Tutorial Statistika Deskriptif Secara Otomatis dengan R

Saya ingat dulu waktu zaman kuliah, setidaknya membutuhkan waktu 30 menit untuk melakukan dan membuat report berisi analisa statistika deskriptif dari suatu data. Di zaman sekarang, untuk melakukan hal yang sama, saya hanya butuh waktu tak lebih dari 1 menit saja.

Berikut ini akan saya tunjukkan beberapa alternatif melakukan dan membuat analisa statistika deskriptif sederhana dengan R. Sebagai contoh, saya akan gunakan data mtcars dari bawaan R sebagai berikut:

df %>% knitr::kable()

	mpg	cyl	disp	hp	drat	wt	qsec	vs	am	gear	carb
Mazda RX4	21.0	6	160.0	110	3.90	2.620	16.46	0	1	4	4
Mazda RX4 Wag	21.0	6	160.0	110	3.90	2.875	17.02	0	1	4	4
Datsun 710	22.8	4	108.0	93	3.85	2.320	18.61	1	1	4	1
Hornet 4 Drive	21.4	6	258.0	110	3.08	3.215	19.44	1	0	3	1
Hornet Sportabout	18.7	8	360.0	175	3.15	3.440	17.02	0	0	3	2
Valiant	18.1	6	225.0	105	2.76	3.460	20.22	1	0	3	1
Duster 360	14.3	8	360.0	245	3.21	3.570	15.84	0	0	3	4
Merc 240D	24.4	4	146.7	62	3.69	3.190	20.00	1	0	4	2
Merc 230	22.8	4	140.8	95	3.92	3.150	22.90	1	0	4	2
Merc 280	19.2	6	167.6	123	3.92	3.440	18.30	1	0	4	4
Merc 280C	17.8	6	167.6	123	3.92	3.440	18.90	1	0	4	4
Merc 450SE	16.4	8	275.8	180	3.07	4.070	17.40	0	0	3	3
Merc 450SL	17.3	8	275.8	180	3.07	3.730	17.60	0	0	3	3
Merc 450SLC	15.2	8	275.8	180	3.07	3.780	18.00	0	0	3	3
Cadillac Fleetwood	10.4	8	472.0	205	2.93	5.250	17.98	0	0	3	4
Lincoln Continental	10.4	8	460.0	215	3.00	5.424	17.82	0	0	3	4
Chrysler Imperial	14.7	8	440.0	230	3.23	5.345	17.42	0	0	3	4
Fiat 128	32.4	4	78.7	66	4.08	2.200	19.47	1	1	4	1
Honda Civic	30.4	4	75.7	52	4.93	1.615	18.52	1	1	4	2
Toyota Corolla	33.9	4	71.1	65	4.22	1.835	19.90	1	1	4	1
Toyota Corona	21.5	4	120.1	97	3.70	2.465	20.01	1	0	3	1
Dodge Challenger	15.5	8	318.0	150	2.76	3.520	16.87	0	0	3	2
AMC Javelin	15.2	8	304.0	150	3.15	3.435	17.30	0	0	3	2
Camaro Z28	13.3	8	350.0	245	3.73	3.840	15.41	0	0	3	4
Pontiac Firebird	19.2	8	400.0	175	3.08	3.845	17.05	0	0	3	2
Fiat X1-9	27.3	4	79.0	66	4.08	1.935	18.90	1	1	4	1
Porsche 914-2	26.0	4	120.3	91	4.43	2.140	16.70	0	1	5	2
Lotus Europa	30.4	4	95.1	113	3.77	1.513	16.90	1	1	5	2
Ford Pantera L	15.8	8	351.0	264	4.22	3.170	14.50	0	1	5	4
Ferrari Dino	19.7	6	145.0	175	3.62	2.770	15.50	0	1	5	6
Maserati Bora	15.0	8	301.0	335	3.54	3.570	14.60	0	1	5	8
Volvo 142E	21.4	4	121.0	109	4.11	2.780	18.60	1	1	4	2

Oke, saya mulai ya!

Menggunakan base R

Kita bisa menggunakan function summary() untuk mendapatkan summary statistic berikut:

df %>% summary()

      mpg             cyl             disp             hp       
 Min.   :10.40   Min.   :4.000   Min.   : 71.1   Min.   : 52.0  
 1st Qu.:15.43   1st Qu.:4.000   1st Qu.:120.8   1st Qu.: 96.5  
 Median :19.20   Median :6.000   Median :196.3   Median :123.0  
 Mean   :20.09   Mean   :6.188   Mean   :230.7   Mean   :146.7  
 3rd Qu.:22.80   3rd Qu.:8.000   3rd Qu.:326.0   3rd Qu.:180.0  
 Max.   :33.90   Max.   :8.000   Max.   :472.0   Max.   :335.0  
      drat             wt             qsec             vs        
 Min.   :2.760   Min.   :1.513   Min.   :14.50   Min.   :0.0000  
 1st Qu.:3.080   1st Qu.:2.581   1st Qu.:16.89   1st Qu.:0.0000  
 Median :3.695   Median :3.325   Median :17.71   Median :0.0000  
 Mean   :3.597   Mean   :3.217   Mean   :17.85   Mean   :0.4375  
 3rd Qu.:3.920   3rd Qu.:3.610   3rd Qu.:18.90   3rd Qu.:1.0000  
 Max.   :4.930   Max.   :5.424   Max.   :22.90   Max.   :1.0000  
       am              gear            carb      
 Min.   :0.0000   Min.   :3.000   Min.   :1.000  
 1st Qu.:0.0000   1st Qu.:3.000   1st Qu.:2.000  
 Median :0.0000   Median :4.000   Median :2.000  
 Mean   :0.4062   Mean   :3.688   Mean   :2.812  
 3rd Qu.:1.0000   3rd Qu.:4.000   3rd Qu.:4.000  
 Max.   :1.0000   Max.   :5.000   Max.   :8.000  

Kita bisa menggunakan function str() untuk mendapatkan informasi struktur data berikut:

df %>% str()

'data.frame':   32 obs. of  11 variables:
 $ mpg : num  21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
 $ cyl : num  6 6 4 6 8 6 8 4 4 6 ...
 $ disp: num  160 160 108 258 360 ...
 $ hp  : num  110 110 93 110 175 105 245 62 95 123 ...
 $ drat: num  3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
 $ wt  : num  2.62 2.88 2.32 3.21 3.44 ...
 $ qsec: num  16.5 17 18.6 19.4 17 ...
 $ vs  : num  0 0 1 1 0 1 0 1 1 1 ...
 $ am  : num  1 1 1 0 0 0 0 0 0 0 ...
 $ gear: num  4 4 4 3 3 3 3 4 4 4 ...
 $ carb: num  4 4 1 1 2 1 4 2 2 4 ...

Menggunakan library(dplyr)

Berikut adalah summary statistics menggunakan library(dplyr).

df %>% glimpse()

Rows: 32
Columns: 11
$ mpg  <dbl> 21.0, 21.0, 22.8, 21.4, 18.7, 18.1, 14.3, 24.4, 22.8, 19.2, 17.8,…
$ cyl  <dbl> 6, 6, 4, 6, 8, 6, 8, 4, 4, 6, 6, 8, 8, 8, 8, 8, 8, 4, 4, 4, 4, 8,…
$ disp <dbl> 160.0, 160.0, 108.0, 258.0, 360.0, 225.0, 360.0, 146.7, 140.8, 16…
$ hp   <dbl> 110, 110, 93, 110, 175, 105, 245, 62, 95, 123, 123, 180, 180, 180…
$ drat <dbl> 3.90, 3.90, 3.85, 3.08, 3.15, 2.76, 3.21, 3.69, 3.92, 3.92, 3.92,…
$ wt   <dbl> 2.620, 2.875, 2.320, 3.215, 3.440, 3.460, 3.570, 3.190, 3.150, 3.…
$ qsec <dbl> 16.46, 17.02, 18.61, 19.44, 17.02, 20.22, 15.84, 20.00, 22.90, 18…
$ vs   <dbl> 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0,…
$ am   <dbl> 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0,…
$ gear <dbl> 4, 4, 4, 3, 3, 3, 3, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 4, 4, 4, 3, 3,…
$ carb <dbl> 4, 4, 1, 1, 2, 1, 4, 2, 2, 4, 4, 3, 3, 3, 4, 4, 4, 1, 2, 1, 1, 2,…

Menggunakan library(skimr)

Jika kalian hendak mendapatkan analisa yang lebih lengkap, kita bisa memanfaatkan library(skimr) berikut ini:

df %>% skim()

Name	Piped data
Number of rows	32
Number of columns	11
_______________________
Column type frequency:
numeric	11
________________________
Group variables	None

Data summary

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
mpg	0	1	20.09	6.03	10.40	15.43	19.20	22.80	33.90	▃▇▅▁▂
cyl	0	1	6.19	1.79	4.00	4.00	6.00	8.00	8.00	▆▁▃▁▇
disp	0	1	230.72	123.94	71.10	120.83	196.30	326.00	472.00	▇▃▃▃▂
hp	0	1	146.69	68.56	52.00	96.50	123.00	180.00	335.00	▇▇▆▃▁
drat	0	1	3.60	0.53	2.76	3.08	3.70	3.92	4.93	▇▃▇▅▁
wt	0	1	3.22	0.98	1.51	2.58	3.33	3.61	5.42	▃▃▇▁▂
qsec	0	1	17.85	1.79	14.50	16.89	17.71	18.90	22.90	▃▇▇▂▁
vs	0	1	0.44	0.50	0.00	0.00	0.00	1.00	1.00	▇▁▁▁▆
am	0	1	0.41	0.50	0.00	0.00	0.00	1.00	1.00	▇▁▁▁▆
gear	0	1	3.69	0.74	3.00	3.00	4.00	4.00	5.00	▇▁▆▁▂
carb	0	1	2.81	1.62	1.00	2.00	2.00	4.00	8.00	▇▂▅▁▁

Menggunakan library(psych)

Alternatif lain mendapatkan summary statistics dalam bentuk tabel adalah dengan memanfaatkan library(psych).

psych::describe(df)

     vars  n   mean     sd median trimmed    mad   min    max  range  skew
mpg     1 32  20.09   6.03  19.20   19.70   5.41 10.40  33.90  23.50  0.61
cyl     2 32   6.19   1.79   6.00    6.23   2.97  4.00   8.00   4.00 -0.17
disp    3 32 230.72 123.94 196.30  222.52 140.48 71.10 472.00 400.90  0.38
hp      4 32 146.69  68.56 123.00  141.19  77.10 52.00 335.00 283.00  0.73
drat    5 32   3.60   0.53   3.70    3.58   0.70  2.76   4.93   2.17  0.27
wt      6 32   3.22   0.98   3.33    3.15   0.77  1.51   5.42   3.91  0.42
qsec    7 32  17.85   1.79  17.71   17.83   1.42 14.50  22.90   8.40  0.37
vs      8 32   0.44   0.50   0.00    0.42   0.00  0.00   1.00   1.00  0.24
am      9 32   0.41   0.50   0.00    0.38   0.00  0.00   1.00   1.00  0.36
gear   10 32   3.69   0.74   4.00    3.62   1.48  3.00   5.00   2.00  0.53
carb   11 32   2.81   1.62   2.00    2.65   1.48  1.00   8.00   7.00  1.05
     kurtosis    se
mpg     -0.37  1.07
cyl     -1.76  0.32
disp    -1.21 21.91
hp      -0.14 12.12
drat    -0.71  0.09
wt      -0.02  0.17
qsec     0.34  0.32
vs      -2.00  0.09
am      -1.92  0.09
gear    -1.07  0.13
carb     1.26  0.29

Menggunakan library(Hmisc)

Alternatif lain mendapatkan summary statistics dalam bentuk narasi per variabel adalah dengan memanfaatkan library(Hmisc).

Hmisc::describe(df)

df 

 11  Variables      32  Observations
--------------------------------------------------------------------------------
mpg 
       n  missing distinct     Info     Mean  pMedian      Gmd      .05 
      32        0       25    0.999    20.09     19.6    6.796    12.00 
     .10      .25      .50      .75      .90      .95 
   14.34    15.43    19.20    22.80    30.09    31.30 

lowest : 10.4 13.3 14.3 14.7 15  , highest: 26   27.3 30.4 32.4 33.9
--------------------------------------------------------------------------------
cyl 
       n  missing distinct     Info     Mean  pMedian      Gmd 
      32        0        3    0.866    6.188        6    1.948 
                            
Value          4     6     8
Frequency     11     7    14
Proportion 0.344 0.219 0.438

For the frequency table, variable is rounded to the nearest 0
--------------------------------------------------------------------------------
disp 
       n  missing distinct     Info     Mean  pMedian      Gmd      .05 
      32        0       27    0.999    230.7    223.4    142.5    77.35 
     .10      .25      .50      .75      .90      .95 
   80.61   120.83   196.30   326.00   396.00   449.00 

lowest : 71.1 75.7 78.7 79   95.1, highest: 360  400  440  460  472 
--------------------------------------------------------------------------------
hp 
       n  missing distinct     Info     Mean  pMedian      Gmd      .05 
      32        0       22    0.997    146.7    142.5    77.04    63.65 
     .10      .25      .50      .75      .90      .95 
   66.00    96.50   123.00   180.00   243.50   253.55 

lowest :  52  62  65  66  91, highest: 215 230 245 264 335
--------------------------------------------------------------------------------
drat 
       n  missing distinct     Info     Mean  pMedian      Gmd      .05 
      32        0       22    0.997    3.597    3.575   0.6099    2.853 
     .10      .25      .50      .75      .90      .95 
   3.007    3.080    3.695    3.920    4.209    4.314 

lowest : 2.76 2.93 3    3.07 3.08, highest: 4.08 4.11 4.22 4.43 4.93
--------------------------------------------------------------------------------
wt 
       n  missing distinct     Info     Mean  pMedian      Gmd      .05 
      32        0       29    0.999    3.217    3.186    1.089    1.736 
     .10      .25      .50      .75      .90      .95 
   1.956    2.581    3.325    3.610    4.048    5.293 

lowest : 1.513 1.615 1.835 1.935 2.14 , highest: 3.845 4.07  5.25  5.345 5.424
--------------------------------------------------------------------------------
qsec 
       n  missing distinct     Info     Mean  pMedian      Gmd      .05 
      32        0       30        1    17.85     17.8    2.009    15.05 
     .10      .25      .50      .75      .90      .95 
   15.53    16.89    17.71    18.90    19.99    20.10 

lowest : 14.5  14.6  15.41 15.5  15.84, highest: 19.9  20    20.01 20.22 22.9 
--------------------------------------------------------------------------------
vs 
       n  missing distinct     Info      Sum     Mean 
      32        0        2    0.739       14   0.4375 

--------------------------------------------------------------------------------
am 
       n  missing distinct     Info      Sum     Mean 
      32        0        2    0.724       13   0.4062 

--------------------------------------------------------------------------------
gear 
       n  missing distinct     Info     Mean  pMedian      Gmd 
      32        0        3    0.841    3.688      3.5   0.7863 
                            
Value          3     4     5
Frequency     15    12     5
Proportion 0.469 0.375 0.156

For the frequency table, variable is rounded to the nearest 0
--------------------------------------------------------------------------------
carb 
       n  missing distinct     Info     Mean  pMedian      Gmd 
      32        0        6    0.929    2.812      2.5    1.718 
                                              
Value          1     2     3     4     6     8
Frequency      7    10     3    10     1     1
Proportion 0.219 0.312 0.094 0.312 0.031 0.031

For the frequency table, variable is rounded to the nearest 0
--------------------------------------------------------------------------------

Menggunakan library(GGally)

Jika kita membutuhkan summary statistics berupa density plot dan korelasi antar variabel, kita bisa menggunakan library(GGally).

GGally::ggpairs(df)

Menggunakan library(DataExplorer)

Ada satu library yang bisa digunakan untuk mendapatkan satu file report berformat .html yakni bernama library(DataExplorer).

Hasilnya meng-cover analisa sebagai berikut:

• Basic Statistics
  • Raw Counts
  • Percentages
• Data Structure
• Missing Data Profile
• Univariate Distribution
  • Histogram
  • QQ Plot
• Correlation Analysis
• Principal Component Analysis

Mohon maaf saya tak bisa menampilkannya dalam blog ini tapi kalian bisa mencobanya sendiri dengan skrip:

DataExplorer::create_report(df)

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