複数のファイルを含むR出力の統計とプロットの取得

Question

このコードを使用して、rに複数のファイルを含む統計とプロットを取得しようとしています。このコードでは、データフレームのプロットとデータ構造を得ることができます（プロットは含まれていません）。コードの下にRの出力を含めました。私のanaylze関数の残りの情報を取得するには、このコードをどのように操作できますか？

getwd() 
options(max.print=100000) 
################## 
#DATA EXPLORATION# 
################## 
#Here we are just stating the type and name of the data set, and that 
# attach(filenames) 
#Function for summary statistics 
anaylze<- function(filename){ 
    YC<-read.csv(file = filename, header = TRUE, sep=",", row.names="number",na.strings="") 
    names(YC) 
    str(YC) #to check breakdown of structure of a data frame 
    summary(YC) #summary stat 
    pairs(YC) #basic scatterplot matrix 
    cor(YC) #determines correlations b/w 2 variables 
    par(mar=rep(2,4)) 
    boxplot(YC) #indicates if variables differ in magnitiude and that they have different variances 
    library(MVN) 
    uniNorm(YC, type="SW", desc=TRUE) #gets the shapiro-wilks test of univariate normality along w/ descriptive stats like skewness and kurtosis 
    par(mar=rep(2,4)) 
    uniPlot(YC, "histogram") #visulization step 
    result<-mardiaTest(YC, qqplot=T)#to get all plot within the same frame 
    result 
} 
#to see the result of the MV normality test for the soil data 
filenames <- list.files(path = "/Users/jblamkey/OneDrive/School/R/R Workspace", pattern = "CodaBrundage", full.names=TRUE) 
filenames <- filenames[1:5] 
for (YC in filenames) { 
    print(YC) 
    anaylze(YC) 
} 

###R OUTPUT### 

> getwd() 
[1] "/Users/jblamkey/OneDrive/School/R/R Workspace" 
> options(max.print=100000) 
> ################## 
> #DATA EXPLORATION# 
> ################## 
> #Here we are just stating the type and name of the data set, and that 
> # attach(filenames) 
> #Function for summary statistics 
> anaylze<- function(filename){ 
+ YC<-read.csv(file = filename, header = TRUE, sep=",", row.names="number",na.strings="") 
+ names(YC) 
+ str(YC) #to check breakdown of structure of a data frame 
+ summary(YC) #summary stat 
+ pairs(YC) #basic scatterplot matrix 
+ cor(YC) #determines correlations b/w 2 variables 
+ par(mar=rep(2,4)) 
+ boxplot(YC) #indicates if variables differ in magnitiude and that they have different variances 
+ library(MVN) 
+ uniNorm(YC, type="SW", desc=TRUE) #gets the shapiro-wilks test of univariate normality along w/ descriptive stats like skewness and kurtosis 
+ par(mar=rep(2,4)) 
+ uniPlot(YC, "histogram") #visulization step 
+ result<-mardiaTest(YC, qqplot=T)#to get all plot within the same frame 
+ result 
+ } 
> #to see the result of the MV normality test for the soil data 
> filenames <- list.files(path = "/Users/jblamkey/OneDrive/School/R/R Workspace", pattern = "CodaBrundage", full.names=TRUE) 
> filenames <- filenames[1:5] 
> for (YC in filenames) { 
+ print(YC) 
+ anaylze(YC) 
+ } 
[1] "/Users/jblamkey/OneDrive/School/R/R Workspace/2016_CodaBrundage-Entry-001.csv" 
'data.frame': 489 obs. of 14 variables: 
$ area_mm2  : num 17.22 10.49 9.43 16.75 18.63 ... 
$ perimeter_mm : num 21 16.6 14.4 19.3 20.3 ... 
$ majellipse_mm : num 6.82 5.44 3.77 5.95 6.28 ... 
$ minellipse_mm : num 3.22 2.47 3.29 3.6 3.78 ... 
$ grain..ch1 : num 216 198 215 211 213 ... 
$ grain..ch2 : num 191 171 192 181 187 ... 
$ grain..ch3 : num 132 113 133 119 132 ... 
$ nonCrease..ch1: num 216 198 215 211 213 ... 
$ nonCrease..ch2: num 189 171 192 181 187 ... 
$ nonCrease..ch3: num 130 113 133 119 132 ... 
$ crease..ch1 : num 206 0 0 0 0 ... 
$ crease..ch2 : num 172 0 0 0 0 ... 
$ crease..ch3 : num 111 0 0 0 0 ... 
$ CreaseUp  : int 1 0 0 0 0 1 0 1 0 0 ... 
[1] "/Users/jblamkey/OneDrive/School/R/R Workspace/2016_CodaBrundage-Entry-002.csv" 
'data.frame': 420 obs. of 14 variables: 
$ area_mm2  : num 15.9 18.3 13.3 12.7 16.4 ... 
$ perimeter_mm : num 19.3 21.6 17.6 18.6 19.1 ... 
$ majellipse_mm : num 6.31 6.95 5.72 6.18 6.04 ... 
$ minellipse_mm : num 3.22 3.36 2.97 2.62 3.47 ... 
$ grain..ch1 : num 211 226 215 226 219 ... 
$ grain..ch2 : num 180 195 188 204 190 ... 
$ grain..ch3 : num 126 137 126 154 133 ... 
$ nonCrease..ch1: num 211 226 215 220 219 ... 
$ nonCrease..ch2: num 180 195 188 198 190 ... 
$ nonCrease..ch3: num 126 137 126 149 133 ... 
$ crease..ch1 : num 0 0 0 194 0 ... 
$ crease..ch2 : num 0 0 0 173 0 ... 
$ crease..ch3 : num 0 0 0 127 0 ... 
$ CreaseUp  : int 0 0 0 1 0 1 0 0 0 1 ... 
[1] "/Users/jblamkey/OneDrive/School/R/R Workspace/2016_CodaBrundage-Entry-003.csv" 
'data.frame': 345 obs. of 14 variables: 
$ area_mm2  : num 14.7 15.3 19.6 19.2 19.5 ... 
$ perimeter_mm : num 19.5 19 20.9 21.8 22.2 ... 
$ majellipse_mm : num 6.52 5.98 6.75 7.36 7.35 ... 
$ minellipse_mm : num 2.88 3.28 3.7 3.32 3.38 ... 
$ grain..ch1 : num 222 229 226 226 226 ... 
$ grain..ch2 : num 197 204 200 201 202 ... 
$ grain..ch3 : num 138 145 141 145 150 ... 
$ nonCrease..ch1: num 222 229 224 226 226 ... 
$ nonCrease..ch2: num 197 204 197 200 201 ... 
$ nonCrease..ch3: num 138 145 138 144 148 ... 
$ crease..ch1 : num 0 0 202 221 211 ... 
$ crease..ch2 : num 0 0 171 191 181 ... 
$ crease..ch3 : num 0 0 114 134 126 ... 
$ CreaseUp  : int 0 0 1 1 1 1 1 1 0 1 ... 
[1] "/Users/jblamkey/OneDrive/School/R/R Workspace/2016_CodaBrundage-Entry-004.csv" 
'data.frame': 461 obs. of 14 variables: 
$ area_mm2  : num 16.2 15.5 16.5 16.3 16.8 ... 
$ perimeter_mm : num 20.2 20.7 19.5 19.8 20.2 ... 
$ majellipse_mm : num 6.39 6.61 5.99 6.59 6.52 ... 
$ minellipse_mm : num 3.27 3.04 3.52 3.16 3.29 ... 
$ grain..ch1 : num 234 211 196 218 218 ... 
$ grain..ch2 : num 205 178 167 191 190 ... 
$ grain..ch3 : num 138 118 116 135 132 ... 
$ nonCrease..ch1: num 228 211 199 218 218 ... 
$ nonCrease..ch2: num 201 178 168 191 190 ... 
$ nonCrease..ch3: num 136 118 117 135 132 ... 
$ crease..ch1 : num 177 0 197 0 0 ... 
$ crease..ch2 : num 160 0 161 0 0 ... 
$ crease..ch3 : num 114 0 109 0 0 ... 
$ CreaseUp  : int 1 0 1 0 0 1 1 0 0 1 ... 
[1] "/Users/jblamkey/OneDrive/School/R/R Workspace/2016_CodaBrundage-Entry-005.csv" 
'data.frame': 439 obs. of 14 variables: 
$ area_mm2  : num 17.2 13.1 15.2 20.1 11.6 ... 
$ perimeter_mm : num 19.3 19.6 18.5 21.8 17.7 ... 
$ majellipse_mm : num 6.63 6.39 5.87 6.9 5.93 ... 
$ minellipse_mm : num 3.32 2.64 3.31 3.73 2.52 ... 
$ grain..ch1 : num 198 207 228 210 212 ... 
$ grain..ch2 : num 170 179 201 181 186 ... 
$ grain..ch3 : num 120 122 144 122 136 ... 
$ nonCrease..ch1: num 197 207 228 210 209 ... 
$ nonCrease..ch2: num 169 179 201 181 182 ... 
$ nonCrease..ch3: num 120 123 144 122 133 ... 
$ crease..ch1 : num 183 197 0 0 197 ... 
$ crease..ch2 : num 153 169 0 0 166 ... 
$ crease..ch3 : num 106 116 0 0 118 ... 
$ CreaseUp  : int 1 1 0 0 1 0 0 1 0 1 ... 

Answer 1

各統計情報を分析のリストに追加し、そのリストを返します。私はあまりにも怠惰で、ファイルがなくてもあなたのサンプルを再現していないという少しの例があります。

analyze <- function (testdata) { 
    results=list() 
    results$mean = mean(testdata) 
    plot(testdata) 
    results$plot = recordPlot() 
    return(results) 
} 

datasets <- list(c(1,5,10), c(10,15,20),c(30,40,50)) 

all_results = list() 
for (dataset in datasets) { 
    # append new results to list 
    all_results[[length(all_results) + 1]] <- analyze(dataset) 
} 


all_results[[1]]$mean 
all_results[[2]]$mean 
all_results[[2]]$plot 

# loop over all_results if you want 
for(results in all_results) { 
    print(results$mean) 
}