- Eksplorasi Data Menggunakan R ================ Muhammad Luqman 2018-04-29
Dalam statistik, Exploratory Data Analysis (EDA) adalah teknik untuk
menganalisis data dengan tujuan untuk melihat karakteristik data
tersebut. EDA seringkali dilakukan dengan menggunakan teknik
visualisasi. Dengan menggunakan library ggplot2, kita dapat melakukan
visualisasi data dalam R untuk melakukan EDA.
library(dplyr)
library(ggplot2)library(hflights)
hflights = as_tibble(hflights)
hflights## # A tibble: 227,496 × 21
## Year Month DayofMonth DayOfWeek DepTime ArrTime UniqueCarrier FlightNum
## <int> <int> <int> <int> <int> <int> <chr> <int>
## 1 2011 1 1 6 1400 1500 AA 428
## 2 2011 1 2 7 1401 1501 AA 428
## 3 2011 1 3 1 1352 1502 AA 428
## 4 2011 1 4 2 1403 1513 AA 428
## 5 2011 1 5 3 1405 1507 AA 428
## 6 2011 1 6 4 1359 1503 AA 428
## 7 2011 1 7 5 1359 1509 AA 428
## 8 2011 1 8 6 1355 1454 AA 428
## 9 2011 1 9 7 1443 1554 AA 428
## 10 2011 1 10 1 1443 1553 AA 428
## # ℹ 227,486 more rows
## # ℹ 13 more variables: TailNum <chr>, ActualElapsedTime <int>, AirTime <int>,
## # ArrDelay <int>, DepDelay <int>, Origin <chr>, Dest <chr>, Distance <int>,
## # TaxiIn <int>, TaxiOut <int>, Cancelled <int>, CancellationCode <chr>,
## # Diverted <int>
str(hflights)## tibble [227,496 × 21] (S3: tbl_df/tbl/data.frame)
## $ Year : int [1:227496] 2011 2011 2011 2011 2011 2011 2011 2011 2011 2011 ...
## $ Month : int [1:227496] 1 1 1 1 1 1 1 1 1 1 ...
## $ DayofMonth : int [1:227496] 1 2 3 4 5 6 7 8 9 10 ...
## $ DayOfWeek : int [1:227496] 6 7 1 2 3 4 5 6 7 1 ...
## $ DepTime : int [1:227496] 1400 1401 1352 1403 1405 1359 1359 1355 1443 1443 ...
## $ ArrTime : int [1:227496] 1500 1501 1502 1513 1507 1503 1509 1454 1554 1553 ...
## $ UniqueCarrier : chr [1:227496] "AA" "AA" "AA" "AA" ...
## $ FlightNum : int [1:227496] 428 428 428 428 428 428 428 428 428 428 ...
## $ TailNum : chr [1:227496] "N576AA" "N557AA" "N541AA" "N403AA" ...
## $ ActualElapsedTime: int [1:227496] 60 60 70 70 62 64 70 59 71 70 ...
## $ AirTime : int [1:227496] 40 45 48 39 44 45 43 40 41 45 ...
## $ ArrDelay : int [1:227496] -10 -9 -8 3 -3 -7 -1 -16 44 43 ...
## $ DepDelay : int [1:227496] 0 1 -8 3 5 -1 -1 -5 43 43 ...
## $ Origin : chr [1:227496] "IAH" "IAH" "IAH" "IAH" ...
## $ Dest : chr [1:227496] "DFW" "DFW" "DFW" "DFW" ...
## $ Distance : int [1:227496] 224 224 224 224 224 224 224 224 224 224 ...
## $ TaxiIn : int [1:227496] 7 6 5 9 9 6 12 7 8 6 ...
## $ TaxiOut : int [1:227496] 13 9 17 22 9 13 15 12 22 19 ...
## $ Cancelled : int [1:227496] 0 0 0 0 0 0 0 0 0 0 ...
## $ CancellationCode : chr [1:227496] "" "" "" "" ...
## $ Diverted : int [1:227496] 0 0 0 0 0 0 0 0 0 0 ...
lookup = c(A = "Carrier", B = "Weather", C = "National Air System", D = "Security")
hflights = hflights %>%
mutate(CancellationReason = lookup[CancellationCode])ct = table(hflights$UniqueCarrier, hflights$Cancelled)
ct##
## 0 1
## AA 3184 60
## AS 365 0
## B6 677 18
## CO 69557 475
## DL 2599 42
## EV 2128 76
## F9 832 6
## FL 2118 21
## MQ 4513 135
## OO 15837 224
## UA 2038 34
## US 4036 46
## WN 44640 703
## XE 71921 1132
## YV 78 1
prop.table(ct)##
## 0 1
## AA 1.399585e-02 2.637409e-04
## AS 1.604424e-03 0.000000e+00
## B6 2.975876e-03 7.912227e-05
## CO 3.057504e-01 2.087949e-03
## DL 1.142438e-02 1.846186e-04
## EV 9.354011e-03 3.340718e-04
## F9 3.657207e-03 2.637409e-05
## FL 9.310054e-03 9.230932e-05
## MQ 1.983771e-02 5.934170e-04
## OO 6.961441e-02 9.846327e-04
## UA 8.958399e-03 1.494532e-04
## US 1.774097e-02 2.022014e-04
## WN 1.962232e-01 3.090164e-03
## XE 3.161418e-01 4.975912e-03
## YV 3.428632e-04 4.395682e-06
prop.table(ct, margin = 1)##
## 0 1
## AA 0.981504316 0.018495684
## AS 1.000000000 0.000000000
## B6 0.974100719 0.025899281
## CO 0.993217386 0.006782614
## DL 0.984096933 0.015903067
## EV 0.965517241 0.034482759
## F9 0.992840095 0.007159905
## FL 0.990182328 0.009817672
## MQ 0.970955250 0.029044750
## OO 0.986053172 0.013946828
## UA 0.983590734 0.016409266
## US 0.988731014 0.011268986
## WN 0.984495953 0.015504047
## XE 0.984504401 0.015495599
## YV 0.987341772 0.012658228
prop.table(ct, margin = 2)##
## 0 1
## AA 0.0141811752 0.0201816347
## AS 0.0016256686 0.0000000000
## B6 0.0030152813 0.0060544904
## CO 0.3097989961 0.1597712748
## DL 0.0115756515 0.0141271443
## EV 0.0094778709 0.0255634040
## F9 0.0037056337 0.0020181635
## FL 0.0094333320 0.0070635721
## MQ 0.0201003906 0.0454086781
## OO 0.0705362034 0.0753447696
## UA 0.0090770211 0.0114362597
## US 0.0179758867 0.0154725866
## WN 0.1988215016 0.2364614867
## XE 0.3203279842 0.3807601749
## YV 0.0003474032 0.0003363606
ggplot(hflights, aes(x = Cancelled)) +
geom_bar()
hflights %>%
filter(Cancelled == 1) %>%
ggplot(aes(x = CancellationReason)) +
geom_bar()
ggplot(hflights, aes(x = UniqueCarrier, fill = Cancelled)) +
geom_bar(position = "dodge")## Warning: The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
ggplot(hflights, aes(x = UniqueCarrier, fill = Cancelled)) +
geom_bar(position = "fill")## Warning: The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
hflights %>%
filter(Cancelled == 1) %>%
ggplot(aes(x = UniqueCarrier,
fill = CancellationCode)) +
geom_bar(position = "fill")
ggplot(hflights, aes(x = AirTime)) +
geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 3622 rows containing non-finite values (`stat_bin()`).
ggplot(hflights, aes(x = AirTime)) +
geom_histogram(binwidth = 60)## Warning: Removed 3622 rows containing non-finite values (`stat_bin()`).
ggplot(hflights, aes(x = AirTime)) +
geom_histogram() +
facet_wrap(~Origin)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 3622 rows containing non-finite values (`stat_bin()`).
ggplot(hflights, aes(x = AirTime)) +
geom_density()## Warning: Removed 3622 rows containing non-finite values (`stat_density()`).
ggplot(hflights, aes(x = AirTime, fill = Origin)) +
geom_density(alpha = 0.3)## Warning: Removed 3622 rows containing non-finite values (`stat_density()`).
ggplot(hflights, aes(x = 1, y = Distance)) +
geom_boxplot()
ggplot(hflights, aes(x = Origin, y = Distance)) +
geom_boxplot()
ggplot(hflights, aes(x = Distance, y = AirTime)) +
geom_point()## Warning: Removed 3622 rows containing missing values (`geom_point()`).
library(gapminder)
gap2007 = gapminder %>%
filter(year == 2007, continent != "Oceania")
gap2007## # A tibble: 140 × 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 Austria Europe 2007 79.8 8199783 36126.
## 7 Bahrain Asia 2007 75.6 708573 29796.
## 8 Bangladesh Asia 2007 64.1 150448339 1391.
## 9 Belgium Europe 2007 79.4 10392226 33693.
## 10 Benin Africa 2007 56.7 8078314 1441.
## # ℹ 130 more rows
gap2007 %>%
ggplot(aes(x = continent, y = lifeExp)) +
geom_boxplot()
gap2007 %>%
ggplot(aes(x = lifeExp, fill = continent)) +
geom_density(alpha = 0.3)
gap2007 %>%
group_by(continent) %>%
summarize(mean(lifeExp),
median(lifeExp))## # A tibble: 4 × 3
## continent `mean(lifeExp)` `median(lifeExp)`
## <fct> <dbl> <dbl>
## 1 Africa 54.8 52.9
## 2 Americas 73.6 72.9
## 3 Asia 70.7 72.4
## 4 Europe 77.6 78.6
gap2007 %>%
group_by(continent) %>%
summarize(varians = var(lifeExp),
std = sd(lifeExp),
iqr = IQR(lifeExp),
n_data = n())## # A tibble: 4 × 5
## continent varians std iqr n_data
## <fct> <dbl> <dbl> <dbl> <int>
## 1 Africa 92.8 9.63 11.6 52
## 2 Americas 19.7 4.44 4.63 25
## 3 Asia 63.4 7.96 10.2 33
## 4 Europe 8.88 2.98 4.78 30