Cleaning_Starwors
Enrico
2024-03-04
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(dplyr)1 Check Dada set or head (starwors) or str(starwors)
View(starwars)2 Check variable type number of column number of row
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 <list> <"A New Hope", "The Empire Strikes Back", "Return of the J…
## $ vehicles <list> <"Snowspeeder", "Imperial Speeder Bike">, <>, <>, <>, "Imp…
## $ starships <list> <"X-wing", "Imperial shuttle">, <>, <>, "TIE Advanced x1",…Type of variable
After checking variabke we decided to change ‘gender’ now in
to factor
check again if the column gender is
class(starwars$gender)## [1] "character"now check the unique variable in the colum gender
unique(starwars$gender)## [1] "masculine" "feminine" NANow changing the column from to factor and check if is
change
#####. if you will write the same variable starwars\(gender1 <- as_factor(starwars\)gender) you will have a new column call gender1
starwars$gender <- as_factor(starwars$gender)
class(starwars$gender)## [1] "factor"Che the levels of the column and count NA in a column
levels(starwars$gender)## [1] "masculine" "feminine"sum(is.na(starwars$gender))## [1] 4Change level of the factor in this case feminine and masculine
starwars$gender<- factor((starwars$gender), levels = c('masculine','feminine'))check levels change
levels(starwars$gender)## [1] "masculine" "feminine"select variables
starwars %>%
select(name,height,ends_with('color')) %>%
names()## [1] "name" "height" "hair_color" "skin_color" "eye_color" # or
starwars %>%
select(name,height,ends_with('color')) ## # A tibble: 87 × 5
## name height hair_color skin_color eye_color
## <chr> <int> <chr> <chr> <chr>
## 1 Luke Skywalker 172 blond fair blue
## 2 C-3PO 167 <NA> gold yellow
## 3 R2-D2 96 <NA> white, blue red
## 4 Darth Vader 202 none white yellow
## 5 Leia Organa 150 brown light brown
## 6 Owen Lars 178 brown, grey light blue
## 7 Beru Whitesun Lars 165 brown light blue
## 8 R5-D4 97 <NA> white, red red
## 9 Biggs Darklighter 183 black light brown
## 10 Obi-Wan Kenobi 182 auburn, white fair blue-gray
## # ℹ 77 more rowslooking for unique value in the column hair_color
unique(starwars$hair_color)## [1] "blond" NA "none" "brown"
## [5] "brown, grey" "black" "auburn, white" "auburn, grey"
## [9] "white" "grey" "auburn" "blonde"using select and filter in the hair_color column using %in% = either blond or brown the row have to contain one of the two value in order to be troue and hieght min than 180
starwars %>%
select(name,height,ends_with('color')) %>%
filter(hair_color %in% c('blond','brown') &
height<180) ## # A tibble: 8 × 5
## name height hair_color skin_color eye_color
## <chr> <int> <chr> <chr> <chr>
## 1 Luke Skywalker 172 blond fair blue
## 2 Leia Organa 150 brown light brown
## 3 Beru Whitesun Lars 165 brown light blue
## 4 Wedge Antilles 170 brown fair hazel
## 5 Wicket Systri Warrick 88 brown brown brown
## 6 Finis Valorum 170 blond fair blue
## 7 Cordé 157 brown light brown
## 8 Dormé 165 brown light brownmissing data .NA option we can remuved all the missing data in order to calculate the mean NOT RACCOMANDAD IF YOU DONT UNDERSTEND THE NA
mean(starwars$height,na.rm = TRUE)## [1] 174.6049#or
starwars %>%
select(name,gender,hair_color,height) %>% na.omit()## # A tibble: 72 × 4
## name gender hair_color height
## <chr> <fct> <chr> <int>
## 1 Luke Skywalker masculine blond 172
## 2 Darth Vader masculine none 202
## 3 Leia Organa feminine brown 150
## 4 Owen Lars masculine brown, grey 178
## 5 Beru Whitesun Lars feminine brown 165
## 6 Biggs Darklighter masculine black 183
## 7 Obi-Wan Kenobi masculine auburn, white 182
## 8 Anakin Skywalker masculine blond 188
## 9 Wilhuff Tarkin masculine auburn, grey 180
## 10 Chewbacca masculine brown 228
## # ℹ 62 more rowssecond option we can try really to understand were the missing data NAis.
filter(!complete.cases(.)) complete.cases= with out NA il puntino si riferisce alla dataset con !complete.cases= with esclamation point means the opposit or gime me all the value missing
starwars %>%
select(name, height,gender,hair_color) %>%
filter(!complete.cases(.))## # A tibble: 15 × 4
## name height gender hair_color
## <chr> <int> <fct> <chr>
## 1 C-3PO 167 masculine <NA>
## 2 R2-D2 96 masculine <NA>
## 3 R5-D4 97 masculine <NA>
## 4 Greedo 173 masculine <NA>
## 5 Jabba Desilijic Tiure 175 masculine <NA>
## 6 Jek Tono Porkins 180 <NA> brown
## 7 Arvel Crynyd NA masculine brown
## 8 Gregar Typho 185 <NA> black
## 9 Cordé 157 <NA> brown
## 10 Sly Moore 178 <NA> none
## 11 Finn NA masculine black
## 12 Rey NA feminine brown
## 13 Poe Dameron NA masculine brown
## 14 BB8 NA masculine none
## 15 Captain Phasma NA feminine noneAfter examing the NA in the dataset we want to drop NA for height but not for hair color because the caracter on the film have no hair
#drop NA for height
starwars %>%
select(name, height,gender,hair_color) %>%
filter(!complete.cases(.)) %>%
drop_na(height)## # A tibble: 9 × 4
## name height gender hair_color
## <chr> <int> <fct> <chr>
## 1 C-3PO 167 masculine <NA>
## 2 R2-D2 96 masculine <NA>
## 3 R5-D4 97 masculine <NA>
## 4 Greedo 173 masculine <NA>
## 5 Jabba Desilijic Tiure 175 masculine <NA>
## 6 Jek Tono Porkins 180 <NA> brown
## 7 Gregar Typho 185 <NA> black
## 8 Cordé 157 <NA> brown
## 9 Sly Moore 178 <NA> nonetransform hair_color for some character in none because they did not have hair
starwars %>%
select(name, height,gender,hair_color) %>%
mutate(hair_color = replace_na(hair_color,'none'))## # A tibble: 87 × 4
## name height gender hair_color
## <chr> <int> <fct> <chr>
## 1 Luke Skywalker 172 masculine blond
## 2 C-3PO 167 masculine none
## 3 R2-D2 96 masculine none
## 4 Darth Vader 202 masculine none
## 5 Leia Organa 150 feminine brown
## 6 Owen Lars 178 masculine brown, grey
## 7 Beru Whitesun Lars 165 feminine brown
## 8 R5-D4 97 masculine none
## 9 Biggs Darklighter 183 masculine black
## 10 Obi-Wan Kenobi 182 masculine auburn, white
## # ℹ 77 more rowsDuplicate
names <- c('Peter','John','Andrew','Peter')
age <- c(22,33,44,22)
friends <- data.frame(names,age)
#this is with the tidyverse to see the duplicated
duplicated(friends)## [1] FALSE FALSE FALSE TRUEglimpse(friends)## Rows: 4
## Columns: 2
## $ names <chr> "Peter", "John", "Andrew", "Peter"
## $ age <dbl> 22, 33, 44, 22# this under is the old R
friends[!duplicated(friends), ]## names age
## 1 Peter 22
## 2 John 33
## 3 Andrew 44####. sama with tidyverse
friends %>% distinct()## names age
## 1 Peter 22
## 2 John 33
## 3 Andrew 44Recording variables
starwars %>% select(name,gender)## # A tibble: 87 × 2
## name gender
## <chr> <fct>
## 1 Luke Skywalker masculine
## 2 C-3PO masculine
## 3 R2-D2 masculine
## 4 Darth Vader masculine
## 5 Leia Organa feminine
## 6 Owen Lars masculine
## 7 Beru Whitesun Lars feminine
## 8 R5-D4 masculine
## 9 Biggs Darklighter masculine
## 10 Obi-Wan Kenobi masculine
## # ℹ 77 more rowsstarwars %>%
select(name, gender) %>%
mutate(gender_value = recode(gender,
'masculine'= 1,
'feminine'= 2))## # A tibble: 87 × 3
## name gender gender_value
## <chr> <fct> <dbl>
## 1 Luke Skywalker masculine 1
## 2 C-3PO masculine 1
## 3 R2-D2 masculine 1
## 4 Darth Vader masculine 1
## 5 Leia Organa feminine 2
## 6 Owen Lars masculine 1
## 7 Beru Whitesun Lars feminine 2
## 8 R5-D4 masculine 1
## 9 Biggs Darklighter masculine 1
## 10 Obi-Wan Kenobi masculine 1
## # ℹ 77 more rows