Data 607 Week 6 -Project 2
Yuen Chun Wong
October 8, 2017
Tidying and Transforming Data
Load libraries
library(tidyr)## Warning: package 'tidyr' was built under R version 3.4.2library(dplyr)## Warning: package 'dplyr' was built under R version 3.4.2##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union#1 NYC Foreclosure Data - Sedo Oh
Source: Table 1 (in the pdf file) http://furmancenter.org/files/sotc/SOC_2016_Full.pdf
Analysis: draw tread line of the foreclosure
Load Data - NYC Foreclosure
NYCForeclosure <- tbl_df(read.csv("foreclosure.csv", stringsAsFactors = FALSE, check.names = FALSE))
#Boro Summary
print(NYCForeclosure)## # A tibble: 6 x 7
## Boro `2011` `2012` `2013` `2014` `2015` `2016`
## <chr> <int> <int> <int> <int> <int> <int>
## 1 Bronx 8770 8956 8456 5459 4324 4904
## 2 Brooklyn 21351 21768 19745 12530 9185 10877
## 3 Manhattan 1142 1154 1213 733 608 901
## 4 Queens 29307 28721 25712 16007 12563 14796
## 5 S.Island 10319 10274 9163 6222 4983 5646
## 6 NYC 70889 70873 64289 40951 31663 37124Tidy the Foreclosure data
#get boro
boroType <- NYCForeclosure$Boro
#tidy data
NYCForeclosure.ByYear <- NYCForeclosure %>%
gather(Yr, ForeclosureCount, 2:7)
print(NYCForeclosure.ByYear)## # A tibble: 36 x 3
## Boro Yr ForeclosureCount
## <chr> <chr> <int>
## 1 Bronx 2011 8770
## 2 Brooklyn 2011 21351
## 3 Manhattan 2011 1142
## 4 Queens 2011 29307
## 5 S.Island 2011 10319
## 6 NYC 2011 70889
## 7 Bronx 2012 8956
## 8 Brooklyn 2012 21768
## 9 Manhattan 2012 1154
## 10 Queens 2012 28721
## # ... with 26 more rowsModifed from http://www.statmethods.net/graphs/line.html
#get the range for x,y
xrange = range(NYCForeclosure.ByYear$Yr)
yrange = range(NYCForeclosure.ByYear$ForeclosureCount)
#setup plot
plot(xrange, yrange, type="n", xlab="Year",
ylab="Foreclosure Count" )
colors <- rainbow(length(boroType))
linetype <- c(1:length(boroType))
plotchar <- seq(18,18+length(boroType),1)
# add lines
for (i in 1:length(boroType)) {
boroforeclosure <- subset(NYCForeclosure.ByYear, NYCForeclosure.ByYear$Boro==boroType[i])
lines(boroforeclosure$Yr, boroforeclosure$ForeclosureCount, type="b", lwd=1.5,
lty=linetype[i], col=colors[i], pch=plotchar[i])
}
# add a title and subtitle
title("NYC Foreclosure", "")
# add a legend
legend( "topright", boroType, cex=0.8, col=colors,
pch=plotchar, lty=linetype, title="Boro")
As we can see above, NYC foreclosure rate gradually decreased but it slightly picked up again from 2015 to 2016.
#2 Oil supply and demand, price - Silverio Vasquez - EIA Short-term Energy Outlook
Tidy data can be used to downstream analysis. I saved the data in the csv format and add grouping for easy calcuation before tidying
EIA_OilSupply <- tbl_df(read.csv("EIA_OilSupply.csv", stringsAsFactors = FALSE, check.names = FALSE))
EIA_OilConsumption <- tbl_df(read.csv("EIA_OilConsumption.csv", stringsAsFactors = FALSE, check.names = FALSE))
EIA_OilPrice <- tbl_df(read.csv("EIA_OilPrice.csv", stringsAsFactors = FALSE, check.names = FALSE))
#all oil data
print(EIA_OilSupply)## # A tibble: 9 x 75
## CODE GROUP DESC `1/31/2013`
## <chr> <chr> <chr> <dbl>
## 1 papr_us OECD U.S. (50 States) 11.59
## 2 papr_ca OECD Canada 4.12
## 3 papr_mx OECD Mexico 2.96
## 4 papr_otheroecd OECD Other OECD 4.40
## 5 copr_opec Non-OECD OPEC Crude Oil Portion 30.40
## 6 opec_nc Non-OECD OPEC Other Liquids (b) 6.43
## 7 papr_fsu Non-OECD Former Soviet Union 13.74
## 8 papr_ch Non-OECD China 4.89
## 9 papr_other_nonoecd Non-OECD Other Non-OECD 11.66
## # ... with 71 more variables: `2/28/2013` <dbl>, `3/31/2013` <dbl>,
## # `4/30/2013` <dbl>, `5/31/2013` <dbl>, `6/30/2013` <dbl>,
## # `7/31/2013` <dbl>, `8/31/2013` <dbl>, `9/30/2013` <dbl>,
## # `10/31/2013` <dbl>, `11/30/2013` <dbl>, `12/31/2013` <dbl>,
## # `1/31/2014` <dbl>, `2/28/2014` <dbl>, `3/31/2014` <dbl>,
## # `4/30/2014` <dbl>, `5/31/2014` <dbl>, `6/30/2014` <dbl>,
## # `7/31/2014` <dbl>, `8/31/2014` <dbl>, `9/30/2014` <dbl>,
## # `10/31/2014` <dbl>, `11/30/2014` <dbl>, `12/31/2014` <dbl>,
## # `1/31/2015` <dbl>, `2/28/2015` <dbl>, `3/31/2015` <dbl>,
## # `4/30/2015` <dbl>, `5/31/2015` <dbl>, `6/30/2015` <dbl>,
## # `7/31/2015` <dbl>, `8/31/2015` <dbl>, `9/30/2015` <dbl>,
## # `10/31/2015` <dbl>, `11/30/2015` <dbl>, `12/31/2015` <dbl>,
## # `1/31/2016` <dbl>, `2/29/2016` <dbl>, `3/31/2016` <dbl>,
## # `4/30/2016` <dbl>, `5/31/2016` <dbl>, `6/30/2016` <dbl>,
## # `7/31/2016` <dbl>, `8/31/2016` <dbl>, `9/30/2016` <dbl>,
## # `10/31/2016` <dbl>, `11/30/2016` <dbl>, `12/31/2016` <dbl>,
## # `1/31/2017` <dbl>, `2/28/2017` <dbl>, `3/31/2017` <dbl>,
## # `4/30/2017` <dbl>, `5/31/2017` <dbl>, `6/30/2017` <dbl>,
## # `7/31/2017` <dbl>, `8/31/2017` <dbl>, `9/30/2017` <dbl>,
## # `10/31/2017` <dbl>, `11/30/2017` <dbl>, `12/31/2017` <dbl>,
## # `1/31/2018` <dbl>, `2/28/2018` <dbl>, `3/31/2018` <dbl>,
## # `4/30/2018` <dbl>, `5/31/2018` <dbl>, `6/30/2018` <dbl>,
## # `7/31/2018` <dbl>, `8/31/2018` <dbl>, `9/30/2018` <dbl>,
## # `10/31/2018` <dbl>, `11/30/2018` <dbl>, `12/31/2018` <dbl>print(EIA_OilConsumption)## # A tibble: 11 x 75
## CODE GROUP DESC `1/31/2013` `2/28/2013`
## <chr> <chr> <chr> <dbl> <dbl>
## 1 patc_us OECD U.S. (50 States) 18.75 18.64
## 2 patc_ust OECD U.S. Territories 0.28 0.28
## 3 patc_ca OECD Canada 2.52 2.48
## 4 patc_oecd_europe OECD Europe 12.82 13.41
## 5 patc_ja OECD Japan 5.08 5.19
## 6 patc_other_oecd OECD Other OECD 6.37 6.48
## 7 patc_fsu Non-OECD Eurasia 4.16 4.42
## 8 patc_nonoecd_europe Non-OECD Europe 0.62 0.64
## 9 patc_ch Non-OECD China 10.88 11.67
## 10 patc_other_asia Non-OECD Other Asia 11.48 11.63
## 11 patc_other_nonoecd Non-OECD Other Non-OECD 17.84 17.67
## # ... with 70 more variables: `3/31/2013` <dbl>, `4/30/2013` <dbl>,
## # `5/31/2013` <dbl>, `6/30/2013` <dbl>, `7/31/2013` <dbl>,
## # `8/31/2013` <dbl>, `9/30/2013` <dbl>, `10/31/2013` <dbl>,
## # `11/30/2013` <dbl>, `12/31/2013` <dbl>, `1/31/2014` <dbl>,
## # `2/28/2014` <dbl>, `3/31/2014` <dbl>, `4/30/2014` <dbl>,
## # `5/31/2014` <dbl>, `6/30/2014` <dbl>, `7/31/2014` <dbl>,
## # `8/31/2014` <dbl>, `9/30/2014` <dbl>, `10/31/2014` <dbl>,
## # `11/30/2014` <dbl>, `12/31/2014` <dbl>, `1/31/2015` <dbl>,
## # `2/28/2015` <dbl>, `3/31/2015` <dbl>, `4/30/2015` <dbl>,
## # `5/31/2015` <dbl>, `6/30/2015` <dbl>, `7/31/2015` <dbl>,
## # `8/31/2015` <dbl>, `9/30/2015` <dbl>, `10/31/2015` <dbl>,
## # `11/30/2015` <dbl>, `12/31/2015` <dbl>, `1/31/2016` <dbl>,
## # `2/29/2016` <dbl>, `3/31/2016` <dbl>, `4/30/2016` <dbl>,
## # `5/31/2016` <dbl>, `6/30/2016` <dbl>, `7/31/2016` <dbl>,
## # `8/31/2016` <dbl>, `9/30/2016` <dbl>, `10/31/2016` <dbl>,
## # `11/30/2016` <dbl>, `12/31/2016` <dbl>, `1/31/2017` <dbl>,
## # `2/28/2017` <dbl>, `3/31/2017` <dbl>, `4/30/2017` <dbl>,
## # `5/31/2017` <dbl>, `6/30/2017` <dbl>, `7/31/2017` <dbl>,
## # `8/31/2017` <dbl>, `9/30/2017` <dbl>, `10/31/2017` <dbl>,
## # `11/30/2017` <dbl>, `12/31/2017` <dbl>, `1/31/2018` <dbl>,
## # `2/28/2018` <dbl>, `3/31/2018` <dbl>, `4/30/2018` <dbl>,
## # `5/31/2018` <dbl>, `6/30/2018` <dbl>, `7/31/2018` <dbl>,
## # `8/31/2018` <dbl>, `9/30/2018` <dbl>, `10/31/2018` <dbl>,
## # `11/30/2018` <dbl>, `12/31/2018` <dbl>print(EIA_OilPrice)## # A tibble: 4 x 75
## CODE GROUP DESC `1/31/2013`
## <chr> <chr> <chr> <dbl>
## 1 WTIPUUS Crude Oil West Texas Intermediate Spot Average 94.76
## 2 BREPUUS Crude Oil Brent Spot Average 112.96
## 3 RAIMUUS Crude Oil U.S. Imported Average 97.91
## 4 RACPUUS Crude Oil U.S. Refiner Average Acquisition Cost 100.78
## # ... with 71 more variables: `2/28/2013` <dbl>, `3/31/2013` <dbl>,
## # `4/30/2013` <dbl>, `5/31/2013` <dbl>, `6/30/2013` <dbl>,
## # `7/31/2013` <dbl>, `8/31/2013` <dbl>, `9/30/2013` <dbl>,
## # `10/31/2013` <dbl>, `11/30/2013` <dbl>, `12/31/2013` <dbl>,
## # `1/31/2014` <dbl>, `2/28/2014` <dbl>, `3/31/2014` <dbl>,
## # `4/30/2014` <dbl>, `5/31/2014` <dbl>, `6/30/2014` <dbl>,
## # `7/31/2014` <dbl>, `8/31/2014` <dbl>, `9/30/2014` <dbl>,
## # `10/31/2014` <dbl>, `11/30/2014` <dbl>, `12/31/2014` <dbl>,
## # `1/31/2015` <dbl>, `2/28/2015` <dbl>, `3/31/2015` <dbl>,
## # `4/30/2015` <dbl>, `5/31/2015` <dbl>, `6/30/2015` <dbl>,
## # `7/31/2015` <dbl>, `8/31/2015` <dbl>, `9/30/2015` <dbl>,
## # `10/31/2015` <dbl>, `11/30/2015` <dbl>, `12/31/2015` <dbl>,
## # `1/31/2016` <dbl>, `2/29/2016` <dbl>, `3/31/2016` <dbl>,
## # `4/30/2016` <dbl>, `5/31/2016` <dbl>, `6/30/2016` <dbl>,
## # `7/31/2016` <dbl>, `8/31/2016` <dbl>, `9/30/2016` <dbl>,
## # `10/31/2016` <dbl>, `11/30/2016` <dbl>, `12/31/2016` <dbl>,
## # `1/31/2017` <dbl>, `2/28/2017` <dbl>, `3/31/2017` <dbl>,
## # `4/30/2017` <dbl>, `5/31/2017` <dbl>, `6/30/2017` <dbl>,
## # `7/31/2017` <dbl>, `8/31/2017` <dbl>, `9/30/2017` <dbl>,
## # `10/31/2017` <dbl>, `11/30/2017` <dbl>, `12/31/2017` <dbl>,
## # `1/31/2018` <dbl>, `2/28/2018` <dbl>, `3/31/2018` <dbl>,
## # `4/30/2018` <dbl>, `5/31/2018` <dbl>, `6/30/2018` <dbl>,
## # `7/31/2018` <dbl>, `8/31/2018` <dbl>, `9/30/2018` <dbl>,
## # `10/31/2018` <dbl>, `11/30/2018` <dbl>, `12/31/2018` <dbl>Tidy the Oil data
#tidy data
EIA_OilSupply.ByMonth <- EIA_OilSupply %>%
gather(Month, MilBarrels, 4:75)
EIA_OilConsumption.ByMonth <- EIA_OilConsumption %>%
gather(Month, MilBarrels, 4:75)
EIA_OilPrice.ByMonth <- EIA_OilPrice %>%
gather(DT, PerBarrel, 4:75)
head(EIA_OilSupply.ByMonth)## # A tibble: 6 x 5
## CODE GROUP DESC Month MilBarrels
## <chr> <chr> <chr> <chr> <dbl>
## 1 papr_us OECD U.S. (50 States) 1/31/2013 11.59
## 2 papr_ca OECD Canada 1/31/2013 4.12
## 3 papr_mx OECD Mexico 1/31/2013 2.96
## 4 papr_otheroecd OECD Other OECD 1/31/2013 4.40
## 5 copr_opec Non-OECD OPEC Crude Oil Portion 1/31/2013 30.40
## 6 opec_nc Non-OECD OPEC Other Liquids (b) 1/31/2013 6.43head(EIA_OilConsumption.ByMonth)## # A tibble: 6 x 5
## CODE GROUP DESC Month MilBarrels
## <chr> <chr> <chr> <chr> <dbl>
## 1 patc_us OECD U.S. (50 States) 1/31/2013 18.75
## 2 patc_ust OECD U.S. Territories 1/31/2013 0.28
## 3 patc_ca OECD Canada 1/31/2013 2.52
## 4 patc_oecd_europe OECD Europe 1/31/2013 12.82
## 5 patc_ja OECD Japan 1/31/2013 5.08
## 6 patc_other_oecd OECD Other OECD 1/31/2013 6.37head(EIA_OilPrice.ByMonth)## # A tibble: 6 x 5
## CODE GROUP DESC DT
## <chr> <chr> <chr> <chr>
## 1 WTIPUUS Crude Oil West Texas Intermediate Spot Average 1/31/2013
## 2 BREPUUS Crude Oil Brent Spot Average 1/31/2013
## 3 RAIMUUS Crude Oil U.S. Imported Average 1/31/2013
## 4 RACPUUS Crude Oil U.S. Refiner Average Acquisition Cost 1/31/2013
## 5 WTIPUUS Crude Oil West Texas Intermediate Spot Average 2/28/2013
## 6 BREPUUS Crude Oil Brent Spot Average 2/28/2013
## # ... with 1 more variables: PerBarrel <dbl>Data above can be merged and summarized. E.g. US import/consumption vs import avg price.
tmp_month <- names(EIA_OilSupply)
Months <- tmp_month[4:75]
US_oil.supply <- EIA_OilSupply.ByMonth %>% filter(DESC == "U.S. (50 States)")## Warning: package 'bindrcpp' was built under R version 3.4.2US_oil.Consume_50_States <- EIA_OilConsumption.ByMonth %>% filter(DESC == "U.S. (50 States)")
US_oil.Consume_US_Territories <- EIA_OilConsumption.ByMonth %>% filter(DESC == "U.S. Territories")
US_oil.import <- EIA_OilPrice.ByMonth %>% filter(CODE == "RAIMUUS")
US_oil <- data.frame(
Record_Months <- Months,
Supply <- US_oil.supply$MilBarrels,
Consume <- US_oil.Consume_50_States$MilBarrels + US_oil.Consume_US_Territories$MilBarrels,
ImportAvgPrice <- US_oil.import$PerBarrel
)
print(US_oil)## Record_Months....Months Supply....US_oil.supply.MilBarrels
## 1 1/31/2013 11.59
## 2 2/28/2013 11.67
## 3 3/31/2013 11.83
## 4 4/30/2013 12.17
## 5 5/31/2013 12.11
## 6 6/30/2013 12.11
## 7 7/31/2013 12.46
## 8 8/31/2013 12.59
## 9 9/30/2013 12.88
## 10 10/31/2013 12.82
## 11 11/30/2013 13.06
## 12 12/31/2013 13.09
## 13 1/31/2014 13.04
## 14 2/28/2014 13.09
## 15 3/31/2014 13.31
## 16 4/30/2014 13.90
## 17 5/31/2014 13.85
## 18 6/30/2014 14.26
## 19 7/31/2014 14.35
## 20 8/31/2014 14.44
## 21 9/30/2014 14.53
## 22 10/31/2014 14.73
## 23 11/30/2014 14.90
## 24 12/31/2014 15.13
## 25 1/31/2015 14.75
## 26 2/28/2015 14.97
## 27 3/31/2015 15.06
## 28 4/30/2015 15.33
## 29 5/31/2015 15.18
## 30 6/30/2015 15.03
## 31 7/31/2015 15.20
## 32 8/31/2015 15.20
## 33 9/30/2015 15.20
## 34 10/31/2015 15.17
## 35 11/30/2015 15.22
## 36 12/31/2015 15.10
## 37 1/31/2016 14.92
## 38 2/29/2016 14.82
## 39 3/31/2016 15.01
## 40 4/30/2016 14.79
## 41 5/31/2016 14.96
## 42 6/30/2016 14.79
## 43 7/31/2016 14.83
## 44 8/31/2016 14.65
## 45 9/30/2016 14.45
## 46 10/31/2016 14.73
## 47 11/30/2016 14.96
## 48 12/31/2016 14.69
## 49 1/31/2017 14.70
## 50 2/28/2017 15.08
## 51 3/31/2017 15.28
## 52 4/30/2017 15.21
## 53 5/31/2017 15.40
## 54 6/30/2017 15.41
## 55 7/31/2017 15.60
## 56 8/31/2017 15.44
## 57 9/30/2017 15.49
## 58 10/31/2017 15.96
## 59 11/30/2017 16.21
## 60 12/31/2017 16.25
## 61 1/31/2018 16.25
## 62 2/28/2018 16.33
## 63 3/31/2018 16.48
## 64 4/30/2018 16.53
## 65 5/31/2018 16.67
## 66 6/30/2018 16.61
## 67 7/31/2018 16.68
## 68 8/31/2018 16.60
## 69 9/30/2018 16.52
## 70 10/31/2018 16.73
## 71 11/30/2018 17.01
## 72 12/31/2018 17.05
## Consume....US_oil.Consume_50_States.MilBarrels...US_oil.Consume_US_Territories.MilBarrels
## 1 19.03
## 2 18.92
## 3 18.81
## 4 18.86
## 5 19.06
## 6 19.09
## 7 19.54
## 8 19.40
## 9 19.53
## 10 19.59
## 11 19.77
## 12 19.26
## 13 19.36
## 14 19.17
## 15 18.72
## 16 19.11
## 17 18.85
## 18 19.15
## 19 19.54
## 20 19.66
## 21 19.51
## 22 19.95
## 23 19.63
## 24 19.72
## 25 19.52
## 26 19.98
## 27 19.65
## 28 19.56
## 29 19.60
## 30 20.14
## 31 20.43
## 32 20.23
## 33 19.72
## 34 19.80
## 35 19.44
## 36 19.90
## 37 19.34
## 38 19.96
## 39 19.90
## 40 19.54
## 41 19.48
## 42 20.08
## 43 19.99
## 44 20.41
## 45 20.14
## 46 19.90
## 47 19.93
## 48 20.26
## 49 19.52
## 50 19.48
## 51 20.32
## 52 19.82
## 53 20.31
## 54 20.78
## 55 20.88
## 56 20.56
## 57 20.29
## 58 20.33
## 59 20.33
## 60 20.59
## 61 20.11
## 62 20.23
## 63 20.48
## 64 20.31
## 65 20.51
## 66 20.91
## 67 21.15
## 68 21.15
## 69 20.81
## 70 20.81
## 71 20.76
## 72 21.00
## ImportAvgPrice....US_oil.import.PerBarrel
## 1 97.91
## 2 99.23
## 3 99.11
## 4 96.45
## 5 98.50
## 6 97.17
## 7 101.56
## 8 104.16
## 9 103.49
## 10 97.84
## 11 90.36
## 12 90.57
## 13 89.71
## 14 96.10
## 15 97.13
## 16 97.33
## 17 98.46
## 18 100.26
## 19 98.75
## 20 93.23
## 21 89.38
## 22 82.75
## 23 74.34
## 24 57.36
## 25 44.74
## 26 47.18
## 27 47.22
## 28 51.62
## 29 57.51
## 30 58.89
## 31 52.42
## 32 43.23
## 33 41.12
## 34 42.03
## 35 39.05
## 36 33.16
## 37 27.48
## 38 26.66
## 39 32.24
## 40 35.90
## 41 40.88
## 42 44.13
## 43 41.48
## 44 41.21
## 45 40.86
## 46 44.76
## 47 41.80
## 48 46.72
## 49 48.12
## 50 49.38
## 51 46.53
## 52 47.47
## 53 46.94
## 54 43.44
## 55 43.13
## 56 44.54
## 57 45.00
## 58 44.00
## 59 44.50
## 60 44.50
## 61 44.50
## 62 44.50
## 63 44.50
## 64 44.50
## 65 44.50
## 66 44.50
## 67 45.50
## 68 46.50
## 69 47.50
## 70 48.50
## 71 48.50
## 72 49.50#3 NYPD Motor Vehicle Collisions - Yuen Chun Wong
http://www1.nyc.gov/site/nypd/stats/traffic-data/traffic-data-collision.page It is Aug 2017 data. I saved the data in csv format and merge the column for location.
NYPDCollision <- tbl_df(read.csv("NYPDAug2017_Motor_Vehicle_Collisions.csv", stringsAsFactors = FALSE, check.names = FALSE))
#snapshot of data
head(NYPDCollision)## # A tibble: 6 x 15
## GeoCode Boro_Location GeoCodeLabel
## <int> <chr> <chr>
## 1 1 Manhattan 1st Precinct 1st Precinct
## 2 5 Manhattan 5th Precinct 5th Precinct
## 3 6 Manhattan 6th Precinct 6th Precinct
## 4 7 Manhattan 7th Precinct 7th Precinct
## 5 9 Manhattan 9th Precinct 9th Precinct
## 6 10 Manhattan 10th Precinct 10th Precinct
## # ... with 12 more variables: Number_of_Motor_Vehicle_Collisions <int>,
## # Vehicles_or_Motorists_Involved <int>,
## # Injury_or_Fatal_Collisions <int>, MotoristsInjured <int>,
## # MotoristsKilled <int>, PassengInjured <int>, PassengKilled <int>,
## # CyclistsInjured <int>, CyclistsKilled <int>, PedestrInjured <int>,
## # PedestrKilled <int>, Bicycle <int>NYPDCollision.ByInjuryType <- NYPDCollision %>% gather(InJury_type, Freq, 4:15)Analysis: Min & Max by Injury Type modified from https://stackoverflow.com/questions/31284794/how-do-i-use-group-by-with-order-function-in-r https://stackoverflow.com/questions/31528981/select-first-and-last-row-from-grouped-data
NYPDCollision.ByInjuryType_Max <- NYPDCollision.ByInjuryType %>% group_by(InJury_type) %>% arrange(-Freq) %>%
filter(row_number()==1)
NYPDCollision.ByInjuryType_Min <- NYPDCollision.ByInjuryType %>% group_by(InJury_type) %>% arrange(Freq) %>%
filter(row_number()==1)
(NYPDCollision.ByInjuryType_Max)## # A tibble: 12 x 5
## # Groups: InJury_type [12]
## GeoCode Boro_Location GeoCodeLabel
## <int> <chr> <chr>
## 1 109 Queens 109th Precinct 109th Precinct
## 2 109 Queens 109th Precinct 109th Precinct
## 3 105 Queens 105th Precinct 105th Precinct
## 4 105 Queens 105th Precinct 105th Precinct
## 5 105 Queens 105th Precinct 105th Precinct
## 6 75 Brooklyn 75th Precinct 75th Precinct
## 7 90 Brooklyn 90th Precinct 90th Precinct
## 8 90 Brooklyn 90th Precinct 90th Precinct
## 9 47 Bronx 47th Precinct 47th Precinct
## 10 112 Queens 112th Precinct 112th Precinct
## 11 45 Bronx 45th Precinct 45th Precinct
## 12 66 Brooklyn 66th Precinct 66th Precinct
## # ... with 2 more variables: InJury_type <chr>, Freq <int>(NYPDCollision.ByInjuryType_Min)## # A tibble: 12 x 5
## # Groups: InJury_type [12]
## GeoCode Boro_Location GeoCodeLabel
## <int> <chr> <chr>
## 1 22 Manhattan Central Park Precinct Central Park Precinct
## 2 1 Manhattan 1st Precinct 1st Precinct
## 3 22 Manhattan Central Park Precinct Central Park Precinct
## 4 1 Manhattan 1st Precinct 1st Precinct
## 5 22 Manhattan Central Park Precinct Central Park Precinct
## 6 1 Manhattan 1st Precinct 1st Precinct
## 7 5 Manhattan 5th Precinct 5th Precinct
## 8 22 Manhattan Central Park Precinct Central Park Precinct
## 9 22 Manhattan Central Park Precinct Central Park Precinct
## 10 30 Manhattan 30th Precinct 30th Precinct
## 11 22 Manhattan Central Park Precinct Central Park Precinct
## 12 22 Manhattan Central Park Precinct Central Park Precinct
## # ... with 2 more variables: InJury_type <chr>, Freq <int>As data above, We can conclude: 1. Queens 109th Precinct has highest collison rate while Manhattan Central Park Precinct has lowerest collison rate.
2. More Traffic police and road design may be required in Queens 109th Precinct. 3. Few vehicles are Manhattan Central Park Precinct area.