writing files -- - read.csv, read.table, write.csv, write.table -- - fwrite, fread, save, load. -- - read_dta, read_sas, read_spss, read_excel --- # data.table -- - data.table is a package that provides an enhanced version of data.frame. -- - It is a fast and efficient way to manipulate data. No dependency is required. -- - It is a powerful and fast package for data manipulation. -- - It is a package that is widely used in the academia and industry. -- - It provides a set of functions that are optimized for speed and memory efficiency. --- # data.table -- - We first need to convert the objects to data.table objects. -- - Several alternatives are available to convert objects to data.table objects. fread(), setDT(), as.data.table(), data.table() -- - data.table avoids creating copies of the data, which is a common problem in data manipulation. --- # Basic data.table syntax -- - data_object[rows, columns, by] -- - data_object is the data.table object -- - "[" and "]" are the functions to select the data. We say to R that we work on the data_object. -- - rows: the rows to be selected -- - columns: the columns to be selected -- - by: the grouping variable --- # Quick example ``` r library(data.table) ``` ``` ## Warning: package 'data.table' was built under R version 4.3.3 ``` ``` r data(starwars, package = "dplyr") starwars_dt <- as.data.table(starwars) starwars_dt[ species=="Human", mean(height, na.rm=T), by = gender] ``` ``` ## gender V1 ## <char> <num> ## 1: masculine 182.3913 ## 2: feminine 163.5714 ``` --- # Subsampling -- - Show me human characters with height greater than 200 cm. ``` r library(data.table) data(starwars, package = "dplyr") starwars_dt <- as.data.table(starwars) starwars_dt[species=="Human" & height > 200,] ``` ``` ## name height mass hair_color skin_color eye_color birth_year sex ## <char> <int> <num> <char> <char> <char> <num> <char> ## 1: Darth Vader 202 136 none white yellow 41.9 male ## gender homeworld species ## <char> <char> <char> ## 1: masculine Tatooine Human ## films ## <list> ## 1: A New Hope,The Empire Strikes Back,Return of the Jedi,Revenge of the Sith ## vehicles starships ## <list> <list> ## 1: TIE Advanced x1 ``` --- # Subsampling (cont'd) -- - Create a new data set showing human characters with height greater than 200 cm. ``` r library(data.table) data(starwars, package = "dplyr") starwars_dt <- as.data.table(starwars) human_200 <- starwars_dt[species=="Human" & height > 200,] head(human_200) ``` ``` ## name height mass hair_color skin_color eye_color birth_year sex ## <char> <int> <num> <char> <char> <char> <num> <char> ## 1: Darth Vader 202 136 none white yellow 41.9 male ## gender homeworld species ## <char> <char> <char> ## 1: masculine Tatooine Human ## films ## <list> ## 1: A New Hope,The Empire Strikes Back,Return of the Jedi,Revenge of the Sith ## vehicles starships ## <list> <list> ## 1: TIE Advanced x1 ``` --- # Subsampling (cont'd) -- - Create a new data set showing first ten rows of the starwars data. ``` r library(data.table) data(starwars, package = "dplyr") starwars_dt <- as.data.table(starwars) first_ten <- starwars_dt[1:10,] ``` --- # Sorting -- - Sort the data by height in descending order. ``` r library(data.table) data(starwars, package = "dplyr") starwars_dt <- as.data.table(starwars) starwars_dt = starwars_dt[,][order(-height)] ``` --- # Column operations -- - We use ":=" to create, delete and change columns. -- - Create a new column showing the height in inches ``` r library(data.table) data(starwars, package = "dplyr") starwars_dt <- as.data.table(starwars) starwars_dt[, height_inch := height * 0.393701] ``` -- - We can change the existing columns as well. ``` r starwars_dt[, height := height * 0.393701] ``` --- # Column operations (cont'd) -- - We can convert the only sub-samples to inches. ``` r starwars_dt[species=="Human", height_inch := height * 0.393701] ``` -- - In this case height_inch values of for non-human species are NA (missing value). --- # Chaining operations -- - We can chain the operations in data.table. ``` r starwars_dt[, height_inch := height * 0.393701][, height_inch_round := round(height_inch, 2)][] ``` ``` ## Index: <species> ## name height mass hair_color skin_color ## <char> <num> <num> <char> <char> ## 1: Luke Skywalker 67.71657 77.0 blond fair ## 2: C-3PO 65.74807 75.0 <NA> gold ## 3: R2-D2 37.79530 32.0 <NA> white, blue ## 4: Darth Vader 79.52760 136.0 none white ## 5: Leia Organa 59.05515 49.0 brown light ## 6: Owen Lars 70.07878 120.0 brown, grey light ## 7: Beru Whitesun Lars 64.96067 75.0 brown light ## 8: R5-D4 38.18900 32.0 <NA> white, red ## 9: Biggs Darklighter 72.04728 84.0 black light ## 10: Obi-Wan Kenobi 71.65358 77.0 auburn, white fair ## 11: Anakin Skywalker 74.01579 84.0 blond fair ## 12: Wilhuff Tarkin 70.86618 NA auburn, grey fair ## 13: Chewbacca 89.76383 112.0 brown unknown ## 14: Han Solo 70.86618 80.0 brown fair ## 15: Greedo 68.11027 74.0 <NA> green ## 16: Jabba Desilijic Tiure 68.89768 1358.0 <NA> green-tan, brown ## 17: Wedge Antilles 66.92917 77.0 brown fair ## 18: Jek Tono Porkins 70.86618 110.0 brown fair ## 19: Yoda 25.98427 17.0 white green ## 20: Palpatine 66.92917 75.0 grey pale ## 21: Boba Fett 72.04728 78.2 black fair ## 22: IG-88 78.74020 140.0 none metal ## 23: Bossk 74.80319 113.0 none green ## 24: Lando Calrissian 69.68508 79.0 black dark ## 25: Lobot 68.89768 79.0 none light ## 26: Ackbar 70.86618 83.0 none brown mottle ## 27: Mon Mothma 59.05515 NA auburn fair ## 28: Arvel Crynyd NA NA brown fair ## 29: Wicket Systri Warrick 34.64569 20.0 brown brown ## 30: Nien Nunb 62.99216 68.0 none grey ## 31: Qui-Gon Jinn 75.98429 89.0 brown fair ## 32: Nute Gunray 75.19689 90.0 none mottled green ## 33: Finis Valorum 66.92917 NA blond fair ## 34: Padmé Amidala 72.83469 45.0 brown light ## 35: Jar Jar Binks 77.16540 66.0 none orange ## 36: Roos Tarpals 88.18902 82.0 none grey ## 37: Rugor Nass 81.10241 NA none green ## 38: Ric Olié 72.04728 NA brown fair ## 39: Watto 53.93704 NA black blue, grey ## 40: Sebulba 44.09451 40.0 none grey, red ## 41: Quarsh Panaka 72.04728 NA black dark ## 42: Shmi Skywalker 64.17326 NA black fair ## 43: Darth Maul 68.89768 80.0 none red ## 44: Bib Fortuna 70.86618 NA none pale ## 45: Ayla Secura 70.07878 55.0 none blue ## 46: Ratts Tyerel 31.10238 15.0 none grey, blue ## 47: Dud Bolt 37.00789 45.0 none blue, grey ## 48: Gasgano 48.03152 NA none white, blue ## 49: Ben Quadinaros 64.17326 65.0 none grey, green, yellow ## 50: Mace Windu 74.01579 84.0 none dark ## 51: Ki-Adi-Mundi 77.95280 82.0 white pale ## 52: Kit Fisto 77.16540 87.0 none green ## 53: Eeth Koth 67.32287 NA black brown ## 54: Adi Gallia 72.44098 50.0 none dark ## 55: Saesee Tiin 74.01579 NA none pale ## 56: Yarael Poof 103.93706 NA none white ## 57: Plo Koon 74.01579 80.0 none orange ## 58: Mas Amedda 77.16540 NA none blue ## 59: Gregar Typho 72.83469 85.0 black dark ## 60: Cordé 61.81106 NA brown light ## 61: Cliegg Lars 72.04728 NA brown fair ## 62: Poggle the Lesser 72.04728 80.0 none green ## 63: Luminara Unduli 66.92917 56.2 black yellow ## 64: Barriss Offee 65.35437 50.0 black yellow ## 65: Dormé 64.96067 NA brown light ## 66: Dooku 75.98429 80.0 white fair ## 67: Bail Prestor Organa 75.19689 NA black tan ## 68: Jango Fett 72.04728 79.0 black tan ## 69: Zam Wesell 66.14177 55.0 blonde fair, green, yellow ## 70: Dexter Jettster 77.95280 102.0 none brown ## 71: Lama Su 90.15753 88.0 none grey ## 72: Taun We 83.85831 NA none grey ## 73: Jocasta Nu 65.74807 NA white fair ## 74: R4-P17 37.79530 NA none silver, red ## 75: Wat Tambor 75.98429 48.0 none green, grey ## 76: San Hill 75.19689 NA none grey ## 77: Shaak Ti 70.07878 57.0 none red, blue, white ## 78: Grievous 85.03942 159.0 none brown, white ## 79: Tarfful 92.12603 136.0 brown brown ## 80: Raymus Antilles 74.01579 79.0 brown light ## 81: Sly Moore 70.07878 48.0 none pale ## 82: Tion Medon 81.10241 80.0 none grey ## 83: Finn NA NA black dark ## 84: Rey NA NA brown light ## 85: Poe Dameron NA NA brown light ## 86: BB8 NA NA none none ## 87: Captain Phasma NA NA none none ## name height mass hair_color skin_color ## eye_color birth_year sex gender homeworld ## <char> <num> <char> <char> <char> ## 1: blue 19.0 male masculine Tatooine ## 2: yellow 112.0 none masculine Tatooine ## 3: red 33.0 none masculine Naboo ## 4: yellow 41.9 male masculine Tatooine ## 5: brown 19.0 female feminine Alderaan ## 6: blue 52.0 male masculine Tatooine ## 7: blue 47.0 female feminine Tatooine ## 8: red NA none masculine Tatooine ## 9: brown 24.0 male masculine Tatooine ## 10: blue-gray 57.0 male masculine Stewjon ## 11: blue 41.9 male masculine Tatooine ## 12: blue 64.0 male masculine Eriadu ## 13: blue 200.0 male masculine Kashyyyk ## 14: brown 29.0 male masculine Corellia ## 15: black 44.0 male masculine Rodia ## 16: orange 600.0 hermaphroditic masculine Nal Hutta ## 17: hazel 21.0 male masculine Corellia ## 18: blue NA <NA> <NA> Bestine IV ## 19: brown 896.0 male masculine <NA> ## 20: yellow 82.0 male masculine Naboo ## 21: brown 31.5 male masculine Kamino ## 22: red 15.0 none masculine <NA> ## 23: red 53.0 male masculine Trandosha ## 24: brown 31.0 male masculine Socorro ## 25: blue 37.0 male masculine Bespin ## 26: orange 41.0 male masculine Mon Cala ## 27: blue 48.0 female feminine Chandrila ## 28: brown NA male masculine <NA> ## 29: brown 8.0 male masculine Endor ## 30: black NA male masculine Sullust ## 31: blue 92.0 male masculine <NA> ## 32: red NA male masculine Cato Neimoidia ## 33: blue 91.0 male masculine Coruscant ## 34: brown 46.0 female feminine Naboo ## 35: orange 52.0 male masculine Naboo ## 36: orange NA male masculine Naboo ## 37: orange NA male masculine Naboo ## 38: blue NA male masculine Naboo ## 39: yellow NA male masculine Toydaria ## 40: orange NA male masculine Malastare ## 41: brown 62.0 male masculine Naboo ## 42: brown 72.0 female feminine Tatooine ## 43: yellow 54.0 male masculine Dathomir ## 44: pink NA male masculine Ryloth ## 45: hazel 48.0 female feminine Ryloth ## 46: unknown NA male masculine Aleen Minor ## 47: yellow NA male masculine Vulpter ## 48: black NA male masculine Troiken ## 49: orange NA male masculine Tund ## 50: brown 72.0 male masculine Haruun Kal ## 51: yellow 92.0 male masculine Cerea ## 52: black NA male masculine Glee Anselm ## 53: brown NA male masculine Iridonia ## 54: blue NA female feminine Coruscant ## 55: orange NA male masculine Iktotch ## 56: yellow NA male masculine Quermia ## 57: black 22.0 male masculine Dorin ## 58: blue NA male masculine Champala ## 59: brown NA <NA> <NA> Naboo ## 60: brown NA <NA> <NA> Naboo ## 61: blue 82.0 male masculine Tatooine ## 62: yellow NA male masculine Geonosis ## 63: blue 58.0 female feminine Mirial ## 64: blue 40.0 female feminine Mirial ## 65: brown NA female feminine Naboo ## 66: brown 102.0 male masculine Serenno ## 67: brown 67.0 male masculine Alderaan ## 68: brown 66.0 male masculine Concord Dawn ## 69: yellow NA female feminine Zolan ## 70: yellow NA male masculine Ojom ## 71: black NA male masculine Kamino ## 72: black NA female feminine Kamino ## 73: blue NA female feminine Coruscant ## 74: red, blue NA none feminine <NA> ## 75: unknown NA male masculine Skako ## 76: gold NA male masculine Muunilinst ## 77: black NA female feminine Shili ## 78: green, yellow NA male masculine Kalee ## 79: blue NA male masculine Kashyyyk ## 80: brown NA male masculine Alderaan ## 81: white NA <NA> <NA> Umbara ## 82: black NA male masculine Utapau ## 83: dark NA male masculine <NA> ## 84: hazel NA female feminine <NA> ## 85: brown NA male masculine <NA> ## 86: black NA none masculine <NA> ## 87: unknown NA female feminine <NA> ## eye_color birth_year sex gender homeworld ## species ## <char> ## 1: Human ## 2: Droid ## 3: Droid ## 4: Human ## 5: Human ## 6: Human ## 7: Human ## 8: Droid ## 9: Human ## 10: Human ## 11: Human ## 12: Human ## 13: Wookiee ## 14: Human ## 15: Rodian ## 16: Hutt ## 17: Human ## 18: <NA> ## 19: Yoda's species ## 20: Human ## 21: Human ## 22: Droid ## 23: Trandoshan ## 24: Human ## 25: Human ## 26: Mon Calamari ## 27: Human ## 28: Human ## 29: Ewok ## 30: Sullustan ## 31: Human ## 32: Neimodian ## 33: Human ## 34: Human ## 35: Gungan ## 36: Gungan ## 37: Gungan ## 38: Human ## 39: Toydarian ## 40: Dug ## 41: Human ## 42: Human ## 43: Zabrak ## 44: Twi'lek ## 45: Twi'lek ## 46: Aleena ## 47: Vulptereen ## 48: Xexto ## 49: Toong ## 50: Human ## 51: Cerean ## 52: Nautolan ## 53: Zabrak ## 54: Tholothian ## 55: Iktotchi ## 56: Quermian ## 57: Kel Dor ## 58: Chagrian ## 59: <NA> ## 60: <NA> ## 61: Human ## 62: Geonosian ## 63: Mirialan ## 64: Mirialan ## 65: Human ## 66: Human ## 67: Human ## 68: Human ## 69: Clawdite ## 70: Besalisk ## 71: Kaminoan ## 72: Kaminoan ## 73: Human ## 74: Droid ## 75: Skakoan ## 76: Muun ## 77: Togruta ## 78: Kaleesh ## 79: Wookiee ## 80: Human ## 81: <NA> ## 82: Pau'an ## 83: Human ## 84: Human ## 85: Human ## 86: Droid ## 87: Human ## species ## films ## <list> ## 1: A New Hope,The Empire Strikes Back,Return of the Jedi,Revenge of the Sith,The Force Awakens ## 2: A New Hope,The Empire Strikes Back,Return of the Jedi,The Phantom Menace,Attack of the Clones,Revenge of the Sith ## 3: A New Hope,The Empire Strikes Back,Return of the Jedi,The Phantom Menace,Attack of the Clones,Revenge of the Sith,... ## 4: A New Hope,The Empire Strikes Back,Return of the Jedi,Revenge of the Sith ## 5: A New Hope,The Empire Strikes Back,Return of the Jedi,Revenge of the Sith,The Force Awakens ## 6: A New Hope,Attack of the Clones,Revenge of the Sith ## 7: A New Hope,Attack of the Clones,Revenge of the Sith ## 8: A New Hope ## 9: A New Hope ## 10: A New Hope,The Empire Strikes Back,Return of the Jedi,The Phantom Menace,Attack of the Clones,Revenge of the Sith ## 11: The Phantom Menace,Attack of the Clones,Revenge of the Sith ## 12: A New Hope,Revenge of the Sith ## 13: A New Hope,The Empire Strikes Back,Return of the Jedi,Revenge of the Sith,The Force Awakens ## 14: A New Hope,The Empire Strikes Back,Return of the Jedi,The Force Awakens ## 15: A New Hope ## 16: A New Hope,Return of the Jedi,The Phantom Menace ## 17: A New Hope,The Empire Strikes Back,Return of the Jedi ## 18: A New Hope ## 19: The Empire Strikes Back,Return of the Jedi,The Phantom Menace,Attack of the Clones,Revenge of the Sith ## 20: The Empire Strikes Back,Return of the Jedi,The Phantom Menace,Attack of the Clones,Revenge of the Sith ## 21: The Empire Strikes Back,Return of the Jedi,Attack of the Clones ## 22: The Empire Strikes Back ## 23: The Empire Strikes Back ## 24: The Empire Strikes Back,Return of the Jedi ## 25: The Empire Strikes Back ## 26: Return of the Jedi,The Force Awakens ## 27: Return of the Jedi ## 28: Return of the Jedi ## 29: Return of the Jedi ## 30: Return of the Jedi ## 31: The Phantom Menace ## 32: The Phantom Menace,Attack of the Clones,Revenge of the Sith ## 33: The Phantom Menace ## 34: The Phantom Menace,Attack of the Clones,Revenge of the Sith ## 35: The Phantom Menace,Attack of the Clones ## 36: The Phantom Menace ## 37: The Phantom Menace ## 38: The Phantom Menace ## 39: The Phantom Menace,Attack of the Clones ## 40: The Phantom Menace ## 41: The Phantom Menace ## 42: The Phantom Menace,Attack of the Clones ## 43: The Phantom Menace ## 44: Return of the Jedi ## 45: The Phantom Menace,Attack of the Clones,Revenge of the Sith ## 46: The Phantom Menace ## 47: The Phantom Menace ## 48: The Phantom Menace ## 49: The Phantom Menace ## 50: The Phantom Menace,Attack of the Clones,Revenge of the Sith ## 51: The Phantom Menace,Attack of the Clones,Revenge of the Sith ## 52: The Phantom Menace,Attack of the Clones,Revenge of the Sith ## 53: The Phantom Menace,Revenge of the Sith ## 54: The Phantom Menace,Revenge of the Sith ## 55: The Phantom Menace,Revenge of the Sith ## 56: The Phantom Menace ## 57: The Phantom Menace,Attack of the Clones,Revenge of the Sith ## 58: The Phantom Menace,Attack of the Clones ## 59: Attack of the Clones ## 60: Attack of the Clones ## 61: Attack of the Clones ## 62: Attack of the Clones,Revenge of the Sith ## 63: Attack of the Clones,Revenge of the Sith ## 64: Attack of the Clones ## 65: Attack of the Clones ## 66: Attack of the Clones,Revenge of the Sith ## 67: Attack of the Clones,Revenge of the Sith ## 68: Attack of the Clones ## 69: Attack of the Clones ## 70: Attack of the Clones ## 71: Attack of the Clones ## 72: Attack of the Clones ## 73: Attack of the Clones ## 74: Attack of the Clones,Revenge of the Sith ## 75: Attack of the Clones ## 76: Attack of the Clones ## 77: Attack of the Clones,Revenge of the Sith ## 78: Revenge of the Sith ## 79: Revenge of the Sith ## 80: A New Hope,Revenge of the Sith ## 81: Attack of the Clones,Revenge of the Sith ## 82: Revenge of the Sith ## 83: The Force Awakens ## 84: The Force Awakens ## 85: The Force Awakens ## 86: The Force Awakens ## 87: The Force Awakens ## films ## vehicles ## <list> ## 1: Snowspeeder,Imperial Speeder Bike ## 2: ## 3: ## 4: ## 5: Imperial Speeder Bike ## 6: ## 7: ## 8: ## 9: ## 10: Tribubble bongo ## 11: Zephyr-G swoop bike,XJ-6 airspeeder ## 12: ## 13: AT-ST ## 14: ## 15: ## 16: ## 17: Snowspeeder ## 18: ## 19: ## 20: ## 21: ## 22: ## 23: ## 24: ## 25: ## 26: ## 27: ## 28: ## 29: ## 30: ## 31: Tribubble bongo ## 32: ## 33: ## 34: ## 35: ## 36: ## 37: ## 38: ## 39: ## 40: ## 41: ## 42: ## 43: Sith speeder ## 44: ## 45: ## 46: ## 47: ## 48: ## 49: ## 50: ## 51: ## 52: ## 53: ## 54: ## 55: ## 56: ## 57: ## 58: ## 59: ## 60: ## 61: ## 62: ## 63: ## 64: ## 65: ## 66: Flitknot speeder ## 67: ## 68: ## 69: Koro-2 Exodrive airspeeder ## 70: ## 71: ## 72: ## 73: ## 74: ## 75: ## 76: ## 77: ## 78: Tsmeu-6 personal wheel bike ## 79: ## 80: ## 81: ## 82: ## 83: ## 84: ## 85: ## 86: ## 87: ## vehicles ## starships ## <list> ## 1: X-wing,Imperial shuttle ## 2: ## 3: ## 4: TIE Advanced x1 ## 5: ## 6: ## 7: ## 8: ## 9: X-wing ## 10: Jedi starfighter,Trade Federation cruiser,Naboo star skiff,Jedi Interceptor,Belbullab-22 starfighter ## 11: Naboo fighter,Trade Federation cruiser,Jedi Interceptor ## 12: ## 13: Millennium Falcon,Imperial shuttle ## 14: Millennium Falcon,Imperial shuttle ## 15: ## 16: ## 17: X-wing ## 18: X-wing ## 19: ## 20: ## 21: Slave 1 ## 22: ## 23: ## 24: Millennium Falcon ## 25: ## 26: ## 27: ## 28: A-wing ## 29: ## 30: Millennium Falcon ## 31: ## 32: ## 33: ## 34: Naboo fighter,H-type Nubian yacht,Naboo star skiff ## 35: ## 36: ## 37: ## 38: Naboo Royal Starship ## 39: ## 40: ## 41: ## 42: ## 43: Scimitar ## 44: ## 45: ## 46: ## 47: ## 48: ## 49: ## 50: ## 51: ## 52: ## 53: ## 54: ## 55: ## 56: ## 57: Jedi starfighter ## 58: ## 59: Naboo fighter ## 60: ## 61: ## 62: ## 63: ## 64: ## 65: ## 66: ## 67: ## 68: ## 69: ## 70: ## 71: ## 72: ## 73: ## 74: ## 75: ## 76: ## 77: ## 78: Belbullab-22 starfighter ## 79: ## 80: ## 81: ## 82: ## 83: ## 84: ## 85: X-wing ## 86: ## 87: ## starships ## height_inch height_inch_round ## <num> <num> ## 1: 26.66008 26.66 ## 2: 25.88508 25.89 ## 3: 14.88005 14.88 ## 4: 31.31010 31.31 ## 5: 23.25007 23.25 ## 6: 27.59008 27.59 ## 7: 25.57508 25.58 ## 8: 15.03505 15.04 ## 9: 28.36509 28.37 ## 10: 28.21009 28.21 ## 11: 29.14009 29.14 ## 12: 27.90009 27.90 ## 13: 35.34011 35.34 ## 14: 27.90009 27.90 ## 15: 26.81508 26.82 ## 16: 27.12508 27.13 ## 17: 26.35008 26.35 ## 18: 27.90009 27.90 ## 19: 10.23003 10.23 ## 20: 26.35008 26.35 ## 21: 28.36509 28.37 ## 22: 31.00010 31.00 ## 23: 29.45009 29.45 ## 24: 27.43508 27.44 ## 25: 27.12508 27.13 ## 26: 27.90009 27.90 ## 27: 23.25007 23.25 ## 28: NA NA ## 29: 13.64004 13.64 ## 30: 24.80008 24.80 ## 31: 29.91509 29.92 ## 32: 29.60509 29.61 ## 33: 26.35008 26.35 ## 34: 28.67509 28.68 ## 35: 30.38009 30.38 ## 36: 34.72011 34.72 ## 37: 31.93010 31.93 ## 38: 28.36509 28.37 ## 39: 21.23507 21.24 ## 40: 17.36005 17.36 ## 41: 28.36509 28.37 ## 42: 25.26508 25.27 ## 43: 27.12508 27.13 ## 44: 27.90009 27.90 ## 45: 27.59008 27.59 ## 46: 12.24504 12.25 ## 47: 14.57004 14.57 ## 48: 18.91006 18.91 ## 49: 25.26508 25.27 ## 50: 29.14009 29.14 ## 51: 30.69009 30.69 ## 52: 30.38009 30.38 ## 53: 26.50508 26.51 ## 54: 28.52009 28.52 ## 55: 29.14009 29.14 ## 56: 40.92013 40.92 ## 57: 29.14009 29.14 ## 58: 30.38009 30.38 ## 59: 28.67509 28.68 ## 60: 24.33507 24.34 ## 61: 28.36509 28.37 ## 62: 28.36509 28.37 ## 63: 26.35008 26.35 ## 64: 25.73008 25.73 ## 65: 25.57508 25.58 ## 66: 29.91509 29.92 ## 67: 29.60509 29.61 ## 68: 28.36509 28.37 ## 69: 26.04008 26.04 ## 70: 30.69009 30.69 ## 71: 35.49511 35.50 ## 72: 33.01510 33.02 ## 73: 25.88508 25.89 ## 74: 14.88005 14.88 ## 75: 29.91509 29.92 ## 76: 29.60509 29.61 ## 77: 27.59008 27.59 ## 78: 33.48010 33.48 ## 79: 36.27011 36.27 ## 80: 29.14009 29.14 ## 81: 27.59008 27.59 ## 82: 31.93010 31.93 ## 83: NA NA ## 84: NA NA ## 85: NA NA ## 86: NA NA ## 87: NA NA ## height_inch height_inch_round ``` --- # List .() -- - We can use .() to create a list of variables. ``` r starwars_dt[, .(height, mass)] ``` ``` ## height mass ## <num> <num> ## 1: 67.71657 77.0 ## 2: 65.74807 75.0 ## 3: 37.79530 32.0 ## 4: 79.52760 136.0 ## 5: 59.05515 49.0 ## 6: 70.07878 120.0 ## 7: 64.96067 75.0 ## 8: 38.18900 32.0 ## 9: 72.04728 84.0 ## 10: 71.65358 77.0 ## 11: 74.01579 84.0 ## 12: 70.86618 NA ## 13: 89.76383 112.0 ## 14: 70.86618 80.0 ## 15: 68.11027 74.0 ## 16: 68.89768 1358.0 ## 17: 66.92917 77.0 ## 18: 70.86618 110.0 ## 19: 25.98427 17.0 ## 20: 66.92917 75.0 ## 21: 72.04728 78.2 ## 22: 78.74020 140.0 ## 23: 74.80319 113.0 ## 24: 69.68508 79.0 ## 25: 68.89768 79.0 ## 26: 70.86618 83.0 ## 27: 59.05515 NA ## 28: NA NA ## 29: 34.64569 20.0 ## 30: 62.99216 68.0 ## 31: 75.98429 89.0 ## 32: 75.19689 90.0 ## 33: 66.92917 NA ## 34: 72.83469 45.0 ## 35: 77.16540 66.0 ## 36: 88.18902 82.0 ## 37: 81.10241 NA ## 38: 72.04728 NA ## 39: 53.93704 NA ## 40: 44.09451 40.0 ## 41: 72.04728 NA ## 42: 64.17326 NA ## 43: 68.89768 80.0 ## 44: 70.86618 NA ## 45: 70.07878 55.0 ## 46: 31.10238 15.0 ## 47: 37.00789 45.0 ## 48: 48.03152 NA ## 49: 64.17326 65.0 ## 50: 74.01579 84.0 ## 51: 77.95280 82.0 ## 52: 77.16540 87.0 ## 53: 67.32287 NA ## 54: 72.44098 50.0 ## 55: 74.01579 NA ## 56: 103.93706 NA ## 57: 74.01579 80.0 ## 58: 77.16540 NA ## 59: 72.83469 85.0 ## 60: 61.81106 NA ## 61: 72.04728 NA ## 62: 72.04728 80.0 ## 63: 66.92917 56.2 ## 64: 65.35437 50.0 ## 65: 64.96067 NA ## 66: 75.98429 80.0 ## 67: 75.19689 NA ## 68: 72.04728 79.0 ## 69: 66.14177 55.0 ## 70: 77.95280 102.0 ## 71: 90.15753 88.0 ## 72: 83.85831 NA ## 73: 65.74807 NA ## 74: 37.79530 NA ## 75: 75.98429 48.0 ## 76: 75.19689 NA ## 77: 70.07878 57.0 ## 78: 85.03942 159.0 ## 79: 92.12603 136.0 ## 80: 74.01579 79.0 ## 81: 70.07878 48.0 ## 82: 81.10241 80.0 ## 83: NA NA ## 84: NA NA ## 85: NA NA ## 86: NA NA ## 87: NA NA ## height mass ``` --- # List .() (cont'd) -- - We can create this list as new data.table object. ``` r starwars_dt_list = starwars_dt[, .(height, mass)] ``` --- # Change variable name -- - "setnames" command is used to change the variable names. setnames(object_name, old = c("old_name1", "old_name2"), new = c("new_name1", "new_name2")) ``` r setnames(starwars_dt, old = "height", new = "height_cm") ``` --- # Aggregating (or collapsing) data -- - We can aggregate the data. ``` r starwars_dt[, mean(height_cm, na.rm=T)] ``` ``` ## [1] 68.74214 ``` -- - We can create this value as value. ``` r mean_height <- starwars_dt[, mean(height_cm, na.rm=T)] ``` --- # by -- - We can use "by" to group the data. -- - We can use "by" to group the data and calculate the mean height for each species. ``` r starwars_dt[, mean(height_cm, na.rm=T), by = species] ``` ``` ## species V1 ## <char> <num> ## 1: Human 70.07878 ## 2: Droid 51.65357 ## 3: Wookiee 90.94493 ## 4: Rodian 68.11027 ## 5: Hutt 68.89768 ## 6: <NA> 68.89768 ## 7: Yoda's species 25.98427 ## 8: Trandoshan 74.80319 ## 9: Mon Calamari 70.86618 ## 10: Ewok 34.64569 ## 11: Sullustan 62.99216 ## 12: Neimodian 75.19689 ## 13: Gungan 82.15228 ## 14: Toydarian 53.93704 ## 15: Dug 44.09451 ## 16: Zabrak 68.11027 ## 17: Twi'lek 70.47248 ## 18: Aleena 31.10238 ## 19: Vulptereen 37.00789 ## 20: Xexto 48.03152 ## 21: Toong 64.17326 ## 22: Cerean 77.95280 ## 23: Nautolan 77.16540 ## 24: Tholothian 72.44098 ## 25: Iktotchi 74.01579 ## 26: Quermian 103.93706 ## 27: Kel Dor 74.01579 ## 28: Chagrian 77.16540 ## 29: Geonosian 72.04728 ## 30: Mirialan 66.14177 ## 31: Clawdite 66.14177 ## 32: Besalisk 77.95280 ## 33: Kaminoan 87.00792 ## 34: Skakoan 75.98429 ## 35: Muun 75.19689 ## 36: Togruta 70.07878 ## 37: Kaleesh 85.03942 ## 38: Pau'an 81.10241 ## species V1 ``` -- - We can create new data.table object. ``` r species <- starwars_dt[, .(height_cm = mean(height_cm, na.rm=T)), by = species] ``` --- # Key -- - We can set the key of the data.table object. Aim is to sort the data. ``` r starwars_dt <- setDT(starwars_dt, key = "height_cm") ``` -- - or ``` r setkey(starwars_dt, height_cm) ``` --- # Next week -- - merge -- - We will continue to data manipulations and see some sources in Economics.