Business Analytics Lab Worksheet 02
CME Group Foundation Business Analytics Lab
Brent Predovich
Summer 2017
Task 1
#Read File
#Name the extracted variable
mydata = (Scoring)
age = mydata$Age#Calculate the average age below. Refer to Worksheet 1 for the correct command
age_mean = mean(age)
age_mean[1] 37.07767#Calculate standard deviation of age below. Refer to Worksheet 1 for the correct command.
age_sd = sd(age)
age_sd[1] 10.98486#Calculate the maximum of age below. The command to find the maximum is max(variable) where variable is the extracted variable.
age_max = max(age)
age_max[1] 68#Calculate the minimum of age below. The command to find the minimum is min(variable) where variable is the extracted variable.
age_min = min(age)
age_min[1] 18#Use the formula above to calculate the upper and lower threshold
age_lower = age_mean - (3) * age_sd
age_upper = age_mean + (3) * age_sd
age_upper[1] 70.03223age_lower[1] 4.123096quantile(age) 0% 25% 50% 75% 100%
18 28 36 45 68 lowerq = quantile(age)[2]
upperq = quantile(age)[4]
iqr = upperq - lowerq
iqr75%
17 Below is the upper threshold:
upperthreshold = (iqr * 1.5) + upperq
upperthreshold 75%
70.5 Below is the lower threshold:
lowerthreshold = lowerq - (iqr * 1.5)
lowerthreshold25%
2.5 age[age>upperthreshold]integer(0)mydata[age>upperthreshold,]age[age<lowerthreshold]integer(0)boxplot(age,horizontal = TRUE)
Task 2
mydata = read.csv(file = "data/creditriskorg.csv")
head(mydata)#tail(mydata)mydata = read.csv("data/creditriskorg.csv",skip = 1)
head(mydata)#str(mydata)
summary(mydata) Loan.Purpose Checking Savings
Small Appliance:105 $- :251 $- : 62
New Car :104 $216.00 : 2 $127.00 : 3
Furniture : 85 $271.00 : 2 $836.00 : 3
Business : 44 $296.00 : 2 $904.00 : 3
Used Car : 40 $305.00 : 2 $922.00 : 3
Education : 23 $497.00 : 2 $104.00 : 2
(Other) : 24 (Other) :164 (Other) :349
Months.Customer Months.Employed Gender Marital.Status
Min. : 5.0 Min. : 0.0 F:135 Divorced:156
1st Qu.:13.0 1st Qu.: 6.0 M:290 Married : 36
Median :19.0 Median : 20.0 Single :233
Mean :22.9 Mean : 31.9
3rd Qu.:28.0 3rd Qu.: 47.0
Max. :73.0 Max. :119.0
Age Housing Years Job
Min. :18.0 Other: 52 Min. :1.00 Management: 54
1st Qu.:26.0 Own :292 1st Qu.:2.00 Skilled :271
Median :32.0 Rent : 81 Median :3.00 Unemployed: 11
Mean :34.4 Mean :2.84 Unskilled : 89
3rd Qu.:41.0 3rd Qu.:4.00
Max. :73.0 Max. :4.00
Credit.Risk
High:211
Low :214
checkings = mydata$Checking
checkings [1] $- $- $- $638.00 $963.00
[6] $2,827.00 $- $- $6,509.00 $966.00
[11] $- $- $322.00 $- $396.00
[16] $- $652.00 $708.00 $207.00 $287.00
[21] $- $101.00 $- $- $-
[26] $141.00 $- $2,484.00 $237.00 $-
[31] $335.00 $3,565.00 $- $16,647.00 $-
[36] $- $- $940.00 $- $-
[41] $218.00 $- $16,935.00 $664.00 $150.00
[46] $- $216.00 $- $- $-
[51] $265.00 $4,256.00 $870.00 $162.00 $-
[56] $- $- $461.00 $- $-
[61] $- $580.00 $- $- $-
[66] $- $758.00 $399.00 $513.00 $-
[71] $- $565.00 $- $- $-
[76] $166.00 $9,783.00 $674.00 $- $15,328.00
[81] $- $713.00 $- $- $-
[86] $- $- $303.00 $900.00 $-
[91] $1,257.00 $- $273.00 $522.00 $-
[96] $- $- $- $514.00 $457.00
[101] $5,133.00 $- $644.00 $305.00 $9,621.00
[106] $- $- $- $- $-
[111] $6,851.00 $13,496.00 $509.00 $- $19,155.00
[116] $- $- $374.00 $- $828.00
[121] $- $829.00 $- $- $939.00
[126] $- $889.00 $876.00 $893.00 $12,760.00
[131] $- $- $959.00 $- $-
[136] $- $- $698.00 $- $-
[141] $- $12,974.00 $- $317.00 $-
[146] $- $- $192.00 $- $-
[151] $- $- $- $942.00 $-
[156] $3,329.00 $- $- $- $-
[161] $- $- $339.00 $- $-
[166] $- $105.00 $- $216.00 $113.00
[171] $109.00 $- $- $8,176.00 $-
[176] $468.00 $7,885.00 $- $- $-
[181] $- $- $- $- $-
[186] $- $734.00 $- $- $172.00
[191] $644.00 $- $617.00 $- $586.00
[196] $- $- $- $- $-
[201] $522.00 $585.00 $5,588.00 $- $352.00
[206] $- $2,715.00 $560.00 $895.00 $305.00
[211] $- $- $- $8,948.00 $-
[216] $- $- $- $- $483.00
[221] $- $- $- $663.00 $624.00
[226] $- $- $152.00 $- $-
[231] $498.00 $- $156.00 $1,336.00 $-
[236] $- $- $2,641.00 $- $-
[241] $- $- $- $887.00 $-
[246] $- $- $- $18,408.00 $497.00
[251] $- $946.00 $986.00 $8,122.00 $-
[256] $778.00 $645.00 $- $682.00 $19,812.00
[261] $- $- $859.00 $- $-
[266] $- $- $- $- $795.00
[271] $- $- $- $- $852.00
[276] $- $- $425.00 $- $-
[281] $- $11,072.00 $- $219.00 $8,060.00
[286] $- $- $- $- $1,613.00
[291] $757.00 $- $- $977.00 $197.00
[296] $- $- $- $- $-
[301] $256.00 $296.00 $- $- $-
[306] $298.00 $- $8,636.00 $- $-
[311] $19,766.00 $- $- $- $-
[316] $4,089.00 $- $271.00 $949.00 $-
[321] $911.00 $- $- $- $-
[326] $271.00 $- $- $- $-
[331] $4,802.00 $177.00 $- $- $996.00
[336] $705.00 $- $- $5,960.00 $-
[341] $759.00 $- $651.00 $257.00 $955.00
[346] $- $8,249.00 $- $956.00 $382.00
[351] $- $842.00 $3,111.00 $- $-
[356] $2,846.00 $231.00 $- $17,366.00 $-
[361] $332.00 $242.00 $- $929.00 $-
[366] $- $- $- $- $-
[371] $- $646.00 $538.00 $- $-
[376] $- $- $135.00 $2,472.00 $-
[381] $10,417.00 $211.00 $16,630.00 $- $642.00
[386] $- $296.00 $898.00 $478.00 $315.00
[391] $122.00 $- $- $- $670.00
[396] $444.00 $3,880.00 $819.00 $- $-
[401] $- $- $- $- $-
[406] $- $- $161.00 $- $-
[411] $789.00 $765.00 $- $- $983.00
[416] $- $- $798.00 $- $193.00
[421] $497.00 $- $- $- $-
168 Levels: $- $1,257.00 $1,336.00 ... $996.00 checkings[1:6][1] $- $- $- $638.00 $963.00
[6] $2,827.00
168 Levels: $- $1,257.00 $1,336.00 ... $996.00 clean = checkings[1:10]
clean = sub(",","",clean)
clean =sub("\\$","",clean)
class(clean)[1] "character"clean = as.numeric(clean)NAs introduced by coercionclass(clean)[1] "numeric"clean [1] NA NA NA 638 963 2827 NA NA 6509 966checkings=sub(",","",checkings)
checkings=sub("\\$","",checkings)
checkings=as.numeric(checkings)NAs introduced by coercioncheckings [1] NA NA NA 638 963 2827 NA NA 6509 966
[11] NA NA 322 NA 396 NA 652 708 207 287
[21] NA 101 NA NA NA 141 NA 2484 237 NA
[31] 335 3565 NA 16647 NA NA NA 940 NA NA
[41] 218 NA 16935 664 150 NA 216 NA NA NA
[51] 265 4256 870 162 NA NA NA 461 NA NA
[61] NA 580 NA NA NA NA 758 399 513 NA
[71] NA 565 NA NA NA 166 9783 674 NA 15328
[81] NA 713 NA NA NA NA NA 303 900 NA
[91] 1257 NA 273 522 NA NA NA NA 514 457
[101] 5133 NA 644 305 9621 NA NA NA NA NA
[111] 6851 13496 509 NA 19155 NA NA 374 NA 828
[121] NA 829 NA NA 939 NA 889 876 893 12760
[131] NA NA 959 NA NA NA NA 698 NA NA
[141] NA 12974 NA 317 NA NA NA 192 NA NA
[151] NA NA NA 942 NA 3329 NA NA NA NA
[161] NA NA 339 NA NA NA 105 NA 216 113
[171] 109 NA NA 8176 NA 468 7885 NA NA NA
[181] NA NA NA NA NA NA 734 NA NA 172
[191] 644 NA 617 NA 586 NA NA NA NA NA
[201] 522 585 5588 NA 352 NA 2715 560 895 305
[211] NA NA NA 8948 NA NA NA NA NA 483
[221] NA NA NA 663 624 NA NA 152 NA NA
[231] 498 NA 156 1336 NA NA NA 2641 NA NA
[241] NA NA NA 887 NA NA NA NA 18408 497
[251] NA 946 986 8122 NA 778 645 NA 682 19812
[261] NA NA 859 NA NA NA NA NA NA 795
[271] NA NA NA NA 852 NA NA 425 NA NA
[281] NA 11072 NA 219 8060 NA NA NA NA 1613
[291] 757 NA NA 977 197 NA NA NA NA NA
[301] 256 296 NA NA NA 298 NA 8636 NA NA
[311] 19766 NA NA NA NA 4089 NA 271 949 NA
[321] 911 NA NA NA NA 271 NA NA NA NA
[331] 4802 177 NA NA 996 705 NA NA 5960 NA
[341] 759 NA 651 257 955 NA 8249 NA 956 382
[351] NA 842 3111 NA NA 2846 231 NA 17366 NA
[361] 332 242 NA 929 NA NA NA NA NA NA
[371] NA 646 538 NA NA NA NA 135 2472 NA
[381] 10417 211 16630 NA 642 NA 296 898 478 315
[391] 122 NA NA NA 670 444 3880 819 NA NA
[401] NA NA NA NA NA NA NA 161 NA NA
[411] 789 765 NA NA 983 NA NA 798 NA 193
[421] 497 NA NA NA NAmean(checkings,na.rm = TRUE)[1] 2559.805sum(checkings,na.rm = TRUE)/length(checkings)[1] 1048.014What are some other ways to clean this data in R? How about in Excel?
Task 3
Now, we will look at Chicago taxi data. Go and explore the interactive dashboard and read the description of the data.
Chicago Taxi Dashboard: https://data.cityofchicago.org/Transportation/Taxi-Trips-Dashboard/spcw-brbq
Chicago Taxi Data Description: http://digital.cityofchicago.org/index.php/chicago-taxi-data-released/
Open in RStudio the csv file is located in the data folder, note the size of the file, the number of columns and of rows here. Use the functions learned in lab00 and lab01 to describe the data, identify unique entities, fields and summarize.
mydata = read.csv(file = "data/Taxi_Trips_sample.csv")
mydata$Trip.ID <- NULL
mydata$Taxi.ID <- NULL
head(mydata)Define a relational business logic for the column field ‘Trip Seconds’.
summary(mydata) Trip.Start.Timestamp Trip.End.Timestamp
07/25/2014 06:45:00 PM: 9 : 16
02/05/2015 07:15:00 PM: 8 02/10/2014 10:30:00 AM: 9
02/27/2015 08:45:00 AM: 8 02/05/2015 07:45:00 PM: 8
04/25/2014 06:45:00 PM: 8 03/03/2014 06:45:00 PM: 8
09/18/2013 07:30:00 PM: 8 03/22/2014 08:15:00 PM: 8
03/15/2014 07:00:00 PM: 7 03/24/2016 07:30:00 PM: 8
(Other) :99951 (Other) :99942
Trip.Seconds Trip.Miles Pickup.Census.Tract
Min. : 0.0 Min. : 0.000 Min. :1.703e+10
1st Qu.: 300.0 1st Qu.: 0.000 1st Qu.:1.703e+10
Median : 540.0 Median : 0.900 Median :1.703e+10
Mean : 739.2 Mean : 2.686 Mean :1.703e+10
3rd Qu.: 900.0 3rd Qu.: 2.400 3rd Qu.:1.703e+10
Max. :74340.0 Max. :1830.000 Max. :1.703e+10
NA's :1327 NA's :1 NA's :38042
Dropoff.Census.Tract Pickup.Community.Area Dropoff.Community.Area
Min. :1.703e+10 Min. : 1.00 Min. : 1.00
1st Qu.:1.703e+10 1st Qu.: 8.00 1st Qu.: 8.00
Median :1.703e+10 Median : 8.00 Median :14.00
Mean :1.703e+10 Mean :22.04 Mean :21.14
3rd Qu.:1.703e+10 3rd Qu.:32.00 3rd Qu.:32.00
Max. :1.703e+10 Max. :77.00 Max. :77.00
NA's :38775 NA's :15534 NA's :17532
Fare Tips Tolls Extras
$6.25 : 2892 $0.00 :63911 $0.00 :99932 $0.00 :62102
$5.25 : 2699 $2.00 :10382 $1.90 : 13 $1.00 :18344
$3.25 : 2629 $3.00 : 3769 $1.50 : 12 $2.00 : 8888
$5.85 : 2390 $1.00 : 3162 $50.00 : 8 $1.50 : 4635
$5.65 : 2389 $5.00 : 1004 $3.00 : 7 $3.00 : 2052
$6.05 : 2367 $4.00 : 991 $2.00 : 5 $4.00 : 1134
(Other):84633 (Other):16780 (Other): 22 (Other): 2844
Trip.Total Payment.Type
$7.25 : 2010 Cash :60760
$6.25 : 1908 Credit Card:38322
$3.25 : 1889 Dispute : 58
$6.65 : 1762 No Charge : 622
$8.25 : 1729 Pcard : 18
$7.05 : 1658 Prcard : 6
(Other):89043 Unknown : 213
Company
:35411
Taxi Affiliation Services :29911
Dispatch Taxi Affiliation : 9417
Blue Ribbon Taxi Association Inc. : 6766
Choice Taxi Association : 5185
Chicago Elite Cab Corp. (Chicago Carriag: 5091
(Other) : 8218
Pickup.Centroid.Latitude Pickup.Centroid.Longitude
Min. :41.66 Min. :-87.91
1st Qu.:41.88 1st Qu.:-87.66
Median :41.89 Median :-87.63
Mean :41.90 Mean :-87.66
3rd Qu.:41.92 3rd Qu.:-87.63
Max. :42.02 Max. :-87.54
NA's :15533 NA's :15533
Pickup.Centroid.Location Dropoff.Centroid.Latitude
:15533 Min. :41.67
POINT (-87.632746 41.880994): 8572 1st Qu.:41.88
POINT (-87.620993 41.884987): 5034 Median :41.89
POINT (-87.633308 41.899602): 3850 Mean :41.90
POINT (-87.626215 41.892508): 3832 3rd Qu.:41.92
POINT (-87.631864 41.892042): 3692 Max. :42.02
(Other) :59486 NA's :17376
Dropoff.Centroid.Longitude Dropoff.Centroid..Location
Min. :-87.91 :17376
1st Qu.:-87.66 POINT (-87.632746 41.880994): 7644
Median :-87.63 POINT (-87.620993 41.884987): 4412
Mean :-87.66 POINT (-87.626215 41.892508): 3073
3rd Qu.:-87.63 POINT (-87.631864 41.892042): 3072
Max. :-87.53 POINT (-87.655998 41.944227): 2850
NA's :17376 (Other) :61572
Community.Areas
Min. : 1.00
1st Qu.:37.00
Median :37.00
Mean :41.18
3rd Qu.:38.00
Max. :77.00
NA's :15533 trip_s = mydata$Trip.Seconds
trip_s [1] 480 420 420 2340 300 1020 360 2220 1020 780
[11] 600 0 0 900 660 300 600 540 0 1140
[21] 1200 540 0 0 1080 1380 780 540 3300 300
[31] 480 960 0 600 1440 840 60 360 360 1020
[41] 0 780 1440 420 960 240 300 600 660 300
[51] 660 300 1020 600 360 480 780 0 240 360
[61] 480 480 720 300 600 1020 180 540 0 780
[71] 300 1680 240 360 2520 540 600 840 480 180
[81] 1020 120 1320 480 0 240 840 120 120 540
[91] 0 660 420 240 540 120 2220 780 840 660
[101] 120 1260 420 300 1020 120 600 300 780 1740
[111] 480 1020 840 300 960 240 360 480 480 840
[121] 780 840 300 660 420 660 0 480 0 0
[131] 1440 240 420 840 720 600 900 720 180 1320
[141] 1320 540 1200 1680 600 1200 600 1860 360 0
[151] 780 240 300 1020 1560 720 420 660 660 1980
[161] 600 1200 240 540 480 NA 0 600 0 840
[171] 60 300 600 360 480 480 840 360 300 0
[181] 3360 360 540 180 540 360 1320 540 840 0
[191] 0 780 780 840 300 0 1740 360 960 540
[201] 600 480 420 0 1800 300 0 60 540 0
[211] 240 720 4500 600 1200 540 300 660 720 420
[221] 0 780 840 2760 480 0 0 1140 420 1080
[231] 420 300 240 1020 300 0 240 420 600 180
[241] 180 240 1320 780 840 1440 900 300 1980 360
[251] 180 240 420 480 240 600 660 240 2280 1860
[261] 1140 660 420 480 1500 900 1080 1440 540 240
[271] 120 540 420 0 1320 60 3420 1980 180 360
[281] 2700 300 1080 600 240 840 480 840 660 120
[291] 660 540 420 720 2640 900 360 900 540 2940
[301] 120 1980 600 240 420 480 180 1260 780 0
[311] 240 0 240 1080 360 300 240 600 120 180
[321] 1440 480 360 1140 33060 120 1920 960 1380 0
[331] 660 900 780 720 0 120 0 540 300 1500
[341] 0 420 3300 780 660 420 660 0 1920 3600
[351] 300 1020 660 420 2220 1140 0 300 2040 660
[361] 540 960 1140 0 540 180 360 660 420 480
[371] 780 780 0 180 0 660 600 480 480 840
[381] 720 1800 540 660 1200 1020 1140 480 240 420
[391] 540 300 180 540 600 660 720 360 1320 2220
[401] 540 840 300 0 2760 960 240 0 360 600
[411] 1500 780 720 NA 660 2340 540 360 420 900
[421] 0 180 780 300 900 360 0 1920 540 420
[431] 240 720 300 420 420 180 900 360 300 600
[441] 0 0 420 NA 600 960 240 2280 1380 600
[451] 120 480 2340 1440 300 480 4020 2640 360 780
[461] 1260 0 420 0 240 9840 360 720 1500 900
[471] 1440 180 480 420 360 2460 540 420 480 0
[481] 0 300 660 60 900 660 1740 240 780 240
[491] 360 720 600 600 300 480 480 420 NA 1380
[501] 720 180 120 0 900 720 2400 1020 1320 1980
[511] 720 0 1980 420 420 360 0 420 840 360
[521] 840 420 720 660 660 840 720 3720 780 360
[531] 2460 660 1380 360 960 660 0 300 0 480
[541] 180 780 960 660 480 2280 NA 1560 420 600
[551] 300 240 2220 240 840 600 660 720 240 1140
[561] NA 420 360 420 1200 120 540 180 660 420
[571] 480 3240 300 540 480 840 540 360 540 0
[581] 1740 420 300 240 780 1920 660 1380 480 3600
[591] 0 540 600 0 420 1800 0 2400 480 360
[601] 1500 300 1440 660 240 1200 3540 240 240 360
[611] NA 360 960 540 600 1380 600 420 0 1260
[621] 660 360 660 540 600 660 300 420 720 0
[631] 540 1320 1080 780 540 600 960 1680 60 300
[641] 480 420 660 840 240 1680 960 2520 300 180
[651] 3600 720 600 840 420 1920 1380 900 960 360
[661] 1080 420 NA 420 1500 540 360 720 660 0
[671] 660 60 420 120 720 900 420 60 660 420
[681] 600 420 1200 1560 420 600 600 4020 720 660
[691] 780 300 540 180 360 600 180 1260 540 780
[701] 240 780 180 960 360 300 360 900 660 1140
[711] 540 420 2160 1260 300 420 300 960 1680 780
[721] 780 240 720 660 1080 660 360 840 660 540
[731] 660 0 240 480 240 960 300 1800 1080 0
[741] 480 780 NA 480 960 1080 420 660 360 540
[751] 300 420 3305 180 540 900 960 300 600 300
[761] 1200 300 660 0 360 600 600 2340 540 720
[771] 360 1440 360 540 2040 600 1200 540 480 780
[781] 420 180 840 491 240 480 1080 840 300 660
[791] 360 480 1260 0 600 360 360 1860 240 1740
[801] 540 360 600 720 600 1200 360 1080 240 660
[811] 240 540 420 3420 360 3120 540 600 0 240
[821] 180 300 0 NA 420 1980 2160 660 540 600
[831] 180 300 540 300 4020 300 300 2100 0 780
[841] 1080 660 360 1680 900 900 180 720 480 360
[851] 1440 3900 240 0 720 0 420 900 180 180
[861] 2280 300 360 840 240 480 420 420 300 600
[871] 240 480 840 600 420 3600 0 480 300 600
[881] 300 2520 840 240 240 540 720 420 180 1860
[891] 360 600 240 0 240 540 540 840 540 360
[901] 780 240 0 0 780 300 180 1500 480 1200
[911] 300 1320 480 120 1440 600 1140 60 480 300
[921] 600 600 300 660 360 300 480 2160 1260 360
[931] 480 720 180 1500 660 600 2460 720 240 660
[941] 360 0 420 600 540 600 0 480 0 60
[951] 600 360 0 300 300 600 300 660 1320 1140
[961] 1980 0 2520 1320 480 300 0 900 720 480
[971] 1080 1680 1080 360 240 840 1080 540 1260 300
[981] 1500 0 1140 180 60 0 NA 300 780 360
[991] 240 1020 720 840 300 1800 1140 360 240 420
[ reached getOption("max.print") -- omitted 98999 entries ]trip_m=mydata$Trip.Miles
trip_m [1] 0.15 0.00 1.70 13.80 0.70 1.40 0.10 13.30 8.00
[10] 7.70 2.10 0.00 0.00 0.10 1.10 1.30 1.40 0.00
[19] 0.00 3.40 17.40 0.00 0.00 2.00 4.70 5.20 0.00
[28] 0.00 17.80 1.10 1.10 6.20 0.00 2.40 4.30 6.80
[37] 0.10 1.30 0.50 0.00 0.00 5.20 17.40 1.19 4.60
[46] 0.80 0.00 2.00 4.90 0.90 0.00 1.40 6.50 2.40
[55] 0.90 0.00 0.00 0.00 0.80 1.50 0.00 0.00 2.70
[64] 0.00 0.10 6.80 0.50 3.10 0.00 3.40 0.50 6.90
[73] 0.00 0.00 0.20 1.50 0.00 0.10 2.10 0.30 7.10
[82] 0.50 17.60 1.70 0.00 4.70 0.60 0.50 0.50 0.00
[91] 0.00 0.00 0.70 0.70 0.10 0.60 14.20 2.80 1.16
[100] 1.20 0.00 0.20 1.50 1.60 4.30 0.60 3.30 0.00
[109] 2.40 0.00 1.20 2.40 0.20 0.80 0.00 0.20 2.00
[118] 1.30 1.70 0.40 2.10 1.37 0.00 0.90 0.00 0.00
[127] 0.00 2.30 0.00 0.00 5.80 0.60 1.22 2.70 0.70
[136] 1.40 0.00 0.40 0.40 2.80 10.90 0.00 0.00 0.00
[145] 1.20 7.20 0.00 17.90 0.90 0.00 0.00 0.00 0.70
[154] 4.60 17.40 2.60 2.20 1.30 3.20 168.00 1.10 3.60
[163] 0.80 2.20 1.30 0.00 0.00 1.40 0.00 5.20 0.00
[172] 0.00 2.60 0.10 18.00 0.10 1.50 1.00 1.40 0.00
[181] 1.00 1.50 2.10 0.56 2.30 0.70 0.60 0.00 5.70
[190] 0.00 0.00 0.10 4.20 2.50 0.10 0.00 0.00 0.00
[199] 0.10 0.00 0.10 1.50 1.20 0.00 8.00 0.00 0.00
[208] 0.00 3.10 0.00 0.00 2.00 20.90 0.10 1.00 3.60
[217] 1.50 0.00 0.00 0.00 0.00 1.90 0.30 14.50 0.70
[226] 0.00 0.00 0.40 1.20 0.00 0.00 0.08 0.50 0.20
[235] 0.40 0.00 0.00 1.00 0.00 0.00 0.00 0.70 5.20
[244] 5.40 0.00 18.00 0.00 0.80 19.00 0.00 0.40 0.50
[253] 1.00 0.10 1.30 3.70 0.00 0.30 26.40 10.20 2.00
[262] 0.30 0.00 1.50 4.30 0.00 3.40 0.00 1.40 0.60
[271] 0.00 1.80 2.20 0.00 17.30 0.20 11.90 8.00 0.00
[280] 1.30 17.30 0.60 0.40 2.10 1.00 4.20 2.00 2.80
[289] 1.30 0.40 1.90 0.00 1.00 4.20 3.60 4.00 1.10
[298] 9.00 2.40 1.10 0.00 16.80 0.00 0.00 0.00 0.70
[307] 0.60 0.20 1.80 0.00 0.50 0.00 0.00 3.40 0.70
[316] 0.60 1.00 1.70 0.30 0.40 17.80 1.30 0.00 0.00
[325] 0.50 0.00 0.00 2.70 14.60 0.00 1.40 7.10 1.50
[334] 5.10 0.00 0.00 0.00 1.20 0.90 4.50 0.00 0.80
[343] 0.30 0.00 2.40 1.80 0.30 0.00 2.60 21.00 0.00
[352] 6.50 2.20 0.00 9.80 3.00 0.00 0.00 15.30 1.90
[361] 1.70 0.00 14.30 0.00 1.90 0.60 0.96 2.10 0.00
[370] 0.20 0.00 5.70 0.00 0.80 0.00 0.10 1.20 3.80
[379] 0.10 0.10 0.10 16.00 2.30 0.20 2.10 0.00 14.80
[388] 1.60 0.00 0.00 1.10 2.00 0.60 1.30 0.00 0.00
[397] 3.10 0.63 15.80 12.60 3.10 0.20 1.20 0.00 0.00
[406] 0.00 0.60 0.00 0.10 0.20 0.00 6.70 5.20 0.00
[415] 0.00 1.70 1.50 0.00 1.00 0.10 0.00 0.60 3.60
[424] 1.40 2.60 0.70 0.00 13.50 1.90 1.00 0.00 0.20
[433] 1.20 2.10 0.70 1.10 0.70 0.80 0.50 1.90 0.00
[442] 0.00 1.70 0.00 1.80 7.40 0.00 0.00 6.20 2.00
[451] 0.30 1.30 17.70 4.20 1.20 1.70 19.10 17.80 0.90
[460] 2.60 0.00 0.00 2.10 0.00 0.70 11.20 1.10 1.40
[469] 17.80 2.80 0.10 0.30 1.10 1.60 0.80 18.40 1.50
[478] 0.00 1.20 0.00 0.00 0.00 1.50 0.00 0.10 2.90
[487] 12.10 0.90 2.80 0.50 0.00 2.20 2.60 0.00 0.00
[496] 0.00 2.90 1.40 0.00 1.62 2.10 0.40 0.50 0.00
[505] 3.30 3.50 17.38 0.00 0.00 3.50 1.70 0.00 17.50
[514] 1.90 0.90 1.30 0.00 0.00 0.00 0.40 2.80 1.10
[523] 0.00 0.10 3.94 1.70 3.90 13.90 0.00 0.90 17.60
[532] 1.10 17.40 0.90 8.80 1.20 0.00 1.00 0.00 1.60
[541] 1.10 0.00 0.20 3.80 1.60 12.40 0.00 5.00 0.90
[550] 0.00 0.80 0.80 19.80 1.00 6.90 1.30 4.90 5.20
[559] 0.90 3.30 0.00 0.00 0.80 1.10 7.20 0.00 1.90
[568] 0.60 0.00 0.97 0.10 16.40 0.80 0.00 2.30 0.00
[577] 2.10 0.80 1.60 0.00 7.60 0.00 0.00 1.10 2.20
[586] 1.40 0.20 18.50 1.90 10.50 0.00 0.00 0.00 0.00
[595] 1.40 18.60 0.00 13.30 2.50 1.50 12.90 0.00 14.80
[604] 19.00 0.00 4.00 17.96 1.00 0.00 2.00 0.00 1.10
[613] 2.10 1.10 0.10 22.00 0.00 0.90 0.00 0.40 2.10
[622] 2.30 1.50 2.70 0.00 0.20 0.90 1.00 0.00 0.00
[631] 1.30 3.40 8.10 1.40 3.60 1.40 0.60 12.90 0.00
[640] 1.00 2.10 1.30 3.10 4.40 0.00 0.00 0.10 17.70
[649] 0.80 0.30 21.20 3.50 0.00 3.40 0.00 0.60 1.10
[658] 5.40 0.00 0.09 10.30 1.70 0.00 0.00 15.00 0.80
[667] 0.00 3.20 2.10 0.00 1.30 0.00 1.10 0.60 1.70
[676] 1.70 1.30 0.00 2.30 0.90 3.00 1.40 0.90 12.90
[685] 1.70 0.00 0.00 12.90 3.10 0.00 0.20 0.70 1.00
[694] 0.30 0.00 0.00 0.63 0.00 1.00 3.60 0.00 2.10
[703] 0.70 3.30 1.30 0.07 0.10 13.80 1.20 5.30 2.20
[712] 0.00 0.00 0.30 0.75 0.00 0.00 7.60 1.10 1.70
[721] 3.40 0.00 2.50 1.10 5.80 0.49 0.00 1.60 4.70
[730] 2.00 2.10 0.00 1.00 2.80 0.00 4.30 0.00 9.70
[739] 0.10 0.00 0.00 0.10 0.00 1.30 2.30 6.80 0.00
[748] 0.00 0.80 0.00 1.30 3.40 34.47 0.00 0.70 1.50
[757] 2.70 0.06 1.50 0.70 4.20 1.10 0.00 0.00 0.00
[766] 0.00 1.30 0.90 0.00 2.20 1.40 14.80 1.20 1.30
[775] 11.10 1.10 2.10 0.27 0.60 1.60 1.50 0.00 1.90
[784] 0.00 0.60 2.70 3.30 3.40 1.20 3.40 1.30 0.90
[793] 4.00 0.00 1.60 1.00 0.10 14.60 1.10 0.00 0.00
[802] 1.52 0.00 2.50 0.00 12.30 0.00 7.70 0.00 4.80
[811] 0.50 1.50 2.10 17.30 0.00 17.90 2.20 0.00 0.00
[820] 0.00 0.60 1.30 0.00 0.00 1.70 0.00 12.50 2.90
[829] 1.80 1.50 0.70 0.00 2.40 0.60 13.91 0.00 0.80
[838] 14.10 0.00 3.30 3.30 1.30 0.00 18.50 1.40 0.00
[847] 0.40 3.60 1.50 0.00 0.00 43.20 0.00 0.00 0.10
[856] 0.00 0.00 6.20 0.50 0.80 13.40 0.90 0.00 0.00
[865] 0.00 2.80 1.70 0.60 1.00 0.00 0.98 1.85 2.60
[874] 3.40 0.00 1.10 1.40 2.40 1.50 1.30 1.00 13.40
[883] 2.30 0.10 0.60 3.20 1.60 0.00 0.00 12.80 0.80
[892] 1.70 0.00 0.00 0.50 2.70 1.10 4.20 1.30 0.69
[901] 0.10 0.50 0.00 0.00 1.70 0.80 0.70 6.00 2.00
[910] 3.70 0.00 27.00 1.60 0.00 17.50 1.50 0.00 0.00
[919] 2.00 1.10 2.40 0.10 0.60 0.00 0.90 0.70 3.30
[928] 13.00 3.80 1.70 0.00 0.00 0.40 17.80 0.30 0.00
[937] 19.00 0.10 0.70 0.20 1.70 0.00 1.90 1.60 0.00
[946] 1.50 0.00 2.00 0.00 0.30 0.00 1.40 0.00 0.90
[955] 0.00 1.20 1.00 1.40 10.10 0.10 1.10 0.00 17.60
[964] 0.00 0.00 1.20 0.00 0.00 0.00 1.50 5.80 1.00
[973] 7.80 0.00 1.00 0.00 5.30 1.30 5.70 0.70 12.80
[982] 0.00 0.00 0.00 0.50 0.00 0.00 0.60 0.20 1.30
[991] 1.50 0.00 6.20 3.20 0.90 12.70 2.10 1.30 0.00
[1000] 0.00
[ reached getOption("max.print") -- omitted 98999 entries ]mean_trip_s = mean(trip_s)
mean_trip_s[1] NAmean_trip_m = mean(trip_m)
as.numeric(mean_trip_m)[1] NAUsing https://erdplus.com/#/standalone draw a star schema using the following three tables:
- A Fact table for Trip
- A Dimension table for Community Area
- A Dimension table for Rider
library(png)package <U+393C><U+3E31>png<U+393C><U+3E32> was built under R version 3.2.5library(grid)
png("my.png",500,300)imgError: object 'img' not 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