pierreqi/r_code_50k · Datasets at Hugging Face

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/R/din.equivalent.class.R

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alexanderrobitzsch/CDM

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din.equivalent.class.R

## File Name: din.equivalent.class.R ## File Version: 0.191 #**** calculation of equivalent skill classes din.equivalent.class <-function( q.matrix, rule="DINA") { Q <- q.matrix # Matrix with all skill classes S <- expand.grid( as.data.frame( t( matrix( rep( c(0,1), each=ncol(Q)), ncol=2 )))) J <- nrow(Q) if ( length(rule)==1){ rule <- rep( rule, J ) } rownames(S) <- paste0("Skills_", apply( S, 1, FUN=function(ll){ paste(ll, collapse="" ) } ) ) # Calculation of latent response of every skill class A <- din_equivalent_class_latent_response(q.matrix=Q,S=S,rule=rule) A <- t(A) I <- nrow(A) # calculate latent responses latent.response <- paste0("LatResp_", sapply( 1:I, FUN=function(ii){ paste( A[ ii, ], collapse="" ) } ) ) skillclasses <- data.frame( "skillclass"=rownames(S) ) skillclasses$latent.response <- latent.response # define distinguishable skill classes skillclasses$distinguish.class <- match( latent.response, unique( latent.response ) ) # calculates how many skill classes correspond to the same latent response latent.response <- table( latent.response ) six <- sort( latent.response, index.return=FALSE, decreasing=TRUE) gini_mod <- cdm_gini( as.numeric(six) ) res <- list( "latent.responseM"=A, "latent.response"=latent.response, "S"=S, "gini"=gini_mod, "skillclasses"=skillclasses ) cat( nrow(S), "Skill classes |", max( skillclasses$distinguish.class ), " distinguishable skill classes |", "Gini coefficient=", round( gini_mod,3 ), "\n") return(res) }

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/mgpd/R/Alog.R

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ingted/R-Examples

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Alog.R

Alog <- function(t1, t2, alpha=2 ,... ) eval({t1<-t1; t2<-t2; alpha<-alpha; .Alog})

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/primer/ejp-1/primero.R

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davidlechuga/Remedia-est-des

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primero.R

library(readxl) #leer archivos en excel library(dplyr) # libreria para manipular frames library(graphics) # libreria de grafos primero <- read_excel("primero.xlsx", col_names = FALSE) #abrimos el documento typeof(primero) # vemos el tipo de variables del objeto View(primero) primero1<-as.matrix(primero) # convertimos una lista en una matriz typeof(primero1) # vemos el tipo de variables del objeto sort(primero1, decreasing = FALSE) # ordenamos los resultados de menor a mayor. segundo<-data.frame(sort(primero1, decreasing = FALSE)) # los convertimos en una lista typeof(segundo) # vemos el tipo de variables del objeto segundo1<-as.matrix(segundo) # convertimos una lista en una matriz typeof(segundo1) # emos el tipo de variables del objeto colnames(segundo)<-c("edades") # le agregamos un nombre a la columna. colnames(segundo1)<-c("edades") length(segundo1) # vemos el tamaño del vector lugardelamediana<-length((segundo1)+1)/2 # lugar de la mediana print(segundo1[15:16]) # los lugares de la mediana son 15,y 16 (46+47)/2 # el valor de la mediana es 46.5 stem(segundo$edades) # we crearte a steam- and -leaf-diagram of edades tabulate(segundo$edades) # de 1-80 cuantas veces se repitio el numero ? table(segundo$edades) # solo de los resultados cuales se repitieron?

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/taylor_sin.R

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Asmith9555/R_Functions

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taylor_sin.R

#--- taylor_sin <- function(x, n){ taylor_approx <- 0 sin_function <- sin(x) for (k in 0:n){ taylor_approx <- taylor_approx + ((-1)^k)*((x^((2*k)+1))/(factorial((2*k)+1))) } return(taylor_approx) }

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/Code/install_pkg.R

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omega-eol/ds_boilerplate

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install_pkg.R

# list of most common packages that I usually use # update all old packages first update.packages(); # to support parallel computation install.packages("codetools"); install.packages("foreach"); install.packages("doMC"); install.packages("parallel"); # sparse matrix install.packages("Matrix"); # c++ support install.packages("Rcpp"); install.packages("RcppEigen"); install.packages("RcppArmadillo"); # DB support # make sure that libpq-dev package is installed! install.packages("RPostgreSQL"); # Optimization install.packages("DEoptim"); install.packages("GenSA"); # classifiers install.packages("randomForest"); install.packages("gbm"); install.packages('rpart'); # ds support install.packages('caret'); install.packages('PRROC');

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/Weekend_Homework.R

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Nkomo1997/Intro_to_R_UWC

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Weekend_Homework.R

# Instructions: # Submit: Tuesday morning (before 10-am) # Answer each of the sections in an individual script # Answer all sections # Add comments and notes thoughout the script # Section 1: # Make use of the rast_feb and rast_aug dataset: # Explore the dataset (Hint* head, tail, glimpse etc) - Make use of google for more functions on exploring a dataset # Create a map by making use of the lat and long variables(for each of the datasets) # Create a colour pallete using the link in the document and make use this colour pallete on the map # Add complete labels and titles to the map # Add the name of the oceans (Atlanic and indian ocean) on the map, increase the size of the labels # The map should include the north arrow and scale bar(both maps) # Bonus marks for insetting (having a smaller map inside another map) # Get creative, try new things. # Section 2:(make details on top, load librariess, load data then explore it before coding) # Make use of the ecklonia.csv dataset: # Explore the data (Hint* head, tail, glimpse functions) # Demonstrate the dimensions of the dataset # Create three graphs; bargraph, line graph and boxplot: Write hypothesis for each of the graphs and answer these hypotheses # Make use of the ggarrange function and arrange these three graphs created above into 1 plot # All graphs must have labels as well as titles # Calculate the mean,max,min,median and variance for the stipe_length, stipe_diameter for each of the sites (Hint* group_by site) # Calculate standard error # Determine the min and maximum frond length and stipe length # Determine the overall summary of the dataset(use summary and put dataset name) # Section 3: # Make use of the SACTN_day1 data: # Here create a graph showing temperature variation between sites(group_by=site) # Select all the temperatures recorded at the site Port Nolloth during August or September. # Select all the monthly temperatures recorded in Port Nolloth during the year 1994 # Calculate the average temperature by depth # Work through the tidyverse section within the document. Show what you have done by creating comments/ notes throughout the script(tidy to tidiest) # Section 4: # Make use of any two built in datasets: # Make use of the summarise, select, group_by functions(include in one code or run seperately) # Create at least two visualisations that were not done in the Intro R workshop(eg.density plot-type geom_ and select any graph that pops up and create) ## Good luck!!! ---------------------------------------------------------------------------------------------

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/server.R

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fleschgordon/Coursera_DevDataProd

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server.R

# # This is the server logic of a Shiny web application. # library(shiny) library(shiny) library(dplyr) library(tidyr) library(ggplot2) library(plotly) library(RSQLite) library(reshape2) library(RColorBrewer) library(gplots) library(data.table) library(d3heatmap) library(viridis) # Define server logic required to draw a histogram shinyServer(function(input, output) { # Connect to data base ---------------------------------------------------- con <- dbConnect(SQLite(), dbname="database.sqlite") player <- tbl_df(dbGetQuery(con,"SELECT * FROM player")) Match <- tbl_df(dbGetQuery(con,"SELECT * FROM Match")) Team <- tbl_df(dbGetQuery(con,"SELECT * FROM Team")) Country <- tbl_df(dbGetQuery(con,"SELECT * FROM Country")) League <- tbl_df(dbGetQuery(con,"SELECT * FROM League")) # select columns player <- select(player,player_api_id, player_name) # use player_api_id as key for join Team <- select(Team, team_api_id, team_long_name, team_short_name) # use team_api_id as key for join Country <-select(Country, id, name) %>% rename(country_id = id) %>% rename(country_name = name) # use country_id as key for join League <- select(League, country_id, name) %>% rename(league_name = name) # use country_id as key for join Match <-select(Match, id, country_id, league_id, season, stage, date, match_api_id, home_team_api_id, away_team_api_id, home_team_goal, away_team_goal, home_player_1, home_player_2, home_player_3, home_player_4, home_player_5, home_player_6, home_player_7, home_player_8, home_player_9, home_player_10, home_player_11, away_player_1, away_player_2, away_player_3, away_player_4, away_player_5, away_player_6, away_player_7, away_player_8, away_player_9, away_player_10, away_player_11, goal, shoton, shotoff, foulcommit, card, cross, corner, possession) dbDisconnect(con) PointsDf <-Match %>% select(1:11) %>% mutate(homePoint = if_else((home_team_goal > away_team_goal),3,if_else((home_team_goal == away_team_goal),1,0))) %>% mutate(awayPoint = if_else((home_team_goal > away_team_goal),0,if_else((home_team_goal == away_team_goal),1,3))) tableHomeDt <- PointsDf %>% group_by(season, league_id, home_team_api_id) %>% summarise(pointsHome = sum(homePoint)) %>% ungroup() %>% data.table keycols = c("season", "league_id", "home_team_api_id" ) setkeyv(tableHomeDt,keycols) tableAwayDt <- PointsDf %>% group_by(season, league_id, away_team_api_id) %>% summarise(pointsAway = sum(awayPoint)) %>% ungroup() %>% data.table keycols = c("season", "league_id", "away_team_api_id" ) setkeyv(tableAwayDt,keycols) tableHomeAwayDt <- tableHomeDt[tableAwayDt, nomatch=0] %>% mutate(points = pointsHome + pointsAway) %>% group_by(season, league_id) %>% mutate(rank = min_rank(desc(points))) tableLong <- tableHomeAwayDt %>% left_join(League, by = c("league_id" = "country_id")) %>% left_join(Team, by = c("home_team_api_id" = "team_api_id")) %>% ungroup() %>% select(season, league_name, rank, team_long_name, points) output$Heatmap <- renderPlotly({ seasonsdata <- subset(tableLong, season %in% input$selSeason) seasonsdata$points <- as.factor(seasonsdata$points) p <- ggplot(filter(seasonsdata, league_name %in% input$selLeague), mapping = aes(x = season, y = team_long_name)) + geom_tile(mapping = aes(fill = points),color="white", size=0.1 ) + facet_grid(league_name~., scales = "free_y") +scale_fill_viridis(discrete=TRUE) + theme(legend.position = "none") # free y scale to avoid that all clubs are on Y axis in all leagues ggplotly(p) #p }) })

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/readcsv.r

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sayefi/test-repo

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2021-01-02T23:05:13.755078

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readcsv.r

fileUrl<-"https://d396qusza40orc.cloudfront.net/getdata%2Fwksst8110.for" download.file(fileUrl,"data/quizdata.csv") list.files("./data") fData<-read.csv("data/quizdata.csv",sep=" ",skip = 3,header = TRUE) fData<-read.fwf("data/quizdata.csv",skip=4,widths=c(12, 7, 4, 9, 4, 9, 4, 9, 4)) sum(fData$V4)

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/test.R

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kalexanderk/TestTask_SAS

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refs/heads/master

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test.R

library("openxlsx") df_initial <- read.xlsx("Data/payments.xlsx", sheet=1) typeof(df_initial) class(df_initial) change_date <- function(x) as.Date(x ,origin='1960-01-01') df <- data.frame(df_initial['lead_id'], lapply(df_initial['dt'], change_date), df_initial['amount']) df['weekday'] <- format(df['dt'], "%A") summary(df) shift <- function(d, k) rbind( tail(d,k), head(d,-k), deparse.level = 0 ) df['id_shifted'] <- shift(df['lead_id'], 1) coun<-0 find_days_not_returned<-function(df){ df['days_not_returned']<-0 for(i in 1:nrow(df)){ if ((df[i, 'lead_id']==df[i, 'id_shifted']) & (df[i, 'amount']==0)){ if ((df[i, 'weekday']=='Saturday') | (df[i, 'weekday']=='Sunday')) { df[i, 'days_not_returned']=coun } else{ coun=coun+1 df[i, 'days_not_returned']=coun } } else if ((df[i, 'lead_id']!=df[i, 'id_shifted']) | (df[i, 'amount']>0)){ if (df[i, 'amount'] != 0){ coun=0 df[i,'days_not_returned']=coun } if (df[i, 'amount'] == 0){ coun=1 df[i, 'days_not_returned']=coun } } } return(df) } df_t<-find_days_not_returned(df) df_t$id_shifted=NULL #df_t is the result write.xlsx(df_t, "Files/result.xlsx")

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/R/sperrorest.R

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cran/sperrorest

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sperrorest.R

#' @title Perform spatial error estimation and variable importance assessment #' #' @description {sperrorest} is a flexible interface for multiple types of #' parallelized spatial and non-spatial cross-validation and bootstrap error #' estimation and parallelized permutation-based assessment of spatial variable #' importance. #' #' @details Custom predict functions passed to `pred_fun`, which consist of #' multiple child functions, must be defined in one function. #' #' @section Parallelization: #' #' Running in parallel is supported via package \CRANpkg{future}. #' Have a look at `vignette("future-1-overview", package = "future")`. #' In short: Choose a backend and specify the number of workers, then call #' `sperrorest()` as usual. Example: #' #' ```r #' future::plan(future.callr::callr, workers = 2) #' sperrorest() #' ``` #' Parallelization at the repetition is recommended when using #' repeated cross-validation. If the 'granularity' of parallelized #' function calls is too fine, the overall runtime will be very #' poor since the overhead for passing arguments and handling #' environments becomes too large. Use fold-level parallelization #' only when the processing time of individual folds is very #' large and the number of repetitions is small or equals 1. #' #' Note that nested calls to `future` are not possible. #' Therefore a sequential `sperrorest` call should be used for #' hyperparameter tuning in a nested cross-validation. #' #' @importFrom future.apply future_lapply #' @importFrom utils packageVersion tail #' @importFrom stringr str_replace_all #' #' @param data a `data.frame` with predictor and response variables. Training #' and test samples will be drawn from this data set by `train_fun` and #' `test_fun`, respectively. #' #' @param formula A formula specifying the variables used by the `model`. Only #' simple formulas without interactions or nonlinear terms should be used, #' e.g. `y~x1+x2+x3` but not `y~x1*x2+log(x3)`. Formulas involving interaction #' and nonlinear terms may possibly work for error estimation but not for #' variable importance assessment, but should be used with caution. #' The formula `y~...` is not supported, but `y~1` (i.e. no predictors) is. #' @param coords vector of length 2 defining the variables in `data` that #' contain the x and y coordinates of sample locations. #' @param model_fun Function that fits a predictive model, such as `glm` or #' `rpart`. The function must accept at least two arguments, the first one #' being a formula and the second a data.frame with the learning sample. #' @param model_args Arguments to be passed to `model_fun` (in addition to the #' `formula` and `data` argument, which are provided by {sperrorest}) #' @param pred_fun Prediction function for a fitted model object created by #' `model`. Must accept at least two arguments: the fitted `object` and a #' `data.frame` `newdata` with data on which to predict the outcome. #' @param pred_args (optional) Arguments to `pred_fun` (in addition to the #' fitted model object and the `newdata` argument, which are provided by #' {sperrorest}). #' @param smp_fun A function for sampling training and test sets from `data`. #' E.g. [partition_kmeans] for spatial cross-validation using spatial #' *k*-means clustering. #' @param smp_args (optional) Arguments to be passed to `smp_fun`. #' @param train_fun (optional) A function for resampling or subsampling the #' training sample in order to achieve, e.g., uniform sample sizes on all #' training sets, or maintaining a certain ratio of positives and negatives in #' training sets. E.g. [resample_uniform] or [resample_strat_uniform]. #' @param train_param (optional) Arguments to be passed to `resample_fun`. #' @param test_fun (optional) Like `train_fun` but for the test set. #' @param test_param (optional) Arguments to be passed to `test_fun`. #' @param err_fun A function that calculates selected error measures from the #' known responses in `data` and the model predictions delivered by #' `pred_fun`. E.g. [err_default] (the default). #' @param imp_variables (optional; used if `importance = TRUE`). Variables for #' which permutation-based variable importance assessment is performed. If #' `importance = TRUE` and `imp_variables` == `NULL`, all variables in #' `formula` will be used. #' @param imp_sample_from (default: `"test"`): specified if the permuted feature #' values should be taken from the test set, the training set (a rather unlikely #' choice), or the entire sample (`"all"`). The latter is useful in #' leave-one-out resampling situations where the test set is simply too small #' to perform any kind of resampling. In any case importances are #' always estimates on the test set. (Note that resampling with replacement is #' used if the test set is larger than the set from which the permuted values #' are to be taken.) #' @param imp_permutations (optional; used if `importance = TRUE`). Number of #' permutations used for variable importance assessment. #' @param importance logical (default: `FALSE`): perform permutation-based #' variable importance assessment? #' @param distance logical (default: `FALSE`): if `TRUE`, calculate mean #' nearest-neighbour distances from test samples to training samples using #' [add.distance.represampling]. #' @param do_gc numeric (default: 1): defines frequency of memory garbage #' collection by calling [gc]; if `< 1`, no garbage collection; if `>= 1`, run #' a [gc] after each repetition; if `>= 2`, after each fold. #' @param progress character (default: `all`): Whether to show progress #' information (if possible). Default shows repetition, fold and (if enabled) #' variable importance progress. Set to `"rep"` for repetition information #' only or `FALSE` for no progress information. #' @param mode_rep,mode_fold character (default: `"future"` and `"sequential"`, #' respectively): specifies whether to parallelize the execution at the repetition #' level, at the fold level, or not at all. #' Parallel execution uses `future.apply::future_lapply()` (see details below). #' It is only possible to parallelize at the repetition level or at #' the fold level. #' The `"loop"` option uses a `for` loop instead of an `lappy` #' function; this option is for debugging purposes. #' @param benchmark (optional) logical (default: `FALSE`): if `TRUE`, perform #' benchmarking and return `sperrorestbenchmark` object. #' @param verbose Controls the amount of information printed while processing. #' Defaults to 0 (no output). #' #' @return A list (object of class {sperrorest}) with (up to) six components: #' - error_rep: `sperrorestreperror` containing #' predictive performances at the repetition level #' - error_fold: `sperroresterror` object containing predictive #' performances at the fold level #' - represampling: [represampling] object #' - importance: `sperrorestimportance` object containing #' permutation-based variable importances at the fold level #' - benchmark: `sperrorestbenchmark` object containing #' information on the system the code is running on, starting and #' finishing times, number of available CPU cores and runtime performance #' - package_version: `sperrorestpackageversion` object containing #' information about the {sperrorest} package version #' #' @references Brenning, A. 2012. Spatial cross-validation and bootstrap for #' the assessment of prediction rules in remote sensing: the R package #' 'sperrorest'. #' 2012 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), #' 23-27 July 2012, p. 5372-5375. #' <https://ieeexplore.ieee.org/document/6352393> #' #' Brenning, A. 2005. Spatial prediction models for landslide hazards: review, #' comparison and evaluation. Natural Hazards and Earth System Sciences, #' 5(6), 853-862. \doi{10.5194/nhess-5-853-2005} #' #' Brenning, A., S. Long & P. Fieguth. 2012. Detecting rock glacier #' flow structures using Gabor filters and IKONOS imagery. #' Remote Sensing of Environment, 125, 227-237. #' \doi{10.1016/j.rse.2012.07.005} #' #' Russ, G. & A. Brenning. 2010a. Data mining in precision agriculture: #' Management of spatial information. In 13th International Conference on #' Information Processing and Management of Uncertainty, IPMU 2010; Dortmund; #' 28 June - 2 July 2010. Lecture Notes in Computer Science, 6178 LNAI: 350-359. #' #' Russ, G. & A. Brenning. 2010b. Spatial variable importance assessment for #' yield prediction in Precision Agriculture. In Advances in Intelligent #' Data Analysis IX, Proceedings, 9th International Symposium, #' IDA 2010, Tucson, AZ, USA, 19-21 May 2010. #' Lecture Notes in Computer Science, 6065 LNCS: 184-195. #' #' @examples #' #' ## ------------------------------------------------------------ #' ## Classification tree example using non-spatial partitioning #' ## ------------------------------------------------------------ #' #' # Muenchow et al. (2012), see ?ecuador #' fo <- slides ~ dem + slope + hcurv + vcurv + log.carea + cslope #' #' library(rpart) #' mypred_part <- function(object, newdata) predict(object, newdata)[, 2] #' ctrl <- rpart.control(cp = 0.005) # show the effects of overfitting #' # show the effects of overfitting #' fit <- rpart(fo, data = ecuador, control = ctrl) #' #' ### Non-spatial cross-validation: #' mypred_part <- function(object, newdata) predict(object, newdata)[, 2] #' nsp_res <- sperrorest( #' data = ecuador, formula = fo, #' model_fun = rpart, #' model_args = list(control = ctrl), #' pred_fun = mypred_part, #' progress = TRUE, #' smp_fun = partition_cv, #' smp_args = list(repetition = 1:2, nfold = 3) #' ) #' summary(nsp_res$error_rep) #' summary(nsp_res$error_fold) #' summary(nsp_res$represampling) #' # plot(nsp_res$represampling, ecuador) #' #' ### Spatial cross-validation: #' sp_res <- sperrorest( #' data = ecuador, formula = fo, #' model_fun = rpart, #' model_args = list(control = ctrl), #' pred_fun = mypred_part, #' progress = TRUE, #' smp_fun = partition_kmeans, #' smp_args = list(repetition = 1:2, nfold = 3) #' ) #' summary(sp_res$error_rep) #' summary(sp_res$error_fold) #' summary(sp_res$represampling) #' # plot(sp_res$represampling, ecuador) #' #' smry <- data.frame( #' nonspat_training = unlist(summary(nsp_res$error_rep, #' level = 1 #' )$train_auroc), #' nonspat_test = unlist(summary(nsp_res$error_rep, #' level = 1 #' )$test_auroc), #' spatial_training = unlist(summary(sp_res$error_rep, #' level = 1 #' )$train_auroc), #' spatial_test = unlist(summary(sp_res$error_rep, #' level = 1 #' )$test_auroc) #' ) #' boxplot(smry, #' col = c("red", "red", "red", "green"), #' main = "Training vs. test, nonspatial vs. spatial", #' ylab = "Area under the ROC curve" #' ) #' @export sperrorest <- function(formula, data, coords = c("x", "y"), model_fun, model_args = list(), pred_fun = NULL, pred_args = list(), smp_fun = partition_cv, smp_args = list(), train_fun = NULL, train_param = NULL, test_fun = NULL, test_param = NULL, err_fun = err_default, imp_variables = NULL, imp_permutations = 1000, imp_sample_from = c("test", "train", "all"), importance = !is.null(imp_variables), distance = FALSE, do_gc = 1, progress = "all", benchmark = FALSE, mode_rep = c("future", "sequential", "loop"), mode_fold = c("sequential", "future", "loop"), verbose = 0) { if (verbose >= 1) { cat("sperrorest version", as.character(packageVersion("sperrorest")), "\n") cat("(c) A. Brenning, P. Schratz, and contributors\n") cat("Cite as Brenning (2012), doi: 10.1109/igarss.2012.6352393\n") } # set global variables for R CMD Check current_res <- NULL pooled_obs_train <- NULL pooled_obs_test <- NULL # if benchmark = TRUE, start clock if (benchmark) { start_time <- Sys.time() } # Some checks: if (missing(model_fun)) { stop("'model_fun' is a required argument") } if (any(all.vars(formula) == "...")) { stop("formula of the form lhs ~ ... not accepted by 'sperrorest'\n specify all predictor variables explicitly") } stopifnot(is.function(model_fun)) stopifnot(is.function(smp_fun)) if (!is.null(train_fun)) { stopifnot(is.function(train_fun)) } if (!is.null(test_fun)) { stopifnot(is.function(test_fun)) } stopifnot(is.function(err_fun)) if (importance) { stopifnot(is.numeric(imp_permutations)) if (!is.null(imp_variables)) { stopifnot(is.character(imp_variables)) # nocov } imp_sample_from <- match.arg(imp_sample_from) } stopifnot(is.character(coords)) stopifnot(length(coords) == 2) mode_rep <- match.arg(mode_rep) mode_fold <- match.arg(mode_fold) if ((mode_rep == "future") & (mode_fold == "future")) { warning("Only parallelization at either the repetition level or the fold level\nis supported. Using mode_fold = 'sequential'.") mode_fold <- "sequential" } mode_dist <- mode_rep # Check if user is trying to bypass the normal mechanism for # generating training and test data sets and for passing formulas: if (any(names(model_args) == "formula")) { stop("'model_args' cannot have a 'formula' element") } if (any(names(model_args) == "data")) { stop("'model_args' cannot have a 'data' element") } if (any(names(pred_args) == "object")) { stop("'pred_args' cannot have an 'object' element:\n this will be generated by 'sperrorest'") } if (any(names(pred_args) == "newdata")) { stop("'pred_args' cannot have a 'newdata' element:\n this will be generated by 'sperrorest'") } # account for tibbles as input if (any(class(data) == "tbl")) { data <- as.data.frame(data) } # Name of response variable: response <- all.vars(formula)[1] if (verbose >= 1) cat(date(), "Creating resampling object...\n") smp_args$data <- data smp_args$coords <- coords resamp <- do.call(smp_fun, args = smp_args) if (distance) { if (verbose >= 1) cat(date(), "Adding distance information to resampling object...\n") resamp <- add.distance(object = resamp, data = data, coords = coords, fun = mean, mode = mode_dist[1]) if (verbose >= 3) { cat("\n-----------------------------\nResampling object:", "\n-----------------------------\n") print(resamp) cat("\n-----------------------------\n") } } res <- lapply(resamp, unclass) class(res) <- "sperroresterror" pooled_error <- NULL ### Permutation-based variable importance assessment (optional): impo <- NULL if (importance) { # Importance of which variables: if (is.null(imp_variables)) { imp_variables <- all.vars(formula)[-1] # imp_variables <- strsplit(as.character(formula)[3], " + ", # fixed = TRUE)[[1]] } if (length(imp_variables) == 0) { importance <- FALSE warning("importance is TRUE, but there are no predictors,\n", "or no predictors have been selected; using importance = FALSE.") } } if (importance) { # Dummy data structure that will later be populated with the results: impo <- resamp # Create a template that will contain results of variable importance # assessment: imp_one_rep <- as.list(rep(NA, length(imp_variables))) names(imp_one_rep) <- imp_variables tmp <- as.list(rep(NA, imp_permutations)) names(tmp) <- as.character(seq_len(imp_permutations)) for (vnm in imp_variables) { imp_one_rep[[vnm]] <- tmp } rm(tmp) } # runreps call Sun Apr 9 13:28:31 2017 ------------------------------ # mode = "future" Sun May 21 12:04:55 2017 ----------------------------- if (verbose >= 1) cat(date(), "Running the model assessment...\n") if (mode_rep == "sequential") { my_res <- lapply(seq_along(resamp), function(x) { runreps( current_sample = resamp[[x]], data = data, formula = formula, response = response, do_gc = do_gc, imp_one_rep = imp_one_rep, pred_fun = pred_fun, model_args = model_args, model_fun = model_fun, imp_permutations = imp_permutations, imp_variables = imp_variables, imp_sample_from = imp_sample_from, importance = importance, current_res = current_res, pred_args = pred_args, coords = coords, progress = progress, mode_fold = mode_fold, pooled_obs_train = pooled_obs_train, train_fun = train_fun, train_param = train_param, test_fun = test_fun, test_param = test_param, pooled_obs_test = pooled_obs_test, err_fun = err_fun, i = x ) } ) } else if (mode_rep == "future") { my_res <- future.apply::future_lapply(seq_along(resamp), function(x) { runreps( current_sample = resamp[[x]], data = data, formula = formula, response = response, do_gc = do_gc, imp_one_rep = imp_one_rep, pred_fun = pred_fun, model_args = model_args, model_fun = model_fun, imp_permutations = imp_permutations, imp_variables = imp_variables, imp_sample_from = imp_sample_from, importance = importance, current_res = current_res, pred_args = pred_args, coords = coords, progress = progress, mode_fold = mode_fold, pooled_obs_train = pooled_obs_train, train_fun = train_fun, train_param = train_param, test_fun = test_fun, test_param = test_param, pooled_obs_test = pooled_obs_test, err_fun = err_fun, i = x ) }, future.seed = TRUE ) } else if (mode_rep == "loop") { # for loop as a safety net for debugging purposes: my_res <- list() for (i_rep in seq_along(resamp)) { my_res[[i_rep]] <- runreps( current_sample = resamp[[i_rep]], data = data, formula = formula, response = response, do_gc = do_gc, imp_one_rep = imp_one_rep, pred_fun = pred_fun, model_args = model_args, model_fun = model_fun, imp_permutations = imp_permutations, imp_variables = imp_variables, imp_sample_from = imp_sample_from, importance = importance, current_res = current_res, pred_args = pred_args, coords = coords, progress = progress, mode_fold = mode_fold, pooled_obs_train = pooled_obs_train, train_fun = train_fun, train_param = train_param, test_fun = test_fun, test_param = test_param, pooled_obs_test = pooled_obs_test, err_fun = err_fun, i = i_rep ) if (verbose >= 3) { cat("\n-----------------------------\nResults:", "\n-----------------------------\n") print(my_res[[i_rep]]) cat("-----------------------------\n\n") } } } else stop("invalid mode_rep") ### format parallel outputs ---- if (verbose >= 1) cat(date(), "Postprocessing...\n") # overwrite resamp object with possibly altered resample object from # runfolds # this applies if a custom test_fun or train_fun with a sub-resampling # method is used if (!is.null(test_fun) | !is.null(train_fun)) { if (verbose >= 2) cat(date(), " - Copy possibly altered resampling object...") for (i in seq_along(resamp)) { for (j in seq_along(resamp[[i]])) { # ...was [[1]], which assumes that all repetitions have equal # number of folds. resamp[[i]][[j]] <- my_res[[i]][["resampling"]][[j]][[j]] } } } ## 2021-06-21: ## removed NA check; NAs should be handled by ## summary methods... # check if any rep is NA in all folds and if, remove entry # this happens e.g. in maxent #nolint # if (verbose >= 2) # cat(date(), " - Check NAs...\n") # # check_na <- lapply(my_res, function(x) all(is.na(x))) # nolint # check_na_flat <- unlist(check_na) # # if (any(check_na_flat) == TRUE) { # check_na <- as.numeric(which(lapply(my_res, function(x) { # all(is.na(x)) # }) )) # # my_res <- my_res[-check_na] # # } # assign names to sublists - otherwise `transfer_parallel_output` doesn't work if (verbose >= 2) cat(date(), " - Rename sublists...\n") for (i in seq_along(my_res)) { names(my_res[[i]]) <- c( "error", "pooled_error", "importance", "non-converged-folds" ) } # flatten list & calc sum if (verbose >= 2) cat(date(), " - Flatten lists...\n") not_converged_folds <- sum( unlist(lapply(my_res, function(x) unlist(x[["non-converged-folds"]])))) # transfer results of lapply() to respective data objects if (verbose >= 2) cat(date(), " - Transfer outputs...\n") my_res_mod <- transfer_parallel_output(my_res, res, impo, pooled_error) # nolint pooled_error <- as.data.frame(my_res_mod$pooled_error) rownames(pooled_error) <- NULL class(pooled_error) <- "sperrorestreperror" if (importance) { impo <- my_res_mod$impo class(impo) <- "sperrorestimportance" } if (benchmark) { end_time <- Sys.time() my_bench <- list( system.info = Sys.info(), t_start = start_time, t_end = end_time, cpu_cores = future::nbrOfWorkers(), # was: parallel::detectCores(), which is the number of physically # available cores, but only nbrOfWorkers() can be used by this process. runtime_performance = end_time - start_time ) class(my_bench) <- "sperrorestbenchmark" } else { my_bench <- NULL } package_version <- packageVersion("sperrorest") class(package_version) <- "sperrorestpackageversion" if (verbose >= 1) cat(date(), "Done.\n") res <- list( error_rep = pooled_error, error_fold = my_res_mod$res, represampling = resamp, importance = impo, benchmark = my_bench, package_version = package_version ) class(res) <- "sperrorest" if (not_converged_folds > 0) { if (length(smp_args$repetition) > 1) { smp_args$repetition <- tail(smp_args$repetition, n = 1) } # print counter cat(sprintf( "%s folds of %s total folds (%s rep * %s folds) caused errors or returned NA (e.g., did not converge).", # nolint not_converged_folds, smp_args$repetition * smp_args$nfold, smp_args$repetition, smp_args$nfold )) } res }

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/R/get-targets.r

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no_license

nrminor/ebird-target-map

080eff43ca5d309e43f18faa357b9e7d52d70938

a4d121d8afc4fd6f2633d263f79afcc9a17b7a94

refs/heads/master

2023-07-06T08:25:41.105670

2019-03-12T15:59:05

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get-targets.r

get_targets <- function(life_list, region_list, freq_thresh, period) { if (period > 0) { region_list <- dplyr::filter(region_list, month == period) } dplyr::group_by(region_list, region_code, species_code) %>% dplyr::summarise(frequency = mean(frequency)) %>% dplyr::ungroup() %>% dplyr::filter(frequency >= freq_thresh) %>% dplyr::mutate(seen = (species_code %in% life_list)) %>% dplyr::arrange(region_code, dplyr::desc(frequency)) }

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/R/RIT.R

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chanzuckerberg/FSInteractX

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ea8a9e7996d720a973ad0b2abca2dc01662b6561

refs/heads/master

2020-04-07T00:24:27.623146

2018-11-20T21:38:29

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RIT.R

RIT <- function(z, z0, branch=5, depth=10L, n_trees=100L, theta0=0.5, theta1=theta0, min_inter_sz=2L, L=100L, n_cores=1L, output_list=FALSE) { ## check L, branch, depth, t, min_inter_sz, n_cores, output_list L <- as.integer(L) branch <- as.double(branch) depth <- as.integer(depth) n_trees <- as.integer(n_trees) theta0 <- as.double(theta0) theta1 <- as.double(theta1) min_inter_sz <- as.integer(min_inter_sz) n_cores <- as.integer(n_cores) output_list <- as.logical(output_list) if (L < 1L) stop("L must be >= 1") if (branch < 1) stop("branch must be >= 1") if (depth <= 1L) stop("depth must be >= 2") if (n_trees <= 0L) stop("n_trees must be >= 0") if (min_inter_sz < 2L) stop("min_inter_sz must be >= 2") if (n_cores <1L) stop("n_cores must be >= 1") if(theta0<0 || theta0>1){ stop("theta0 must be between 0 and 1") } if(theta1<0 || theta1>1){ stop("theta1 must be between 0 and 1") } ## check z,z0 and convert to suitable data types for 'ExportedcppFunctions.cpp', then carry out RIT is_2_class <- !missing(z0) is_sparse <- is(z,"Matrix") # If z or z0 is sparse then also make the other one sparse, so that they are of the same type if (is_2_class) { if (is_sparse && !is(z0,"Matrix")) { z0 <- Matrix(z0, sparse=TRUE) } else if (is(z0,"Matrix") && !is_sparse) { z <- Matrix(z, sparse=TRUE) is_sparse <- TRUE } } if (is_sparse) { if (is_2_class) { if (nrow(z0) == 0) stop("z0 must have at least one row") if (ncol(z0) != ncol(z)) stop("z and z0 must have the same number of columns") z0 <- Matrix::t(z0) z0 <- list(z0@i, z0@p) } if (nrow(z) == 0) stop("z must have at least one row") z <- t(z) z <- list(z@i, z@p) } ## if z, z0 are not sparse matrices, check that they are matrices if (!is_sparse) { if (!is.matrix(z)) stop("z must be a matrix") if (nrow(z) == 0) stop("z must have more than 0 rows") if (is_2_class) { if (!is.matrix(z0)) stop("z0 must be a matrix") if (nrow(z0) == 0) stop("z0 must have more than 0 rows") if (ncol(z) != ncol(z0)) stop("z and z0 must have the same number of columns") } } # carry out RIT_2class if (is_2_class) { output <- RIT_2class(z, z0, L, branch, depth, n_trees, theta0, theta1, min_inter_sz, n_cores, is_sparse) # reorder output in decreasing prevalence prev_order <- order(output$Class1$Prevalence1, decreasing=TRUE) prev_order0 <- order(output$Class0$Prevalence0, decreasing=TRUE) output$Class1$Prevalence1 <- output$Class1$Prevalence1[prev_order] output$Class1$Prevalence0 <- output$Class1$Prevalence0[prev_order] output$Class1$Interactions <- output$Class1$Interactions[prev_order] output$Class0$Prevalence0 <- output$Class0$Prevalence0[prev_order0] output$Class0$Prevalence1 <- output$Class0$Prevalence1[prev_order0] output$Class0$Interactions <- output$Class0$Interactions[prev_order0] # check whether output should be a list or a data.frame if(!output_list) { data1 <- convert.to.data.frame(output$Class1) data0 <- convert.to.data.frame(output$Class0) output <- list("Class1"=data1, "Class0"=data0) } return(output) } # carry out RIT_1class output<-RIT_1class(z, L, branch, depth, n_trees, min_inter_sz, n_cores, is_sparse) # reorder output in decreasing prevalence prev_order <- order(output$Prevalence, decreasing=TRUE) output$Prevalence <- output$Prevalence[prev_order] output$Interactions <- output$Interactions[prev_order] # check whether output should be a list or a data.frame if (!output_list) { output<-convert.to.data.frame(output) } return(output) }

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/pda.R

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smartbenben/platform

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2022-12-26T06:29:27.682053

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pda.R

# Closed-form probability distribution of treatment allocation # N: number of patients (scalar) # p: success probabilities (vector) # priora, priorb: priors for beta(alpha, beta) pda <- function(N, p, nmin, early.stop=FALSE, priora=NULL, priorb=NULL) { # number of treatment arms k <- length(p) print(noquote(c("Number of design points", choose(N + k - 1, k - 1)))) # set for first patient EN <- firstEN(p, nmin, early.stop, priora, priorb) p_alloc <- c(1, Ea(buildp(EN))) # expected allocation EaN <- c(1, Ea(buildp(EN))) # we are done if N = 1 if(N == 1) return(summary(buildp(EN))) # for each additional patient for(i in 2 : N) { # tree set for next patient EN <- nextEN(EN, p, nmin, early.stop, priora, priorb) # build table of expected treatment arm allocation alloc <- buildp(EN) p_new <- rowSums(apply(alloc, 1, function(x){ x[length(p)+1]*ERN1(n=unlist(x[1:length(p)]), p, nmin, early.stop, priora, priorb) })) p_alloc <- rbind(p_alloc, c(i,p_new)) EaN <- rbind(EaN,c(i, Ea(alloc))) # progress report print(noquote(c("N: ", i))) # print it out before completion of loop flush.console() } # output, summarized pda <- summarize(buildp(EN)) colnames(EaN) <- c("N", LETTERS[1 : k]) # expected treatment allocations pda$EaN <- EaN pda$p_alloc <- p_alloc return(pda) }

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/code.R

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atamagnini/r-project-happiness-report

1c4831b09205e058f5c46ffe1858adae70fbb89a

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refs/heads/main

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2021-09-29T20:19:05

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code.R

library(tidyverse) library(highcharter) library(webshot) library(htmlwidgets) dataset <- read.csv('~/dataset.csv') #Reshape dataframe df <- as.data.frame(dataset) df <- df[, -c(3:5)] colnames(df)[1] <- 'Country' glimpse(df) df$Country <- as.character(as.factor(df$Country)) #Replace value in specific cells df[df == 'United States'] <- 'United States of America' #map map_data <- get_data_from_map(download_map_data("custom/world")) p <- hcmap(map = 'custom/world', download_map_data = getOption("highcharter.download_map_data"), data = df, value = 'Ladder', joinBy = c('name', 'Country')) %>% hc_tooltip(useHTML = TRUE, headerFormat = '<b>World ranking position of<br>', pointFormat = '{point.name}: N°{point.value}') %>% hc_title(text = 'World happiness report (2019)', style = list(fontWeight = 'bold', fontSize = '20px'), align = 'left') %>% hc_credits(enabled = TRUE, text = 'Map by Antonela Tamagnini <br> Source: WHO, Minsitry of Health Department') %>% hc_mapNavigation(enabled = TRUE) %>% hc_colorAxis(stops = color_stops(8, c("#0000CD","#8ba9fe","#fee08b","#434348"))) p saveWidget(widget = p, file = "plot.html") webshot(url = 'plot.html', file = 'plot.png')

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/man/subsetTxi.Rd

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hcnh174/hlsgr

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refs/heads/master

2023-04-13T18:44:28.519716

2023-04-03T05:38:45

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subsetTxi.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/rnaseq.R \name{subsetTxi} \alias{subsetTxi} \title{Subset txi data by sample or gene} \usage{ subsetTxi(txi, samples, include_genes = rownames(txi$counts)) } \arguments{ \item{include_genes}{} } \value{ } \description{ Subset txi data by sample or gene }

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/source/Windows/R-Portable-Win/library/digest/tinytest/test_raw.R

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test_raw.R

## tests for raw output suppressMessages(library(digest)) current <- hmac("Jefe", 'what do ya want for nothing?', "md5", raw=TRUE) expected <- as.raw(c(0x75, 0x0c, 0x78, 0x3e, 0x6a, 0xb0, 0xb5, 0x03, 0xea, 0xa8, 0x6e, 0x31, 0x0a, 0x5d, 0xb7, 0x38)) expect_equal(current, expected) current <- digest("The quick brown fox", algo="sha1", raw=TRUE) expected <- as.raw(c(0x5f, 0x79, 0x8c, 0xb4, 0xd8, 0x14, 0x4e, 0xec, 0x35, 0xf4, 0xd0, 0x79, 0x3e, 0xf2, 0x1e, 0x55, 0xce, 0xb6, 0xa7, 0x88)) expect_equal(current, expected) ## feed raw to sha1() to test sha1.raw() as well expect_true(sha1(expected) == "75a2995eeec0fcb5d7fa97c676a37f4e224981a1")

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/bpca/demo/rock.var.rd.R

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ingted/R-Examples

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rock.var.rd.R

oask <- devAskNewPage(dev.interactive(orNone=TRUE)) bp <- bpca(rock, var.rb=TRUE, var.rd=TRUE) summary(bp) # The total variance explained is satisfactory (>= .80)! plot(bp) # A more accurate diagnostic bp$var.rd # It is possible to observe that the variable 'perm' # has not a good representation (bpca.2d)! # Observed correlations: cor(rock) # Projected correlations: bp$var.rb # Aditional diagnostic plot(qbpca(rock, bp)) # This variable reamains as important in a dimension not contemplated # by the biplot reduction (PC3): bp$eigenvectors bp1 <- bpca(rock, d=3:4) summary(bp1) plot(bp1) # The recommendation, knowing that this variable has a poor # representaion is: # 1- Avoid to discut it; # 2- Consider to incorporate the information with a bpca.3d bp2 <- bpca(rock, d=1:3, var.rb=TRUE, var.rd=TRUE) summary(bp2) plot(bp2) # Static plot(bp2, rgl.use=TRUE) # Dinamic bp2$var.rd # Nice! # Aditional diagnostic plot(qbpca(rock, bp2)) devAskNewPage(oask)

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/pkgs/oce/tests/testthat/test_flags.R

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vaguiar/EDAV_Project_2017

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test_flags.R

library(oce) context("Flags") test_that("[[ and [[<- work with ctd flags", { data(section) ctd <- section[["station", 100]] expect_equal(c(2,2,2,2,2,3), ctd[["salinityFlag"]][1:6]) ctd[["salinity"]][2] <- -999 ctd[["salinityFlag"]] <- ifelse(ctd[["salinity"]] < 0, 3, ctd[["salinityFlag"]]) expect_equal(c(2,3,2,2,2,3), ctd[["salinityFlag"]][1:6]) ctd[["salinity"]] <- ifelse(ctd[["salinityFlag"]]!=2, NA, ctd[["salinity"]]) expect_equal(is.na(ctd[["salinity"]][1:6]), c(FALSE, TRUE, FALSE, FALSE, FALSE, TRUE)) }) test_that("handleFLags works with ctd data", { data(section) ctd <- section[["station", 100]] ## this stn has a few points with salinityFlag==3 ctdNew <- handleFlags(ctd, flags=list(salinity=c(1, 3:9))) ##cat("ctd salinity: orig had", sum(is.na(ctd[['salinity']])), "NA values; new has", ## sum(is.na(ctdNew[['salinity']])), "\n") expect_equal(sum(is.na(ctd[["salinity"]])), 0) nbad <- sum(ctd[['salinityFlag']] != 2) expect_equal(2, nbad) ## test replacement via function f <- function(object) rep(30, length.out=length(object[['salinity']])) ctdNew2 <- handleFlags(ctd, flags=list(salinity=4:5), actions=list(salinity=f)) expect_equal(sum(ctdNew[['salinity']]==30, na.rm=TRUE), sum(ctd[['salinityFlag']] == 4 | ctd[['salinityFlag']] == 5, na.rm=TRUE)) }) test_that("handleFLags works with the built-in argo dataset", { data(argo) argoNew <- handleFlags(argo, flags=list(salinity=4:5)) ## Test a few that are identified by printing some values ## for argo[["salinityFlag"]]. expect_true(is.na(argoNew[["salinity"]][13, 2])) expect_true(is.na(argoNew[["salinity"]][53, 8])) ## Test whether data with salinity flag of 4 get changed to NA expect_true(all(is.na(argoNew[["salinity"]][4==argo[["salinityFlag"]]]))) expect_true(!all(is.na(argoNew[["salinity"]][1==argo[["salinityFlag"]]]))) ## Similar for temperature. First, check that it is *not* NA, with ## the call to handleFlags() above, which was restricted to salinity. expect_true(!is.na(argoNew[["temperature"]][10, 2])) ## Now, handle *all* the flags, and check temperature again, and also salinity. argoNew2 <- handleFlags(argo) expect_true(is.na(argoNew2[["temperature"]][10, 2])) expect_true(all(is.na(argoNew2[["temperature"]][4==argo[["temperatureFlag"]]]))) # Tests of overall numbers expect_equal(sum(is.na(argo[["salinity"]])), 90) expect_equal(sum(is.na(argoNew[["salinity"]])), 110) ## test replacement via function f <- function(object) rep(30, length.out=length(object[['salinity']])) argoNew3 <- handleFlags(argo, flags=list(salinity=4:5), actions=list(salinity=f)) expect_equal(sum(argoNew3[['salinity']]==30, na.rm=TRUE), sum(argo[['salinityFlag']] == 4 | argo[['salinityFlag']] == 5, na.rm=TRUE)) }) test_that("handleFLags works with the built-in section dataset", { data(section) SECTION <- handleFlags(section) ## Inspection reveals that salinity are triggered in the first CTD entry, i.e. ## the station named "3" in this dataset. ## The default for `handleFlags,ctd-method` is the WOCE standard, with 2=good, 3=bad, ... stn1 <- section[["station", 1]] STN1 <- SECTION[["station", 1]] expect_equal(c(2, 3, 3, 2, 2), stn1[["salinityFlag"]]) ok <- which(2 == stn1[["salinityFlag"]]) expect_equal(stn1[["salinity"]][ok], STN1[["salinity"]][ok]) replace <- which(2 != stn1[["salinityFlag"]]) expect_equal(stn1[["salinityBottle"]][replace], STN1[["salinity"]][replace]) })

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/man/gene_pos_counts.Rd

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gene_pos_counts.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/gene_pos_counts.R \name{gene_pos_counts} \alias{gene_pos_counts} \title{gene position counts} \usage{ gene_pos_counts(dt_gen,dt_snp,dt_gene,keep_indiv=NULL, extract_SNP=NULL,filter_gene=NULL, impute_missing=FALSE,impute_method="mean") } \arguments{ \item{dt_gen}{a dataframe for genetic data that follows PLINK format (.raw)} \item{dt_snp}{a dataframe for SNP information with SNP BP as column names.} \item{dt_gene}{a dataframe for gene boundaries with CHR START END GENE as column names. Where CHR should be integer 1-22. START and END column should be integer. GENE column contains gene names} \item{keep_indiv}{an option to specify individuals to retain. Mutation counts will be provided for individuals provided in the list only. Default is all individuals.} \item{extract_SNP}{an option to specify SNPs for which mutation counts are needed. Mutation counts will be provided for SNPs included in the list only. Default is all SNPs.} \item{filter_gene}{an option to filter in Genes. Mutation counts will be provided for genes included in the list only. Default is all genes.} \item{impute_missing}{an option to impute missing genotypes. Default is FALSE.} \item{impute_method}{an option to specify method to specify imptuation method. Default method is impute to the mean. Alternatively imputation can be carried out by median. Function accepts method in quotes: "mean" or "median". Data are rounded to the second decimal places (e.g. 0.1234 will become 0.12.).} } \value{ Returns an object of data.table class as an output with allelic gene counts within each sample where each row corresponds to gene and column to individual IDs from column second. The first column contains gene names. } \description{ Function returns matrix with allelic counts per gene per individual for SNP and gene coordinates as inputs } \details{ Inputs needed are: recoded genetic data formatted in PLINK format, SNP name with BP (position) and gene name with START and END position. The first six columns of the input genetic data follow standard PLINK .raw format. Column names as FID, IID, PAT, MAT, SEX and PHENOTYPE followed by SNP information as recoded by the PLINK software. The function returns allelic counts per gene per sample (where each row represents a gene and each column represents an individual starting with the second column where first column contains gene information). } \examples{ #Package provides sample data that are loaded with package loading. #not RUN data(recodedgen) #PLINK raw formatted data of 10 individiduals with 10 SNPs data(genecoord) #gene coordinates with START, END, CHR and GENE names. #Five genes with start and end genomic coordinates data(snppos) #SNP and BP column names with SNP names and SNP genomic location in BP. #10 SNPs with genomic location gene_pos_counts(recodedgen, snppos, genecoord) #run the function #subset individuals gene_pos_counts(recodedgen, snppos, genecoord,keep_indiv=c("IID_sample2","IID_sample4")) #subset genes gene_pos_counts(recodedgen,snppos,genecoord,filter_gene=c("GENE1","GENE2")) #subset genes and individual iids gene_pos_counts(recodedgen,snppos,genecoord,filter_gene=c("GENE1","GENE2"), keep_indiv=c("IID_sample10","IID_sample4")) ##impute by mean gene_pos_counts(recodedgen,snppos,genecoord,impute_missing=TRUE,impute_method="mean") #end not RUN } \author{ Sanjeev Sariya }

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/man/addIntercept.Rd

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addIntercept.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/utilities.R \name{addIntercept} \alias{addIntercept} \title{Internal function to add a column for the intercept to a matrix.} \usage{ addIntercept(x) } \arguments{ \item{x}{A \code{matrix} or \code{data.frame}.} } \value{ A \code{matrix} (if \code{x} is a \code{matrix}) or \code{data.frame} (if \code{x} is a \code{data.frame}) guaranteed to have an intercept column. } \description{ Given a \code{matrix} or \code{data.frame} of size nxp, returns an nx(p+1) object of the same type where the first column is an intercept if the intercept is not already in the object (defined as a first column being all 1's). }

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pop.r

d<- read.csv(file="pop.csv") head(d) class(d$pop) b <- read.csv(file="bairro2AP.csv") names(b)[2]<-"BAIRRO" head(b) bd <-merge(d,b,all.d=TRUE) head(bd) class(bd$pop) popap<-aggregate(bd$pop,by=list(bd$APS),FUN=sum) names(popap)<-c("APS","Pop2010") write.csv(popap,file="populacao2010porAPS_RJ.csv",row.names=FALSE)

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/RSI NORMAL.R

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VivekVerma25/codingground

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RSI NORMAL.R

library(zoo) library(TTR) # library(timeSeries) # library(slam) # library(quantmod) # library(quantreg) # library(allelematch) path_input<-"C:/Users/YashV/Desktop/Framework Data/" path_output<-"C:/Users/YashV/Desktop/Framework Data/" stck_lvl<-data.frame(read.csv(paste(path_input,"Roll adjusted returns.csv",sep=""))) stck_lvl$Returns<-as.Date(stck_lvl$Returns,"%d/%m/%Y") tc<-(0) # This is one side trnsaction cost minp=2 #min of 5 stocks should be there or else no trade fa=1 #whether fixed allocation or not alloc=1 lookback_period=c(14,21) rsi_entry_long = c(70,80) #entry for long rsi_entry_short = c(30,20) #entry for short for(li in 1:length(lookback_period)) { for(lo in 1:length(rsi_entry_long)){ pric<-read.csv(paste(path_input,"PX_LAST.csv",sep="")) #Closing prices CAsh clafic<-data.frame(read.csv(paste(path_input,"Nifty 50.csv",sep="")))[,-1] #Universe stck_lvl<-data.frame(read.csv(paste(path_input,"Roll adjusted returns.csv",sep=""))) stck_lvl$Returns<-as.Date(stck_lvl$Returns,"%d/%m/%Y") names<-colnames(pric) dt<-pric[(lookback_period[li]+1):nrow(pric),1] #Dates from where NAs are not present pric<-pric[,-1] #calculating rsi for each stock for(j in 1:ncol(pric)){ if(j==1) { roll<-RSI(pric[,j],lookback_period[li]) rsi<-data.frame(roll) } else{ roll<-RSI(pric[,j],lookback_period[li]) rsi<-cbind(rsi,roll) } } rsi<-rsi[(lookback_period[li]+1):nrow(rsi),] #from where NAs arent present fin<-matrix(0,nrow(rsi),ncol(rsi)) #matrix of long and short signal rsi[rsi==0] <- NA clafic<-clafic[(lookback_period[li]+1):nrow(clafic),] rsi[clafic==0]<-NA #removing stocks which arent in universe #matrix of long and short signal fin[1,which(as.matrix(rsi[1,])>rsi_entry_long[lo])]<-1 fin[2,which(as.matrix(rsi[2,])>rsi_entry_long[lo])]<-1 for(i in 3:nrow(fin)){ fin[i,which(fin[i-1,]==0 & fin[i-2,]==0 & as.matrix(rsi[i,])>rsi_entry_long[lo])]<-1 } fin[1,which(as.matrix(rsi[1,])<(rsi_entry_short[lo]))]<--1 fin[2,which(as.matrix(rsi[2,])<(rsi_entry_short[lo]))]<--1 for(i in 3:nrow(fin)){ fin[i,which(fin[i-1,]==0 & fin[i-2,]==0 & as.matrix(rsi[i,])<(rsi_entry_short[lo]))]<--1 } new<-data.frame(DATE = dt,fin) write.csv(new,"long short signal.csv",row.names = FALSE) pric<-data.frame(read.csv(paste(path_input,"PX_LAST.csv",sep="")))[,-1] pricn<-pric[(lookback_period[li]+1):nrow(pric),] pricn[is.na(pricn)] =0 pricn[which(!is.finite(as.matrix(pricn)))] <- 0 #Code for entry exit with stoploss st<-matrix(0,dim(as.matrix(pricn))[1],dim(as.matrix(pricn))[2]) high<-matrix(0,dim(as.matrix(pricn))[1],dim(as.matrix(pricn))[2]) entry_l<-matrix(0,dim(as.matrix(pricn))[1],dim(as.matrix(pricn))[2]) st_short<-matrix(0,dim(as.matrix(pricn))[1],dim(as.matrix(pricn))[2]) low<-matrix(0,dim(as.matrix(pricn))[1],dim(as.matrix(pricn))[2]) entry_s<-matrix(0,dim(as.matrix(pricn))[1],dim(as.matrix(pricn))[2]) test_shortprice<-as.matrix(pricn) testprice<-as.matrix(pricn) if(minp!=0) netp<-0 if(sum(fin[1,]==1 | fin[1,]==-1)>=minp){ entry_l[1,which(fin[1,]==1)]=1 entry_s[1,which(fin[1,]==(-1))]=(-1) low[1,which(fin[1,]==(-1))]<-test_shortprice[1,which(fin[1,]==(-1))] high[1,which(fin[1,]==(1))]<-testprice[1,which(fin[1,]==(1))] if(sum(entry_l[1,]==1 | fin[1,]==(-1) )>0) { st[1,which(entry_l[1,]==1)]<-0.97*testprice[1,which(entry_l[1,]==1)] st_short[1,which(fin[1,]==(-1))]<-1.03*test_shortprice[1,which(fin[1,]==(-1))] } netp<-netp+sum(fin[1,]==1)+sum(fin[1,]==-1) } for(i in 2:nrow(testprice)){ if(sum(entry_l[i-1,]==1 | entry_s[i-1,]==-1)>0){ j<-sum(entry_l[i-1,]==1) while(j!=0){ c<-which(entry_l[i-1,]==1) if(testprice[i,c[j]]>high[i-1,c[j]]) { st[i,c[j]]<-0.97* (testprice[i,c[j]]) high[i,c[j]]<- (testprice[i,c[j]]) } else { st[i,c[j]]<-st[i-1,c[j]] high[i,c[j]]<-high[i-1,c[j]] } if(testprice[i,c[j]]<st[i,c[j]]){ #exit criteria entry_l[i,c[j]]=0 netp=netp-1 } else{ entry_l[i,c[j]]=1 } j=j-1 } j<-sum(entry_s[i-1,]==-1) while(j!=0){ c<-which(entry_s[i-1,]==-1) if(test_shortprice[i,c[j]]<low[i-1,c[j]]) { st_short[i,c[j]]<-1.03* (test_shortprice[i,c[j]]) low[i,c[j]]<- (test_shortprice[i,c[j]]) } else { st_short[i,c[j]]<-st_short[i-1,c[j]] low[i,c[j]]<-low[i-1,c[j]] } if(test_shortprice[i,c[j]]>st_short[i,c[j]]){ #exit criteria entry_s[i,c[j]]=0 netp=netp-1 } else{ entry_s[i,c[j]]=-1 } j=j-1 } } if((sum(fin[i,]==1 | fin[i,]==-1)+netp)>=minp) { entry_l[i,which(fin[i,]==1)]=1 st[i,which(entry_l[i-1,]==0 & entry_l[i,]==1)]<-0.97*(testprice[i,which(entry_l[i-1,]==0 & entry_l[i,]==1)]) high[i,which(entry_l[i-1,]==0 & entry_l[i,]==1)]<-testprice[i,which(entry_l[i-1,]==0 & entry_l[i,]==1)] entry_s[i,which(fin[i,]==-1)]=-1 st_short[i,which(entry_s[i-1,]==0 & entry_s[i,]==-1)]<-1.03*(test_shortprice[i,which(entry_s[i-1,]==0 & entry_s[i,]==-1)]) low[i,which(entry_s[i-1,]==0 & entry_s[i,]==-1)]<-test_shortprice[i,which(entry_s[i-1,]==0 & entry_s[i,]==-1)] netp<-netp+(sum(fin[i,]==1 & entry_l[i-1,]==0)) +(sum(fin[i,]==-1 & entry_s[i-1,]==0)) } } write.csv(entry_l,paste(path_output,"entry_l.csv"),row.names = FALSE) write.csv(entry_s,paste(path_output,"entry_s.csv"),row.names = FALSE) #Final Matrix with both L & S entry<-matrix(0,dim(as.matrix(pricn))[1],dim(as.matrix(pricn))[2]) for(i in 1:nrow(entry)){ if(i==1){ entry[i,which(entry_l[i,]==1)]=1 entry[i,which(entry_s[i,]==-1)]=-1 } else{ entry[i,which(entry_l[i,]==1 & entry_s[i,]==0 )]=1 # entry[i,which(entry_l[i,]==1 & entry_s[i,]==0 & entry_s[i-1,]==-1 & fin[i,]==1)]=1 entry[i,which(entry_s[i,]==-1 & entry_l[i,]==0)]=-1 # entry[i,which(entry_s[i,]==-1 & entry_l[i,]==0 & entry_l[i-1,]==1 & fin[i,]==-1)]=-1 entry[i,which(entry_l[i,]==1 & entry_s[i,]==-1 & entry_l[i-1,]==0)]=1 entry[i,which(entry_l[i,]==1 & entry_s[i,]==-1 & entry_s[i-1,]==0)]=-1 entry[i,which(entry_l[i,]==1 & entry_s[i,]==-1 & entry_s[i-1,]==-1 & entry_l[i-1,]==1 )]=entry[i-1,which(entry_l[i,]==1 & entry_s[i,]==-1 & entry_s[i-1,]==-1 & entry_l[i-1,]==1 )] } } names<-colnames(clafic) colnames(entry)<-names newentry<-data.frame(DATE =dt,entry) names<-colnames(newentry) write.csv(newentry,paste(path_output,"entry.csv"),row.names = FALSE) #Entry and exit matrix start_date<-matrix(0,dim(entry)[1],dim(entry)[2]) for(i in 1:nrow(entry)){ if(i==1){ start_date[i,which(entry[i,]==1)]=paste(dt[i]) start_date[i,which(entry[i,]==-1)]=paste(dt[i]) } else{ start_date[i,which(entry[i,]==1 & entry[i-1,]==0)]=paste(dt[i]) start_date[i,which(entry[i,]==-1 & entry[i-1,]==0)]=paste(dt[i]) } } start_date<-data.frame(DATE = dt,start_date) colnames(start_date)=names write.csv(start_date,paste(path_output,"start date.csv"),row.names = FALSE) end_date<-matrix(0,dim(entry)[1],dim(entry)[2]) for(i in 2:nrow(entry)){ end_date[i,which(entry[i,]==0 & entry[i-1,]==1)]=paste(dt[i]) end_date[i,which(entry[i,]==0 & entry[i-1,]==-1)]=paste(dt[i]) } end_date<-data.frame(DATE = dt,end_date) colnames(end_date)=names write.csv(end_date,paste(path_output,"end date.csv"),row.names = FALSE) LorS<-matrix(0,dim(entry)[1],dim(entry)[2]) for(i in 1:nrow(entry)){ if(i==1){ LorS[i,which(entry[i,]==1)]="L" LorS[i,which(entry[i,]==-1)]="S" } else{ LorS[i,which(entry[i,]==1 & entry[i-1,]==0)]="L" LorS[i,which(entry[i,]==-1 & entry[i-1,]==0)]="S" } } LorS<-data.frame(DATE = dt,LorS) colnames(LorS)=names write.csv(LorS,paste(path_output,"LorS.csv"),row.names = FALSE) #NetPosition np<-as.matrix(rowSums(abs(entry))) np<-data.frame(DATE =dt,np) write.csv(np,paste(path_output,"net_position_Long_Short.csv"),row.names = FALSE) #netPositions # newst<-data.frame(DATE=dt,st) # write.csv(newst,"stop_loss.csv",row.names = FALSE) # # newp<-data.frame(DATE=dt,pricn) # # write.csv(newp,paste(path_output,"prices.csv"),row.names = FALSE) # # # stock_names<-matrix(0,nrow = nrow(entry),120) # for(i in 1:nrow(entry)){ # k=1 # j<-sum(entry[i,]==1) # while(j!=0){ # c<-which(entry[i,]==1) # stock_names[i,k]=names[c[j]] # k=k+1 # j=j-1 # } # } # stock_names<-data.frame(DATE = dt,stock_names) # write.csv(stock_names,paste(path_output,"stock in zpcr.csv"),row.names=FALSE) # # #TO Calculate Returns entry_final<-entry[-1*dim(entry)[1],] stck_lvl<-stck_lvl[(lookback_period[li]+1):nrow(stck_lvl),] stck_lvl_calc<-stck_lvl[-1,-1] #FIXED ALLOCATION #ALLOCATION ON EACH STOCK exposure_long<-as.matrix(rowSums(abs(entry_final))) for(i in 1:ncol(entry_final)){ entry_final[,i]<-entry_final[,i]/exposure_long } entry_final[is.na(entry_final)] =0 entry_final<-as.matrix(entry_final) entry_final[which(!is.finite(entry_final))] <- 0 exposure_long_og<-as.matrix(rowSums(abs(entry))) for(i in 1:ncol(entry)){ entry[,i]<-entry[,i]/exposure_long_og } entry[is.na(entry)] =0 entry<-as.matrix(entry) entry[which(!is.finite(entry))] <- 0 write.csv(entry,paste(path_output,"entry_after.csv"),row.names=FALSE) #For Both Longs And Shorts #traded Value tv<-matrix(0,dim(entry)[1],dim(entry)[2]) tv[1,]<-entry[1,] for(i in 2:nrow(entry)){ tv[i,]=abs(entry[i,]-entry[i-1,]) } tot_tv<-rowSums(abs(tv)) tot_tv<-data.frame(DATE = dt,Tv = tot_tv) write.csv(tot_tv,paste(path_output,"GTV each day.csv"),row.names=FALSE) # write.csv(stck_lvl_calc,paste(path_output,"stcklvlcalc.csv"),row.names=FALSE) # fr<-data.frame(entry_final*stck_lvl_calc) lng_ret_tb<-data.frame(rowSums(entry_final*stck_lvl_calc)) cst_tb<-data.frame(rowSums(abs(entry[-1,]-entry[-1*dim(entry)[1],])*tc)) # alloc<-rowSums(abs(clafic[-1])) # exposure_long<-as.matrix(rowSums(abs(entry_final))) tot_ret_tb<-data.frame((lng_ret_tb -cst_tb)) tot_ret_tb[is.na(tot_ret_tb)] =0 tot_ret_tb<-as.matrix(tot_ret_tb) tot_ret_tb[which(!is.finite(tot_ret_tb))] <- 0 mtm<-tot_ret_tb mtm<-data.frame(DATE=dt[-1],MTM_Daily = mtm) name<-c("DATE","MTM") colnames(mtm)=name write.csv(mtm,paste(path_output,"MTM.csv"),row.names=FALSE) ann_mtm<-data.frame(aggregate(mtm[,2],by=list((substr(mtm[,1],7,10))),sum)) ann_tv<-data.frame(aggregate(tot_tv[,2],by=list((substr(tot_tv[,1],7,10))),sum)) ann_mtmtv<-ann_mtm[,2]/ann_tv[,2] ann_mtmtv<-data.frame(DATE = ann_mtm[,1],MTMTV<-ann_mtmtv) ret_fin_tb<-data.frame(stck_lvl[2:nrow(stck_lvl),1], tot_ret_tb, lng_ret_tb) colnames(ret_fin_tb) <- c("Date","Total Ret","Ret without tc") write.csv(ret_fin_tb,paste(path_output,"Long Only.csv"),row.names=FALSE) ann_ret_rsi<-data.frame(aggregate(ret_fin_tb[,2],by=list((substr(ret_fin_tb[,1],1,4))),sum)) ann_mean_rsi<-data.frame(aggregate(ret_fin_tb[,(2)],by=list((substr(ret_fin_tb[,1],1,4))),mean)) ann_sd_rsi<-data.frame(aggregate(ret_fin_tb[,(2)],by=list((substr(ret_fin_tb[,1],1,4))),sd)) t1<-ann_mean_rsi[,2] t2<-ann_sd_rsi[,2] ann_sharpe_rsi<- data.frame(DATE=ann_mean_rsi[,1],Sharpe = (t1/t2)*sqrt(252)) #calculation Of cumalative returns cum_ret<-matrix(0,dim(fr)[1],dim(fr)[2]) #ret per day fr<-as.matrix(fr) entry_final<-as.matrix(entry_final) for(i in 1:nrow(fr)){ if(i==1){ cum_ret[i,which(entry_final[i,]!=0)]=fr[i,which(entry_final[i,]!=0)] } else{ cum_ret[i,which(entry_final[i-1,]==0 & entry_final[i,]!=0)]=fr[i,which(entry_final[i-1,]==0 & entry_final[i,]!=0)] c=which(entry_final[i-1,]!=0 & entry_final[i,]!=0) j=length(c) if(j>0){ for(k in 1:j){ cum_ret[i,c[k]]=fr[i,c[k]]+cum_ret[i-1,c[k]] } } } } cum_ret_dt<-data.frame(DATE = dt[-1],cum_ret) write.csv(cum_ret_dt,paste(path_output,"cum_returns.csv"),row.names=FALSE) cum_rt<-matrix(0,length(dt),ncol = ncol(cum_ret)) cum_rt[2:nrow(cum_rt),]=cum_ret trade_ret<-matrix(0,dim(cum_rt)[1],dim(cum_rt)[2]) for(i in 2:nrow(entry)){ c=which(cum_rt[i,]==0 & cum_rt[i-1,]!=0) j=length(c) if(j>0){ for(k in 1:j){ trade_ret[i-1,c[k]]=cum_rt[i-1,c[k]] } } } trade_ret[nrow(entry),]=cum_rt[nrow(entry),] trade_ret<-data.frame(DATE = dt,trade_ret) colnames(trade_ret)<-names write.csv(trade_ret,paste(path_output,"Trade Ret.csv"),row.names = FALSE) no_of_pos_trades<-matrix(0,dim(cum_ret)[1],dim(cum_ret)[2]) no_of_neg_trades<-matrix(0,dim(cum_ret)[1],dim(cum_ret)[2]) for(i in 2:nrow(no_of_pos_trades)){ if(i==nrow(no_of_pos_trades)){ no_of_pos_trades[i,which(cum_ret[i,]>0)]=1 no_of_neg_trades[i,which(cum_ret[i,]<0)]=1 } c=which(cum_ret[i-1,]!=0 & cum_ret[i,]==0) j=length(c) if(j>0){ for(k in 1:j){ if(cum_ret[i-1,c[k]]>0) no_of_pos_trades[i-1,c[k]]=1 if(cum_ret[i-1,c[k]]<0) no_of_neg_trades[i-1,c[k]]=1 } } } no_of_trades=no_of_pos_trades+no_of_neg_trades write.csv(no_of_trades,paste(path_output,"Total trades.csv"),row.names = FALSE) pos<-data.frame((apply(no_of_pos_trades,1,sum,na.rm=TRUE))) neg<-data.frame((apply(no_of_neg_trades,1,sum,na.rm=TRUE))) pos<-data.frame(DATE = dt[-1],pos_No_OF_TRADES = pos[,1]) neg<-data.frame(DATE = dt[-1],neg_No_OF_TRADES = neg[,1]) ann_pos<-data.frame(aggregate(pos[,2],by=list((substr(pos[,1],7,10))),sum)) ann_neg<-data.frame(aggregate(neg[,2],by=list((substr(neg[,1],7,10))),sum)) ann_no_of_trades<-ann_pos[,2]+ann_neg[,2] ann_no_of_trades<-data.frame(DATE = ann_pos[,1], ann_no_of_trades = ann_no_of_trades) success_ratio<-ann_pos[,2]/ann_no_of_trades[,2] success_ratio<-data.frame(DATE = ann_pos[,1], suc_ratio = success_ratio) avg_pos<-matrix(0,dim(cum_ret)[1],dim(cum_ret)[2]) avg_neg<-matrix(0,dim(cum_ret)[1],dim(cum_ret)[2]) for(i in 2:nrow(avg_pos)){ if(i==nrow(avg_pos)){ avg_pos[i,which(cum_ret[i,]>0)]=cum_ret[i,which(cum_ret[i,]>0)] avg_neg[i,which(cum_ret[i,]<0)]=cum_ret[i,which(cum_ret[i,]<0)] } c=which(cum_ret[i-1,]!=0 & cum_ret[i,]==0) j=length(c) if(j>0){ for(k in 1:j){ if(cum_ret[i-1,c[k]]>0) avg_pos[i-1,c[k]]=cum_ret[i-1,c[k]] else avg_neg[i-1,c[k]]=cum_ret[i-1,c[k]] } } } write.csv(avg_pos,paste(path_output,"avg_pos.csv"),row.names=FALSE) write.csv(avg_neg,paste(path_output,"avg_neg.csv"),row.names=FALSE) avg_pos<-data.frame((apply(avg_pos,1,sum,na.rm=TRUE))) avg_neg<-data.frame((apply(avg_neg,1,sum,na.rm=TRUE))) avg_pos<-data.frame(DATE = dt[-1],avg_pos_ret = avg_pos[,1]) avg_neg<-data.frame(DATE = dt[-1],avg_neg_ret = avg_neg[,1]) ann_avg_pos<-data.frame(aggregate(avg_pos[,2],by=list((substr(avg_pos[,1],7,10))),sum)) ann_avg_neg<-data.frame(aggregate(avg_neg[,2],by=list((substr(avg_neg[,1],7,10))),sum)) #Divide total ret pos/total pos ann_avg_pos<-ann_avg_pos[,2]/ann_pos[,2] ann_avg_neg<-ann_avg_neg[,2]/ann_neg[,2] ann_avg_neg<-data.frame(DATE = ann_neg[,1],ann_avg_neg) ann_avg_pos<-data.frame(DATE = ann_pos[,1],ann_avg_pos) wl<-abs(ann_avg_pos[,2]/ann_avg_neg[,2]) wl<-data.frame(DATE = ann_avg_pos[,1],Win_to_lose = wl) #for long side entry_long<-matrix(0,dim(entry)[1],dim(entry)[2]) for(i in 1:nrow(entry)){ entry_long[i,which(entry[i,]>0)]<-entry[i,which(entry[i,]>0)] } write.csv(entry_long,paste(path_output,"entry_long.csv"),row.names=FALSE) entry_long_final<-entry_long[-1*dim(entry_long)[1],] fr<-data.frame(entry_long_final*stck_lvl_calc) lng_ret_tb<-data.frame(rowSums(entry_long_final*stck_lvl_calc)) cst_tb<-data.frame(rowSums(abs(entry_long[-1,]-entry_long[-1*dim(entry_long)[1],])*tc)) # alloc<-rowSums(abs(clafic[-1])) # exposure_long<-as.matrix(rowSums(abs(entry_long_final))) tot_ret_tb<-data.frame((lng_ret_tb -cst_tb)) tot_ret_tb[is.na(tot_ret_tb)] =0 tot_ret_tb<-as.matrix(tot_ret_tb) tot_ret_tb[which(!is.finite(tot_ret_tb))] <- 0 mtm_long<-tot_ret_tb mtm_long<-data.frame(DATE=dt[-1],mtm_long_Daily = mtm_long) name<-c("DATE","mtm_long") colnames(mtm_long)=name write.csv(mtm_long,paste(path_output,"Long_mtm.csv"),row.names=FALSE) ann_mtm_long<-data.frame(aggregate(mtm_long[,2],by=list((substr(mtm_long[,1],7,10))),sum)) ann_tv<-data.frame(aggregate(tot_tv[,2],by=list((substr(tot_tv[,1],7,10))),sum)) ann_mtm_longtv<-ann_mtm_long[,2]/ann_tv[,2] ann_mtm_longtv<-data.frame(DATE = ann_mtm_long[,1],mtm_longTV<-ann_mtm_longtv) ann_ret_rsi_long<-data.frame(aggregate(ret_fin_tb[,2],by=list((substr(ret_fin_tb[,1],1,4))),sum)) ann_mean_rsi_long<-data.frame(aggregate(ret_fin_tb[,(2)],by=list((substr(ret_fin_tb[,1],1,4))),mean)) ann_sd_rsi_long<-data.frame(aggregate(ret_fin_tb[,(2)],by=list((substr(ret_fin_tb[,1],1,4))),sd)) t1<-ann_mean_rsi_long[,2] t2<-ann_sd_rsi_long[,2] ann_sharpe_rsi_long<- data.frame(DATE=ann_mean_rsi_long[,1],Sharpe = (t1/t2)*sqrt(252)) cum_ret<-matrix(0,dim(fr)[1],dim(fr)[2]) #ret per day fr<-as.matrix(fr) entry_long_final<-as.matrix(entry_long_final) for(i in 1:nrow(fr)){ if(i==1){ cum_ret[i,which(entry_long_final[i,]!=0)]=fr[i,which(entry_long_final[i,]!=0)] } else{ cum_ret[i,which(entry_long_final[i-1,]==0 & entry_long_final[i,]!=0)]=fr[i,which(entry_long_final[i-1,]==0 & entry_long_final[i,]!=0)] c=which(entry_long_final[i-1,]!=0 & entry_long_final[i,]!=0) j=length(c) if(j>0){ for(k in 1:j){ cum_ret[i,c[k]]=fr[i,c[k]]+cum_ret[i-1,c[k]] } } } } no_of_pos_trades<-matrix(0,dim(cum_ret)[1],dim(cum_ret)[2]) no_of_neg_trades<-matrix(0,dim(cum_ret)[1],dim(cum_ret)[2]) for(i in 2:nrow(no_of_pos_trades)){ if(i==nrow(no_of_pos_trades)){ no_of_pos_trades[i,which(cum_ret[i,]>0)]=1 no_of_neg_trades[i,which(cum_ret[i,]<0)]=1 y=i } c=which(cum_ret[i-1,]!=0 & cum_ret[i,]==0) j=length(c) if(j>0){ for(k in 1:j){ if(cum_ret[i-1,c[k]]>0) no_of_pos_trades[i-1,c[k]]=1 if(cum_ret[i-1,c[k]]<0) no_of_neg_trades[i-1,c[k]]=1 } } } no_of_trades=no_of_pos_trades+no_of_neg_trades write.csv(no_of_trades,paste(path_output,"Total long trades.csv"),row.names = FALSE) pos<-data.frame((apply(no_of_pos_trades,1,sum,na.rm=TRUE))) neg<-data.frame((apply(no_of_neg_trades,1,sum,na.rm=TRUE))) pos<-data.frame(DATE = dt[-1],pos_No_OF_TRADES = pos[,1]) neg<-data.frame(DATE = dt[-1],neg_No_OF_TRADES = neg[,1]) ann_pos_long<-data.frame(aggregate(pos[,2],by=list((substr(pos[,1],7,10))),sum)) ann_neg_long<-data.frame(aggregate(neg[,2],by=list((substr(neg[,1],7,10))),sum)) ann_no_of_trades_long<-ann_pos_long[,2]+ann_neg_long[,2] ann_no_of_trades_long<-data.frame(DATE = ann_pos_long[,1], ann_no_of_trades_long = ann_no_of_trades_long) #for short side entry_short<-matrix(0,dim(entry)[1],dim(entry)[2]) for(i in 1:nrow(entry)){ entry_short[i,which(entry[i,]<0)]<-entry[i,which(entry[i,]<0)] } entry_short_final<-entry_short[-1*dim(entry_short)[1],] write.csv(entry_short,paste(path_output,"entry_short_after.csv"),row.names=FALSE) fr<-data.frame(entry_short_final*stck_lvl_calc) lng_ret_tb<-data.frame(rowSums(entry_short_final*stck_lvl_calc)) cst_tb<-data.frame(rowSums(abs(entry_short[-1,]-entry_short[-1*dim(entry_short)[1],])*tc)) # alloc<-rowSums(abs(clafic[-1])) # exposure_short<-as.matrix(rowSums(abs(entry_short_final))) tot_ret_tb<-data.frame((lng_ret_tb -cst_tb)) tot_ret_tb[is.na(tot_ret_tb)] =0 tot_ret_tb<-as.matrix(tot_ret_tb) tot_ret_tb[which(!is.finite(tot_ret_tb))] <- 0 mtm_short<-tot_ret_tb mtm_short<-data.frame(DATE=dt[-1],mtm_short_Daily = mtm_short) name<-c("DATE","mtm_short") colnames(mtm_short)=name write.csv(mtm_short,paste(path_output,"Long_mtm.csv"),row.names=FALSE) ann_mtm_short<-data.frame(aggregate(mtm_short[,2],by=list((substr(mtm_short[,1],7,10))),sum)) ann_tv<-data.frame(aggregate(tot_tv[,2],by=list((substr(tot_tv[,1],7,10))),sum)) ann_mtm_shorttv<-ann_mtm_short[,2]/ann_tv[,2] ann_mtm_shorttv<-data.frame(DATE = ann_mtm_short[,1],mtm_shortTV<-ann_mtm_shorttv) ann_ret_rsi_short<-data.frame(aggregate(ret_fin_tb[,2],by=list((substr(ret_fin_tb[,1],1,4))),sum)) ann_mean_rsi_short<-data.frame(aggregate(ret_fin_tb[,(2)],by=list((substr(ret_fin_tb[,1],1,4))),mean)) ann_sd_rsi_short<-data.frame(aggregate(ret_fin_tb[,(2)],by=list((substr(ret_fin_tb[,1],1,4))),sd)) t1<-ann_mean_rsi_short[,2] t2<-ann_sd_rsi_short[,2] ann_sharpe_rsi_short<- data.frame(DATE=ann_mean_rsi_short[,1],Sharpe = (t1/t2)*sqrt(252)) cum_ret<-matrix(0,dim(fr)[1],dim(fr)[2]) #ret per day fr<-as.matrix(fr) entry_short_final<-as.matrix(entry_short_final) for(i in 1:nrow(fr)){ if(i==1){ cum_ret[i,which(entry_short_final[i,]!=0)]=fr[i,which(entry_short_final[i,]!=0)] } else{ cum_ret[i,which(entry_short_final[i-1,]==0 & entry_short_final[i,]!=0)]=fr[i,which(entry_short_final[i-1,]==0 & entry_short_final[i,]!=0)] c=which(entry_short_final[i-1,]!=0 & entry_short_final[i,]!=0) j=length(c) if(j>0){ for(k in 1:j){ cum_ret[i,c[k]]=fr[i,c[k]]+cum_ret[i-1,c[k]] } } } } no_of_pos_trades<-matrix(0,dim(cum_ret)[1],dim(cum_ret)[2]) no_of_neg_trades<-matrix(0,dim(cum_ret)[1],dim(cum_ret)[2]) for(i in 2:nrow(no_of_pos_trades)){ if(i==nrow(no_of_pos_trades)){ no_of_pos_trades[i,which(cum_ret[i,]>0)]=1 no_of_neg_trades[i,which(cum_ret[i,]<0)]=1 y=i } c=which(cum_ret[i-1,]!=0 & cum_ret[i,]==0) j=length(c) if(j>0){ for(k in 1:j){ if(cum_ret[i-1,c[k]]>0) no_of_pos_trades[i-1,c[k]]=1 if(cum_ret[i-1,c[k]]<0) no_of_neg_trades[i-1,c[k]]=1 } } } no_of_trades=no_of_pos_trades+no_of_neg_trades write.csv(no_of_trades,paste(path_output,"Total short trades.csv"),row.names = FALSE) pos<-data.frame((apply(no_of_pos_trades,1,sum,na.rm=TRUE))) neg<-data.frame((apply(no_of_neg_trades,1,sum,na.rm=TRUE))) pos<-data.frame(DATE = dt[-1],pos_No_OF_TRADES = pos[,1]) neg<-data.frame(DATE = dt[-1],neg_No_OF_TRADES = neg[,1]) ann_pos_short<-data.frame(aggregate(pos[,2],by=list((substr(pos[,1],7,10))),sum)) ann_neg_short<-data.frame(aggregate(neg[,2],by=list((substr(neg[,1],7,10))),sum)) ann_no_of_trades_short<-ann_pos_short[,2]+ann_neg_short[,2] ann_no_of_trades_short<-data.frame(DATE = ann_pos_short[,1], ann_no_of_trades_short = ann_no_of_trades_short) final_res<-data.frame(Date=ann_avg_pos[,1],Number_Of_trades=ann_no_of_trades[,2],No_of_pos_Trades = ann_pos[,2], No_of_Neg_Trades = ann_neg[,2], Average_pos_Trade=ann_avg_pos[,2], Avg_Neg_trade = ann_avg_neg[,2], Success_Ratio=success_ratio[,2], Win_to_Lose = wl[,2], Total_mtm=ann_mtm[,2],Tot_tv=ann_tv[,2],Mtm_tv=ann_mtmtv[,2], Sharpe= ann_sharpe_rsi[,2],No_of_long_Trades=ann_no_of_trades_long[,2],Mtm_long = ann_mtm_long[,2],No_of_short_Trades=ann_no_of_trades_short[,2],Mtm_short = ann_mtm_short[,2]) write.csv(final_res[1:8,],paste("C:/Users/YashV/Desktop/Framework Data/Results/","final_res RSI ",lookback_period[li] ,"entry exit",rsi_entry_long[lo]," ",rsi_entry_short[lo],"Nifty 50_5tst_exit.csv"),row.names=FALSE) } }

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/R/Healthcareai/Diabetes/diabetes.R

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nicolaumatarrodona/AI

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library(healthcareai) str(pima_diabetes) #-------------------------------------------------------- # Easy Machine Learning #-------------------------------------------------------- quick_models <- machine_learn(pima_diabetes, patient_id, outcome = diabetes) quick_models predictions <- predict(quick_models) predictions plot(predictions) quick_models %>% predict(outcome_groups = 2) %>% plot() # Data Profiling missingness(pima_diabetes) %>% plot() #-------------------------------------------------------- # Data Preparation #-------------------------------------------------------- split_data <- split_train_test(d = pima_diabetes, outcome = diabetes, p = 0.8, seed = 1) prepped_training_data <- prep_data(split_data$train, patient_id, outcome = diabetes, center = TRUE, scale = TRUE, collapse_rare_factors = FALSE) # write.csv(prepped_training_data,'diabetes_prepared.csv') head(prepped_training_data) # prep_data object with only center, scale and impute missing values set to true. Rest is set to false (default is true) while (FALSE) { prepped_training_data <- prep_data(split_data$train, patient_id, outcome = diabetes, center = TRUE, scale = TRUE, collapse_rare_factors = FALSE, impute = TRUE, remove_near_zero_variance = FALSE, add_levels = FALSE, logical_to_numeric = FALSE, factor_outcome = FALSE) } #-------------------------------------------------------- # Model Training #-------------------------------------------------------- models <- tune_models(d = prepped_training_data, outcome = diabetes, tune_depth = 25, metric = "PR") evaluate(models, all_models = TRUE) models["Random Forest"] %>% plot() #-------------------------------------------------------- # Faster Model Training # flash_models use fixed sets of hyperparameter values to train the models # so you still get a model customized to your data, # but without burning the electricity and time to precisely optimize all the details. # Here we’ll use models = "RF" to train only a random forest. # If you want to train a model on fixed hyperparameter values, but you want to choose those values, # you can pass them to the hyperparameters argument of tune_models. # Run get_hyperparameter_defaults() to see the default values and get a list you can customize. #-------------------------------------------------------- #-------------------------------------------------------- # Model Interpretation #-------------------------------------------------------- # In this plot, the low value of weight_class_normal signifies that people with normal weight # are less likely to have diabetes. Similarly, plasma glucose is associated with increased risk of # diabetes after accounting for other variables. interpret(models) %>% plot() # Tree based methods such as random forest and boosted decision trees can’t provide coefficients # like regularized regression models can, but they can provide information about how important each # feature is for making accurate predictions. get_variable_importance(models) %>% plot() # The explore function reveals how a model makes its predictions. It takes the most important features # in a model, and uses a variety of “counterfactual” observations across those features to see what # predictions the model would make at various combinations of the features. explore(models) %>% plot() #-------------------------------------------------------- # Prediction #-------------------------------------------------------- predict(models) test_predictions <- predict(models, split_data$test, risk_groups = c(low = 30, moderate = 40, high = 20, extreme = 10) ) # > Prepping data based on provided recipe plot(test_predictions) #-------------------------------------------------------- # Saving, Moving, and Loading Models #-------------------------------------------------------- save_models(models, file = "my_models.RDS") models <- load_models("my_models.RDS") #-------------------------------------------------------- # A Regression Example: # # All the examples above have been classification tasks, # redicting a yes/no outcome. Here’s an example of a full # regression modeling pipeline on a silly problem: # predicting individuals’ ages. The code is very similar to classification. #-------------------------------------------------------- regression_models <- machine_learn(pima_diabetes, patient_id, outcome = diabetes) summary(regression_models) # Let’s make a prediction on a hypothetical new patient. Note that the model handles missingness in # insulin and a new category level in weight_class without a problem (but warns about it). new_patient <- data.frame( pregnancies = 0, plasma_glucose = 80, diastolic_bp = 55, skinfold = 24, insulin = NA, weight_class = "???", pedigree = .2, age = 24) predict(regression_models, new_patient)

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library(shinythemes) library(datasets) library(rsconnect) InputData<-read.csv("D:/Analysis.csv") # Use a fluid Bootstrap layout fluidPage( theme = shinytheme("slate"), # Give the page a title titlePanel("ONLINE SHOPPING ANALYSIS"), # Generate a row with a sidebar sidebarLayout( # Define the sidebar with one input sidebarPanel( selectInput("category", "Select Category", choices=colnames(InputData)), hr(), helpText("Data") ), # Create a spot for the barplot mainPanel( plotOutput("storePlot") ) ) )

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cat(sapply(as.double(readLines(tail(commandArgs(), n=1))), function(t) { r <- 0 if (t >= 2^31) { t <- t - 2^31 r <- 1 } s <- as.integer(t) while (s > 0) { s <- bitwAnd(s, s - 1) r <- r + 1 } r %% 3 }), sep="\n")

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make_tpm_files.R

# Code created by Matthew Elliott # Contact: melliot1@ucsc.edu (valid until 2023) # Phone: 231-392-1263 (just in case) ############# ### Set Up Notebook ############# library(plyr) library(biomaRt) ############# ### Load Data ############# # We reharmnonize the wholeblood dataset using harmony ran method # We load in the datasets from CSV files: raw_guinea= read.table("~/test_data/guinea_rna.csv", header=TRUE , sep =",", stringsAsFactors=FALSE) #quote = input$quote, raw_tanzi = read.table("~/test_data/tanzania_rna.csv", header=TRUE , sep =",", stringsAsFactors=FALSE) raw_mali = read.table("~/test_data/mali_rna.csv", header=TRUE , sep =",", stringsAsFactors=FALSE) # we create the south africa dataset using resutls from the unharmonized_counts file # The south africa data is counts, so the unharmonized counts should have th correctly formatted data raw_all = read.table("~/test_data/unharmonized_counts.csv", header=TRUE , sep =",", stringsAsFactors=FALSE) raw_sa = raw_all[ , c(2,111:355) ] raw_guinea[1:10,1:10] # check that mali is actually counts per million raw_mali[1:10,1:10] dim(raw_mali) colSums(raw_mali[,2:81] ) # IT's currently not summing to 1 million dim(raw_sa) raw_sa[1:10,1:10] ############# ### compare to unharmonized_data.csv ############# #Compare data agianst the unharmonized counts file length( unique( raw_guinea[,1] )) length( raw_guinea[,1] ) raw_all = read.table("~/test_data/unharmonized_counts.csv", header=TRUE , sep =",", stringsAsFactors=FALSE) # check raw_all against raw_guinea dim(raw_guinea) dim(raw_tanzi) names(raw_all) raw_all[1:10,1:4] raw_guinea[1:10,1:3] raw_guinea[ which( raw_guinea$hgnc=="AARS" ), 1:3] # The datasets are the same just in different order # The mali dataset is changed names(raw_mali) raw_mali[1:10,1:3] names(raw_all) raw_all[1:10,c(2,31,32)] ############# ### Remove Duplicates ############# # Guinea - Duplicates #length(unique(raw_guinea$hgnc)) #length(raw_guinea$hgnc) raw_guinea2 = ddply( raw_guinea,"hgnc", numcolwise(sum) ) # check that it works #length(unique(raw_guinea$hgnc)) #length(raw_guinea$hgnc) #raw_guinea$hgnc[ which( duplicated(raw_guinea$hgnc) ) ] #raw_guinea[ which(raw_guinea$hgnc=="RPS27") ,] #raw_guinea2[ which(raw_guinea2$hgnc=="RPS27") ,] # Tanzania - No duplicates length(unique(raw_tanzi$GeneSymbol)) length(raw_tanzi$GeneSymbo) raw_tanzi[1:10,1:10] # South Africa - No duplicates #length( raw_sa$hgnc ) #length( unique(raw_sa$hgnc )) # Mali - No duplicates #length( raw_mali$hgnc ) #length( unique(raw_mali$hgnc )) ############# ### TPM Function ############# #counts = raw_guinea2 # for testing #counts = raw_tanzi # for testing # TPM Function TPM_Converter <- function( counts ) { # setup: Get gene lengths human <- useMart("ensembl", dataset="hsapiens_gene_ensembl") # Get gene lengths for guinea gene_coords=getBM(attributes=c("hgnc_symbol", "start_position","end_position"), filters="hgnc_symbol", values=counts[,1], mart=human) gene_coords$size=gene_coords$end_position - gene_coords$start_position gene_coords$end_position = NULL gene_coords$start_position = NULL #dim(gene_coords2) #dim(counts) dim(counts_small) # Only keep genes with corresponding gene lengths gene_coords2 = gene_coords[ !duplicated(gene_coords$hgnc_symbol) ,] # remove duplicates from gene sizes counts_small = counts[ counts[,1] %in% gene_coords2$hgnc_symbol ,] # only keep genes with gene sizes counts_small = counts_small[order(counts_small[,1]),] # order alphabetically gene_coords3 = gene_coords2[ gene_coords2$hgnc_symbol %in% counts_small[,1] ,] # only keep genes with gene sizes gene_coords3 = gene_coords3[order(gene_coords3$hgnc_symbol),] # order alphabetically print( all( gene_coords3$hgnc_symbol == counts_small[,1] ) ) # Create counts varibale counts_gene = sweep( counts_small[,-1], MARGIN = 1, STATS = gene_coords3$size, FUN = "/") scaling_factor=colSums(counts_gene)/1000000 TPM = sweep( counts_gene, MARGIN = 2, STATS = scaling_factor, FUN = "/") TPM_final = cbind( counts_small[,1], TPM ) names(TPM_final)[1] = "hgnc" return(TPM_final) #counts[1:6,1:10] #head(gene_coords2$size) #141403.840 / 1491100 #TPM2[1:6,1:10] #counts2 = round( counts ) #counts2[1:10,1:10] } ############# ### Convert Data to TPM ############# # Convert Guinea, Tanzania, and South Africa tpm_guinea = TPM_Converter(raw_guinea2) tpm_tanzi = TPM_Converter( raw_tanzi ) tpm_sa = TPM_Converter( raw_sa ) # Make datasets look slightly different # Guinea: shuffle rows and rename first column and add a duplicate row rows <- sample(nrow(tpm_guinea)) tpm_guinea2 = tpm_guinea[rows,] names(tpm_guinea2)[1]="gene_symbol" tpm_guinea2 = rbind( tpm_guinea2, tpm_guinea2[1,] ) # duplicate a row # Tanzania: shuffle rows rows <- sample(nrow(tpm_tanzi)) tpm_tanzi2 = tpm_tanzi[rows,] # Rescale Mali scaling_factor=colSums(raw_mali[,-1])/1000000 tpms = sweep( raw_mali[,-1], MARGIN = 2, STATS = scaling_factor, FUN = "/") tpm_mali = cbind( raw_mali[,1], tpms ) names(tpm_mali)[1] = "hgnc" #tpm_mali[1:20,1:10] #dim(raw_mali) #olSums( tpm_mali[,2:81] ) #head(raw_mali[,-1]) #colSums(tpm_guinea[,2:5]) #colSums(tpm_tanzi[,3:6]) #colSums(tpm_sa[,33:35]) # Check it works #tpm_guinea[1:10,1:10] #dim(TPM_final) #TPM_final[1:10,1:10] #colSums(TPM_final[,-1]) #safety=TPM_final ############# ### Save Datasets ############# write.csv( tpm_tanzi2, "tpm_data/tanzania_tpm.csv", row.names=FALSE) write.csv( tpm_guinea2, "tpm_data/guinea_tpm.csv", row.names=FALSE) write.csv( tpm_mali, "tpm_data/mali_tpm.csv", row.names=FALSE) write.csv( tpm_sa, "tpm_data/south_africa_tpm.csv", row.names=FALSE)

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/proc_CITIPM_v1_0.R

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castillosebastian/JUSTAT

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proc_CITIPM_v1_0.R

# JUSTAT is a free software developed for official statistics of Supreme Court # of Entre Ríos (STJER), Argentina, Office of Planification Management and # Statistics (APGE) # V.1.0 # 13-06-17 # Authors # JUSTAT: Lic. Sebastián Castillo, Ing. Federico Caballaro y Lic. Paolo Orundes ############################ proc_CITIPM ################################### # Librerías library(data.table) library(lubridate) library(stringr) library(purrr) library(stringdist) library(dplyr) library(tidyr) library(tibble) proc_CITIPM <- function(id_archivo) { BD_adm_ingresos <- fread("~/JUSTAT/BD/BD_adm_ingresos.csv") BD_CITIPM <- fread("~/JUSTAT/BD/BD_CITIPM.csv") ultimo_procesado <- BD_CITIPM$id_archivos[which.max(BD_CITIPM$id_archivos)] BD_adm_ingresos <- BD_adm_ingresos[BD_adm_ingresos$id_archivos > ultimo_procesado & BD_adm_ingresos$id_operacion == "CITIPM" & BD_adm_ingresos$rec_estado == "admitido" , ] lista_tbs_prim_CITIPM <- map(BD_adm_ingresos$ruta, fread, encoding = "Latin-1", na.strings = "" ) for (i in seq_along(lista_tbs_prim_CITIPM)) { if (length(lista_tbs_prim_CITIPM[[i]]) == 6) { colnames(lista_tbs_prim_CITIPM[[i]]) <- try(c("caratula", "tproc", "finic", "nro_receptoria", "radicacion", "origenOmedpriv")) } else { next() } } # filtar tablas de 6 variables indice_6col <- lengths(lista_tbs_prim_CITIPM) == 6 lista_tbs_prim_CITIPM <- lista_tbs_prim_CITIPM[indice_6col] lista_inic_xorgano <- list() for (i in seq_along(lista_tbs_prim_CITIPM)) { lista_inic_xorgano[[i]] <- lista_tbs_prim_CITIPM[[i]] %>% group_by(radicacion, tproc) %>% summarise(cantidad_procesos = n()) lista_inic_xorgano[[i]]$periodo <- BD_adm_ingresos$id_periodo[indice_6col][[i]] lista_inic_xorgano[[i]]$id_archivos <- BD_adm_ingresos$id_archivos[indice_6col][[i]] lista_inic_xorgano[[i]]$informante <- BD_adm_ingresos$id_organismo[indice_6col][[i]] lista_inic_xorgano[[i]] } BD_dfs <- bind_rows(lista_inic_xorgano) BD_dfs <- select(BD_dfs, id_archivos, informante, periodo, radicacion, tproc, cantidad_procesos) write.table(BD_dfs, file= "~/JUSTAT/BD/BD_CITIPM.csv", sep = ",", row.names = F, col.names = F, append = T) }

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BelhalK/AccelerationTrainingAlgorithms

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require(ggplot2) require(gridExtra) require(reshape2) mixt.simulate <-function(n,weight,mu,sigma) { G <- length(mu) Z <- sample(1:G, n, prob=weight, replace=T) x<-NULL for (g in 1:G) { x<-c(x,rnorm(length(which(Z==g)),mu[g],sigma[g])) } return(x) } #------------------------------------- mixt.em <- function(x, theta0, K) { G<-length(mu) col.names <- c("iteration", paste0("p",1:G), paste0("mu",1:G), paste0("sigma",1:G)) theta.est <- matrix(NA,K+1,3*G+1) theta.est[1,] <- c(0, theta0$p, theta0$mu, theta0$sigma) theta<-theta0 for (k in 1:K) { s<-step.E(x,theta) theta<-step.M(s,n) theta.est[k+1,] <- c(k, theta$p, theta$mu, theta$sigma) } df <- as.data.frame(theta.est) names(df) <- col.names return(df) } mixt.iem <- function(x, theta0, K,nbr) { G<-length(mu) col.names <- c("iteration", paste0("p",1:G), paste0("mu",1:G), paste0("sigma",1:G)) theta.est <- matrix(NA,K+1,3*G+1) theta.est[1,] <- c(0, theta0$p, theta0$mu, theta0$sigma) tau <- compute.tau(x,theta0) theta<-theta0 tau.old <- compute.tau(x[1],theta0) s <- compute.stat_iem(x,tau, tau.old,1) l <- rep(sample(1:n,n), K/n) i <- 1:nbr for (k in 1:K) { if (k%%(n/nbr) == 1) { # l<-sample(1:n,n) # l<-1:n i<-1:nbr } # tau.new <- compute.tau(x[i],theta) # s <- compute.stat_iem(x,tau, tau.new, i) tau[l[i],] <- compute.tau(x[l[i]],theta) s <- compute.stat(x,tau) theta<-step.M(s,n) theta.est[k+1,] <- c(k, theta$p, theta$mu, theta$sigma) i = i+nbr } df <- as.data.frame(theta.est) names(df) <- col.names return(df) } # mixt.oem <- function(x, theta0, K,nbr) # { # G<-length(mu) # kiter = 1:K # rho = 1/(kiter+1) # col.names <- c("iteration", paste0("p",1:G), paste0("mu",1:G), paste0("sigma",1:G)) # theta.est <- matrix(NA,K+1,3*G+1) # theta.est[1,] <- c(0, theta0$p, theta0$mu, theta0$sigma) # theta<-theta0 # tau<-compute.tau(x,theta) # s<-compute.stat(x,tau) # s.old <- s # theta<-step.M(s,n) # n<-length(x) # l <- NULL # l <- rep(sample(1:n,n), K/n) # i <- 1:nbr # for (k in 1:K) # { # if (k%%(n/nbr) == 1) # { # i<-1:nbr # } # tau.new <- compute.tau(x[l[i]],theta) # s <- compute.stat_oem(x,tau,s.old, tau.new, l[i],rho[k]) # s.old <- s # i <- i+nbr # theta<-step.M(s,n) # theta.est[k+1,] <- c(k, theta$p, theta$mu, theta$sigma) # } # df <- as.data.frame(theta.est) # names(df) <- col.names # return(df) # } # mixt.oemvr <- function(x, theta0, K,nbr,rho) # { # G<-length(mu) # col.names <- c("iteration", paste0("p",1:G), paste0("mu",1:G), paste0("sigma",1:G)) # rho = 0.0001 # theta.est <- matrix(NA,K+1,3*G+1) # theta.est[1,] <- c(0, theta0$p, theta0$mu, theta0$sigma) # tau <- compute.tau(x,theta0) # tau.old.init <- tau[1,] # theta<-theta0 # s<-compute.stat(x,tau) # s.old <- s # s.old.init <- s # l <- rep(sample(1:n,n), K/n) # i <- 1:nbr # for (k in 1:K) # { # if (k%%(n/nbr) == 1) # { # i<-1:nbr # tau.old.init <- compute.tau(x[i],theta) # s.old.init <- compute.stat(x,tau) # } # tau.new <- compute.tau(x[i],theta) # s <- compute.stat_oemvr(x,tau, tau.new,s.old,s.old.init,tau.old.init, i,rho) # s.old <- s # i <- i+nbr # theta<-step.M(s,n) # theta.est[k+1,] <- c(k, theta$p, theta$mu, theta$sigma) # } # df <- as.data.frame(theta.est) # names(df) <- col.names # return(df) # } mixt.oem <- function(x, theta0, K,nbr,rho) { G<-length(mu) col.names <- c("iteration", paste0("p",1:G), paste0("mu",1:G), paste0("sigma",1:G)) theta.est <- matrix(NA,K+1,3*G+1) theta.est[1,] <- c(0, theta0$p, theta0$mu, theta0$sigma) theta<-theta0 #Init tau <- compute.tau(x,theta) s<-compute.stat(x,tau) l <- NULL l <- rep(sample(1:n,n), K/n) i <- 1:nbr for (k in 1:K) { if (k%%(n/nbr) == 1) { i<-1:nbr } tau.indiv.new <- compute.tau(x[l[i]],theta) #Update statistic s$s1 <- s$s1 + rho[k]*(tau.indiv.new - s$s1) s$s2 <- s$s2 + rho[k]*(x[l[i]]*tau.indiv.new - s$s2) s$s3 <- s$s3 + rho[k]*(x[l[i]]^2*tau.indiv.new - s$s3) theta <- step.M(s,n) theta.est[k+1,] <- c(k, theta$p, theta$mu, theta$sigma) #Update index i <- i+nbr } df <- as.data.frame(theta.est) names(df) <- col.names return(df) } mixt.oemvr <- function(x, theta0, K,nbr,rho) { G<-length(mu) col.names <- c("iteration", paste0("p",1:G), paste0("mu",1:G), paste0("sigma",1:G)) theta.est <- matrix(NA,K+1,3*G+1) theta.est[1,] <- c(0, theta0$p, theta0$mu, theta0$sigma) theta<-theta0 #Init tau <- compute.tau(x,theta) s<-compute.stat(x,tau) l <- NULL l <- rep(sample(1:n,n), K/n) i <- 1:nbr for (k in 1:K) { if (k%%(n/nbr) == 1) { i<-1:nbr theta.e.0 <- theta } tau.indiv.new <- compute.tau(x[l[i]],theta) s.indiv.new <- x[l[i]]*tau.indiv.new tau.indiv.e.0 <- compute.tau(x[l[i]],theta.e.0) s.indiv.e.0 <- x[l[i]]*tau.indiv.e.0 tau.e.0 <- compute.tau(x,theta.e.0) s.e.0 <- x%*%tau.e.0 #Update statistic s$s1 <- s$s1 + rho*(tau.indiv.new - tau.indiv.e.0 + colSums(tau.e.0) - s$s1) s$s2 <- s$s2 + rho*(s.indiv.new - s.indiv.e.0 + s.e.0 - s$s2) s$s3 <- s$s3 + rho*(x[l[i]]*s.indiv.new - x[l[i]]*s.indiv.e.0 + (x^2)%*%tau.e.0 - s$s3) theta <- step.M(s,n) theta.est[k+1,] <- c(k, theta$p, theta$mu, theta$sigma) #Update index i <- i+nbr } df <- as.data.frame(theta.est) names(df) <- col.names return(df) }

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#' find a cluster of diploid cells with integrative clustering method #' #' @param norm.mat.smooth smoothed data matrix; genes in rows; cell names in columns. #' @param min.cells minimal number of cells per cluster. #' @param n.cores number of cores for parallel computing. #' #' @return 1) predefined diploid cell names; 2) clustering results; 3) inferred baseline. #' #' @examples #' test.bnc <- baseline.norm.cl(norm.mat.smooth=norm.mat.smooth, min.cells=5, n.cores=10) #' #' test.bnc.cells <- test.bnc$preN #' @export baseline.norm.cl <- function(norm.mat.smooth, min.cells=5, n.cores=n.cores){ d <- parallelDist::parDist(t(norm.mat.smooth), threads = n.cores) ##use smooth and segmented data to detect intra-normal cells km <- 6 fit <- hclust(d, method="ward.D2") ct <- cutree(fit, k=km) while(!all(table(ct)>min.cells)){ km <- km -1 ct <- cutree(fit, k=km) if(km==2){ break } } SDM <-NULL SSD <-NULL for(i in min(ct):max(ct)){ data.c <- apply(norm.mat.smooth[, which(ct==i)],1, median) sx <- max(c(0.05, 0.5*sd(data.c))) GM3 <- mixtools::normalmixEM(data.c, lambda = rep(1,3)/3, mu = c(-0.2, 0, 0.2), sigma = sx,arbvar=FALSE,ECM=FALSE,maxit=5000) SDM <- c(SDM, GM3$sigma[1]) SSD <- c(SSD, sd(data.c)) i <- i+1 } wn <- mean(cluster::silhouette(cutree(fit, k=2), d)[, "sil_width"]) #### PDt <- pf(max(SDM)^2/min(SDM)^2, nrow(norm.mat.smooth), nrow(norm.mat.smooth), lower.tail = FALSE) #PDt <- dt((min(SDM)-max(SDM))/mad(SDM),df=km-1) # print(c("low sigma pvalue:", PDt)) #print(c("low sd pvalue:", dt((min(SSD)-max(SSD))/mad(SSD),df=km-1))) if(wn <= 0.15|(!all(table(ct)>min.cells))| PDt > 0.05){ WNS <- "unclassified.prediction" print("low confidence in classification") }else { WNS <- "" } basel <- apply(norm.mat.smooth[, which(ct %in% which(SDM==min(SDM)))], 1, median) preN <- colnames(norm.mat.smooth)[which(ct %in% which(SDM==min(SDM)))] ### return both baseline and warning message RE <- list(basel, WNS, preN, ct) names(RE) <- c("basel", "WNS", "preN", "cl") return(RE) }

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/helper_function.R \name{choose_rho} \alias{choose_rho} \title{Draw error curve} \usage{ choose_rho(data, n_fold, rho) } \arguments{ \item{data}{This is a matrix.} \item{n_fold}{This parameter specifies the n number in n-fold cross_validation.} \item{rho}{This is the regularization parameter values to be evalueated in terms their errors.} } \value{ A list of errors and their corresponding \eqn{log(rho)}. } \description{ This function draws error curve using cross-validation. }

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/generators.R \name{ip_random} \alias{ip_random} \title{generate random IPv4 IP addresses} \usage{ ip_random(n) } \arguments{ \item{n}{the number of IP addresses to randomly generate.} } \value{ a character vector of randomly-generated IPv4 addresses, in their dotted-decimal form. } \description{ \code{ip_random} generates random IP addresses. These currently only follow IPv4 standards, since IPv6 addresses are too large to be stored in R in their numeric form. All IPs generated this way are valid. } \examples{ ip_random(1) #[1] "49.20.57.31" } \seealso{ \code{\link{ip_to_numeric}} for converting \code{random_ips}' output to its numeric form, and \code{\link{range_generate}} for generating all IP addresses within a specific range. }

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# 4.2 Building a Scatterplot library(tidyverse) ggplot(mpg, aes(displ, hwy, color=factor(cyl))) + geom_point() ggplot(mpg, aes(displ, hwy, color=factor(cyl))) + geom_line() + theme(legend.position = "none") ggplot(mpg, aes(displ, hwy, color=factor(cyl))) + geom_bar(stat="identity", position = "identity", fill=NA) + theme(legend.position="none") ggplot(mpg, aes(displ, hwy, color=factor(cyl))) + geom_point() + geom_smooth(method = "lm") ggplot(mpg, aes(displ, hwy, color=factor(cyl))) + geom_point() + geom_smooth() # 4.2.2 Scaling vignette("ggplot2-specs") # 4.3 Adding Complexity ggplot(mpg, aes(displ, hwy)) + geom_point() + geom_smooth() + facet_wrap(~year) # 4.4 Components of the Layered Grammar

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#'Internal Functions #' #'@description INTERNAL FUNCTIONS: \code{ggtern} makes use of several non-exported internal functions, list are as follows: #'@keywords internal #'@name zzz-internal #'@rdname undocumented NULL #' \code{ifthenelse} function takes input arguments \code{x}, \code{a} and \code{b} and returns \code{a} if \code{x} is \code{TRUE}, else, returns \code{b} #' @param x logical input to check #' @param a value to return if \code{x} is TRUE #' @param b value to return if \code{x} is FALSE #' @keywords internal #' @rdname undocumented ifthenelse <- function(x,a,b){ if(!is.logical(x))stop("x argument must be logical") if(x){a}else{b} } #' \code{is.numericor} function takes input arguments \code{A} and \code{B} and returns \code{A} if \code{A} is numeric, else, returns \code{B} #' @param A value to return if numeric #' @param B numeric value to return if \code{A} is NOT numeric #' @keywords internal #' @rdname undocumented is.numericor <- function(A,B){ if(!is.numeric(B)){stop("b must be numeric")} if(is.numeric(A)){A}else{B} } "%||%" <- function(a, b) {if (!is.null(a)) a else b} #' \code{find_global_tern} is a function that conducts a named search for the \code{name} object instance, within the \code{env} environment. #' If an instance doesn't exist within the \code{env} environment, a search is then conducted within the \code{ggtern} and \code{ggplot2} #' namespaces \emph{(in that order)}. This is a modified version of the original source as provided in \code{ggplot2}, which has the same functionality, however, the modification is such that the function #' now additionally searches within the \code{ggtern} namespace prior to the \code{ggplot2} namespace. #' @param name character name of object to search for #' @param env environment to search within as first priority #' @keywords internal #' @rdname undocumented find_global_tern <- function (name, env=environment()){ if(!is.character(name)){stop("'name' must be provided as a character")} if(!inherits(environment(),"environment")){stop("'env' must inherit the environment class")} if (exists(name, env)){return(get(name, env))} nsenv <- asNamespace("ggtern") if(exists(name, nsenv)){return(get(name, nsenv))} nsenv <- asNamespace("ggplot2") if(exists(name, nsenv)){return(get(name, nsenv))} NULL } #' \code{getBreaks} is a function that calculates the Breaks for Major or Minor Gridlines based #' on the input limits. #' @param limits the scale limits #' @param isMajor major or minor grids #' @param nMajor number of major breaks #' @param nMinor number of minor breaks #' @keywords internal #' @rdname undocumented getBreaks <- function(limits,isMajor,nMajor=5,nMinor=2*nMajor){ if(is.null(limits)){ limits = c(0,1) } if(!all(is.numeric(limits))){ limits=c(0,1) } if(diff(range(limits)) == 0){ return(if(isMajor){getOption("tern.breaks.default")}else{getOption("tern.breaks.default.minor")}) }else{ ret = pretty(limits,n=nMajor) if(!isMajor){ r = range(ret) d = diff(r)/(length(ret)-1) minor = seq(min(ret)-d,max(ret)+d,by=d/2) minor = minor[which(minor >= min(limits) & minor <= max(limits))] ret = minor[which(!minor %in% ret)] } ret = ret[which(!ret %in% min(limits))] ret } } #' #' #' \code{tern_dep} is a function that gives a deprecation error, warning, or messsage, #' depending on version number, it is based of the \code{\link[ggplot2]{gg_dep}} function which is #' used inside the \code{ggplot2} package #' @inheritParams ggplot2::gg_dep #' @keywords internal #' @rdname undocumented tern_dep <- function(version, msg) { v <- as.package_version(version) cv <- packageVersion("ggtern") # If current major number is greater than last-good major number, or if # current minor number is more than 1 greater than last-good minor number, # give error. if (cv[[1,1]] > v[[1,1]] || cv[[1,2]] > v[[1,2]] + 1) { stop(msg, " (Defunct; last used in version ", version, ")", call. = FALSE) # If minor number differs by one, give warning } else if (cv[[1,2]] > v[[1,2]]) { warning(msg, " (Deprecated; last used in version ", version, ")", call. = FALSE) # If only subminor number is greater, give message } else if (cv[[1,3]] > v[[1,3]]) { message(msg, " (Deprecated; last used in version ", version, ")") } invisible() } #internal .makeValid <- function(x){ x = x[[1]] if(class(x) == 'character'){ x = gsub("%","'%'",x) x = gsub('([[:punct:]])\\1+', '\\1', x) x = gsub(" ","~",x) } x } #' \code{arrow_label_formatter} is a function that formats the labels directly adjacent to the ternary arrows. #' @param label character label #' @param suffix chacater suffix behind each label #' @param sep the seperator between label and suffix #' @keywords internal #' @rdname undocumented arrow_label_formatter = function(label,suffix=NULL,sep="/") UseMethod("arrow_label_formatter") arrow_label_formatter.default = function(label,suffix=NULL,sep="/") arrow_label_formatter.character( as.character(label), suffix,sep) arrow_label_formatter.call = function(label,suffix=NULL,sep="/") arrow_label_formatter.expression(as.expression(label),suffix,sep) arrow_label_formatter.expression = function(label,suffix=NULL,sep="/"){ suffix = if(suffix == "") NULL else suffix sep = if(is.null(suffix)) "" else .trimAndPad(sep) parse(text=paste(as.character(label),suffix,sep)) } arrow_label_formatter.character = function(label,suffix=NULL,sep="/") { suffix = if(suffix == "") NULL else suffix sep = if(is.null(suffix)) "" else .trimAndPad(sep) TeX(paste(label,suffix,sep=sep)) } .trimAndPad <- function(x){ x = gsub("^(\\s+)","",gsub("(\\s+)$","",x)) if(nchar(x) == 1) x = sprintf(" %s ",x) x } #' \code{label_formatter} is a function that formats / parses labels for use in the grid. #' @param label character label label_formatter = function(label){ arrow_label_formatter(label,suffix="",sep="") } #' \code{joinCharacterSeries} is a function will turn a character vector #' from the format \code{c('a','b','c')} to a single string #' in the following format: \code{"'a','b' and 'c'"} #' @param x character vector #' @author Nicholas Hamilton #' @keywords internal #' @rdname undocumented joinCharacterSeries <- function(x,lastWord='and'){ if(!is.character(x) | !is.vector(x)) stop("'x' must be character vector",call.=FALSE) if(length(x) > 1){ x = paste(paste(x[-length(x)],collapse="', '"),x[length(x)],sep=sprintf("' %s '",lastWord)) } sprintf("'%s'",x) } #' \code{identityInv} is a function which returns exactly the same as \code{\link{identity}} however #' it can be used within transformation logic via \code{do.call(...)} in the same way as for example #' \code{\link{ilrInv}} is to \code{\link{ilr}}. #' @param x input object #' @author Nicholas Hamilton #' @keywords internal #' @rdname undocumented identityInv = function(z) identity(z) #' \code{getFormulaVars} is a function that returns a list of either dependent or independent variables used #' in an input formula #' @param x formula object #' @param dependent whether to return the dependent variables (TRUE) or the indpenedent variables (FALSE) #' @rdname undocumented #' @keywords internal #' @author Nicholas Hamilton getFormulaVars = function(x,dependent=TRUE) { if(class(x) != 'formula') stop("x argument must be a formula",call.=FALSE) all.vars(x[[if(dependent) 3 else 2]]) } #' Function to add missing scales and other items to the plot and its coordinates sytem #' @param ggplot object #' @rdname undocumented #' @keywords internal #' @author Nicholas Hamilton scales_add_missing_tern <- function(plot){ #Run some checks stopifnot(inherits(plot,'ggplot')) stopifnot(inherits(plot$coordinates,'CoordTern')) #Ensure required scales have been added rs = plot$coordinates$required_scales ggint$scales_add_missing(plot,rs,plot$plot_env) ##NH #plot$scales$scales = plot$scales$scales[!sapply(plot$scales$scales,is.null)] #Push some details to the coordinates plot$coordinates$scales = sapply(rs,plot$scales$get_scales) ##NH for(r in rs) plot$coordinates$limits[[r]] = plot$scales$get_scales(r)$limits plot$coordinates$labels_coord = plot$labels plot$coordinates$theme = ggint$plot_theme(plot) #NH #done plot } #' Function to add clipping mask if it isn't already present #' @param plot ggplot object #' @rdname undocumented #' @keywords internal #' @author Nicholas Hamilton layers_add_missing_mask = function(plot){ if(!"GeomMask" %in% unlist(lapply(plot$layers,function(x){ class(x$geom) }))) plot = plot + geom_mask() plot }

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/figures-worm.R \name{plot_worms_grid} \alias{plot_worms_grid} \title{Make a Kobe "worm" timeseries plot with uncertainty} \usage{ plot_worms_grid(object_list, prob = 0.5, include_historical = TRUE) } \arguments{ \item{object_list}{A named list of MSE objects from DLMtool. Names become scenario names.} \item{prob}{Tail probability for the quantiles. E.g., 0.5 refers to an interquartile range.} \item{include_historical}{Logical: include the historical time?} } \value{ A ggplot object } \description{ Make a Kobe "worm" timeseries plot with uncertainty } \examples{ x <- list() x[[1]] <- mse_example x[[2]] <- mse_example names(x) <- c("Scenario 1", "Scenario 2") plot_worms_grid(x) }

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WAIC.Rd

\name{WAIC} \alias{WAIC} \title{ Watanabe-Akaike Information Criterion (WAIC) } \description{ Function returning the Watanabe-Akaike Information Criterion (WAIC) of a fitted model object. } \usage{ WAIC(object, ..., newdata = NULL) } \arguments{ \item{object}{A fitted model object which contains MCMC samples.} \item{\dots}{Optionally more fitted model objects.} \item{newdata}{Optionally, use new data for computing the WAIC.} } \value{ A data frame containing the WAIC and estimated number of parameters. } \references{ Watanabe S. (2010). Asymptotic Equivalence of {B}ayes Cross Validation and Widely Applicable Information Criterion in Singular Learning Theory. \emph{The Journal of Machine Learning Research}, \bold{11}, 3571--3594. \url{https://jmlr.org/papers/v11/watanabe10a.html} } \examples{ \dontrun{d <- GAMart() b1 <- bamlss(num ~ s(x1), data = d) b2 <- bamlss(num ~ s(x1) + s(x2), data = d) WAIC(b1, b2) } } \keyword{regression}

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/content/mapk/mapk_VP.R

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metrumresearchgroup/ub-cdse-2019

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refs/heads/master

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mapk_VP.R

library(mrgsolve) library(tidyverse) library(magrittr) library(reshape2) library(R.utils) sourceDirectory("content/script/VPop/") # Load simulation results (We are trying to match this Population) sims <- readRDS("content/mapk/mapk_sims.RDS") sims %<>% filter(label == "GDC") # Load treatment regimens reg <- readRDS("content/mapk/mapk_setup.RDS") reg %<>% filter(label == "GDC")%>%pull(object) reg <- reg[[1]] model <- mread("mapk", 'content/mapk/', soloc = 'content/mapk') m_params <- names(param(model)) # What parameters do we need to explore? foo <- readRDS("content/mapk/s10vpop_pk.RDS") (foo %>% summarise_all(sd)%>%gather()%>%filter(value!=0))%>%pull(key) length((foo %>% summarise_all(sd)%>%gather()%>%filter(value!=0))%>%pull(key)) p_names <- foo %>% summarise_all(sd)%>%gather()%>%filter(value!=0) # 33 of them ICs <- foo %>% summarise_all(sd)%>%gather()%>%filter(value==0) ICnames <- ICs$key ICs <- foo%>%summarise_all(mean)%>%gather()%>%filter(key %in% ICnames) ICs %<>% spread(key,value) # Filter only to ones present in model p_names %<>% filter(key %in% m_params)%>%pull(key) # Left with 27 # parameters <- foo%>%group_by(VPOP)%>%slice(1)%>%filter(VPOP==910)%>%gather(key="Name",value="Values")%>%filter(Name %in% p_names) # Get Parameter Limits pLower <- foo %>% summarise_all(.funs = function(x){min(x)*0.85}) pUpper <- foo %>% summarise_all(.funs = function(x){max(x)*1.15}) pLower %<>% gather(key="Name",value="Lower") pUpper %<>% gather(key="Name",value="Upper") paramLims <- left_join(pLower,pUpper,by="Name") paramLims %<>% filter(Name %in% p_names) # Load model # Set up simulation function sim_fcn <- function(parameters=NULL,model,pnames,dosing,ICs,simulate=0){ loadso(model) if(!is.null(parameters)){ param_in <- data.frame(Names = pnames,Values=parameters) param_in <- spread(param_in,key=Names,value=Values) }else{ param_in <- data.frame(ID=1) } param_in %<>% cbind(ICs) output <- model%>%idata_set(param_in) %>%Req(TUMOR)%>%obsonly%>%mrgsim(delta=56,end=56,events=as.ev(dosing))%>% filter(time==56)%>%as.data.frame() if(simulate==0){ return(list(NSS=output)) }else{ return(output) } } stateLims <- data.frame(Name = 'TUMOR','Lower'=0.0,'Upper'=4.0,Time=56) model_args = list(model=model,pnames=p_names,dosing=reg,ICs=ICs) control=list(runParallel="parallel",nCores = 4, parallel_libs="mrgsolve") plausiblePatients <- generatePPs(model_fn = sim_fcn, NP=1e2, paramLims=paramLims,stateLims = stateLims, method='SA', model_args = model_args, scoreThreshold = 0) hist_data <- plausiblePatients$simulation_results%>%mutate(Source="Plausible") hist_data %<>% rbind(sims%>%select(ID,time,TUMOR)%>%mutate(Source="Simulation")) ggplot(hist_data,aes(x=TUMOR))+geom_density(aes(x=TUMOR,y=..scaled..,fill=Source),alpha=0.5) VPs <- getVPs(plausiblePatients, sims%>%select(ID,time,TUMOR),runs=20,plausible_pdf = 'auto',data_pdf='auto', alpha_algorithm = 'PSO') hist_data <- plausiblePatients$simulation_results%>%mutate(Source="Plausible") hist_data %<>% rbind(sims%>%select(ID,time,TUMOR)%>%mutate(Source="Simfaculation")) hist_data %<>% rbind((VPs$VPs)%>%mutate(Source="VP")) unique <- hist_data%>%group_by(Source)%>%count(Source) hist_data <- merge(hist_data,y = unique, by='Source',all.x=TRUE) ggplot(hist_data,aes(x=TUMOR, color=Source,fill=Source)) + geom_histogram(alpha=0.4, size = 1.5 ) ggplot(hist_data) + geom_density(aes(x=TUMOR,y=..scaled..,fill=Source),alpha=0.5)

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/man/binQTLScan.Rd

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YaoLab-Bioinfo/binQTL

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2022-01-11T19:57:21.731539

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binQTLScan.Rd

\name{binQTLScan} \alias{binQTLScan} \title{QTL mapping with binmap data} \usage{ binQTLScan(phenotype="", genotype="", population="RIL") } \arguments{ \item{phenotype}{A data frame representing the phenotype of a population.} \item{genotype}{A data frame representing the genotype of a population.} \item{population}{A character string indicating the type of the population.} } \description{ The phenotype data frame should contain two columns. The first column is the ID of each accession of a population. The second column contains the phenotypic value of specific trait for each accession. For RIL (recombinant Inbred lines) or F2 population, each row of the genotype data frame represents a Bin. The first column is the ID of each Bin. The second column is the chromosome ID of each Bin. The 3rd column is the start coordinate of each Bin. The 4th column is the end coordinate of each Bin. Each of the rest columns gives the genotype of each accession at different bins. The names of the rest columns can be any characters specified by the User. For MAGIC population with 8 parents, each bin should be represented as 8 lines. The population type can be RIL, F2 or magic. } \author{Wen Yao, Shizhong Xu} \examples{ ril.phe <- read.csv(system.file("examples/ril.phe.csv", package="binQTL"), as.is=TRUE) dim(ril.phe) head(ril.phe) ril.geno <- read.csv(system.file("examples/ril.geno.csv", package="binQTL"), as.is=TRUE) dim(ril.geno) ril.geno[1:2, 1:9] qtl.res <- binQTLScan(phenotype = ril.phe, genotype = ril.geno, population="RIL") head(qtl.res) }

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/tests/test.matharray.R

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cran/xtable

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2021-06-08T07:10:15.108553

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test.matharray.R

require(xtable) V <- matrix(c(1.140380e-03, 3.010497e-05, 7.334879e-05, 3.010497e-05, 3.320683e-04, -5.284854e-05, 7.334879e-05, -5.284854e-05, 3.520928e-04), nrow = 3) ### Simple test of print.xtableMatharray print.xtableMatharray(xtable(V, display = rep("E", 4))) class(V) <- c("xtableMatharray") class(V) ### Test without any additional arguments mth <- xtableMatharray(V) str(mth) print(mth) ### Test with arguments to xtable mth <- xtableMatharray(V, display = rep("E", 4)) str(mth) print(mth) mth <- xtableMatharray(V, digits = 6) str(mth) print(mth) ### Test with additional print.xtableMatharray arguments mth <- xtableMatharray(V, digits = 6) str(mth) print(mth, format.args = list(decimal.mark = ",")) print(mth, scalebox = 0.5) print(mth, comment = TRUE) print(mth, timestamp = "2000-01-01") print(mth, comment = TRUE, timestamp = "2000-01-01")

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/TestFunctions.R

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MarkBell1310/PGSMs

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refs/heads/master

2021-01-24T21:25:23.786728

2018-04-16T13:09:58

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TestFunctions.R

#**************************************************** # #**** Particle Gibbs for SBMs - Test functions ****** # #**************************************************** rm(list = ls()) library(Matrix) library(igraph) library(matrixStats) #library(blockmodels) source("PGSMsFunctions.R") #**************************************************** # generate SBM num.nodes <- 20 num.clusters <- 4 sbm <- sample_sbm(n = num.nodes, pref.matrix = forceSymmetric(matrix(runif(num.clusters^2), (c(num.clusters, num.clusters)))), #pref.matrix = matrix(c(0.99, 0.01, 0.01, 0.99), c(num.clusters, num.clusters)), block.sizes = c(6, 4, 7, 3), directed = FALSE, loops = FALSE) plot(sbm) adj <- as_adj(sbm) # define clusters: 20 data points in 4 clusters all.clusters <- list(c(18, 14, 3, 5), c(12, 16, 20), c(1, 4, 19), c(7, 9, 13, 15), c(2, 6, 8, 10, 11, 17)) # select anchors at random s <- SelectAnchors(all.clusters) # calculate c.bar and s.bar closure <- CalculateClosureOfAnchors(s, all.clusters) c.bar <- closure$c.bar s.bar <- closure$s.bar # uniform permutation on closure of anchors sigma <- SamplePermutation(s, s.bar) # get particle from c.bar particle <- MapClustersToAllocations(sigma, c.bar) MapAllocationsToClusters(sigma, particle, s) # should have same elements as c.bar c.bar # intermediate targets alpha <- 1 beta1 <- 0.1 beta2 <- 0.2 t <- 3 n <- length(particle) log.previous.weight <- log(0.1) LogIntermediateTarget(sigma, s, particle, all.clusters, c.bar, adj, tau1, tau2, t, alpha, beta1, beta2) LogImprovedIntermediateTarget(sigma, s, particle, all.clusters, c.bar, adj, tau1, tau2, t, n, alpha, beta1, beta2) # proposal and weights PossibleAllocations(sigma, s, particle, all.clusters, c.bar, adj, tau1, tau2, t, n, alpha, beta1, beta2) Proposal(sigma, s, particle, all.clusters, c.bar, adj, tau1, tau2, t, n, alpha, beta1, beta2) LogUnnormalisedWeight(sigma, s, particle, log.previous.weight, all.clusters, c.bar, adj, tau1, tau2, t, n, alpha, beta1, beta2) # PGSM N <- 10 resampling.threshold <- 0.5 ParticleGibbsSplitMerge(all.clusters, adj, s, s.bar, c.bar, N, resampling.threshold, alpha, beta1, beta2) SplitMerge(all.clusters, adj, N, resampling.threshold, alpha, beta1, beta2)

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/Scripts/Benchmark_script.R

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mftth/Deko_Projekt

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Benchmark_script.R

### benchmark runs # missing_samples = c("105103","130002","PNET08","130003","145702","1344","127403","PNET55") library(devtools) load_all("~/artdeco") source("~/Deko_Projekt/CIBERSORT_package/CIBERSORT.R") library("stringr") library("bseqsc") library("MuSiC") ### #transcriptome_data = read.table("~/Deko_Projekt/Data/Bench_data/Scarpa.S29.tsv",sep = "\t",header = T,row.names = 1) #colnames(transcriptome_data) = str_replace(colnames(transcriptome_data) , pattern ="^X","") #transcriptome_data[1:5,1:5] #models_ductal = list(c("Alpha_Beta_Gamma_Delta_Baron","Alpha_Beta_Gamma_Delta_Hisc_Baron")) models_ductal = c( list(c("Alpha_Beta_Gamma_Delta_Baron","Alpha_Beta_Gamma_Delta_Acinar_Ductal_Baron")), list(c("Alpha_Beta_Gamma_Delta_Segerstolpe","Alpha_Beta_Gamma_Delta_Acinar_Ductal_Segerstolpe")), list(c("Alpha_Beta_Gamma_Delta_Lawlor","Alpha_Beta_Gamma_Delta_Acinar_Ductal_Lawlor")) ) models_hisc = c( list(c("Alpha_Beta_Gamma_Delta_Baron","Alpha_Beta_Gamma_Delta_Acinar_Ductal_Hisc_Baron")), list(c("Alpha_Beta_Gamma_Delta_Segerstolpe","Alpha_Beta_Gamma_Delta_Acinar_Ductal_Hisc_Segerstolpe")), list(c("Alpha_Beta_Gamma_Delta_Lawlor","Alpha_Beta_Gamma_Delta_Acinar_Ductal_Hisc_Lawlor")) ) nr_models = length(models_ductal) transcriptome_files = list.files("~/Deko_Projekt/Data/Bench_data/",full.names = T,pattern = "[0-9].tsv") transcriptome_files = as.character(sapply(transcriptome_files,FUN=rep,3)) visualization_files = str_replace_all(transcriptome_files,pattern ="\\.tsv",".vis.tsv") #meta_info = read.table("~/MAPTor_NET/Misc/Meta_information.tsv",sep = "\t",header = T,stringsAsFactors = F) meta_info = read.table("~/Deko_Projekt/Misc/Meta_information.tsv",sep = "\t",header = T,stringsAsFactors = F) rownames(meta_info) = meta_info$Name colnames(meta_info) = str_replace(colnames(meta_info),pattern = "\\.","_") source("~/Deko_Projekt/Scripts/Benchmark.R") algorithm = "bseqsc" # NMF # music # bseqsc type = "hisc" high_threshold = 66 low_threshold = 33 confidence_threshold = 1.1 transcriptome_files i = 10 fractions <<- matrix( as.character(), ncol = 6) #for( i in 1:length(transcriptome_files)){ #for( i in 16:18){ dataset_query = tail(as.character(unlist(str_split(transcriptome_files[i],pattern = "/"))),1) dataset_query = str_replace_all(dataset_query,".tsv","") if (type == "ductal") { models = models_ductal#[[1]] } else if (type == "hisc") { models = models_hisc#[[1]] } models = models[((i-1) %% 3) + 1] dataset_training = as.character(unlist(models))[2] path_benchmark_files = paste0( "~/Deko_Projekt/Results/Cell_fraction_predictions/", paste0( c(dataset_query, dataset_training, #paste0(models[2], collapse = ".", sep =""), algorithm,"tsv" ), collapse = "." ) ) path_benchmark_files_dec_res = paste0( "~/Deko_Projekt/Results/Cell_fraction_predictions/", paste0( c(dataset_query, dataset_training, #paste0(models[2], collapse = ".", sep =""), algorithm,".dec_res.tsv" ), collapse = "." ) ) transcriptome_file = transcriptome_files[i] visualization_file = visualization_files[i] print(i) print(dataset_query) print(dataset_training) res = run_benchmark( dataset_query = dataset_query, dataset_training = dataset_training, type = type, algorithm = algorithm, transcriptome_file = transcriptome_file, visualization_file = visualization_file, path_benchmark_files = path_benchmark_files, high_threshold = high_threshold, low_threshold = low_threshold, confidence_threshold = confidence_threshold, path_benchmark_files_dec_res = path_benchmark_files_dec_res ) write.table(res, path_benchmark_files, sep ="\t", quote = F, row.names = F) #fractions = rbind(fractions,res) #}

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/Impute NA.R

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ravindrareddytamma/EDA-Techniques

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2020-03-14T08:11:58.542173

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Impute NA.R

impute.na <- function(colname) { if(!is.numeric(colname)) stop("Required Numeric Column as Input!") suppressWarnings(require(zoo)) return(na.approx(colname)) }

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/scripts/original_simulations/4-plotResults.R

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jfiksel/ReVAMP_Simulations

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refs/heads/master

2020-03-28T04:52:57.748212

2018-09-17T15:08:55

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4-plotResults.R

library(ggplot2) library(dplyr) results <- readRDS("../../data/simulation_results.rds") results$csmf.acc.split <- round(results$csmf.acc.split, 2) ### relevel results results$method <- factor(results$method, levels = c('tariff_calib', 'tariff_train_and_calib', 'tariff_train', 'tariff_mle', 'tariff_revamp', 'insilico_calib', 'insilico_train_and_calib', 'insilico_train', 'insilico_mle', 'insilico_revamp')) results$is.revamp <- grepl("revamp", results$method) csmf.plot <- results %>% filter(measure == "csmf") %>% ggplot(aes(x = method, y = accuracy, color = is.revamp)) + facet_grid(calib.size ~ csmf.acc.split) + geom_boxplot() + theme(axis.text.x = element_text(angle = 90, hjust = 1)) ccc.plot <- results %>% filter(measure == "ccc") %>% ggplot(aes(x = method, y = accuracy, color = is.revamp)) + facet_grid(calib.size ~ csmf.acc.split) + geom_boxplot() + theme(axis.text.x = element_text(angle = 90, hjust = 1)) viz.dir <- "../../visualizations" if(!dir.exists(viz.dir)){ dir.create(viz.dir) } ggsave(filename = file.path(viz.dir, "csmf_results.jpg"), plot = csmf.plot, width = 14, height = 12) ggsave(filename = file.path(viz.dir, "ccc_results.jpg"), plot = ccc.plot, width = 14, height = 12)

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/R/plugin.R

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cran/ROI.plugin.scs

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refs/heads/master

2023-08-05T04:06:41.381302

2023-07-07T11:40:02

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plugin.R

## ROI plugin: SCS ## based on scs interface cone_dims <- function(x, ctype) { wcol <- which(colnames(x) == ctype) if ( length(wcol) == 0 ) return(NULL) as.integer(table(x$v[x$j == wcol])) } cone_counts <- function(x, ctype) { wcol <- which(colnames(x) == ctype) if ( length(wcol) == 0 ) return(0) sum(x$v[x$j == wcol]) } ## SLAM - VECH ## ## unvech ## ====== unvech <- function(x) { ## length(vech(M)) := m * (m-1) / 2; where n is the dimension of the m x m matrix M n <- as.integer((- 1 + sqrt(1 + 8 * length(x))) / 2) k <- scale_which(n) x[k] <- x[k] / sqrt(2) idx <- seq_len(n) i <- unlist(lapply(idx, seq.int, to=n), recursive=FALSE, use.names=FALSE) j <- unlist(mapply(rep_len, idx, rev(idx), SIMPLIFY=FALSE, USE.NAMES=FALSE)) simple_triplet_matrix(c(i, j[k]), c(j, i[k]), c(x, x[k])) } ## ## svec ## ==== ## @param x a symmetric matrix of type numeric ## @return a numeric vector ## example: Let A be defined as, ## a11 a12 a13 ## a21 a22 a23 ## a31 a32 a33 ## then svec(A) returns ## s2 <- sqrt(2) ## c(a11, s2*a21, s2*a31, a22, s2*a32, a33) ## n x (n+1) / 2 = 3 * 2 = 6 ## multiplies a lower triangular matrix with sqrt(2) svec <- function(x) { x <- as.matrix(x) x[lower.tri(x)] <- sqrt(2) * x[lower.tri(x)] as.numeric(x[lower.tri(x, TRUE)]) } ## ## svec_inv ## ======== ## ## is the backward transformation ## svec_inv <- function(x) { m <- length(x) n <- as.integer((- 1 + sqrt(1 + 8 * m)) / 2) M <- matrix(0, nrow=n, ncol=n) M[lower.tri(M, TRUE)] <- x M[lower.tri(M)] <- M[lower.tri(M)] / sqrt(2) M[upper.tri(M)] <- t(M)[upper.tri(M)] M } ## ## vector_to_psd ## ============= ## ## is the backward transformation ## vector_to_psd <- function(x) { n <- as.integer((- 1 + sqrt(1 + 8 * length(x))) / 2) M <- matrix(0, nrow=n, ncol=n) M[lower.tri(M, TRUE)] <- x M[upper.tri(M)] <- t(M)[upper.tri(M)] M } ## scale_which ## =========== ## ## gives the indices which should be scaled in an vectorized n x n matrix ## ## @param n an integer giving the dimension of the n x n matrix ## scale_which <- function(n) { fun <- function(x) cbind( ((x - 1) * n) + seq.int(x+1, n), rep.int(x, n - x) ) x <- do.call(rbind, lapply(seq_len(n-1), fun)) vec_correction <- cumsum(seq_len(n) - 1) x[,1] - vec_correction[x[,2]] } ## sym_vec_scale_lower_tri ## scales the off diagonal elements of a vectorized lower triangular matrix ## by a given vector ## @param x a numeric vector containing a lower triangular matrix ## @returns the scaled vector sym_vec_scale_lower_tri <- function(x, scale=sqrt(2)) { i <- 1L n <- calc_psd_matrix_dim(length(x)) fun <- function(y) { if ( i == 1 ) { ## do nothing } else if ( i > n ) { i <<- 1L n <<- n - 1 } else { y <- y * scale } i <<- i + 1L return(y) } sapply(x, fun) } to_dense_vector <- function(x, len) { y <- rep.int(0L, len) if ( is.null(x$ind) ) return(y) y[x$ind] <- x$val return(y) } calc_expp_dims <- function(x) { y <- x$id[x$cone == scs_cones['expp']] if ( !length(y) ) return(NULL) length(unique(y)) } calc_expd_dims <- function(x) { y <- x$id[x$cone == scs_cones['expd']] if ( !length(y) ) return(NULL) length(unique(y)) } calc_soc_dims <- function(x) { y <- x$id[x$cone == scs_cones['soc']] if ( !length(y) ) return(NULL) as.integer(table(y)) } calc_psd_matrix_dim <- function(m) as.integer((- 1 + sqrt(1 + 8 * m)) / 2) calc_psd_dims <- function(x) { y <- x$id[x$cone == scs_cones['psd']] if ( !length(y) ) return(NULL) sapply(table(y), calc_psd_matrix_dim) } calc_pow_dims <- function(x) { powp <- powd <- NULL ids <- unique(x$id[x$cone == scs_cones['powp']]) if ( length(ids) ) powp <- sapply(as.character(ids), function(id) x$params[[id]]['a'], USE.NAMES = FALSE) ids <- unique(x$id[x$cone == scs_cones['powd']]) if ( length(ids) ) powd <- sapply(as.character(ids), function(id) -x$params[[id]]['a'], USE.NAMES = FALSE) unname(c(powp, powd)) } calc_dims <- function(cones) { dims <- list() dims$z <- sum(cones$cone == scs_cones["zero"]) dims$l <- sum(cones$cone == scs_cones["nonneg"]) dims$ep <- calc_expp_dims(cones) dims$ed <- calc_expd_dims(cones) dims$q <- calc_soc_dims(cones) dims$s <- calc_psd_dims(cones) dims$p <- calc_pow_dims(cones) dims } which_scs_default_lower_bounds <- function(lower_bounds) { which_inf <- which(is.infinite(lower_bounds$val)) if ( length(which_inf) ) return(lower_bounds$ind[which_inf]) return(NULL) } ## get the indices of the conic bounds which are not the free cone get_indizes_nonfree <- function(bo) { if ( is.null(bo) ) return(integer()) if ( is.null(bo$cones) ) return(integer()) c(unlist(bo$cones$nonneg), unlist(bo$cones$soc), unlist(bo$cones$psd), unlist(bo$cones$expp), unlist(bo$cones$expd), unlist(lapply(bo$cones$powp, "[[", "i")), unlist(lapply(bo$cones$powd, "[[", "i"))) } scs_cones <- c("zero" = 1L, "nonneg" = 2L, "soc" = 3L, "psd" = 4L, "expp" = 5L, "expd" = 6L, "powp" = 7L, "powd" = 8L) solve_OP <- function(x, control = list()) { constr <- as.C_constraint(constraints(x)) ## check if "scs" supports the provided cone types stopifnot(all(constr$cones$cone %in% scs_cones)) obj <- as.vector(terms(objective(x))[["L"]]) if ( maximum(x) ) obj <- -obj AL <- AU <- NULL AL.rhs <- AU.rhs <- double() ## lower bounds lower_bounds <- to_dense_vector(bounds(x)$lower, length(objective(x))) not_is_scs_default <- !is.infinite(lower_bounds) if ( any(not_is_scs_default) ) { li <- which(not_is_scs_default) AL <- simple_triplet_matrix(i = seq_along(li), j = li, v = rep.int(-1, length(li)), nrow = length(li), ncol = length(obj)) AL.rhs <- -lower_bounds[not_is_scs_default] } ## upper bounds ui <- bounds(x)$upper$ind ub <- bounds(x)$upper$val if ( length(ui) ) { AU <- simple_triplet_matrix(i = seq_along(ui), j = ui, v = rep.int(1, length(ui)), nrow = length(ui), ncol = length(obj)) AU.rhs <- ub } A <- rbind(constr$L, AL, AU) A.rhs <- c(constr$rhs, AL.rhs, AU.rhs) cones <- c(constr$cones, K_lin(length(AL.rhs)), K_lin(length(AU.rhs))) if ( nrow(constr) > 0 ) { i <- with(cones, order(cone, id)) ordered_cones <- list(cone = cones$cone[i], id = cones$id[i]) A <- A[i,] A.rhs <- A.rhs[i] dims <- calc_dims(cones) } else { dims <- calc_dims(cones) } ## The NO_PSD_SCALING mode is only for testing purposes if ( !is.null(dims$s) & is.null(control$NO_PSD_SCALING) ) { psd_j <- list() b <- ordered_cones$cone == scs_cones["psd"] roi_cones <- split(seq_along(ordered_cones$cone)[b], ordered_cones$id[b]) for ( i in seq_along(roi_cones) ) { psd_dim <- dims$s[i] psd_j[[i]] <- roi_cones[[i]][scale_which( psd_dim )] k <- A$i %in% psd_j[[i]] A$v[k] <- sqrt(2) * A$v[k] A.rhs[psd_j[[i]]] <- sqrt(2) * A.rhs[psd_j[[i]]] } } if ( is.null(control$verbose) ) control$verbose <- FALSE if ( is.null(control$eps_rel) ) control$eps_rel <- 1e-6 solver_call <- list(scs, A = A, b = A.rhs, obj = obj, cone = dims, control = control) mode(solver_call) <- "call" if ( isTRUE(control$dry_run) ) return(solver_call) out <- eval(solver_call) out$len_objective <- length(objective(x)) out$len_dual_objective <- nrow(constraints(x)) if ( "s" %in% names(dims) ) { out$psd <- lapply(roi_cones, function(j) unvech(out$y[j])) } else { out$psd <- NULL } optimum <- (-1)^x$maximum * tryCatch({as.numeric(out$x %*% obj)}, error=function(e) as.numeric(NA)) ROI_plugin_canonicalize_solution(solution = out$x, optimum = optimum, status = out[["info"]][["status_val"]], solver = "scs", message = out) } ROI_plugin_solution_dual.scs_solution <- function(x) { x$message$y[seq_len(x$message$len_dual_objective)] } ROI_plugin_solution_psd.scs_solution <- function(x) { x$message$psd }

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/gluedatabrew_operations.R \name{gluedatabrew_delete_schedule} \alias{gluedatabrew_delete_schedule} \title{Deletes the specified DataBrew schedule} \usage{ gluedatabrew_delete_schedule(Name) } \arguments{ \item{Name}{[required] The name of the schedule to be deleted.} } \description{ Deletes the specified DataBrew schedule. See \url{https://www.paws-r-sdk.com/docs/gluedatabrew_delete_schedule/} for full documentation. } \keyword{internal}

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#' Split List to CSV by Factor #' #' This function allows you to split a data frame into a list of data frames by a chosen factor and write them out as separate CSV files in the chosen directory. #' @param x the vector or data frame containing values to be divided into groups. #' @param f a ‘factor’ in the sense that as.factor(f) defines the grouping, or a list of such factors in which case their interaction is used for the grouping. #' @param directory the directory that the CSV files will be written out to. Default is current working directory. #' Credit: agstudy, Tyler Rinker # https://stackoverflow.com/questions/9713294/split-data-frame-based-on-levels-of-a-factor-into-new-data-frames # https://stackoverflow.com/questions/17018138/using-lapply-to-apply-a-function-over-list-of-data-frames-and-saving-output-to-f #' @keywords split data frame df csv list factor #' @export #' @examples #' split2csv(mtcars, 'carb') # writes CSV files to the current working directory using the element name as filenames. split2csv <- function(x, f, directory = getwd()) { setwd(directory) df_list <- split(x, as.factor(x[[f]])) lapply(1:length(df_list), function(i) write.csv(df_list[[i]], file = paste0(names(df_list[i]), ".csv"), row.names = FALSE))}

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Binomial Distribution in R.R

# dbinom(x,size,prob) --> This function gives the probability density distribution at each point where prob = probability of success # Create a sample of 100 numbers which are incremented by 1. x1 <- seq(0,100,by = 1) # Create the binomial distribution. y <- dbinom(x1,100,0.5) # Plot the graph for this sample. plot(x1,y) # pbinom(x, size, prob) --> This function gives the cumulative probability of an event. It is a single value representing the probability. # Probability of getting 26 or less heads from a 51 tosses of a coin. x2 <- pbinom(26,51,0.5) print(x2) # qbinom(p, size, prob) --> This function takes the probability value and gives a number whose cumulative value matches the probability value. # How many heads will have a probability of 0.25 will come out when a coin is tossed 51 times. x3 <- qbinom(0.25,51,0.5) print(x3) # rbinom(n, size, prob) --> This function generates required number of random values of given probability from a given sample. # Find 10 random values from a sample of 500 with probability of 0.4. x4 <- rbinom(10,500,.4) print(x4)

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testUI <- function(id) { ns <- NS(id) tagList( tags$div( shinyWidgets::pickerInput( ns('tidyselector'), label = 'Pivot variables selection helper', choices = c("Starts With", "Ends With", "Select All"), width = "100%" ) ), tags$div( style = "width:100%;", conditionalPanel( sprintf("input['%s'] != 'Select All'", ns("tidyselector")), # textInput("helper", "Enter helper text"), checkboxInput(ns("dropna"), "Drop NA values?"), verbatimTextOutput(ns("dropout")) ) ), actionButton( inputId = ns("valid_helpers"), label = "Apply selection rule", width = "100%", class = "btn-primary", disabled = "disabled" ) ) } testUIServer <- function(input, output, session) { observeEvent(input$tidyselector, { updateActionButton( session = session, inputId = "valid_helpers", label = "Apply selection rule" ) }) output$dropout <- renderText({ input$dropna }) } new_ui <- fluidPage( testUI('myNamespace') ) server <- function(input, output, session) { callModule(testUIServer, "myNamespace") } shinyApp(ui = new_ui, server = server)

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library(phangorn) ### Name: cladePar ### Title: Utility function to plot.phylo ### Aliases: cladePar ### Keywords: plot ### ** Examples tree <- rtree(10) plot(tree) nodelabels() x <- cladePar(tree, 12) cladePar(tree, 18, "blue", "blue", x=x, plot=TRUE)

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classify_with_words.R

library(caret) library(dplyr) library(readr) library(pamr) library(tidyr) library(pROC) library(ROCR) # Load the wide form word frequencies data load("data/wide_form_word_freqs.RData") # load the metadata load("data/metadata_word_punc.RData") # Ignore very short documents: e.g. docs < 1000 wds word_threshold <- 1000 long_docs <- filter(metadata, NumWords >= word_threshold) %>% mutate(ID=paste(Author, Text_ID, sep="_")) %>% select(Author, Text_ID, ID, NumWords) long_doc_data <- wide_df[which(wide_df$ID %in% long_docs$ID),] # Merge in the meta data meta_full <- merge(long_docs, long_doc_data) # Now winnow the data to high frequency words that are used by all authors. # Calculate the means for winnowing the_means <- colMeans(meta_full[, 5:ncol(meta_full)]) # First set a maximum number of possible features to retain # We tested values from 100 to 1000. Ideally the feature set # should be small and limited to high frequency "context insensative" # features. Here we begin with 150 and then winnow further in the # next step max_cols <- 150 if(length(the_means) < max_cols){ max_cols <- length(the_means) } # We'll need to know the names of the meta columns metacols <- colnames(meta_full)[1:4] # we collect the 150 most frequent features into a vector "keepers" keepers <- names(sort(the_means, decreasing = TRUE)[1:max_cols]) # form a new dataframe with just the metadata and feature columns temp_data <- meta_full[,c(metacols, keepers)] # now check that every feature appears at least once in every author # and also in the unknown doc. Second winnowing step zero_value_test <- group_by(temp_data, Author) %>% select(-ID, -Text_ID, -NumWords) %>% summarise_each(funs(sum)) # reset any 0 values to NA, so we can use a function to find # any columns containing an "NA" (i.e. zero value) zero_value_test[zero_value_test == 0] <- NA # This function will identify columns that have NA values nacols <- function(df) { colnames(df)[unlist(lapply(df, function(x) any(is.na(x))))] } # Sending zero_value_test to the function returns a set of features # that were not present in all three authors and also in the # unknown file. we will remove these remove <- nacols(zero_value_test) # remove any features that are not common to all authors. if(length(remove) > 0){ classing_data_full <- temp_data[, -which(colnames(temp_data) %in% remove)] } else { classing_data_full <- temp_data } # Balance the classes by undersampling # Setting seed for repeatability during testing. set.seed(8675309) # go Jenny! # figure out which rows are which and then sample from the # larger classes based on the size of the smaller class r_ids <- which(classing_data_full$Author == "Rehnquist") s_ids <- which(classing_data_full$Author == "Scalia") t_ids <- which(classing_data_full$Author == "Thomas") u_ids <- which(classing_data_full$Author == "Unknown") small_class_size <- min(c(length(r_ids), length(s_ids), length(t_ids))) r_keep <- sample(r_ids, small_class_size) s_keep <- sample(s_ids, small_class_size) t_keep <- sample(t_ids, small_class_size) # a new data frame from the sampled data classing_data <- classing_data_full[c(r_keep, s_keep, t_ids),] # compare composition of authors before balancing . . . table(classing_data_full$Author) # . . . to composition after Balancing table(classing_data$Author) ################################################################################ # Classify the data using SVM and 3/4 of data for training ################################################################################ # TRAIN ON 3/4 OF DATA trainIndex <- createDataPartition(factor(classing_data$Author), p = .75, list = FALSE, times = 1) training <- classing_data[trainIndex,5:ncol(classing_data)] testing <- classing_data[-trainIndex,5:ncol(classing_data)] unknown <- classing_data_full[u_ids, 5:ncol(classing_data_full)] # 10-fold x-validation with 5 repeats fitControl <- trainControl(method = "repeatedcv", repeats = 5, classProbs = T) # Build an SVM model from training data svmFit <- train(x=training, y = factor(classing_data$Author[trainIndex]), method = "svmRadial", preProcess = c("center","scale"),trControl = fitControl) # Examine the model svmFit # Examine how the training data was classified in x-validation # This is not the best measure of performance. We'll also look at # the performace on the held out data (below). training_data_class_pred <- predict(svmFit, newdata = training, type = "raw") confusionMatrix(data = training_data_class_pred, reference = factor(classing_data$Author[trainIndex])) ################################################################################ # NOTE: # Good explanation of the kappa statistic here: # https://stats.stackexchange.com/questions/82162/cohens-kappa-in-plain-english ################################################################################ # Now make predictions using the unseen data and examine performance again class_pred <- predict(svmFit, newdata = testing, type = "raw") class_probs <- predict(svmFit, newdata = testing, type = "prob") confusionMatrix(data = class_pred, reference = factor(classing_data$Author[-trainIndex])) ################################################################################ # NOTE: # Some find the multi-class AUC a useful performance metric. # This function builds multiple ROC curve to compute the multi-class # AUC as defined by Hand and Till. A multiclass AUC is a mean of AUCs. # See David J. Hand and Robert J. Till (2001). A Simple Generalisation # of the Area Under the ROC Curve for Multiple Class Classification Problems. # Machine Learning 45(2), p. 171–186. DOI: 10.1023/A:1010920819831. mx <- multiclass.roc(factor(classing_data$Author[-trainIndex]), as.numeric(factor(class_pred)), percent=TRUE) mx$auc ################################################################################ # now classify the "unknown" Bush V. Gore Document class_pred <- predict(svmFit, newdata = unknown, type = "raw") class_probs <- predict(svmFit, newdata = unknown, type = "prob") # Show final classification result and probabilities class_probs ################################################################################ # Rerun the same test using all available data for model training ################################################################################ trainIndex <- createDataPartition(factor(classing_data$Author), p = 1, list = FALSE, times = 1) training <- classing_data[trainIndex,5:ncol(classing_data)] testing <- classing_data[-trainIndex,5:ncol(classing_data)] unknown <- classing_data_full[u_ids, 5:ncol(classing_data_full)] fitControl <- trainControl(method = "repeatedcv", repeats = 5, classProbs = T) svmFit <- train(x=training, y = factor(classing_data$Author[trainIndex]), method = "svmRadial", preProcess = c("center","scale"),trControl = fitControl) svmFit # Examine the model # Examine how the training data was classified in x-validation training_data_class_pred <- predict(svmFit, newdata = training, type = "raw") confusionMatrix(data = training_data_class_pred, reference = factor(classing_data$Author[trainIndex])) # Predict the Bush V. Gore Doc. class_pred <- predict(svmFit, newdata = unknown, type = "raw") class_probs <- predict(svmFit, newdata = unknown, type = "prob") # Show final classification result and probabilities class_probs training_data_class_pred <- predict(svmFit, newdata = training, type = "raw") mx <- multiclass.roc(factor(classing_data$Author[trainIndex]), as.numeric(factor(training_data_class_pred)), percent=TRUE) mx$auc # Save the probabilities for each document?: training_data_class_probs <- predict(svmFit, newdata = training, type = "prob") ################################################################################ # Rerun clasification USING NSC instead of SVM ################################################################################ # TRAIN ON 3/4 OF DATA set.seed(8675309) # Hi Jenny! trainIndex <- createDataPartition(factor(classing_data$Author), p = .75, list = FALSE, times = 1) training <- classing_data[trainIndex,5:ncol(classing_data)] testing <- classing_data[-trainIndex,5:ncol(classing_data)] unknown <- classing_data_full[u_ids, 5:ncol(classing_data_full)] # 10 x 10-fold x-validation fitControl <- trainControl(method = "repeatedcv", repeats = 5, classProbs = T) # Build the NSC model nscFit <- train(x=training, y = factor(classing_data$Author[trainIndex]), method = "pam", preProcess = c("center","scale"),trControl = fitControl) # Examine the model nscFit # Examine how the training data was classified in x-validation training_data_class_pred <- predict(nscFit, newdata = training, type = "raw") confusionMatrix(data = training_data_class_pred, reference = factor(classing_data$Author[trainIndex])) # Now make predictions using the unseen and examine class_pred <- predict(nscFit, newdata = testing, type = "raw") class_probs <- predict(nscFit, newdata = testing, type = "prob") confusionMatrix(data = class_pred, reference = factor(classing_data$Author[-trainIndex])) # now classify the Bush V. Gore Document class_pred <- predict(nscFit, newdata = unknown, type = "raw") class_probs <- predict(nscFit, newdata = unknown, type = "prob") # Show final classification result and probabilities class_probs ################################################################################ # Rerun USING out of the box NSC so that we can access the feature weights ################################################################################ # Author column is a factor and needs to be character vector for this algo classing_data$Author <- as.character(classing_data$Author) set.seed(8675309) # set see for repeatability during testing. trainIndex <- createDataPartition(factor(classing_data$Author), p = .75, list = FALSE, times = 1) training <- classing_data[trainIndex,5:ncol(classing_data)] testing <- classing_data[-trainIndex,5:ncol(classing_data)] unknown <- classing_data_full[u_ids, 5:ncol(classing_data_full)] feature_cols <- 5:ncol(classing_data) train_data <- classing_data[trainIndex, feature_cols] test_data <- classing_data[-trainIndex, feature_cols] train_signal_colors <- classing_data[trainIndex, "Author"] test_signal_colors <- classing_data[-trainIndex, "Author"] unknown_data <- classing_data_full[u_ids, feature_cols] features <- colnames(train_data) data.train <- list(x=t(train_data), y=train_signal_colors, geneid=features) data.test <- list(x=t(test_data), y=test_signal_colors, geneid=features) data.unk <- list(x=t(unknown_data), y="UNK", geneid=features) prior <- rep(1/length(levels(as.factor(data.train$y))), length(levels(as.factor(data.train$y)))) pamr.train.out <- pamr.train(data.train, prior=prior) # pamr.cv.out <- pamr.cv(pamr.train.out, data.train) new.scales <- pamr.adaptthresh(pamr.train.out) pamr.train.out <- pamr.train(data.train, prior=prior, threshold.scale=new.scales) pamr.cv.out <- pamr.cv(pamr.train.out, data.train) thresh.row <- which(pamr.cv.out$error == min(pamr.cv.out$error))[1] the.thresh <- pamr.cv.out$threshold[thresh.row] tt <- pamr.confusion(pamr.cv.out, threshold=the.thresh, FALSE) tt1 <- tt diag(tt1) <- 0 tt <- cbind(tt, apply(tt1, 1, sum)/apply(tt, 1, sum)) dimnames(tt)[[2]][ncol(tt)] <- "Class Error rate" tt # Held out data. . . pamr.test.pred <- pamr.predict(pamr.train.out, data.test$x, threshold=0, type="class") theProbs <- as.data.frame(pamr.predict(pamr.train.out, data.test$x, threshold=0, type="posterior")) signalskey <- c("Rehnquist", "Scalia", "Thomas") predicted.class <- as.character(signalskey[as.numeric(pamr.test.pred)]) pred.results <- as.data.frame(cbind(Author=classing_data[-trainIndex, "Author"], predicted.class, theProbs)) colnames(pred.results)[3:5] <- c("Rehnquist", "Scalia", "Thomas") confusionMatrix(data = pred.results$predicted.class, reference = pred.results$Author) # Unknown Text pamr.test.pred <- pamr.predict(pamr.train.out, data.unk$x, threshold=0, type="class") theProbs <- as.data.frame(pamr.predict(pamr.train.out, data.unk$x, threshold=0, type="posterior")) signalskey <- c("Rehnquist", "Scalia", "Thomas") predicted.class <- as.character(signalskey[as.numeric(pamr.test.pred)]) pred.results <- as.data.frame(cbind(Author="Unknown", predicted.class, theProbs)) colnames(pred.results)[3:5] <- c("Rehnquist", "Scalia", "Thomas") pred.results pamr.listgenes(pamr.train.out, data.train, the.thresh, pamr.cv.out)

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mevo_daily_bikes.R

## http://www.statmethods.net/stats/regression.html ## http://www.cookbook-r.com/Statistical_analysis/Regression_and_correlation/ ## google:: trust wvs Heston et al. ## http://tdhock.github.io/animint/geoms.html require(ggplot2) require(ggpubr) d <- read.csv("MEVO_DAILY_BIKES.csv", sep = ';', header=T, na.string="NA"); ##rains <- read.csv("mevo_rains_daily.csv", sep = ';', header=T, na.string="NA"); ## ##d["rains"] <- rains$opad nzb <- d$bikes - d$zb d["nzb"] <- nzb p1 <- ggplot(d, aes(x = as.Date(day))) + ggtitle("MEVO: rowery jeżdżone (nzb) vs niejeżdżone (zb)") + geom_point(aes(y = bikes, colour = 'bikes'), size=1) + geom_point(aes(y = zb, colour = 'zb'), size=1) + geom_point(aes(y = nzb, colour = 'nzb'), size=1) + ##geom_line(aes(y = rains, colour = 'nzb'), size=1) + geom_smooth(aes(x = as.Date(day), y=bikes, colour='bikes'), method="loess", size=.5) + geom_smooth(aes(x = as.Date(day), y=zb, colour='zb'), method="loess", size=.5) + geom_smooth(aes(x = as.Date(day), y=nzb, colour='nzb'), method="loess", size=1) + ylab(label="#") + ##theme(legend.title=element_blank()) + labs(colour = "Rowery: ") + theme(legend.position="top") + theme(legend.text=element_text(size=10)); p2 <- ggplot(d, aes(x = as.Date(day))) + ggtitle("MEVO: dzienny dystans (Gdańsk/Gdynia)") + geom_point(aes(y = ga, colour = 'ga'), size=1) + geom_point(aes(y = gd, colour = 'gd'), size=1) + geom_smooth(aes(x = as.Date(day), y=ga, colour='ga'), method="loess", size=.5) + geom_smooth(aes(x = as.Date(day), y=gd, colour='gd'), method="loess", size=.5) + ylab(label="km") + labs(colour = "Miasta: ") + theme(legend.position="top") + theme(legend.text=element_text(size=10)); p3 <- ggplot(d, aes(x = as.Date(day))) + ggtitle("MEVO: dzienny dystans (Tczew/Rumia/Sopot)") + geom_point(aes(y = sop, colour = 'sop'), size=1) + geom_point(aes(y = tczew, colour = 'tczew'), size=1) + geom_point(aes(y = rumia, colour = 'rumia'), size=1) + geom_smooth(aes(x = as.Date(day), y=sop, colour='sop'), method="loess", size=.5) + geom_smooth(aes(x = as.Date(day), y=tczew, colour='tczew'), method="loess", size=.5) + geom_smooth(aes(x = as.Date(day), y=rumia, colour='rumia'), method="loess", size=.5) + ylab(label="km") + labs(colour = "Miasta: ") + theme(legend.position="top") + theme(legend.text=element_text(size=10)); p4 <- ggplot(d, aes(x = as.Date(day))) + ggtitle("MEVO: dzienny dystans łącznie") + geom_line(aes(y = dist.total, colour = 'dist.total'), size=.5) + geom_smooth(aes(x = as.Date(day), y=dist.total, colour='dist.total'), method="loess", size=1) + ylab(label="km") + labs(colour = "") + theme(legend.position="top") + theme(legend.text=element_text(size=10)); p1;p2;p3;p4 ggarrange(p1, p2, p3, p4, ncol = 2, nrow = 2) ggsave(file="mevo_daily_bikes.pdf", width=12) # https://stackoverflow.com/questions/16652199/compute-monthly-averages-from-daily-data d$day <- as.Date(d$day); d$mm <- months(d$day) d$yy <- format(d$day, format="%y") aggregate(nzb ~ mm + yy, d, mean) aggregate(zb ~ mm + yy, d, mean) aggregate(bikes ~ mm + yy, d, mean) d$nzbp <- d$nzb/d$bikes * 100 ## udział średni jeżdżonych w całości aggregate(nzbp ~ mm + yy, d, mean) ## gdańsk gdynia aggregate(gd ~ mm + yy, d, mean) aggregate(ga ~ mm + yy, d, mean) ## mean(d$zstat) mean(d$sstat) mean(d$gd0p) mean(d$ga0p) mean(d$sop0p) mean(d$tczew0p) mean(d$rumia0p) mean(d$gd1p) mean(d$ga1p) mean(d$sop1p) mean(d$tczew1p) mean(d$rumia1p)

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miltoss/HWZ_DataAnalysis_2015

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central_limit_theorem_homework.R

# HOMEWORK EXCERCISE # This is a simulation of the central limit theorem # (Chapter 6.4 of the book) # The idea is the following: Iteratively generate a vector data_dist of random numbers # following a probability density function (for example the uniform distribution). # There will be n_samples iterations (i.e. generated vectors) # The vector has length (n_observations). Add each vector to a sum vector # data_sum (update the sum vector). When finished plot the resulting vector # as a histogram and check if it is a normal distribution. # This is the size of one random sample n_observations = 5000 # This is the number of random samples to draw. Vary this number # from 1 to 5000 and see what happens. For example, try the following # value sfor n: 1, 2, 3, 5, 10, 100, 1,000, 10,000 n_samples = 1000 # Take n_samples samples each of sample_size # Initialise vector with zeros data_sum = rep(0, n_observations) # Check some things out length(data_sum) head(data_sum) # Iteratively generate a sample and add it to the sum for (i in 1:n_samples) { # Fill in the code here. Generate vector data_dist and # add it to data_sum } # Let's see what came out: # Split the plot window in two columns par(mfrow = c(1, 2)) # Plot the resulting distribution hist(data_sum) # Test resulting distribution for normality in a graphical way. # If a straight line is drawn, then a normal distribution (data_sum) # has been created qqnorm(data_sum) # Reset the plot window par(mfrow = c(1, 1)) # Another way to test normality # If the p-value printed on the console # is under 0.05, then we know distribution is not normal shapiro.test(data_sum)

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tkmckenzie/ikde-scripts

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data.R

#' Randomly generated multivariate linear model data #' #' A dataset for estimation of linear models #' #' @format A list with two components: #' \describe{ #' \item{X}{Matrix of independent variables} #' \item{y}{Vector of dependent variable observations} #' } #' @details #' Generated with the following code: #' \preformatted{ #' set.seed(100) #' #' N <- 100 #' k <- 4 #' sd <- 10 #' #' X <- cbind(1, matrix(runif(N * (k - 1), -10, 10), ncol = k - 1)) #' beta <- runif(k, -5, 5) #' y <- X %*% beta + rnorm(N, sd = sd) #' y <- c(y) #' } "lm.generated" #' Prostatic nodal development data #' #' A dataset replicated from Chib (1995) indicating presence of prostatic nodal development among patients prostate cancer #' #' @format A data.frame with 53 observations of 7 variables: #' \describe{ #' \item{Case}{Patient identifier} #' \item{y}{Binary outcome indicating nodal development} #' \item{X.1}{Explanatory variable} #' \item{X.2}{Explanatory variable} #' \item{X.3}{Binary explanatory variable} #' \item{X.4}{Binary explanatory variable} #' \item{X.5}{Binary explanatory variable} #' } #' @details #' These data were replicated from Chib (1995) #' @references #' \insertRef{Chib}{ikde} "prostatic.nodes"

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cran/faraway

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ozone.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/faraway-package.R \docType{data} \name{ozone} \alias{ozone} \title{Ozone in LA in 1976} \format{ A data frame with 330 observations on the following 10 variables. \describe{ \item{O3}{Ozone conc., ppm, at Sandbug AFB.} \item{vh}{a numeric vector} \item{wind}{wind speed} \item{humidity}{a numeric vector} \item{temp}{temperature} \item{ibh}{inversion base height} \item{dpg}{Daggett pressure gradient} \item{ibt}{a numeric vector} \item{vis}{visibility} \item{doy}{day of the year} } } \source{ Breiman, L. and J. H. Friedman (1985). Estimating optimal transformations for multiple regression and correlation. Journal of the American Statistical Association 80, 580-598. } \description{ A study the relationship between atmospheric ozone concentration and meteorology in the Los Angeles Basin in 1976. A number of cases with missing variables have been removed for simplicity. } \examples{ data(ozone) ## maybe str(ozone) ; plot(ozone) ... } \keyword{datasets}

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getVersion.Rd

\name{getVersion} \alias{getVersion} %- Also NEED an '\alias' for EACH other topic documented here. \title{ getVersion} \description{ Internal function. } \usage{ getVersion(InputFile) } \arguments{ \item{InputFile}{Input file name.} } \keyword{file}

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pierremichelhardy/Master-Thesis

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MAIN - Additional Data Wrangling.R

# This code is written by Pierre Michel B. Hardy in partial fulfillment of his master thesis # This code contains additional data wrangling before running the regression # Namely, it's about taking out the effects of individual artist effects from the variables. # Packages ######### library(readxl) library(dplyr) library(xlsx) library(varhandle) # Data ######### dat <- read_excel("DATA COLLECTED - READY FOR ANALYSIS.xlsx") # Main Code ######## # turn T2H into numeric. Apparently, it's in character dat$T2H <- as.numeric(dat$T2H) # make another copy to reduce risk of having to repeat dat2 <- dat # group by artist, take the mean value of variable, then subtract the mean from each # this first one is my test to make sure the code does what I want it to dat3 <- dat2 %>% group_by(arist.id) %>% transmute(dance.mean = mean(danceability), danceability2 = danceability - mean(danceability), dancebility.orig = danceability) # this code snippet has been tested against excel on a few artists # the results are consistent with what I want to happen # this code works! # okay, time to to do this for all columns dat4 <- dat2 %>% group_by(arist.id) %>% transmute(danceability2 = danceability - mean(danceability), loudness2 = loudness - mean(loudness), speechiness2 = speechiness - mean(speechiness), acousticness2 = acousticness - mean(acousticness), instrumentalness2 = instrumentalness - mean(instrumentalness), liveness2 = liveness - mean(liveness), tempo2 = tempo - mean(tempo), track.duration_ms2 = track.duration_ms - mean(track.duration_ms), track.popularity2 = track.popularity - mean(track.popularity), T2H2 = T2H - mean(T2H),) # just going to put back the columns I didn't transform but lost from the transmutation leftover <- select(dat2, c("key","mode","track.id","track.name","track.album.id", "track.album.name","artist.name","time.signature.dummy")) # Last job: turning the moods into binary mood.binary <- select(dat2, c("track.mood")) mood.binary <- to.dummy(mood.binary$track.mood, "mood") # volt em in dat5 <- cbind(as.data.frame(dat4), leftover, mood.binary) # Export the dataset ######## write.xlsx(dat5, "DATA COLLECTED - READY FOR ANALYSIS - ARTIST EFFECTS.xlsx") # Next Steps ##### # That's it! Artist effects are taken care off and now we're ready for analysis

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stormy-ua/humanactivityrecognition

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run_analysis.R

library(dplyr) library(magrittr) # loads tidy train or test data set loadDataSet <- function(xFile, yFile, subjectsFiles) { # load list of all features from features.txt featureNames <- as.character(read.table("features.txt", stringsAsFactors = F)[, 2]) # we want to load only std and mean features so filter out other featuresToLoad <- grepl("std", featureNames) | grepl("mean", featureNames) colClassesToLoad <- rep("NULL", times = length(featureNames)) colClassesToLoad[featuresToLoad] <- "numeric" # load measurements data data <- read.table(xFile, colClasses = colClassesToLoad) # set column names names(data) <- featureNames[featuresToLoad] # load subjects subjects <- read.table(subjectsFiles, colClasses = c("numeric"), col.names = c("SubjectId")) data <- cbind(subjects, data) # load activity labels from activity_labels.txt activityLabels <- read.table("activity_labels.txt", col.names = c("ActivityLabelIndex", "ActivityLabel")) # load activities activities <- read.table(yFile, stringsAsFactors = T , colClasses = c("numeric"), col.names = c("ActivityLabelIndex")) # append activities to data set activities <- merge(activities, activityLabels, by = "ActivityLabelIndex")[, 2] data$Activity <- activities # return tidt data set data } # load and merge tidy train and tidy test data sets data <- rbind(loadDataSet("train\\X_train.txt", "train\\y_train.txt", "train\\subject_train.txt"), loadDataSet("test\\X_test.txt", "test\\y_test.txt", "test\\subject_test.txt")) #summarize data <- data %>% group_by(SubjectId, Activity) %>% summarise_each(funs(mean)) head(data) # save tidy data set to tidy.txt write.table(data, "tidy.txt", row.names = F, quote = F)

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kaixinhuaihuai/OncoCast

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predict.R

#' predIncomingSurv #' #' This function let's user predict the genetic risk score of new/incoming patients. This function #' makes use of the output of the OncoCast function and an inputed data set similar to the one use to generate #' those results. The user can retrieve the genetic risk score of those new patients and their predicted survival curves. #' #' @param OC_object Output of the OncoCast function. #' @param new.data New data set containing the information of the incoming patients. Should be a dataframe #' with patients as rows and features as columns. #' @param surv.print A numeric vector indicating the patients for which the user wishes to print the predicted #' survival curves. #' @param riskRefit The refitted cox proportional hazard model with the risk score as predictor. #' #' @return data.out : The data frame inputted in the function with an additional column giving the predicted risk #' score of the incoming patients. #' @return RiskHist : A histogram of the distribution of the risk scores of patients in the given dataset. #' @return IncKM : An interactive Kaplan-Meier plot of the selected patients in the surv.print argument. #' @export #' @examples library(OncoCast) #' test <- OncoCast(data=survData,formula = Surv(time,status)~., #' method = "LASSO",runs = 30, #' save = FALSE,nonPenCol = NULL,cores =1) #' results <- getResults_OC(OC_object=test$LASSO,data=survData, #' cuts=c(0.2,0.4,0.6,0.8), #' geneList=NULL,mut.data=TRUE) #' new.data <- as.data.frame(matrix(rbinom(5*20,1,0.5),nrow=20,ncol = 5)) #' colnames(new.data) <- c("ImpCov1","ImpCov2","ImpCov3","ImpCov4","Cov7") #' rownames(new.data) <- paste0("Incoming",1:20) #' Incoming <- predIncomingSurv(test$LASSO,new.data=new.data, #' surv.print = c(5,10,15),riskRefit = results$RiskRefit) #' @import #' magrittr #' dtplyr #' ggplot2 #' survminer #' reshape2 #' scales #' pheatmap #' @importFrom plotly plot_ly layout toRGB add_ribbons #' @importFrom dplyr select filter mutate group_by rename summarise arrange predIncomingSurv <- function(OC_object,new.data,surv.print= NULL,riskRefit){ OC_object <- Filter(Negate(is.null), OC_object) # get all information needed from the oncocast object # 1. risk final.pred <- sapply(OC_object,"[[","predicted") ori.risk <- apply(final.pred,1,function(x){ mean(as.numeric(x),na.rm = TRUE) }) ### FOR PENALIZED REG ### if(OC_object[[1]]$method %in% c("LASSO","RIDGE","ENET")){ # 2. Fits LassoFits <- t(sapply(OC_object,"[[","fit")) LassoFits[is.na(LassoFits)] <- 0 ################################ features <- colnames(LassoFits) dums <- apply(new.data,2,function(x){anyNA(as.numeric(as.character(x)))}) if(sum(dums) > 0){ tmp <- new.data %>% select(which(dums)) %>% fastDummies::dummy_cols(remove_first_dummy = T) %>% select(-one_of(names(which(dums)))) new.data <- as.data.frame(cbind( new.data %>% select(-one_of(names(which(dums)))), tmp ) %>% mutate_all(as.character) %>% mutate_all(as.numeric) ) warning("Character variables were transformed to dummy numeric variables. If you didn't have any character variables make sure all columns in your input data are numeric. The transformed data will be saved as part of the output.") } if(!all(is.na(match(colnames(new.data),features)))){ matched.genes <- c(na.omit(match(colnames(new.data),features))) new.dat <- new.data[,which(!is.na(match(colnames(new.data),features)))] ## ADD ALL MISSING GENES TO BE ALL zero ## missing <- features[which(is.na(match(features,colnames(new.dat))))] to.add <- as.data.frame(matrix(0L,nrow=nrow(new.dat),ncol=length(missing))) colnames(to.add) <- missing rownames(to.add) <- rownames(new.dat) new.dat <- as.data.frame(cbind(new.dat,to.add)) new.dat <- new.dat[,match(features,colnames(new.dat))] ############################################# all.pred <- lapply(1:nrow(LassoFits),function(x){ ### Subset to the coefs of that cv ### coefs <- LassoFits[x,LassoFits[x,] != 0] new.temp <- select(new.dat,names(coefs)) ## substract mean mutation rate of TRAINING SET !!!### new.x <- new.temp - rep(OC_object[[x]]$means[match(names(coefs),names(OC_object[[x]]$means))], each = nrow(new.temp)) cal.risk.test <- drop(as.matrix(new.x) %*% coefs) return(cal.risk.test) }) } else{ stop("No gene overlapped be sure they are correctly matched.") } } ### For GBM ### if(OC_object[[1]]$method %in% c("GBM","RF","SVM","NN")){ if(OC_object[[1]]$method == "GBM") features <- OC_object[[1]]$GBM$var.names if(OC_object[[1]]$method == "RF") features <- OC_object[[1]]$RF$forest$independent.variable.names if(OC_object[[1]]$method == "SVM") features <- names(OC_object[[1]]$Vars) if(OC_object[[1]]$method == "NN") features <- names(OC_object[[1]]$Vars) dums <- apply(new.data,2,function(x){anyNA(as.numeric(as.character(x)))}) if(sum(dums) > 0){ tmp <- new.data %>% select(which(dums)) %>% fastDummies::dummy_cols(remove_first_dummy = T) %>% select(-one_of(names(which(dums)))) new.data <- as.data.frame(cbind( new.data %>% select(-one_of(names(which(dums)))), tmp ) %>% mutate_all(as.character) %>% mutate_all(as.numeric) ) warning("Character variables were transformed to dummy numeric variables. If you didn't have any character variables make sure all columns in your input data are numeric. The transformed data will be saved as part of the output.") } if(!all(is.na(match(colnames(new.data),features)))){ matched.genes <- c(na.omit(match(colnames(new.data),features))) new.dat <- new.data[,which(!is.na(match(colnames(new.data),features)))] ## ADD ALL MISSING GENES TO BE ALL zero ## missing <- features[which(is.na(match(features,colnames(new.dat))))] to.add <- as.data.frame(matrix(0L,nrow=nrow(new.dat),ncol=length(missing))) colnames(to.add) <- missing rownames(to.add) <- rownames(new.dat) new.dat <- as.data.frame(cbind(new.dat,to.add)) new.dat <- new.dat[,match(features,colnames(new.dat))] if(OC_object[[1]]$method == "GBM") { all.pred <- lapply(OC_object,function(x){ predict(x$GBM,newdata=new.dat, n.trees = x$bestTreeForPrediction, type="response") })} if(OC_object[[1]]$method == "RF") { all.pred <- lapply(OC_object,function(x){ predict(x$RF,new.dat)$predictions })} if(OC_object[[1]]$method == "SVM") { all.pred <- lapply(OC_object,function(x){ predict(x$SVM,new.dat) })} if(OC_object[[1]]$method == "NN") { all.pred <- lapply(OC_object,function(x){ predict(x$NN,new.dat) })} } else{ stop("No gene overlapped be sure they are correctly matched.") } } all.pred <- do.call("cbind",all.pred) Risk <- apply(all.pred,1,mean) names(Risk) <- rownames(new.dat) # Risk.all <- as.matrix(coefs) %*% as.matrix(t(new.dat)) # Risk <- apply(Risk.all,2,mean) #new.data$Risk <- Risk ########################################## ori.risk.range <- range(ori.risk) new.data$OncoCastRiskScore <- rescale(Risk, to = c(0, 10), from = ori.risk.range) #WithOriginal #new.data$rescaledRisk <- rescale(new.data$Risk, to = c(0, 10), from = range(new.data$Risk, na.rm = TRUE, finite = TRUE)) RiskHistogram.new <- ggplot(new.data, aes(x = OncoCastRiskScore, y = ..density..)) + geom_histogram(show.legend = FALSE, aes(fill=..x..), breaks=seq(min(new.data$OncoCastRiskScore,na.rm = T), max(new.data$OncoCastRiskScore,na.rm = T))) +#, by=20/nrow(new.data))) + geom_density(show.legend = FALSE) + theme_minimal() + labs(x = "Average risk score", y = "Density") + scale_fill_gradient(high = "red", low = "green") #return(list("RiskHistogram.new"=RiskHistogram.new,"out.data"=new.data)) #################################################### ## Creat survival curves for patients of interest ## #################################################### if(is.null(surv.print)) surv.print <- 1:nrow(new.data) mut <- new.data[surv.print,] colnames(mut)[ncol(mut)] <- "RiskScore" allSurvs <- data.frame(nrow= 5) for(j in 1:nrow(mut)){ survival.probs <- as.data.frame(matrix(nrow=6,ncol=15)) rownames(survival.probs) <- c("Patient","Surv","Lower","Upper","Time","OncoRiskScore") surv.temp <- survfit(riskRefit, newdata = mut[j,]) for(i in 1:ncol(survival.probs)){ survival.probs[,i] <- try(c(rownames(mut)[j],as.numeric(summary(surv.temp, times = (i*3-3))$surv), round(summary(surv.temp, times = (i*3-3))$lower,digits=2), round(summary(surv.temp, times = (i*3-3))$upper,digits=2), i*3-3,as.numeric(mut$RiskScore[j])),silent=T) } allSurvs <- cbind(allSurvs,survival.probs) } allSurvs <- allSurvs[,-1] a <- list( autotick = FALSE, dtick = 6, tickcolor = toRGB("black") ) t.survival.probs <- as.data.frame(t(allSurvs)) for(k in 2:ncol(t.survival.probs)){ t.survival.probs[,k] <- as.numeric(as.character(t.survival.probs[,k])) } y <- list( title = "Survival Probability" ) IndSurvKM <- plot_ly(t.survival.probs, x = ~Time, y = ~Surv, name = ~Patient, type = 'scatter', mode = 'lines+markers',hoverinfo="hovertext",color = ~Patient, hovertext = ~paste("Genetic Risk Score :",round(OncoRiskScore,digits=3)) ) %>% layout(yaxis = y,xaxis = ~a) %>% layout(xaxis = list(title = paste0("Time (Months)"), showgrid = TRUE),showlegend = FALSE) %>% add_ribbons(data = t.survival.probs, ymin = ~Lower, ymax = ~Upper, line = list(color = 'rgba(7, 164, 181, 0.05)'), fillcolor = 'rgba(7, 164, 181, 0.2)', name = "Confidence Interval") #%>% layout(showlegend = FALSE) # } # else{ # IndSurvKM = NULL # t.survival.probs # } return(list("data.out" = new.data,"RiskHist"=RiskHistogram.new,"IncKM" = IndSurvKM,"survivalEst"=t.survival.probs)) }

889e9e6fdffb9c5e862e057934f0426afa98b116

d11d7fe9df513536af20898f7dd8c36beec2ed06

/US_Scrapping.R

8cfb5f9e4e0a4ee2ccc23f9eb954facfd78fd6c5

[]

no_license

urosgodnov/Trademarks

b54a1b3e913825ad95c6563504709e1392d707b8

3cd8e9685e54c961b96bd13a591c527355ecaf36

refs/heads/master

2021-09-06T10:48:14.471968

2018-02-05T19:08:24

105,199,426

0

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UTF-8

R

false

19,526

r

US_Scrapping.R

GetOwner<-function(dataOwner) { assignements <- dataOwner %>% html_nodes(xpath = "//div[@class='assignmentsContainer persist-area' and (@data-assign-type='Ownership and Name Change' or @data-assign-type='Others')]/@id")%>% html_text() assignementsStatus <- dataOwner %>% html_nodes(xpath = "//div[@class='assignmentsContainer persist-area' and (@data-assign-type='Ownership and Name Change' or @data-assign-type='Others')]/@data-assign-type")%>% html_text() assignementsL<-as.list(gsub(".*-([0-9]+).*", "\\1", assignements)) if (length(assignementsL)>0 && class(assignementsL)=="list") { for (i in length(assignementsL):1) { #Command x<-paste("//div[@id='assignments-",assignementsL[[i]],"']//div[@class='value']",sep="") name<-paste("//div[@id='assignments-",assignementsL[[i]],"']//div[contains(text(),'Assignee')]/following::div[1]",sep="") address<-paste("//div[@id='assignments-",assignementsL[[i]],"']//div[contains(text(),'Address')]/following::div[1]",sep="") Conveyance<-dataOwner %>% html_nodes(xpath = x) %>% html_text() Conveyance<-Conveyance[1] if (grepl("Legal",Conveyance) && assignementsStatus[[i]]=='Others' || assignementsStatus[[i]]=='Ownership and Name Change') { OwnerName<-dataOwner %>% html_node(xpath = name) %>% html_text() OwnerName<-gsub("\r","",OwnerName) OwnerName<-gsub("\n","",OwnerName) OwnerName<-gsub("Name:","",OwnerName) OwnerName<-trimws(OwnerName) OwnerAddr<-dataOwner %>% html_node(xpath = address) %>% html_text() OwnerAddr<-gsub("\r","",OwnerAddr) OwnerAddr<-sub("\n","",OwnerAddr) break } } } else {OwnerName<-NA OwnerAddr<-NA} return(data.frame(OwnerName,OwnerAddr, stringsAsFactors = FALSE)) } USClasses<-function(data) { tmpDF <- data.frame(matrix(ncol = 18, nrow = 1)) class <- sapply(as.list(1:9), function(x) { return(paste("class", x, sep = "")) }) desc <- sapply(as.list(1:9), function(x) { return(paste("description", x, sep = "")) }) colnames(tmpDF) <- c(class, desc) classdesc<-data %>% html_nodes(xpath = "//div[text()='For:']/following::div[1]") %>% html_text() classdesc<-gsub("\r\n","",classdesc) #removing text between characters classdesc<-gsub('\\[.*?\\]', '', classdesc) classdesc<-gsub('\\*', '', classdesc) classdesc<-gsub(' ,', ',', classdesc) classStatus<-data %>% html_nodes(xpath = "//div[text()='Class Status:']/following::div[1]") %>% html_text() classStatus<-gsub("\r\n","",classStatus) classn<-data %>% html_nodes(xpath = "//div[text()='International Class(es):']/following::div[1]") %>% html_text() if (length(classn)==1 && grepl(",",classn)) { classn<-gsub("(^|[^0-9])0+","\\1",unlist(str_split(classn,",",simplify = FALSE))) classn<-gsub("\r\n", "", classn, perl = TRUE) classes<-as.data.frame(cbind.fill(classn,classdesc,classStatus), stringsAsFactors = FALSE) colnames(classes)<-c("classn","classdesc","classStatus") } else { classn<-gsub("\\D", "", classn) classn<-gsub("(^|[^0-9])0+", "\\1", classn, perl = TRUE) classes<-data.frame(classn,classdesc,classStatus, stringsAsFactors = FALSE) } rows<-nrow(classes) classes<-classes[classes$classStatus=="ACTIVE",] if (nrow(classes)==0 && rows>0) { classes<-data.frame(classn,classdesc,classStatus, stringsAsFactors = FALSE) classes<-head(classes,1) } if (length(classes)>0 && nrow(classes)>0) { for (i in 1:nrow(classes)) { tmpDF[, i] <- classes[i,1] tmpDF[, i + 9] <- classes[i,2] } return(tmpDF) } else {return(tmpDF<-as.data.frame(NULL))} } USScrap <- function(AppNo) { #AppNo <-"73802871" #Making URL and Reading data current<-Sys.getlocale("LC_TIME") Sys.setlocale("LC_TIME","English") AppNo<-gsub(",","",AppNo) AppNo<-gsub("/","",AppNo) AppNo<-gsub("-","",AppNo, fixed=TRUE) try(rm("data"), silent = TRUE) try(rm("tmpDF"), silent = TRUE) try(rm("dataOwner"), silent = TRUE) try(rm("statusURL"), silent = TRUE) try(rm("imageUrl"), silent = TRUE) if (!grepl('^[0-9]+$', AppNo)) { tmpDF = as.data.frame(NULL) return(tmpDF) } url <- paste( "http://tsdr.uspto.gov/#caseNumber=", AppNo, "&caseType=SERIAL_NO&searchType=statusSearch", sep = "" ) statusURL<-paste("http://tsdr.uspto.gov/statusview/sn",AppNo,sep="") try(data <- statusURL %>% read_html(), silent=TRUE) if (!(class(data)[1] %in% "xml_document")) { tmpDF = as.data.frame(NULL) return(tmpDF) } application <- as.Date( gsub("\r\n","",data %>% html_nodes(xpath = "//div[text()='Application Filing Date:']/following::div[1]") %>% html_text()), "%B. %d, %Y" ) if (is.na(application) && length(application)>0) { application <- as.Date( gsub("\r\n","",data %>% html_nodes(xpath = "//div[text()='Application Filing Date:']/following::div[1]") %>% html_text()), "%B %d, %Y" ) } application<-format(application, "%d.%m.%Y") if (length(application)==0 || is.na(application)) { application<-format(as.Date("1800-01-01","%Y-%m-%d"),"%d.%m.%Y") } registrationNo<-gsub("\r\n","",data %>% html_nodes(xpath = "//div[text()='US Registration Number:']/following::div[1]") %>% html_text()) if (length(registrationNo)==0) { registrationNo<-NA } acceptance <- as.Date( gsub("\r\n","",data %>% html_nodes(xpath = "//div[text()='Registration Date:']/following::div[1]") %>% html_text()), "%B. %d, %Y" ) if (is.na(acceptance) && length(acceptance)>0) { acceptance <- as.Date( gsub("\r\n","",data %>% html_nodes(xpath = "//div[text()='Registration Date:']/following::div[1]") %>% html_text()), "%B %d, %Y" ) } acceptance<-format(acceptance, "%d.%m.%Y") if (length(acceptance)==0 || is.na(acceptance)) { acceptance<-format(as.Date("1800-01-01","%Y-%m-%d"),"%d.%m.%Y") } priority <- as.Date( gsub("\r\n","",data %>% html_nodes(xpath = "//div[text()='Foreign Application Filing Date:']/following::div[1]") %>% html_text()), "%B. %d, %Y" ) if (is.na(priority) && length(priority)>0) { priority <- as.Date( gsub("\r\n","",data %>% html_nodes(xpath = "//div[text()='Foreign Application Filing Date:']/following::div[1]") %>% html_text()), "%B %d, %Y" ) } priority<-format(priority, "%d.%m.%Y") if (length(priority)==0 || is.na(priority)) { priority<-format(as.Date("1800-01-01","%Y-%m-%d"),"%d.%m.%Y") } NoticeOfAllowanceDate <- as.Date( gsub("\r\n","",data %>% html_nodes(xpath = "//div[text()='Notice of Allowance Date:']/following::div[1]") %>% html_text()), "%B. %d, %Y" ) if (is.na(NoticeOfAllowanceDate) && length(NoticeOfAllowanceDate)>0) { NoticeOfAllowanceDate <- as.Date( gsub("\r\n","",data %>% html_nodes(xpath = "//div[text()='Notice of Allowance Date:']/following::div[1]") %>% html_text()), "%B %d, %Y" ) } NoticeOfAllowanceDate<-format(NoticeOfAllowanceDate, "%d.%m.%Y") if (length(NoticeOfAllowanceDate)==0 || is.na(NoticeOfAllowanceDate)) { NoticeOfAllowanceDate<-format(as.Date("1800-01-01","%Y-%m-%d"),"%d.%m.%Y") } priorityNo<-gsub("\r\n","",data %>% html_nodes(xpath = "//div[text()='Foreign Application Number:']/following::div[1]") %>% html_text()) priorityNo<-tail(priorityNo,1) if (length(priorityNo)==0) { priorityNo<-NA } if (is.na(priorityNo)) { priorityNo<-gsub("\r\n","",data %>% html_nodes(xpath = "//div[text()='Foreign Registration Number:']/following::div[1]") %>% html_text()) priorityNo<-tail(priorityNo,1) if (length(priorityNo)==0) { priorityNo<-NA } priority <- as.Date( gsub("\r\n","",data %>% html_nodes(xpath = "//div[text()='Foreign Registration Date:']/following::div[1]") %>% html_text()), "%B. %d, %Y" ) if (is.na(priority) && length(priority)>0) { priority <- as.Date( gsub("\r\n","",data %>% html_nodes(xpath = "//div[text()='Foreign Registration Date:']/following::div[1]") %>% html_text()), "%B %d, %Y" ) } priority<-format(priority, "%d.%m.%Y") if (length(priority)==0 || is.na(priority)) { priority<-format(as.Date("1800-01-01","%Y-%m-%d"),"%d.%m.%Y") } } priorityCountry<-gsub("\r\n","",data %>% html_nodes(xpath = "//div[text()='Foreign Application/Registration Country:']/following::div[1]") %>% html_text()) priorityCountry<-tail(priorityCountry,1) if (length(priorityCountry)==0) { priorityCountry<-NA } publication <- as.Date( gsub("\r\n","",data %>% html_nodes(xpath = "//div[text()='Publication Date:']/following::div[1]") %>% html_text()), "%B. %d, %Y" ) if (is.na(publication) && length(publication)>0) { publication <- as.Date( gsub("\r\n","",data %>% html_nodes(xpath = "//div[text()='Publication Date:']/following::div[1]") %>% html_text()), "%B %d, %Y" ) } publication<-format(publication, "%d.%m.%Y") if (length(publication)==0 || is.na(publication)) { publication<-format(as.Date("1800-01-01","%Y-%m-%d"),"%d.%m.%Y") } #First use date FirstUseDate<- as.Date(gsub("\r\n","",data %>% html_nodes(xpath = "//div[text()='First Use:']/following::div[1]") %>% html_text()), "%B. %d, %Y" ) if (is.na(FirstUseDate) && length(FirstUseDate)>0) { FirstUseDate<- as.Date(gsub("\r\n","",data %>% html_nodes(xpath = "//div[text()='First Use:']/following::div[1]") %>% html_text()), "%B %d, %Y" ) } FirstUseDate<-format(FirstUseDate, "%d.%m.%Y") if (length(FirstUseDate)==0 || is.na(FirstUseDate)) { FirstUseDate<-format(as.Date("1800-01-01","%Y-%m-%d"),"%d.%m.%Y") } FirstUseDate<-min(FirstUseDate) #TM Type kind<- gsub("\r\n","",data %>% html_nodes(xpath = "//div[text()='Mark Drawing Type:']/following::div[1]") %>% html_text()) kind<-gsub(".*([0-9]+).*$", "\\1", kind) kind<-ifelse(kind %in% c("1","2","3","4","5"),kind,"") kind<-switch(as.numeric(kind),"WORD","DEVICE","WORD-DEVICE","WORD","WORD-DEVICE") kind<-ifelse(is.null(kind),NA,kind) AppType<- try(gsub("\r\n","",data %>% html_nodes(xpath = "//span[contains(@data-sectiontitle,'International Registration')]/following::div[1]") %>% html_text()), silent=TRUE) AppType<-ifelse(length(AppType)>0,"International","National") agentOnRecord<-gsub("\r\n","",data %>% html_nodes(xpath = "//div[text()='Attorney Name:']/following::div[1]") %>% html_text()) if (length(agentOnRecord)==0) { agentOnRecord<-NA } agentOnRecordAddr<-gsub("\r","",data %>% html_nodes(xpath = "//div[text()='Correspondent Name/Address:']/following::div[1]") %>% html_text()) agentOnRecordAddr<-gsub(agentOnRecord,"",agentOnRecordAddr) if (length(agentOnRecordAddr)==0) { agentOnRecordAddr<-NA } agentOnRecord<-paste(agentOnRecord,agentOnRecordAddr,sep="") associatedTMs<-gsub("\r\n","",data %>% html_nodes(xpath = "//div[contains(text(),'Claimed Ownership')]/following::div[1]//a")%>% html_text()) associatedTMs<-paste(associatedTMs,collapse = ",") if (length(associatedTMs)==0) { associatedTMs<-NA } ###Dealing with images imageUrl<-paste("http://tmsearch.uspto.gov/ImageAgent/ImageAgentProxy?getImage=",AppNo,sep="") if (length(imageUrl) == 1 && !is.na(imageUrl)) { cat(paste("\n","Downloading image...",sep="")) imageName<-paste("./logos/", AppNo, ".jpeg", sep ="") try(download.file(imageUrl,imageName, mode = 'wb',cacheOK=FALSE), silent = TRUE) size<-file.info(imageName)$size #delete files with problems if (size<400) { file.remove(imageName) } } else {imageUrl<-NA} #####Classes tmpDF<-USClasses(data) if (length(tmpDF)==0) { tmpDF <- data.frame(matrix(ncol = 18, nrow = 1)) class <- sapply(as.list(1:9), function(x) { return(paste("class", x, sep = "")) }) desc <- sapply(as.list(1:9), function(x) { return(paste("description", x, sep = "")) }) colnames(tmpDF) <- c(class, desc) } #Color color<- gsub("\r\n","",data %>% html_nodes(xpath = "//div[text()='Color(s) Claimed:']/following::div[1]") %>% html_text()) if (length(color)>0) { color<-ifelse(grepl('not',color),"Black and white",color) } else {color<-NA} #Owner na ta zajeban način #I have to call this tab urlA<-paste("http://tsdr.uspto.gov/assignments/",AppNo,"?searchprefix=sn",sep="") try(dataOwner <- urlA %>% read_html(), silent=TRUE) if ( !exists("dataOwner")) { owner<-gsub("\r\n","",data %>% html_nodes(xpath = "//div[text()='Owner Name:']/following::div[1]") %>% html_text()) if (length(owner)==0) { owner<-NA } ownerAddr<-gsub("\r","",data %>% html_nodes(xpath = "//div[text()='Owner Address:']/following::div[1]") %>% html_text()) ownerAddr<-sub("\n","",ownerAddr) if (length(ownerAddr)==0) { ownerAddr<-NA } } else { owner1<-GetOwner(dataOwner) if (nrow(na.omit(owner1))==0) { owner<-gsub("\r\n","",data %>% html_nodes(xpath = "//div[text()='Owner Name:']/following::div[1]") %>% html_text()) if (length(owner)==0) { owner<-NA } ownerAddr<-gsub("\r","",data %>% html_nodes(xpath = "//div[text()='Owner Address:']/following::div[1]") %>% html_text()) ownerAddr<-sub("\n","",ownerAddr) if (length(ownerAddr)==0) { ownerAddr<-NA } } else { ownerAddr<-owner1$OwnerAddr owner<-trimws(gsub("Name:","",owner1$OwnerName)) } } LimDis<-gsub('"','',data %>% html_nodes(xpath = "//div[text()='Disclaimer:']/following::div[1]") %>% html_text()) if (length(LimDis)==0) { LimDis<-NA } statusw<-data %>% html_nodes(xpath = "//div[text()='TM5 Common Status Descriptor:']/../div") %>% html_text() statusw<-paste(statusw,collapse = ",") if (grepl("LIVE/REGISTRATION",statusw)) { status<-"REGISTERED" } else if (grepl("LIVE/APPLICATION",statusw)) { status<-"FILED" }else if (grepl("DEAD/",statusw)) { status<-"INACTIVE" } else {status<-NA} if (acceptance!="01.01.1800" && status!="INACTIVE") { x <- 0 while (x<100) { x<-x+10 tmpDate<-as.Date(acceptance,"%d.%m.%Y") %m+% years(x) if (tmpDate>today()) { renewal<- tmpDate break } } tmpDAU<-tmpDate<-as.Date(acceptance,"%d.%m.%Y") %m+% years(6) if (tmpDAU>today()) { DAU<-tmpDAU renewalGP<-as.Date(renewal,"%d.%m.%Y") %m+% months(6) DAUGP<-as.Date(DAU,"%d.%m.%Y") %m+% months(6) } else { DAU<- renewal renewalGP<-as.Date(renewal,"%d.%m.%Y") %m+% months(6) DAUGP<-as.Date(DAU,"%d.%m.%Y") %m+% months(6) } } else { renewal <-as.Date("01.01.1800", "%d.%m.%Y") renewalGP<-as.Date("01.01.1800", "%d.%m.%Y") DAU <-as.Date("01.01.1800", "%d.%m.%Y") DAUGP <-as.Date("01.01.1800", "%d.%m.%Y") } renewal<-format(renewal, "%d.%m.%Y") if (length(renewal)==0 || is.na(renewal)) { renewal<-format(as.Date("1800-01-01","%Y-%m-%d"),"%d.%m.%Y") } renewalGP<-format(renewalGP, "%d.%m.%Y") if (length(renewalGP)==0 || is.na(renewalGP)) { renewalGP<-format(as.Date("1800-01-01","%Y-%m-%d"),"%d.%m.%Y") } DAU<-format(DAU, "%d.%m.%Y") if (length(DAU)==0 || is.na(DAU)) { DAU<-format(as.Date("1800-01-01","%Y-%m-%d"),"%d.%m.%Y") } DAUGP<-format(DAUGP, "%d.%m.%Y") if (length(DAUGP)==0 || is.na(DAUGP)) { DAUGP<-format(as.Date("1800-01-01","%Y-%m-%d"),"%d.%m.%Y") } words<-NA image<-NA trademark<-data %>% html_nodes(xpath = "//div[@id='summary']//div[text()='Mark:']/following::div[1]") %>% html_text() trademark<-gsub("\r","",trademark) trademark<-gsub("\t","",trademark) trademark<-gsub("\n","",trademark) trademark<-trimws(trademark) agComment<-data %>% html_nodes(xpath = "//div[text()='Status:']/following::div[1]") %>% html_text() agComment<-gsub("\r","",agComment) agComment<-gsub("\t","",agComment) agComment<-gsub("\n","",agComment) agComment<-trimws(agComment) ##AppNumber AppNumber<-data %>% html_nodes(xpath = "//div[text()='US Serial Number:']/following::div[1]")%>% html_text() AppNumber<-gsub("\r","",AppNumber) AppNumber<-gsub("\n","",AppNumber) AppNumber<-trimws(gsub("\t","",AppNumber)) AppNumber<-gsub(",","",AppNumber) AppNumber<-gsub("/","",AppNumber) AppNumber<-gsub("-","",AppNumber, fixed=TRUE) #return DF tmpDF <- cbind( data.frame( AppNumber, trademark, registrationNo, renewal, application, acceptance, FirstUseDate, priority, priorityNo, priorityCountry, NoticeOfAllowanceDate, publication, agentOnRecord, associatedTMs, status, kind, AppType, DAU, renewalGP, DAUGP, color, words, image, imageUrl, agComment, LimDis, owner, ownerAddr, stringsAsFactors = FALSE ), tmpDF ) tmpDF<-tmpDF%>%dplyr::rename( `Application no.`=AppNumber, Trademark=trademark, `Application date`=application, `Registration no.`=registrationNo, `Registration date`=acceptance, `First use date`=FirstUseDate, `Next renewal date`=renewal, `Priority date`=priority, `Priority no.`=priorityNo, `Priority country`=priorityCountry, `Notice of allowance date`=NoticeOfAllowanceDate, `Publication date`=publication, `TM Type`=kind, `Application type`=AppType, `Next DAU date`=DAU, `Is color`=color, `Next renewal date: end of grace period`=renewalGP, `Next DAU date: end of grace period`=DAUGP, Status=status, `Limitations & Disclaimers`=LimDis, `Agent on record`=agentOnRecord, Owner=owner, `Owner address`=ownerAddr, `Associated TMs`=associatedTMs, `1st Class`= class1, `1st Goods & Services` =description1, `2nd Class` = class2, `2nd Goods & Services`=description2, `3rd Class` =class3, `3rd Goods & Services`=description3, `4th Class` =class4, `4th Goods & Services`=description4, `5th Class` =class5, `5th Goods & Services`=description5, `6th Class` =class6, `6th Goods & Services`=description6, `7th Class` =class7, `7th Goods & Services`=description7, `8th Class` =class8, `8th Goods & Services`=description8, `9th Class` =class9, `9th Goods & Services`=description9, `Agent's comment`=agComment ) if (class(tmpDF) != "data.frame") { tmpDF = as.data.frame(NULL) } rm(list=setdiff(ls(), "tmpDF")) Sys.setlocale("LC_TIME",current) return(tmpDF) }

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#!/usr/bin/env Rscript # # Make table of medians (or means) and t-test p-values for several key # comparisons. Has a lot of overlap with the gene_count_table. # # Also reports some correlations. # projdir = '/home/tiffinp/epste051/project/symbiosis_gene_evolution' indir = file.path(projdir, 'notes/table') genes_files = list('Classic Signaling'='symbiosis_signaling_genes.edited.2020-05-07.txt', 'Classic Fixation'='symbiosis_fixation_genes.edited.2020-05-07.txt', 'Host Benefit'=c('gwas_candidates/2020-05-11/plant_biomass.A17.top10.txt', 'gwas_candidates/2020-05-11/plant_biomass.R108.top10.txt'), 'Symbiont Fitness'=c('gwas_candidates/2020-05-11/rhizobial_fitness.A17.top10.txt', 'gwas_candidates/2020-05-11/rhizobial_fitness.R108.top10.txt')) sym_sets = names(genes_files) genes = structure(vector('list', length=length(genes_files)), names=names(genes_files)) for(geneset in names(genes)) { for(gf in genes_files[[geneset]]) { file.copy(file.path(indir, gf), '.', overwrite=TRUE) } genes[[geneset]] = unique(as.character(unlist(sapply(genes_files[[geneset]], function(fn) { scan(basename(fn), what='character', sep='\n') }, USE.NAMES=FALSE)))) } file.copy(file.path(projdir, 'results/gene_comparison/73strains_alpha_complete/2020-05-11/data.tsv'), '.', overwrite=TRUE) gene_data = read.csv('data.tsv', sep='\t', comment.char='#', header=TRUE, as.is=TRUE, check.names=FALSE) gene_data[, 'ka_ks.n.all'] = ifelse(is.na(gene_data[, 'ka_ks.n.all']), 0, gene_data[, 'ka_ks.n.all']) genes[['Genome']] = unique(gene_data[, 'gene']) genes[['Non-Symbiosis']] = genes[['Genome']][!(genes[['Genome']] %in% unlist(genes[sym_sets]))] output = matrix(ncol=14, nrow=7, data=NaN, dimnames=list(c('Non-Symbiosis', 'Classic Signaling', 'Classic Fixation', 't_test_sig_fix', 'Host Benefit', 'Symbiont Fitness', 't_test_ben_fit'), c('genomes', 'copies_per_genome', 'duplication', 'transfer', 'median_ka_ks', 'mean_ka_ks', 'median_ka_ks_3', 'mean_ka_ks_3', 'r2', 'delta', 'delta_pav', 'delta_pav_3', 'loss', 'n_gene'))) output_n = matrix(ncol=2, nrow=7, data=NaN, dimnames=list(c('Non-Symbiosis', 'Classic Signaling', 'Classic Fixation', 't_test_sig_fix', 'Host Benefit', 'Symbiont Fitness', 't_test_ben_fit'), c( 'median_ka_ks_3', 'delta_pav_3'))) output_mean = matrix(ncol=14, nrow=5, data=NaN, dimnames=list(c('Non-Symbiosis', 'Classic Signaling', 'Classic Fixation', 'Host Benefit', 'Symbiont Fitness'), c('genomes', 'copies_per_genome', 'duplication', 'transfer', 'median_ka_ks', 'mean_ka_ks', 'median_ka_ks_3', 'mean_ka_ks_3', 'r2', 'delta', 'delta_pav', 'delta_pav_3', 'loss', 'n_gene'))) # P-values for test for difference with Non-Symbiosis output_p = matrix(ncol=15, nrow=10, data=NaN, dimnames=list(c('Classic Signaling', 'Classic Fixation', 'Host Benefit', 'Symbiont Fitness', 'fix_vs_sig', 'ben_vs_fit', 'sig_vs_ben', 'sig_vs_fit', 'fix_vs_ben', 'fix_vs_fit'), c('genomes', 'copies_per_genome', 'duplication', 'transfer', 'transfer_minus_dup', 'median_ka_ks', 'mean_ka_ks', 'median_ka_ks_3', 'mean_ka_ks_3', 'r2', 'delta', 'delta_pav', 'delta_pav_3', 'loss', 'n_gene'))) # Correlations with median pairwise Ka/Ks output_kaks_cor = matrix(ncol=12, nrow=5, data=NaN, dimnames=list(c('Non-Symbiosis', 'Classic Signaling', 'Classic Fixation', 'Host Benefit', 'Symbiont Fitness'), c('transfer', 'duplication', 'delta', 'delta_pav', 'delta_pav_3', 'r2', 'transfer_df', 'duplication_df', 'delta_df', 'delta_pav_df', 'delta_pav_3_df', 'r2_df'))) ## Median and mean stats for each measurement for(ss in c('Non-Symbiosis', sym_sets)) { d = gene_data[gene_data[, 'gene'] %in% genes[[ss]], ] output[ss, 'genomes'] = median(d[, 'n_strains']) output[ss, 'copies_per_genome'] = median(d[, 'n_genes'] / d[, 'n_strains']) output[ss, 'duplication'] = median(d[, 'duplication'], na.rm=TRUE) output[ss, 'transfer'] = median(d[, 'transfer'], na.rm=TRUE) output[ss, 'median_ka_ks'] = median(d[, 'ka_ks.median.all'], na.rm=TRUE) output[ss, 'mean_ka_ks'] = median(d[, 'ka_ks.mean.all'], na.rm=TRUE) output[ss, 'median_ka_ks_3'] = median(d[d[, 'ka_ks.n.all'] > 2, 'ka_ks.median.all'], na.rm=TRUE) output[ss, 'mean_ka_ks_3'] = median(d[d[, 'ka_ks.n.all'] > 2, 'ka_ks.mean.all'], na.rm=TRUE) output[ss, 'r2'] = median(d[, 'r2'], na.rm=TRUE) output[ss, 'delta'] = median(d[, 'delta'], na.rm=TRUE) output[ss, 'delta_pav'] = median(d[, 'delta_pav'], na.rm=TRUE) output[ss, 'delta_pav_3'] = median(d[d[, 'n_genes'] > 2, 'delta_pav'], na.rm=TRUE) output[ss, 'loss'] = median(d[, 'loss'], na.rm=TRUE) output[ss, 'n_gene'] = median(d[, 'n_genes'], na.rm=TRUE) output_n[ss, 'median_ka_ks_3'] = sum(!is.na(d[d[, 'ka_ks.n.all'] > 2, 'ka_ks.median.all'])) output_n[ss, 'delta_pav_3'] = sum(!is.na(d[d[, 'n_genes'] > 2, 'delta_pav'])) } for(ss in c('Non-Symbiosis', sym_sets)) { d = gene_data[gene_data[, 'gene'] %in% genes[[ss]], ] output_mean[ss, 'genomes'] = mean(d[, 'n_strains']) output_mean[ss, 'copies_per_genome'] = mean(d[, 'n_genes'] / d[, 'n_strains']) output_mean[ss, 'duplication'] = mean(d[, 'duplication'], na.rm=TRUE) output_mean[ss, 'transfer'] = mean(d[, 'transfer'], na.rm=TRUE) output_mean[ss, 'median_ka_ks'] = mean(d[, 'ka_ks.median.all'], na.rm=TRUE) output_mean[ss, 'mean_ka_ks'] = mean(d[, 'ka_ks.mean.all'], na.rm=TRUE) output_mean[ss, 'median_ka_ks_3'] = mean(d[d[, 'ka_ks.n.all'] > 2, 'ka_ks.median.all'], na.rm=TRUE) output_mean[ss, 'mean_ka_ks_3'] = mean(d[d[, 'ka_ks.n.all'] > 2, 'ka_ks.mean.all'], na.rm=TRUE) output_mean[ss, 'r2'] = mean(d[, 'r2'], na.rm=TRUE) output_mean[ss, 'delta'] = mean(d[, 'delta'], na.rm=TRUE) output_mean[ss, 'delta_pav'] = mean(d[, 'delta_pav'], na.rm=TRUE) output_mean[ss, 'delta_pav_3'] = mean(d[d[, 'n_genes'] > 2, 'delta_pav'], na.rm=TRUE) output_mean[ss, 'loss'] = mean(d[, 'loss'], na.rm=TRUE) output_mean[ss, 'n_gene'] = mean(d[, 'n_genes'], na.rm=TRUE) } for(ss in rownames(output_kaks_cor)) { d = gene_data[gene_data[, 'gene'] %in% genes[[ss]], ] d = d[d[, 'ka_ks.n.all'] > 2, ] for(st in grep('_df', colnames(output_kaks_cor), value=TRUE, invert=TRUE)) { stc = st if(st == 'delta_pav_3') stc = 'delta_pav' if(sum(!is.na(d[, stc]) & !is.na(d[, 'ka_ks.median.all'])) < 3) { cat('Skipping', st, 'correlation because not enough complete observations\n') } else { ct = cor.test(d[, stc], d[, 'ka_ks.median.all']) output_kaks_cor[ss, st] = ct[['estimate']] output_kaks_cor[ss, paste0(st, '_df')] = ct[['parameter']] } } } ## T-test (unequal variances) tt = function(x1, x2, ...) { t.test(x1, x2, var.equal=FALSE, paired=FALSE, alternative='two.sided', ...)[['p.value']] } dns = gene_data[gene_data[, 'gene'] %in% genes[['Non-Symbiosis']], ] for(ss in sym_sets) { d = gene_data[gene_data[, 'gene'] %in% genes[[ss]], ] output_p[ss, 'genomes'] = tt(d[, 'n_strains'], dns[, 'n_strains']) output_p[ss, 'copies_per_genome'] = tt(d[, 'n_genes'] / d[, 'n_strains'], dns[, 'n_genes'] / dns[, 'n_strains']) output_p[ss, 'duplication'] = tt(d[, 'duplication'], dns[, 'duplication']) output_p[ss, 'transfer'] = tt(d[, 'transfer'], dns[, 'transfer']) output_p[ss, 'median_ka_ks'] = tt(d[, 'ka_ks.median.all'], dns[, 'ka_ks.median.all']) output_p[ss, 'median_ka_ks_3'] = tt(d[d[, 'ka_ks.n.all'] > 2, 'ka_ks.median.all'], dns[dns[, 'ka_ks.n.all'] > 2, 'ka_ks.median.all']) output_p[ss, 'r2'] = tt(d[, 'r2'], dns[, 'r2']) output_p[ss, 'delta'] = tt(d[, 'delta'], dns[, 'delta']) output_p[ss, 'delta_pav'] = tt(d[, 'delta_pav'], dns[, 'delta_pav']) output_p[ss, 'delta_pav_3'] = tt(d[d[, 'n_genes'] > 2, 'delta_pav'], dns[dns[, 'n_genes'] > 2, 'delta_pav']) output_p[ss, 'transfer_minus_dup'] = tt(d[, 'transfer_minus_dup'], dns[, 'transfer_minus_dup']) output_p[ss, 'loss'] = tt(d[, 'loss'], dns[, 'loss']) output_p[ss, 'n_gene'] = tt(d[, 'n_genes'], dns[, 'n_genes']) } d1 = gene_data[gene_data[, 'gene'] %in% genes[['Classic Fixation']], ] d2 = gene_data[gene_data[, 'gene'] %in% genes[['Classic Signaling']], ] output['t_test_sig_fix', 'genomes'] = tt(d1[, 'n_strains'], d2[, 'n_strains']) output['t_test_sig_fix', 'copies_per_genome'] = tt(d1[, 'n_genes']/d1[, 'n_strains'], d2[, 'n_genes']/d2[, 'n_strains']) output['t_test_sig_fix', 'duplication'] = tt(d1[, 'duplication'], d2[, 'duplication']) output['t_test_sig_fix', 'transfer'] = tt(d1[, 'transfer'], d2[, 'transfer']) output['t_test_sig_fix', 'median_ka_ks'] = tt(d1[, 'ka_ks.median.all'], d2[, 'ka_ks.median.all']) output['t_test_sig_fix', 'median_ka_ks_3'] = tt(d1[d1[, 'ka_ks.n.all'] > 2, 'ka_ks.median.all'], d2[d2[, 'ka_ks.n.all'] > 2, 'ka_ks.median.all']) output['t_test_sig_fix', 'r2'] = tt(d1[, 'r2'], d2[, 'r2']) output['t_test_sig_fix', 'delta'] = tt(d1[, 'delta'], d2[, 'delta']) output['t_test_sig_fix', 'delta_pav'] = tt(d1[, 'delta_pav'], d2[, 'delta_pav']) output['t_test_sig_fix', 'delta_pav_3'] = tt(d1[d[, 'n_genes'] > 2, 'delta_pav'], d2[d2[, 'n_genes'] > 2, 'delta_pav']) for(comp in list(c('Classic Fixation', 'Classic Signaling', 'fix_vs_sig'), c('Host Benefit', 'Symbiont Fitness', 'ben_vs_fit'), c('Classic Fixation', 'Host Benefit', 'fix_vs_ben'), c('Classic Fixation', 'Symbiont Fitness', 'fix_vs_fit'), c('Classic Signaling', 'Host Benefit', 'sig_vs_ben'), c('Classic Signaling', 'Symbiont Fitness', 'sig_vs_fit'))) { ss = comp[3] d1 = gene_data[gene_data[, 'gene'] %in% genes[[comp[1]]], ] d2 = gene_data[gene_data[, 'gene'] %in% genes[[comp[2]]], ] output_p[ss, 'genomes'] = tt(d1[, 'n_strains'], d2[, 'n_strains']) output_p[ss, 'copies_per_genome'] = tt(d1[, 'n_genes'] / d1[, 'n_strains'], d2[, 'n_genes'] / d2[, 'n_strains']) output_p[ss, 'duplication'] = tt(d1[, 'duplication'], d2[, 'duplication']) output_p[ss, 'transfer'] = tt(d1[, 'transfer'], d2[, 'transfer']) output_p[ss, 'median_ka_ks'] = tt(d1[, 'ka_ks.median.all'], d2[, 'ka_ks.median.all']) output_p[ss, 'median_ka_ks_3'] = tt(d1[d1[, 'ka_ks.n.all'] > 2, 'ka_ks.median.all'], d2[d2[, 'ka_ks.n.all'] > 2, 'ka_ks.median.all']) output_p[ss, 'r2'] = tt(d1[, 'r2'], d2[, 'r2']) output_p[ss, 'delta'] = tt(d1[, 'delta'], d2[, 'delta']) output_p[ss, 'delta_pav'] = tt(d1[, 'delta_pav'], d2[, 'delta_pav']) output_p[ss, 'delta_pav_3'] = tt(d1[d1[, 'n_genes'] > 2, 'delta_pav'], d2[d2[, 'n_genes'] > 2, 'delta_pav']) output_p[ss, 'transfer_minus_dup'] = tt(d1[, 'transfer_minus_dup'], d2[, 'transfer_minus_dup']) output_p[ss, 'loss'] = tt(d1[, 'loss'], d2[, 'loss']) output_p[ss, 'n_gene'] = tt(d1[, 'n_genes'], d2[, 'n_genes']) } write_readable_table = function(object, fname) { handle = file(fname, 'w') for(i in 1:ncol(object)) { cat('\n\t', file=handle) write.table(object[, i, drop=FALSE], handle, sep='\t', col.names=TRUE, row.names=TRUE, quote=FALSE) } close(handle) } write_readable_table(output, 'medians.tsv') write_readable_table(output_n, 'n.tsv') write_readable_table(output_mean, 'means.tsv') write_readable_table(output_p, 'pvalues_vs_all.tsv') write_readable_table(output_kaks_cor, 'correlations_with_median_kaks.tsv')

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message('library paths:\n', paste('... ', .libPaths(), sep='', collapse='\n')) shiny::runApp('./App/app.R', launch.browser = TRUE)

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ChicagoChlaModel.R

library(pracma) library(ggplot2) library(MASS) library(compositions) library(ggmap) library(rgdal) library(rgeos) library(maptools) library(dplyr) library(tidyr) library(tmap) setwd( "/Users/marlinfiggins/Desktop/ChicagoChlamydiaModel/") Chicago= readOGR(dsn = "chicomm", layer = "chicomm") ExtraData = read.csv(file="Chicago Pop Data.csv", header=TRUE, sep=",") ExtraData=ExtraData[as.numeric(Chicago@data[["DISTNAME"]]),] DistanceMat=spDists(Chicago, Chicago) alpha=0.015 ##########Switch to Area to Area GTransProb=function(GridPositions,ExtraData, DistanceMat, alpha){ ProbMat=matrix(NA, nrow=nrow(GridPositions), ncol=nrow(GridPositions)) GP=GridPositions ##from J to K####### #####Find Distance Classes for each neighborhood, find the probability (summed for each neighborhood in that class) and weigh it by the size of the neighborhood DistanceClasses= seq(0, max(DistanceMat+0.001), length.out = 20) for (J in 1:nrow(GridPositions)){ for (i in 2:length(DistanceClasses)){ DistanceClassProb=0 NeighClass=which(DistanceMat[J,]<=DistanceClasses[i] & DistanceMat[J,]>=DistanceClasses[i-1]) for (K in NeighClass){ DistanceClassProb=exp(-sum(((GridPositions[K,]-GridPositions[J,])/alpha)^2))+DistanceClassProb } for (K in NeighClass){ ProbMat[J,K]=DistanceClassProb*ExtraData$Land.Area..Acres.[K]/sum(ExtraData$Land.Area..Acres.[NeighClass]) } } } ######Normalizing Transition Probs####### for (J in 1:nrow(GridPositions)){ ProbMat[J,]=ProbMat[J,]/sum(ProbMat[J,]) } return(ProbMat) ########Want to return matrix of prob from going to J to K } P=GTransProb(coordinates(Chicago), ExtraData, DistanceMat, alpha)

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/plotGrid (woodgrain).R

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jrevenaugh/Riquity

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plotGrid (woodgrain).R

# plotGrid # # Make a simple plot of a Riquity grid plotGrid <- function(grid) { centers <- gCenter centers$gc <- grid openCenters <- centers %>% filter(gc == FALSE) filledCenters <- centers %>% filter(gc == TRUE) g <- ggplot() + scale_y_continuous(limits = c(-1, 5)) + scale_x_continuous(limits = c(-1, 5)) + coord_equal(expand = FALSE) + # Add woodgrain background annotation_custom(grob, xmin = -Inf, xmax = Inf, ymin = -Inf, ymax = Inf) + # Mask off woodgrain outside of board are geom_polygon(data = mask1, aes(x, y), fill = "white", color = "white") + geom_polygon(data = mask2, aes(x, y), fill = "white", color = "white") + # Outline board geom_path(data = triangle, aes(x, y), color = "black", size = 2) + # Add diagonals geom_path(data = diagonals, aes(x, y), color = "steelblue", size = 3) + # Add open holes geom_point(data = openCenters, aes(x, y), size = 10, color = "gray20", fill = "gray20", pch = 21) + # Add "pegged" holes geom_point(data = filledCenters, aes(x, y), size = 30, color = "black", fill = "darkgoldenrod", pch = 21) + geom_point(data = filledCenters, aes(x, y), size = 20, color = "black", fill = "goldenrod", pch = 21) + theme_void() return(g) }

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/Plot5.R

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## This solution uses the "downloader" package from CRAN. If you already have it loaded, ## then please ignore the next section. ########## install.packages("downloader") library(downloader) ########## url <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2FNEI_data.zip" download(url, dest="dataset.zip", mode="wb") unzip("dataset.zip") NEI <- readRDS("summarySCC_PM25.rds") SCC <- readRDS("Source_Classification_Code.rds") ########## install.packages("dplyr") library(dplyr) library(ggplot2) ########## E <- filter(NEI, type == "ON-ROAD", fips == "24510") g <- ggplot(E, aes(year, Emissions)) g + geom_point(color = "steelblue", size = 2, alpha = 1/3) + geom_smooth(method="lm") + xlab("Year") + ggtitle("Annual Motor Vehicle PM25 Emmisions - Baltimore City")

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ex4.R

Q1. library(ElemStatLearn) data(vowel.train) data(vowel.test) set.seed(33833) summary(vowel.train)

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df<- read.table("household_power_consumption.txt", header = TRUE, sep=";", as.is=TRUE, na.strings="?") DATE1 <- as.Date("01/02/2007", "%d/%m/%Y") DATE2 <- as.Date("02/02/2007", "%d/%m/%Y") df$Date <- as.Date(df$Date, "%d/%m/%Y") df1 <- df[df$Date >= DATE1 & df$Date <= DATE2, ] x <- paste(df1$Date, df1$Time) x <- strptime(x, "%Y-%m-%d %H:%M:%S") plot(x, df1$Global_active_power, type = "l", ylab = "Global Active Power(kilowatts", xlab = "") dev.copy(png, file = "plot2.png", width = 480, height = 480) dev.off()

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b_parsing.R

####### Get code from source chunk ##### chunk_code_get <- function(chunk_name){ paste(knitr::knit_code$get(chunk_name), collapse = "\n") } correct_py <- function(lang){ if (lang == "py") {lang <- "python"} } code_remove_omit <- function(code, omit = "#OMIT"){ code %>% stringr::str_split(pattern = "\n") %>% .[[1]] %>% .[!stringr::str_detect(., omit)] %>% paste(collapse = "\n") } # # create_code() %>% # code_as_table() %>% # code_as_table_process_break_messages() #### Code parsing ######### code_as_table <- function(code, omit = "#OMIT"){ code %>% code_remove_omit(omit = omit) %>% stringr::str_split(pattern = "\n") %>% .[[1]] %>% tibble::tibble(raw_code = .) %>% dplyr::mutate(line = 1:dplyr::n()) } code_as_table_process_break_messages <- function(code_as_table){ code_as_table %>% dplyr::mutate(raw_code = stringr::str_remove(raw_code, "\\s+$")) %>% dplyr::mutate(non_seq = stringr::str_extract(raw_code, "#BREAK\\-?\\d+")) %>% dplyr::mutate(non_seq = stringr::str_extract(non_seq, "-?\\d+")) %>% dplyr::mutate(non_seq = as.numeric(non_seq)) %>% dplyr::mutate(non_seq = tidyr::replace_na(non_seq, 1)) %>% dplyr::mutate(user = stringr::str_detect(raw_code, "#BREAK$")) %>% dplyr::mutate(rotate = stringr::str_detect(raw_code, "#ROTATE$")) } # create_code() %>% # code_as_table() %>% # code_as_table_process_break_messages() # create_code() %>% # code_as_table() %>% # code_as_table_process_break_messages() # # create_code_remove() %>% # code_simple_parse() code_simple_parse <- function(code, omit = "#OMIT"){ code %>% code_as_table(omit = omit) %>% code_as_table_process_break_messages() } #### Real Parsing #### r_code_base_parse <- function(code, omit = "#OMIT") { code <- code_remove_omit(code = code, omit = omit) # code <- stringr::str_remove_all(code, "#BREAK\\d+|#BREAK|#ROTATE|#OMIT") sf <- srcfile(code) try(parse(text = code, srcfile = sf)) utils::getParseData(sf) } r_base_parsed_count_parentheses <- function(base_parsed){ num_lines <- max(base_parsed$line1) tibble::tibble(line = 1:num_lines) -> all_lines base_parsed %>% dplyr::rename(line = line1) %>% dplyr::mutate(open_par = text == "(") %>% dplyr::mutate(closed_par = text == ")") %>% dplyr::mutate(open_curly = text == "{") %>% dplyr::mutate(closed_curly = text == "}") %>% dplyr::mutate(open_square = text == "[") %>% dplyr::mutate(open_square = ifelse(text == "[[", 2, open_square)) %>% dplyr::mutate(closed_square = text == "]") %>% dplyr::group_by(line) %>% dplyr::summarise( full_line = paste0(text, collapse = ""), comment = stringr::str_trim(paste0(ifelse(token == "COMMENT", text, ""), collapse = " ")), num_open_par = sum(open_par), num_closed_par = sum(closed_par), num_open_curly = sum(open_curly), num_closed_curly = sum(closed_curly), num_open_square = sum(open_square), num_closed_square = sum(closed_square) ) %>% dplyr::full_join(all_lines, by = "line") %>% dplyr::arrange(line) %>% dplyr::mutate( full_line = tidyr::replace_na(full_line, ""), comment = tidyr::replace_na(comment, ""), num_open_par = tidyr::replace_na(num_open_par, 0), num_closed_par = tidyr::replace_na(num_closed_par, 0), num_open_curly = tidyr::replace_na(num_open_curly, 0), num_closed_curly = tidyr::replace_na(num_closed_curly, 0), num_open_square = tidyr::replace_na(num_open_square, 0), num_closed_square = tidyr::replace_na(num_closed_square, 0) ) %>% dplyr::mutate(balanced_paren = (cumsum(num_open_par) - cumsum(num_closed_par)) == 0) %>% dplyr::mutate(balanced_curly = (cumsum(num_open_curly) - cumsum(num_closed_curly)) == 0) %>% dplyr::mutate(balanced_square = (cumsum(num_open_square) - cumsum(num_closed_square)) == 0) %>% dplyr::mutate(all_parentheses_balanced = balanced_paren & balanced_curly & balanced_square) %>% dplyr::select(line, full_line, comment, all_parentheses_balanced) } # create_code() %>% # r_code_base_parse() %>% # r_base_parsed_count_parentheses() # create_code_remove() %>% # r_code_full_parse() #### Full parse R, python, stata #### r_code_full_parse <- function(code = code, omit = "#OMIT"){ arithmetic <- "\\+$|-$|\\/$|\\*$|\\^$|%%$|%\\/%$" matrix <- "%\\*%$|%o%$" ggplot_change_data <- "%\\+%$" the_magrittr <- "%>%$|%\\$%$" base_pipe <- "\\|\\>$" right_assign <- "->$" combine_booleans <- "\\|$|\\&$" connectors <- paste(arithmetic, matrix, ggplot_change_data, the_magrittr, base_pipe, right_assign, combine_booleans, sep = "|") raw_code_table <- code_simple_parse(code = code, omit = omit) parsed_code_table <- code %>% r_code_base_parse(omit = omit) %>% r_base_parsed_count_parentheses() raw_code_table %>% dplyr::full_join(parsed_code_table, by = "line") %>% # we need this XXXXXXX so that we don't get a bunch of warnings dplyr::mutate(comment = tidyr::replace_na(comment, "XXXXXXXXX")) %>% dplyr::mutate(comment = stringr::str_replace(comment, "^$", "XXXXXXXXX")) %>% dplyr::mutate(code = stringr::str_remove(raw_code, comment)) %>% dplyr::mutate(connector = stringr::str_extract(stringr::str_trim(code), connectors)) %>% dplyr::mutate(connector = tidyr::replace_na(connector, "")) %>% # delete comments understood as dplyr::mutate(comment = stringr::str_remove(comment, "#BREAK-?\\d?\\d?")) %>% dplyr::mutate(comment = stringr::str_remove(comment, "#ROTATE")) %>% dplyr::mutate(comment = stringr::str_remove(comment, "#[[A-Z]]+")) %>% dplyr::mutate(comment = stringr::str_remove(comment, "XXXXXXXXX")) %>% dplyr::mutate(code = stringr::str_remove(stringi::stri_trim_right(code), connectors)) %>% dplyr::mutate(auto = all_parentheses_balanced & code != "") %>% dplyr::select(line, raw_code, code, connector, comment, auto, user, non_seq, rotate) } # create_python_code() %>% # python_code_full_parse() # create_python_code_pipeline() %>% # python_code_full_parse() # # code <- create_python_code_pipeline() python_code_full_parse <- function(code, omit = "#OMIT"){ connectors <- "\\\\" code %>% code_simple_parse(omit = omit) %>% dplyr::mutate(code = raw_code) %>% dplyr::mutate(open_par = stringr::str_count(code, "\\{|\$|\\[")) %>% dplyr::mutate(closed_par = stringr::str_count(code, "\\}|\$|\\]")) %>% dplyr::mutate(auto = cumsum(open_par) == cumsum(closed_par)) %>% dplyr::mutate(auto = ifelse(raw_code == "", FALSE, auto)) %>% dplyr::mutate(auto = ifelse(stringr::str_detect(raw_code, ":\\s?$"), FALSE, auto)) %>% dplyr::mutate(indented = stringr::str_detect(code, "^\\s+")) %>% # dplyr::mutate(indented_follows = dplyr::lead(indented, default = FALSE)) %>% # dplyr::mutate(auto = ifelse(indented_follows, FALSE, auto)) %>% dplyr::mutate(connector = stringr::str_extract(stringr::str_trim(code), connectors)) %>% dplyr::mutate(connector = tidyr::replace_na(connector, "")) %>% # dplyr::mutate(connector = stringr::str_replace(connector, "\\\\", "\\")) %>% dplyr::mutate(code = stringr::str_remove(stringi::stri_trim_right(code), connectors)) %>% dplyr::mutate(comment = "") } stata_code_full_parse <- function(code, omit = "#OMIT"){ code %>% code_simple_parse(omit = omit) %>% dplyr::mutate(code = raw_code) %>% dplyr::mutate(auto = ifelse(raw_code == "", FALSE, TRUE)) %>% dplyr::mutate(connector = "") %>% dplyr::mutate(comment = "") } #### Combined code parsing all languages #### code_parse <- function(code = create_code(), lang = "r", omit = "#OMIT") { if (lang == "r") { r_code_full_parse(code = code, omit = omit) %>% dplyr::mutate(func = stringr::str_extract(code, "\\w+\\(")) %>% dplyr::mutate(func = stringr::str_remove(func, "\\(")) } else if (lang %in% c("python", "py")) { python_code_full_parse(code = code, omit = omit) } else if (lang == "stata") { NULL } }

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get_cache_site.R

.get_cache_site <- function() { cache_site <- "AquaCache" cache_site }

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pkg_ref_class_extract.R

#' @export `$.pkg_ref` <- function(x, name) { `[[`(x, as.character(name)) } #' @export `$<-.pkg_ref` <- function(x, name, value) { `[[<-`(x, as.character(name), value = value) } #' Lazily instantiated, immutable metadata access #' #' If errors are thrown upon instantiation, they are saved and rethrown any time #' the value is attempted to be accessed. These then propegate through #' assessment and scoring functions to affect any downstream metrics. #' #' @param x pkg_ref object to extract metadata from #' @param name name of metadata field to extract #' @param ... additional arguments used to extract from internal environment #' #' @return a pkg_ref object #' @export #' @keywords internal `[[.pkg_ref` <- function(x, name, ...) { if (!name %in% bare_env(x, names(x))) { allow_mutation(x, { pkg_ref_cache(x, name) ret <- tryCatch(pkg_ref_cache(x, name), error = function(e) e) x[[name]] <- ret if (inherits(ret, "error")) stop(ret) ret }) } else { bare_env(x, { ret <- x[[name, ...]] if (inherits(ret, "error")) stop(ret) ret }) } } #' @export `[[<-.pkg_ref` <- function(x, name, value) { if (is.null(attr(x, "allowed_mutations"))) stop(pkg_ref_mutability_error(name)) bare_env(x, x[[name]] <- value) } #' @export `[.pkg_ref` <- function(x, names, ...) { lapply(names, function(n, ...) x[[n, ...]], ...) } #' @export `[<-.pkg_ref` <- function(x, names, value) { invisible(Map(function(name, value) { `[[<-`(x, name = name, value = value) }, names, value)) } #' evaluate an expression with a pkg_ref object reclassed as a bare environment #' object, used to sidestep pkg_ref assignment guardrails #' #' @param x a \code{pkg_ref} object #' @param expr an expression to evaluate, avoiding \code{pkg_ref} extraction #' handlers #' @param envir an environment in which the expression is to be evaluated #' #' @return the result of \code{expr} #' @keywords internal bare_env <- function(x, expr, envir = parent.frame()) { old_class <- class(x) class(x) <- "environment" on.exit(class(x) <- old_class) eval(expr, envir = envir) } #' pretty printing for a pkg_ref mutability error caused by trying to do #' assignment within the pkg_ref without permission #' #' @param name name of field for which mutation was attempted #' @return a \code{simplError} with subclasses \code{pkg_ref_mutability_error}, #' \code{pkg_ref_error} #' @keywords internal pkg_ref_mutability_error <- function(name) { message <- list(paste0( "Assignment to a pkg_ref environment can only be done in a ", "pkg_ref_cache call.")) if (!missing(name)) message <- append(message, list(paste0( "Extend the pkg_ref class by implementing function '", "pkg_ref_cache.", name, "'"))) e <- simpleError(message = paste(message, collapse = " ")) class(e) <- c("pkg_ref_mutability_error", "pkg_ref_error", class(e)) e } #' a wrapper to assert that a pkg_ref has been permitted to do an additional #' mutation, used to handle recursive initialization of cached fields #' #' @param x a \code{pkg_ref} object #' @param expr an expression to evaluate, and possible do a mutation within #' @param envir an environment in which the expression is to be evaluated #' #' @return the result of \code{expr} #' @keywords internal allow_mutation <- function(x, expr, envir = parent.frame()) { inc_mutations_count(x) on.exit(dec_mutations_count(x)) expr <- substitute(expr) eval(expr, envir = envir) } #' increment the number of allowed mutations #' #' @param x pkg_ref object to increment mutation counter for #' @return a pkg_ref object #' @keywords internal inc_mutations_count <- function(x) { if (is.null(attr(x, "allowed_mutations"))) attr(x, "allowed_mutations") <- 0 attr(x, "allowed_mutations") <- attr(x, "allowed_mutations") + 1 } #' decrement the number of allowed mutations #' #' @param x pkg_ref object to decrement mutation counter for #' @return pkg_ref object #' @keywords internal dec_mutations_count <- function(x) { attr(x, "allowed_mutations") <- attr(x, "allowed_mutations") - 1 if (attr(x, "allowed_mutations") <= 0) attr(x, "allowed_mutations") <- NULL }

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devtools::load_all() library(fasttime) library(Matrix) ## GLOBAL data_names <- c("Votes", "Postings", "Following") # store data as .fst read_csv_data(user = "tobias", data_names = data_names, save_as_fst = TRUE, return_df = FALSE) # read .fst votes <- read_fst_data(user = "tobias", data_name = "Votes") posts <- read_fst_data(user = "tobias", data_name = "Postings") # combine data df <- create_user_vote_user_data(df_votes = votes, df_posts = posts) ## Prepare Data # map user ids from random ids (199, 30, 5000000,...) to (1, 2, 3, 4...) # date columns to actual dates users_uq <- unique(c(df$ID_PostUser, df$ID_VoteUser)) df1 <- df[, .(ID_Posting = map_id(ID_Posting), ID_PostUser = map_id(ID_PostUser, levels = users_uq), ID_VoteUser = map_id(ID_VoteUser, levels = users_uq), ID_Article = map_id(ID_Article), ArticleChannel = ArticleChannel, VoteCreated = fastdate(VoteCreatedAt), PostingCreated = fastdate(PostingCreatedAt), ArticleCreated = fastdate(ArticlePublishingDate))] ### insights all data cat_("Dimension = ", collapse_(c("Rows:", "Cols:"), dim(df1))) for (i in grep_get("ID_", colnames(df1))) cat_("Unique ", i, ": ", length(unique(df1[[i]]))) head(df1) ### Create Sparse Matrices at each timepoint adj_mat <- create_adj_mat_time(df = df1, time_var = "VoteCreated", x = "ID_VoteUser", y = "ID_PostUser") ### Insights time Points y_n <- sapply(adj_mat, sum) plot(time_points, y_n, main = "Votes Per Day", type = "l") points(time_points, y_n, pch = 19, cex = 1, col = "white") points(time_points, y_n, pch = 19, cex = .4) ### path_temp <- paste0(get_path("tobias"), "adj_mat_inv.rds") B <- store_alpha(A = adj_mat, a = .3, path = path_temp) DCM <- dcm_simple(B = B)

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Funny_String.R

# Enter your code here. Read input from STDIN. Print output to STDOUT library(stringr) nums <- suppressWarnings(readLines(file("stdin"))) nums2<-nums[-1] asc <- function(x) { strtoi(charToRaw(x),16L) } strReverse <- function(x) sapply(lapply(strsplit(x, NULL), rev), paste, collapse="") allSame <- function(x) length(unique(x)) == 5 for(i in 1:length(nums2)) { nums3<-tolower(str_replace_all(as.matrix(nums2[i]), fixed(" "), "")) nums3<-str_replace_all(unlist(strsplit(unlist(nums3),"")),"[^[:alnum:]]", "") nums3<-gsub("[^A-Za-z ]", "", nums3) nums3<-na.omit(nums3) nums4<-length(nums3) rev1<-strReverse(nums2[i]) revs3<-tolower(str_replace_all(as.matrix(rev1), fixed(" "), "")) revs3<-str_replace_all(unlist(strsplit(unlist(revs3),"")),"[^[:alnum:]]", "") revs3<-gsub("[^A-Za-z ]", "", revs3) revs3<-na.omit(revs3) revs4<-length(revs3) # write.table(rev1, sep = " ", append=T, row.names = F, col.names = F,quote = FALSE,) isFunny<-"Funny" for(j in 2:nums4) { nums5<-asc(as.character(nums3[j])) nums6<-asc(as.character(nums3[j-1])) revs5<-asc(as.character(revs3[j])) revs6<-asc(as.character(revs3[j-1])) # write.table(nums5-nums6, sep = " ", append=T, row.names = F, col.names = F,quote = FALSE,) # write.table(revs5-revs6, sep = " ", append=T, row.names = F, col.names = F,quote = FALSE,) if(abs(nums5-nums6)==abs(revs5-revs6)) { funornot<-"Funny" #write.table("Funny", sep = " ", append=T, row.names = F, col.names = F,quote = FALSE,) } else { funornot<-"Not Funny" isFunny<-"Not Funny" break; } } # write.table(isFunny, sep = " ", append=T, row.names = F, col.names = F,quote = FALSE,) write.table(funornot, sep = " ", append=T, row.names = F, col.names = F,quote = FALSE,) }

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/CCM/CCMBaseExperiment.R

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CCMBaseExperiment.R

CCMBaseExperiment <- function(data, mats, E, num_libs, tau=1, num_trials=dim(data)[3], num_samples=100, preprocfn=identity, freq=1, result_path=NULL, data_obs_idx=NULL) { # return: votingMats, est, tableResults save_data <- TRUE if (is.null(result_path)) { save_data <- FALSE } # Make directory to hold results files if one does not already exist if (save_data && !dir.exists(result_path)) { print(sprintf("Result path not found: %s", result_path)) stop() } nvars <- dim(mats)[1] # number of variables / oscillators num_mats <- 1 if (length(dim(mats)) == 3) { num_mats <- dim(mats)[3] # number of matrices we try } if (is.null(data_obs_idx)) { data_obs_idx <- matrix(TRUE, nrow=num_mats, ncol=nvars) } # Tables to hold results table_results_TPR <- numeric(length=num_mats) table_results_FPR <- numeric(length=num_mats) table_results_acc <- numeric(length=num_mats) # est holds CCM's estimate of the networks est <- array(NaN, c(nvars, nvars, num_mats)) ccm_rho_graphs <- array(NaN, c(nvars, nvars, num_libs, num_trials, num_mats)) count <- 1 # number of times we have run network inference method (so know how often to save work) # Loop over the networks for (i in 1:num_mats) { obs_idx <- data_obs_idx[i,] num_obs <- sum(obs_idx) hasMats = length(dim(mats)) == 3 truth <- mats if (hasMats) { truth <- mats[,,i] } num_positives <- sum(truth[obs_idx, obs_idx] & !diag(nvars)) num_negatives <- sum(!truth[obs_idx, obs_idx] & !diag(nvars)) hasTrials = length(dim(data)) >= 3 if (is.list(data)) { preproc_data <- preprocfn(data[[i]][[1]][obs_idx,,]) } else if (!hasTrials && !hasMats) { preproc_data <- preprocfn(data[obs_idx,]) } else if (hasTrials && !hasMats) { preproc_data <- preprocfn(data[obs_idx,,]) } else { preproc_data <- preprocfn(data[obs_idx,,,i]) } time_length <- dim(preproc_data)[2] delta <- floor(time_length/(num_libs + 1)) lib_sizes <- delta * 1:num_libs # Run network inference on this data graphs <- get_ccm_rho(preproc_data, E, lib_sizes, tau, num_trials, num_samples) ccm_rho_graphs[,,,, i] <- graphs # Compute the adjacency matrix predicted by averaging the CCM trials voted_graphs <- apply(graphs, c(1, 2, 3), function(x) mean(na.omit(x))) predMat <- apply(voted_graphs, c(1, 2), function(x) mean(na.omit(x))) > 0.5 diag(predMat) <- 0 est[,, i] <- predMat # Save results table_results_TPR[i] <- sum((est[,, i] + truth == 2) * !diag(nvars)) / num_positives table_results_FPR[i] = sum((est[,, i] - truth == 1) * !diag(nvars)) / num_negatives table_results_acc[i] = sum((est[,, i] == truth) * !diag(nvars)) / (num_obs^2-num_obs) if (save_data && count %% freq == 0) { # save necessary files } count <- count + 1 } # TODO: Save whole workspace (including all those tables of results) if (save_data) { } table_results <- list("tpr"=table_results_TPR, "fpr"=table_results_FPR, "acc"=table_results_acc) result <- list("pred_mats"=est, "graphs"=ccm_rho_graphs, "table_results"=table_results) return(result) }

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sesion000_crearGuia.R

##R --vanilla setwd("~/tmp/Provita") require(foreign) require(gdata) require(vegan) require(xtable) require(rgeos) require(rgdal) require(XML) require(RColorBrewer) hoy <- format(Sys.time(), "%Y%m%d") mi.path <- "~/doc/" mi.path <- "~/Dropbox/Provita/doc/" mi.path <- "~/Provita_JRFP/doc/" mi.dir <- "000_BDRLE" mi.arch <- "Document1_RLE_XMLguide" titulo <- "Report1_RLE_XMLdevelopment" xml.db <- "~/Provita_JRFP/xml/" cdg.doc <- sprintf("Provita.RLE.2017.%s",1) ## colocar código aquí... options(width=80) ##system(sprintf("rm %s.*",mi.arch)) Sweave(file=paste(mi.path,mi.dir,"/",mi.arch,".Rnw",sep=""),eps=F) ##Stangle(file=paste(mi.path,mi.dir,"/",mi.arch,".Rnw",sep="")) tools::texi2dvi(paste(mi.arch,".tex",sep=""), pdf=TRUE) ##system(sprintf("evince %s.pdf &",mi.arch)) ##system(sprintf("mv %s.pdf %s/%s/%s_%s.pdf",mi.arch,mi.path,mi.dir,hoy,titulo))

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make_stan_model.R

## -*- coding: utf-8 -*- #' Creates the SDT stan model code. #' #' \code{make_stan_model} function creates the stan model code #' defining the SDT model with additional regression structure and #' random effects if these are specified using the \code{random} #' argument. See \code{\link{make_stan_data}} for details on how to #' specify a random effects structure. The model is described in detail #' in the paper (TODO reference) #' #' @param random an optional list specifying random effects #' structure. See \code{\link{make_stan_data}} for details. #' @param gamma_link is either 'softmax' (described in the paper), 'log_distance' or 'log_ratio' #' (See the Readme file in the github repository) #' @param metad if TRUE meta-d' model code (only with the softmax gamma link function) is created, #' default is FALSE. #' @return a string containing the full model definition in the stan #' modelling language. #' @examples #' data(gabor) #' model = make_stan_model(list(list(group = ~ id, delta = ~ -1 + duration, gamma = ~ 1))) #' cat(model) #' @export make_stan_model = function(random = NULL, gamma_link = 'softmax', metad = FALSE){ if(!(gamma_link %in% c('softmax', 'log_ratio', 'log_distance'))) stop("The gamma_link function must be one of the following: 'softmax', 'log_ratio', 'log_distance'") model = '' if(!metad){ f = file(paste(path.package('bhsdtr'), '/stan_templates/sdt_template.stan', sep = '')) }else{ f = file(paste(path.package('bhsdtr'), '/stan_templates/metad_template.stan', sep = '')) } ## We go line by line for(part in readLines(f)){ ## If this is part of the random effects' specification ... if(rmatch('//(common|delta|gamma)', part)){ ## ... and there are some random effects in the model ... if(length(random) > 0) for(l in 1:length(random)){ ## ... then add the line read from the template with % replaced by the grouping factor number ... if(rmatch('//common', part)) model[length(model)+1] = gsub('%', l, part) ## ... and do the same with parts of delta/gamma ## random effects' specification if delta/gamma is ## associated with random effects of the given ## grouping factor ... for(par in c('delta', 'gamma')) if(!is.null(random[[l]][[par]]) & rmatch(sprintf('//%s', par), part)) model[length(model)+1] = gsub('%', l, part) } }else if(rmatch('//link-gamma', part)){ ## Replace the gamma link function specification with the ## chosen link function model = c(model, readLines((f2 = file(paste(path.package('bhsdtr'), sprintf('/stan_templates/link_%s.stan', gamma_link), sep = ''))))) close(f2) }else{ ## This line is not about the random effects' specification model[length(model)+1] = part } } close(f) paste(model, collapse = '\n') } rmatch = function (pattern, vector){ res = TRUE for (i in 1:length(vector)) { if (length(grep(pattern, vector[i])) > 0) { res[i] = TRUE } else { res[i] = FALSE } } res }

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funcs.R

g_legend <- function(a.gplot){ tmp <- ggplot_gtable(ggplot_build(a.gplot)) leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box") legend <- tmp$grobs[[leg]] return(legend) } #' wqdat is input data #' station is station character string #' plo_fun <- function(wqdat, station, cols, parms){ # scale ranges rngs <- wqdat %>% group_by(var) %>% summarise( minv = min(val, na.rm = T), maxv = max(val, na.rm = T), .groups = 'keep' ) %>% nest %>% deframe %>% lapply(., unlist) # parameter labels lbs <- parms %>% select(var, lbs) %>% deframe # subset to statio toplo <- wqdat %>% filter(station %in% !!station) %>% spread(var, val) ylbsz <- 13 p1 <- plot_ly(toplo) %>% add_markers(x = ~date, y = ~tp, type = 'scatter', color = I(cols[6]), mode = 'markers', line = list(shape = 'linear'), showlegend = F) %>% layout( yaxis = list(title = lbs['tp'], titlefont = list(size = ylbsz))#, range = rngs$tp) ) p2 <- plot_ly(toplo) %>% add_markers(x = ~date, y = ~tn, type = 'scatter', color = I(cols[5]), mode = 'markers', line = list(shape = 'linear'), showlegend = F) %>% layout( yaxis = list(title = lbs['tn'], titlefont = list(size = ylbsz))#, range = rngs$tn) ) p3 <- plot_ly(toplo) %>% add_markers(x = ~date, y = ~nh34, type = 'scatter', color = I(cols[4]), mode = 'markers', line = list(shape = 'linear'), showlegend = F) %>% layout( yaxis = list(title = lbs['nh34'], titlefont = list(size = ylbsz))#, range = rngs$nh34) ) p4 <- plot_ly(toplo) %>% add_markers(x = ~date, y = ~chla, type = 'scatter', color = I(cols[3]), mode = 'markers', line = list(shape = 'linear'), showlegend = F) %>% layout( yaxis = list(title = lbs['chla'], titlefont = list(size = ylbsz))#, range = rngs$chla) ) p5 <- plot_ly(toplo) %>% add_markers(x = ~date, y = ~ph, type = 'scatter', color = I(cols[2]), mode = 'markers', line = list(shape = 'linear'), showlegend = F) %>% layout( yaxis = list(title = lbs['ph'], titlefont = list(size = ylbsz))#, range = rngs$ph) ) p6 <- plot_ly(toplo) %>% add_markers(x = ~date, y = ~sal, type = 'scatter', color = I(cols[1]), mode = 'markers', line = list(shape = 'linear'), showlegend = F) %>% layout( yaxis = list(title = lbs['sal'], titlefont = list(size = ylbsz))#, range = rngs$sal) ) p <- subplot(p1, p2, p3, p4, p5, p6, nrows = 6, shareX = T, shareY = F, titleY = T) %>% layout( xaxis = list(title = NA, range = c(as.Date('1995-01-01'), as.Date('2022-01-01'))) ) %>% plotly::config( toImageButtonOptions = list( format = "svg", filename = "myplot" ) ) return(p) } # function for plotting rapid response transect data # modified from show_transect in tbpetools show_rstransect <- function(savdat, mcrdat, savsel, mcrsel, base_size = 12){ savlevs <- c('Thalassia testudinum', 'Halodule wrightii', 'Syringodium filiforme', 'Ruppia maritima', 'Halophila engelmannii', 'Halophila decipiens') grplevs <- c('Red', 'Green', 'Brown', 'Cyanobacteria') abulabs <- c('<1%', '1-5%', '6-25%', '26-50%', '51-75%', '76-100%') abubrks <- c(0, 1, 2, 3, 4, 5) colpal <- colorRampPalette(RColorBrewer::brewer.pal(n = 8, name = 'Dark2')) szrng <- c(2, 16) # xlims savxlms <- savdat %>% pull(location) %>% unique %>% sort mcrxlms <- mcrdat %>% pull(location) %>% unique %>% sort xlms <- range(savxlms, mcrxlms) # get dates for factor levels # this makes sure that y values on plots are shared dts1 <- savdat %>% pull(date) dts2 <- mcrdat %>% pull(date) dts <- c(dts1, dts1) %>% unique %>% sort %>% format('%b %d, %Y') # prep sav plot data savdatfrm <- savdat %>% dplyr::mutate( Year = lubridate::year(date), location = as.numeric(as.character(location)), pa = ifelse(bb == 0, 0, 1), date = format(date, '%b %d, %Y'), date = factor(date, levels = dts) ) %>% dplyr::select(Year, date, location, taxa, abundance, pa, bb) # sort color palette so its the same regardless of species selected savcol <- colpal(length(savlevs)) names(savcol) <- savlevs savcol <- savcol[savsel] # legend labels leglab <- 'Abundance (bb)' # data with species toplo1a <- savdatfrm %>% dplyr::filter(taxa %in% !!savsel) %>% dplyr::filter(pa == 1) %>% dplyr::mutate( bb = round(bb, 1), tltp = paste0(taxa, ', ', abundance) ) %>% dplyr::arrange(date, location) # find overplots dups1 <- duplicated(toplo1a[, c('date', 'location')]) dups2 <- duplicated(toplo1a[, c('date', 'location')], fromLast = T) dups <- apply(cbind(dups1, dups2), 1, any) toplo1a <- toplo1a %>% mutate( dups = dups ) %>% group_by(date, location) %>% mutate( location = case_when( dups ~ location + seq(-1 * length(dups) / 3, length(dups) / 3, length.out = length(dups)), T ~ location ) ) %>% ungroup() # data w/o species, no facet toplo2a <- savdatfrm %>% group_by(date, location) %>% filter(sum(pa) == 0) %>% ungroup() %>% select(date, location) %>% unique() pa <- ggplot2::ggplot(toplo1a, ggplot2::aes(y = date, x = location)) + ggplot2::geom_point(data = toplo2a, alpha = 1, colour = 'black', size = 2) + ggplot2::geom_point(aes(size = bb, fill = taxa), alpha = 0.8, pch = 21) + ggplot2::scale_fill_manual(values = savcol) + ggplot2::scale_radius(limits = range(abubrks), labels = abulabs, breaks = abubrks, range = szrng) + ggplot2::theme_minimal(base_size = base_size, base_family = 'Roboto') + ggplot2::scale_y_discrete(limits = dts, breaks = dts) + ggplot2::scale_x_continuous(breaks = savxlms) + ggplot2::coord_cartesian(xlim = xlms) + ggplot2::theme( panel.grid.major.y = ggplot2::element_blank(), panel.grid.minor.y = ggplot2::element_blank(), panel.grid.minor.x = ggplot2::element_blank(), legend.title = ggplot2::element_blank(), strip.text = ggplot2::element_text(hjust = 0), axis.title.y = element_blank(), axis.title.x = element_blank() ) + ggplot2::labs( x = 'Transect distance (m)', title = 'Submerged aquatic vegetation' ) + guides(fill = guide_legend(override.aes = list(size = 7), order = 1)) # prep mcr plot data mcrdatfrm <- mcrdat %>% dplyr::mutate( Year = lubridate::year(date), location = as.numeric(as.character(location)), pa = ifelse(bb == 0, 0, 1), date = format(date, '%b %d, %Y'), date = factor(date, levels = dts) ) %>% dplyr::select(Year, date, location, taxa, abundance, pa, bb) # sort color palette so its the same regardless of species selected mcrcol <- c('tomato1', 'lightgreen', 'burlywood3', 'lightblue') names(mcrcol) <- grplevs mcrcol <- mcrcol[mcrsel] # legend labels leglab <- 'Abundance (bb)' # data with species toplo1b <- mcrdatfrm %>% dplyr::filter(taxa %in% mcrsel) %>% dplyr::filter(pa == 1) %>% dplyr::mutate( bb = round(bb, 1), tltp = paste0(taxa, ', ', abundance) ) # jitter duplicates dups1 <- duplicated(toplo1b[, c('date', 'location')]) dups2 <- duplicated(toplo1b[, c('date', 'location')], fromLast = T) dups <- apply(cbind(dups1, dups2), 1, any) toplo1b <- toplo1b %>% mutate( dups = dups ) %>% group_by(date, location) %>% mutate( location = case_when( dups ~ location + seq(-1 * length(dups) / 3, length(dups) / 3, length.out = length(dups)), T ~ location ) ) %>% ungroup() # data w/o species, no facet toplo2b <- mcrdatfrm %>% group_by(date, location) %>% filter(sum(pa) == 0) %>% ungroup() %>% select(date, location) %>% unique() pb <- ggplot2::ggplot(toplo1b, ggplot2::aes(y = date, x = location)) + ggplot2::geom_point(data = toplo2b, colour = 'black', alpha = 1, size = 2) + ggplot2::geom_point(inherit.aes = F, aes(colour = 'Empty sample'), x = NA, y = NA) + ggplot2::geom_point(aes(size = bb, fill = taxa), alpha = 0.8, pch = 21) + ggplot2::scale_fill_manual(values = mcrcol) + ggplot2::scale_colour_manual(values = 'black') + ggplot2::scale_radius(limits = range(abubrks), labels = abulabs, breaks = abubrks, range = szrng, guide = F) + ggplot2::theme_minimal(base_size = base_size, base_family = 'Roboto') + ggplot2::scale_y_discrete(limits = dts, breaks = dts) + ggplot2::scale_x_continuous(breaks = mcrxlms) + ggplot2::coord_cartesian(xlim = xlms) + ggplot2::theme( panel.grid.major.y = ggplot2::element_blank(), panel.grid.minor.y = ggplot2::element_blank(), panel.grid.minor.x = ggplot2::element_blank(), legend.title = ggplot2::element_blank(), strip.text = ggplot2::element_text(hjust = 0), axis.title.y = element_blank() ) + ggplot2::labs( x = 'Transect distance (m)', title = 'Macroalgae' ) + guides( fill = guide_legend(override.aes = list(size = 7), order = 1), colour = guide_legend(override.aes = list(size = 2)) ) # out p <- pa + pb + plot_layout(ncol = 1, heights = c(0.9, 1), guides = 'collect') return(p) } # function for creating popup plot with mapview click rswqpopup_plo <- function(station, datin){ # monitoring data toplo1 <- datin %>% filter(station == !!station) %>% mutate( minrng = gsub('(^.*)\\-.*$', '\\1', nrmrng), maxrng = gsub('^.*\\-(.*)$', '\\1', nrmrng) ) %>% select(-nrmrng) %>% mutate( minrng = as.numeric(minrng), maxrng = as.numeric(maxrng) ) # reference data toplo2 <- toplo1 %>% select(date, minrng, maxrng) %>% mutate( mo = lubridate::month(date) ) %>% group_by(mo) %>% mutate( datestr = floor_date(date, unit = 'month'), dateend = ceiling_date(date, unit = 'month') ) %>% ungroup %>% select(-date) %>% unique ylb <- unique(toplo1$lbs) minrng <- unique(toplo1$minrng) maxrng <- unique(toplo1$maxrng) ylm <- range(c(minrng, maxrng, toplo1$val), na.rm = TRUE) out <- ggplot() + geom_line(data = toplo1, aes(x = date, y = val)) + geom_point(data = toplo1, aes(x = date, y = val), size = 2) + geom_rect(data = toplo2, aes(xmin = datestr, xmax = dateend, ymin = minrng, ymax = maxrng, fill = 'Monthly normal range', group = mo), alpha = 0.2) + coord_cartesian(ylim = ylm, xlim = range(toplo1$date)) + scale_fill_manual(values = 'blue') + theme_minimal(base_size = 18) + theme( legend.title = element_blank(), axis.title.x = element_blank(), legend.position = 'top' ) + labs( y = ylb, subtitle = paste('Station', station) ) return(out) } # estimate TBBI, used in dat_proc, source code from tbeptools tbbi_fun <- function(datin, salin){ taxdat <- datin %>% select(date = Date, station = StationNumber, FAMILY, NAME, TaxaCount = Count, AdjCount) %>% filter(FAMILY != 'NULL') %>% mutate( date = as.Date(date) ) # taxa counts aggregated by station & taxa list id taxasums <- taxdat %>% dplyr::group_by(station, FAMILY, NAME) %>% dplyr::summarise( SumofCount = sum(TaxaCount, na.rm = T), SumofAdjCount = sum(AdjCount, na.rm = T), .groups = 'drop' ) # biology stats aggregated by station biostats <- taxasums %>% dplyr::group_by(station) %>% dplyr::summarise( SpeciesRichness = length(na.omit(NAME)), RawCountAbundance = sum(SumofCount, na.rm = T), AdjCountAbundance = sum(SumofAdjCount, na.rm = T), .groups = 'drop' ) spionid <- taxasums %>% dplyr::filter(FAMILY %in% 'Spionidae') %>% dplyr::group_by(station) %>% dplyr::summarise(SpionidAbundance = sum(SumofAdjCount, na.rm = T), .groups = 'drop') capitellid <- taxasums %>% dplyr::filter(FAMILY %in% 'Capitellidae') %>% dplyr::group_by(station) %>% dplyr::summarise(CapitellidAbundance = sum(SumofAdjCount, na.rm = T), .groups = 'drop') # calculate biology populations/abundance by station biostatspopulation <- biostats %>% dplyr::left_join(spionid, by = 'station') %>% dplyr::left_join(capitellid, by = 'station') %>% dplyr::mutate( Salinity = salin, StandPropLnSpecies = dplyr::case_when( is.na(SpeciesRichness) | is.na(Salinity) ~ 0, T ~ ((log(SpeciesRichness + 1) / log(10)) / ((( 3.2983 - 0.23576 * Salinity ) + 0.01081 * Salinity^2) - 0.00015327 * Salinity^3) - 0.84227 ) / 0.18952 ), SpeciesRichness = ifelse(is.na(SpeciesRichness), 0, SpeciesRichness), RawCountAbundance = ifelse(is.na(RawCountAbundance), 0, RawCountAbundance), TotalAbundance = ifelse(is.na(AdjCountAbundance), 0, AdjCountAbundance), SpionidAbundance = ifelse(is.na(SpionidAbundance), 0, SpionidAbundance), CapitellidAbundance = ifelse(is.na(CapitellidAbundance), 0, CapitellidAbundance) ) %>% dplyr::select(station, SpeciesRichness, RawCountAbundance, TotalAbundance, SpionidAbundance, CapitellidAbundance, StandPropLnSpecies) biostatstbbi <- biostatspopulation %>% dplyr::mutate( TBBI = dplyr::case_when( CapitellidAbundance == 0 & SpionidAbundance != 0 ~ (((-0.11407) + (StandPropLnSpecies * 0.32583 ) + (((asin(SpionidAbundance / TotalAbundance) - 0.11646 ) / (0.18554)) * (-0.1502)) + ((-0.51401) * (-0.60943))) - (-3.3252118)) / (0.7578544 + 3.3252118), CapitellidAbundance != 0 & SpionidAbundance == 0 ~ (((-0.11407) + (StandPropLnSpecies * 0.32583) + ((-0.62768) * (-0.1502)) + ((( asin( CapitellidAbundance / TotalAbundance) - 0.041249) / 0.08025) * (-0.60943))) - (-3.3252118)) / (0.7578544 + 3.3252118), CapitellidAbundance == 0 & SpionidAbundance == 0 & TotalAbundance != 0 ~ (((-0.11407) + (StandPropLnSpecies * 0.32583) + ((-0.62768) * (-0.1502)) + ((-0.51401) * (-0.60943))) - (-3.3252118)) / ( 0.7578544 + 3.3252118), TotalAbundance == 0 ~ 0, T ~ ((( -0.11407) + (StandPropLnSpecies * 0.32583) + (((asin(SpionidAbundance / TotalAbundance) - 0.11646) / 0.18554) * (-0.1502)) + (((asin( CapitellidAbundance / TotalAbundance) - 0.041249) / 0.08025) * (-0.60943))) - (-3.3252118)) / (0.7578544 + 3.3252118) ), TBBI = round(100 * TBBI, 2) ) %>% dplyr::select(station, TotalAbundance, SpeciesRichness, TBBI) %>% dplyr::filter(!is.na(station)) dts <- taxdat %>% select(station, date) %>% unique # family sums taxfams <- taxdat %>% group_by(station, FAMILY) %>% summarise( TotalAbundance = sum(AdjCount, na.rm = T), .groups = 'drop' ) %>% group_by(station) %>% nest() %>% rename(family_abu = data) # total abundance is individuals/m2 out <- biostatstbbi %>% dplyr::mutate( TBBICat = dplyr::case_when( TBBI == 0 ~ 'Empty Sample', TBBI < 73 ~ 'Degraded', TBBI >= 73 & TBBI < 87 ~ 'Intermediate', TBBI >= 87 ~ 'Healthy', T ~ NA_character_ ), col = dplyr::case_when( TBBICat == 'Empty Sample' ~ 'grey', TBBICat == 'Degraded' ~ 'red', TBBICat == 'Intermediate' ~ 'yellow', TBBICat == 'Healthy' ~ 'darkgreen', ) ) %>% left_join(dts, ., by = 'station') %>% mutate( modt = paste0(as.character(month(date, abbr = F, label = T)), ' 2021') ) %>% left_join(taxfams, by = 'station') return(out) } #' Convert DO into % saturation for 1-m depth #' Use convention of expressing saturation at 1 atm. #' data(sfbay) #' #' @param do, dissolved oxygen mg/l #' @param t tem temperature, degrees C #' @param S salinity, on the Practical Salinity Scale #' @param P pressure, atm DOsat <- function (do, t, S, P = NULL) { T = t + 273.15 lnCstar = -139.34411 + 157570.1/T - 66423080/T^2 + 1.2438e+10/T^3 - 862194900000/T^4 - S * (0.017674 - 10.754/T + 2140.7/T^2) Cstar1 <- exp(lnCstar) if (is.null(P)) { out <- Cstar1 } else { Pwv = (1 - 0.000537 * S) * exp(18.1973 * (1 - 373.16/T) + 3.1813e-07 * (1 - exp(26.1205 * (1 - T/373.16))) - 0.018726 * (1 - exp(8.03945 * (1 - 373.16/T))) + 5.02802 * log(373.16/T)) theta = 0.000975 - 1.426e-05 * t + 6.436e-08 * t^2 out <- Cstar1 * P * (1 - Pwv/P) * (1 - theta * P)/((1 - Pwv) * (1 - theta)) } out <- 100 * do / Cstar1 return(out) }

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/man/Bonferroni_m.Rd

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no_license

alexmarzel/svisits

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9b6f13e251a3eb443096a772331447b8b7874035

refs/heads/master

2020-05-25T23:25:32.143389

2017-01-22T17:37:00

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2016-07-15T13:55:59

2016-07-14T16:45:09

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Bonferroni_m.Rd

\name{Bonferroni_m} \alias{Bonferroni_m} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Detecting the pairs using Bonferroni correction } \description{ This method is much faster than the shuffling method, but less precise. } \usage{ Bonferroni_m(unadjusted_pairs, ids=ids, prob = 1/75, alpha = 0.01, only_significant = TRUE) } \arguments{ \item{unadjusted_pairs}{ a \code{"table"} object containing the pairs and the number of shared visits. See \code{\link{get_observed_pairs}}. } \item{ids}{a \code{"data.table"} object with columns "ids" and "N_visits". The "ids" column represents patient identifiers and is the reference (see \code{setkey} from package \code{data.table} for more details). The "N_visits" contains the number of visits for each patient.} \item{prob}{ probability of sharing a single visit. Default \code{1/75}. } \item{alpha}{ type I error } \item{only_significant}{ returns only significant pairs above the threshold (default) or all the pairs} } \details{ } \value{a data frame with \describe{ \item{\code{"allPairs"}}{pair identifier} \item{\code{"Freq"}}{the unadjusted number of shared visits} \item{\code{"id_1"}}{identifier of the first pair member} \item{\code{"id_2"}}{identifier of the second pair member} \item{\code{"N_visits.x"}}{total number of visits of the first pair member} \item{\code{"N_visits.y"}}{total number of visits of the second pair member} \item{\code{"Prob_for_Bonferr"}}{probability of sharing the given number of shared visits} \item{\code{"BP"}}{row number} \item{\code{"ltp"}}{the minus log10-transformed probablity of sharing the given number shared visits} } } \examples{ # load data data("simulated_data") db_dates <- prepare_db(your_database = simulated_data, ids_column = "subject", dates_column = "sim_dates") # first get unadjusted pairs unadjusted_observed_pairs <- get_observed_pairs(db_dates) # prepare ids ids <- data.table(ids = as.character(names(table(db_dates$subject))), N_visits = as.character(as.numeric(table(db_dates$subject)))) setkey(ids, "ids") # run Bonferroni_m_output <- Bonferroni_m(unadjusted_observed_pairs, ids = ids, prob = 1/75, alpha = 0.01) # number of significant pairs nrow(Bonferroni_m_output) }

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tkachenia/multimedia_course

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base.R

hexAbc <- function(str_abc, prefix = "") { abc <- strsplit(str_abc, "")[[1]] abc_utf8 <- enc2utf8(abc) hex <- sapply(abc_utf8, charToRaw, simplify = TRUE) hex_str <- paste0(prefix, toupper(hex)) utf8 <- sapply(hex_str, as.character) dim(utf8) <- dim(hex) if (!is.null(dim(utf8))) utf8 <- apply(utf8, 2, paste, collapse = "\t") names(utf8) <- abc utf8 } hexStr <- function(str, prefix = "") { str_utf8 <- enc2utf8(str) hex <- charToRaw(str_utf8) utf8 <- paste0(prefix, toupper(hex)) paste(utf8, collapse = " ") } hexInt <- function(int, bytesCount = 4, endian = c("little", "big"), prefix = "") { bits <- intToBits(int) bytes <- packBits(bits, type = "raw") bytes <- as.character(bytes) if (length(bytes) > bytesCount) { bytes <- bytes[1:bytesCount] } else { bytes <- c(bytes, rep("00", bytesCount - length(bytes))) } bytes <- paste0(prefix, toupper(bytes)) if (endian[1] == "big") { bytes <- rev(bytes) } paste(bytes, collapse = " ") } makeField <- function(values = c(""), ...) { if ( is.character(values[1]) ) { field <- sapply(values, hexStr) } else { field <- sapply(values, hexInt, ...) } names(field) <- values list(field) }

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adamleerich/alr3-aa1f43f9

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residual.plots.Rd

\name{residual.plots} \alias{residual.plots} \alias{residual.plots.lm} \alias{resplot} \alias{resid.curv.test} \alias{tukey.nonadd.test} \title{Residual plots and curvature tests for linear model fits} \description{ Plots the residuals versus each term in a mean function and versus fitted values. Also computes a curvature test for each of the plots by adding a quadratic term and testing the quadratic to be zero. This is Tukey's test for nonadditivity when plotting against fitted values. } \usage{ ### This is a generic function with only one required argument: residual.plots (m, ...) ### When the first argument is a linear model (of class lm), the form of the ### function is \method{residual.plots}{lm}(m,vars=~.,fitted=TRUE,plot=TRUE, layout=NULL,ask,...) ### The following are three related functions: resplot(m,varname="tukey",type="pearson", plot=TRUE,add.quadratic=TRUE, ylab=paste(string.capitalize(type),"Residuals"),...) resid.curv.test(m,varname) tukey.nonadd.test(m) } \arguments{ \item{m}{ \code{lm} regression object } \item{vars}{ A one-sided formula that specifies a subset of the predictors. One residual plot is drawn for each column specified. The default \code{~.} is to plot against all predictors. For example, the specification \code{vars = ~.-X3} would plot against all predictors except for \code{X3}.} \item{fitted}{If TRUE, the default, plot against fitted values.} \item{tukey}{If TRUE, draw plot of residuals versus fitted values and compute Tukey's test of non-additivity.} \item{layout}{ If set to a value like \code{c(1,1)} or \code{c(4,3)}, the layout of the graph will have this many rows and columns. If not set, the program will select an appropriate layout. If the number of graphs exceed nine, you must select the layout yourself, or you will get a maximum of nine per page.} \item{ask}{If TRUE, ask the user before drawing the next plot; FALSE if don't ask.} \item{\dots}{\code{residual.plots} passes these arguments to \code{resplot}. \code{resplot} passes them to \code{plot}. } \item{varname}{Quoted variable name for the horizontal axis, \code{"tukey"} by default for Tukey's test and the plot versus fitted values.} \item{type}{Type of residuals to be used. Pearson residuals are appropriate for \code{lm} objects since there are equivalent to ordinary residuals with ols and correctly weighted residuals with wls.} \item{ylab}{Label for the yaxis. The default is the residual type.} \item{add.quadratic}{if TRUE, fits the quadratic regression of the vertical axis on the horizontal axis.} \item{plot}{If TRUE, draw the plot(s).} } \details{ \code{residual.plots} draws all residuals plots, versus each term specified first-order term in the model (interactions are automatically skipped) and versus fitted values, and returns all the curvature tests. \code{resplot}, which is called by \code{residual.plots}, should be viewed as an internal function, and is included here to display its arguments, which can be used with \code{residual.plots} as well. \code{resid.curv.test} computes the curvature test only. For any factors a boxplot will be drawn. } \value{ Returns a data.frame with one row for each plot drawn, one column for the curvature test statistic, and a second column for the corresponding p-value. This function is used primarily for its side effect of drawing residual plots. } \references{S. Weisberg (2005), \emph{Applied Linear Regression}, third edition, Wiley, Chapter 8} \author{Sanford Weisberg, \email{sandy@stat.umn.edu}} \seealso{See Also \code{\link{lm}}} \examples{ data(highway) highway$Sigs <- (round(highway$Sigs*highway$Len)+1)/highway$Len attach(highway) d <- data.frame(Rate=Rate,logLen=logb(Len,2), logADT=logb(ADT,2),logTrks=logb(Trks,2), Slim=Slim,Shld=Shld,logSigs1=logb(Sigs,2)) attach(d) m2 <- lm(Rate~logLen+logADT+logTrks+Slim+Shld+logSigs1,d) residual.plots(m2) } \keyword{ hplot }% at least one, from doc/KEYWORDS \keyword{ regression }% __ONLY ONE__ keyword per line

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rodmel/ExData_Plotting1

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plot2.R

## Exploratory Data Analysis ## version 1.0 ## By Rodrigo Melgar - July 2014 ##================================================================## ## Household energy usage over 2-day period in February 2007 ## ## Based on Individual household electric power consumption data ## ##================================================================## ## Step 1: use the data.table function "fread" to quickly read the large file # and filter only those required data (Dates within Feb.1-2, 2007). ##---------------------------------------------------------------------------- library(data.table) # save to "tmp" the subset data tmp <- fread("household_power_consumption.txt", sep=";" , header=TRUE, colClasses="character" , na.strings=c("?","NA") )[Date=="1/2/2007" | Date=="2/2/2007"] # create the data by correctly formatting the "tmp" subset data # Note: Date and Time are combined as one variable DateTime using as.POSIXct data <- data.table( DateTime=as.POSIXct(strptime( paste(tmp$Date,tmp$Time) , format="%d/%m/%Y %H:%M:%S")) ,Global_active_power = as.numeric(tmp$Global_active_power) ) rm(tmp) ## release the memory used by "tmp" as it is no longer needed setkey(data, DateTime) ## create DateTime as the Key of the Data Table ## Step 2: extract data points to be plotted ##---------------------------------------------------------------------------- points = as.data.frame( data[, list(DateTime, Global_active_power)]) rm(data) ## tidy up unwanted object ## Step 3: Create the graph to png file ##---------------------------------------------------------------------------- # open device png(filename="plot2.png", width=480, height=480, bg="transparent") # plot and annotate the graph plot( points$DateTime, points$Global_active_power, type="n" ,xlab="", ylab="Global Active Power (kilowatts)") lines(points$DateTime,points$Global_active_power, col="black") # close the device dev.off() rm(points) ## tidy up unwanted object

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/man/nl_reverse.Rd

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cran/nlgeocoder

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2020-03-31T10:13:44.864225

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nl_reverse.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/nl_reverse.R \name{nl_reverse} \alias{nl_reverse} \title{experimental reverse api} \usage{ nl_reverse() } \description{ experimental reverse api }

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KWB-R/aquanes.report

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test-function-calculate_operational_parameters_berlin_t.R

# # This test file has been generated by kwb.test::create_test_files() # test_that("calculate_operational_parameters_berlin_t() works", { expect_error(aquanes.report:::calculate_operational_parameters_berlin_t()) })

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OMahoneyM/GGBN_sample_query

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refs/heads/main

2023-03-13T21:24:55.633404

2021-03-11T19:01:13

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scratch.R

# needed to run fromJSON(), library('jsonlite') # needed to run flatten() library('purrr') #library('tidyverse') # needed to run RCurl() library('RCurl') # needed to run rbind.fill() library('plyr') # needed to run the progress_bar library('progress') # needed to run str_replace_all() library('stringr') # needed to run write_tsv() library('readr') # The base URL used to query the API base <- "http://data.ggbn.org/ggbn_portal/api/search?getSampletype&name=" # Load in the data containing the taxonomy to query data <- read.csv('GP_kozloff_edits.csv', header = TRUE, sep = ",") # Remove the header from "data" names(data) <- NULL # Extract species from "data" and store it as a vector "taxa" ## Note the double brackets "[[]]" in "data[[2]]" as it extracts the column ## from the data frame and returns it as a vector. You could also write it as ## "data[,2]" and get the same result. Simply writing "data[2]" returns a ## a sublist of the "data" with the class data.frame. test <- c("Hermissenda crassicornis", "Arthropoda", "Polychaeta","Cnidaria", "Annelida", "Echinodermata","Nudibranchia", "Asteroidea", "Nemertea","Nematoda", "Mollusca", "Copepoda") taxa <- data[[2]] # trims off leading and trailing whitespace taxa <- trimws(taxa) # Create an empty data frame to add the query output to result <- data.frame() # Create new instance of the progress bar with ETA pb <- progress_bar$new( format = " downloading [:bar] :percent eta: :eta", total = length(taxa), clear = FALSE, width= 60) # For loop that builds URL, makes GET request, and uses rbind.fill # to append "request" variable to the data frame "result" # the flatten() function allows null results to be coerced into dataframes for (i in 1:length(taxa)) { # Initiate progress bar with ETA pb$tick(0) pb$tick() # Query GGBN API and store flattened JSON response in request request <- flatten(fromJSON(getURL(URLencode(paste(base,taxa[i], sep = ""))))) # use rbind.fill to append each request to the result dataframe and # autopopulate the null values with NA result <- rbind.fill(result, as.data.frame(request)) } # Remove "fullScientificName_nc=" from the species query result$filters <- str_replace_all(result$filters, "^.*=", "") # write "result" to TSV write_tsv(result, 'GGBN_Query_results_Complete.tsv', na = "NA") # DONE ################################################################################ # --------------------- FUNCTIONIZE THE SCRIPT ABOVE --------------------------# ################################################################################ # turn above into two functions. one function for the loop # and the second function for the data cleanup GGBN_query <- function(taxa) { # The base URL used to query the API base <- "http://data.ggbn.org/ggbn_portal/api/search?getSampletype&name=" # Create an empty data frame to add the query output to result <- data.frame() # Create new instance of the progress bar with ETA pb <- progress_bar$new( format = " downloading [:bar] :percent eta: :eta", total = length(taxa), clear = FALSE, width= 60) # For loop that builds URL, makes GET request, and uses rbind.fill # to append "request" variable to the data frame "result" # the flatten() function allows null results to be coerced into dataframes for (i in 1:length(taxa)) { # Initiate progress bar with ETA pb$tick(0) pb$tick() # Query GGBN API and store flattened JSON response in request request <- base %>% paste0(taxa[i]) %>% URLencode() %>% getURL() %>% fromJSON() %>% flatten() # use rbind.fill to append each request to the result dataframe and # autopopulate the null values with NA result <- request %>% as.data.frame() %>% rbind.fill(result) } return(result) } df <- GGBN_query(test) # DONE ################################################################################ # --------------------------- USING SAPPLY ------------------------------------# ################################################################################ # Turning the above code into a function that uses lapply instead of a for loop # Load in the data containing the taxonomy to query data <- read.csv('GP_kozloff_edits.csv', header = TRUE, sep = ",") # The base URL used to query the API base <- "http://data.ggbn.org/ggbn_portal/api/search?getSampletype&name=" # Extract species from "data" and store it as a vector "taxa" ## Note the double brackets "[[]]" in "data[[2]]" as it extracts the column ## from the data frame and returns it as a vector. You could also write it as ## "data[,2]" and get the same result. Simply writing "data[2]" returns a ## a sublist of the "data" with the class data.frame. # trims off leading and trailing whitespace with trimws() taxa_list <- data[[2]] %>% trimws() # Create new instance of the progress bar with ETA pb <- progress_bar$new( format = " downloading [:bar] :percent eta: :eta", total = length(taxa_list), clear = FALSE, width= 60) # function that queries GGBN for available sample type data GGBN_query <- function(taxa) { # Initiate progress bar with ETA pb$tick(0) # Update progress bar each time the function is run pb$tick() # Query GGBN API and store flattened JSON response in request request <- base %>% paste0(taxa) %>% URLencode() %>% getURL() %>% fromJSON() %>% flatten() %>% as.data.frame() } # Run sapply() to query each species in the taxa list using GGBN_query() df <- taxa_list %>% sapply(FUN = GGBN_query) # Convert df from a list of lists to a data.frame df <- do.call(rbind.fill, df) # Remove "fullScientificName_nc=" from the species query df$filters <- str_replace_all(df$filters, "^.*=", "") # write "result" to TSV write_tsv(df, 'GGBN_Query_results_Complete.tsv', na = "NA") # DONE ################################################################################ # -----------------If/else check on doc type and header -----------------------# ################################################################################ # writing function to check if data inputed was csv, tsv/txt # also check is data has a header or not wrapper_test <- function(data_file = NULL, head = NULL, column = NULL) { # if else check for null values. if (is.null(data_file) & is.null(head) & is.null(column)) { print("You didn't enter any arguments... Are you even trying to use this function?") } else if (is.null(data_file) & is.null(head)) { print("data_file argument and head argument are both NULL Enter the correct values for both to proceed with function") } else if (is.null(data_file) & is.null(column)) { print("data_file argument and column argument are both NULL Enter the correct values for both to proceed with function") } else if (is.null(head) & is.null(column)) { print("head argument and column argument are both NULL Enter the correct values for both to proceed with function") } else if (is.null(data_file)) { print("data_file argument is NULL Please supply appropriate data file") } else if (is.null(head)) { print("head argument is NULL Please indicate the presence of table header with TRUE or FALSE") } else if (is.null(column)) { print("column argument is NULL Please indicate the header name or numeric column position of the taxonomic names to be queried") } else { if (data_file == 1) { data <- data.frame(x = 1:4, y = 5:8, z = 9:12) } else if (data_file == 2) { data <- "neatwo" } else { print("Hello. We have been trying to reach you about your car's extended warranty") } if (column == 1) { taxa_list <- data[[column]] %>% trimws() } else { print("Please enter TRUE or FALSE for the header argument and check the spelling of the column argument") } } } wrapper_test() wrapper_test(column = 1) wrapper_test(head = TRUE) wrapper_test(data_file = 1) wrapper_test(head = TRUE, column = 1) wrapper_test(data_file = 1, column = 1) wrapper_test(data_file = 1, head = TRUE) x <- wrapper_test(data_file = 1, head = TRUE, column = 1) # Load in the data containing the taxonomy to query data <- read.csv('GP_kozloff_edits_test.csv', header = TRUE, sep = ",") column <- "scientificname_verbatim" data4 <- data[[2]] test_df <- data.frame(uno = 1:4, test_header = 5:8) test <- "test_header" test2 <- 2 head <- "poo" is.logical(head) if (!is.logical(head)) { print("do it right") } else { print("cool") } if (grepl("^.*\\.tsv|^.*\\.txt", test) == TRUE) { print("success") } else { print("Hello. We have been trying to reach you about your car's extended warranty") } if (grepl("^.*\\.csv", data_file) == TRUE) { data <- read.table(file = data_file, header = head, sep = ",") } else if (grepl("^.*\\.tsv|^.*\\.txt", data_file) == TRUE) { data <- read.table(file = data_file, header = head, sep = "\t") } else { print("Incorrect data format. Please load .csv, .tsv, or .txt file") } is.character(test2) is.integer(test2) test2 %% 1 == 0 is.numeric() if (test %in% names(test_df) | is.numeric(column)) { zoop <- "pass" } else { print("Please enter TRUE or FALSE for the header argument and check the spelling of the column argument") } if (head == FALSE & is.character(test) == TRUE) { print("You entered FALSE for the header argument and a string for the column argument. Please check your file again and re-enter a vaild combination") } else { print("Please enter TRUE or FALSE for the header argument and check the spelling of your column argument") } # if column string is found in data_file colnames | column is int grepl(column, colnames(data_file)) == TRUE | is.integer(column) == TRUE # else if head == FALSE and column is string # --------------------------------------------------------------------------- # QC'd if statements to check if entered arguments are valid # --------------------------------------------------------------------------- if (grepl("^.*\\.csv", data_file) == TRUE) { data <- read.table(file = data_file, header = head, sep = ",") } else if (grepl("^.*\\.tsv|^.*\\.txt", data_file) == TRUE) { data <- read.table(file = data_file, header = head, sep = "\t") } else { print("Incorrect data format. Please load .csv, .tsv, or .txt file") } if (column %in% names(data_file) || is.numeric(column)) { taxa_list <- data[[column]] %>% trimws() } else if (head == FALSE && is.character(column) == TRUE) { print("You entered FALSE for the header argument and a string for the column argument. Please check your file again and re-enter a vaild combination") } else { print("Please enter TRUE or FALSE for the header argument and check the spelling of the column argument") } ################################################################################ ################# PUTTING EVERYTHING TOGETHER FOR REAL ######################### ################################################################################ wrapper_test <- function(data_file = NULL, head = NULL, column = NULL) { # Validation check for NULL values function arguments if (is.null(data_file) & is.null(head) & is.null(column)) { print("You didn't enter any arguments... Are you even trying to use this function?") } else if (is.null(data_file) & is.null(head)) { print("data_file argument and head argument are both NULL Enter the correct values for both to proceed with function") } else if (is.null(data_file) & is.null(column)) { print("data_file argument and column argument are both NULL Enter the correct values for both to proceed with function") } else if (is.null(head) & is.null(column)) { print("head argument and column argument are both NULL Enter the correct values for both to proceed with function") } else if (is.null(data_file)) { print("data_file argument is NULL Please supply appropriate data file") } else if (is.null(head)) { print("head argument is NULL Please indicate the presence of table header with TRUE or FALSE") } else if (is.null(column)) { print("column argument is NULL Please indicate the header name or numeric column position of the taxonomic names to be queried") } else { # Validation check on file type for data_file argument and Boolean entry for # head argument. If pass the file is loaded as a data frame if (!is.logical(head)) { print("head argument was provided in incorrect format. Please enter either TRUE or FALSE") } else if (grepl("^.*\\.csv", data_file)) { data <- read.table(file = data_file, header = head, sep = ",") } else if (grepl("^.*\\.tsv|^.*\\.txt", data_file)) { data <- read.table(file = data_file, header = head, sep = "\t") } else { print("Incorrect data format. Please load .csv, .tsv, or .txt file") } # Validation check that column value can be found in header values if it is # a string or if it is a numeric value instead. If pass the taxa column is # extracted, white space is trimmed, and stored as taxa_list vector if (column %in% names(data_file) || is.numeric(column)) { taxa_list <- data[[column]] %>% trimws() # Create new instance of the progress bar with ETA pb <- progress_bar$new( format = " downloading [:bar] :percent eta: :eta", total = length(taxa_list), clear = FALSE, width= 60) # The base URL used to query the API base <- "http://data.ggbn.org/ggbn_portal/api/search?getSampletype&name=" # function that queries GGBN for available sample type data GGBN_query <- function(taxa) { # Initiate progress bar with ETA pb$tick(0) # Update progress bar each time the function is run pb$tick() # Query GGBN API and store flattened JSON response in request request <- base %>% paste0(taxa) %>% URLencode() %>% getURL() %>% fromJSON() %>% flatten() %>% as.data.frame() } # Run sapply() to query each species in the taxa list using GGBN_query() df <- taxa_list %>% sapply(FUN = GGBN_query) # Convert df from a list of lists to a data.frame df <- do.call(rbind.fill, df) # Remove "fullScientificName_nc=" from the species query df$filters <- str_replace_all(df$filters, "^.*=", "") return(df) } else if (head == FALSE && is.character(column) == TRUE) { print("You entered FALSE for the head argument and a string for the column argument. Please check your file again and re-enter a vaild combination") } else { print("Please enter TRUE or FALSE for the head argument and check the spelling of the column argument") } } } df <- wrapper_test(data_file = 'GP_kozloff_edits_test.csv', head = TRUE, column = 2) # write "result" to TSV write_tsv(df, 'GGBN_Query_results_Complete.tsv', na = "NA")

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library(HWEintrinsic) ### Name: HWEdata-class ### Title: Class "HWEdata". Data specification for the Hardy-Weinberg ### Testing Problem Using the Intrinsic Prior Approach. ### Aliases: HWEdata-class HWEdata initialize,HWEdata-method ### Keywords: classes methods ### ** Examples data.tmp <- c(3, 9, 8) dataset <- new("HWEdata", data = data.tmp)

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##Plot 3 - shows total PM2.5 emission for each of the years (1999,2002,2005,2008) in baltimore broken down by source #get required library library(ggplot2) #set working directory setwd("../Exploratory Data Analysis/Project 2") #import data from PM2.5 emissions RDS file NEI <- readRDS("summarySCC_PM25.rds") #import data from source classification code table RDS file SCC <- readRDS("Source_Classification_Code.rds") #Subset baltimore only data (fips=24510) baltimore_Subset <- subset(NEI, fips == "24510") #Use aggregate function to find emission for 1999,2002,2005, and 2008 using baltimore subset of the data broken down by source data_TotalEmissions <- aggregate(baltimore_Subset[c("Emissions")], list(type=baltimore_Subset$type, year=baltimore_Subset$year), sum) #create the plot utilizing ggplot2 qplot method. Using smoother (method loess) to smooth connections between points png("plot3.png") chart <- qplot(year, Emissions, data = data_TotalEmissions, geom=c("point","smooth"), method="loess", colour=type, ylab= "Emissions (PM 2.5)", xlab= "Year", main= "Total Emissions for Baltimore by Type") print(chart) dev.off()

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get_county_pop.R

tidycensus::census_api_key("34ee793ce93ee686718193c8f108cb8de08dfc91") # create table of county demographics options(tigris_use_cache = TRUE) #county_map <- tigris::counties(class = "sf", cb = TRUE, resolution = "20m") county_pop <- tidycensus::get_acs( geography = "county", variables = "B01003_001", geometry = TRUE, resolution = "5m" ) #county_pop <- sf::st_transform(county_pop, crs = 2163 ) state_map <- tigris::states( cb = TRUE, resolution = "5m", class = "sf" ) save( county_pop, state_map, file = "stco_maps.RData" )

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stat-identity.r

#' Identity statistic. #' #' The identity statistic leaves the data unchanged. #' #' @param mapping The aesthetic mapping, usually constructed with #' \code{\link{aes}} or \code{\link{aes_string}}. Only needs to be set #' at the layer level if you are overriding the plot defaults. #' @param data A layer specific dataset - only needed if you want to override #' the plot defaults. #' @param geom The geometric object to use display the data #' @param position The position adjustment to use for overlapping points #' on this layer #' @param show.legend logical. Should this layer be included in the legends? #' \code{NA}, the default, includes if any aesthetics are mapped. #' \code{FALSE} never includes, and \code{TRUE} always includes. #' @param inherit.aes If \code{FALSE}, overrides the default aesthetics, #' rather than combining with them. This is most useful for helper functions #' that define both data and aesthetics and shouldn't inherit behaviour from #' the default plot specification, e.g. \code{\link{borders}}. #' @param ... other arguments passed on to \code{\link{layer}}. This can #' include aesthetics whose values you want to set, not map. See #' \code{\link{layer}} for more details. #' @export #' @examples #' p <- ggplot(mtcars, aes(wt, mpg)) #' p + stat_identity() stat_identity <- function(mapping = NULL, data = NULL, geom = "point", position = "identity", ..., show.legend = NA, inherit.aes = TRUE) { layer( data = data, mapping = mapping, stat = StatIdentity, geom = geom, position = position, show.legend = show.legend, inherit.aes = inherit.aes, params = list( na.rm = FALSE, ... ) ) } #' @rdname ggplot2-ggproto #' @format NULL #' @usage NULL #' @export StatIdentity <- ggproto("StatIdentity", Stat, compute_layer = function(data, scales, params) { data } )

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superSeq_summary.R

#! /usr/bin/env Rscript library(subSeq) library(DESeq2) library(dplyr) library(qvalue) library(data.table) args <- commandArgs(trailingOnly = TRUE) experiment <- args[1] contrast <- args[2] presample = as.numeric(args[3]) outfolder = args[4] infolder = args[5] trial = as.numeric(args[6]) load(paste0(infolder, "/", experiment, "_", contrast, "_", trial, ".rda")) ss_out <- summary(ss) ss_out$experiment <- experiment ss_out$contrast <- contrast ss_out$presample <- presample ss_out$replication <- trial write.table(ss_out, file = paste0(outfolder, "/", experiment, "_", contrast, "_", trial, ".summary"))

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filter.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/filter.livereg.R \name{filter} \alias{filter} \title{Filter CSO Live Register data} \usage{ filter(object, gender = c("both", "male", "female"), age.group = c("all", "under_25", "over_and_25"), start.year = 1967, end.year = 2018) } \arguments{ \item{object}{\code{livereg} S3 object} \item{gender}{a character vector of gender choices: "both", "male", "female"} \item{age.group}{a character vector of age group choices: "all", "under_25", "over_and_25"} \item{start.year}{the start year of the data} \item{end.year}{the end year of the data} } \value{ A filtered \code{livereg} S3 object which contain the live register unemployment data based on the input filtering parameters. } \description{ \code{filter} filters the CSO Live Register unemployment data based on certain input options. These options are gender, age group, start year, and end year. } \examples{ dat = load_livereg(use.offline.data = TRUE) the_1990s_data = filter(dat, start.year = 1990, end.year = 1999) female_data = filter(dat, gender = c("female")) }

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parse_taxonomy_silva_128.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/phyloseq.R \name{parse_taxonomy_silva_128} \alias{parse_taxonomy_silva_128} \title{Parse elements of a taxonomy vector} \usage{ parse_taxonomy_silva_128(char.vec) } \arguments{ \item{char.vec}{(Required). A single character vector of taxonomic ranks for a single OTU, unprocessed (ugly).} } \value{ A character vector in which each element is a different taxonomic rank of the same OTU, and each element name is the name of the rank level. For example, an element might be \code{"Firmicutes"} and named \code{"phylum"}. These parsed, named versions of the taxonomic vector should reflect embedded information, naming conventions, desired length limits, etc; or in the case of \code{\link{parse_taxonomy_default}}, not modified at all and given dummy rank names to each element. } \description{ These are provided as both example and default functions for parsing a character vector of taxonomic rank information for a single taxa. As default functions, these are intended for cases where the data adheres to the naming convention used by greengenes the naming convention used by greengenes and silva. (\url{http://greengenes.lbl.gov/cgi-bin/nph-index.cgi}) or where the convention is unknown, respectively. To work, these functions -- and any similar custom function you may want to create and use -- must take as input a single character vector of taxonomic ranks for a single OTU, and return a \strong{named} character vector that has been modified appropriately (according to known naming conventions, desired length limits, etc. The length (number of elements) of the output named vector does \strong{not} need to be equal to the input, which is useful for the cases where the source data files have extra meaningless elements that should probably be removed, like the ubiquitous ``Root'' element often found in greengenes/QIIME taxonomy labels. In the case of \code{parse_taxonomy_default}, no naming convention is assumed and so dummy rank names are added to the vector. More usefully if your taxonomy data is based on greengenes, the \code{parse_taxonomy_greengenes} function clips the first 3 characters that identify the rank, and uses these to name the corresponding element according to the appropriate taxonomic rank name used by greengenes (e.g. \code{"p__"} at the beginning of an element means that element is the name of the phylum to which this OTU belongs). If you taxonomy data is based on SILVA, the \code{parse_taxonomy_silva_128} function clips the first 5 characters that identify rank, and uses these to name the corresponding element according to the appropriate taxonomic rank name used by SILVA (e.g. \code{"D_1__"} at the beginning of an element means that element is the name of the phylum to which this OTU belongs. Alternatively you can create your own function to parse this data. Most importantly, the expectations for these functions described above make them compatible to use during data import, specifically the \code{\link{import_biom}} function, but it is a flexible structure that will be implemented soon for all phyloseq import functions that deal with taxonomy (e.g. \code{\link{import_qiime}}). } \details{ This function is currently under PR review by phyloseq in a well supported pull request: \url{https://github.com/joey711/phyloseq/pull/854}. If you use this function, then please comment on the GitHub PR to encourage merging this feature. } \examples{ \dontrun{ > taxvec1 = c("Root", "k__Bacteria", "p__Firmicutes", "c__Bacilli", "o__Bacillales", "f__Staphylococcaceae") > parse_taxonomy_default(taxvec1) > parse_taxonomy_greengenes(taxvec1) > taxvec2 = c("Root;k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacillales;f__Staphylococcaceae") > parse_taxonomy_qiime(taxvec2) > taxvec3 = c("D_0__Bacteria", "D_1__Firmicutes", "D_2__Bacilli", "D_3__Staphylococcaceae") > parse_taxonomy_silva_128(taxvec3) } } \seealso{ \code{\link[phyloseq:parseTaxonomy-functions]{parse_taxonomy_default}} \code{\link[phyloseq:parseTaxonomy-functions]{parse_taxonomy_greengenes}} \code{\link[phyloseq:parseTaxonomy-functions]{parse_taxonomy_qiime}} \code{\link[phyloseq]{import_biom}} \code{\link[phyloseq]{import_qiime}} }

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# program spuRs/resources/scripts/binom.sim.r binom.sim <- function(n, p) { X <- 0 px <- (1-p)^n Fx <- px U <- runif(1) while (Fx < U) { X <- X + 1 px <- px*p/(1-p)*(n-X+1)/X Fx <- Fx + px } return(X) }

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#' Round value based on magnitude #' #' @param x A numeric value to be rounded. Values >= 100 are rounded to 1 #' decimal place. Values < 100 and >=1 are rounded to two decimal places. #' Values <1 are rounded to three decimal places. #' #' @return A numeric value rounded based on the magnitude of `x` #' @export #' #' @examples #' library(curios) #' # Note: R will display right padded zero's below. This is a display manifestation only. #' roundx_n(c(0.1234, 1.1234, 100.123)) #' # Verify rounding does not add right hand padding #' roundx_n(c(0.1234, 1.1234, 100.123))[3] roundx_n <- function(x) { dplyr::case_when(abs(x) >= 100 ~ round(x, 1), abs(x) >= 1 ~ round(x, 2), TRUE ~ round(x, 3)) }

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#Assignment 3: Incarceration trends # Loading data ----------------------------------------------------------------- # Install and load the packages library(tidyverse) install.packages("maps") install.packages("mapproj") install.packages("patchwork") library(maps) library(mapproj) library(patchwork) # Load incarceration_trends.csv file incarceration_trends <- read.csv( "https://raw.githubusercontent.com/vera-institute/incarceration-trends/master/incarceration_trends.csv") # Variable: Black Jail Population ---------------------------------------------- #What is the average value of black jail population across all the counties most #recently? `recent_average_black_jail_pop` recent_average_black_jail_pop <- incarceration_trends %>% filter(year == max(year, na.rm = T)) %>% summarize(mean_black_jail = mean(black_jail_pop, na.rm = T)) # When is the highest black jail population from the dataset? #`year_highest_black_jail_pop` year_highest_black_jail_pop <- incarceration_trends %>% filter(black_jail_pop == max(black_jail_pop, na.rm = T)) %>% pull(year) # Where is the highest black jail population from the dataset? #`place_highest_black_jail_pop` place_highest_black_jail_pop <- incarceration_trends %>% filter(black_jail_pop == max(black_jail_pop, na.rm = T)) %>% select(state, county_name) #Top 5 unique places have highest black jail population? `top_5_places` top_5_places <- incarceration_trends %>% group_by(county_name) %>% summarize(total_in_each_county = sum(black_jail_pop, na.rm = T)) %>% arrange(desc(total_in_each_county)) %>% slice(1:5) top_5_chart <- top_5_places %>% left_join(incarceration_trends, by = "county_name", na.rm = T) %>% filter(black_jail_pop != "NA") # What is total black jail population in Washington in the most recent year? #`total_wa` total_wa <- incarceration_trends %>% filter(state == "WA") %>% filter(year == max(year, na.rm = T)) %>% summarize(total_black_jail = sum(black_jail_pop, na.rm = T)) # Trends over time chart ------------------------------------------------------ black_jail_pop_over_time_in_top_5 <- ggplot(data = top_5_chart) + geom_point(mapping = aes(x = year, y = black_jail_pop, color = county_name)) + geom_smooth(mapping = aes(x = year, y = black_jail_pop, color = county_name), se = FALSE) + labs(x = "Year", y = "Black Jail Population", title = "Top 5 Counties Has Highest Black Jail Population Over Time") + scale_color_discrete("County Name") # Variable comparison charts-------------------------------------------------- #Make a data frame for black and white jail pop? `two variables data` two_variables_data <- data.frame(incarceration_trends$black_jail_pop, incarceration_trends$white_jail_pop) #Graph the two variables comparison chart? `variables_comparison_chart` variable_comparison_chart <- ggplot(data = two_variables_data) + geom_point(mapping = aes(x = incarceration_trends.black_jail_pop, y = incarceration_trends.white_jail_pop)) + labs(x = "Black Jail Pop", y = "White Jail Pop", title = "Comparison between Black Jail Population and White Jail Population in All Counties") #Map ------------------------------------------------------------------------- #Get the `black_jail_pop` most recent data from the dataset? `black_jail_data` black_jail_data <- incarceration_trends %>% filter(year == max(year, na.rm = T)) #Use map_data function to join `incarceration_trends` dataset with the map_data? # `join_map` join_map <- map_data("county") %>% unite(polyname, region, subregion, sep = ",") %>% left_join(county.fips, by = "polyname") #Merge map data and incarceration data?`merge_map` merge_map <- join_map %>% left_join(black_jail_data, by = "fips") %>% filter(black_jail_pop != "NA") # Incorporation blank theme blank_theme <- theme_bw() + theme( axis.line = element_blank(), # remove axis lines axis.text = element_blank(), # remove axis labels axis.ticks = element_blank(), # remove axis ticks axis.title = element_blank(), # remove axis titles plot.background = element_blank(), # remove gray background panel.grid.major = element_blank(), # remove major grid lines panel.grid.minor = element_blank(), # remove minor grid lines panel.border = element_blank() # remove border around plot ) # Create map `black_jail_map` black_jail_map <- ggplot(merge_map) + geom_polygon( mapping = aes(x = long, y = lat, group = group, fill = black_jail_pop), color = "gray", size = 0.3) + coord_map() + scale_fill_continuous(limits = c(0, max(merge_map$black_jail_pop)), na.value = "white", low = "yellow", high = "red") + blank_theme + ggtitle("Black Jail Population in the U.S")

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% Generated by roxygen2 (4.0.1): do not edit by hand \name{extract_tags} \alias{extract_tags} \title{Extract the tags of an org file.} \usage{ extract_tags(x, inherit = TRUE) } \arguments{ \item{x}{org object as character vector.} \item{inherit}{logical; if \code{TRUE} (default), inherit tags from level one headlines.} } \value{ the tags of an orgfile as a character vector. } \description{ This function is used to extract the tags matching the regexp pattern. } \details{ If there is more than one tag for a headline, they will be bundled in one character object separated by one space character. The headlines without any tag will give a \code{NA} value, effectively resulting in a vector of as many elements as the number of headlines in the org file. If the \code{inherit} parameter is set to TRUE (default), the function will make use of tag inheritance given by the org-mode variable \code{org-use-tag-inheritance}. So far, the inheritance only works for level one tags. } \examples{ system.file("extdata", "sample.org", package = "orgclockr") \%>\% readLines() \%>\% extract_tags(inherit = FALSE) ## [1] "TagOne" "TagTwo" NA ## [4] NA "TagThree" "TagTwo" ## [7] "TagTwo" "TagOne TagThree TagTwo" NA ## [10] NA NA NA }

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Pop Mod code(1).R

# It's good practice to start your code with a line that clears R's memory: rm(list=ls()) # 2. Simple population models Years <- 100 # sets the duration of the simulation R <- 0.2 # sets the population’s growth rate N <- c(10,rep(0,Years-1)) # defines a vector to store the population sizes (an initial # size of 10, followed by 0 repeated for every other year) for (t in 2:Years) N[t] <- N[t-1]+N[t-1]*R # loops through the remaining years of the simulation, # calculating the new population size each time plot(1:Years,N, bty= "l", xlab= "Year", ylab= "Population size", type= "l") Years <- 100 # sets the duration of the simulation R <- 3.0 # sets the population’s maximum growth rate K <- 100 # sets the carrying capacity N <- c(10,rep(0,Years-1)) # defines a vector to store the population sizes (an initial # size of 10, followed by 0 repeated for every other year) for (t in 2:Years) N[t] <- N[t-1]+N[t-1]*R*(1 - N[t-1]/K) # loops through the remaining years of the # simulation, calculating the new # population size each time plot(1:Years,N, bty= "l", xlab= "Year", ylab= "Population size", type= "l") # 3. The effect of growth rate Rs <- seq(1.5,3,0.01) Years <- 250 # sets the duration of the simulation K <- 100 # sets the carrying capacity stable.sizes <- lapply(Rs,function(R){ N <- c(10,rep(0,Years-1)) # defines a vector to store the population sizes (an initial # size of 10, followed by 0 repeated for every other year) for (t in 2:Years) N[t] <- N[t-1]+N[t-1]*R*(1 - N[t-1]/K) # loops through the remaining years of the # simulation, calculating the new # population size each time sizes <- N[(Years-99):Years] # record only the last 100 years' worth of population sizes return(data.frame(PGR=R,Pop.sizes=sizes)) # store those population sizes as a data frame # within the "stable.sizes" object; the first column contains the population growth rate }) stable.sizes <- do.call('rbind',stable.sizes) # bind all the data frames together as a single object stable.sizes$colour <- 'red' # make another column in the data frame, with the word 'red' in each row stable.sizes$colour[stable.sizes$Pop.sizes < K] <- 'blue' # change the colour column to be 'blue' for all # rows in which the population sizes are less than the carrying capacity par(las=1) # change the graphical parameter that controls axis tick-mark label orientation # and plot the graph: plot(stable.sizes$PGR,stable.sizes$Pop.sizes,cex=0.3,pch=19,col=stable.sizes$colour,xlab='Population growth rate',ylab='Stable sizes') # 4. Matrices, simulations and population characteristics rm(list=ls()) setwd("C:/My documents/R") # or whatever represents a good directory for you to work in M <- matrix(c(0,0.024,0,52,0.08,0.25,279.5,0,0.43),nrow=3) N <- c(10,10,10) # note that this can also be written N <- rep(10,3) N <- M%*%N # NB. Matrix multiplication is sensitive to order, unlike normal # multiplication (i.e. M%*%N ≠ N%*%M) # set the duration of the simulation Years <- 15 # define the transition matrix M <- matrix(c(0,0.024,0,52,0.08,0.25,279.5,0,0.43),nrow=3) # define a matrix with 3 rows and ‘Years’ columns to store population sizes. Make the first 3 # numbers (to fill the first column) 10 (the initial population sizes) and all subsequent numbers # zero (because we’ll calculate what those numbers should be in subsequent steps) N <- matrix( c(rep(10,3), rep(0,(Years-1)*3) ), nrow=3) # compute population sizes for (t in 2:Years) N[,t] <- M%*%N[,t-1] # plot the outcome par(las=1) plot(c(0,Years),c(1,max(N)),xlab="Time",ylab="Number",type="n",log="y",bty="l") cols=c("black","brown","darkgreen") for (L in 1:3) lines(1:Years,N[L,],col=cols[L],lwd=3) # add a legend labs <- c("Pre-juveniles","Juveniles","Adults") legend( x=0, # an x coordinate for the legend’s top left corner y=3000000, # a y coordinate for the legend’s top left corner legend=labs, # tells R that the text for the legend is in the vector ‘labs’ lty=1, lwd=3, # specifies the line characteristics col=cols, # tells R that the colours for the legend are in the vector ‘cols’ box.lty=0) # suppresses a surrounding box lambda.est = sum(N[,Years])/sum(N[,Years-1]) round(lambda.est,2) # reduces the number of decimal places to 2 SSD.est = N[,Years]/sum(N[,Years]) round(SSD.est,3) Final <- numeric(3) # this is one way to declare an empty vector of numbers for (s in 1:3) { N[,1] <- c(0,0,0) N[s,1] = 1 for (t in 2:Years) N[,t] = M%*%N[,t-1] Final[s] = sum(N[,Years]) } RV.est <- Final/Final[1] round(RV.est,2) # 5. Population characteristics and matrix properties lambda.true <- eigen(M)$values[1] ( lambda.true <- round(Re(eigen(M)$values[1]),2) ) SSD.true <- eigen(M)$vectors[,1]/sum(eigen(M)$vectors[,1]) ( SSD.true <- round(Re(SSD.true),3) ) RV.true <- eigen(t(M))$vectors[,1] RV.true <- Re(RV.true/RV.true[1]) # this makes all reproductive values relative to that of the 1st stage class round(RV.true,3) # 6. Sensitivities, elasticities and other uses of matrix models M <- matrix(c(0,0.024,0,52,0.08,0.25,279.5,0,0.43),nrow=3) # the matrix w <- eigen(M)$vectors[,1]/sum(eigen(M)$vectors[,1]) # the SSD v <- eigen(t(M))$vectors[,1]/eigen(t(M))$vectors[1,1] # reproductive values s = 3 # number of life stages # Summed product of RV and SSD for each stage class: v.w <- sum(v*w) # Now calculate sensitivity for each matrix element: sensitivity <- matrix(nrow=s,ncol=s,0) for (i in 1:s) for (j in 1:s) sensitivity[i,j] = v[i]*w[j]/v.w ( sensitivity <- round(Re(sensitivity),4) ) # Elasticities can then be computed by: Lambda <- eigen(M)$values[1] elasticity <- sensitivity * M / Lambda ( elasticity <- round(Re(elasticity),4) )

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LM_Model_for_edr.R

#### LM Model for edr (Presinct Level) #### Ziyi Lu # Load Packages library(tidyverse) library(janitor) library(skimr) library(readxl) # Load data data <- readRDS("data/processed/competition_data.rds") %>% select(-pctname,-signatures,-county,-date) #total = signatures + ab_mb data$mailballot <- ifelse(data$mailballot =="Yes", 1, 0) data <- mutate_all(data, funs(as.numeric)) data2018 <- readRDS("data/processed/county_dat_2018.rds") %>% rename(countycode = COUNTYCODE) voter2018_reg7am <- readRDS("data/processed/voter2018.rds") #%>% select_if(~sum(!is.na(.)) > 0) data_2018 <- voter2018_reg7am %>% left_join(data2018, by = c("countycode")) #### Presinct Level Data df2010 <- data %>% filter(year == 2010) df2012 <- data %>% filter(year == 2012) df2014 <- data %>% filter(year == 2014) df2016 <- data %>% filter(year == 2016) # LM Model with all FRED variables mod_2010 <- df2010 %>% lm(edr~reg7am+age_med+assoc_deg+buildings+burd_house+ commute+discon_yth+eq_sprime+hisp_latin+ homeowner+house_singp+inc_ineq+income_med+ income_percap+income_person+perc_pop_u18+pop_est+ pop_perc+pop_res+pop_u18+poverty+poverty_u18+prec_rel_chil_518+ privat_ests+race_white+racical_dissim+rel_chil_518+undergrad+unemployment,data = .) mod_2012 <- df2012 %>% lm(edr~reg7am+age_med+assoc_deg+buildings+burd_house+ commute+discon_yth+eq_sprime+hisp_latin+ homeowner+house_singp+inc_ineq+income_med+ income_percap+income_person+perc_pop_u18+pop_est+ pop_perc+pop_res+pop_u18+poverty+poverty_u18+prec_rel_chil_518+ privat_ests+race_white+racical_dissim+rel_chil_518+undergrad+unemployment,data = .) mod_2014 <- df2014 %>% lm(edr~reg7am+age_med+assoc_deg+buildings+burd_house+ commute+discon_yth+eq_sprime+hisp_latin+ homeowner+house_singp+inc_ineq+income_med+ income_percap+income_person+perc_pop_u18+pop_est+ pop_perc+pop_res+pop_u18+poverty+poverty_u18+prec_rel_chil_518+ privat_ests+race_white+racical_dissim+rel_chil_518+undergrad+unemployment,data = .) mod_2016 <- df2016 %>% lm(edr~reg7am+age_med+assoc_deg+buildings+burd_house+ commute+discon_yth+eq_sprime+hisp_latin+ homeowner+house_singp+inc_ineq+income_med+ income_percap+income_person+perc_pop_u18+pop_est+ pop_perc+pop_res+pop_u18+poverty+poverty_u18+prec_rel_chil_518+ privat_ests+race_white+racical_dissim+rel_chil_518+undergrad+unemployment,data = .) lm_coef_2010 <- as.matrix(mod_2010$coefficients) lm_coef_2012 <- as.matrix(mod_2012$coefficients) lm_coef_2014 <- as.matrix(mod_2014$coefficients) lm_coef_2016 <- as.matrix(mod_2016$coefficients) lm_coef <- (1/3)*lm_coef_2014 + (1/3)*lm_coef_2010 + (1/6)*lm_coef_2016 + (1/6)*lm_coef_2012 test_data <- data_2018 %>% ungroup() %>% select(reg7am,age_med,assoc_deg,buildings,burd_house, commute,discon_yth,eq_sprime,hisp_latin, homeowner,house_singp,inc_ineq,income_med, income_percap,income_person,perc_pop_u18,pop_est, pop_perc,pop_res,pop_u18,poverty,poverty_u18,prec_rel_chil_518, privat_ests,race_white,racical_dissim,rel_chil_518,undergrad,unemployment) test_data <- as.matrix(data.frame(1, test_data)) edr_hat <- as.vector(test_data %*% lm_coef) edr_hat <- ifelse(edr_hat>0,edr_hat,0) voter2018_edr_lm <- voter2018_reg7am %>% add_column(edr=edr_hat) saveRDS(voter2018_edr_lm,"data/processed/voter2018_edr_lm.rds")

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plot4.R

FileUrl <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip" Dest_1 <- "Elec_UCI.zip" if(!file.exists(Dest_1)){download.file(FileUrl,destfile = Dest_1, method = "curl")} if(file.exists(Dest_1)){unzip(Dest_1)} library(grDevices) #loading data data_full <- read.csv("household_power_consumption.txt", header=T, sep=';', na.strings="?", nrows=2075259, check.names=F, stringsAsFactors=F, comment.char="", quote='\"') data_1 <- subset(data_full, Date %in% c("1/2/2007","2/2/2007")) data_1$Date <- as.Date(data_1$Date, format="%d/%m/%Y") datetime <- paste(as.Date(data_1$Date), data_1$Time) data_1$Datetime <- as.POSIXct(datetime) #plot 4 par(mfcol = c(2,2)) "Subgraph1" plot(data_1$Datetime, data_1$Global_active_power, xlab = "date", ylab = "Global Active Power(kilowatts)", type = "l") "Subgraph2" with(data_1, {plot(data_1$Sub_metering_1~data_1$Datetime, col = "black", xlab = "", ylab = "Energy Sub Metering", type = "l") lines(Sub_metering_2~Datetime, col ="red") lines(Sub_metering_3~Datetime, col = "blue") legend("topright", col = c("black","red","blue"), lty = 1, lwd = 2, legend = c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3")) }) "Subgraph3" plot(data_1$Voltage~data_1$Datetime, type = "l", xlab = "datetime", ylab = "Voltage") "Subgraph4" plot(data_1$Global_reactive_power~data_1$Datetime, type = "l", xlab = "datetime", ylab = "Global Reactive Power") dev.copy(png, file="plot4.png", height=480, width=480) dev.off()

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/R/metaROC.R

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metaROC.R

metaROC <- function(data, ...) { UseMethod("metaROC") } metaROC.default <- function(data, Ni=1000, model=c("fixed-effects", "random-effects"), plot.Author=FALSE, plot.bands=TRUE, plot.inter.var=FALSE, cex.Author=0.7, lwd.Author=12, col.curve='blue', col.bands='light blue', alpha.trans=0.5, col.border='blue', ...){ # Check if input arguments are correct model <- match.arg(model) if(!is.numeric(Ni) || length(Ni)!=1 || Ni%%1!=0 || Ni <= 0){ stop("Ni should be a positive integer.") } if(!is.numeric(alpha.trans) || length(alpha.trans)!=1 || alpha.trans < 0 || alpha.trans > 1){ stop("alpha.trans should be a number in the unit interval.") } if(!is.data.frame(data) || sum(names(data)=="Author")!=1 || sum(names(data)=="TP")!=1 || sum(names(data)=="TN")!=1 || sum(names(data)=="FP")!=1 || sum(names(data)=="FN")!=1){ stop("data should be a data frame with at least 5 variables called 'Author', 'TP', 'TN', 'FP' and 'FN'.") } n <- data$n <- data$TP+data$FN m <- data$m <- data$FP+data$TN data$tpr <- data$TP/n data$fpr <- data$FP/m S <- unique(data$Author) cat("Number of papers included in meta-analysis:", length(S), '\n', sep=' ') sapply(S, function(i){ data.S <- data[data$Author==i,] n.S <- dim(data.S)[1] if(!identical(data.S$n, rep(data.S$n[1], n.S))){ stop("The number of positives does not coincide for some Author. Check it.") }else if(!identical(data.S$m, rep(data.S$m[1], n.S))){ stop("The number of negatives does not coincide for some Author. Check it.") } }) # Plot pairs (False-Positive Rate, True-Positive Rate) for each Author if(plot.Author){ plot(data$fpr, data$tpr, xlim=c(0,1), ylim=c(0,1), lwd=lwd.Author, pch=1, col='gray', xlab="False-Positive Rate", ylab="True-Positive Rate", cex.lab=1.5, main=paste("ROC curve (",model," model)", sep=''), ...) axis(1, at=seq(0,1,0.01), labels=F, tck=-0.01) axis(1, at=seq(0,1,0.1), labels=F, tck=-0.02) axis(2, at=seq(0,1,0.01), labels=F, tck=-0.01) axis(2, at=seq(0,1,0.1), labels=F, tck=-0.02) } index.ord <- order(data$Author, data$fpr, data$tpr) data <- data[index.ord,] n.points <- dim(data)[1] t <- seq(0,1,length=Ni) roc.j <- sapply(S, function(j){ f <- approxfun(c(0,data$fpr[data$Author==j],1), c(0,data$tpr[data$Author==j],1), ties="ordered") # If there exist a point (1,Se) with Se<1, it will not be considered if(plot.Author){lines(c(0,t,1), c(0,f(t),1), col='gray')} f(t)}) if(plot.Author){ for (i in 1:n.points) { text(data$fpr[i],data$tpr[i],data$Author[i],cex=cex.Author) } } # Variance of fixed-effects model ROC estimate (for each Author) der.roc.j <- matrix(1, Ni, length(S)) size.j <- sapply(S, function(j){ M <- m[min(which(data$Author==j))] N <- n[min(which(data$Author==j))] c(M,N)}) M <- matrix(rep(size.j[1,],Ni),Ni,length(S),byrow=TRUE) N <- matrix(rep(size.j[2,],Ni),Ni,length(S),byrow=TRUE) T <- matrix(rep(t,length(S)),Ni,length(S)) var.j <- (der.roc.j)^2*T*(1-T)/M + (roc.j)*(1-roc.j)/N var.j[1,] <- var.j[2,] # To avoid infinite weights (var.j=0 iff t=0 or t=1) var.j[Ni,] <- var.j[(Ni-1),] w.j <- 1/var.j W <- apply(w.j, 1, sum) cat("Model considered:", model, '\n', sep=' ') # ROC curve estimate (fixed-effects model) RA.fem <- apply(w.j*roc.j, 1, sum)/W sRA.fem <- RA.fem for(i in 1:(Ni-1)){sRA.fem[i+1] <- max(sRA.fem[i],sRA.fem[i+1])} # Plot ROC curve estimate if(model=="fixed-effects"){ se.RA.fem <- W^(-1/2) if(!plot.Author){ plot(t, sRA.fem, 'l', xlim=c(0,1), ylim=c(0,1), lwd=1, col=col.curve, xlab="False-Positive Rate", ylab="True-Positive Rate", cex.lab=1.5, main=paste("ROC curve (",model," model)", sep=''), ...) abline(0,1,col='gray', lty=2) axis(1, at=seq(0,1,0.01), labels=F, tck=-0.01) axis(1, at=seq(0,1,0.1), labels=F, tck=-0.02) axis(2, at=seq(0,1,0.01), labels=F, tck=-0.01) axis(2, at=seq(0,1,0.1), labels=F, tck=-0.02) abline(0, 1, col='gray') } RA <- RA.fem; sRA <- sRA.fem; se.RA <- se.RA.fem }else if(model=="random-effects"){ inter.var <- apply(w.j*(roc.j - RA.fem)^2, 1, sum)/W # RA.fem is considered to compute tau^2 inter.var[1] <- inter.var[2] inter.var[Ni] <- inter.var[Ni-1] w.j.rem <- 1/(var.j + inter.var) W.rem <- apply(w.j.rem, 1, sum) # ROC curve estimate (random-effects model) RA.rem <- apply(w.j.rem*roc.j, 1, sum)/W.rem sRA.rem <- RA.rem for(i in 1:(Ni-1)){sRA.rem[i+1] <- max(sRA.rem[i],sRA.rem[i+1])} se.RA.rem <- (apply((w.j.rem)^2*var.j, 1, sum)/(W.rem^2))^(1/2) if(!plot.Author){ plot(t, sRA.rem, 'l', xlim=c(0,1), ylim=c(0,1), lwd=1, col=col.curve, xlab="False-Positive Rate", ylab="True-Positive Rate", cex.lab=1.5, main=paste("ROC curve (",model," model)", sep=''), ...) abline(0, 1, col='gray', lty=2) axis(1, at=seq(0,1,0.01), labels=F, tck=-0.01) axis(1, at=seq(0,1,0.1), labels=F, tck=-0.02) axis(2, at=seq(0,1,0.01), labels=F, tck=-0.01) axis(2, at=seq(0,1,0.1), labels=F, tck=-0.02) abline(0, 1, col='gray') } RA <- RA.rem; sRA <- sRA.rem; se.RA <- se.RA.rem } makeTransparent <- function(Color, alpha=255){ newColor <- col2rgb(Color) apply(newColor, 2, function(col){ rgb(red=col[1], green=col[2], blue=col[3], alpha=alpha, maxColorValue=255) }) } # Plot ROC interval confidence intervals if(plot.bands){ polygon(c(t,rev(t)),c(sRA-1.96*se.RA,rev(sRA+1.96*se.RA)),col=makeTransparent(col.bands,alpha.trans*255), border=makeTransparent(col.border,alpha.trans*255)) } lines(c(0,t,1), c(0,sRA,1), lwd=2, col=col.curve) abline(0, 1, col='gray', lty=2) # Area under the curve estimate (AUC) auc <- mean(sRA[-1]+sRA[-Ni])/2 text(0.8, 0.1, paste("AUC =", round(auc,3))) cat("The area under the summary ROC curve (AUC) is ", round(auc,3),".\n", sep="") # Youden index ind.youden <- which.max(1-t+sRA) youden.index <- c(1-t[ind.youden], sRA[ind.youden]) cat("The optimal specificity and sensitivity (in the Youden index sense) for summary ROC curve are ", round(youden.index[1],3)," and ", round(youden.index[2],3),", respectively.\n", sep="") # Plot inter-study variability estimate if(model=="random-effects" && plot.inter.var){ dev.new() par(mar=c(6,5,4,2)) plot(t, inter.var, 'l', lwd=2, xlab="t", ylab=expression(tau[M]^2~(t)), cex.lab=1.2, main="Inter-study variability") } results <- list(data=data, t=t, sRA=sRA, RA=RA, se.RA=se.RA, youden.index=youden.index, auc=auc, roc.j=roc.j, w.j=w.j, model=model) if(model=="random-effects"){ results <- list(data=data, t=t, sRA=sRA, RA=RA, se.RA=se.RA, youden.index=youden.index, auc=auc, roc.j=roc.j, w.j=w.j, w.j.rem=w.j.rem, inter.var=inter.var, model=model) } results }

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/data/genthat_extracted_code/ramps/examples/corRExp2.Rd.R

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surayaaramli/typeRrh

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corRExp2.Rd.R

library(ramps) ### Name: corRExp2 ### Title: Non-Separable Exponential Spatio-Temporal Correlation Structure ### Aliases: corRExp2 ### Keywords: models ### ** Examples sp1 <- corRExp2(form = ~ x + y + t) spatDat <- data.frame(x = (0:4)/4, y = (0:4)/4, t=(0:4)/4) cs1Exp <- corRExp2(c(1, 1, 1), form = ~ x + y + t) cs1Exp <- Initialize(cs1Exp, spatDat) corMatrix(cs1Exp) cs2Exp <- corRExp2(c(1, 1, 1), form = ~ x + y + t, metric = "man") cs2Exp <- Initialize(cs2Exp, spatDat) corMatrix(cs2Exp)

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/Leaflet.R

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ChanningC12/Developing-Data-Products

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refs/heads/master

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Leaflet.R

# Leaflet: Javascript libraries for creating interactive map install.packages("leaflet") library(leaflet) my_map = leaflet() %>% # pipe notation in R %>%, equals my_map = addTiles(leaflet()) addTiles() my_map # Adding Markers my_map = my_map %>% addMarkers(lat=39.2980803, lng=-76.5898801, popup="Jeff Leek's Office") my_map # Adding many markers set.seed(2016-11-20) df = data.frame(lat=runif(20,min=39.2,max=39.2), lng=runif(20,min=-76.6,max=-76.5)) df %>% leaflet() %>% addTiles() %>% addMarkers() # Making Custom Markers hopkinsIcon = makeIcon( iconUrl = "http://brand.jhu.edu/content/uploads/2014/06/university.shield.small_.blue_.png", iconWidth = 31*215*230, iconHeight=31, iconAnchorX=31*215/230/2, iconAnchorY=16 ) hopkinsLatLong = data.frame(lat=c(39.2973166,39.3288851,39.2906617,39.2970681,39.2824806), lng=c(-76.5929798,-76.6206598,-76.5469683,-76.6150537,-76.6016766)) hopkinsLatLong %>% leaflet() %>% addTiles() %>% addMarkers(icon=hopkinsIcon) # Mapping Clusters df = data.frame(lat=runif(500,min=39.25,max=39.35), lng=runif(500,min=-76.65,max=-76.55)) df %>% leaflet() %>% addTiles() %>% addMarkers(clusterOptions = markerClusterOptions()) df_20 = data.frame(lat=runif(20,min=39.25,max=39.35), lng=runif(20,min=-76.65,max=-76.55)) df_20 %>% leaflet() %>% addTiles() %>% addCircleMarkers() # Drawing Circles md_cities = data.frame(name=c("Baltimore","Frederick","Rockville","Gaithersburg","Bowie","Hagerstown", "Annapolis","College Park", "Salisbury","Laurel"), pop=c(619493,66169,62334,61045,55232,39890,38880,30587,30484,25346), latitude=c(39.292592,39.4143921,39.0840,39.1434,39.0068,39.6418,38.9784,38.9897, 38.3607,39.0993), longitude=c(-76.6077852,-77.4204875,-77.1528,-77.2014,-76.7791,-77.7200,-76.4922, -76.9378,-75.5994,-76.8483)) md_cities %>% leaflet() %>% addTiles() %>% addCircles(weight=1,radius=sqrt(md_cities$pop)*30) # Drawing Rectangles leaflet() %>% addTiles() %>% addRectangles(lat1 = 37.3858, lng1 = -122.0595, lat2 = 37.3890, lng2 = -122.0625) # Adding Legends df = data.frame(lat = runif(20,min=39.25,max=39.35), lng = runif(20,min=-76.65,max=-76.55), col = sample(c("red","blue","green"),20,replace=T), stringsAsFactors = F) str(df) df %>% leaflet() %>% addTiles() %>% addCircleMarkers(color=df$col) %>% addLegend(labels=LETTERS[1:3],colors=c("blue","red","green"))