R what does 2 commas mean? - r

I'm looking at an example for the knnflex package and they setup a training and test set using the following:
train <- rbind(iris3[1:25,,1], iris3[1:25,,2], iris3[1:25,,3])
test <- rbind(iris3[26:50,,1], iris3[26:50,,2], iris3[26:50,,3])
My questions is how does this differ from :
train <- rbind(iris3[1:25,1], iris3[1:25,2], iris3[1:25,3])
test <- rbind(iris3[26:50,1], iris3[26:50,2], iris3[26:50,3])


Two commas means there were more than two dimensions and you selected all of the items in the dimension that could have been specified between the two commas. For example, imagine a cube instead of a square, with all of the data in it. You can select row, height, and depth. If you select [row,,depth], then you will have selected an entire column in the cube at that row and depth. The principle is the same up to larger dimensions but harder to describe.


Why don't you just try?
> train <- rbind(iris3[1:25,,1], iris3[1:25,,2], iris3[1:25,,3])
> test <- rbind(iris3[26:50,,1], iris3[26:50,,2], iris3[26:50,,3])
> train <- rbind(iris3[1:25,1], iris3[1:25,2], iris3[1:25,3])
Error in iris3[1:25, 1] : incorrect number of dimensions
> test <- rbind(iris3[26:50,1], iris3[26:50,2], iris3[26:50,3])
Error in iris3[26:50, 1] : incorrect number of dimensions
More generally, leaving an index unspecified selects all entries for that index:
> mtx<-matrix(c(1,2,3,4),nrow=2)
> mtx
[,1] [,2]
[1,] 1 3
[2,] 2 4
> mtx[1,]
[1] 1 3
> mtx[,1]
[1] 1 2


The difference is between iris and iris3, iris3 is a 3- dimensional matrix, contains the same data as iris. But it stores it differently. You can see iris as a bidimensional matrix
Check the link below
https://stat.ethz.ch/R-manual/R-devel/library/datasets/html/iris.html

Related

How to coerce a corpus to be assessed on the same terms as another one

Problem
I need to have a corpus of text, applied the same terms as another, so I can get a term document matrix with the same values.
What I am attempting, is to classify different corpus of texts between 2 groups, using a logistic regression, but I need both of these corpus to have the same variables from the function DocumentTermMatrix().
Current attempt and explanation with code
I can't wrap my head about how to approach the issue, for example, this gives me a first matrix of terms with their frequency:
data("crude")
crude_1 <- crude[1:10]
dtm_1 <- DocumentTermMatrix(crude_1)
dtm_1$dimnames$Terms
# [1] "..." "\"(it)" "\"demand" "\"for"
# [5] "\"growth" "\"if" "\"is" "\"may" ...
How can I use the same terms for the second part of the crude dataset, because so far the Terms are different:
crude_2 <- crude[11:20]
dtm_2 <- DocumentTermMatrix(crude_2)
dtm_2$dimnames$Terms
# [1] "..." "\"expansion" "\"is" "\"may"
# [5] "\"none" "\"this" "\"we" "\"will" ...
I could try to run a frequency on the same terms on crude_2. However, it would be expensive in terms of computation, and you might know a practical solution to this problem.
Question
I would like to coerce dtm_2 to have the same terms as in dtm_1. Only with the frequency of the crude_2 dataset. Is there a practical way to do this in R ?
Or more easily for example: Say I want to find out, how many times zebra or girafe appears in these text, and I want to do it explicitly, how can I proceed ?
Libraries used: library(tm)

Ok so I found a solution to my question by using the function tm_term_score() from the package tm and it works well I'd say, though any different implementation would be welcome.
Normal solution
This is the solution, first we capture the terms in a separate variable and then we apply the term list to create a matrix upon the other Document Term Matrix with the workds frequency:
data("crude")
# First dataset and term list
crude_1 <- crude[1:10]
dtm_1 <- DocumentTermMatrix(crude_1)
term_list <- dtm_1$dimnames$Terms
# Second dataset
crude_2 <- crude[11:20]
dtm_2 <- DocumentTermMatrix(crude_2)
# Creating a dummy column to remove at the end
X <- data.frame(dummy_col = 1:dtm_2$nrow)
for (term in term_list) {
temp_col <- tm_term_score(dtm_2, term)
# Attaching the column to the DF
X$temp_col<-temp_col
names(X)[length(names(X))] <- term
}
# Removing the dummy column
X$dummy_col <- NULL
# The variable X now contains the term frequency of the first data set, but applied to the second
Specific terms
And if you are looking for specific terms (like zebra or girafe, which there are none here), you can do as follow:
term_list <- c("zebra", "girafe")
X <- data.frame(dummy_col = 1:dtm_2$nrow)
for (term in term_list) {
temp_col <- tm_term_score(dtm_2, term)
# Attaching the column to the DF
X$temp_col<-temp_col
names(X)[length(names(X))] <- term
}
X$dummy_col <- NULL
X
# zebra girafe
# 1 0 0
# 2 0 0
# 3 0 0
# ...

R studio doesn't find objects in my function

I’m new to programming and I’m currently writing a function to go through hundreds of csv files in the working directory.
The files have tons of NA values in it.
The function (which I call it corr) has two parameters, the directory, and a threshold value (numeric vector of length 1 indicating the number of complete cases).
The purpose of the function is to take the complete cases for two columns that are sulfate and nitrate(second and third column in the spreadsheet) and calculate the correlation between them if the number of complete cases is greater than the threshold parameter.
The function should return a vector with the correlation if it met the threshold requirement (the default threshold value is 0).
When I run the code I get back two of the following:
A + sign in the console
OR
2.The objects I created in the function can't be found.
Any help would be much appreciated. Thank you in advance!
corr <- function(directory, threshold=0){
filelist2<- data.frame(list.files(path=directory,
pattern=".csv", full.names=TRUE))
corvector <- numeric()
for(i in 1:length(filelist2)){
data <-data.frame(read.csv(filelist2[i]))
removedNA<-complete.cases(data)
newdata<-data[removedNA,2:3]
if(nrow(removedNA) > threshold){
corvector<-c(corvector, cor(data$sulfate, data$nitrate ))
}
}
corvector
}

I don't think your nrow(removedNA) does what you think it does. To replicate the example I use the mtcars dataset.
data <- mtcars # create dataset
data[2:4, 2] <- NA # create some missings in column 2
data[15:17, 3] <- NA # create some missing in column 3
removedNA <- complete.cases(data)
table(removedNA) # 6 missings indeed
nrow(removedNA) # NULL removedNA is no data.frame, so nrow() doesn't work
newdata <- data[removedNA, 2:3] # this works though
nrow(newdata) # and this shows the rows in 'newdata'
#---- therefore instead of nrow(removedNA) try
if(nrow(data)-nrow(newdata) < threshold) {
...
}
NB: I changed the > in < in the line with threshold. I guess it depends on whether you want to set an absolute minimum number of lines (in which cases you could simply use nrow(newdata) > threshold) as threshold, or whether you want the threshold to reflect the different number of lines in the original data and 'new' data.

How to save complete inspect output in R's tm package instead of sample matrix of size 10 * 10?

# My TermDocumentMatrix (TDM)
Nepal.tdm
# Structure of my TDM
str(Nepal.tdm)
# My locality vector
localities
# Structure of my locality vector
str(localities)
#chr [1:344] "kalyan" "surkhet" "chhinchu" "harre" "pyuthan" "thapdada" "khola" ...
# inspecting matching localities in my TDM
locality.matches <- inspect(Nepal.tdm[localities[localities %in% Terms(Nepal.tdm)], ])
# I have tried following things but without success because the output is always 10 * 10 sample matrix when I want complete matrix of 200 * 92
as.data.frame(as.matrix(inspect(Nepal.tdm[localities[localities %in% Terms(Nepal.tdm)], ])))
capture.output(out <- data.frame(inspect(Nepal.tdm[localities[localities %in% Terms(Nepal.tdm)], ])))

Since tm v.0.7 inspect.TermDocumentMatrix() displays a sample instead of the full matrix. The full dense representation is available via as.matrix().
# This will give you the entire matrix, from here you can filter it as you want
as.matrix(Nepal.tdm)

How does one process the results from replicate in R?

Say I have the following code which essentially gives me random simulations for revenue and cost for 12 months
simulate.revenue<-function() {
return(sapply(rnorm(12,100000,30000),function(x) max(0,x)))
}
simulate.cost<-function() {
return(sapply(rnorm(12,50000,20000),function(x) max(0,x)))
}
sim.run<-function() {
revenue<-simulate.revenue()
cost<-simulate.cost()
profit<-revenue-cost
year.simulation<-data.frame(revenue,cost,profit)
return(year.simulation)
}
Now to run the above simulation function 10 times I am aware that I should:
sim.results<-replicate(10,sim.run())
So the question is how do I further process sim.results to say:
find the mean for total yearly profit over each run
find the mean for profit by month over each of the runs (mean(profit[1], mean(profit[2]), ...)

Structure of replicate result:
replicate(1, sim.run()) easily gives you the structure of what is returned: A list item for each column of the data.frame (here 3 list items). Running two simulations adds another 3 list items.
Convert it into proper format:
To convert the list into a data.frame use:
result <- data.frame(matrix(unlist(sim.results), nrow = 12, byrow = FALSE))
In your case every 3 columns of the resulting data.frame correspond to one simulation. To separate the simulations into a list again:
result_list <- list()
m <- 1
n_simulations <- 10
n_columnsPerSimulation <- 3
for (i in seq(1, n_simulations * n_columnsPerSimulation, n_columnsPerSimulation)){
result_list[[m]] <- result[,seq(i, i+n_columnsPerSimulation-1)]
m <- m + 1
}
This is very ugly but seems to work.
Analyze result:
Now you can analyze each simulation e.g. with sapply/lapply like the following example shows:
sapply(result_list, function(x) mean(x[,1]))

R understanding behaviour of as.matrix and matrix while plotting an image

I am reading data from MNIST handwritten digit dataset. Its first column is the digit label and rest 784 columns (28 X 28) are pixel values. Pixel values are between [0, 255]. Thus, there are a total of 1+784=785 columns. To plot a row as an image, I have to convert the 784 pixel values into a matrix of 28X28. In this conversion process, I am not clear about the behaviour of as.matrix() and matrix() functions. The code and comments are as follows:
> train <- read.csv("train.csv", header=TRUE)
# Read 2nd row. Ignore the label col and read rest of 784 columns
> data<-train[2,2:785]
# Convert data into matrix of 28X28
> data<-as.matrix(data,nrow=28,ncol=28)
> dim(data)
[1] 1 784 <= Failed to convert to 28X28
> class(data)
[1] "matrix" <= But class is matrix
# as.matrix() failed. Use matrix()
> data<-matrix(data,nrow=28,ncol=28)
> dim(data)
[1] 28 28 <= Matrix conversion success
Probably, the solution is to ignore as.matrix() altogether and just use matrix(). But, it so happens that in plotting the image as.matrix() does serve as a necessary intermediary. For example, the following code works to plot the image of digit zero:
train <- read.csv("train.csv", header=TRUE)
data<-train[2,2:785]
data<-as.matrix(data,nrow=28,ncol=28)
data<-matrix(data,nrow=28,ncol=28)
##Color ramp def.
colors<-c('white','black')
cus_col<-colorRampPalette(colors=colors)
image(1:28,1:28,data,main="IInd row",col=cus_col(256))
But, in the following code where I have removed as.matrix(), the code gives an error:
> train <- read.csv("train.csv", header=TRUE)
> data<-train[2,2:785]
> data<-matrix(data,nrow=28,ncol=28)
> ##Color ramp def.
> colors<-c('white','black')
> cus_col<-colorRampPalette(colors=colors)
> image(1:28,1:28,data,main="IInd row",col=cus_col(256))
Error in is.finite(z) : default method not implemented for type 'list'
I am unable to understand the role of as.matrix() and the meaning of this error. Kindly do let me know what is wrong.
EDITED
Here is what the first three rows of data look like:
label,pixel0,pixel1,pixel2,pixel3,pixel4,pixel5,pixel6,pixel7,pixel8,pixel9,pixel10,pixel11,pixel12,pixel13,pixel14,pixel15,pixel16,pixel17,pixel18,pixel19,pixel20,pixel21,pixel22,pixel23,pixel24,pixel25,pixel26,pixel27,pixel28,pixel29,pixel30,pixel31,pixel32,pixel33,pixel34,pixel35,pixel36,pixel37,pixel38,pixel39,pixel40,pixel41,pixel42,pixel43,pixel44,pixel45,pixel46,pixel47,pixel48,pixel49,pixel50,pixel51,pixel52,pixel53,pixel54,pixel55,pixel56,pixel57,pixel58,pixel59,pixel60,pixel61,pixel62,pixel63,pixel64,pixel65,pixel66,pixel67,pixel68,pixel69,pixel70,pixel71,pixel72,pixel73,pixel74,pixel75,pixel76,pixel77,pixel78,pixel79,pixel80,pixel81,pixel82,pixel83,pixel84,pixel85,pixel86,pixel87,pixel88,pixel89,pixel90,pixel91,pixel92,pixel93,pixel94,pixel95,pixel96,pixel97,pixel98,pixel99,pixel100,pixel101,pixel102,pixel103,pixel104,pixel105,pixel106,pixel107,pixel108,pixel109,pixel110,pixel111,pixel112,pixel113,pixel114,pixel115,pixel116,pixel117,pixel118,pixel119,pixel120,pixel121,pixel122,pixel123,pixel124,pixel125,pixel126,pixel127,pixel128,pixel129,pixel130,pixel131,pixel132,pixel133,pixel134,pixel135,pixel136,pixel137,pixel138,pixel139,pixel140,pixel141,pixel142,pixel143,pixel144,pixel145,pixel146,pixel147,pixel148,pixel149,pixel150,pixel151,pixel152,pixel153,pixel154,pixel155,pixel156,pixel157,pixel158,pixel159,pixel160,pixel161,pixel162,pixel163,pixel164,pixel165,pixel166,pixel167,pixel168,pixel169,pixel170,pixel171,pixel172,pixel173,pixel174,pixel175,pixel176,pixel177,pixel178,pixel179,pixel180,pixel181,pixel182,pixel183,pixel184,pixel185,pixel186,pixel187,pixel188,pixel189,pixel190,pixel191,pixel192,pixel193,pixel194,pixel195,pixel196,pixel197,pixel198,pixel199,pixel200,pixel201,pixel202,pixel203,pixel204,pixel205,pixel206,pixel207,pixel208,pixel209,pixel210,pixel211,pixel212,pixel213,pixel214,pixel215,pixel216,pixel217,pixel218,pixel219,pixel220,pixel221,pixel222,pixel223,pixel224,pixel225,pixel226,pixel227,pixel228,pixel229,pixel230,pixel231,pixel232,pixel233,pixel234,pixel235,pixel236,pixel237,pixel238,pixel239,pixel240,pixel241,pixel242,pixel243,pixel244,pixel245,pixel246,pixel247,pixel248,pixel249,pixel250,pixel251,pixel252,pixel253,pixel254,pixel255,pixel256,pixel257,pixel258,pixel259,pixel260,pixel261,pixel262,pixel263,pixel264,pixel265,pixel266,pixel267,pixel268,pixel269,pixel270,pixel271,pixel272,pixel273,pixel274,pixel275,pixel276,pixel277,pixel278,pixel279,pixel280,pixel281,pixel282,pixel283,pixel284,pixel285,pixel286,pixel287,pixel288,pixel289,pixel290,pixel291,pixel292,pixel293,pixel294,pixel295,pixel296,pixel297,pixel298,pixel299,pixel300,pixel301,pixel302,pixel303,pixel304,pixel305,pixel306,pixel307,pixel308,pixel309,pixel310,pixel311,pixel312,pixel313,pixel314,pixel315,pixel316,pixel317,pixel318,pixel319,pixel320,pixel321,pixel322,pixel323,pixel324,pixel325,pixel326,pixel327,pixel328,pixel329,pixel330,pixel331,pixel332,pixel333,pixel334,pixel335,pixel336,pixel337,pixel338,pixel339,pixel340,pixel341,pixel342,pixel343,pixel344,pixel345,pixel346,pixel347,pixel348,pixel349,pixel350,pixel351,pixel352,pixel353,pixel354,pixel355,pixel356,pixel357,pixel358,pixel359,pixel360,pixel361,pixel362,pixel363,pixel364,pixel365,pixel366,pixel367,pixel368,pixel369,pixel370,pixel371,pixel372,pixel373,pixel374,pixel375,pixel376,pixel377,pixel378,pixel379,pixel380,pixel381,pixel382,pixel383,pixel384,pixel385,pixel386,pixel387,pixel388,pixel389,pixel390,pixel391,pixel392,pixel393,pixel394,pixel395,pixel396,pixel397,pixel398,pixel399,pixel400,pixel401,pixel402,pixel403,pixel404,pixel405,pixel406,pixel407,pixel408,pixel409,pixel410,pixel411,pixel412,pixel413,pixel414,pixel415,pixel416,pixel417,pixel418,pixel419,pixel420,pixel421,pixel422,pixel423,pixel424,pixel425,pixel426,pixel427,pixel428,pixel429,pixel430,pixel431,pixel432,pixel433,pixel434,pixel435,pixel436,pixel437,pixel438,pixel439,pixel440,pixel441,pixel442,pixel443,pixel444,pixel445,pixel446,pixel447,pixel448,pixel449,pixel450,pixel451,pixel452,pixel453,pixel454,pixel455,pixel456,pixel457,pixel458,pixel459,pixel460,pixel461,pixel462,pixel463,pixel464,pixel465,pixel466,pixel467,pixel468,pixel469,pixel470,pixel471,pixel472,pixel473,pixel474,pixel475,pixel476,pixel477,pixel478,pixel479,pixel480,pixel481,pixel482,pixel483,pixel484,pixel485,pixel486,pixel487,pixel488,pixel489,pixel490,pixel491,pixel492,pixel493,pixel494,pixel495,pixel496,pixel497,pixel498,pixel499,pixel500,pixel501,pixel502,pixel503,pixel504,pixel505,pixel506,pixel507,pixel508,pixel509,pixel510,pixel511,pixel512,pixel513,pixel514,pixel515,pixel516,pixel517,pixel518,pixel519,pixel520,pixel521,pixel522,pixel523,pixel524,pixel525,pixel526,pixel527,pixel528,pixel529,pixel530,pixel531,pixel532,pixel533,pixel534,pixel535,pixel536,pixel537,pixel538,pixel539,pixel540,pixel541,pixel542,pixel543,pixel544,pixel545,pixel546,pixel547,pixel548,pixel549,pixel550,pixel551,pixel552,pixel553,pixel554,pixel555,pixel556,pixel557,pixel558,pixel559,pixel560,pixel561,pixel562,pixel563,pixel564,pixel565,pixel566,p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Difference is explained in the documentation.
https://stat.ethz.ch/R-manual/R-devel/library/base/html/matrix.html
Also all rows & columns of a matrix must have the same class (numeric, character, etc). In a dataframe, you can have some of each. Sometimes R is not able to convert different classes when using as.matrix()

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