Data Wrangling in R
Alexander Matrunich
14 December 2017, R-Ladies Tbilisi
Agenda
- Introduction
- Basics of base R
- Data types in R
- Mysterious square brackets
- Basics of tidyverse
- Pipe %>%
- dplyr
- tydir
Intro
Alexander Matrunich: who am I?
- Specialist degree in Sociology, 2003
- Head of SocLab, Pskov Volny Institute (2005-2010)
- Market Research expert, Danish Economical Support Programme (2008-2010)
- Data Analyst, Institute of Regional Development (2011-2013)
- R trainer, Statistian, Food and Agricultural Organization of the United Nations (2013-2017)
Questions to audience
Who does use anything from the following:
- Excel
- R
- dplyr, tidyr, purrr
- reshape, reshap2, plyr
- ggplot2
- lapply, vapply, sapply
- Any other stat software?
Required software today
- R + RStudio IDE
- packages: dplyr, tidyr
Some basics of base R
Data types
| Dimensions | Homogeneous | Heterogeneous |
|---|---|---|
| 1D | Atomic vector | List |
| 2D | Matrix | Data frame |
| nD | Array | Nested data frames (purrr) |
Atomic (not recursive) data types
- logical:
TRUE\FALSE - integer:
1L,2L,100L - double (numeric): 1, 1.1, 2.1, 3.1415
- character:
I am a string - null object:
NULL
Extract or Replace Parts of an Object
[returns candy with paper,[[returns the candy only[can select more than one element[[and$select only one element- Subsetting drops all attributes except
names,dimanddimnames. [[supports computed indices
Your turn
- Create a list.
- Extract first element with
[. - Extract first element with
[[. - Compare.
Data processing in modern R
Pipe %>%
read_csv("/path/to/my.csv") %>%
select(age, sex) %>%
group_by(sex) %>%
summarize(age = mean(age))
summarize(
group_by(
select(
read_csv("/path/to/my.csv"), age, sex),
sex),
age = mean(age))
A Grammar of Data Manipulation: dplyr
- Similar tools to work with different data sources (R data frames, SQL data bases)
- Many packages follow dplyr's philosophy
- Easy to read code
Source code as a mean of communication
tibble - modern reimagining of the data.frame
- does less (no changes of types or column names);
- complains more (when a variable doesn't exist);
- enhanced print method (also check
dplyr::glimpse()).
Old-style data.frame
library(dplyr)
as.data.frame(starwars)
name height mass
1 Luke Skywalker 172 77.0
2 C-3PO 167 75.0
3 R2-D2 96 32.0
4 Darth Vader 202 136.0
5 Leia Organa 150 49.0
6 Owen Lars 178 120.0
7 Beru Whitesun lars 165 75.0
8 R5-D4 97 32.0
9 Biggs Darklighter 183 84.0
10 Obi-Wan Kenobi 182 77.0
11 Anakin Skywalker 188 84.0
12 Wilhuff Tarkin 180 NA
13 Chewbacca 228 112.0
14 Han Solo 180 80.0
15 Greedo 173 74.0
16 Jabba Desilijic Tiure 175 1358.0
17 Wedge Antilles 170 77.0
18 Jek Tono Porkins 180 110.0
19 Yoda 66 17.0
20 Palpatine 170 75.0
21 Boba Fett 183 78.2
22 IG-88 200 140.0
23 Bossk 190 113.0
24 Lando Calrissian 177 79.0
25 Lobot 175 79.0
26 Ackbar 180 83.0
27 Mon Mothma 150 NA
28 Arvel Crynyd NA NA
29 Wicket Systri Warrick 88 20.0
30 Nien Nunb 160 68.0
31 Qui-Gon Jinn 193 89.0
32 Nute Gunray 191 90.0
33 Finis Valorum 170 NA
34 Jar Jar Binks 196 66.0
35 Roos Tarpals 224 82.0
36 Rugor Nass 206 NA
37 Ric Olié 183 NA
38 Watto 137 NA
39 Sebulba 112 40.0
40 Quarsh Panaka 183 NA
41 Shmi Skywalker 163 NA
42 Darth Maul 175 80.0
43 Bib Fortuna 180 NA
44 Ayla Secura 178 55.0
45 Dud Bolt 94 45.0
46 Gasgano 122 NA
47 Ben Quadinaros 163 65.0
48 Mace Windu 188 84.0
49 Ki-Adi-Mundi 198 82.0
50 Kit Fisto 196 87.0
51 Eeth Koth 171 NA
52 Adi Gallia 184 50.0
53 Saesee Tiin 188 NA
54 Yarael Poof 264 NA
55 Plo Koon 188 80.0
56 Mas Amedda 196 NA
57 Gregar Typho 185 85.0
58 Cordé 157 NA
59 Cliegg Lars 183 NA
60 Poggle the Lesser 183 80.0
61 Luminara Unduli 170 56.2
62 Barriss Offee 166 50.0
63 Dormé 165 NA
64 Dooku 193 80.0
65 Bail Prestor Organa 191 NA
66 Jango Fett 183 79.0
67 Zam Wesell 168 55.0
68 Dexter Jettster 198 102.0
69 Lama Su 229 88.0
70 Taun We 213 NA
71 Jocasta Nu 167 NA
72 Ratts Tyerell 79 15.0
73 R4-P17 96 NA
74 Wat Tambor 193 48.0
75 San Hill 191 NA
76 Shaak Ti 178 57.0
77 Grievous 216 159.0
78 Tarfful 234 136.0
79 Raymus Antilles 188 79.0
80 Sly Moore 178 48.0
81 Tion Medon 206 80.0
82 Finn NA NA
83 Rey NA NA
84 Poe Dameron NA NA
85 BB8 NA NA
86 Captain Phasma NA NA
87 Padmé Amidala 165 45.0
hair_color skin_color
1 blond fair
2 <NA> gold
3 <NA> white, blue
4 none white
5 brown light
6 brown, grey light
7 brown light
8 <NA> white, red
9 black light
10 auburn, white fair
11 blond fair
12 auburn, grey fair
13 brown unknown
14 brown fair
15 <NA> green
16 <NA> green-tan, brown
17 brown fair
18 brown fair
19 white green
20 grey pale
21 black fair
22 none metal
23 none green
24 black dark
25 none light
26 none brown mottle
27 auburn fair
28 brown fair
29 brown brown
30 none grey
31 brown fair
32 none mottled green
33 blond fair
34 none orange
35 none grey
36 none green
37 brown fair
38 black blue, grey
39 none grey, red
40 black dark
41 black fair
42 none red
43 none pale
44 none blue
45 none blue, grey
46 none white, blue
47 none grey, green, yellow
48 none dark
49 white pale
50 none green
51 black brown
52 none dark
53 none pale
54 none white
55 none orange
56 none blue
57 black dark
58 brown light
59 brown fair
60 none green
61 black yellow
62 black yellow
63 brown light
64 white fair
65 black tan
66 black tan
67 blonde fair, green, yellow
68 none brown
69 none grey
70 none grey
71 white fair
72 none grey, blue
73 none silver, red
74 none green, grey
75 none grey
76 none red, blue, white
77 none brown, white
78 brown brown
79 brown light
80 none pale
81 none grey
82 black dark
83 brown light
84 brown light
85 none none
86 unknown unknown
87 brown light
eye_color birth_year gender
1 blue 19.0 male
2 yellow 112.0 <NA>
3 red 33.0 <NA>
4 yellow 41.9 male
5 brown 19.0 female
6 blue 52.0 male
7 blue 47.0 female
8 red NA <NA>
9 brown 24.0 male
10 blue-gray 57.0 male
11 blue 41.9 male
12 blue 64.0 male
13 blue 200.0 male
14 brown 29.0 male
15 black 44.0 male
16 orange 600.0 hermaphrodite
17 hazel 21.0 male
18 blue NA male
19 brown 896.0 male
20 yellow 82.0 male
21 brown 31.5 male
22 red 15.0 none
23 red 53.0 male
24 brown 31.0 male
25 blue 37.0 male
26 orange 41.0 male
27 blue 48.0 female
28 brown NA male
29 brown 8.0 male
30 black NA male
31 blue 92.0 male
32 red NA male
33 blue 91.0 male
34 orange 52.0 male
35 orange NA male
36 orange NA male
37 blue NA male
38 yellow NA male
39 orange NA male
40 brown 62.0 male
41 brown 72.0 female
42 yellow 54.0 male
43 pink NA male
44 hazel 48.0 female
45 yellow NA male
46 black NA male
47 orange NA male
48 brown 72.0 male
49 yellow 92.0 male
50 black NA male
51 brown NA male
52 blue NA female
53 orange NA male
54 yellow NA male
55 black 22.0 male
56 blue NA male
57 brown NA male
58 brown NA female
59 blue 82.0 male
60 yellow NA male
61 blue 58.0 female
62 blue 40.0 female
63 brown NA female
64 brown 102.0 male
65 brown 67.0 male
66 brown 66.0 male
67 yellow NA female
68 yellow NA male
69 black NA male
70 black NA female
71 blue NA female
72 unknown NA male
73 red, blue NA female
74 unknown NA male
75 gold NA male
76 black NA female
77 green, yellow NA male
78 blue NA male
79 brown NA male
80 white NA female
81 black NA male
82 dark NA male
83 hazel NA female
84 brown NA male
85 black NA none
86 unknown NA female
87 brown 46.0 female
homeworld species
1 Tatooine Human
2 Tatooine Droid
3 Naboo Droid
4 Tatooine Human
5 Alderaan Human
6 Tatooine Human
7 Tatooine Human
8 Tatooine Droid
9 Tatooine Human
10 Stewjon Human
11 Tatooine Human
12 Eriadu Human
13 Kashyyyk Wookiee
14 Corellia Human
15 Rodia Rodian
16 Nal Hutta Hutt
17 Corellia Human
18 Bestine IV Human
19 <NA> Yoda's species
20 Naboo Human
21 Kamino Human
22 <NA> Droid
23 Trandosha Trandoshan
24 Socorro Human
25 Bespin Human
26 Mon Cala Mon Calamari
27 Chandrila Human
28 <NA> Human
29 Endor Ewok
30 Sullust Sullustan
31 <NA> Human
32 Cato Neimoidia Neimodian
33 Coruscant Human
34 Naboo Gungan
35 Naboo Gungan
36 Naboo Gungan
37 Naboo <NA>
38 Toydaria Toydarian
39 Malastare Dug
40 Naboo <NA>
41 Tatooine Human
42 Dathomir Zabrak
43 Ryloth Twi'lek
44 Ryloth Twi'lek
45 Vulpter Vulptereen
46 Troiken Xexto
47 Tund Toong
48 Haruun Kal Human
49 Cerea Cerean
50 Glee Anselm Nautolan
51 Iridonia Zabrak
52 Coruscant Tholothian
53 Iktotch Iktotchi
54 Quermia Quermian
55 Dorin Kel Dor
56 Champala Chagrian
57 Naboo Human
58 Naboo Human
59 Tatooine Human
60 Geonosis Geonosian
61 Mirial Mirialan
62 Mirial Mirialan
63 Naboo Human
64 Serenno Human
65 Alderaan Human
66 Concord Dawn Human
67 Zolan Clawdite
68 Ojom Besalisk
69 Kamino Kaminoan
70 Kamino Kaminoan
71 Coruscant Human
72 Aleen Minor Aleena
73 <NA> <NA>
74 Skako Skakoan
75 Muunilinst Muun
76 Shili Togruta
77 Kalee Kaleesh
78 Kashyyyk Wookiee
79 Alderaan Human
80 Umbara <NA>
81 Utapau Pau'an
82 <NA> Human
83 <NA> Human
84 <NA> Human
85 <NA> Droid
86 <NA> <NA>
87 Naboo Human
films
1 Revenge of the Sith, Return of the Jedi, The Empire Strikes Back, A New Hope, The Force Awakens
2 Attack of the Clones, The Phantom Menace, Revenge of the Sith, Return of the Jedi, The Empire Strikes Back, A New Hope
3 Attack of the Clones, The Phantom Menace, Revenge of the Sith, Return of the Jedi, The Empire Strikes Back, A New Hope, The Force Awakens
4 Revenge of the Sith, Return of the Jedi, The Empire Strikes Back, A New Hope
5 Revenge of the Sith, Return of the Jedi, The Empire Strikes Back, A New Hope, The Force Awakens
6 Attack of the Clones, Revenge of the Sith, A New Hope
7 Attack of the Clones, Revenge of the Sith, A New Hope
8 A New Hope
9 A New Hope
10 Attack of the Clones, The Phantom Menace, Revenge of the Sith, Return of the Jedi, The Empire Strikes Back, A New Hope
11 Attack of the Clones, The Phantom Menace, Revenge of the Sith
12 Revenge of the Sith, A New Hope
13 Revenge of the Sith, Return of the Jedi, The Empire Strikes Back, A New Hope, The Force Awakens
14 Return of the Jedi, The Empire Strikes Back, A New Hope, The Force Awakens
15 A New Hope
16 The Phantom Menace, Return of the Jedi, A New Hope
17 Return of the Jedi, The Empire Strikes Back, A New Hope
18 A New Hope
19 Attack of the Clones, The Phantom Menace, Revenge of the Sith, Return of the Jedi, The Empire Strikes Back
20 Attack of the Clones, The Phantom Menace, Revenge of the Sith, Return of the Jedi, The Empire Strikes Back
21 Attack of the Clones, Return of the Jedi, The Empire Strikes Back
22 The Empire Strikes Back
23 The Empire Strikes Back
24 Return of the Jedi, The Empire Strikes Back
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 Attack of the Clones, The Phantom Menace, Revenge of the Sith
33 The Phantom Menace
34 Attack of the Clones, The Phantom Menace
35 The Phantom Menace
36 The Phantom Menace
37 The Phantom Menace
38 Attack of the Clones, The Phantom Menace
39 The Phantom Menace
40 The Phantom Menace
41 Attack of the Clones, The Phantom Menace
42 The Phantom Menace
43 Return of the Jedi
44 Attack of the Clones, The Phantom Menace, Revenge of the Sith
45 The Phantom Menace
46 The Phantom Menace
47 The Phantom Menace
48 Attack of the Clones, The Phantom Menace, Revenge of the Sith
49 Attack of the Clones, The Phantom Menace, Revenge of the Sith
50 Attack of the Clones, The Phantom Menace, Revenge of the Sith
51 The Phantom Menace, Revenge of the Sith
52 The Phantom Menace, Revenge of the Sith
53 The Phantom Menace, Revenge of the Sith
54 The Phantom Menace
55 Attack of the Clones, The Phantom Menace, Revenge of the Sith
56 Attack of the Clones, The Phantom Menace
57 Attack of the Clones
58 Attack of the Clones
59 Attack of the Clones
60 Attack of the Clones, Revenge of the Sith
61 Attack of the Clones, Revenge of the Sith
62 Attack of the Clones
63 Attack of the Clones
64 Attack of the Clones, Revenge of the Sith
65 Attack of the Clones, Revenge of the Sith
66 Attack of the Clones
67 Attack of the Clones
68 Attack of the Clones
69 Attack of the Clones
70 Attack of the Clones
71 Attack of the Clones
72 The Phantom Menace
73 Attack of the Clones, Revenge of the Sith
74 Attack of the Clones
75 Attack of the Clones
76 Attack of the Clones, Revenge of the Sith
77 Revenge of the Sith
78 Revenge of the Sith
79 Revenge of the Sith, A New Hope
80 Attack of the Clones, Revenge of the Sith
81 Revenge of the Sith
82 The Force Awakens
83 The Force Awakens
84 The Force Awakens
85 The Force Awakens
86 The Force Awakens
87 Attack of the Clones, The Phantom Menace, Revenge of the Sith
vehicles
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 Sith speeder
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64 Flitknot speeder
65
66
67 Koro-2 Exodrive airspeeder
68
69
70
71
72
73
74
75
76
77 Tsmeu-6 personal wheel bike
78
79
80
81
82
83
84
85
86
87
starships
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 Trade Federation cruiser, Jedi Interceptor, Naboo fighter
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
35
36
37 Naboo Royal Starship
38
39
40
41
42 Scimitar
43
44
45
46
47
48
49
50
51
52
53
54
55 Jedi starfighter
56
57 Naboo fighter
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77 Belbullab-22 starfighter
78
79
80
81
82
83
84 T-70 X-wing fighter
85
86
87 H-type Nubian yacht, Naboo star skiff, Naboo fighter
tibble
starwars
# A tibble: 87 x 13
name height mass hair_color
<chr> <int> <dbl> <chr>
1 Luke Skywalker 172 77 blond
2 C-3PO 167 75 <NA>
3 R2-D2 96 32 <NA>
4 Darth Vader 202 136 none
5 Leia Organa 150 49 brown
6 Owen Lars 178 120 brown, grey
7 Beru Whitesun lars 165 75 brown
8 R5-D4 97 32 <NA>
9 Biggs Darklighter 183 84 black
10 Obi-Wan Kenobi 182 77 auburn, white
# ... with 77 more rows, and 9 more variables:
# skin_color <chr>, eye_color <chr>,
# birth_year <dbl>, gender <chr>,
# homeworld <chr>, species <chr>, films <list>,
# vehicles <list>, starships <list>
glimpse() from dplyr
glimpse(starwars)
Observations: 87
Variables: 13
$ name <chr> "Luke Skywalker", "C-3PO",...
$ height <int> 172, 167, 96, 202, 150, 17...
$ mass <dbl> 77, 75, 32, 136, 49, 120, ...
$ hair_color <chr> "blond", NA, NA, "none", "...
$ skin_color <chr> "fair", "gold", "white, bl...
$ eye_color <chr> "blue", "yellow", "red", "...
$ birth_year <dbl> 19.0, 112.0, 33.0, 41.9, 1...
$ gender <chr> "male", NA, NA, "male", "f...
$ homeworld <chr> "Tatooine", "Tatooine", "N...
$ species <chr> "Human", "Droid", "Droid",...
$ films <list> [<"Revenge of the Sith", ...
$ vehicles <list> [<"Snowspeeder", "Imperia...
$ starships <list> [<"X-wing", "Imperial shu...
dplyr's verbs (some)
filter(): filter rows based on conditionsselect(): select columnsmutate(): create new variablesarrange(): reorder rowsgroup_by(): group rows by categoriessummarize(): calculate [groups] totals*_join(): family of functions to merge tables
filter()
starwars %>%
filter(species == "Droid")
# A tibble: 5 x 13
name height mass hair_color skin_color
<chr> <int> <dbl> <chr> <chr>
1 C-3PO 167 75 <NA> gold
2 R2-D2 96 32 <NA> white, blue
3 R5-D4 97 32 <NA> white, red
4 IG-88 200 140 none metal
5 BB8 NA NA none none
# ... with 8 more variables: eye_color <chr>,
# birth_year <dbl>, gender <chr>,
# homeworld <chr>, species <chr>, films <list>,
# vehicles <list>, starships <list>
filter()
starwars %>%
select(name, ends_with("color"))
# A tibble: 87 x 4
name hair_color skin_color
<chr> <chr> <chr>
1 Luke Skywalker blond fair
2 C-3PO <NA> gold
3 R2-D2 <NA> white, blue
4 Darth Vader none white
5 Leia Organa brown light
6 Owen Lars brown, grey light
7 Beru Whitesun lars brown light
8 R5-D4 <NA> white, red
9 Biggs Darklighter black light
10 Obi-Wan Kenobi auburn, white fair
# ... with 77 more rows, and 1 more variables:
# eye_color <chr>
mutate() & select()
starwars %>%
mutate(name, bmi = mass / ((height / 100) ^ 2)) %>%
select(name:mass, bmi)
# A tibble: 87 x 4
name height mass bmi
<chr> <int> <dbl> <dbl>
1 Luke Skywalker 172 77 26.02758
2 C-3PO 167 75 26.89232
3 R2-D2 96 32 34.72222
4 Darth Vader 202 136 33.33007
5 Leia Organa 150 49 21.77778
6 Owen Lars 178 120 37.87401
7 Beru Whitesun lars 165 75 27.54821
8 R5-D4 97 32 34.00999
9 Biggs Darklighter 183 84 25.08286
10 Obi-Wan Kenobi 182 77 23.24598
# ... with 77 more rows
arrange()
starwars %>%
arrange(desc(mass))
# A tibble: 87 x 13
name height mass hair_color
<chr> <int> <dbl> <chr>
1 Jabba Desilijic Tiure 175 1358 <NA>
2 Grievous 216 159 none
3 IG-88 200 140 none
4 Darth Vader 202 136 none
5 Tarfful 234 136 brown
6 Owen Lars 178 120 brown, grey
7 Bossk 190 113 none
8 Chewbacca 228 112 brown
9 Jek Tono Porkins 180 110 brown
10 Dexter Jettster 198 102 none
# ... with 77 more rows, and 9 more variables:
# skin_color <chr>, eye_color <chr>,
# birth_year <dbl>, gender <chr>,
# homeworld <chr>, species <chr>, films <list>,
# vehicles <list>, starships <list>
Let's rock!
starwars %>%
group_by(species) %>%
summarise(
n = n(),
mass = mean(mass, na.rm = TRUE)
) %>%
filter(n > 1)
# A tibble: 9 x 3
species n mass
<chr> <int> <dbl>
1 Droid 5 69.75000
2 Gungan 3 74.00000
3 Human 35 82.78182
4 Kaminoan 2 88.00000
5 Mirialan 2 53.10000
6 Twi'lek 2 55.00000
7 Wookiee 2 124.00000
8 Zabrak 2 80.00000
9 <NA> 5 48.00000
tidyr
What is tidy data?
- Each variable is in a column.
- Each observation is a row.
- Each value is a cell.
The patient
df1 <- tribble(
~student, ~biology, ~math,
"Alex", 82, 70,
"Sandro", NA, 85,
"Shura", 71, 68
)
df1
# A tibble: 3 x 3
student biology math
<chr> <dbl> <dbl>
1 Alex 82 70
2 Sandro NA 85
3 Shura 71 68
A twin of the patient
(df2 <- tribble(
~Alex, ~Sandro, ~Shura,
82, NA, 71,
70, 85, 68
))
# A tibble: 2 x 3
Alex Sandro Shura
<dbl> <dbl> <dbl>
1 82 NA 71
2 70 85 68
df1
# A tibble: 3 x 3
student biology math
<chr> <dbl> <dbl>
1 Alex 82 70
2 Sandro NA 85
3 Shura 71 68
Scalpel, please!
library(tidyr)
df1 %>%
gather("subject", "mark", -student)
# A tibble: 6 x 3
student subject mark
<chr> <chr> <dbl>
1 Alex biology 82
2 Sandro biology NA
3 Shura biology 71
4 Alex math 70
5 Sandro math 85
6 Shura math 68
What about the twin?
df2 %>%
mutate(subject = row_number()) %>%
glimpse() %>%
gather("student", "mark", -subject) %>%
select(2, 1, 3)
Observations: 2
Variables: 4
$ Alex <dbl> 82, 70
$ Sandro <dbl> NA, 85
$ Shura <dbl> 71, 68
$ subject <int> 1, 2
# A tibble: 6 x 3
student subject mark
<chr> <int> <dbl>
1 Alex 1 82
2 Alex 2 70
3 Sandro 1 NA
4 Sandro 2 85
5 Shura 1 71
6 Shura 2 68
Sources
- Official
dplyrweb-site dplyr.tidyverse.org - Official
tidyrweb-site tidyr.tidyverse.org - Tidy data vignette
- R manual pages:
vector,is.atomic,Extract, etc. - Brad Boehmke. Data Wrangling in R. Slides
- R and Microsoft R Workflows for Data Science.
Contacts
Alexander Matrunich
- a@rresults.consulting
- Twitter: @matrunich
- LinkedIn: matrunich
- These slides: rpubs.com/malexan/tidydataslides
- Source code of the slides: gitlab.com/rresults/rladies_tbs_dplyr