我尝试使用此代码来获取r中多个文件的统计信息和绘图。使用此代码，我可以获得数据框的图和数据结构（不包括图）。我在代码下面加了R输出。如何操作此代码以获取我的anaylze函数中的其他信息？
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 ...
答案 0 :(得分：0)
将每个统计信息添加到analyze中的列表中，然后返回该列表。这是一个小例子，因为我懒得在没有文件的情况下重现你的例子。
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)
}