第II部

第11章 統計解析で分かること・分からないこと

In [1]:
sapply(c("pipeR", "dplyr", "tidyr", "ggplot2", "readr", "readxl", "Cairo", "grid", "gridExtra"), 
       require, character.only = TRUE)

Loading required package: pipeR
Loading required package: dplyr
Warning message:
: package 'dplyr' was built under R version 3.3.1
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

Loading required package: tidyr
Loading required package: ggplot2
Loading required package: readr
Loading required package: readxl
Loading required package: Cairo
Loading required package: grid
Loading required package: gridExtra
Warning message:
: package 'gridExtra' was built under R version 3.3.1
Attaching package: 'gridExtra'

The following object is masked from 'package:dplyr':

    combine
pipeR
TRUE
dplyr
TRUE
tidyr
TRUE
ggplot2
TRUE
readr
TRUE
readxl
TRUE
Cairo
TRUE
grid
TRUE
gridExtra
TRUE
  • x: 子供のゲーム時間
  • y: 親の認識したゲーム時間
In [2]:
d <- read_csv("chap11_1.csv", locale = locale(encoding = "cp932"))
d
親子xy
1 1290350
2 25070
3 3 80100
4 4100130
5 5200250
6 6350430
7 7430520
8 8 80100
9 9210260
10 10110140
11117090
12 12260320
13 13220270
14 14330400
15 15170210
16 16420510
17 17 80100
18 18300370
19 19290350
20 20230280

（1.1）x, y それぞれについて度数分布，代表値，散布度

In [3]:
options(repr.plot.width = 8, repr.plot.height = 6)
In [4]:
d %>>% gather(key, val, -親子) %>>% 
    ggplot(aes(x = val)) + geom_histogram(breaks = seq(0, 600, 60)) + facet_wrap(~ key, nrow = 2)




































    

  • 平均値
    • 














In [5]:



    


d %>>% summarise_each(funs(mean), -親子)
























xy


	
1213.5262.5





























    

  • 中央値
    • 














In [6]:



    


d %>>% summarise_each(funs(median), -親子)
























xy


	
1215265





























    

  • 標本分散
    • 














In [7]:



    


varp <- function(x) {
    ret <- var(x) * (length(x) - 1) / length(x)
    ret
}

















In [8]:



    


d %>>% summarise_each(funs(varp), -親子)
























xy


	
113492.7519328.75





























    

  • 標準偏差
    • 














In [9]:



    


d %>>% summarise_each(funs(sqrt(varp(.))), -親子)
























xy


	
1116.1583139.0279





























（1.2）xとyの差について，度数分布，代表値，散布度













In [10]:



    


d <- d %>>% mutate(yx = y - x)

















In [11]:



    


options(repr.plot.width = 4, repr.plot.height = 4)

















In [12]:



    


d %>>% ggplot(aes(x = yx)) + geom_histogram(breaks = seq(20, 90, 10))




















































In [13]:



    


d %>>% summarise_each(funs(mean, median, varp, sqrt(varp(.))), yx)
























meanmedianvarpsqrt


	
1 49 50529 23





























（1.3）xとyの相関係数













In [14]:



    


with(cor(x, y), data = d)





















0.999814777240536
























（1.4）y~xの回帰式













In [15]:



    


res.lm <- lm(y~x, data = d)
res.lm %>>% summary()























Call:
lm(formula = y ~ x, data = d)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.0446 -2.3701 -0.3618  2.1800  4.1558 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) 7.012840   1.319652   5.314 4.73e-05 ***
x           1.196661   0.005429 220.401  < 2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 2.82 on 18 degrees of freedom
Multiple R-squared:  0.9996,	Adjusted R-squared:  0.9996 
F-statistic: 4.858e+04 on 1 and 18 DF,  p-value: < 2.2e-16




















In [16]:



    


res.lm$coefficients























	
(Intercept)

		
7.01284022901187

	
x

		
1.19666116988753






















In [17]:



    


d %>>% ggplot(aes(x = x, y = y)) + geom_point() + 
    geom_abline(intercept = res.lm$coefficients["(Intercept)"], slope = res.lm$coefficients["x"])


























































（2）（1）からわかること，わからないこと



















（3）（2）より，実態と認識のずれを調べる方法は













In [18]:



    


devtools::session_info()





















Session info -------------------------------------------------------------------
Packages -----------------------------------------------------------------------
Session info -------------------------------------------------------------------
Packages -----------------------------------------------------------------------














 setting  value                       
 version  R version 3.3.0 (2016-05-03)
 system   x86_64, mingw32             
 ui       RTerm                       
 language (EN)                        
 collate  Japanese_Japan.932          
 tz       Asia/Tokyo                  
 date     2016-07-07                  

 package    * version    date       source                             
 assertthat   0.1        2013-12-06 CRAN (R 3.3.0)                     
 base64enc    0.1-3      2015-07-28 CRAN (R 3.3.0)                     
 Cairo      * 1.5-9      2015-09-26 CRAN (R 3.3.0)                     
 colorspace   1.2-6      2015-03-11 CRAN (R 3.3.0)                     
 DBI          0.4-1      2016-05-08 CRAN (R 3.3.0)                     
 devtools     1.11.1     2016-04-21 CRAN (R 3.3.0)                     
 digest       0.6.9      2016-01-08 CRAN (R 3.3.0)                     
 dplyr      * 0.5.0      2016-06-24 CRAN (R 3.3.1)                     
 evaluate     0.9        2016-04-29 CRAN (R 3.3.0)                     
 ggplot2    * 2.1.0      2016-03-01 CRAN (R 3.3.0)                     
 gridExtra  * 2.2.1      2016-02-29 CRAN (R 3.3.1)                     
 gtable       0.2.0      2016-02-26 CRAN (R 3.3.0)                     
 IRdisplay    0.3.0.9000 2016-06-20 Github (IRkernel/IRdisplay@69fd5d8)
 IRkernel     0.6        2016-06-20 Github (IRkernel/IRkernel@735d4ff) 
 jsonlite     0.9.22     2016-06-15 CRAN (R 3.3.0)                     
 labeling     0.3        2014-08-23 CRAN (R 3.3.0)                     
 lazyeval     0.2.0      2016-06-12 CRAN (R 3.3.0)                     
 magrittr     1.5        2014-11-22 CRAN (R 3.3.0)                     
 memoise      1.0.0      2016-01-29 CRAN (R 3.3.0)                     
 munsell      0.4.3      2016-02-13 CRAN (R 3.3.0)                     
 pbdZMQ       0.2-3      2016-05-20 CRAN (R 3.3.0)                     
 pipeR      * 0.6.1.3    2016-04-04 CRAN (R 3.3.0)                     
 plyr         1.8.4      2016-06-08 CRAN (R 3.3.0)                     
 R6           2.1.2      2016-01-26 CRAN (R 3.3.0)                     
 Rcpp         0.12.5     2016-05-14 CRAN (R 3.3.0)                     
 readr      * 0.2.2      2015-10-22 CRAN (R 3.3.0)                     
 readxl     * 0.1.1      2016-03-28 CRAN (R 3.3.0)                     
 repr         0.7        2016-05-13 CRAN (R 3.3.0)                     
 scales       0.4.0      2016-02-26 CRAN (R 3.3.0)                     
 stringi      1.1.1      2016-05-27 CRAN (R 3.3.0)                     
 stringr      1.0.0      2015-04-30 CRAN (R 3.3.0)                     
 tibble       1.0        2016-03-23 CRAN (R 3.3.0)                     
 tidyr      * 0.5.1      2016-06-14 CRAN (R 3.3.0)                     
 uuid         0.1-2      2015-07-28 CRAN (R 3.3.0)                     
 withr        1.0.1      2016-02-04 CRAN (R 3.3.0)