R
!!!table(): クロス集計
{{outline}}
----



!!例

!サンプル・データ
*https://www.openintro.org/data/index.php?data=acs12
**Results from the US Census American Community Survey, 2012
**2012年のアメリカ人口統計からの2,000のサンプル

!データの取り込み
{{pre
acs.dat <- read.csv("https://www.openintro.org/data/csv/acs12.csv")
head(acs.dat)

  income         employment hrs_work  race age gender citizen time_to_work    lang married         edu disability   birth_qrtr
1  60000 not in labor force       40 white  68 female     yes           NA english      no     college         no jul thru sep
2      0 not in labor force       NA white  88   male     yes           NA english      no hs or lower        yes jan thru mar
3     NA               <NA>       NA white  12 female     yes           NA english      no hs or lower         no oct thru dec
4      0 not in labor force       NA white  17   male     yes           NA   other      no hs or lower         no oct thru dec
5      0 not in labor force       NA white  77 female     yes           NA   other      no hs or lower        yes jul thru sep
6   1700           employed       40 other  35 female     yes           15   other     yes hs or lower        yes jul thru sep

}}


{{pre
str(acs.dat)

'data.frame':	2000 obs. of  13 variables:
 $ income      : int  60000 0 NA 0 0 1700 NA NA NA 45000 ...
 $ employment  : chr  "not in labor force" "not in labor force" NA "not in labor force" ...
 $ hrs_work    : int  40 NA NA NA NA 40 NA NA NA 84 ...
 $ race        : chr  "white" "white" "white" "white" ...
 $ age         : int  68 88 12 17 77 35 11 7 6 27 ...
 $ gender      : chr  "female" "male" "female" "male" ...
 $ citizen     : chr  "yes" "yes" "yes" "yes" ...
 $ time_to_work: int  NA NA NA NA NA 15 NA NA NA 40 ...
 $ lang        : chr  "english" "english" "english" "other" ...
 $ married     : chr  "no" "no" "no" "no" ...
 $ edu         : chr  "college" "hs or lower" "hs or lower" "hs or lower" ...
 $ disability  : chr  "no" "yes" "no" "no" ...
 $ birth_qrtr  : chr  "jul thru sep" "jan thru mar" "oct thru dec" "oct thru dec" ...
}}

!クロス集計：table(データフレームのカラム, データフレームのカラム)
*人種と教育

{{pre
table(acs.dat$race, acs.dat$edu)

        college grad hs or lower
  asian      22   11          53
  black      26    9         162
  other      12    9         125
  white     299  115        1099
}}

!比率で表示： prop.table(table())
*全体の内訳の比率
{{pre
prop.table(table(acs.dat$race, acs.dat$edu))

            college        grad hs or lower
  asian 0.011328527 0.005664264 0.027291452
  black 0.013388260 0.004634398 0.083419156
  other 0.006179197 0.004634398 0.064366632
  white 0.153964985 0.059217302 0.565911432

}}

!比率で表示（四捨五入）： round(prop.table(table()),2)
*小数点以下2桁
{{pre
round(prop.table(table(acs.dat$race, acs.dat$edu)),2)

        college grad hs or lower
  asian    0.01 0.01        0.03
  black    0.01 0.00        0.08
  other    0.01 0.00        0.06
  white    0.15 0.06        0.57

}}

!全体の比率ではなく、横で集計した比率：margin = 1
*この場合は、人種ごとに学歴の比率を出す
{{pre
round(prop.table(table(acs.dat$race, acs.dat$edu), margin=1),2)

        college grad hs or lower
  asian    0.26 0.13        0.62
  black    0.13 0.05        0.82
  other    0.08 0.06        0.86
  white    0.20 0.08        0.73
}}
*白人の73%は高卒
*大卒の比率はアジア系が一番高い
!全体の比率ではなく、縦で集計した比率：margin = 2
*学歴ごとに、それを構成する人種の割合
{{pre
round(prop.table(table(acs.dat$race, acs.dat$edu), margin=2),2)

        college grad hs or lower
  asian    0.06 0.08        0.04
  black    0.07 0.06        0.11
  other    0.03 0.06        0.09
  white    0.83 0.80        0.76

}}
*大卒の83%は白人