JHU Coursera Datatools Course Project

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Datatools Course Project 数据工具项目

JHU DataScience Specialization/Cousers The Data Scientist’s Toolbox/Week??/Course Project

由于这个非常入门的课程，共4Week每周一个小quiz，这个应该是最后的Project忘记了
主要是熟练掌握一些R语言的数据处理工具例如 xlsx,XML等格式，以及readr这些有用的R包用法
以下代码下载资源比较大暂不执行有兴趣的读者可以自己尝试

读取csv格式 2006年美国社区调查（ACS）

The American Community Survey distributes downloadable data about United States communities. Download the 2006 microdata survey about housing for the state of Idaho using download.file() from here:

  • (pid, Population CSV file)
  • (hid, Household CSV file)

说明书 PUMS 说明书 DATA DICTIONARY - 2006 HOUSING PDF

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fileUrl <- "https://d396qusza40orc.cloudfront.net/getdata%2Fdata%2Fss06pid.csv"
download.file(fileUrl,destfile = "Fss06pid.csv",method = "libcurl")
fileUrl <- "https://d396qusza40orc.cloudfront.net/getdata%2Fdata%2Fss06hid.csv"
download.file(fileUrl,destfile = "Fss06hid.csv",method = "libcurl")

读取几十上百m的数据算大文件，运算应该考虑花销

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## Unit: milliseconds
##                                           expr       min        lq
##  PID <- fread(file = "Fss06pid.csv", fill = T)  13.40045  13.40045
##                read_csv(file = "Fss06pid.csv") 627.03159 627.03159
##                read.csv(file = "Fss06pid.csv") 422.56456 422.56456
##       mean    median        uq       max neval
##   13.40045  13.40045  13.40045  13.40045     1
##  627.03159 627.03159 627.03159 627.03159     1
##  422.56456 422.56456 422.56456 422.56456     1
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## Unit: milliseconds
##                                           expr       min        lq
##  HID <- fread(file = "Fss06hid.csv", fill = T)  17.08404  17.08404
##                read_csv(file = "Fss06hid.csv") 519.85311 519.85311
##                read.csv(file = "Fss06hid.csv") 971.56116 971.56116
##       mean    median        uq       max neval
##   17.08404  17.08404  17.08404  17.08404     1
##  519.85311 519.85311 519.85311 519.85311     1
##  971.56116 971.56116 971.56116 971.56116     1
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## Unit: microseconds
##                                       expr      min        lq       mean
##         tapply(PID$pwgtp15, PID$SEX, mean)  159.973  203.6820  262.39322
##  sapply(split(PID$pwgtp15, PID$SEX), mean)  137.322  162.9805  250.35640
##          mean(PID[PID$SEX == 1, ]$pwgtp15) 1533.888 1615.2890 2570.51404
##            mean(PID$pwgtp15, by = PID$SEX)   11.326   16.1045   20.48577
##          median(PID$pwgtp15, by = PID$SEX)   49.550   64.7685   91.37941
##         mean(PID[PID$SEX == 1, ]$SERIALNO) 1548.398 1624.1370 3693.22785
##       median(PID[PID$SEX == 1, ]$SERIALNO) 1630.508 1709.9625 2321.75111
##     median        uq        max neval cld
##   267.5640  289.3300    441.692   100  a 
##   215.8915  246.1520   3571.401   100  a 
##  1694.3900 4332.1510   6311.799   100   b
##    18.2280   22.4745     78.571   100  a 
##    85.2955  109.8930    249.160   100  a 
##  1740.4000 2707.6610 127876.038   100   b
##  1751.5480 2145.2830   6184.035   100   b
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pander(arrange(A,mean))
Table continues below
expr min lq mean
PID <- fread(file = “Fss06pid.csv”, fill = T) 13.4 13.4 13.4
read.csv(file = “Fss06pid.csv”) 422.6 422.6 422.6
read_csv(file = “Fss06pid.csv”) 627 627 627
median uq max neval
13.4 13.4 13.4 1
422.6 422.6 422.6 1
627 627 627 1 
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pander(arrange(B,mean))
Table continues below
expr min lq mean
HID <- fread(file = “Fss06hid.csv”, fill = T) 17.08 17.08 17.08
read_csv(file = “Fss06hid.csv”) 519.9 519.9 519.9
read.csv(file = “Fss06hid.csv”) 971.6 971.6 971.6
median uq max neval
17.08 17.08 17.08 1
519.9 519.9 519.9 1
971.6 971.6 971.6 1

简单计算求和时间

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pander(arrange(select(C,expr,mean),mean))
expr mean
mean(PID\(pwgtp15, by = PID\)SEX) 20.49
median(PID\(pwgtp15, by = PID\)SEX) 91.38
sapply(split(PID\(pwgtp15, PID\)SEX), mean) 250.4
tapply(PID\(pwgtp15, PID\)SEX, mean) 262.4
median(PID[PID$SEX == 1, ]$SERIALNO) 2322
mean(PID[PID$SEX == 1, ]$pwgtp15) 2571
mean(PID[PID$SEX == 1, ]$SERIALNO) 3693

Quiz 1

How many housing units in this survey were worth more than $1,000,000?

  • 159
  • 164
  • 53
  • 24

读取xls,xlsx格式 美国国家天然气采购计划

说明书 Natural Gas Acquisition Program

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fileUrl <- "https://d396qusza40orc.cloudfront.net/getdata%2Fdata%2FDATA.gov_NGAP.xlsx"
download.file(fileurl,destfile = "gov_NGAP.xlsx",mode='wb')
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NGAP <- read.xlsx(file="gov_NGAP.xlsx",
                  sheetIndex=1,
                  colIndex=7:15,
                  rowIndex = 18:23,
                  header=T)
NGAP <- as.tbl(NGAP)
pander(head(NGAP))
Table continues below
Zip CuCurrent PaCurrent PoCurrent Contact Ext
74136 0 1 0 918-491-6998 0
30329 1 0 0 404-321-5711 NA
74136 1 0 0 918-523-2516 0
80203 0 1 0 303-864-1919 0
80120 1 0 0 345-098-8890 456
Fax email Status
918-491-6659 NA 1
NA NA 1
918-523-2522 NA 1
NA NA 1
NA NA 1 
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cat("Sum of Zipcode and")
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## Sum of Zipcode and
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sum(NGAP$Zip*NGAP$Ext,na.rm=T)
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## [1] 36534720

Quiz 3

Read rows 18-23 and columns 7-15 into R and assign the result to a variable called:

What is the value of:

sum(dat(Zip*dat)Ext,na.rm=T)

(original data source: http://catalog.data.gov/dataset/natural-gas-acquisition-program)

  1. 36534720
  2. 338924
  3. 33544718
  4. 0

读取xml格式 巴的摩尔餐厅数据

说明书 data.baltimorecity.gov

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fileUrl <- "https://d396qusza40orc.cloudfront.net/getdata%2Fdata%2Frestaurants.xml"
download.file(fileUrl,destfile = "Frestaurants.xml",mode='wb')
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system.time(DOC <- xmlTreeParse("Frestaurants.xml", useInternal = TRUE))
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##    user  system elapsed 
##    0.02    0.00    0.02
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rootNode <- xmlRoot(DOC)
# 邮编是"21231" 的记录有多少条
sum(xpathSApply(rootNode, "//zipcode", xmlValue) == "21231")
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## [1] 127

Quiz 4

How many restaurants have zipcode 21231?

  • 127
  • 17
  • 28
  • 156
THE END