data summary

restData <- read.csv("./data/restaurants.csv", encoding = "UTF-8")

Look at a bit of the data

head(restData,n=3)

##    name zipCode neighborhood councilDistrict policeDistrict
## 1   410   21206    Frankford               2   NORTHEASTERN
## 2  1919   21231  Fells Point               1   SOUTHEASTERN
## 3 SAUTE   21224       Canton               1   SOUTHEASTERN
##                          Location.1
## 1 4509 BELAIR ROAD\nBaltimore, MD\n
## 2    1919 FLEET ST\nBaltimore, MD\n
## 3   2844 HUDSON ST\nBaltimore, MD\n

tail(restData,n=3)

##                    name zipCode  neighborhood councilDistrict
## 1325 ZINK'S CAF<U+0090>   21213 Belair-Edison              13
## 1326       ZISSIMOS BAR   21211       Hampden               7
## 1327             ZORBAS   21224     Greektown               2
##      policeDistrict                         Location.1
## 1325   NORTHEASTERN 3300 LAWNVIEW AVE\nBaltimore, MD\n
## 1326       NORTHERN      1023 36TH ST\nBaltimore, MD\n
## 1327   SOUTHEASTERN  4710 EASTERN Ave\nBaltimore, MD\n

Make summary

summary(restData)

##                            name         zipCode             neighborhood
##  MCDONALD'S                  :   8   Min.   :-21226   Downtown    :128  
##  POPEYES FAMOUS FRIED CHICKEN:   7   1st Qu.: 21202   Fells Point : 91  
##  SUBWAY                      :   6   Median : 21218   Inner Harbor: 89  
##  KENTUCKY FRIED CHICKEN      :   5   Mean   : 21185   Canton      : 81  
##  BURGER KING                 :   4   3rd Qu.: 21226   Federal Hill: 42  
##  DUNKIN DONUTS               :   4   Max.   : 21287   Mount Vernon: 33  
##  (Other)                     :1293                    (Other)     :863  
##  councilDistrict       policeDistrict
##  Min.   : 1.000   SOUTHEASTERN:385   
##  1st Qu.: 2.000   CENTRAL     :288   
##  Median : 9.000   SOUTHERN    :213   
##  Mean   : 7.191   NORTHERN    :157   
##  3rd Qu.:11.000   NORTHEASTERN: 72   
##  Max.   :14.000   EASTERN     : 67   
##                   (Other)     :145   
##                           Location.1    
##  1101 RUSSELL ST\nBaltimore, MD\n:   9  
##  201 PRATT ST\nBaltimore, MD\n   :   8  
##  2400 BOSTON ST\nBaltimore, MD\n :   8  
##  300 LIGHT ST\nBaltimore, MD\n   :   5  
##  300 CHARLES ST\nBaltimore, MD\n :   4  
##  301 LIGHT ST\nBaltimore, MD\n   :   4  
##  (Other)                         :1289

More in depth information

str(restData)

## 'data.frame':    1327 obs. of  6 variables:
##  $ name           : Factor w/ 1277 levels "#1 CHINESE KITCHEN",..: 9 3 992 1 2 4 5 6 7 8 ...
##  $ zipCode        : int  21206 21231 21224 21211 21223 21218 21205 21211 21205 21231 ...
##  $ neighborhood   : Factor w/ 173 levels "Abell","Arlington",..: 53 52 18 66 104 33 98 133 98 157 ...
##  $ councilDistrict: int  2 1 1 14 9 14 13 7 13 1 ...
##  $ policeDistrict : Factor w/ 9 levels "CENTRAL","EASTERN",..: 3 6 6 4 8 3 6 4 6 6 ...
##  $ Location.1     : Factor w/ 1210 levels "1 BIDDLE ST\nBaltimore, MD\n",..: 835 334 554 755 492 537 505 530 507 569 ...

Quantiles of quantitative variables

quantile(restData$councilDistrict,na.rm=TRUE)

##   0%  25%  50%  75% 100% 
##    1    2    9   11   14

quantile(restData$councilDistrict,probs=c(0.5,0.75,0.9))

## 50% 75% 90% 
##   9  11  12

Make table

table(restData$zipCode,useNA="ifany")

## 
## -21226  21201  21202  21205  21206  21207  21208  21209  21210  21211 
##      1    136    201     27     30      4      1      8     23     41 
##  21212  21213  21214  21215  21216  21217  21218  21220  21222  21223 
##     28     31     17     54     10     32     69      1      7     56 
##  21224  21225  21226  21227  21229  21230  21231  21234  21237  21239 
##    199     19     18      4     13    156    127      7      1      3 
##  21251  21287 
##      2      1

Make table

table(restData$councilDistrict,restData$zipCode)

##     
##      -21226 21201 21202 21205 21206 21207 21208 21209 21210 21211 21212
##   1       0     0    37     0     0     0     0     0     0     0     0
##   2       0     0     0     3    27     0     0     0     0     0     0
##   3       0     0     0     0     0     0     0     0     0     0     0
##   4       0     0     0     0     0     0     0     0     0     0    27
##   5       0     0     0     0     0     3     0     6     0     0     0
##   6       0     0     0     0     0     0     0     1    19     0     0
##   7       0     0     0     0     0     0     0     1     0    27     0
##   8       0     0     0     0     0     1     0     0     0     0     0
##   9       0     1     0     0     0     0     0     0     0     0     0
##   10      1     0     1     0     0     0     0     0     0     0     0
##   11      0   115   139     0     0     0     1     0     0     0     1
##   12      0    20    24     4     0     0     0     0     0     0     0
##   13      0     0     0    20     3     0     0     0     0     0     0
##   14      0     0     0     0     0     0     0     0     4    14     0
##     
##      21213 21214 21215 21216 21217 21218 21220 21222 21223 21224 21225
##   1      2     0     0     0     0     0     0     7     0   140     1
##   2      0     0     0     0     0     0     0     0     0    54     0
##   3      2    17     0     0     0     3     0     0     0     0     0
##   4      0     0     0     0     0     0     0     0     0     0     0
##   5      0     0    31     0     0     0     0     0     0     0     0
##   6      0     0    15     1     0     0     0     0     0     0     0
##   7      0     0     6     7    15     6     0     0     0     0     0
##   8      0     0     0     0     0     0     0     0     2     0     0
##   9      0     0     0     2     8     0     0     0    53     0     0
##   10     0     0     0     0     0     0     1     0     0     0    18
##   11     0     0     0     0     9     0     0     0     1     0     0
##   12    13     0     0     0     0    26     0     0     0     0     0
##   13    13     0     1     0     0     0     0     0     0     5     0
##   14     1     0     1     0     0    34     0     0     0     0     0
##     
##      21226 21227 21229 21230 21231 21234 21237 21239 21251 21287
##   1      0     0     0     1   124     0     0     0     0     0
##   2      0     0     0     0     0     0     1     0     0     0
##   3      0     1     0     0     0     7     0     0     2     0
##   4      0     0     0     0     0     0     0     3     0     0
##   5      0     0     0     0     0     0     0     0     0     0
##   6      0     0     0     0     0     0     0     0     0     0
##   7      0     0     0     0     0     0     0     0     0     0
##   8      0     2    13     0     0     0     0     0     0     0
##   9      0     0     0    11     0     0     0     0     0     0
##   10    18     0     0   133     0     0     0     0     0     0
##   11     0     0     0    11     0     0     0     0     0     0
##   12     0     0     0     0     2     0     0     0     0     0
##   13     0     1     0     0     1     0     0     0     0     1
##   14     0     0     0     0     0     0     0     0     0     0

Check for missing values

sum(is.na(restData$councilDistrict))

## [1] 0

any(is.na(restData$councilDistrict))

## [1] FALSE

all(restData$zipCode > 0)

## [1] FALSE

Row and column sums

colSums(is.na(restData))

##            name         zipCode    neighborhood councilDistrict 
##               0               0               0               0 
##  policeDistrict      Location.1 
##               0               0

all(colSums(is.na(restData))==0)

## [1] TRUE

Values with specific characteristics

table(restData$zipCode %in% c("21212"))

## 
## FALSE  TRUE 
##  1299    28

table(restData$zipCode %in% c("21212","21213"))

## 
## FALSE  TRUE 
##  1268    59

Values with specific characteristics

head(restData[restData$zipCode %in% c("21212","21213"),], 2)

##                 name zipCode  neighborhood councilDistrict policeDistrict
## 29 BAY ATLANTIC CLUB   21212      Downtown              11        CENTRAL
## 39       BERMUDA BAR   21213 Broadway East              12        EASTERN
##                         Location.1
## 29 206 REDWOOD ST\nBaltimore, MD\n
## 39 1801 NORTH AVE\nBaltimore, MD\n

Cross tabs

data(UCBAdmissions)
DF = as.data.frame(UCBAdmissions)
summary(DF)

##       Admit       Gender   Dept       Freq      
##  Admitted:12   Male  :12   A:4   Min.   :  8.0  
##  Rejected:12   Female:12   B:4   1st Qu.: 80.0  
##                            C:4   Median :170.0  
##                            D:4   Mean   :188.6  
##                            E:4   3rd Qu.:302.5  
##                            F:4   Max.   :512.0

Cross tabs

xt <- xtabs(Freq ~ Gender + Admit,data=DF)
xt

##         Admit
## Gender   Admitted Rejected
##   Male       1198     1493
##   Female      557     1278

Flat tables

warpbreaks$replicate <- rep(1:9, len = 54)
xt = xtabs(breaks ~.,data=warpbreaks)
xt

## , , replicate = 1
## 
##     tension
## wool  L  M  H
##    A 26 18 36
##    B 27 42 20
## 
## , , replicate = 2
## 
##     tension
## wool  L  M  H
##    A 30 21 21
##    B 14 26 21
## 
## , , replicate = 3
## 
##     tension
## wool  L  M  H
##    A 54 29 24
##    B 29 19 24
## 
## , , replicate = 4
## 
##     tension
## wool  L  M  H
##    A 25 17 18
##    B 19 16 17
## 
## , , replicate = 5
## 
##     tension
## wool  L  M  H
##    A 70 12 10
##    B 29 39 13
## 
## , , replicate = 6
## 
##     tension
## wool  L  M  H
##    A 52 18 43
##    B 31 28 15
## 
## , , replicate = 7
## 
##     tension
## wool  L  M  H
##    A 51 35 28
##    B 41 21 15
## 
## , , replicate = 8
## 
##     tension
## wool  L  M  H
##    A 26 30 15
##    B 20 39 16
## 
## , , replicate = 9
## 
##     tension
## wool  L  M  H
##    A 67 36 26
##    B 44 29 28

Flat tables

ftable(xt)

##              replicate  1  2  3  4  5  6  7  8  9
## wool tension                                     
## A    L                 26 30 54 25 70 52 51 26 67
##      M                 18 21 29 17 12 18 35 30 36
##      H                 36 21 24 18 10 43 28 15 26
## B    L                 27 14 29 19 29 31 41 20 44
##      M                 42 26 19 16 39 28 21 39 29
##      H                 20 21 24 17 13 15 15 16 28

Size of a data set

fakeData = rnorm(1e5)
object.size(fakeData)

## 800040 bytes

print(object.size(fakeData),units="Mb")

## 0.8 Mb

data summary

Xshi0001

2018年4月9日

Look at a bit of the data

Make summary

More in depth information

Quantiles of quantitative variables

Make table

Make table

Check for missing values

Row and column sums

Values with specific characteristics

Values with specific characteristics

Cross tabs

Cross tabs

Flat tables

Flat tables

Size of a data set

operation

Built-in Functions