Use dictionary to collect items based on their first two elements: expressions = {} for left, op, right in list1 + list2: expressions.setdefault((left, op), []).append(right) then build a new list with the mean of the collected right-hand-side operands: [[left, op] + [sum(rights) // len(rights)] for (left, op), rights in expressions.items()]...

Here's a possible data.table solution library(data.table) setDT(df)[, .(Y = Y[1L], Z = mean(Z)), by = .(X, indx = cumsum(substr(Y, 11, 12) == '00'))] # X indx Y Z # 1: FRI 1 200101010000 -6.510 # 2: FRI 2 200101010100 -6.040 # 3: FRI 3 200101010200 -5.465 # 4: FRI 4...

Transferred from comments. RET is an xts object and rollapply.xts in the xts package does not support vector widths. Try this to cause it to use rollapply.zoo in the zoo package: rollapplyr(as.zoo(RET), 1:nrow(RET), mean.geometric) ...

angularjs,node.js,mongodb,express,mean

If you look in menus.client.services.js in the core module of mean.js the last line looks like this: this.addMenu('topbar');. If you change it to this.addMenu('topbar', true);. You will see all your menu items showing on the topbar when you aren't logged in. Then you can add your menu item like in...

You haven't explicitly asked a question so I'll try and comply to the "set me in the right direction" part. I'd suggest re-formatting the loop structure to a cleaner one, like this: double total; for(int student = 1; student <= 4; student++) { System.out.printf("Student %d\n", student); double sum = 0,...

The problem is with how you're using lapply. Here's the correct code: lapply(eu[eu$plicht=='ja',2:13], weighted.mean, eu[eu$plicht=='ja','inwoners'], na.rm=TRUE) lapply(eu[eu$plicht=='nee',2:13], weighted.mean, eu[eu$plicht=='nee','inwoners'], na.rm=TRUE) Notice how weighted.mean is used as an argument, rather than inside an anonymous function with x as an argument. You could equivalently do: lapply(eu[eu$plicht=='ja',2:13], function(x) weighted.mean(x, eu[eu$plicht=='ja','inwoners'], na.rm=TRUE)) lapply(eu[eu$plicht=='nee',2:13], function(x)...

Here's a quick data.table solution (assuming coef is a) library(data.table) setDT(df)[, .(MeanASmall = mean(b[-40 <= a & a < 0]), MeanABig = mean(b[0 <= a & a <= 40]))] # MeanASmall MeanABig # 1: 33.96727 89.46 If a range is limited, you could do this quickly with base R too...

Given those data structures that you use, this is not trivial, but it will become much easier if you use a single dictionary mapping items to their values, instead. First, let's try to re-structure your data in that way: values = {entry['ptid']: entry['Value'] for entry in dctData} items = {}...

r,ggplot2,mean,facet,standard-deviation

Here's my approach to the solution outlined by @DavidArenburg (though I simplified the data a little, using simple cumulative statistics and a plain index): library(tidyr) library(dplyr) library(TTR) v <- rnorm(1000) df <- data.frame(index = 1:1000, variable = v, mean = runMean(v, n=1, cumulative=TRUE), sd = runSD(v, n=1, cumulative=TRUE)) dd <-...

You can check these ones : https://developer.forecast.io/docs/v2 http://www.openweathermap.com/API http://api.accuweather.com/developers/ Besides, you can use Google with the following keywords : "weather forecast api"...

(df.final <- data.frame(ford = sample(0:100, 5), toyota = sample(0:50, 5))) # ford toyota # 1 42 5 # 2 30 46 # 3 45 29 # 4 69 48 # 5 18 14 col.name # [1] "df.final$ford" typeof(col.name) # [1] "character" Currently, col.name is a character vector, so taking its...

matlab,image-processing,matrix,mean,subsetting

Method 1 Using mat2cell and cellfun AC = mat2cell(A, repmat(2,size(A,1)/2,1), repmat(2,size(A,2)/2,1)); out = cellfun(@(x) mean(x(:)), AC); Method 2 using im2col out = reshape(mean(im2col(A,[2 2],'distinct')),size(A)./2); Method 3 Using simple for loop out(size(A,1)/2,size(A,2)/2) = 0; k = 1; for i = 1:2:size(A,1) l = 1; for j = 1:2:size(A,2) out(k,l) = mean(mean(A(i:i+1,j:j+1)));...

Use e.g. the readxl package: library(readxl) df <- read_excel(filename, sheetnumber) rowMeans(df[, -1]) # [1] 21.66667 45.00000 66.33333 43.00000 ...

You need to do the following: #since you don't care about the Infs convert them to NAs #so that they get removed at the mean function #since we have set na.rm=TRUE df$amihud[df$amihud==Inf] <- NA library(dplyr) #you need to use summarise to calculate the means as below: res <- df %>%...

You could use rowMeans transform(mix, Mean=rowMeans(mix[,2:3]), check.names=FALSE) # agrp 1998-1999 2000-2001 tot Mean #1 1 140903 72208 213111 106555.5 #2 2 88322 33704 122026 61013.0 #3 3 18175 3804 21979 10989.5 #4 4 6125 797 6922 3461.0 ...

Updated: generalized to allow for scaling of the response as well as the predictors. Here's a fairly crude implementation. If our original (unscaled) regression is Y = b0 + b1*x1 + b2*x2 ... Then our scaled regression is (Y0-mu0)/s0 = b0' + (b1'*(1/s1*(x1-mu1))) + b2'*(1/s2*(x2-mu2))+ ... This is equivalent to...

You can use zip within a list comprehension: >>> from __future__ import division >>> [[set(i).pop(),round(sum(j)/len(j),0)] for i,j in [zip(*i) for i in data]] [['apple', 2.0], ['cake', 7.0], ['chocolate', 7.0], ['grapes', 6.0]] The zip function here would separate your values from names in nested lists: >>> [zip(*i) for i in data]...

r,data.frame,plyr,mean,summary

I think you're looking for numcolwise? ddply(diamonds,.(cut),numcolwise(mean,na.rm = TRUE)) cut carat depth table price x y z 1 Fair 1.0461366 64.04168 59.05379 4358.758 6.246894 6.182652 3.982770 2 Good 0.8491847 62.36588 58.69464 3928.864 5.838785 5.850744 3.639507 3 Very Good 0.8063814 61.81828 57.95615 3981.760 5.740696 5.770026 3.559801 4 Premium 0.8919549 61.26467 58.74610...

matlab,time-series,covariance,mean

I'm not sure that I understand all of the details of your question, but if you just want to operate on a delayed version of a signal, you can do something like this... %xcorr with a 1-sample shifted version of itself [c,lags]=xcorr(t(1:end-1),t(2:end)); %xcorr with a 2-sample shifted version of itself...

You were close, try this mean(iris$Sepal.Length[which(iris$Species != 'setosa')]) or mean(iris$Sepal.Length[iris$Species != 'setosa']) or mean(iris[iris$Species!= "setosa", "Sepal.Length"]) ...

statistics,mean,angle,direction

The values don't look right Angles that differ by 360 are equivalent. So, -28.8551147 == 331.145, which is the arithmetic mean of the two values you provided. If you would like to ensure that your values are always in [0,360), you should add 360 if the values are less...

Hm, I guess reusing group_by with add=TRUE is one way: dta %>% group_by(isex) %>% mutate( ng = n_distinct(idno) ) %>% group_by(value,add=TRUE) %>% summarise( smn = 10*n(), mean = 10*n()/ng[1] ) # isex value smn mean # 1 FEMALE a Sleep 150 75 # 2 FEMALE j Child care 10 5...

It's very simple. First, you should calculate mean for each column, then you should reshape resulting 1x25 vector into 5x5 matrix: matrix(apply(data,2,mean), nrow=5, ncol=5, byrow=TRUE) apply(data,2,mean) calculates mean values for each column of data and matrix constructs a 5x5 matrix from its results....

I think this is because in the mean(dat$x, na.rm=T) version, each NA that is removed, reduces the number of observations by 1, whereas if you aggregate first, in your example you have an NA in row 10 (ID 11) which is removed but since the other rows with ID 11...

Just count the number of doubles as you go along, and then work out the mean once you're done scanning the file. int count = 0; while (inputStream.hasNextDouble ()) { count ++; x = inputStream.nextDouble (); y += x; System.out.println (x); } // divide y by count for the mean...

What I think you're asking is this: given two vectors of numbers, how can I find the mean of the first items in each vector, the mean of the second items in each vector, and so on. If that's the case, then here is a way to do that. First,...

r,time-series,average,mean,moving-average

For returning the last value try: values$last = c(NA,values[-nrow(values),1]) or the lag function could be used as well I believe....

That's just a display thing, the grouped column now becomes the index and this is just the way that it is displayed, you will notice here that even when you set pd.set_option('display.notebook_repr_html', False) you still get this line, it has no effect on operations on the goruped df: In [30]:...

The display of your "example" object and the attempts you've made indicate to me that even though you are calling your object a data.frame, it's actually a matrix. My hints that you're actually using a matrix? data.frames don't generally print the quotes around strings. as.numeric(some_data_frame) will give you an error...

javascript,angularjs,function,angularjs-scope,mean

Your first function contains this line: $scope.selected.project = [$model]; So, if you set $model to 1 (which you do in the line above), it will, of course, return [1]. In your second function, I can't see $scope.selected.project on the left-hand side of any assignment, so it simply isn't assigned the...

python,python-3.x,dictionary,mean,median

I don't know how to manipulate the values to find the names of all the students whose grades are above the mean for the class. (the mean being the sum of the values divided by the number of values). You can use a list comprehension to create a sublist...

bodyParser() no longer deals with grabbing uploaded files (multipart was removed from it sometime mid-2013), so by itself there's no issue with temp files. If you are using one of the multipart middleware packages (busboy, formidable, et al.), you'll have to take care of temp files explicitly. This usually isn't...

It seems you want to know whether each entry is greater than its column-mean. This is done efficiently with bsxfun: result = bsxfun(@gt, a, mean(a,1)); Example: a = 3 1 3 2 5 2 3 1 1 3 5 2 The column-means, given by mean(a,1), are ans = 3.000000000000000 2.000000000000000...

[Original Solution (see Update 2 for the faster solutions)] f.m <- function(Q1) { z <- matrix(nrow=nrow(Q1),ncol=nrow(Q1)) b <- row(z) < col(z) z[b] <- (Q1$A[col(z)[b]] + Q1$B[row(z)[b]])/2 z } [Sample output] f.m(Q1) # [,1] [,2] [,3] [,4] [,5] # [1,] NA 2 2.5 3.0 3.5 # [2,] NA NA 3.0 3.5...

# Setup problem import pandas as pd import numpy as np num_samples = 100 s = pd.Series(np.random.randint(0, 500, num_samples), index=pd.date_range('03/06/2015', periods=num_samples, freq='10min')) mask = np.random.rand(num_samples) < .7 s[mask] = np.nan # Loop through index # Note the perc_nan variable can be changed depending on what percentage of the interval must...

I would approach such a task using aggregate or plyr. With aggregate you get the group means (of Fracht I assume) with the following call: groupMeans <- aggregate(Fracht ~ Bewirtschaftungsform, daten, mean) Rounding is suggested for printing: groupMeans$Fracht <- round(groupMeans$Fracht, 2) Within the ggplot object you can then just add:...

Having this file: $ cat infile "ID1","Cpd_number","ID2","ID3","activity" "95","123","4","5","10" "95","123","4","5","100" "95","123","4","5","1" "95","123","4","6","10" "95","123","4","6","100" "95","456","4","6","10" "95","456","4","6","100" This piece: awk -F\" 'BEGIN{print} # Print headers last != $4""$8 && last{ # ONLY When last key "Cpd_number + ID3" print line,exp(C/D) # differs from actual , print line + average C=D=0} # reset acumulators...

angularjs,mongodb,express,mongoose,mean

The solution I found (I am not sure if it is the correct solution or a workaround) is to populate the .idea field of a poll with its corresponding ._id: var poll = new Polls ({ idea: this.idea._id, vote1: 5, vote2: 3, vote3: 3, vote4: 1, vote5: 2 }); At...

javascript,mongodb,express,mongoose,mean

You will need to test the error returned from the save method to see if it was thrown for a duplicative username. app.post('/authenticate', function(req, res) { var user = new User({ username: req.body.username }); user.save(function(err) { if (err) { if (err.name === 'MongoError' && err.code === 11000) { // Duplicate...

node.js,gulp,mean,nodemon,gulp-livereload

Looks like the issue is this .on('start', ['watch']) .on('change', ['watch']) .on('restart', function () { console.log('restarted!'); }); Everytime a file changes, you're triggering watch which attaches additional listeners. After a few restarts, you will exceed the max number of listeners. This is a standard nodemon setup, and is all you need...

The code in the express.js file executes the contents of your model files. The anatomy of a MEAN.js model file is the following; Load the mongoose and schema packages, along with all your included models (if any). Declare the schema for the given model Register the given schema under the...

python,numpy,statistics,mean,weighted

You actually have 2 different questions. How to make data discrete, and How to make a weighted average. It's usually better to ask 1 question at a time, but anyway. Given your specification: xmin = -100 xmax = 100 binsize = 20 First, let's import numpy and make some data:...

assuming you data is named df, you can try aggregate aggregate(Value~Month+Station, data=df, FUN = mean) Month Station Value 1 1 ANT 83.5 2 2 ANT 118.5 3 3 ANT 108.5 4 1 ARO 52.0 5 2 ARO 66.5 6 3 ARO 78.5 ...

As indicated by @Khashaa in the comments, you can use rowMeans after using embed or you can use rollmean from the "zoo" package. Example: a <- 1:4 rowMeans(embed(a, 2)) # [1] 1.5 2.5 3.5 library(zoo) rollmean(a, 2) # [1] 1.5 2.5 3.5 If these are too obscure, a similar concept...

Try this: Just to clear the confusion. dat1=as.data.frame(matrix(rnorm(25),ncol=5)) dat5=as.data.frame(matrix(rnorm(25),ncol=5)) dat7=as.data.frame(matrix(rnorm(25),ncol=5)) my_fun <- function(dataframe){ rowMeans( dataframe[ , c("V1","V2")],na.rm=TRUE) } dfList<-list(dat1,dat5,dat7) Vars <- grep("dat", ls(), value=TRUE) Vars #[1] "dat1" "dat5" "dat7" res <- lapply(dfList, function(x) transform(x,V6=my_fun(x))) for(i in 1:length(Vars)){ assign(Vars[i], res[[i]],envir=.GlobalEnv) } A Second function: my_funSD <-...

We could use rleid to create another grouping variable, get the mean of 'y', and assign the 'indx' to NULL library(data.table) # v 1.9.5+ DT[, .(my = mean(y)), by = .(indx = rleid(x), x)][, indx := NULL] # x my #1: 19 54 #2: 21 38 #3: 19 47 #4:...

You can do (A+B)/((A!=0) + (B!=0)) to get [,1] [,2] [,3] [1,] 1 1 2 [2,] 2 3 2 [3,] 3 2 2 Here != tests for equality with zero returning TRUE or FALSE. When we add those up, the TRUEs are treated like 1 and the FALSEs become 0....

javascript,angularjs,express,mean

Angular works with async proccessing and callback digestions, which mean your http call retrun a promise object who get resolved when data arrived. You should use angular's $http service. As mentioned in the documentation: The $http service is a function which takes a single argument — a configuration object —...

r,function,subset,mean,missing-data

That's the way I fixed it: pollutantmean <- function(directory, pollutant, id = 1:332) { #set the path path = directory #get the file List in that directory fileList = list.files(path) #extract the file names and store as numeric for comparison file.names = as.numeric(sub("\\.csv$","",fileList)) #select files to be imported based on...

MATLAB is performing the calculation correctly, but the default number format (short-fixed) for the display makes the 3 and 9 appear to be 0 since the other numbers are large. You can change the format using format. When you change it to short-exponential format, you get Octave's output (since it...

You want to use the aggregate function. Probably something like this: example_data <- data.frame(age=c(3,3,3,3,4,4,4,4), Year=c(2004,2004,2005,2005,2004,2004,2005,2005), Length=c(23.2,27.6,25.4,22.2,37.6,31.3,29.9,30.1)) aggregate(x=example_data$Length, by=list(example_data$age,example_data$Year), FUN=mean) Group.1 Group.2 x 1 3 2004 25.40 2 4 2004 34.45 3 3 2005 23.80 4 4 2005 30.00 ...

Too long for a comment. The reason your results look different is that aggregate(...) sorts the results by your grouping variable(s). In the first case, strftime(data.15min$posix, format="%m/%d/%y %H") is a character vector with poorly formatted dates (they do not sort properly). So the first row corresponds to the "date" "01/01/96...

If you want to first take mean on ['cluster', 'org'] combination and then again take mean on cluster groups In [59]: (df.groupby(['cluster', 'org'], as_index=False).mean() .groupby('cluster')['time'].mean()) Out[59]: cluster 1 15 2 54 3 6 Name: time, dtype: int64 If you wan't mean values by cluster only, then you could In [58]:...

When you do this Dim DblArray(lstbxInput.Items.Count - 1) As Double All of DblArray items are 0, but you never change any of the DblArray items in your code, so dblmean would also be 0. You need to assign each item of DblArray before calculating the mean. I would guess this...

You can use datetime.timedelta d={'one' : Series([1, 2, 3], index=['a', 'b', 'c']), 'two' :Series([datetime.datetime(2014, 7, 9) , datetime.datetime(2014, 7, 10) , datetime.datetime(2014, 7, 11) ], index=['a', 'b', 'c'])} df = pd.DataFrame(d) def avg_datetime(series): dt_min = series.min() deltas = [x-dt_min for x in series] return dt_min + functools.reduce(operator.add, deltas) / len(deltas)...

For your two questions, you can save key strokes by using paste or grepl such as rowMeans(df[grepl("^no2", names(df))]) Or rowMeans(df[paste0("no2_", 1:6)]) Or rowMeans(df[paste0("bc_", 1:5)]) For the last question, use rowSums instead, and divide by the number of columns temp <- df[c("no2_1","no2_2")] rowSums(temp, na.rm = TRUE)/ncol(temp) # 1 2 3 4...

The skipna arg is a boolean specifying whether or not to exclude NA/null values, not which values to ignore: skipna : boolean, default True Exclude NA/null values. If an entire row/column is NA, the result will be NA Assuming I understand what you're trying to do, you could replace -9999...

In addition to @akrun's advice, another option is to convert the columns to numeric yourself (rather than having read.csv do it): dati <- data.frame( lapply(dataGSR[-c(1:3),-9],as.numeric)) ## R> colMeans(dati) Shimmer Shimmer.1 Shimmer.2 Shimmer.3 Shimmer.4 Shimmer.5 Shimmer.6 Shimmer.7 33004.2924 18647.4609 707.4335 718.3989 2521.3626 3672.1383 2497.9013 3659.9287 Where dataGSR was read in with...

you can add the two DataFrames together to get a new one: (a+b)/2 Output: 0 1 0 1 1 1 6 6 [2 rows × 2 columns] Or if you wanted to do some more complicate processing on the result: c = a+b c.std() output: 0 7.071068 1 7.071068 ...

I guess you need mean(z[,1][70 <= z[,2] & z[,2] <= 72], na.rm=TRUE) data set.seed(45) z <- matrix(sample(c(NA,70:80), 5*10, replace=TRUE), ncol=5, nrow=10) ...

javascript,node.js,mongodb,mongoose,mean

By adding an attribute in the ChartSchema or an Array of Chart in the UserSchema. Add and _id field on both Schema. ChartSchema = new Schema({ // ... _creator: { type: Number, ref: 'User' }, }); Or UserSchema = new Schema({ // ... chartsCreated: [{ type: Number, ref: 'Chart' }]...

I would suggest the "data.table" package for this: sdcols <- names(DF)[-1] ## A vector of the new columns we want to add as.data.table(DF)[, paste(sdcols, "mean", sep = "_") := lapply(.SD, mean), by = ID][] ## you can also be more specific and specify sdcols # ID C1 C2 C1_mean C2_mean...

Try mapply(function(x,y) tapply(x,y, FUN=mean) , Example[seq(1, ncol(Example), 2)], Example[seq(2, ncol(Example), 2)]) Or instead of seq(1, ncol(Example), 2) just use c(TRUE, FALSE) and c(FALSE, TRUE) for the second case...

why don't you do it this way instead: User.save(function (err, user) { if (err) { return res.status(400).json({ _status: 400, _content: { message: err.toString() } }); } res.status(200).json({ _status: 200, _content: { message: "OK", data: user } }); }); Your code won't work because try catch won't be able to catch...

node.js,express,mean,mean-stack,meanjs

I cloned the 0.4.0 branch and here's what I did to make the routing by slug working as it should in all scenarios. 1- Add the slug to the article schema and ensure its unique article.server.model.js slug: { type: String, default: '', trim: true, unique: true, required: 'Slug cannot be...

javascript,jquery,html,css,mean

I believe the problem is that since you are doing this inside an Angular controller, Angular thinks that the #yes anchor is referring to a specific route, not just a plain old anchor within the page. If I remember correctly, specifying target="_self" on the link will tell Angular to just...

You could try data.table library(data.table) as.data.table(work1)[, .(dead_sum=sum(dead), surv_sum=sum(surv), age_mean=mean(age)), keyby=.(period, diagnosis)] Or dplyr library(dplyr) work1 %>% group_by(period, diagnosis) %>% summarise(dead_sum=sum(dead), surv_sum=sum(surv), age_mean=mean(age)) # result period diagnosis dead_sum surv_sum age_mean 1: 1 1 1 3407 57.00000 2: 2 1 0 3783 25.00000 3: 3 1 0 9262 33.33333 4: 4 1...

You need a single regex in strsplit for this task (removing letters and splitting): s4[] <- lapply(s4, function(x) { if (is.numeric(x)) x else sapply(strsplit(as.character(x), "-|[[:alpha:]]"), function(y) mean(as.numeric(y))) }) The result: > s4 X1a X1b X1a.1 X1b.1 1 5.27 4.76 5.090 4.750 2 2.47 2.74 2.770 2.800 4 1.14 1.38 1.120...

The number of elements in your original series will always be a multiple of 12, so you can use reshape() to yield an 12 x n matrix. From there using mean() is straightforward to get the vector that you want. nSessions = 20; % Choose an integer to test number...

rnorm(100) gives you a random sample of 100 values from distribution mean = 0 and sd = 1. Because it is random, the actual value of mean(rnorm(100)) depends on which particular values you get back. There is no guarantee that the mean will be 0, but statistically it should converge...

with rowMeans > rowMeans(f[-1]) ## [1] 6.6 17.4 10.8 You can also add another column to of means to the data set > f$AvgSales <- rowMeans(f[-1]) > f ## name day1sales day1sales.1 day1sales.2 day1sales.3 day1sales.4 AvgSales ## 1 apple 2 2 2 22 5 6.6 ## 2 orange 5 8...

Perhaps you want: rnorm2 <- function(n,mean,sd) { mean+sd*scale(rnorm(n)) } set.seed(101) r <- rnorm2(100,4,1) x <- seq_along(r) ## sets up a vector from 1 to length(r) par(las=1,bty="l") ## cosmetic preferences plot(x, r, col = "green", pch=16) ## draws the points ## if you don't want points at all, use ## plot(x,...

Use as a numpy array as such: include numpy as np np.std(array) Example of grabbing parts of array: a = [1 2 3] a[0:1] = [1] a[0:2] = [1 2] Also for 2D (or higher) array, a = [[1,2,3,4,5,6],[1,2,3,4,5,6]] b = np.array(a) print b[:,:3] [[1 2 3 4] [1 2...

node.js,mongoose,npm,mean,bson

Finally I am able to install mongoose on windows 7 below are my findings/solutions I found out problem was with mongodb which is itself a node module and a dependency for mongoose.The mongodb module was not build properly becasue I had not install Visual Studio c++ 2010. So I install...

Well, I would distinguish between the cases of NA/NaN/Infinity and the rest. I would certainly not convert them to zero as this would distort the result significantly while at the same time, not having any real mathematical sense. If a value is NA, then it is not, as the name...

Just keep track of the highest average in another variable. Then in your score == -1 condition check to see if the current average is higher than the highest average, and if so make current the new highest. Then after your for loop you could print out the highest average.

You could try Map. lst <- Map(function(x,y) {x1 <- x[y:length(x)] tapply(x1,as.numeric(gl(length(x1), 5, length(x1))), FUN=mean)}, df, df1) lst # $X1 # 1 2 3 4 5 6 #-0.16500158 0.11339623 -0.86961872 -0.54985564 0.19958461 0.35234983 # 7 8 9 10 11 12 #0.32792769 0.65989801 -0.30409184 -0.53264725 -0.45792792 -0.59139844 # 13 14 15 16...

It's generally unwise to use subset within functions, but even more unwise to index with a vector that is of a different length than the number of rows of the data-object. The boot function passes a series of index vectors of row-names that have been sampled with replacement from the...

Do you mean like this? library(knitr) dfc[c(-1,-2)] <- signif(dfc[c(-1,-2)], digits = 4) dfc_str <- transmute(dfc, TRA, Comp1 = paste(meanComp1, " +/-", sdComp1), Comp2 = paste(meanComp2, " +/-", sdComp2), Comp3 = paste(meanComp3, " +/-", sdComp3)) kable(dfc_str) ...

To remove the outliers, you must set the option outline to FALSE. Let's assume that your data are the following: data <- data.frame(a = c(seq(0,1,0.1),3)) Then, you use the boxplot function: res <- boxplot(data, outline=FALSE) In the res object, you have several pieces of information about your data. Among these,...

You may want to play around with aggregate For instance: aggregate(DF$mw3wuus, FUN=mean, by=list(y1=DF$y1, y0=DF$y0, y3=DF$y3, y4=DF$y4)) Will give you: y1 y0 y3 y4 x 1 AG 2 1994 AA 45.5 2 AI 1 1997 AA 22.0 3 AI 1 1994 EB 78.0 4 AI 1 1999 EB 21.0 5 AG...

I assume that you want to calculate the mean value of entries (0,0), (0,1), (1,0), (1,1), then of (2,2), (2,3), (3,2), (3,3), and so on. You select every second column and row, four times, starting with indices (0,0), (0,1), (1,0) and (1,1), respectively. Then, you divide by 4 and round...

r,if-statement,for-loop,matrix,mean

I reckon this is what you need: A<-c(1,2,3,4,5) B<-c(2,3,4,5,6) Q1<-data.frame(cbind(A,B)) res <- outer(A, B, "+")/2 diag(res) <- NA res The outer() function does all the magic. Result: [,1] [,2] [,3] [,4] [,5] [1,] NA 2.0 2.5 3.0 3.5 [2,] 2.0 NA 3.0 3.5 4.0 [3,] 2.5 3.0 NA 4.0 4.5...

Mean does not work on data.frames. It only works on vectors and matrices. If you try mean(as.matrix(tQs)) it should work - assuming there is no problem with the underlying data....

It's not totally clear what exactly you want to do, but it looks like what you mean is taking the per-row average of columns A-D. In which case, you're just giving the wrong axis argument. DF['means']=DF[['A','B','C','D']].mean(axis=1) should work fine....

I guess the answer was a bit more complicated. This is working, although there may be a better way to do it. I had to create a new function in one of the app user controllers: exports.userByEmail = function(req, res, next, email) { console.log('userByEmail ' + email); User.findOne({ email: email...

javascript,node.js,express,mean

You're sending a response twice via res.end(). Get rid of the second one and you should be fine. Also, calling res.end() after res.render() is redundant since res.render() automatically ends the response with the rendered result by default.

Unpacked, this single line might look like: row_indices = find(Y==y); new_X = X(row_indices,:); params.means(k,:) = mean(new_X); So, as you can see, the Y==y is simply being used to find a subset of X over which the mean is taken. Given that you said that this was for computing multivariate Gaussian...

OP managed to fix this for themselves with: sudo xcode-select -switch /Applications/Xcode.app/Contents/Developer/ but graciously accepted my answer anyway. Original answer below: There are two distinct problems here. One is that your node is slightly out of date and your npm is substantially out of date. You're using OSX, so there...

You could use ave from base R test$meanbyname <- with(test, ave(value, name)) Or using mutate from dplyr or := in data.table, can get the results i.e. library(dplyr) group_by(test, name) %>% mutate(meanbyname=mean(value)) Or library(data.table) setDT(test)[, meanbyname:= mean(value), by=name] ...

Actually its not the mean of the first line, its the mean from the last. After the first list comprehension, row assumes the value of the last row, and when you use row[2] to create numbers list, you create a static list using the values from the last row. You...

javascript,angularjs,mean,atom

This actually bit me once before and I STILL was stumped by it when it bit me again just now. Two curly brackets ({{...}}) tells AngularJS to evaluate the contents as an expression. Beautifier breaks this by helpfully breaking up the doubled curly brackets into separate lines. Because that's what...

1 You can aggregate with a data.table library(data.table) # This turns all Jans to 1 and Decs to 12 for example mth <- month(as.Date(df$date)) dt2 <- as.data.table(df) # turn df into data table dt dt2[, mth := mth] # pop month into your data frame setkey(dt2, "mth") # data tables...

r,time-series,mean,moving-average

I am not completely clear on how you want to do the filling but here are some ways: 1) Add the argument fill = NA to rollmean to add NA's to the ends. 2) If you want partial means at the ends then use this where x is your data,...