Week 1 Materials and Homeworks
Data Processing, Wrangling and Visualization with ggplot2 and ggpubr
2023-07-09
Intruction
- Before you run this file, create
datafolder insidefirst_projectfolder. NOTmaterialsfolder - Save data
starwarsin data folder. - Installing
ggpubrpackage byinstall.packages("ggpubr") - Installing
RColorBrewerpackage byinstall.packages("RColorBrewer") - Loading these following R packages.
- Save this file
week_1_materials.Rmdinmaterialsfolder. - Save
data-visualization.pdfinpicsfolder, which is undermaterialsfolder
Source
Homework
Follow the Intruction and run
week1_materials.RmdsucsessfulPublishing
week1_materials.htmlto yourrpubaccountOpen the new working file, name
week1_homework.R, practicing writing r codes, look at the rcodes inblockofweek1_materials.Rmd. I suggest copy all r block toweek1_homework.R, then working onweek1_homework.R
library(dplyr)
library(ggpubr)
library(ggplot2)
library(RColorBrewer)
library(reactable)
library(reactablefmtr)
library(htmltools)
library(fontawesome)
library(DT)
library(klippy) Data Manipulation
We are using library dplyr and data name
starwars for data manipulation. starwars
stored in data folder. Before you run the
r_codes, remember call library(dplyr)
Read data
- Read data
starwarsfrom data folder.
starwars <- read.csv("../data/starwars.csv")
- Print the first 6 rows of
starwars
head(starwars)
# 1. Read data `starwars` from data folder.
starwars <- read.csv("../data/starwars.csv")
# 2. Print the first 6 rows of `starwars`
head(starwars)## name height mass hair_color skin_color eye_color birth_year
## 1 Luke Skywalker 172 77 blond fair blue 19.0
## 2 C-3PO 167 75 <NA> gold yellow 112.0
## 3 R2-D2 96 32 <NA> white, blue red 33.0
## 4 Darth Vader 202 136 none white yellow 41.9
## 5 Leia Organa 150 49 brown light brown 19.0
## 6 Owen Lars 178 120 brown, grey light blue 52.0
## sex gender homeworld species
## 1 male masculine Tatooine Human
## 2 none masculine Tatooine Droid
## 3 none masculine Naboo Droid
## 4 male masculine Tatooine Human
## 5 female feminine Alderaan Human
## 6 male masculine Tatooine Human
## films
## 1 The Empire Strikes Back, Revenge of the Sith, Return of the Jedi, A New Hope, The Force Awakens
## 2 The Empire Strikes Back, Attack of the Clones, The Phantom Menace, Revenge of the Sith, Return of the Jedi, A New Hope
## 3 The Empire Strikes Back, Attack of the Clones, The Phantom Menace, Revenge of the Sith, Return of the Jedi, A New Hope, The Force Awakens
## 4 The Empire Strikes Back, Revenge of the Sith, Return of the Jedi, A New Hope
## 5 The Empire Strikes Back, Revenge of the Sith, Return of the Jedi, A New Hope, The Force Awakens
## 6 Attack of the Clones, Revenge of the Sith, A New Hope
## vehicles starships
## 1 Snowspeeder, Imperial Speeder Bike X-wing, Imperial shuttle
## 2
## 3
## 4 TIE Advanced x1
## 5 Imperial Speeder Bike
## 6Structure glimpse()
- Checking structure of
starwars.
dplyr::glimpse(starwars)
- Checking summary of
starwars.
summary(starwars)
- List of column names of
starwars.
colnames(starwars)
# 1. Checking structure of `starwars`. you can you use str(starwars)
dplyr::glimpse(starwars)## Rows: 87
## Columns: 14
## $ name <chr> "Luke Skywalker", "C-3PO", "R2-D2", "Darth Vader", "Leia Or…
## $ height <int> 172, 167, 96, 202, 150, 178, 165, 97, 183, 182, 188, 180, 2…
## $ mass <dbl> 77.0, 75.0, 32.0, 136.0, 49.0, 120.0, 75.0, 32.0, 84.0, 77.…
## $ hair_color <chr> "blond", NA, NA, "none", "brown", "brown, grey", "brown", N…
## $ skin_color <chr> "fair", "gold", "white, blue", "white", "light", "light", "…
## $ eye_color <chr> "blue", "yellow", "red", "yellow", "brown", "blue", "blue",…
## $ birth_year <dbl> 19.0, 112.0, 33.0, 41.9, 19.0, 52.0, 47.0, NA, 24.0, 57.0, …
## $ sex <chr> "male", "none", "none", "male", "female", "male", "female",…
## $ gender <chr> "masculine", "masculine", "masculine", "masculine", "femini…
## $ homeworld <chr> "Tatooine", "Tatooine", "Naboo", "Tatooine", "Alderaan", "T…
## $ species <chr> "Human", "Droid", "Droid", "Human", "Human", "Human", "Huma…
## $ films <chr> "The Empire Strikes Back, Revenge of the Sith, Return of th…
## $ vehicles <chr> "Snowspeeder, Imperial Speeder Bike", "", "", "", "Imperial…
## $ starships <chr> "X-wing, Imperial shuttle", "", "", "TIE Advanced x1", "", …# 2. Checking summary of `starwars`.
summary(starwars)## name height mass hair_color
## Length:87 Min. : 66.0 Min. : 15.00 Length:87
## Class :character 1st Qu.:167.0 1st Qu.: 55.60 Class :character
## Mode :character Median :180.0 Median : 79.00 Mode :character
## Mean :174.4 Mean : 97.31
## 3rd Qu.:191.0 3rd Qu.: 84.50
## Max. :264.0 Max. :1358.00
## NA's :6 NA's :28
## skin_color eye_color birth_year sex
## Length:87 Length:87 Min. : 8.00 Length:87
## Class :character Class :character 1st Qu.: 35.00 Class :character
## Mode :character Mode :character Median : 52.00 Mode :character
## Mean : 87.57
## 3rd Qu.: 72.00
## Max. :896.00
## NA's :44
## gender homeworld species films
## Length:87 Length:87 Length:87 Length:87
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## vehicles starships
## Length:87 Length:87
## Class :character Class :character
## Mode :character Mode :character
##
##
##
## # 3. List of column names of `starwars`.
colnames(starwars)## [1] "name" "height" "mass" "hair_color" "skin_color"
## [6] "eye_color" "birth_year" "sex" "gender" "homeworld"
## [11] "species" "films" "vehicles" "starships"Selecting select()
- Selecting rows and columns using R Base
starwars[c(1:4), c('name', 'homeworld')]
- Selecting rows and columns using
dplyrpackage
- Select the firs 4 rows and columns name, homeworld
starwars %>% slice(1:4) %>% dplyr::select(name, homeworld)
- Select the firs 4 rows and columns name, homeworld with index
starwars %>% slice(1:4) %>% dplyr::select(1, 9)
- Select the firs 4 rows and choose all columns BUT name
starwars %>% slice(1:4) %>% dplyr::select(-name)
- Select the firs 4 rows and choose only columns containing an underscore
starwars %>% slice(1:4) %>% dplyr::select(contains("_"))
- Select the firs 4 rows and choose only columns beginning with “s”
starwars %>% slice(1:4) %>% dplyr::select(starts_with("s"))
# Select the firs 4 rows and columns name, homeworld with R base
starwars[c(1:4), c('name', 'homeworld')]## name homeworld
## 1 Luke Skywalker Tatooine
## 2 C-3PO Tatooine
## 3 R2-D2 Naboo
## 4 Darth Vader Tatooine# Select the firs 4 rows and columns name, homeworld with R base using index
starwars[c(1:4), c(1, 9)]## name gender
## 1 Luke Skywalker masculine
## 2 C-3PO masculine
## 3 R2-D2 masculine
## 4 Darth Vader masculine# using dplyr
# Select the firs 4 rows and columns name, homeworld
starwars %>% slice(1:4) %>% dplyr::select(name, homeworld)## name homeworld
## 1 Luke Skywalker Tatooine
## 2 C-3PO Tatooine
## 3 R2-D2 Naboo
## 4 Darth Vader Tatooine# Select the firs 4 rows and columns name, homeworld with index
starwars %>% slice(1:4) %>% dplyr::select(1, 9)## name gender
## 1 Luke Skywalker masculine
## 2 C-3PO masculine
## 3 R2-D2 masculine
## 4 Darth Vader masculine# Select the firs 4 rows and choose all columns BUT name
starwars %>% slice(1:4) %>% dplyr::select(-name)## height mass hair_color skin_color eye_color birth_year sex gender
## 1 172 77 blond fair blue 19.0 male masculine
## 2 167 75 <NA> gold yellow 112.0 none masculine
## 3 96 32 <NA> white, blue red 33.0 none masculine
## 4 202 136 none white yellow 41.9 male masculine
## homeworld species
## 1 Tatooine Human
## 2 Tatooine Droid
## 3 Naboo Droid
## 4 Tatooine Human
## films
## 1 The Empire Strikes Back, Revenge of the Sith, Return of the Jedi, A New Hope, The Force Awakens
## 2 The Empire Strikes Back, Attack of the Clones, The Phantom Menace, Revenge of the Sith, Return of the Jedi, A New Hope
## 3 The Empire Strikes Back, Attack of the Clones, The Phantom Menace, Revenge of the Sith, Return of the Jedi, A New Hope, The Force Awakens
## 4 The Empire Strikes Back, Revenge of the Sith, Return of the Jedi, A New Hope
## vehicles starships
## 1 Snowspeeder, Imperial Speeder Bike X-wing, Imperial shuttle
## 2
## 3
## 4 TIE Advanced x1# Select the firs 4 rows and choose only columns containing an underscore
starwars %>% slice(1:4) %>% dplyr::select(contains("_"))## hair_color skin_color eye_color birth_year
## 1 blond fair blue 19.0
## 2 <NA> gold yellow 112.0
## 3 <NA> white, blue red 33.0
## 4 none white yellow 41.9# Select the firs 4 rows and choose only columns beginning with "s"
starwars %>% slice(1:4) %>% dplyr::select(starts_with("s"))## skin_color sex species starships
## 1 fair male Human X-wing, Imperial shuttle
## 2 gold none Droid
## 3 white, blue none Droid
## 4 white male Human TIE Advanced x1Filtering filter()
- Select columns name, mass, homeworld and filter only characters with their Weight greater than the average of the Weight for all characters.
starwars %>% filter(mass > mean(mass, na.rm = TRUE)) %>% dplyr::select(name,mass, homeworld)
- Select columns name, species, homeworld and filter only characters with species is “Human”
filter(starwars, species == "Human") %>% dplyr::select(name, species, homeworld)
- Select columns name, hair_color, homeworld, eye_color and filter only characters with hair_color is “none” and eye_color is “black”
filter(starwars, hair_color == "none" & eye_color == "black") %>% dplyr::select(name, hair_color,homeworld, eye_color )
- Select columns name, hair_color, homeworld, eye_color and filter only characters with weight greater than 1000 and height greater than 150
starwars %>% filter(mass >1000 & height > 150) %>% dplyr::select(name, hair_color,homeworld, eye_color )
- Within group Gender, select columns gender, mass, name, hair_color, eye_color eye_color and filter only characters with their Weight greater than the average of the Weight for all characters.
starwars %>% group_by(gender) %>% filter(mass > mean(mass, na.rm = TRUE)) %>% dplyr::select(gender, mass, name, hair_color, eye_color )
# 1. Select columns name, mass, homeworld and filter only characters with their Weight greater than the average of the Weight for all characters using dplyr
starwars %>% filter(mass > mean(mass, na.rm = TRUE)) %>% dplyr::select(name,mass, homeworld)## name mass homeworld
## 1 Darth Vader 136 Tatooine
## 2 Owen Lars 120 Tatooine
## 3 Chewbacca 112 Kashyyyk
## 4 Jabba Desilijic Tiure 1358 Nal Hutta
## 5 Jek Tono Porkins 110 Bestine IV
## 6 IG-88 140 <NA>
## 7 Bossk 113 Trandosha
## 8 Dexter Jettster 102 Ojom
## 9 Grievous 159 Kalee
## 10 Tarfful 136 Kashyyyk# 2. Select columns name, species, homeworld and and filter only characters with species == "Human"
filter(starwars, species == "Human") %>% dplyr::select(name, species, homeworld)## name species homeworld
## 1 Luke Skywalker Human Tatooine
## 2 Darth Vader Human Tatooine
## 3 Leia Organa Human Alderaan
## 4 Owen Lars Human Tatooine
## 5 Beru Whitesun lars Human Tatooine
## 6 Biggs Darklighter Human Tatooine
## 7 Obi-Wan Kenobi Human Stewjon
## 8 Anakin Skywalker Human Tatooine
## 9 Wilhuff Tarkin Human Eriadu
## 10 Han Solo Human Corellia
## 11 Wedge Antilles Human Corellia
## 12 Jek Tono Porkins Human Bestine IV
## 13 Palpatine Human Naboo
## 14 Boba Fett Human Kamino
## 15 Lando Calrissian Human Socorro
## 16 Lobot Human Bespin
## 17 Mon Mothma Human Chandrila
## 18 Arvel Crynyd Human <NA>
## 19 Qui-Gon Jinn Human <NA>
## 20 Finis Valorum Human Coruscant
## 21 Shmi Skywalker Human Tatooine
## 22 Mace Windu Human Haruun Kal
## 23 Gregar Typho Human Naboo
## 24 Cordé Human Naboo
## 25 Cliegg Lars Human Tatooine
## 26 Dormé Human Naboo
## 27 Dooku Human Serenno
## 28 Bail Prestor Organa Human Alderaan
## 29 Jango Fett Human Concord Dawn
## 30 Jocasta Nu Human Coruscant
## 31 Raymus Antilles Human Alderaan
## 32 Finn Human <NA>
## 33 Rey Human <NA>
## 34 Poe Dameron Human <NA>
## 35 Padmé Amidala Human Naboo# 3. Select columns name, hair_color, homeworld, eye_color and and filter only characters with hair_color is "none" and eye_color is "black"
filter(starwars, hair_color == "none" & eye_color == "black") %>% dplyr::select(name, hair_color,homeworld, eye_color )## name hair_color homeworld eye_color
## 1 Nien Nunb none Sullust black
## 2 Gasgano none Troiken black
## 3 Kit Fisto none Glee Anselm black
## 4 Plo Koon none Dorin black
## 5 Lama Su none Kamino black
## 6 Taun We none Kamino black
## 7 Shaak Ti none Shili black
## 8 Tion Medon none Utapau black
## 9 BB8 none <NA> black# 4. Select columns name, hair_color, homeworld, eye_color and and filter only characters with weight greater than 1000 and height greater than 150
starwars %>% filter(mass >1000 & height > 150) %>% dplyr::select(name, hair_color,homeworld, eye_color )## name hair_color homeworld eye_color
## 1 Jabba Desilijic Tiure <NA> Nal Hutta orange# 5. Within group Gender, select columns gender, mass, name, hair_color, eye_color eye_color and filter only characters with their Weight greater than the average of the Weight for all characters.
starwars %>% group_by(gender) %>% filter(mass > mean(mass, na.rm = TRUE)) %>% dplyr::select(gender, mass, name, hair_color, eye_color )## # A tibble: 14 × 5
## # Groups: gender [2]
## gender mass name hair_color eye_color
## <chr> <dbl> <chr> <chr> <chr>
## 1 masculine 136 Darth Vader none yellow
## 2 masculine 120 Owen Lars brown, grey blue
## 3 feminine 75 Beru Whitesun lars brown blue
## 4 masculine 112 Chewbacca brown blue
## 5 masculine 1358 Jabba Desilijic Tiure <NA> orange
## 6 masculine 110 Jek Tono Porkins brown blue
## 7 masculine 140 IG-88 none red
## 8 masculine 113 Bossk none red
## 9 feminine 55 Ayla Secura none hazel
## 10 feminine 56.2 Luminara Unduli black blue
## 11 feminine 55 Zam Wesell blonde yellow
## 12 feminine 57 Shaak Ti none black
## 13 masculine 159 Grievous none green, yellow
## 14 masculine 136 Tarfful brown blueCreate new variables mutate()
- Select variable name, mass, species. Create a new variable name mass_norm, which use mass divide by mean of mass
starwars %>% select(name, mass, species) %>% mutate(mass_norm = mass / mean(mass, na.rm = TRUE))
# without group
# Select variable name, mass, species. Create a new variable name mass_norm, which use mass divide by mean of mass
starwars %>%
select(name, mass, species) %>%
mutate(mass_norm = mass / mean(mass, na.rm = TRUE))## name mass species mass_norm
## 1 Luke Skywalker 77.0 Human 0.7912704
## 2 C-3PO 75.0 Droid 0.7707179
## 3 R2-D2 32.0 Droid 0.3288397
## 4 Darth Vader 136.0 Human 1.3975685
## 5 Leia Organa 49.0 Human 0.5035357
## 6 Owen Lars 120.0 Human 1.2331487
## 7 Beru Whitesun lars 75.0 Human 0.7707179
## 8 R5-D4 32.0 Droid 0.3288397
## 9 Biggs Darklighter 84.0 Human 0.8632041
## 10 Obi-Wan Kenobi 77.0 Human 0.7912704
## 11 Anakin Skywalker 84.0 Human 0.8632041
## 12 Wilhuff Tarkin NA Human NA
## 13 Chewbacca 112.0 Wookiee 1.1509388
## 14 Han Solo 80.0 Human 0.8220991
## 15 Greedo 74.0 Rodian 0.7604417
## 16 Jabba Desilijic Tiure 1358.0 Hutt 13.9551329
## 17 Wedge Antilles 77.0 Human 0.7912704
## 18 Jek Tono Porkins 110.0 Human 1.1303863
## 19 Yoda 17.0 Yoda's species 0.1746961
## 20 Palpatine 75.0 Human 0.7707179
## 21 Boba Fett 78.2 Human 0.8036019
## 22 IG-88 140.0 Droid 1.4386735
## 23 Bossk 113.0 Trandoshan 1.1612150
## 24 Lando Calrissian 79.0 Human 0.8118229
## 25 Lobot 79.0 Human 0.8118229
## 26 Ackbar 83.0 Mon Calamari 0.8529279
## 27 Mon Mothma NA Human NA
## 28 Arvel Crynyd NA Human NA
## 29 Wicket Systri Warrick 20.0 Ewok 0.2055248
## 30 Nien Nunb 68.0 Sullustan 0.6987843
## 31 Qui-Gon Jinn 89.0 Human 0.9145853
## 32 Nute Gunray 90.0 Neimodian 0.9248615
## 33 Finis Valorum NA Human NA
## 34 Jar Jar Binks 66.0 Gungan 0.6782318
## 35 Roos Tarpals 82.0 Gungan 0.8426516
## 36 Rugor Nass NA Gungan NA
## 37 Ric Olié NA <NA> NA
## 38 Watto NA Toydarian NA
## 39 Sebulba 40.0 Dug 0.4110496
## 40 Quarsh Panaka NA <NA> NA
## 41 Shmi Skywalker NA Human NA
## 42 Darth Maul 80.0 Zabrak 0.8220991
## 43 Bib Fortuna NA Twi'lek NA
## 44 Ayla Secura 55.0 Twi'lek 0.5651932
## 45 Dud Bolt 45.0 Vulptereen 0.4624308
## 46 Gasgano NA Xexto NA
## 47 Ben Quadinaros 65.0 Toong 0.6679556
## 48 Mace Windu 84.0 Human 0.8632041
## 49 Ki-Adi-Mundi 82.0 Cerean 0.8426516
## 50 Kit Fisto 87.0 Nautolan 0.8940328
## 51 Eeth Koth NA Zabrak NA
## 52 Adi Gallia 50.0 Tholothian 0.5138120
## 53 Saesee Tiin NA Iktotchi NA
## 54 Yarael Poof NA Quermian NA
## 55 Plo Koon 80.0 Kel Dor 0.8220991
## 56 Mas Amedda NA Chagrian NA
## 57 Gregar Typho 85.0 Human 0.8734803
## 58 Cordé NA Human NA
## 59 Cliegg Lars NA Human NA
## 60 Poggle the Lesser 80.0 Geonosian 0.8220991
## 61 Luminara Unduli 56.2 Mirialan 0.5775246
## 62 Barriss Offee 50.0 Mirialan 0.5138120
## 63 Dormé NA Human NA
## 64 Dooku 80.0 Human 0.8220991
## 65 Bail Prestor Organa NA Human NA
## 66 Jango Fett 79.0 Human 0.8118229
## 67 Zam Wesell 55.0 Clawdite 0.5651932
## 68 Dexter Jettster 102.0 Besalisk 1.0481764
## 69 Lama Su 88.0 Kaminoan 0.9043091
## 70 Taun We NA Kaminoan NA
## 71 Jocasta Nu NA Human NA
## 72 Ratts Tyerell 15.0 Aleena 0.1541436
## 73 R4-P17 NA Droid NA
## 74 Wat Tambor 48.0 Skakoan 0.4932595
## 75 San Hill NA Muun NA
## 76 Shaak Ti 57.0 Togruta 0.5857456
## 77 Grievous 159.0 Kaleesh 1.6339220
## 78 Tarfful 136.0 Wookiee 1.3975685
## 79 Raymus Antilles 79.0 Human 0.8118229
## 80 Sly Moore 48.0 <NA> 0.4932595
## 81 Tion Medon 80.0 Pau'an 0.8220991
## 82 Finn NA Human NA
## 83 Rey NA Human NA
## 84 Poe Dameron NA Human NA
## 85 BB8 NA Droid NA
## 86 Captain Phasma NA <NA> NA
## 87 Padmé Amidala 45.0 Human 0.4624308- Select variable name, height, mass, homeworld, species. Create a new variable name mass_norm within group of species, which use mass divide by mean of mass
starwars %>% select(name, height, mass, homeworld, species) %>% group_by(species) %>% mutate(mass_norm = mass / mean(mass, na.rm = TRUE))
# Select variable name, height, mass, homeworld, species. Create a new variable name mass_norm within group of species, which use mass divide by mean of mass
starwars %>%
select(name, height, mass, homeworld, species) %>%
group_by(species) %>%
mutate(mass_norm = mass / mean(mass, na.rm = TRUE))## # A tibble: 87 × 6
## # Groups: species [38]
## name height mass homeworld species mass_norm
## <chr> <int> <dbl> <chr> <chr> <dbl>
## 1 Luke Skywalker 172 77 Tatooine Human 0.930
## 2 C-3PO 167 75 Tatooine Droid 1.08
## 3 R2-D2 96 32 Naboo Droid 0.459
## 4 Darth Vader 202 136 Tatooine Human 1.64
## 5 Leia Organa 150 49 Alderaan Human 0.592
## 6 Owen Lars 178 120 Tatooine Human 1.45
## 7 Beru Whitesun lars 165 75 Tatooine Human 0.906
## 8 R5-D4 97 32 Tatooine Droid 0.459
## 9 Biggs Darklighter 183 84 Tatooine Human 1.01
## 10 Obi-Wan Kenobi 182 77 Stewjon Human 0.930
## # ℹ 77 more rows- Select variable name, height, mass, homeworld. Create a variable mass2 by mass x 2 then mass2 x mass2
starwars %>% select(name, height, mass, homeworld) %>% mutate( mass2 = mass * 2, mass2_squared = mass2 * mass2)
# Select variable name, height, mass, homeworld. Create a variable mass2 by mass x 2 then mass2 x mass2
starwars %>%
select(name, height, mass, homeworld) %>%
mutate(
mass2 = mass * 2,
mass2_squared = mass2 * mass2
)## name height mass homeworld mass2 mass2_squared
## 1 Luke Skywalker 172 77.0 Tatooine 154.0 23716.00
## 2 C-3PO 167 75.0 Tatooine 150.0 22500.00
## 3 R2-D2 96 32.0 Naboo 64.0 4096.00
## 4 Darth Vader 202 136.0 Tatooine 272.0 73984.00
## 5 Leia Organa 150 49.0 Alderaan 98.0 9604.00
## 6 Owen Lars 178 120.0 Tatooine 240.0 57600.00
## 7 Beru Whitesun lars 165 75.0 Tatooine 150.0 22500.00
## 8 R5-D4 97 32.0 Tatooine 64.0 4096.00
## 9 Biggs Darklighter 183 84.0 Tatooine 168.0 28224.00
## 10 Obi-Wan Kenobi 182 77.0 Stewjon 154.0 23716.00
## 11 Anakin Skywalker 188 84.0 Tatooine 168.0 28224.00
## 12 Wilhuff Tarkin 180 NA Eriadu NA NA
## 13 Chewbacca 228 112.0 Kashyyyk 224.0 50176.00
## 14 Han Solo 180 80.0 Corellia 160.0 25600.00
## 15 Greedo 173 74.0 Rodia 148.0 21904.00
## 16 Jabba Desilijic Tiure 175 1358.0 Nal Hutta 2716.0 7376656.00
## 17 Wedge Antilles 170 77.0 Corellia 154.0 23716.00
## 18 Jek Tono Porkins 180 110.0 Bestine IV 220.0 48400.00
## 19 Yoda 66 17.0 <NA> 34.0 1156.00
## 20 Palpatine 170 75.0 Naboo 150.0 22500.00
## 21 Boba Fett 183 78.2 Kamino 156.4 24460.96
## 22 IG-88 200 140.0 <NA> 280.0 78400.00
## 23 Bossk 190 113.0 Trandosha 226.0 51076.00
## 24 Lando Calrissian 177 79.0 Socorro 158.0 24964.00
## 25 Lobot 175 79.0 Bespin 158.0 24964.00
## 26 Ackbar 180 83.0 Mon Cala 166.0 27556.00
## 27 Mon Mothma 150 NA Chandrila NA NA
## 28 Arvel Crynyd NA NA <NA> NA NA
## 29 Wicket Systri Warrick 88 20.0 Endor 40.0 1600.00
## 30 Nien Nunb 160 68.0 Sullust 136.0 18496.00
## 31 Qui-Gon Jinn 193 89.0 <NA> 178.0 31684.00
## 32 Nute Gunray 191 90.0 Cato Neimoidia 180.0 32400.00
## 33 Finis Valorum 170 NA Coruscant NA NA
## 34 Jar Jar Binks 196 66.0 Naboo 132.0 17424.00
## 35 Roos Tarpals 224 82.0 Naboo 164.0 26896.00
## 36 Rugor Nass 206 NA Naboo NA NA
## 37 Ric Olié 183 NA Naboo NA NA
## 38 Watto 137 NA Toydaria NA NA
## 39 Sebulba 112 40.0 Malastare 80.0 6400.00
## 40 Quarsh Panaka 183 NA Naboo NA NA
## 41 Shmi Skywalker 163 NA Tatooine NA NA
## 42 Darth Maul 175 80.0 Dathomir 160.0 25600.00
## 43 Bib Fortuna 180 NA Ryloth NA NA
## 44 Ayla Secura 178 55.0 Ryloth 110.0 12100.00
## 45 Dud Bolt 94 45.0 Vulpter 90.0 8100.00
## 46 Gasgano 122 NA Troiken NA NA
## 47 Ben Quadinaros 163 65.0 Tund 130.0 16900.00
## 48 Mace Windu 188 84.0 Haruun Kal 168.0 28224.00
## 49 Ki-Adi-Mundi 198 82.0 Cerea 164.0 26896.00
## 50 Kit Fisto 196 87.0 Glee Anselm 174.0 30276.00
## 51 Eeth Koth 171 NA Iridonia NA NA
## 52 Adi Gallia 184 50.0 Coruscant 100.0 10000.00
## 53 Saesee Tiin 188 NA Iktotch NA NA
## 54 Yarael Poof 264 NA Quermia NA NA
## 55 Plo Koon 188 80.0 Dorin 160.0 25600.00
## 56 Mas Amedda 196 NA Champala NA NA
## 57 Gregar Typho 185 85.0 Naboo 170.0 28900.00
## 58 Cordé 157 NA Naboo NA NA
## 59 Cliegg Lars 183 NA Tatooine NA NA
## 60 Poggle the Lesser 183 80.0 Geonosis 160.0 25600.00
## 61 Luminara Unduli 170 56.2 Mirial 112.4 12633.76
## 62 Barriss Offee 166 50.0 Mirial 100.0 10000.00
## 63 Dormé 165 NA Naboo NA NA
## 64 Dooku 193 80.0 Serenno 160.0 25600.00
## 65 Bail Prestor Organa 191 NA Alderaan NA NA
## 66 Jango Fett 183 79.0 Concord Dawn 158.0 24964.00
## 67 Zam Wesell 168 55.0 Zolan 110.0 12100.00
## 68 Dexter Jettster 198 102.0 Ojom 204.0 41616.00
## 69 Lama Su 229 88.0 Kamino 176.0 30976.00
## 70 Taun We 213 NA Kamino NA NA
## 71 Jocasta Nu 167 NA Coruscant NA NA
## 72 Ratts Tyerell 79 15.0 Aleen Minor 30.0 900.00
## 73 R4-P17 96 NA <NA> NA NA
## 74 Wat Tambor 193 48.0 Skako 96.0 9216.00
## 75 San Hill 191 NA Muunilinst NA NA
## 76 Shaak Ti 178 57.0 Shili 114.0 12996.00
## 77 Grievous 216 159.0 Kalee 318.0 101124.00
## 78 Tarfful 234 136.0 Kashyyyk 272.0 73984.00
## 79 Raymus Antilles 188 79.0 Alderaan 158.0 24964.00
## 80 Sly Moore 178 48.0 Umbara 96.0 9216.00
## 81 Tion Medon 206 80.0 Utapau 160.0 25600.00
## 82 Finn NA NA <NA> NA NA
## 83 Rey NA NA <NA> NA NA
## 84 Poe Dameron NA NA <NA> NA NA
## 85 BB8 NA NA <NA> NA NA
## 86 Captain Phasma NA NA <NA> NA NA
## 87 Padmé Amidala 165 45.0 Naboo 90.0 8100.00- Select variable name, height, mass, homeworld. Update a variable height by converting height to feet
starwars %>% select(name, height, mass, homeworld) %>% mutate( mass = NULL, height = height * 0.0328084 # convert to feet)
# As well as adding new variables, you can use mutate() to
# remove variables and modify existing variables.
starwars %>%
select(name, height, mass, homeworld) %>%
mutate(
mass = NULL,
height = height * 0.0328084 # convert to feet
)## name height homeworld
## 1 Luke Skywalker 5.643045 Tatooine
## 2 C-3PO 5.479003 Tatooine
## 3 R2-D2 3.149606 Naboo
## 4 Darth Vader 6.627297 Tatooine
## 5 Leia Organa 4.921260 Alderaan
## 6 Owen Lars 5.839895 Tatooine
## 7 Beru Whitesun lars 5.413386 Tatooine
## 8 R5-D4 3.182415 Tatooine
## 9 Biggs Darklighter 6.003937 Tatooine
## 10 Obi-Wan Kenobi 5.971129 Stewjon
## 11 Anakin Skywalker 6.167979 Tatooine
## 12 Wilhuff Tarkin 5.905512 Eriadu
## 13 Chewbacca 7.480315 Kashyyyk
## 14 Han Solo 5.905512 Corellia
## 15 Greedo 5.675853 Rodia
## 16 Jabba Desilijic Tiure 5.741470 Nal Hutta
## 17 Wedge Antilles 5.577428 Corellia
## 18 Jek Tono Porkins 5.905512 Bestine IV
## 19 Yoda 2.165354 <NA>
## 20 Palpatine 5.577428 Naboo
## 21 Boba Fett 6.003937 Kamino
## 22 IG-88 6.561680 <NA>
## 23 Bossk 6.233596 Trandosha
## 24 Lando Calrissian 5.807087 Socorro
## 25 Lobot 5.741470 Bespin
## 26 Ackbar 5.905512 Mon Cala
## 27 Mon Mothma 4.921260 Chandrila
## 28 Arvel Crynyd NA <NA>
## 29 Wicket Systri Warrick 2.887139 Endor
## 30 Nien Nunb 5.249344 Sullust
## 31 Qui-Gon Jinn 6.332021 <NA>
## 32 Nute Gunray 6.266404 Cato Neimoidia
## 33 Finis Valorum 5.577428 Coruscant
## 34 Jar Jar Binks 6.430446 Naboo
## 35 Roos Tarpals 7.349082 Naboo
## 36 Rugor Nass 6.758530 Naboo
## 37 Ric Olié 6.003937 Naboo
## 38 Watto 4.494751 Toydaria
## 39 Sebulba 3.674541 Malastare
## 40 Quarsh Panaka 6.003937 Naboo
## 41 Shmi Skywalker 5.347769 Tatooine
## 42 Darth Maul 5.741470 Dathomir
## 43 Bib Fortuna 5.905512 Ryloth
## 44 Ayla Secura 5.839895 Ryloth
## 45 Dud Bolt 3.083990 Vulpter
## 46 Gasgano 4.002625 Troiken
## 47 Ben Quadinaros 5.347769 Tund
## 48 Mace Windu 6.167979 Haruun Kal
## 49 Ki-Adi-Mundi 6.496063 Cerea
## 50 Kit Fisto 6.430446 Glee Anselm
## 51 Eeth Koth 5.610236 Iridonia
## 52 Adi Gallia 6.036746 Coruscant
## 53 Saesee Tiin 6.167979 Iktotch
## 54 Yarael Poof 8.661418 Quermia
## 55 Plo Koon 6.167979 Dorin
## 56 Mas Amedda 6.430446 Champala
## 57 Gregar Typho 6.069554 Naboo
## 58 Cordé 5.150919 Naboo
## 59 Cliegg Lars 6.003937 Tatooine
## 60 Poggle the Lesser 6.003937 Geonosis
## 61 Luminara Unduli 5.577428 Mirial
## 62 Barriss Offee 5.446194 Mirial
## 63 Dormé 5.413386 Naboo
## 64 Dooku 6.332021 Serenno
## 65 Bail Prestor Organa 6.266404 Alderaan
## 66 Jango Fett 6.003937 Concord Dawn
## 67 Zam Wesell 5.511811 Zolan
## 68 Dexter Jettster 6.496063 Ojom
## 69 Lama Su 7.513124 Kamino
## 70 Taun We 6.988189 Kamino
## 71 Jocasta Nu 5.479003 Coruscant
## 72 Ratts Tyerell 2.591864 Aleen Minor
## 73 R4-P17 3.149606 <NA>
## 74 Wat Tambor 6.332021 Skako
## 75 San Hill 6.266404 Muunilinst
## 76 Shaak Ti 5.839895 Shili
## 77 Grievous 7.086614 Kalee
## 78 Tarfful 7.677166 Kashyyyk
## 79 Raymus Antilles 6.167979 Alderaan
## 80 Sly Moore 5.839895 Umbara
## 81 Tion Medon 6.758530 Utapau
## 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 5.413386 Naboo- Select variable name, homeworld, species and then convert only homeworld, species to factor
starwars %>% select(name, homeworld, species) %>% mutate(across(!name, as.factor))
# Use across() with mutate() to apply a transformation
# to multiple columns in a tibble.
starwars %>%
select(name, homeworld, species) %>%
mutate(across(!name, as.factor))## name homeworld species
## 1 Luke Skywalker Tatooine Human
## 2 C-3PO Tatooine Droid
## 3 R2-D2 Naboo Droid
## 4 Darth Vader Tatooine Human
## 5 Leia Organa Alderaan Human
## 6 Owen Lars Tatooine Human
## 7 Beru Whitesun lars Tatooine Human
## 8 R5-D4 Tatooine Droid
## 9 Biggs Darklighter Tatooine Human
## 10 Obi-Wan Kenobi Stewjon Human
## 11 Anakin Skywalker Tatooine Human
## 12 Wilhuff Tarkin Eriadu Human
## 13 Chewbacca Kashyyyk Wookiee
## 14 Han Solo Corellia Human
## 15 Greedo Rodia Rodian
## 16 Jabba Desilijic Tiure Nal Hutta Hutt
## 17 Wedge Antilles Corellia Human
## 18 Jek Tono Porkins Bestine IV Human
## 19 Yoda <NA> Yoda's species
## 20 Palpatine Naboo Human
## 21 Boba Fett Kamino Human
## 22 IG-88 <NA> Droid
## 23 Bossk Trandosha Trandoshan
## 24 Lando Calrissian Socorro Human
## 25 Lobot Bespin Human
## 26 Ackbar Mon Cala Mon Calamari
## 27 Mon Mothma Chandrila Human
## 28 Arvel Crynyd <NA> Human
## 29 Wicket Systri Warrick Endor Ewok
## 30 Nien Nunb Sullust Sullustan
## 31 Qui-Gon Jinn <NA> Human
## 32 Nute Gunray Cato Neimoidia Neimodian
## 33 Finis Valorum Coruscant Human
## 34 Jar Jar Binks Naboo Gungan
## 35 Roos Tarpals Naboo Gungan
## 36 Rugor Nass Naboo Gungan
## 37 Ric Olié Naboo <NA>
## 38 Watto Toydaria Toydarian
## 39 Sebulba Malastare Dug
## 40 Quarsh Panaka Naboo <NA>
## 41 Shmi Skywalker Tatooine Human
## 42 Darth Maul Dathomir Zabrak
## 43 Bib Fortuna Ryloth Twi'lek
## 44 Ayla Secura Ryloth Twi'lek
## 45 Dud Bolt Vulpter Vulptereen
## 46 Gasgano Troiken Xexto
## 47 Ben Quadinaros Tund Toong
## 48 Mace Windu Haruun Kal Human
## 49 Ki-Adi-Mundi Cerea Cerean
## 50 Kit Fisto Glee Anselm Nautolan
## 51 Eeth Koth Iridonia Zabrak
## 52 Adi Gallia Coruscant Tholothian
## 53 Saesee Tiin Iktotch Iktotchi
## 54 Yarael Poof Quermia Quermian
## 55 Plo Koon Dorin Kel Dor
## 56 Mas Amedda Champala Chagrian
## 57 Gregar Typho Naboo Human
## 58 Cordé Naboo Human
## 59 Cliegg Lars Tatooine Human
## 60 Poggle the Lesser Geonosis Geonosian
## 61 Luminara Unduli Mirial Mirialan
## 62 Barriss Offee Mirial Mirialan
## 63 Dormé Naboo Human
## 64 Dooku Serenno Human
## 65 Bail Prestor Organa Alderaan Human
## 66 Jango Fett Concord Dawn Human
## 67 Zam Wesell Zolan Clawdite
## 68 Dexter Jettster Ojom Besalisk
## 69 Lama Su Kamino Kaminoan
## 70 Taun We Kamino Kaminoan
## 71 Jocasta Nu Coruscant Human
## 72 Ratts Tyerell Aleen Minor Aleena
## 73 R4-P17 <NA> Droid
## 74 Wat Tambor Skako Skakoan
## 75 San Hill Muunilinst Muun
## 76 Shaak Ti Shili Togruta
## 77 Grievous Kalee Kaleesh
## 78 Tarfful Kashyyyk Wookiee
## 79 Raymus Antilles Alderaan Human
## 80 Sly Moore Umbara <NA>
## 81 Tion Medon Utapau Pau'an
## 82 Finn <NA> Human
## 83 Rey <NA> Human
## 84 Poe Dameron <NA> Human
## 85 BB8 <NA> Droid
## 86 Captain Phasma <NA> <NA>
## 87 Padmé Amidala Naboo HumanData Visualization with ggplot2 and
ggpubr
Scatter plot
- Using
mpgdata fromggplot2package, plotdislandhwywith color byclass
ggplot(data = mpg, aes(x = displ, y = hwy, colour = class)) + geom_point(size = 3)
Using
mpgdata fromggplot2package, plotdislandhwywith color byclassand add color style and linear regression lineUsing
mpgdata fromggplot2package, plotdislandhwywith color byclassandfacet_grid(vars(cyl)
# 1. Using `mpg` data from `ggplot2` package, plot `disl` and `hwy` with color by `class`
p <- ggplot(data = mpg, aes(x = displ, y = hwy, colour = class)) +
geom_point(size = 3)
p
# 2. Using `mpg` data from `ggplot2` package, plot `disl` and `hwy` with color by `class` and add color style and linear regression line
p <- ggplot(data = mpg, aes(x = displ, y = hwy, colour = class)) +
geom_point(size = 3) +
geom_smooth(method = "lm", formula = "y ~ x", alpha = 0.3) +
scale_color_viridis_d() +
scale_fill_viridis_d() +
labs(x = "Display", y = "Highway", fill = "Class", color = "Class")
p
# 3. Using `mpg` data from `ggplot2` package, plot `disl` and `hwy` with color by `class` and `facet_grid(vars(cyl)`
p <- ggplot(mpg, aes(displ, hwy, colour = class)) +
geom_point(size = 3) +
scale_color_viridis_d() +
scale_fill_viridis_d() +
facet_grid(vars(cyl), scales = "free")
p
Bar plot
Using
mpgdata fromggplot2package, plot the number of manufacturer by colorclassUsing
mpgdata fromggplot2package, plot the propotion of manufacturer by colorclass
# 1. Using `mpg` data from `ggplot2` package, plot the number of manufacturer by color `class`
p <- ggplot(data = mpg, aes(x = manufacturer,fill = as.character(cyl))) +
geom_bar(position = "stack") +
scale_fill_brewer(palette = "BuPu")
p
# 2. Using `mpg` data from `ggplot2` package, plot the propotion of manufacturer by color `class`
p <- ggplot(data = mpg, aes(x = manufacturer,fill = as.character(cyl))) +
geom_bar(position = "fill")
p
# Load data
data("mtcars")
dfm <- mtcars
# Convert the cyl variable to a factor
dfm$cyl <- as.factor(dfm$cyl)
# Add the name colums
dfm$name <- rownames(dfm)
ggbarplot(dfm, x = "name", y = "mpg",
fill = "cyl", # change fill color by cyl
color = "white", # Set bar border colors to white
palette = "jco", # jco journal color palett. see ?ggpar
sort.val = "desc", # Sort the value in dscending order
sort.by.groups = FALSE, # Don't sort inside each group
x.text.angle = 90 # Rotate vertically x axis texts
)
ggbarplot(dfm, x = "name", y = "mpg",
fill = "cyl", # change fill color by cyl
color = "white", # Set bar border colors to white
palette = "jco", # jco journal color palett. see ?ggpar
sort.val = "asc", # Sort the value in dscending order
sort.by.groups = TRUE, # Sort inside each group
x.text.angle = 90 # Rotate vertically x axis texts
)
Density plot
- Create a dataset name
wdata - Density plot with mean lines and marginal rug
- Change outline and fill colors by groups (“sex”)
- Use custom palette
# Create some data format
set.seed(1234)
wdata = data.frame(
sex = factor(rep(c("F", "M"), each=200)),
weight = c(rnorm(200, 55), rnorm(200, 58)))
head(wdata, 4)## sex weight
## 1 F 53.79293
## 2 F 55.27743
## 3 F 56.08444
## 4 F 52.65430# Density plot with mean lines and marginal rug
# Change outline and fill colors by groups ("sex")
# Use custom palette
ggdensity(data = wdata, x = "weight",
add = "mean", rug = TRUE,
color = "sex", fill = "sex",
palette = c("#00AFBB", "#E7B800"))
Histogram plot
- Using a dataset name
wdata - Histogram plot with mean lines and marginal rug
- Change outline and fill colors by groups (“sex”)
- Use custom palette
# Histogram plot with mean lines and marginal rug
# Change outline and fill colors by groups ("sex")
# Use custom color palette
gghistogram(data = wdata, x = "weight",
add = "mean", rug = TRUE,
color = "sex", fill = "sex",
palette = c("#00AFBB", "#E7B800"))
Box plots
- Loading data
ToothGrowth - Box plots with jittered points
- Change outline colors by groups: dose
- Use custom color palette
- Add jitter points and change the shape by groups
- Add p-values comparing groups
- Specify the comparisons you want
# Load data
data("ToothGrowth")
df <- ToothGrowth
head(df, 4)## len supp dose
## 1 4.2 VC 0.5
## 2 11.5 VC 0.5
## 3 7.3 VC 0.5
## 4 5.8 VC 0.5# Box plots with jittered points
# Change outline colors by groups: dose
# Use custom color palette
# Add jitter points and change the shape by groups
p <- ggboxplot(df, x = "dose", y = "len",
color = "dose", palette =c("#00AFBB", "#E7B800", "#FC4E07"),
add = "jitter", shape = "dose")
p
# Add p-values comparing groups
# Specify the comparisons you want
my_comparisons <- list( c("0.5", "1"), c("1", "2"), c("0.5", "2") )
p + stat_compare_means(comparisons = my_comparisons)+ # Add pairwise comparisons p-value
stat_compare_means(label.y = 50) 
Violin plots
- Violin plots with box plots inside
- Change fill color by groups: dose
- add boxplot with white fill color
# Violin plots with box plots inside
# Change fill color by groups: dose
# add boxplot with white fill color
ggviolin(df, x = "dose", y = "len", fill = "dose",
palette = c("#00AFBB", "#E7B800", "#FC4E07"),
add = "boxplot", add.params = list(fill = "white"))+
stat_compare_means(comparisons = my_comparisons, label = "p.signif")+ # Add significance levels
stat_compare_means(label.y = 50) 
Deviation graphs
The deviation graph shows the deviation of quantitatives values to a reference value. In the R code below, we’ll plot the mpg z-score from the mtcars dataset.
Calculate the z-score of the mpg data:
# Calculate the z-score of the mpg data
dfm$mpg_z <- (dfm$mpg -mean(dfm$mpg))/sd(dfm$mpg)
dfm$mpg_grp <- factor(ifelse(dfm$mpg_z < 0, "low", "high"),
levels = c("low", "high"))
# Inspect the data
head(dfm[, c("name", "wt", "mpg", "mpg_z", "mpg_grp", "cyl")])## name wt mpg mpg_z mpg_grp cyl
## Mazda RX4 Mazda RX4 2.620 21.0 0.1508848 high 6
## Mazda RX4 Wag Mazda RX4 Wag 2.875 21.0 0.1508848 high 6
## Datsun 710 Datsun 710 2.320 22.8 0.4495434 high 4
## Hornet 4 Drive Hornet 4 Drive 3.215 21.4 0.2172534 high 6
## Hornet Sportabout Hornet Sportabout 3.440 18.7 -0.2307345 low 8
## Valiant Valiant 3.460 18.1 -0.3302874 low 6ggbarplot(dfm, x = "name", y = "mpg_z",
fill = "mpg_grp", # change fill color by mpg_level
color = "white", # Set bar border colors to white
palette = "jco", # jco journal color palett. see ?ggpar
sort.val = "asc", # Sort the value in ascending order
sort.by.groups = FALSE, # Don't sort inside each group
x.text.angle = 90, # Rotate vertically x axis texts
ylab = "MPG z-score",
xlab = FALSE,
legend.title = "MPG Group"
)
ggbarplot(dfm, x = "name", y = "mpg_z",
fill = "mpg_grp", # change fill color by mpg_level
color = "white", # Set bar border colors to white
palette = "jco", # jco journal color palett. see ?ggpar
sort.val = "desc", # Sort the value in descending order
sort.by.groups = FALSE, # Don't sort inside each group
x.text.angle = 90, # Rotate vertically x axis texts
ylab = "MPG z-score",
legend.title = "MPG Group",
rotate = TRUE,
ggtheme = theme_minimal()
)
ggdotchart(dfm, x = "name", y = "mpg",
color = "cyl", # Color by groups
palette = c("#00AFBB", "#E7B800", "#FC4E07"), # Custom color palette
sorting = "ascending", # Sort value in descending order
add = "segments", # Add segments from y = 0 to dots
ggtheme = theme_pubr() # ggplot2 theme
)
ggdotchart(dfm, x = "name", y = "mpg",
color = "cyl", # Color by groups
palette = c("#00AFBB", "#E7B800", "#FC4E07"), # Custom color palette
sorting = "descending", # Sort value in descending order
add = "segments", # Add segments from y = 0 to dots
rotate = TRUE, # Rotate vertically
group = "cyl", # Order by groups
dot.size = 6, # Large dot size
label = round(dfm$mpg), # Add mpg values as dot labels
font.label = list(color = "white", size = 9,
vjust = 0.5), # Adjust label parameters
ggtheme = theme_pubr() # ggplot2 theme
)
ggdotchart(dfm, x = "name", y = "mpg_z",
color = "cyl", # Color by groups
palette = c("#00AFBB", "#E7B800", "#FC4E07"), # Custom color palette
sorting = "descending", # Sort value in descending order
add = "segments", # Add segments from y = 0 to dots
add.params = list(color = "lightgray", size = 2), # Change segment color and size
group = "cyl", # Order by groups
dot.size = 6, # Large dot size
label = round(dfm$mpg_z,1), # Add mpg values as dot labels
font.label = list(color = "white", size = 9,
vjust = 0.5), # Adjust label parameters
ggtheme = theme_pubr() # ggplot2 theme
)+
geom_hline(yintercept = 0, linetype = 2, color = "lightgray")
Dot charts
Lollipop chart
Lollipop chart is an alternative to bar plots, when you have a large set of values to visualize.
Lollipop chart colored by the grouping variable “cyl”:
ggdotchart(dfm, x = "name", y = "mpg",
color = "cyl", # Color by groups
palette = c("#00AFBB", "#E7B800", "#FC4E07"), # Custom color palette
sorting = "ascending", # Sort value in descending order
add = "segments", # Add segments from y = 0 to dots
ggtheme = theme_pubr() # ggplot2 theme
)
Sort in decending order. sorting = “descending”.
Rotate the plot vertically, using rotate = TRUE.
Sort the mpg value inside each group by using group = “cyl”.
Set dot.size to 6.
Add mpg values as label. label = “mpg” or label = round(dfm$mpg).
ggdotchart(dfm, x = "name", y = "mpg",
color = "cyl", # Color by groups
palette = c("#00AFBB", "#E7B800", "#FC4E07"), # Custom color palette
sorting = "descending", # Sort value in descending order
add = "segments", # Add segments from y = 0 to dots
rotate = TRUE, # Rotate vertically
group = "cyl", # Order by groups
dot.size = 6, # Large dot size
label = round(dfm$mpg), # Add mpg values as dot labels
font.label = list(color = "white", size = 9,
vjust = 0.5), # Adjust label parameters
ggtheme = theme_pubr() # ggplot2 theme
)
Deviation graph:
Use y = “mpg_z” Change segment color and size: add.params = list(color = “lightgray”, size = 2)
ggdotchart(dfm, x = "name", y = "mpg_z",
color = "cyl", # Color by groups
palette = c("#00AFBB", "#E7B800", "#FC4E07"), # Custom color palette
sorting = "descending", # Sort value in descending order
add = "segments", # Add segments from y = 0 to dots
add.params = list(color = "lightgray", size = 2), # Change segment color and size
group = "cyl", # Order by groups
dot.size = 6, # Large dot size
label = round(dfm$mpg_z,1), # Add mpg values as dot labels
font.label = list(color = "white", size = 9,
vjust = 0.5), # Adjust label parameters
ggtheme = theme_pubr() # ggplot2 theme
)+
geom_hline(yintercept = 0, linetype = 2, color = "lightgray")
Cleveland’s dot plot
Color y text by groups. Use y.text.col = TRUE.
ggdotchart(dfm, x = "name", y = "mpg",
color = "cyl", # Color by groups
palette = c("#00AFBB", "#E7B800", "#FC4E07"), # Custom color palette
sorting = "descending", # Sort value in descending order
rotate = TRUE, # Rotate vertically
dot.size = 2, # Large dot size
y.text.col = TRUE, # Color y text by groups
ggtheme = theme_pubr() # ggplot2 theme
)+
theme_cleveland() # Add dashed grids
RColorBrewer
- Display color RColorBrewer
display.brewer.all(colorblindFriendly = TRUE)
display.brewer.all(colorblindFriendly = TRUE) 