EDA
2540120585_KevinaNugrahaEleeas
2022-06-02
#source('create_datasets.R')
library(readr)## Warning: package 'readr' was built under R version 4.1.3library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
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## intersect, setdiff, setequal, unionlibrary(ggplot2)## Warning: package 'ggplot2' was built under R version 4.1.3library(openintro)## Warning: package 'openintro' was built under R version 4.1.3## Loading required package: airports## Warning: package 'airports' was built under R version 4.1.3## Loading required package: cherryblossom## Warning: package 'cherryblossom' was built under R version 4.1.3## Loading required package: usdata## Warning: package 'usdata' was built under R version 4.1.3library(Hmisc)## Warning: package 'Hmisc' was built under R version 4.1.3## Loading required package: lattice##
## Attaching package: 'lattice'## The following objects are masked from 'package:openintro':
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## ethanol, lsegments## Loading required package: survival##
## Attaching package: 'survival'## The following object is masked from 'package:openintro':
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## Attaching package: 'Hmisc'## The following objects are masked from 'package:dplyr':
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## format.pval, unitslibrary(gapminder)## Warning: package 'gapminder' was built under R version 4.1.3cars <- read.csv("https://assets.datacamp.com/production/course_1796/datasets/cars04.csv")
comics <- read.csv("https://assets.datacamp.com/production/course_1796/datasets/comics.csv")
life <- read.csv("https://assets.datacamp.com/production/course_1796/datasets/life_exp_raw.csv")Exploring Categorical Data
Bar chart expectations
Bar charts with categorical variables on the x axis and in the fill are a common way to see a contingency table visually.
It essentialy what you would get if you used the
tablefunction with two variablesWhich way you show the data can change the perception.
Which variable you use for the fill or the position of the bars (fill, dodge, stack) all can give different perceptions
head(comics)## name id align eye hair
## 1 Spider-Man (Peter Parker) Secret Good Hazel Eyes Brown Hair
## 2 Captain America (Steven Rogers) Public Good Blue Eyes White Hair
## 3 Wolverine (James \\"Logan\\" Howlett) Public Neutral Blue Eyes Black Hair
## 4 Iron Man (Anthony \\"Tony\\" Stark) Public Good Blue Eyes Black Hair
## 5 Thor (Thor Odinson) No Dual Good Blue Eyes Blond Hair
## 6 Benjamin Grimm (Earth-616) Public Good Blue Eyes No Hair
## gender gsm alive appearances first_appear publisher
## 1 Male <NA> Living Characters 4043 Aug-62 marvel
## 2 Male <NA> Living Characters 3360 Mar-41 marvel
## 3 Male <NA> Living Characters 3061 Oct-74 marvel
## 4 Male <NA> Living Characters 2961 Mar-63 marvel
## 5 Male <NA> Living Characters 2258 Nov-50 marvel
## 6 Male <NA> Living Characters 2255 Nov-61 marvel#comicsDataframe comics memiliki 19856 baris dan 11 kolom yaitu kolom name, id, align, eye, hair, gender, gsm, alive, appearance, first_appear, and publisher. Data berisi setiap karakter komik yang di terbitkan oleh Marvel dan dc - name: Nama karakter - id: Status identitas karakter (Secret Identity, Public identity, [on marvel only: No Dual Identity]) - align: Sifat - eye: Warna mata - hair: Warna rambut - gender: Jenis kelamin - gsm: Jika karakter memiliki minoritas seksual (Homosexual dan bisexual characters) - alive: Mati atau masih hidup - appearances: Jumlah kemunculan karakter - first_appear: Waktu karakter muncul pertama kali - publisher: Penerbit karakter
Semua kolom bertipe data character, kecuali kolom ‘appearances’ yang bertipe data integer. Semua kolom gsm memiliki nilai kosong.
describe(comics)## comics
##
## 11 Variables 23272 Observations
## --------------------------------------------------------------------------------
## name
## n missing distinct
## 23272 0 23272
##
## lowest : 'Spinner (Earth-616) \"Spider-Girl\" (Mutant\/Spider Clone) (Earth-616) \"Thumper\" Morgan (Earth-616) \u00c4kr\u00e4s (Earth-616) 107 (Earth-616)
## highest: Zzlrrrzzzm (New Earth) Zzutak's Enemy (Earth-616) Zzutak (Earth-616) Zzxz (Earth-616) Zzzax (Earth-616)
## --------------------------------------------------------------------------------
## id
## n missing distinct
## 17489 5783 4
##
## Value No Dual Public Secret Unknown
## Frequency 1788 6994 8698 9
## Proportion 0.102 0.400 0.497 0.001
## --------------------------------------------------------------------------------
## align
## n missing distinct
## 19859 3413 4
##
## Value Bad Good Neutral
## Frequency 9615 7468 2773
## Proportion 0.484 0.376 0.140
##
## Value Reformed Criminals
## Frequency 3
## Proportion 0.000
## --------------------------------------------------------------------------------
## eye
## n missing distinct
## 9877 13395 26
##
## lowest : Amber Eyes Auburn Hair Black Eyeballs Black Eyes Blue Eyes
## highest: Variable Eyes Violet Eyes White Eyes Yellow Eyeballs Yellow Eyes
## --------------------------------------------------------------------------------
## hair
## n missing distinct
## 16734 6538 28
##
## lowest : Auburn Hair Bald Black Hair Blond Hair Blue Hair
## highest: Strawberry Blond Hair Variable Hair Violet Hair White Hair Yellow Hair
## --------------------------------------------------------------------------------
## gender
## n missing distinct
## 22293 979 3
##
## Value Female Male Other
## Frequency 5804 16421 68
## Proportion 0.260 0.737 0.003
## --------------------------------------------------------------------------------
## gsm
## n missing distinct
## 154 23118 6
##
## lowest : Bisexual Characters Genderfluid Characters Homosexual Characters Pansexual Characters Transgender Characters
## highest: Genderfluid Characters Homosexual Characters Pansexual Characters Transgender Characters Transvestites
##
## Value Bisexual Characters Genderfluid Characters Homosexual Characters
## Frequency 29 1 120
## Proportion 0.188 0.006 0.779
##
## Value Pansexual Characters Transgender Characters Transvestites
## Frequency 1 2 1
## Proportion 0.006 0.013 0.006
## --------------------------------------------------------------------------------
## alive
## n missing distinct
## 23266 6 2
##
## Value Deceased Characters Living Characters
## Frequency 5458 17808
## Proportion 0.235 0.765
## --------------------------------------------------------------------------------
## appearances
## n missing distinct Info Mean Gmd .05 .10
## 21821 1451 442 0.978 19.01 30.62 1 1
## .25 .50 .75 .90 .95
## 1 4 10 30 63
##
## lowest : 1 2 3 4 5, highest: 2961 3061 3093 3360 4043
## --------------------------------------------------------------------------------
## first_appear
## n missing distinct
## 22388 884 1606
##
## lowest : 1935, October 1936, February 1936, March 1936, September 1937, March
## highest: Sep-95 Sep-96 Sep-97 Sep-98 Sep-99
## --------------------------------------------------------------------------------
## publisher
## n missing distinct
## 23272 0 2
##
## Value dc marvel
## Frequency 6896 16376
## Proportion 0.296 0.704
## --------------------------------------------------------------------------------comics$align <- as.factor(comics$align)
levels(comics$align)## [1] "Bad" "Good" "Neutral"
## [4] "Reformed Criminals"Kolom align memiliki 4 tipe nilai Bad, Good, Neutral, dan Reformed Criminals
comics$gender <- as.factor(comics$gender)
levels(comics$gender)## [1] "Female" "Male" "Other"Kolom gender memiliki 3 tipe nilai yaitu female, male, dan other
tabel <- table(comics$align, comics$gender)
tabel##
## Female Male Other
## Bad 1573 7561 32
## Good 2490 4809 17
## Neutral 836 1799 17
## Reformed Criminals 1 2 0membuat table antara align dan gender
comics <- comics %>%
filter(align != 'Reformed Criminals') %>%
droplevels()
levels(comics$align)## [1] "Bad" "Good" "Neutral"Karena nilai reformed criminal pada kolom align sangat kecil dibandingkan 3 tipe lainnya maka dihapus untuk mempermudah proses analisis data
ggplot(comics, aes(x = align, fill = gender)) + geom_bar(position = "dodge")
Dari histogram tersebut, gender male memiliki nilai align bad yang tertinggi yaitu lebih dari 7000, gender female memiliki nilai align tertinggi pada good yaitu lebih dari 2000, dan other hampir tak nampak nilai align nya. Gender male memiliki nilai tertinggi dalam setiap tipe align.
ggplot(comics, aes(x = gender, fill = align)) +
geom_bar(positio = "dodge") +
theme(axis.text.x = element_text(angle = 90))
Histogram ini adalah kebalikan dari histogram sebelumnya, ini lebih menjelaskan pada tiap gendernya. Gender Female memiliki align good tertinggi, gender male memiliki align bad tertinggi, dan other dengan align kecil.
Bar chart interpretation
Among characters with “Neutral” alignment, males are the most common.
In general, there is an association between gender and alignment.
There are more male characters than female characters in this dataset.
print("Count dari comics kolom id dan align")## [1] "Count dari comics kolom id dan align"options(scipen = 999, digits = 3)
tbl_cnt <- table(comics$id, comics$align)
tbl_cnt##
## Bad Good Neutral
## No Dual 474 647 390
## Public 2172 2930 965
## Secret 4493 2475 959
## Unknown 7 0 2print("Proportions dari comics kolom id dan align")## [1] "Proportions dari comics kolom id dan align"prop.table(tbl_cnt)##
## Bad Good Neutral
## No Dual 0.030553 0.041704 0.025139
## Public 0.140003 0.188862 0.062202
## Secret 0.289609 0.159533 0.061815
## Unknown 0.000451 0.000000 0.000129prop.table() adalah fungsi untuk mengekspresikan entri tabel sebagai Pecahan Tabel Marginal. Untuk mendapatkan proporsi diseluruh baris atau kolom bawah hanya perlu menambah margin.print("hasil dari sum pasti bernilai 1")## [1] "hasil dari sum pasti bernilai 1"sum(prop.table(tbl_cnt))## [1] 1prop.table(tbl_cnt, 1)##
## Bad Good Neutral
## No Dual 0.314 0.428 0.258
## Public 0.358 0.483 0.159
## Secret 0.567 0.312 0.121
## Unknown 0.778 0.000 0.222margin 1 dan nilai sum 4
prop.table(tbl_cnt, 2)##
## Bad Good Neutral
## No Dual 0.066331 0.106907 0.168394
## Public 0.303946 0.484137 0.416667
## Secret 0.628743 0.408956 0.414076
## Unknown 0.000980 0.000000 0.000864margin 2 dan nilai sum 3
ggplot(comics, aes(x = id, fill = align)) +
geom_bar(position = "fill") +
ylab("proportion")
Id unknown memiliki kecendurungan memiliki sifat buruk (75%) tanpa sifat baik, sementara ke 4 id lain memiliki sifat baik dan id no dual memiliki sifat buruk terkecil. Id NA dan secret memiliki proporsi sifat yang hampir sama yaitu lebih dari 50% jahat dan sekitar 30% baik.
ggplot(comics, aes(x = align, fill = id)) +
geom_bar(position = "fill") +
ylab("proportion")
proporsi tokoh yang memiliki sifat buruk terbanyak adalah secret, tokoh dengan sifat neutral dan baik mayoritas adalah public dan secret dengan sedikit tokoh no dual dan NA.
tab <- table(comics$align, comics$gender)
options(scipen = 999, digits = 3)
prop.table(tab)##
## Female Male Other
## Bad 0.082210 0.395160 0.001672
## Good 0.130135 0.251333 0.000888
## Neutral 0.043692 0.094021 0.000888prop.table(tab, 2)##
## Female Male Other
## Bad 0.321 0.534 0.485
## Good 0.508 0.339 0.258
## Neutral 0.171 0.127 0.258ggplot(comics, aes(x = align, fill = gender)) +
geom_bar()
Terdapat 3 sifat yaitu bad, good, dan netral. Ketiganya mayoritas gender male karena memang dalam data tersebut tokoh pria memang lebih banyak dan wanita memiliki kecenderungan sifat good. Tokoh other tak nampak dan NA kecil.
ggplot(comics, aes(x = align, fill = gender)) +
geom_bar(position = "fill")
ketiga sifat tersebut mayoritas merujuk kepada gender male, ini merujuk pada karakter pria dalam komik yang lebih banyak.
table(comics$id)##
## No Dual Public Secret Unknown
## 1511 6067 7927 9dalam dataframe secret kebanyakan identitas tokohnya adalah secret dan public, sementara identitas unknown hanya terdapat 9 tokoh.
ggplot(comics, aes(x = id)) +
geom_bar()
dari histogram tersebut, tampak tokoh dengan identitas secret kurang lebih 8000, public sekitar 6000, NA lebih dari 4000, dan no dual kurang dari 2000
ggplot(comics, aes(x = id)) +
geom_bar() +
facet_wrap(~align)
dari histogram tersebut, tampak tokoh dengan sifat buruk terbanyak pada identitas secret sebanyak 4500 dan sekitar masing-masing 2000 beridentitas NA dan public. Tokoh bersifat baik mayoritas beridentitas public dan secret sejumlah 2500an. Tokoh dengan sifat netral beridentitas public dan secret sebanyak 1000 dan kurang dari 500 beridentitas No dual dan NA
comics$align <- factor(comics$align,
levels = c("Bad", "Neutral", "Good"))
ggplot(comics, aes(x = align)) +
geom_bar()
Dari hisogram tersebut, dapat kita lihat bahwa kebanyakan dari karakter dalam comics memiliki sifat buruk (sekitar 9000 karakter) dan sebagian bersifat baik (sekitar 7500 karakter) dan netral cukup kecil hanya berkisar 2000 karakter
ggplot(comics, aes(x = align)) +
geom_bar() +
facet_wrap(~ gender)
dari histogram diatas, dapat diketahui bahwa gender wanita biasanya bersifat baik dan gender pria mayoritas jahat,
lev <- c("apple", "key lime", "boston creme", "blueberry", "cherry", "pumpkin", "strawberry")
pies <- data.frame(flavors = as.factor(rep(c("apple", "blueberry", "boston creme", "cherry", "key lime", "pumpkin", "strawberry"), times = c(17, 14, 15, 13, 16, 12, 11))))
pies$flavor <- factor(pies$flavor, levels = lev)
head(pies$flavor)## [1] apple apple apple apple apple apple
## Levels: apple key lime boston creme blueberry cherry pumpkin strawberry#pies
ggplot(pies, aes(x = flavor)) +
geom_bar(fill = "chartreuse") +
theme(axis.text.x = element_text(angle = 90))
terdapat 98 baris dan 2 kolom, dimana apple adalah yang terbanyak dan strawberry yang jumlahnya terkecil, meski tidak terdapat perbedaan yang signifikan
Exploring Numerical Data
ggplot(cars, aes(x = weight)) +
geom_dotplot(dotsize = 0.4)## Bin width defaults to 1/30 of the range of the data. Pick better value with `binwidth`.## Warning: Removed 2 rows containing non-finite values (stat_bindot).
ggplot(cars, aes(x = weight)) +
geom_histogram(dotsize = 0.4, binwidth = 500)## Warning: Ignoring unknown parameters: dotsize## Warning: Removed 2 rows containing non-finite values (stat_bin).
ggplot(cars, aes(x = weight)) +
geom_density()## Warning: Removed 2 rows containing non-finite values (stat_density).
dari dataframe cars visualisasi kolom weight dapat terlihat perubahannya ketika menggunakan line chart
ggplot(cars, aes(x = 1, y = weight)) +
geom_boxplot() +
coord_flip()## Warning: Removed 2 rows containing non-finite values (stat_boxplot).
dari boxplot diatas terlihat bahwa terdapat cukup banyak outliers dan boxplot memiliki range 3000 sampai 4000
str(cars)## 'data.frame': 428 obs. of 19 variables:
## $ name : chr "Chevrolet Aveo 4dr" "Chevrolet Aveo LS 4dr hatch" "Chevrolet Cavalier 2dr" "Chevrolet Cavalier 4dr" ...
## $ sports_car : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
## $ suv : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
## $ wagon : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
## $ minivan : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
## $ pickup : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
## $ all_wheel : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
## $ rear_wheel : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
## $ msrp : int 11690 12585 14610 14810 16385 13670 15040 13270 13730 15460 ...
## $ dealer_cost: int 10965 11802 13697 13884 15357 12849 14086 12482 12906 14496 ...
## $ eng_size : num 1.6 1.6 2.2 2.2 2.2 2 2 2 2 2 ...
## $ ncyl : int 4 4 4 4 4 4 4 4 4 4 ...
## $ horsepwr : int 103 103 140 140 140 132 132 130 110 130 ...
## $ city_mpg : int 28 28 26 26 26 29 29 26 27 26 ...
## $ hwy_mpg : int 34 34 37 37 37 36 36 33 36 33 ...
## $ weight : int 2370 2348 2617 2676 2617 2581 2626 2612 2606 2606 ...
## $ wheel_base : int 98 98 104 104 104 105 105 103 103 103 ...
## $ length : int 167 153 183 183 183 174 174 168 168 168 ...
## $ width : int 66 66 69 68 69 67 67 67 67 67 ...ggplot(cars, aes(x = city_mpg)) +
geom_histogram() +
facet_wrap(~ suv)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 14 rows containing non-finite values (stat_bin).
unique(cars$ncyl)## [1] 4 6 3 8 5 12 10 -1table(cars$ncyl)##
## -1 3 4 5 6 8 10 12
## 2 1 136 7 190 87 2 3common_cyl <- filter(cars, ncyl %in% c(4,6,8))
ggplot(common_cyl, aes(x = as.factor(ncyl), y = city_mpg)) +
geom_boxplot()## Warning: Removed 11 rows containing non-finite values (stat_boxplot).
dari boxplot diatas, kolom ncyl dengan nilai 4 memiliki beberapa outliers, ncyl dengan nilai 6 hanya terdapat 1 outlier, sementara ncyl bernilai 8 tidak terdpat outlier
ggplot(common_cyl, aes(x = city_mpg, fill = as.factor(ncyl))) +
geom_density(alpha = .3)## Warning: Removed 11 rows containing non-finite values (stat_density).
kolom ncyl dengan nilai 4 dan 6 cukup tinggi sementara nilai 4 hanya 0.1
cars %>%
ggplot(aes(horsepwr)) +
geom_histogram() +
ggtitle("Horsepower distribution")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
cars %>%
filter(msrp < 25000) %>%
ggplot(aes(horsepwr)) +
geom_histogram() +
xlim(c(90, 550)) +
ggtitle("Horsepower distribtion for msrp < 25000")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 1 rows containing non-finite values (stat_bin).## Warning: Removed 2 rows containing missing values (geom_bar).
cars %>%
ggplot(aes(horsepwr)) +
geom_histogram(binwidth = 3) +
ggtitle("binwidth = 3")
cars %>%
ggplot(aes(horsepwr)) +
geom_histogram(binwidth = 30) +
ggtitle("binwidth = 30")
cars %>%
ggplot(aes(horsepwr)) +
geom_histogram(binwidth = 60) +
ggtitle("binwidth = 60")
cars %>%
ggplot(aes(x = 1, y = msrp)) +
geom_boxplot()
cars_no_out <- cars %>%
filter(msrp < 100000)
cars_no_out %>%
ggplot(aes(x = 1, y = msrp)) +
geom_boxplot()
dari boxplot kolom msrp terdapat cukup banyak outliers
cars %>%
ggplot(aes(x = 1, y = city_mpg)) +
geom_boxplot()## Warning: Removed 14 rows containing non-finite values (stat_boxplot).
cars %>%
ggplot(aes(city_mpg)) +
geom_density()## Warning: Removed 14 rows containing non-finite values (stat_density).
cars %>%
ggplot(aes(x = 1, y = width)) +
geom_boxplot()## Warning: Removed 28 rows containing non-finite values (stat_boxplot).
cars %>%
ggplot(aes(x = width)) +
geom_density()## Warning: Removed 28 rows containing non-finite values (stat_density).
common_cyl %>%
ggplot(aes(x = hwy_mpg)) +
geom_histogram() +
facet_grid(ncyl ~ suv) +
ggtitle("hwy_mpg by ncyl and suv")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 11 rows containing non-finite values (stat_bin).
Numerical Summaries
head(life)## State County fips Year Female.life.expectancy..years.
## 1 Alabama Autauga County 1001 1985 77.0
## 2 Alabama Baldwin County 1003 1985 78.8
## 3 Alabama Barbour County 1005 1985 76.0
## 4 Alabama Bibb County 1007 1985 76.6
## 5 Alabama Blount County 1009 1985 78.9
## 6 Alabama Bullock County 1011 1985 75.1
## Female.life.expectancy..national..years.
## 1 77.8
## 2 77.8
## 3 77.8
## 4 77.8
## 5 77.8
## 6 77.8
## Female.life.expectancy..state..years. Male.life.expectancy..years.
## 1 76.9 68.1
## 2 76.9 71.1
## 3 76.9 66.8
## 4 76.9 67.3
## 5 76.9 70.6
## 6 76.9 66.6
## Male.life.expectancy..national..years. Male.life.expectancy..state..years.
## 1 70.8 69.1
## 2 70.8 69.1
## 3 70.8 69.1
## 4 70.8 69.1
## 5 70.8 69.1
## 6 70.8 69.1x <- head(round(life$Female.life.expectancy..years.), 11)
x## [1] 77 79 76 77 79 75 77 77 77 78 77sum(x)/11## [1] 77.2mean(x)## [1] 77.2sort(x)## [1] 75 76 77 77 77 77 77 77 78 79 79median(x)## [1] 77table(x)## x
## 75 76 77 78 79
## 1 1 6 1 2str(gapminder)## tibble [1,704 x 6] (S3: tbl_df/tbl/data.frame)
## $ country : Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ continent: Factor w/ 5 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ year : int [1:1704] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## $ lifeExp : num [1:1704] 28.8 30.3 32 34 36.1 ...
## $ pop : int [1:1704] 8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
## $ gdpPercap: num [1:1704] 779 821 853 836 740 ...gap2007 <- filter(gapminder, year == 2007)
gap2007## # A tibble: 142 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 2007 43.8 31889923 975.
## 2 Albania Europe 2007 76.4 3600523 5937.
## 3 Algeria Africa 2007 72.3 33333216 6223.
## 4 Angola Africa 2007 42.7 12420476 4797.
## 5 Argentina Americas 2007 75.3 40301927 12779.
## 6 Australia Oceania 2007 81.2 20434176 34435.
## 7 Austria Europe 2007 79.8 8199783 36126.
## 8 Bahrain Asia 2007 75.6 708573 29796.
## 9 Bangladesh Asia 2007 64.1 150448339 1391.
## 10 Belgium Europe 2007 79.4 10392226 33693.
## # ... with 132 more rows#gap2007$lifeExp
#gap2007 %>%
# group_by(continent) %>%
# summarize(mean(lifeExp),
# median(lifeExp))gap2007 %>%
ggplot(aes(x = continent, y = lifeExp)) +
geom_boxplot()
x## [1] 77 79 76 77 79 75 77 77 77 78 77sum(x - mean(x))## [1] -0.0000000000000568sum((x - mean(x))^2)## [1] 13.6print("Variance")## [1] "Variance"sum((x - mean(x))^2)/(length(x) - 1)## [1] 1.36var(x)## [1] 1.36print("Standard Deviation")## [1] "Standard Deviation"sqrt(sum((x - mean(x))^2)/(length(x) - 1))## [1] 1.17sd(x)## [1] 1.17summary(x)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 75.0 77.0 77.0 77.2 77.5 79.0print("interquartile")## [1] "interquartile"IQR(x)## [1] 0.5print("MAX")## [1] "MAX"max(x)## [1] 79print("MIN")## [1] "MIN"min(x)## [1] 75print("perbedaan range antara max dan min")## [1] "perbedaan range antara max dan min"diff(range(x))## [1] 4str(gap2007)## tibble [142 x 6] (S3: tbl_df/tbl/data.frame)
## $ country : Factor w/ 142 levels "Afghanistan",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ continent: Factor w/ 5 levels "Africa","Americas",..: 3 4 1 1 2 5 4 3 3 4 ...
## $ year : int [1:142] 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 ...
## $ lifeExp : num [1:142] 43.8 76.4 72.3 42.7 75.3 ...
## $ pop : int [1:142] 31889923 3600523 33333216 12420476 40301927 20434176 8199783 708573 150448339 10392226 ...
## $ gdpPercap: num [1:142] 975 5937 6223 4797 12779 ...print("Error in is.data.frame(x) : object 'lifeExp' not found")## [1] "Error in is.data.frame(x) : object 'lifeExp' not found"#gap2007 %>%
# group_by(continent) %>%
# summarize(sd(lifeExp),
# IQR(lifeExp),
# n())gap2007 %>%
ggplot(aes(x = lifeExp, fill = continent)) +
geom_density(alpha = 0.3)
head(gap2007)## # A tibble: 6 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 2007 43.8 31889923 975.
## 2 Albania Europe 2007 76.4 3600523 5937.
## 3 Algeria Africa 2007 72.3 33333216 6223.
## 4 Angola Africa 2007 42.7 12420476 4797.
## 5 Argentina Americas 2007 75.3 40301927 12779.
## 6 Australia Oceania 2007 81.2 20434176 34435.print("Error in mean(lifeExp) : object 'lifeExp' not found")## [1] "Error in mean(lifeExp) : object 'lifeExp' not found"#gap2007 %>%
# filter(continent == "Americas") %>%
# summarize(mean(lifeExp),
# sd(lifeExp))print("Error in median(pop) : object 'pop' not found")## [1] "Error in median(pop) : object 'pop' not found"#gap2007 %>%
# summarize(median(pop),
# IQR(pop))gap2007 %>%
ggplot(aes(x = pop)) +
geom_density()
gap2007 <- gap2007 %>%
mutate(log_pop = log(pop))
gap2007 %>%
ggplot(aes(x = log_pop)) +
geom_density()
str(gapminder)## tibble [1,704 x 6] (S3: tbl_df/tbl/data.frame)
## $ country : Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ continent: Factor w/ 5 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ year : int [1:1704] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## $ lifeExp : num [1:1704] 28.8 30.3 32 34 36.1 ...
## $ pop : int [1:1704] 8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
## $ gdpPercap: num [1:1704] 779 821 853 836 740 ...gap_asia <- gap2007 %>%
filter(continent == "Asia") %>%
mutate(is_outlier = lifeExp < 50)
gap_asia %>%
filter(!is_outlier) %>%
ggplot(aes(x = 1, y = lifeExp)) +
geom_boxplot()
boxplot diatas tidak memiliki outliers karena sudah difilter dan boxplot tersebut memiliki range lebih dari 65 hingga sekitar 75
email <- read.csv("C:/Users/VANIA/Downloads/email.csv")
str(email)## 'data.frame': 3921 obs. of 21 variables:
## $ spam : int 0 0 0 0 0 0 0 0 0 0 ...
## $ to_multiple : int 0 0 0 0 0 0 1 1 0 0 ...
## $ from : int 1 1 1 1 1 1 1 1 1 1 ...
## $ cc : int 0 0 0 0 0 0 0 1 0 0 ...
## $ sent_email : int 0 0 0 0 0 0 1 1 0 0 ...
## $ time : chr "2012-01-01T06:16:41Z" "2012-01-01T07:03:59Z" "2012-01-01T16:00:32Z" "2012-01-01T09:09:49Z" ...
## $ image : int 0 0 0 0 0 0 0 1 0 0 ...
## $ attach : int 0 0 0 0 0 0 0 1 0 0 ...
## $ dollar : int 0 0 4 0 0 0 0 0 0 0 ...
## $ winner : chr "no" "no" "no" "no" ...
## $ inherit : int 0 0 1 0 0 0 0 0 0 0 ...
## $ viagra : int 0 0 0 0 0 0 0 0 0 0 ...
## $ password : int 0 0 0 0 2 2 0 0 0 0 ...
## $ num_char : num 11.37 10.5 7.77 13.26 1.23 ...
## $ line_breaks : int 202 202 192 255 29 25 193 237 69 68 ...
## $ format : int 1 1 1 1 0 0 1 1 0 1 ...
## $ re_subj : int 0 0 0 0 0 0 0 0 0 0 ...
## $ exclaim_subj: int 0 0 0 0 0 0 0 0 0 0 ...
## $ urgent_subj : int 0 0 0 0 0 0 0 0 0 0 ...
## $ exclaim_mess: int 0 1 6 48 1 1 1 18 1 0 ...
## $ number : chr "big" "small" "small" "small" ...email## spam to_multiple from cc sent_email time image attach
## 1 0 0 1 0 0 2012-01-01T06:16:41Z 0 0
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## 4 0 0 1 0 0 2012-01-01T09:09:49Z 0 0
## 5 0 0 1 0 0 2012-01-01T10:00:01Z 0 0
## 6 0 0 1 0 0 2012-01-01T10:04:46Z 0 0
## 7 0 1 1 0 1 2012-01-01T17:55:06Z 0 0
## 8 0 1 1 1 1 2012-01-01T18:45:21Z 1 1
## 9 0 0 1 0 0 2012-01-01T21:08:59Z 0 0
## 10 0 0 1 0 0 2012-01-01T18:12:00Z 0 0
## 11 0 0 1 0 1 2012-01-01T18:23:44Z 0 0
## 12 0 0 1 1 0 2012-01-01T21:40:51Z 0 0
## 13 0 0 1 0 0 2012-01-02T03:00:18Z 0 0
## 14 0 0 1 1 1 2012-01-01T19:38:32Z 0 0
## 15 0 0 1 2 0 2012-01-01T23:32:53Z 0 0
## 16 0 0 1 1 1 2012-01-01T23:40:14Z 0 0
## 17 0 0 1 0 0 2012-01-02T05:42:16Z 0 0
## 18 0 0 1 2 0 2012-01-02T00:54:46Z 0 0
## 19 0 0 1 0 1 2012-01-02T01:40:40Z 0 0
## 20 0 0 1 0 0 2012-01-02T01:58:14Z 0 0
## 21 0 0 1 0 0 2012-01-02T02:05:45Z 0 0
## 22 0 0 1 0 0 2012-01-02T02:05:47Z 2 2
## 23 0 0 1 0 0 2012-01-02T07:07:22Z 0 0
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## 30 0 0 1 0 0 2012-01-02T04:43:43Z 0 0
## 31 0 0 1 2 1 2012-01-02T02:58:13Z 0 1
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## dollar winner inherit viagra password num_char line_breaks format re_subj
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## 1765 0 no 0 0 0 2.128 45 1 1
## 1766 0 no 0 0 0 7.568 208 1 1
## 1767 0 no 0 0 0 5.504 102 1 1
## 1768 0 no 0 0 22 10.113 203 1 0
## 1769 0 no 0 0 22 10.113 203 1 0
## 1770 2 no 0 0 0 18.649 388 1 0
## 1771 44 no 0 0 0 190.087 4022 1 0
## 1772 0 no 0 0 0 1.259 36 1 1
## 1773 4 no 0 0 0 11.648 172 1 0
## 1774 0 no 0 0 0 1.448 32 1 0
## 1775 0 no 0 0 0 38.198 577 1 0
## 1776 0 no 0 0 0 5.127 120 1 1
## 1777 0 no 0 0 0 8.937 211 1 1
## 1778 0 no 0 0 0 0.740 18 1 1
## 1779 0 no 0 0 0 2.286 60 1 1
## 1780 0 no 0 0 11 9.521 201 1 0
## 1781 0 no 0 0 11 9.517 201 1 0
## 1782 0 no 0 0 0 13.337 256 1 1
## 1783 0 no 0 0 0 0.082 6 0 1
## 1784 0 no 0 0 0 0.109 4 0 1
## 1785 0 no 0 0 0 7.347 98 0 0
## 1786 0 no 0 0 0 0.355 23 0 1
## 1787 0 no 0 0 0 0.080 6 0 1
## 1788 63 no 0 0 0 37.104 1332 1 0
## 1789 0 no 0 0 0 7.767 150 1 0
## 1790 0 no 0 0 0 0.244 8 0 1
## 1791 0 no 0 0 0 1.840 46 1 1
## 1792 2 no 0 0 0 14.249 353 1 0
## 1793 0 no 0 0 0 73.318 1425 1 0
## 1794 0 no 0 0 0 15.402 422 1 0
## 1795 0 no 0 0 0 0.667 26 0 1
## 1796 0 no 0 0 0 4.818 58 0 0
## 1797 0 no 0 0 0 0.790 17 0 0
## 1798 2 no 0 0 0 8.526 147 1 0
## 1799 0 no 0 0 0 6.843 199 1 0
## 1800 15 no 0 0 0 16.763 379 1 0
## 1801 20 no 1 0 0 17.677 380 1 0
## 1802 0 no 0 0 0 13.225 255 1 0
## 1803 0 no 0 0 0 25.340 639 1 0
## 1804 27 no 0 0 0 19.133 701 1 0
## 1805 0 no 0 0 0 0.262 8 0 1
## 1806 9 no 0 0 0 1.854 47 0 1
## 1807 0 no 0 0 0 10.419 198 1 0
## 1808 2 no 0 0 0 14.263 209 1 0
## 1809 20 no 0 0 0 46.751 922 1 0
## 1810 15 no 0 0 0 17.700 398 1 0
## 1811 0 no 0 0 0 0.046 3 0 0
## 1812 0 no 0 0 0 0.723 15 0 1
## 1813 0 no 0 0 0 0.907 20 0 1
## 1814 0 no 0 0 0 2.599 86 1 0
## 1815 0 no 0 0 0 24.422 622 1 0
## 1816 0 no 0 0 0 25.173 635 1 0
## 1817 0 no 0 0 0 24.835 630 1 0
## 1818 0 no 0 0 0 25.476 640 1 0
## 1819 0 no 0 0 0 24.947 632 1 0
## 1820 0 no 0 0 0 7.525 178 1 0
## 1821 2 yes 1 0 0 10.218 235 1 0
## 1822 0 no 0 0 0 16.193 306 1 0
## 1823 0 no 0 0 0 8.577 119 1 0
## 1824 4 no 1 0 0 7.875 190 1 0
## 1825 0 no 0 0 0 25.926 649 1 0
## 1826 11 no 0 0 0 19.503 424 1 0
## 1827 16 no 0 0 0 26.765 295 1 0
## 1828 26 no 0 0 0 16.976 216 1 0
## 1829 0 no 0 0 0 4.177 103 1 1
## 1830 0 no 0 0 0 0.649 20 0 0
## 1831 0 no 0 0 0 1.468 40 0 0
## 1832 15 no 0 0 0 22.389 489 1 0
## 1833 0 no 0 0 0 25.022 633 1 0
## 1834 0 no 0 0 0 26.093 651 1 0
## 1835 13 no 0 0 0 16.179 366 1 0
## 1836 0 no 0 0 0 31.362 750 1 0
## 1837 0 no 0 0 0 9.337 179 1 0
## 1838 0 no 0 0 0 25.555 643 1 0
## 1839 0 no 0 0 0 31.462 753 1 0
## 1840 0 no 0 0 0 33.004 778 1 0
## 1841 0 no 0 0 0 6.676 166 1 0
## 1842 0 no 0 0 0 9.404 188 1 0
## 1843 12 no 1 0 0 12.116 270 1 0
## 1844 0 no 0 0 0 0.887 27 0 0
## 1845 0 no 0 0 0 1.027 26 0 0
## 1846 0 no 0 0 0 0.917 28 0 1
## 1847 0 no 0 0 0 2.887 84 1 1
## 1848 0 no 0 0 0 7.449 199 1 1
## 1849 0 no 0 0 0 1.590 35 0 1
## 1850 0 no 0 0 0 1.589 31 1 0
## 1851 0 no 0 0 0 4.187 89 1 1
## 1852 0 no 0 0 0 13.157 320 1 1
## 1853 0 no 0 0 0 3.100 72 0 1
## 1854 0 no 0 0 0 3.503 81 0 1
## 1855 4 no 0 0 0 3.135 68 1 0
## 1856 4 no 0 0 0 4.902 117 1 1
## 1857 0 no 0 0 0 0.651 19 0 0
## 1858 0 no 0 0 0 7.039 182 1 1
## 1859 0 no 0 0 0 9.291 253 1 0
## 1860 0 no 0 0 0 12.151 348 1 1
## 1861 0 no 0 0 0 1.167 33 1 1
## 1862 0 no 0 0 0 14.745 449 1 1
## 1863 0 no 0 0 0 17.162 536 1 1
## 1864 0 no 0 0 0 17.926 560 1 1
## 1865 0 no 0 0 0 6.900 188 1 0
## 1866 0 no 0 0 0 6.491 190 1 1
## 1867 2 no 0 0 0 10.731 357 1 0
## 1868 0 no 0 0 0 3.637 75 1 0
## 1869 0 no 0 0 0 4.946 175 1 0
## 1870 1 no 0 0 0 1.551 45 0 0
## 1871 1 no 0 0 0 1.595 59 0 1
## 1872 0 no 0 0 0 1.176 28 0 0
## 1873 0 no 0 0 0 1.213 34 0 0
## 1874 2 no 0 0 0 7.818 162 1 1
## 1875 0 no 0 0 0 0.900 29 0 1
## 1876 1 no 0 0 0 4.809 88 1 1
## 1877 0 no 0 0 0 5.495 143 1 1
## 1878 0 no 0 0 0 3.568 81 1 0
## 1879 0 no 0 0 0 33.828 437 0 0
## 1880 0 no 0 0 0 9.178 127 0 0
## 1881 0 no 0 0 0 3.153 90 1 1
## 1882 0 no 0 0 0 0.829 31 0 1
## 1883 0 no 0 0 0 1.063 23 1 0
## 1884 0 no 0 0 0 13.224 174 0 0
## 1885 0 no 0 0 0 1.380 51 0 1
## 1886 0 no 0 0 0 1.195 25 1 0
## 1887 0 no 0 0 0 2.734 84 1 0
## 1888 0 no 0 0 0 0.601 16 1 0
## 1889 1 no 0 0 0 11.289 153 0 0
## 1890 2 no 0 0 0 8.678 149 1 0
## 1891 0 no 0 0 0 25.739 646 1 0
## 1892 0 no 0 0 0 17.318 311 1 0
## 1893 0 no 0 0 0 26.316 656 1 0
## 1894 0 no 0 0 0 0.266 11 0 0
## 1895 0 no 0 0 0 34.408 807 1 0
## 1896 0 no 0 0 0 0.076 6 0 1
## 1897 0 no 0 0 0 4.560 70 0 0
## 1898 6 no 0 0 0 7.501 145 1 0
## 1899 0 no 0 0 0 9.491 233 1 1
## 1900 0 no 0 0 0 0.331 16 0 1
## 1901 0 no 0 0 0 3.096 42 1 1
## 1902 0 no 0 0 0 1.643 48 1 1
## 1903 0 no 0 0 0 3.451 91 1 1
## 1904 0 no 0 0 0 1.447 55 0 0
## 1905 0 no 0 0 0 2.977 81 1 1
## 1906 0 no 0 0 0 4.316 127 1 1
## 1907 0 no 0 0 0 1.446 40 1 0
## 1908 0 no 0 0 0 10.761 299 1 0
## 1909 0 no 0 0 0 25.189 636 1 0
## 1910 0 no 0 0 0 3.967 100 1 0
## 1911 0 no 0 0 0 26.877 667 1 0
## 1912 0 no 0 0 0 0.349 13 0 0
## 1913 6 no 0 0 0 19.838 195 1 0
## 1914 0 no 0 0 0 24.957 632 1 0
## 1915 0 no 0 0 0 24.542 624 1 0
## 1916 0 no 0 0 0 25.200 636 1 0
## 1917 0 no 0 0 0 10.486 234 1 0
## 1918 0 no 0 0 0 1.028 22 1 0
## 1919 0 no 0 0 0 26.630 663 1 0
## 1920 0 no 0 0 2 25.209 725 1 0
## 1921 0 no 0 0 0 24.656 822 1 0
## 1922 16 no 1 0 0 14.096 312 1 0
## 1923 0 no 0 0 0 10.091 136 0 0
## 1924 0 no 0 0 0 4.312 124 1 1
## 1925 0 no 0 0 0 9.861 179 1 0
## 1926 5 no 0 0 0 20.966 498 1 0
## 1927 0 no 0 0 0 32.008 499 1 0
## 1928 0 no 0 0 0 15.472 302 1 0
## 1929 0 no 0 0 0 14.043 255 1 0
## 1930 0 no 0 0 0 2.529 79 1 0
## 1931 0 no 0 0 0 4.197 98 1 1
## 1932 0 no 0 0 0 2.014 50 1 1
## 1933 0 no 0 0 0 6.071 157 1 1
## 1934 0 no 0 0 0 1.307 35 0 0
## 1935 0 no 0 0 0 10.081 214 1 1
## 1936 0 no 0 0 0 8.006 214 1 1
## 1937 0 no 0 0 0 2.784 71 1 1
## 1938 0 no 0 0 0 0.732 20 1 0
## 1939 4 no 0 0 0 7.278 201 1 1
## 1940 0 no 0 0 0 13.622 311 1 1
## 1941 1 no 0 0 0 23.601 408 1 0
## 1942 0 no 0 0 0 24.540 624 1 0
## 1943 0 no 0 0 0 3.778 98 1 0
## 1944 4 no 0 0 0 9.049 254 1 1
## 1945 0 no 0 0 0 5.569 95 1 0
## 1946 0 no 0 0 0 0.232 12 0 0
## 1947 0 no 0 0 0 0.340 12 0 0
## 1948 0 no 0 0 0 12.290 139 1 0
## 1949 0 no 0 0 0 0.006 3 0 0
## 1950 0 no 0 0 0 19.343 446 1 0
## 1951 0 no 0 0 0 4.350 62 1 0
## 1952 0 no 0 0 0 1.485 36 1 0
## 1953 0 no 0 0 0 24.846 539 1 0
## 1954 0 no 0 0 0 19.986 452 1 0
## 1955 0 no 0 0 0 0.054 3 0 0
## 1956 0 no 0 0 0 19.997 452 1 0
## 1957 0 no 0 0 0 1.881 26 1 1
## 1958 0 no 0 0 0 24.709 541 1 0
## 1959 0 no 0 0 0 1.212 29 0 1
## 1960 4 no 0 0 0 22.227 356 1 0
## 1961 2 yes 0 0 0 2.152 41 0 1
## 1962 0 no 0 0 0 0.299 4 1 0
## 1963 1 no 0 0 0 7.714 138 1 1
## 1964 12 no 0 0 0 17.778 188 1 0
## 1965 0 no 0 0 0 5.242 56 0 0
## 1966 4 no 0 0 0 10.286 296 1 1
## 1967 0 no 0 0 0 19.985 424 1 0
## 1968 0 no 0 0 0 7.015 170 1 1
## 1969 0 no 0 0 0 0.545 13 1 1
## 1970 0 no 0 0 0 1.732 45 1 1
## 1971 0 no 0 0 0 7.505 168 1 0
## 1972 0 no 0 0 0 24.433 621 1 0
## 1973 20 no 1 0 0 20.623 407 1 0
## 1974 0 no 0 0 0 11.933 165 0 0
## 1975 4 yes 1 0 0 8.575 199 1 0
## 1976 0 no 0 0 2 2.100 53 1 1
## 1977 4 no 0 0 0 11.743 347 1 1
## 1978 0 no 0 0 0 25.653 645 1 0
## 1979 4 yes 0 0 0 7.661 92 1 0
## 1980 2 no 0 0 0 8.118 136 1 0
## 1981 2 no 0 0 0 11.630 175 1 1
## 1982 0 no 0 0 0 0.705 21 1 0
## 1983 0 no 0 0 0 3.859 82 1 1
## 1984 6 no 0 0 0 46.305 1345 1 0
## 1985 0 no 0 0 0 1.407 32 0 1
## 1986 0 no 0 0 0 5.324 129 1 1
## 1987 0 no 0 0 0 2.043 50 1 0
## 1988 0 no 0 0 0 33.360 439 0 0
## 1989 0 no 0 0 0 7.111 150 1 1
## 1990 0 no 0 0 0 0.812 24 0 0
## 1991 2 no 0 0 0 12.388 228 1 1
## 1992 1 no 0 0 0 9.089 152 1 0
## 1993 0 no 0 0 0 13.176 184 0 1
## 1994 0 no 0 0 0 12.974 168 0 0
## 1995 0 no 0 0 0 0.999 36 0 0
## 1996 1 no 0 0 0 26.636 661 1 0
## 1997 0 no 0 0 0 2.575 53 1 0
## 1998 1 no 0 0 0 27.995 687 1 0
## 1999 0 no 0 0 0 0.775 24 0 0
## 2000 0 no 0 0 0 2.969 80 1 0
## 2001 1 no 0 0 0 29.379 711 1 0
## 2002 0 no 0 0 0 17.339 311 1 0
## 2003 10 no 0 0 0 13.975 166 1 0
## 2004 1 no 0 0 0 30.652 735 1 0
## 2005 0 no 0 0 0 7.922 229 1 1
## 2006 0 no 0 0 0 3.184 77 1 1
## 2007 0 no 0 0 0 3.021 73 1 1
## 2008 0 no 0 0 0 2.538 61 1 1
## 2009 0 no 0 0 0 23.050 537 1 1
## 2010 0 no 0 0 0 5.905 94 1 0
## 2011 0 no 0 0 0 24.707 626 1 0
## 2012 0 no 0 0 0 25.542 642 1 0
## 2013 0 no 0 0 0 26.255 654 1 0
## 2014 0 no 0 0 0 26.807 664 1 0
## 2015 0 no 0 0 0 27.266 673 1 0
## 2016 9 no 0 0 0 39.245 667 1 0
## 2017 0 no 0 0 0 26.158 652 1 0
## 2018 0 no 0 0 0 46.703 752 1 0
## 2019 0 no 0 0 0 8.664 211 1 0
## 2020 0 no 0 0 0 3.720 84 1 0
## 2021 8 no 1 0 0 11.427 249 1 0
## 2022 0 no 0 0 0 9.786 196 1 0
## 2023 0 no 0 0 0 25.415 639 1 0
## 2024 0 no 0 0 0 2.342 63 1 0
## 2025 0 no 0 0 0 5.731 96 0 0
## 2026 0 no 0 0 0 5.528 61 0 0
## 2027 21 no 0 0 0 16.182 553 1 0
## 2028 0 no 0 0 0 3.529 80 1 0
## 2029 0 no 0 0 0 6.988 156 1 0
## 2030 0 no 0 0 0 24.439 621 1 0
## 2031 0 no 0 0 0 4.266 78 1 0
## 2032 0 no 0 0 0 0.458 14 1 1
## 2033 0 no 0 0 4 0.413 19 0 0
## 2034 0 no 0 0 0 25.959 648 1 0
## 2035 0 no 0 0 0 25.572 643 1 0
## 2036 0 no 0 0 0 25.093 635 1 0
## 2037 0 no 0 0 0 3.199 26 1 0
## 2038 0 no 0 0 0 18.539 524 1 1
## 2039 0 no 0 0 0 0.517 20 1 0
## 2040 0 no 0 0 0 25.101 635 1 0
## 2041 0 no 0 0 0 1.132 24 1 0
## 2042 1 no 0 0 0 0.517 17 0 0
## 2043 0 no 0 0 0 1.791 41 1 1
## 2044 0 no 0 0 0 2.266 68 1 1
## 2045 0 no 0 0 0 0.574 17 0 1
## 2046 0 no 0 0 0 0.715 13 0 0
## 2047 0 no 0 0 0 11.002 209 1 0
## 2048 0 no 0 0 0 26.904 665 1 0
## 2049 2 no 0 0 0 9.585 150 1 0
## 2050 0 no 0 0 0 2.117 36 1 1
## 2051 0 no 0 0 0 0.239 4 1 0
## 2052 0 no 0 0 0 7.011 183 1 0
## 2053 0 no 0 0 0 2.077 19 0 0
## 2054 0 no 0 0 0 28.464 693 1 0
## 2055 0 no 0 0 0 1.763 45 1 1
## 2056 0 no 0 0 0 6.457 134 1 0
## 2057 0 no 0 0 0 0.353 19 0 0
## 2058 0 no 0 0 0 25.579 642 1 0
## 2059 0 no 0 0 0 3.233 48 0 0
## 2060 11 no 0 0 0 16.341 357 1 0
## 2061 0 no 0 0 0 4.777 187 1 0
## 2062 0 no 0 0 0 25.385 638 1 0
## 2063 2 yes 0 0 0 10.907 153 1 0
## 2064 0 no 0 0 0 22.428 498 1 0
## 2065 0 no 0 0 0 27.182 671 1 0
## 2066 0 no 0 0 0 72.806 1045 1 0
## 2067 1 no 0 0 0 2.531 39 0 1
## 2068 16 no 1 0 0 20.340 404 1 0
## 2069 0 no 0 0 0 9.610 196 1 0
## 2070 0 no 0 0 0 34.473 446 0 0
## 2071 1 no 0 0 0 38.726 513 1 0
## 2072 4 no 0 0 0 9.989 299 1 0
## 2073 0 no 0 0 0 5.950 168 1 0
## 2074 0 no 0 0 0 1.019 31 0 1
## 2075 0 no 0 0 0 0.654 18 1 0
## 2076 0 no 0 0 0 0.266 4 1 0
## 2077 0 no 0 0 2 10.810 295 1 0
## 2078 0 no 0 0 0 7.116 121 1 0
## 2079 0 no 0 0 0 0.781 22 1 0
## 2080 0 no 0 0 0 7.183 254 1 1
## 2081 0 no 0 0 2 11.355 314 1 0
## 2082 0 yes 0 0 0 18.315 417 1 0
## 2083 0 no 0 0 0 2.026 62 1 1
## 2084 0 no 0 0 0 5.967 247 1 1
## 2085 0 no 0 0 0 1.102 27 1 0
## 2086 0 no 0 0 0 24.482 623 1 0
## 2087 4 no 0 0 0 10.144 148 1 0
## 2088 0 no 0 0 0 10.159 258 1 1
## 2089 0 no 0 0 0 24.761 627 1 0
## 2090 0 no 0 0 0 25.907 650 1 0
## 2091 0 no 0 0 0 25.690 644 1 0
## 2092 0 no 0 0 0 3.592 91 1 1
## 2093 0 no 0 0 0 95.308 2355 1 0
## 2094 0 no 0 0 0 7.538 137 1 0
## 2095 0 no 0 0 0 25.354 638 1 0
## 2096 0 no 0 0 0 1.034 41 0 0
## 2097 0 no 0 0 0 6.912 155 1 0
## 2098 0 no 0 0 0 9.548 394 1 1
## 2099 0 no 0 0 0 24.644 626 1 0
## 2100 0 no 0 0 0 25.731 642 1 0
## 2101 16 no 0 0 0 66.956 1235 1 0
## 2102 0 no 0 0 0 0.004 2 0 0
## 2103 0 no 0 0 0 1.138 58 0 1
## 2104 0 no 0 0 0 1.033 26 0 0
## 2105 0 no 0 0 0 3.708 75 1 0
## 2106 0 no 0 0 0 9.383 176 1 0
## 2107 0 no 0 0 0 2.432 55 1 0
## 2108 2 no 0 0 0 0.592 17 0 0
## 2109 6 no 1 0 0 8.651 198 1 0
## 2110 0 no 0 0 3 1.422 36 1 0
## 2111 0 no 0 0 0 9.929 200 1 0
## 2112 0 no 0 0 0 27.884 682 1 0
## 2113 0 no 0 0 0 7.521 126 1 0
## 2114 2 no 0 0 0 28.441 907 1 0
## 2115 0 no 0 0 0 26.586 662 1 0
## 2116 0 no 0 0 0 6.428 206 1 0
## 2117 0 no 0 0 0 27.723 682 1 0
## 2118 0 no 0 0 0 29.643 715 1 0
## 2119 0 no 0 0 0 31.042 740 1 0
## 2120 0 no 0 0 0 10.786 231 1 1
## 2121 0 no 0 0 5 1.999 42 0 0
## 2122 14 no 1 0 0 17.796 356 1 0
## 2123 0 no 0 0 0 9.603 196 1 0
## 2124 0 no 0 0 0 25.460 513 1 0
## 2125 8 no 0 0 0 8.691 304 1 0
## 2126 0 no 0 0 0 10.747 264 1 1
## 2127 0 no 0 0 0 12.862 326 1 1
## 2128 0 no 0 0 0 5.778 146 1 0
## 2129 4 no 0 0 0 8.120 208 1 1
## 2130 2 no 0 0 0 2.317 53 1 1
## 2131 0 no 0 0 0 10.383 137 0 0
## 2132 0 no 0 0 0 12.531 300 1 1
## 2133 6 no 0 0 0 9.246 146 1 0
## 2134 0 no 0 0 0 0.720 17 0 0
## 2135 0 no 0 0 0 0.623 25 0 0
## 2136 0 no 0 0 0 24.518 625 1 0
## 2137 0 no 0 0 0 24.751 630 1 0
## 2138 0 no 0 0 0 3.856 81 1 0
## 2139 0 no 0 0 0 17.429 320 1 0
## 2140 10 no 1 0 0 17.383 353 1 0
## 2141 0 no 0 0 0 25.131 634 1 0
## 2142 0 no 0 0 0 2.855 63 1 1
## 2143 0 no 0 0 0 1.300 26 1 0
## 2144 0 no 0 0 0 13.629 341 1 1
## 2145 0 no 0 0 0 7.284 135 1 0
## 2146 0 no 0 0 0 24.681 626 1 0
## 2147 0 no 0 0 0 2.883 93 0 1
## 2148 0 no 0 0 0 15.588 390 1 1
## 2149 0 no 0 0 0 26.175 656 1 0
## 2150 2 no 0 0 0 24.904 632 1 0
## 2151 2 no 0 0 0 26.687 663 1 0
## 2152 0 no 0 0 0 14.526 316 1 1
## 2153 0 no 0 0 0 6.282 227 0 1
## 2154 0 no 0 0 0 5.242 56 0 0
## 2155 0 no 0 0 0 4.001 132 0 1
## 2156 0 no 0 0 0 2.375 50 0 1
## 2157 46 yes 0 0 0 25.588 282 1 0
## 2158 0 no 0 0 0 29.098 704 1 0
## 2159 0 no 0 0 0 0.667 9 1 0
## 2160 0 no 0 0 0 16.573 309 1 0
## 2161 0 no 0 0 0 0.397 14 0 1
## 2162 0 no 0 0 0 10.103 279 1 1
## 2163 0 no 0 0 0 24.503 623 1 0
## 2164 0 no 0 0 0 25.894 650 1 0
## 2165 0 no 0 0 0 24.889 629 1 0
## 2166 0 no 0 0 0 0.693 25 1 0
## 2167 0 no 0 0 0 1.643 18 1 0
## 2168 0 no 0 0 0 3.889 84 1 0
## 2169 16 no 1 0 0 17.603 356 1 0
## 2170 0 no 0 0 0 7.485 89 0 0
## 2171 4 no 0 0 0 7.721 164 1 0
## 2172 0 no 0 0 0 16.593 435 1 0
## 2173 0 no 0 0 0 2.500 61 1 1
## 2174 0 no 0 0 0 2.061 73 1 0
## 2175 0 no 0 0 0 6.190 129 1 0
## 2176 0 no 0 0 0 5.246 56 0 0
## 2177 0 no 0 0 0 2.928 79 1 1
## 2178 0 no 0 0 0 3.618 49 0 0
## 2179 0 no 0 0 0 2.984 28 1 0
## 2180 0 no 0 0 0 11.910 312 1 0
## 2181 0 no 0 0 0 24.580 626 1 0
## 2182 0 no 0 0 0 1.888 35 0 0
## 2183 0 no 0 0 0 11.240 159 0 0
## 2184 0 no 0 0 0 3.746 98 1 0
## 2185 0 no 0 0 0 16.510 428 1 1
## 2186 0 no 0 0 0 4.047 98 1 1
## 2187 0 no 0 0 0 0.741 27 0 0
## 2188 0 no 0 0 0 5.389 136 1 1
## 2189 0 no 0 0 0 16.925 382 1 0
## 2190 0 no 0 0 0 11.780 348 1 1
## 2191 0 no 0 0 0 5.506 170 1 1
## 2192 0 no 0 0 0 0.361 15 0 0
## 2193 0 no 0 0 0 1.225 45 0 1
## 2194 0 no 0 0 0 5.462 122 1 0
## 2195 0 no 0 0 0 7.912 179 1 1
## 2196 0 no 0 0 0 11.569 260 1 1
## 2197 0 no 0 0 0 5.895 148 1 0
## 2198 0 no 0 0 0 8.267 195 1 1
## 2199 0 no 0 0 0 11.151 276 1 1
## 2200 0 no 0 0 0 2.148 54 0 1
## 2201 0 no 0 0 0 10.914 294 1 1
## 2202 0 no 0 0 0 15.586 376 1 1
## 2203 0 no 0 0 0 14.420 417 1 1
## 2204 0 no 0 0 0 18.172 551 1 1
## 2205 0 no 0 0 0 13.704 340 1 1
## 2206 0 no 0 0 0 5.227 124 0 1
## 2207 0 no 0 0 0 19.395 482 1 1
## 2208 0 no 0 0 0 23.519 515 1 1
## 2209 0 no 0 0 0 23.466 552 1 1
## 2210 0 no 0 0 4 4.033 59 1 1
## 2211 0 no 0 0 0 25.254 812 1 0
## 2212 0 no 0 0 0 29.007 926 1 1
## 2213 0 no 0 0 0 8.879 203 0 1
## 2214 0 no 0 0 0 1.583 38 1 0
## 2215 0 no 0 0 0 19.437 253 1 0
## 2216 0 no 0 0 4 6.890 129 1 1
## 2217 0 no 0 0 0 0.963 20 0 0
## 2218 0 no 0 0 0 26.217 633 1 1
## 2219 0 no 0 0 0 0.661 12 0 0
## 2220 0 no 0 0 2 1.982 49 0 0
## 2221 0 no 0 0 0 1.275 23 0 0
## 2222 0 no 0 0 0 31.113 720 1 1
## 2223 0 no 0 0 0 4.742 167 1 0
## 2224 0 no 0 0 0 0.781 28 1 1
## 2225 0 no 0 0 0 5.727 139 1 1
## 2226 0 no 0 0 0 12.987 168 0 0
## 2227 0 no 0 0 0 30.279 983 1 1
## 2228 6 no 0 0 0 9.692 152 1 0
## 2229 0 no 0 0 0 0.417 20 0 0
## 2230 0 no 0 0 0 64.202 1139 1 0
## 2231 0 no 0 0 0 1.690 47 0 0
## 2232 0 no 0 0 0 4.659 104 1 0
## 2233 0 no 0 0 0 23.352 782 1 0
## 2234 20 no 1 0 0 23.612 455 1 0
## 2235 0 no 0 0 0 8.645 184 1 0
## 2236 0 no 0 0 0 1.027 21 0 1
## 2237 2 no 0 0 0 31.398 652 1 0
## 2238 0 no 0 0 0 5.574 61 0 0
## 2239 0 no 0 0 0 1.122 32 1 1
## 2240 0 no 0 0 0 7.777 237 1 1
## 2241 4 no 0 0 0 9.027 148 1 0
## 2242 0 no 0 0 0 0.083 6 0 0
## 2243 0 no 0 0 0 16.280 396 1 0
## 2244 0 no 0 0 0 0.822 40 0 0
## 2245 0 no 0 0 0 33.343 436 0 0
## 2246 0 no 0 0 2 24.474 601 1 1
## 2247 2 no 0 0 0 28.468 575 1 0
## 2248 0 no 0 0 0 0.286 11 0 0
## 2249 0 no 0 0 0 0.325 10 1 0
## 2250 0 no 0 0 0 9.129 232 1 1
## 2251 0 no 0 0 0 1.926 56 1 1
## 2252 4 no 0 0 0 64.401 1167 1 0
## 2253 0 no 0 0 0 2.110 44 0 1
## 2254 0 no 0 0 0 7.441 147 1 0
## 2255 0 no 0 0 0 11.778 323 1 1
## 2256 0 no 0 0 0 1.698 41 1 0
## 2257 0 no 0 0 0 0.008 3 0 0
## 2258 0 no 0 0 0 0.272 13 0 1
## 2259 0 no 0 0 0 1.524 48 1 1
## 2260 0 no 0 0 0 31.707 609 1 0
## 2261 0 no 0 0 0 22.189 699 1 1
## 2262 2 no 0 0 0 28.651 605 1 0
## 2263 0 no 0 0 0 1.692 41 1 1
## 2264 0 no 0 0 0 1.096 26 0 0
## 2265 0 no 0 0 5 1.222 35 0 0
## 2266 0 no 0 0 0 11.545 325 1 1
## 2267 0 no 0 0 0 3.890 100 1 0
## 2268 0 no 0 0 0 0.298 12 0 0
## 2269 0 no 0 0 0 16.779 396 1 1
## 2270 0 no 0 0 0 1.110 48 0 1
## 2271 0 no 0 0 0 22.929 570 1 1
## 2272 0 no 0 0 0 0.522 13 0 1
## 2273 0 no 0 0 0 1.991 44 1 0
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## 3472 0 0 0 small
## 3473 0 0 1 small
## 3474 0 0 1 small
## 3475 0 0 1 small
## 3476 0 0 1 small
## 3477 0 0 0 small
## 3478 0 0 1 small
## 3479 0 0 0 big
## 3480 0 0 1 small
## 3481 0 0 0 small
## 3482 0 0 1 small
## 3483 0 0 1 small
## 3484 0 0 1 small
## 3485 0 0 12 small
## 3486 0 0 22 small
## 3487 0 0 0 small
## 3488 0 0 1 small
## 3489 0 0 1 small
## 3490 0 0 1 small
## 3491 0 0 0 small
## 3492 0 0 1 small
## 3493 0 0 1 small
## 3494 0 0 0 small
## 3495 0 0 1 small
## 3496 1 0 0 none
## 3497 0 0 14 small
## 3498 0 0 1 small
## 3499 0 0 0 small
## 3500 0 0 2 small
## 3501 0 0 0 small
## 3502 0 0 3 small
## 3503 0 0 20 small
## 3504 0 0 1 small
## 3505 1 0 14 small
## 3506 0 0 1 small
## 3507 0 0 0 small
## 3508 0 0 0 none
## 3509 0 0 0 small
## 3510 0 0 0 small
## 3511 0 0 0 small
## 3512 0 0 5 big
## 3513 0 0 2 none
## 3514 0 0 14 small
## 3515 0 0 0 small
## 3516 0 0 0 small
## 3517 0 0 0 none
## 3518 0 0 2 small
## 3519 0 0 0 small
## 3520 1 0 24 small
## 3521 0 0 0 small
## 3522 0 0 0 none
## 3523 0 0 0 small
## 3524 0 0 2 small
## 3525 0 0 0 none
## 3526 0 0 0 none
## 3527 0 0 0 small
## 3528 0 0 0 big
## 3529 0 0 0 small
## 3530 0 0 9 big
## 3531 0 0 1 big
## 3532 0 0 1 small
## 3533 0 0 0 small
## 3534 0 0 6 small
## 3535 0 0 0 small
## 3536 0 0 2 small
## 3537 0 0 0 small
## 3538 0 0 0 small
## 3539 0 0 6 small
## 3540 0 0 26 small
## 3541 0 0 0 small
## 3542 0 0 7 big
## 3543 0 0 3 big
## 3544 0 0 8 small
## 3545 0 0 13 big
## 3546 1 0 26 small
## 3547 0 0 0 none
## 3548 0 0 4 small
## 3549 0 0 1 big
## 3550 0 0 24 small
## 3551 0 0 40 small
## 3552 0 0 0 small
## 3553 0 0 0 small
## 3554 0 0 0 small
## 3555 0 0 0 small
## 3556 0 0 2 none
## 3557 0 0 4 small
## 3558 0 0 5 small
## 3559 0 0 0 small
## 3560 0 0 0 big
## 3561 0 0 2 small
## 3562 0 0 1 small
## 3563 0 0 0 small
## 3564 1 0 20 small
## 3565 0 0 0 small
## 3566 0 0 8 small
## 3567 0 0 0 big
## 3568 0 0 1 none
## 3569 0 0 27 small
## 3570 0 0 1 none
## 3571 0 0 1 small
## 3572 0 0 26 small
## 3573 0 0 8 small
## 3574 0 0 4 small
## 3575 0 0 0 small
## 3576 0 0 0 small
## 3577 1 0 0 small
## 3578 0 0 0 small
## 3579 0 0 0 small
## 3580 0 0 0 small
## 3581 0 0 14 big
## 3582 0 0 0 small
## 3583 0 0 0 big
## 3584 0 0 0 small
## 3585 0 0 1 small
## 3586 0 0 1 small
## 3587 0 0 4 big
## 3588 0 0 0 small
## 3589 0 0 3 small
## 3590 0 0 4 small
## 3591 0 0 2 small
## 3592 0 0 2 big
## 3593 0 0 0 small
## 3594 0 0 5 big
## 3595 0 0 1 small
## 3596 0 0 4 small
## 3597 1 0 14 small
## 3598 0 0 2 small
## 3599 0 0 2 small
## 3600 0 0 14 small
## 3601 0 0 54 small
## 3602 0 0 4 small
## 3603 0 0 4 small
## 3604 0 0 3 small
## 3605 0 0 0 big
## 3606 0 0 2 small
## 3607 0 0 3 small
## 3608 0 0 2 small
## 3609 0 0 0 small
## 3610 0 0 1 small
## 3611 0 0 2 small
## 3612 0 0 3 small
## 3613 0 0 0 none
## 3614 0 0 3 small
## 3615 0 0 0 small
## 3616 0 0 0 small
## 3617 0 0 5 big
## 3618 0 0 0 none
## 3619 0 0 0 small
## 3620 0 0 2 small
## 3621 0 0 5 small
## 3622 0 0 2 small
## 3623 1 0 18 small
## 3624 0 0 2 small
## 3625 1 0 2 small
## 3626 0 0 0 small
## 3627 0 0 12 small
## 3628 0 0 0 small
## 3629 0 0 5 small
## 3630 0 0 3 big
## 3631 0 0 0 small
## 3632 0 0 1 small
## 3633 0 0 8 small
## 3634 0 0 0 small
## 3635 1 0 12 small
## 3636 0 0 5 small
## 3637 0 0 6 small
## 3638 0 0 0 none
## 3639 0 0 1 small
## 3640 0 0 3 small
## 3641 0 0 1 small
## 3642 0 0 1 none
## 3643 0 0 1 small
## 3644 0 0 1 big
## 3645 1 0 0 small
## 3646 0 0 0 small
## 3647 0 0 4 small
## 3648 0 0 6 small
## 3649 0 0 0 small
## 3650 0 0 12 small
## 3651 1 0 12 small
## 3652 0 0 0 small
## 3653 0 0 4 big
## 3654 0 0 1 small
## 3655 0 0 1 small
## 3656 0 0 0 none
## 3657 0 0 10 big
## 3658 0 0 0 big
## 3659 0 0 0 none
## 3660 0 0 0 small
## 3661 0 0 4 big
## 3662 1 0 16 small
## 3663 0 0 0 small
## 3664 0 0 13 small
## 3665 0 0 4 small
## 3666 0 0 2 small
## 3667 0 0 16 small
## 3668 0 0 0 small
## 3669 0 0 6 small
## 3670 0 0 0 small
## 3671 0 0 0 small
## 3672 0 0 2 small
## 3673 0 0 0 small
## 3674 0 0 0 small
## 3675 0 0 9 big
## 3676 0 0 0 small
## 3677 0 0 31 small
## 3678 0 0 0 small
## 3679 0 0 1 small
## 3680 0 0 4 small
## 3681 0 0 2 big
## 3682 0 0 1 small
## 3683 0 0 0 none
## 3684 0 0 2 small
## 3685 0 0 2 small
## 3686 0 0 55 small
## 3687 0 0 2 small
## 3688 0 0 0 small
## 3689 0 0 0 none
## 3690 0 0 0 small
## 3691 0 0 7 small
## 3692 0 0 0 none
## 3693 0 0 8 small
## 3694 0 0 0 small
## 3695 0 0 2 none
## 3696 0 0 8 small
## 3697 0 0 0 small
## 3698 1 0 6 small
## 3699 0 0 2 none
## 3700 0 0 52 big
## 3701 0 0 30 small
## 3702 1 0 8 small
## 3703 0 0 0 big
## 3704 0 0 11 small
## 3705 0 0 0 small
## 3706 0 0 0 big
## 3707 0 0 5 big
## 3708 0 0 5 big
## 3709 1 0 3 none
## 3710 1 0 2 small
## 3711 0 0 5 big
## 3712 1 0 14 small
## 3713 0 0 1 small
## 3714 0 0 5 small
## 3715 0 0 947 big
## 3716 0 0 5 small
## 3717 0 0 0 small
## 3718 0 0 2 small
## 3719 0 0 0 none
## 3720 0 0 0 small
## 3721 0 0 2 small
## 3722 1 0 16 small
## 3723 0 0 16 small
## 3724 0 0 0 small
## 3725 1 0 25 big
## 3726 1 0 14 small
## 3727 0 0 2 none
## 3728 1 0 31 big
## 3729 0 0 0 small
## 3730 0 0 0 small
## 3731 0 0 0 none
## 3732 0 0 0 small
## 3733 0 0 0 small
## 3734 0 0 2 small
## 3735 0 0 0 small
## 3736 0 0 0 small
## 3737 0 0 0 small
## 3738 0 0 2 small
## 3739 0 0 0 small
## 3740 0 0 2 small
## 3741 0 0 2 small
## 3742 0 0 1 small
## 3743 0 0 2 small
## 3744 0 0 0 small
## 3745 0 0 2 small
## 3746 0 0 0 small
## 3747 0 0 10 small
## 3748 0 0 0 small
## 3749 0 0 1 small
## 3750 0 0 0 none
## 3751 0 0 0 small
## 3752 0 0 0 small
## 3753 0 0 2 small
## 3754 0 0 3 small
## 3755 0 0 3 small
## 3756 0 0 3 small
## 3757 0 0 0 small
## 3758 0 0 0 small
## 3759 0 0 0 big
## 3760 0 0 12 small
## 3761 0 0 12 small
## 3762 0 0 6 big
## 3763 0 0 8 small
## 3764 0 0 1 small
## 3765 1 0 8 small
## 3766 0 0 12 small
## 3767 0 0 4 small
## 3768 0 0 0 small
## 3769 0 0 10 small
## 3770 0 0 2 small
## 3771 0 0 0 small
## 3772 0 0 1 small
## 3773 0 0 1 small
## 3774 0 0 1 small
## 3775 0 0 0 small
## 3776 0 0 1 none
## 3777 0 0 0 small
## 3778 0 0 4 small
## 3779 1 0 3 small
## 3780 1 0 2 small
## 3781 0 0 7 big
## 3782 0 0 1 small
## 3783 0 0 1 small
## 3784 0 0 2 small
## 3785 0 0 2 small
## 3786 0 0 2 small
## 3787 1 0 35 big
## 3788 0 0 19 small
## 3789 0 0 0 small
## 3790 0 0 2 small
## 3791 1 0 7 small
## 3792 0 0 1 small
## 3793 1 0 18 small
## 3794 0 0 15 big
## 3795 0 0 0 big
## 3796 0 0 1 small
## 3797 0 0 2 small
## 3798 0 0 1 small
## 3799 0 0 0 none
## 3800 0 0 2 none
## 3801 0 0 1 small
## 3802 0 0 0 big
## 3803 0 0 2 small
## 3804 0 0 0 none
## 3805 1 0 0 none
## 3806 0 0 12 big
## 3807 0 0 0 small
## 3808 0 0 1 none
## 3809 0 0 7 big
## 3810 0 0 0 none
## 3811 0 0 0 none
## 3812 0 0 1 small
## 3813 0 0 2 small
## 3814 0 0 0 none
## 3815 0 0 1 none
## 3816 0 0 0 none
## 3817 1 0 0 small
## 3818 0 0 9 small
## 3819 1 0 0 none
## 3820 0 0 1 none
## 3821 0 0 0 none
## 3822 1 0 3 big
## 3823 0 0 0 none
## 3824 0 0 0 none
## 3825 0 0 0 none
## 3826 0 0 0 none
## 3827 0 0 1 none
## 3828 0 0 1 small
## 3829 0 0 1 small
## 3830 0 0 0 none
## 3831 0 0 3 small
## 3832 0 0 1 none
## 3833 0 0 0 big
## 3834 0 0 2 small
## 3835 0 0 0 none
## 3836 0 0 1 small
## 3837 0 0 0 none
## 3838 0 0 0 none
## 3839 0 0 3 small
## 3840 0 0 2 small
## 3841 0 0 0 none
## 3842 0 0 0 none
## 3843 0 0 1 small
## 3844 0 0 0 none
## 3845 0 0 1 small
## 3846 0 0 2 big
## 3847 0 0 0 big
## 3848 0 0 1 big
## 3849 0 0 0 none
## 3850 0 0 16 small
## 3851 0 0 1 small
## 3852 0 0 1 small
## 3853 0 0 0 none
## 3854 0 0 1 big
## 3855 0 0 0 small
## 3856 0 0 1 small
## 3857 0 0 1 small
## 3858 0 0 0 none
## 3859 0 0 1 big
## 3860 0 0 0 none
## 3861 1 0 9 small
## 3862 0 0 2 big
## 3863 0 0 0 none
## 3864 0 0 1 small
## 3865 0 0 4 big
## 3866 1 0 2 small
## 3867 0 0 1 small
## 3868 0 0 1 small
## 3869 0 0 0 none
## 3870 0 0 17 small
## 3871 0 0 1 none
## 3872 0 0 0 none
## 3873 0 0 0 none
## 3874 0 0 0 small
## 3875 0 0 1 small
## 3876 0 0 3 small
## 3877 0 0 0 none
## 3878 0 0 0 small
## 3879 0 0 0 small
## 3880 0 0 0 none
## 3881 1 0 1 small
## 3882 0 0 1 small
## 3883 0 0 0 none
## 3884 0 0 0 none
## 3885 0 0 0 none
## 3886 0 0 0 small
## 3887 0 0 0 big
## 3888 0 0 0 big
## 3889 0 0 0 none
## 3890 0 0 0 none
## 3891 0 0 1 small
## 3892 0 0 3 small
## 3893 0 0 14 big
## 3894 0 1 9 small
## 3895 0 0 0 none
## 3896 0 0 0 small
## 3897 0 0 0 none
## 3898 0 1 1 none
## 3899 0 0 0 none
## 3900 0 0 1 small
## 3901 0 0 2 small
## 3902 0 0 0 none
## 3903 0 0 0 small
## 3904 1 0 10 small
## 3905 0 0 0 small
## 3906 0 0 0 small
## 3907 1 0 1 none
## 3908 0 0 0 none
## 3909 0 0 0 none
## 3910 0 0 0 none
## 3911 0 0 0 none
## 3912 0 0 1 small
## 3913 0 0 2 small
## 3914 1 0 3 small
## 3915 0 0 0 big
## 3916 0 0 1 small
## 3917 0 0 0 small
## 3918 0 0 0 small
## 3919 0 0 5 small
## 3920 0 0 0 small
## 3921 1 0 1 smallterdapat 21 kolom dan 3921 baris dengan mayoritas tipe data integer
table(email$spam)##
## 0 1
## 3554 367spam ditunjukkan dengan angka 1 dalam boolean sejumlah 367 dan not spam ditujukkan dengan angka nol atau false sebanyak 3554
email <- email %>%
mutate(spam = factor(ifelse(spam == 0, "not-spam", "spam")))
email %>%
mutate(log_num_char = log(num_char)) %>%
ggplot(aes(x = spam, y = log_num_char)) +
geom_boxplot()
dari boxplot tersebut, not spam terdapat banyak outliers sementara spam hanya terdapat beberapa outliers
print("Error in median(exclaim_mess) : object 'exclaim_mess' not found")## [1] "Error in median(exclaim_mess) : object 'exclaim_mess' not found"#email %>%
# group_by(spam) %>%
# summarize(
# median(exclaim_mess),
# IQR(exclaim_mess)) table(email$exclaim_mess)##
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
## 1435 733 507 128 190 113 115 51 93 45 85 17 56 20 43 11
## 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
## 29 12 26 5 29 9 15 3 11 6 11 1 6 8 13 12
## 32 33 34 35 36 38 39 40 41 42 43 44 45 46 47 48
## 13 3 3 2 3 3 1 2 1 1 3 3 5 3 2 1
## 49 52 54 55 57 58 62 71 75 78 89 94 96 139 148 157
## 3 1 1 4 2 2 2 1 1 1 1 1 1 1 1 1
## 187 454 915 939 947 1197 1203 1209 1236
## 1 1 1 1 1 1 2 1 1email %>%
mutate(log_exclaim_mess = log(exclaim_mess)) %>%
ggplot(aes(x = log_exclaim_mess)) +
geom_histogram() +
facet_wrap(~ spam)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 1435 rows containing non-finite values (stat_bin).
Nilai paling umum dari exclaim_mess di kedua kelas email adalah nol (log(exclaim_mess) dari -4.6 setelah menambahkan .01).
table(email$image)##
## 0 1 2 3 4 5 9 20
## 3811 76 17 11 2 2 1 1email %>%
mutate(has_image = image > 0) %>%
ggplot(aes(x = has_image, fill = spam)) +
geom_bar(position = "fill")
dari histogram diatas terlihat bahwa spam lebih banyak tidak menggunakan gambar, sementara email yang bukan spam hampir semua mencantumkan gambar.
sum(email$image > email$attach)## [1] 0print("Error in median(num_char) : object 'num_char' not found")## [1] "Error in median(num_char) : object 'num_char' not found"#email %>%
# filter(spam == "not-spam") %>%
# group_by(to_multiple) %>%
# summarize(median(num_char))print("Error in median(dollar) : object 'dollar' not found")## [1] "Error in median(dollar) : object 'dollar' not found"#email %>%
# filter(dollar > 0) %>%
# group_by(spam) %>%
# summarize(median(dollar))email %>%
filter(dollar > 10) %>%
ggplot(aes(x = spam)) +
geom_bar()
boxplot dengan dollar lebih dari 10 terlihat bahwa hampir semua bukan spam dengan jumlah yang sangat kecil spam
email$number <- as.factor(email$number)
levels(email$number)## [1] "big" "none" "small"table(email$number)##
## big none small
## 545 549 2827email$number <- factor(email$number, levels = c("none","small","big"))
ggplot(email, aes(x = number)) +
geom_bar() +
facet_wrap( ~ spam)
dari histogram diatas dapat terlihat bahwa email yang bukan spam kebanyakan memiliki number small dan sedikit none dan big, sementara email spam memiliki number yang kecil pada ke 3 tipe nilainya hal ini juga dikarenakan jumlah email spam yang memang kecil.