Prediction by neural network in R (nnet package) - r

I can't get why the testing of dataset is not working in R neural networks (nnet package).
I have two datasets with similar structures - for training (trainset, 17 cases) and prediction (testset, 9 cases). Each dataset has columns: Age, Gender, Height, Weight. In the testing dataset the age is unknown (NaN).
The formula for training is obtained successfully below:
library(nnet)
trainednetwork<-nnet(age~gender+emLength+action5cnt,trainset, size=17)
Anyway, if I try to use test dataset for prediction in the next string of the code,
prediction<-predict(trainednetwork,testset)
I get mistake "No component terms, no attribute". Can anyone help?
The data (obtained with dput() function):
testset:
structure(list(
age = c(NA_integer_, NA_integer_, NA_integer_, NA_integer_, NA_integer_,
NA_integer_, NA_integer_, NA_integer_, NA_integer_),
gender = structure(
c(2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L),
.Label = c("f", "m"),
class = "factor"),
Height= c(9L, 11L, 9L, 11L, 9L, 11L, 9L, 11L, 9L),
Weight= c(1L, 41L, 2L, 1L, 2L, 29L, 12L, 6L, 12L)),
.Names = c("age", "gender", "Height", "Weight"),
class = "data.frame",
row.names = c(NA, 9L))
trainset:
structure(list(
age = c(43L, 35L, 22L, 28L, 20L, 47L, 41L, 23L,
42L, 27L, 22L, 60L, 62L, 47L, 42L, 26L, 54L),
gender = structure(
c(2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L),
.Label = c("f", "m"),
class = "factor"),
Height= c(7L, 9L, 11L, 11L, 11L, 9L, 11L, 9L, 23L, 9L,
9L, 9L, 10L, 7L, 7L, 11L, 7L),
Weight= c(2L, 2L, 9L, 9L, 28L, 8L, 6L, 3L, 1L, 2L, 40L,
1L, 9L, 1L, 7L, 4L, 35L)),
.Names = c("age", "gender", "Height", "Weight"),
class = "data.frame",
row.names = c(NA, 17L))


I think in the R neuralnet package the command to use for prediction is "compute", not predict, which is very confusing. A

Related

Change x axis labels to hours (time) on geom_tile()

Here is a geom_tile displaying hours and days of the week, how can it made to display each hour (i.e. 00:00 through to 23:00 on the x axis)?
library(tidyverse)
df %>%
ggplot(aes(hour, day, fill = value)) +
geom_tile(colour = "ivory")
Currently it displays every fifth hour:
I have tried a bunch of different things, and would prefer a 'best practice' way (i.e. without manually generating labels), but in case labels are needed, here's one way to produce them hour_labs <- 0:23 %>% { ifelse(nchar(.) == 1, paste0("0", .), .) } %>% paste0(., ":00")
Data for reproducible example
df <- structure(list(day = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L), .Label = c("Sunday",
"Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday"
), class = c("ordered", "factor")), hour = c(0L, 2L, 3L, 5L,
6L, 7L, 8L, 10L, 11L, 12L, 13L, 18L, 21L, 22L, 23L, 0L, 1L, 2L,
3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 20L, 21L, 22L,
23L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L,
20L, 21L, 22L, 23L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
11L, 13L, 14L, 20L, 21L, 22L, 23L, 0L, 1L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 9L, 10L, 11L, 12L, 13L, 15L, 20L, 21L, 22L, 23L, 0L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 11L, 13L, 14L, 15L, 16L,
19L, 21L, 0L, 1L, 2L, 3L, 7L, 8L, 10L, 13L, 14L, 22L, 23L), value = c(1L,
1L, 1L, 2L, 1L, 3L, 1L, 1L, 2L, 1L, 3L, 1L, 2L, 13L, 13L, 24L,
39L, 21L, 17L, 25L, 22L, 27L, 28L, 19L, 6L, 2L, 2L, 1L, 2L, 2L,
7L, 23L, 38L, 18L, 26L, 21L, 20L, 31L, 40L, 35L, 22L, 5L, 3L,
2L, 7L, 4L, 3L, 3L, 3L, 17L, 13L, 23L, 24L, 19L, 31L, 13L, 35L,
50L, 22L, 13L, 7L, 2L, 1L, 1L, 1L, 1L, 3L, 14L, 17L, 33L, 32L,
32L, 25L, 29L, 27L, 38L, 26L, 11L, 8L, 4L, 5L, 5L, 3L, 1L, 1L,
3L, 14L, 21L, 24L, 22L, 25L, 26L, 23L, 58L, 36L, 26L, 6L, 3L,
1L, 5L, 3L, 1L, 1L, 3L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L,
1L, 1L)), row.names = c(NA, -116L), groups = structure(list(day = structure(1:7, .Label = c("Sunday",
"Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday"
), class = c("ordered", "factor")), .rows = structure(list(1:15,
16:33, 34:51, 52:69, 70:88, 89:105, 106:116), ptype = integer(0), class = c("vctrs_list_of",
"vctrs_vctr"))), row.names = c(NA, 7L), class = c("tbl_df", "tbl",
"data.frame"), .drop = TRUE), class = c("grouped_df", "tbl_df",
"tbl", "data.frame"))

Here's one way using sprintf to construct labels.
library(dplyr)
library(ggplot2)
df %>%
mutate(lab = sprintf('%02d:00', hour)) %>%
ggplot() + aes(lab, day, fill = value) +
geom_tile(colour = "ivory") +
theme(axis.text.x = element_text(angle = 90, hjust = 1))
To complete the missing times apart from #Eric Watt's suggestion we can also use complete.
df %>%
mutate(lab = sprintf('%02d:00', hour)) %>%
tidyr::complete(lab = sprintf('%02d:00', 0:23)) %>%
ggplot() + aes(lab, day, fill = value) +
geom_tile(colour = "ivory") +
theme(axis.text.x = element_text(angle = 90, hjust = 1))

I would suggest making sure your data type is correctly representing your data. If your hour column is representing time in hours, then it should be a time based structure. For example:
df$hour <- as.POSIXct(as.character(df$hour), format = "%H", tz = "UTC")
Then you can tell ggplot that the x axis is a datetime variable using scale_x_datetime.
ggplot(df, aes(hour, day, fill = value)) +
geom_tile(colour = "ivory") +
scale_x_datetime(labels = date_format("%H:%M")) +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
If you want a break for every hour, you can input that as breaks:
ggplot(df, aes(hour, day, fill = value)) +
geom_tile(colour = "ivory") +
scale_x_datetime(breaks = as.POSIXct(as.character(0:23), format = "%H", tz = "UTC"),
labels = date_format("%H:%M")) +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
You can also use the scales package which has handy formatting options such as date_breaks:
library(scales)
ggplot(df, aes(hour, day, fill = value)) +
geom_tile(colour = "ivory") +
scale_x_datetime(breaks = date_breaks("1 hour"),
labels = date_format("%H:%M")) +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))

Re-assemble dataframe by index

What I'm trying to do is getting a dataframe where the repeated rows in the first column act as an index to copy the corresponding rows of other columns. I know this sound messy, and my inability to accurately state the issue is one of the reasons I'm having so many problems with this.
I'll provide a reproducible example below.
structure(list(Var1 = structure(c(1L, 2L, 3L, 4L, 5L, 6L, 7L,
8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 1L, 2L, 3L, 4L,
5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L,
16L, 17L), .Label = c("2016-01", "2016-02", "2016-03", "2016-04",
"2016-05", "2016-06", "2016-07", "2016-08", "2016-09", "2016-10",
"2016-11", "2016-12", "2017-01", "2017-02", "2017-03", "2017-04",
"2017-05"), class = "factor"), Var2 = structure(c(1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L
), .Label = c("B2B", "B2C", "B2K"), class = "factor"), Freq = c(5L,
13L, 8L, 13L, 36L, 5L, 18L, 1L, 12L, 24L, 22L, 6L, 24L, 15L,
11L, 26L, 1L, 338L, 285L, 291L, 232L, 142L, 42L, 92L, 9L, 46L,
34L, 45L, 35L, 30L, 31L, 36L, 56L, 9L, 0L, 1L, 0L, 0L, 0L, 0L,
7L, 0L, 13L, 0L, 1L, 0L, 0L, 0L, 0L, 2L, 0L)), .Names = c("Var1",
"Var2", "Freq"), class = "data.frame", row.names = c(NA, -51L
))
basically what I want is:
On Var1 no repeated dates
On the row where the date is repeated, take the value of Var2 and Freq and copy them in two new columns to the index of the unique date
This must be done for every distinct level of Var2
Thank you in advance!

I think what your trying to explain is a dcast. Does this end up how you want it?
library(reshape2)
dcast(x,Var1~Var2,value.var="Freq")

A base R option would be
xtabs(Freq~Var1 + Var2, df1)

plot group and category means with group_by

I am new to R and trying to figure out a way to plot means for individual samples as well as group means with ggplot.
I am following this articles on R-bloggers (last paragraph):
https://www.r-bloggers.com/plotting-individual-observations-and-group-means-with-ggplot2/
This is my code:
gd <- meanplot1 %>%
group_by(treatment, value) %>%
summarise(measurement = mean(measurement))
ggplot(meanplot1, aes(x=value, y=measurement, color=treatment)) +
geom_line(aes(group=sample), alpha=0.3) +
geom_line(data=gd, size=3, alpha=0.9) +
theme_bw()
Whilst the sample means are being shown, the group means aren´t. I get the error
geom_path: Each group consists of only one observation. Do you need
to adjust the group aesthetic?
Upon adding group=1, I get a weirdly mixed category mean, but not what I am looking for..
I scrolled through a lot of articles already, but couldnt find an answer - I would be so happy if somebody could help me out here!! :)
My data (meanplot1) is formatted like this:
treatment sample value measurement
1 control, control 1, initial, 20,
2 control, control 1, 26, NA,
3 control, control 1, 26', 28,
12 control, control 2, initial, 22,
13 control control 2, 26, NA,
14 control control 2, 26', 36,
15 control control 2, 28, 45,
67 stressed, stress 1, initial, 37,
68 stressed, stress 1, 26, NA,
69 stressed, stress 1, 26', 17,
78 stressed, stress 2, initial, 36,
79 stressed, stress 2, 26, NA,
80 stressed, stress 2, 26', 25,
I am hoping to see 6 lines, one mean for stress 1, stress 2, control 1 and control 2, and one mean for all treatment=control, and one for all treatment=stressed
output dput(gd):
structure(list(treatment = structure(c(1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L
), .Label = c("control", "stressed"), class = "factor"), value = structure(c(1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 11L), .Label = c("26", "26'", "28", "28'",
"30", "30'", "32", "32'", "34", "34'", "initial"), class = "factor"),
measurement = c(NA, 32.3333333333333, 39.5, 30.3333333333333,
31.8333333333333, 31.8333333333333, NA, 36, 34.6666666666667,
36, 24.6666666666667, NA, 25.3333333333333, 33.3333333333333,
32, 50.1666666666667, 39.1666666666667, NA, 33.5, 24.3333333333333,
27.3333333333333, 36)), class = c("grouped_df", "tbl_df",
"tbl", "data.frame"), row.names = c(NA, -22L), vars = list(treatment), drop = TRUE, .Names = c("treatment",
"value", "measurement"))
output dput(meanplot1):
structure(list(treatment = structure(c(1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("control",
"stressed"), class = "factor"), sample = structure(c(1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L), .Label = c("control 1",
"control 2", "control 3", "control 4", "control 5", "control 6",
"stress 1", "stress 2", "stress 3", "stress 4", "stress 5", "stress 6"
), class = "factor"), value = structure(c(11L, 1L, 2L,
3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 1L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 9L, 10L, 11L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
11L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 1L, 2L, 3L,
4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 1L, 2L, 3L, 4L, 5L, 6L, 7L,
8L, 9L, 10L, 11L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 1L, 2L, 3L, 4L,
5L, 6L, 7L, 8L, 9L, 10L, 11L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L), .Label = c("26", "26'",
"28", "28'", "30", "30'", "32", "32'", "34", "34'", "initial"
), class = "factor"), measurement = c(20L, NA, 28L, 18L, 17L,
19L, 34L, NA, 23L, 29L, 27L, 22L, NA, 36L, 45L, 31L, 40L, 44L,
NA, 49L, 40L, 39L, 32L, NA, 35L, 57L, 30L, 37L, 29L, NA, 44L,
37L, 46L, 20L, NA, 39L, 27L, 30L, 40L, 25L, NA, 29L, 50L, 30L,
26L, NA, 28L, 45L, 47L, 27L, 35L, NA, 24L, 22L, 35L, 28L, NA,
28L, 45L, 27L, 28L, 24L, NA, 47L, 30L, 39L, 37L, NA, 17L, 29L,
29L, 31L, 29L, NA, 37L, 21L, 27L, 36L, NA, 25L, 41L, 51L, 66L,
50L, NA, 33L, 25L, 22L, 36L, NA, 33L, 45L, 26L, 72L, 59L, NA,
33L, 26L, 25L, 33L, NA, 21L, 33L, 25L, 29L, 21L, NA, 26L, 20L,
16L, 22L, NA, 30L, 27L, 28L, 57L, 41L, NA, 28L, 23L, 17L, 52L,
NA, 26L, 25L, 33L, 46L, 35L, NA, 44L, 31L, 57L)), .Names = c("treatment",
"sample", "value", "measurement"), class = "data.frame", row.names = c(NA,
-132L))

I suppose you are aiming to plot the treatment means.
By default, since you are using a categorical x-axis, the grouping is set to the interaction between x and color. You only want to group by treatment, however. So we'll add the correct grouping to the call.
ggplot(meanplot1, aes(x = value, y = measurement, color=treatment)) +
geom_line(aes(group=sample), alpha=0.3) +
geom_line(aes(group = treatment), gd, size=3, alpha=0.9) +
theme_bw()
Also note that
ggplot(meanplot1, aes(x=value, y=measurement, color=treatment)) +
geom_line(aes(group=sample), alpha=0.3) +
stat_summary(aes(group = treatment), fun.y = mean, geom = 'line', size=3, alpha=0.9) +
theme_bw()
Gives the same plot, without the interruption.

Anova with repeated replicated design

How can the analysis of repeated replicated design given on this page ( https://stats.stackexchange.com/questions/115135/repeated-measures-anova-with-replicated-measurements ) be done in R? I can perform ANOVA using aov() but I have some doubts as to the Error term there.
The data is as follows:
mydf = structure(list(User = c(1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L),
Mode = c(1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L), Trial1Time = c(20L,
5L, 40L, 10L, 15L, 30L, 13L, 11L, 35L), Trial2Time = c(30L,
7L, 25L, 20L, 17L, 35L, 26L, 11L, 38L)), .Names = c("User",
"Mode", "Trial1Time", "Trial2Time"), class = "data.frame", row.names = c(NA,
-9L))

Converting object of class rules to data frame in R

I have an output of apriori function, which mines data and gives set of rules. I want to convert it to data frame for further processing.
The rules object looks like this:
> inspect(output)
lhs rhs support confidence lift
1 {curtosis=(846,1.27e+03]} => {skewness=(-0.254,419]} 0.2611233 0.8044944 2.418776
2 {variance=(892,1.34e+03]} => {notes.class=FALSE} 0.3231218 0.9888393 1.781470
3 {variance=(-0.336,446]} => {notes.class=TRUE} 0.2859227 0.8634361 1.940608
4 {skewness=(837,1.26e+03]} => {notes.class=FALSE} 0.2924872 0.8774617 1.580815
5 {entropy=(-0.155,386],
class=FALSE} => {skewness=(837,1.26e+03]} 0.1597374 0.9521739 2.856522
6 {variance=(-0.336,446],
curtosis=(846,1.27e+03]} => {skewness=(-0.254,419]} 0.1378556 0.8325991 2.503275
We can create rules object using data frame. Data frame looks like this:
> data
variance skewness curtosis entropy notes.class
1 (892,1.34e+03] (837,1.26e+03] (-0.268,424] (386,771] FALSE
2 (892,1.34e+03] (-0.254,419] (424,846] (771,1.16e+03] FALSE
3 (892,1.34e+03] (837,1.26e+03] (-0.268,424] (-0.155,386] FALSE
4 (446,892] (-0.254,419] (846,1.27e+03] (386,771] FALSE
Than we can get output variable using this:
> output <- apriori(data)
There was used arules package. dput(output) gives this:
new("rules"
, lhs = new("itemMatrix"
, data = new("ngCMatrix"
, i = c(8L, 2L, 0L, 5L, 9L, 12L, 0L, 8L, 0L, 3L, 0L, 8L, 8L, 13L, 8L,
10L, 3L, 10L, 8L, 11L, 8L, 13L, 3L, 12L, 2L, 5L, 2L, 6L, 2L,
5L, 2L, 6L, 2L, 10L, 2L, 7L, 2L, 11L, 0L, 3L, 0L, 10L, 0L, 7L,
11L, 13L, 5L, 6L, 6L, 12L, 5L, 10L, 1L, 5L, 4L, 6L, 6L, 13L,
0L, 3L, 8L, 0L, 8L, 13L, 3L, 8L, 13L, 0L, 3L, 13L, 2L, 5L, 6L,
2L, 5L, 12L, 2L, 6L, 12L)
, p = c(0L, 1L, 2L, 3L, 4L, 6L, 8L, 10L, 12L, 14L, 16L, 18L, 20L, 22L,
24L, 26L, 28L, 30L, 32L, 34L, 36L, 38L, 40L, 42L, 44L, 46L, 48L,
50L, 52L, 54L, 56L, 58L, 61L, 64L, 67L, 70L, 73L, 76L, 79L)
, Dim = c(14L, 38L)
, Dimnames = list(NULL, NULL)
, factors = list()
)
, itemInfo = structure(list(labels = structure(c("variance=(-0.336,446]",
"variance=(446,892]", "variance=(892,1.34e+03]", "skewness=(-0.254,419]",
"skewness=(419,837]", "skewness=(837,1.26e+03]", "curtosis=(-0.268,424]",
"curtosis=(424,846]", "curtosis=(846,1.27e+03]", "entropy=(-0.155,386]",
"entropy=(386,771]", "entropy=(771,1.16e+03]", "notes.class=FALSE",
"notes.class=TRUE"), class = "AsIs"), variables = structure(c(5L,
5L, 5L, 4L, 4L, 4L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L), .Label = c("curtosis",
"entropy", "notes.class", "skewness", "variance"), class = "factor"),
levels = structure(c(4L, 8L, 12L, 2L, 6L, 10L, 3L, 7L, 11L,
1L, 5L, 9L, 13L, 14L), .Label = c("(-0.155,386]", "(-0.254,419]",
"(-0.268,424]", "(-0.336,446]", "(386,771]", "(419,837]",
"(424,846]", "(446,892]", "(771,1.16e+03]", "(837,1.26e+03]",
"(846,1.27e+03]", "(892,1.34e+03]", "FALSE", "TRUE"), class = "factor")), .Names = c("labels",
"variables", "levels"), row.names = c(NA, -14L), class = "data.frame")
, itemsetInfo = structure(list(), .Names = character(0), row.names = integer(0), class = "data.frame")
)
, rhs = new("itemMatrix"
, data = new("ngCMatrix"
, i = c(3L, 12L, 13L, 12L, 5L, 3L, 8L, 13L, 0L, 3L, 8L, 3L, 3L, 8L,
6L, 5L, 12L, 12L, 12L, 12L, 12L, 13L, 13L, 13L, 3L, 12L, 5L,
12L, 12L, 13L, 4L, 13L, 3L, 0L, 8L, 12L, 6L, 5L)
, p = 0:38
, Dim = c(14L, 38L)
, Dimnames = list(NULL, NULL)
, factors = list()
)
, itemInfo = structure(list(labels = structure(c("variance=(-0.336,446]",
"variance=(446,892]", "variance=(892,1.34e+03]", "skewness=(-0.254,419]",
"skewness=(419,837]", "skewness=(837,1.26e+03]", "curtosis=(-0.268,424]",
"curtosis=(424,846]", "curtosis=(846,1.27e+03]", "entropy=(-0.155,386]",
"entropy=(386,771]", "entropy=(771,1.16e+03]", "notes.class=FALSE",
"notes.class=TRUE"), class = "AsIs"), variables = structure(c(5L,
5L, 5L, 4L, 4L, 4L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L), .Label = c("curtosis",
"entropy", "notes.class", "skewness", "variance"), class = "factor"),
levels = structure(c(4L, 8L, 12L, 2L, 6L, 10L, 3L, 7L, 11L,
1L, 5L, 9L, 13L, 14L), .Label = c("(-0.155,386]", "(-0.254,419]",
"(-0.268,424]", "(-0.336,446]", "(386,771]", "(419,837]",
"(424,846]", "(446,892]", "(771,1.16e+03]", "(837,1.26e+03]",
"(846,1.27e+03]", "(892,1.34e+03]", "FALSE", "TRUE"), class = "factor")), .Names = c("labels",
"variables", "levels"), row.names = c(NA, -14L), class = "data.frame")
, itemsetInfo = structure(list(), .Names = character(0), row.names = integer(0), class = "data.frame")
)
, quality = structure(list(support = c(0.261123267687819, 0.323121808898614,
0.285922684172137, 0.292487235594457, 0.159737417943107, 0.137855579868709,
0.137855579868709, 0.142231947483589, 0.142231947483589, 0.110138584974471,
0.110138584974471, 0.12399708242159, 0.153902261123268, 0.107221006564551,
0.13056163384391, 0.13056163384391, 0.150984682713348, 0.139314369073669,
0.100656455142232, 0.107221006564551, 0.154631655725748, 0.165572574762947,
0.112326768781911, 0.105762217359592, 0.12180889861415, 0.181619256017505,
0.181619256017505, 0.102844638949672, 0.105762217359592, 0.12837345003647,
0.12837345003647, 0.137855579868709, 0.137855579868709, 0.137855579868709,
0.137855579868709, 0.13056163384391, 0.13056163384391, 0.13056163384391
), confidence = c(0.804494382022472, 0.988839285714286, 0.863436123348018,
0.87746170678337, 0.952173913043478, 0.832599118942731, 0.832599118942731,
0.859030837004405, 0.898617511520737, 0.853107344632768, 0.915151515151515,
0.80188679245283, 0.972350230414747, 0.885542168674699, 0.864734299516908,
0.913265306122449, 1, 0.974489795918367, 1, 1, 0.990654205607477,
1, 0.980891719745223, 0.873493975903614, 0.814634146341463, 0.943181818181818,
0.950381679389313, 1, 0.92948717948718, 0.931216931216931, 0.897959183673469,
1, 0.969230769230769, 0.895734597156398, 0.832599118942731, 1,
0.864734299516908, 0.93717277486911), lift = c(2.41877587226493,
1.78146998779801, 1.94060807395104, 1.580814717477, 2.85652173913043,
2.50327498261071, 2.56515369004603, 1.93070701234925, 2.71366653809456,
2.56493458221826, 2.81948927477017, 2.41093594836147, 2.92344773223381,
2.72826587247868, 2.58853870008227, 2.73979591836735, 1.80157687253614,
1.75561827884899, 1.80157687253614, 1.80157687253614, 1.78473970550309,
2.24754098360656, 2.20459434060771, 1.96321350977681, 2.44926187419769,
1.69921455023295, 2.85114503816794, 1.80157687253614, 1.67454260588295,
2.09294821753838, 2.68799572230639, 2.24754098360656, 2.91406882591093,
2.70496064471679, 2.56515369004603, 1.80157687253614, 2.58853870008227,
2.81151832460733)), row.names = c(NA, 38L), .Names = c("support",
"confidence", "lift"), class = "data.frame")
, info = structure(list(data = data, ntransactions = 1371L, support = 0.1,
confidence = 0.8), .Names = c("data", "ntransactions", "support",
"confidence"))
)

We can't duplicate your data from your question (oh, you just added your data as I was typing this! Sorry!), so I'll use the example from the arules package:
library('arules');
data("Adult")
## Mine association rules.
rules <- apriori(Adult,
parameter = list(supp = 0.5, conf = 0.9,
target = "rules"))
Then I can duplicate the stuff output from inspect(rules):
> ruledf = data.frame(
lhs = labels(lhs(rules))$elements,
rhs = labels(rhs(rules))$elements,
rules#quality)
> head(ruledf)
lhs rhs support confidence lift
1 {} {capital-gain=None} 0.9173867 0.9173867 1.0000000
2 {} {capital-loss=None} 0.9532779 0.9532779 1.0000000
3 {hours-per-week=Full-time} {capital-gain=None} 0.5435895 0.9290688 1.0127342
4 {hours-per-week=Full-time} {capital-loss=None} 0.5606650 0.9582531 1.0052191
5 {sex=Male} {capital-gain=None} 0.6050735 0.9051455 0.9866565
6 {sex=Male} {capital-loss=None} 0.6331027 0.9470750 0.9934931
and do stuff like order by decreasing lift:
head(ruledf[order(-ruledf$lift),])
The help for the rules class: http://www.rdocumentation.org/packages/arules/functions/rules-class.html will tell you what you can get from your rules object - I just used that information to build a data frame. If its not exactly what you want, then cook one up using your own recipe!

Run apriori in data Adult
rules <- apriori(Adult, parameter = list(supp = 0.5, conf = 0.9, target =
"rules"))
Inspect LHS, RHS, support, confidence and lift
arules::inspect(rules)
Create a dataframe
df = data.frame(
lhs = labels(lhs(rules)),
rhs = labels(rhs(rules)),
rules#quality)
View top 6 lines in new dataframe
head(df)

This does the trick
rules_dataframe <- as(output, 'data.frame')

Resources