pierreqi/r_code_50k · Datasets at Hugging Face

a4ce2f028ea218c926292f2a4da5420071af4f54

29585dff702209dd446c0ab52ceea046c58e384e

/expectreg/R/expectreg.ls.R

cbd5aa2fc72cb3058abca113a468e86b443a8c73

[]

no_license

ingted/R-Examples

825440ce468ce608c4d73e2af4c0a0213b81c0fe

d0917dbaf698cb8bc0789db0c3ab07453016eab9

refs/heads/master

2020-04-14T12:29:22.336088

2016-07-21T14:01:14

null

0

null

UTF-8

R

false

12,361

r

expectreg.ls.R

expectreg.ls <- function (formula, data = NULL, estimate = c("laws", "restricted", "bundle", "sheets"), smooth = c("schall", "gcv", "cvgrid", "aic", "bic", "lcurve", "fixed"), lambda = 1, expectiles = NA, ci = FALSE) { smooth = match.arg(smooth) estimate = match.arg(estimate) if (!is.na(charmatch(expectiles[1], "density")) && charmatch(expectiles[1], "density") > 0) { pp <- seq(0.01, 0.99, by = 0.01) } else if (any(is.na(expectiles)) || !is.vector(expectiles) || any(expectiles > 1) || any(expectiles < 0)) { pp <- c(0.01, 0.02, 0.05, 0.1, 0.2, 0.5, 0.8, 0.9, 0.95, 0.98, 0.99) } else { pp <- expectiles } np <- length(pp) yy = eval(as.expression(formula[[2]]), envir = data, enclos = environment(formula)) attr(yy, "name") = deparse(formula[[2]]) m = length(yy) design = list() x = list() types = list() bnd = list() Zspathelp = list() nb = vector() krig.phi = list() center = TRUE varying = list() Blist = list() Plist = list() if (formula[[3]] == "1") { design[[1]] = rb(matrix(1, nrow = m, ncol = 1), "parametric", center = F) smooth = "fixed" } else if (formula[[3]] == ".") { design[[1]] = rb(data[, names(data) != all.vars(formula[[2]])], "parametric") smooth = "fixed" } else for (i in 1:length(labels(terms(formula)))) { types[[i]] = strsplit(labels(terms(formula))[i], "(", fixed = TRUE)[[1]][1] if (types[[i]] == labels(terms(formula))[i]) { design[[i]] = rb(eval(parse(text = labels(terms(formula))[i]), envir = data, enclos = environment(formula)), "parametric") types[[i]] = "parametric" design[[i]]$xname = labels(terms(formula))[i] } else design[[i]] = eval(parse(text = labels(terms(formula))[i]), envir = data, enclos = environment(formula)) } nterms = length(design) varying[[1]] = design[[1]][[9]] if (any(!is.na(varying[[1]]))) { B = design[[1]][[1]] * varying[[1]] Blist[[1]] = design[[1]][[1]] * varying[[1]] } else { B = design[[1]][[1]] Blist[[1]] = design[[1]][[1]] } DD = as.matrix(design[[1]][[2]]) Plist[[1]] = DD x[[1]] = design[[1]][[3]] names(x)[1] = design[[1]]$xname[1] types[[1]] = design[[1]][[4]] bnd[[1]] = design[[1]][[5]] Zspathelp[[1]] = design[[1]][[6]] nb[1] = ncol(design[[1]][[1]]) krig.phi[[1]] = design[[1]][[7]] center = center && design[[1]][[8]] constmat = as.matrix(design[[1]]$constraint) if (length(design) > 1) for (i in 2:length(labels(terms(formula)))) { varying[[i]] = design[[i]][[9]] if (any(!is.na(varying[[i]]))) { B = cbind(B, design[[i]][[1]] * varying[[i]]) Blist[[i]] = design[[i]][[1]] * varying[[i]] } else { B = cbind(B, design[[i]][[1]]) Blist[[i]] = design[[i]][[1]] } design[[i]][[2]] = as.matrix(design[[i]][[2]]) Plist[[i]] = design[[i]][[2]] DD = rbind(cbind(DD, matrix(0, nrow = nrow(DD), ncol = ncol(design[[i]][[2]]))), cbind(matrix(0, nrow = nrow(design[[i]][[2]]), ncol = ncol(DD)), design[[i]][[2]])) constmat = rbind(cbind(constmat, matrix(0, nrow = nrow(constmat), ncol = ncol(design[[i]]$constraint))), cbind(matrix(0, nrow = nrow(design[[i]]$constraint), ncol = ncol(constmat)), design[[i]]$constraint)) x[[i]] = design[[i]][[3]] names(x)[i] = design[[i]]$xname[1] types[[i]] = design[[i]][[4]] bnd[[i]] = design[[i]][[5]] Zspathelp[[i]] = design[[i]][[6]] nb[i] = ncol(design[[i]][[1]]) krig.phi[[i]] = design[[i]][[7]] center = center && design[[i]][[8]] } if (center) { B = cbind(1, B) DD = rbind(0, cbind(0, DD)) constmat = rbind(0, cbind(0, constmat)) } if (estimate == "laws") coef.vector = laws(B, DD, yy, pp, lambda, smooth, nb, center, constmat, types) else if (estimate == "restricted") { coef.vector = restricted(B, DD, yy, pp, lambda, smooth, nb, center, constmat, types) trend.coef = coef.vector[[4]] residual.coef = coef.vector[[5]] asymmetry = coef.vector[[6]] } else if (estimate == "bundle") { coef.vector = bundle(B, DD, yy, pp, lambda, smooth, nb, center, constmat, types) trend.coef = coef.vector[[4]] residual.coef = coef.vector[[5]] asymmetry = coef.vector[[6]] } else if (estimate == "sheets") coef.vector = sheets(Blist, Plist, yy, pp, lambda, smooth, nb, center, types) vector.a.ma.schall = coef.vector[[1]] lala = coef.vector[[2]] diag.hat = coef.vector[[3]] covariance = NULL if (ci) { W = list() covariance = list() for (i in 1:np) { W = as.vector(ifelse(yy > B %*% vector.a.ma.schall[, i], pp[i], 1 - pp[i])) square.dev = (yy - B %*% vector.a.ma.schall[, i])^2 correct = 1/(1 - diag.hat[, i]) if (any(is.na(W))) { correct[!is.na(W)] = correct[1:(length(correct) - length(which(is.na(W))))] correct[is.na(W)] = 1 W[which(is.na(W))] = 1 square.dev[which(is.na(square.dev))] = 0 } lahmda = rep(lala[, i], times = nb) if (center) lahmda = c(0, lahmda) K = lahmda * t(DD) %*% DD helpmat = solve(t(W * B) %*% B + K) covariance[[i]] = helpmat %*% (t(B * (W^2 * (correct^2 * square.dev)[, 1])) %*% B) %*% helpmat } } Z <- list() coefficients <- list() final.lambdas <- list() helper <- list() fitted = B %*% vector.a.ma.schall if (center) { intercept = vector.a.ma.schall[1, ] B = B[, -1, drop = FALSE] vector.a.ma.schall = vector.a.ma.schall[-1, , drop = FALSE] } else intercept = rep(0, np) for (k in 1:length(design)) { final.lambdas[[k]] = lala[k, ] names(final.lambdas)[k] = design[[k]]$xname partbasis = (sum(nb[0:(k - 1)]) + 1):(sum(nb[0:k])) if (types[[k]] == "pspline") { Z[[k]] <- matrix(NA, m, np) coefficients[[k]] = matrix(NA, nrow = nb[k], ncol = np) helper[[k]] = NA for (i in 1:np) { Z[[k]][, i] <- design[[k]][[1]] %*% vector.a.ma.schall[partbasis, i, drop = FALSE] + intercept[i] coefficients[[k]][, i] = vector.a.ma.schall[partbasis, i, drop = FALSE] } } else if (types[[k]] == "markov") { Z[[k]] <- matrix(NA, m, np) coefficients[[k]] = matrix(NA, nrow = nb[k], ncol = np) helper[[k]] = list(bnd[[k]], Zspathelp[[k]]) for (i in 1:np) { Z[[k]][, i] = design[[k]][[1]] %*% vector.a.ma.schall[partbasis, i, drop = FALSE] + intercept[i] coefficients[[k]][, i] = vector.a.ma.schall[partbasis, i, drop = FALSE] } } else if (types[[k]] == "2dspline") { Z[[k]] <- matrix(NA, m, np) coefficients[[k]] = matrix(NA, nrow = nb[k], ncol = np) helper[[k]] = NA for (i in 1:np) { Z[[k]][, i] = design[[k]][[1]] %*% vector.a.ma.schall[partbasis, i, drop = FALSE] + intercept[i] coefficients[[k]][, i] = vector.a.ma.schall[partbasis, i, drop = FALSE] } } else if (types[[k]] == "radial") { Z[[k]] <- matrix(NA, m, np) coefficients[[k]] = matrix(NA, nrow = nb[k], ncol = np) helper[[k]] = Zspathelp[[k]] for (i in 1:np) { Z[[k]][, i] = design[[k]][[1]] %*% vector.a.ma.schall[partbasis, i, drop = FALSE] + intercept[i] coefficients[[k]][, i] = vector.a.ma.schall[partbasis, i, drop = FALSE] } } else if (types[[k]] == "krig") { Z[[k]] <- matrix(NA, m, np) coefficients[[k]] = matrix(NA, nrow = nb[k], ncol = np) helper[[k]] = list(krig.phi[[k]], Zspathelp[[k]]) for (i in 1:np) { Z[[k]][, i] = design[[k]][[1]] %*% vector.a.ma.schall[partbasis, i, drop = FALSE] + intercept[i] coefficients[[k]][, i] = vector.a.ma.schall[partbasis, i, drop = FALSE] } } else if (types[[k]] == "random") { Z[[k]] <- matrix(NA, m, np) coefficients[[k]] = matrix(NA, nrow = nb[k], ncol = np) helper[[k]] = NA for (i in 1:np) { Z[[k]][, i] <- design[[k]][[1]] %*% vector.a.ma.schall[partbasis, i, drop = FALSE] + intercept[i] coefficients[[k]][, i] = vector.a.ma.schall[partbasis, i, drop = FALSE] } } else if (types[[k]] == "ridge") { Z[[k]] <- matrix(NA, m, np) coefficients[[k]] = matrix(NA, nrow = nb[k], ncol = np) helper[[k]] = NA for (i in 1:np) { Z[[k]][, i] <- design[[k]][[1]] %*% vector.a.ma.schall[partbasis, i, drop = FALSE] + intercept[i] coefficients[[k]][, i] = vector.a.ma.schall[partbasis, i, drop = FALSE] } } else if (types[[k]] == "parametric") { Z[[k]] <- matrix(NA, m, np) coefficients[[k]] = matrix(NA, nrow = nb[k], ncol = np) helper[[k]] = NA for (i in 1:np) { Z[[k]][, i] <- design[[k]][[1]] %*% vector.a.ma.schall[partbasis, i, drop = FALSE] + intercept[i] coefficients[[k]][, i] = vector.a.ma.schall[partbasis, i, drop = FALSE] } } else if (types[[k]] == "special") { Z[[k]] <- matrix(NA, m, np) coefficients[[k]] = matrix(NA, nrow = nb[k], ncol = np) helper[[k]] = NA for (i in 1:np) { Z[[k]][, i] <- design[[k]][[1]] %*% vector.a.ma.schall[partbasis, i, drop = FALSE] + intercept[i] coefficients[[k]][, i] = vector.a.ma.schall[partbasis, i, drop = FALSE] } } names(Z)[k] = design[[k]]$xname[1] names(coefficients)[k] = design[[k]]$xname[1] } desmat = B if (center) desmat = cbind(1, B) result = list(lambda = final.lambdas, intercepts = intercept, coefficients = coefficients, values = Z, response = yy, covariates = x, formula = formula, asymmetries = pp, effects = types, helper = helper, design = desmat, bases = design, fitted = fitted, covmat = covariance) if (estimate == "restricted" || estimate == "bundle") { result$trend.coef = trend.coef result$residual.coef = residual.coef result$asymmetry.coef = asymmetry } result$predict <- function(newdata = NULL) { BB = list() values = list() bmat = NULL for (k in 1:length(coefficients)) { BB[[k]] = predict(design[[k]], newdata) values[[k]] <- BB[[k]] %*% coefficients[[k]] values[[k]] = t(apply(values[[k]], 1, function(x) { x + intercept })) bmat = cbind(bmat, BB[[k]]) } if (center) { bmat = cbind(1, bmat) vector.a.ma.schall = rbind(intercept, vector.a.ma.schall) } fitted = bmat %*% vector.a.ma.schall names(values) = names(coefficients) list(fitted = fitted, values = values) } class(result) = c("expectreg", estimate) result }

6a0ccef30043e0ff1c6a9609210d22eae7a9caac

75b265e23131ce614640a804d1d7a4f18695bee3

/man/period_ends.Rd

747abe904635ea6b8327f8873368c990569bfb92

[ "MIT" ]

permissive

ppd-dpp/calpr

0db70d3dc182147fa73de9d7d670f902463f8f0f

88884e93812d9d0d5a861d8f4c96b8bb6f6a5786

refs/heads/master

2022-11-04T13:07:49.311293

2020-06-15T16:53:38

271,619,398

0

null

UTF-8

R

false

548

rd

period_ends.Rd

\name{period_ends} \alias{period_ends} \title{Period ends} \description{ End points for calendar periods } \usage{ period_ends(x, period, start.mon) } \arguments{ \item{x}{A vector of dates.} \item{period}{One of 'month', 'quarter', or 'year'.} \item{start.mon}{Starting month of a year, beginning at 1 for January.} } \details{ This is a simple helper function to make a sequence of dates to cut \code{x}. There is no argument checking, and it should probably not be called directly. } \value{ A pair of POSIXlt that bound \code{x}. }

def0758566e720f97fe71cf4253ad237b5985c5e

5db254fa65b50a095f4b44f9346bbb743aa894d1

/R/method_EM.r

b0c6540961eb1ed09438545fbf5d3bec72bf7ca7

[]

no_license

cran/nowcasting

5866a701e83f61b063b337967fd64f75882499a5

55e3e19dad1bd6528116422b6fca2b12b4479fe7

refs/heads/master

2021-06-25T05:11:10.040964

2019-08-01T04:00:02

100,689,288

3

1

null

UTF-8

R

false

27,679

r

method_EM.r

#' @importFrom magic adiag remNaNs_spline <-function(X,options){ TT <- dim(X)[1] N <- dim(X)[2] k <- options$k indNaN <- is.na(X) if(options$method == 1){ # replace all the missing values (método Giannone et al. 2008) for (i in 1:N){ x = X[,i] x[indNaN[,i]] = median(x,na.rm = T); x_MA<-filter(x = c(rep(x[1],k),x,rep(x[length(x)],k)),filter = rep(1,2*k+1)/(2*k+1),sides = 1) x_MA=x_MA[(2*k+1):length(x_MA)] x[indNaN[,i]]=x_MA[indNaN[,i]] X[,i]=x; } }else if(options$method == 2){ # replace missing values after removing leading and closing zeros rem1 <- (rowSums(indNaN)>N*0.8) nanLead <- which(rem1) # nanEnd <- which(rem1[length(rem1):1]) # nanLE <- c(nanEnd,nanLead) nanLE<-nanLead X<-X[-nanLE,] indNaN=is.na(X) for (i in 1:N){ x = X[,i] isnanx = is.na(x) t1 = min(which(!isnanx)) t2 = max(which(!isnanx)) x1<-stats::spline(x[t1:t2],xout = 1:(t2-t1+1)) xx<-x1$y x[t1:t2]<-x1$y isnanx<-is.na(x) x[isnanx] <- median(x,na.rm = T) x_MA<-filter(x = c(rep(x[1],k),x,rep(x[length(x)],k)),filter = rep(1,2*k+1)/(2*k+1),sides = 1) x_MA=x_MA[(2*k+1):length(x_MA)] x[indNaN[,i]]=x_MA[indNaN[,i]] X[,i]=x; } }else if(options$method == 3){ # only remove rows with leading and closing zeros rem1 <- (rowSums(indNaN)==N) nanLead <- which(rem1) # nanEnd <- which(rem1[length(rem1):1]) # nanLE <- c(nanEnd,nanLead) nanLE<-nanLead if(length(nanLE) != 0){ X <- X[-nanLE,] } indNaN=is.na(X) }else if(options$method == 4){ # remove rows with leading and closing zeros & replace missing values rem1 <- (rowSums(indNaN)==N) nanLead <- which(rem1) # nanEnd <- which(rem1[length(rem1):1]) # nanLE <- c(nanEnd,nanLead) nanLE<-nanLead X<-X[-nanLE,] indNaN=is.na(X) for (i in 1:N){ x = X[,i] isnanx = is.na(x) t1 = min(which(!isnanx)) t2 = max(which(!isnanx)) x1<-stats::spline(x[t1:t2],xout = 1:(t2-t1+1)) xx<-x1$y x[t1:t2]<-x1$y isnanx<-is.na(x) x[isnanx] <- median(x,na.rm = T) x_MA<-filter(x = c(rep(x[1],k),x,rep(x[length(x)],k)),filter = rep(1,2*k+1)/(2*k+1),sides = 1) x_MA=x_MA[(2*k+1):length(x_MA)] x[indNaN[,i]]=x_MA[indNaN[,i]] X[,i]=x; } }else if(options$method == 5){ indNaN=is.na(X) for (i in 1:N){ x = X[,i] isnanx = is.na(x) t1 = min(which(!isnanx)) t2 = max(which(!isnanx)) x1<-stats::spline(x[t1:t2],xout = 1:(t2-t1+1)) xx<-x1$y x[t1:t2]<-x1$y isnanx<-is.na(x) x[isnanx] <- median(x,na.rm = T) x_MA<-filter(x = c(rep(x[1],k),x,rep(x[length(x)],k)),filter = rep(1,2*k+1)/(2*k+1),sides = 1) x_MA=x_MA[(2*k+1):length(x_MA)] x[indNaN[,i]]=x_MA[indNaN[,i]] X[,i]=x; } } return(list(X = X,indNaN=indNaN)) } InitCond<-function(xNaN,r,p,blocks,optNaN,R_mat,q,nQ,i_idio){ x<-xNaN Rcon<-R_mat # library(magic) pC = size(Rcon,2) ppC = max(p,pC) n_b = size(blocks,2) res_remNaNs_spline <- remNaNs_spline(x,optNaN) xBal<- res_remNaNs_spline$X indNaN <- res_remNaNs_spline$indNaN TT <- dim(xBal)[1] # time T of series N <- dim(xBal)[2] # number of series NM <- N-nQ # number of monthy frequency series xNaN = xBal for(i in 1:N){ xNaN[indNaN[,i],i] <- NA } # Initialize model coefficient output C = {} A = {} Q = {} initV = {} res = xBal resNaN = xNaN # Set the first observations as NaNs: For quarterly-monthly aggreg. scheme indNaN[1:pC-1,] <- T for(i in 1:n_b){ # loop for each block r_i<-r[i] ######################## # Observation equation # ######################## C_i = zeros(N,r_i*ppC) # Initialize state variable matrix helper idx_i = find(blocks[,i]) # returns the series loaded in block i # Note that the variables have been reshuffled so as to have all quarterly at the last columns of X idx_iM = idx_i[idx_i<NM+1]; # index for monthly variables idx_iQ = idx_i[idx_i>NM]; # index for quarterly variables eig<-eigen(cov(res[,idx_iM])) v<-eig$vectors[,1:r_i] d<-eig$values[1:r_i] C_i[idx_iM,1:r_i] = v f = as.matrix(res[,idx_iM])%*%as.matrix(v) for(kk in 0:(max(p+1,pC)-1)){ if(kk == 0){ FF<-f[(pC-kk):(dim(f)[1]-kk),] }else{ FF <- cbind(FF,f[(pC-kk):(dim(f)[1]-kk),]) } } Rcon_i = kronecker(Rcon,eye(r_i)) q_i = kronecker(q,zeros(r_i,1)); ff = FF[,1:(r_i*pC)] for(j in idx_iQ){ # Coeficiente "loadings" de Variáveis trimestrais xx_j = resNaN[pC:dim(resNaN)[1],j] if(sum(!is.na(xx_j)) < size(ff,2)+2){ xx_j = res[pC:dim(res)[1],j] } ff_j = ff[!is.na(xx_j),] xx_j = xx_j[!is.na(xx_j)] iff_j = solve(t(ff_j)%*%ff_j) Cc = iff_j%*%t(ff_j)%*%xx_j Cc = Cc - iff_j%*%t(Rcon_i)%*%solve(Rcon_i%*%iff_j%*%t(Rcon_i))%*%(Rcon_i%*%Cc-q_i); C_i[j,1:(pC*r_i)] <- t(Cc) } ff = rbind(zeros(pC-1,pC*r_i),ff) res = res - ff%*%t(C_i) resNaN = res for(i_aux in 1:dim(indNaN)[2]){ resNaN[indNaN[,i_aux],i_aux] <- NA } C <- cbind(C,C_i) ####################### # Transition Equation # ####################### z <- FF[,1:r_i] Z <- FF[,(r_i+1):(r_i*(p+1))] A_i = t(zeros(r_i*ppC,r_i*ppC)) A_temp = solve(t(Z)%*%Z)%*%t(Z)%*%z A_i[1:r_i,1:(r_i*p)] <- t(A_temp) A_i[(r_i+1):dim(A_i)[1],1:(r_i*(ppC-1))] <- eye(r_i*(ppC-1)) ########################## Q_i = zeros(ppC*r_i,ppC*r_i) e = z - Z%*%A_temp # VAR residuals Q_i[1:r_i,1:r_i] = cov(e); # VAR covariance matrix initV_i = matrix(solve(eye((r_i*ppC)^2)-kronecker(A_i,A_i))%*%c(Q_i),r_i*ppC,r_i*ppC); if(is.null(A)){ A<-A_i }else{ A <- magic::adiag(A,A_i) } if(is.null(Q)){ Q<-Q_i }else{ Q <- magic::adiag(Q,Q_i) } if(is.null(initV)){ initV<-initV_i }else{ initV <- magic::adiag(initV,initV_i) } } R = diag(apply(resNaN, 2, stats::var, na.rm = T)) eyeN = eye(N) eyeN<-eyeN[,i_idio] # Initial conditions C <- cbind(C,eyeN) ii_idio = find(i_idio) n_idio = length(ii_idio) B = zeros(n_idio) S = zeros(n_idio) BM <- zeros(n_idio) SM <- zeros(n_idio) # Loop for monthly variables for (i in 1:n_idio){ # Set observation equation residual covariance matrix diagonal R[ii_idio[i],ii_idio[i]] <- 1e-04 # Subsetting series residuals for series i res_i <- resNaN[,ii_idio[i]] # number of leading zeros # leadZero = max( find( t(1:TT) == cumsum(is.na(res_i)) ) ) # endZero = max( find( TT:1 == cumsum(is.na(res_i[length(res_i):1])) ) ); # res_i<-res_i[(leadZero+1):(length(res_i)-endZero)] res_i<-res_i[!is.na(res_i)] # Linear regression: AR 1 process for monthly series residuals BM[i,i] = solve(t(res_i[1:(length(res_i)-1)])%*%res_i[1:(length(res_i)-1)])%*%t(res_i[1:(length(res_i)-1)])%*%res_i[2:length(res_i)] SM[i,i] = stats::var(res_i[2:length(res_i)]-res_i[1:(length(res_i)-1)]*BM[i,i]) } # blocks for covariance matrices initViM = diag(1/diag(eye(size(BM,1))-BM^2))%*%SM; if(!nQ==0){ C<-cbind(C,rbind(zeros(NM,5*nQ),t(kronecker(eye(nQ),c(1,2,3,2,1))))) Rdiag<-diag(R) sig_e <- Rdiag[(NM+1):N]/19 Rdiag[(NM+1):N] <- 1e-04 R <- diag(Rdiag) # Covariance for obs matrix residuals # for BQ, SQ rho0 <- 0.1 temp <- zeros(5) temp[1,1] <- 1 # BQ and SQ BQ <- kronecker(eye(nQ),rbind(cbind(rho0,zeros(1,4)),cbind(eye(4),zeros(4,1)))) if(length(sig_e)>1){ SQ = kronecker(diag((1-rho0^2)*sig_e),temp) }else{ SQ = kronecker((1-rho0^2)*sig_e,temp) } initViQ = matrix(solve(eye((5*nQ)^2)-kronecker(BQ,BQ))%*%c(SQ),5*nQ,5*nQ) }else{ BQ <- NULL SQ <- NULL initViQ <- NULL } A1<-magic::adiag(A,BM,BQ) Q1<-magic::adiag(Q, SM, SQ) A<-A1 # Observation matrix Q<-Q1 # Residual covariance matrix # Initial conditions initZ = zeros(size(A,1),1); # States initV = magic::adiag(initV, initViM, initViQ) # Covariance of states return(list(A = A, C = C, Q = Q, R = R, initZ = initZ, initV = initV)) } EM_DFM_SS_block_idioQARMA_restrMQ<-function(X,Par){ # library(matlab) thresh = 1e-4 r = Par$r p = Par$p max_iter = Par$max_iter i_idio = Par$i_idio R_mat = Par$Rconstr q = Par$q nQ = Par$nQ blocks = Par$blocks ### Prepara??o dos dados TT <- dim(X)[1] N <- dim(X)[2] ### Standardise X Mx = colMeans(X,na.rm=T) Wx = sapply(1:N,function(x) sd(X[,x],na.rm = T)) xNaN <- (X-kronecker(t(Mx),rep(1,TT)))/kronecker(t(Wx),rep(1,TT)) ### Initial conditions # Removing missing values (for initial estimator) optNaN<-list() optNaN$method = 2; # Remove leading and closing zeros optNaN$k = 3; res_InitCond<-InitCond(xNaN,r,p,blocks,optNaN,R_mat,q,nQ,i_idio); A<-res_InitCond$A C<-res_InitCond$C Q<-res_InitCond$Q R<-res_InitCond$R Z_0<-res_InitCond$initZ V_0<-res_InitCond$initV # some auxiliary variables for the iterations previous_loglik = -Inf num_iter = 0 LL = -Inf converged = F # y for the estimation is WITH missing data y = t(xNaN) #-------------------------------------------------------------------------- #THE EM LOOP #-------------------------------------------------------------------------- #The model can be written as #y = C*Z + e; #Z = A*Z(-1) + v #where y is NxT, Z is (pr)xT, etc #remove the leading and ending nans for the estimation optNaN$method = 3 y_est <- remNaNs_spline(xNaN,optNaN) y_est_indNaN<-t(y_est$indNaN) y_est<-t(y_est$X) num_iter = 0 loglik_aux = -Inf while ((num_iter < max_iter) & !converged){ res_EMstep = EMstep(y_est, A, C, Q, R, Z_0, V_0, r,p,R_mat,q,nQ,i_idio,blocks) # res_EMstep <- list(C_new, R_new, A_new, Q_new, Z_0, V_0, loglik) C = res_EMstep$C_new; R = res_EMstep$R_new; A = res_EMstep$A_new; Q = res_EMstep$Q_new; Z_0<-res_EMstep$Z_0 V_0<-res_EMstep$V_0 loglik<-res_EMstep$loglik # updating the user on what is going on if((num_iter%% 5)==0&&num_iter>0){message(num_iter,"th iteration: \nThe loglikelihood went from ",round(loglik_aux,4)," to ",round(loglik,4))} loglik_aux <- loglik # Checking convergence if (num_iter>2){ res_em_converged = em_converged(loglik, previous_loglik, thresh,1) converged<-res_em_converged$converged decreasse<-res_em_converged$decrease } LL <- cbind(LL, loglik) previous_loglik <- loglik num_iter <- num_iter + 1 } # final run of the Kalman filter res_runKF = runKF(y, A, C, Q, R, Z_0, V_0) Zsmooth<-t(res_runKF$xsmooth) x_sm <- Zsmooth[2:dim(Zsmooth)[1],]%*%t(C) Res<-list() Res$x_sm <- x_sm Res$X_sm <- kronecker(t(Wx),rep(1,TT))*x_sm + kronecker(t(Mx),rep(1,TT)) Res$FF <- Zsmooth[2:dim(Zsmooth)[1],] #-------------------------------------------------------------------------- # Loading the structure with the results #-------------------------------------------------------------------------- Res$C <- C; Res$R <- R; Res$A <- A; Res$Q <- Q; Res$Mx <- Mx; Res$Wx <- Wx; Res$Z_0 <- Z_0; Res$V_0 <- V_0; Res$r <- r; Res$p <- p; return(Res) } EMstep <- function(y = NULL, A = NULL, C = NULL, Q = NULL, R = NULL, Z_0 = NULL, V_0 = NULL, r = NULL, p = NULL, R_mat = NULL, q = NULL, nQ = NULL, i_idio = NULL, blocks = NULL){ n <- size(y,1) TT <- size(y,2) nM <- n - nQ pC <- size(R_mat,2) ppC <- max(p,pC) n_b <- size(blocks,2) # Compute the (expected) sufficient statistics for a single Kalman filter sequence. #Running the Kalman filter with the current estimates of the parameters res_runKF = runKF(y, A, C, Q, R, Z_0, V_0); Zsmooth<-res_runKF$xsmooth Vsmooth<-res_runKF$Vsmooth VVsmooth<-res_runKF$VVsmooth loglik<-res_runKF$loglik A_new <- A Q_new <- Q V_0_new <- V_0 for(i in 1:n_b){ r_i <- r[i] rp <- r_i*p if(i == 1){ rp1 <- 0*ppC }else{ rp1 <- sum(r[1:(i-1)])*ppC } A_i <- A[(rp1+1):(rp1+r_i*ppC), (rp1+1):(rp1+r_i*ppC)] Q_i <- Q[(rp1+1):(rp1+r_i*ppC), (rp1+1):(rp1+r_i*ppC)] if(rp==1){ EZZ <- t(Zsmooth[(rp1+1):(rp1+rp),2:ncol(Zsmooth)]) %*% Zsmooth[(rp1+1):(rp1+rp),2:ncol(Zsmooth)] + sum(Vsmooth[(rp1+1):(rp1+rp),(rp1+1):(rp1+rp),2:dim(Vsmooth)[3]]) # E(Z'Z) EZZ_BB <- t(Zsmooth[(rp1+1):(rp1+rp),1:(ncol(Zsmooth)-1)]) %*% Zsmooth[(rp1+1):(rp1+rp),1:(ncol(Zsmooth)-1)] + sum(Vsmooth[(rp1+1):(rp1+rp),(rp1+1):(rp1+rp),1:(dim(Vsmooth)[3]-1)]) #E(Z(-1)'Z_(-1)) EZZ_FB <- t(Zsmooth[(rp1+1):(rp1+rp),2:ncol(Zsmooth)]) %*% Zsmooth[(rp1+1):(rp1+rp),1:(ncol(Zsmooth)-1)] + sum(VVsmooth[(rp1+1):(rp1+rp),(rp1+1):(rp1+rp),]) #E(Z'Z_(-1)) }else{ EZZ <- (Zsmooth[(rp1+1):(rp1+rp),2:ncol(Zsmooth)]) %*% t(Zsmooth[(rp1+1):(rp1+rp),2:ncol(Zsmooth)]) + apply(Vsmooth[(rp1+1):(rp1+rp),(rp1+1):(rp1+rp),2:dim(Vsmooth)[3]],c(1,2),sum) # E(Z'Z) EZZ_BB <- (Zsmooth[(rp1+1):(rp1+rp),1:(ncol(Zsmooth)-1)]) %*% t(Zsmooth[(rp1+1):(rp1+rp),1:(ncol(Zsmooth)-1)]) + apply(Vsmooth[(rp1+1):(rp1+rp),(rp1+1):(rp1+rp),1:(dim(Vsmooth)[3]-1)],c(1,2),sum) #E(Z(-1)'Z_(-1)) EZZ_FB <- (Zsmooth[(rp1+1):(rp1+rp),2:ncol(Zsmooth)]) %*% t(Zsmooth[(rp1+1):(rp1+rp),1:(ncol(Zsmooth)-1)]) + apply(VVsmooth[(rp1+1):(rp1+rp),(rp1+1):(rp1+rp),],c(1,2),sum) #E(Z'Z_(-1)) } A_i[1:r_i,1:rp] <- EZZ_FB[1:r_i,1:rp] %*% solve(EZZ_BB[1:rp,1:rp]) Q_i[1:r_i,1:r_i] <- (EZZ[1:r_i,1:r_i] - A_i[1:r_i,1:rp] %*% t(matrix(EZZ_FB[1:r_i,1:rp],r_i,rp))) / TT A_new[(rp1+1):(rp1+r_i*ppC),(rp1+1):(rp1+r_i*ppC)] <- A_i Q_new[(rp1+1):(rp1+r_i*ppC),(rp1+1):(rp1+r_i*ppC)] <- Q_i; V_0_new[(rp1+1):(rp1+r_i*ppC),(rp1+1):(rp1+r_i*ppC)] <- Vsmooth[(rp1+1):(rp1+r_i*ppC),(rp1+1):(rp1+r_i*ppC),1] } rp1 <- sum(sum(r))*ppC niM <- sum(i_idio[1:nM]) # idiosyncratic EZZ <- diag(diag(Zsmooth[(rp1+1):nrow(Zsmooth),2:ncol(Zsmooth)] %*% t(Zsmooth[(rp1+1):nrow(Zsmooth),2:ncol(Zsmooth)]))) + diag(diag(apply(Vsmooth[(rp1+1):dim(Vsmooth)[1],(rp1+1):dim(Vsmooth)[2],2:dim(Vsmooth)[3]], MARGIN = 1:2, FUN = sum))) #E(Z'Z) EZZ_BB <- diag(diag(Zsmooth[(rp1+1):nrow(Zsmooth),1:(ncol(Zsmooth)-1)] %*% t(Zsmooth[(rp1+1):nrow(Zsmooth),1:(ncol(Zsmooth)-1)]))) + diag(diag(apply(Vsmooth[(rp1+1):dim(Vsmooth)[1],(rp1+1):dim(Vsmooth)[2],1:(dim(Vsmooth)[3]-1)], MARGIN = 1:2, FUN = sum))) #E(Z(-1)'Z_(-1)) EZZ_FB <- diag(diag(Zsmooth[(rp1+1):nrow(Zsmooth),2:ncol(Zsmooth)] %*% t(Zsmooth[(rp1+1):nrow(Zsmooth),1:(ncol(Zsmooth)-1)]))) + diag(diag(apply(VVsmooth[(rp1+1):dim(VVsmooth)[1],(rp1+1):dim(VVsmooth)[2],], MARGIN = 1:2, FUN = sum))) #E(Z'Z_(-1)) A_i <- EZZ_FB %*% diag(1/diag(EZZ_BB)) Q_i <- (EZZ - A_i %*% t(EZZ_FB)) / TT A_new[(rp1+1):(rp1+niM),(rp1+1):(rp1+niM)] <- A_i[1:niM,1:niM] Q_new[(rp1+1):(rp1+niM),(rp1+1):(rp1+niM)] <- Q_i[1:niM,1:niM] V_0_new[(rp1+1):(rp1+niM),(rp1+1):(rp1+niM)] <- diag(diag(Vsmooth[(rp1+1):(rp1+niM),(rp1+1):(rp1+niM),1])) Z_0 <- Zsmooth[,1] #zeros(size(Zsmooth,1),1); # # nanY <- is.nan(y) nanY<-is.na(y) y[nanY] <- 0 # LOADINGS C_new <- C # Blocks bl <- unique(blocks) n_bl <- size(bl,1) bl_idxM <- NULL bl_idxQ <- NULL R_con <- NULL q_con <- NULL for(i in 1:n_b){ bl_idxQ <- cbind(bl_idxQ, repmat(bl[,i],1,r[i]*ppC)) bl_idxM <- cbind(bl_idxM, repmat(bl[,i],1,r[i]), zeros(n_bl,r[i]*(ppC-1))) if(i == 1){ R_con <- kronecker(R_mat,eye(r[i])) }else{ R_con <- as.matrix(Matrix::bdiag(R_con, kronecker(R_mat,eye(r[i])))) } q_con <- rbind(q_con, zeros(r[i]*size(R_mat,1),1)) } bl_idxM <- bl_idxM == 1 bl_idxQ <- bl_idxQ == 1 #idio i_idio_M <- i_idio[1:nM] n_idio_M <- length(find(i_idio_M)) c_i_idio <- cumsum(i_idio) for(i in 1:n_bl){ bl_i <- bl[i,] rs <- sum(r[bl_i == 1]) idx_i <- NULL for(k in 1:nrow(blocks)){ idx_i[k] <- sum(blocks[k,] == bl_i) == size(blocks,2) } idx_i <- find(idx_i) # MONTHLY idx_iM <- idx_i[idx_i < (c(nM) + 1)] n_i <- length(idx_iM) if(n_i==0){ denom <- NULL nom <- NULL } else { denom <- zeros(n_i*rs,n_i*rs) nom <- zeros(n_i,rs) i_idio_i <- i_idio_M[idx_iM] == 1 i_idio_ii <- c_i_idio[idx_iM] i_idio_ii <- i_idio_ii[i_idio_i] for(t in 1:TT){ nanYt <- diag(!nanY[idx_iM,t]) nn <- sum(bl_idxM[i,]) denom <- denom + kronecker(Zsmooth[bl_idxM[i,],t+1][1:nn] %*% t(Zsmooth[bl_idxM[i,],t+1][1:nn]) + Vsmooth[bl_idxM[i,],bl_idxM[i,],t+1][1:nn,1:nn], nanYt) nom <- nom + y[idx_iM,t] %*% t(Zsmooth[bl_idxM[i,],t+1][1:nn]) - nanYt[,i_idio_i] %*% (Zsmooth[rp1+i_idio_ii,t+1] %*% t(Zsmooth[bl_idxM[i,],t+1][1:nn]) + Vsmooth[rp1+i_idio_ii,bl_idxM[i,],t+1][,1:nn]) } vec_C <- solve(denom) %*% c(nom) C_new[idx_iM,bl_idxM[i,]][,1:nn] <- matrix(vec_C,n_i,rs) } # QUARTERLY idx_iQ <- idx_i[idx_i>c(nM)] rps <- rs*ppC R_con_i <- R_con[,bl_idxQ[i,]] q_con_i <- q_con no_c <- !(rowSums(R_con_i == 0) == ncol(R_con_i)) R_con_i <- R_con_i[no_c,] q_con_i <- q_con_i[no_c,] for(j in idx_iQ){ denom <- zeros(rps,rps) nom <- zeros(1,rps) idx_jQ <- j-c(nM) if(i != 1){ i_idio_jQ <- (rp1+n_idio_M+5*(idx_jQ-1)+1):(rp1+n_idio_M+5*idx_jQ) V_0_new[i_idio_jQ,i_idio_jQ] <- Vsmooth[i_idio_jQ,i_idio_jQ,1] A_new[i_idio_jQ[1],i_idio_jQ[1]] <- A_i[i_idio_jQ[1]-rp1,i_idio_jQ[1]-rp1] Q_new[i_idio_jQ[1],i_idio_jQ[1]] <- Q_i[i_idio_jQ[1]-rp1,i_idio_jQ[1]-rp1] for(t in 1:TT){ nanYt <- as.vector(!nanY[j,t])*1 nn2 <- sum(bl_idxQ[i,]) denom <- denom + kronecker(Zsmooth[bl_idxQ[i,],t+1][1:nn2] %*% t(Zsmooth[bl_idxQ[i,],t+1][1:nn2]) + Vsmooth[bl_idxQ[i,],bl_idxQ[i,],t+1][1:nn2,1:nn2],nanYt) nom <- nom + y[j,t] %*% t(Zsmooth[bl_idxQ[i,],t+1][1:nn2]) nom <- nom - nanYt %*% (matrix(c(1,2,3,2,1), nrow = 1) %*% Zsmooth[i_idio_jQ,t+1] %*% t(Zsmooth[bl_idxQ[i,],t+1][1:nn2]) + matrix(c(1,2,3,2,1), nrow = 1) %*% Vsmooth[i_idio_jQ,bl_idxQ[i,],t+1][,1:nn2]) } C_i <- solve(denom) %*% t(nom) C_i_constr <- C_i - solve(denom) %*% t(R_con_i) %*% solve(R_con_i %*% solve(denom) %*% t(R_con_i)) %*% (R_con_i %*% C_i - q_con_i) nn3 <- sum(bl_idxQ[i,]) C_new[j,bl_idxQ[i,]][1:nn3] <- C_i_constr } } } R_new <- zeros(n,n) for(t in 1:TT){ nanYt <- diag(!nanY[,t])*1 == 1 R_new <- R_new + (y[,t] - nanYt %*% C_new %*% Zsmooth[,t+1]) %*% t(y[,t] - nanYt %*% C_new %*% Zsmooth[,t+1]) + nanYt %*% C_new %*% Vsmooth[,,t+1] %*% t(C_new) %*% nanYt + (eye(n)-nanYt) %*% R %*% (eye(n)-nanYt) } R_new <- R_new/TT RR <- diag(R_new) #RR(RR<1e-2) = 1e-2; RR[i_idio_M] <- 1e-04 if(nM<length(RR)){ RR[(nM+1):length(RR)] <- 1e-04 } R_new <- diag(RR) if(!is.matrix(Z_0)){ Z_0<-matrix(Z_0,length(Z_0),1) } # output return(list(C_new = C_new, R_new = R_new, A_new = A_new, Q_new = Q_new, Z_0 = Z_0, V_0 = V_0, loglik = loglik)) } FIS <- function(Y,Z,R,TT,Q,S){ # library(corpcor) # %______________________________________________________________________ # % Fixed intervall smoother (see Harvey, 1989, p. 154) # % FIS returns the smoothed state vector AmT and its covar matrix PmT # % Use this in conjunction with function SKF # %______________________________________________________________________ # % INPUT # % Y Data (nobs x n) # % S Estimates from Kalman filter SKF # % S.Am : Estimates a_t|t-1 (nobs x m) # % S.Pm : P_t|t-1 = Cov(a_t|t-1) (nobs x m x m) # % S.AmU : Estimates a_t|t (nobs x m) # % S.PmU : P_t|t = Cov(a_t|t) (nobs x m x m) # % OUTPUT # % S Smoothed estimates added to above # % S.AmT : Estimates a_t|T (nobs x m) # % S.PmT : P_t|T = Cov(a_t|T) (nobs x m x m) # % S.PmT_1 : Cov(a_ta_t-1|T) # % where m is the dim of state vector and t = 1 ...T is time m<-dim(S$Am)[1] nobs<-dim(S$Am)[2] S$AmT = zeros(m,nobs+1) S$PmT = array(0,c(m,m,nobs+1)) S$AmT[,nobs+1] <- S$AmU[,nobs+1] S$PmT[,,nobs+1] <- S$PmU[,,nobs+1] S$PmT_1<-array(0,c(m,m,nobs)) S$PmT_1[,,nobs] <- (eye(m)-S$KZ)%*%TT%*%S$PmU[,,nobs] pinv<-corpcor::pseudoinverse(S$Pm[,,nobs]) J_2 <- S$PmU[,,nobs]%*%t(TT)%*%pinv for (t in nobs:1){ PmU <- S$PmU[,,t] Pm1 <- S$Pm[,,t] P_T <- S$PmT[,,t+1] P_T1 <- S$PmT_1[,,t] J_1 <- J_2 S$AmT[,t] <- S$AmU[,t] + J_1%*%(S$AmT[,t+1] - TT%*%S$AmU[,t]) S$PmT[,,t] <- PmU + J_1%*%(P_T - Pm1)%*%t(J_1) if(t>1){ pinv<-corpcor::pseudoinverse(S$Pm[,,t-1]) J_2 <- S$PmU[,,t-1]%*%t(TT)%*%pinv S$PmT_1[,,t-1] = PmU%*%t(J_2)+J_1%*%(P_T1-TT%*%PmU)%*%t(J_2) } } return(S) } SKF <-function(Y,Z,R,TT,Q,A_0,P_0){ #Y = y; Z = C; TT = A; A_0 = x_0; P_0 = Sig_0 # %______________________________________________________________________ # % Kalman filter for stationary systems with time-varying system matrices # % and missing data. # % # % The model is y_t = Z * a_t + eps_t # % a_t+1 = TT * a_t + u_t # % # %______________________________________________________________________ # % INPUT # % Y Data (nobs x n) # % OUTPUT # % S.Am Predicted state vector A_t|t-1 (nobs x m) # % S.AmU Filtered state vector A_t|t (nobs+1 x m) # % S.Pm Predicted covariance of A_t|t-1 (nobs x m x m) # % S.PmU Filtered covariance of A_t|t (nobs+1 x m x m) # % S.loglik Value of likelihood function # # % Output structure & dimensions n <- dim(Z)[1] m <- dim(Z)[2] nobs <- size(Y,2) S<-list() S$Am <- array(NA,c(m,nobs)) S$Pm <- array(NA,c(m,m,nobs)) S$AmU <- array(NA,c(m,nobs+1)) S$PmU <- array(NA,c(m,m,nobs+1)) S$loglik <- 0 # ______________________________________________________________________ Au <- A_0; # A_0|0; Pu <- P_0; # P_0|0 S$AmU[,1] = Au; S$PmU[,,1] = Pu; for(t in 1:nobs){ # t # A = A_t|t-1 & P = P_t|t-1 A <- TT%*%Au; P <- TT%*%Pu%*%t(TT) + Q; P <- 0.5*(P+t(P)) # handling the missing data res_MissData <- MissData(Y[,t],Z,R) y_t <- res_MissData$y Z_t <- res_MissData$C R_t <- res_MissData$R L_t <- res_MissData$L # if(is.null(y_t)){ if(sum(is.na(y_t))==length(y_t)){ Au <- A Pu <- P } else { if(!is.matrix(Z_t)){ Z_t<-t(as.matrix(Z_t)) } PZ <- P%*%t(Z_t) iF <- solve(Z_t%*%PZ + R_t) PZF <- PZ%*%iF V <- y_t - Z_t%*%A Au <- A + PZF%*%V Pu <- P - PZF%*%t(PZ) Pu <- 0.5*(Pu+t(Pu)) S$loglik <- S$loglik + 0.5*(log(det(iF)) - t(V)%*%iF%*%V) } S$Am[,t] <- A S$Pm[,,t] <- P # Au = A_t|t & Pu = P_t|t S$AmU[,t+1] <- Au S$PmU[,,t+1] <- Pu } # t if(sum(is.na(y_t))==length(y_t)){ S$KZ <- zeros(m,m) }else{ S$KZ <- PZF%*%Z_t } return(S) } MissData <- function(y,C,R){ # library(matlab) # ______________________________________________________________________ # PROC missdata # PURPOSE: eliminates the rows in y & matrices Z, G that correspond to # missing data (NaN) in y # INPUT y vector of observations at time t (n x 1 ) # S KF system matrices (structure) # must contain Z & G # OUTPUT y vector of observations (reduced) (# x 1) # Z G KF system matrices (reduced) (# x ?) # L To restore standard dimensions (n x #) # where # is the nr of available data in y # ______________________________________________________________________ # [y,C,R,L] ix <- !is.na(y) e <- eye(length(y)) L <- e[,ix] y <- y[ix] C <- C[ix,] R <- R[ix,ix] return(list(y=y,C=C,R=R,L=L)) } # %%% Replication files for: # %%% ""Nowcasting", 2010, (by Marta Banbura, Domenico Giannone and Lucrezia Reichlin), # %%% in Michael P. Clements and David F. Hendry, editors, Oxford Handbook on Economic Forecasting. # %%% # %%% The software can be freely used in applications. # %%% Users are kindly requested to add acknowledgements to published work and # %%% to cite the above reference in any resulting publications # %-------------------------------------------------------------------------- # % KALMAN FILTER # %-------------------------------------------------------------------------- runKF <- function(y, A, C, Q, R, x_0, Sig_0){ # x_0 = Z_0; Sig_0 = V_0 S <- SKF(y,C,R,A,Q, x_0, Sig_0); S <- FIS(y,C,R,A,Q,S); xsmooth <- S$AmT; Vsmooth <- S$PmT; VVsmooth <- S$PmT_1; loglik <- S$loglik; return(list(xsmooth = xsmooth,Vsmooth = Vsmooth,VVsmooth = VVsmooth,loglik = loglik)) } em_converged <- function(loglik = NULL, previous_loglik = NULL, threshold = NULL, check_increased = NULL){ # EM_CONVERGED Has EM converged? # [converged, decrease] = em_converged(loglik, previous_loglik, threshold) # # We have converged if the slope of the log-likelihood function falls below 'threshold', # i.e., |f(t) - f(t-1)| / avg < threshold, # where avg = (|f(t)| + |f(t-1)|)/2 and f(t) is log lik at iteration t. # 'threshold' defaults to 1e-4. # # This stopping criterion is from Numerical Recipes in C p423 # # If we are doing MAP estimation (using priors), the likelihood can decrase, # even though the mode of the posterior is increasing. nargin <- 4 - sum(c(is.null(loglik), is.null(previous_loglik), is.null(threshold), is.null(check_increased))) if(nargin < 3){threshold <- 1e-4} if(nargin < 4){check_increased <- 1} converged <- 0 decrease <- 0 if(!is.null(check_increased)){ if(loglik - previous_loglik < -1e-3){ # allow for a little imprecision message(paste("******likelihood decreased from",round(previous_loglik,4),"to",round(loglik,4))) decrease <- 1 } } delta_loglik <- abs(loglik - previous_loglik) avg_loglik <- (abs(loglik) + abs(previous_loglik) + 2.2204e-16)/2 if((delta_loglik / avg_loglik) < threshold){converged <- 1} # output list(converged = converged, decrease = decrease) }

6d42a46c99998e1abf73f918a157618b1e914653

b6e49b79809de5adc9ade2863db75bc3200b164f

/seurat_object_analysis_v1_and_v2.R

15584a86fef03cb2f61c3e3dc8df9244501f1395

[]

no_license

guokai8/2018-Developmental-single-cell-RNA-sequencing

764b32c3a0c053f20869dd2a5a9064b0ddd486b6

3662d349cfedc11ea1da7e6525ec07a0e31666cd

refs/heads/master

2022-02-24T17:42:25.710686

2019-09-17T15:56:24

null

0

null

UTF-8

R

false

4,511

r

seurat_object_analysis_v1_and_v2.R

##### Analyze Seurat Object (or Subclustered Seurat Object) #### ### sections do not necessarily need to be run in order library(Seurat) ## version 2.3.0. Can still be used with seurat v1, some default settings change library(dplyr) #### Inputs for Program #### input_file <- "path_to_seurat_object.Rdata" output="~/Documents/" load(input_file) ## rename seurat object seurat_ob <- seurat_object ## if using object created in seurat v1, can update object with seurat_ob=UpdateSeuratObject(seurat_object) ####################################################################################### ########## Choose Resolution for downstream analysis, if required ######### seurat_ob <- SetAllIdent(seurat_ob, id = "res.0.8") ######### PCA Analysis ############ png(paste(output,"pca1_2.png",sep="")) PCAPlot(seurat_ob,1,2) dev.off() pdf(paste(output,"pcaheatmap_top10.pdf",sep="")) PCHeatmap(seurat_ob, pc.use = 1:10, cells.use=500,do.balanced = TRUE, cexRow=0.5) dev.off() ProjectPCA(seurat_ob) PrintPCA(seurat_ob, pcs.print = 1:2, use.full=T) png(paste(root_dir,file_name, "_vizpca_1_2.png",sep="")) VizPCA(seurat_ob, 2:3) dev.off() ########## Plot TSNE results ########## png(paste(output, "_res08.png",sep=""), width=695, height=538) TSNEPlot(seurat_ob,do.label=T,pt.size=3,do.return=T, label.size=8) dev.off() ########## Find Markers of Clusters ################ ## Find Cluster Markers ## ### seurat v1 - default is "bimod" ### seurat v2 - default is "wilcox", "MAST" is also available ### can alter with parameter test.use all_markers <- FindAllMarkers(seurat_ob,only.pos=T, min.pct=0.25, thresh.use=0.25) write.csv(all_markers, file=paste(output, "_res08_markers.csv",sep="")) df <- NULL for(x in unique(all_markers$cluster)){clusx <- all_markers[which(all_markers$cluster==x),] sorted_clus <- clusx[order(clusx$avg_diff,decreasing=TRUE),] df <- rbind(df, sorted_clus)} write.csv(df, file=paste(output,"_res08_sorted_markers_avgdiff.csv",sep="")) # Find Pairwise Markers # ## between two clusters markers <- FindMarkers(seurat_ob,2,3,min.pct=0.25, thresh.use=0.25, only.pos=F) sorted_markers <- markers[order(markers$avg_diff,decreasing=TRUE),] ## 1 cluster vs. 2 others markers <- FindMarkers(seurat_ob,1,c(2:3),min.pct=0, thresh.use=0, only.pos=F) write.csv(sorted_markers, file=paste(output, "_clus1_clus2_3_avgdiff_markers.csv",sep="")) ######## Feature and Violin Plots for Differentially Expressed Genes ####### ### load in already calculated markers if needed ### all_markers <- read.csv("path_to_markers.csv") ## from FindAllMarkers sorted <- all_markers %>% group_by(cluster) %>% top_n(50, avg_diff) genes <- unique(sorted$gene) endo_genes=c("Gcg","Ins1","Fev","Neurog3") feature_plots <- function(genes){ for(x in genes){ pdf(paste(output, "feature_plot_",x,".pdf",sep=""), width=7, height=5,useDingbats = F) FeaturePlot(seurat_ob,x,cols.use = c("gray","red"), pt.size=2,no.legend=T,no.axes=T) dev.off() } } feature_plots(endo_genes) feature_plots(genes) violin_plots <- function(genes){ for(x in genes){ png(paste(output, "_violin_",x,".png",sep=""),width=1000,height=200) VlnPlot(seurat_ob, x,size.x.use = 12,size.y.use=10,size.title.use=20) dev.off() } } violin_plots(endo_genes) violin_plots(genes) ######## Dot Plots ########### pdf(paste(output, "_dotplot.pdf"),width=15, height=4) DotPlot(seurat_ob,genes,cols.use=myPalette(low = "blue",high="red"), cex.use = 2) dev.off() ########################### Change TSNE Resolution ############################### seurat_ob <- StashIdent(seurat_ob, save.name = "orig.res") seurat_ob <- FindClusters(seurat_ob,resolution=1,print.output = F) png(paste(output, "_res1.png",sep=""),width=695, height=538) TSNEPlot(seurat_ob, do.label=T, do.return=T, pt.size=2, label.size=8, no.legend=F) dev.off() ### get cell names of particular cluster ### new_ob = SubsetData(seurat_ob,ident.use = 15) new_cell_names = colnames(new_ob@data) ########################## Adding Metadata ################################## current.cluster.ids <- levels(seurat_ob@ident) new.cluster.ids <- c("Acinar","Mature Acinar","Prolif. Acinar","Prolif. Ductal","Ductal","Ngn3","Fev","Beta","Alpha","Epsilon") seurat_ob@ident <- plyr::mapvalues(seurat_ob@ident, from = current.cluster.ids, to = new.cluster.ids) meta <- data.frame(Cluster_Names=seurat_ob@ident) seurat_ob <- AddMetaData(seurat_ob,metadata=meta) save(seurat_ob, file=paste(output,"analysis.Rdata",sep=""))

4a699018c982a33f9854609fad8d4c9c9a7a3309

afd52451e8845963de4ad1243005834fa0958beb

/sta_r/sta_glm.R

b13566ba37a5050b95aca00a50c5973e8223c017

[]

no_license

plus4u/R

7c0d867767ae948b24a15322df11b500abcfd920

c8c25313567bd8bcf5142a04187a24e0d5ad12d1

refs/heads/master

2021-09-19T13:52:40.115595

2021-08-11T06:47:22

155,179,952

1

0

null

UHC

R

false

9,448

r

sta_glm.R

getwd() setwd("C:/Users/beomc/OneDrive/바탕 화면/SPSS_DATA") data <- read.csv(file = "drama_genre.csv") # 데이터 필터링 data <- subset(data, genre %in% c("막장드라마", "멜로드라마")) # factor의 레벨을 3에서 2로 바꾸어 주기 위함줄여주기 위함 data$genre <- as.factor(as.character(data$genre)) # 색 투명도 설정을 위한 alpha 함수를 사용하기 위해 scales 패키지를 설치하고 라이브러리 불러오기 install.packages("scales") library(scales) #1. 산점도 그리기 plot(formula = sum_age_mainactors ~ avg_slap_face, data = data, col = alpha(c("blue", "green"), 0.8)[data$genre], xlab = "회당 뺨 맞는 횟수", ylab = "주연배우 나이 합계", main = "드라마 장르 분포") # 범례 그리기 legend("topleft", legend = levels(data$genre), pch = 1, col = alpha(c("blue", "green"), 0.8), cex = 0.9, bty = "n") # 2. 재현성을 위한 seed 설정 set.seed(9876) # idx 설정 (6 : 4) idx <- sample(x = c("train_valid", "test"), size = nrow(data), replace = TRUE, prob = c(6, 4)) # idx에 따라 데이터 나누기 train_valid <- data[idx == "train_valid", ] test <- data[idx == "test", ] # test 데이터 설명변수/반응변수 나누기 test_x <- test[, -3] test_y <- test[, 3] # alpha 함수를 사용하려면 미리 scales 라이브러리를 불러와야 한다. library(scales) # train_valid 산점도 그리기 plot(formula = sum_age_mainactors ~ avg_slap_face, data = train_valid, col = alpha(c("blue", "green"), 0.8)[train_valid$genre], pch = 0, main = "드라마 장르 분포", xlab = "회당 뺨 맞는 횟수", ylab = "주연배우 나이 합계") # test 데이터 표시하기 points(formula = sum_age_mainactors ~ avg_slap_face, data = test, pch = 16, cex = 1.2, col = alpha(c("blue", "green"), 0.5)[test$genre]) # 범례 그리기 legend("topleft", c(paste0("train_valid ", levels(train_valid$genre)), paste0("test ", levels(test$genre))), pch = c(0, 0, 16, 16), col = c(alpha(c("blue", "green"), 0.8), alpha(c("blue", "green"), 0.5)), cex = 0.9, bty = "n") ## result <- data.frame(fold = rep(c(1, 2, 3), each = 82), mdl = rep(c("full", "step"), 41), i = rep(seq(0, 1, length.out = 41), 6), accuracy = rep(rep(NA, 41), 6)) ## idx <- sample(x = c(1:3), size = nrow(data), replace = TRUE, prob = c(1, 1, 1)) for(k in c(1, 2, 3)){ # idx에 따라 train vs. valid 데이터 나누기 valid <- train_valid[idx == k, ] train <- train_valid[idx != k, ] # valid 데이터 설명변수/반응변수 나누기 valid_x <- valid[, -3] valid_y <- valid[, 3] } ### 3. # 로지스틱 회귀분석 모델 생성 full <- glm(formula = genre ~ ., data = train, family = "binomial") # Stepwise step <- step(object = full, trace = F) # full과 step 모델별 확률 예측 full_pred_p <- as.numeric(predict(object = full, newdata = valid_x, type = "response")) step_pred_p <- as.numeric(predict(object = step, newdata = valid_x, type = "response")) # # 4. 분류 정확도의 분모 l <- length(valid_y) for(i in unlist(unique(result$i))){ # i를 기준으로 0 또는 1로 분류 full_pred_class <- ifelse(full_pred_p < i, levels(valid_y)[1], levels(valid_y)[2]) step_pred_class <- ifelse(step_pred_p < i, levels(valid_y)[1], levels(valid_y)[2]) # 분류 정확도 계산 full_accuracy <- sum(full_pred_class == valid_y) / l step_accuracy <- sum(step_pred_class == valid_y) / l # result 테이블에 분류 정확도 입력 result[result$fold == k & result$mdl == "full" & result$i == i, "accuracy"] <- full_accuracy result[result$fold == k & result$mdl == "step" & result$i == i, "accuracy"] <- step_accuracy } ## # Stepwise step <- step(object = full, trace = F) # full과 step 모델별 확률 예측 full_pred_p <- as.numeric(predict(object = full, newdata = valid_x, type = "response")) step_pred_p <- as.numeric(predict(object = step, newdata = valid_x, type = "response")) # 분류 정확도의 분모 l <- length(valid_y) for(i in unlist(unique(result$i))){ # i를 기준으로 0 또는 1로 분류 full_pred_class <- ifelse(full_pred_p < i, levels(valid_y)[1], levels(valid_y)[2]) step_pred_class <- ifelse(step_pred_p < i, levels(valid_y)[1], levels(valid_y)[2]) # 분류 정확도 계산 full_accuracy <- sum(full_pred_class == valid_y) / l step_accuracy <- sum(step_pred_class == valid_y) / l # result 테이블에 분류 정확도 입력 result[result$fold == k & result$mdl == "full" & result$i == i, "accuracy"] <- full_accuracy result[result$fold == k & result$mdl == "step" & result$i == i, "accuracy"] <- step_accuracy } ## # full 모델 k-Fold = 1 plot(accuracy ~ i, result[result$mdl == "full" & result$fold == 1, ], type = "l", col = alpha("purple", 0.4), ylim = c(0.3, 1), xlab = "임계치", ylab = "분류 정확도", main = "분류 정확도 in full/step 3-Fold CV") # full 모델 k-Fold = 2 lines(accuracy ~ i, result[result$mdl == "full" & result$fold == 2, ], col = alpha("orange", 0.4)) # full 모델 k-Fold = 3 lines(accuracy ~ i, result[result$mdl == "full" & result$fold == 3, ], col = alpha("green", 0.4)) # step 모델 k-Fold = 1 lines(accuracy ~ i, result[result$mdl == "step" & result$fold == 1, ], col = alpha("purple", 0.5), lty = 2) # step 모델 k-Fold = 2 lines(accuracy ~ i, result[result$mdl == "step" & result$fold == 2, ], col = alpha("orange", 0.5), lty = 2) # step 모델 k-Fold = 3 lines(accuracy ~ i, result[result$mdl == "step" & result$fold == 3, ], col = alpha("green", 0.5), lty = 2) # 범례 그리기 legend("topleft", c("full k=1", "full k=2", "full k=3", "step k=1", "step k=2", "step k=3"), col = c(alpha(c("purple", "orange", "green"), 0.4), alpha(c("purple", "orange", "green"), 0.5)), lty = rep(c(1, 2), each = 3), bty = "n", cex = 0.9) ## # 그룹핑을 위해 plyr 패키지를 설치하고 라이브러리 불러오기 install.packages("plyr") library(plyr) # 모델별 임계치별 평균 분류 정확도 계산하기 tmp <- ddply(result, .(mdl, i), summarise, avg_accuracy = mean(accuracy)) # full 모델 lines(avg_accuracy ~ i, tmp[tmp$mdl == "full", ], col = alpha("red", 0.7), lty = 1, type = "o", pch = 20) # step 모델 lines(avg_accuracy ~ i, tmp[tmp$mdl == "step", ], col = alpha("red", 0.7), lty = 2, type = "o", pch = 20) # # 범례 그리기 legend("topright", c("full avg accuracy", "step avg accuracy"), pch = 20, col = alpha("red", 0.7), lty = rep(c(1, 2)), bty = "n", cex = 0.9) # tmp[tmp$avg_accuracy == max(tmp$avg_accuracy), ] # test_x가 멜로드라마일 확률을 step 모델로 구하기 test_p <- as.numeric(predict(object = step, newdata = test_x, type = "response")) # 임계치 0.45를 기준으로 막장드라마와 멜로드라마 분류하기 test_class <- ifelse(test_p < 0.45, levels(data$genre)[1], levels(data$genre)[2]) # 분류 정확도 계산하기 sum(test_class == test_y) / length(test_y) ## # 정답/오답 만들기 (pch 옵션을 위해) ox <- as.factor(ifelse(test_class == test_y, "O", "X")) # train_valid 산점도 그리기 plot(formula = sum_age_mainactors ~ avg_slap_face, data = train_valid, col = alpha(c("blue", "green"), 0.8)[train_valid$genre], xlab = "회당 뺨 맞는 횟수", ylab = "주연배우 나이 합계", main = "드라마 장르 분포") # test 산점도 그리기 points(formula = sum_age_mainactors ~ avg_slap_face, data = test, col = alpha(c("blue", "green"), 0.6)[test$genre], pch = c(19, 17)[ox]) # 범례 그리기 legend("topleft", legend = c(paste0("train_valid ", levels(train_valid$genre)), paste0("test ", levels(test$genre), " 정답"), paste0("test ", levels(test$genre), " 오답")), pch = c(1, 1, 19, 19, 17, 17), col = c(alpha(c("blue", "green"), 0.8), alpha(c("blue", "green"), 0.6), alpha(c("blue", "green"), 0.6)), cex = 0.9, bty = "n")

d1606f459e894c68d1d52f8912559ca5afd2d721

8511b55f2f66301c434fca6594d9c6df79644419

/plot2.R

0619cfc6fcb46a73683718787be3fa1a89e74387

[]

no_license

hpykala/ExData_Plotting2

8a3965f288b407080fc6b0e69ad7860c87f95ea0

252d8e0237f77d70843f09b66a8f450e4ab08bf3

refs/heads/master

2016-09-14T08:04:51.457790

2016-04-25T08:08:00

56,834,817

0

null

UTF-8

R

false

1,077

r

plot2.R

##Have total emissions from PM2.5 decreased in the Baltimore City, Maryland (fips == "24510") ##from 1999 to 2008? Use the base plotting system to make a plot answering this question. library(data.table) #download data if needed if(!file.exists("summarySCC_PM25.rds")) { download.file("https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2FNEI_data.zip", "FNEI_data.zip", mode="wb") unzip("FNEI_data.zip") } NEI <- readRDS("summarySCC_PM25.rds") #subset Baltimore City baltimore <- data.table(subset(NEI, fips == "24510")) #calculate yearly sums to find total pm2.5 emissions yearly <- baltimore[,sum(Emissions), by = year] setnames(yearly, "V1", "pm25tot") #scale total emissions to thousands of tons yearly[,pm25tot := pm25tot/10^3] png("plot2.png") #scatterplot plot(yearly$year, yearly$pm25tot, pch = 19, xlab = "Year", ylab = "Total PM2.5 emissions (Thousand tons)", main = "Total yearly PM2.5 emissions in the Baltimore City") #add trend line to show clear downward trend abline(lm(pm25tot ~ year, data = yearly)) dev.off()

e65b5249b630e052ac00404fe046282e72fe00aa

3a91fc71b1ecfdda05598d535000444a6808e1fa

/functions/get_sri.R

0b681d4fe3306b6207f64e475f9a8b391a4d682c

[]

no_license

qwebber/social-issa

bd870201f3266de6092c505f173b58f74b394969

a98a4eb1d223ecc9acc0684a9a204c91e55f95bf

refs/heads/master

2023-04-10T11:58:09.840312

2023-02-22T17:01:26

340,049,482

1

0

null

UTF-8

R

false

1,644

r

get_sri.R

#' Dynamic network #' #' @inheritParams hr_network #' #' @return Graph strength for each individual. #' @export #' get_sri <- function(DT = NULL, id = NULL, by = NULL) { if (is.null(DT) | is.null(id)) { stop('DT, and id must be provided') } DT[, { d <- data.table::dcast(.SD, formula = groupEnd ~ get(id), fun.aggregate = length, value.var = 'groupEnd') gbi_df <- data.matrix(d[, !'groupEnd', with = FALSE]) rownames(gbi_df) <- d$groupEnd gbi.net_df <- asnipe::get_network(gbi_df, data_format = "GBI", association_index = "SRI") gbi.net_df[lower.tri(gbi.net_df)] <- NA diag(gbi.net_df) <- NA gbi.grph_df <- igraph::graph_from_adjacency_matrix(gbi.net_df, mode = "undirected", diag = FALSE, weighted = TRUE) out <- na.omit(reshape2::melt(gbi.net_df)) out <- data.table(ID1 = out$Var1, ID2 = out$Var2, sri = out$value) #memb <- data.table(membershipID1 = membership(fastgreedy.community(gbi.grph_df)), # ID1 = names(igraph::degree(gbi.grph_df))) #memb2 <- data.table(membershipID2 = membership(fastgreedy.community(gbi.grph_df)), # ID2 = names(igraph::degree(gbi.grph_df))) #all <- merge(out, memb, by = "ID1") #all2 <- merge(all, memb2, by = "ID2") }, by = by] }

fe35e171659ee526c185b7718e7b3eb578f6fd59

ffdea92d4315e4363dd4ae673a1a6adf82a761b5

/data/genthat_extracted_code/rvgtest/examples/plot.ierror.Rd.R

ffd81c5d94446bfd98bfb6c1b8d511ef3a20c734

[]

no_license

surayaaramli/typeRrh

d257ac8905c49123f4ccd4e377ee3dfc84d1636c

66e6996f31961bc8b9aafe1a6a6098327b66bf71

refs/heads/master

2023-05-05T04:05:31.617869

2019-04-25T22:10:06

null

0

null

UTF-8

R

false

917

r

plot.ierror.Rd.R

library(rvgtest) ### Name: plot.rvgt.ierror ### Title: Plot Errors in Inversion Methods ### Aliases: plot.rvgt.ierror ### Keywords: distribution hplot htest ### ** Examples ## Create a table of u-errors for spline interpolation of ## the inverse CDF of the standard normal distribution and ## the beta distribution aqn <- splinefun(x=pnorm((-100:100)*0.05), y=(-100:100)*0.05, method="monoH.FC") uerrn <- uerror(n=1e5, aqdist=aqn, pdist=pnorm) aqb <- splinefun(x=pbeta((0:100)*0.01,shape1=2,shape2=5), y=(0:100)*0.01, method="monoH.FC") uerrb <- uerror(n=1e5, aqdist=aqb, pdist=pbeta, shape1=2, shape2=5) ## Plot u-errors of the normal distribution plot(uerrn) ## Plot maximal u-errors of the normal distribution plot(uerrn,maxonly=TRUE) ## Compare the u-errors of these two distributions and ## draw maximal tolerated error plot.rvgt.ierror(list(uerrn,uerrb),tol=1.e-6)

9b5f32e139428591c9000d802b405ea951da0ce2

43fd4395e3ed61a40ae4283757a9ae6fadbd6c52

/R/fitted.uniNmix.R

efa19f9f773885e3d72c1b212df1b59dc08efe18

[]

no_license

cran/jointNmix

f96d01a741bc466b6ee925263d62e8a65441dfaf

e142b6d98fce3c774e9498160656d42a8b234a46

refs/heads/master

2020-09-06T19:31:55.212810

2016-11-12T00:38:21

73,520,306

0

null

UTF-8

R

false

78

r

fitted.uniNmix.R

fitted.uniNmix <- function(object, ...) { return(list("sp1"=object$fv1)) }

14a4d51436cc074a0951cf7452a75cd645c347cf

52ae69c4fb2943e06e335cbd22490ae5085f6ca3

/day06.R

fe6ac028f47dc84ac97d791d798f6306579d062d

[]

no_license

kwichmann/AdventOfCode2020

aa91d06e442a4e88adea4512952ae38a36690ed6

4b3fe46e426bcde0603e6ceb931c372503ee8dd1

refs/heads/main

2023-02-03T13:07:39.441697

2020-12-17T19:46:38

318,871,274

0

null

UTF-8

R

false

1,060

r

day06.R

# Make sure to setwd to the right directory library(readr) library(stringr) library(dplyr) dataString <- read_file("day06.txt") # Part 1 questionnaires <- str_split(dataString, "\n\n") questionnaires_some <- questionnaires %>% sapply(function(q) gsub("\n", "", q)) q_some_df <- as.data.frame(matrix(rep(NA, 26 * length(questionnaires_some)), ncol = 26)) colnames(q_some_df) <- letters for (i in 1:length(questionnaires_some)) { for (letter in letters) { q_some_df[[letter]][i] <- str_detect(questionnaires_some[i], letter) } } print(sum(as.matrix(q_some_df))) # Part 2 questionnaires_all <- (questionnaires %>% lapply(function(q) str_split(q, "\n")))[[1]] %>% lapply(function(q) str_split(q, "")) %>% lapply(function(q) Reduce(intersect, q)) q_all_df <- as.data.frame(matrix(rep(NA, 26 * length(questionnaires_all)), ncol = 26)) colnames(q_all_df) <- letters for (i in 1:length(questionnaires_all)) { for (letter in letters) { q_all_df[[letter]][i] <- letter %in% questionnaires_all[[i]] } } print(sum(as.matrix(q_all_df)))

89be4e60890f5fae92501624905860e719f37c9a

704fb9134e11ff44bbf5393bb4749c9205543f5e

/run_analysis.R

b25546debdbb781b7d93bbf7a1a72e8a22c396e5

[]

no_license

pavarit/coursera-data-cleaning

837cc2e2e25ac348e71ccc29d0c455d590a20720

f82c2b7d796ffecacf10afde8e42f7ba0e4cc350

refs/heads/master

2020-05-22T09:05:11.828369

2017-03-11T23:53:22

84,686,288

0

null

UTF-8

R

false

1,863

r

run_analysis.R

## 1) Merges the training and the test sets to create one data set. ## 2) Extracts only the measurements on the mean and standard deviation for each measurement. ## 3) Uses descriptive activity names to name the activities in the data set ## 4) Appropriately labels the data set with descriptive variable names. ## 5) From the data set in step 4, creates a second, independent tidy data set with the average of each variable for each activity and each subject. ##Need to set working directory to the location of UCI HAR Dataset folder before running library(dplyr) ##Read all relavant data files setwd("UCI HAR Dataset/train") subjtrain <- read.table("subject_train.txt", colClasses = "numeric") xtrain <- read.table("X_train.txt", colClasses = "numeric") ytrain <- read.table("y_train.txt", colClasses = "numeric") setwd("..") setwd("test") subjtest <- read.table("subject_test.txt", colClasses = "numeric") xtest <- read.table("X_test.txt", colClasses = "numeric") ytest <- read.table("y_test.txt", colClasses = "numeric") setwd("..") actlabel <- read.table("activity_labels.txt", colClasses = "character") features <- read.table("features.txt", colClasses = "character") ##Bind data together alltrain <- cbind(subjtrain, ytrain, xtrain) alltest <- cbind(subjtest, ytest, xtest) dt <- rbind(alltrain, alltest) colnames(dt) <- c("subject", "activity", features[, 2]) ##Extract only columns with mean or std data dtmeanstd <- dt[, c("subject", "activity", grep("mean\$\$|std\$\$", features[, 2], value = TRUE))] ##Replace activity number with activity names for(i in 1:length(dtmeanstd$activity)) { dtmeanstd$activity[i] <- actlabel[dtmeanstd$activity[i], 2] } #Create tidy data dttidy <- dtmeanstd %>% group_by(subject, activity) %>% summarize_each(funs(mean)) ##Create txt file write.table(dttidy, file = "tidy data.txt", row.names = FALSE)

cdc226f713e19cc09ef4c856a814d3d035ef7cdf

5e3a624a4c869bf7a03419a71ab6fcf904d69108

/R/optimizeMbo.R

a8e06fa8e6b0e3af3ba13c804ec2e32433c64166

[]

no_license

ascheppach/EBO

20296345296f15c202485dc4c18480cd14404f34

f7d9d90a935887266d31a16269b664973d3f2e12

refs/heads/master

2022-12-24T13:16:22.036546

2020-07-16T09:21:32

302,585,497

0

null

UTF-8

R

false

569

r

optimizeMbo.R

optimizeMBO = function(configuration, objNormal, info, funcEvals) { # create MBOControl and MBOsurrogate (infillcrit + surrogate) mboControlLearner = createMboControlSurrogate(x = configuration) # compute mlrMBO res = configMboFunc(instance = list(objNormal, info), funcEvals = funcEvals, design = configuration$design, amountDesign = configuration$amountDesign, control = mboControlLearner[[1]], surrogate = mboControlLearner[[2]]) return(res) }

6d7ff87f28420d5647cb384f09cb9c7c215caca1

29e424922b3d0e510736f71314795500e8e99341

/00a_functions_ml_approach_20180625.R

c4a49a806a8e179125fbde12432982cdff381608

[]

no_license

holgersr/Bayesian-inference-and-simulation-of-Elo-scores-in-analysis-of-social-behaviour

f9e31801820658500b0f679d16ec40c3991c1b9b

2a0c67188c8c1be98b7ad65de83590cbd9150022

refs/heads/master

2021-09-19T09:20:20.299115

2018-06-25T06:41:06

95,112,337

2

0

null

UTF-8

R

false

2,220

r

00a_functions_ml_approach_20180625.R

## Functions needed to implement the ML approach: pAfun <- function(EloA, EloB){ 1/(1 + exp(EloB - EloA)) } Elo_pA <- function(EloStart_logk, X, show_k = FALSE){ EloStart <- EloStart_logk[-length(EloStart_logk)] EloStart <- EloStart - mean(EloStart) k <- exp(EloStart_logk[length(EloStart_logk)]) if (show_k) { cat(paste0(round(k, 3), paste(rep(" ", 20), collapse = " "))) cat("\r") } EloNow <- EloStart Elo <- matrix(nrow = nrow(X), ncol = length(EloStart), 0) colnames(Elo) <- colnames(X) pA <- rep(0, nrow(X)) for (i in 1:nrow(X)) { A <- which(X[i, ] == 1) B <- which(X[i, ] == -1) pA[i] <- pAfun(EloA = EloNow[A], EloB = EloNow[B]) toAdd <- (1 - pA[i]) * k EloNow[A] <- EloNow[A] + toAdd EloNow[B] <- EloNow[B] - toAdd Elo[i, ] <- EloNow } return(list(pA = pA, Elo = Elo)) } logLik <- function(EloStart_logk, X, show_k = FALSE){ pA <- Elo_pA(EloStart_logk = EloStart_logk, X = X, show_k = show_k)$pA return(-sum(log(pA))) } logLik_model1 <- function(logk, X, show_k = FALSE){ pA <- Elo_pA(EloStart_logk = c(rep(0, ncol(X)), logk), X = X, show_k = show_k)$pA return(-sum(log(pA))) } pAfun_001factor <- function(EloA, EloB){ 1/(1 + exp(0.01*(EloB - EloA))) } Elo_pA_001factor <- function(EloStart_logk, X, show_k = FALSE){ EloStart <- EloStart_logk[-length(EloStart_logk)] EloStart <- EloStart - mean(EloStart) k <- exp(EloStart_logk[length(EloStart_logk)]) if (show_k) { cat(paste0(round(k, 3), paste(rep(" ", 20), collapse = " "))) cat("\r") } EloNow <- EloStart Elo <- matrix(nrow = nrow(X), ncol = length(EloStart), 0) colnames(Elo) <- colnames(X) pA <- rep(0, nrow(X)) for (i in 1:nrow(X)) { A <- which(X[i, ] == 1) B <- which(X[i, ] == -1) pA[i] <- pAfun_001factor(EloA = EloNow[A], EloB = EloNow[B]) toAdd <- (1 - pA[i]) * k EloNow[A] <- EloNow[A] + toAdd EloNow[B] <- EloNow[B] - toAdd Elo[i, ] <- EloNow } return(list(pA = pA, Elo = Elo)) } logLik_001factor <- function(EloStart_logk, X, show_k = FALSE){ pA <- Elo_pA_001factor(EloStart_logk = EloStart_logk, X = X, show_k = show_k)$pA return(-sum(log(pA))) }

85748a1b997fe697ea301c7ca22ef19d5af7532e

982dcc77db4a58e81527649b33db10014fd2edb3

/day03/day3.R

5d513721096cf79a9dab8abb370081f07ac337aa

[]

no_license

jaewon-jun9/R-in-multi

c734aaa1bce11b583a64b2309b852989924e51c4

acd5f828557b8c54fcfc64a799a80340d3e6a086

refs/heads/master

2020-12-03T01:17:14.655310

2020-01-01T03:10:51

231,172,519

1

0

null

2020-01-01T03:47:20

2020-01-01T03:47:19

null

UTF-8

R

false

3,044

r

day3.R

y <- c(0,25,50,75,100) z <- c(50,50,50,50,50) y == z y != z y > z y < z y >= z y <= z y == 50 #알아서 복사 ex) 50 50 50 50 50 y > 50 num1 <- 11 #c(11) num2 <- 3 #c(3) num1/num2 num1%%num2 num1 %/% num2 #LIST 리스트 lds <- list(1,2,3) #각각 묶음 lds lds +100 #오류 lds[1] #첫번째 보따리 lds[1]+10 #오류 lds[[1]]+10 #가능 names(lds) <- LETTERS[1:3] lds lds[[2]] lds[["B"]] lds$B a <- list( a=1:3, b="a string", c=pi, d=list(-1,-5) ) a a[1] a[[1]] a$a a[1]+1 #오류 unlist(a[1])+1 (O) a[[1]]+1 a[[1]][2] +100 new_a <-unlist(a[1]) a[1]; new_a names(new_a) <- NULL new_a names(a) <- NULL a ls() length(ls()) save(list=ls(),file="all.rda") rm(list=ls()) ls() load("all.rda") ls() #read file data nums<- scan("data/sample_num.txt", what="") word_ansi <- scan("data/sample_ansi.txt",what="") word_utf8 <- scan("data/sample_utf8.txt",what="",encoding = "UTF-8") word_utf8 word_utf8_new <- scan("data/sample_utf8.txt",what="") word_utf8_new lines_ansi <- readLines("data/sample_ansi.txt") lines_utf8 <- readLines("data/sample_utf8.txt",encoding = "UTF-8") #if else randomNum <- sample(1:10,1) if(randomNum>5){ cat(randomNum,":5보다 크군요","\n") }else{ cat(randomNum,":5보다 작거나 같군요","\n") } if(randomNum%%2){ cat(randomNum,";홀수","\n") }else{ cat(randomNum,";짝수","\n") } score <- c(50) if (score >= 90) { cat(score, "는 A등급입니다","\n") }else if (score >=80){ cat(score, "는 B등급입니다","\n") }else if (score >=70){ cat(score, "는 C등급입니다","\n") }else if (score >=60){ cat(score, "는 D등급입니다","\n") }else{ cat(score, "는 F등급입니다","\n") } score <- sample(0:100,1) if (score >= 90){ cat(score, "는 A등급입니다","\n") }else if (score >=80){ cat(score, "는 B등급입니다","\n") }else if (score >=70){ cat(score, "는 C등급입니다","\n") }else if (score >=60){ cat(score, "는 D등급입니다","\n") }else{ cat(score, "는 F등급입니다","\n") } #for 실습 for(data in month.name) print(data) for(data in month.name) print(data); print("ㅋㅋ") for(data in month.name){print(data);print("ㅋㅋ")} for(n in 1:5) cat("hello?","\n") for(i in 1:5){ for(j in 1:5){ cat("i",i,"j=",j,"\n") } } #구구단 for(dan in 1:9){ for(num in 1:9){ cat(dan,"x",num,"=",dan*num,"\t") } cat("\n") } #switch 문을 대신하는 함수 month <- sample(1:12,1) month <- paste(month,"월",sep="") #sep="" 생략하면 "3 월"로 나옴. "3 월"과"3월"은 다름. result <- switch(EXPR = month, "12월"=,"1월"=,"2월"="겨울", "3월"=,"4월"=,"5월"="봄", "6월"=,"7월"=,"8월"="여름", "가을") cat(month,"은"," ",result,"입니다.\n",sep="") num <- sample(1:10,1) num switch(EXPR = num,"A","B","C","D") for(num in 1:10){ cat(num,":",switch(EXPR = num,"A","B","C","D"),"\n") } for(num in 1:10){ num<- as.character(num) cat(num,":",switch(EXPR = num,"7"="A","8"="B","9"="C","10"="D","ㅋ"),"\n") }

0405e9525b0062293293ae421ccfe01aeac482e4

db12b990924703cd74748d8585cd9c11fafa6746

/h2o-r/tests/testdir_autoGen/runit_complexFilterTest_iris_test_numeric_missing_extra_40.R

2382248085c4bf1fd5ba12800811d396ce26827c

[ "Apache-2.0" ]

permissive

h2oai/h2o-3

919019a8f297eec676011a9cfd2cc2d97891ce14

d817ab90c8c47f6787604a0b9639b66234158228

refs/heads/master

2023-08-17T18:50:17.732191

2023-08-17T16:44:42

17,371,412

6,872

2,345

Apache-2.0

2023-09-14T18:05:40

2014-03-03T16:08:07

Jupyter Notebook

UTF-8

R

false

1,933

r

runit_complexFilterTest_iris_test_numeric_missing_extra_40.R

setwd(normalizePath(dirname(R.utils::commandArgs(asValues=TRUE)$"f"))) source("../../scripts/h2o-r-test-setup.R") ## # Author: Autogenerated on 2013-12-18 17:01:19 # gitHash: 2581a0dfa12a51892283830529a5126ea49f0cb9 # SEED: 2481425483200553751 ## complexFilterTest_iris_test_numeric_missing_extra_40 <- function() { Log.info("A munge-task R unit test on data <iris_test_numeric_missing_extra> testing the functional unit <['!', '!=', '&', '>=', '|', '>=']> ") Log.info("Uploading iris_test_numeric_missing_extra") hex <- h2o.importFile(locate("smalldata/iris/iris_test_numeric_missing_extra.csv"), "riris_test_numeric_missing_extra.hex") Log.info("Performing compound task !( ( hex[,c(\"species\")] != 1.2108297567 ) & ( hex[,c(\"petal_len\")] >= 5.18451212374 ) | ( ( hex[,c(\"petal_len\")] >= 3.39830058306 )) ) on dataset <iris_test_numeric_missing_extra>") filterHex <- hex[!( ( hex[,c("species")] != 1.2108297567 ) & ( hex[,c("petal_len")] >= 5.18451212374 ) | ( ( hex[,c("petal_len")] >= 3.39830058306 )) ),] Log.info("Performing compound task !( ( hex[,c(\"petal_len\")] != 4.56344348577 ) & ( hex[,c(\"petal_len\")] >= 3.54674974992 ) | ( ( hex[,c(\"species\")] >= 2.26145385905 )) ) on dataset iris_test_numeric_missing_extra, and also subsetting columns.") filterHex <- hex[!( ( hex[,c("petal_len")] != 4.56344348577 ) & ( hex[,c("petal_len")] >= 3.54674974992 ) | ( ( hex[,c("species")] >= 2.26145385905 )) ), c("petal_wid","petal_len","sepal_len","species")] Log.info("Now do the same filter & subset, but select complement of columns.") filterHex <- hex[!( ( hex[,c("petal_len")] != 4.56344348577 ) & ( hex[,c("petal_len")] >= 3.54674974992 ) | ( ( hex[,c("species")] >= 2.26145385905 )) ), c("sepal_wid")] } doTest("compoundFilterTest_ on data iris_test_numeric_missing_extra unit= ['!', '!=', '&', '>=', '|', '>=']", complexFilterTest_iris_test_numeric_missing_extra_40)

cbeecfbcdbe25fdebc364e69a83efabdf384caa6

73591225035cc34cf9995a857f84e8f991535ae0

/cachematrix.R

38242627b1da5b2528503e37c144f3693a0590dd

[]

no_license

HeinrichZiegler/ProgrammingAssignment2

984428339d16851a8004292f279e1d748d124236

8380f895b589f9d6b3cd602a33993b3f8a6880fb

refs/heads/master

2022-12-17T02:10:59.248750

2020-09-15T18:22:57

295,770,090

0

null

2020-09-15T15:26:27

2020-09-15T15:26:26

null

UTF-8

R

false

1,321

r

cachematrix.R

## This file provides two R functions to facilitate the caching of the ## inverse matrix or a given matrix ## Assumption: the provided matrix is always valid! ## makeCacheMatrix is a function which creates an object containing the ## matrix and adding the ability to remember the inverse matrix ## it will also allow reset the inverse matrix value, or change the matrix ## Whenever the matrix contained is changed, the inverse matrix will be ## reset as well makeCacheMatrix <- function(x = matrix()) { i <- NULL set <- function(y) { x <<- y i <<- NULL } get <- function() x setInverse <- function(solve) i <<- solve getInverse <- function() i list(set = set, get = get, setInverse = setInverse, getInverse = getInverse) } ## This function "cacheSolve" will calculate the inverse matrix of "special" ## matrix provided with makeCacheMatrix, and store the inverse in the object ## provided ## However, if an inverse matrix already exists it will use the buffered value ## and return it without performing the solve() operation cacheSolve <- function(x, ...) { i <- x$getInverse() if(!is.null(i)) { message("getting cached data") return(i) } data <- x$get() i <- solve(data, ...) x$setInverse(i) ## Return a matrix that is the inverse of 'x' i }

b0173b02bb3c0f5932287b29a39f3a9ed36b64a7

f1bc608c0dd10fba6ec997ab774c17632073933b

/Skeletons/rfe.R

d9d085dde8b2a89567213bdbc92a54a5a83b3337

[]

no_license

junonforthis/JUSMASTERARBEIT

9312926099bb8337fcddd384eeaf9a27aa3fec66

82c9ecb9867d2c9df1520af987969d8e536af69d

refs/heads/master

2022-07-22T22:53:54.835339

2019-04-25T23:03:49

null

0

null

UTF-8

R

false

5,342

r

rfe.R

setwd("D:/") cal <- readRDS("E:/calfinal.rds") length(colnames(cal)) sapply(cal,function(x) any(is.na(x))) # reorder columns of cal to set target variables first sapply(cal,function(x) any(is.na(x))) colnames(cal) # choose numeric X-variables calx <- colnames(cal)[c(3:30)] set.seed(7) num <- as.vector(which(sapply(cal[,c(calx)],class)=="numeric")) # calculate correlation matrix correlationMatrix <- cor(as.matrix(cal[,calx[num]]), method = "pearson") # summarize the correlation matrix #print(correlationMatrix) # find attributes that are highly corrected (ideally >0.75) highlyCorrelated <- findCorrelation(correlationMatrix, cutoff=0.5) # print indexes of highly correlated attributes print(highlyCorrelated) sort(highlyCorrelated) calfilter <- calx[num][-highlyCorrelated] calgone <- calx[num][highlyCorrelated] fac <- as.vector(which(sapply(cal[,c(calx)],class)=="factor")) calfac <- colnames(cal[,calx])[fac] chisq.test(table(cal[,"Month"], cal[,"Quarter"])) chisq.test(table(cal[,"Weekday_No"], cal[,"Weekend"])) chisq.test(table(cal[,"Holiday"], cal[,"HolidayWeek"]), simulate.p.value = T) chis = list() twofac <- combn(calfac,2) for (i in 1:ncol(twofac)){ chis[i] <- chisq.test(table(cal[,twofac[1,i]], cal[,twofac[2,i]]), simulate.p.value = T)$p.value } sigchis <- which(chis < 0.05) twofac[,sigchis] # prepare training scheme controlimp <- trainControl(method="timeslice", initialWindow = 7, horizon=1, fixedWindow=T) # train the model model <- train(Weight~., data=cal3, method="cforest", trControl=controlimp) # estimate variable importance importance <- varImp(model, scale=FALSE) # summarize importance print(importance) # plot importance plot(importance) set.seed(84) controlrfe <- rfeControl(functions=caretFuncs, method="timeslice") # run the RFE algorithm results <- rfe(x=cal3[,nonerr], y=cal3[,26], rfeControl=controlrfe, preProc = c("center", "scale"),method="svmRadial", initialWindow=1) # summarize the results print(results) # list the chosen features predictors(results) # plot the results plot(results, type=c("g", "o")) myTimeControl <- trainControl(method = "timeslice", initialWindow = 7, horizon = 1, fixedWindow = F) plsFitTime <- train(Weight ~ ., data = cal3[,c(nonerr,"Weight")], method = "svmRadial", preProc = c("center", "scale"), trControl = myTimeControl) # leave out one variable: cal3 <- cal[,c(calx,"Weight")] saveRDS(cal3, "E:/cal3.rds") fac <- as.vector(which(sapply(cal3,class)=="factor")) cal3[,c(fac)] <- sapply(cal3[,c(fac)], as.numeric) num <- as.vector(which(sapply(cal3[,c(calx)],class)=="numeric")) loo <- function(xvars) { loo <- list() for (i in 1:length(xvars)){ loo[[i]] <- xvars[-i] } return(loo) } foo <- loo(calx) calts <- ts(cal3) nonerr <- colnames(cal3[,c(1:14,16:25)]) iteratevars <- function(target, cal3, nonerr) { firstmod <- auto.arima(target, xreg=as.matrix(cal3[,nonerr ])) nonerr <- loo(nonerr) for (j in 1:length(nonerr)) { model <- auto.arima(target, xreg=as.matrix(cal3[,nonerr[[j]]])) if (model$aic < firstmod$aic) { firstmod <- model thisisit <- j } } return(firstmod) } whazzup <- iteratevars(calts[,26], cal3, nonerr) answer <- names(whazzup$coef) answer <- answer[4:length(answer)] huh <- which(calx %in% answer) calx[huh] answer whazzup$aic firstmod <- auto.arima(calts[,26], xreg=as.matrix(cal3[,nonerr ])) firstmod$aic far1 <- function(target,h) {forecast(auto.arima(target, xreg=as.matrix(cal3[,nonerr])), h = h)} firstmod <- tsCV(calts[,26], forecastfunction = far1, h=1, window=1) itvars <- function(target, cal3, nonerr) { nonerr1 <- nonerr far1 <- function(target1,h) {forecast(auto.arima(target1, xreg=as.matrix(cal3[,nonerr1]), h = h))} firstmod <- tsCV(target, forecastfunction = far1, h = 1, window=1) nonerr2 <- loo(nonerr) for (j in 1:length(nonerr2)) { nonerr1 <- nonerr2[[j]] model <- tsCV(target, forecastfunction = far1, h = 1, window=1) if (rmse(model) < rmse(firstmod)) { firstmod <- model thisisit <- j } } return(firstmod) } whazzupi <- itvars(calts[,26], cal3, nonerr) nonerr1 <- nonerr far1 <- function(target,h) {forecast(auto.arima(target, xreg=as.matrix(cal3[,nonerr1])), h = h)} firstmod <- tsCV(calts[,26], forecastfunction = far1, h=1, window=1) nonerr <- loo(nonerr) nonerr1 <- nonerr[[1]] model <- tsCV(calts[,26], forecastfunction = far1, h = 1, window=1) if (rmse(model) < rmse(firstmod)) { firstmod <- model thisisit <- j } } # # nonerr2 <- loo(nonerr1[[thisisit]]) # for (k in 1:length(nonerr2)) { # model <- auto.arima(calts[,26], xreg=as.matrix(cal3[,nonerr2[[k]]])) # if (model$aic < firstmod$aic) { # firstmod <- model # thisisit <- k # } else{print(thisisit); break} # } # firstmod # nonerr1[[j-1]] # #print(any(is.na(cal[complete.cases(cal[,j]),j]))) # # # model <- auto.arima(calts, xreg=as.matrix(cal[complete.cases(cal[,foo[[1]]]),foo[[1]]])) # # # calts <- ts(cal$Weight) # sapply(cal[complete.cases(cal[,foo[[1]]]),foo[[1]]], class) # sapply(foo, class) # # computationally intense: # # install.packages("sets") # # library(sets) # # set_power(calx) # # set_power(calx[1:5])

1d5c4aec708100adaf57467c1d06cb182afb7e4f

ebe618be227430b4764203e06ae805b6d0fe0d40

/plot3.R

15eb3121ba526ded91eb4177763f965f300e29ff

[]

no_license

goodnick/ExData_Plotting1

492ed1cb679b8d14c48d936dd85925300d7a7a6d

bf55d48597d39459e06cabfe009e78af262d64b0

refs/heads/master

2021-01-18T05:56:53.539395

2016-02-08T03:01:50

51,243,178

0

null

2016-02-07T10:07:54

2016-02-07T10:07:51

null

UTF-8

R

false

906

r

plot3.R

## source the data loading file if it hasn't been sourced already if(!exists("readHousePowerConsumptionData", mode="function")) { source("ReadData.R") } ## Create plot 3 and create png file createPlot3File <- function() { filename <- "plot3.png" png(filename = filename, bg = "transparent") generatePlot3() dev.off() message(paste("plot3 created -", filename)) } ## Generate plot 3 on the current device. generatePlot3 <- function () { houseData <- readHousePowerConsumptionData() plot(houseData$Time, houseData$Sub_metering_1, type = "l", xlab = "", ylab = "Energy sub metering") lines(houseData$Time, houseData$Sub_metering_2, col = "red") lines(houseData$Time, houseData$Sub_metering_3, col = "blue") legend("topright", legend = c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3"), col = c("black", "red", "blue"), lty = "solid") }

55e777abe81e0bc6b3fbc36c3a36f331672b859d

91c6a5300abe4c27e113582d028f5ea7442cd144

/user_dist_DHMM.R

afe589d315ce91abd017fd639b967a926d135592

[]

no_license

bw4sz/Nimble

fa66d7e0dfb9ece7ebaeba97be2ac0cc2be45fa5

4c27441edb47e88ecd7f27e4532e10b962f78a5b

refs/heads/master

2021-01-09T20:05:38.785219

2016-06-27T22:40:00

62,092,944

1

0

null

UTF-8

R

false

5,402

r

user_dist_DHMM.R

## ----eval = FALSE-------------------------------------------------------- ## { ## ## # ------------------------------------------------- ## # Parameters: ## # s: survival probability ## # psiV: transitions from vegetative ## # psiF: transitions from flowering ## # psiD: transitions from dormant ## # ------------------------------------------------- ## # States (S): ## # 1 vegetative ## # 2 flowering ## # 3 dormant ## # 4 dead ## # Observations (O): ## # 1 seen vegetative ## # 2 seen flowering ## # 3 not seen ## # ------------------------------------------------- ## ## # Priors and constraints ## # Survival: uniform ## for (t in 1:(n.occasions-1)){ ## s[t] ~ dunif(0, 1) ## } ## # Transitions: gamma priors ## for (i in 1:3){ ## a[i] ~ dgamma(1, 1) ## psiD[i] <- a[i]/sum(a[]) ## b[i] ~ dgamma(1, 1) ## psiV[i] <- b[i]/sum(b[]) ## c[i] ~ dgamma(1, 1) ## psiF[i] <- c[i]/sum(c[]) ## } ## ## # Define state-transition and observation matrices ## for (i in 1:nind){ ## # Define probabilities of state S(t+1) given S(t) ## for (t in 1:(n.occasions-1)){ ## ps[1,i,t,1] <- s[t] * psiV[1] ## ps[1,i,t,2] <- s[t] * psiV[2] ## ps[1,i,t,3] <- s[t] * psiV[3] ## ps[1,i,t,4] <- 1-s[t] ## ps[2,i,t,1] <- s[t] * psiF[1] ## ps[2,i,t,2] <- s[t] * psiF[2] ## ps[2,i,t,3] <- s[t] * psiF[3] ## ps[2,i,t,4] <- 1-s[t] ## ps[3,i,t,1] <- s[t] * psiD[1] ## ps[3,i,t,2] <- s[t] * psiD[2] ## ps[3,i,t,3] <- s[t] * psiD[3] ## ps[3,i,t,4] <- 1-s[t] ## ps[4,i,t,1] <- 0 ## ps[4,i,t,2] <- 0 ## ps[4,i,t,3] <- 0 ## ps[4,i,t,4] <- 1 ## ## # Define probabilities of O(t) given S(t) ## po[1,i,t,1] <- 1 ## po[1,i,t,2] <- 0 ## po[1,i,t,3] <- 0 ## po[2,i,t,1] <- 0 ## po[2,i,t,2] <- 1 ## po[2,i,t,3] <- 0 ## po[3,i,t,1] <- 0 ## po[3,i,t,2] <- 0 ## po[3,i,t,3] <- 1 ## po[4,i,t,1] <- 0 ## po[4,i,t,2] <- 0 ## po[4,i,t,3] <- 1 ## } #t ## } #i ## ## # Likelihood ## for (i in 1:nind){ ## # Define latent state at first capture ## z[i,f[i]] <- y[i,f[i]] ## for (t in (f[i]+1):n.occasions){ ## # State process: draw S(t) given S(t-1) ## z[i,t] ~ dcat(ps[z[i,t-1], i, t-1,]) ## # Observation process: draw O(t) given S(t) ## y[i,t] ~ dcat(po[z[i,t], i, t-1,]) ## } #t ## } #i ## } ## ----eval=FALSE---------------------------------------------------------- ## ## Filter MCMC for the orchid model ## ## ## load nimble library ## library(nimble) ## ## ## define custom distribution ## dDHMMorchid <- nimbleFunction( ## run = function(x = double(1), length = double(), ## prior = double(1), Z = double(2), ## T = double(3), log.p = double()) { ## pi <- prior ## logL <- 0 ## for(t in 1:length) { ## Zpi <- Z[x[t], ] * pi ## sumZpi <- sum(Zpi) ## logL <- logL + log(sumZpi) ## if(t != length) ## pi <- (T[,,t] %*% Zpi / sumZpi)[ ,1] ## } ## returnType(double()) ## return(logL) ## } ## ) ## ## # this is just a stump that doesn't simulate anything, ## # as we know the MCMC we'll use doesn't need to simulate() ## # from DHMMorchid ## rDHMMorchid <- nimbleFunction( ## run = function(n = integer(), length = double(), ## prior = double(1), Z = double(2), ## T = double(3)) { ## declare(x, double(1, length)) ## returnType(double(1)) ## return(x) ## } ## ) ## ## registerDistributions(list( ## dDHMMorchid = list( ## BUGSdist = 'dDHMMorchid(length, prior, Z, T)', ## types = c('value = double(1)', 'length = double()', 'prior = double(1)', ## 'Z = double(2)', 'T = double(3)'), ## discrete = TRUE ## ) ## )) ## ## ## define hierarchical model ## code <- nimbleCode({ ## for (t in 1:(k-1)) { ## s[t] ~ dunif(0, 1) ## } ## for (i in 1:3) { ## a[i] ~ dgamma(1, 1) ## psiD[i] <- a[i]/sum(a[1:3]) ## b[i] ~ dgamma(1, 1) ## psiV[i] <- b[i]/sum(b[1:3]) ## c[i] ~ dgamma(1, 1) ## psiF[i] <- c[i]/sum(c[1:3]) ## } ## for (t in 1:(k-1)) { ## T[1,1,t] <- s[t] * psiV[1] ## T[2,1,t] <- s[t] * psiV[2] ## T[3,1,t] <- s[t] * psiV[3] ## T[4,1,t] <- 1-s[t] ## T[1,2,t] <- s[t] * psiF[1] ## T[2,2,t] <- s[t] * psiF[2] ## T[3,2,t] <- s[t] * psiF[3] ## T[4,2,t] <- 1-s[t] ## T[1,3,t] <- s[t] * psiD[1] ## T[2,3,t] <- s[t] * psiD[2] ## T[3,3,t] <- s[t] * psiD[3] ## T[4,3,t] <- 1-s[t] ## T[1,4,t] <- 0 ## T[2,4,t] <- 0 ## T[3,4,t] <- 0 ## T[4,4,t] <- 1 ## } ## T[1,1,k] <- 1 ## T[2,1,k] <- 0 ## T[3,1,k] <- 0 ## T[4,1,k] <- 0 ## T[1,2,k] <- 0 ## T[2,2,k] <- 1 ## T[3,2,k] <- 0 ## T[4,2,k] <- 0 ## T[1,3,k] <- 0 ## T[2,3,k] <- 0 ## T[3,3,k] <- 1 ## T[4,3,k] <- 0 ## T[1,4,k] <- 0 ## T[2,4,k] <- 0 ## T[3,4,k] <- 0 ## T[4,4,k] <- 1 ## for (i in 1:nind) { ## y[i, f[i]:k] ~ dDHMMorchid(length = k-f[i]+1, ## prior = prior[1:4], ## Z = Z[1:3,1:4], ## T = T[1:4,1:4,f[i]:k]) ## } ## }) ##

479c2797ee2ea53495d46c97afb09a87c818e158

6c9ca22438b72e8037abe069397b26002ad6761e

/youbike/1105youbike.R

1dcd190b0fbdc795e36caad2f8b296feb7ff1d3c

[]

no_license

m0904104/myR

4a5e72f537838f686b7f8eff56da1db710c97814

a5c3d27db477966eeb7e65d4eb32e05fc675e2e1

refs/heads/main

2023-01-14T01:13:55.975082

2020-11-26T10:33:11

306,333,536

0

null

UTF-8

R

false

595

r

1105youbike.R

setwd("C:/myR1027/Youbike") install.packages('jsonlite') library(jsonlite) file <- "https://quality.data.gov.tw/dq_download_json.php?nid=123026&md5_url=4d8de527a0bcd8a7b1aeae91120f021d" download.file(file,"ubikeNTP.json") jdata <- fromJSON("ubikeNTP.json") str(jdata) jdata$sarea x=table(jdata$sarea) y=sort(x,decreasing = TRUE) z=as.data.frame(y) summary(z$Fr) boxplot(z$Freq) install.packages('data.table') library(data.table) file <- "https://tcgbusfs.blob.core.windows.net/blobyoubike/YouBikeTP.json" download.file(file,"ubikeTP.json") m <- fromJSON("ubikeTP.json") n <- rbindlist(m$retVal)

8b582c2280f2b8224432ee0ea688629fe92d1b4b

347f49f7b0133687424cbd7567bdaef94ef9a02e

/02-Lab exercise - Body Image and Academic Performance of College Students/01_Question1.R

4be8d71d1f908623825aed4b9a31759710b05a22

[]

no_license

kayomotunde/probability-and-statistics

48af600b5f7b320e0daec5b7129c228d2a0c6b91

efb94675646baf28bafdb0b2896256e6abb2b182

refs/heads/master

2022-12-05T01:20:12.682290

2020-08-31T16:49:42

291,742,911

0

null

UTF-8

R

false

1,865

r

01_Question1.R

# Question 1 # Is there a relationship between students' college GPAs and their high school GPAs? # ---- # reflect on question # Typically, there should be a positive trend and moderate to high strength # of the relationship between the two variables, as it is expected that # High School performance is a pre-cursor to results in College # ---- # variables selected - HS_GPA and GPA # both variables are quantitative # HS_GPA - explanatory variable ('x') # GPA - response variable ('y') # Exploratory Data Analysis # Using a scatter-plot and correlation r (if applicable), # let's describe the relationship between the two variables # conduct analysis x_scale <- c(1.2, 4.0) y_scale <- c(1.2, 4.5) plot(data$HS_GPA, data$GPA, xlim = x_scale, ylim = y_scale) # Scatter-plot cor(data$HS_GPA, data$GPA, use = "c") # ---- # The results indicate that in general the students who have a higher # high school GPA will tend to also have a higher college GPA, and vice versa. # The results further indicate that this relationship is linear # and that it is moderately strong. # ---- # Since the relationship between the students' high school GPAs and college GPAs # is linear (as displayed by the scatterplot and supported by the # correlation coefficient r being relatively close to 1), # it makes sense to go ahead and find the regression line - # the line that best fits the linear pattern of our data L=lm(data$GPA~data$HS_GPA); abline(L); cf=coefficients(L); lt=paste("GPA = ",round(cf[1],2),"+",round(cf[2],2),"HS_GPA") legend(1.7,4.3,lt) # The results are consistent with what can be expected. # ---- # Suggestion # We should intervene and counsel students while still in high school. # Knowing that students with low GPAs are at risk of not doing well in college, # colleges should develop programs, such as peer or faculty mentors, for these students.

a5824b406394ae4aa24b6c6113060ce9e0ed4af0

f4f588a622c6c93e3261dbf4b90852c5a3833757

/upload_to_trello.R

33f3c7d1e52fd6432df7ccb37713bd4e864a025d

[]

no_license

SteveOhh/trello_trade_board

82e9185c3e4d4cb78c3ce267e9b7353ecc8fa818

c864b59de9d07f5a2cc568eaaa29b38e29791ae1

refs/heads/main

2023-03-04T16:57:12.568411

2021-02-04T19:54:35

336,013,948

2

0

null

UTF-8

R

false

4,628

r

upload_to_trello.R

require(trelloR) ### get key & secret from https://trello.com/app-key ### use personal Trello account # Only do this once https://github.com/jchrom/trelloR/issues/32 my_token = get_token(appname = "Trade board project", key = "30278732032ff952a83401244dd94968", secret = NULL, # should read from cache scope = c("read", "write"), expiration = "never" ) ### Load most recent data load("./Data/coffees.Rdata") ############ ############ ############ ############ Set up ########### ############ ############ ############ ### Navigated in browser to # create the board # delete the lists I don't want, # create a list I do want ("Trade coffee list) # get the board ID # create custom fields ###################### Make card function ######################### ### <final> Create one card for each coffee, with names a concat of roaster/product names # For each... # Make card name # Make card description # Create card # Store card ID in the same ? dataframe (new_card$id) ################################################################## ##### ##### ##### ##### ##### Prep ##### ##### ##### ##### ##### ##### ### Global variables # Store board ID board_id <- "5fe8d603949d7e3f69354b90" # Target list list_ids <- get_board_lists(board_id) coffee_list_id <- list_ids[list_ids$name=="Trade coffee list","id"] %>% as.character() # Get card custom fields' IDs board_custom_fields <- get_board_cards(board_id)[1,1] %>% get_card_fields() # Store field names cfs <- c("Price", "Producer", "Sub_region", "Process", "Variety", "Elevation", "URL") # Function for attachments add_attachments <- function(card_id, coffee) { # global variables token <- my_token$credentials$oauth_token key <- "30278732032ff952a83401244dd94968" # obviously not global, but it could be # defined by inputs put_url <- paste0("https://api.trello.com/1/cards/", card_id, "/attachments/") image_payload <- list(name = "PNG", url = coffee$image, mimeType = "image/png") url_payload <- coffee$url httr::POST(url = paste0(put_url, '?key=', key, '&token=', token), body = image_payload, encode = "json" ) httr::POST(url = paste0(put_url, '?key=', key, '&token=', token), body = url_payload, encode = "json" ) } ##### ##### ##### ##### ##### Make a card ##### ##### ##### ##### ##### make_coffee_card <- function(coffee) { # Description function source("./populate_card_description.R") # Card title roaster <- coffee$roaster_name product_name <- coffee$product_name # Description roaster_taste_notes <- coffee$roaster_taste_notes trade_taste_notes <- coffee$trade_taste_notes taste_profile <- coffee$taste_profile description <- populate_card_description(roaster_taste_notes, trade_taste_notes, taste_profile) gps_coordinates <- coffee$source_gps_string # not sure how to handle NAs; trying it out # Card list, name, description, and map coordindates to send with request card_details <- list(name = paste0(product_name, " - ", roaster), desc = description, coordinates = gps_coordinates, pos = "bottom") # Create card & store details (so you can call the ID) new_card <- add_card(list = coffee_list_id, body = card_details, token = my_token) # Update custom fields for(cf in cfs) { column_name <- tolower(cf) field_id <- board_custom_fields[board_custom_fields$name==cf,"id"]%>% as.character() field_key <- "text" raw_field_value <- coffee[1,column_name] %>% # coerce tibble to character as.character() %>% # handle empty result ifelse(.=="character(0)", "", .) # field_value <- ifelse(raw_field_value=="character(0)", "", raw_field_value) # # this is deprecated, but I don't see how to use update_resource() update_card_field(card = new_card$id, field = field_id, key = field_key, value = field_value, token = my_token) } # Attach image and URL (* need to learn how to add attachment) add_attachments(card_id = new_card$id, coffee = coffee) # Add decaf label (*need to learn) if(coffee$decaf==TRUE) { add_label(card = new_card$id, color = "yellow", name = "Decaf") } return(new_card$id) } ### Upload from all rows in the df for(i in 1:nrow(coffees)) { df <- tibble(coffees[i,]) make_coffee_card(df) }

57ed6fb54c9ce904a9d17ce1b3e69b8d4d309f86

3f0a9af0e7d37102e6872fed916ddf0b670b0164

/man/SAVE.controls.Rd

e9f9689a354afd1e4816e2bbdefd774f99909eca

[]

no_license

cran/SAVE

530468b32e40f5d5031f9b37185a42cbe9cd8dae

c59892f2db42057e6be88aa895673257c7751f66

refs/heads/master

2016-09-06T07:51:42.532014

2015-03-14T00:00:00

17,693,483

0

1

null

UTF-8

R

false

2,707

rd

SAVE.controls.Rd

\name{SAVE.controls} \alias{SAVE.controls} \title{ Control parameters for kriging process. } \description{ Returns a list suitable as the \code{kriging.controls} argument of the \code{SAVE} function.} \usage{SAVE.controls(lower = NULL, upper = NULL, optim.method = "BFGS", parinit = NULL,...)} \arguments{ \item{lower}{(see below)} \item{upper}{optional vector containing the bounds of the correlation parameters for optimization in function \code{\link[DiceKriging]{km}} in package \code{DiceKriging}. The default values are given by \code{\link[DiceKriging]{covParametersBounds}}.} \item{optim.method}{an optional character string indicating which optimization method is chosen for the likelihood maximization. \code{"BFGS"} is the \code{optim} quasi-Newton procedure of package \code{stats}, with the method "L-BFGS-B". \code{"gen"} is the \code{genoud} genetic algorithm (using derivatives) from package \code{rgenoud} (>= 5.3.3).} \item{parinit}{an optional vector containing the initial values for the variables to be optimized over. If no vector is given, an initial point is generated as follows. For method \code{"gen"}, the initial point is generated uniformly inside the hyper-rectangle domain defined by \code{lower} and \code{upper}. For method \code{"BFGS"}, some points (see \code{control} below) are generated uniformly in the domain. Then the best point with respect to the likelihood (or penalized likelihood, see \code{penalty}) criterion is chosen.} \item{\dots}{arguments for \code{SAVE.controls} if they are not given explicitly. Current version deprecates any other argument different than those listed above.} } \value{ a list of parameters that are allowed to be passed to the \code{\link[DiceKriging]{km}} function in package \code{\link[DiceKriging]{DiceKriging}}. \item{lower}{(see above)} \item{upper}{(see above)} \item{optim.method}{(see above)} \item{parinit}{(see above)} } \author{ Jesus Palomo, Rui Paulo and Gonzalo Garcia-Donato } \seealso{See Also \code{\link[DiceKriging]{DiceKriging}} for more details on the parametes.} \examples{ \dontrun{ library(SAVE) sc <- SAVE.controls(lower = 0.2, upper = 0.7, optim.method = "BFGS") sc1 <- SAVE.controls(lower = 0.2, upper = 0.7, optim.method = "BFGS", coef.var=1, coef.trend=0) ### In this case, the parameters \code{coef.var} and \code{coef.trend} ### will be deprecated since \code{\link{SAVE}} does not allow to fix ### any parameter different than: \code{lower}, \code{upper}, ### \code{optim.method} and \code{parinit}. } } \keyword{ internal }

353baa8b9c99a98c90407c591492cb15aa114a85

50899f7d432f8604984301a86936f580cab012cd

/analyses/examine_batch_ethnicity_effects_by_gene_wrap_parallelized.R

0024d18c98e7e682c68a962e62c0a90ad4ae5fb4

[ "MIT" ]

permissive

Huang-lab/SequenceVariantCallEvaluation

e4476419b1cbdaaf4fbd4eadbcb87c7fd9b0ac15

994e52d87f40c781ebe55a80fd6109f8aa9ec7de

refs/heads/master

2020-04-04T20:18:38.711915

2018-12-04T19:53:21

156,241,982

0

null

UTF-8

R

false

3,807

r

examine_batch_ethnicity_effects_by_gene_wrap_parallelized.R

##### examine_batch_ethnicity_effects.R ##### # Kuan-lin Huang @ MSSM # implementation of LMG to find independent contribution of regressors # library("relaimpo") # couldn't get this to work, seems to require all quantitative variables library("hier.part") system("mkdir out") # take input arguments args = commandArgs(trailingOnly=TRUE) # read in files if (length(args)<1) { stop("At least two argument must be supplied [input file] [output tag name].n", call.=FALSE) } input_file = args[1] out_name = args[2] # read in clinical data file clin_f = "PanCan_ClinicalData_V4_wAIM_filtered10389.txt" clin = read.table(header=T, quote = "", sep="\t", fill =T, file = clin_f, stringsAsFactors=FALSE) # read in and process meta data file meta_f = "tcga_meta_data.normals_only.Cases_N10389.txt" meta = read.table(header=F, sep="\t", file=meta_f, fill=T) colnames(meta) = c("bcr_patient_barcode","ID","cancer","X","assay","center","date","platform","Y","reagent","capture") meta$capture_brief = gsub("\\|.*","",meta$capture) meta$year = gsub(".*/.*/([0-9]+)","\\1",meta$date) meta = meta[order(meta$year,decreasing = T),]# keep only the later year meta_uniq = meta[!duplicated(meta$bcr_patient_barcode),] meta_uniq$analyte = gsub("TCGA-..-....-...-..(.)-.*","\\1",meta_uniq$ID) meta_uniq$analyte[meta_uniq$analyte=="X"] = "D" # read in and process istat variant count file istat_all = read.table(header=F, sep="\t", file=gzfile(input_file), fill=T) colnames(istat_all) = c("ID","NALT","NMIN","NHET","NVAR","RATE","SING","TITV","PASS","PASS_S","QUAL","DP","geneName","fileName") istat_all$geneName = gsub("istat/","",istat_all$geneName) istat_all$bcr_patient_barcode = substr(istat_all$ID,1,12) ##### individual level stats ##### for (gene in unique(istat_all$geneName)){ tryCatch({ # gene = unique(istat_all$geneName)[1] # trouble-shoot istat = istat_all[istat_all$geneName==gene,] istat_clin = merge(istat,clin[,c("bcr_patient_barcode","consensus_call")],by="bcr_patient_barcode") istat_clin_meta = merge(istat_clin, meta_uniq[,-which(colnames(meta_uniq) %in% c("bcr_sample_barcode","X","Y","assay"))], by=c("ID")) # note: can consider using capture_brief instead to avoid over-fitting/attribution # LMG test istat_clin_eur = istat_clin_meta[istat_clin_meta$consensus_call=="eur",] # pdf("variation_BRCA1.pdf",width=10) variation_explained = hier.part(istat_clin_meta$NVAR, istat_clin_meta[,c("capture","platform","reagent","year","center","analyte","consensus_call")], family = "gaussian", gof = "Rsqu")$I.perc # dev.off() variation_explained_eur = hier.part(istat_clin_eur$NVAR, istat_clin_eur[,c("capture","platform","reagent","year","center","analyte")], family = "gaussian", gof = "Rsqu")$I.perc # # regression test # fit = lm(data = istat_clin, NVAR ~ consensus_call + type + Center + Analyte) # the order of importance found using LMG # summary(fit) # results = data.frame(anova(fit)) # # fit = lm(data = istat_clin, NVAR ~ consensus_call + type + Center + Analyte) # the order of importance found using LMG # summary(fit) # results = data.frame(anova(fit)) # # fit = lm(data = istat_clin_eur, NVAR ~ type + Center + Analyte) # the order of importance found using LMG # summary(fit) # results = data.frame(anova(fit)) # concatenate gene name into output variation_explained$gene = gene variation_explained_eur$gene = gene write.table(variation_explained, quote=F,col.names = F,file = paste("out/all_gene_istat_full_exon_",out_name,".tsv",sep=""), append = TRUE, sep = "\t") write.table(variation_explained_eur, quote=F,col.names = F,file = paste("out/all_gene_istat_eur_full_exon_",out_name,".tsv",sep=""), append = TRUE, sep = "\t") }, error=function(e){cat("ERROR for gene:",gene, "\n")}) }

6606ba45b4ee4543f88f4cd75c0e8771db390573

8d119dec4eee3d38e8a72456b09e2f95002bbf93

/Bike_SF/pull.R

c5e52d3f991df990de37d7f1f138f75eeeb40c91

[]

no_license

ksk8/SFbiking

a918470c7abd873d568864c280da94441d76fd4c

7cd0a8d508b3c0a0281f5f46a15030e45193e670

refs/heads/master

2020-04-01T20:27:37.625868

2016-07-11T19:22:41

63,095,624

0

null

UTF-8

R

false

104

r

pull.R

pull <- function(x,y) {x[,if(is.name(substitute(y))) deparse(substitute(y)) else y, drop = FALSE][[1]]}

0cc3b2cde1d20e0bc5ba521e1dc0b6aea37f1eba

62ee1070ede548d33df78210a1aafa0831309dc5

/global.R

0239f847071b0cdc1bbb45473ff5bb94bb705e0d

[]

no_license

brianmwangy/Teen-pregnancy-application

fd4666283dab4cf0c0e7185b4ba06e2af3c2bd8f

ed8b8fe357e8ee16b73a36533e1a236377ff657e

refs/heads/main

2023-01-13T17:32:46.603982

2020-11-21T06:16:21

null

0

null

UTF-8

R

false

848

r

global.R

library(dplyr) library(readr) library(zoo) library(tidyr) library(rgdal) library(raster) #loading national teen pregnancy stats national<-read_csv("./www/nation.csv") national$period<-as.yearqtr(national$period,"%Y Q%q") #loading ward teen pregnancy stats wardf<-read_csv("./www/wardf2.csv") wardf$period<-as.yearqtr(wardf$period,"%Y Q%q") yr<-unique(wardf$period) cnty<-unique(wardf$county) preg<-unique(wardf$Type) #loading county stats county<-read_csv("./www/county.csv") county$period<-as.yearqtr(county$period,"%Y Q%q") cnty2<-unique(county$county) #loading subcounty stats subcounty<-read_csv("./www/subcounty.csv") subcounty$period<-as.yearqtr(subcounty$period,"%Y Q%q") cnty3<-unique(subcounty$county) preg2<-unique(subcounty$type) #loading the kenya basemap bsmap<-readOGR("./www/year2.shp")

6d4a9d9b40580c1f5e4361ad1c6c429c7eaf95c9

58f46e3a93ef5369ad8dc73ea3b21db829f1dcac

/tests/test-all.R

fe37b93d3a6f37bc90cfeb9db3902c734d95cf3d

[]

no_license

firebitsbr/sslsaran

4702023b7eae80a60e95e8021aa7ee67b488e332

059dce4fb35e18596642216e05895ca3e18f9e6c

refs/heads/master

2020-04-07T21:23:27.975802

2018-02-20T17:51:43

null

0

null

UTF-8

R

false

41

r

test-all.R

library(testthat) test_check("sslsaran")

b4c7304abf4b1c8e960bf5b7b9dad3b3aba625eb

f18e1210ca120c9116e356a8549e89e04219dc75

/tests/by_hand_tests/test_trelliscope.R

5ba37aa8d5cc00b277e78a8664705b3f066660db

[ "BSD-2-Clause" ]

permissive

EMSL-Computing/ftmsRanalysis

46c73a727d7c5d5a5320bf97a07e9dac72abd281

dd3bc3afbf6d1250d1f86e22b936dcc154f4101d

refs/heads/master

2023-07-21T18:13:26.355313

2023-02-09T17:03:09

122,233,846

14

10

NOASSERTION

2023-07-11T16:34:15

2018-02-20T17:52:18

R

UTF-8

R

false

4,837

r

test_trelliscope.R

## Tests of creating trelliscope displays with the convenience functions in ftmsRanalysis library(ftmsRanalysis) library(trelliscope) vdbDir <- vdbConn(file.path(tempdir(), "trell_test"), autoYes = TRUE) data('exampleProcessedPeakData') ########################################################## ## SAMPLE PLOTS sampleDdo <- divideBySample(exampleProcessedPeakData) ## TEST S-1: VK plot, color by VK category panelFnS1 <- panelFunctionGenerator("vanKrevelenPlot", vkBoundarySet="bs2", title="Test") makeDisplay(sampleDdo, panelFn=panelFnS1, cogFn=vanKrevelenCognostics(vkBoundarySet="bs2"), name = "Trelliscope test S_1 with VK", group = "Sample") ## TEST S-2: Kendrick plot, color by VK category panelFnS2 <- panelFunctionGenerator("kendrickPlot", vkBoundarySet="bs1") makeDisplay(sampleDdo, panelFn=panelFnS2, cogFn=kendrickCognostics(vkBoundarySet="bs1"), name = "Trelliscope test S_2 with Kendrick", group = "Sample") ## TEST S-3: VK plot, color by Intensity panelFnS3 <- panelFunctionGenerator("vanKrevelenPlot", colorCName="Intensity") makeDisplay(sampleDdo, panelFn=panelFnS3, cogFn=vanKrevelenCognostics(), name = "Trelliscope test S_3 with VK", group = "Sample") ## TEST S-4: densityPlot of NOSC panelFn4 <- panelFunctionGenerator("densityPlot", variable="NOSC") makeDisplay(sampleDdo, panelFn=panelFn4, cogFn=densityCognostics("NOSC"), name = "Trelliscope test S_4 with density", group = "Sample") ########################################################## ## GROUP PLOTS groupDdo <- divideByGroup(exampleProcessedPeakData) groupSummaryDdo <- summarizeGroups(groupDdo, summary_functions = c("prop_present", "n_present")) ## TEST G-1: VK group plot, color by proportion present panelFnG1 <- panelFunctionGenerator("vanKrevelenPlot", colorCName=expr(paste0(getSplitVar(v, "Group"), "_prop_present")), legendTitle="Proportion Present") makeDisplay(groupSummaryDdo, panelFn=panelFnG1, cogFn=vanKrevelenCognostics(), name = "Trelliscope test G_1 with VK plot per group", group = "Group") ## TEST G-2: Kendrick group plot, color by n present panelFnG2 <- panelFunctionGenerator("kendrickPlot", colorCName=expr(paste0(getSplitVar(v, "Group"), "_n_present")), legendTitle="Number Present") makeDisplay(groupSummaryDdo, panelFn=panelFnG2, cogFn=kendrickCognostics(), name = "Trelliscope test G_2 with Kendrick plot per group", group = "Group") ## TEST G-3: classes plot for each group panelFnG3 <- panelFunctionGenerator("classesPlot") makeDisplay(groupDdo, panelFn=panelFnG3, name = "Trelliscope test G_3 with classesPlot", group = "Group") ## TEST G-4: density plot for each group panelFnG4 <- panelFunctionGenerator("densityPlot", variable="NOSC", groups=NA) makeDisplay(groupDdo, panelFn=panelFnG4, cogFn=densityCognostics("NOSC"), name = "Trelliscope test G_4 with densityPlot", group = "Group") view() ########################################################## ## GROUP COMPARISON PLOTS exampleProcessedPeakData <- assign_class(exampleProcessedPeakData, "bs1") grpCompDdo <- divideByGroupComparisons(exampleProcessedPeakData, "all") grpCompSummaryDdo <- summarizeGroupComparisons(grpCompDdo, summary_functions="uniqueness_gtest", summary_function_params=list(uniqueness_gtest=list(pres_fn="nsamps", pres_thresh=2, pvalue_thresh=0.05))) ## TEST GC-1: VK group comparison plot panelFnGC1 <- panelFunctionGenerator("vanKrevelenPlot", colorCName="uniqueness_gtest") makeDisplay(grpCompSummaryDdo, panelFn=panelFnGC1, cogFn=vanKrevelenCognostics(), name = "Trelliscope test GC_1 with VK group comparison", group = "Group_Comparison") ## TEST GC-2: Kendrick group comparison plot panelFnGC2 <- panelFunctionGenerator("kendrickPlot", colorCName="uniqueness_gtest") makeDisplay(grpCompSummaryDdo, panelFn=panelFnGC2, cogFn=kendrickCognostics(), name = "Trelliscope test GC_2 with Kendrick group comparison", group = "Group_Comparison") ## TEST GC-3: Density group comparison plot panelFnGC3 <- panelFunctionGenerator("densityPlot", variable="NOSC", samples=FALSE, groups=NA) makeDisplay(grpCompDdo, panelFn=panelFnGC3, cogFn=densityCognostics("NOSC"), name = "Trelliscope test GC_3 with density on group comparison", group = "Group_Comparison")

9fd435e8320de866c44a3947a51a83e4e9077202

747564318a56ac683165b03ee6f1157712c2e4ce

/inst/examples/list_experiments.R

78e8d5bd92d6155c4e73659821804e3b32250311

[]

no_license

dataandcrowd/gamar

626a9758ec58b81db71f3363a77e732cf092eecc

3e9dd2b61b55816f79c16d152d5de32d6e9ea71d

refs/heads/master

2023-04-02T18:00:01.423668

2021-04-09T14:44:43

null

0

null

UTF-8

R

false

94

r

list_experiments.R

gaml_file <- system.file("models", "sir.gaml", package = "gamar") list_experiments(gaml_file)

df1980c294cb72c38e496743aad1c58eec9cacba

3a5fa834091a8fd9d9749fcd6cb2a0bfea46ac62

/trying_so_hard.R

147a3899e2bc1c9c6043b577d54f8be33d67a082

[]

no_license

foundinblank/study1adults

47100eb05ef9237ef452bb8e6e5497a6a17241e2

16239dbdb1a2b88678a072eb398a6d1a025c7d9c

refs/heads/master

2022-08-03T17:41:14.491830

2020-05-23T12:11:23

102,015,531

0

null

UTF-8

R

false

3,949

r

trying_so_hard.R

# Run to line 300 monica_y <- monica %>% pull(y_ma5) rhand_y <- rhand_expanded %>% pull(y) monica_new <- monica_y[121:2400] rhand_new <- rhand_y[121:2400] mlpar <- list(lgM = 120, radiusspan = 100, radiussample = 10, normalize = 2, rescale = 2, mindiagline = 2, minvertline = 2, tw = 0, whiteline = FALSE, recpt = FALSE, fnnpercent = 10, typeami = "maxlag") lists <- data_ma %>% group_by(name) %>% fill(y_ma5, .direction = "down") %>% fill(y_ma5, .direction = "up") %>% # slice(121:(n()-120)) %>% summarise(y = list(y_ma5)) library(furrr) plan(multiprocess) optimize_params <- lists %>% mutate(params = future_map(y, ~ optimizeParam(.x, rhand_y, mlpar))) output_params <- optimize_params %>% group_by(name) %>% mutate(r = pluck(params, 1, 1), dim = pluck(params, 1, 2), delay = pluck(params, 1, 3)) param_means <- output_params %>% ungroup() %>% summarise(r_mean = ceiling(mean(r)), dim_mean = ceiling(mean(dim)), delay_mean = ceiling(mean(delay))) crqa_data <- lists %>% group_by(name) %>% mutate(rhand = future_map(y, ~ crqa(.x, rhand_y, delay = param_means$delay_mean, embed = param_means$dim_mean, rescale = 2, radius = param_means$r_mean+2, normalize = 2, mindiagline = 2, minvertline = 2, tw = 0, whiteline = FALSE, recpt = FALSE, side = 'both'))) write_rds(crqa_data, "~/Desktop/crqa.rds") crqa_results <- crqa_data %>% mutate(rhand_rr = map_dbl(rhand, pluck, "RR"), rhand_det = map_dbl(rhand, pluck, "DET")) %>% left_join(participants) %>% select(name, maingroup, rhand_rr, rhand_det) crqa_results %>% ggplot(aes(x = maingroup, y = rhand_rr, color = maingroup)) + geom_jitter() crqa_results %>% ggplot(aes(x = rhand_rr)) + geom_histogram() params_and_output <- crqa_results %>% left_join(output_params, by = "name") %>% select(-y, -params) params_and_output %>% ggplot(aes(x = rhand_det, y = rhand_rr, label = name)) + geom_point() library(GGally) ggpairs(params_and_output[,3:7]) # fitler out one extreme value... params_and_output %>% ungroup() %>% filter(rhand_rr < 60) %>% ggstatsplot::ggbetweenstats(x = maingroup, y = rhand_rr) that1 <- optimizeParam(monica_y, rhand_y, mlpar) that1 rqaAns <- crqa(monica_y, rhand_y, radius = 33, embed = 14, delay = 117, rescale = 2, normalize = 2, mindiagline = 2, minvertline = 2, tw = 0, whiteline = FALSE, recpt = FALSE, side = "both") rqaMetrics <- c(rqaAns[1], rqaAns[2], rqaAns[5]); rqaMetrics mRP <- data.table::melt(as.matrix(rqaAns$RP), varnames=c("TimeV1", "TimeV2"), value.name="Recurrence") binary <- ggplot(mRP, aes(x = TimeV1, y = TimeV2, fill = Recurrence)) + geom_raster() + theme(axis.line = element_blank(), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_blank(), axis.text = element_blank(), axis.ticks = element_blank(), axis.title = element_blank()) + ggtitle("Binary Cross Recurrence Plot") + scale_fill_manual(values = c("#9999ff","#000066"), breaks = c(TRUE, FALSE)) + theme(legend.position = "none", plot.title = element_text(size = 16, face = "bold")) binary library(tseriesChaos) lists %>% group_by(name) %>% mutate(ami = map(y, ~ mutual(.x, lag.max = 240, plot = F))) %>% mutate(that = map_dbl(ami, min)) OKAY BREAKTHROUGH

42bada5f61d1b7f0587017bb00a44f42c2f036e8

441f9a4ab2fd14bd529dc2edcc12b15495c034fb

/src/eval_shared_pathway_auc.R

10c67321d9878c05854257b9f04555e7cdf3df65

[ "MIT" ]

permissive

alorchhota/spice_analysis

9e6670225c69be7ece92ed9b5f81d1c0659b995b

f66b43f9aa75e4bed416abdaaa5327bc3c27c6f9

refs/heads/master

2023-03-22T04:07:41.109076

2021-03-11T22:10:46

300,128,702

0

null

UTF-8

R

false

2,168

r

eval_shared_pathway_auc.R

library(argparser) library(spice) args <- arg_parser("program"); args <- add_argument(args, "--net", help="network file (rds)", default="/Users/ashissaha/github/spice_analysis/results/spice_results/gtex_v8/results/Muscle_Skeletal/corrected/AXPVAR/1500/pearson_network.rds") args <- add_argument(args, "--pathway", help="pathway file (rds)", default="/Users/ashissaha/github/spice_analysis/results/spice_results/shared_data/msigdb/kegg_genesets.rds") args <- add_argument(args, "--curve", help="Should the curves be returned?", default=TRUE) args <- add_argument(args, "--max", help="compute max auc?", default=TRUE) args <- add_argument(args, "--min", help="compute min auc?", default=TRUE) args <- add_argument(args, "--rand", help="compute random auc?", default=FALSE) args <- add_argument(args, "--dg", help="compute auc using Davis and Goadrich algorithm?", default=FALSE) args <- add_argument(args, "--na_rm", help="remove NA?", default=FALSE) args <- add_argument(args, "--neg", help="negative value handling", default="error") args <- add_argument(args, "--o", help="Output file (rds)", default="results/shared_pathway_auc.rds") ### parse args argv = parse_args(args) net_fn = argv$net pathway_fn = argv$pathway curve = as.logical(argv$curve) max_compute = as.logical(argv$max) min_compute = as.logical(argv$min) rand_compute = as.logical(argv$rand) dg_compute = as.logical(argv$dg) na_rm = as.logical(argv$na_rm) neg_treat = argv$neg out_fn = argv$o ### check variables stopifnot(file.exists(net_fn)) stopifnot(file.exists(pathway_fn)) stopifnot(neg_treat %in% c("none", "warn", "error")) stopifnot(dir.exists(dirname(out_fn))) ### load from inputs net_mat = readRDS(net_fn) pathways = readRDS(pathway_fn) ### check network stopifnot(is.matrix(net_mat)) stopifnot(length(rownames(net_mat))>0) ### compute shared-pathway auc shared_pathway_auc = spice::coexpression_shared_pathway_auc( net = net_mat, pathways = pathways, curve = curve, max.compute = max_compute, min.compute = min_compute, rand.compute = rand_compute, dg.compute = dg_compute, na.rm = na_rm, neg.treat = neg_treat) ### save saveRDS(shared_pathway_auc, file = out_fn)

0af33358309e438be8e1511cadaee92107651510

4d07eecae0429dc15066b34fbe512b8ff2ae53ea

/mds/aral/pointplot.R

6a1880e4a0f8852a5a9b16302231f4148d681669

[]

no_license

distanceModling/phd-smoothing

7ff8ba7bace1a7d1fa9e2fcbd4096b82a126c53c

80305f504865ce6afbc817fff83382678864b11d

refs/heads/master

2020-12-01T09:31:24.448615

2012-03-27T18:35:45

null

0

null

UTF-8

R

false

348

r

pointplot.R

# load the fitted model data file and look at what happened. load("aral/aralfit.RData") pdf(file="../thesis/mds/figs/aral-pp.pdf",width=6,height=3) par(mfrow=c(1,2),las=1,mgp=c(1.5,0.75,0),mar=c(3,3,2,2),cex.axis=0.5,cex.lab=0.7) plot(pred.grid,pch=".",xlab="x",ylab="y",asp=1) plot(pred.grid.mds,pch=".",xlab="x*",ylab="y*",asp=1) dev.off()

23ae3ae9b2e0b92ec844d4b92d7247385a3c6d0a

d97e2169ce9cd893920a54cffa3e754d1e309e6f

/R/gen_coreDataManip.r

0767953b7b157a3508bb2c246bb0ea52c9111741

[]

no_license

bpollner/aquap2

5ccef0ba4423413e56df77a1d2d83967bffd1d04

7f7e2cf84056aad4c8a66f55d099b7bdaa42c0be

refs/heads/master

2021-07-22T15:07:22.912086

2021-05-27T12:50:22

30,932,899

2

1

null

UTF-8

R

false

36,467

r

gen_coreDataManip.r

#' @title Perform Smoothing and / or m-th Derivative #' @description Performs a smoothing and / or the calculation of the m-th #' derivative on the given dataset using the Savitzky-Golay filter. #' @details The underlying function is \code{\link[signal]{sgolayfilt}}. #' @param dataset An object of class 'aquap_data" as produced by #' \code{\link{gfd}} or as can be extracted from a 'cube' via #' \code{\link{getcd}}. #' @param p Numeric length one, the order of the filter. Default = 2. #' @param n Numeric length one, the filter length, must be odd. Default = 21. #' @param m Numeric length one, Return the m-th derivative of the filter #' coefficients. Default = 0. #' @param exportModel Logical. If a possible model should be stored in the set, #' (leave at \code{FALSE}). #' @return Returns the dataset with the NIR-data smoothed or transformed. #' @examples #' \dontrun{ #' fd <- gfd() # get the full dataset #' fd <- selectWls(fd, 1100, 1800) #' fd_avg <- do_avg(fd, n=71) # apply strong smoothing #' plot(fd - fd_avg, pg.where="", pg.main="| smoothed subtracted") #' } #' @family Data pre-treatment functions #' @family dpt modules documentation #' @export do_sgolay <- function(dataset, p=2, n=21, m=0, exportModel=FALSE) { autoUpS() cns <- colnames(dataset$NIR) rns <- rownames(dataset$NIR) NIR <- t(apply(dataset$NIR, 1, signal::sgolayfilt, p=p, n=n, m=m)) exportAdditionalModelToAp2Env(doExport=exportModel, thisMod=NULL, thisType=pv_dptModules[1]) # sgol colnames(NIR) <- cns rownames(NIR) <- rns dataset$NIR <- I(NIR) return(dataset) } # EOF # is used when we have no access to .ap2 like in parallel operations do_sgolay_sys <- function(dataset, p=2, n=21, m=0) { cns <- colnames(dataset$NIR) rns <- rownames(dataset$NIR) NIR <- t(apply(dataset$NIR, 1, signal::sgolayfilt, p=p, n=n, m=m)) colnames(NIR) <- cns rownames(NIR) <- rns dataset$NIR <- I(NIR) return(dataset) } # EOF ### for baseline removal #' @title Calculate Standard Normal Variation SNV #' @description Calculate the standard normal variation (SNV) by autoscaling the #' transformed NIR-data #' @inheritParams do_sgolay #' @return Returns the dataset with the transformed NIR data. #' @examples #' \dontrun{ #' fd <- gfd() # get the full dataset #' fd <- selectWls(fd, 1100, 1800) #' fd_snv <- do_snv(fd) #' plot(fd - fd_snv, pg.where="", pg.main="| snv subtracted") #' } #' @family Data pre-treatment function #' @family dpt modules documentation #' @export do_snv <- function(dataset, exportModel=FALSE) { autoUpS() NIR <- t(scale(t(dataset$NIR),center=TRUE,scale=TRUE)) exportAdditionalModelToAp2Env(doExport=exportModel, thisMod=NULL, thisType=pv_dptModules[2]) # snv colnames(NIR) <- colnames(dataset$NIR) rownames(NIR) <- rownames(dataset$NIR) dataset$NIR <- I(NIR) return(dataset) } # EOF #' @title Perform MSC #' @description Performs MSC (multiplicative scatter correction) on the #' provided dataset. #' @details If no reference is provided, the average of all spectra of the provided #' dataset is used as a reference for the baseline correction. Provide a dataset #' with a single spectrum (as e.g. produced by \code{\link{do_avg}}) to use this #' as a reference for baseline correction. Internally, the function #' \code{\link[pls]{msc}} is used. #' @inheritParams do_sgolay #' @param ref An object of class 'aquap_data' containing a single spectrum, #' i.e. a single row (as e.g. produced by \code{\link{do_avg}}, or by #' subscripting via '[]'). If no reference is provided, the average of all spectra #' of the provided dataset is used as a reference for the baseline correction. #' @param extMscModel Provide an external msc model to use this for predicting #' the data instead of running msc on the data. Experimental feature. #' @return Returns the dataset with the transformed NIR data. #' @seealso \code{\link{ssc}} for sub-selecting in a datset using a specific #' criterion. #' @examples #' \dontrun{ #' fd <- gfd() # get the full dataset #' fd <- selectWls(fd, 1100, 1800) #' fd_msc <- do_msc(fd) #' plot(fd - fd_msc, pg.where="", pg.main="| msc subtracted") #' fd_msc_ref <- do_msc(fd, ref=fd[4]) # use the 4th row of fd as a reference #' plot(fd - fd_msc_ref, pg.where="", pg.main="| 4th row as reference") #' fd_msc_mq <- do_msc(fd, ref=do_avg(ssc(fd, C_Group=="MQ"))) # is assuming the #' # existence of a column name "C_Group" and one or more of its values being "MQ" #' plot(fd - fd_msc_mq, pg.where="", pg.main="| average of MQ as reference") #' } #' @seealso \code{\link{getcd}} #' @family Data pre-treatment functions #' @family dpt modules documentation #' @export do_msc <- function(dataset, ref=NULL, extMscModel=NULL, exportModel=FALSE) { autoUpS() if (!is.null(ref)) { if (class(ref) != "aquap_data") { stop("Please provide an object of class 'aquap_data' to the argument 'ref'.", call.=FALSE) } if (nrow(ref) != 1) { stop("Please provide a dataset with only one single row, i.e. only one single spectrum, to the argument 'ref'.", call.=FALSE) } if (ncol(ref$NIR) != ncol(dataset$NIR)) { stop("Please provide a dataset containing the same number of wavelengths to the argument 'ref'", call.=FALSE) } refInput <- as.numeric(ref$NIR) } else { refInput <- NULL } if (is.null(extMscModel)) { NIR <- pls::msc(dataset$NIR, refInput) } else { NIR <- predict(extMscModel, as.matrix(dataset$NIR)) } exportAdditionalModelToAp2Env(doExport=exportModel, thisMod=NIR, thisType=pv_dptModules[3]) # msc colnames(NIR) <- colnames(dataset$NIR) rownames(NIR) <- rownames(dataset$NIR) dataset$NIR <- I(NIR) return(dataset) } # EOF #' @title Average spectra in a dataset #' @description Calculate the average spectrum of all the given spectra in a #' dataset, or define groups to be averaged by providing one or more #' class-variable. #' @details The total and group-wise average spectrum is returned each in a #' single row with the header-data taken from the first row of the subset of #' the original dataset as defined by the grouping. If parameter \code{clv} is #' left at the default NULL, all the spectra in the dataset will be averaged #' together into a single row. #' @inheritParams do_sgolay #' @param clv Character vector, one or more valid class-variables defining the #' subsets of the dataset to be averaged. #' @param inParallel Logical, if the averaging of spectra should be done in #' parallel. Defaults to TRUE. #' @return The transformed dataset. #' @examples #' \dontrun{ #' fd <- gfd() # get the full dataset #' fd <- selectWls(fd, 1100, 1800) #' fd_avg <- do_avg(fd) #' fd_avg2 <- do_avg(ssc(fd, C_water=="MQ")) # assumes the existence of column "C_water" #' plot(fd_avg, pg.where="", pg.main="| averaged") #' plot(fd - fd_avg, pg.where="", pg.main="| avg subtracted") #' ####### #' fd_avg_group <- do_avg(fd, "C_Group") # averaging within groups #' fd_avg_time_group <- do_avg(fd, c("C_Time", "C_Group")) # averaging within a #' # subset defined by "C_Time" and "C_Group" #' } #' @family Data pre-treatment functions #' @seealso \code{\link{getcd}} #' @export do_avg <- function(dataset, clv=NULL, inParallel=TRUE) { autoUpS() if (is.null(clv)) { NIR <- matrix(apply(dataset$NIR, 2, mean), nrow=1) colnames(NIR) <- colnames(dataset$NIR) dsNew <- dataset[1,] dsNew$NIR <- I(NIR) return(dsNew) } # end is.null(clv) # cns <- colnames(dataset$header) if (!all(clv %in% cns)) { ind <- which(!clv %in% cns) stop(paste0("Sorry, the class-variables '", paste(clv[ind], collapse="', '"), "' seem not to be present in the provided dataset."), call.=FALSE) } fdf <- makeFlatDataFrameMulti(dataset, clv) if (inParallel) { registerParallelBackend() } else { registerDoSEQ() } ## fdfMean <- plyr::ddply(fdf, .variables=clv, .fun=plyr::colwise(mean), .parallel=inParallel) ##### CORE ##### NIR <- as.matrix(fdfMean[,-(1:length(clv))]) smh <- fdfMean[, 1:length(clv)] # XXX problem with collapsing !! XXX ## header <- as.data.frame(matrix(rep(NA, ncol(dataset$header)), nrow=1)) colnames(header) <- colnames(dataset$header) colRep <- as.data.frame(matrix(rep(NA, ncol(dataset$colRep)), nrow=1)) colnames(colRep) <- colnames(dataset$colRep) ds <- dataset for (i in 1: nrow(smh)) { for (k in 1: ncol(smh)) { ds <- ssc_s(ds, clv[k], smh[i,k]) # search the matching data to construct the header } # end for k ds <- ds[1,] # only take the first row siH <- ds$header class(siH) <- "data.frame" header <- rbind(header, siH) siCol <- ds$colRep class(siCol) <- "data.frame" colRep <- rbind(colRep, siCol) ds <- dataset } # end for i header <- header[-1,] # leave out all the NAs colRep <- colRep[-1,] rownames(NIR) <- rownames(header) newData <- data.frame(I(header), I(colRep), I(NIR)) dataset@.Data <- newData dataset@row.names <- rownames(header) for (i in 1: ncol(dataset$header)) { if (all(is.character(dataset$header[,i]))) { dataset$header[i] <- factor(dataset$header[,i]) } } return(dataset) } # EOF ### input is a data frame with one or 2 loading vectors or one regression vector calc_emsc <- function(dataset, input) { ## this one possibly used "external" autoUpS() cnsWls <- colnames(dataset$NIR) rns <- rownames(dataset$NIR) NIRdata <- dataset$NIR class(NIRdata) <- "matrix" wls <- getWavelengths(dataset) ColMeansNIR <- colMeans(NIRdata) if (ncol(input) == 1) { Xcal1 <- cbind(rep(1, ncol(NIRdata)), ColMeansNIR, input[1]) Ycor2_tmp <- t(rep(NA, ncol(NIRdata))) for (i in 1: nrow(NIRdata)) { Ycal <- as.data.frame(matrix(NIRdata[i,], ncol=1)) tmp <- lm(Ycal$V1 ~ Xcal1[,1] + Xcal1[,2] + Xcal1[,3]) b <- tmp$coefficients[-2] Ycor2_tmp <- rbind(Ycor2_tmp, t(((Ycal-b[1])-b[3]*Xcal1[,3])/b[2])) ## here things are happening !! } # end for i Ycor2 <- out <- Ycor2_tmp[-1,] } else { Xcal1 <- cbind(rep(1, ncol(NIRdata)), ColMeansNIR, input[1], input[2]) Ycal <- NA Ycor2_tmp <- t(rep(NA, ncol(NIRdata))) for ( i in 1: nrow(NIRdata)) { Ycal <- as.data.frame(matrix(NIRdata[i,], ncol=1)) tmp <- lm(Ycal$V1 ~ Xcal1[,1] + Xcal1[,2] + Xcal1[,3] + Xcal1[,4] ) b <- tmp$coefficients[-2] Ycor2_tmp <- rbind(Ycor2_tmp, t(((Ycal-b[1]) - b[3]*Xcal1[,3]-b[4]*Xcal1[,4]) / b[2]) ) } # end for i Ycor2 <- out <- Ycor2_tmp[-1,] } # all is done colnames(Ycor2) <- cnsWls rownames(Ycor2) <- rns out <- as.data.frame(Ycor2) return(out) } # EOF ### input the vecLoad directly from the model via: pcaModel$loadings[, c(x,y)] #' @title Perform EMSC #' @description Performs EMSC with up to two signals on the provided dataset #' @details A data frame with one or two loadings or with one regression vector #' can be used as input to (try to) remove the effect on the data described by #' said loadings / regr. vector. For example, from a pca-model the first and #' second loading vector can be extracted with \code{pcaModel$loadings[, c(1,2)]}. #' @inheritParams do_sgolay #' @param vecLoad A data frame x (\code{ncol(x) <= 2}) with one or two loading #' vectors or one regression vector. #' @return Returns the dataset with the transformed NIR data. #' @seealso \code{\link{getcm}} for easy extraction of single models where #' loading vectors or a regression vector can be obtained. #' @examples #' \dontrun{ #' fd <- gfd() #' cu <- gdmm(fd, getap(do.pca=TRUE)) # assumes no split #' loadings <- getcm(cu, 1, what="pca")$loadings[, c(1,2)] #' fd_emsc <- do_emsc(fd, loadings) #' } #' @family Data pre-treatment functions #' @family dpt modules documentation #' @export do_emsc <- function(dataset, vecLoad=NULL, exportModel=FALSE) { autoUpS() input <- as.data.frame(vecLoad) if (ncol(input) > 2) { stop("At the moment, not more than 2 effects can be removed from the data. Please be a bit more content.", call.=FALSE) } if (is.null(vecLoad)) { stop("Please provide a data frame with one or two loading vectors or one regression vector to the argument 'vecLoad' (do_emsc).", call.=FALSE) } NIR <- as.matrix(calc_emsc(dataset, input)) exportAdditionalModelToAp2Env(doExport=exportModel, thisMod=NULL, thisType=pv_dptModules[4]) # emsc rownames(NIR) <- rownames(dataset) colnames(NIR) <- colnames(dataset$NIR) dataset$NIR <- I(NIR) return(dataset) } #EOF do_scale <- function(dataset) { NIR <- som::normalize(dataset$NIR, byrow=FALSE) colnames(NIR) <- colnames(dataset$NIR) rownames(NIR) <- rownames(dataset$NIR) dataset$NIR <- NIR return(dataset) } # EOF do_scale_fc <- function(dataset, calibAvgTable) { # used in aquagram norm foreign center avg <- apply(calibAvgTable, 2, mean) NIR <- scale(as.matrix(dataset$NIR), center=avg, scale=TRUE) colnames(NIR) <- colnames(dataset$NIR) rownames(NIR) <- rownames(dataset$NIR) dataset$NIR <- I(NIR) return(dataset) } # EOF #' @title Perform gap-segment derivatives #' @description Performs gap-segment derivatives on the provided dataset. The #' behaviour of the filter can be modified via its arguments, please see also #' the documentation for \code{\link[prospectr]{gapDer}}. #' @details The first column of the wavelengths and the last one get doubled in #' order to have the same number of wavelengths in the resulting dataset as in #' the original dataset. The underlying function is \code{\link[prospectr]{gapDer}}. #' @inheritParams do_sgolay #' @param m Numeric length one. The order of the derivative, can be between 1 and #' 4. Default is 1. #' @param w Numeric length one. The filter length (should be odd), ie. the spacing #' between points #' over which the derivative is computed. Default is 1. #' @param s Numeric length one. The segment size, i.e. the range over which the #' points are averaged.Default is 1. #' @param deltaW Numeric length one. The sampling interval / band spacing. Default #' is 1. #' @section Note: #' The documentation for the parameters was mostly taken from #' \code{\link[prospectr]{gapDer}} by Antoine Stevens. #' @seealso \code{\link[prospectr]{gapDer}} #' @examples #' \dontrun{ #' fd <- gfd() #' fd_gsd <- do_gapDer(fd) #' fd_gsd2 <- do_gapDer(fd, 1,11,13,1) #' plot(fd_gsd - fd_gsd2) #' } #' @family Data pre-treatment functions #' @family dpt modules documentation #' @export do_gapDer <- function(dataset, m=1, w=1, s=1, deltaW, exportModel=FALSE) { autoUpS() nir <- getNIR(dataset) NIR <- prospectr::gapDer(nir, m=m, w=w, s=s, delta.wav=deltaW) # gives back a matrix with one columne less at the beginning and end, so in total two wavelengths missing !! # NIR <- as.matrix(NIR) # wlsGap <- as.numeric(gsub("w", "", colnames(NIR))) # wlsD <- getWavelengths(dataset) # indLow <- which(wlsD == min(wlsGap)) # indHigh <- which(wlsD == max(wlsGap)) # nrLostLow <- length(1:indLow) # nrLostHigh <- length(indHigh: length(wlsD)) # cat(paste("Low wls lost:", nrLostLow, "\n")) # cat(paste("High wls lost:", nrLostHigh, "\n")) # return(NIR) # first <- as.data.frame(NIR[,1]) # last <- as.data.frame(NIR[, ncol(NIR)]) # NIR <- cbind(first, NIR, last) # colnames(NIR) <- colnames(dataset$NIR) # rownames(NIR) <- rownames(dataset$NIR) # NIR <- as.matrix(NIR) # because otherwise the "AsIs" is behaving strange exportAdditionalModelToAp2Env(doExport=exportModel, thisMod=NULL, thisType=pv_dptModules[7]) # gapDer dataset$NIR <- I(NIR) return(dataset) } # EOF checkDeTrendSrcTrgInput <- function(dataset, src=NULL, trg="src") { addInfo <- "\n(For the latter, please see ?dpt_modules for further information.)" checkNumLengthTwo <- function(arg) { argName <- deparse(substitute(arg)) if (!all(is.numeric(arg)) | length(arg) != 2) { stop(paste0("Please provide a numeric length two to the argument '", argName, "' resp. in the de-trend argument in the analysis procedure / your input.", addInfo), call.=FALSE) } } # EOIF ## checkWls <- function(arg, allWls= getWavelengths(dataset)) { argName <- deparse(substitute(arg)) if (min(arg) < min(allWls) | max(arg) > max(allWls)) { stop(paste0("Sorry, the range in the argument '", argName, "' is not within the wavelength-range of the provided dataset [", min(allWls) , " to ", max(allWls), "]. Please check your input at the argument '", argName, "' resp. in the de-trend argument in the analysis procedure / your input.", addInfo), call.=FALSE) } } # EOIF checkRange <- function(arg) { argName <- deparse(substitute(arg)) if (diff(arg) <= 0) { stop(paste0("Please provide a wavelength range greater than zero for the argument '", argName, "' resp. in the de-trend argument in the analysis procedure / your input.", addInfo), call.=FALSE) } } if (!is.null(src)) { checkNumLengthTwo(src) checkWls(src) checkRange(src) } if (any(is.character(trg))) { if (!trg %in% c("src", "all")) { stop(paste0("Please provide one of 'src', 'all' or a numeric length two to the argument 'trg' resp. in the de-trend argument in the analysis procedure / your input.", addInfo), call.=FALSE) } } if (all(trg == "src")) { trg <- src } if (all(trg == "all")) { trg <- range(getWavelengths(dataset)) } if (!is.null(trg)) { checkNumLengthTwo(trg) checkWls(trg) checkRange(trg) } assign("trg", trg, pos=parent.frame(n=1)) ## } # EOF #' @title Perform De-Trend #' @description Perform de-trending of the given dataset. For each observation, #' the linear model \code{lm(absorbance ~ wavelength)} is calculated. The #' coefficients of the resulting model are then used to modify the absorbance #' values after the formula #' \code{newAbsorbance = absorbance - intercept - k*wavelength}. It is possible #' to separately specify the source and the target of the de-trend operation. #' @details Via the arguments \code{src} ('source') and \code{trg} ('target') #' it is possible to specify separately from which wavelength-range the values #' for the de-trend should be taken resp. to which wavelength-range the #' resulting de-trend should be applied. Please see documentation for the #' arguments \code{src} and \code{trg} for further details. If the target #' wavelengths are only a part of the whole wavelength-range in the dataset, #' the de-trend will be applied only to this wavelengths, and the rest of the #' wavelengths will remain unchanged. Abrupt steps in the resulting spectra can #' be the result of this. If both arguments \code{src} and \code{trg} are left #' at their defaults, the full range of wavelengths in the dataset is used for #' calculating the de-trend values, i.e. the linear models, and the resulting #' de-trend is also applied to the full range of wavelengths present in the #' dataset. #' @inheritParams do_sgolay #' @param dataset An object of class 'aquap_data' as produced e.g. by #' \code{\link{gfd}}. #' @param src 'source'; the wavelength-range from where the values for de-trend #' should be calculated (i.e. the linear models). Leave at the default NULL #' to use the full range of wavelengths in the provided dataset, or provide #' a numeric length two to use this wavelength range for calculating the values #' for de-trend. #' @param trg 'target'; character length one or numeric length two. The wavelengths #' where the de-trend should be applied. If left at the default 'src' the same #' wavelength as specified in \code{src} is used. Possible values are: #' \describe{ #' \item{src}{Apply the de-trend to the same wavelength-range as specified in #' argument \code{src} (the default).} #' \item{all}{Apply the de-trend to all the wavelengths in the provided dataset.} #' \item{Numeric length two}{Provide a numeric length two to the argument #' \code{trg} to apply the de-trend only to this wavelength range.} #' } #' @return The transformed dataset #' @examples #' \dontrun{ #' fd <- gfd() #' fdDT <- do_detrend(fd) # use the whole wavelength range of 'fd' as source and #' # target for the de-trend #' plot(fd) #' plot(fdDT) #' ### #' fdc <- selectWls(fd, 1300, 1600) #' plot(fdc) #' plot(do_detrend(fdc)) # whole range as source and target #' plot(do_detrend(fdc, src=c(1400, 1500))) # same target as soruce #' plot(do_detrend(fdc, src=c(1400, 1500), trg="all")) # apply to full range #' plot(do_detrend(fdc, src=c(1400, 1500), trg=c(1300, 1600))) # same as above #' plot(do_detrend(fdc, src=c(1300, 1400), trg=c(1380, 1580))) #' } #' @family Data pre-treatment functions #' @family dpt modules documentation #' @export do_detrend<- function(dataset, src=NULL, trg="src", exportModel=FALSE) { autoUpS() checkDeTrendSrcTrgInput(dataset, src, trg) # is assigning trg # source if (is.null(src)) { absSrc <- getNIR(dataset) wlsSrc <- getWavelengths(dataset) } else { ds <- selectWls(dataset, src[1], src[2]) absSrc <- getNIR(ds) wlsSrc <- getWavelengths(ds) } mods <- apply(absSrc, 1, function(x) lm(x ~ wlsSrc)$coefficients) ### calculate the models !! one for every observation ### gives back a matrix with two rows and one column for every observation # target if (is.null(trg)) { nirTrg <- getNIR(dataset) wlsTrg <- getWavelengths(dataset) } else { if (is.numeric(trg)) { ds <- selectWls(dataset, trg[1], trg[2]) nirTrg <- getNIR(ds) wlsTrg <- getWavelengths(ds) } else { } } # NIR <- matrix(NA, nrow(nirTrg), ncol(nirTrg)) # for (i in 1: nrow(nirTrg)) { # NIR[i,] <- as.numeric(nirTrg[i,]) - mods[1,i] - mods[2,i]*wlsTrg # } NIRnew <- t(sapply(1:nrow(nirTrg), function(i) as.numeric(nirTrg[i,]) - mods[1,i] - mods[2,i]*wlsTrg)) exportAdditionalModelToAp2Env(doExport=exportModel, thisMod=NULL, thisType=pv_dptModules[6]) # deTrend colnames(NIRnew) <- cnsNew <- colnames(nirTrg) cnsOld <- colnames(dataset$NIR) indHere <- which(cnsOld %in% cnsNew) dataset$NIR[, indHere] <- NIRnew return(dataset) } # EOF #' @title Perform Data-Pretreatment Sequence #' @description Manually perform a sequence of data pre-treatments defined in a #' string. #' @details For internal use. #' @param dataset An object of class 'aquap_data'. #' @param dptSeq A character holding at least one valid string for data pre-treatment. #' @param extraModelList A list of possible external models to hand over to the #' data pre-treatment process. #' @param silent Logical. If status info should be printed or not. #' @return The transformed dataset. #' @export do_dptSeq <- function(dataset, dptSeq, extraModelList=NULL, silent=TRUE) { autoUpS(cfs=FALSE) .ap2$stn$allSilent <- silent return(performDPT_Core(dataset, dptSeq, extraModelList, allExportModels=FALSE)) } # EOF #' @title Resample data to new Wavelengths #' @description Resample the data in the provided dataset to new, possibly #' evenly spaced, wavelengths. #' @details If nothing is provided for the argument \code{targetWls}, an evenly #' spaced vector from the lowest to the highest available wavelength #' is automatically generated as the target wavelengths to be resampled to. #' @param dataset An object of class 'aquap_data' as produced e.g. by #' \code{\link{gfd}}. #' @param targetWls Numeric vector, the target wavelengths. If left at the #' default \code{NULL}, an evenly spaced vector from the lowest to the #' highest available wavelength is automatically generated as the target #' wavelengths to be resampled to. #' @param tby Numeric length one, the spacing in nm of the automatically #' generated target wavelength. Only applies if \code{targetWls} is left at #' \code{NULL}. #' @param method The resampling method. For details see #' \code{\link[pracma]{interp1}}. Defaults to 'cubic'; the default can be #' changed in the settings.r file (parameter \code{gen_resample_method}). #' @return The resampled dataset. #' @examples #' \dontrun{ #' fd <- gfd() #' fdR <- do_resampleNIR(fd) #' } #' @family Data pre-treatment functions #' @export do_resampleNIR <- function(dataset, targetWls=NULL, tby=0.5, method=get("stn", envir=.ap2)$gen_resample_method) { ncpwl <- dataset@ncpwl charPrevWls <- substr(colnames(dataset$NIR)[1], 1, (ncpwl)) # x <- getWavelengths(dataset) if (is.null(targetWls)) { xNew <- seq(ceiling(x[1]/2) * 2, floor(x[length(x)]/2) * 2, tby) } else { xNew <- targetWls # if (identical(x, xNew)) { # return(dataset) # To save possibly time. In the merging, it could be that we provide the same target than the present wls. # } # end if } # end else NIR <- t(apply(dataset$NIR, 1, pracma::interp1, x = x, xi = xNew, method = method)) colnames(NIR) <- paste0(charPrevWls, xNew) dataset$NIR <- NIR return(dataset) } # EOF checkBlowUpInput_grouping <- function(header, grp) { cns <- colnames(header) cPref <- .ap2$stn$p_ClassVarPref cns <- cns[grep(cPref, cns)] ### checkClassExistence <- function(X, char, ppv=TRUE) { if (!all(is.character(X))) { stop(paste0("Please only provide characters as input for the argument '", char, "'. Please check your input."), call.=FALSE) } for (i in 1: length(X)) { if (!X[i] %in% cns) { msg1 <- paste0("Sorry, it seems that the class-variable `", X[i], "` as grouping variable (", char, ") does not exist in the provided dataset.") msg2 <- ""; if (ppv) { msg2 <- paste0("\nPossible values are: '", paste(cns, collapse="', '"), ".") } stop(paste0(msg1, msg2), call.=FALSE) } } # end for i } # EOIF checkClassExistence(grp, "grp") } # EOF checkTransformBlowUpInput_TnAn <- function(minPart, tn, an) { if (is.character(tn)) { if (substr(tn,1,1) != "x") { stop(paste0("Please provide `tn` in the format `xN`, with N being a positive integer"), call.=FALSE) } options(warn=-1) tn <- round(as.numeric(substr(tn, 2, nchar(tn)))) * minPart # multiply with minPart, as we want to have it xFold options(warn=0) if (!is.numeric(tn)) { stop(paste0("Please provide an integer after the character `x`in the argument `tn`."), call.=FALSE) } } else { if (!is.numeric(tn)) { stop(paste0("Please provide either an integer or a character in the format `xN`, with N being a positive integer, to the argument `tn`."), call.=FALSE) } rn <- round(tn) } # end else if (tn < minPart) {tn <- minPart} # if (is.character(an)) { if (substr(an, nchar(an), nchar(an)) != "%") { stop(paste0("Please provide `an` in the format `N%`, with N being a positive integer"), call.=FALSE) } options(warn=-1) an <- round(as.numeric(substr(an, 1, nchar(an)-1))) options(warn=0) if (an > 100) { an <- 100} an <- round((an * minPart) / 100) # as we want to have a percentage if (!is.numeric(an)) { stop(paste0("Please provide an integer before the character `%`in the argument `an`."), call.=FALSE) } } else { if (!is.numeric(an)) { stop(paste0("Please provide either an integer or a character in the format `N%`, with N being a positive integer, to the argument `an`."), call.=FALSE) } an <- round(an) if (an < 1) {an <- 1} } # end else ### return(list(tn=tn, an=an)) } # EOF lotto_loop <- function(tn, an, size, n=2000) { lotto <- function(tn, an=3, size=8) { nums <- lapply(1:tn, function(x) sample(1:size, an, replace = TRUE)) nums <- lapply(nums, sort) numsChar <- unlist(lapply(nums, function(x) paste(x, collapse="-"))) out <- vector("logical", length(numsChar)) for (i in 1: length(numsChar)){ out[i] <- numsChar[i] %in% numsChar[-i] } le <- length(which(out)) perc <- round((le / tn)*100,0) return(invisible(perc)) } # EOIF percOut <- vector("numeric", n) for (i in 1:n) { percOut[i] <- lotto(tn, an, size) } percOut <- round(mean(percOut),1) # cat(paste0("total average: ", out)) return(invisible(percOut)) } #' @title Increase the Numbers of Observation in Dataset #' @description Use random observations of the provided dataset (within a possible #' grouping), calculate their average and add the resulting spectrum as new #' observation to the dataset. #' @details The random observations are sampled with replacement. The provenience #' of each observation is marked in an additional column in the header named #' `blowup`, where the character `orig` denotes an original observation, while the #' character `artif` is marking an artificially generated observation. #' @param dataset An object of class 'aquap_data' as produced e.g. by #' \code{\link{gfd}}. #' @param tn Numeric or character length one. If numeric, \code{tn} is denoting #' the target-number, i.e. the desired number of observations in the expanded #' dataset. If a grouping is provided via \code{grp}, the target number is #' defining the desired number of observations within each subgroup obtained #' by the grouping. If \code{tn} is a character it hast to be in the format #' \code{xN}, with \code{N} being a positive integer. In this case it means #' an N-fold increase of the dataset resp. of each subgroup as defined by the #' grouping in \code{grp}. Defaults to \code{x10}. #' @param an Numeric or character length one. If numeric, \code{an} is denoting #' the "average-number", i.e. the number of observations resp. their spectra that #' should be drawn (with replacement) from the dataset resp. from the respective #' subgroup. These drawn samples then are averaged together into a new spectrum. #' If \code{an} is a character it has to be in the format \code{N\%}, with #' \code{N} being a positive integer. In this case it denotes the percentage of #' observations in the dataset resp. in each of the subgroups possibly obtained #' via \code{grp} that should be drawn (with replacement) and averaged together #' into a new observation. Defaults to \code{100\%}, what is the recommended value. #' @param grp Character. One ore more valid class-variable names that should be #' used to from subgroups within the dataset. Similar to the \code{spl.var} #' argument in the analysis procedure. #' @param cst Logical. If consecutive scans should always be kept together. #' NOT YET IMPLEMENTED - value is ignored, and consec. scans are NOT kept together. #' @param conf Logical. If numbers should be presented and confirmation requested #' before commencing the actual calculations. #' @section Warning: Do take care of a correct grouping, otherwise the inclusion #' of observations into the same group that do not belong together will destroy #' any information. #' @param replace Logical. If the sample drawing should be done with replacement. #' Recommended value is TRUE. #' @param pred Logical. If an estimation of the number of identical spectra should #' be made. Only presented if \code{conf} is TRUE. #' @return The dataset with increased numbers of observations. #' @examples #' \dontrun{ #' fd <- gfd() #' fdPlus <- do_blowup(fd, tn="x4", an=4) #' fdPlus <- do_blowup(fd, tn="x8", an="10%", grp=c("C_Foo", "C_Bar")) #' fdPlus <- do_blowup(fd, tn=1000, an=5) #' } #' @family Data pre-treatment functions #' @export do_blowup <- function(dataset, grp=NULL, tn="x10", an="100%", cst=TRUE, conf=TRUE, pred=TRUE, replace=TRUE) { cPref <- .ap2$stn$p_ClassVarPref yPref <- .ap2$stn$p_yVarPref snColSet <- .ap2$stn$p_sampleNrCol snCol <- paste0(yPref, snColSet) txtOrig <- "orig" txtBlow <- "artif" colNameBlow <- "blowup" colOrig <- 1 colBlow <- 2 lottoLoopN <- .ap2$stn$cl_extDatPred_N_lottoLoop # header <- headerFac <- getHeader(dataset) # headerFac only used for grouping colRep <- getColRep(dataset) blowupDf <- data.frame(X=rep(txtOrig, nrow(header))) ## add a new column to the header telling us what samples are new and what are old colnames(blowupDf) <- paste0(cPref, colNameBlow) header <- cbind(header, blowupDf) blowCol <- data.frame(X=rep(colOrig, nrow(header))) colnames(blowCol) <- paste0(cPref, colNameBlow) colRep <- cbind(colRep, blowCol) # # first get out the factors facInd <- vector("logical", length=ncol(header)) for (i in 1: ncol(header)) { facInd[i] <- isFac <- is.factor(header[,i]) if (isFac) { header[,i] <- as.character(header[,i]) } } # end for i # minPart <- nrow(header) splitList <- list(header) NIR <- getNIR(dataset) NIRsplitList <- list(NIR) colRepSplitList <- list(colRep) if (!is.null(grp)) { checkBlowUpInput_grouping(header, grp) # stops if wrong or so splitList <- split(header, headerFac[,grp]) # returns a list with a dataframe in each element NIRsplitList <- split(NIR, headerFac[,grp]) colRepSplitList <- split(colRep, headerFac[,grp]) minPart <- min(unlist(lapply(splitList, nrow))) # get the minium number of participants from all the splitelements } aa <- checkTransformBlowUpInput_TnAn(minPart, tn, an) tn <- aa$tn an <- aa$an # print(tn); print(an); print(minPart); print("-----") # now we have the correct target and average number requiredN <- tn - minPart if (requiredN == 0) { message("No dataset expansion has been performed.") return(dataset) } eachNPart <- lapply(splitList, function(x) 1: nrow(x)) # gives a list with a vector from 1:N, with N being the number of participants from each element of the splitList indList <- newNirList <- newHeaderList <- newColRepList <- vector("list", length=length(splitList)) for (i in 1: length(splitList)) { # now for every element within the splitList (could be only 1) we perform tn iterations of the resampling ###### CORE ###### indList[[i]] <- lapply(1:requiredN, function(x, npa, ana) sample(npa[[i]], ana, replace=replace), npa=eachNPart, ana=an) # gives a list for each element of the splitList ###### CORE ###### newNirList[[i]] <- matrix(NA, nrow=requiredN , ncol=ncol(NIR)) newHeaderList[[i]] <- data.frame(matrix(NA, nrow=requiredN, ncol=ncol(header))) newColRepList[[i]] <- data.frame(matrix(NA, nrow=requiredN, ncol=ncol(colRep))) } # end i if (conf) { avgDoubleMsg <- "" if (pred) { avgDoubles <- lotto_loop(tn=requiredN, an=an, size=minPart, n=lottoLoopN) avgDoubleMsg <- paste0("The expected percentage of identical spectra is ~", avgDoubles, "%.") } cat(paste0(requiredN, " observations will be added to each of ", length(indList), " subgroups (the smallest containing ", minPart, " observations) by drawing from ", an, " observations.\n", avgDoubleMsg, "\n\nPress enter to continue or escape to abort:")) scan(file = "", n = 1, quiet = TRUE) } # future: check here the IndList for doubles !! # now we have indList containing the header segments, NIRsplitList containing the NIR segments, and the indList that contains the indices to be averaged for each segment if (!.ap2$stn$allSilent) {cat(paste0("Calculating (draw from ", an, ") and adding ", requiredN, " new observations to each of ", length(indList), " subgroups:\n"))} for (i in 1: length(indList)) { # i is the number of the splitSegment as defined by the grouping for (k in 1: length(indList[[i]])) { # k is the number of spectra we will add to each subgroup (k can be very large) ######(parallelize this, NOT above !!) inds <- indList[[i]][[k]] newNirList[[i]][k,] <- apply(NIRsplitList[[i]][inds,], 2, mean) # subselect from the NIR segment and calculate colwise average newHeaderList[[i]][k,] <- splitList[[i]][inds[1],] # (splitList contains the header segments) just get the first of the obervations to be averaged newColRepList[[i]][k,] <- colRepSplitList[[i]][inds[1],] # same } # end for k if (!.ap2$stn$allSilent) {cat(".")} } # end for i # now we have to get the new elements out of the list into each one object allNewHeader <- do.call("rbind", newHeaderList) allNewHeader[,ncol(allNewHeader)] <- rep(txtBlow, nrow(allNewHeader)) colnames(allNewHeader) <- colnames(header) allNewColRep <- do.call("rbind", newColRepList) allNewColRep[,ncol(allNewColRep)] <- rep(colBlow, nrow(allNewColRep)) colnames(allNewColRep) <- colnames(colRep) allNewNir <- do.call("rbind", newNirList) colnames(allNewNir) <- colnames(NIR) # (? maybe a problem with rownames?) # now fuse together with the original data tsi <- which(colnames(header) == "Timestamp") if (length(tsi) != 0) { allNewHeader[,tsi] <- as.POSIXct(allNewHeader[,tsi], origin="1970-01-01") } header <- rbind(header, allNewHeader) snrs <- range(header[,snCol]) header[,snCol] <- seq(min(snrs), max(snrs), length.out=nrow(header)) colRep <- rbind(colRep, allNewColRep) NIR <- as.matrix(rbind(NIR, allNewNir)) for (i in 1: ncol(header)) { if (facInd[i]) { header[,i] <- as.factor(header[,i]) # re-factorize if necessary } } # end for i out <- new("aquap_data", data.frame(I(header), I(colRep), I(NIR)), metadata=dataset@metadata, anproc=dataset@anproc, ncpwl=dataset@ncpwl, version=dataset@version) if (!.ap2$stn$allSilent) {cat(" ok.\n")} return(out) ## XXX still required: # include consec. scans together } # EOF

074697051eac2b3e23737f2360c4a25fdb4d686e

ee77fb8b2c8a4de8aad82de953a462ae9b38668e

/man/update_rating.Rd

5aef30aee34499d319b0fd183a50a22393fc34d2

[]

no_license

lazappi/aflelo

700a281c9eb086887128f3ea968de8d0bbbd87a4

a5c3d964140fbcd092b13a6672fffd474fb67692

refs/heads/master

2021-04-12T12:24:49.305211

2018-04-19T11:45:22

126,563,142

2

0

null

UTF-8

R

false

true

520

rd

update_rating.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/model.R \name{update_rating} \alias{update_rating} \title{Update rating} \usage{ update_rating(model, team, new_rating) } \arguments{ \item{model}{aflelo_model to update} \item{team}{name of team to update} \item{new_rating}{new rating value} } \value{ afelo_object with updated rating } \description{ Update rating for a single team in an AFLELO Model } \examples{ model <- aflelo_model() aflelo:::update_rating(model, "Richmond", 1600) }

22b40186010c52e9b99153e8f055081039087d82

39e89c03c14590143dc3543b58766ec12ca7432f

/man/pdb.quaternary.Rd

225c5d15620b0e818fe51fe6fbdf704a355376ad

[]

no_license

jcaledo/ptm_0.1.1

65a4733284a3d2e11bfe4a949fb54dfe356b05b8

ad4f1f04b87a53d6e49ab90d3fe391ec011cced7

refs/heads/master

2021-03-25T22:56:30.250750

2020-03-27T13:19:31

247,651,998

0

null

UTF-8

R

false

true

877

rd

pdb.quaternary.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/pdb_ancillary.R \name{pdb.quaternary} \alias{pdb.quaternary} \title{Protein Subunit Composition} \usage{ pdb.quaternary(pdb, keepfiles = FALSE) } \arguments{ \item{pdb}{the path to the PDB of interest or a 4-letter identifier.} \item{keepfiles}{logical, if TRUE the fasta file containing the alignment of the subunits is saved in the current directory, as well as the splitted pdb files.} } \value{ This function returns a list with four elements: (i) a distances matrix, (ii) the sequences, (iii) chains id, (iv) the PDB ID used. } \description{ Determines the subunit composition of a given protein. } \details{ A fasta file containing the alignment among the subunit sequences can be saved in the current directory if required. } \examples{ pdb.quaternary('1bpl') } \author{ Juan Carlos Aledo }

38bfcb3222e5b58b2413d6938d347b452ffabb35

8ac06e475183e8519f543fce41e72ec0e7226309

/R/shadePlot.R

d61ec913f638314503abc27ba766d21c5bad6dc7

[]

no_license

kashenfelter/Dmisc

0a43b7fbd83c874996501c83f54b2f46ca050af7

7e8ed7c1477f67376de6832fa1bfaf20170e5136

refs/heads/master

2020-03-13T22:06:00.018345

2017-08-21T17:08:19

null

0

null

UTF-8

R

false

2,035

r

shadePlot.R

#' Shade a section of a pdf #' #' Plot a pdf and shade a section of it. #' #' @param func The density function to be plotted (dnorm, dt, df) #' @param xlim The x-limits for the plotting region #' @param ylim The y-limits for the plotting region. If left #' as NULL then it will automatically use a reasonable ylimit #' for the upper bound by figuring out the highest point #' in your plotting region #' @param shadelim A vector containing the upper endpoint #' and the lower endpoint of the region you want to shade #' @param col The color of the shaded region #' @param xlab Label for the x-axis #' @param ylab Label for the y-axis #' @param main Title for the plot #' @param \ldots Additional parameters to pass onto the density #' function. For instance if you wanted to plot a normal density #' with a mean of 5 and sd of 2 you would also pass in mean=5, sd=2 #' in the parameter list (assuming you used dnorm as your 'func'). #' #' @export shadePlot <- function(func = dnorm, xlim = c(-3,3), ylim = NULL, shadelim = c(0, 1), col = "red", xlab = "x", ylab = expression(paste(f[X](x))), main = "", ...){ xs <- seq(xlim[1], xlim[2], length.out = 1000) ys <- func(xs, ...) # Find appropriate shading region lb <- max(shadelim[1], xlim[1]) ub <- min(shadelim[2], xlim[2]) # Make sure plot has lower bound of y = 0 xy <- xy.coords(xs, ys) if (is.null(ylim)){ ylim <- range(xy$y[is.finite(xy$y)]) ylim[1] <- 0 } # Plot the function plot(xs, ys, type = "l", xlim = xlim, ylim = ylim, xlab = xlab, ylab = ylab, main = main) # Find which xs are inside of our bounds idx <- ((xs >= lb) & (xs <= ub)) # Shade the appropriate region. polygon(c(lb, lb, xs[idx], ub, ub), c(0, func(lb, ...), ys[idx], func(ub, ...), 0), col = col) }

3a4b823471088e83557c757758162e2b88cca823

ed1ace377a08f57aa4f5ba25797861ff67598f7e

/UK/analyse.R

cedf67db7eaf3fafc6c83ff00c857a32a258423d

[]

no_license

haosifan/turnout_rightwingpopulists

a66702dbe3c85f59d1d4512679d93a99e3191fd4

e8fec2c18f06b0b3ecd86677fb1fb7cc3f73547c

refs/heads/master

2021-01-21T11:23:15.512271

2017-03-01T14:45:21

83,562,062

0

null

UTF-8

R

false

1,478

r

analyse.R

############################################# ##### Grafiken UK ########################### ############################################# source("datenaufbereitung.R") library(ggplot2) library(ggthemes) corr_df <- uk_df[uk_df$Election.Year==2015,] corr_uk1 <- round(cor(corr_df$turnout,corr_df$UKIP, use = "complete.obs"),2) p_uk1 <- ggplot(uk_df[uk_df$Election.Year==2015,], aes(x = turnout, y = UKIP)) + geom_point(alpha = 0.7, size=2, color="gray25") + geom_smooth(method = 'lm', se = F, color = 'black', show.legend = F, linetype="dashed") + geom_text(aes(label=paste("Pearson R: ", corr_uk1)),x=-Inf, y=Inf, hjust=-0.2, vjust=1.2) + theme_minimal() + scale_color_economist() + xlab('Wahlbeteiligung (%)') + ylab('UKIP-Stimmanteil (%)') #ggtitle('General Election UK') ggsave("../zgfx/UK_GE2015_wb.tif", dpi = 300, device = "tiff") corr_uk2 <- round(cor(corr_df$diff_turnout,corr_df$diff_ukip, use = "complete.obs"),2) p_uk2 <- ggplot(uk_df, aes(x = diff_turnout, y = diff_ukip)) + geom_point(alpha = 0.7, size=2, color="gray25") + geom_smooth(method = 'lm', se = F, color = 'black', show.legend = F, linetype="dashed") + geom_text(aes(label=paste("Pearson R: ", corr_uk2)),x=-Inf, y=Inf, hjust=-0.2, vjust=1.2) + theme_minimal() + scale_color_economist() + xlab('Differenz in der Wahlbeteiligung (%)') + ylab('Differenz UKIP-Stimmanteil (%)') #ggtitle('General Election UK') ggsave("../zgfx/UK_GE2015_diff.tif", dpi = 300, device = "tiff")

fbb3cd931f80f1c68b25b4eba48be7850ade7963

9cce02f026cbdb383bed192efdf8ffb03af1234d

/Mapper/Mapper examples 1.R

83a6c86e30943b56eca53f7e14f3b67514335809

[ "MIT" ]

permissive

lancelotdacosta/CMP_project

32669f2ef8029a9d4e75131d48b08b1aab4e6485

0eb6befc2aec485a92187be5de76c3cf9876e339

refs/heads/master

2020-03-25T02:07:08.612571

2018-08-10T14:48:09

143,274,534

0

null

UTF-8

R

false

2,187

r

Mapper examples 1.R

library(networkD3) library(TDAmapper) library(igraph) First.Example.data = data.frame( x=1:1000, y=sin(1:1000) ) plot(First.Example.data$x,First.Example.data$y) First.Example.dist = dist(First.Example.data) First.Example.mapper <- mapper1D(distance_matrix = First.Example.dist, filter_values = First.Example.data$y, num_intervals = 20, percent_overlap = 50, num_bins_when_clustering = 2) First.Example.graph <- graph.adjacency(First.Example.mapper$adjacency, mode="undirected") plot(First.Example.graph, layout = layout.auto(First.Example.graph) ) #Mean value of First.Example.data$x in each vertex: y.mean.vertex <- rep(0,First.Example.mapper$num_vertices) for (i in 1:First.Example.mapper$num_vertices){ points.in.vertex <- First.Example.mapper$points_in_vertex[[i]] y.mean.vertex[i] <-mean((First.Example.data$x[points.in.vertex])) } #Vertex size: vertex.size <- rep(0,First.Example.mapper$num_vertices) for (i in 1:First.Example.mapper$num_vertices){ points.in.vertex <- First.Example.mapper$points_in_vertex[[i]] vertex.size[i] <- length((First.Example.mapper$points_in_vertex[[i]])) } #Mapper graph with the vertices colored in function of First.Example.data$y and vertex size proportional to the number of points inside: y.mean.vertex.grey <- grey(1-(y.mean.vertex - min(y.mean.vertex))/(max(y.mean.vertex) - min(y.mean.vertex) )) V(First.Example.graph)$color <- y.mean.vertex.grey V(First.Example.graph)$size <- vertex.size plot(First.Example.graph,main ="Mapper Graph") legend(x=-2, y=-1, c("y small","y medium", "large y"),pch=21, col="#777777", pt.bg=grey(c(1,0.5,0)), pt.cex=2, cex=.8, bty="n", ncol=1) #Interactive library(networkD3) MapperNodes <- mapperVertices(First.Example.mapper, 1:2000 ) MapperLinks <- mapperEdges(First.Example.mapper) forceNetwork(Nodes = MapperNodes, Links = MapperLinks, Source = "Linksource", Target = "Linktarget", Value = "Linkvalue", NodeID = "Nodename", Group = "Nodegroup", opacity = 1, linkDistance = -1, charge = -400)

519330bdb3325f7a6c27eb03c7c022e66507446a

d08d401957330f5a5326544c02d53ecfaa1fa6ae

/macro/tests/testthat/test-continuous_step.R

cbd9c4031e557c94bdf436fd1300edfc6f597cdf

[]

no_license

dd-harp/MASH

103733fa1b0cc0f0f2f4fdeef41b84e33b828349

3786b6904a82fc25cd5709df73f195302a9d9b08

refs/heads/main

2021-08-11T09:15:46.371074

2021-06-08T18:44:48

204,227,705

0

null

2021-06-08T18:44:49

2019-08-25T00:32:06

C++

UTF-8

R

false

4,131

r

test-continuous_step.R

test_that("individual two state bounce", { set.seed(32432) is_enabled <- list( infect = function(state, time) with(state, disease == "S"), recover = function(state, time) with(state, disease == "I") ) when <- list( infect = function(state, time) rexp(1, 1/50), recover = function(state, time) rexp(1, 1/200) ) fire <- list( infect = function(state, time) {within(state, {disease = "I"})}, recover = function(state, time) {within(state, {disease = "S"})} ) transitions <- list( is_enabled = is_enabled, when = when, fire = fire ) individual <- list(disease = "S", when = 0.32, infect = 0.32, recover = Inf) status <- update_individual(individual, transitions, observe_continuous)$individual expect_equal(status$disease, "I") expect_equal(status$infect, Inf) expect_gt(status$recover, 0.32) expect_equal(status$when, status$recover) individual <- list(disease = "I", when = 0.44, infect = Inf, recover = 0.44) status <- update_individual(individual, transitions, observe_continuous)$individual expect_equal(status$disease, "S") expect_equal(status$recover, Inf) expect_gt(status$infect, 0.44) expect_equal(status$when, status$infect) }) #' The individual is in an absorbing state, so there is nothing #' else to fire. Check that it goes to Inf for the next time. test_that("individual removed goes to inf", { set.seed(32432) is_enabled <- list( infect = function(state, time) with(state, disease == "S") ) when <- list( infect = function(state, time) rexp(1, 1/50) ) fire <- list( infect = function(state, time) {within(state, {disease = "I"})} ) transitions <- list( is_enabled = is_enabled, when = when, fire = fire ) individual <- list(disease = "S", when = 0.32, infect = 0.32) # browser() status <- update_individual(individual, transitions, observe_continuous)$individual expect_equal(status$disease, "I") expect_equal(status$infect, Inf) expect_equal(status$when, Inf) }) #' A transition was enabled and fired, but it's now enabled again #' in the new state. Check that it gets scheduled again. test_that("individual transition fires again", { set.seed(32432) is_enabled <- list( infect = function(state, time) with(state, disease == "S") ) when <- list( infect = function(state, time) rexp(1, 1/50) ) # Returns to same state when it fires. fire <- list( infect = function(state, time) {within(state, {disease = "S"})} ) transitions <- list( is_enabled = is_enabled, when = when, fire = fire ) individual <- list(disease = "S", when = 0.32, infect = 0.32) # browser() status <- update_individual(individual, transitions, observe_continuous)$individual expect_equal(status$disease, "S") expect_gt(status$infect, 0.32) expect_equal(status$when, status$infect) }) test_that("can use data.table", { set.seed(97234) # This sets up the transitions. program_globals <- new.env() program_globals$b <- 0.2 program_globals$c <- 3.7 transitions <- list( infect = list( is_enabled = function(state, time) with(state, disease == "S"), when = function(state, time) rexp(1, 1/50), fire = function(state, time) { b <<- 0.3 within(state, {disease = "I"}) } ), recover = list( is_enabled = function(state, time) with(state, disease == "I"), when = function(state, time) rexp(1, 1/200), fire = function(state, time) { b <<- 0.25 within(state, {disease = "S"}) } ) ) # This sets up the data.table of individuals. individuals <- data.table::data.table( disease = factor(c("S", "I", "S"), levels = c("S", "I")) ) simulation <- continuous_simulation( individuals, transitions, observe_continuous, program_globals ) simulation <- init_continuous(simulation) simulation <- run_continuous(simulation, 200) trajectory <- simulation$trajectory[1:simulation$trajectory_cnt] expect_gt(length(trajectory), 1) for (check_idx in 1:length(trajectory)) { expect_true(trajectory[[check_idx]]$name %in% c("infect", "recover")) } })

c63213881e4648b6e61578666aefd4270968e7a4

de15a984ea7b066a452ad48b01c3b7b6eca2d12d

/scripts/01-data_cleaning-post-strat.R

35d9661209be64c41b01ad594ed6d04793f8f3d9

[ "MIT" ]

permissive

Xeon0312/forecasting_us_election

e092f1ccf3362aaf322f0f68d91383b413114149

376e7ba5025ae8efb9ca0ef386a2612cdfcbfe73

refs/heads/forecasting_us_election

2023-01-03T12:52:18.103725

2020-11-04T03:54:19

308,555,098

0

1

MIT

2020-11-04T02:53:25

2020-10-30T07:25:10

R

UTF-8

R

false

10,247

r

01-data_cleaning-post-strat.R

#### Preamble #### # Purpose: Prepare and clean the survey data downloaded from IPUMS # Author: Boyu Cao # Data: 22 October 2020 # Contact: boyu.cao@mail.utoronto.ca # License: MIT # Pre-requisites: # - Need to have downloaded the ACS data and saved it to inputs/data # - Don't forget to gitignore it! #### Workspace setup #### library(haven) library(tidyverse) # Read in the raw data. raw_data <- read_dta("../../inputs/data/usa_00001.dta" ) # Add the labels raw_data <- labelled::to_factor(raw_data) # Just keep some variables that may be of interest (change # this depending on your interests) names(raw_data) reduced_data_p <- raw_data %>% select(age, sex, educd, stateicp, inctot, race, perwt, citizen, labforce) # Cleaning data # Remove people who can't vote reduced_data_p$age<-as.numeric(reduced_data_p$age) reduced_data_p<-reduced_data_p %>% filter(age>=18 & (citizen=="naturalized citizen"|citizen=="born abroad of american parents") ) # Adjust the NA reduced_data_p$inctot<-ifelse(reduced_data_p$inctot==9999999, NaN,reduced_data_p$inctot) reduced_data_p$labforce<-ifelse(reduced_data_p$labforce=="n/a", NA,reduced_data_p$labforce) # Drop NA reduced_data_p<-na.omit(reduced_data_p) reduced_data_p <- labelled::to_factor(reduced_data_p) # Clean memory rm(raw_data) #### What's next? #### # Making some age-groups reduced_data_p<-reduced_data_p %>% mutate(agegroup = case_when(age <=20 ~ '20 or less', age >20 & age <= 30 ~ '21 to 30', age >30 & age <= 40 ~ '31 to 40', age >40 & age <= 50 ~ '41 to 50', age >50 & age <= 60 ~ '51 to 60', age >60 & age <= 70 ~ '61 to 70', age >70 & age <= 80 ~ '71 to 80', age >80 ~ 'above 80' )) # Unified the columns name ## gender reduced_data_p$sex<-ifelse(reduced_data_p$sex=="female","Female","Male") reduced_data_p$labforce<-ifelse(reduced_data_p$labforce==2,"No","Yes") reduced_data_p<-rename(reduced_data_p,gender=sex) ## education grade_3toless<-c("no schooling completed","nursery school, preschool","kindergarten","grade 1","grade 2","grade 3") grade_4to8<-c("grade 4","grade 5","grade 6","grade 7","grade 8") grade_9to12<-c("grade 9","grade 10","grade 11","12th grade, no diploma") high_school_grad<-c("ged or alternative credential","regular high school diploma") col_not_grad<-c("some college, but less than 1 year", "1 or more years of college credit, no degree") reduced_data_p<-reduced_data_p %>% mutate(educd2 = case_when(educd =="associate's degree, type not specified" ~ 'Associate Degree', educd=="doctoral degree"~'Doctorate degree', educd =="master's degree" ~ 'Masters degree', educd=="professional degree beyond a bachelor's degree" ~ "College Degree (such as B.A., B.S.)", educd =="bachelor's degree" ~ "College Degree (such as B.A., B.S.)", educd %in% col_not_grad~"Completed some college, but no degree", educd %in% high_school_grad~"High school graduate", educd %in% grade_9to12~"Completed some high school", educd %in% grade_4to8~"Middle School - Grades 4 - 8", educd %in% grade_3toless ~"3rd Grade or less" )) ### drop educd & rename educd2 reduced_data_p<-rename(reduced_data_p,education=educd2) reduced_data_p$educd<-NULL ## race reduced_data_p<-reduced_data_p %>% mutate(race2 = case_when(race=="white"~"White", race=="chinese"~"Chinese", race=="black/african american/negro"~"Black, or African American", race=="two major races"~"Other race", race=="other race, nec"~"Other race", race=="japanese"~"Japanese", race=="american indian or alaska native"~"American Indian or Alaska Native", race=="three or more major races"~"Other race", race=="other asian or pacific islander"~"other asian or pacific islander" )) reduced_data_p$race<-reduced_data_p$race2 reduced_data_p$race2<-NULL ## Short the states name reduced_data_p<-reduced_data_p %>% mutate(state = case_when(stateicp=="alabama"~"AL", stateicp=="alaska"~"AK", stateicp=="arizona"~"AZ", stateicp=="arkansas"~"AR", stateicp=="california"~"CA", stateicp=="colorado"~"CO", stateicp=="connecticut"~"CT", stateicp=="delaware"~"DE", stateicp=="florida"~"FL", stateicp=="georgia"~"GA", stateicp=="hawaii"~"HI", stateicp=="idaho"~"ID", stateicp=="illinois"~"IL", stateicp=="indiana"~"IN", stateicp=="iowa"~"IA", stateicp=="kansas"~"KS", stateicp=="kentucky"~"KY", stateicp=="louisiana"~"LA", stateicp=="maine"~"ME", stateicp=="maryland"~"MD", stateicp=="massachusetts"~"MA", stateicp=="michigan"~"MI", stateicp=="minnesota"~"MN", stateicp=="mississippi"~"MS", stateicp=="missouri"~"MO", stateicp=="montana"~"MT", stateicp=="nebraska"~"NE", stateicp=="nevada"~"NV", stateicp=="new hampshire"~"NH", stateicp=="new jersey"~"NJ", stateicp=="new mexico"~"NM", stateicp=="new york"~"NY", stateicp=="north carolina"~"NC", stateicp=="north dakota"~"ND", stateicp=="ohio"~"OH", stateicp=="oklahoma"~"OK", stateicp=="oregon"~"OR", stateicp=="pennsylvania"~"PA", stateicp=="rhode island"~"RI", stateicp=="south carolina"~"SC", stateicp=="south dakota"~"SD", stateicp=="tennessee"~"TN", stateicp=="texas"~"TX", stateicp=="utah"~"UT", stateicp=="vermont"~"VT", stateicp=="virginia"~"VA", stateicp=="washington"~"WA", stateicp=="west virginia"~"WV", stateicp=="wisconsin"~"WI", stateicp=="wyoming"~"WY", stateicp=="district of columbia"~"DC")) reduced_data_p$stateicp<-NULL # reference: https://www.50states.com/abbreviations.htm ## House hold income reduced_data_p<-reduced_data_p %>% mutate(household_income = case_when(inctot<=14999 ~ "Less than $14,999", inctot>=15000 & inctot<=19999~"$15,000 to $19,999", inctot>=20000 & inctot<=24999~"$20,000 to $24,999", inctot>=25000 & inctot<=29999~"$25,000 to $29,999", inctot>=30000 & inctot<=34999~"$30,000 to $34,999", inctot>=35000 & inctot<=39999~"$35,000 to $39,999", inctot>=40000 & inctot<=44999~"$40,000 to $44,999", inctot>=45000 & inctot<=49999~"$45,000 to $49,999", inctot>=50000 & inctot<=54999~"$50,000 to $54,999", inctot>=55000 & inctot<=59999~"$55,000 to $59,999", inctot>=60000 & inctot<=64999~"$60,000 to $64,999", inctot>=65000 & inctot<=69999~"$65,000 to $69,999", inctot>=70000 & inctot<=74999~"$70,000 to $74,999", inctot>=75000 & inctot<=79999~"$75,000 to $79,999", inctot>=80000 & inctot<=84999~"$80,000 to $84,999", inctot>=85000 & inctot<=89999~"$85,000 to $89,999", inctot>=90000 & inctot<=94999~"$90,000 to $94,999", inctot>=95000 & inctot<=99999~"$95,000 to $99,999", inctot>=100000 & inctot<=124999~"$100,000 to $124,999", inctot>=125000 & inctot<=149999~"$125,000 to $149,999", inctot>=150000 & inctot<=174999~"$150,000 to $174,999", inctot>=175000 & inctot<=199999~"$175,000 to $199,999", inctot>=200000 & inctot<=249999~"$200,000 to $249,999", inctot>=250000~"$250,000 and above" )) reduced_data_p$inctot<-NULL reduced_data_p%>% select(perwt,agegroup,gender,education,state,household_income,race,labforce)->census_data # Add cells census_data$cell<-paste(census_data$agegroup,census_data$gender) # Convert variables to factors f.cols.census<-c("agegroup","gender","education","state","household_income" ,"race", "cell","labforce") census_data[f.cols.census] <- lapply(census_data[f.cols.census], factor) rm(reduced_data_p)

23c47f62694db63cd76df9ede30edea833574f1c

74651f98951cbaf136fc2d2d8ad03b6f8bc0f958

/zi_pi.r

4d5a96fd82a6d3743124ef8fdc4dd30b99f34bb1

[]

no_license

Xuyifei-NAU/MyScript

65de9c1876ac3ed1c30a9be98d58c2ba296c492b

c7e63b661029a16cbe076ea2859942482b6af4d5

refs/heads/main

2023-06-26T10:40:41.103806

2021-07-23T00:45:16

311,651,078

0

null

UTF-8

R

false

2,959

r

zi_pi.r

##定义函数 zi.pi<-function(nodes_bulk, z.bulk, modularity_class, degree){ z.bulk[abs(z.bulk)>0]<-1 module<-which(colnames(nodes_bulk)==modularity_class) module.max<-max(nodes_bulk[,module]) degree<-which(colnames(nodes_bulk)==degree) #按照模块将相关矩阵分割 bulk.module<-list(NA) length(bulk.module)<-module.max for(i in 1:max(nodes_bulk[,module])){ bulk.module[[i]]<-z.bulk[which(nodes_bulk[,module]==i),which(nodes_bulk[,module]==i)] bulk.module[[i]]<-as.data.frame(bulk.module[[i]]) rownames(bulk.module[[i]])<-rownames(z.bulk)[which(nodes_bulk[,module]==i)] colnames(bulk.module[[i]])<-colnames(z.bulk)[which(nodes_bulk[,module]==i)] } # within-module degree z z_bulk<-list(NA) length(z_bulk)<-module.max for(i in 1:length(z_bulk)){ z_bulk[[i]]<-bulk.module[[i]][,1] z_bulk[[i]]<-as.data.frame(z_bulk[[i]]) colnames(z_bulk[[i]])<-"z" rownames(z_bulk[[i]])<-rownames(bulk.module[[i]]) } #计算z值 for(i in 1:max(nodes_bulk[,module])){ if(length(bulk.module[[i]])==1){ z_bulk[[i]][,1]<-0 }else if(sum(bulk.module[[i]])==0){ z_bulk[[i]][,1]<-0 }else{ k<-rowSums(bulk.module[[i]]) mean<-mean(k) sd<-sd(k) if (sd==0){ z_bulk[[i]][,1]<-0 }else{ z_bulk[[i]][,1]<-(k-mean)/sd } } } #z值合并 for(i in 2:max(nodes_bulk[,module])) { z_bulk[[i]]<-rbind(z_bulk[[i-1]],z_bulk[[i]]) } z_bulk<-z_bulk[[module.max]] #按照模块将相关矩阵列分割 bulk.module1<-list(NA) length(bulk.module1)<-module.max for(i in 1:max(nodes_bulk[,module])){ bulk.module1[[i]]<-z.bulk[,which(nodes_bulk[,module]==i)] bulk.module1[[i]]<-as.data.frame(bulk.module1[[i]]) rownames(bulk.module1[[i]])<-rownames(z.bulk) colnames(bulk.module1[[i]])<-colnames(z.bulk)[which(nodes_bulk[,module]==i)] } #among-module connectivity c c_bulk<-list(NA) length(c_bulk)<-module.max for(i in 1:length(c_bulk)){ c_bulk[[i]]<-z.bulk[,1] c_bulk[[i]]<-as.matrix(c_bulk[[i]]) colnames(c_bulk[[i]])<-"c" rownames(c_bulk[[i]])<-rownames(z.bulk) c_bulk[[i]][,1]<-NA } #每个节点各模块连接数平方 for(i in 1:max(nodes_bulk[,module])){ c_bulk[[i]]<-rowSums(bulk.module1[[i]]) c_bulk[[i]]<-as.matrix(c_bulk[[i]]) c_bulk[[i]]<-c_bulk[[i]]*c_bulk[[i]] colnames(c_bulk[[i]])<-"c" rownames(c_bulk[[i]])<-rownames(z.bulk) } #平方和 for(i in 2:max(nodes_bulk[,module])){ c_bulk[[i]]<-c_bulk[[i]]+c_bulk[[i-1]] } c_bulk<-c_bulk[[module.max]] c_bulk1<-1-(c_bulk/(nodes_bulk[,degree]*nodes_bulk[,degree])) colnames(c_bulk1)<-"c" #z,c整合 z_c_bulk<-c_bulk1 z_c_bulk<-as.data.frame(z_c_bulk) z_c_bulk$z<-z_bulk[match(rownames(c_bulk1),rownames(z_bulk)),] z_c_bulk<-z_c_bulk[,c(2,1)] names(z_c_bulk)[1:2]<-c('within_module_connectivities','among_module_connectivities') z_c_bulk$nodes_id<-rownames(z_c_bulk) nodes_bulk$nodes_id<-rownames(nodes_bulk) z_c_bulk<-merge(z_c_bulk,nodes_bulk,by='nodes_id') z_c_bulk }

b1da38b273f4ad7d13a7606e0a1f552bc1494657

ffdea92d4315e4363dd4ae673a1a6adf82a761b5

/data/genthat_extracted_code/yorkr/examples/bowlerWicketsAgainstOpposition.Rd.R

59be0cf55e6299f78e2ca8d9c501d808c6b3a44b

[]

no_license

surayaaramli/typeRrh

d257ac8905c49123f4ccd4e377ee3dfc84d1636c

66e6996f31961bc8b9aafe1a6a6098327b66bf71

refs/heads/master

2023-05-05T04:05:31.617869

2019-04-25T22:10:06

null

0

null

UTF-8

R

false

376

r

bowlerWicketsAgainstOpposition.Rd.R

library(yorkr) ### Name: bowlerWicketsAgainstOpposition ### Title: Bowler wickets versus different teams ### Aliases: bowlerWicketsAgainstOpposition ### ** Examples ## Not run: ##D # Get the data frame for RA Jadeja ##D jadeja <- getBowlerWicketDetails(team="India",name="Jadeja",dir=pathToFile) ##D bowlerWicketsAgainstOpposition(jadeja,"RA Jadeja") ## End(Not run)

73735b73816713afe37be15f71fa767bc177360b

503284431cfffe00693e6b52931c614b235887fb

/QuantStuff.r

baf14857da3dfc9244229fb2b2c583db564cbd4b

[]

no_license

tpopenfoose/quantstuff

5cb37f4c7b4a8bc148a97d6b173aef9867caaf0b

031775f639394276882b7557e6dbd6e4fedbe857

refs/heads/master

2021-01-13T03:46:55.359239

2016-06-30T15:43:49

null

0

null

UTF-8

R

false

1,849

r

QuantStuff.r

library('quantmod') #library('zoo') #library('xts') #library('PerformanceAnalytics') techSector <- c("XLK","AAPL","GOOG") #,"MSFT","VZ","IBM","T","ORCL","QCOM","CSCO","INTC","V","FB","MA","EBAY","EMC","TXN","ACN","HPQ","ADP","YHOO","CRM") #loadSymbols <- c("XPH","XBI","IHF","IHI","JO","MSFT") getSymbols(techSector,src="yahoo") mySymbol <- XLK dayRange <- 500 #candleChart(last(mySymbol,dayRange),dn.col="black",up.col="white",theme="white") chartSeries(last(mySymbol,dayRange), type="candlesticks", name="XLK Tech Sector SPDR Fund", theme=chartTheme("white"), up.col="white", dn.col="black", grid.col="gray" ) addBBands() addMACD() #chartStock("XLK","90") getChart("XLK",500,XLK) #My Indicators # not used: addTA(LoHi(last(mySymbol,dayRange))) #Absolute daysBody <- abs(XLK$XLK.Close - XLK$XLK.Open) # Body Size daysBody <- XLK$XLK.Close - XLK$XLK.Open # Body Size daysRange <- XLK$XLK.High - XLK$XLK.Low # Low to High daysUpShadow <- ifelse(XLK$XLK.Close > XLK$XLK.Open, XLK$XLK.High - XLK$XLK.Close, XLK$XLK.High - XLK$XLK.Open) # Upper Shadow Size daysLoShadow <- ifelse(XLK$XLK.Close > XLK$XLK.Open, XLK$XLK.Open - XLK$XLK.Low, XLK$XLK.Close - XLK$XLK.Low) # Lower Shadow Size daySummary <- daysBody + daysRange + daysUpShadow + daysLoShadow addTA(last(daysBody,dayRange), col="blue", overlay=TRUE) addTA(last(daysRange,dayRange), col="brown") addTA(last(daysUpShadow,dayRange), col="green") addTA(last(daysLoShadow,dayRange), col="red") addTA(last(daySummary,dayRange), col="purple") # Cross Correlation #ccf(last(drop(AUY$AUY.Open), 90),last(drop(daysRange), 90)) #ccf(last(drop(AUY$AUY.Open), 400),last(drop(GLD$GLD.Open), 400)) #Plot various metrics... #plot(last(daysRange,90),col="blue") #lines(last(daysBody,90),col="red")

7264d6f7b8a312f0ad0f8052099a877fbaf47440

3be7a6917ae3e03701954a4360a6c5b612ddbf32

/R/nature.R

bfda9e058d3b0a585d6101bdd48c2aff42e29cbb

[ "MIT" ]

permissive

zambujo/editorials

9fa6abcb86dedea268ef949d7d9d40f44c9c4ba1

2f4c8c71e93c5d59748f750324350b814d15a932

refs/heads/main

2023-01-25T04:06:51.366937

2020-12-09T11:16:57

311,373,661

0

null

UTF-8

R

false

5,117

r

nature.R

#' Retrieve selected data from a Nature journal URL #' #' @param addr Relative path to page. #' @param polite_bow An HTTP polite::bow session object. #' @param csv_path Path to CSV file. #' #' @return A tibble with scraped data, if `csv_path` is missing. #' @family nature scraping function #' @name get_nature #' @examples #' \dontrun{ #' get_nature_volumes(bow("https://www.nature.com")) #' } #' NULL #' @rdname get_nature #' @export get_nature_volumes <- function(addr, polite_bow, csv_path) { if (missing(addr)) addr <- "nature/volumes" volumes_html <- get_page(addr, polite_bow) volumes <- rvest::html_nodes(volumes_html, "#volume-decade-list") res <- tibble::tibble( volume_key = volumes %>% rvest::html_nodes("a") %>% rvest::html_attr("href"), date = volumes %>% rvest::html_nodes("time") %>% rvest::html_text() ) # cast time res <- dplyr::mutate(res, year = as.numeric(stringr::str_extract(date, "\\d{4}"))) return_df(res, csv_path) } #' @rdname get_nature #' @export get_nature_issues <- function(addr, polite_bow, csv_path) { issues_html <- get_page(addr, polite_bow) issues <- rvest::html_nodes(issues_html, ".flex-box-item") res <- tibble::tibble( issue_key = issues %>% rvest::html_attr("href"), issue_date = issues %>% rvest::html_nodes(".text-gray") %>% rvest::html_text() ) return_df(res, csv_path) } #' @rdname get_nature #' @export get_nature_contents <- function(addr, polite_bow, csv_path) { toc_html <- get_page(addr, polite_bow) contents <- toc_html %>% rvest::html_nodes("#ThisWeek-content") if (length(contents) == 0) { contents <- toc_html %>% rvest::html_nodes("#ResearchHighlights-section") } res <- tibble::tibble( issue_key = addr, article_key = contents %>% rvest::html_nodes("a") %>% rvest::html_attr("href") %>% stringr::str_subset("^/articles/"), contents_labels = contents %>% rvest::html_nodes(".mb4 span:nth-child(1)") %>% rvest::html_text() ) if (nrow(res) > 0) return_df(res, csv_path) } #' @rdname get_nature #' @export get_nature_articles <- function(addr, polite_bow, csv_path) { article_html <- get_page(addr, polite_bow) article_title <- article_html %>% rvest::html_node(".c-article-title, .article-item__title") %>% rvest::html_text() article_subject <- article_html %>% rvest::html_node(".c-article-title__super, .article-item__subject") %>% rvest::html_text() doi_txt <- NA_character_ doi_link <- NA_character_ if (stringr::str_to_lower(article_title) == "research highlights") { # early volumes ----------------------------------------- coda <- c("journal club", "rights and permissions", "about this article", "comments") article_title <- article_html %>% rvest::html_nodes(".c-article-section__title") %>% html_text stop_at <- which(stringr::str_to_lower(article_title) %in% coda) %>% min() ## stop short of journal club article_title <- head(article_title, stop_at - 1) external_refs <- article_html %>% rvest::html_nodes(".c-article-section__content") link_list <- external_refs %>% purrr::map(rvest::html_nodes, css = "a") %>% head(length(article_title)) %>% purrr::map(rvest::html_attr, name = "href") txt_list <- external_refs %>% purrr::map(rvest::html_node, css = "p") %>% head(length(article_title)) %>% purrr::map(rvest::html_text) %>% stringr::str_squish() df_refs <- tibble::tibble(ttl = article_title, txt = txt_list, a = link_list) %>% unnest(a, keep_empty = TRUE) article_title <- dplyr::pull(df_refs, ttl) doi_txt <- dplyr::pull(df_refs, txt) doi_link <- dplyr::pull(df_refs, a) } else { # later volumes ----------------------------------------- external_refs <- article_html %>% rvest::html_nodes(".c-article-section__content a, .serif") doi_idx <- external_refs %>% rvest::html_attr("href") %>% stringr::str_detect("doi[.]org") if (any(doi_idx)) { doi_txt <- external_refs %>% rvest::html_text() %>% subset(doi_idx) %>% head(1) %>% ## ! leap of faith stringr::str_squish() doi_link <- external_refs %>% rvest::html_attr("href") %>% subset(doi_idx) %>% head(1) ## ! leap of faith } } if (is.na(article_subject)) { # try to parse subject from title subject_title <- stringr::str_split(article_title, ": ", n = 2) article_subject <- purrr::map_chr(subject_title, head, 1) article_title <- purrr::map_chr(subject_title, tail, 1) if (identical(article_subject, article_title)) article_subject <- NA_character_ } res <- tibble::tibble( article_key = addr, title = article_title, topic = article_subject, citation = doi_txt, doi = doi_link ) return_df(res, csv_path) }

7e4869d1f844482512fa50639c7abb6b720ffed7

ddb6801f17ad79f09dfccb1a17af980dc432bedc

/usefulFunctions/numGenesChanged.R

f0f5aaeae95b4368166d3d6817d6c1c87dd89cec

[]

no_license

alexsanchezpla/scripts

49177da789965d71a4bf9453a740482070d37f6b

d833be5653d1aac657379f0e8ef49511d5fa5082

refs/heads/master

2023-03-05T02:49:56.718712

2023-02-19T11:10:14

41,293,582

0

11

null

2016-12-14T11:20:46

2015-08-24T09:11:10

HTML

UTF-8

R

false

2,098

r

numGenesChanged.R

#' Given a limma-outputted topTable (TT) it returns the number of up or down regulated genes that woud be returned if the cutoff was set at different values. #' @param TT A top table object such as produced by the limma package or the UEB pipeline. #' @param cName Name to the comparison that produced the top Table. Defaults to "comparison". #' @keywords genelists, filtering #' @seealso limma #' @export #' @examples #' AvsB <- read.table("https://raw.githubusercontent.com/alexsanchezpla/scripts/master/Gene_List_Management/dades/ExpressAndTop_AvsB.csv2", head=T, sep=";", dec=",", row.names=1) #' genesChanged <- numGenesChanged (AvsB, "Group A vs group B") numGenesChanged <- function (TT, cName="comparison"){ Bup <- sum(TT$t>0 & TT$B >0 ) Bdown <- sum(TT$t <=0 & TT$B >0 ) adjP001Up <- sum(TT$t>0 & TT$adj.P.Val < 0.01 ) adjP001Down <- sum(TT$t< 0 & TT$adj.P.Val < 0.01 ) adjP005Up <- sum(TT$t>0 & TT$adj.P.Val < 0.05 ) adjP005Down <- sum(TT$t< 0 & TT$adj.P.Val < 0.05 ) adjP025Up <- sum(TT$t>0 & TT$adj.P.Val < 0.25 ) adjP025Down <- sum(TT$t< 0 & TT$adj.P.Val < 0.25 ) P001Up <- sum(TT$t>0 & TT$P.Value < 0.01 ) P001Down <- sum(TT$t< 0 & TT$P.Value < 0.01 ) P005Up <- sum(TT$t>0 & TT$P.Value < 0.05 ) P005Down <- sum(TT$t< 0 & TT$P.Value < 0.05 ) nGenes <- data.frame(comparisonName= c(Bup, Bdown, adjP001Up, adjP001Down, adjP005Up, adjP005Down, adjP025Up, adjP025Down, P001Up, P001Down, P005Up, P005Down)) rowNames <- c("upReg-B>0", "downReg-B>0", "upReg-Adjusted-p-val < 0.01", "downReg-Adjusted-p-val < 0.01", "upReg-Adjusted-p-val < 0.05", "downReg-Adjusted-p-val < 0.05", "upReg-Adjusted-p-val < 0.25", "downReg-Adjusted-p-val < 0.25", "upReg-P value < 0.01 ", "downReg-P value < 0.01", "upReg-P value < 0.05", "downReg-P value < 0.05") rownames(nGenes) <-rowNames colnames(nGenes)[1] <- cName return(nGenes) }

d2c556c551566fb12ff81d5bbb3daf304b4b0f0b

0ab0d9377260d2a6f37503fc1de64f1111cbc6b4

/RProgramming/week4.R

474380d110d8a24ff68be23ebac59ba50747c5ae

[]

no_license

a-igo/DataAnalysis

4aaa7df63714431f02d0b8f060be303cb7dbbe1b

55a7f5f865325fa1a998d4914ee5f3be31a89f94

refs/heads/master

2020-04-17T09:17:34.376359

2016-04-10T07:53:56

null

0

null

UTF-8

R

false

294

r

week4.R

outcome <- read.csv('outcome-of-care-measures.csv', colClasses = 'character') head(outcome) #Hist of "Hospital.30.Day.Death..Mortality..Rates.from.Heart.Attack" outcome[, 11] <- as.numeric(outcome[, 11]) hist(outcome[, 11]) options(warn = -1) source('rankall.R') rankall("heart failure", 10)

c9a055bd728ef74eaa50a95047d110c1336ca1be

9fe31d58c07d8d667e08e41c0213d84d4587e721

/viz_stacked_bar.R

2e92637e2110274a80d423db1d4e33c9ec62f7c8

[]

no_license

Ecboxer/roi_sports

b8fe46f3c05052d01fd87080e79e5e8304e02766

6cc8c506b00a5d82df90f7f92d1bdb1f86730eb2

refs/heads/master

2020-04-26T11:28:44.211328

2019-04-09T16:42:57

173,517,846

1

0

null

UTF-8

R

false

3,972

r

viz_stacked_bar.R

library(tidyverse) library(Lahman) library(baseballr) library(plotly) df_action <- read_csv('df_action.csv') df_action %>% filter(Sport == 'Baseball') action_dur <- df_action %>% filter(Sport == 'Baseball') %>% select(action_duration) action_min <- action_dur$action_duration / 60 df_mlb <- read_csv('mlb_misc.csv') df_mlb %>% head() df_innings <- read_csv('innings.csv') df_innings %>% head() br_teams <- c('LAA','ARI','ATL','BAL','BOS','CHC', 'CHW','CIN','CLE','COL','DET','MIA', 'HOU','KCR','LAD','MIL','MIN','NYM', 'NYY','OAK','PHI','PIT','SDP','SEA', 'SFG','STL','TBR','TEX','TOR','WSN') df_action %>% filter(Sport=='Baseball') %>% select(perc_action, perc_comm) %>% mutate(perc_other=1-perc_action-perc_comm) %>% gather(key=Key, value=Value) %>% ggplot() + geom_bar(aes(x=1, y=Value, fill=Key), stat='identity', position='fill') df_mlb %>% filter(Year > 2013) %>% select(Year, Time) action_duration_sec <- df_action %>% filter(Sport=='Baseball') %>% select(action_duration) %>% pull df_innings$`Seconds of Action` <- action_duration_sec df_innings %>% filter(Inning==9) %>% select(Year, `Commercials`=`Commercial time (game)`, `Action`=`Seconds of Action`, `Other`=`Time of game`) g_stacked <- df_innings %>% filter(Inning==9) %>% select(Year, `Commercials`=`Commercial time (game)`, `Action`=`Seconds of Action`, `Other`=`Time of game`) %>% gather(key=Category, value=Value, -Year) %>% ggplot() + geom_bar(aes(x=Year, y=Value, fill=Category), width=28, stat='identity', position='fill') + theme_eric() + xlab('Year') + ylab('Proportion of the game') + ggtitle('Have modern baseball broadcasts lost action?') + scale_fill_brewer(type='qual', palette=2, 'Share') + scale_x_discrete(limits=c(1984,2014)) + theme(legend.title=element_blank()) g_stacked_p <- ggplotly(g_stacked) %>% add_annotations(text="Category", xref="paper", yref="paper", x=1.03, xanchor="left", y=0.81, yanchor="bottom", legendtitle=T, showarrow=F) %>% layout(legend=list(y=0.8, yanchor='top')) g_stacked_p # Slopegraph h_slope <- df_innings %>% filter(Inning==9) %>% select(Year, `Commercials`=`Commercial time (game)`, `Action`=`Seconds of Action`, `Other`=`Time of game`) %>% gather(key=Category, value=Value, -Year) %>% group_by(Year) %>% mutate(Total=sum(Value)) %>% ungroup() %>% mutate(Proportion=round(Value/Total,2)) %>% ggplot() + geom_line(aes(x=Year, y=Proportion, color=Category), size=4, alpha=.5) + geom_point(aes(x=Year, y=Proportion, fill=Category), size=8, shape=21, color='black') + theme_eric() + xlab('Year') + ylab('Proportion of the game') + ggtitle('Have modern baseball broadcasts lost action?') + scale_color_brewer(type='qual', palette=2, 'Share', labels=c('Action', 'Commercials', 'Other')) + scale_fill_brewer(type='qual', palette=2, 'Share', labels=c('Action', 'Commercials', 'Other')) + scale_x_continuous(limits=c(1980,2018), breaks=c(1984, 2014)) + scale_y_continuous(breaks=seq(0.1, 0.8, by=0.1)) + theme(legend.title=element_blank(), axis.title.x=element_text(size=20)) h_slope_p <- ggplotly(h_slope) %>% add_annotations(text="Category", xref="paper", yref="paper", x=1.023, xanchor="left", y=0.81, yanchor="bottom", legendtitle=T, showarrow=F) %>% layout(legend=list(y=0.8, yanchor='top')) h_slope_p

b8d5267bf4bef3e15a0ea92552ffce00e6ea6f43

7c4ecb3b2102146d5dfbca7dcd11500e9536c42f

/cachematrix.R

756010593b1db86aae1e644597be0a8130ca6bf6

[]

no_license

vldmr-voznyuk/ProgrammingAssignment2

08e979def662a059cf5b3d5f34bc88293334fe72

3f1fb015d27b02f56e85a861555882b882cd80e2

refs/heads/master

2020-09-07T17:11:06.876694

2019-11-12T07:22:46

220,856,151

0

null

2019-11-10T21:51:52

2019-11-10T21:51:51

null

UTF-8

R

false

2,062

r

cachematrix.R

# The below functions are created to avoid costly re-computing of the inverse # of a matrix. # Function "makeCacheMatrix" creates a list of funcions to: # 1.set the value of the matrix # 2.get the value of the matrix # 3.set the value of the inverse matrix (uses a built-in solve() function) # 4.get the value of the inverse matrix # Set functions from the list uses <<- operator to store values in the parent function enviroment. makeCacheMatrix <- function(x = matrix()) { m <- NULL set <- function(y) { x <<- y m <<- NULL } get <- function() x setinverse <- function(inverse) m <<- inverse getinverse <- function() m list(set = set, get = get, setinverse = setinverse, getinverse = getinverse) } # The "cacheSolve" function takes the output of "makeCacheMatrix" as an argument. It checks # if the inverse of the matrix was stored in cache, and either takes the result from cache or # calculates the inverse of the matrix and stores it there. # If "makeCacheMatrix" is called with a matrix as an argument, the inverse of that matrix is # calculated. If the "makeCacheMatrix" is called without an argument (or for calculating the # inverse of a new matrix), the "set" function from the list should be called before executing # the "cacheSolve" function. Please, see use-examples below. # NOTE: it is assumed the matrix supplied is always invertible. # Example 1. --------------- # > mx<-makeCacheMatrix() # > mx$set(matrix(c(1:4),2,2)) # > cacheSolve(mx) # Example 2. --------------- # > mx<-cacheSolve( makeCacheMatrix( matrix( c(1:4),2,2) ) ) # Now, when the inverse of the same matrix is needed again, the "cacheSolve" function # may be called or the $getinverse function used. In both cases the cashed value will be used. # > cacheSolve(mx) # > mx$getinverse() cacheSolve <- function(x, ...) { m <- x$getinverse() if(!is.null(m)) { message("getting cached data") return(m) } data <- x$get() m <- solve(data, ...) x$setinverse(m) m # Return a matrix that is the inverse of 'x' }

531ba296bf37ea9aee9bf266709e91568e2cff0e

b50c76b10eeae84c3e972deab7effdecb22ebb43

/02_ dados/02_ RFB/scripts/function extract table unid.r

04ad332a5d5a5f3a2595d6b8cd09559a0580aa60

[ "MIT" ]

permissive

matth3us/tccENAP

9002cfcd3825e989a235ba5190d9112ebd9d46ca

bd3b607dee01ea82cef2a45b48815dd147ff5113

refs/heads/master

2022-05-25T17:45:34.538088

2020-04-26T22:33:26

176,340,283

0

null

UTF-8

R

false

322

r

function extract table unid.r

tabularUnid <- function(url){ remDr$navigate(url) df <- readHTMLTable(htmlParse(remDr$getPageSource()[[1]], encoding = 'UTF-8')) [[1]] %>% spread(key = V1, value = V2) %>% select(Unidade, Estado, Cidade, Bairro, CEP, Logradouro, Atendimento, Titular, 'Telefone(s)', Observações) return(df) }

7053fad7d8ab5536dd3b63560a3b5cbf786ab2a7

a94fafa0e1ba5fefc8c711db78468baca7179374

/Code/Data Analysis/main_doube_stance_analysis.R

2f5fc9f5c833960e25dc54e537a1b2b9b31d4138

[]

no_license

CatStrain/Cat_skin

1c76fe7870e1ace2e1f9612fd166ac5135771785

9b6823cf68467a87d3af39b3e1cb0709dd432815

refs/heads/master

2023-04-10T11:16:52.024737

2021-02-25T04:01:43

273,569,581

1

2

null

2021-04-26T00:33:50

2020-06-19T19:16:47

C++

UTF-8

R

false

5,167

r

main_doube_stance_analysis.R

#Initialization: rm(list = ls()); # clear workspace variables cat("\014") # clear window library(rgl) library(class) library(ggplot2) library(caret) # "R package that provides general interface to +- 150 ML alg" library("lattice") ####### # loading RAW files: mypath_1 <- "C:/Users/dario/Documents/Github/Cat_skin/Data/Backup/Biped/4_points_5V_Sensors_in_Center_V2/initial_force_plate_data_2_021321.txt" forceplate_trainingdata <- read.csv(mypath_1) # Creating dataframe from csv file mypath_2 <- "C:/Users/dario/Documents/Github/Cat_skin/Data/Backup/Biped/4_points_5V_Sensors_in_Center_V2/force_plate_with_biped_data_2_021321.txt" labels_CoP <- read.csv(mypath_2) mypath_3 <- "C:/Users/dario/Documents/Github/Cat_skin/Data/Backup/Biped/4_points_5V_Sensors_in_Center_V2/biped_leg_test_simut_0213_2.txt" features_strainin_signals <- read.csv(mypath_3) ######## #DOWNSAMPLING data, and adding column names downsample_with_labels <- function(x){ # downsampling funtion CoP_posotions_all = rep(c(1:4), times = ceiling(nrow(x)/(25*3))) # generating label patterns data.downsampled = x[seq(12,nrow(x),25),] # down sampling prebiped_data seq.int(from, to, by, length.out, along.with, ...) data.downsampled[,5] = CoP_posotions_all[1:250] # selecting labels to fit the prebiped_data size return (data.downsampled) } downsample_no_labels <- function(x){ # downsampling funtion data.downsampled = x[seq(12,nrow(x),25),] # down sampling prebiped_data seq.int(from, to, by, length.out, along.with, ...) return (data.downsampled) } forceplate_trainingdata <- downsample_with_labels(forceplate_trainingdata) #Downsampling with labels for Force Plate Data labels_CoP <- downsample_no_labels(labels_CoP) #Downsampling with no labels for using as ground truth provided by Force Plate features_strainin_signals <- downsample_no_labels(features_strainin_signals) #Downsampling with no labels for left foot biped newheaders <- c("LC_1", "LC_2", "LC_3", "LC_4","CoP_location") colnames(forceplate_trainingdata) <- newheaders newheaders <- c("LC_1", "LC_2", "LC_3", "LC_4") colnames(labels_CoP) <- newheaders biped_data <- features_strainin_signals newheaders <- c("SL_1", "SL_2", "SL_3", "SL_4","SL_5", "SL_6", "SL_7", "SL_8") colnames(biped_data) <- newheaders ######## #Reprocessing (normalizing) trans_1 <- preProcess(forceplate_trainingdata, method = c("range")) forceplate_trainingdata = predict(trans_1, forceplate_trainingdata[,1:5]) trans_2 <- preProcess(labels_CoP, method = c("range")) labels_CoP = predict(trans_2, labels_CoP[,1:4]) trans_3 <- preProcess(biped_data, method = c("range")) biped_data = predict(trans_3, biped_data[,1:8]) ###### # Training force plate (with KNN): set.seed(99) # required to reproduce the results forceplate_trainingdata['CoP_location'] = factor(forceplate_trainingdata[,'CoP_location']) trControl <- trainControl(method = "cv", number = 5) # 5 fold Cross-Validation fit <- train(CoP_location ~ ., method = "knn", tuneGrid = expand.grid(k = 1:20), trControl = trControl, metric = "Accuracy", data = forceplate_trainingdata) # test KNN for K values: 1:20 print(fit) # print results print(confusionMatrix(fit)) levelplot(confusionMatrix(fit)$table) # show the confusion matrix ######### #Using force plate as ground truth for the incoming biped data test_pred <- predict(fit, newdata = labels_CoP) #Labels for biped sensory data (GROUND TRUTH) #test_pred print(test_pred) biped_data[,9] = test_pred # adding labels to biped data newheaders <- c("SL_1", "SL_2", "SL_3", "SL_4","SL_5", "SL_6", "SL_7", "SL_8","CoP_location") colnames(biped_data) <- newheaders ######### # Training and testing biped set.seed(99) biped_data['CoP_location'] = factor(biped_data[,'CoP_location']) trControl <- trainControl(method = "cv", number = 5) fit <- train(CoP_location ~ ., method = "knn", tuneGrid = expand.grid(k = 1:20), trControl = trControl, metric = "Accuracy", data = biped_data) print(fit) print(confusionMatrix(fit)) levelplot(confusionMatrix(fit)$table)

90de2cb61190f6f53e9471ba84984578f2f038cb

1e9c9f2a9639db7cdb032aae69cb4d99aef1d3a5

/codeSchool/tryR/chapter-2.R

fd85ab4119ac4d5cdc911bf87980880d636b3601

[ "MIT" ]

permissive

sagarnikam123/learnNPractice

f0da3f8acf653e56c591353ab342765a6831698c

1b3b0cb2cff2f478006626a4c37a99102acbb628

refs/heads/master

2023-02-04T11:21:18.211654

2023-01-24T14:47:52

61,184,927

2

1

MIT

2022-03-06T11:07:18

2016-06-15T06:57:19

Python

UTF-8

R

false

10,280

r

chapter-2.R

################# # Chapter-2 # ################# # - basics of manipulating vectors - creating and accessing them, doing math with them, and making sequences. # - how to make bar plots and scatter plots with vectors. # - how R treats vectors where one or more values are not available # Data structures in R # All R objects have a type or mode, as well as a class, which can be determined with typeof, mode & class. # 1- Vectors & its Access # Vector is a basic structure,it is list of values # in the following atomic modes (data types): # numeric, integer, character, logical, complex, raw # A vector's values can be numbers, strings, logical values, or any other type, as long as they're all the same type c(12,39,45) # "c" function (c is short for Combine) creates a new vector by combining/concanating a list of values. c('a','b','c') a = c(1, 2, 3, 4) # Basic Arithmetic Operations a+5 a - 10 a/5 b <- a - 10 exp(a) log(a) sin(a) a^2 # squre a*b # multiplication #Basic Statistics min(a,b) max(a,b) mean(a) median(a) quantile(a) var(a) sd(b) # Vectors cannot hold values with different modes (types) # All the values were converted to a single mode (characters) so that the vector can hold them all. > c(22,"Trying ...R",'z') [1] "22" "Trying ...R" "z" #Accessing:- retrieve an individual value within a vector by providing its numeric index in square brackets. > alphaList <- c('a','b','c','d','e','f') > alphaList [1] "a" "b" "c" "d" "e" "f" > str(alphaList) # internal structure of alphaList chr [1:6] "a" "b" "c" "d" "e" "f" mode(alphaList) #view storage mode of object passed i.e. alphaList > # Many languages start array indices at 0, but R's vector indices start at 1 > alphaList[1] [1] "a" > alphaList[2:5] [1] "b" "c" "d" "e" > #assign new values within an existing vector > alphaList[1] <- 'FirstElement' > alphaList [1] "FirstElement" "b" "c" "d" "e" [6] "f" > alphaList[7] = "Seventh Element" > alphaList [1] "FirstElement" "b" "c" "d" [5] "e" "f" "Seventh Element" > line= c ('one','two','three','four','five','six') > line [1] "one" "two" "three" "four" "five" "six" > line[2:6] # retrieve ranges of values [1] "two" "three" "four" "five" "six" > line[c(1,3)]# You can use a vector within the square brackets to access multiple values [1] "one" "three" > line[5:6] <- c("Fifth-5","Sixth-6") # set ranges of values; just provide the values in a vector. > line [1] "one" "two" "three" "four" "Fifth-5" "Sixth-6" # Assigning names to a vector's elements (can be used as labels for your data.) # by passing a second vector filled with names to the names assignment function numbers<- c(1,2,3,4,5) names(numbers)<-c('one','two','three','four','five') numbers # You can also use the names to access the vector's values numbers['four'] # 2- Sequence Vectors #vector with a sequence of numbers, with start:end notation 3:8 seq(40,47,by=1) # using seq function seq(1,4,by =0.5) # you can increments elements in seq other than 1 20:15 # reverse vector # 3- Plotting One Vector # barplot function draws a bar chart with a vector's values. plotValues <- c(4, 5, 1) barplot(plotValues) names(plotValues) <- c("ShriLanka", "Australia", "India") barplot(plotValues) barplot(1:20) # 4- Vector Math # If you add a scalar (a single value) to a vector, # the scalar will be added to each value in the vector, returning a new vector with the results. a <- c(1, 2, 3) a + 1 #addition a / 2 # division a * 2 #Multiplication # If you add two vectors, R will take each value from each vector and add them b <- c(4, 5, 6) a + b a - b #Compairing two vectors a == c(1, 99, 3) # Notice that R didn't test whether the whole vectors were equal; it checked each value in the a vector against the value at the same index in our new vector. sin(a) # Trignometric function # 5- Scatter Plots # takes two vectors, one for X values and one for Y values, and draws a graph of them. x <- seq(1, 20, 0.1) y <- sin(x) plot(x, y) # first argument (x) are used for the horizontal axis, and values from the second (y) for the vertical. values <- -10:10 absolutes <- abs(values) plot(values, absolutes) # 6 -NA Values (dealing with Missing Data) # when working with sample data, a given value isn't available # R explicitly indicates value as 'NA' if sample was not available a <- c(1, 3, NA, 7, 9) is.na(a) # returns FALSE FALSE TRUE FALSE FALSE sum(a) # We can explicitly tell sum (and many other functions) to remove NA values before they do their calculations, sum(a, na.rm = TRUE) mean(a,na.rm=T) newA = na.omit(a) # Handle Missing Values in Objects na.exclude(a) # 7- Histogram # First, generate a set of 10,000 Gaussian distributed random numbers data <- rnorm(1e4) # Gaussian distributed numbers with mean=0 & sigma=1 hist(data) # Plots a histogram, with sensible bin choices by default hist(data, breaks=7) # 8 -Plotting ?plot # Help page for plot command ?par # Help page for graphics parameter control ?Devices # or "?device" # (R can output in postscript, PDF, bitmap, PNG, JPEG and more formats) dev.list() # list graphics devices colours() # or "colors()" List all available colours ?plotmath # To create an output file copy of a plot for printing or including in a document etc. # 9- Functions cat # Type function name without brackets to list contents args(cat) # Return arguments of any function body(cat) # Return main body of function formals(fun) # Get or set the formal arguments of a function debug(fun); undebug(fun) # Set or unset the debugging flag on a function # Create your own function fun <- function(x, a, b, c) (a*x^2) + (b*x^2) + c fun(3, 1, 2, 3) fun(5, 1, 2, 3) # A more complicated example of a function. First, create some data: set.seed(123) # allow reproducible random numbers a <- rnorm(1000, mean=10, sd=1) # 1000 Gaussian random numbers b <- rnorm(100, mean=50, sd=15) # smaller population of higher numbers x <- c(a, b) # Combine datasets hist(x) # Shows outlier population clearly sd(x) # Strongly biased by outliers mad(x) # Robustly estimates sd of main sample mean(x) # biased median(x) # robust # 10- Simple Graphs # Dot Plots # Simple Dotplot dotchart(mtcars$mpg,labels=row.names(mtcars),cex=.7,main="Gas Milage for Car Models", xlab="Miles Per Gallon") # Dotplot: Grouped Sorted and Colored # Sort by mpg, group and color by cylinder x <- mtcars[order(mtcars$mpg),] # sort by mpg x$cyl <- factor(x$cyl) # it must be a factor x$color[x$cyl==4] <- "red" x$color[x$cyl==6] <- "blue" x$color[x$cyl==8] <- "darkgreen" dotchart(x$mpg,labels=row.names(x),cex=.7,groups= x$cyl,main="Gas Milage for Car Models\ngrouped by cylinder", xlab="Miles Per Gallon", gcolor="black", color=x$color) # Bar Plots # Simple Bar Plot counts <- table(mtcars$gear) barplot(counts, main="Car Distribution",xlab="Number of Gears") # Stacked Bar Plot with Colors and Legend counts <- table(mtcars$vs, mtcars$gear) barplot(counts, main="Car Distribution by Gears and VS", xlab="Number of Gears", col=c("darkblue","red"),legend = rownames(counts)) # Grouped Bar Plot counts <- table(mtcars$vs, mtcars$gear) barplot(counts, main="Car Distribution by Gears and VS", xlab="Number of Gears", col=c("darkblue","red"),legend = rownames(counts), beside=TRUE) # Line Graphs # Define the cars vector with 5 values cars <- c(1, 3, 6, 4, 9) # trucks <- c(2, 5, 4, 5, 12) # Graph the cars vector with all defaults plot(cars) # Graph cars using blue points overlayed by a line plot(cars, type="o", col="blue") # Create a title with a red, bold/italic font title(main="Autos", col.main="red", font.main=4) # Graph trucks with red dashed line and square points lines(trucks, type="o", pch=22, lty=2, col="red") # Create a title with a red, bold/italic font title(main="Autos", col.main="red", font.main=4) # Start PNG device driver to save output to figure.png png(filename="LinePlot.png", height=295, width=300,bg="white") #More Advanced Line Graphs x <- c(1:5); y <- x # create some data par(pch=22, col="red") # plotting symbol and color par(mfrow=c(2,4)) # all plots on one page opts = c("p","l","o","b","c","s","S","h") for(i in 1:length(opts)){ heading = paste("type=",opts[i]) plot(x, y, type="n", main=heading) lines(x, y, type=opts[i]) } #Pie chart # Pie Chart with Percentages slices <- c(10, 12, 4, 16, 8) lbls <- c("US", "UK", "Australia", "Germany", "France") pct <- round(slices/sum(slices)*100) lbls <- paste(lbls, pct) # add percents to labels lbls <- paste(lbls,"%",sep="") # ad % to labels pie(slices,labels = lbls, col=rainbow(length(lbls)),main="Pie Chart of Countries") #Box plots # Boxplots can be created for individual variables or for variables by group. # Boxplot of MPG by Car Cylinders boxplot(mpg~cyl,data=mtcars, main="Car Milage Data",xlab="Number of Cylinders", ylab="Miles Per Gallon") # Notched Boxplot of Tooth Growth Against 2 Crossed Factors # boxes colored for ease of interpretation boxplot(len~supp*dose, data=ToothGrowth, notch=TRUE, col=(c("gold","darkgreen")),main="Tooth Growth", xlab="Suppliment and Dose") # Histograms # Colored Histogram with Different Number of Bins hist(mtcars$mpg, breaks=12, col="red") # Add a Normal Curve (Thanks to Peter Dalgaard) x <- mtcars$mpg h<-hist(x, breaks=10, col="red", xlab="Miles Per Gallon", main="Histogram with Normal Curve") xfit<-seq(min(x),max(x),length=40) yfit<-dnorm(xfit,mean=mean(x),sd=sd(x)) yfit <- yfit*diff(h$mids[1:2])*length(x) lines(xfit, yfit, col="blue", lwd=2) # Scatterplots # Simple Scatterplot attach(mtcars) plot(wt, mpg, main="Scatterplot Example",xlab="Car Weight ", ylab="Miles Per Gallon ", pch=19) # Add fit lines abline(lm(mpg~wt), col="red") # regression line (y~x) lines(lowess(wt,mpg), col="blue") # lowess line (x,y)

a2014f7d98be626d2fb489735a9f8416d4dd4227

dec53b9fae27d2e2f71b937f6e77a7d8c82b7aec

/Biomod2_singleSpecies.R

dc2ada09bc38819f4bdc2ebf5c905758ab753c32

[]

no_license

npcastaneda/_playground

6219eb7b09e81a12a866e7ca4cf213aa77a03643

25e7df35e47193df29e5a1208a0ebf00a07313e5

refs/heads/master

2021-01-22T06:36:57.914690

2014-10-03T09:03:53

21,495,995

0

null

UTF-8

R

false

7,020

r

Biomod2_singleSpecies.R

################################################## # SINGLE-SPECIES DISTRIBUTION MODELING WITH BIOMOD2 ################################################## # set dir dir <- "D:/CWR/Biomod2/Test.apr.24.2013" setwd(dir) # load the library library(biomod2) # load our species data DataSpecies <- read.csv(system.file("external/species/mammals_table.csv", package="biomod2")) head(DataSpecies) # the name of studied species myRespName <- 'GuloGulo' # the presence/absences data for our species myResp <- as.numeric(DataSpecies[,myRespName]) # the XY coordinates of species data myRespXY <- DataSpecies[,c("X_WGS84","Y_WGS84")] # load the environmental raster layers (could be .img, ArcGIS # rasters or any supported format by the raster package) # Environmental variables extracted from Worldclim (bio_3, bio_4, # bio_7, bio_11 & bio_12) myExpl = stack( system.file( "external/bioclim/current/bio3.grd", package="biomod2"), system.file( "external/bioclim/current/bio4.grd", package="biomod2"), system.file( "external/bioclim/current/bio7.grd", package="biomod2"), system.file( "external/bioclim/current/bio11.grd", package="biomod2"), system.file( "external/bioclim/current/bio12.grd", package="biomod2")) # Formating data myBiomodData <- BIOMOD_FormatingData(resp.var = myResp, expl.var = myExpl, resp.xy = myRespXY, resp.name = myRespName) # This is my version myBiomodData <- BIOMOD_FormatingData(resp.var = myResp, expl.var = myExpl, resp.xy = myRespXY, resp.name = myRespName, eval.resp.var = NULL, eval.expl.var = NULL, eval.resp.xy = NULL, PA.nb.rep = 0, PA.nb.absences = 1000, PA.strategy = 'SRE', PA.dist.min = 0, PA.dist.max = NULL, PA.sre.quant = 0.025, PA.table = NULL, na.rm = TRUE) # Check the format of your current data myBiomodData plot(myBiomodData) # ----------- OJO: the function BIOMOD_FormatingData prepares the pseudo-abscences of data # (use preferably: SRE) #?BIOMOD_FormatingData # Modeling #------------ OJO: BIOMOD_ModelingOptions: Use this for setting the parameters of each model ?BIOMOD_ModelingOptions #------------------------------------------------------ # DO NOT RUN THIS PART! # default BIOMOD.model.option object myBiomodOptions <- BIOMOD_ModelingOptions() # print the object myBiomodOptions # you can copy a part of the print, change it and custom your options # here we want to compute quadratic GLM and select best model with 'BIC' criterium myBiomodOptions <- BIOMOD_ModelingOptions( GLM = list( type = 'quadratic', interaction.level = 0, myFormula = NULL, test = 'BIC', family = 'binomial', control = glm.control(epsilon = 1e-08, maxit = 1000, trace = FALSE) )) #------------------------------------------------------ # 2. Defining Models Options using default options. myBiomodOption <- BIOMOD_ModelingOptions() # 3. Computing the models myBiomodModelOut <- BIOMOD_Modeling( myBiomodData, models = c('SRE','CTA','RF','MARS','FDA'), models.options = myBiomodOption, NbRunEval=3, DataSplit=80, Prevalence=0.5, VarImport=3, models.eval.meth = c('TSS','ROC'), SaveObj = TRUE, rescal.all.models = TRUE, do.full.models = FALSE, modeling.id = paste(myRespName,"FirstModeling",sep="")) # Modeling summary myBiomodModelOut # get all models evaluation myBiomodModelEval <- getModelsEvaluations(myBiomodModelOut) # print the dimnames of this object dimnames(myBiomodModelEval) # let's print the TSS scores of Random Forest myBiomodModelEval["TSS","Testing.data","RF",,] # let's print the ROC scores of all selected models myBiomodModelEval["ROC","Testing.data",,,] # print variable importances getModelsVarImport(myBiomodModelOut) # Ensamble modeling myBiomodEM <- BIOMOD_EnsembleModeling( modeling.output = myBiomodModelOut, chosen.models = 'all', em.by='all', eval.metric = c('TSS'), eval.metric.quality.threshold = c(0.7), prob.mean = T, prob.cv = T, prob.ci = T, prob.ci.alpha = 0.05, prob.median = T, committee.averaging = T, prob.mean.weight = T, prob.mean.weight.decay = 'proportional' ) # print summary myBiomodEM # get evaluation scores getEMeval(myBiomodEM) # Model projection # projection over the globe under current conditions myBiomodProj <- BIOMOD_Projection( modeling.output = myBiomodModelOut, new.env = myExpl, proj.name = 'current', selected.models = 'all', binary.meth = 'TSS', compress = 'xz', clamping.mask = F, output.format = '.grd') # summary of crated projections myBiomodProj # files created on hard drive list.files("GuloGulo/proj_current/") # make some plots sub-selected by str.grep argument plot(myBiomodProj, str.grep = 'MARS') # if you want to make custom plots, you can also get the projected map myCurrentProj <- getProjection(myBiomodProj) myCurrentProj # load environmental variables for the future. myExplFuture = stack( system.file( "external/bioclim/future/bio3.grd", package="biomod2"), system.file( "external/bioclim/future/bio4.grd", package="biomod2"), system.file( "external/bioclim/future/bio7.grd", package="biomod2"), system.file( "external/bioclim/future/bio11.grd", package="biomod2"), system.file( "external/bioclim/future/bio12.grd", package="biomod2")) myBiomodProjFuture <- BIOMOD_Projection( modeling.output = myBiomodModelOut, new.env = myExplFuture, proj.name = 'future', selected.models = 'all', binary.meth = 'TSS', compress = 'xz', clamping.mask = T, output.format = '.grd') # make some plots, sub-selected by str.grep argument plot(myBiomodProjFuture, str.grep = 'MARS') # Ensemble forecast myBiomodEF <- BIOMOD_EnsembleForecasting( projection.output = myBiomodProj, EM.output = myBiomodEM, binary.meth = 'TSS') proj_current_GuloGulo_TotalConsensus_EMbyTSS <- stack("GuloGulo/proj_current/proj_current_GuloGulo_TotalConsensus_EMbyTSS.grd") proj_current_GuloGulo_TotalConsensus_EMbyTSS # reduce layer names for plotting convegences names(proj_current_GuloGulo_TotalConsensus_EMbyTSS) <- sapply(strsplit(names(proj_current_GuloGulo_TotalConsensus_EMbyTSS),"_"), tail, n=1) levelplot(proj_current_GuloGulo_TotalConsensus_EMbyTSS)

a4f42c3e88a90125107735e7489795ea90cb3fba

364dcb95aac6dff3f8548768dc99bba945ec81b6

/R/plot.R

4494b774bc682546f526d3a7e5e7b08834969a56

[ "MIT" ]

permissive

tidyverse/ggplot2

3ef62b72861c246b13ffc2d95678079984fe65c0

c76b9aeda648e9b6022b7169021e854c3d3890cb

refs/heads/main

2023-08-31T07:08:20.846510

2023-08-17T16:19:44

19,438

4,632

1,971

NOASSERTION

2023-09-14T13:25:40

2008-05-25T01:21:32

R

UTF-8

R

false

7,531

r

plot.R

#' Create a new ggplot #' #' `ggplot()` initializes a ggplot object. It can be used to #' declare the input data frame for a graphic and to specify the #' set of plot aesthetics intended to be common throughout all #' subsequent layers unless specifically overridden. #' #' `ggplot()` is used to construct the initial plot object, #' and is almost always followed by a plus sign (`+`) to add #' components to the plot. #' #' There are three common patterns used to invoke `ggplot()`: #' #' * `ggplot(data = df, mapping = aes(x, y, other aesthetics))` #' * `ggplot(data = df)` #' * `ggplot()` #' #' The first pattern is recommended if all layers use the same #' data and the same set of aesthetics, although this method #' can also be used when adding a layer using data from another #' data frame. #' #' The second pattern specifies the default data frame to use #' for the plot, but no aesthetics are defined up front. This #' is useful when one data frame is used predominantly for the #' plot, but the aesthetics vary from one layer to another. #' #' The third pattern initializes a skeleton `ggplot` object, which #' is fleshed out as layers are added. This is useful when #' multiple data frames are used to produce different layers, as #' is often the case in complex graphics. #' #' The `data =` and `mapping =` specifications in the arguments are optional #' (and are often omitted in practice), so long as the data and the mapping #' values are passed into the function in the right order. In the examples #' below, however, they are left in place for clarity. #' #' @param data Default dataset to use for plot. If not already a data.frame, #' will be converted to one by [fortify()]. If not specified, #' must be supplied in each layer added to the plot. #' @param mapping Default list of aesthetic mappings to use for plot. #' If not specified, must be supplied in each layer added to the plot. #' @param ... Other arguments passed on to methods. Not currently used. #' @param environment `r lifecycle::badge("deprecated")` Used prior to tidy #' evaluation. #' @export #' @examples #' # Create a data frame with some sample data, then create a data frame #' # containing the mean value for each group in the sample data. #' set.seed(1) #' #' sample_df <- data.frame( #' group = factor(rep(letters[1:3], each = 10)), #' value = rnorm(30) #' ) #' #' group_means_df <- setNames( #' aggregate(value ~ group, sample_df, mean), #' c("group", "group_mean") #' ) #' #' # The following three code blocks create the same graphic, each using one #' # of the three patterns specified above. In each graphic, the sample data #' # are plotted in the first layer and the group means data frame is used to #' # plot larger red points on top of the sample data in the second layer. #' #' # Pattern 1 #' # Both the `data` and `mapping` arguments are passed into the `ggplot()` #' # call. Those arguments are omitted in the first `geom_point()` layer #' # because they get passed along from the `ggplot()` call. Note that the #' # second `geom_point()` layer re-uses the `x = group` aesthetic through #' # that mechanism but overrides the y-position aesthetic. #' ggplot(data = sample_df, mapping = aes(x = group, y = value)) + #' geom_point() + #' geom_point( #' mapping = aes(y = group_mean), data = group_means_df, #' colour = 'red', size = 3 #' ) #' #' # Pattern 2 #' # Same plot as above, passing only the `data` argument into the `ggplot()` #' # call. The `mapping` arguments are now required in each `geom_point()` #' # layer because there is no `mapping` argument passed along from the #' # `ggplot()` call. #' ggplot(data = sample_df) + #' geom_point(mapping = aes(x = group, y = value)) + #' geom_point( #' mapping = aes(x = group, y = group_mean), data = group_means_df, #' colour = 'red', size = 3 #' ) #' #' # Pattern 3 #' # Same plot as above, passing neither the `data` or `mapping` arguments #' # into the `ggplot()` call. Both those arguments are now required in #' # each `geom_point()` layer. This pattern can be particularly useful when #' # creating more complex graphics with many layers using data from multiple #' # data frames. #' ggplot() + #' geom_point(mapping = aes(x = group, y = value), data = sample_df) + #' geom_point( #' mapping = aes(x = group, y = group_mean), data = group_means_df, #' colour = 'red', size = 3 #' ) ggplot <- function(data = NULL, mapping = aes(), ..., environment = parent.frame()) { UseMethod("ggplot") } #' @export ggplot.default <- function(data = NULL, mapping = aes(), ..., environment = parent.frame()) { if (!missing(mapping) && !inherits(mapping, "uneval")) { cli::cli_abort(c( "{.arg mapping} should be created with {.fn aes}.", "x" = "You've supplied a {.cls {class(mapping)[1]}} object" )) } data <- fortify(data, ...) p <- structure(list( data = data, layers = list(), scales = scales_list(), guides = guides_list(), mapping = mapping, theme = list(), coordinates = coord_cartesian(default = TRUE), facet = facet_null(), plot_env = environment ), class = c("gg", "ggplot")) p$labels <- make_labels(mapping) set_last_plot(p) p } #' @export ggplot.function <- function(data = NULL, mapping = aes(), ..., environment = parent.frame()) { # Added to avoid functions end in ggplot.default cli::cli_abort(c( "{.arg data} cannot be a function.", "i" = "Have you misspelled the {.arg data} argument in {.fn ggplot}" )) } plot_clone <- function(plot) { p <- plot p$scales <- plot$scales$clone() p } #' Reports whether x is a ggplot object #' @param x An object to test #' @keywords internal #' @export is.ggplot <- function(x) inherits(x, "ggplot") #' Explicitly draw plot #' #' Generally, you do not need to print or plot a ggplot2 plot explicitly: the #' default top-level print method will do it for you. You will, however, need #' to call `print()` explicitly if you want to draw a plot inside a #' function or for loop. #' #' @param x plot to display #' @param newpage draw new (empty) page first? #' @param vp viewport to draw plot in #' @param ... other arguments not used by this method #' @keywords hplot #' @return Invisibly returns the original plot. #' @export #' @method print ggplot #' @examples #' colours <- list(~class, ~drv, ~fl) #' #' # Doesn't seem to do anything! #' for (colour in colours) { #' ggplot(mpg, aes_(~ displ, ~ hwy, colour = colour)) + #' geom_point() #' } #' #' # Works when we explicitly print the plots #' for (colour in colours) { #' print(ggplot(mpg, aes_(~ displ, ~ hwy, colour = colour)) + #' geom_point()) #' } print.ggplot <- function(x, newpage = is.null(vp), vp = NULL, ...) { set_last_plot(x) if (newpage) grid.newpage() # Record dependency on 'ggplot2' on the display list # (AFTER grid.newpage()) grDevices::recordGraphics( requireNamespace("ggplot2", quietly = TRUE), list(), getNamespace("ggplot2") ) data <- ggplot_build(x) gtable <- ggplot_gtable(data) if (is.null(vp)) { grid.draw(gtable) } else { if (is.character(vp)) seekViewport(vp) else pushViewport(vp) grid.draw(gtable) upViewport() } if (isTRUE(getOption("BrailleR.VI")) && rlang::is_installed("BrailleR")) { print(asNamespace("BrailleR")$VI(x)) } invisible(x) } #' @rdname print.ggplot #' @method plot ggplot #' @export plot.ggplot <- print.ggplot

0b0446adc37e55ef5cf7064ac13a5fd1928e0a16

77e9997f1d751ec975c98d4a01b29cf512c47688

/factors.R

adeb178395b6ab99e97db88391aa10b47882352c

[]

no_license

Khan996/Hello-R

e52ef1ee3f0a73ed608f079299a58f4ed3ff5a57

acb2f22ca68d5a72b6c1647356692f1368133135

refs/heads/main

2023-08-27T13:54:03.935191

2021-10-27T14:34:49

410,268,353

0

null

UTF-8

R

false

621

r

factors.R

x <- factor(c("Yes", "Yes", "No", "No", "Yes")) x table(x) unclass(x) z <- factor(c("Punab", "KPk", "KPk", "Sindh")) z unclass(z) c <- factor(c("Yes","Yes","No","No", "Yes"), levels = c("Yes", "No")) c weeks <- factor(c("Monday","Tuesday","Saturday", "Wednesday","Friday", "Thursday", "Sunday"), levels = c("Monday","Tuesday","Wednesday", "Thursday","Friday","Saturday", "Sunday")) weeks #NA & NaN are used for Null where data is not present D <- c(1,2,3, NA, 3, NaN, 4, NA) D is.na(D) is.nan(D)

0dd5f225f76f76511bf9b6521837c15d277bbc8c

c4ca20a12315b278cda1875867d399b15adc4337

/Analysis.R

48ceec23ad46f7fecc9f39fc9afc544438cae6ba

[]

no_license

StefanoAllesina/stima_nepo

167fca5420c1da01154a11f6a6161bb110a032cd

87caaeba0b05a7e0606f20e8b840323b57599f82

refs/heads/master

2021-01-18T17:57:37.537850

2016-09-26T14:37:37

69,259,339

0

null

UTF-8

R

false

746

r

Analysis.R

library(dplyr) # Read names names <- read.csv("ITA-Names.csv", stringsAsFactors = FALSE) # How many unique names in a sample of x academics? for_random <- matrix(0, 1000, nrow(names)) for (i in 1:nrow(for_random)) for_random[i,] <- cumsum(!duplicated(sample(names$Last))) # Read the input sectors <- read.csv("Input.csv") # Calculate expected number of people sectors$Expected_people <- rep(NA, nrow(sectors)) for (i in 1:nrow(sectors)){ num_names <- sectors$Names[i] tmp <- apply(for_random, 1, function(x) which(x == num_names)[1]) sectors$Expected_people[i] <- mean(tmp) print(sectors[i,]) } sectors$Excess_people <- sectors$People - sectors$Expected_people #Save output write.csv(sectors, file = "Output.csv", row.names = FALSE)

2f1a2c8503137990f8dd717f3677308cacfd2f6f

11641bb10056da1bf416315b255cb9326553d667

/R/rmd2ipynb.R

fcebc457cdbb3c8e2a4ae8be2af1ccaef490405e

[ "MIT" ]

permissive

jullybobble/rmd2ipynb

ee655d8db2f3a85ec3df92ecd7ad82e6917bc79f

14b2cc494b68eeece21df4ccb6753052983ab33d

refs/heads/master

2023-03-06T12:01:51.897302

2021-02-19T18:06:56

336,350,624

0

null

UTF-8

R

false

3,927

r

rmd2ipynb.R

#' Convert an RMarkdown HTML notebook to an IPython (or Jupyter) notebook. #' #' Note that you must pass the HTML file with extension `.nb.html` that is rendered #' automatically for RMarkdown documents with output with `html_notebook` output. #' Passing the RMarkdown document directly will fail. #' #' @param html_notebook_file path to the RMarkdown HTML document to be converted #' @param ipynb_file path to the desired output IPython notebook to be generated #' #' @return the resulting JSON of the IPython notebook, invisibly, as a character #' vector #' #' @export #' @importFrom magrittr `%>%` #' @importFrom readr write_file #' @importFrom purrr map map_df #' @importFrom stringr str_replace #' @importFrom rmarkdown parse_html_notebook #' @importFrom xml2 read_html xml_find_first #' @importFrom rvest html_text #' @import dplyr tidyr html_nb_to_ipynb <- function(html_notebook_file, ipynb_file = str_replace(html_notebook_file, "\\.nb\\.html$", ".ipynb"), verbose = F, remove_html_comments = T) { stopifnot(file.exists(html_notebook_file)) parsed <- parse_html_notebook(html_notebook_file) title <- parsed$header %>% parse_html() %>% xml_find_first("/html/head/title") %>% html_text() json <- parsed %>% rmd_annotations() %>% ipynb_from_rmd(title, remove_html_comments) write_file(json, ipynb_file) if(verbose) { message("wrote file ", ipynb_file) } invisible(json) } #' @importFrom jsonlite toJSON ipynb_from_rmd <- function(annotations, title = NULL, remove_html_comments = T) { cells <- annotations %>% rowwise %>% group_map( ~ ipynb_cell_from_rmd_part(.x$label, .x$source, .x$outputs, remove_html_comments)) %>% discard(is.null) title_cell <- if(is.null(title)) { list() } else { list(ipynb_cell_markdown(paste("#", title))) } list( nbformat = 4, nbformat_minor = 4, metadata = list( kernelspec = list( display_name = "R", language = "R", name = "ir" ), language_info = list( codemirror_mode = "r", file_extension = ".r", mimetype = "text/x-r-source", name = "R", pygments_lexer = "r", version = paste(version$major, sep = ".", version$minor) ) ), cells = c(title_cell, cells) ) %>% toJSON(auto_unbox = T, pretty = T, na = "null") } #' @importFrom xml2 xml_find_first #' @importFrom rvest html_text html_attr #' @importFrom stringr str_detect str_remove str_remove_all #' @importFrom purrr discard ipynb_cell_from_rmd_part <- function(label, source, outputs, remove_html_comments) { if (label == "text") { if(source %>% str_detect('^<div id="rmd-source-code">')) { NULL } else { md <- html_to_md(source) if(remove_html_comments) { md <- str_remove_all(md, "(?m)^:::.*\n") } if(str_detect(md, "^\\s*$")) { NULL } else { ipynb_cell_markdown(md) } } } else if (label == "chunk") { code <- if(source == "") { NULL } else { source %>% read_html() %>% html_text() } out <- if(!is.null(outputs[[1]])) { outputs[[1]] %>% rowwise %>% group_map( ~ ipynb_cell_from_rmd_part(.x$label, .x$source, .x$outputs, remove_html_comments)) %>% discard(is.null) } else { NULL } ipynb_cell_code(code, out) } else if (label == "plot") { img_base64 <- source %>% read_html %>% xml_find_first("//img") %>% html_attr("src") %>% str_remove("data:image/png;base64,") ipynb_out_img(img_base64) } else if (label == "output") { text <- source %>% read_html %>% xml_find_first("//pre/code") %>% html_text() ipynb_out_stream(text) } else if (label == "frame") { table <- source %>% read_html() %>% html_text() %>% parse_pagedtable_json() ipynb_out_table(table) } else { NULL } }

9c9dfd69a2299dfa4f4dee7ff426328af084439c

7ab0b6d8bad7e7824528d1f05c10792759cabab1

/R/get-scene.R

430eacf2998b32e19f8187946380cc31c3500e43

[]

permissive

tbradley1013/dundermifflin

a4711e3cd02d494885a30a34ddca877f04cb2ff9

691045dbfe6ab526caa4db4240ea378f81f5262d

refs/heads/master

2020-05-01T06:33:37.546458

2020-02-27T13:18:15

177,332,858

20

1

MIT

2020-02-04T14:45:54

2019-03-23T19:42:59

R

UTF-8

R

false

2,421

r

get-scene.R

# Get Scene #' Get a random scene dialog from the Office #' #' @examples #' #' \dontrun{random_scene()} #' #' @export random_scene <- function(){ ep_n_scene <- dundermifflin::ep_n_scene office_quotes <- dundermifflin::office_quotes season <- sample(c(1:4, 6:9), 1) episode <- sample(ep_n_scene$episode[ep_n_scene$season == season], 1) scene <- sample(ep_n_scene$scene[ep_n_scene$season == season & ep_n_scene$episode == episode], 1) scene_quotes <- office_quotes[office_quotes$season == season & office_quotes$episode == episode & office_quotes$scene == scene,] class(scene_quotes) <- c("dunder_scene", class(scene_quotes)) return(scene_quotes) } #' @export print.dunder_scene <- function(x, ...){ screen_width <- options()$width max_char_length <- max(nchar(x$character)) if (length(unique(x$character)) <= 7){ cols <- c("red", "green", "yellow", "blue", "magenta", "cyan", "silver") cols <- sample(cols, length(unique(x$character)), replace = FALSE) names(cols) <- unique(x$character) } else { cols <- c("red", "green", "yellow", "blue", "magenta", "cyan", "silver") cols <- sample(cols, length(unique(x$character)), replace = TRUE) names(cols) <- unique(x$character) } header <- paste0("THE OFFICE - ", unique(x$name), " (Season ", unique(x$season), ", Episode ", unique(x$episode), ", Scene ", unique(x$scene), ")\n") header_len <- nchar(header) cat(rep("=", min(c(header_len, screen_width))), "\n", sep = "") cat("THE OFFICE - ", crayon::red(unique(x$name)), " (Season ", unique(x$season), ", Episode ", unique(x$episode), ", Scene ", unique(x$scene), ")\n", sep = "") cat(rep("=", min(c(header_len, screen_width))), "\n", sep = "") for (i in 1:nrow(x)){ qt <- x[i,] cat( char_color(qt$character, cols), ": ", rep(" ", (max_char_length - nchar(qt$character))), qt$quote, "\n", sep = "" ) } } #' @export as.character.dunder_scene <- function(x, ...){ max_char_length <- max(nchar(x$character)) out <- character(nrow(x)) for (i in 1:nrow(x)){ qt <- x[i,] out[i] <- paste0( qt$character, ": ", paste(rep(" ", (max_char_length - nchar(qt$character))), collapse = ""), qt$quote ) } out <- paste(out, collapse = "\n") return(out) } char_color <- function(character, cols){ color <- cols[names(cols) == character] crayon::style(character, crayon::make_style(color)) }

6fbabf808672411b693c059201a82aa7c5f48fa4

edf2d3864db8751074133b2c66a7e7995a960c6b

/man/MCNearestMeanClassifier.Rd

5efbeb3f5d38e471f1a89472e9a255de9f8f89ae

[]

no_license

jkrijthe/RSSL

78a565b587388941ba1c8ad8af3179bfb18091bb

344e91fce7a1e209e57d4d7f2e35438015f1d08a

refs/heads/master

2023-04-03T12:12:26.960320

2023-03-13T19:21:31

7,248,018

65

24

null

2023-03-28T06:46:23

2012-12-19T21:55:39

R

UTF-8

R

false

true

2,611

rd

MCNearestMeanClassifier.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/MCNearestMeanClassifier.R \name{MCNearestMeanClassifier} \alias{MCNearestMeanClassifier} \title{Moment Constrained Semi-supervised Nearest Mean Classifier} \usage{ MCNearestMeanClassifier(X, y, X_u, update_sigma = FALSE, prior = NULL, x_center = FALSE, scale = FALSE) } \arguments{ \item{X}{matrix; Design matrix for labeled data} \item{y}{factor or integer vector; Label vector} \item{X_u}{matrix; Design matrix for unlabeled data} \item{update_sigma}{logical; Whether the estimate of the variance should be updated after the means have been updated using the unlabeled data} \item{prior}{matrix; Class priors for the classes} \item{x_center}{logical; Should the features be centered?} \item{scale}{logical; Should the features be normalized? (default: FALSE)} } \description{ Update the means based on the moment constraints as defined in Loog (2010). The means estimated using the labeled data are updated by making sure their weighted mean corresponds to the overall mean on all (labeled and unlabeled) data. Optionally, the estimated variance of the classes can be re-estimated after this update is applied by setting update_sigma to \code{TRUE}. To get the true nearest mean classifier, rather than estimate the class priors, set them to equal priors using, for instance \code{prior=matrix(0.5,2)}. } \references{ Loog, M., 2010. Constrained Parameter Estimation for Semi-Supervised Learning: The Case of the Nearest Mean Classifier. In Proceedings of the 2010 European Conference on Machine learning and Knowledge Discovery in Databases. pp. 291-304. } \seealso{ Other RSSL classifiers: \code{\link{EMLeastSquaresClassifier}}, \code{\link{EMLinearDiscriminantClassifier}}, \code{\link{GRFClassifier}}, \code{\link{ICLeastSquaresClassifier}}, \code{\link{ICLinearDiscriminantClassifier}}, \code{\link{KernelLeastSquaresClassifier}}, \code{\link{LaplacianKernelLeastSquaresClassifier}()}, \code{\link{LaplacianSVM}}, \code{\link{LeastSquaresClassifier}}, \code{\link{LinearDiscriminantClassifier}}, \code{\link{LinearSVM}}, \code{\link{LinearTSVM}()}, \code{\link{LogisticLossClassifier}}, \code{\link{LogisticRegression}}, \code{\link{MCLinearDiscriminantClassifier}}, \code{\link{MCPLDA}}, \code{\link{MajorityClassClassifier}}, \code{\link{NearestMeanClassifier}}, \code{\link{QuadraticDiscriminantClassifier}}, \code{\link{S4VM}}, \code{\link{SVM}}, \code{\link{SelfLearning}}, \code{\link{TSVM}}, \code{\link{USMLeastSquaresClassifier}}, \code{\link{WellSVM}}, \code{\link{svmlin}()} } \concept{RSSL classifiers}

9b66537874dbd64f89f0948955119b02e5a3980f

f43ff1e09138649558c2e90a75bd2d4f3cbbdbb6

/source/Windows/R-Portable-Win/library/plotly/examples/shiny/proxy_relayout/app.R

8f4da760fdffa1cf9b9f5930edafdc81402bf816

[ "MIT", "CC-BY-3.0", "GPL-2.0-only", "LicenseRef-scancode-unknown-license-reference" ]

permissive

romanhaa/Cerebro

5b2d9371403c52f60341894f84cd0f6a006cc930

946ed178c986027d60af6013e63d1fc51ae8b371

refs/heads/master

2022-12-02T15:49:57.705873

2021-11-20T11:47:12

2021-11-21T17:09:37

164,686,297

87

23

MIT

2022-11-10T18:21:44

2019-01-08T16:09:59

HTML

UTF-8

R

false

1,317

r

app.R

library(shiny) library(plotly) ui <- fluidPage( plotlyOutput("plot") ) server <- function(input, output, session) { p <- ggplot(txhousing) + geom_line(aes(date, median, group = city)) output$plot <- renderPlotly({ ggplotly(p, dynamicTicks = TRUE) %>% rangeslider() }) observeEvent(event_data("plotly_relayout"), { d <- event_data("plotly_relayout") # unfortunately, the data structure emitted is different depending on # whether the relayout is triggered from the rangeslider or the plot xmin <- if (length(d[["xaxis.range[0]"]])) d[["xaxis.range[0]"]] else d[["xaxis.range"]][1] xmax <- if (length(d[["xaxis.range[1]"]])) d[["xaxis.range[1]"]] else d[["xaxis.range"]][2] if (is.null(xmin) || is.null(xmax)) return(NULL) # compute the y-range based on the new x-range idx <- with(txhousing, xmin <= date & date <= xmax) yrng <- extendrange(txhousing$median[idx]) plotlyProxy("plot", session) %>% plotlyProxyInvoke("relayout", list(yaxis = list(range = yrng))) }) yRange <- range(txhousing$median, na.rm = TRUE) observeEvent(event_data("plotly_doubleclick"), { plotlyProxy("plot", session) %>% plotlyProxyInvoke("relayout", list(yaxis = list(range = yRange))) }) } shinyApp(ui, server)

4b0a4ce981c0b9876c1b29689bd219cb04c50cc8

49373eee9ed92cbe9db5229309d591b0a8d54b27

/R/app_server.R

aa4fe0e2e4573e65fca2452c7ee08a58d7e19004

[ "MIT" ]

permissive

cparsania/FungiExpresZ

263520cf9c4f02b6d3110a9e73759aff5722631f

8a18f17feedade5ecf4e9a4fe995530998f3b527

refs/heads/master

2023-08-24T17:38:06.221566

2023-07-21T12:47:58

212,790,225

12

0

NOASSERTION

2023-05-26T01:29:03

2019-10-04T10:30:38

R

UTF-8

R

false

159,219

r

app_server.R

#' app server #' #' @param input session input #' @param output session output #' @param session session #' @importFrom dplyr %>% #' @importFrom dplyr add_count #' @importFrom dplyr add_row #' @importFrom dplyr add_rownames #' @importFrom dplyr add_tally #' @importFrom dplyr all_vars #' @importFrom dplyr any_vars #' @importFrom dplyr arrange #' @importFrom dplyr arrange_all #' @importFrom dplyr arrange_at #' @importFrom dplyr arrange_if #' @importFrom dplyr as_data_frame #' @importFrom dplyr as_tibble #' @importFrom dplyr bind_cols #' @importFrom dplyr bind_rows #' @importFrom dplyr case_when #' @importFrom dplyr count #' @importFrom dplyr count_ #' @importFrom dplyr data_frame #' @importFrom dplyr distinct #' @importFrom dplyr distinct_all #' @importFrom dplyr distinct_at #' @importFrom dplyr distinct_if #' @importFrom dplyr distinct_prepare #' @importFrom dplyr enquo #' @importFrom dplyr enquos #' @importFrom dplyr everything #' @importFrom dplyr expr #' @importFrom dplyr filter #' @importFrom dplyr filter_all #' @importFrom dplyr filter_at #' @importFrom dplyr filter_if #' @importFrom dplyr first #' @importFrom dplyr funs #' @importFrom dplyr glimpse #' @importFrom dplyr group_by #' @importFrom dplyr group_by_ #' @importFrom dplyr group_by_all #' @importFrom dplyr group_by_at #' @importFrom dplyr if_else #' @importFrom dplyr intersect #' @importFrom dplyr last #' @importFrom dplyr last_col #' @importFrom dplyr left_join #' @importFrom dplyr mutate #' @importFrom dplyr mutate_ #' @importFrom dplyr mutate_all #' @importFrom dplyr mutate_at #' @importFrom dplyr mutate_each #' @importFrom dplyr mutate_if #' @importFrom dplyr n #' @importFrom dplyr n_distinct #' @importFrom dplyr n_groups #' @importFrom dplyr na_if #' @importFrom dplyr order_by #' @importFrom dplyr pull #' @importFrom dplyr quo #' @importFrom dplyr quo_name #' @importFrom dplyr quos #' @importFrom dplyr rename #' @importFrom dplyr rename_all #' @importFrom dplyr rename_at #' @importFrom dplyr rename_if #' @importFrom dplyr rename_vars #' @importFrom dplyr rename_vars_ #' @importFrom dplyr right_join #' @importFrom dplyr row_number #' @importFrom dplyr rowwise #' @importFrom dplyr same_src #' @importFrom dplyr sample_frac #' @importFrom dplyr sample_n #' @importFrom dplyr select #' @importFrom dplyr select_ #' @importFrom dplyr select_all #' @importFrom dplyr select_at #' @importFrom dplyr select_if #' @importFrom dplyr select_var #' @importFrom dplyr select_vars #' @importFrom dplyr select_vars_ #' @importFrom dplyr slice #' @importFrom dplyr slice_ #' @importFrom dplyr setdiff #' @importFrom dplyr starts_with #' @importFrom dplyr summarise #' @importFrom dplyr summarise_ #' @importFrom dplyr summarise_all #' @importFrom dplyr summarise_at #' @importFrom dplyr summarise_each #' @importFrom dplyr summarise_each_ #' @importFrom dplyr summarise_if #' @importFrom dplyr summarize #' @importFrom dplyr summarize_ #' @importFrom dplyr summarize_all #' @importFrom dplyr summarize_at #' @importFrom dplyr summarize_each #' @importFrom dplyr summarize_each_ #' @importFrom dplyr summarize_if #' @importFrom dplyr tally #' @importFrom dplyr tally_ #' @importFrom dplyr tbl #' @importFrom dplyr tbl_df #' @importFrom dplyr tibble #' @importFrom dplyr top_n #' @importFrom dplyr tribble #' @importFrom dplyr ungroup #' @importFrom dplyr union #' @importFrom dplyr union_all #' @importFrom dplyr vars #' @importFrom dplyr contains #' @importFrom dplyr if_else #' @importFrom tidyr %>% #' @importFrom tidyr drop_na #' @importFrom tidyr gather #' @importFrom tidyr nest #' @importFrom tidyr replace_na #' @importFrom tidyr separate #' @importFrom tidyr separate_rows #' @importFrom tidyr spread #' @importFrom tidyr unite #' @importFrom tidyr unnest #' @importFrom magrittr %>% #' @importFrom readr read_csv #' @importFrom readr read_delim #' @importFrom readr read_file #' @importFrom readr read_lines #' @importFrom readr read_rds #' @importFrom readr read_tsv #' @importFrom readr type_convert #' @importFrom readr write_csv #' @importFrom readr write_csv2 #' @importFrom readr write_delim #' @importFrom readr write_file #' @importFrom readr write_lines #' @importFrom readr write_rds #' @importFrom readr write_tsv #' @importFrom rlang as_function #' @importFrom purrr as_mapper #' @importFrom purrr as_vector #' @importFrom purrr compact #' @importFrom purrr compose #' @importFrom purrr is_list #' @importFrom purrr keep #' @importFrom purrr map #' @importFrom purrr map_at #' @importFrom purrr map_chr #' @importFrom purrr map_dbl #' @importFrom purrr map_depth #' @importFrom purrr map_df #' @importFrom purrr map_dfc #' @importFrom purrr map_dfr #' @importFrom purrr map_if #' @importFrom purrr map_int #' @importFrom purrr map_lgl #' @importFrom purrr map_raw #' @importFrom purrr map2 #' @importFrom purrr map2_chr #' @importFrom purrr map2_dbl #' @importFrom purrr map2_df #' @importFrom purrr map2_dfc #' @importFrom purrr map2_dfr #' @importFrom purrr map2_int #' @importFrom purrr map2_lgl #' @importFrom purrr map2_raw #' @importFrom purrr negate #' @importFrom purrr partial #' @importFrom purrr safely #' @importFrom purrr set_names #' @importFrom purrr splice #' @importFrom tibble add_column #' @importFrom tibble add_row #' @importFrom tibble as_data_frame #' @importFrom tibble as_tibble #' @importFrom tibble as.tibble #' @importFrom tibble column_to_rownames #' @importFrom tibble data_frame #' @importFrom tibble enframe #' @importFrom tibble glimpse #' @importFrom tibble repair_names #' @importFrom tibble rowid_to_column #' @importFrom tibble rownames_to_column #' @importFrom tibble tibble #' @importFrom glue glue #' @importFrom stringr str_c #' @importFrom stringr str_extract #' @importFrom stringr str_extract_all #' @importFrom stringr str_glue #' @importFrom stringr str_length #' @importFrom stringr str_match #' @importFrom stringr str_match_all #' @importFrom stringr str_order #' @importFrom stringr str_pad #' @importFrom stringr str_remove #' @importFrom stringr str_remove_all #' @importFrom stringr str_replace #' @importFrom stringr str_replace_all #' @importFrom stringr str_replace_na #' @importFrom stringr str_sort #' @importFrom stringr str_split #' @importFrom stringr str_wrap #' @importFrom forcats %>% #' @importFrom forcats as_factor #' @importFrom forcats fct_c #' @importFrom forcats fct_drop #' @importFrom forcats fct_expand #' @importFrom forcats fct_inorder #' @importFrom forcats fct_match #' @importFrom forcats fct_other #' @importFrom forcats fct_recode #' @importFrom forcats fct_relabel #' @importFrom forcats fct_relevel #' @importFrom forcats fct_reorder #' @importFrom forcats fct_reorder2 #' @importFrom clipr clear_clip #' @importFrom clipr read_clip #' @importFrom clipr read_clip_tbl #' @importFrom clipr write_clip #' @importFrom clipr write_last_clip #' @importFrom rlang := #' @importFrom rlang !! #' @importFrom rlang !!! #' @importFrom rlang %@% #' @importFrom rlang %@%<- #' @importFrom rlang %|% #' @importFrom rlang %||% #' @importFrom rlang enquo #' @importFrom rlang enquos #' @importFrom rlang quo #' @importFrom rlang quos #' @importFrom rlang set_names #' @importFrom tm Corpus #' @importFrom tm Docs #' @importFrom tm DocumentTermMatrix #' @importFrom tm removeNumbers #' @importFrom tm removePunctuation #' @importFrom tm removeSparseTerms #' @importFrom tm removeWords #' @importFrom tm SimpleCorpus #' @importFrom tm stemDocument #' @importFrom tm stopwords #' @importFrom tm TermDocumentMatrix #' @importFrom tm termFreq #' @importFrom tm Terms #' @importFrom tm tm_filter #' @importFrom tm tm_index #' @importFrom tm tm_map #' @importFrom tm tm_parLapply #' @importFrom tm tm_parLapply_engine #' @importFrom tm tm_reduce #' @importFrom tm tm_term_score #' @importFrom tm VCorpus #' @importFrom tm VectorSource #' @importFrom tm content_transformer #' @importFrom tm stripWhitespace #' @importFrom MASS kde2d #' @importFrom janitor clean_names #' @importFrom broom tidy #' @importFrom rio convert #' @importFrom scales squish #' @import ggupset #' @keywords internal app_server <- function(input, output,session) { # List the first level callModules here options(shiny.maxRequestSize = 30 * 1024^2) ## prepare user applied filters' (while selecting pre loaded data) expressions . ## NOTE : following filter expressions will be used to filter the sample_info (pre loaded data) table. ## Therefore, column names - `Organism` and `strain` (given as names of vector) must be identical to the column names of the sample_info table. ## @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ ## Display, filter and select existing data ---- ## @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ ### strains by species strains_by_species <- reactive({ req(sample_info) sample_info %>% dplyr::select(species, strain, genotype) %>% dplyr::group_by(species) %>% dplyr::summarise(genotype = list(genotype) , strain = list(strain)) }) ## Update species on sra sample info table observe({ req(strains_by_species()) ## all species available_species <- base::unique(strains_by_species()$species) shinyWidgets::updatePickerInput(session = session, inputId = "select_species", choices = available_species, selected = "Aspergillus nidulans") }) ## get selected species strain selected_species_strains <- reactive({ req(input$select_species ,strains_by_species()) ## strains for selected species available_strains <- strains_by_species() %>% dplyr::filter(species == input$select_species) %>% dplyr::select(strain) %>% tidyr::unnest(cols = strain) %>% tidyr::drop_na() %>% pull(1) %>% unique() %>% sort() return(available_strains) }) ## update strain info observe({ req(selected_species_strains()) shinyWidgets::updatePickerInput(session = session, inputId = "select_strain", choices = selected_species_strains()) }) ## reset strain filter observeEvent(input$reset_strain_filter , { shinyWidgets::updatePickerInput(session = session, inputId = "select_strain", choices = selected_species_strains()) }) ## get genotypes for selected species selected_species_genotype <- reactive({ req(input$select_species ,strains_by_species()) ## genotype for selected species available_genotype <- strains_by_species() %>% dplyr::filter(species == input$select_species) %>% dplyr::select(genotype) %>% tidyr::unnest(cols = genotype) %>% tidyr::drop_na() %>% pull(1) %>% unique() %>% sort() return(available_genotype) }) ## update genotype info observe({ req(selected_species_genotype()) shinyWidgets::updatePickerInput(session = session, inputId = "select_genotype", choices = selected_species_genotype()) }) ## reset genotype filter observeEvent(input$reset_genotype_filter , { shinyWidgets::updatePickerInput(session = session, inputId = "select_genotype", choices = selected_species_genotype()) }) ## show number of rows selected user_selected_rows <- reactive({ if(is.null(input$pre_loaded_data_sra_sample_info_rows_selected)){ return(0) }else{ return(length(input$pre_loaded_data_sra_sample_info_rows_selected)) } }) ## Render text for number of rows selected output$sra_info_number_of_rows_selected <- renderText( paste(user_selected_rows() ,"row(s) selected") ) ## Update strain on sra sample info table user_filters_expressions <- reactive({ u_filters_vals <- c(species = input$select_species, strain = input$select_strain , genotype = input$select_genotype) u_filters_expr <- lapply(names(u_filters_vals), function(col) { my_filter(col, "==", value = u_filters_vals[[col]]) }) return(u_filters_expr) }) ## SRA sample info table user display sra_sample_info_user_display <- reactive({ ## add ncbi urls whereever possible dd <- sample_info %>% dplyr::filter(!!!user_filters_expressions()) return(dd) }) ## pre loaded data output$pre_loaded_data_sra_sample_info <- DT::renderDataTable({ return(sra_sample_info_user_display()) #%>% # dplyr::mutate(run_accession = # map_chr(run_accession , # ~ tags$a( .x, href = paste("https://www.ncbi.nlm.nih.gov/sra/" , # .x , # sep = "") , target = "blank") %>% # as.character() # )) }, selection = "multiple", class = "cell-border stripe", rownames = FALSE, extensions = c("Buttons"), server = F, options = list( scrollX = TRUE, dom = "Blfrtip", autoWidth = TRUE, searchHighlight = TRUE, columnDefs = list( list(width = '700px', targets = c(5, 6,9)), # Fix width columns list(targets = c(2,3, 8:18), visible = FALSE)# The column number must be identical to the columns in colvis extension ), buttons = list("copy", list(extend = "collection", buttons = c("csv", "excel", "pdf"), text = "Download"), list(extend = "colvis", columns = c(2,3,8:18)) ), # end of buttons customization lengthMenu = list( c(10, 20 ,50, 70, 100,500 ) # declare values , c(10, 20 ,50, 70, 100,500 ) # declare titles ), pageLength = 10 ), ## end of options ) ## reset rows selection in preloaded data sample information observeEvent(input$deselect_all_rows, { proxy <- DT::dataTableProxy("pre_loaded_data_sra_sample_info") DT::selectRows(proxy = proxy, NULL) }) ## select all rows in preloaded data sample information observe({ #req(pre_loaded_data_sra_sample_info()) proxy = DT::dataTableProxy("pre_loaded_data_sra_sample_info") if (is.null(input$sra_sample_info_select_all_rows)) { DT::selectRows(proxy = proxy, NULL) } else if(input$sra_sample_info_select_all_rows == "TRUE") { DT::selectRows(proxy = proxy, input$pre_loaded_data_sra_sample_info_rows_all) } }) ## user selected SRA id sample info user_selected_sra_id_sample_info <- eventReactive(input$submit_user_sra_samples, { req(input$pre_loaded_data_sra_sample_info_rows_selected) selected_rows <- input$pre_loaded_data_sra_sample_info_rows_selected n_select <- selected_rows %>% length() n_max_sra_limit <-1000 if(n_select > n_max_sra_limit){ shinyWidgets::sendSweetAlert(session = session , type = "warning", title = "Warning...!!" , text = paste("Due to memory limit, current version allows maximum", n_max_sra_limit, "samples. You have selected " , n_select, "." , "Application will continue with first", n_max_sra_limit, "samples.", sep = " ")) selected_rows <- selected_rows[1:n_max_sra_limit] } ## process user applied filter expressions user_selected_data_sra_ids <- sra_sample_info_user_display() %>% dplyr::slice(selected_rows) }) ## prepare tibble having sra_id and bioproject_id columns user_selected_sra_id_to_bioproject_id <- reactive({ user_selected_sra_id_sample_info() %>% dplyr::select(bio_project , run_accession) %>% dplyr::mutate_all( ~replace_na(. ,"--NA--")) }) ## @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ ## User selected SRA data ---- ## @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ ## user selected data user_selected_sra_id_data <- eventReactive(input$submit_user_sra_samples,{ req(user_selected_sra_id_sample_info() ) ## process user applied filter expressions user_selected_data_sra_ids <- user_selected_sra_id_sample_info()%>% dplyr::select(23) %>% ## column 23 is Run/SRA id. IMP: change this, if column order in the sample_info table changed. dplyr::pull(1) ## Objective : load species specific data. ## Reference annotation name of respective species will be used to load species specific data ## 1st step is to get the user reference annotation name (e.g FungiDB-39_AnidulansFGSCA4 for Aspergillus nidulans and FungiDB-39_CalbicansSC5314 for Candida albicans) ## Once reference annotation name obtained, use it to get the respective species expression .rds file name. ## Reference annotations to .rds file name mapping given in the rds object "reference_annotation_to_expr_map.rds" ## once the name of .rds file obtained, load it and get the user selected data user_selected_species_ref_annot <- sra_sample_info_user_display()%>% dplyr::select(2) %>% ## column 2 is get the name of ref annot dplyr::pull(1) %>% as.character() %>% .[1] #ref_annot_to_expr_rds contains two cols ---> 'reference_annotation' , 'expression_mat_data_file' ## load respective expression mat expr_data_mat_rds_file <- ref_annot_to_expr_rds %>% dplyr::filter(.data$reference_annotation == !!user_selected_species_ref_annot) %>% dplyr::select(2) %>% ## 2nd column is expression_mat_data_file pull(1) %>% .[1] ### it make sure that if reference annot match to multiple rows, it only returns .rds data mat file for first match. ## load expr data withProgress(message = "Loadind data from .rds" , { incProgress(0.5) #print("expr_mat_file_path") #print(paste(get_expression_mats_dir_path() , expr_data_mat_rds_file , sep = "/")) expr_data_mat <- readr::read_rds(paste(get_expression_mats_dir_path() , expr_data_mat_rds_file , sep = "/")) incProgress(1) }) user_selected_sra_id_data <- expr_data_mat %>% dplyr::select(1, !!user_selected_data_sra_ids) return(user_selected_sra_id_data) }) ##@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ ### display active group info in the in side panel ---- ##@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ #observe(group_info,{ callModule(module = display_active_groups_server , id = "display_active_groups", group_data = reactive(group_info) ) ##@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ ### display sample info for user selected sample info in side panel ---- ##@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ observeEvent(user_selected_sra_id_sample_info(),{ req(user_selected_sra_id_sample_info()) callModule(module = display_sra_sample_info_server , id = "user_selected_sra_id_sample_info" , user_selected_sra_sample_info_tibble = reactive(user_selected_sra_id_sample_info()) ) }) ## set output status for sra sample info display on the side panel. ## UI will displayed only if sra samples selected by users output$display_sra_sample_info_in_side_panel <- reactive({ req(user_selected_sra_id_sample_info()) return(TRUE) }) outputOptions(x = output , name = "display_sra_sample_info_in_side_panel" , suspendWhenHidden = FALSE) ## @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ ## Process user uploaded data ---- ## @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ ## user upoaded data user_uploaded_data <- eventReactive(input$submit_user_uploaded_data,{ req(input$upload_data_source) upload_data_source <- input$upload_data_source data_from_user = tryCatch({ if(upload_data_source == "upload_from_file"){ req(input$file1) data_from_user <- readr::read_delim(file = input$file1$datapath, delim = input$sep, col_names = T, trim_ws = T) }else if(upload_data_source == "upload_from_clipboard") { data_from_user <- text_to_tibble(text = input$user_pasted_data , sep = input$sep) } }, error = function(e) { print(paste("File uploading error." , e, sep = " ")) return(NULL) } , warning = function(w){ print(paste("File uploaded with warnings." , w, sep = " ")) return(NULL) }) ## show alert/error if error produced while reading file. if(is.null(data_from_user)){ shinyWidgets::sendSweetAlert(session = session ,type = "error",title = "Error...!!" ,text = paste("Error while reading the data. Probably unsupported file format (if uploaded from file) or uncorrect pasted data.", sep = " ")) return(NULL) } ## error if first column is not type char if(!purrr::map_lgl(data_from_user[1], is.character) ){ shinyWidgets::sendSweetAlert(session = session ,type = "error",title = "Error...!!" ,text = paste("First column must be of type character.", sep = " ")) return(NULL) } ## other than 1st column, all must be of type nueric if( ! all(purrr::map_lgl(data_from_user[-1], is.numeric)) ){ shinyWidgets::sendSweetAlert(session = session ,type = "error",title = "Error...!!" ,text = paste("Other than first column, all must be of type numeric.", sep = " ")) return(NULL) } ## column 1 must not have duplicates. if(any(base::duplicated(data_from_user[[1]]))){ shinyWidgets::sendSweetAlert(session = session ,type = "error" ,title = "Error...!!" ,text = paste("Column 1 must have unique values.", sep = " ")) return(NULL) } ## uploded data must have atleast two columns if(base::ncol(data_from_user) < 2){ shinyWidgets::sendSweetAlert(session = session ,type = "error",title = "Error...!!" ,text = paste("Uploaded data must have atleast 2 columns.\n Make sure that you have selected correct separator.", sep = " ")) return(NULL) } ## remove genes which are 0 in all samples processed_data <- data_from_user %>% tidyr::drop_na() %>% ## genes will be dropped if any column contain NA dplyr::filter_if(is.numeric , .vars_predicate = dplyr::any_vars(. != 0 )) ## select genes which have atleast one non zero value ## log2 trasform user uploaded data if(input$log_transform_user_upload_data == "log2"){ processed_data <- processed_data %>% dplyr::filter_if(is.numeric , dplyr::any_vars( . > 0)) %>% ## remove genes having negative values (value < 0) in any sample dplyr::mutate_if(is.numeric , ~(log2( . + 1))) } ## log10 trasform user uploaded data if(input$log_transform_user_upload_data == "log10"){ processed_data <- processed_data %>% dplyr::filter_if(is.numeric , dplyr::any_vars( . > 0)) %>%## remove genes having negative values (value < 0) in any sample dplyr::mutate_if(is.numeric , ~(log10( . + 1))) } ## remove na and round the values. processed_data <- processed_data %>% dplyr::mutate_if(is.numeric, round, 4) %>% ## 4 digits after decimal as_tibble() %>% tidyr::drop_na() ## rows containing NA (after log transform) will be removed ## check the number of rows in the processed data. if number of row remain zero throw error. if(base::nrow(processed_data) == 0 ){ warning_text <- paste("All genes have value NA after log transformation (if selected) in one of the sample or value 0 in all the samples. Please upload correct data. If log transformation used, make sure that values are non-negative. ") shinyWidgets::sendSweetAlert(session = session , type = "error", title = "Error...!!" , text = warning_text) return(NULL) } ## Update alert type depending on number of genes remained after removing NA. ## if number of rows in the processed data less than user uploaded rows, throw warning. if(base::nrow(processed_data) < base::nrow(data_from_user)){ genes_removed <- dplyr::setdiff(data_from_user[[1]] , processed_data[[1]]) type = "warning" title = "Data upload with warning...!!!" numb_of_genes_removed <- base::nrow(data_from_user) - base::nrow(processed_data) text <- tags$h5(numb_of_genes_removed, " genes have been removed due to one of the following reasons...", tags$br(),tags$br(), "1). Genes have value NA in one of the sample. NA found either directly from uploaded data or due to log transformation (if selected).", tags$br(),tags$br(), "2). Genes have value 0 in all the samples.", tags$br(),tags$br(), "Removed genes are ...", tags$br(),tags$br(), tags$h5(paste0(genes_removed , collapse = ","), style = "height: 50px;white-space:nowrap;overflow-x:auto;"), style = "text-align:justify;") ## if number of rows in the processed data equal to the user uploaded rows, show success. } else if(base::nrow(processed_data) == base::nrow(data_from_user)) { type = "success" title = "Success...!!!" text <- paste("Data uploaded successfully.") } shinyWidgets::sendSweetAlert(session = session , type = type, title = title , text = text ) ## column names replace space to "_", this must be applied to groups also when group info uploaded from file user_uploaded_processed_data <- processed_data %>% rename_all(function(.){gsub(pattern = " " , "_" , .)}) return(user_uploaded_processed_data) }) ## disable UI elems if upload example data checked on observe({ if(input$upload_sample_data){ shinyjs::hide(id ="user_data_upload_section") shinyWidgets::updateProgressBar(session = session , id = "upload_sample_data_pb" , value = 100) } else { shinyjs::show(id ="user_data_upload_section") shinyWidgets::updateProgressBar(session = session , id = "upload_sample_data_pb" , value = 0) } ## enable disable data upload. at a time either of the upload from file or upload from clipboard will be enabled if(input$upload_data_source == "upload_from_file"){ shinyjs::enable(id = "file1") } else{ shinyjs::disable(id = "file1") } }) ## @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ ## Finalize active plot data ---- ## @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ plot_data <- reactiveVal(NULL) ## when user hit action button on sra sample info page, update plot data. observeEvent(input$submit_user_sra_samples ,{ req(user_selected_sra_id_data()) final_data <- user_selected_sra_id_data() %>% tidyr::drop_na() %>% dplyr::filter_if(is.numeric , .vars_predicate = dplyr::any_vars(. != 0 )) ## select genes which have atleast one non zero value if(nrow(final_data) == 0 ){ ## send error if number of rows remained after removing na is 0. sendSweetAlert( session = session, title = "Error...", text = "All the rows have atleast one missing or NA value found. Input file needs to be fixed.", type = "error" ) plot_data(NULL) } ## display success alert when data uploaded sendSweetAlert( session = session, title = paste(final_data %>% ncol() - 1 , "data selected"), text = paste("Selected data now available for analysis.\n\nRun accession will be used as identifier for downstream analysis.\n\nClose selection window to use data further.\n\n"), type = "success" ) plot_data(final_data) }) ## if user upload the data , make it plot data observeEvent(user_uploaded_data(),{ req(user_uploaded_data()) ## to execute this user data must be available final_data <- user_uploaded_data() %>% tidyr::drop_na() %>% dplyr::filter_if(is.numeric , .vars_predicate = dplyr::any_vars(. != 0 )) ## select genes which have atleast one non zero value plot_data(final_data) }) ## make plot data to example data when th eaction button input$upload_sample_data triggered observeEvent(input$upload_sample_data,{ if(input$upload_sample_data){ example_data_file = system.file("app", "cartoon_data" ,"cartoon_log2fpkm_log2fc.txt" ,package = "FungiExpresZ" ) example_data <- readr::read_delim(file = example_data_file , delim = "\t") %>% tidyr::drop_na() %>% dplyr::filter_if(is.numeric , .vars_predicate = dplyr::any_vars(. != 0 )) ## select genes which have atleast one non zero value plot_data(example_data) } ## if input$upload_sample_data == FALSE set NULL else { plot_data(NULL) } }) ## when submit_user_uploaded_data triggered and join data button is on observeEvent(input$submit_user_uploaded_data,{ req(input$join_user_data, user_uploaded_data()) ## to execute this user data must be available user_upload_join_by <- colnames(user_uploaded_data())[1] merge_data <- user_uploaded_data() %>% dplyr::left_join(user_selected_sra_id_data() , by = set_names("geneName" , user_upload_join_by)) merge_data <- merge_data %>% tidyr::drop_na() ## rows with NA after merger will be removed. ## send warning if number of rows remained after removing na is < user_uploaded_data() if(merge_data %>% nrow() < user_uploaded_data() %>% nrow() && merge_data %>% nrow() > 0){ sendSweetAlert( session = session, title = "Warning...!", text = paste0("Only ", merge_data %>% nrow(), " out of " , user_uploaded_data() %>% nrow() , " genes matched to database ID. Application will continue with ", merge_data %>% nrow() , " genes."), type = "warning" ) final_data <- merge_data %>% dplyr::filter_if(is.numeric , .vars_predicate = dplyr::any_vars(. != 0 )) ## select genes which have atleast one non zero value plot_data(final_data) } ## send warning if number of rows remained after removing na is 0 return user uploaded data if(nrow(merge_data) == 0 ){ sendSweetAlert( session = session, title = "Warning...", text = "None of the ID from first column of uploaded data match with database id. Either fix the input id or application continue with uploaded data.", type = "warning" ) final_data <- user_uploaded_data() %>% tidyr::drop_na() %>% dplyr::filter_if(is.numeric , .vars_predicate = dplyr::any_vars(. != 0 )) ## select genes which have atleast one non zero value plot_data(final_data) } ## send success message if All the IDs from the first column of uploaded data match with database id if(merge_data %>% nrow() >= user_uploaded_data() %>% nrow()){ sendSweetAlert( session = session, title = "Data joined successfully...", text = "All the IDs from the first column of uploaded data matched with database id.", type = "success" ) final_data <- merge_data %>% tidyr::drop_na() %>% dplyr::filter_if(is.numeric , .vars_predicate = dplyr::any_vars(. != 0 )) ## select genes which have atleast one non zero value plot_data(final_data) } }) ## print final plot data on terminal observe({ req(plot_data()) # print("plot_data dims") # print(dim(plot_data())) # print("plot_data") # print(plot_data()) }) ## preserve plot data original row order plot_data_original_row_order <- reactive({ req(plot_data()) orig_row_ord <- plot_data() %>% dplyr::select(1) %>% dplyr::mutate(!!generate_random_strings(1) := dplyr::row_number()) return(orig_row_ord) }) ## @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ ## Define groups ---- ## @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ ##reactive values containing group info group_info <- reactiveValues() ## User defined column groups user_defined_column_groups <- callModule(module = add_sample_group , id = "sample_group" , sample_names = reactive(base::colnames(plot_data())[-1]) , bioproject_groups = reactive(user_selected_sra_id_to_bioproject_id())) ## Assign user groups to reactive values. Below observe will be executed if user_defined_groups changed observe({ group_info$column_groups <- user_defined_column_groups() }) ## User defined row groups user_defined_row_groups <- callModule(module = add_gene_groups , session = session, id = "gene_groups" , gene_names = reactive( plot_data() %>% .[[1]] )) ## Assign user defined row groups observe({ group_info$row_groups <- user_defined_row_groups() }) ## @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ ## Reset UI elems of each plot panel based on data being uploaded or selected from pre-loaded ---- ## @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ ### if no rows selected, remove x/y choices observe({ # if ((input$data_selection == "select_sample" & # base::is.null(input$pre_loaded_data_sra_sample_info_rows_selected)) | # (input$data_selection == "upload_user_data" & base::is.null(input$file1)) # ) { if(is.null(plot_data())){ ## update scatter x updateSelectInput( session = session, inputId = "scatter_x", choices = character(0) ) ## update scatter y updateSelectInput( session = session, inputId = "scatter_y", choices = character(0) ) ## update multi scatter x updateSelectInput( session = session, inputId = "multi_scatter_vars", choices = character(0) ) ## update density x updateSelectInput( session = session, inputId = "density_x", choices = character(0) ) ## update box x updateSelectInput( session = session, inputId = "box_x", choices = character(0) ) ## update line x updateSelectInput( session = session, inputId = "lineplot_x", choices = character(0) ) ## joy plot updateSelectInput( session = session, inputId = "joy_plot_x", choices = character(0) ) ## heatmap vars updateSelectInput( session = session, inputId = "heatmap_vars", choices = character(0) ) ## pca plot updateSelectInput( session = session, inputId = "pca_plot_vars", choices = character(0) ) ## corr heat box updateSelectInput( session = session, inputId = "corr_heatbox_vars", choices = character(0) ) ## violin updateSelectInput( session = session, inputId = "violin_x", choices = character(0) ) ## histogram updateSelectInput( session = session, inputId = "histogram_x", choices = character(0) ) ## bar updateSelectInput( session = session, inputId = "barplot_vars", choices = character(0) ) } ### if no file uploaded, remove x/y choices # if (input$data_selection == "upload_user_data" & base::is.null(input$file1)) { # # ## update scatter x # updateSelectInput( # session = session, # inputId = "scatter_x", # choices = character(0) # ) # # ## update scatter y # updateSelectInput( # session = session, # inputId = "scatter_y", # choices = character(0) # ) # # ## update multi scatter x # updateSelectInput( # session = session, # inputId = "multi_scatter_vars", # choices = character(0) # ) # # # ## update density x # updateSelectInput( # session = session, # inputId = "density_x", # choices = character(0) # ) # # ## update box x # updateSelectInput( # session = session, # inputId = "box_x", # choices = character(0) # ) # # ## update line x # updateSelectInput( # session = session, # inputId = "lineplot_x", # choices = character(0) # ) # # ## joy plot # updateSelectInput( # session = session, # inputId = "joy_plot_x", # choices = character(0) # ) # # ## heatmap vars # updateSelectInput( # session = session, # inputId = "heatmap_vars", # choices = character(0) # ) # # ## pca plot # updateSelectInput( # session = session, # inputId = "pca_plot_vars", # choices = character(0) # ) # # ## corr heat box # updateSelectInput( # session = session, # inputId = "corr_heatbox_vars", # choices = character(0) # ) # # ## violin # updateSelectInput( # session = session, # inputId = "violin_x", # choices = character(0) # ) # ## histogram # updateSelectInput( # session = session, # inputId = "histogram_x", # choices = character(0) # ) # # ## bar # updateSelectInput( # session = session, # inputId = "barplot_vars", # choices = character(0) # ) # # # } }) ## @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ ## Fix reference annotations ---- ## @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ genome_for_annotations <- reactiveVal(NULL) ## reference annot when user choose SRA data observeEvent(input$submit_user_sra_samples , { #if(input$data_selection == "select_sample"){ ## when data selected from existing one req(input$pre_loaded_data_sra_sample_info_rows_selected) genome_for_annotations <- sample_info %>% ## sample_info is pre loaded data dplyr::filter(!!!user_filters_expressions()) %>% ## filter by user applied filters dplyr::select(2) %>% ## column 2 is reference_annotation columns. it contains genome name identical to fungidb annotations. IMP: change this, if column order in the sample_info table changed. dplyr::slice(input$pre_loaded_data_sra_sample_info_rows_selected)%>% ## user selected rows dplyr::pull(1) genome_for_annotations(genome_for_annotations[1]) }) ## reference annot when user upload data or select example data observe({ genome_for_annotations(input$user_selected_species) if(input$upload_sample_data){ genome_for_annotations("FungiDB-64_CalbicansSC5314") } }) ## Check current annotations observe({ req(genome_for_annotations()) #print(paste("User selected geneome", genome_for_annotations() ,sep = " ")) }) ## For selected genome sample ids output$sample_selected_species_gene_id <- renderText({ req(genome_for_annotations()) sample_ids_to_display <- tryCatch({ ah_data_summary2 %>% dplyr::filter(genome == genome_for_annotations()) %>% dplyr::select(gr_cols) %>% tidyr::unnest(cols = gr_cols) %>% dplyr::pull(ID) %>% sample(20) %>% paste0(collapse = ", ") }, error = function(x){ return("Trouble to get Ids") }) return(sample_ids_to_display) }) ## @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ # scatter plot server---- ## @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ # ## Question : Both evenReactive and observeEvent can be invalidated by action button. ## Then, why am I using eventReactive here ? Why not observeEvent ?? ## Ans :: eventReactive is there to delay the reactions. Given the reactive value, it triggers the chunk of code and ## return some values, which can be cached and used further in the app. While, observeEvent is also there to trigger the action ## on the basis of given reactive values but it does not return anything. It has side effects. Side effects are the actions due ## to functions but without returning anything. ## check whether x and y are numeric type scatter_vars_validated <- eventReactive(input$generate_scatter, { req(input$scatter_x, input$scatter_y, plot_data()) x_vals <- plot_data()[, input$scatter_x][[1]] y_vals <- plot_data()[, input$scatter_y][[1]] ## validate user selected variables if (!is.numeric(x_vals)) { shinyWidgets::sendSweetAlert( session = session, title = "Error...", text = "X must be of type numeric", type = "error" ) return(FALSE) } else if (!is.numeric(y_vals)) { shinyWidgets::sendSweetAlert( session = session, title = "Error...", text = "Y must be of type numeric", type = "error" ) return(FALSE) } return(TRUE) }) ## update plot axis limits, reason to use observe event is, ## it won't cache the values in the computer memory. Whenever user clicks on "Generate Plot" (input$plot) invalidated ## the axis limits will be updated. observeEvent(input$generate_scatter, { req(scatter_vars_validated()) # selected x y cols x_vals <- plot_data()[, input$scatter_x][[1]] y_vals <- plot_data()[, input$scatter_y][[1]] # calculate axis limits # xlim x_range <- round(range(x_vals), 3) # ylim y_range <- round(range(y_vals), 3) # update UI # X min and max updateNumericInput( session = session, inputId = "plot_xmin", min = x_range[1], max = x_range[2], value = x_range[1] ) updateNumericInput( session = session, inputId = "plot_xmax", min = x_range[1], max = x_range[2], value = x_range[2] ) # Y min and max updateNumericInput( session = session, inputId = "plot_ymin", min = y_range[1], max = y_range[2], value = y_range[1] ) updateNumericInput( session = session, inputId = "plot_ymax", min = y_range[1], max = y_range[2], value = y_range[2] ) }) ## update x, y variables observe({ ## update x updatePickerInput( session = session, inputId = "scatter_x", choices = base::colnames(plot_data())[-1], selected = base::colnames(plot_data())[2] ) ## update y updatePickerInput( session = session, inputId = "scatter_y", choices = base::colnames(plot_data())[-1], selected = base::colnames(plot_data())[2] ) }) ## scatter plot user selected gene group data scatter_plot_data <- callModule(module = gene_group_selection , id = "scatter_plot_select_gene_groups" , gene_group_info = reactive(group_info$row_groups), generate_plot_action = reactive(input$generate_scatter), current_session_data_matrix = plot_data) ### current scatter plot genome, updates only when generate scatter hits. current_scatter_plot_genome <- eventReactive(input$generate_scatter , { return(genome_for_annotations()) }) ## get scatter plot elements : corr value, density, intercept , slope and base ggplot scatter_plot_elems <- eventReactive(input$generate_scatter, { req(scatter_plot_data()) xvar <- scatter_plot_data() %>% dplyr::pull(input$scatter_x) yvar <- scatter_plot_data() %>% dplyr::pull(input$scatter_y) # linear model param mm <- lm(formula = yvar ~ xvar) linear_model_param <- broom::tidy(mm) # calculte corr value xvar <- scatter_plot_data() %>% dplyr::pull(input$scatter_x) yvar <- scatter_plot_data() %>% dplyr::pull(input$scatter_y) corr <- cor(xvar, yvar) scatter_xy_corr <- format(round(corr, 3), nsmall = 2) ## restric digits after decimal # calculate density x_y_density <- get_density(scatter_plot_data()[, input$scatter_x][[1]], scatter_plot_data()[, input$scatter_y][[1]]) # xy scatter plot gp <- ggplot(scatter_plot_data(), aes_string(x = as.symbol(input$scatter_x), y = as.symbol(input$scatter_y))) gff_feat = ah_data_summary2 %>% dplyr::filter(genome == current_scatter_plot_genome()) %>% dplyr::select(gr_cols) %>% tidyr::unnest(cols = gr_cols) return(list(linear_model_param = linear_model_param, scatter_xy_corr = scatter_xy_corr, gp = gp, x_y_density = x_y_density, gff_feat = gff_feat)) }) # observe({ # req(scatter_plot_data()) # #print(scatter_plot_data()) # }) ## scatterplot condition panel:show only if variable (x, y) validated and displayed on UI output$scatter_plot_status <- reactive({ req(final_scatter()) return(TRUE) }) outputOptions(output, "scatter_plot_status", suspendWhenHidden = FALSE) ## final scatter plot final_scatter <- reactive({ req(scatter_vars_validated() , scatter_plot_elems() , input$plot_xmin, input$plot_xmax,input$plot_ymin, input$plot_ymax ) # req(input$plot_xmax,input$plot_xmin) ## all four xmin, xmax, ymin and ymax must be there to generate the plot withProgress(message = "Preparing scatter plot", { incProgress(0.5) ## color by density, default colors if (input$scatter_color_chooser == "default") { gp <- scatter_plot_elems()$gp + geom_point(aes(color = scatter_plot_elems()$x_y_density), alpha = input$scatter_alpha , size= input$scatter_point_size) + viridis::scale_color_viridis() } ## color by density, user defined colors if (input$scatter_color_chooser == "manual") { gp <- scatter_plot_elems()$gp + geom_point(aes(color = scatter_plot_elems()$x_y_density), alpha = input$scatter_alpha , size= input$scatter_point_size) + scale_color_gradientn(colours = c(input$scatter_col_low, input$scatter_col_medium, input$scatter_col_high)) } ## color by genesets , default colors if (input$scatter_color_chooser == "by_gene_groups") { gp <- scatter_plot_elems()$gp + geom_point(aes(color = gene_groups), alpha = input$scatter_alpha , size= input$scatter_point_size) + scale_color_discrete() } # real time change of axis limits. gp <- gp + xlim(input$plot_xmin, input$plot_xmax) + ylim(input$plot_ymin, input$plot_ymax) ## default plot title gp <- callModule(module = plot_title_and_axis_label_server , id = "scatter_plot_title_and_labels", my_ggplot = gp , plot_title = base::paste("Pearson correlation:", scatter_plot_elems()$scatter_xy_corr, sep = ""), axis_x_title = shiny::isolate(input$scatter_x), axis_y_title = shiny::isolate(input$scatter_y), x_tick_angle = 0, color_legend_title = ifelse(input$scatter_color_chooser == "by_gene_groups" , "Groups", "Density") ) ## add / remove regression line if (input$scatter_diagonal_line == "manual") { gp <- gp + geom_abline(slope = input$manual_slope, intercept = input$manual_intercept) } if (input$scatter_diagonal_line == "from_data") { i_cept <- scatter_plot_elems()$linear_model_param %>% dplyr::filter(term == "(Intercept)") %>% pull(estimate) slope <- scatter_plot_elems()$linear_model_param %>% dplyr::filter(term == "xvar") %>% pull(estimate) gp <- gp + geom_abline(intercept = i_cept, slope = slope ) } incProgress(1) }) return(gp) }) ## render scatter plot output$scatter_plot <- renderPlot({ req(final_scatter()) final_scatter() }, height = function() { return(session$clientData$output_scatter_plot_width) ## dynamic height }, width = function() { return(session$clientData$output_scatter_plot_width) ## dynamic height }, res = 96 ) ## dynamic resolution ## render corr value output$scatter_xy_corr <- renderText({ return(scatter_plot_elems()$scatter_xy_corr) }) ## brush table for scatter plot scatter_plot_brushed_points <- eventReactive(input$plot_brush, { req(scatter_plot_data()) scatter_plot_brushed_points <- brushedPoints( df = scatter_plot_data(), brush = input$plot_brush, xvar = scatter_plot_elems()$gp$labels$x, #input$scatter_x yvar = scatter_plot_elems()$gp$labels$y ) #map metadata info to geneNames join_col_x <- base::colnames(scatter_plot_brushed_points)[1] ## first column containing geneNames join_col_y <- base::colnames(scatter_plot_elems()$gff_feat)[7] ## "id" column display_data <- scatter_plot_brushed_points %>% left_join(scatter_plot_elems()$gff_feat, by = setNames(join_col_y, join_col_x)) %>% dplyr::select(1,c("seqnames", "start", "end","width", "strand", "description"), dplyr::everything()) return(display_data) }) ## render brushed data output$brush_table <- DT::renderDataTable({ req(scatter_plot_brushed_points()) return(scatter_plot_brushed_points() %>% dplyr::mutate_all(funs(replace_na(as.character(.),"--NA--"))) %>% dplyr::mutate_if(is.numeric , round , 2)) },selection = "none", server = F, extensions = c("Buttons"), options = list( deferRender = TRUE, scrollX = TRUE, dom = "Blfrtip", searchHighlight = TRUE, buttons = list("copy", list(extend ="collection", buttons = c("csv", "excel", "pdf"), text = "Download" )), # end of buttons customization # customize the length menu lengthMenu = list( c(10, 50, 100, 500 ) # declare values , c(10, 50, 100, 500) # declare titles ), pageLength = 10 ) ) ## call module to download scatter plot callModule(module = export_plot, id = "export_scatter", file_name = "scatter", plot = final_scatter ) ## call module for scatter plot functional analysis callModule(module = functional_analysis_server , id = "scatterplot_functional_analysis_ui" , ui_id = "scatterplot_functional_analysis_ui", session = session, gene_set = reactive(split(x = scatter_plot_brushed_points()[[1]], ## genename f = "1")) , genome = reactive(genome_for_annotations()) ) ## @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ # multi scatter plot server---- ## @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ ## check whether x and y are numeric type multi_scatter_vars_validated <- eventReactive(input$generate_multi_scatter, { req(input$multi_scatter_vars) selected_vars <- plot_data() %>% dplyr::select(input$multi_scatter_vars) col_class <- plot_data() %>% dplyr::select(input$multi_scatter_vars) %>% dplyr::summarise_all(is.numeric) %>% tidyr::gather() if (!all(col_class$value)) { non_numeric_cols <- col_class %>% dplyr::filter(!value) shinyWidgets::sendSweetAlert( session = session, title = "Error...", text = paste0(paste0(non_numeric_cols$key, collapse = " "), " must be of type numeric", collapse = ""), type = "error" ) return(FALSE) } if (length(input$multi_scatter_vars) > 5) { shinyWidgets::sendSweetAlert( session = session, title = "Error...", text = paste0("Due to memory limit, maximum 5 variables allowed at moment. Try CorrHeatBox for more than 5 variables."), type = "error" ) return(FALSE) } else { return(TRUE) } }) ## multi scatterplot condition panel:show only if variable (x, y) validated and displayed on UI output$multi_scatter_plot_status <- reactive({ req(final_multi_scatter_plot()) return(TRUE) }) outputOptions(output, "multi_scatter_plot_status", suspendWhenHidden = FALSE) ## update x variables observe({ req(plot_data()) ## update x updatePickerInput( session = session, inputId = "multi_scatter_vars", choices = base::colnames(plot_data())[-1], selected = base::colnames(plot_data())[2] ) }) ## multi scatter plot user selected gene group data multi_scatter_plot_data <- callModule(module = gene_group_selection , id = "multi_scatter_plot_gene_group_selection" , gene_group_info = reactive(group_info$row_groups), generate_plot_action = reactive(input$generate_multi_scatter), current_session_data_matrix = plot_data ) ## prepare multi scatter plot ggpair_plot <- eventReactive(input$generate_multi_scatter, { req(multi_scatter_plot_data() , multi_scatter_vars_validated()) gp <- GGally::ggpairs(multi_scatter_plot_data(), columns = c(input$multi_scatter_vars), upper = list(continuous = GGally::wrap("cor", size = input$multi_scatter_corr_text_text_size , color = input$multi_scatter_corr_text_col))) return(gp) }) ## multi scatter final plot final_multi_scatter_plot <- reactive({ req(ggpair_plot()) multi_scatter_gp <- ggpair_plot() ## decorate multi scatter through module multi_scatter_gp <- callModule(module = plot_title_and_axis_label_server , id = "multi_scatter_plot_title_and_labels" , my_ggplot = multi_scatter_gp , axis_x_title = "Value" , axis_y_title = "Value", x_tick_angle = 0 , aspect_ratio =NULL) return(multi_scatter_gp) }) output$multi_scatter_plot <- renderPlot({ req(final_multi_scatter_plot()) return( withProgress(message = "Display multi-scatter plot in progress",{ incProgress(0.5) print(final_multi_scatter_plot()) incProgress(1) }) ) }, height = function() { return(session$clientData$output_multi_scatter_plot_width) }, ## dynamic height width = function() { return(session$clientData$output_multi_scatter_plot_width) } ) ## dynamic width ## call module to export multi scatter plot callModule(module = export_plot, id = "export_multi_scatter" , file_name = "multi_scatter_plot", plot = final_multi_scatter_plot ) ## @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ # CorrHeatBox server---- ## @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ ## update x, y variables observe({ ## update x updatePickerInput( session = session, inputId = "corr_heatbox_vars", choices = base::colnames(plot_data())[-1], selected = base::colnames(plot_data())[2] ) }) ## check whether selected x var(s) are of numeric type corr_heatbox_vars_validated <- eventReactive(input$generate_corr_heatbox, { req(input$corr_heatbox_vars) ## validate selected variables selected_non_num_vars <- plot_data() %>% dplyr::select(c(input$corr_heatbox_vars)) %>% dplyr::summarise_all(is.numeric) %>% tidyr::gather() %>% dplyr::filter(value == F) if (nrow(selected_non_num_vars) >= 1) { shinyWidgets::sendSweetAlert( session = session, title = "Error...", text = ifelse(length(selected_non_num_vars$key) == 1, base::paste0(selected_non_num_vars$key, " is non numeric varibales"), base::paste0(base::paste0(selected_non_num_vars$key, collapse = ","), " are non numeric varibales") ), type = "error" ) return(FALSE) } else { return(TRUE) } }) ## corr_heatbox status output$corr_heatbox_status <- reactive({ req(final_corr_heatbox()) return(TRUE) }) outputOptions(output, "corr_heatbox_status", suspendWhenHidden = FALSE) ## corr heat box user selected gene group data corr_heatbox_user_selected_gene_group_data <- callModule(module = gene_group_selection , id = "corr_heat_box_gene_group_selection" , gene_group_info = reactive(group_info$row_groups), generate_plot_action = reactive(input$generate_corr_heatbox), current_session_data_matrix = plot_data ) ## prepare corr_heatbox data corr_heatbox_data <- eventReactive(input$generate_corr_heatbox, { req(input$corr_heatbox_vars, corr_heatbox_vars_validated() , corr_heatbox_user_selected_gene_group_data()) withProgress(message = "Preparing CorrHeatBox " , { incProgress(0.5) ## tibble to data.frame with row names corr_heatbox_data <- corr_heatbox_user_selected_gene_group_data() %>% dplyr::select(1,c(input$corr_heatbox_vars)) %>% as.data.frame() %>% tibble::column_to_rownames(colnames(corr_heatbox_user_selected_gene_group_data())[1]) ## calculate correlation corr_mat <- tryCatch({ cor_mat <- stats::cor(corr_heatbox_data) },error = function(e){ return(NULL) }) incProgress(1) }) return(corr_mat) }) ## show error if corr data is NULL observe({ if(is.null(corr_heatbox_data())){ shinyWidgets::sendSweetAlert(session = session,title = "Error..!", text = "Error while calculating correlation (stats::cor). Please check the data.") } }) ## final corrHeatBox final_corr_heatbox <- reactive({ req(corr_heatbox_data()) chb <- tryCatch({ chb <- ggcorrplot::ggcorrplot(corr = corr_heatbox_data() , method = input$heatbox_method , type = input$heatbox_type, hc.order = input$cluster_heatbox, show.diag = as.logical(input$heatbox_show_diag), lab_col = input$heatbox_corr_text_col , lab_size = input$heatbox_corr_text_text_size, lab = input$heatbox_show_corr_value ) },error = function(e){ return(NULL) }) ## return NULL (at function) if ggcorrplot::ggcorrplot throws an error if(is.null(chb)){ return(NULL) } ## set color scale auto: if(input$corr_heatbox_scale_manual == "auto"){ chb <- chb + scale_fill_gradientn(colours = viridis::viridis(n = input$corr_heatbox_colors) %>% rev()) } ## set color scale manual if(input$corr_heatbox_scale_manual == "manual"){ chb <- chb + scale_fill_gradientn(colours = viridis::viridis(n = input$corr_heatbox_colors) %>% rev(), limits = c(input$corr_heatbox_scale_manual_min , input$corr_heatbox_scale_manual_max) , oob=scales::squish) } ## decorate plot chb <- callModule(plot_title_and_axis_label_server, id = "corr_heatbox_title_and_labels" , my_ggplot = chb, x_tick_angle = 45 , fill_legend_title = "Corr", apply_theme =FALSE) return(chb) }) ## show error if corr data is NULL observe({ if(is.null(final_corr_heatbox())){ shinyWidgets::sendSweetAlert(session = session,title = "Error..!", type = "error", text = "Error while generating corr plot (ggcorrplot::ggcorrplot). Make sure that you selected at least two samples or else check input data.") } }) ## render corr_heatbox output$corr_heatbox <- renderPlot({ req(final_corr_heatbox()) return(print(final_corr_heatbox())) }, height = function() { return(session$clientData$output_corr_heatbox_width) }, width = function() { return(session$clientData$output_corr_heatbox_width) }, res = 90) ## render corr heat box data output$corr_heatbox_data <- DT::renderDataTable({ req(corr_heatbox_data()) return(corr_heatbox_data() %>% as.data.frame() %>% rownames_to_column("RowNames") %>% as_tibble() %>% dplyr::mutate_if(is.numeric, function(i){round(i,2)})) },selection = "none", server = F, extensions = "Buttons", options = list( scrollX = TRUE, dom = "Blfrtip", searchHighlight = TRUE, buttons = list("copy", list(extend ="collection", buttons = c("csv", "excel", "pdf"), text = "Download" )), # end of buttons customization # customize the length menu lengthMenu = list( c(10, 50, 100, 500, -1) # declare values , c(10, 50, 100, 500, "All") # declare titles ), pageLength = 10 ) ) ## export corr heat box callModule(module = export_plot, id = "export_corr_heatbox" , file_name = "corr_heatbox", plot = final_corr_heatbox ) ## @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ # density plot server---- ## @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ ## update x, y variables observe({ ## update x updatePickerInput( session = session, inputId = "density_x", choices = base::colnames(plot_data())[-1], selected = base::colnames(plot_data())[2] ) }) ## check whether selected x var(s) are of numeric type density_vars_validated <- eventReactive(input$generate_density, { req(input$density_x) ## validate selected variables selected_non_num_vars <- plot_data() %>% dplyr::select(c(input$density_x)) %>% dplyr::summarise_all(is.numeric) %>% tidyr::gather() %>% dplyr::filter(value == F) if (nrow(selected_non_num_vars) >= 1) { shinyWidgets::sendSweetAlert( session = session, title = "Error...", text = ifelse(length(selected_non_num_vars$key) == 1, base::paste0(selected_non_num_vars$key, " is non numeric varibales"), base::paste0(base::paste0(selected_non_num_vars$key, collapse = ","), " are non numeric varibales") ), type = "error" ) return(FALSE) } else { return(TRUE) } }) ## density plot status output$density_plot_status <- reactive({ req(final_density_plot()) return(TRUE) }) outputOptions(output, "density_plot_status", suspendWhenHidden = FALSE) ## density plot user selected gene group data density_plot_data <- callModule(module = gene_group_selection , id = "density_plot_gene_group_selection" , gene_group_info = reactive(group_info$row_groups), generate_plot_action = reactive(input$generate_density), current_session_data_matrix = plot_data ) ## density plot dp <- eventReactive(input$generate_density, { req(density_plot_data() , density_vars_validated()) withProgress(message = "Preparing density plot" , { incProgress(0.5) plot_data <- density_plot_data() %>% dplyr::select(c(input$density_x , !!as.symbol("gene_groups"))) melted <- plot_data %>% tidyr::gather(samples, value , -gene_groups) %>% left_join(group_info$column_groups , by = c (samples = "group_members")) %>% ## addd sample groups tidyr::replace_na(list(groups = "No groups assigned")) %>% ## NA will be converted to "No groups assigned" dplyr::arrange(groups , samples)%>% dplyr::mutate(samples = forcats::fct_inorder(samples)) incProgress(1) }) ## base density plot dp <- ggplot(melted, aes(x = value, fill = samples)) return(dp) }) ## final density plot final_density_plot <- reactive({ ## dynamic alpha req(dp()) dp <- dp() + geom_density(alpha = input$density_plot_alpha) #+ theme_bw() ## fill and facet density plot out <- callModule(module = ggplot_fill_and_facet , id = "density_plot_fill_and_facet" , ggplot = dp , allow_x_var_selection = FALSE) dp <- out$plot ## decorate plot dp <- callModule(plot_title_and_axis_label_server , id = "decorate_density_plot" , my_ggplot = dp , axis_x_title = "Value", x_tick_angle = 0, axis_y_title = "Density", fill_legend_title = out$fill_var, color_legend_title = out$fill_var ) return(dp) }) ## render density plot output$density_plot <- renderPlot({ return(print(final_density_plot())) }, height = function() { return(session$clientData$output_density_plot_width) }, width = function() { return(session$clientData$output_density_plot_width) }, res = 90) ## prepare density plot data to be shown as table density_filtered_column <- eventReactive(dp(), { dd <- plot_data() return(dd) }) ## render density plot data output$density_filtered_column_display <- DT::renderDataTable({ return(density_filtered_column()) }) callModule(module = export_plot, id = "export_density" , file_name = "density_plot", plot = final_density_plot ) ## @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ # histogram server---- ## @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ ## update x, y variables observe({ ## update x updatePickerInput( session = session, inputId = "histogram_x", choices = base::colnames(plot_data())[-1], selected = base::colnames(plot_data())[2] ) }) ## check whether selected x var(s) are of numeric type histogram_vars_validated <- eventReactive(input$generate_histogram, { req(input$histogram_x) ## validate selected variables selected_non_num_vars <- plot_data() %>% dplyr::select(c(input$histogram_x)) %>% dplyr::summarise_all(is.numeric) %>% tidyr::gather() %>% dplyr::filter(value == F) if (nrow(selected_non_num_vars) >= 1) { shinyWidgets::sendSweetAlert( session = session, title = "Error...", text = ifelse(length(selected_non_num_vars$key) == 1, base::paste0(selected_non_num_vars$key, " is non numeric varibales"), base::paste0(base::paste0(selected_non_num_vars$key, collapse = ","), " are non numeric varibales") ), type = "error" ) return(FALSE) } else { return(TRUE) } }) ## histogram status output$histogram_status <- reactive({ req(final_histogram()) return(TRUE) }) outputOptions(output, "histogram_status", suspendWhenHidden = FALSE) ## get histogram gene group specific data histogram_user_selected_gene_group_data <- callModule(module = gene_group_selection , id = "histogram_gene_group_selection" , gene_group_info = reactive(group_info$row_groups), generate_plot_action = reactive(input$generate_histogram), current_session_data_matrix = plot_data) ## histogram hg <- eventReactive(input$generate_histogram, { req(input$histogram_x, histogram_vars_validated() , histogram_user_selected_gene_group_data()) withProgress(message = "Preparing histogram" , { incProgress(0.5) plot_data <- histogram_user_selected_gene_group_data() %>% dplyr::select(c(input$histogram_x , !!as.symbol("gene_groups"))) melted <- plot_data %>% tidyr::gather(samples, value , -gene_groups) %>% left_join(group_info$column_groups , by = c (samples = "group_members")) %>% tidyr::replace_na(list(groups = "No groups assigned")) %>% ## NA will be converted to "No groups assigned" dplyr::arrange(groups , samples)%>% dplyr::mutate(samples = forcats::fct_inorder(samples)) incProgress(1) }) hg <- ggplot(melted, aes(x = value, fill = samples, col = samples)) return(hg) }) ## final histogram final_histogram <- reactive({ ## dynamic alpha req(hg()) hg <- hg() + geom_histogram(alpha = input$histogram_alpha , position = input$histogram_positions , bins = input$histogram_number_of_bins , col ="black") ## fill and facet histogram out <- callModule(module = ggplot_fill_and_facet , id = "histogram_fill_and_facet" , ggplot = hg , allow_x_var_selection = FALSE) hg <- out$plot ## decorate plot hg <- callModule(plot_title_and_axis_label_server , id = "decorate_histogram" , my_ggplot = hg , axis_x_title = "Value", x_tick_angle = 0, axis_y_title = "Count", fill_legend_title = out$fill_var, color_legend_title = out$fill_var ) ## remove legend title return(hg) }) ## render histogram output$histogram <- renderPlot({ return(print(final_histogram())) }, height = function() { return(session$clientData$output_histogram_width) }, width = function() { return(session$clientData$output_histogram_width) }, res = 90) callModule(module = export_plot, id = "export_histogram" , file_name = "histogram", plot = final_histogram) ## @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ ## Joy plot server---- ## @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ ## update x, y variables observe({ ## update x updatePickerInput( session = session, inputId = "joy_plot_x", choices = base::colnames(plot_data())[-1], selected = base::colnames(plot_data())[2] ) }) ## check whether selected x var(s) are of numeric type joy_plot_vars_validated <- eventReactive(input$generate_joy_plot, { req(input$joy_plot_x) ## validate selected variables selected_non_num_vars <- plot_data() %>% dplyr::select(c(input$joy_plot_x)) %>% dplyr::summarise_all(is.numeric) %>% tidyr::gather() %>% dplyr::filter(value == F) if (nrow(selected_non_num_vars) >= 1) { shinyWidgets::sendSweetAlert( session = session, title = "Error...", text = ifelse(length(selected_non_num_vars$key) == 1, base::paste0(selected_non_num_vars$key, " is non numeric varibales"), base::paste0(base::paste0(selected_non_num_vars$key, collapse = ","), " are non numeric varibales") ), type = "error" ) return(FALSE) } else { return(TRUE) } }) ## multi scatterplot condition panel:show only if variable (x, y) validated and displayed on UI output$joy_plot_status <- reactive({ req(final_joy_plot()) return(TRUE) }) outputOptions(output, "joy_plot_status", suspendWhenHidden = FALSE) ## joy plot user selected gene group data joy_plot_user_selected_gene_group_data <- callModule(module = gene_group_selection , id = "joy_plot_gene_group_selection" , gene_group_info = reactive(group_info$row_groups), generate_plot_action = reactive(input$generate_joy_plot), current_session_data_matrix = plot_data) ## joy plot ggjoy_plot <- eventReactive(input$generate_joy_plot, { req(input$joy_plot_x, joy_plot_vars_validated() , joy_plot_user_selected_gene_group_data()) plot_data <- joy_plot_user_selected_gene_group_data() %>% dplyr::select(c(input$joy_plot_x) , gene_groups) %>% tidyr::gather(samples, value , -gene_groups) %>% left_join(group_info$column_groups , by = c(samples = "group_members")) %>% tidyr::replace_na(list(groups = "No groups assigned")) %>% ## NA will be converted to "No groups assigned" dplyr::mutate(samples = forcats::fct_inorder(samples)) %>% ## preserve original order dplyr::mutate(groups = forcats::fct_inorder(groups))# %>% ggjoy_plot <- ggplot(plot_data) + aes(x = value) return(ggjoy_plot) }) ## final joy plot final_joy_plot <- reactive({ req(ggjoy_plot()) ## fill and facet ggjoy_plot <- ggjoy_plot() out <- callModule(module = joyplot_fill_and_facet , id = "joy_plot_fill_and_facet" , joyplot = ggjoy_plot) ggjoy_plot <- out$plot withProgress(message = "Preparing joy plot",{ incProgress(0.5) ggjoy_plot <- ggjoy_plot + scale_x_continuous(expand = c(0.01, 0)) + scale_y_discrete(expand = c(0.01, 0)) #+ #theme_bw() ## decorate joy plot ggjoy_plot <- callModule(plot_title_and_axis_label_server , id = "decorate_joy_plot" , my_ggplot = ggjoy_plot , axis_x_title = ggjoy_plot$mapping$x %>% rlang::quo_text(), axis_y_title = ggjoy_plot$mapping$y %>% rlang::quo_text(), x_tick_angle = 0, fill_legend_title = out$fill_var) incProgress(1) }) return(ggjoy_plot) }) ## render density plot output$joy_plot <- renderPlot({ ## dynamic alpha req(final_joy_plot()) return(print(final_joy_plot())) }, height = function() { return(session$clientData$output_joy_plot_width) }, width = function() { return(session$clientData$output_joy_plot_width) }, res = 90) callModule(module = export_plot , id = "export_joy_plot" , file_name = "joy_plot" , plot = final_joy_plot) ## @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ # box plot server---- ## @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ ## update box plot variables observeEvent(plot_data(), { updatePickerInput( session = session, inputId = "box_x", choices = base::colnames(plot_data())[-1], selected = base::colnames(plot_data())[2] ) }) ## validate boxplot selected variables box_vars_validated <- eventReactive(input$generate_box, { req(input$box_x) selected_non_num_vars <- plot_data() %>% dplyr::select(c(input$box_x)) %>% dplyr::summarise_all(is.numeric) %>% tidyr::gather() %>% dplyr::filter(value == F) if (nrow(selected_non_num_vars) >= 1) { shinyWidgets::sendSweetAlert( session = session, title = "Error...", text = ifelse(length(selected_non_num_vars$key) == 1, base::paste0(selected_non_num_vars$key, " is non numeric varibales"), base::paste0(base::paste0(selected_non_num_vars$key, collapse = ","), " are non numeric varibales") ), type = "error" ) return(FALSE) } else { return(TRUE) } }) ## boxplot condition panel : show only if selected variable(s) validated and displayed on UI output$box_plot_status <- reactive({ req(final_box_plot()) return(TRUE) }) outputOptions(output, "box_plot_status", suspendWhenHidden = FALSE) ## box plot user selected gene group data box_plot_user_selected_gene_group_data <- callModule(module = gene_group_selection , id = "box_plot_gene_group_selection" , gene_group_info = reactive(group_info$row_groups), generate_plot_action = reactive(input$generate_box), current_session_data_matrix = plot_data) ## box plot boxplt <- eventReactive(input$generate_box, { req(input$box_x , box_vars_validated() , box_plot_user_selected_gene_group_data()) box_plot_data <- box_plot_user_selected_gene_group_data() %>% dplyr::select(c(input$box_x , "gene_groups")) %>% tidyr::gather(key = samples , value = value , -gene_groups) %>% dplyr::left_join(group_info$column_groups , by = c(samples = "group_members")) %>% tidyr::replace_na(list(groups = "No groups assigned")) %>% ## NA will be converted to "No groups assigned" dplyr::mutate(samples = forcats::fct_inorder(samples)) %>% dplyr::mutate(groups = forcats::fct_inorder(groups))# %>% ## base plot bp <- ggplot(box_plot_data) return(bp) }) final_box_plot <- reactive({ req(boxplt()) withProgress(message = "Preparing box plot" , { incProgress(0.5) boxplt <- boxplt() ## y axis and alpha boxplt <- boxplt + geom_boxplot(alpha = input$box_plot_alpha) + aes(y = value) ## fill and facet box plot out <- callModule(module = ggplot_fill_and_facet , id = "box_plot_fill_and_facet" , ggplot = boxplt) boxplt <- out$plot # dynamic display pvalue if (input$box_plot_pvalue == "TRUE") { box_x_var_type <- boxplt$data %>% dplyr::pull(!!boxplt$mapping$x) %>% unique() %>% as.character() if(length(box_x_var_type) >= 2){ all_combin <- combn(box_x_var_type, 2, simplify = F) boxplt <- boxplt + ggpubr::stat_compare_means(comparisons = all_combin, method = input$box_plot_pval_method) } } ## box plot dots if(input$box_plot_show_dots %>% as.logical()){ boxplt <- boxplt + geom_jitter(width = input$boxplot_dots_width , color = input$boxplot_dots_color , alpha = input$boxplot_dots_transprancy) } ## decorate box plot boxplt <- callModule(module = plot_title_and_axis_label_server , id = "box_plot_label_and_title", my_ggplot = boxplt, axis_x_title = boxplt$mapping$x %>% rlang::quo_text(), axis_y_title = boxplt$mapping$y %>% rlang::quo_text(), fill_legend_title = boxplt$mapping$fill %>% rlang::quo_text(), x_tick_angle = 90, ) incProgress(1) }) return(boxplt) }) ## render boxplot output$box_plot <- renderPlot({ req(final_box_plot()) return(print(final_box_plot())) }, height = function() { return(session$clientData$output_box_plot_width) }, width = function() { return(session$clientData$output_box_plot_width) }, res = 96) ## export box plot callModule(module = export_plot , id = "export_box_plot" , file_name = "box_plot" ,plot = final_box_plot) ## @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ # violin plot server---- ## @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ ## update violin plot variables observeEvent(plot_data(), { updatePickerInput( session = session, inputId = "violin_x", choices = base::colnames(plot_data())[-1], selected = base::colnames(plot_data())[2] ) }) ## validate violin plot selected variables violin_vars_validated <- eventReactive(input$generate_violin, { req(input$violin_x) selected_non_num_vars <- plot_data() %>% dplyr::select(c(input$violin_x)) %>% dplyr::summarise_all(is.numeric) %>% tidyr::gather() %>% dplyr::filter(value == F) if (nrow(selected_non_num_vars) >= 1) { shinyWidgets::sendSweetAlert( session = session, title = "Error...", text = ifelse(length(selected_non_num_vars$key) == 1, base::paste0(selected_non_num_vars$key, " is non numeric varibales"), base::paste0(base::paste0(selected_non_num_vars$key, collapse = ","), " are non numeric varibales") ), type = "error" ) return(FALSE) } else { return(TRUE) } }) ## violin plot condition panel : show only if selected variable(s) validated and displayed on UI output$violin_plot_status <- reactive({ req(final_violin_plot()) return(TRUE) }) outputOptions(output, "violin_plot_status", suspendWhenHidden = FALSE) ## violin plot data violin_plot_user_selected_gene_group_data <- callModule(module = gene_group_selection , id = "violin_plot_gene_group_selection" , gene_group_info = reactive(group_info$row_groups), generate_plot_action = reactive(input$generate_violin), current_session_data_matrix = plot_data) ## violin plot violinplt <- eventReactive(input$generate_violin, { req(input$violin_x , violin_vars_validated() , violin_plot_user_selected_gene_group_data()) violin_plot_data <- violin_plot_user_selected_gene_group_data() %>% dplyr::select(c(input$violin_x) , gene_groups) %>% tidyr::gather(key = samples , value = value , -gene_groups) %>% left_join(group_info$column_groups , by = c(samples = "group_members")) %>% tidyr::replace_na(list(groups = "No groups assigned")) %>% ## NA will be converted to "No groups assigned" dplyr::mutate(samples = forcats::fct_inorder(samples)) %>% dplyr::mutate(groups = forcats::fct_inorder(groups))# %>% ## fill violin plot by variable, default vp <- ggplot(violin_plot_data) return(vp) }) final_violin_plot <- reactive({ req(violinplt()) withProgress(message = "Preparing violin plot" , { incProgress(0.5) violin_plot_vars <- isolate(input$violin_x) violinplt <- violinplt() #violinplt <- violinplt + aes(x = samples, y = value , fill = samples ) ## y axis and alpha violinplt <- violinplt + geom_violin(alpha = input$violin_plot_alpha , draw_quantiles = c(input$violin_show_quantile))+ aes(y = value) # # dynamic fill and alpha # violinplt <- violinplt + # geom_violin(alpha = input$violin_plot_alpha , draw_quantiles = c(input$violin_show_quantile)) # # ## fill and facet violin plot out <- callModule(module = ggplot_fill_and_facet , id = "violin_plot_fill_and_facet" , ggplot = violinplt) violinplt <- out$plot # dynamic display pvalue if (input$violin_plot_pvalue == "TRUE") { violin_x_var_type <- violinplt$data %>% dplyr::pull(!!violinplt$mapping$x) %>% unique() %>% as.character() if(length(violin_x_var_type) >= 2){ all_combin <- combn(violin_x_var_type, 2, simplify = F) violinplt <- violinplt + ggpubr::stat_compare_means(comparisons = all_combin, method = input$violin_plot_pval_method) } } # decorate box plot violinplt <- callModule(module = plot_title_and_axis_label_server , id = "violin_plot_label_and_title", my_ggplot = violinplt, axis_x_title = violinplt$mapping$x %>% rlang::quo_text(), axis_y_title = violinplt$mapping$y %>% rlang::quo_text(), x_tick_angle = 90, fill_legend_title = violinplt$mapping$fill %>% rlang::quo_text() ) incProgress(1) }) return(violinplt) }) ## render violine output$violin_plot <- renderPlot({ req(final_violin_plot()) return(print(final_violin_plot())) }, height = function() { return(session$clientData$output_violin_plot_width) }, width = function() { return(session$clientData$output_violin_plot_width) }, res = 96) ## export box plot callModule(module = export_plot , id = "export_violin_plot" , file_name = "violin_plot" ,plot = final_violin_plot) ## @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ # bar plot server---- ## @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ ## update bar plot variables observeEvent(plot_data(), { updatePickerInput( session = session, inputId = "barplot_vars", choices = base::colnames(plot_data())[-1], selected = base::colnames(plot_data())[2] ) }) ## update bar plot genes observeEvent(plot_data(), { updatePickerInput( session = session, inputId = "barplot_select_genes", choices = plot_data() %>% dplyr::pull(1), ## first column is gene name selected = plot_data() %>% dplyr::pull(1) %>% .[1] ) }) ## validate barplot selected variables barplot_vars_validated <- eventReactive(input$generate_barplot, { req(input$barplot_vars) selected_non_num_vars <- plot_data() %>% dplyr::select(c(input$barplot_vars)) %>% dplyr::summarise_all(is.numeric) %>% tidyr::gather() %>% dplyr::filter(value == F) if (nrow(selected_non_num_vars) >= 1) { shinyWidgets::sendSweetAlert( session = session, title = "Error...", text = ifelse(length(selected_non_num_vars$key) == 1, base::paste0(selected_non_num_vars$key, " is non numeric varibales"), base::paste0(base::paste0(selected_non_num_vars$key, collapse = ","), " are non numeric varibales") ), type = "error" ) return(FALSE) } else { return(TRUE) } }) ## barplot condition panel : show only if selected variable(s) validated and displayed on UI output$barplot_status <- reactive({ req(final_barplot()) return(TRUE) }) outputOptions(output, "barplot_status", suspendWhenHidden = FALSE) ## barplot barplot <- eventReactive(input$generate_barplot, { req(input$barplot_vars , barplot_vars_validated(),input$barplot_select_genes) barplot_data <- plot_data() %>% dplyr::rename_at(1 , funs(return("GeneNames"))) %>%## set first column name as geneNames dplyr::select(1, c(input$barplot_vars)) %>% dplyr::filter(GeneNames %in% input$barplot_select_genes) %>% tidyr::gather(key = samples , value = value , -GeneNames) %>% left_join(group_info$column_groups , by = c(samples = "group_members")) %>% tidyr::replace_na(list(groups = "No groups assigned")) %>% ## NA will be converted to "No groups assigned" dplyr::mutate(samples = forcats::fct_inorder(samples)) %>% ## order by first appearance dplyr::mutate(groups = forcats::fct_inorder(groups))# %>% ## order by first appearance ## if user groups uploaded chunk below make sure that order in each group is as per user uploaded samples if(TRUE){ ## original (user supplied) sample order barplot_data <- barplot_data %>% dplyr::mutate(samples = forcats::fct_relevel(samples ,group_info$column_groups %>% pull(2) %>% as.character() %>% unique() )) #%>% ## order by group members } if(TRUE){ ## original (user supplied) column group order barplot_data <- barplot_data %>% dplyr::mutate(groups = forcats::fct_relevel(groups ,group_info$column_groups %>% pull(1) %>% as.character() %>% unique())) } ## barplot base ggplot barplot <- ggplot(barplot_data) return(barplot) }) final_barplot <- reactive({ req(barplot()) withProgress(message = "Preparing bar plot" , { incProgress(0.5) barplot <- barplot() ## current bar plot data current_bar_plot_data <- barplot$data ## dynamic color aesthatic (color_by_var) is opposite to x axis aesthatic. ## can be one of the : samples or GeneNames. is the gene column is always named as GeneName ? color_by_var <- ifelse(input$barplot_xaxis_choices == "samples" , "GeneNames" , "samples") barplot <- barplot + geom_col(aes(x = !!as.symbol(input$barplot_xaxis_choices), y = value , col = !!(as.symbol(color_by_var)) ) , position = "dodge" , alpha = input$barplot_alpha) + scale_color_manual(breaks = current_bar_plot_data[[color_by_var]] %>% unique(), values = c(rep("black", current_bar_plot_data[[color_by_var]] %>% unique() %>% length() ))) + guides(color = FALSE, size = FALSE) ## fill by groups or variable. if(input$fill_barplot != "identical"){ barplot <- barplot + aes(fill = !!as.name(input$fill_barplot)) } ## fill bar plot identical if (input$fill_barplot == "identical") { barplot <- barplot + aes(fill = input$barplot_color_chooser) + ## fake aesthatic scale_fill_manual(breaks = current_bar_plot_data[[color_by_var]] %>% unique(), values = c(rep(input$barplot_color_chooser, current_bar_plot_data[[color_by_var]] %>% unique() %>% length() ))) } ## facet bar plot if(input$barplot_facet_value != "none") { barplot <- barplot + facet_wrap(~ eval(as.symbol(input$barplot_facet_value)) , scales = c(input$barplot_yscale )) } ## decorate box plot barplot <- callModule(module = plot_title_and_axis_label_server , id = "barplot_label_and_title", my_ggplot = barplot, axis_x_title = input$barplot_xaxis_choices, axis_y_title = "Value", x_tick_angle = 90, fill_legend_title = input$fill_barplot ) incProgress(1) }) return(barplot) }) ## render barplot output$barplot <- renderPlot({ req(final_barplot()) return(print(final_barplot())) }, height = function() { return(session$clientData$output_barplot_width) }, width = function() { return(session$clientData$output_barplot_width) }, res = 96) ## export barplot callModule(module = export_plot , id = "export_barplot" , file_name = "barplot" ,plot = final_barplot) ## @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ # line plot server---- ## @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ ## update x variables observe({ # update x updatePickerInput( session = session, inputId = "lineplot_x", choices = base::colnames(plot_data())[-1], selected = base::colnames(plot_data())[2] ) }) ## validate line plot parameters line_plot_params_validated <- eventReactive(input$generate_lineplot, { req(input$lineplot_x) selected_non_num_vars <- plot_data() %>% dplyr::select(c(input$lineplot_x)) %>% dplyr::summarise_all(is.numeric) %>% tidyr::gather() %>% dplyr::filter(value == F) ## check if selected variables are of type numeric if (nrow(selected_non_num_vars) >= 1) { shinyWidgets::sendSweetAlert( session = session, title = "Error...", text = ifelse(length(selected_non_num_vars$key) == 1, base::paste0(selected_non_num_vars$key, " is non numeric varibales"), base::paste0(base::paste0(selected_non_num_vars$key, collapse = ","), " are non numeric varibales") ), type = "error" ) return(FALSE) } ## atleast 2 variable req to generate line plot if(length(input$lineplot_x) == 1) { shinyWidgets::sendSweetAlert( session = session, title = "Error...", text = paste0("Select atleast two columns to draw line plot"), type = "error" ) return(FALSE) } ## value given for the `lineplot_top_n_genes` must be of type numeric if(!is.numeric(input$lineplot_top_n_genes)) { shinyWidgets::sendSweetAlert( session = session, title = "Error...", text = paste0("`# top variable genes to show` value must be of type numeric"), type = "error" ) return(FALSE) } ## value given for the `line_plot_nclust` must be of type numeric if(!is.numeric(input$line_plot_nclust)) { shinyWidgets::sendSweetAlert( session = session, title = "Error...", text = paste0("`# of clusters (k-means)` value must be of type numeric"), type = "error" ) return(FALSE) } else { return(TRUE) } }) ## line plot condition panel : show only if selected variable(s) validated and displayed on UI output$line_plot_status <- reactive({ req(line_plt()) return(TRUE) }) outputOptions(output, "line_plot_status", suspendWhenHidden = FALSE) ## line plot user selected gene group data line_plot_user_selected_gene_group_data <- callModule(module = gene_group_selection , id = "line_plot_gene_group_selection" , gene_group_info = reactive(group_info$row_groups), generate_plot_action = reactive(input$generate_lineplot), current_session_data_matrix = plot_data) ## preapre line plot data. line_plot_data <- eventReactive(eventExpr = input$generate_lineplot, { req(line_plot_params_validated()) withProgress(message = 'Preparing line plot data', { # Update progress incProgress(0.5) ## display 50% task finished when it start # Long Running Task set.seed(1234) ## handle error if generated while performing clustering clustered <- tryCatch({ clustered <- tibble_to_row_clusters(x = line_plot_user_selected_gene_group_data(), row_ident = 1 , cols_to_use = input$lineplot_x, use_z_score_for_clustering = ifelse(input$lineplot_cluster_value_type == 'zscore' , TRUE, FALSE) , num_of_top_var_rows = ifelse(input$lineplot_genes_selection == 'top_variable_genes',input$lineplot_top_n_genes , -1), nclust = ifelse(input$line_plot_cluster_genes_by == "kmeans", as.numeric(input$line_plot_nclust) , 1 ) ## if user selects cluster by gene groups, value of nclust = 1 ) ## add count to cluster name }, error = function(x){ shinyWidgets::sendSweetAlert(session = session , type = "error", title = "Error" , text = tags$h5(tags$code(x))) return(NULL) }) # Update progress incProgress(1) }) ## with progress ends ## return NULL if oject clustered in NULL if(is.null(clustered)){ return(NULL) } ## select which value to display on plot, zscore or raw value. if(input$lineplot_display_value_type == "zscore"){ forPlot <- clustered$zscore %>% left_join(clustered$clusters) } else{ forPlot <- clustered$raw_value %>% left_join(clustered$clusters) } ## if cluster by gene_groups assign user defined groups if(input$line_plot_cluster_genes_by == "gene_groups"){ ## tibble of two cols, where 1st column is gene names and 2nd is user supplied gene_groups user_gene_groups <- line_plot_user_selected_gene_group_data() %>% dplyr::select(1, dplyr::last_col()) ## get column names to use them inside left_join left_col <- colnames(forPlot)[1] right_col <- colnames(user_gene_groups)[1] ## get the name of target column which is going to join as a result of left join. ## the name of resulted joined column will be used later to remove column from the df. column_to_be_joined <- colnames(user_gene_groups)[2] forPlot <- forPlot %>% left_join(user_gene_groups , setNames(right_col, left_col)) %>% dplyr::mutate(clust = !!as.symbol(column_to_be_joined)) %>% dplyr::select(-!!as.symbol(column_to_be_joined)) } ## Add gene count in cluster name forPlot <- forPlot %>% dplyr::group_by(clust) %>% dplyr::add_count() %>% dplyr::ungroup() %>% dplyr::mutate(clust = paste(clust, ":(N=" ,n , ")" , sep = "")) %>% dplyr::select(-n) ## plot all lines vs avg line based on tick of activate_avg_line_plot if (input$activate_avg_line_plot == "average") { forPlot <- forPlot %>% dplyr::group_by(clust) %>% dplyr::summarise_if(is.numeric, input$avg_line_type) } forPlot <- forPlot %>% tidyr::gather(key = "variable", value = "value" , input$lineplot_x) %>% dplyr::mutate(variable = forcats::fct_inorder(variable)) %>% ## preserve original order of samples dplyr::group_by(variable) return(forPlot) }) ## plot line_plt <- eventReactive(eventExpr = input$generate_lineplot, { req(line_plot_data()) ### plot gp <- line_plot_data() %>% dplyr::mutate(clust = forcats::fct_relevel(clust, as.numeric(clust) %>% unique %>% sort %>% as.character)) %>% ## numeric cluster to ordered factor dplyr::mutate(row_num = 1:n()) %>% ggplot2::ggplot(aes(x = variable, y = value, group = row_num , alpha = 1)) return(gp) }) ## final line plot final_line_plot <- reactive({ req(line_plt()) validate(need(is.numeric(input$line_plot_ncols) , message = "Number of columns must be numeric")) ## line plot geoms line_plt <- line_plt() + geom_point(size = input$lineplot_point_size) + #geom_line(aes(col = clust) , size = input$lineplot_line_size) + ggalt::geom_xspline(aes(col = clust), spline_shape=input$line_plot_splin_shape, size=input$lineplot_line_size)+ theme(legend.position = "none", axis.text.x = element_text(angle = 90, vjust = 0.4)) ## line plot color identical if(input$line_plot_color_by == 'identical'){ ## get number of clusters n_clust <- line_plt$data %>% dplyr::pull(clust) %>% ## pull cluster var column base::unique() %>% as.character() %>% length() ## generate colors for each cluster colrs <- c(rep(input$line_plot_color_chooser, n_clust)) ## color and fill manual line_plt <- line_plt + scale_fill_manual(values = colrs) + scale_color_manual(values = colrs) } ## line plot manual alpha line_plt <- line_plt + scale_alpha(range = input$line_plot_line_transparancy) + ## override alpha in the plot guides(alpha = FALSE , color = guide_legend(override.aes = list(alpha = input$line_plot_line_transparancy))) ## override alpha in the legend ## lie plot facet if(input$line_plot_separate_by == 'gene_groups'){ line_plt <- line_plt + facet_wrap(~clust, ncol = input$line_plot_ncols , scales = input$line_plot_facet_scale_free) } ## decorate line plot line_plot <- callModule(module = plot_title_and_axis_label_server , id = "decorate_line_plot" , my_ggplot = line_plt , axis_x_title = "Samples", axis_y_title = "Value", color_legend_title = "Cluster", x_tick_angle = 90 ) # ## fill color identical # line_plot <- callModule(module = ggplot_fill_colour , "line_plot_color_identical" , # gp = line_plot , times = input$line_plot_nclust) return(line_plot) }) ## render line plot output$line_plot <- renderPlot({ req(final_line_plot()) return(print(final_line_plot())) #return( # withProgress(message = "Display line plot in progress", { # incProgress(0.5) #print(final_line_plot()) #incProgress(1) #}) #) }, height = function() { return(session$clientData$output_line_plot_width) }, width = function() { return(session$clientData$output_line_plot_width) }, res = 96) ## lineplot data to display line_plot_data_disply <- eventReactive(input$generate_lineplot , { req(line_plot_data()) display_data <- line_plot_data() %>% tidyr::spread(key = "variable" , value = "value")%>% dplyr::select(1,input$lineplot_x,dplyr::everything()) }) ## genome annotation for line plot ## global genome annotations object cannot be used in following scenario ## For example : user has generated one lineplot Now accidently or purposely user selected / uploaded data ## for different species than the previous lineplot generated. In this scenario, global reference genome will ## be updated , and therefore, annotations in the existing lineplot will also be affected. ## To prepvent this seprate heatmap ref genome from global annot. lineplot ref annot will be updated only when ## plot lineplot submit button hit lineplot_reference_annot <- eventReactive(input$generate_lineplot , { req(genome_for_annotations()) return(genome_for_annotations()) }) ## line plot output table output$line_plot_clustred_data <- DT::renderDataTable({ ## map gene features if average line not asked if(isolate(input$activate_avg_line_plot != "average")){ user_selected_species_annot <- ah_data_summary2 %>% dplyr::filter(genome == lineplot_reference_annot())%>% dplyr::select(gr_cols) %>% tidyr::unnest(cols = gr_cols) join_col_x <- base::colnames(line_plot_data_disply())[1] ## first column containing geneNames join_col_y <- base::colnames(user_selected_species_annot)[7] ## "id" column line_plot_data_to_show <- line_plot_data_disply()%>% left_join(user_selected_species_annot, by = setNames(join_col_y, join_col_x)) %>% dplyr::select(1,c("seqnames", "start", "end", "strand", "description"), colnames(line_plot_data_disply())) %>% dplyr::mutate_if(is.numeric, round, 4)%>% dplyr::mutate_all(funs(replace_na(as.character(.),"--NA--"))) }else{ line_plot_data_to_show <- line_plot_data_disply() } return(line_plot_data_to_show %>% dplyr::arrange(clust) %>% dplyr::mutate_if(is.numeric , round , 3)) }, selection = "none", server = F, extensions = "Buttons", options = list( scrollX = TRUE, dom = "Blfrtip", searchHighlight = TRUE, buttons = list("copy", list( extend = "collection", buttons = c("csv", "excel", "pdf"), text = "Download" )), # end of buttons customization # customize the length menu lengthMenu = list( c(10, 50, 100, 500, -1) # declare values , c(10, 50, 100, 500, "All") # declare titles ), pageLength = 10 ) ## end of options ) ## export line plot callModule(module = export_plot ,id = "export_line_plot" ,file_name = "line_plot" , plot = final_line_plot) ## line plot functional analysis callModule(module = functional_analysis_server , id = "lineplot_functional_analysis_ui" , ui_id = "lineplot_functional_analysis_ui", session = session, gene_set = reactive(split(x = line_plot_data_disply()[[1]], ## genename f = line_plot_data_disply()$clust)) , genome = reactive({lineplot_reference_annot()})) ## @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ # PCA plot server---- ## @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ ## check whether x and y are numeric type pca_plot_vars_validated <- eventReactive(input$generate_pca_plot, { req(input$pca_plot_vars) withProgress(message = "validate data" , { incProgress(0.3) selected_vars <- plot_data() %>% dplyr::select(input$pca_plot_vars) col_class <- plot_data() %>% dplyr::select(input$pca_plot_vars) %>% dplyr::summarise_all(is.numeric) %>% tidyr::gather() incProgress(0.5) if (!all(col_class$value)) { non_numeric_cols <- col_class %>% dplyr::filter(!value) shinyWidgets::sendSweetAlert( session = session, title = "Error...", text = paste0(paste0(non_numeric_cols$key, collapse = " "), " must be of type numeric", collapse = ""), type = "error" ) return(FALSE) } incProgress(0.7) if(length(input$pca_plot_vars) == 1) { shinyWidgets::sendSweetAlert( session = session, title = "Error...", text = paste0("Select atleast two columns to perfrom PCA."), type = "error" ) return(FALSE) } else { return(TRUE) } incProgress(1) }) }) ## update x variables observe({ req(plot_data()) # update x updatePickerInput( session = session, inputId = "pca_plot_vars", choices = base::colnames(plot_data())[-1], selected = base::colnames(plot_data())[2] ) }) ## pca plot user selected gene group data pca_plot_user_selected_gene_group_data <- callModule(module = gene_group_selection , id = "pca_plot_gene_group_selection" , gene_group_info = reactive(group_info$row_groups), generate_plot_action = reactive(input$generate_pca_plot), current_session_data_matrix = plot_data) ## prepare PCA input matrix , where columns are gene names and rows are sample names pca_input_matrix <- eventReactive(eventExpr = input$generate_pca_plot,{ req(pca_plot_vars_validated() , pca_plot_user_selected_gene_group_data()) withProgress(message = "Prepare PCA data",{ incProgress(0.3) plot_data_t <- pca_plot_user_selected_gene_group_data() %>% dplyr::select(1, input$pca_plot_vars) %>% dplyr::select_if(function(.) { !all(. == 0) }) %>% ## remove if all values are 0 in the sample tidyr::gather(key = sname, value, -1) %>% tidyr::spread(1, value) ## transpose data --> genes will be column and samples will be row ## conver dt to matrix for prcomp input pca_input_data <- plot_data_t %>% as.data.frame() %>% column_to_rownames("sname") }) return(pca_input_data) }) ## prform PCA pca_plot_pr_comp <- eventReactive(eventExpr = input$generate_pca_plot,{ req(pca_input_matrix()) withProgress(message = "Perform PCA" , { # perform pca pr <- tryCatch({ prcomp(pca_input_matrix()) ## remove last column having sample groups } , error = function(x){ sendSweetAlert(session = session ,title = "Error while performing PCA" , text = tags$h4("Error message from function :" , tags$code("prcomp()"),br(),br(), tags$code(x)) , type = "error") return(NULL) }) }) return(pr) }) ### perform pca sample k-means pca_data_sample_kmeans <- reactive({ req(pca_input_matrix()) validate( need(is.numeric(input$pca_sample_sample_kmeans_n) && (input$pca_sample_sample_kmeans_n > 0 ) , message = "Number of cluster must be numeric") ) ## perform kmenas km_data <- tryCatch({ set.seed(12345) km <- kmeans(pca_input_matrix() , centers = input$pca_sample_sample_kmeans_n ) tt <- tibble::tibble(sname = rownames(pca_input_matrix()) , kmeans_clust = km$cluster ) }, error = function(x){ sendSweetAlert(session = session ,title = "PCA sample K-means error!!!" , text = tags$h4("Putting all samples in one group.",br(), "Error message from function :" , tags$code("kmeans()"),br(),br(), tags$code(x)) , type = "error") tt <- tibble::tibble(sname = rownames(pca_input_matrix()) , kmeans_clust = "All samples") return(tt) }) return(km_data) }) ## Attach % variance with each PC my_pr_comp_names <- reactive({ req(pca_plot_pr_comp()) ## get proportion of variance for each PC pc_prop_of_var <- summary(pca_plot_pr_comp())$importance %>% #summary(pr)$importance %>% as.data.frame() %>% rownames_to_column(var = "feature_type") %>% tidyr::gather(pc , value , -feature_type) %>% dplyr::filter(feature_type == "Proportion of Variance") %>% dplyr::select(-feature_type) %>% dplyr::mutate(value = round(value * 100 ,1)) %>% ## convert to percentage dplyr::mutate(with_var = paste(pc ," (" , value ,"%",")" , sep = "")) %>% dplyr::select(-value) ## return named vector pc_prop_of_var %>% dplyr::pull(with_var) %>% rlang::set_names( pc_prop_of_var$pc) }) ## Get PCs from prcomp obtained object. pr_comp_derieved_tibble is a tibble where columns are PCs and rows are samples. pr_comp_derieved_tibble <- reactive({ req(pca_plot_pr_comp()) ## get pr_comp tibble, and map sample and group info pr_comp_tbl <- pca_plot_pr_comp()$x %>% as.data.frame() %>% dplyr::rename_all(~(my_pr_comp_names())) %>% tibble::rownames_to_column("sname") %>% as_tibble() ## rename column names. new column names are with 'prop of var' attached. ## create column sname (sample names ) from row names ## prevent rendering from global sample group update isolate({ pr_comp_tbl_grps <- pr_comp_tbl %>% left_join(group_info$column_groups , by = c (sname = "group_members")) %>% ## add column groups denoting sample groups tidyr::replace_na(list(groups = "No groups assigned")) ## in the column groups replace NA to "No groups assigned" }) ## add sample kmeans data pr_comp_tbl_grps <- pr_comp_tbl_grps %>% left_join(pca_data_sample_kmeans() , by = "sname") %>% tidyr::replace_na(list(kmeans_clust = "No groups assigned")) ## in the column groups return(pr_comp_tbl_grps) }) ## PCA plot update show / hide sample groups. It allows user to exclude sample groups directly from PCA plot observe({ req(pr_comp_derieved_tibble()) if(input$pca_plot_colour == "groups"){ shinyWidgets::updateMultiInput(inputId = "pca_plot_hide_sample_groups" , session = session, choices = pr_comp_derieved_tibble() %>% pull("groups") %>% unique() ) } if(input$pca_plot_colour == "kmeans"){ shinyWidgets::updateMultiInput(inputId = "pca_plot_hide_sample_groups" , session = session, choices = pr_comp_derieved_tibble() %>% pull("kmeans_clust") %>% unique() ) } }) ### Remove user excluded groups before final PCA plot pca_data_after_groups_excluded_by_user <- reactive({ #req(input$pca_plot_hide_sample_groups) user_excluded_groups <- input$pca_plot_hide_sample_groups plot_data <- pr_comp_derieved_tibble() %>% dplyr::filter(! xor((groups %in% user_excluded_groups),(kmeans_clust %in% user_excluded_groups))) ## make sure pca_data_after_groups_excluded_by_user() > 1 row. ### This is necessary to check when user exclude all the groups validate( need(plot_data %>% nrow() > 1 , "Atleast one sample group must be selected" ) ) return(plot_data) }) ## update PC choices observe({ req(my_pr_comp_names()) available_pc <- my_pr_comp_names() default_pc <- available_pc[1:2] ## default PCs updateSelectizeInput(session = session , inputId = "select_pcs_to_plot" , choices = available_pc %>% unname(), selected = default_pc ,server = TRUE) }) ## get names of PC columns pcs_to_plot <- reactive({ validate( need(input$select_pcs_to_plot %>% length() == 2 , "Select 2 Pcs.") ) return(input$select_pcs_to_plot) }) ## pca plot pca_gp <- reactive({ req(all(pcs_to_plot() %in% my_pr_comp_names())) withProgress(message = "Preparing PCA plot",{ incProgress(0.5) ## add group information plot_data <- pca_data_after_groups_excluded_by_user() #print("PCA plot data") #print(plot_data %>% dplyr::select(sname, groups)) ## PCs to plot pc_x = pcs_to_plot()[1]## PC1 pc_y = pcs_to_plot()[2] ## PC2 ## plot , default PC1 and PC2 pca_gp <- ggplot(data = plot_data , aes_string(x = as.symbol(pc_x), y = as.symbol(pc_y) )) + geom_point(size = input$pca_sample_dot_size) #+ theme_bw() ## whether sample names should be shown or not if(input$pca_display_sample_names) { pca_gp <- pca_gp + ggrepel::geom_text_repel(aes(label = sname), size = input$pca_plot_sample_names_size) } ### color groups if(input$pca_plot_colour == "groups") { pca_gp <- pca_gp + aes(color = groups) + scale_color_manual(values = get_gg_colors(pr_comp_derieved_tibble() %>% pull(groups) %>% as_factor() %>% levels()) ) } ## color by sample kmeans if(input$pca_plot_colour == "kmeans") { pca_gp <- pca_gp + aes(color = factor(kmeans_clust)) + scale_color_manual(values = get_gg_colors(pr_comp_derieved_tibble() %>% dplyr::pull(kmeans_clust) %>% forcats::as_factor() %>% levels())) } ## color identical , NOTE: using breaks = NULL explicitly will be helpful to getrid of legends if(input$pca_plot_colour == "identical") { pca_gp <- pca_gp + aes(col = "") + scale_color_manual( breaks = NULL , values = input$pca_sample_name_color) } incProgress(1) }) return(pca_gp) }) ## final pca plot final_pca_plot <- reactive({ req(pca_gp()) withProgress(message = "Decorate PCA plot",{ incProgress(0.5) pca_gp <- callModule(module = plot_title_and_axis_label_server , id = "decorate_pca_plot" , my_ggplot = pca_gp() , axis_x_title = pca_gp()$labels$x, axis_y_title = pca_gp()$labels$y , x_tick_angle = 0, color_legend_title = input$pca_plot_colour) pca_gp <- pca_gp + scale_x_continuous(expand = expand_scale(mult = 0.2)) + # expand x scale scale_y_continuous(expand = expand_scale(mult = 0.2)) # expand y scale incProgress(1) return(pca_gp) }) }) ## render pca output$pca_plot <- renderPlot({ req(final_pca_plot()) final_pca_plot() #return(print(final_pca_plot())) }, height = function() { req(final_pca_plot()) return(session$clientData$output_pca_plot_width) }, ## dynamic height width = function() { req(final_pca_plot()) return(session$clientData$output_pca_plot_width) }, res = 96 ) ## dynamic width ## export pca plot callModule(module = export_plot , id = "export_pca_plot" , file_name = "pca_plot" , plot = final_pca_plot) ## current pca plot SRA sample info, update only when generate pca plot submit button hit current_pca_plot_sra_sample_info <- eventReactive(eventExpr = input$generate_pca_plot,{ return(user_selected_sra_id_sample_info()) }) ### pla plot brush data pca_brushed_data <- eventReactive(input$pca_plot_brush , { pc_x = pcs_to_plot()[1] ## PC1 pc_y = pcs_to_plot()[2] ## PC2 ## subset brushed points data bp <- brushedPoints(df = pca_data_after_groups_excluded_by_user() , brush = input$pca_plot_brush , xvar = pc_x, yvar = pc_y) ## map required sample info display_pca_data <- current_pca_plot_sra_sample_info() %>% dplyr::filter(run_accession %in% bp$sname) %>% dplyr::select(-c("reference_annot" , "taxon_id","scientific_name" , "instrument_model" , "updated_date")) return(display_pca_data) }) ## render PCA brushed data output$pca_brushed_datatable <- DT::renderDataTable({ req(pca_brushed_data()) return(pca_brushed_data() %>% dplyr::mutate_all(funs(replace_na(as.character(.),"--NA--"))) %>% dplyr::mutate_if(is.numeric , round , 2)) },selection = "none", server = F, extensions = c("Buttons"), options = list( deferRender = TRUE, scrollX = TRUE, dom = "Blfrtip", searchHighlight = TRUE, columnDefs = list( list(targets = c(6:15), visible = FALSE) # The column number must be identical to the columns in colvis extension ), buttons = list("copy", list(extend = "collection", buttons = c("csv", "excel", "pdf"), text = "Download" ), list(extend = "colvis", columns = c(6:15) )), # end of buttons customization # customize the length menu lengthMenu = list( c(10, 50, 100, 500, -1) # declare values , c(10, 50, 100, 500, "All") # declare titles ), pageLength = 10 ) ) ## pca plot brushed data table status , decide whether should be displayed or not output$pac_plot_brushed_data_table_status <- reactive({ req(pca_brushed_data()) return(TRUE) }) outputOptions(output,name = "pac_plot_brushed_data_table_status" , suspendWhenHidden = FALSE) ## pca plot condition panel:show only if variable (x, y) validated and displayed on UI output$pca_plot_status <- reactive({ req(pca_plot_pr_comp()) return(TRUE) }) outputOptions(output, "pca_plot_status", suspendWhenHidden = FALSE) ## @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ # heatmap server---- ## @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ ## check whether x and y are numeric type heatmap_parmas_validated <- eventReactive(input$generate_heatmap, { req(input$heatmap_vars) selected_vars <- plot_data() %>% dplyr::select(input$heatmap_vars) col_class <- plot_data() %>% dplyr::select(input$heatmap_vars) %>% dplyr::summarise_all(is.numeric) %>% tidyr::gather() withProgress(message = "Validating inputs", { incProgress(1/10) if (!all(col_class$value)) { non_numeric_cols <- col_class %>% dplyr::filter(!value) shinyWidgets::sendSweetAlert( session = session, title = "Error...", text = paste0(paste0(non_numeric_cols$key, collapse = " "), " must be of type numeric", collapse = ""), type = "error" ) return(FALSE) } incProgress(2/10) ## atleast two vars req to generate hm if(length(input$heatmap_vars) == 1) { shinyWidgets::sendSweetAlert( session = session, title = "Error...", text = paste0("Select atleast two columns to draw heatmap"), type = "error" ) return(FALSE) } incProgress(3/10) # check if number of genes given are of type numeric if ((!is.numeric(input$heatmap_top_n_genes)) & (input$hm_genes_selection == "top_variable_genes")) { sendSweetAlert( session = session, title = "Error...", text = "'# top variable genes to show' must be numeric", type = "error" ) return(NULL) } incProgress(4/10) ## check if user given color scale min is valid if (!is.numeric(input$heatmap_scale_min)) { sendSweetAlert( session = session, title = "Error...", text = "Heatmap color scale (minimum) must be numeric value", type = "error" ) return(NULL) } incProgress(5/10) ## check if user given color scale max valid or not if (!is.numeric(input$heatmap_scale_max)) { sendSweetAlert( session = session, title = "Error...", text = "Heatmap color scale (maximum) must be numeric value", type = "error" ) return(NULL) } incProgress(6/10) ## check if number of cluster given are of type numeric. if (!is.numeric(input$heatmap_row_nclust) || !is.numeric(input$heatmap_coulm_nclust)) { sendSweetAlert( session = session, title = "Error...", text = "'# of clusters (column / row k-means)' must be of type numeric", type = "error" ) return(NULL) } incProgress(7/10) ## check if number of cluster given are > 0 and <= total rows in the data or if (input$heatmap_row_nclust == 0 || input$heatmap_coulm_nclust == 0 || input$heatmap_row_nclust >= nrow(plot_data()) || input$heatmap_row_nclust >= input$heatmap_top_n_genes || input$heatmap_coulm_nclust >= ncol(plot_data()) || input$heatmap_coulm_nclust >= length(input$heatmap_vars) ) { sendSweetAlert( session = session, title = "Error...", text = "'# of clusters (column / row k-means)' must be > 0 and < total rows (row clusters) or total columns (column clusters)", type = "error" ) return(NULL) } else { return(TRUE) } incProgress(10/10) }) }) ## heatmap conditional panel output$heatmap_status <- reactive({ req(heatmap_to_display()) return(TRUE) }) outputOptions(output, "heatmap_status", suspendWhenHidden = FALSE) ## update x variables observe({ req(plot_data()) ## update x updatePickerInput( session = session, inputId = "heatmap_vars", choices = base::colnames(plot_data())[-1], selected = base::colnames(plot_data())[2] ) }) ## heatmap user selected gene group data heatmap_user_selected_gene_group_data <- callModule(module = gene_group_selection , id = "heatmap_select_gene_groups" , gene_group_info = reactive(group_info$row_groups), generate_plot_action = reactive(input$generate_heatmap), current_session_data_matrix = plot_data) ## prepare heatmap data. heatmap_top_variable_genes_df <- eventReactive(eventExpr = input$generate_heatmap, { req(heatmap_parmas_validated(), heatmap_user_selected_gene_group_data()) withProgress(message = "Preparing heatmap data " , { incProgress(0.1) # fix number of genes to plot, default all genes (if any gene group selected, all the genes from selected gene group will be plotted) num_of_genes_for_hm <- nrow(heatmap_user_selected_gene_group_data()) if (input$hm_genes_selection == "top_variable_genes") { ## if user given number of genes argument < total genes, all genes will be used in the plot. if (nrow(heatmap_user_selected_gene_group_data()) < input$heatmap_top_n_genes) { num_of_genes_for_hm <- nrow(heatmap_user_selected_gene_group_data()) } else { num_of_genes_for_hm <- input$heatmap_top_n_genes } } # max genes cannot be higher than 20k max_hm_genes <- 20000 if (num_of_genes_for_hm > max_hm_genes) { sendSweetAlert( session = session, title = "Error...", text = paste("Number of genes cannot be higher than ", max_hm_genes, ".", sep = ""), type = "error" ) return(NULL) } incProgress(0.3 , message = "row wise clustering") ## get clusters and zscore data clustered <- tryCatch({ tibble_to_row_clusters(x = heatmap_user_selected_gene_group_data(), row_ident = 1 , cols_to_use = input$heatmap_vars, use_z_score_for_clustering = ifelse(input$heatmap_cluster_value_type == 'zscore' , TRUE, FALSE), #use_z_score_for_clustering = input$hm_use_zscore , num_of_top_var_rows = ifelse(input$hm_genes_selection == 'top_variable_genes', num_of_genes_for_hm , -1), nclust = as.numeric(input$heatmap_row_nclust) ) } , error = function(x){ return(x) }) ## if error generated while clustering if(inherits(clustered , "error")){ sendSweetAlert( session = session, title = "Error...", text = paste0("Row k-means error : ", clustered, collapse = " "), type = "error" ) return(NULL) } ## select which value to display on plot, zscore or raw value. if(input$heatmap_display_value_type == "zscore"){ heatmap_data <- clustered$zscore #print(heatmap_data) } else{ heatmap_data <- clustered$raw_value #print(heatmap_data) } ##@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ ## arrange heatmap input data in original order is important when user wants to have same order of genes in the heatmap which was uploaded. ## Following things must be kept in mind when arranging heatmap data matrix in the original order ## 1) output of the tibble_to_row_clusters function throws list containing raw_data_matrix, zscore_matrix and row_clusters ## if order of the rows in heatmap data matrix (zscore or raw data) change, order in the row clusters must change. ##@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ gene_name_col_names <- plot_data_original_row_order() %>% colnames() %>% .[1] row_order_col_name <- plot_data_original_row_order() %>% colnames() %>% .[2] heatmap_data <- heatmap_data %>% left_join(plot_data_original_row_order() , by = setNames(gene_name_col_names , colnames(heatmap_data)[1])) %>% dplyr::arrange(.data[[row_order_col_name]]) %>% ## arrange by original gene order dplyr::select(-.data[[row_order_col_name]]) ## remove column ## as mentioned above change order in row clusters row_splt <- clustered$clusters %>% left_join(plot_data_original_row_order() , by = setNames(gene_name_col_names , colnames(clustered$clusters)[1])) %>% dplyr::arrange(.data[[row_order_col_name]]) %>% dplyr::select(-.data[[row_order_col_name]]) ### which clusters to use ? kmeans or user defined if(input$heatmap_row_clusters_choices == "kmeans") { row_splt <- row_splt%>% pull(2) } else if(input$heatmap_row_clusters_choices == "gene_groups"){ ## get user given row groups row_splt = heatmap_data %>% as_tibble() %>% dplyr::left_join(group_info$row_groups , by = setNames(colnames(group_info$row_groups)[2] , colnames(heatmap_data)[1])) %>% pull(colnames(group_info$row_groups)[1]) ## set levels in the orginal order of user supplied groups row_splt <- row_splt %>% as.factor() %>% fct_relevel(., group_info$row_groups %>% pull(1) %>% unique()) } ## number of genes in each cluster gene_count <- table(row_splt) ## Fix row cluster names row_splt <- row_splt %>% tibble(x = . ) %>% dplyr::add_count(x) %>% dplyr::mutate(cluster_name = paste(input$hm_cluster_prefix , x ,":(N=", n,")" , sep = "")) %>% pull(cluster_name) ## convert tibble to data frame heatmap_data <- heatmap_data %>% as.data.frame() ##generate matrix with rownames as gene name rownames(heatmap_data) <- heatmap_data[[1]] heatmap_data <- heatmap_data[-1] incProgress(0.7 , message = "column wise clustering") ## column clusters, which clusters to use ? kmeans or user defined if(input$heatmap_column_clusters_choices == "kmeans"){ # column_splt <- tryCatch({ set.seed(1234) column_km_out <- kmeans(t(heatmap_data) ,centers = input$heatmap_coulm_nclust) column_km_out$cluster } , error = function(x){ return(x) }) } else if(input$heatmap_column_clusters_choices == "sample_groups"){ # use sample groups as column cluster ## heatmap column names to user supplied grp names tibble column_splt_ss = heatmap_data %>% colnames() %>% tibble(sname = .) %>% left_join(group_info$column_groups , by = c (sname = "group_members")) #%>% ## arrange by user supplied column order column_splt_ss <- column_splt_ss %>% dplyr::mutate(sname = forcats::fct_relevel(as.character(sname) , group_info$column_groups %>% pull(2) %>% unique() )) ## get named vector of heatmap column names, where vector elems are group names and names are column names. column_splt <- column_splt_ss %>% pull(colnames(group_info$column_groups)[1]) %>% ## pull user supplied group columns rlang::set_names( . ,column_splt_ss$sname) ## rearrange heatmap data as split order changed heatmap_data <- heatmap_data %>% dplyr:: select(names(column_splt)) } ## Fix column cluster name column_splt <- column_splt %>% tibble(x = .) %>% dplyr::add_count(x) %>% dplyr::mutate(cluster_name = paste(input$hm_column_cluster_prefix , x ,"\n(N=", n,")" , sep = "")) %>% pull(cluster_name) %>% set_names(names(column_splt)) ## set orig names if it has ## handle column split error if(inherits(column_splt , "error")){ sendSweetAlert( session = session, title = "Error...", text = paste0("Column k-means error : ", column_splt, collapse = " "), type = "error" ) return(NULL) } incProgress(0.9 , message = "final data") ## set heatmap data to a list rr= NULL rr$mat <- heatmap_data rr$clust <- row_splt rr$column_clusters <- column_splt rr$row_sd <- rr$mat %>% rownames %>% tibble(geneName = . ) %>% left_join(clustered$std_dev , by =c("geneName" = colnames(clustered$std_dev)[1])) %>% as.data.frame() %>% column_to_rownames("geneName") %>% as.matrix() incProgress(0.1) return(rr) }) }) ## update heatmap legend range observe({ #req(heatmap_top_variable_genes_df()) if(input$heatmap_display_value_type == "zscore"){ updateNumericInput(session = session , inputId = "heatmap_scale_min", value = -1.5) updateNumericInput(session = session , inputId = "heatmap_scale_max", value = 1.5) } if(input$heatmap_display_value_type != "zscore"){ updateNumericInput(session = session , inputId = "heatmap_scale_min", value = 0) updateNumericInput(session = session , inputId = "heatmap_scale_max", value = 10) } }) ## generate heatmap heatmap <- eventReactive(input$generate_heatmap, { req(heatmap_top_variable_genes_df()) withProgress(message = "Preparing heatmap",{ incProgress(0.5) # generate heatmap hm_row_threshold <- 2000 ##if number of genes higher than this, gene names will be disabled. # ## hm colors hm_colors <- tryCatch({ circlize::colorRamp2( breaks = seq(from = input$heatmap_scale_min, to = input$heatmap_scale_max , length.out = 3), colors = c(input$heatmap_col_low, input$heatmap_col_medium, input$heatmap_col_high) ) }, error = function(e){ ## send warning if color function give error sendSweetAlert(session = session , title = "Warning" , type = "warning" , text = tags$h4("Error while generating user defined colors", br(), "Error from" , tags$code("circlize::colorRamp2()"), br(), tags$code(e),br(), "Default scale (-2 to 2) will be used." )) ## default color : get minimum and maximum from circlize::colorRamp2( breaks = seq(from = -2, to = 2 , length.out = 3), colors = c(input$heatmap_col_low, input$heatmap_col_medium, input$heatmap_col_high) ) }) ## prepare column annotations column_ha = NULL ## default column annotations ## boxplot and column name annotations if(!is.null(input$heatmap_anno_type)){ ## make sure atleast one annot selected ## convert NA to "No groups assigned" no_grp_label <- "NA" ## get groups for hm cols hm_selected_column_groups <- heatmap_top_variable_genes_df()$mat %>% colnames() %>% tibble(hm_cols = . ) %>% left_join(group_info$column_groups , by = c(hm_cols = "group_members")) %>% pull(groups) %>% replace_na(no_grp_label) ### column grp annot grp_colors <- get_gg_colors(x = hm_selected_column_groups %>% as_factor() %>% levels()) ## column box annot column_box_anno <- ComplexHeatmap::anno_boxplot(heatmap_top_variable_genes_df()$mat , axis = T , border = T , axis_param = list(gp = grid::gpar(fontsize = 8), side = "left", facing = "outside" ), height = unit(input$heatmap_top_annot_height,"cm") ) ## display both grp and box annot if(all(c("show_heatmap_column_groups" , "show_heatmap_column_boxplot") %in% input$heatmap_anno_type)){ ## all annotations column_ha = ComplexHeatmap::HeatmapAnnotation(Groups = hm_selected_column_groups , Box = column_box_anno, col = list(Groups = grp_colors) ) ## only grp annot }else if(input$heatmap_anno_type == "show_heatmap_column_groups") {## sample group annotations column_ha = ComplexHeatmap::HeatmapAnnotation(Groups = hm_selected_column_groups , col = list(Groups = grp_colors)) ## only box annot }else if(input$heatmap_anno_type == "show_heatmap_column_boxplot"){ ## box plot annotations column_ha = ComplexHeatmap::HeatmapAnnotation(box = column_box_anno) } } ## final hm hm <- ComplexHeatmap::Heatmap(heatmap_top_variable_genes_df()$mat, #row_title = "cluster_%s", cluster_column_slices = F,#input$hm_cluster_column_slice %>% as.logical() , ## it make sure that column clusters arrange in order column_split = heatmap_top_variable_genes_df()$column_clusters %>% factor(., levels = gtools::mixedsort(unique(.))), show_column_names = input$show_hm_colum_names %>% as.logical(), show_column_dend = input$show_hm_column_dend %>% as.logical(), cluster_columns = input$hm_cluster_columns %>% as.logical(), column_names_gp = grid::gpar(fontsize = input$hm_column_names_font_size), top_annotation = column_ha, column_title_rot = 90, column_title_gp = grid::gpar(fontsize = 10), # if SD heatmap AND sort by SD both on together, turn off row clustering. cluster_rows = ifelse(input$sort_hm_by_std_dev %>% as.logical() && input$show_std_dev_hm %>% as.logical() , FALSE , as.logical(input$hm_cluster_rows)), # if number of genes higher than hm_row_threshold disable genenames show_row_names = ifelse(nrow(heatmap_top_variable_genes_df()$mat) <= hm_row_threshold, as.logical(input$show_hm_row_names), FALSE), show_row_dend = input$show_hm_row_dend %>% as.logical() , #row_split = paste0("Clust_",heatmap_top_variable_genes_df()$clust), row_split = heatmap_top_variable_genes_df()$clust %>% factor(., levels = gtools::mixedsort(unique(.))) , row_names_gp = grid::gpar(fontsize = input$hm_row_names_font_size), row_order = if(all(input$sort_hm_by_std_dev %>% as.logical() , input$show_std_dev_hm %>% as.logical())) order(heatmap_top_variable_genes_df()$row_sd[,1]) else NULL, cluster_row_slices = F, #input$hm_cluster_row_slice, row_title_rot = 0, row_title_gp = grid::gpar(fontsize = 10), name = ifelse(input$heatmap_display_value_type == "zscore" , "zscore", input$heatmap_legend_name), heatmap_legend_param = list(legend_direction = input$heatmap_legened_direction), col = hm_colors, border = input$hm_border %>% as.logical() #width = unit(5 , "in"), #height = unit(5 , "in") ) incProgress(1) }) return(hm) }) ## standard deviation heatmap sd_heatmap <- eventReactive(input$generate_heatmap, { req(heatmap_top_variable_genes_df()) hm_row_threshold = 2000 sd_hm <- ComplexHeatmap::Heatmap(heatmap_top_variable_genes_df()$row_sd, row_split = heatmap_top_variable_genes_df()$clust, show_row_names = ifelse(nrow(heatmap_top_variable_genes_df()$mat) <= hm_row_threshold, as.logical(input$show_hm_row_names), FALSE), row_names_gp = grid::gpar(fontsize = input$hm_row_names_font_size), heatmap_legend_param = list(legend_direction = input$heatmap_legened_direction), name = "std_dev") return(sd_hm) }) ## final heatmap : data heatmap or combined with SD heatmap heatmap_to_display <- eventReactive(input$generate_heatmap , { req(heatmap()) if(input$show_std_dev_hm %>% as.logical()) { show_hm <- heatmap() + sd_heatmap() ## temparory SD heatmap de activted }else{ show_hm <- heatmap() } return(show_hm) }) ## render heatmap output$heatmap <- renderPlot({ #req(heatmap_to_display()) # heatmap display in progress # withProgress(message = "Display heatmap in progress" , { # incProgress(0.5) # set.seed(123) # #hm <- shiny::isolate(ComplexHeatmap::draw(heatmap_to_display() , heatmap_legend_side = input$heatmap_legend_pos)) # incProgress(1) # }) return(heatmap_to_display()) }, height = function() { req(heatmap_top_variable_genes_df()) minimum_height <- 400 ht <- minimum_height + (input$hm_row_height * nrow(heatmap_top_variable_genes_df()$mat)) return(ht) }, ## dynamic height width = function() { req(heatmap_top_variable_genes_df()) minimum_width <- 500 wd <- minimum_width + (input$hm_col_width * ncol(heatmap_top_variable_genes_df()$mat)) return(wd) }, res = 108 ) ## dynamic width # Export heatmap callModule(module = export_base_graphics , id = "export_heatmap" , file_name = "heatmap" , plot = as_mapper(~heatmap_to_display()), ## pass as a function does not open device locally legend_pos = reactive(input$heatmap_legend_pos)) ## prepare heatmap data for display active_heatmap_data <- reactive({ req(heatmap_to_display()) # clusts must be a list. However, if only 1 cluster (rowwise) given , `clusts` becomes vector. # To overcome this, convert clusts to list explicitly when number of row clusters == 1. if(heatmap()@matrix_param$row_split %>% pull(row_split) %>% unique() %>% length() == 1) { ## this can be improved clusts <- list("1" = ComplexHeatmap::row_order(heatmap())) } else { ## return list where names of each elem indicates the cluster name displyed in heatmap clusts <- ComplexHeatmap::row_order(heatmap()) } ## prepare the tibble of two column where first column is cluster names and second column is row number of original data matrix given to the ComplexHeatmap::Heatmap function names(clusts) <- as.character(names(clusts)) hm_clust <- data_frame(clust = names(clusts), row_num = clusts) %>% tidyr::unnest() ##column order hm_coloum_ord <- ComplexHeatmap::column_order(heatmap()) %>% unlist(use.names = F) ## final data to be shown below heatmap as heatmap data hm_data <- heatmap_top_variable_genes_df()$mat %>% ## this is the original data supplied to the heatmap as.data.frame() %>% dplyr::select(hm_coloum_ord) %>% rownames_to_column(var = "gene_name") %>% as_data_frame() %>% dplyr::mutate(row_num = dplyr::row_number()) %>% right_join(hm_clust , by = "row_num") %>% ## two tibble will be join by row_num column dplyr::select(-row_num) %>% left_join(heatmap_top_variable_genes_df()$row_sd %>% as.data.frame() %>% tibble::rownames_to_column("gene_name") , by = "gene_name")## add std dev return(hm_data %>% dplyr::mutate_if(is.numeric, round, 4)) }) ## genome annotation for heatmap ## global genome annotations object cannot be used in following scenario ## For example : user has generated one heatmap. Now accidently or purposely user selected / uploaded data ## for different species than the previous heatmap generated. In this scenario, global reference genome will ## be updated , and therefore, annotations in the existing heatmap will also be affected. ## To prepvent this seprate heatmap ref genome from global annot. Heatmap ref annot will be updated only when ## plot heatmap submit button hit heatmap_reference_annot <- eventReactive(input$generate_heatmap , { req(genome_for_annotations()) return(genome_for_annotations()) }) ## render heatmap data output$heatmap_data <- DT::renderDataTable({ ## map species annotations while displaying heatmap data user_selected_species_annot <- ah_data_summary2 %>% dplyr::filter(genome == heatmap_reference_annot()) %>% dplyr::select(gr_cols) %>% tidyr::unnest(cols = gr_cols) join_col_x <- base::colnames(active_heatmap_data())[1] ## first column containing geneNames join_col_y <- base::colnames(user_selected_species_annot)[7] ## "id" column heat_map_data_to_show <- active_heatmap_data() %>% left_join(user_selected_species_annot, by = setNames(join_col_y, join_col_x)) %>% dplyr::select(1,c("seqnames", "start", "end", "strand", "description"), colnames(active_heatmap_data()))%>% dplyr::mutate_all(funs(replace_na(as.character(.),"--NA--"))) return(heat_map_data_to_show) },rownames = T, selection = "none", server = T, extensions = "Buttons", options = list( scrollX = TRUE, dom = "Blfrtip", searchHighlight = TRUE, buttons = list("copy", list( extend = "collection", buttons = c("csv", "excel"), text = "Download" )), # end of buttons customization # customize the length menu lengthMenu = list( c(10, 50, 100, 500, -1) # declare values , c(10, 50, 100, 500, "All") # declare titles ), pageLength = 10 ) ) ## prepare column cluster data heatmap_column_cluster <- eventReactive(input$generate_heatmap,{ req(heatmap()) hm_column_labels <- heatmap()@column_names_param$labels hm_column_ord <- ComplexHeatmap::column_order(heatmap()) named_list_from_vector <- purrr::as_mapper(~ if(!is_list(.x)){ list(`1` = .x) }else{ .x } ) ## given a list of a char vector it returns a char vector where each elem of list collpase in a single vector elem. colum_list_to_chr = as_mapper(~map_chr(., paste0 , collapse =",")) hm_column_ord_lst <- named_list_from_vector(hm_column_ord) ## wide format wide <- tibble(cluster = names(hm_column_ord_lst) , column_order = hm_column_ord_lst) %>% tidyr::unnest() %>% dplyr::mutate(column_labels = hm_column_labels[column_order]) %>% dplyr::select(-column_order) %>% dplyr::group_by(cluster) %>% dplyr::summarise(column_labels = list(column_labels)) %>% dplyr::mutate(count = lengths(column_labels)) %>% dplyr::mutate(column_labels = colum_list_to_chr(column_labels)) %>% dplyr::select(cluster, count, column_labels) %>% dplyr::slice(match(names(hm_column_ord_lst) , .$cluster )) ## arrange clusters in original heatmap order ## given a char vector , reeturns list of vectors. Each elem of char will be splitted by 'split' and all splitted elems will be strored in a vector colum_chr_to_list = as_mapper(~map(., function(.) {strsplit(. , split = ",") %>% unlist() })) ## Long format long <- wide %>% dplyr::mutate(column_labels = colum_chr_to_list(column_labels)) %>% tidyr::unnest(cols = column_labels) list(wide = wide, long = long) }) ## prepare row cluster data heatmap_row_cluster <-eventReactive(input$generate_heatmap,{ req(active_heatmap_data()) ## get data from reactive elems active_hm_data <- active_heatmap_data() cols_of_interest <- c(colnames(active_hm_data)[1] , "clust") wide <- active_hm_data %>% dplyr::select(cols_of_interest) %>% ## select first (gene name) and row cluster column : 'clust' dplyr::group_by(clust) %>% dplyr::summarise(row_labels = list(!!as.symbol(cols_of_interest[1] ))) %>% dplyr::mutate(count = lengths(row_labels)) %>% dplyr::mutate(row_labels = map_chr(row_labels, ~paste0(.x , collapse = ","))) %>% dplyr::select( clust, count , row_labels) %>% dplyr::slice(match( active_heatmap_data()$clust %>% unique(), .$clust )) ## arrange in orginal order ## given a char vector , reeturns list of vectors. Each elem of char will be splitted by 'split' and all splitted elems will be strored in a vector colum_chr_to_list = as_mapper(~map(., function(.) {strsplit(. , split = ",") %>% unlist() })) long <- wide %>% dplyr::mutate(row_labels = colum_chr_to_list(row_labels)) %>% tidyr::unnest(cols = row_labels) list(wide = wide, long = long ) }) # render heatmap cluster data output$heatmap_display_cluster_data <- DT::renderDataTable({ if(input$heatmap_cluster_type == 'show_hm_row_side_clusters'){ return(heatmap_row_cluster()[[input$heatmap_cluster_data_format]]) } else if (input$heatmap_cluster_type == 'show_hm_column_side_clusters'){ return(heatmap_column_cluster()[[input$heatmap_cluster_data_format]] ) } }, rownames = T, selection = "none", server = T, extensions = "Buttons", options = list( scrollX = TRUE, dom = "Blfrtip", searchHighlight = TRUE, buttons = list("copy", list(extend = "collection", buttons =c("csv", "excel"), text = "Download" ) ), # end of buttons customization # customize the length menu pageLength = 10 )) ## heatmap functional analysis callModule(module = functional_analysis_server , id = "heatmap_functional_analysis_ui" , ui_id = "heatmap_functional_analysis_ui", session = session, gene_set = reactive(split(x = active_heatmap_data()$gene_name, f = factor(active_heatmap_data()$clust , levels = active_heatmap_data()$clust %>% unique()))) , genome = reactive({heatmap_reference_annot()}) ) ## update heatmap column cluster in the wordcloud observeEvent(input$generate_heatmap,{ shinyWidgets::updatePickerInput(session = session , inputId = "wordcloud_column_cluster", choices = heatmap_column_cluster()$wide$cluster %>% gtools::mixedsort()) }) ## heatmap word cloud hm_cluster_wise_sample_infor_ll <- eventReactive(input$generate_heatmap, { ## get samples / column labels for user selected cluster cluster_name_column_lable_tbl <- heatmap_column_cluster()$long %>% dplyr::select(cluster, column_labels ) ## get abstract from selected labels join_col_x <- cluster_name_column_lable_tbl %>% colnames() %>% .[2] ## column having hm column labels join_col_y <- user_selected_sra_id_sample_info() %>% colnames()%>% .[23] ## column having SRA id cluster_wise_sample_infor_tbl <- cluster_name_column_lable_tbl %>% left_join(user_selected_sra_id_sample_info() , by = setNames(join_col_y,join_col_x)) %>% dplyr::group_by(cluster) %>% tidyr::nest() cluster_wise_sample_infor_ll <- cluster_wise_sample_infor_tbl %>% dplyr::pull(2) names(cluster_wise_sample_infor_ll) <- cluster_wise_sample_infor_tbl %>% pull(1) return(cluster_wise_sample_infor_ll) }) observe({ callModule(id = "hm_sample_infor" , module = cluster_wise_sample_information_server , parent_id = "hm_sample_infor", cluster_wise_sample_information = reactive(hm_cluster_wise_sample_infor_ll())) }) ####@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ ## Download page server code ---- ####@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ ## download GO data callModule(module = download_go_data_server , id = "download_go_data" , ah_data = ah_data_summary2) ## download gene expression matrix callModule(module = download_gene_expression_matrix_server , id = "download_gene_expression_data") }

0b2d4717d566a4f4aa87f43062c552089b41bc2c

471b0bb52ba7fce30bbdf908362646ea352fddfa

/plot3.R

548c2fefea048a801e06f169a5c06948d84ee013

[]

no_license

ShankerGCEO/ExploratoryDataCourseProject1

32958c768ab60b8f7a84a31bdc5b93267ca9ec57

f48a172c9771eedd24b4bfa91048bf59ffa40409

refs/heads/master

2021-01-23T19:44:14.052094

2014-07-13T15:49:14

2014-07-13T15:53:35

null

0

null

UTF-8

R

false

838

r

plot3.R

setwd("V:/Data Analytics/RPractice/coursera") a<-read.table("household_power_consumption.txt",sep=";",heade=T,na.strings = c("?")) a[,"Date"]<-as.Date(a$Date,format="%d/%m/%Y") a1<-a[a[,"Date"]=="2007-02-01"|a[,"Date"]== "2007-02-02",] a1$DateTime<-strptime(do.call(paste, c(a1[c("Date", "Time")], sep = " ")),format="%Y-%m-%d %H:%M:%S",tz="") png("plot3.png",width=480,height=480) with(a1,plot(DateTime,Sub_metering_1,type="n",xlab="",ylab="Energy sub metering")) with(a1, points(DateTime, as.numeric(Sub_metering_1), col = "black",type="l")) with(a1, points(DateTime, as.numeric(Sub_metering_2), col = "red",type="l")) with(a1, points(DateTime, as.numeric(Sub_metering_3), col = "blue",type="l")) legend("topright", pch = "______", col = c("black","red","blue"), legend = c("Sub_metering_1","Sub_metering_2","Sub_metering_3")) dev.off()

0c984732ae8f019a94f9c4e2ae962d58777c9258

9420cae72566bc3881ddadac1728f1b75c967d35

/man/deweather-package.Rd

acafec89a35b3f243ce9e240f51e9289f5732fd6

[]

no_license

davidcarslaw/deweather

6b9482c81914a4a0d9e18a528f770b2b7f7ae76d

12d5a11f70223f8c528575274929f721162642e0

refs/heads/master

2023-08-27T16:47:03.618721

2023-08-07T15:10:58

30,747,167

29

9

null

2023-07-20T06:28:23

2015-02-13T08:22:40

R

UTF-8

R

false

true

903

rd

deweather-package.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/deweather-package.R \docType{package} \name{deweather-package} \alias{deweather} \alias{deweather-package} \title{deweather: Remove the influence of weather on air quality data} \description{ \if{html}{\figure{logo.png}{options: style='float: right' alt='logo' width='120'}} Model and account for (or remove) the effect of meteorology on atmospheric composition data. The technique uses boosted regression trees based on the gbm package. Code can also be run using multiple cores if available. } \seealso{ Useful links: \itemize{ \item \url{https://davidcarslaw.github.io/deweather/} \item \url{https://github.com/davidcarslaw/deweather} \item Report bugs at \url{https://github.com/davidcarslaw/deweather/issues} } } \author{ \strong{Maintainer}: David Carslaw \email{david.carslaw@york.ac.uk} } \keyword{internal}

18f2c628782172c4ea434ceeb06cea89889b0a57

e6eae169a196ee2a9e71e54620bde8f145a73fa7

/scale.R

ae3fa9670c88ee2fe7254025bda0c65d6e14dfc1

[]

no_license

rhesus123/rcns_scripts

1871dad8b09a5eea79a876023fd77bf788787f5a

5e351aaf5f52c5278018e360c19d1b2381e065a0

refs/heads/master

2020-04-27T07:40:49.166066

2019-03-05T10:19:53

174,143,960

0

null

2019-03-06T12:46:53

2019-03-06T12:46:52

null

UTF-8

R

false

424

r

scale.R

for(i in list.files(pattern = "exp.+tsv")){ data <- read.table(i, header = T, check.names = F, sep = "\t") keep <- grep("-", data[,1], invert = T) data <- data[keep,] for(col in 2:ncol(data)){ data[, col] <- floor(data[, col] / mean(data[, col]) * 1000) } data <- data[rowSums(data[,2:ncol(data)]) > 50,] write.table(data, paste("../scaled/", i, sep = ""), quote = F, sep = "\t", row.names = F) cat(i,sep="\n") }

5a1b1363c742ab3a4a601071be041b207b2a7d59

a1e2905336de2218b7198456ccc18c4c5a774e79

/man/SimulateSMD.Rd

ca9076815317cfdb4548276dd19b2a2327b3f483

[]

no_license

cran/metaforest

c07e7e5b8033293a4faddc275598cdd2bbf302c6

75bd2037e1a6bb2019a664d9030b5bb80519d12f

refs/heads/master

2022-04-01T21:51:43.638562

2020-01-08T03:50:02

102,970,968

0

1

null

UTF-8

R

false

true

3,277

rd

SimulateSMD.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/SimulateSMD.R \name{SimulateSMD} \alias{SimulateSMD} \title{Simulates a meta-analytic dataset} \usage{ SimulateSMD(k_train = 20, k_test = 100, mean_n = 40, es = 0.5, tau2 = 0.04, moderators = 5, distribution = "normal", model = es * x[, 1]) } \arguments{ \item{k_train}{Atomic integer. The number of studies in the training dataset. Defaults to 20.} \item{k_test}{Atomic integer. The number of studies in the testing dataset. Defaults to 100.} \item{mean_n}{Atomic integer. The mean sample size of each simulated study in the meta-analytic dataset. Defaults to 40. For each simulated study, the sample size n is randomly drawn from a normal distribution with mean mean_n, and sd mean_n/3.} \item{es}{Atomic numeric vector. The effect size, also known as beta, used in the model statement. Defaults to .5.} \item{tau2}{Atomic numeric vector. The residual heterogeneity. For a range of realistic values encountered in psychological research, see Van Erp, Verhagen, Grasman, & Wagenmakers, 2017. Defaults to 0.04.} \item{moderators}{Atomic integer. The number of moderators to simulate for each study. Make sure that the number of moderators to be simulated is at least as large as the number of moderators referred to in the model parameter. Internally, the matrix of moderators is referred to as "x". Defaults to 5.} \item{distribution}{Atomic character. The distribution of the moderators. Can be set to either "normal" or "bernoulli". Defaults to "normal".} \item{model}{Expression. An expression to specify the model from which to simulate the mean true effect size, mu. This formula may use the terms "es" (referring to the es parameter of the call to SimulateSMD), and "x[, ]" (referring to the matrix of moderators, x). Thus, to specify that the mean effect size, mu, is a function of the effect size and the first moderator, one would pass the value \code{model = es * x[ , 1]}. Defaults to es * x[ , 1].} } \value{ List of length 4. The "training" element of this list is a data.frame with k_train rows. The columns are the variance of the effect size, vi; the effect size, yi, and the moderators, X. The "testing" element of this list is a data.frame with k_test rows. The columns are the effect size, yi, and the moderators, X. The "housekeeping" element of this list is a data.frame with k_train + k_test rows. The columns are n, the sample size n for each simulated study; mu_i, the mean true effect size for each simulated study; and theta_i, the true effect size for each simulated study. } \description{ This function simulates a meta-analytic dataset based on the random-effects model. The simulated effect size is Hedges' G, an estimator of the Standardized Mean Difference (Hedges, 1981; Li, Dusseldorp, & Meulman, 2017). The functional form of the model can be specified, and moderators can be either normally distributed or Bernoulli-distributed. See Van Lissa, in preparation, for a detailed explanation of the simulation procedure. } \examples{ set.seed(8) SimulateSMD() SimulateSMD(k_train = 50, distribution = "bernoulli") SimulateSMD(distribution = "bernoulli", model = es * x[ ,1] * x[ ,2]) }

efa42aed320921ee2c1b519540c538199e5778b8

99a695f64f28d9da2981e3d8447b5b6a287d69d0

/Plot3.R

8393dfad66656b2c6bd61241ada0c280c746f12e

[]

no_license

JimCallahanOrlando/ExData_Plotting2

5ce37891809a7aa36ae076d18034449e01997871

50b4c3267665e8d1cd42fc95410fd9a855238592

refs/heads/master

2020-04-21T09:02:43.874118

2015-09-23T18:55:52

41,051,321

0

null

UTF-8

R

false

2,711

r

Plot3.R

# Plot3.R # PURPOSE: Load EPA Air Pollution Data files into R AND draw plot 3 # Set Directory to project directory for plots setwd("~/GitHub/ExData_Plotting2/") getwd() ## This first line will likely take a few seconds. Be patient! NEI <- readRDS(".data/summarySCC_PM25.rds") SCC <- readRDS(".data/Source_Classification_Code.rds") str(NEI) # before removal # Convert all of the NEI columns EXCEPT Emissions, fips and SCC to factor. # NEI$fips <- factor(NEI$fips) # NEI$SCC <- factor(NEI$SCC) NEI$Pollutant <- factor(NEI$Pollutant) NEI$type <- factor(NEI$type) NEI$year <- factor(NEI$year) str(NEI) # after removal # Number of observations for each year (before NA and INF removal). table(NEI$year) # Remove incomplete, missing, infinite, negative and zero observations. # Only computing sums(not averages) and zeros appear to be missing data. NEI <- NEI[complete.cases(NEI), ] NEI <- NEI[!is.na(NEI$Emissions), ] NEI <- NEI[!is.infinite(NEI$Emissions), ] NEI <- NEI[NEI$Emissions > 0, ] # will be taking log() -- can't have neg value. # Number of observations for each year (after NA and INF removal). table(NEI$year) # Summarize NEI$Emissions by NEI$year NEI$year <- factor(NEI$year) Year <- levels(NEI$year) summary(NEI$Emissions) # Question 3: # Of the four types of sources indicated by the type # (point, nonpoint, onroad, nonroad) variable, which of these four sources # have seen decreases in emissions from 1999–2008 for Baltimore City? # Which have seen increases in emissions from 1999–2008? # Use the ggplot2 plotting system to make a plot answer this question. # NEI$type factor (point, nonpoint, onroad, nonroad) NEI$type <- factor(NEI$type) NEI$year <- factor(NEI$year) Year <- levels(NEI$year) BaltimoreEI <- NEI[NEI$fips == "24510", ] BaltimoreEI <- BaltimoreEI[!is.na(BaltimoreEI$Emissions), ] require(ggplot2) # use same box plot, but faceted. # use facets to display type. # This is same boxplot as above; need to add facets for "type" # BaltimoreEI$EmissionsLog10 <- log10(BaltimoreEI$Emissions) # HONOR CODE: "R Graphics Cookbook" by Winston Chang for PNG parameters and qplot # Argument to log must be in quotes: log = "y" par(mar = c(4, 4, 4, 1) ) ppi <- 150 png(filename = "plot3.png", height = 3.5*ppi, width=3.5*ppi, units = "px", pointsize = 14) bp2 <- qplot( data = BaltimoreEI, x = year, y = Emissions, log = "y" ) + geom_boxplot() # Add facets (note: tilda, "~" is required!) bp3 <- bp2 + facet_wrap( ~ type ) + ggtitle("Baltimore Particulate Matter 2.5 Emission Trends \nBy Source") bp3 <- bp3 + ylab("Emissions - log scale") + xlab("Year\nSource: US EPA National Emissions Inventory (NEI)") bp3 dev.off() # End of Plot3.R

201812d72ad6a6c1b4c75dc0d15cea4cb92deddf

1ce5a3554f494371e909ddfd18f3463168522afd

/Week1_assignment_project_1/plot1.R

315c63679d2a32f05aec95bf4ab4af7e69274000

[]

no_license

SriNithin965/ExData_Plotting1

336ac77e88c3508c0bf8d6afb018731a718d295c

c510c84f15d9935b8084044b09684a61440aaf9f

refs/heads/master

2022-06-21T07:43:27.197004

2020-05-01T06:14:15

260,389,729

0

null

2020-05-01T05:53:37

null

UTF-8

R

false

451

r

plot1.R

library(data.table) library(dplyr) library(lubridate) data<-fread("household_power_consumption.txt",na.strings="?") data[, dateTime := as.POSIXct(paste(Date, Time), format = "%d/%m/%Y %H:%M:%S")] data <- data[(dateTime >= "2007-02-01") & (dateTime < "2007-02-03")] png("plot1.png",width=480,height=480) with(data,hist(data[["Global_active_power"]],main="Global active power",col="red",xlab="Global active power(KiloWatts)",ylab="frequency")) dev.off()

b49b6ebc0518a2254d96b42312da75d334261708

90570592e0b39ed04999798d6a2b294762004838

/data/Rate_1.data.R

6fa31caaa89964816e3fac013e07fa6a492b5e85

[]

no_license

wmmurrah/cognitivemodeling

d7fa6d081ee16b4c45642e807f495345c7926fa1

38e98cabdfaa3f659f821e2fdb27396e49d16f84

refs/heads/master

2021-07-12T06:05:07.816085

2020-10-11T23:01:54

210,068,736

0

null

UTF-8

R

false

68

r

Rate_1.data.R

k <- 50 n <- 100 # beta_prior_shape1 <- 1 # beta_prior_shape2 <- 1

183914d55926fc95b56f44981f35b17c7ae52393

0053e50cff6a87c3190dc00fbeb4b931b53a162c

/server.R

bedc842ec8ccb94c1399bd9d83ff3f0104bfe897

[]

no_license

flow-r/flow_creator

6eec0373cdb951e05d534ea581f1706b164de57e

b7f3d7a3693242768f9f9e54756badff5f7d4a8f

refs/heads/master

2021-01-10T11:41:42.571883

2015-11-25T02:04:51

null

0

null

UTF-8

R

false

4,632

r

server.R

# This is the server logic for a Shiny web application. # You can find out more about building applications with Shiny here: # # http://shiny.rstudio.com # library(shiny) library(gplots) require(reshape2) library(flowr) source("funcs2.R") as.c=as.character #source("~/Dropbox/public/github_flow/R/plot-funcs.R") ## http://shiny.rstudio.com/gallery/widgets-gallery.html ## http://stackoverflow.com/questions/22160372/r-shiny-how-to-build-dynamic-ui-text-input ## for each job we have: ## jobname ## cmd ## previous_job ## submission_type ## dependency_type shinyServer(func = function(input, output, session){ ##print(url_fields_to_sync) firstTime <- TRUE source("funcs2.R") output$hash <- renderText({ #url_fields_to_sync <- as.c(names(input)) #if(length(url_fields_to_sync) == 0) url_fields_to_sync = c("num_jobs", "refresh") newHash = paste(collapse=",", Map(function(field) { paste(sep="=", field, input[[field]]) }, url_fields_to_sync)) # the VERY FIRST time we pass the input hash up. return( if (!firstTime) { newHash } else { if (is.null(input$hash)) { NULL } else { firstTime<<-F; isolate(input$hash) } } ) }) output$jobs = renderUI({ ## make a new UI, when ever either of these change their value input$num_jobs; input$refresh #print(get_jobnames(input)) isolate({ get_jobforms(input) }) }) #outputOptions(output, 'jobs', suspendWhenHidden=FALSE) #----------- get the dat to be used in the later steps #debug(get_flow_table) current_def <- reactive({ if (input$num_jobs < 1) return("") # get this only if we have more than 0 jobs message("class of input is: ") #print(class(input)) to_flowdef(input) }) # --------- flow summary table, this is really a flowdef output$flow_def <- renderTable({ ## need atleast one job #browser() if (input$num_jobs < 1) return() def <- current_def() return(as.data.frame(def)) }, include.rownames=FALSE) # ------------------------------------- output flowdef ------------------------------- output$download_flowdef <- downloadHandler( filename = function() { paste(input$flow_name, '.def', sep='') }, content = function(file) { # get this only if we have more than 2 jobs def <- current_def() params::write_sheet(def, file) }) # -------------------------- flow summary plot ---------------------------------------------------- #debug(.plot_flow_dat) output$flow_diagram <- renderPlot({ #browser() # ------- proceed only when we have more than 2 jobs if (input$num_jobs < 2) return(textplot(c('Add some jobs using the slider below\n', 'Say more than 3 ...'))) def <- try(current_def()) if(class(def)[1] == "try-error"){ textplot("please refresh ...") } else{ plot_flow(def) } }) # ------------------------------------- download the plot made earlier ------------------------------- output$downloadPlot <- downloadHandler( filename = function() { paste(input$flow_name, '.pdf', sep='') }, content = function(file) { # get this only if we have more than 2 jobs if (input$num_jobs < 2) return("") pdf(file) def <- current_def() plot_flow(def) dev.off() }) current_flow_code <- reactive({ to_flowcode(input=input) }) # ------------------------------------- output code ------------------------------- output$code <- renderUI({ #debug(get_dat_flowcode) flow_code <- current_flow_code() #print(flow_code) ret <- pre(class="shiny-code", # we need to prevent the indentation of <code> ... </code> HTML(format(tags$code( class="language-r", #paste(readLines(file.path.ci(getwd(), rFile), warn=FALSE),collapse="\n") paste(flow_code, collapse = "\n") ), indent = FALSE))) return(ret) }) output$rscript <- downloadHandler( filename = function () { paste(input$flow_name, '.R', sep='') }, content = function(file) { flow_code <- current_flow_code() write(flow_code, file) }) }) ## server

a6c6b0e3bb220ec3fd4179a12f4f183f558ee305

f1000eab041e19d6194b84a11d24297115332dda

/man/add.zeros.noCount.Rd

fcbbdf3533846312ec92d6bde8b551fddacc2ec3

[]

no_license

pointblue/RMN.functions

902a1a41e0463bf871f2eaacb321622a69f6f77c

ef89702f869ea3a9b338dccc0c7f520b498bac8c

refs/heads/master

2020-09-22T12:57:28.781442

2020-04-10T01:10:14

225,205,393

0

1

null

2020-01-25T18:49:16

2019-12-01T18:06:43

R

UTF-8

R

false

true

514

rd

add.zeros.noCount.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/add.zeros.noCount.r \name{add.zeros.noCount} \alias{add.zeros.noCount} \title{Adds zeros to dataframe for statistical analysis} \usage{ add.zeros.noCount(pc) } \arguments{ \item{df}{A data frame object} } \value{ A data frame } \description{ Takes bird data that has been prepared by bird_prepare function and turned into the same information but with zeros where no detections were found. } \examples{ df2 = bird_species.common(pc) }

41ef464fd158f393e4552ba8df901cfb1e8c32c6

19526246d42b3fd8fdf67750b57909e6e6a99114

/man/rmspfc.Rd

3647a00d08c773a5e70852134485ce1d1eb8dc6a

[]

no_license

cran/nlirms

3b864e5b6a54bd39bf9a0530ef6fe40fd8776f79

4c4e32251c750735ae29a5a2eaf88eddf6b0e1d0

refs/heads/master

2020-03-10T13:05:38.733788

2018-04-13T10:16:48

null

0

null

UTF-8

R

false

3,975

rd

rmspfc.Rd

\name{rmspfc} \alias{rmspfc} \alias{rmspfc} \title{Rate-making system based on the posteriori freuency component} \usage{ rmspfc(time = 5, claim = 5, fmu = .2, fsigma = 2, fnu = 1, family = "NO", round = 2, size = 8, padlength = 4, padwidth = 2, ...) } \arguments{ \item{time}{time period to designing of rate-making system based on the posteriori freuency component} \item{claim}{number of claims to designing of rate-making system based on the posteriori freuency component} \item{fmu}{mu parameter of frequency model in designing of rate-making system} \item{fsigma}{sigma parameter of frequency model in designing of rate-making system} \item{fnu}{nu parameter of frequency model in designing of rate-making system} \item{family}{a nlirms.family object, which is used to define the frequency model to designing of rate-making system} \item{round}{rounds the rate-making system values to the specified number of decimal places } \item{size}{indicates the size of graphical table for rate-making system} \item{padlength}{indicates the length of each graphical table cells} \item{padwidth}{indicates the width of each graphical table cells} \item{...}{for further arguments} } \value{ rmspfc() function return the expected number of claims of policyholders based on the different models. } \description{ rmspfc() function gives the rate-making system based on the posteriori freuency component. Values given by rmspfc() function is equal to with expected number of claims given by enc.family (i.e. enc.PGA, enc.PIGA, enc.PGIG) for different amounts of time and claim. } \details{ rmspfc() function gives the rate-making system in the form of a table where each table cells is related to the one time and claim. for example the cell with time=2 and claim=1, shows the expected number of claims in next year for a ploicyholder that who had a one claim in past two years. } \author{ Saeed Mohammadpour (s.mohammadpour1111@gmail.com), Soodabeh Mohammadpoor Golojeh (s.mohammadpour@gmail.com) } \references{ Frangos, N. E., & Vrontos, S. D. (2001). Design of optimal bonus-malus systems with a frequency and a severity component on an individual basis in automobile insurance. ASTIN Bulletin: The Journal of the IAA, 31(1), 1-22. Lemaire, J. (1995) Bonus-Malus Systems in Automobile Insurance, Kluwer Academic Publishers, Massachusetts. MohammadPour, S., Saeedi, K., & Mahmoudvand, R. (2017). Bonus-Malus System Using Finite Mixture Models. Statistics, Optimization & Information Computing, 5(3), 179-187. Najafabadi, A. T. P., & MohammadPour, S. (2017). A k-Inflated Negative Binomial Mixture Regression Model: Application to Rate--Making Systems. Asia-Pacific Journal of Risk and Insurance, 12. Rigby, R. A., & Stasinopoulos, D. M. (2005). Generalized additive models for location, scale and shape. Journal of the Royal Statistical Society: Series C (Applied Statistics), 54(3), 507-554. Stasinopoulos, D. M., Rigby, B. A., Akantziliotou, C., Heller, G., Ospina, R., & Motpan, N. (2010). gamlss. dist: Distributions to Be Used for GAMLSS Modelling. R package version, 4-0. Stasinopoulos, D. M., & Rigby, R. A. (2007). Generalized additive models for location scale and shape (GAMLSS) in R. Journal of Statistical Software, 23(7), 1-46. } \examples{ # rate-Making system based on the Poisson-Gamma model for frequency component rmspfc(time = 5, claim = 5, fmu = .2, fsigma = 2, fnu = 1, family = "PGA", round = 2, size = 8, padlength = 4, padwidth = 2) # rate-Making system based on the Poisson-Inverse Gamma model for frequency component rmspfc(time = 5, claim = 5, fmu = .2, fsigma = 2, fnu = 1, family = "PIGA", round = 2, size = 8, padlength = 4, padwidth = 2) # rate-Making system based on the Poisson-Generalized Inverse Gaussian model for frequency rmspfc(time = 5, claim = 5, fmu = .2, fsigma = 2, fnu = 1, family = "PGIG", round = 2, size = 8, padlength = 4, padwidth = 2) }

5dc21cd3083b622047f5a4a5dcdfe1a31ab857bc

2a7e77565c33e6b5d92ce6702b4a5fd96f80d7d0

/fuzzedpackages/pvar/man/ChangePoints.Rd

9b3399e71ed20c633e1e2ad28085184547bc46f7

[]

no_license

akhikolla/testpackages

62ccaeed866e2194652b65e7360987b3b20df7e7

01259c3543febc89955ea5b79f3a08d3afe57e95

refs/heads/master

2023-02-18T03:50:28.288006

2021-01-18T13:23:32

329,981,898

7

1

null

UTF-8

R

false

true

591

rd

ChangePoints.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/RcppExports.R \name{ChangePoints} \alias{ChangePoints} \title{Change Points of a \code{numeric} vector} \usage{ ChangePoints(x) } \arguments{ \item{x}{\code{numeric} vector.} } \value{ The vector of index of change points. } \description{ Finds changes points (i.e. corners) in the \code{numeric} vector. } \details{ The end points of the vector will be always included in the results. } \examples{ x <- rwiener(100) cid <- ChangePoints(x) plot(x, type="l") points(time(x)[cid], x[cid], cex=0.5, col=2, pch=19) }

6b856fa609e048152ac1a2f8347f3ffbde8b0273

4ba396a905823ed536d62bfaa45aafe6dc6b2715

/man/loadHMM.Rd

38d330852ba97bb0321bf34fea15ec878e0d3efe

[]

no_license

msesia/snpknock

2570aa5c9f0c4af9d0dc4953a3f9f51a65df65d9

f0d027b4891fa9a0ec2a541c231fb80a8dbbabc8

refs/heads/master

2020-05-18T14:13:26.987921

2019-11-07T02:22:08

2019-11-07T02:46:05

184,463,949

2

null

UTF-8

R

false

true

2,533

rd

loadHMM.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/fastphase.R \name{loadHMM} \alias{loadHMM} \title{Load HMM parameters fitted by fastPHASE} \usage{ loadHMM(r_file, alpha_file, theta_file, char_file, compact = TRUE, phased = FALSE) } \arguments{ \item{r_file}{a string with the path of the "_rhat.txt" file produced by fastPHASE.} \item{alpha_file}{a string with the path of the "_alphahat.txt" file produced by fastPHASE.} \item{theta_file}{a string with the path of the "_thetahat.txt" file produced by fastPHASE.} \item{char_file}{a string with the path of the "_origchars" file produced by fastPHASE.} \item{compact}{whether to assemble the explicit transition and emission matrices for the HMM (default: FALSE).} \item{phased}{whether to assemble a model for phased haplotypes, if compact==FALSE (default: FALSE).} } \value{ A structure containing the parameters from the Li and Stephens HMM for phased haplotypes. } \description{ This function loads the parameter estimates obtained by fastPHASE (see \link{runFastPhase}) and assembles the Li and Stephens HMM, in the format required by the knockoff generation functions \link{knockoffHaplotypes} and \link{knockoffGenotypes}. } \details{ This function by default returns a structure with three fields: \itemize{ \item{"r": a numerical array of length p.} \item{"alpha": a numerical array of size (p,K).} \item{"theta": a numerical array of size (p,K).} } If the parameter compact is FALSE, this function assembles the HMM model for the genotype data (either unphased or phased), in the format required by the knockoff generation function \link{knockoffHMM}. } \examples{ # Specify the location of the fastPHASE output files containing the parameter estimates. # Example files can be found in the package installation directory. r_file = system.file("extdata", "genotypes_rhat.txt", package = "SNPknock") alpha_file = system.file("extdata", "genotypes_alphahat.txt", package = "SNPknock") theta_file = system.file("extdata", "genotypes_thetahat.txt", package = "SNPknock") char_file = system.file("extdata", "genotypes_origchars", package = "SNPknock") # Read the parameter files and load the HMM hmm = loadHMM(r_file, alpha_file, theta_file, char_file) # Read the parameter files and load the HMM hmm.large = loadHMM(r_file, alpha_file, theta_file, char_file, compact=FALSE) } \references{ \insertRef{scheet2006}{SNPknock} } \seealso{ Other fastPHASE: \code{\link{runFastPhase}}, \code{\link{writeXtoInp}} } \concept{fastPHASE}

967336c14ce76b8209d8321ba77875147cd32409

fe7a611013804f9ba61a53816ec3e60b96491547

/e_practice in text mining_w4/datacamp_online practice.R

943d7c63b7490cddb83122641e8dfc2da635868d

[]

no_license

cafepeng/R-course-2018

9dba30ad1a073dfa64bbd111e6bf3bbd658fd7e2

87c46eeb79c8cbc02f6bc9bd0513626f645751eb

refs/heads/master

2021-01-24T12:12:18.482070

2018-04-25T14:22:31

123,124,274

0

null

UTF-8

R

false

2,365

r

datacamp_online practice.R

##learn from datacamp (text mining Bag of Words) #need package qdap #install.packages("qdap") library(qdap) new_Text<-"DataCamp is the first online learning platform that focuses on building the best learning experience specifically for Data Science. We have offices in Boston and Belgium and to date, we trained over 250,000 (aspiring) data scientists in over 150 countries. These data science enthusiasts completed more than 9 million exercises. You can take free beginner courses, or subscribe for $25/month to get access to all premium courses" new_Text2<-read.csv(new_Text) #check is it is classified as character class(new_Text2) #count the frequency of words require(qdap) term_count<-freq_terms(new_Text2,1) ##---parsing from tweet of starbucks-----------## library(rvest) #輸入網址，爬取此網頁 title<-read_html("https://mobile.twitter.com/starbucks") #標題內含css title<-html_nodes(title,".dir-ltr") title<-html_text(title) title<-list(title) str(title) ##------------------------------------------## #practice on datacamp# ##1ST TRY## # Create the object: text text<-"She woke up at 6 A.M. It\'s so early! She was only 10% awake and began drinking coffee in front of her computer." library(tm) library(qdap) # All lowercase text<-tolower(text) text # Remove punctuation text<-removePunctuation(text) text # Remove numbers text<-removeNumbers(text) text # Remove whitespace text<-stripWhitespace(text) text ##2ND TRY## # Create the object: text library(tm) library(qdap) text00<-list("She woke up at 6 A.M. It\'s so early! She was only 10% awake and began drinking coffee in front of her computer.") class(text) text01<-VectorSource(text00) text02<-VCorpus(text01) # All lowercase text02<-tm_map(text02,content_transformer(tolower)) text02 # Remove punctuation text02<-tm_map(text02,removePunctuation) text02 # Remove numbers text02<-tm_map(text02,removeNumbers) text02 # Remove whitespace text02<-tm_map(text02,stripWhitespace) text02 #=====RIGHT ANSWER TO ABOVE=============# # Create the object: text text <- "She woke up at 6 A.M. It\'s so early! She was only 10% awake and began drinking coffee in front of her computer." # All lowercase tolower(text) # Remove punctuation removePunctuation(text) # Remove numbers removeNumbers(text) # Remove whitespace stripWhitespace(text)

43be123b9544b3730089223e2627a2c5895c5adc

b61458d49215f3208eaa855a04f6ee730f68733d

/man/trackNames.Rd

46025f7cd690e6b543803acf506177e554deae05

[]

no_license

pneuvial/c3co

2c22de1990f29755cfb0b8547f65c320d6316cf8

c20d9de94476bc7a6f4f405d04437ed6876fd088

refs/heads/master

2021-08-16T17:13:10.765020

2019-03-03T16:10:54

59,739,091

3

2

null

UTF-8

R

false

true

249

rd

trackNames.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/segmentData.R \name{trackNames} \alias{trackNames} \title{Get track names} \usage{ trackNames(x) } \arguments{ \item{x}{A segmentation.} } \description{ Get track names }

c1da95bbbca55f7da9143284d77b92e188a889ba

5a9d2ee3ee9e1f5153108ba5c9a9ffbd7647ee1d

/run_analysis.R

eda7c3a471b1756fb6afb8c2e3e0f333a92168de

[]

no_license

mattsedlar/GetData_CourseProject

7af69806706285c567af028d9cd7f7f6ecbdb53e

99e2a68cd54b5aa18c8001e12a9ae765b5e3d2eb

refs/heads/master

2021-01-19T06:36:41.690603

2015-07-23T17:29:57

39,090,997

0

null

UTF-8

R

false

1,475

r

run_analysis.R

library(dplyr) source("acquire_data.R") # label and merge an individual dataset. label_n_merge <- function(df, lbl, subj) { names(df) <- make.names(features[,2], unique=TRUE) df <- cbind(lbl,df) colnames(df)[1] <- "Activities" df <- cbind(subj,df) colnames(df)[1] <- "Subjects" df } test_df <- label_n_merge(test_data, test_labels, test_subjects) train_df <- label_n_merge(train_data, train_labels, train_subjects) # combine both sets df <- rbind(test_df, train_df) # extract only subjects, activities, means and standard deviations df <- df %>% select(Subjects,Activities, contains(".mean."), contains(".std.")) # descriptive activity names y <- 1 while (y <= length(activities)) { df$Activities[df$Activities == y] <- activities[y] y <- y + 1 } # clean up the column names names(df) <- gsub("^t", "Time ", names(df)) names(df) <- gsub("^f", "Frequency ", names(df)) names(df) <- gsub("Acc", " Accelerometer ", names(df)) names(df) <- gsub("Gyro", " Gyroscope ", names(df)) names(df) <- gsub("Mag", " Magnitude ", names(df)) names(df) <- gsub(".mean", "Mean ", names(df)) names(df) <- gsub(".std", "Standard Deviation ", names(df)) names(df) <- gsub("...X", "X", names(df)) names(df) <- gsub("...Y", "Y", names(df)) names(df) <- gsub("...Z", "Z", names(df)) # TIDY DATA SET tidy_df <- df %>% group_by(Subjects, Activities) %>% summarise_each(funs(mean)) # Write table write.table(tidy_df, "tidydata.txt", sep="\t", row.names=FALSE, quote=FALSE)

39d7dc5ad430d20a6057ad6a3661a572b8d7df01

1e39fc5bc9e4f53f63655269d203fd896caeeb00

/R/write.transects.R

b1e10928a18dd55d263788acd32233eb4d2d8459

[]

no_license

m-murchie/dht-bootstrap

0405c921dd2e5dffee3b32e5123fe7eeb672ebb8

9b64c13593b63cfbfbea6756c756b0ea8177fb64

refs/heads/master

2020-04-05T23:14:16.058224

2016-07-07T15:35:08

60,344,043

1

null

UTF-8

R

false

1,090

r

write.transects.R

## taken from DL Miller, spatlaugh repo # write out a transect as a shapefile # this is a completely Byzantine process library(sp) library(rgdal) # transect should have the following columns: # x,y x,y coordinates of **endpoints** # leg survey leg associated # file filename (well actually foldername) to write to write.transects <- function(transect, file){ llist <- list() Sample.Label <- c() # make each leg a different Line object for(this_leg in unique(transect$leg)){ # get the transect bits for this leg tr <- transect[transect$leg==this_leg,] for(i in 1:(nrow(tr)-1)){ this_label <- paste0(this_leg, "-", i) ll <- Line(tr[,c("x","y")][i:(i+1),]) llist <- c(llist, Lines(ll, ID=this_label)) Sample.Label <- c(Sample.Label, this_label) } } ll <- SpatialLines(llist) dat <- data.frame(Sample.Label=Sample.Label) rownames(dat) <- Sample.Label ll <- SpatialLinesDataFrame(ll, data=dat) writeOGR(ll, file, "data", "ESRI Shapefile" ) }

eb4c5e181cd29d04758beeb26ea7063303138dce

86535689d126480c5982255305c1de70deb8e09c

/air_pollution_stunting/scripts/nfhs_data_analysis.R

f4c94ba261f33cadaf9645510c9b5683b66d6229

[]

no_license

prateek149/data_visualization

fc2469bff447bc83f50d5b3b61f5e06f7500cf0f

368bec576f5aad7df38ead0193701d190b378478

refs/heads/master

2021-04-22T06:47:20.530780

2018-04-03T08:17:50

58,259,378

0

2

null

2017-03-30T09:10:54

2016-05-07T09:32:26

HTML

UTF-8

R

false

2,592

r

nfhs_data_analysis.R

library("plyr") library("sqldf") library("readstata13") library("foreign") setwd("/Users/prateekmittal/Dropbox/IndiaSpend_Prateek/Datasets") a <- read.dta("newdataset.dta") a$haz <- a$hw5/100 a <- a[which(a$haz<6),] a$stunting <- 0 a[which(a$haz<=-2),]$stunting <- 1 a$fuel_type <- "-999" a[which(a$v161 == 2),]$fuel_type <- "LPG" a[which(a$v161 == 5),]$fuel_type <- "Kerosene" a[which(a$v161 == 8),]$fuel_type <- "Wood" a[which(a$v161 == 10),]$fuel_type <- "Crop Residue" a[which(a$v161 == 9),]$fuel_type <- "Crop Residue" a[which(a$v161 == 11),]$fuel_type <- "Dung" a$separate_kitchen <- 0 a[which(a$hv242==1),]$separate_kitchen <- 1 a$window <- 0 a[which(a$s56a==1),]$window <- 1 a$chimney <- 0 a[which(a$s50==1),]$chimney <- 1 b <- sqldf("SELECT fuel_type, COUNT(fuel_type) AS total, SUM(stunting) AS stunting FROM a GROUP BY fuel_type") b$stunting_rate <- b$stunting/b$total b$pm10 <- -999 b[which(b$fuel_type=="Dung"),]$pm10 <- 1.6 b[which(b$fuel_type=="Wood"),]$pm10 <- 0.4 b[which(b$fuel_type=="Crop Residue"),]$pm10 <- 1.4 b[which(b$fuel_type=="Kerosene"),]$pm10 <- 0.1 b[which(b$fuel_type=="LPG"),]$pm10 <- 0 write.csv(b[which(b$fuel_type!="-999"),], "/Users/prateekmittal/Dropbox/data_visualization/air_pollution_stunting/data/stunting_fuel.csv") c <- sqldf("SELECT fuel_type, separate_kitchen, window,COUNT(fuel_type) AS total, SUM(stunting) AS stunting FROM a GROUP BY fuel_type,separate_kitchen, window ") c <- c[which(c$fuel_type != "-999"),] for (i in unique(c$fuel_type)) { total <- sum(c[which(c$fuel_type==i),]$total) stunting <- sum(c[which(c$fuel_type==i),]$stunting) c <- rbind(c, data.frame(fuel_type=i,separate_kitchen=-1,window=-1,total=total, stunting=stunting)) #Window records total <- sum(c[which(c$fuel_type==i & c$window==1),]$total) stunting <- sum(c[which(c$fuel_type==i & c$window==1),]$stunting) stunting_rate <- stunting/total c <- rbind(c, data.frame(fuel_type=i,separate_kitchen=-1,window=1,total=total, stunting=stunting)) total <- sum(c[which(c$fuel_type==i & c$separate_kitchen==1),]$total) stunting <- sum(c[which(c$fuel_type==i & c$separate_kitchen==1),]$stunting) stunting_rate <- stunting/total c <- rbind(c, data.frame(fuel_type=i,separate_kitchen=1,window=-1,total=total, stunting=stunting)) } c$stunting_rate <- c$stunting/c$total write.csv(c, "/Users/prateekmittal/Dropbox/data_visualization/air_pollution_stunting/data/stunting_fuel_ventilation.csv")

ce5120a26c951d63277e187d2743bbf11c0c07e1

5aa6740910caf6d233fd9d9995c1ba779ca71e69

/R/mscale.R

d01872c46d1089a9401ac1a9cfd18d95f71c0212

[]

no_license

jdgonzalezwork/ktaucenters

39f812e231235d4e0417cdc8e7ec078801f9b7bd

7cdeb89bf534bba595edbb00e1a6b7d957026233

refs/heads/master

2023-02-10T01:33:09.251935

2023-01-29T23:04:18

198,319,763

0

1

null

2023-01-31T01:42:08

2019-07-23T00:10:09

R

UTF-8

R

false

7,110

r

mscale.R

#----------------------------------------------------------------------------------- constC1=function(p){rep(1,length(p))}; constC2=function(p){ aMaronna <- 2.9987 bMaronna <- -0.4647 aMaronna*p^bMaronna } #----------------------------------------------------------------------------------- # CODIGO desarrollado por mi, (y mas rapido) Mscale <- function(u, b=0.5, c){ #' Mscale #' the M scale of an univariate sample (see reference below) #' #' @param u an univariate sample of size n. #' @param b the desired break down point #' @param c a tuning constant, if consistency to standard normal distribution is desired use #' \code{\link{normal_consistency_constants}} #' @return the Mscale value #' @examples #' Mscale(u=rnorm(100),c=1) #' #' @importFrom stats median #' @references Maronna, R. A., Martin, R. D., Yohai, V. J., & Salibian-Barrera, M. (2018). #' Robust statistics: theory and methods (with R). Wiley. #' @export sn <- median(abs(u)) / .6745 if (sn==0){return(sn)} quantity <- mean(rhoOpt(u/sn,cc=c)) - b # indicates if the root is on the right while (quantity>0){ sn <- 1.5 * sn quantity=mean(rhoOpt(u/sn,cc=c)) - b # } if (quantity==0){return(sn)} i <- 0 err <- 1 while (( i < 1000 ) & (err > 1e-10)) { var <- u/sn; AA <- mean(rhoOpt(var,cc=c)); BB <- mean(psiOpt(var,cc=c)*var); factorAB <- (AA -BB -b) / (2*AA-BB-2*b); snplus1 <- sn*factorAB; err <- abs(snplus1/sn - 1) sn <- abs(snplus1) i <- i+1 } sn } #----------------------------------------------------------------------------------- #' normal_consistency_constants #' #' @description constants previously computed in order the M scale value is consistent #' with the standard normal distribution for the optimal rho function considered in \code{\link{rhoOpt}}. #' (Constant were computed from p=1 till p =400) #' #' @param p dimension where observation lives #' @return cvalue #' #' @examples #' p=5; #' n=1000 #' X=matrix(rnorm(n*p), ncol=p) #' dist=apply(X,1,function(t){sqrt(sum(t^2))}) #' s= Mscale(dist,b=0.5, c=normal_consistency_constants(p)) #' #' ### variable s should be near from one for all p values between 1 and 400. #' #' @references [1] Maronna, R. A., Martin, R. D., Yohai, V. J., & Salibián-Barrera, M. (2018). #' Robust statistics: theory and methods (with R). Wiley. #' [2] Salibian-Barrera, M., Willems, G., & Zamar, R. (2008). #' The fast-tau estimator for regression. #' Journal of Computational and Graphical Statistics, 17(3), 659-682. #' #' #' #' @export normal_consistency_constants <- function(p){ vaux= c(0.404629,0.6944748,0.8985921,1.063144,1.204321,1.329791, 1.443817,1.548994,1.647149,1.739537,1.827075,1.910406, 1.99017,2.066772,2.140529,2.211772,2.280742,2.347639,2.412622, 2.475882,2.537545,2.597723,2.656494,2.714016,2.770276,2.825434, 2.879547,2.932612,2.984741,3.035955,3.08632,3.135869,3.184648, 3.232684,3.279986,3.326633,3.372634,3.418005,3.462781,3.506981, 3.550627,3.593741,3.636342,3.678449,3.720075,3.761236,3.80195, 3.842231,3.88208,3.921557,3.960609,3.999286,4.037581,4.075532, 4.113132,4.15039,4.187306,4.223932,4.260211,4.296178,4.331845, 4.367245,4.402347,4.437173,4.471721,4.506001,4.540023,4.573796, 4.60732,4.6406,4.673648,4.706454,4.739037,4.771396,4.803549, 4.835481,4.867181,4.898696,4.930009,4.961124,4.992046,5.022776, 5.053319,5.083678,5.113838,5.143877,5.173678,5.203352,5.232814, 5.262137,5.291296,5.320304,5.34915,5.377833,5.406382,5.434777, 5.463004,5.491069,5.519042,5.546857,5.574513,5.602029,5.629455, 5.656713,5.683843,5.710848,5.737726,5.764478,5.791107,5.817614, 5.844001,5.870269,5.89642,5.922455,5.948373,5.974178,5.999901, 6.025461,6.05095,6.076328,6.101599,6.126763,6.151823,6.176781, 6.201638,6.226395,6.251055,6.275624,6.300084,6.324457,6.348727, 6.372907,6.397029,6.421037,6.444946,6.468764,6.492492,6.516168, 6.539737,6.563222,6.586623,6.609943,6.63318,6.656336,6.679371, 6.702367,6.725285,6.748124,6.770886,6.793581,6.816188,6.83873, 6.861204,6.883556,6.905896,6.928129,6.950294,6.972394,6.994428, 7.016405,7.038284,7.060111,7.081862,7.103564,7.12519,7.146749, 7.168252,7.189681,7.211047,7.232349,7.253587,7.274746,7.295884, 7.316926,7.337911,7.358844,7.379737,7.400556,7.421316,7.442017, 7.462661,7.483248,7.503778,7.524252,7.54467,7.565039,7.585339, 7.605592,7.625797,7.645945,7.666039,7.68608,7.706069,7.726007, 7.74591,7.765748,7.785535,7.805273,7.82496,7.844558,7.864147, 7.883689,7.903179,7.922623,7.942019,7.961367,7.980669,7.999924, 8.019132,8.038295,8.057414,8.076486,8.095513,8.114495,8.133433, 8.152326,8.171176,8.189983,8.208746,8.227467,8.246149,8.264782, 8.283357,8.301944,8.320429,8.338918,8.35733,8.375742,8.394071, 8.412383,8.430632,8.448894,8.46707,8.485221,8.503334,8.521408, 8.539443,8.557449,8.575411,8.593294,8.61118,8.629047,8.646855, 8.664627,8.682362,8.700059,8.717721,8.735375,8.752975,8.7705, 8.788031,8.805526,8.822969,8.840417,8.857788,8.875119,8.892458, 8.909744,8.926998,8.94422,8.961409,8.978539,8.995658,9.012743, 9.029797,9.046818,9.063807,9.080764,9.097691,9.114586,9.131449, 9.148281,9.165083,9.181853,9.198593,9.215303,9.231982,9.248631, 9.265251,9.28184,9.2984,9.314931,9.331432,9.347904,9.364348, 9.380762,9.397148,9.413505,9.429834,9.446134,9.462407,9.478651, 9.494868,9.511057,9.527219,9.543353,9.55946,9.57554,9.591593, 9.607619,9.623618,9.639591,9.655538,9.671458,9.687352,9.70322, 9.719062,9.734879,9.75067,9.766435,9.782175,9.797895,9.813593, 9.829224,9.84487,9.86049,9.876084,9.891652,9.907196,9.922714, 9.938208,9.953677,9.969121,9.984542,9.999938,10.01531,10.03066, 10.04598,10.06129,10.07656,10.09182,10.10705,10.12226,10.13745, 10.15261,10.16776,10.18288,10.19798,10.21304,10.22809,10.24312, 10.25813,10.27312,10.2881,10.30303,10.31794,10.33285,10.34773, 10.3626,10.37743,10.39222,10.40702,10.42181,10.43656,10.45129, 10.46599,10.48068,10.49537,10.51,10.52463,10.53923,10.55382, 10.56839,10.58294,10.59746,10.61197,10.62646,10.64092,10.65537, 10.6698,10.68421,10.6986,10.71297,10.72732,10.74165,10.75597, 10.77026,10.78454,10.79879,10.81303,10.82725,10.84145,10.85564, 10.8698,10.88395,10.89807,10.91218,10.92627,10.9403,10.95436, 10.96841,10.98243,10.99643,11.01041,11.02437,11.03832,11.05225, 11.06617,11.08006,11.09393) ret1=vaux[length(vaux)]; if (p <length(vaux) ){ ret1 = vaux[p] } ret1 }

72b88c569a7f5ac3e65515b7a991d9fe7f6f979d

6dd8ef4c301d743bf479f5ba1c302d56c179b113

/tcga_codes/Figures_for_Poster.R

aa5418788c7fe8540391d4bede282ff62509e9b7

[]

no_license

Fengithub/Genomics_Project_DCGs

00b55c2fd226461a291d154f658b0d661f886b70

677f2291705973ccdbb5b957c26cafb9a118800d

refs/heads/master

2020-05-03T05:33:10.633680

2019-09-17T19:18:56

178,450,154

0

null

UTF-8

R

false

18,195

r

Figures_for_Poster.R

#source("Figures_for_Poster.R") ##################### ## Set Working Dir ## ##################### #install.packages("ggrepel") library(ggplot2) library(ggrepel) setwd("~/Box Sync/Genomics_Project/") source("dirinfo.R") ########## ## Data ## ########## ## Covariance Matrix Summary cov.normal <- read.csv(file.path(sparsematrixdir, "GLASSO_step1_cov_normal_summary.csv"), header = T) cov.cancer <- read.csv(file.path(sparsematrixdir, "GLASSO_step1_cov_cancer_summary.csv"), header = T) cov.normal2 <- read.csv(file.path(sparsematrixdir, "GLASSO_step1_cov_normal_summary2.csv"), header = T) cov.cancer2 <- read.csv(file.path(sparsematrixdir, "GLASSO_step1_cov_cancer_summary2.csv"), header = T) cov.normal <- as.data.frame(cbind(cov.normal, cov.normal2)) cov.cancer <- as.data.frame(cbind(cov.cancer, cov.cancer2)) ## Commuting Time Distance (ECTD) Summary ctd.normal <- read.csv(file.path(ECTDdir, "ECTD_MoorePenrose_normal_summary.csv"), header = T) ctd.cancer <- read.csv(file.path(ECTDdir, "ECTD_MoorePenrose_cancer_summary.csv"), header = T) ctd.normal2 <- read.csv(file.path(ECTDdir, "ECTD_MoorePenrose_normal_summary2.csv"), header = T) ctd.cancer2 <- read.csv(file.path(ECTDdir, "ECTD_MoorePenrose_cancer_summary2.csv"), header = T) ctd.normal <- as.data.frame(cbind(ctd.normal, ctd.normal2)) ctd.cancer <- as.data.frame(cbind(ctd.cancer, ctd.cancer2)) ## Matrix Measures cov.measure <- read.csv(file.path(resultdir, "Cov_GraphicalMeasures.csv"), header = T) ctd.measure <- read.csv(file.path(resultdir, "ECTD_GraphicalMeasures.csv"), header = T) #Gene DensityStat DensityStatRank BtwnessStat BtwnessStatRank ClonessStat ClonessStatRank ClonessStatscale # Gene DensityStat DensityStatRank BtwnessStat BtwnessStatRank ClonessStat ClonessStatRank ClonessStatscale ClonessStatRankscale CluCoefStat CluCoefStatRank #1 CREB3L1 0.05268447 3477 0 2002 483.3401 982 0.27198879 2110 0.1630802 3335 #2 C10orf90 0.03660332 3655 0 2002 483.4422 857 0.37416214 1840 0.1307714 3504 ########### ## Graph ## ########### ## Ven Diagram thres = 20 for (thres in c(10, 20, 30, 40, 50, 100)) { venda <- ctd.measure venda$DensityStatvenda <- 0 idx = c(order(ctd.measure[,3])[1:thres]) venda$DensityStatvenda[idx] <- 1 venda$BtwnessStatvenda <- 0 idx = order(cov.measure[,5])[1:thres] venda$BtwnessStatvenda[idx] <- 1 venda$ClonessStatvenda <- 0 idx = order(ctd.measure[,9])[1:thres] venda$ClonessStatvenda[idx] <- 1 venda$CluCoefStatvenda <- 0 idx = order(ctd.measure[,11])[1:thres] venda$CluCoefStatvenda[idx] <- 1 myvenda = venda[c("Gene", "DensityStatvenda", "BtwnessStatvenda", "ClonessStatvenda", "CluCoefStatvenda")] myvenda$sum = apply(myvenda[,2:5], 1, sum) myvenda = myvenda[myvenda$sum>0,] myvenda = myvenda[order(-myvenda$sum),] apply(myvenda[,2:5], 2, sum) dim(myvenda) deg.res <- read.table(file.path(datdir, "tcga_DEGresult.txt"), header = T) colnames(deg.res) <- c("Gene", "Statistic", "Pvalue", "BHPvalue", "BonferPvalue") deg.res$DEGrank <- rank(-abs(deg.res$Statistic)) myvenda <- merge(myvenda, deg.res, by = "Gene") write.csv(myvenda, file.path(resultdir, paste0("VenDiagram_overappedGenes_top",thres,".csv")), row.names = F, quote = F) } ven10 <- read.csv(file.path(resultdir, "VenDiagram_overappedGenes_top10.csv"), header = T) ven20 <- read.csv(file.path(resultdir, "VenDiagram_overappedGenes_top20.csv"), header = T) ven30 <- read.csv(file.path(resultdir, "VenDiagram_overappedGenes_top30.csv"), header = T) ven40 <- read.csv(file.path(resultdir, "VenDiagram_overappedGenes_top40.csv"), header = T) ven50 <- read.csv(file.path(resultdir, "VenDiagram_overappedGenes_top50.csv"), header = T) ven100 <- read.csv(file.path(resultdir, "VenDiagram_overappedGenes_top100.csv"), header = T) ## Graph of Measures cov.measure <- read.csv(file.path(resultdir, "Cov_GraphicalMeasures.csv"), header = T) ctd.measure <- read.csv(file.path(resultdir, "ECTD_GraphicalMeasures.csv"), header = T) # plot1 Btw vs Den thre = cov.measure$BtwnessStat[order(-cov.measure$BtwnessStat)] thre = thre[20] dat <- data.frame(gene = as.character(cov.measure$Gene), btw = cov.measure$BtwnessStat, den = ctd.measure$DensityStat) rownames(dat) = dat$gene dat$Significant <- ifelse(dat$btw >= thre, "Y", "N") png("densityVSbtwness.png", height = 1000, width = 1000) p1<-ggplot(dat, aes(x = den, y = btw)) + theme(legend.position = "none") + geom_point(aes(color = Significant), size = 5) + labs(x = "Density Stat.", y = "Betweenness Stat.") + scale_color_manual(values = c("grey", "red")) + theme_bw(base_size = 20) + geom_text_repel( data = subset(dat, btw >= thre), aes(label = gene), size = 10, box.padding = unit(0.35, "lines"), point.padding = unit(0.5, "lines") ) p1 dev.off() thre = ctd.measure$DensityStat[order(-ctd.measure$DensityStat)] thre = thre[20] dat <- data.frame(gene = as.character(cov.measure$Gene), den = cov.measure$BtwnessStat, btw = ctd.measure$DensityStat) rownames(dat) = dat$gene dat$Significant <- ifelse(dat$btw >= thre, "Y", "N") png("densityVSbtwness_inv.png", height = 1000, width = 1000) p1<-ggplot(dat, aes(x = den, y = btw)) + theme(legend.position = "none") + geom_point(aes(color = Significant), size = 5) + labs(y = "Density Stat.", x = "Betweenness Stat.") + scale_color_manual(values = c("grey", "red")) + theme_bw(base_size = 20) + geom_text_repel( data = subset(dat, btw >= thre), aes(label = gene), size = 10, box.padding = unit(0.35, "lines"), point.padding = unit(0.5, "lines") ) p1 dev.off() # plot2: Clo vs Den thre = ctd.measure$ClonessStatscale[order(-ctd.measure$ClonessStatscale)] thre = thre[20] dat <- data.frame(gene = as.character(cov.measure$Gene), btw = ctd.measure$ClonessStatscale, den = ctd.measure$DensityStat) rownames(dat) = dat$gene dat$Significant <- ifelse(dat$btw >= thre, "Y", "N") png("densityVScloness.png", height = 1000, width = 1000) p2 <- ggplot(dat, aes(x = den, y = btw)) + theme(legend.position = "none") + geom_point(aes(color = Significant), size = 5) + labs(x = "Density Stat.", y = "Closenness Stat. (Scaled)") + scale_color_manual(values = c("grey", "red")) + theme_bw(base_size = 20) + geom_text_repel( data = subset(dat, btw >= thre), aes(label = gene), size = 10, box.padding = unit(0.35, "lines"), point.padding = unit(0.5, "lines") ) p2 dev.off() thre = ctd.measure$DensityStat[order(-ctd.measure$DensityStat)] thre = thre[20] dat <- data.frame(gene = as.character(cov.measure$Gene), den = ctd.measure$ClonessStatscale, btw = ctd.measure$DensityStat) rownames(dat) = dat$gene dat$Significant <- ifelse(dat$btw >= thre, "Y", "N") png("densityVScloness_inv.png", height = 1000, width = 1000) p2 <- ggplot(dat, aes(x = den, y = btw)) + theme(legend.position = "none") + geom_point(aes(color = Significant), size = 5) + labs(y = "Density Stat.", x = "Closenness Stat. (Scaled)") + scale_color_manual(values = c("grey", "red")) + theme_bw(base_size = 20) + geom_text_repel( data = subset(dat, btw >= thre), aes(label = gene), size = 10, box.padding = unit(0.35, "lines"), point.padding = unit(0.5, "lines") ) p2 dev.off() # plot3: CC vs Den thre = ctd.measure$CluCoefStat[order(-ctd.measure$CluCoefStat)] thre = thre[20] dat <- data.frame(gene = as.character(cov.measure$Gene), btw = ctd.measure$CluCoefStat, den = ctd.measure$DensityStat) rownames(dat) = dat$gene dat$Significant <- ifelse(dat$btw >= thre, "Y", "N") png("densityVScluscoef.png", height = 1000, width = 1000) p3 <- ggplot(dat, aes(x = den, y = btw)) + theme(legend.position = "none") + geom_point(aes(color = Significant), size = 5) + labs(x = "Density Stat.", y = "Clustering Coefficient Stat.") + scale_color_manual(values = c("grey", "red")) + theme_bw(base_size = 20) + geom_text_repel( data = subset(dat, btw >= thre), aes(label = gene), size = 10, box.padding = unit(0.35, "lines"), point.padding = unit(0.5, "lines") ) p3 dev.off() thre = ctd.measure$DensityStat[order(-ctd.measure$DensityStat)] thre = thre[20] dat <- data.frame(gene = as.character(cov.measure$Gene), den = ctd.measure$CluCoefStat, btw = ctd.measure$DensityStat) rownames(dat) = dat$gene dat$Significant <- ifelse(dat$btw >= thre, "Y", "N") png("densityVScluscoef_inv.png", height = 1000, width = 1000) p3 <- ggplot(dat, aes(x = den, y = btw)) + theme(legend.position = "none") + geom_point(aes(color = Significant), size = 5) + labs(y = "Density Stat.", x = "Clustering Coefficient Stat.") + scale_color_manual(values = c("grey", "red")) + theme_bw(base_size = 20) + geom_text_repel( data = subset(dat, btw >= thre), aes(label = gene), size = 10, box.padding = unit(0.35, "lines"), point.padding = unit(0.5, "lines") ) p3 dev.off() # plot4: CC vs gene thre = ctd.measure$CluCoefStat[order(-ctd.measure$CluCoefStat)] thre = thre[20] dat <- data.frame(gene = as.character(cov.measure$Gene), cc = ctd.measure$CluCoefStat, ccidx = 1:length(ctd.measure$CluCoefStat)) rownames(dat) = dat$gene dat$Significant <- ifelse(ctd.measure$CluCoefStat >= thre, "Y", "N") png("ClusteringCoefStatPlot.png", height = 800, width = 1200) p4<-ggplot(dat, aes(x = ccidx, y = cc)) + theme(legend.position = "none") + geom_point(aes(color = Significant), size = 5) + labs(y = "Clustering Coefficient Stat.", x = "Gene") + scale_color_manual(values = c("grey", "red")) + theme_bw(base_size = 15) + geom_text_repel( data = subset(dat, cc >= thre), aes(label = gene), size = 10, box.padding = unit(0.5, "lines"), point.padding = unit(0.3, "lines") ) p4 dev.off() # plot5: Btw vs Btw dat <- data.frame(gene = as.character(cov.measure$Gene), btw = cov.measure$BtwnessStat, den = ctd.measure$DensityStat, btw2 = ctd.measure$BtwnessStat) rownames(dat) = dat$gene dat$Significant <- ifelse(dat$btw2 > 10^4, "Y", "N") png("btwnessVSbtwness.png", height = 1000, width = 1000) p5<-ggplot(dat, aes(x = btw, y = btw2)) + theme(legend.position = "none", axis.text = element_text(size=40)) + geom_point(aes(color = Significant), size = 5) + labs(x = "Betweenness Stat. (Covariance Mat)", y = "Betweenness Stat. (ECTD)") + scale_color_manual(values = c("grey", "red")) + theme_bw(base_size = 30) + geom_text_repel( data = subset(dat, btw2 > 10^4), aes(label = gene), size = 25, box.padding = unit(0.8, "lines"), point.padding = unit(0.3, "lines") ) p5 dev.off() # plot6: Density Plot thre = ctd.measure$DensityStat[order(-ctd.measure$DensityStat)] thre = thre[20] dat <- data.frame(gene = as.character(cov.measure$Gene), cc = ctd.measure$DensityStat, ccidx = 1:length(ctd.measure$DensityStat)) rownames(dat) = dat$gene dat$Significant <- ifelse(ctd.measure$DensityStat >= thre, "Y", "N") png("DensityStatPlot.png", height = 800, width = 1200) p6<-ggplot(dat, aes(x = ccidx, y = cc)) + theme(legend.position = "none") + geom_point(aes(color = Significant), size = 5) + labs(y = "Density Stat.", x = "Gene") + scale_color_manual(values = c("grey", "red")) + theme_bw(base_size = 15) + geom_text_repel( data = subset(dat, cc >= thre), aes(label = gene), size = 10, box.padding = unit(0.5, "lines"), point.padding = unit(0.3, "lines") ) p6 dev.off() # plot7: Btw Plot thre = cov.measure$BtwnessStat[order(-cov.measure$BtwnessStat)] thre = thre[20] dat <- data.frame(gene = as.character(cov.measure$Gene), cc = cov.measure$BtwnessStat, ccidx = 1:length(cov.measure$BtwnessStat)) rownames(dat) = dat$gene dat$Significant <- ifelse(cov.measure$BtwnessStat >= thre, "Y", "N") png("BetweennessStatPlot.png", height = 800, width = 1200) p7<-ggplot(dat, aes(x = ccidx, y = cc)) + theme(legend.position = "none") + geom_point(aes(color = Significant), size = 5) + labs(y = "Betweenness Stat.", x = "Gene") + scale_color_manual(values = c("grey", "red")) + theme_bw(base_size = 15) + geom_text_repel( data = subset(dat, cc >= thre), aes(label = gene), size = 10, box.padding = unit(0.5, "lines"), point.padding = unit(0.3, "lines") ) p7 dev.off() # plot8: Closeness Plot (Scaled) thre = ctd.measure$ClonessStatscale[order(-ctd.measure$ClonessStatscale)] thre = thre[20] dat <- data.frame(gene = as.character(cov.measure$Gene), cc = ctd.measure$ClonessStatscale, ccidx = 1:length(ctd.measure$ClonessStatscale)) rownames(dat) = dat$gene dat$Significant <- ifelse(ctd.measure$ClonessStatscale >= thre, "Y", "N") png("ClonessStatPlot.png", height = 800, width = 1200) p8<-ggplot(dat, aes(x = ccidx, y = cc)) + theme(legend.position = "none") + geom_point(aes(color = Significant), size = 5) + labs(y = "Closeness Stat. (Scaled)", x = "Gene") + scale_color_manual(values = c("grey", "red")) + theme_bw(base_size = 15) + geom_text_repel( data = subset(dat, cc >= thre), aes(label = gene), size = 10, box.padding = unit(0.5, "lines"), point.padding = unit(0.3, "lines") ) p8 dev.off() # plot9: Closen vs Btwness thre = ctd.measure$ClonessStatscale[order(-ctd.measure$ClonessStatscale)] thre = thre[20] dat <- data.frame(gene = as.character(cov.measure$Gene), btw = ctd.measure$ClonessStatscale, den = cov.measure$BtwnessStat) rownames(dat) = dat$gene dat$Significant <- ifelse(dat$btw >= thre, "Y", "N") png("btwnessVScloness.png", height = 1000, width = 1000) p9 <- ggplot(dat, aes(x = den, y = btw)) + theme(legend.position = "none") + geom_point(aes(color = Significant), size = 5) + labs(x = "Betweenness Stat.", y = "Closenness Stat. (Scaled)") + scale_color_manual(values = c("grey", "red")) + theme_bw(base_size = 20) + geom_text_repel( data = subset(dat, btw >= thre), aes(label = gene), size = 10, box.padding = unit(0.35, "lines"), point.padding = unit(0.5, "lines") ) p9 dev.off() thre = cov.measure$BtwnessStat[order(-cov.measure$BtwnessStat)] thre = thre[20] dat <- data.frame(gene = as.character(cov.measure$Gene), den = ctd.measure$ClonessStatscale, btw = cov.measure$BtwnessStat) rownames(dat) = dat$gene dat$Significant <- ifelse(dat$btw >= thre, "Y", "N") png("btwnessVScloness_inv.png", height = 1000, width = 1000) p9 <- ggplot(dat, aes(x = den, y = btw)) + theme(legend.position = "none") + geom_point(aes(color = Significant), size = 5) + labs(y = "Betweenness Stat.", x = "Closenness Stat. (Scaled)") + scale_color_manual(values = c("grey", "red")) + theme_bw(base_size = 20) + geom_text_repel( data = subset(dat, btw >= thre), aes(label = gene), size = 10, box.padding = unit(0.35, "lines"), point.padding = unit(0.5, "lines") ) p9 dev.off() # plot10: CC vs Btwness thre = ctd.measure$CluCoefStat[order(-ctd.measure$CluCoefStat)] thre = thre[20] dat <- data.frame(gene = as.character(cov.measure$Gene), btw = ctd.measure$CluCoefStat, den = cov.measure$BtwnessStat) rownames(dat) = dat$gene dat$Significant <- ifelse(dat$btw >= thre, "Y", "N") png("btwnessVScluscoef.png", height = 1000, width = 1000) p10 <- ggplot(dat, aes(x = den, y = btw)) + theme(legend.position = "none") + geom_point(aes(color = Significant), size = 5) + labs(x = "Betweenness Stat.", y = "Clustering Coefficient Stat.") + scale_color_manual(values = c("grey", "red")) + theme_bw(base_size = 20) + geom_text_repel( data = subset(dat, btw >= thre), aes(label = gene), size = 10, box.padding = unit(0.35, "lines"), point.padding = unit(0.5, "lines") ) p10 dev.off() thre = cov.measure$BtwnessStat[order(-cov.measure$BtwnessStat)] thre = thre[20] dat <- data.frame(gene = as.character(cov.measure$Gene), den = ctd.measure$CluCoefStat, btw = cov.measure$BtwnessStat) rownames(dat) = dat$gene dat$Significant <- ifelse(dat$btw >= thre, "Y", "N") png("btwnessVScluscoef_inv.png", height = 1000, width = 1000) p10 <- ggplot(dat, aes(x = den, y = btw)) + theme(legend.position = "none") + geom_point(aes(color = Significant), size = 5) + labs(y = "Betweenness Stat.", x = "Clustering Coefficient Stat.") + scale_color_manual(values = c("grey", "red")) + theme_bw(base_size = 20) + geom_text_repel( data = subset(dat, btw >= thre), aes(label = gene), size = 10, box.padding = unit(0.35, "lines"), point.padding = unit(0.5, "lines") ) p10 dev.off() # plot11: CC vs Closen thre = ctd.measure$CluCoefStat[order(-ctd.measure$CluCoefStat)] thre = thre[20] dat <- data.frame(gene = as.character(cov.measure$Gene), btw = ctd.measure$CluCoefStat, den = ctd.measure$ClonessStatscale) rownames(dat) = dat$gene dat$Significant <- ifelse(dat$btw >= thre, "Y", "N") png("cluscoeffVScloseness.png", height = 1000, width = 1000) p11 <- ggplot(dat, aes(x = den, y = btw)) + theme(legend.position = "none") + geom_point(aes(color = Significant), size = 5) + labs(x = "Closenness Stat. (Scaled).", y = "Clustering Coefficient Stat.") + scale_color_manual(values = c("grey", "red")) + theme_bw(base_size = 20) + geom_text_repel( data = subset(dat, btw >= thre), aes(label = gene), size = 10, box.padding = unit(0.35, "lines"), point.padding = unit(0.5, "lines") ) p11 dev.off() thre = ctd.measure$ClonessStatscale[order(-ctd.measure$ClonessStatscale)] thre = thre[20] dat <- data.frame(gene = as.character(cov.measure$Gene), den = ctd.measure$CluCoefStat, btw = ctd.measure$ClonessStatscale) rownames(dat) = dat$gene dat$Significant <- ifelse(dat$btw >= thre, "Y", "N") png("cluscoeffVScloseness_inv.png", height = 1000, width = 1000) p11 <- ggplot(dat, aes(x = den, y = btw)) + theme(legend.position = "none") + geom_point(aes(color = Significant), size = 5) + labs(y = "Closenness Stat. (Scaled).", x = "Clustering Coefficient Stat.") + scale_color_manual(values = c("grey", "red")) + theme_bw(base_size = 20) + geom_text_repel( data = subset(dat, btw >= thre), aes(label = gene), size = 10, box.padding = unit(0.35, "lines"), point.padding = unit(0.5, "lines") ) p11 dev.off()

0d95a63d913140a2e55999a3bd59dba7ced6cfa1

f6990f4746aca0291bc6970286ad6d46b9866a55

/man/mle.Rd

8300423b6f07aff91581d1c9e12f17e453759f37

[]

no_license

cran/merror

3c730db7c55bb309b055f14a23ce120647aacd3d

9b606d2e3ed3b6f89df35c0a4e73a5b8b50584a2

refs/heads/master

2023-08-31T05:10:07.313831

2023-08-29T13:20:02

2023-08-29T14:30:47

17,697,402

0

null

UTF-8

R

false

824

rd

mle.Rd

\name{mle} \alias{mle} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Compute maximum likelihood estimates of precision. } \description{ This is an internal function that computes the maximum likelihood estimates of precision for the constant bias model using paired data. } \usage{ mle(v, r, ni) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{v}{ Variance-Covariance matrix for the n x N items by methods measurement data. } \item{r}{ Initial estimates of imprecision, usually Grubbs. } \item{ni}{ No. of items measured. } } \value{ An N vector containing the maximum likelihood estimates of precision. } \references{ Jaech, J. L. (1985) \emph{Statistical Analysis of Measurement Errors}. New York: Wiley. } \author{ Richard A. Bilonick } \keyword{ htest }

e0dfe4007f7d5a45c7a42971b45ed52bfdc009d5

390ad289fbf96b3dcf90f88acfc71531801391ec

/man/dot-get_reduced_dims.Rd

f99a42f070cca50e312e638a27949bb13670e392

[ "MIT" ]

permissive

keshav-motwani/scanalysis

8f7e30f1207b35dca0424c6c6856e15c624d36fa

2276832c42224efd5d059b5d327aa6468e62a053

refs/heads/master

2023-02-27T14:43:44.118883

2021-02-03T04:24:13

232,159,917

4

1

null

UTF-8

R

false

true

438

rd

dot-get_reduced_dims.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plot_reduced_dimensions.R \name{.get_reduced_dims} \alias{.get_reduced_dims} \title{Get reduced dimensions of object} \usage{ .get_reduced_dims(sce, type) } \arguments{ \item{sce}{SingleCellExperiment object} \item{type}{Name of reduction type in reducedDims} } \value{ } \description{ Get reduced dimensions of object } \examples{ NULL } \keyword{internal}

d95ab5c8a7847902bbc16e35162e824db5ab8290

cb96d3d279e96a8803fa0fb04b20328c4f603c1e

/R/duplicateFinder.R

4e0874996e3206685ec13d190005f0634970681c

[]

no_license

Hossein-Fallahi/MetaGx

06b599f6739409705726a8eaef31478e7804b42a

53e9c40891c5f0f8ab94fe1fda693e48d17a6aff

refs/heads/master

2023-03-15T08:39:33.740280

2017-01-02T22:45:16

null

0

null

UTF-8

R

false

2,991

r

duplicateFinder.R

######################## ## Benjamin Haibe-Kains ## All rights Reserved ## September 1, 2013 ######################## `duplicateFinder` <- function (eset, topvar.genes=1000, dupl.cor=0.99, method=c("pearson", "spearman", "kendall"), cor.matrix=FALSE, nthread=1) { ## find duplicates based on correlation of gene expresison profiles # # Arga: # eset: an expressionSet object # topvar.genes: number of most variant genes used to define the expression profiles # # Returns # list of duplicates sample names method <- match.arg(method) if (topvar.genes < 3) { topvar.genes <- length(featureNames(eset)) } ## select the most variant genes ## at least in 80% of the datasets iix <- apply(exprs(eset), 1, function (x, y) { return ((sum(is.na(x)) / length(x)) < ( 1 - y)) }, y=0.8) varg <- Biobase::featureNames(eset)[iix][order(apply(exprs(eset)[iix, , drop=FALSE], 1, var, na.rm=TRUE), decreasing=TRUE)[1:topvar.genes]] ## alternative, inefficient approach # pairs <- t(combn(1:length(Biobase::sampleNames(eset)), 2, simplify=TRUE)) # splitix <- parallel::splitIndices(nx=nrow(pairs), ncl=nthread) # splitix <- splitix[sapply(splitix, length) > 0] # mcres <- parallel::mclapply(splitix, function(x, pairs, expr, method) { # res <- apply(pairs[x, , drop=FALSE], 1, function (x, expr, method) { # return (cor(x=expr[ , x[1], drop=FALSE], y=expr[ , x[2], drop=FALSE], method=method, use="complete.obs")) # }, expr=expr, method=method) # return (res) # }, pairs=pairs, expr=Biobase::exprs(eset)[varg, , drop=FALSE], method=method) # res <- t(do.call(cbind, mcres)) # res <- unlist(apply(res, 2, function (x, y, gid) { # rr <- matrix(NA, nrow=length(gid), ncol=length(gid), dimnames=list(gid, gid)) # rr[y] <- x # rr[y[ , 2:1]] <- x # diag(rr) <- 1 # return (list(rr)) # }, y=pairs, gid), recursive=FALSE) ## more efficient but still slow approach # splitix <- parallel::splitIndices(nx=length(Biobase::sampleNames(eset)), ncl=nthread) # splitix <- splitix[sapply(splitix, length) > 0] # mcres <- parallel::mclapply(splitix, function(splitix, expr, method) { # cores <- cor(x=expr[ , splitix, drop=FALSE], y=expr, method=method, use="pairwise.complete.obs") # }, expr=Biobase::exprs(eset)[varg, , drop=FALSE], method=method) # cor.samples <- do.call(rbind, mcres) ## using mRMRe nn <- mRMRe::get.thread.count() mRMRe::set.thread.count(nthread) expr <- mRMRe::mRMR.data(data=data.frame(Biobase::exprs(eset)[varg, , drop=FALSE])) cor.samples <- mRMRe::mim(object=expr, continuous_estimator=method, method="cor") mRMRe::set.thread.count(nn) if (cor.matrix) { return (cor.samples) } diag(cor.samples) <- NA ## create list of duplicates for each sample duplix <- apply(cor.samples, 1, function (x, y) { res <- names(x)[!is.na(x) & x > y] return (res) }, y=dupl.cor) duplix <- duplix[sapply(duplix, length) > 0] return (duplix) }

ffef10f787225cd49420f78027b57259ec987930

f6328efe81b7bd86c6e160fad49359f32568a8c1

/R_Scripts/analysisforMarch16.R

1af5950db81c76df99c05101a95d6f52edd2a214

[]

no_license

robertipk/Entrainment

7ef84fb37b93210f2f9c81236b31fe6bba190cef

69566591d166e265f2efa49fc324cac75ab306cd

refs/heads/master

2021-01-22T08:39:01.090811

2016-08-23T18:45:27

66,334,475

0

null

UTF-8

R

false

1,716

r

analysisforMarch16.R

library(dplyr) source("superscript.R") origTable <- read.csv("correcteddata.csv",header=TRUE) origTable$speaker = paste(origTable$speaker,origTable$letter,sep="") origTable$letter <- NULL head(origTable) loudness <- subset(origTable,select=c("speaker","loudness")) F0finEnv <- subset(origTable,select=c("speaker","F0finEnv")) jitterLocal <- subset(origTable,select=c("speaker","jitterLocal")) shimmerLocal <- subset(origTable,select=c("speaker","shimmerLocal")) F0finEnv <- F0finEnv %>% filter(F0finEnv != 0) jitterLocal <- jitterLocal %>% filter(jitterLocal != 0) shimmerLocal <- shimmerLocal %>% filter(shimmerLocal != 0) loudnessAvg <- aggregate(loudness$loudness, by=list(loudness$speaker), FUN=mean) F0finEnvAvg <- aggregate(F0finEnv$F0finEnv, by=list(F0finEnv$speaker), FUN=mean) jitterLocalAvg <- aggregate(jitterLocal$jitterLocal, by=list(jitterLocal$speaker), FUN=mean) shimmerLocalAvg <- aggregate(shimmerLocal$shimmerLocal, by=list(shimmerLocal$speaker), FUN=mean) template<-read.csv("newtoplevel.csv",header=TRUE) Avg <- select(loudnessAvg,name,loudness) colnames(Avg)[1]<-"name" colnames(Avg)[2]<-"loudness" loudnessAvg$name<-lapply(loudnessAvg$name,function(x)substr(x,4,8)) template$self_feature <- apply(template,MARGIN=1,function(x)findValueSelf(Avg,x)) template$partner_feature <- apply(template,MARGIN=1,function(x)findValuePartner(Avg,x)) template$partner_diff <- apply(template,MARGIN=1,function(x)calcPartnerDifference(x)) template$nonpartner_diff <- apply(template,MARGIN=1,function(x)calcNonPartnerDifferenceMeanOfDifferences(template,x)) template$outlier<-addOutlierColumn(Avg,template) template$normdiff <-apply(template,MARGIN=1,function(x)calcNormdiff(x)) all_ttests(template)

e8594434d286a7f453194c7365c7d6d5209d9fd6

bbe69e27263454120aa4ada60eaed9116fccb6da

/R/import_from_base.R

78a94126669165cef73122c10edd3100fb9131be

[]

no_license

ropensci-archive/outsider.devtools

702812e099b9983f022f985ac607e5004c620489

787f92887435e7aedf4721170511ca4e790d2df2

refs/heads/master

2023-04-17T17:31:03.227335

2022-06-17T07:11:38

null

0

null

UTF-8

R

false

1,763

r

import_from_base.R

# import outsider.base functions #' @importFrom outsider.base arglist_get #' @export outsider.base::arglist_get #' @importFrom outsider.base arglist_parse #' @export outsider.base::arglist_parse #' @importFrom outsider.base dirpath_get #' @export outsider.base::dirpath_get #' @importFrom outsider.base filestosend_get #' @export outsider.base::filestosend_get #' @importFrom outsider.base wd_get #' @export outsider.base::wd_get #' @importFrom outsider.base docker_img_ls #' @export outsider.base::docker_img_ls #' @importFrom outsider.base image_install #' @export outsider.base::image_install #' @importFrom outsider.base is_docker_available #' @export outsider.base::is_docker_available #' @importFrom outsider.base outsider_init #' @export outsider.base::outsider_init #' @importFrom outsider.base run #' @export outsider.base::run #' @importFrom outsider.base is_installed #' @export outsider.base::is_installed #' @importFrom outsider.base meta_get #' @export outsider.base::meta_get #' @importFrom outsider.base modules_list #' @export outsider.base::modules_list #' @importFrom outsider.base pkg_install #' @export outsider.base::pkg_install #' @importFrom outsider.base uninstall #' @export outsider.base::uninstall #' @importFrom outsider.base cat_line #' @export outsider.base::cat_line #' @importFrom outsider.base char #' @export outsider.base::char #' @importFrom outsider.base func #' @export outsider.base::func #' @importFrom outsider.base log_set #' @export outsider.base::log_set #' @importFrom outsider.base stat #' @export outsider.base::stat #' @importFrom outsider.base server_connect #' @export outsider.base::server_connect #' @importFrom outsider.base server_disconnect #' @export outsider.base::server_disconnect

a8044abcf7acdeba9c753b01dea55ec0cee3134c

11d8fbce4f2c9a45a1fa68d0a00919d6a063cba7

/Data-Science/3_Getting_and_Cleaning_Data/Lecture/Week_2/Examples/Reading from HDF5.R

58519667d5663e840f1bf560c91bd96d19d17558

[ "LicenseRef-scancode-unknown-license-reference", "MIT" ]

permissive

shanky0507/Coursera-John-Hopkins

e03c9c8234ff634a047aaf0aea9912c8b96358bd

1b555bf9d3aaef3bfdd5d4bcd0e3b5c0e734cf66

refs/heads/master

2020-08-12T06:42:10.942297

2019-10-12T20:08:52

214,708,337

0

MIT

2019-10-12T20:06:29

2019-10-12T20:06:28

null

UTF-8

R

false

853

r

Reading from HDF5.R

## Install Package source("http://bioconductor.org/biocLite.R") biocLite("rhdf5") ## Create Groups library(rhdf5) created = h5createFile("example.h5") created created = h5createGroup("example.h5","foo") created = h5createGroup("example.h5","baa") created = h5createGroup("example.h5","foo/foobaa") h5ls("example.h5") ## Write to Groups A = matrix(1:10,nr=5,nc=2) h5write(A, "example.h5","foo/A") B = array(seq(0.1,2.0,by=0.1),dim=c(5,2,2)) attr(B, "scale") <- "liter" h5write(B, "example.h5","foo/foobaa/B") h5ls("example.h5") ## Write a data set df = data.frame(1L:5L,seq(0,1,length.out=5), c("ab","cde","fghi","a","s"), stringsAsFactors=FALSE) h5write(df, "example.h5","df") h5ls("example.h5") ## Reading data readA = h5read("example.h5","foo/A") readB = h5read("example.h5","foo/foobaa/B") readdf= h5read("example.h5","df") readA

65702cdc6924dc7db936a38304b7da4b5aa09cd8

12a10c22bf3d08e70f9830a22e94a47632a71d82

/man/allmods.Rd

7b7561e38069176c3eca3ff314c05a91c38846a4

[]

no_license

dsambrano/PsyTools

973136448bd3c3eb5127cf13152a811846cc4133

7884c5b51cdc853fc7377d9737eafee2640dec14

refs/heads/master

2021-01-20T12:41:17.671181

2017-05-05T15:46:40

90,392,060

0

null

UTF-8

R

false

true

1,082

rd

allmods.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/allmods.R \name{allmods} \alias{allmods} \title{All Possible Models} \usage{ allmods(dv="dv", ivs=c("iv1","iv2","iv3"), data=dataset, plot = FALSE) } \arguments{ \item{dv}{A string of the variable name of you dependent variable.} \item{ivs}{A list of strings of the names of you independent variables.} \item{data}{A \code{data.frame} object.} \item{plot}{A \code{logical} statement indicating whether you want the affiliated plots.} } \value{ A matrix with the R^2, adj R^2, PRESS, and Mallow Cp for all possible (non interaction) models given the variables. } \description{ This function produces the coeffiecients and the anova summary table for Hierarchical regression. } \examples{ dv <- rnorm(500, 47, 12) iv1 <- rnorm(500, 0, 6) iv2 <- rnorm(500, 100, 5) iv3 <- rnorm(500, 12, 3) iv4 <- rnorm(500, 12, 100) dataset <- data.frame(dv, iv1, iv2, iv3, iv4) allmods(dv ="dv", ivs=c("iv1", "iv2", "iv3"), data = dataset, plots = FALSE) } \author{ K. Preston and D. Sambrano }

31d59dacf7b683afd0a294522be0dc24aa2f106d

c1d359cdf0281885744cdcd85d41a21e91218b43

/man/KYCG_plotMetaEnrichment.Rd

e8faa4f8bad5367d690022c5eccb1568b4ef7e33

[ "MIT" ]

permissive

zwdzwd/sesame

20b2d29578661487db53432c8991d3c4478aa2c1

62fe6ef99a02e7f94b121fb601c3f368b8a4c1a8

refs/heads/master

2023-08-08T01:45:02.112492

2023-07-26T13:23:03

122,086,019

37

26

MIT

2023-01-05T16:02:38

2018-02-19T16:00:34

R

UTF-8

R

false

true

656

rd

KYCG_plotMetaEnrichment.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/KYCG_plot.R \name{KYCG_plotMetaEnrichment} \alias{KYCG_plotMetaEnrichment} \title{Plot meta gene or other meta genomic features} \usage{ KYCG_plotMetaEnrichment(result_list) } \arguments{ \item{result_list}{one or a list of testEnrichment} } \value{ a grid plot object } \description{ Plot meta gene or other meta genomic features } \examples{ cg_lists <- KYCG_getDBs("MM285.TFBS") queries <- cg_lists[(sapply(cg_lists, length) > 40000)] result_list <- lapply(queries, testEnrichment, "MM285.metagene", silent=TRUE, platform="MM285") KYCG_plotMetaEnrichment(result_list) }

64fb8b1bd25216ed1c154813e38600c0843c78fb

43e6ce29a417f19475aecb90945e80dd2e797040

/shopping-cart.R

61446ab381047b2971d547e8c3b0bc80106cdf1d

[]

no_license

wolfkden/BigData-R

8e5725a5d9da2bc65124dc8d14a8e9cc837e1be4

51392eaf3e01930e9157a387fca6fc042090c94a

refs/heads/master

2021-01-13T14:27:57.819783

2015-02-18T00:48:38

30,945,645

0

null

UTF-8

R

false

1,788

r

shopping-cart.R

for(x in as.character(alldays$ITEM_NAME)) nlist[length(nlist)+1]<-paste(unlist(strsplit(x,",")),collapse="+") ax<-aggregate(cbind(ITEM_LIST=ITEM_ID,ITEM_COUNT=ITEM_ID,ITEM_NAME=ITEM_NAME)~CHK_NUM,alldays,FUN=function(x) { return(paste(sort(union(NULL,x)),collapse="__")) }) ax$ITEM_COUNT<-unlist(lapply(strsplit(ax$ITEM_LIST,"__"),FUN=length)) agg<-aggregate(CHK_NUM~ITEM_COUNT,ax,FUN=length) agg<-agg[order(agg$CHK_NUM,decreasing=TRUE),] agg<-aggregate(CHK_NUM~ITEM_COUNT,ax,FUN=length) agg<-agg[order(agg$CHK_NUM,decreasing=TRUE),] agg<-aggregate(CHK_NUM~ITEM_LIST+ITEM_COUNT+ITEM_NAME,ax,FUN=length) agg<-aggregate(CHK_NUM~ITEM_LIST,ax,FUN=length) agg<-aggregate(CHK_NUM~ITEM_LIST+ITEM_COUNT+ITEM_NAME,ax,FUN=length) agg<-agg[order(agg$CHK_NUM,decreasing=TRUE),] ax<-aggregate(cbind(ITEM_LIST=ITEM_ID,ITEM_COUNT=ITEM_ID,ITEM_NAME=ITEM_NAME)~CHK_NUM,alldays,FUN=function(x) { return(paste(sort(union(NULL,x)),collapse="__")) }) a1<-aggregate(CHECK_COUNT~CHK_NUM+ITEM_ID,alldays,FUN=function(x) { return(c(mx=max(x),n=length(x))) }) a1<-cbind(a1,a1$CHECK_COUNT) a1<-subset(a1,n==1 & mx==1) a1$CHECK_COUNT<-NULL a1<-aggregate(ITEM_ID~CHK_NUM, a1, FUN=function(x) { return(c(ITEM_ID=max(x), n=length(x))) }) a1<-cbind(a1,a1$ITEM_ID) a1[[2]]<-NULL a1<-subset(a1,n==1) x1<-subset(axx[,c(1:2,5)],ITEM_COUNT>3) x1<-melt(data.frame(t(apply(subset(axx[,c(1:2,5)],ITEM_COUNT==3), 1, FUN=function(x) { return(unlist(x)) }))),id=c("CHK_NUM","ITEM_COUNT")) x2<-melt(data.frame(t(apply(subset(axx[,c(1:2,6)],ITEM_COUNT==3), 1, FUN=function(x) { return(unlist(x)) }))),id=c("CHK_NUM","ITEM_COUNT")) x1$variable<-NULL x2$variable<-NULL names(x1)[3]<-"ITEM_PAIR" names(x2)[3]<-"ITEM_WTS" xx<-merge(x1,x2)

8debb9de8673ac3d8a7dbfbb6112a5546186bc1a

b626eca042800ee5572344262a165c1989235f73

/paper/Rcode_eps/03-contour-plot.R

0500fd350b1ba0b241d215be2409803d18787803

[]

no_license

nicoballarini/SubgrPlots

38f04f07478c672cae761b1a9cd1fa5f79062d6e

61229c9f36c9650f71b178b970a80d2a7bf9471d

refs/heads/master

2020-03-13T12:59:46.419273

2020-01-29T02:26:45

131,130,461

3

0

null

UTF-8

R

false

2,712

r

03-contour-plot.R

###############################################################################- ## ## This program creates the figures for the manuscript using the ## prca data that is included in this package ## ## ## Instead of using rm(list = ls()), make sure you start with a fresh R ## by restarting R -> Control+Shift+F10 # cat("\014") # Cleans the console ## Load needed libraries ## If SubgrPlots package is not installed, then open project and use following lines or ## in the build window, click Install and Restart # devtools::build() # devtools::install() library(SubgrPlots) # Loads this package. Install it first library(dplyr) library(grid) library(gridGraphics) # Load the data to be used data(prca) dat <- prca setup.ss = c(10,60,15,30) dat %>% rename(Weight = weight, Age = age) -> dat setEPS() postscript(file = "paper/figures_eps/03-contour-plot.eps", width = 7.1, height = 3) graphics::layout(matrix(c(6,6,7,8,8, 1,2,3,4,5), nrow=2, ncol=5, byrow = TRUE), heights = c(.075, .925), widths = c(4, .9, .25, 4, .9)) # graphics::layout(matrix(c(1, 2), nrow=1, ncol=2), widths=c(4,1)) plot_contour(dat, covari.sel = c(8,9), trt.sel = 3, resp.sel = c(1,2), outcome.type = "survival", setup.ss = setup.ss, n.grid = c(100,100), brk.es = seq(-4.5,4.5,length.out = 101), n.brk.axis = 7, para.plot = c(0.5, 2, 6), font.size = c(1, 1, .75, 1, .9), title = NULL, strip = paste("Treatment effect size (log hazard ratio)"), show.overall = T,show.points = T, filled = T, palette = "hcl", col.power = 0.75, new.layout = FALSE) graphics::par(mar=c(0,0,0,0)) plot(0, type = "n", axes = F, xlab = "", ylab = "") plot_contour_localreg(dat, covari.sel = c(8,9), trt.sel = 3, resp.sel = c(1,2), n.grid = c(100,100), font.size = c(1, 1, .75, 1, .9), brk.es = seq(-4.5,4.5,length.out = 101), n.brk.axis = 7, strip = "Treatment effect size (log hazard ratio)", outcome.type = "survival", new.layout = FALSE) gridGraphics::grid.echo() p = grid.grab() graphics::par(mar=c(0,0,0,0), xpd=TRUE) plot(0, 0, type = "n", axes = F, xlab = "", ylab = "") text(-1, -.5, "(a)", cex = 1.5) plot(0, 0, type = "n", axes = F, xlab = "", ylab = "") plot(0, 0, type = "n", axes = F, xlab = "", ylab = "") text(-1, -.5, "(b)", cex = 1.5) dev.off()

e327aac8af9e5696be11832f4d167b824cd7e584

567d8f2a240cd3b7f3899b3fd5e3bd9328b2b895

/R/simulate_cvd.R

870bb87a39e4e646e5aba901a8d1d117819b9069

[]

no_license

cran/colorspace

ec9123555d9a820c2a2c639b94d28734563df6e0

fadb043aeb85048a0a2b9daddbb258002d0a4dfc

refs/heads/master

2023-01-24T18:54:37.214672

2023-01-23T10:40:02

17,695,192

7

3

null

2017-08-29T03:43:34

2014-03-13T04:18:48

R

UTF-8

R

false

9,060

r

simulate_cvd.R

#' Simulate Color Vision Deficiency #' #' Transformation of R colors by simulating color vision deficiencies, #' based on a CVD transform matrix. #' #' Using the physiologically-based model for simulating color vision deficiency (CVD) #' of Machado et al. (2009), different kinds of limitations can be #' emulated: deuteranope (green cone cells defective), protanope (red cone cells defective), #' and tritanope (blue cone cells defective). #' The workhorse function to do so is \code{simulate_cvd} which can take any vector #' of valid R colors and transform them according to a certain CVD transformation #' matrix (see \code{\link{cvd}}) and transformation equation. #' #' The functions \code{deutan}, \code{protan}, and \code{tritan} are the high-level functions for #' simulating the corresponding kind of colorblindness with a given severity. #' Internally, they all call \code{simulate_cvd} along with a (possibly interpolated) #' version of the matrices from \code{\link{cvd}}. Matrix interpolation can be carried out with #' the function \code{interpolate_cvd_transform} (see examples). #' #' If input \code{col} is a matrix with three rows named \code{R}, \code{G}, and #' \code{B} (top down) they are interpreted as Red-Green-Blue values within the #' range \code{[0-255]}. Then the CVD transformation is applied directly to these #' coordinates avoiding any further conversions. #' #' Finally, if \code{col} is a formal \code{\link[colorspace]{color-class}} object, then its #' coordinates are transformed to (s)RGB coordinates, as described above, and returned as a formal #' object of the same class after the color vision deficiency simulation. #' #' Up to version 2.0-3 of the package, the CVD transformations had been applied #' directly to the gamma-corrected sRGB coordinates (corresponding to the hex coordinates #' of the colors), following the illustrations of Machado et al. (2009). However, #' the paper implicitly relies on a linear RGB space (see page 1294, column 1) where their #' linear matrix transformations for simulating color vision deficiencies are applied. #' Therefore, starting from version 2.1-0 of the package, a new argument \code{linear = TRUE} #' has been added that first maps the provided colors to linearized RGB coordinates, applies #' the color vision deficiency transformation, and then maps back to gamma-corrected sRGB #' coordinates. Optionally, \code{linear = FALSE} can be used to restore the behavior #' from previous versions. For most colors the difference between the two strategies is #' negligible but for some highly-saturated colors it becomes more noticable, e.g., for #' red, purple, or orange. #' #' @param col vector of R colors. Can be any of the three kinds of R colors, #' i.e., either a color name (an element of \code{\link[grDevices]{colors}}), a hexadecimal (hex) #' string of the form \code{"#rrggbb"} or \code{"#rrggbbaa"} (see \code{\link[grDevices]{rgb}}), or #' an integer \code{i} meaning \code{palette()[i]}. Additionally, \code{col} can be #' a formal \code{\link[colorspace]{color-class}} object or a matrix with three named #' rows (or columns) containing R/G/B (0-255) values. #' @param severity numeric. Severity of the color vision defect, a number between 0 and 1. #' @param cvd_transform numeric 3x3 matrix, specifying the color vision deficiency transform matrix. #' @param linear logical. Should the color vision deficiency transformation be applied to the #' linearized RGB coordinates (default)? If \code{FALSE}, the transformation is applied to the #' gamma-corrected sRGB coordinates (which was the default up to version 2.0-3 of the package). #' @param cvd list of cvd transformation matrices. See \code{\link{cvd}} for available options. #' #' @return A color object as specified in the input \code{col} (hexadecimal string, RGB matrix, #' or formal color class) with simulated color vision deficiency. #' #' @references Machado GM, Oliveira MM, Fernandes LAF (2009). #' \dQuote{A Physiologically-Based Model for Simulation of Color Vision Deficiency.} #' \emph{IEEE Transactions on Visualization and Computer Graphics}. \bold{15}(6), 1291--1298. #' \doi{10.1109/TVCG.2009.113} #' Online version with supplements at #' \url{http://www.inf.ufrgs.br/~oliveira/pubs_files/CVD_Simulation/CVD_Simulation.html}. #' #' Zeileis A, Fisher JC, Hornik K, Ihaka R, McWhite CD, Murrell P, Stauffer R, Wilke CO (2020). #' \dQuote{colorspace: A Toolbox for Manipulating and Assessing Colors and Palettes.} #' \emph{Journal of Statistical Software}, \bold{96}(1), 1--49. #' \doi{10.18637/jss.v096.i01} #' @keywords colors cvd colorblind #' @seealso \code{\link{cvd}} #' @export #' @examples #' # simulate color-vision deficiency by calling `simulate_cvd` with specified matrix #' simulate_cvd(c("#005000", "blue", "#00BB00"), tritanomaly_cvd["6"][[1]]) #' #' # simulate color-vision deficiency by calling the shortcut high-level function #' tritan(c("#005000", "blue", "#00BB00"), severity = 0.6) #' #' # simulate color-vision deficiency by calling `simulate_cvd` with interpolated cvd matrix #' simulate_cvd(c("#005000", "blue", "#00BB00"), #' interpolate_cvd_transform(tritanomaly_cvd, severity = 0.6)) #' #' # apply CVD directly on wide RGB matrix (with R/G/B channels in rows) #' RGB <- diag(3) * 255 #' rownames(RGB) <- c("R", "G", "B") #' deutan(RGB) #' #' @importFrom grDevices col2rgb simulate_cvd <- function(col, cvd_transform, linear = TRUE) { ## determine input type input_type <- if (inherits(col, "color")) { ## S4 colorspace class "colorspace" } else if (is.matrix(col)) { ## named RGB matrix (0-255) "matrix" } else if (is.character(col) && (all(substr(col, 1L, 1L) == "#") & all(nchar(col) %in% c(7L, 9L)))) { ## all hex "hex" } else { ## assume built-in colors "other" } ## indexes of missing values (if hex) NAidx <- NULL ## convert input to wide RGB matrix (0-255) if (input_type == "colorspace") { color_class <- class(col) col <- t(coords(as(col, if(linear) "RGB" else "sRGB"))) * 255 } else if (input_type == "matrix") { if(NROW(col) != 3L && NCOL(col) == 3L && all(toupper(colnames(col)) == c("R", "G", "B"))) { col <- t(col) transpose <- TRUE } else { transpose <- FALSE } stopifnot(all(toupper(rownames(col)) == c("R", "G", "B"))) } else if (input_type == "hex") { # Save transparency value for later alpha <- substr(col, 8L, 9L) # keep indizes of NA colors NAidx <- which(is.na(col)) col <- substr(col, 1L, 7L) col <- grDevices::col2rgb(col) } else { # keep indizes of NA colors NAidx <- which(is.na(col)) col <- grDevices::col2rgb(col, alpha = TRUE) ## extract alpha values (if non-FF) alpha <- format(as.hexmode(col[4L, ]), width = 2L, upper.case = TRUE) alpha[alpha == "FF"] <- "" ## retain only RGB col <- col[1L:3L, ] } if (linear && input_type %in% c("hex", "other")) { sRGB_to_linearRGB <- function(x) { x <- x/255 y <- ((x + 0.055)/1.055)^2.4 small <- x <= 0.03928 y[small] <- x[small]/12.92 return(y * 255) } col <- sRGB_to_linearRGB(col) } ## transform color RGB <- cvd_transform %*% col rownames(RGB) <- c("R", "G", "B") ## bound RGB values RGB[RGB < 0] <- 0 RGB[RGB > 255] <- 255 if (linear && input_type %in% c("hex", "other")) { linearRGB_to_sRGB <- function(y) { y <- y/255 x <- 1.055 * y^(1/2.4) - 0.055 small <- y <= 0.03928/12.92 x[small] <- 12.92 * y[small] return(x * 255) } RGB <- linearRGB_to_sRGB(RGB) } ## convert back to input type if (input_type == "colorspace") { col <- t(RGB/255) col <- if(linear) RGB(col) else sRGB(col) col <- as(col, color_class) } else if (input_type == "matrix") { col <- if(transpose) t(RGB) else RGB } else { RGB <- round(RGB) col <- paste(grDevices::rgb(RGB[1L, ], RGB[2L, ], RGB[3L, ], maxColorValue = 255), alpha, sep = "") if(length(NAidx) > 0L) col[NAidx] <- NA } return(col) } #' @rdname simulate_cvd #' @export deutan <- function(col, severity = 1, linear = TRUE) { simulate_cvd(col, cvd_transform = interpolate_cvd_transform(deutanomaly_cvd, severity), linear = linear) } #' @rdname simulate_cvd #' @export protan <- function(col, severity = 1, linear = TRUE) { simulate_cvd(col, cvd_transform = interpolate_cvd_transform(protanomaly_cvd, severity), linear = linear) } #' @rdname simulate_cvd #' @export tritan <- function(col, severity = 1, linear = TRUE) { simulate_cvd(col, cvd_transform = interpolate_cvd_transform(tritanomaly_cvd, severity), linear = linear) } #' @rdname simulate_cvd #' @export interpolate_cvd_transform <- function(cvd, severity = 1) { if (severity <= 0) { cvd[[1]] } else if (severity >= 1) { cvd[[11]] } else { s <- 10*severity i1 <- floor(s) i2 <- ceiling(s) if (i1 == i2) { cvd[[i1+1]] } else { (i2-s)*cvd[[i1+1]] + (s-i1)*cvd[[i2+1]] } } }

99a09d7182ae3578277d7fe48fd6fc0c39c1f1ef

f2384c2f83c77b900e493be75d711fc5e8a561f9

/K_Nearest_Neigbour.R

32c3e7c5649bb336e114d28650673f8fa8664dc1

[]

no_license

RituUW/Data-mining-using-R

d0e32a38daad8a2b62076cdc0e2653c730bdca06

4039e4f7be08042b110d941bc49533257b5c1d8f

refs/heads/main

2023-02-02T08:56:37.962907

2020-12-18T06:56:13

322,501,531

0

null

UTF-8

R

false

892

r

K_Nearest_Neigbour.R

#K NEAREST NEIGBOURS # load data mower.df <- read.csv("RidingMowers.csv", stringsAsFactors = TRUE) set.seed(11) # partition data train.index <- sample(1:nrow(mower.df), 0.6*nrow(mower.df)) train.df <- mower.df[train.index, ] valid.df <- mower.df[-train.index,] train.norm.df <- train.df valid.norm.df <- valid.df # normalize data library(caret) # compute mean and standard deviation of each column norm.values <- preProcess(train.df[, 1:2], method=c("center", "scale")) train.norm.df[, 1:2] <- predict(norm.values, train.df[, 1:2]) valid.norm.df[, 1:2] <- predict(norm.values, valid.df[, 1:2]) #install.packages("FNN") library(FNN) # use ?knn to find out more information # It worth noting that the input argument cl must be a factor! knn.pred <- knn(train.norm.df[, 1:2], valid.norm.df[, 1:2], cl = train.norm.df[, 3], k = 5) confusionMatrix(knn.pred, valid.norm.df[, 3])

b5b76a06b15b3082d70de6d7956114f0789903cf

a3db37c4dfebd618aeeb78065dd38af3f3490869

/cachematrix.R

2345c4aa7f144b570e945a7f8208f01ecdead501

[]

no_license

souvik82/ProgrammingAssignment2

ae9dae0da1081381328192ba4aca15e5dec722c8

9e171dc10d4856fc5249799b22cc28a7a69f9f4a

refs/heads/master

2021-01-13T16:16:09.901463

2016-04-29T09:36:45

57,369,380

1

0

null

2016-04-29T08:36:03

null

UTF-8

R

false

1,133

r

cachematrix.R

## makeCacheMatrix(): creates a special “matrix” object that can cache its inverse. makeCacheMatrix <- function(x = matrix()) { inv <- NULL set <- function(y){ # use `<<-` to assign a value to an object in an environment # different from the current environment. x <<- y inv <<- NULL } get <- function() x setinverse <- function(inverse) inv <<- inverse getinverse <- function() inv list(set =set,get=get,setinverse=setinverse,getinverse=getinverse) } ## cacheSolve(): computes the inverse of the “matrix” returned by makeCacheMatrix(). ## If the inverse has already been calculated and the matrix has not changed, it’ll return the inverse from the cache directly. cacheSolve <- function(x, ...) { ## Return a matrix that is the inverse of 'x' inv <- x$getinverse() if(!is.null(inv)) { ## get it from the cache and skips the computation. message("getting cached data") return(inv) } data <- x$get() inv <- solve(data, ...) x$setinverse(inv) inv } ## Test the result ## r <- rnorm(48) ## x <- matrix(r, nrow=4, ncol=4) ## y <- makeCacheMatrix(x) ## cacheSolve(y)

7e2881986d2bd118ea536fc8288e3389eed5f545

ffdea92d4315e4363dd4ae673a1a6adf82a761b5

/data/genthat_extracted_code/hdrcde/examples/plot.cde.Rd.R

cd79162f910b063ac236418d2ae37eeb01788015

[]

no_license

surayaaramli/typeRrh

d257ac8905c49123f4ccd4e377ee3dfc84d1636c

66e6996f31961bc8b9aafe1a6a6098327b66bf71

refs/heads/master

2023-05-05T04:05:31.617869

2019-04-25T22:10:06

null

0

null

UTF-8

R

false

318

r

plot.cde.Rd.R

library(hdrcde) ### Name: plot.cde ### Title: Plots conditional densities ### Aliases: plot.cde ### Keywords: distribution hplot smooth ### ** Examples faithful.cde <- cde(faithful$waiting,faithful$eruptions, x.name="Waiting time", y.name="Duration time") plot(faithful.cde) plot(faithful.cde,plot.fn="hdr")

ec3326d6356c9fda4748fa785b2dee44ed1b364e

c2f8c87cf965423cd563a5fcd7bd7a38bf9200d0

/man/run_civis.Rd

75d0756dcc6e50a97585dbe0c7aaee0528bd105f

[]

no_license

mheilman/civis-r

18f6a1831879f1d85daa45e6d05a8d5fa004248d

68acbc93bb2168bccfea93ab0494121c220c93ce

refs/heads/master

2020-12-19T22:14:11.218094

2019-10-01T14:10:00

235,868,128

0

null

2020-01-23T19:18:05

2020-01-23T19:18:04

null

UTF-8

R

false

true

1,036

rd

run_civis.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/scripts.R \name{run_civis} \alias{run_civis} \title{Evaluate an R expression in a Civis Platform container} \usage{ run_civis(expr, ...) } \arguments{ \item{expr}{code to evaluate} \item{...}{arguments to \code{\link{CivisFuture}}} } \description{ Evaluate an R expression in a Civis Platform container } \details{ \code{run_civis} blocks until completion. For non-blocking calls, use futures directly with \code{\link{civis_platform}}. Attempts are made at detecting and installing necessary packages within the container, and detecting global variables required in \code{expr}. } \examples{ \dontrun{ run_civis(2+2) # specify required resources, and a specific image run_civis(2+2, required_resources = list(cpu = 1024, memory = 2048), docker_image_name='image', docker_image_tag = 'latest') } } \seealso{ Other script_utils: \code{\link{civis_script}}, \code{\link{fetch_output_file_ids}}, \code{\link{run_template}} } \concept{script_utils}

1cdb58e38f9396cfeaf32a91165fef531f5c7f90

67eea017c474ab14755b8476ad29c19124766038

/misc/evaluate_glm_logistic.R

9a14b3de2b35894796180b0a102503ad4f8be5d9

[ "MIT" ]

permissive

mutazag/mdsi

dd6a457c7269401d552cda162df8a37df92ca6fc

efecc8f650ddf6866154389f98d4ce0a9803db18

refs/heads/master

2021-06-01T20:23:12.920707

2020-03-29T01:21:32

125,200,143

0

MIT

2018-05-26T16:58:12

2018-03-14T11:07:29

R

UTF-8

R

false

1,792

r

evaluate_glm_logistic.R

## evalulate a simple logistic regression using glm() # https://stats.stackexchange.com/questions/25389/obtaining-predicted-values-y-1-or-0-from-a-logistic-regression-model-fit # data y simulated from a logistic regression model # with with three predictors, n=10000 x = matrix(rnorm(30000),10000,3) lp = 0 + x[,1] - 1.42*x[2] + .67*x[,3] + 1.1*x[,1]*x[,2] - 1.5*x[,1]*x[,3] +2.2*x[,2]*x[,3] + x[,1]*x[,2]*x[,3] p = 1/(1+exp(-lp)) y = runif(10000)<p # fit a logistic regression model mod = glm(y~x[,1]*x[,2]*x[,3],family="binomial") # using a cutoff of cut, calculate sensitivity, specificity, and classification rate perf = function(cut, mod, y) { yhat = (mod$fit>cut) w = which(y==1) sensitivity = mean( yhat[w] == 1 ) specificity = mean( yhat[-w] == 0 ) c.rate = mean( y==yhat ) d = cbind(sensitivity,specificity)-c(1,1) d = sqrt( d[1]^2 + d[2]^2 ) out = t(as.matrix(c(sensitivity, specificity, c.rate,d))) colnames(out) = c("sensitivity", "specificity", "c.rate", "distance") return(out) } s = seq(.01,.99,length=1000) OUT = matrix(0,1000,4) for(i in 1:1000) OUT[i,]=perf(s[i],mod,y) plot(s,OUT[,1],xlab="Cutoff",ylab="Value",cex.lab=1.5,cex.axis=1.5,ylim=c(0,1),type="l",lwd=2,axes=FALSE,col=2) axis(1,seq(0,1,length=5),seq(0,1,length=5),cex.lab=1.5) axis(2,seq(0,1,length=5),seq(0,1,length=5),cex.lab=1.5) lines(s,OUT[,2],col="darkgreen",lwd=2) lines(s,OUT[,3],col=4,lwd=2) lines(s,OUT[,4],col="darkred",lwd=2) box() legend(0.5,.2,col=c(2,"darkgreen",4,"darkred"), lwd=c(2,2,2,2), c("Sensitivity","Specificity","Classification Rate","Distance")) colnames(OUT) <- c("Sensitivity","Specificity","Classification Rate","Distance") library(dplyr) OUT[OUT[,"sensitivity"] == OUT[,"specificity"]] mean(OUT[,"Sensitivity"] == OUT[,"Specificity"])

79cae1c023cd5457b0faa9e1658f8d1f973a54ca

ee976af89b21fa5a3b16c5eb9ffb99fd9b11ad91

/plot4.R

97423d453b6e942d8ea34a1a0087e5de7a128e2c

[]

no_license

jbrant/ExData_Project2

51b95f1cc4ff74fc21716874b61546b270960c3d

75510d85f93f61ccb1152dfc88e71a25efc1d691

refs/heads/master

2021-01-19T16:58:44.074063

2014-07-26T03:40:34

null

0

null

UTF-8

R

false

1,981

r

plot4.R

####################################################### ## Plot 4 ####################################################### ### Begin Setup ### ## Load Required libraries library(ggplot2) library(grid) library(data.table) ## Read in National Emissions Inventory Dataset if (!exists("NEI.data")) { NEI.data <- as.data.table(readRDS("summarySCC_PM25.rds")) } ## Read in Source Code Classification Dataset if (!exists("SCC.data")) { SCC.data <- as.data.table(readRDS("Source_Classification_Code.rds")) } ### End Setup ### ## Merge the NEI and SCC datasets, including only the following columns: ## 1. SCC ## 2. Emissions ## 3. year ## 4. EI.Sector data.merged <- merge( x = NEI.data[, c("SCC", "Emissions", "year"), with = FALSE], y = SCC.data[, c("SCC", "EI.Sector"), with = FALSE], by = "SCC") ## Extract emissions data for coal combustion sources data.coal <- data.merged[EI.Sector %in% data.merged[grep("Coal", data.merged$EI.Sector)]$EI.Sector] ## Calculate total emissions from coal combustion sources per year total.coal.emissions <- aggregate(data.coal$Emissions, list(data.coal$year), sum) ## Reset the names names(total.coal.emissions) <- c("Year", "Emissions") ## Open the PNG file device png(filename = "plot4.png", width = 640, height = 640, units = "px") ## Plot the total emissions from coal for each of the four years ggplot(total.coal.emissions, aes(x = Year, y = Emissions/1e5)) + geom_point(color = "indianred", size = 3) + geom_line(color = "steelblue") + theme(panel.margin = unit(0.8, "lines"), plot.title = element_text(face = "bold", size = 16, vjust = 1), axis.title.x = element_text(face = "bold"), axis.title.y = element_text(face = "bold")) + scale_x_continuous(breaks = seq(1999, 2008, by = 3)) + scale_y_continuous(limits = c(0, 6)) + labs(x = "Year", y = "Total Emissions (in 100,000 tons)", title = "Total Emissions from Coal Combustion (for U.S.)") ## Close the file device dev.off()

6d11fa558921e1222daa9e80f1aa35165e20ec2f

622c68ff62c4bf91664c4157fdcadcc1d7359a0a

/man/multiply.p.s.epsilon.Rd

19f7e5b6e8735389f2fe8f7863785890639f4575

[]

no_license

kdkorthauer/MADGiC

791b40130ea9df515cbd1f1ccd9aed90e730e378

4e763b526ebb8cbc87bdd6688761794e55b854cb

refs/heads/master

2021-01-10T08:52:45.487014

2020-06-09T16:38:51

47,279,073

3

2

null

UTF-8

R

false

2,419

rd

multiply.p.s.epsilon.Rd

\name{multiply.p.s.epsilon} \alias{multiply.p.s.epsilon} \title{Multiply the constants in \code{exome.constants} by the relative rate parameters of the background mutation model} \usage{ multiply.p.s.epsilon(X, p, s, epsilon, delta, gene) } \arguments{ \item{X}{a list with one entry for each chromosome, where each entry is a matrix containing a row for each coding base pair and the following columns: 1. base pair position, 2. nucleotide number (see \code{\link{convert.seq.to.num}}), 3. number of possible nonsilent transitions, 4. number of possible nonsilent transversions, 5. number of possible silent transitions, 6. number of possible silent transversions, and 7. whether the position is in a CpG dinucleotide.} \item{p}{a vector of length 7 containing the mutation type relative rate parameters in the following order: A/T transition, A/T transversion, non-CpG transition, non-CpG transversion, CpG transition, CpG transversion, and Indel.} \item{s}{a vector of length 3 containing the relative rate parameters for the three replication timing regions (1=Early, 2=Middle, 3=Late)} \item{epsilon}{a vector of length 3 containing the relative rate parameters for the three expression levels (1=Low, 2=Medium, 3=High)} \item{delta}{vector of length 2 where the second element represents the relative rate of mutation in olfactory receptor (OR) genes compared to all others within a similar replication timing and expression level category. First element set to 1 (reference category).} \item{gene}{a list with one entry for each gene, each entry is another list of 5 elements: Ensembl name, chromosome, base pairs, replication timing region (1=Early, 2=Middle, 3=Late), and expression level (1=Low, 2=Medium, 3=High).} } \value{ an object of the same structure as exome.constants, but columns 3-6 (e, f, c, d) have been multiplied by relative rate parameters \code{p}, \code{s}, and \code{epsilon}. } \description{ A function to multiply the constants e, f, c, and d in \code{exome.constants} by the relative rate parameters of the background mutation model. The parameters used depend on the mutation type, nucleotide context of the position, and the replication timing region and expression level of the gene that the position resides in. } \note{ This internal function is not intended to be called by the user. }

46582ce4101f2d03f91de3bb2d2ee32f92c647c7

386708f184c5e25d44f1fb877fae9b4ebc3de286

/ui.R

7b21a5a070429986acd9d04b35a24640af92f241

[]

no_license

DevinEnglish/bad-drivers-project

7aeda0eb5834014911040eaea68f829abba8aab4

62f88cf7f667ed0b2a1afbb36cb3fcae7e7fb26c

refs/heads/master

2020-05-21T13:19:28.064371

2019-06-05T23:14:14

186,068,779

0

null

UTF-8

R

false

19,595

r

ui.R

# # This is the user-interface definition of a Shiny web application. You can # run the application by clicking 'Run App' above. # # Find out more about building applications with Shiny here: # # http://shiny.rstudio.com/ # library(shiny) library(leaflet) library(htmltools) library(plotly) bad_driving <- read.csv("data/bad-drivers.csv", stringsAsFactors = FALSE) state_accidents <- read.csv("data/accidents-per-state-2017.csv", stringsAsFactors = FALSE) alcohol_levels <- read.csv("data/BAC-levels-of-drivers-in-accidents.csv", stringsAsFactors = FALSE) month <- read.csv("data/crashes-by-month-2017.csv", stringsAsFactors = FALSE) severity <- read.csv("data/Crashes-by-severity.csv", stringsAsFactors = FALSE) time_of_day <- read.csv("data/crashes-time-of-day-2017.csv", stringsAsFactors = FALSE) driver_demographic <- read.csv("data/driver-demographic-2017.csv", stringsAsFactors = FALSE) victim_types <- read.csv("data/injurys-and-victim-type.csv", stringsAsFactors = FALSE) # Define UI for application that draws a histogram shinyUI(fluidPage( div(img(src='banner.png'), align="center"), navbarPage(theme = shinythemes::shinytheme("slate"), "Bad Drivers", #Introduction page tabPanel("Home", fluidRow(column(7,offset = 3, h1("Bad Drivers and Insurance Statistics in the United States of America 2017", align="center") )), p("On average 3,287 die from a car accident every day, that is a lot of lives lost. Bad driving is a big problem in the United states taking the 3rd place spot on leading causes of death. While we have come a long way, increasing car safety through seatbelts and airbags, and educating more of the population on the importance of safe driving habits, there is still more to be done. The information in this application was gathered by the National Highway Traffic Safety Administration 2017 anual motor safety report. This application's aim is to educate insurance companies and other services related to road safety on trends to better implement their services, as well as provide information to the general public to be more aware of their own safety efforts."), div(img(src='photo1.jpg', width="1000px", height="500px"), align="center") ), #Interactive maps tabPanel("State Data", h2("Number of drivers involved in fatal collisions (per billion miles)"), leafletOutput("map1"), div("As Shown in this map, Southern, Central, and Central-Northern states have the highest number of drivers involved in fatal collisions per 1 billion miles. As a reminder, this is just correlation so further investigation is necessary in order to establish a well-established conclusion."), h2("Percentage of drivers who were speeding"), leafletOutput("map2"), div("While many drivers were speeding at the time of the accident, only Pennsylvania had a percentage that was 50 percent or above. Perhaps this suggests that most acts of incompetence that cause accidents are not speeding-related and that there are other signifiers to take into account when it comes to predicting what kinds of accidents to expect."), h2("Percentage of drivers who were alcohol impaired"), leafletOutput("map3"), div("On average, between 20 and 35 percent of fatal accidents were attributed to alcohol impairment in most states. However, States like Texas, South Carolina, Hawaii, Montana, Rhode Island and North Dakota had percentages that were in the high thirties or even low forties."), h2("Percentage of drivers who were NOT distracted"), leafletOutput("map4"), div("Surprisingly, The vast majority of drivers at the time of accidents were not classified as distracted. Wisconsin and Mississippi are exceptions as only 39 and 10 percent of drivers in accidents respectively were not classified as distracted."), h2("Percentage of drivers who had not been involved in previous accidents"), leafletOutput("map5"), div("The majority of drivers involved in such accidents do not have a history of accidents in the past. However, the percentage of those who were not involved in previous accidents does vary by state. The East Coast seems to have noticeably lower percentages of drivers who were not involved in previous accidents than other states. Maybe this could be due to higher population density leading to a higher concentrations of motor vehicles on the roads that is responsible for people being more likely to be involved in accidents in the past."), h2("Price of car insurance premiums"), leafletOutput("map6"), div("Most of the North Eastern USA, Florida, and Louisiana have the highest car insurance premiums. Overall, states in the south have higher insurance premiums than states in the north. The midwest is notable for having relatively low car insurance premiums in comparison to other states. Another interesting, detail is that Washington and Montana are notable for having higher insurance premiums in comparison to \ nearby states. "), h2("Cost of losses incurred by insurance companies per insured driver"), leafletOutput("map7"), div("The East coast and Southern states are shown to have the highest costs of losses incurred by insurance companies per insured drivers. This could be linkd to the number of drivers involved in fatal collisions per 1 billion miles per state. However, Montana, North Dakota, and South Dakota have some of the highest numbers of drivers involved in fatal collisions per 1 billion miles but still has some of the lowest costs of losses incurred by insurance. Further investigation would be necessary to find a better sense of correlation and causation.") ), tabPanel("Drunk Driving", navbarPage("The Data", tabPanel("Drunk Driving", sidebarLayout( sidebarPanel( sliderInput("yearForAlcGraph", label = h3("Year"), min = 1988, #fix scaling to be in years max = 2017, value = 2000, animate = TRUE), p("While many accidents happened when the driver had a BAC of 0, these accidents were likely caused by other distractions such as texting while driving or other distracted driving. While those accidents are a majority accidents caused by those legally intoxicated have large numbers as well. Since 1988 the number of accidents caused by drunk driving has gone down significantly as research and awareness of achohol's harm has increased. Services such as Uber and Lift have also helped to decrease the number of intoxiated people on the roads, by providing them a safe way to get home.")), mainPanel(plotlyOutput("alcoholLevelsGraph"), p(" Getting behind the wheel of a vehicle after a night of brinking is a crime. Driving under the influence (DUI) is defined as operating a vehicle with a blood alcohol content of 0.08% or above. A DUI is classified as a misdemeanor in all states, and punishment usually means up to six months in jail on a first offense, time may be increased by situation. Even drinking a small amount of alcohol can be harmful. The effects of alcohol put you at a higher risk of accident or road injury. It takes a lot of ability to drive safely such as concentration and quick judgments both of which alcohol affects"), p(" Slow reaction times: Drinking slows your response time which increases the likelihood of an accident."), p(" Reduced coordination: Drinking affects your vision, and hand and foot coordination, all of which are very important when driving."), p(" Decreased concentration: Alcohol greatly affects concentration, something very important when driving such as staying in your lane, paying attention to other cars and understanding traffic signs.") ) ) ), tabPanel("Drunk Driving Table",mainPanel(tableOutput("alcoholLevelsTable"))) ) ), tabPanel("Trends in 2017", navbarPage("The Data", tabPanel("Monthly Trends", sidebarLayout( sidebarPanel(radioButtons("byMonthAccidentType", label = h3("Type of Accident"), choices = list("Fatal" = "Fatal", "Injury Only" = "Injury Only", "Property Damage" = "Property Damage"), selected = "Fatal"), p("Fatal accidents seem to decline when transitioning from Fall to winter and rising significantly from early winter to mid summer. Injuries also appear to be more common during the spring, summer, and fall compared to winter months. Another interesting observation is that mid summer shows noticeably fewer instances of propoerty damage compared to winter, spring, and fall. ") ), mainPanel(plotlyOutput("byMonthGraph")) ) ), tabPanel("Weekly Trends", sidebarLayout( sidebarPanel(selectInput("dayOfWeek", label = h3("Day of the Week"), choices=list("Monday"=3,"Tuesday"=4,"Wednesday"=5,"Thursday"=6,"Friday"=7, "Saturday"=8,"Sunday"=2), selected = 3), p("The average number of accidents seems to rise from early moorning to 3-6 PM and declines shortly after. Perhaps this is due to rush hour leading to a higher concentration of cars on the road") ), mainPanel(plotlyOutput("TODGraph")) ) ), navbarMenu("Tables", tabPanel("Monthly Trends Table", tableOutput("monthTable") ), tabPanel("Weekly Trends Table", tableOutput("timeOfDayTable")) ) ) ), tabPanel("Trends Through the Years", navbarPage("The Data", tabPanel("Accident Severity", sidebarLayout( sidebarPanel(radioButtons("severityAccidentType", label = h3("Type of Accident"), choices = list("Fatal" = "Fatal", "Injury Only" = "Injury Only", "Property Damage" = "Property Damage"), selected = "Fatal"), p("Over the years, fatal injuries have decreased and/or plateaued. However, starting in 2010, the number of fatal accidents has begun to rise. Injury-only accidents also experienced similar declines and raises except without the same plateauing phenomenon. However, for instances of property damage, relatively consistent numbers have been maintained between the mid 1990s and late 2000's. ") ), mainPanel(plotlyOutput("severityGraph"), p("Many inovative technologies have helped reduce the number of accidents on our roads. Some of those technologies are..."),p("Passenger restraints such as seat belts"),p("Airbags"), p("Crash avoidance equipment such as lights and reflectors"), p("Driver assistance systems such as Electronic Stability Control"), p("Safety glass"), p("Car services such as Uber help get intoxicated people off the road")) ) ), tabPanel("Severity Table", tableOutput("severityTable")) )), tabPanel("Accident Demographic", navbarPage("The Data", tabPanel("Driver Demographic", sidebarLayout( sidebarPanel(radioButtons("age", label = h3("Age"), choices = list("<16" = "<16", "16-20" = "16-20", "21-24" = "21-24", "25-34" = "25-34", "35-44" = "35-44", "45-54" = "45-54", "55-64" = "55-64", "65-74" = "65-74", ">75" = ">74"), selected = "<16"), p("In any given age group, males were more likely to be involved in car accidents than females. The age group that had the most number of accidents were males and females age 25-34") ), mainPanel(plotlyOutput("demoGraph")) ) ), tabPanel("Victim Demographic", sidebarLayout( sidebarPanel(radioButtons("victimType", label = h3("Type of Transportation Victim Was Using"), choices = list("Passenger Car"= "Passenger Car", "Light Truck"= "Light Truck", "Large Truck"= "Large Truck", "Bus"= "Bus","Motorcycle"="Motorcycle", "Pedestrian"="Pedestrian", "Cyclist"="Cyclist", "Total"= "Total"), selected = "Passenger Car"), p("In 1997, the total number of vehiclees victimized in accidents peaked at 3.348 Million vehicles. Every year, the majority of vehicles victimized in accidents were Light Trucks.") ), mainPanel(plotlyOutput("victimDemo")) )), navbarMenu("Tables", tabPanel("Driver Demographic Table", tableOutput("driverDemographicTable") ), tabPanel("Victim Demogaphic", tableOutput("victimTypeTable") ) ) ) ) ) ))

dbe36f579fd6ba0db7f89ef5bb2fc9d0ad4eb41d

b05bd9f10c51f8115043fcfd0e42caf8c7a0e555

/assignment5/run_periodically.R

1f2cb636667667d095394949742c1143c51b8433

[]

no_license

johngonz/datascience_uwash

56e440d34e95bb2899033624d978fd7f4b527918

9f2a11f0083e1885fb0d591c1136b31d0cd3a981

refs/heads/master

2021-01-10T09:55:50.717129

2015-12-09T09:24:22

43,198,338

0

null

UTF-8

R

false

345

r

run_periodically.R

# Run this code periodically #T if(!require(installr)) {install.packages("installr"); require(installr)} #load / install+load installr updateR() # This code updates all R packages x <- packageStatus(repositories="http://cran.r-project.org/src/contrib") st <- x$avai["Status"] install.packages(rownames(st)[which(st$Status=="not installed")])

8498fef2b90b1d815942ba54bb17096365fe055d

e68e99f52f3869c60d6488f0492905af4165aa64

/man/torch_greater.Rd

887819a8ad43c7104a9243f22610c1a295557684

[ "MIT" ]

permissive

mlverse/torch

a6a47e1defe44b9c041bc66504125ad6ee9c6db3

f957d601c0295d31df96f8be7732b95917371acd

refs/heads/main

2023-09-01T00:06:13.550381

2023-08-30T17:44:46

232,347,878

448

86

NOASSERTION

2023-09-11T15:22:22

2020-01-07T14:56:32

C++

UTF-8

R

false

true

501

rd

torch_greater.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/gen-namespace-docs.R, % R/gen-namespace-examples.R, R/gen-namespace.R \name{torch_greater} \alias{torch_greater} \title{Greater} \usage{ torch_greater(self, other) } \arguments{ \item{self}{(Tensor) the tensor to compare} \item{other}{(Tensor or float) the tensor or value to compare} } \description{ Greater } \section{greater(input, other, *, out=None) -> Tensor }{ Alias for \code{\link[=torch_gt]{torch_gt()}}. }