数据整理

1 tidyverse系统

https://www.math.pku.edu.cn/teachers/lidf/docs/Rbook/html/_Rbook/summary-manip.html#summm-tidyv
(完整版）
载入tidyverse包，则magrittr包，readr包，dplyr包和tidyr包都会被自动载入:

library(tidyverse)

下面的例子中用如下的一个班的学生数据作为例子，保存在如下class.csv文件中：

name,sex,age,height,weight
Alice,F,13,56.5,84
Becka,F,13,65.3,98
Gail,F,14,64.3,90
Karen,F,12,56.3,77
Kathy,F,12,59.8,84.5
Mary,F,15,66.5,112
Sandy,F,11,51.3,50.5
Sharon,F,15,62.5,112.5
Tammy,F,14,62.8,102.5
Alfred,M,14,69,112.5
Duke,M,14,63.5,102.5
Guido,M,15,67,133
James,M,12,57.3,83
Jeffrey,M,13,62.5,84
John,M,12,59,99.5
Philip,M,16,72,150
Robert,M,12,64.8,128
Thomas,M,11,57.5,85
William,M,15,66.5,112

读入为tibble:

d.class <- read_csv(
  "class.csv", 
  col_types=cols(
  .default = col_double(),
  name=col_character(),
  sex=col_factor(levels=c("M", "F"))
))

这个数据框有19个观测，有如下5个变量：

name
sex
age
height
weight

R的NHANES扩展包提供了一个规模更大的示例数据框NHANES，可以看作是美国扣除住院病人以外的人群的一个随机样本，有10000个观测，有76个变量，主题是个人的健康与营养方面的信息。仅作为教学使用而不足以作为严谨的科研用数据。原始数据的情况详见http://www.cdc.gov/nchs/nhanes.htm。载入NHANES数据框：

library(NHANES)
data(NHANES)

print(dim(NHANES))

## [1] 10000    76

print(names(NHANES))

##  [1] "ID"               "SurveyYr"         "Gender"           "Age"             
##  [5] "AgeDecade"        "AgeMonths"        "Race1"            "Race3"           
##  [9] "Education"        "MaritalStatus"    "HHIncome"         "HHIncomeMid"     
## [13] "Poverty"          "HomeRooms"        "HomeOwn"          "Work"            
## [17] "Weight"           "Length"           "HeadCirc"         "Height"          
## [21] "BMI"              "BMICatUnder20yrs" "BMI_WHO"          "Pulse"           
## [25] "BPSysAve"         "BPDiaAve"         "BPSys1"           "BPDia1"          
## [29] "BPSys2"           "BPDia2"           "BPSys3"           "BPDia3"          
## [33] "Testosterone"     "DirectChol"       "TotChol"          "UrineVol1"       
## [37] "UrineFlow1"       "UrineVol2"        "UrineFlow2"       "Diabetes"        
## [41] "DiabetesAge"      "HealthGen"        "DaysPhysHlthBad"  "DaysMentHlthBad" 
## [45] "LittleInterest"   "Depressed"        "nPregnancies"     "nBabies"         
## [49] "Age1stBaby"       "SleepHrsNight"    "SleepTrouble"     "PhysActive"      
## [53] "PhysActiveDays"   "TVHrsDay"         "CompHrsDay"       "TVHrsDayChild"   
## [57] "CompHrsDayChild"  "Alcohol12PlusYr"  "AlcoholDay"       "AlcoholYear"     
## [61] "SmokeNow"         "Smoke100"         "Smoke100n"        "SmokeAge"        
## [65] "Marijuana"        "AgeFirstMarij"    "RegularMarij"     "AgeRegMarij"     
## [69] "HardDrugs"        "SexEver"          "SexAge"           "SexNumPartnLife" 
## [73] "SexNumPartYear"   "SameSex"          "SexOrientation"   "PregnantNow"

变量ID是受试者编号， SurveyYr是调查年份，同一受试者可能在多个调查年份中有数据。变量中包括性别、年龄、种族、收入等人口学数据，包括体重、身高、脉搏、血压等基本体检数据，以及是否糖尿病、是否抑郁、是否怀孕、已生产子女数等更详细的健康数据，运动习惯、饮酒、性生活等行为方面的数据。这个教学用数据集最初的使用者是Cashmere高中的Michelle Dalrymple 和新西兰奥克兰大学的Chris Wild。

2 用filter()选择行子集

行子集可以用行下标选取，如d.class[8:12,]。函数head()取出数据框的前面若干行， tail()取出数据框的最后若干行。
从d.class中选出年龄在13岁和13岁以下的女生：

d.class %>%
  filter(sex=="F", age<=13) %>%
  knitr::kable()