pierreqi/r_code_50k · Datasets at Hugging Face

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AgronomiaR/seedreg

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N_model.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/N_model.R \name{N_model} \alias{N_model} \title{Analysis: graph for not significant trend} \usage{ N_model( trat, resp, ylab = "Germination (\%)", error = "SE", xlab = expression("Temperature ("^"o" * "C)"), theme = theme_classic(), legend.position = "top" ) } \arguments{ \item{trat}{Numerical vector with treatments (Declare as numeric)} \item{resp}{Numerical vector containing the response of the experiment.} \item{ylab}{Dependent variable name (Accepts the \emph{expression}() function)} \item{error}{Error bar (It can be SE - \emph{default}, SD or FALSE)} \item{xlab}{Independent variable name (Accepts the \emph{expression}() function)} \item{theme}{ggplot2 theme (\emph{default} is theme_classic())} \item{legend.position}{legend position (\emph{default} is "top")} } \value{ The function returns an exploratory graph of segments } \description{ Ggraph for non-significant trend. Can be used within the multicurve command } \examples{ library(seedreg) data("aristolochia") attach(aristolochia) N_model(trat,resp) } \author{ Gabriel Danilo Shimizu Leandro Simoes Azeredo Goncalves } \keyword{non-significant}

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/service/paws.cloudsearchdomain/man/search.Rd

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search.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/paws.cloudsearchdomain_operations.R \name{search} \alias{search} \title{Retrieves a list of documents that match the specified search criteria} \usage{ search(cursor = NULL, expr = NULL, facet = NULL, filterQuery = NULL, highlight = NULL, partial = NULL, query, queryOptions = NULL, queryParser = NULL, return = NULL, size = NULL, sort = NULL, start = NULL, stats = NULL) } \arguments{ \item{cursor}{Retrieves a cursor value you can use to page through large result sets. Use the \code{size} parameter to control the number of hits to include in each response. You can specify either the \code{cursor} or \code{start} parameter in a request; they are mutually exclusive. To get the first cursor, set the cursor value to \code{initial}. In subsequent requests, specify the cursor value returned in the hits section of the response. For more information, see \href{http://docs.aws.amazon.com/cloudsearch/latest/developerguide/paginating-results.html}{Paginating Results} in the \emph{Amazon CloudSearch Developer Guide}.} \item{expr}{Defines one or more numeric expressions that can be used to sort results or specify search or filter criteria. You can also specify expressions as return fields. You specify the expressions in JSON using the form \code{{"EXPRESSIONNAME":"EXPRESSION"}}. You can define and use multiple expressions in a search request. For example: \code{{"expression1":"_score*rating", "expression2":"(1/rank)*year"}} For information about the variables, operators, and functions you can use in expressions, see \href{http://docs.aws.amazon.com/cloudsearch/latest/developerguide/configuring-expressions.html#writing-expressions}{Writing Expressions} in the \emph{Amazon CloudSearch Developer Guide}.} \item{filterQuery}{Specifies a structured query that filters the results of a search without affecting how the results are scored and sorted. You use \code{filterQuery} in conjunction with the \code{query} parameter to filter the documents that match the constraints specified in the \code{query} parameter. Specifying a filter controls only which matching documents are included in the results, it has no effect on how they are scored and sorted. The \code{filterQuery} parameter supports the full structured query syntax. For more information about using filters, see \href{http://docs.aws.amazon.com/cloudsearch/latest/developerguide/filtering-results.html}{Filtering Matching Documents} in the \emph{Amazon CloudSearch Developer Guide}.} \item{partial}{Enables partial results to be returned if one or more index partitions are unavailable. When your search index is partitioned across multiple search instances, by default Amazon CloudSearch only returns results if every partition can be queried. This means that the failure of a single search instance can result in 5xx (internal server) errors. When you enable partial results, Amazon CloudSearch returns whatever results are available and includes the percentage of documents searched in the search results (percent-searched). This enables you to more gracefully degrade your users' search experience. For example, rather than displaying no results, you could display the partial results and a message indicating that the results might be incomplete due to a temporary system outage.} \item{query}{[required] Specifies the search criteria for the request. How you specify the search criteria depends on the query parser used for the request and the parser options specified in the \code{queryOptions} parameter. By default, the \code{simple} query parser is used to process requests. To use the \code{structured}, \code{lucene}, or \code{dismax} query parser, you must also specify the \code{queryParser} parameter. For more information about specifying search criteria, see \href{http://docs.aws.amazon.com/cloudsearch/latest/developerguide/searching.html}{Searching Your Data} in the \emph{Amazon CloudSearch Developer Guide}.} \item{queryOptions}{Configures options for the query parser specified in the \code{queryParser} parameter. You specify the options in JSON using the following form \code{{\"OPTION1\":\"VALUE1\",\"OPTION2\":VALUE2\"...\"OPTIONN\":\"VALUEN\"}.} The options you can configure vary according to which parser you use: \itemize{ \item \code{defaultOperator}: The default operator used to combine individual terms in the search string. For example: \code{defaultOperator: 'or'}. For the \code{dismax} parser, you specify a percentage that represents the percentage of terms in the search string (rounded down) that must match, rather than a default operator. A value of \code{0\%} is the equivalent to OR, and a value of \code{100\%} is equivalent to AND. The percentage must be specified as a value in the range 0-100 followed by the percent (%) symbol. For example, \code{defaultOperator: 50\%}. Valid values: \code{and}, \code{or}, a percentage in the range 0%-100% (\code{dismax}). Default: \code{and} (\code{simple}, \code{structured}, \code{lucene}) or \code{100} (\code{dismax}). Valid for: \code{simple}, \code{structured}, \code{lucene}, and \code{dismax}. \item \code{fields}: An array of the fields to search when no fields are specified in a search. If no fields are specified in a search and this option is not specified, all text and text-array fields are searched. You can specify a weight for each field to control the relative importance of each field when Amazon CloudSearch calculates relevance scores. To specify a field weight, append a caret (\code{^}) symbol and the weight to the field name. For example, to boost the importance of the \code{title} field over the \code{description} field you could specify: \code{"fields":["title^5","description"]}. Valid values: The name of any configured field and an optional numeric value greater than zero. Default: All \code{text} and \code{text-array} fields. Valid for: \code{simple}, \code{structured}, \code{lucene}, and \code{dismax}. \item \code{operators}: An array of the operators or special characters you want to disable for the simple query parser. If you disable the \code{and}, \code{or}, or \code{not} operators, the corresponding operators (\code{+}, \code{|}, \code{-}) have no special meaning and are dropped from the search string. Similarly, disabling \code{prefix} disables the wildcard operator (\code{*}) and disabling \code{phrase} disables the ability to search for phrases by enclosing phrases in double quotes. Disabling precedence disables the ability to control order of precedence using parentheses. Disabling \code{near} disables the ability to use the ~ operator to perform a sloppy phrase search. Disabling the \code{fuzzy} operator disables the ability to use the ~ operator to perform a fuzzy search. \code{escape} disables the ability to use a backslash (\code{\}) to escape special characters within the search string. Disabling whitespace is an advanced option that prevents the parser from tokenizing \item \code{phraseFields}: An array of the \code{text} or \code{text-array} fields you want to use for phrase searches. When the terms in the search string appear in close proximity within a field, the field scores higher. You can specify a weight for each field to boost that score. The \code{phraseSlop} option controls how much the matches can deviate from the search string and still be boosted. To specify a field weight, append a caret (\code{^}) symbol and the weight to the field name. For example, to boost phrase matches in the \code{title} field over the \code{abstract} field, you could specify: \code{"phraseFields":["title^3", "plot"]} Valid values: The name of any \code{text} or \code{text-array} field and an optional numeric value greater than zero. Default: No fields. If you don't specify any fields with \code{phraseFields}, proximity scoring is disabled even if \code{phraseSlop} is specified. Valid for: \code{dismax}. \item \code{phraseSlop}: An integer value that specifies how much matches can deviate from the search phrase and still be boosted according to the weights specified in the \code{phraseFields} option; for example, \code{phraseSlop: 2}. You must also specify \code{phraseFields} to enable proximity scoring. Valid values: positive integers. Default: 0. Valid for: \code{dismax}. \item \code{explicitPhraseSlop}: An integer value that specifies how much a match can deviate from the search phrase when the phrase is enclosed in double quotes in the search string. (Phrases that exceed this proximity distance are not considered a match.) For example, to specify a slop of three for dismax phrase queries, you would specify \code{"explicitPhraseSlop":3}. Valid values: positive integers. Default: 0. Valid for: \code{dismax}. \item \code{tieBreaker}: When a term in the search string is found in a document's field, a score is calculated for that field based on how common the word is in that field compared to other documents. If the term occurs in multiple fields within a document, by default only the highest scoring field contributes to the document's overall score. You can specify a \code{tieBreaker} value to enable the matches in lower-scoring fields to contribute to the document's score. That way, if two documents have the same max field score for a particular term, the score for the document that has matches in more fields will be higher. The formula for calculating the score with a tieBreaker is \code{(max field score) + (tieBreaker) * (sum of the scores for the rest of the matching fields)}. Set \code{tieBreaker} to 0 to disregard all but the highest scoring field (pure max): \code{"tieBreaker":0}. Set to 1 to sum the scores from all fields (pure sum): \code{"tieBreaker":1}. Valid values: 0.0 to 1.0. D }} \item{queryParser}{Specifies which query parser to use to process the request. If \code{queryParser} is not specified, Amazon CloudSearch uses the \code{simple} query parser. Amazon CloudSearch supports four query parsers: \itemize{ \item \code{simple}: perform simple searches of \code{text} and \code{text-array} fields. By default, the \code{simple} query parser searches all \code{text} and \code{text-array} fields. You can specify which fields to search by with the \code{queryOptions} parameter. If you prefix a search term with a plus sign (+) documents must contain the term to be considered a match. (This is the default, unless you configure the default operator with the \code{queryOptions} parameter.) You can use the \code{-} (NOT), \code{|} (OR), and \code{*} (wildcard) operators to exclude particular terms, find results that match any of the specified terms, or search for a prefix. To search for a phrase rather than individual terms, enclose the phrase in double quotes. For more information, see \href{http://docs.aws.amazon.com/cloudsearch/latest/developerguide/searching-text.html}{Searching for Text} in the \emph{Amazon CloudSearch Developer Guide}. \item \code{structured}: perform advanced searches by combining multiple expressions to define the search criteria. You can also search within particular fields, search for values and ranges of values, and use advanced options such as term boosting, \code{matchall}, and \code{near}. For more information, see \href{http://docs.aws.amazon.com/cloudsearch/latest/developerguide/searching-compound-queries.html}{Constructing Compound Queries} in the \emph{Amazon CloudSearch Developer Guide}. \item \code{lucene}: search using the Apache Lucene query parser syntax. For more information, see \href{http://lucene.apache.org/core/4_6_0/queryparser/org/apache/lucene/queryparser/classic/package-summary.html#package_description}{Apache Lucene Query Parser Syntax}. \item \code{dismax}: search using the simplified subset of the Apache Lucene query parser syntax defined by the DisMax query parser. For more information, see \href{http://wiki.apache.org/solr/DisMaxQParserPlugin#Query_Syntax}{DisMax Query Parser Syntax}. }} \item{return}{Specifies the field and expression values to include in the response. Multiple fields or expressions are specified as a comma-separated list. By default, a search response includes all return enabled fields (\code{_all_fields}). To return only the document IDs for the matching documents, specify \code{_no_fields}. To retrieve the relevance score calculated for each document, specify \code{_score}.} \item{size}{Specifies the maximum number of search hits to include in the response.} \item{sort}{Specifies the fields or custom expressions to use to sort the search results. Multiple fields or expressions are specified as a comma-separated list. You must specify the sort direction (\code{asc} or \code{desc}) for each field; for example, \code{year desc,title asc}. To use a field to sort results, the field must be sort-enabled in the domain configuration. Array type fields cannot be used for sorting. If no \code{sort} parameter is specified, results are sorted by their default relevance scores in descending order: \code{_score desc}. You can also sort by document ID (\code{_id asc}) and version (\code{_version desc}). For more information, see \href{http://docs.aws.amazon.com/cloudsearch/latest/developerguide/sorting-results.html}{Sorting Results} in the \emph{Amazon CloudSearch Developer Guide}.} \item{start}{Specifies the offset of the first search hit you want to return. Note that the result set is zero-based; the first result is at index 0. You can specify either the \code{start} or \code{cursor} parameter in a request, they are mutually exclusive. For more information, see \href{http://docs.aws.amazon.com/cloudsearch/latest/developerguide/paginating-results.html}{Paginating Results} in the \emph{Amazon CloudSearch Developer Guide}.} \item{stats}{Specifies one or more fields for which to get statistics information. Each specified field must be facet-enabled in the domain configuration. The fields are specified in JSON using the form: \code{{"FIELD-A":{},"FIELD-B":{}}} There are currently no options supported for statistics.} } \description{ Retrieves a list of documents that match the specified search criteria. How you specify the search criteria depends on which query parser you use. Amazon CloudSearch supports four query parsers: } \details{ \itemize{ \item \code{simple}: search all \code{text} and \code{text-array} fields for the specified string. Search for phrases, individual terms, and prefixes. \item \code{structured}: search specific fields, construct compound queries using Boolean operators, and use advanced features such as term boosting and proximity searching. \item \code{lucene}: specify search criteria using the Apache Lucene query parser syntax. \item \code{dismax}: specify search criteria using the simplified subset of the Apache Lucene query parser syntax defined by the DisMax query parser. } For more information, see \href{http://docs.aws.amazon.com/cloudsearch/latest/developerguide/searching.html}{Searching Your Data} in the \emph{Amazon CloudSearch Developer Guide}. The endpoint for submitting \code{Search} requests is domain-specific. You submit search requests to a domain's search endpoint. To get the search endpoint for your domain, use the Amazon CloudSearch configuration service \code{DescribeDomains} action. A domain's endpoints are also displayed on the domain dashboard in the Amazon CloudSearch console. } \section{Accepted Parameters}{ \preformatted{search( cursor = "string", expr = "string", facet = "string", filterQuery = "string", highlight = "string", partial = TRUE|FALSE, query = "string", queryOptions = "string", queryParser = "simple"|"structured"|"lucene"|"dismax", return = "string", size = 123, sort = "string", start = 123, stats = "string" ) } }

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/R/lhs_getpairs.r

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CenterForPeaceAndSecurityStudies/MeasuringLandscape

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# These functions involve retrieving string suggestions based on locality sensitive hashing # # # # # library(textreuse) # library(LSHR) lhs_getpairs <- function(strings, grams, bands_number=400, rows_per_band=5, mc.cores = parallel::detectCores()) { pairs <- LSHR:::get_similar_pairs_cosine( grams, bands_number = bands_number, # increase this number, and you'll get more false positives but fewer misses rows_per_band = rows_per_band, # basically shrink this number #distance = "cosine", #redundant if you use pairs_cosine seed = 1, mc.cores = parallel::detectCores(), verbose = T ) dim(pairs) # 426,604 That's about 9.4 matches per which is almost exactly what we were looking for. 20 bands, 6 rows 344,923,166 suggestions <- as.data.table(pairs) # pairs[order(-N)] dim(suggestions) # 426,604 That's about 9.4 matches per which is almost exactly what we were looking for. 20 bands, 6 rows 344,923,166 suggestions$a <- strings[suggestions$id1] suggestions$b <- strings[suggestions$id2] suggestions[, ab := paste(a, b, sep = "_")] suggestions[, ba := paste(b, a, sep = "_")] return(suggestions) } eval_lshr <- function(strings, grams, data, bands_number=400, rows_per_band=5) { suggestions <- lhs_getpairs(strings, grams, bands_number, rows_per_band) hits <- data$stemmed_ab[data$rex_match == 1] %in% suggestions$ab | data$stemmed_ab[data$rex_match == 1] %in% suggestions$ba # data$ab[data$rex_match==1][hits] misses <- !data$stemmed_ab[data$rex_match == 1] %in% suggestions$ab & !data$stemmed_ab[data$rex_match == 1] %in% suggestions$ba # data$ab[data$rex_match==1][misses] #Anything that's an exact match is being counted as a miss because suggestions don't count themselves results <- data.frame( hits = sum(hits), misses = sum(misses), suggestions = nrow(suggestions), qgrams = ncol(grams), bands_number = bands_number, rows_per_band = rows_per_band, suggestions_per = round(nrow(suggestions) / nrow(grams)) ) return(results) }

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/R/aussim_df.R

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aussim_df.R

make_aussim_df <- function(aus.sim, maxyear = 2050) { # Combine simulation results into one tibble dimnames(aus.sim[["male"]]) <- dimnames(aus.sim[["female"]]) <- list( Age = dimnames(aus.sim[[1]])[[1]], Year = as.numeric(dimnames(aus.sim[[1]])[[2]]) + 1, Rep = dimnames(aus.sim[[1]])[[3]] ) aussim_df <- bind_rows( as.tbl_cube(aus.sim[["male"]] / 1e3, met_name = "Population") %>% as_tibble() %>% mutate(Sex = "Male"), as.tbl_cube(aus.sim[["female"]] / 1e3, met_name = "Population") %>% as_tibble() %>% mutate(Sex = "Female") ) %>% filter(Year <= maxyear) return(aussim_df) }

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/cran/paws.machine.learning/man/comprehend_start_events_detection_job.Rd

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/comprehend_operations.R \name{comprehend_start_events_detection_job} \alias{comprehend_start_events_detection_job} \title{Starts an asynchronous event detection job for a collection of documents} \usage{ comprehend_start_events_detection_job( InputDataConfig, OutputDataConfig, DataAccessRoleArn, JobName = NULL, LanguageCode, ClientRequestToken = NULL, TargetEventTypes, Tags = NULL ) } \arguments{ \item{InputDataConfig}{[required] Specifies the format and location of the input data for the job.} \item{OutputDataConfig}{[required] Specifies where to send the output files.} \item{DataAccessRoleArn}{[required] The Amazon Resource Name (ARN) of the IAM role that grants Amazon Comprehend read access to your input data.} \item{JobName}{The identifier of the events detection job.} \item{LanguageCode}{[required] The language code of the input documents.} \item{ClientRequestToken}{An unique identifier for the request. If you don't set the client request token, Amazon Comprehend generates one.} \item{TargetEventTypes}{[required] The types of events to detect in the input documents.} \item{Tags}{Tags to associate with the events detection job. A tag is a key-value pair that adds metadata to a resource used by Amazon Comprehend. For example, a tag with "Sales" as the key might be added to a resource to indicate its use by the sales department.} } \description{ Starts an asynchronous event detection job for a collection of documents. See \url{https://www.paws-r-sdk.com/docs/comprehend_start_events_detection_job/} for full documentation. } \keyword{internal}

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giabaio/survHE

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/data.R \docType{data} \name{data} \alias{data} \title{A fictional survival trial.} \format{ A data frame with 367 rows and 8 variables: \describe{ \item{ID_patient}{The individual level identifier} \item{time}{The observed time at which the event happens} \item{censored}{An indicator to describe whether the event is fully observed or censored} \item{arm}{An indicator for the treatment arm, with 0 = control and 1 = active treatment} \item{sex}{An indicator for the individual's sex, with 0 = male and 1 = female} \item{age}{A numeric variable with the individual's age} \item{imd}{A categorical variable representing a measure of area-level social deprivation} \item{ethnic}{A categorical variable representing the individual's ethnic group, as measured from a Census} } } \usage{ data } \description{ A dataset containing fictional data from a trial, where the main outcome is in terms of time-to-event and censoring indicator and with additional covariates. } \keyword{datasets}

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#' Load stage from a .ngl file #' #' @description #' Load stage from a .ngl file #' #' @param NGLVieweR NGLVIeweR object. #' @param stage data.frame of selections loaded from .ngl file. #' #' @import NGLVieweR #' @export loadStage <- function(NGLVieweR, stage) { viewer <- NGLVieweR if (!is.null(stage)) { viewer <- stageParameters( viewer, cameraType = stage$cameraType, backgroundColor = stage$backgroundColor, lightIntensity = stage$lightIntensity, clipNear = stage$clipNear, clipFar = stage$clipFar ) } else{ viewer <- stageParameters( viewer, cameraType = "perspective", backgroundColor = "black", lightIntensity = 1, clipNear = 0, clipFar = 100 ) } return(viewer) }

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1610130943-test.R

testlist <- list(a = -171L, b = -256L, x = c(-1L, -16383233L, -16320513L, 505085951L, 16777471L, 63996L, -114883070L, -63753L, -54785L, 1845557756L, -109874144L, 1685026146L, 1818569827L, 1862271015L, 704612488L, -2004318072L, -2004318072L, -2004318072L, -2004318072L, -2004353024L, 0L, 0L, 30L, 452984576L, 0L, 16777216L, 0L, 16383225L, 654311424L, -604023506L, -57830L, -42470L, 452984831L, -15007970L, 437911771L, 436263686L, -134217984L, 255L, 505085951L, -1L, -1L, -1L, -41502L, 41L, -64769L, 1442781951L, -1L, -1L, -250L, -65310L, -501343518L, -1L, 505085951L, -1L, -15007745L, -1L, -16384000L, 0L, 851967L, -246L, -1L, -16777216L, 0L, 218103807L, -1L, -1L )) result <- do.call(grattan:::anyOutside,testlist) str(result)

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/man/reviewNeuronsMoreFrames.Rd

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reviewNeuronsMoreFrames.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/SCALPEL_reviewNeurons.R \name{reviewNeuronsMoreFrames} \alias{reviewNeuronsMoreFrames} \title{Save additional frames for manually classifying the identified neurons from SCALPEL.} \usage{ reviewNeuronsMoreFrames(scalpelOutput, neuronSet, numFrames = 10) } \arguments{ \item{scalpelOutput}{An object returned by one of the SCALPEL functions: \code{\link{scalpel}}, \code{\link{scalpelStep2}}, or \code{\link{scalpelStep3}}.} \item{neuronSet}{The set of neurons that should be reviewed: use \code{"A"} for those resulting from \code{\link{scalpelStep2}} and saved as \code{scalpelOutput$A}, or use \code{"Afilter"} for those resulting from \code{\link{scalpelStep3}} and saved as \code{scalpelOutput$Afilter}. This argument is ignored if the class of \code{scalpelOutput} is \code{scalpelStep2}.} \item{numFrames}{The maximum number of frames that should be saved for each neuron being considered. Each neuron has a number of frames equal to the number of members in that neuron's cluster that can be plotted. All frames will be saved when the total number of available frames for the neuron is less than \code{numFrames}. The default value is 10.} } \value{ None } \description{ We use this function after running \code{\link{reviewNeurons}} or \code{\link{reviewNeuronsInteractive}} to plot additional frames for neurons whose classification was unclear from the single frame plotted. The additional frames are saved, and the classification for the neurons can then be updated using \code{\link{updateNeurons}} or \code{\link{updateNeuronsInteractive}}. } \examples{ \dontrun{ ### many of the functions in this package are interconnected so the ### easiest way to learn to use the package is by working through the vignette, ### which is available at ajpete.com/software #assumes you have run the example for the "reviewNeuronsInteractive" function #we save frames for the neurons previously classified #as "?" using the "reviewNeuronsInteractive" function reviewNeuronsMoreFrames(scalpelOutput = scalpelOutput, neuronSet = "A") } } \seealso{ \code{\link{reviewNeurons}}, \code{\link{updateNeurons}}, \code{\link{reviewNeuronsInteractive}}, \code{\link{updateNeuronsInteractive}} }

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fghjorth/vkme16

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06_matching.R

setwd("~/GitHub/vkme16/") require(haven) require(stargazer) require(cem) #coarsened exact matching require(dplyr) #indlæs data ld<-read_dta("data/6_laddlenz.dta") #bivariat model ols1<-lm(vote_l_97~tolabor,data=ld) summary(ols1) #model med kontroller (jf Ladd/Lenz s 402) ols2<-lm(vote_l_97~tolabor+vote_l_92+vote_c_92+vote_lib_92+ labor+conservative+liberal+labfel92+confel92+know_3,data=ld) summary(ols2) #sammenlign koefficienterne stargazer(ols1,ols2,type="text",keep="tolabor") #reducer data til de variable der er med i ols2 ld<-dplyr::select(ld,vote_l_97,tolabor,vote_l_92,vote_c_92,vote_lib_92,labor,conservative,liberal,labfel92,confel92,know_3) #konverter fra havens importformat til data frame (som nogle ældre pakker forudsætter) ld<-as.data.frame(ld) #evaluer balance imbalance(group=ld$tolabor,data=ld,drop=c("tolabor","vote_l_97")) #kør matching cemmatch1<-cem(treatment="tolabor",data=ld,drop="vote_l_97") #ny data frame med kun de matchede observationer ldmatched<-ld[cemmatch1$matched,] #evaluer balance på det matchede data imbalance(group=ldmatched$tolabor,data=ldmatched,drop=c("tolabor","vote_l_97")) #vi kan nu køre regressionen på det matchede data ols2matched<-lm(vote_l_97~tolabor+vote_l_92+vote_c_92+vote_lib_92+ labor+conservative+liberal+labfel92+confel92+know_3,data=ldmatched) #sammenlign koefficienterne stargazer(ols1,ols2,ols2matched,type="text",keep="tolabor")

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marhenriq/cgmquantify

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eA1c.R

#' Compute Estimated A1c #' #' This function computes the estimated A1c, according to #' the American Diabetes Association calculator #' #' @param df Data frame read through readfile #' @return A numeric value representing eA1c #' @examples #' mydatafile <- system.file("extdata", "my_data_file.csv", package = "cgmquantify") #' mydata <- readfile(mydatafile) #' eA1c(mydata) #' @export eA1c <- function(df) { eA1c = (46.7 + mean(df$glucose))/28.7 return(eA1c) }

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/data/genthat_extracted_code/Opt5PL/examples/Deff.Rd.R

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surayaaramli/typeRrh

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Deff.Rd.R

library(Opt5PL) ### Name: Deff ### Title: Obtaining D-efficiency for estimating model parameters ### Aliases: Deff ### ** Examples ##Under the 5PL model with the parameter values ##T5=(30000,0.5,800,0.5,2) and the dose range [1.95,32000], ##find the D-efficiency of the broad range design. ##The broad range design dose=c(1.95,7.8,31.25,125,500,2000,8000,32000) dlength=length(dose) weight=rep(1/dlength,dlength-1) ##Parameter values T5=c(30000,0.5,800,0.5,2) ##Compute D-efficiency of the broad range design Deff(weight,dose,model=5,P=T5,LB=1.95,UB=32000,grid=.01)

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chopley/timHuntSentimentAnalysis

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analysis.R

require(twitter) require(sentiment) require(plyr) require(ggplot2) require(wordcloud) require(RColorBrewer) require(tm) #get a list of words that have emotions attached to them wordList<-read.csv2('AFINN/AFINN-111.txt',sep='\t') filenameList<-c('2015-06-08_2015-06-09_timhunt','2015-06-09_2015-06-10_timhunt','2015-06-10_2015-06-11_timhunt','2015-06-12_2015-06-13_timhunt', '2015-06-13_2015-06-14_timhunt','2015-06-15_2015-06-16_timhunt','2015-06-16_2015-06-17_timhunt','2015-06-21_2015-06-22_timhunt', '2015-06-22_2015-06-23_timhunt','2015-06-23_2015-06-24_timhunt','2015-06-26_2015-06-27_timhunt','2015-06-27_2015-06-28_timhunt', '2015-06-28_2015-06-29_timhunt') load(filenameList[1]) df1<-dfTimHunt for(i in 2:length(filenameList)){ load(filenameList[i]) df1<-rbind(df1,dfTimHunt) } # remove retweet entities some_txt = gsub("(RT|via)((?:\\b\\W*@\\w+)+)", "", df1$text) # remove at people some_txt = gsub("@\\w+", "", some_txt) # remove punctuation some_txt = gsub("[[:punct:]]", "", some_txt) # remove numbers some_txt = gsub("[[:digit:]]", "", some_txt) # remove html links some_txt = gsub("http\\w+", "", some_txt) # remove unnecessary spaces some_txt = gsub("[ \t]{2,}", " ", some_txt) some_txt = gsub("^\\s+|\\s+$", "", some_txt) some_txt<-tolower(some_txt) #split the strings up splitStrings<-strsplit(some_txt," ") nSamples<-length(splitStrings) myCorpus <- Corpus(VectorSource(some_txt)) my_stopwords <- c(stopwords('english')) myCorpus <- tm_map(myCorpus, removeWords, my_stopwords,lazy=TRUE) for(i in 1:length(myCorpus)){ i #we use try to handle the cases where our cleaning has thrown out everything in the tweet :( try({ #get the list of words remaining after cleaning a specific tweet a<-as.character(myCorpus[[i]]) #split the words by space words<-strsplit(a," ") #find how many words are in this particular tweet nWords <-length(words[[1]]) aSum<-0 #now go through those words and create a vector that contains the emotional value of each word for(j in 1:nWords){ #find the values for words contained in the lists aSum<-cbind(aSum,wordList[words[[1]][j]==wordList[1]][2]) } #now try and find the mean of that vector- this should give an idea of how the tweeter felt about Tim Hun df1$emotion[i]<-mean(as.numeric(aSum),na.rm=TRUE) },silent=TRUE) } #now we plot histograms of the data par(mfrow=c(4,4)) tstart <- strptime("2015-06-09 02:54:17 UTC", format="%Y-%m-%d %H:%M:%S",tz="UTC") tend<-tstart+ 60*60*24 meanEmotion<-mean(df1$emotion[((df1$created<tend)&(df1$created>tstart))]) Title<-paste(tstart,'\nEmotion',round(meanEmotion,3)) hist(df1$emotion[((df1$created<tend)&(df1$created>tstart))],30,main=Title,xlab='Emotion') for(i in 1:20){ tstart<-tstart + 60*60*24 tend<-tend+ 60*60*24 meanEmotion<-mean(df1$emotion[((df1$created<tend)&(df1$created>tstart))]) Title<-paste(tstart,'\nEmotion',round(meanEmotion,3)) try(hist(df1$emotion[((df1$created<tend)&(df1$created>tstart))],30,main=Title,xlab='Emotion')) } #create a corpus from the strings myCorpus <- Corpus(VectorSource(df1$text)) #transfomr to lower myCorpus <- tm_map(myCorpus, content_transformer(tolower),lazy=TRUE) #remove punctuation myCorpus <- tm_map(myCorpus, content_transformer(removePunctuation),lazy=TRUE) my_stopwords <- c(stopwords('english')) myCorpus <- tm_map(myCorpus, removeWords, my_stopwords,lazy=TRUE) myCorpus <- tm_map(myCorpus, stripWhitespace,lazy=TRUE) inspect(myCorpus[1:2]) myDTM <- TermDocumentMatrix(myCorpus) dataframe<-data.frame(text=unlist(sapply(myCorpus, `[`)), stringsAsFactors=F) # classify emotion class_emo = classify_emotion(some_txt, algorithm="bayes", prior=1.0) # get emotion best fit emotion = class_emo[,7] # substitute NA's by "unknown" emotion[is.na(emotion)] = "unknown"

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improvement.R

improvement <- function(rules, t = 0.1) { # This function determines whether the OR of a rule is interesting or if it just # a consequence of a parent rule. The goal is to eliminate redundant rules with a # Occam's Razor strategy # Exctract interesting data from rules structure i <- rules@lhs@data@i #tags p <- rules@lhs@data@p #indexes length <- length(p)-1 #total rules Rules <- list() # First, rules are organized in a list structure for (ind in 1:length) { size <- p[ind+1] - p[ind] start<-p[ind]+1 end <- p[ind]+size s <- start:end temp <- i[s] Rules <- c(Rules,list(temp)) } # initialize logical array "imp" -> tag improved rules imp <- logical(length(Rules)) imp[1:length(imp)] <- TRUE for (i in 1:length(Rules)) { if (length(Rules[[i]])>1) { # rules of length 1 are all significant Sets <- list() # produce Sets: list of all subset of current rule for (j in 1:length(Rules[[i]])-1) Sets <- c(Sets, combn(Rules[[i]],j,simplify=FALSE)) # look up elements of Sets in Rules and evaluate improvement for (j in Sets) { if (length(j) > 0) { pos <- match(list(j),Rules) if (!is.na(pos)) { if (abs(rules@quality$oddsRatio[i]-rules@quality$oddsRatio[pos])<t) imp[i] <- FALSE } } } } } return(data.frame(imp=imp)) }

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/data/genthat_extracted_code/condmixt/examples/gpd.mme.Rd.R

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surayaaramli/typeRrh

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gpd.mme.Rd.R

library(condmixt) ### Name: gpd.mme ### Title: Moment Estimator for the Generalized and the Hybrid Pareto ### Distribution ### Aliases: gpd.mme hpareto.mme ### ** Examples r<-rhpareto(1000,0.1,0,1,trunc=FALSE) hpareto.mme(r,p=0.991)

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/Problem Sets/Student Submissions/Problem Set 3/1830531/e3_q1_1830531.R

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NikoStein/ADS19

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e3_q1_1830531.R

# Problem Set 3 # Question 1 library(tidyverse) library(readr) library(RColorBrewer) # 1 a) data <- read_csv2("https://www.bundeswahlleiter.de/dam/jcr/5441f564-1f29-4971-9ae2-b8860c1724d1/ew19_kerg2.csv", skip = 9) # consider only big parties Parteien <- c("CDU","CSU", "GRÜNE", "SPD", "AfD", "DIE LINKE", "FDP") # 1 b) #filter out the state level and performance of all the big parties data %>% filter(UegGebietsart == "BUND", Gruppenart == "Partei") -> laender # col-graph for every state and election result colored in laender %>% filter(Gruppenname %in% Parteien) %>% ggplot(aes(x = Gebietsname, y = Prozent, fill = Gruppenname)) + geom_col() + theme_light() + theme(axis.text.x = element_text(angle = 90, hjust = 1, size = 7), axis.text.y = element_text(size = 5), axis.title.y = element_text(size = 8), axis.title.x = element_text(size = 8), legend.title = element_text(size = 5), legend.text = element_text(size = 6)) # faceting, to show the results for every state separately laender %>% filter(Gruppenname %in% Parteien) %>% ggplot(aes(x = Gruppenname, y = Prozent)) + geom_col() + facet_wrap(~Gebietsname) # 1 c) # turn it around and show the parties' performance for every state pal = colorRampPalette(brewer.pal(9, "Set1")) laender %>% filter(Gruppenname %in% Parteien[-2]) %>% ggplot(aes(x = Gebietsname, y = Prozent, fill = factor(Gebietsname)))+ scale_fill_manual(values = pal(16)) + geom_col() + facet_wrap(~Gruppenname) + theme_light()+ theme(axis.text.x = element_text(angle = 90, hjust = 1, size = 7), axis.text.y = element_text(size = 5), axis.title.y = element_text(size = 8), axis.title.x = element_text(size = 8), legend.title = element_text(size = 5), legend.text = element_text(size = 6), legend.position = "none") # 1 d) library(leaflet) library(raster) laender %>% group_by(Gebietsname) %>% filter(Prozent == max(Prozent)) -> winner # load level 1 map for germany germany <- getData("GADM", country="DEU", level=1) # create dataframe with information we want to map onto the germany map election = winner %>% ungroup() %>% dplyr::select(Gruppenname, Gebietsname, Prozent) # merge the spdf with the election results merge(germany, election, by.x = "NAME_1", by.y = "Gebietsname") -> germany_joined pal <- colorFactor("Set1", NULL) polygon_popup <- paste0("Bundesland: ", germany_joined$NAME_1, " ", "Gewinnerpartei: ", germany_joined$Gruppenname, " ", "Anteil: ", round(germany_joined$Prozent, 1), " % ") leaflet() %>% addProviderTiles("CartoDB.Positron") %>% addPolygons(data = germany_joined, fillOpacity = 0.65, fillColor= ~pal(Gruppenname), weight = 1, color = "white", popup = polygon_popup) # 1 e) # filter the data for the winner of every constituency with the respective share of votes data %>% filter(Gebietsart == "Kreis", Gruppenart == "Partei") %>% group_by(Gebietsname) %>% filter(Prozent == max(Prozent)) -> kreise # load the map of level 2 for germany germanyKreise <- getData("GADM", country="DEU", level=2) germanyKreise$NAME_2 # map is not up to data, in the meantime Göttingen and Osterode merged into one constituency germanyKreise[["CC_2"]][229] = "03159" germanyKreise[["CC_2"]][211] = "03159" germanyKreise[["NAME_2"]][229] = "Göttingen" # extract information about the elections into one dataframe electionKreise = kreise %>% ungroup() %>% dplyr::select(Gruppenname, Gebietsname, Prozent, Gebietsnummer) # merge the dataframe with the spatial_df merge(germanyKreise, electionKreise, by.x = "CC_2", by.y = "Gebietsnummer") -> germanyKreise_joined # create the colors for the seven parties pals <- colorFactor(c("black","#6495ed","#32CD32", "red","#0000ee","#FF6961","yellow"), levels = Parteien, Parteien) # normalize the percentages in order to fill in the opacity normal <- function(x, ...){(x - min(x, ...)) / (max(x, ...) - min(x, ...))} # produce the pop-up display polygon_popup <- paste0("Kreis: ", germanyKreise_joined$NAME_2, " ", "Gewinnerpartei: ", germanyKreise_joined$Gruppenname, " ", "Anteil: ", round(germanyKreise_joined$Prozent, 2), " % ") # create the mapping via leaflet leaflet() %>% addProviderTiles("CartoDB.Positron") %>% addPolygons(data = germanyKreise_joined, fillOpacity = ~normal(Prozent, na.rm = TRUE), fillColor= ~pals(Gruppenname), weight = 2, color = "white", popup = polygon_popup)

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vallotlab/ChromSCape

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mm10.chromosomes.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/data.R \docType{data} \name{mm10.chromosomes} \alias{mm10.chromosomes} \title{Data.frame of chromosome length - mm10} \format{ mm10.chromosomes - a data frame with 24 rows and 3 variables: \describe{ \item{chr}{Chromosome - character} \item{start}{Start of the chromosome (bp) - integer} \item{end}{End of the chromosome (bp) - integer} } } \usage{ data("mm10.chromosomes") } \description{ This data frame provides the length of each "canonical" chromosomes of Mus Musculus (Mouse) genome build mm10. } \keyword{datasets}

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akhikolla/updatedatatype-list1

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1612725998-test.R

testlist <- list(a = 2405990150192.13, b = 4.94065645841247e-324) result <- do.call(BayesMRA::rmvn_arma_scalar,testlist) str(result)

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ethanduncan65/20-Years-Later

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#install package for cv.glm() library(boot) # load the data df = read.csv('MLB Team Stats 2015-2019.csv', header = TRUE) # reduce data to variables of interest df_playoff = df[-c(3:6, 8:9, 13, 15:20, 27)] # quick EDA pairs(df_playoff) ### {win_pct vs. run_diff} # plot made_playoffs against likely predictors par(mfrow=c(2,2)) plot(df_playoff$win_pct, df_playoff$made_playoffs) plot(df_playoff$vsWO_win_pct, df_playoff$made_playoffs) plot(df_playoff$road_win_pct, df_playoff$made_playoffs) plot(df_playoff$vsLHP_win_pct, df_playoff$made_playoffs) # SLR model for win_pct = payroll_M slr_model_payroll = lm(df_playoff$win_pct ~ df_playoff$payroll_M) slr1_summ = summary(slr_model_payroll) # plot data against regression eq. plot(df_playoff$payroll_M, df_playoff$win_pct) abline(a=slr1_summ$coefficients[1], b=slr1_summ$coefficients[2], col='red') # make prediction for Mariner's win_pct pred_1 = slr1_summ$coefficients[1] + slr1_summ$coefficients[2]*(81.257217) pred_1 # SLR model for win_pct = Rdiff slr_model_runDiff = lm(df_playoff$win_pct ~ df_playoff$Rdiff) summary(slr_model_runDiff) # LOOCV on orders of payroll_M x = df_playoff$payroll_M y = df_playoff$win_pct # df consisting of payroll_M and win_pct only df = data.frame(x,y) # vector to store error for each model errors = c() # for loop over the 7 models we want to fit for (i in 1:7){ set.seed(99) # fit model based on degree of polynomial fit.glm.i <- glm(y~poly(x,i)) # extract and append error to storage vector i_err = cv.glm(df, fit.glm.i)$delta[1] errors = c(errors, i_err) } # plot the errors plot(c(1:7),errors, xlab = "degree of polynomial", ylab = "error") lines(c(1:7),errors) # logistic regression model to predict odds of making playoffs using only payroll_M logit_modelX = glm(df_playoff$made_playoffs ~df_playoff$payroll_M, family = binomial(link = "logit")) summary(logit_modelX) cv.glm(df_playoff, logit_modelX)$delta[1] # logistic regression model to predict odds of making playoffs logit_model = glm(df_playoff$made_playoffs ~ df_playoff$road_win_pct + df_playoff$vsWO_win_pct + df_playoff$payroll_M, family = binomial(link = "logit")) logit_1 = summary(logit_model) cv.glm(df_playoff, logit_model)$delta[1] # calc. current season stats for Mariners needed for prediction SEA2021_road_win_pct = 14/(14+20) SEA2021_vsWO_win_pct = 15/(15+20) # prediction for Seattle Mariners lnOdds = logit_1$coefficients[1] + logit_1$coefficients[2]*(SEA2021_road_win_pct) + logit_1$coefficients[3]*(SEA2021_vsWO_win_pct) + logit_1$coefficients[4]*(81.257217) pred_prob = exp(lnOdds)/(1+exp(lnOdds)) # calc. current season stats for Boston Red Sox needed for prediction BOS2021_road_win_pct = 20/(20+10) BOS2021_vsWO_win_pct = 17/(17+11) # prediction for Boston Red Sox lnOdds_BOS = logit_1$coefficients[1] + logit_1$coefficients[2]*(BOS2021_road_win_pct) + logit_1$coefficients[3]*(BOS2021_vsWO_win_pct) + logit_1$coefficients[4]*(176.846501) pred_prob_BOS = exp(lnOdds_BOS)/(1+exp(lnOdds_BOS))

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gradrate.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/datadoc.R \docType{data} \name{gradrate} \alias{gradrate} \title{2015-2019 4-year graduation rates in Nevada} \format{ a tibble with 40 rows and 3 columns: \describe{ \item{Group:}{Race/Ethnicity} \item{rate:}{4-year graduation rate} \item{year:}{Graduating class year (e.g. 2015 = class of 2015} } } \source{ \url{https://www.samhsa.gov/data/report/2017-2018-nsduh-state-specific-tables} } \usage{ gradrate } \description{ A dataset showing the 4-year graduation rate by race in Nevada } \keyword{datasets}

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source("read_data.R") do_plot <- function(data = get_data()){ png(filename="plot4.png", width = 480, height = 480) par(mfrow = c(2, 2)) source("plot2.R") do_plot(data) with(data, plot(data$TimeTime, data$Voltage, xlab = "datetime", ylab = "Voltage", type="l")) source("plot3.R") do_plot(data) with(data, plot(data$TimeTime, data$Global_reactive_power, xlab = "datetime", ylab = "Global_reactive_power", type="l")) dev.off() }

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/stable_kendall_fit.R \name{plot.kendall_fit} \alias{plot.kendall_fit} \title{QQ-plot for the result of fitting stable Kendall distribtion.} \usage{ \method{plot}{kendall_fit}(x, ...) } \arguments{ \item{x}{List returned by fit_separate or fit_kendall function.} \item{...}{Aditional arguments.} } \value{ ggplot2 object } \description{ QQ-plot for the result of fitting stable Kendall distribtion. }

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ui <- fluidPage( tags$head(tags$style("#c_typing{color: blue; font-size: 40px; font-style: bold; } #c_title{color: black; font-size: 40px; font-style: bold; } #c_typing2{color: grey; font-size: 40px font-style: bold }" ) ), tags$script(' $(document).on("keypress", function (e) { Shiny.onInputChange("letterpress", e.which); }); '), # Application title br(), column(4, offset = 4, align = "center", titlePanel("Name that country!")), # plot and other parts plotOutput("map"), br(), br(), br(), br(), column(6, offset = 3, align = "center", h1(strong(textOutput("c_title")))), column(6, offset = 3, align = "center", h1(strong(textOutput("c_typing")))), # column(6, offset = 3, align = "center", h1(strong(textOutput("c_typing2")))), column(4, offset = 4, align = "center", actionButton("update", "Next")), column(4, offset = 4, align = "center", checkboxInput(inputId = "hard", label = "Hard Mode", value = FALSE)) )

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models_stan_nsc.R

### 14 October 2019 - Cat ## Now with real data... ### Main Question: # This is really what I want to show - # that concentrations vary seasonally A LOT in shallower increments, and only a little in deeper increments. ## And this varies between diffuse and ring-porous wood anatomies ## Main Issues with data: # Right skew of data for total and total sugar concentration ## zero-inflated starch concentration ## housekeeping rm(list=ls()) options(stringsAsFactors = FALSE) library(ggplot2) library(dplyr) library(tidyr) library(brms) library(rstanarm) ## Load the data setwd("~/Documents/git/nscradiocarbon/analyses/") ### Ring-porous first: ring <- read.csv("input/ring.csv") diff <- read.csv("input/diff.csv") ring$season <- ifelse(ring$season=="spring", "aspring", ring$season) ring$season <- ifelse(ring$season=="summer", "bsummer", ring$season) ring$season <- ifelse(ring$season=="autumn", "cautumn", ring$season) diff$season <- ifelse(diff$season=="spring", "aspring", diff$season) diff$season <- ifelse(diff$season=="summer", "bsummer", diff$season) diff$season <- ifelse(diff$season=="autumn", "cautumn", diff$season) ring.total <- ring[(ring$method=="total"),] diff.total <- diff[(diff$method=="total"),] ring.sugar <- ring[(ring$method=="sugar"),] diff.sugar <- diff[(diff$method=="sugar"),] ring.starch <- ring[(ring$method=="starch"),] diff.starch <- diff[(diff$method=="starch"),] ringtot.mod <- brm(conc ~ season + (season | increment), data=ring.total, control=list(max_treedepth = 15,adapt_delta = 0.99), prior = prior(normal(0, 30), class = "Intercept") + prior(normal(0, 10), class = "sd") + prior(normal(0,10), class = "b")) save(ringtot.mod, file="stan/ringtotalconc_randincr.Rdata") difftot.mod <- brm(conc ~ season + (season | increment), data=diff.total, control=list(max_treedepth = 15,adapt_delta = 0.99), prior = prior(normal(0, 30), class = "Intercept") + prior(normal(0, 10), class = "sd") + prior(normal(0,10), class = "b")) save(difftot.mod, file="stan/difftotalconc_randincr.Rdata") ringsug.mod <- brm(conc ~ season + (season | increment), data=ring.sugar, control=list(max_treedepth = 15,adapt_delta = 0.99), prior = prior(normal(0, 30), class = "Intercept") + prior(normal(0, 10), class = "sd") + prior(normal(0,10), class = "b")) save(ringsug.mod, file="stan/ringsugarconc_randincr.Rdata") diffsug.mod <- brm(conc ~ season + (season | increment), data=diff.sugar, control=list(max_treedepth = 15,adapt_delta = 0.99), prior = prior(normal(0, 30), class = "Intercept") + prior(normal(0, 10), class = "sd") + prior(normal(0,10), class = "b")) save(diffsug.mod, file="stan/diffsugarconc_randincr.Rdata") ringstar.mod <- brm(conc ~ season + (season | increment), data=ring.starch, control=list(max_treedepth = 15,adapt_delta = 0.99), prior = prior(normal(0, 30), class = "Intercept") + prior(normal(0, 10), class = "sd") + prior(normal(0,10), class = "b")) save(ringstar.mod, file="stan/ringstarchconc_randincr.Rdata") diffstar.mod <- brm(conc ~ season + (season | increment), data=diff.starch, control=list(max_treedepth = 15,adapt_delta = 0.99), prior = prior(normal(0, 30), class = "Intercept") + prior(normal(0, 10), class = "sd") + prior(normal(0,10), class = "b")) save(diffstar.mod, file="stan/diffstarchconc_randincr.Rdata") if(FALSE){ alltot <- full_join(ring.total, diff.total) alltot$season <- ifelse(alltot$season=="spring", "aspring", alltot$season) alltot$season <- ifelse(alltot$season=="summer", "bsummer", alltot$season) alltot$season <- ifelse(alltot$season=="autumn", "cautumn", alltot$season) allseas.mod <- brm(conc ~ increment*wood + (increment*wood|season), data=alltot, control=list(max_treedepth = 15,adapt_delta = 0.99)) save(allseas.mod, file="stan/allwood_seasrand.Rdata") #alltot$ring <- ifelse(alltot$wood=="ring", 1, 0) allincr.mod <- brm(conc ~ season*wood + (season*wood | increment), data=alltot, control=list(max_treedepth = 15,adapt_delta = 0.99)) save(allincr.mod, file="stan/allwood_incrrand.Rdata") #loo1 <- loo(tot.arm.inc) #loo2 <- loo(tot.arm.student) #compare_models(loo1, loo2) launch_shinystan(ringtot.mod) }

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### Author: Khushnud Sapaev ### Project: MovieLens Project ### Course: HarvardX: PH125.9x - Capstone Project ### GitHub: https://github.com/ksapaev/ ############################### ### Dataset and Preparation ### ############################### # Create edx set and validation set # Note: this process could take a couple of minutes # Loading libraries, if does not exist then installing first if(!require(tidyverse)) install.packages("tidyverse", repos = "http://cran.us.r-project.org") if(!require(caret)) install.packages("caret", repos = "http://cran.us.r-project.org") # MovieLens 10M dataset: # https://grouplens.org/datasets/movielens/10m/ # http://files.grouplens.org/datasets/movielens/ml-10m.zip dl <- tempfile() download.file("http://files.grouplens.org/datasets/movielens/ml-10m.zip", dl) ratings <- read.table(text = gsub("::", "\t", readLines(unzip(dl, "ml-10M100K/ratings.dat"))), col.names = c("userId", "movieId", "rating", "timestamp")) movies <- str_split_fixed(readLines(unzip(dl, "ml-10M100K/movies.dat")), "\\::", 3) colnames(movies) <- c("movieId", "title", "genres") movies <- as.data.frame(movies) %>% mutate(movieId = as.numeric(levels(movieId))[movieId], title = as.character(title), genres = as.character(genres)) movielens <- left_join(ratings, movies, by = "movieId") # Validation set will be 10% of MovieLens data set.seed(1) test_index <- createDataPartition(y = movielens$rating, times = 1, p = 0.1, list = FALSE) edx <- movielens[-test_index,] temp <- movielens[test_index,] # Make sure userId and movieId in validation set are also in edx set validation <- temp %>% semi_join(edx, by = "movieId") %>% semi_join(edx, by = "userId") # Add rows removed from validation set back into edx set removed <- anti_join(temp, validation) edx <- rbind(edx, removed) rm(dl, ratings, movies, test_index, temp, movielens, removed) ############################ ### Methods and Analysis ### ############################ #Getting the summary of the dataset. Counting number of rows without NA's summary(edx) sum(complete.cases(edx)) summary(validation) sum(complete.cases(validation)) #Number of rows by genres and ratings edx %>% group_by(genres) %>% summarize(count = n()) %>% arrange(desc(count)) edx %>% group_by(rating) %>% summarize(count = n()) %>% arrange(desc(count)) #Visualization of ratings ggplot(edx, aes(rating)) + geom_bar(fill = "orange") + scale_x_continuous(breaks = c(seq(0.5,5,0.5))) + scale_y_continuous(breaks = c(seq(0, 2500000, 500000))) + theme_bw() + theme(panel.border = element_blank()) + ggtitle("Rating distribution of movies") # Quantity of unique users and movies edx %>% summarize(Users = length(unique(userId)), Movies = length(unique(movieId))) #Distribution of number of ratings per movie edx %>% group_by(movieId) %>% summarize(count = n()) %>% filter(count == 1) %>% left_join(edx, by = "movieId") %>% group_by(title) %>% summarize(rating = rating, count = count) #Visualization of number of ratings per movie edx %>% count(movieId) %>% ggplot(aes(n)) + geom_histogram(bins = 25) + scale_x_log10() + xlab("Number of ratings") + ylab("Number of movies") + ggtitle("Number of ratings per movie") #Visualization of number of ratings per user edx %>% count(userId) %>% ggplot(aes(n)) + geom_histogram(bins = 25) + scale_x_log10() + xlab("Number of ratings") + ylab("Number of users") + ggtitle("Number of ratings per user") ############################ ### Modelling Approach ### ############################ ########################################## ### Movie and user effect model # Mean rating of edx subset, movie and user averages mu <- mean(edx$rating) movie_avgs <- edx %>% group_by(movieId) %>% summarize(m_a = mean(rating - mu)) user_avgs <- edx %>% left_join(movie_avgs, by='movieId') %>% group_by(userId) %>% summarize(u_a = mean(rating - mu - m_a)) # Testing and saving the RMSE result predicted_ratings <- validation %>% left_join(movie_avgs, by='movieId') %>% left_join(user_avgs, by='userId') %>% mutate(predict = mu + m_a + u_a) %>% pull(predict) movie_user_model_rmse <- RMSE(predicted_ratings, validation$rating) # Checking the result rmse_results <- tibble(method="Movie and user effect model", RMSE = movie_user_model_rmse) rmse_results ########################################## ## Regularized movie and user effect model # lambda is a tuning parameter lambdas <- seq(0, 10, 0.25) # m_a and u_a for each lambda followed by prediction and testing the rating rmses <- sapply(lambdas, function(l){ mu <- mean(edx$rating) m_a <- edx %>% group_by(movieId) %>% summarize(m_a = sum(rating - mu)/(n()+l)) u_a <- edx %>% left_join(m_a, by="movieId") %>% group_by(userId) %>% summarize(u_a = sum(rating - mu - m_a)/(n()+l)) predicted_ratings <- validation %>% left_join(m_a, by = "movieId") %>% left_join(u_a, by = "userId") %>% mutate(predict = mu + m_a + u_a) %>% pull(predict) return(RMSE(predicted_ratings, validation$rating)) }) # Visualization of RMSEs vs lambdas to select the optimal lambda qplot(lambdas, rmses) # The optimal lambda is the one with the minimal RMSE lambda <- lambdas[which.min(rmses)] # Checking the result with the result from the previous model rmse_results <- tibble(method="Regularized movie and user effect model", RMSE = min(rmses)) rmse_results %>% knitr::kable()

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naive-bayes-prediction.R

# Naive Bayes predictions download.file(url = "https://assets.datacamp.com/production/course_2906/datasets/locations.csv", destfile = "bretts-location.csv", method = "curl") where9am <- read.csv(file = "bretts-location.csv", stringsAsFactors = TRUE) # Doing a basic predicated prediction # Compute P(A) p_A <- nrow(subset(where9am, location == "office"))/nrow(where9am) # Compute P(B) p_B <- nrow(subset(where9am, daytype == "weekday"))/nrow(where9am) # Compute the observed P(A and B) p_AB <- nrow(subset(where9am, location == "office", daytype == "weekday"))/nrow(where9am) # Compute P(A | B) p_A_given_B <- p_AB/p_B print(p_A_given_B) ## Using the Naivebays package to build a model (NOT AVAILABLE FOR R 3.3) # Load the naivebayes package library(e1071) # Build the location prediction model locmodel <- naive_bayes(location ~ daytype, data = where9am) # Predict Thursday's 9am location predict(locmodel, newdata = data.frame(daytype = as.factor("weekday"))) # Predict Saturdays's 9am location predict(locmodel, data.frame(daytype = as.factor("weekend"))) ## Hashing out some of these probs # The 'naivebayes' package is loaded into the workspace # and the Naive Bayes 'locmodel' has been built # Examine the location prediction model print(locmodel) # Obtain the predicted probabilities for Thursday at 9am predict(locmodel, newdata = thursday9am , type = "prob") # Obtain the predicted probabilities for Saturday at 9am predict(locmodel, newdata = saturday9am , type = "prob") ## Building a multi-feature bayes model # Build a NB model of location locmodel <- naive_bayes(location ~ daytype + hourtype, data = locations) # Predict Brett's location on a weekday afternoon predict(locmodel, data.frame(daytype = "weekday", hourtype = "afternoon")) # Predict Brett's location on a weekday evening predict(locmodel, weekday_evening) ## Adding the Laplace correction # Observe the predicted probabilities for a weekend afternoon predict(locmodel, weekend_afternoon, type = "prob") # Build a new model using the Laplace correction locmodel2 <- naive_bayes(location ~ daytype + hourtype, data = locations, laplace = 1) # Observe the new predicted probabilities for a weekend afternoon predict(locmodel2, weekend_afternoon, type = "prob")

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% Generated by roxygen2 (4.0.1): do not edit by hand \name{get_superclasses_information} \alias{get_superclasses_information} \title{Get superclass information.} \usage{ get_superclasses_information(contains, where) } \arguments{ \item{contains}{character. A character vector of super class names.} \item{where}{environment. The environment in which to look for superClasses. # TODO: (RK) Provide ability to look for super classes in multiple environments?} } \value{ a list with keys \code{superClasses}, \code{isRefSuperClass} indicating a character vector of super class names, whether each is a defined reference class or not, and the super class definitions, respectively. } \description{ Provide a list containing \code{superClasses}, (the actual class names) \code{isRefSuperClass}, a vector of logicals indicating whether each superclass is a reference class, and \code{superClassDefs}, containing the actual superclass definitions, obtained using \code{methods::getClass}. } \details{ Note that \code{envRefClass} will always be one of the returned superclasses. }

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runDnDsCv.R

#written by noah friedman, help from craig to run #a template for R scripts for plotting library(ggplot2) library(grid) require(cowplot) library(egg) library(dplyr) library(data.table); setDTthreads(6) library(dndscv) library(data.table) library(ggrepel) library(plyr) fix_maf_cols <- function(maf){ maf <- data.table(maf) mutations <- maf[Variant_Type %in% c("SNP", "INS", "DEL"),. (sampleID = Tumor_Sample_Barcode, chr = Chromosome, pos = Start_Position, ref = Reference_Allele, mut = Tumor_Seq_Allele2, gene= Hugo_Symbol, hgvs =HGVSp_Short, status=hypermutantStatus, cancerType=cancerType )] #mutations$chr = paste("chr", as.vector(mutations$chr), sep='') #gsub("chr","",as.vector(mutations$chr)) return(mutations) } run_dnds_on_normal_and_hypermutated_tumors <- function(inputMaf, cancerTypes){ gene_list468 <- c('ABL1', 'ACVR1', 'AGO2', 'AKT1', 'AKT2', 'AKT3', 'ALK', 'ALOX12B', 'AMER1', 'ANKRD11', 'APC', 'AR', 'ARAF', 'ARID1A', 'ARID1B', 'ARID2', 'ARID5B', 'ASXL1', 'ASXL2', 'ATM', 'ATR', 'ATRX', 'AURKA', 'AURKB', 'AXIN1', 'AXIN2', 'AXL', 'B2M', 'BABAM1', 'BAP1', 'BARD1', 'BBC3', 'BCL10', 'BCL2', 'BCL2L1', 'BCL2L11', 'BCL6', 'BCOR', 'BIRC3', 'BLM', 'BMPR1A', 'BRAF', 'BRCA1', 'BRCA2', 'BRD4', 'BRIP1', 'BTK', 'CALR', 'CARD11', 'CARM1', 'CASP8', 'CBFB', 'CBL', 'CCND1', 'CCND2', 'CCND3', 'CCNE1', 'CD274', 'CD276', 'CD79A', 'CD79B', 'CDC42', 'CDC73', 'CDH1', 'CDK12', 'CDK4', 'CDK6', 'CDK8', 'CDKN1A', 'CDKN1B', 'CDKN2A', 'CDKN2B', 'CDKN2C', 'CEBPA', 'CENPA', 'CHEK1', 'CHEK2', 'CIC', 'CREBBP', 'CRKL', 'CRLF2', 'CSDE1', 'CSF1R', 'CSF3R', 'CTCF', 'CTLA4', 'CTNNB1', 'CUL3', 'CXCR4', 'CYLD', 'CYSLTR2', 'DAXX', 'DCUN1D1', 'DDR2', 'DICER1', 'DIS3', 'DNAJB1', 'DNMT1', 'DNMT3A', 'DNMT3B', 'DOT1L', 'DROSHA', 'DUSP4', 'E2F3', 'EED', 'EGFL7', 'EGFR', 'EIF1AX', 'EIF4A2', 'EIF4E', 'ELF3', 'EP300', 'EPAS1', 'EPCAM', 'EPHA3', 'EPHA5', 'EPHA7', 'EPHB1', 'ERBB2', 'ERBB3', 'ERBB4', 'ERCC2', 'ERCC3', 'ERCC4', 'ERCC5', 'ERF', 'ERG', 'ERRFI1', 'ESR1', 'ETV1', 'ETV6', 'EZH1', 'EZH2', 'FAM175A', 'FAM46C', 'FAM58A', 'FANCA', 'FANCC', 'FAT1', 'FBXW7', 'FGF19', 'FGF3', 'FGF4', 'FGFR1', 'FGFR2', 'FGFR3', 'FGFR4', 'FH', 'FLCN', 'FLT1', 'FLT3', 'FLT4', 'FOXA1', 'FOXL2', 'FOXO1', 'FOXP1', 'FUBP1', 'FYN', 'GATA1', 'GATA2', 'GATA3', 'GLI1', 'GNA11', 'GNAQ', 'GNAS', 'GPS2', 'GREM1', 'GRIN2A', 'GSK3B', 'GTF2I', 'H3F3A', 'H3F3B', 'H3F3C', 'HGF', 'HIST1H1C', 'HIST1H2BD', 'HIST1H3A', 'HIST1H3B', 'HIST1H3C', 'HIST1H3D', 'HIST1H3E', 'HIST1H3F', 'HIST1H3G', 'HIST1H3H', 'HIST1H3I', 'HIST1H3J', 'HIST2H3C', 'HIST2H3D', 'HIST3H3', 'HLA-A', 'HLA-B', 'HNF1A', 'HOXB13', 'HRAS', 'ICOSLG', 'ID3', 'IDH1', 'IDH2', 'IFNGR1', 'IGF1', 'IGF1R', 'IGF2', 'IKBKE', 'IKZF1', 'IL10', 'IL7R', 'INHA', 'INHBA', 'INPP4A', 'INPP4B', 'INPPL1', 'INSR', 'IRF4', 'IRS1', 'IRS2', 'JAK1', 'JAK2', 'JAK3', 'JUN', 'KDM5A', 'KDM5C', 'KDM6A', 'KDR', 'KEAP1', 'KIT', 'KLF4', 'KNSTRN', 'KRAS', 'LATS1', 'LATS2', 'LMO1', 'LYN', 'MALT1', 'MAP2K1', 'MAP2K2', 'MAP2K4', 'MAP3K1', 'MAP3K13', 'MAP3K14', 'MAPK1', 'MAPK3', 'MAPKAP1', 'MAX', 'MCL1', 'MDC1', 'MDM2', 'MDM4', 'MED12', 'MEF2B', 'MEN1', 'MET', 'MGA', 'MITF', 'MLH1','KMT2A', 'KMT2D', 'KMT2C', 'KMT2B', 'MPL', 'MRE11A', 'MSH2', 'MSH3', 'MSH6', 'MSI1', 'MSI2', 'MST1', 'MST1R', 'MTOR', 'MUTYH', 'MYC', 'MYCL1', 'MYCN', 'MYD88', 'MYOD1', 'NBN', 'NCOA3', 'NCOR1', 'NEGR1', 'NF1', 'NF2', 'NFE2L2', 'NFKBIA', 'NKX2-1', 'NKX3-1', 'NOTCH1', 'NOTCH2', 'NOTCH3', 'NOTCH4', 'NPM1', 'NRAS', 'NSD1', 'NTHL1', 'NTRK1', 'NTRK2', 'NTRK3', 'NUF2', 'NUP93', 'PAK1', 'PAK7', 'PALB2', 'PARK2', 'PARP1', 'PAX5', 'PBRM1', 'PDCD1', 'PDCD1LG2', 'PDGFRA', 'PDGFRB', 'PDPK1', 'PGR', 'PHOX2B', 'PIK3C2G', 'PIK3C3', 'PIK3CA', 'PIK3CB', 'PIK3CD', 'PIK3CG', 'PIK3R1', 'PIK3R2', 'PIK3R3', 'PIM1', 'PLCG2', 'PLK2', 'PMAIP1', 'PMS1', 'PMS2', 'PNRC1', 'POLD1', 'POLE', 'PPARG', 'PPM1D', 'PPP2R1A', 'PPP4R2', 'PPP6C', 'PRDM1', 'PRDM14', 'PREX2', 'PRKAR1A', 'PRKCI', 'PRKD1', 'PTCH1', 'PTEN', 'PTP4A1', 'PTPN11', 'PTPRD', 'PTPRS', 'PTPRT', 'RAB35', 'RAC1', 'RAC2', 'RAD21', 'RAD50', 'RAD51', 'RAD51B', 'RAD51C', 'RAD51D', 'RAD52', 'RAD54L', 'RAF1', 'RARA', 'RASA1', 'RB1', 'RBM10', 'RECQL', 'RECQL4', 'REL', 'RET', 'RFWD2', 'RHEB', 'RHOA', 'RICTOR', 'RIT1', 'RNF43', 'ROS1', 'RPS6KA4', 'RPS6KB2', 'RPTOR', 'RRAGC', 'RRAS', 'RRAS2', 'RTEL1', 'RUNX1', 'RXRA', 'RYBP', 'SDHA', 'SDHAF2', 'SDHB', 'SDHC', 'SDHD', 'SESN1', 'SESN2', 'SESN3', 'SETD2', 'SETD8', 'SF3B1', 'SH2B3', 'SH2D1A', 'SHOC2', 'SHQ1', 'SLX4', 'SMAD2', 'SMAD3', 'SMAD4', 'SMARCA4', 'SMARCB1', 'SMARCD1', 'SMO', 'SMYD3', 'SOCS1', 'SOS1', 'SOX17', 'SOX2', 'SOX9', 'SPEN', 'SPOP', 'SPRED1', 'SRC', 'SRSF2', 'STAG2', 'STAT3', 'STAT5A', 'STAT5B', 'STK11', 'STK19', 'STK40', 'SUFU', 'SUZ12', 'SYK', 'TAP1', 'TAP2', 'TBX3', 'TCEB1', 'TCF3', 'TCF7L2', 'TEK', 'TERT', 'TET1', 'TET2', 'TGFBR1', 'TGFBR2', 'TMEM127', 'TMPRSS2', 'TNFAIP3', 'TNFRSF14', 'TOP1', 'TP53', 'TP53BP1', 'TP63', 'TRAF2', 'TRAF7', 'TSC1', 'TSC2', 'TSHR', 'U2AF1', 'UPF1', 'VEGFA', 'VHL', 'VTCN1', 'WHSC1', 'WHSC1L1', 'WT1', 'WWTR1', 'XIAP', 'XPO1', 'XRCC2', 'YAP1', 'YES1', 'ZFHX3', 'ZRSR2') impactGenesThatDontWorkForDnds <- c("MAP3K14", "MYCL1", "RYBP", "KMT5A", "PAK5", "FAM123B") gene_list468 <- setdiff(gene_list468, impactGenesThatDontWorkForDnds) dndsResults <- vector(mode="list", length=length(cancerTypes)) names(dndsResults) <- cancerTypes #MAIN LOOP TO RUN DNDS on all cohorts for(cType in cancerTypes){ print(paste('working on', cType)) localList <- vector(mode="list", length=2) names(localList) <- c('Hypermutated', 'Normal') normalMaf <- inputMaf[(inputMaf$cancerType == cType) & (inputMaf$status == 'Normal'),] #read.table(normalPath, sep = '\t', header=TRUE) hyperMaf <- maf[(inputMaf$cancerType == cType) & (inputMaf$status == 'Hypermutated'),]#read.table(hyperPath, sep = '\t', header=TRUE) normalMaf <- normalMaf[,c('sampleID', 'chr' ,'pos' ,'ref' ,'mut')] hyperMaf <- hyperMaf[,c('sampleID', 'chr' ,'pos' ,'ref' ,'mut')] print(paste('running normal, nmut:', dim(normalMaf))) dndsNormal <- dndscv(normalMaf, gene_list = gene_list468, max_muts_per_gene_per_sample = Inf, max_coding_muts_per_sample = Inf, use_indel_sites=F) print(paste('running hyper, nmut:', dim(hyperMaf))) dndsHyper <- dndscv(hyperMaf, gene_list = gene_list468, max_muts_per_gene_per_sample = Inf, max_coding_muts_per_sample = Inf, use_indel_sites=F) localList$Hypermutated <- dndsHyper localList$Normal <- dndsNormal dndsResults[[cType]] <- localList } return(dndsResults) } #TODO there is an error similar to: #https://github.com/im3sanger/dndscv/issues/14 cancerTypes= c('Colorectal Cancer', 'Endometrial Cancer', 'Glioma', 'Prostate Cancer', 'Esophagogastric Cancer', 'Bladder Cancer') maf <- read.table('/Users/friedman/Desktop/hypermutationProjectFinal/files/mafs/impact_mutations_maf_synonymous_included.maf', sep = ',', header=TRUE) maf <- fix_maf_cols(maf) dndsResults <- run_dnds_on_normal_and_hypermutated_tumors(maf, cancerTypes) dndsMerged <- vector(mode="list", length=length(cancerTypes)) for(cancerType in cancerTypes){ sel_cvN <- dndsResults[[cancerType]]$Normal$sel_cv sel_cvH <- dndsResults[[cancerType]]$Hypermutated$sel_cv mergedCtype <- merge(sel_cvN, sel_cvH, suffixes = c(".Normal",".Hypermutated"), by='gene_name') mergedCtype$cancerType <- cancerType dndsMerged[[cancerType]] <- mergedCtype } combinedDf <- bind_rows(dndsMerged) colnames(combinedDf) plt <- make_dnds_plot(combinedDf, title='DNDS Hypermutated vs\nNon-hypermutated') ggsave('~/Desktop/plot.pdf', plt, width = 8, height = 8, units = c("in")) write.table(combinedDf, file='/Users/friedman/Desktop/mnt/juno/work/taylorlab/friedman/myAdjustedDataFiles/dndsHypermutants.tsv', quote=FALSE, sep='\t') #### ######## ############## ################## ######################## ################## ############## ########### ######### ### #TODO move this analysis to its own neighborhood df <- read.table('/Users/friedman/Desktop/WORK/dataForLocalPlotting/dndscv_pancan_vs_local_genes.tsv', sep = '\t', header=TRUE) endoNormalGenes <- unique(sel_cvEdnoN[sel_cvEdnoN$qglobal_cv < .01,]$gene_name) coloNormalGenes <- unique(sel_cvColoN[sel_cvColoN$qglobal_cv < .01,]$gene_name) #dfLim <- subset(df, !(gene %in% endoNormalGenes)) dfLim <- subset(df, !(gene %in% coloNormalGenes)) ggplot(dfLim, aes(x=nOncogenicMuts, y=1- log10(qVal), label=displayLabelOncogenic))+ geom_point()+ geom_text_repel()+ geom_segment(aes(xend=0, yend=2, y=2, x=40))+ geom_segment(aes(xend=0, yend=3, y=3, x=40))+ xlim(0,50)+ xlab('N oncogenic mutations across hypermutated cancer-type cohort')+ ylab('1 - log10(pan-cancer dndscv q value)')+ ggtitle('Oncogenic mutations in colorectal cancer\nin non-colorectal cancer related genes') #PLOT THE DNDSCV information df <- read.table('/Users/friedman/Desktop/WORK/dataForLocalPlotting/dndscvSummary.tsv', sep = '\t', header=TRUE) make_dnds_plot(df, 'DNDS-CV Comparing Hypermutated and\n Non-Hypermutated Cancers')

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/Assignment2/pollutantmean.R

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pollutantmean.R

pollutantmean <- function(directory, pollutant, id = 1:332) { cwd <- getwd() setwd(directory) fileList = paste0(formatC(id, width = 3, flag = "0"), ".csv") data = lapply(fileList, read.csv) polList <- list() for(d in data) { polList <- append(polList, d[[pollutant]]) } polVec <- as.numeric(polList) setwd(cwd) mean(polVec, na.rm = TRUE) }

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/scripts/05_tibble_adatbeolvasas.R

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2022-10-28T18:03:31.763798

2022-10-24T08:46:45

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05_tibble_adatbeolvasas.R

# A tibble. Fájlok olvasása és írása. library(tidyverse) # 1. A tibble ---------------------------------------------------------------- # A tibble csomaggal lehet őket kezelni - ez is benne van a tidyverse szupercsomagban # data.frame-ből tibble: iris class(iris) as_tibble(iris) # Vektorokbol tibble tibble( x = 1:5, y = 1, z = x ^ 2 + y ) # Egyelemű bemeneteket automatikusan újra felhasználja / a példában y # Lehet hivatkozni az éppen létrehozott változókra / a példában z # A tibble-ben a valtozonevek lehetnek ervenytelen R valtozonevek # ` ` (magyar bill: Alt Gr + 7) hasznalataval lehet rajuk hivatkozni tb <- tibble( `:)` = "smile", ` ` = "space", `2000` = "number" ) tb # Transzponalt adatbevitel a tribble() fuggvennyel: # ez akkor nagyon jó, ha kódban akarunk bevinni egy kisebb adattáblát. # Oszlopok neveit formulával adjuk meg ( ~ <név>), # az értékeket pedig vesszővel elválasztva soroljuk fel tribble( ~x, ~y, ~z, #--/--/---- olvashatóbb, ha a fejléc alá egy komment sort teszünk "a", 2, 3.6, "b", 1, 8.5 ) # tibble nyomtatasa a konzolra: alapbeállítás szerint 10 sor, # és annyi oszlop, amennyi a konzolra kifér # minden oszlop alatt látjuk a típusát is mpg # Megjelenített sorok számának és a konzolra nyomtatott adatok szélességének szabályozása: print(mpg, n = 20, width = 100) # Modosithatjuk az alapbeallitasokat is # Ha tobb mint m sor van, nyomtasson n sort # options(tibble.print_max = n, tibble.print_min = m) # Mindig minden sor nyomtatasa: options(dplyr.print_min = Inf) # Mindig minden oszlop nyomtatasa, a konzol szelessegetol fuggetlenul: options(tibble.width = Inf) # További opciók: package?tibble ?print.tbl # Megtekintés az RStudio-ban: View(mpg) # Hogyan tudunk kinyerni egyetlen változót? df <- tibble( x = runif(5), y = rnorm(5) ) # $ - csak nev alapjan df$x # [[]] - nev (string!) es pozicio alapjan is df[["x"]] df[[1]] # Ha egy pipe-ban szeretnénk használni, használjuk a . helyőrzőt! df %>% .$x df %>% .[[1]] # Ha nem létező oszlopot próbálunk elérni, # figyelmeztetést kapunk, eredményül pedig NULL-t df$z # Nehany regebbi csomag nem kompatibilis a tibble-lel. # Ilyenkor konvertaljuk az adatainkat data.frame-é: as.data.frame(df) # 1.1 tibble - gyakorlas ---------------- # 1) Nyerd ki a diamonds táblából a color változót # a) név alapján! # b) pozíció alapján! # 2) Ha egy valtozo neve egy változóban tarolva, hogyan lehet a valtozot kinyerni # egy tibble-bol? mpg adatokban van a cty valtozo var <- "cty" # 3) Hasonlítsd össze a következő műveleteket data.frame-n és # egy egyező tibble-n! Mi a különbség? # Mi lehet a gond a data.frame működésében? df <- data.frame(abc = 1, xyz = "a") df$x df[, "xyz"] df[, c("abc", "xyz")] df_t <- tibble(abc = 1, xyz = "a") df_t$x df_t[, "xyz"] df_t[, c("abc", "xyz")] # 2. Adatok importálása ------------------------------------ # 2.1 utils ---- # read.csv(), read.delim(), read.table() # A beolvásófüggvények első argumentuma a fájl elérési útja: # meg kell mondani a függvénynek, hogy hol van a fájl, amit # be szeretnénk tölteni az R-be. magas <- read.csv("C://Users/Berci/teaching/introduction_to_R/data/magassagok_1.txt") # Ez azonban kényelmetlen lehet, ha szeretnénk, # hogy több gépen is működjön a kód. # Például a te gépeden elég valószínű, hogy nem fog működni. # Megoldás, ha RStudio projektet használunk. # Ilyenkor az .RProj fájlt tartalmazó projektmappa lesz a "kiindulópont". # Ez az elérési út működni fog más gépen is, ha az introduction_to_R.RProj projekt meg van nyitva. magas <- read.csv("data/magassagok_1.txt") # Elérési utak meghatározása op.rendszerenként eltér. # A file.path az adott op.rendszernek megfelelő formátumú elérési utat ad: path <- file.path("data", "magassagok_1.txt") magas <- read.csv(path) # Honnan tudja a file.path a helyes formátumot? # Nézzük meg a forráskódot! file.path # A .Platform listából nézi meg .Platform # A read.csv jól működik, ha a csv fájlban a tizedesvessző ponttal, # az oszlopok elválasztása pedig vesszővel van jelölve (angolszász) # DE: a tizedesvesszőt van, ahol vesszővel jelölik (pl. magyar) # és az oszlopokat ;-vel választják el egymástól, # ezeket a read.csv "nem érti": path <- file.path("data", "magassagok_2.txt") (magas_2 <- read.csv(path)) str(magas_2) # Erre van kitalálva a read.csv2: (magas_2 <- read.csv2(path)) str(magas_2) # Mit csinál a read.csv és a read.csv2 ? read.csv read.csv2 # A read.table függvényt hívják meg, # a sep és a dec argumentumokat a célnak megfelelően beállítva # 2.2 readr ---- # A readr csomag legtöbb függvénye szövegfájlokat olvas be: # read_csv (vesszővel tagolt) # read_tsv (tabulátorral tagolt) # read_csv2 (pontosvesszovel tagolt) # read_delim (bármilyen tagoló megadható) # read_fwf (fix szélességű fájlok) # a read fuggvenyek elso argumentuma a beolvasando fajl eleresi utvonala read_csv("data/magassagok_1.txt") data_path <- "data" path <- file.path(data_path, "magassagok_1.txt") read_csv(path) # Meg lehet adni a bemenet csv fajlt a fuggveny hivasakor is # Igy lehet probalgatni a readr mukodeset # Es masoknak lehet reprodukalhato peldakat adni (pl stackoverflow-n) read_csv( "a,b,c 1,2,3 4,5,6" ) # A beolvasott fajl elso sorat oszlopnevkent ertelmezi a readr # Felulirhato! Mikor lehet erre szukseg? # 1) metaadatok a fájl tetején read_csv( "The first line of metadata The second line of metadata x,y,z 1,2,3", skip = 2 ) read_csv( "# A comment I want to skip x,y,z 1,2,3", comment = "#" ) # 2) nincs fejlec az adatokban # a, megmondjuk, hogy nincs fejléc (\n sortorest jeloli) read_csv("1,2,3\n4,5,6", col_names = FALSE) # b, megadunk egy fejlécet read_csv("1,2,3\n4,5,6", col_names = c("x", "y", "z")) # hianyzo adatok jelolesenek meghatarozasa: na argumentum read_csv("a,b,c\n1,2,.", na = ".") # Az esetek 75%-ában elég a fentieket tudni # a csv fájlok olvasásához! # A readr a base R beolvasóihoz képest: # - gyorsabb # - tibble-be olvasnak be # - karakter oszlopokból nem lesz automatikusan faktor # - nem használ sorneveket # - jobb a megismételhetőségük (base R függ op.rendsz.-től és R beállításoktól) # 2.2.1 readr - gyakorlás ------------------------------------------------- # 1) A data könyvtárban a digitspan_data.txt-ben négy személy életkora és számterjedelme van. # Az első két sorban az adatgyűjtés időpontja, és a kurzus neve van. Ezeket nem szeretnénk beolvasni. # Viszont azt szeretnénk, hogy az első oszlop neve "age", a második oszlop neve pedig "digitspan" legyen. # Olvasd be egy tibble-be ennek megfelelően a digitspan_data.txt-t! # 2) Mi a gond ezekkel a kódból megadott csv fájlokkal? read_csv("a,b\n1,2,3\n4,5,6") read_csv("a,b,c\n1,2\n1,2,3,4") read_csv("a,b\n\"1") read_csv("a,b\n1,2\na,b") read_csv("a;b\n1;3") # 2.3 Vektorok elemzése --------------------------------------------------- # Hogyan olvassa be a readr a lemezről a fájlokat? # Ehhez előbb nézzük meg, hogyan dolgoznak a parse_* függvények! # Adunk nekik egy karaktervektort, és visszaadnak egy speciális vektort. str(parse_logical(c("TRUE", "FALSE", "NA"))) str(parse_integer(c("1", "2", "3"))) str(parse_date(c("2010-01-01", "1979-10-14"))) # 1. arg.: elemzendő karaktervektor, 2. arg.: NA jelölésének meghatározása parse_integer(c("1", "231", ".", "456"), na = ".") # Mi történik, ha nem sikerül az elemzés? x <- parse_integer(c("123", "345", "abc", "123.45")) # Amit nem sikerült elemezni, ott NA-t kapunk x # Sikertelen elemzések áttekintése: problems(x) # 2.3.1 Számok ------------------------------------------------------------ # Miért nem olyan egyszerű? # a, tizedesvessző lehet . és , is parse_double("1.23") # Helyi sajátosságok beállítása: locale parse_double("1,23", locale = locale(decimal_mark = ",")) # b, számok körül néha spec. karakterek $ % # parse_number nagyon hasznos: elenged minden karaktert, ami nem szám parse_number("$100") parse_number("20%") parse_number("It cost $123.45") # c, különböző csoportosító karakterek 1'000 1,000 1 000 # parse_number, locale-ban pedig megadjuk a csoportosítót: # USA parse_number("$123,456,789") # Európában gyakori: parse_number("123.456.789", locale = locale(grouping_mark = ".")) # Svájc: parse_number("123'456'789", locale = locale(grouping_mark = "'")) # 2.3.2 Karakterláncok (strings) ------------------------------------------ # Ugyanaz a string többféleképpen is reprezentálható # Hogyan reprezentál az R egy stringet? charToRaw("Hadley") # Minden hexadecimális szám egy bájtnyi információt reprezentál. # A karakterkódolás a hexadecimális számok és karakterek megfeleltetése. # ASCII kódolás az angol nyelvre jó # Egyéb nyelvek: # Latin1 / ISO-8859-2 Ny-Eu nyelvek # Latin2 / ISO-8859-2- K-Eu nyelvek # # Nagyon elterjedt és szinte mindent kódol: UTF-8 # A readr alapértelmezésként UTF-8-at használ beolvasáshoz és íráshoz is # Probléma akkor lehet, ha nem UTF-8-ban van kódolva az adat, # ilyenkor furcsa stringeket fogunk látni: x1 <- "\x82\xb1\x82\xf1\x82\xc9\x82\xbf\x82\xcd" x1 parse_character(x1, locale = locale(encoding = "Shift-JIS")) # Hogyan jövünk rá a kódolásra? # Jobb esetben a dokumentációból kiderül. # Ha nem, segíthet a guess_encoding (sok szöveggel jobban működik) szoveg <- "Ez egy magyar nyelvű szöveg." guess_encoding(charToRaw(szoveg)) # 2.3.3 Faktorok (kategorikus változók) ----------------------------------- # Lehetséges értékek halmaza ismert fruit <- c("apple", "banana") parse_factor(c("apple", "banana", "bananana"), levels = fruit) # Ha sok problémás eset van, érdemes faktor helyett # inkább stringnek beolvasni, és később kipucolni # 2.3.4 Dátum és idő ------------------------------------------------------ # Alapbeállítás szerint: # a, parse_datetime ISO8601 formátumra számít év, hónap, nap, óra, perc, másodperc parse_datetime("2010-10-20 141345") # Ha az idő hiányzik, azt éjfélnek veszi parse_datetime("20101010") # b, parse_date év (4 számj.), majd - vagy /, hónap, majd - or /, és a nap: parse_date("2010-10-01") # c, parse_time óra : perc (opcionális : másodperc és am/pm) parse_time("01:10 am") parse_time("20:10:01") # Ha az alapbeállítások nem működnek, megadhatunk egyedi formátumot # részleteket lásd https://r4ds.had.co.nz/data-import.html#readr-datetimes parse_date("01/02/15", "%d/%m/%y") # nap/hónap/év, tehát február 1. parse_date("01/02/15", "%m/%d/%y") # hónap/nap/év, tehát január 2. parse_date("Jan 23 2015", "%b %d %Y") # <hónap rövid neve> <nap> <év> # 2.4 Fájlok elemzése ----------------------------------------------------- # Hogyan találja ki a readr a változók típusát? # Megnézi az első 1000 sort, és heurisztikákat alkalmaz # FRISSÍTÉS: read_csv újabb verziója alapbeállítás szerint az összes sort megnézi guess_parser("2010-10-01") guess_parser("15:01") guess_parser(c("TRUE", "FALSE")) guess_parser(c("1", "5", "9")) guess_parser(c("12,352,561")) # De lehet, hogy az első 1000 sor speciális, pl. # - első 1000 sorban egész szám van, de később jönnek tizedestörtek is # - első 1000 sorban NA van, később jönnek az értékek # Lássunk egy példát! challenge <- read_csv(readr_example("challenge.csv")) problems(challenge) # Mi volt az oszlopok meghatározása az előző beolvasásnál? # Módosítsuk az oszlopok típusát! challenge <- read_csv( readr_example("challenge.csv"), col_types = cols( x = col_double(), y = col_character() ) ) # Mi a helyzet az y-nal? challenge tail(challenge) # Adjuk meg helyesen típuását ennek az oszlopnak is! challenge <- read_csv( readr_example("challenge.csv"), col_types = cols( x = col_double(), y = col_date() ) ) challenge tail(challenge) # Ha az adat már R-ben van mint karaktervektor: parse_xyz # Ha a readr beolvasást akarjuk irányítani: col_xyz # Érdemes mindig explicit megadni az oszlopok típusát, # következetes és reprodukálhatóbb lesz az adatimportálás. # Megoldás lehet még, ha több sor alapján következtetünk # az oszlop típusára. challenge_2 <- read_csv( readr_example("challenge.csv"), guess_max = 3000 ) # Vagy ha minden oszlopot karakternek olvasunk be, challenge_2 <- read_csv( readr_example("challenge.csv"), col_types = cols(.default = col_character()) ) # És utána a type_convert-tel felismertetjük az oszlopok típusát type_convert(challenge_2) # Hogyan találja ki a type_convert a típust? # http://r4ds.had.co.nz/data-import.html#parsing-a-file # 2.4 fájlok elemzése - gyakorlás # 1) A data könyvtárban a recog_data.csv fájl egy felismerési teszt eredményeit tartalmazza egy sajátos formátumban. # Az első oszlopban a résztvevő azonosítója, a másodikban a felismerési pontosság %-ban megadva, # a harmadik oszlopban pedig a résztvevő életkora. # Akinél nincs meg az életkor, annál azt írták a táblázatba, hogy "nem ismert". # A legfelső sorban a kísérlet neve szerepel. # Olvasd be és dolgozd fel az adatokat, ügyelve arra, hogy # - a kísérlet neve ne legyen része a beolvasott tibble-nek # - adj az oszlopoknak értelmes neveket már a beolvasásnál # - a hiányzó adatok NA-nak legyenek beolvasva # - a beolvasás után a százalékos pontosság legyen 0 és 1 közötti tizedestörtté alakítva (mutate) # 3. Fájlba írás ---------------------------------------------------------- # readr: write_tsv és write_csv # Könnyen olvasható fájlokat írnak: # - karakterláncokat UTF-8-ban kódolják # - dátum, dátum-idő ISO8601 formátumban # Két arg-t mindenképp meg kell adni: melyik adatot, és hová írjuk write_csv(challenge, "challenge_out.csv") ch_again <- read_csv("challenge_out.csv") # Az oszlopok típusa elveszett időközben! # Mikor lehet ez zavaró? # Alternatívák: fájlba írunk komplett objektumokat # a, RDS (R saját bináris fájlformátuma) write_rds(challenge, "challenge_out.RDS") (ch_rds <- read_rds("challenge_out.RDS")) # b, feather (gyors bináris fájlformátum, más nyelvek is "értik") (feather csomag kell hozzá!) # install.packages("feather) # library(feather) write_feather(challenge, "challenge.feather") read_feather("challenge.feather") # További adattípusok (xls, xlsx, SPSS, SAS, Stata, ...) # http://r4ds.had.co.nz/data-import.html#other-types-of-data # https://cran.r-project.org/web/packages/XLConnect/vignettes/XLConnect.pdf

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/hierarchical_CleanedData.R

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AlexandrosPetropoulos/BankMarketing

c00fc4ea271f9abd6abf330fac3e22fc58e97e63

e5f185205bf52eb1ff14f83b71237a3223b20907

refs/heads/main

2023-01-30T14:38:14.507393

2020-12-18T13:22:10

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hierarchical_CleanedData.R

# Clear plots if(!is.null(dev.list())) dev.off() # Clean workspace rm(list=ls()) # Clear console cat("\014") # Set working directory setwd("~/Big_Data") # Load libraries (mporei na ksemine kapoia apo tis dokimes kai tora na min xreiazetai) # entoli gia install packages install.packages("caret") #library(rpart) #DECISION TREES #library(rpart.plot) #DECISION TREES #library(mice) #library(VIM) #library(gdata) #library(plyr)#prosoxi prota prepei na ginei load i plyr kai meta i dplyr #library(dplyr) library(ggplot2) #library(FSelector) #library(e1071) #NAIVE BAYES library(caret) #library(pander) #library(class) library(MLmetrics)# xreiazetai gia to confusionMatrix #library(mlbench) #library(klaR) library(class) #KNN #library(ROCR) library(cluster)#KMEANS,HIERARCHICAL #library(factoextra) #library(NbClust) #library(pracma) #library(mixtools) #library(dbscan) #library(DataExplorer) #create_report(train) #hkmeans # Import data from csv bank_data_original=read.table(file ="bank-additional/bank-additional-full.csv",header=TRUE,sep=";") bank <- bank_data_original #Data preparation cleaning #shuffle Data #PROSOXI PREPEI OPOSDIPOTE NA EINAI FALSE, allios vgainoun xiliades duplicates #search google for sampling with replacement set.seed(248) bank <- bank[sample(1:nrow(bank),replace=FALSE), ] #split in train and validation test stratified #createDataPartition epistrefei mia lista me FALSE kai TRUE inBank <- createDataPartition(y = bank$y, p = .70, list = FALSE) #inBank <- createDataPartition(y = bank, p = .70, list = FALSE) training <- bank[inBank,] testing <- bank[-inBank,] #library(ROSE) #balance data #data.balanced.ou <- ovun.sample(y~., data=training,N=nrow(training), p=0.5, seed=1, method="both")$data #training<-data.balanced.ou #prota kanoume scale ta data kai meta binarization #prosxi training kai test prepei na ginoun me siggekrimeno tropo, opos edo preProcValues <- preProcess(training, method = c("center", "scale")) #preProcValues <- preProcess(training, method = "range",rangeBounds = c(0, 1)) trainTransformed <- predict(preProcValues, training) testTransformed <- predict(preProcValues, testing) trainTransformed <- training testTransformed <- testing # dummify the data #kano dummy to y tote pernei diafores times metaksi 0 kai 1 #dmy <- dummyVars(" ~ .", data = trainTransformed) dmy <- dummyVars(" ~ .", data = trainTransformed, fullRank=T) trainset <- data.frame(predict(dmy, newdata = trainTransformed)) #epidi to target egine y.no kai y.yes kratame mono to ena apo ta 2 #trainset <-trainset[,-dim(trainset)[2]] colnames(trainset)[dim(trainset)[2]] <- "y" #dmy <- dummyVars(" ~ .", data = testTransformed) dmy <- dummyVars(" ~ .", data = testTransformed, fullRank=T) testset <- data.frame(predict(dmy, newdata = testTransformed)) #epidi to target egine y.no kai y.yes kratame mono to ena apo ta 2 #testset <-testset[,-dim(testset)[2]] colnames(testset)[dim(testset)[2]] <- "y" #trainset<-trainset[,c(1:40,54)] #testset<-testset[,c(1:40,54)] ###################UNSUPERVISED # #Hierarchical #load("dist_un") load("dist_unscaled.Rdata") #d = dist(trainset[,-ncol(trainset)]) #dim(trainset) 27364 54 range 0-1 #save(d, file = "dist_hierarchical_70_30.Rdata") #poia methodo #p.x. spherical clusters me wards hc_single = hclust(d, method = "single") #plot(hc_single) clusters = cutree(hc_single, k = 8) hc_complete = hclust(d, method = "complete") #plot(hc_complete) clusters = cutree(hc_complete, k = 8) hc_ave = hclust(d, method = "ave") #plot(hc_complete) clusters = cutree(hc_ave, k = 3) # Silhouette vgenei veltisto 2 slc = c() for (i in 2:10){ # Create clusters clusters = cutree(hc_ave, k = i) # Calculate and store silhouette values slc [i-1] = mean(silhouette(clusters, d)[, 3]) } plot(2:10, slc, type="b", xlab="Number of Clusters", ylab="Silhouette") abline(v=3, lty=2) #Elbow gia hierarchical #enonoume ta dedomena train kai test #? #? #? nCluster = 3 #clusters = cutree(hc_single, k = nCluster) #head(clusters) majorityClass <- NULL for (i in 1:nCluster){ #trainset[clusters == i &,,drop=FALSE] if(dim(trainset[clusters==i & trainset$y==0,])[1]>dim(trainset[clusters==i & trainset$y==1,])[1]){ majorityClass <- c(majorityClass,0) } else{ majorityClass <- c(majorityClass,1) } } majorityClass #anatheto san neo y ta clusters sta opoia anikei i kathe grammi #temptrainset <-trainset #temptrainset$y<-NULL #temptrainset$yy <- clusters #apla edo anti gia to y tou train vazoume ta clusters pou dimiourgithikan #prediction = knn(trainset[,-ncol(trainset)], testset[,-ncol(testset)], temptrainset$yy, k = 1) #head(prediction) #arkei kai to parakato , mporo na sviso ta temp(to clusters isos thelei na ginei dataframe , alla kai etsi douleui) prediction = knn(trainset[,-ncol(trainset)], testset[,-ncol(testset)], clusters, k = 1) #gia error sto train set #prediction = knn(trainset[,-ncol(trainset)], trainset[,-ncol(trainset)], clusters, k = 1) ######## #den xreiazetai #for (i in 1:size(clusters)[2]){ # clusters[[i]] <- majorityClass[clusters[[i]]] # #} ###### #peritto i metatropi , alla auto skeftika pio grigora #ggplot2 gia ti size prediction_int<-as.numeric(prediction) for(i in 1:length(prediction_int)){ prediction_int[i] <- majorityClass[prediction_int[i]] } results <- confusionMatrix(factor(prediction_int,levels = c(0,1)), factor(testset[,ncol(testset)]),positive="1") setNames(data.frame(results$overall["Accuracy"],results$byClass["Precision"],results$byClass["Recall"],results$byClass["F1"],row.names = NULL),c("Accuracy","Precision","Recall","F1")) source("draw_confusion_matrix.R") draw_confusion_matrix(results) #error gia trainset results <- confusionMatrix(factor(prediction_int,levels = c(0,1)), factor(trainset[,ncol(trainset)]),positive="1") setNames(data.frame(results$overall["Accuracy"],results$byClass["Precision"],results$byClass["Recall"],results$byClass["F1"],row.names = NULL),c("Accuracy","Precision","Recall","F1")) ############################################################################################ #den xrisimopoiithike telika , alla einai kali sinartisi , tin afino gia na tin exo #gia na anatheso nea stoixeia ipologizo tin apostasi #https://stackoverflow.com/questions/39005958/r-how-to-get-row-column-subscripts-of-matched-elements-from-a-distance-matri ## 1D index to 2D index finv <- function (k, dist_obj) { if (!inherits(dist_obj, "dist")) stop("please provide a 'dist' object") n <- attr(dist_obj, "Size") valid <- (k >= 1) & (k <= n * (n - 1) / 2) k_valid <- k[valid] j <- rep.int(NA_real_, length(k)) j[valid] <- floor(((2 * n + 1) - sqrt((2 * n - 1) ^ 2 - 8 * (k_valid - 1))) / 2) i <- j + k - (2 * n - j) * (j - 1) / 2 cbind(i, j) } #dd is dist object #finv(which.min(dd),dd) head(trainset[clusters==i,-ncol(trainset)]) #pososta yes kai no se kathe cluster gia na poume poso kala einai #single #gia to 1 dim(trainset[clusters==1 & trainset$y==1,])[1] # 2572 yes dim(trainset[clusters==1 & trainset$y==0,])[1] #25206 no 2572/(25206+2572) # 9% yes 90% no #gia to 2 dim(trainset[clusters==2 & trainset$y==1,])[1] #375 dim(trainset[clusters==2 & trainset$y==0,])[1] #gia to 3 dim(trainset[clusters==3 & trainset$y==1,])[1] #0 dim(trainset[clusters==3 & trainset$y==0,])[1] #1 #gia to 4 dim(trainset[clusters==4 & trainset$y==1,])[1] #0 dim(trainset[clusters==4 & trainset$y==0,])[1] #1 #complete #gia to 1 dim(trainset[clusters==1 & trainset$y==1,])[1] # 3042 yes dim(trainset[clusters==1 & trainset$y==0,])[1] #25408 no #gia to 2 dim(trainset[clusters==2 & trainset$y==1,])[1] #201 dim(trainset[clusters==2 & trainset$y==0,])[1] #95 dim(trainset[clusters==3 & trainset$y==1,])[1] #5 dim(trainset[clusters==3 & trainset$y==0,])[1] #7 #gia to 4 dim(trainset[clusters==4 & trainset$y==1,])[1] #0 dim(trainset[clusters==4 & trainset$y==0,])[1] #74 for (i in 1:nCluster){ a=dim(trainset[clusters==i & trainset$y==1,])[1] b=dim(trainset[clusters==i & trainset$y==0,])[1] if(majorityClass[i] == 0){ x= (b/(b+a)) } else{ x = (a/(b+a)) } print(x) }

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/script/rml/instacard/tools.R

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genpack/tutorials

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refs/heads/master

2023-06-10T21:18:27.491378

2023-06-06T00:15:14

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tools.R

## Tools for instacard project: # Version: 2 # Product Within-Cluster Features Added: next_last = function(v){ if(length(v) < 2) return(-1) else return(v[length(v) - 1]) } uncumsum = function(v){ nn = length(v) if(nn < 2) return(-1) else return(c(v[1], v[-1] - v[-nn])) } ## input dataset must contain all orders of each customer upto the current order number build_features = function(dataset){ dataset %>% arrange(user_id, order_number) %>% distinct(user_id, order_number, days_since_prior_order) %>% group_by(user_id) %>% summarise(avgDaysBetweenCustomerOrders = mean(days_since_prior_order, na.rm = T), varDaysBetweenCustomerOrders = var(days_since_prior_order, na.rm = T)) %>% ungroup -> customer_order_pace # Customer Profile: dataset %>% group_by(user_id) %>% summarise(TotalCountCustomerProductsOrdered = length(product_id), current_order_id = last(order_id), current_order_number = max(order_number), current_day_number = max(day_number), TotalCountCustomerOrders = max(order_number), TotalCountCustomerReorders = sum(reordered), avgCustomerOrderHOD = mean(order_hour_of_day %>% as.numeric, na.rm = T), maxCustomerOrderHOD = max(order_hour_of_day %>% as.numeric, na.rm = T), minCustomerOrderHOD = min(order_hour_of_day %>% as.numeric, na.rm = T), avgCustomerOrderDOW = mean(order_dow), modCustomerOrderDOW = most_frequent(order_dow)) %>% ungroup %>% left_join(customer_order_pace, by = 'user_id') -> customer_profile # Customer-aisle profile: dataset %>% group_by(user_id, aisle_id) %>% summarise(CountCustomerWithinAisleOrdered = length(order_id)) %>% ungroup -> customer_aisle_profile # Customer-department profile: dataset %>% group_by(user_id, department_id) %>% summarise(CountCustomerWithinDepartmentOrdered = length(order_id)) -> customer_department_profile # Customer-Cluster profile: (By cluster we mean product cluster) dataset %>% group_by(user_id, cluster_id) %>% summarise(CountCustomerWithinClusterOrdered = length(order_id)) %>% ungroup -> customer_cluster_profile # Product Profile: dataset %>% group_by(product_id) %>% summarise(TotalProductOrdered = length(order_id), TotalProductReordered = sum(reordered), avgProductOrderHOD = mean(order_hour_of_day %>% as.numeric, na.rm = T), maxProductOrderHod = max(order_hour_of_day %>% as.numeric, na.rm = T), minProductOrderHod = min(order_hour_of_day %>% as.numeric, na.rm = T), varProductOrderHod = var(order_hour_of_day %>% as.numeric, na.rm = T), avgProductOrderDOW = mean(order_dow), modProductOrderDOW = most_frequent(order_dow), varProductOrderDOW = var(order_dow) ) %>% ungroup -> product_profile # Aisle Profile: dataset %>% group_by(aisle_id) %>% summarise(AbsoluteAislePopularity = length(order_id), TotalWithinAisleReorders = sum(reordered)) %>% ungroup -> aisle_profile # Department Profile: dataset %>% group_by(department_id) %>% summarise(AbsoluteDepartmentPopularity = length(order_id), TotalWithinDepartmentReorders = sum(reordered)) %>% ungroup -> department_profile # Cluster Profile: dataset %>% group_by(cluster_id) %>% summarise(AbsoluteClusterPopularity = length(order_id), TotalWithinClusterReorders = sum(reordered)) %>% ungroup -> cluster_profile # Feature 1: # CountProductOrdered: dataset %>% left_join(customer_profile, by = 'user_id') %>% arrange(user_id, order_number) %>% group_by(user_id, product_id, aisle_id, department_id, cluster_id) %>% summarise(CountCustomerProductOrdered = length(order_id), CountCustomerProductReordered = sum(reordered), CountCustomerProductFirstSelection = sum(order_number == 1), CountCustomerProductFirstTwoSelections = sum(order_number < 3), CountCustomerProductFirstThreeSelections = sum(order_number < 4), IsProductInCurrentCustomersOrder = sum(order_id == current_order_id), day_number_last_product_ordered = last(day_number), day_number_nextlast_product_ordered = next_last(day_number), order_number_last_product_ordered = last(order_number), order_number_nextlast_product_ordered = next_last(order_number), avgDaysBetweenProductOrders = mean(uncumsum(day_number), na.rm = T), varDaysBetweenProductOrders = var(uncumsum(day_number), na.rm = T) ) %>% ungroup %>% left_join(customer_aisle_profile, by = c('user_id', 'aisle_id')) %>% left_join(customer_department_profile, by = c('user_id', 'department_id')) %>% left_join(customer_cluster_profile, by = c('user_id', 'cluster_id')) %>% left_join(product_profile, by = 'product_id') %>% left_join(aisle_profile, by = 'aisle_id') %>% left_join(department_profile, by = 'department_id') %>% left_join(cluster_profile, by = 'cluster_id') %>% left_join(customer_profile, by = 'user_id') -> train_data ### Computed Features: relative poularities, percentages, ratios and rates ... train_data %>% mutate(CustomerProductPerOrder = CountCustomerProductOrdered/TotalCountCustomerOrders, CustomerWithinAislePerOrder = CountCustomerWithinAisleOrdered/TotalCountCustomerOrders, CustomerWithinDepartmentPerOrder = CountCustomerWithinDepartmentOrdered/TotalCountCustomerOrders, CustomerWithinClusterPerOrder = CountCustomerWithinClusterOrdered/TotalCountCustomerOrders, CustomerProductPerAisle = CountCustomerProductOrdered/CountCustomerWithinAisleOrdered, CustomerProductPerDepartment = CountCustomerProductOrdered/CountCustomerWithinDepartmentOrdered, CustomerProductPerCluster = CountCustomerProductOrdered/CountCustomerWithinClusterOrdered, CustomerProductFirstSelectionRate = CountCustomerProductFirstSelection/CountCustomerProductOrdered, CustomerWithinAisleRate = CountCustomerWithinAisleOrdered/TotalCountCustomerProductsOrdered, CustomerWithinDepartmentRate = CountCustomerWithinDepartmentOrdered/TotalCountCustomerProductsOrdered, CustomerWithinClusterRate = CountCustomerWithinClusterOrdered/TotalCountCustomerProductsOrdered, CustomerAislePerDepartment = CountCustomerWithinAisleOrdered/CountCustomerWithinDepartmentOrdered, ProductPerAislePopularity = TotalProductOrdered/AbsoluteAislePopularity, ProductPerDepartmentPopularity = TotalProductOrdered/AbsoluteDepartmentPopularity, ProductPerClusterPopularity = TotalProductOrdered/AbsoluteClusterPopularity, AislePerDepartmentPopularity = AbsoluteAislePopularity/AbsoluteDepartmentPopularity, DaysSinceLastProductOrdered = day_number_last_product_ordered - day_number_nextlast_product_ordered, OrdersSinceLastProductOrdered = order_number_last_product_ordered - order_number_nextlast_product_ordered - 1) } # input id_set is dataset containing user_id and product_id # input cutpoint_dataset must have the same columns as dataset passed to function build_features() build_labels = function(id_set, label_dataset){ id_set %>% left_join(label_dataset[, c('user_id', 'product_id', 'reordered')], by = c('user_id', 'product_id')) %>% mutate(label = as.numeric(!is.na(reordered))) %>% pull(label) } build_training_pack = function(orders, products, order_products_prior, cutpoint_split = 'last_order', customer_subset = NULL){ if(is.null(products$cluster_id)){products$cluster_id <- 1} products$cluster_id[is.na(products$cluster_id)] <- 0 order_products = order_products_prior %>% left_join(products, by = 'product_id') %>% left_join(orders %>% # filter(eval_set == 'prior') %>% select(-eval_set) %>% arrange(user_id, order_number) %>% mutate(day_number = days_since_prior_order %>% na2zero) %>% rutils::column.cumulative.forward(col = 'day_number', id_col = 'user_id') , by = 'order_id') if(!is.null(customer_subset)){ order_products %<>% filter(user_id %in% customer_subset) } ### Pick cutpoint order numbers: order_products %>% arrange(user_id, order_number) %>% group_by(user_id) %>% summarise(cutpoint_order_number = max(order_number), cutpoint_order_id = last(order_id)) %>% ungroup -> cutpoint_orders order_products %<>% left_join(cutpoint_orders, by = 'user_id') %>% mutate(flag_label = order_number == cutpoint_order_number, flag_feature = order_number < cutpoint_order_number) ind_features = which(order_products$flag_feature) ind_labels = which(order_products$flag_label) cat('\n', '%s rows total, %s rows for feature aggregation, %s rows for labeling, %s rows removed!' %>% sprintf(nrow(order_products), length(ind_features), length(ind_labels), nrow(order_products) - length(ind_features) - length(ind_labels))) return(list(feature_dataset = order_products[ind_features,], label_dataset = order_products[ind_labels,], cutpoint_orders = cutpoint_orders)) } add_cutpoint_features = function(X_train, orders, cutpoint_orders){ X_train %>% left_join(cutpoint_orders, by = 'user_id') %>% left_join(orders %>% select(-user_id, -eval_set, -order_number) %>% rename(cutpoint_order_id = order_id, cutpoint_order_dow = order_dow, cutpoint_order_hod = order_hour_of_day, cutpoint_order_dsp = days_since_prior_order), by = 'cutpoint_order_id') } ##### Event-Oriented Modelling: ## Create Eventlogs from order_product table: create_eventlog = function(orders, products, order_products, keywords){ products$title = products$product_name %>% gsub(pattern = '\\s', replacement = '') %>% gsub(pattern = '-', replacement = '') # keywords %<>% tolower # List of keyword products: keyword_products = list() for(kw in keywords){ keyword_products[[kw]] <- products$title %>% charFilter(kw) } products_filtered = products %>% dplyr::filter(title %in% unlist(keyword_products)) order_products_filtered = order_products %>% dplyr::filter(product_id %in% products_filtered$product_id) %>% left_join(products_filtered, by = 'product_id') %>% left_join(orders, by = 'order_id') eventlogs = list() # Creating events: ordering products in which keywords are used: for(kw in names(keyword_products)){ cat('\n', 'Creating eventlog for keyword: ', kw, ' ... ') order_products_filtered %>% dplyr::filter(title %in% keyword_products[[kw]]) %>% rename(caseID = user_id, eventTime = order_number) %>% group_by(caseID, eventTime) %>% summarise(value = length(product_id)) %>% ungroup %>% mutate(eventType = paste0(kw, 'Ordered'), attribute = 'Count') %>% select(caseID, eventTime, eventType, attribute, value) -> eventlogs[[kw]] cat('Done!', '\n') } users = order_products_filtered$user_id %>% unique orders %>% filter(user_id %in% users) %>% mutate(eventType = 'OrderIssued', attribute = 'DSPO') %>% select(caseID = user_id, eventTime = order_number, eventType, attribute, value = days_since_prior_order) %>% na2zero -> eventlogs[['orders']] return(eventlogs) } create_dynamic_features = function(eventlog, aggregators = c('sum', 'max'), types = c('s', 'e'), win_sizes = c(2,3,6)){ # eventlog = els$chocolate eventlog %>% mutate(eventTime = as.Date('2010-07-01') + eventTime) %>% promer::dfg_pack(event_funs = c('count', 'count_cumsum', 'elapsed', 'tte', 'censored'), var_funs = c('sum', 'sum_cumsum', 'last'), horizon = 1) -> dfp swf_tables = c('event_count', 'var_sum', 'var_last') dfp %<>% promer::add_swf(tables = swf_tables, aggregators = aggregators, win_sizes = win_sizes) } extract_dataset_from_dfpack = function(dfp, target_keyword){ ftables = names(dfp) %-% c("case_timends", "event_attr_map", "event_time", "event_tte", "event_censored", "event_label") df = dfp[[ftables[1]]] for(tn in ftables[-1]){ df %<>% left_join(dfp[[tn]], by = c('caseID', 'eventTime')) } return(list(X = df, y = dfp$event_label[[sprintf("%sOrdered_label", target_keyword)]])) }

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#Set 1 1 Sample Z- test ########################## #Question 1 1 Sample Z- test --------------- #ho: u=130 #h1: u!=130 n=9 mu0=130 x_bar=131.08 sigma=1.5 alpha=0.01 z=(x_bar-mu0)/(sigma/sqrt(n)) #two tail test qnorm(alpha/2) # as z is not in CR , we will NOT reject Ho ------------------------------------------------------------ #Question 2 #ho: u=75 #h1: u<75 n=25 mu0=75 x_bar=72.3 sigma=9 alpha=0.05 z=(x_bar-mu0)/(sigma/sqrt(n)) #one tail test qnorm(alpha) # CR for Z (-inf,-1.644] # As z doesnt lie within CR , we will NOT Reject Ho #part b alpha=0.1 mu1=71 cp=qnorm(alpha) #1.28 #P(z>-1.28|u=71) type_2_error=1-pnorm(cp-(mu1-mu0)/(sigma/sqrt(n))) # 0.1734 ---------------------------------------------------------------------- # question 3 #ho: u=750 #h1:u<750 mu0=750 sigma=38 n=50 x_bar=738.44 alpha=0.05 z=(x_bar-mu0)/(sigma/sqrt(n)) # -2.15 qnorm(alpha) # -1.644 # CR for z is (-inf,-1.644] # as z lies within CR , we Reject Ho # Thus average life time is less than 750hrs, do not buy them

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## Download extract and load data into R # check if complete unziped data is pressent allready if not download and unzip if (!file.exists("./input/summarySCC_PM25.rds") | !file.exists("./input/Source_Classification_Code.rds")) { # creat directory for data if (!dir.exists("./input")) { dir.create("./input") } # download zip file from url if not pressent in directory if (!file.exists("./input/proj2Data.zip")) { download.file( "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2FNEI_data.zip", "./input/proj2Data.zip" ) } # unzip file. if (file.exists("./input/proj2Data.zip")) { cat("extracting file") unzip("./input/proj2Data.zip",exdir = "./input") }else{ stop("file proj2Data.zip not found cannot extract") } # check to make sure everything worked ok if (!file.exists("./input/summarySCC_PM25.rds")) { stop("file summarySCC_PM25.rds not found possible unzip error") } if (!file.exists("./input/Source_Classification_Code.rds")) { stop("file Source_Classification_Code.rds not found possible unzip error") } } # load data into R nei <- readRDS("./input/summarySCC_PM25.rds") scc <- readRDS("./input/Source_Classification_Code.rds")

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#' @title Function retrieves the DCA scores of the first two axes and plots them #' against two environmental tables #' @details Function applies a DCA, retrieves the scores of the first axes and #' plots the scores against the environmental variables. #' @param londo Plot-species matrix with an id column named \code{pnr} which #' will be used to rename the rownames, and then will be removed prior to #' executing the ordination. #' @param expl Dataframe containing environmental variables. The dataframe #' should have an id column which should have a correspondence in the #' species-plot-matrix with the same name. #' @param choices Number of DCA axes to be retained. Only \code{1:2} is #' currently supported. #' @return A \code{list} containing two [lattice::xyplot()]s. #' @author Jannes Muenchow #' @importFrom dplyr select inner_join #' @importFrom vegan scores decorana #' @importFrom reshape2 melt #' @importFrom lattice xyplot panel.xyplot panel.loess explore_dca = function(londo, id = "pnr", choices = 1:2, expl) { # just keep observations found in response and explanatory tables expl = dplyr::inner_join(dplyr::select(londo, id), expl, by = id) # get rid off id but keep the id as rownames rownames(londo) = londo[, id] londo = dplyr::select(londo, -id) if (any(colSums(londo) == 0)) { stop("There are empty columns") } # check if each plot has at least one observation if (!all(rowSums(londo) > 0)) { stop("There are empty rows!") } dca = vegan::decorana(londo, iweigh = TRUE) # extract scores and add id dca = dplyr::select(as.data.frame(vegan::scores(dca, display = "sites")), choices) dca = cbind(as.integer(row.names(dca)), dca) names(dca)[1] = id # join environmental data tmp = dplyr::inner_join(dca, expl, by = id) tmp = dplyr::select(tmp, -id) dca1 = reshape2::melt(dplyr::select(tmp, -DCA2), id.var = "DCA1") dca2 = reshape2::melt(dplyr::select(tmp, -DCA1), id.var = "DCA2") p_1 = lattice::xyplot(DCA1 ~ value | variable, data = dca1, col = "salmon", pch = 16, scales = list(relation = "free"), panel = function(x, y, ...) { lattice::panel.xyplot(x, y, ...) lattice::panel.loess(x, y, span = 0.9, lwd = 2.5, col = "gray") }) p_2 = lattice::xyplot(DCA2 ~ value | variable, data = dca2, col = "salmon", pch = 16, scales = list(relation = "free"), panel = function(x, y, ...) { lattice::panel.xyplot(x, y, ...) lattice::panel.loess(x, y, span = 0.9, lwd = 2.5, col = "gray") }) # return your result list(p_1, p_2) } #' @title Variation partitioning applied to an ordination result or #' Hellinger-transformed matrix #' @details Function applies variation partitioning to the scores of a DCA or #' hellinger-transforms the input matrix prior to the variation paritioning. #' @param londo Plot-species matrix with an id column which will be used to join #' the environmental data and to rename the rownames prior to its deletion #' before executing the ordination. #' @param expl_1 Dataframe containing environmental variables. The dataframe #' should have an id column which should have a correspondence in the #' species-plot-matrix with the same name. #' @param expl_2 Dataframe containing environmental variables. The dataframe #' should have an id column which should have a correspondence in the #' species-plot-matrix with the same name. #' @param choices Number of DCA axes to be retained (default: 1:2). #' @return Output of [vegan::varpart()]. #' @author Jannes Muenchow #' @importFrom dplyr inner_join select #' @importFrom vegan decorana anova.cca rda varpart ordi_varpart = function(londo, choices = 1:2, id = "pnr", expl_1, expl_2, dca = TRUE) { # just keep observations found in response and explanatory tables expl_1 = dplyr::inner_join(dplyr::select(londo, id), expl_1, by = id) expl_2 = dplyr::inner_join(dplyr::select(londo, id), expl_2, by = id) # get rid off id but keep the id as rownames rownames(londo) = londo[, id] londo = dplyr::select(londo, -id) if (any(colSums(londo) == 0)) { stop("There are empty columns") } # check if each plot has at least one observation if (!all(rowSums(londo) > 0)) { stop("There are empty rows!") } # apply DCA if (isTRUE(dca)) { # d = decostand(londo, "pa") dca = vegan::decorana(londo, iweigh = TRUE) # plot(dca, display = "sites") # Single axis contribution sc = round(dca$evals / sum(dca$evals), 2) # Cumulative contribution cc = round(cumsum(dca$evals / sum(dca$evals)), 2) # extract scores and add id dca = dplyr::select(as.data.frame(vegan::scores(dca, display = "sites")), choices) dca = cbind(as.integer(row.names(dca)), dca) names(dca)[1] = id vp = varpart(dplyr::select(dca, -id), dplyr::select(expl_1, -id), dplyr::select(expl_2, -id)) # # you can apply a weight to the result (but the result is more or less the # # same just with the difference that the explained variance is much smaller) # d = vp$part$indfract[1:3, 3] # weight = cc[ncol(dplyr::select(dca, -id))] # vp$part$indfract[1:3, 3] = d * weight # vp$part$indfract[4, 3] = vp$part$indfract[4, 3] + sum(d - (d * weight)) # test fraction a & c a = vegan::anova.cca(vegan::rda(dplyr::select(dca, -id), dplyr::select(expl_1, -id), dplyr::select(expl_2, -id), step = 1000)) #Test of fraction c c = vegan::anova.cca(vegan::rda(dplyr::select(dca, -id), dplyr::select(expl_2, -id), dplyr::select(expl_1, -id), step = 1000)) breaks = c(0, 0.001, 0.01, 0.05, 0.1, 1) labs = labels = c("***", "**", "*", ".", "") a = cut(a$`Pr(>F)`[1], breaks, labs, include.lowest = FALSE) c = cut(c$`Pr(>F)`[1], breaks, labs, include.lowest = FALSE) return(list("vp" = vp, "a" = a, "c" = c)) } else { return(vegan::varpart(londo, expl_1, expl_2, transfo = "hel")) } }

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#install.packages('devtools') #devtools::install_github('shiny-incubator', 'rstudio') library(shinyIncubator) shinyUI( pageWithSidebar( headerPanel("Stock Market Simulator"), sidebarPanel( h4("Simulator Controls"), actionButton('playGame', 'Play Simulator', icon = icon("play")), br(),br(), actionButton('resetGame', 'Reset Simulator', icon = icon("repeat")), br(),br(), actionButton('pauseGame', 'Pause Simulator', icon = icon("pause")), br(),br(), actionButton('buyStock', 'Buy Stock', icon = icon("money")), br(),br(), actionButton('sellStock', 'Sell Stock', icon = icon("trash-o")), br(),br(), sliderInput("speedSlider", "Change How Quickly Time Passes", 20, 5000, value = 1000), textOutput("gameEndingOutput") ), mainPanel( tabsetPanel( tabPanel("Docs", h4("Introduction"), p("Welcome to the Stock Market Simulator!"), p("Did I say simulator? I meant real life, of course! Your grandfather has just passed away and left behind a generous sum of money in your name. It's up to you to carry out the family name and become a billionaire from the stock market! (well, you can try at least)"), br(), br(), h4("How to Invest"), p("Over on the \"Simulator\" tab, you can track the stock prices over time and track how much money you currently have both in stocks and in the bank. You start with $1,000,000 and can choose to buy stocks by clicking the \"Buy Stock\" button which will convert everything in your bank into stocks, based on the current market price. If you feel like the market price is about to drop, sell those stocks! You can do this with the \"Sell Stocks\" button up on the left. Feel free to use your supernatural powers to influence the passage of time by changing the slider on the left. The number on the slider is the number of milliseconds between each market quarter. If time is going too quickly, feel free to click the \"Pause Simulator\" button to stop time. When you're ready, click the \"Play Simulator\" button to start. "), br(), br(), h4("Explanation of Simulator Value"), p(strong("Year"), ": The current year and quarter"), p(strong("Stock Price"), ": The current price of stock on the market"), p(strong("Stock Owned"), ": The number of stocks that you currently own"), p(strong("Stock Value"), ": The total value of all the stocks that you currently own (current stock price * stocks owned)"), p(strong("Bank"), ": The amount of money you own that is not in stocks"), p(strong("Neutral Bank"), ": The Neutral Stock Value and Bank measures how much money you could have made if you bought all the stock you could right at the beginning and then never traded again"), p(strong("Perfect Bank"), ": The Perfect Stock Value and Bank measures how much money you could have made if you traded at the perfect time, every time as in you sold every time the stock price was about to drop and bought every time the stock was about to rise"), p(strong("Imperfect Bank"), ": The Imperfect Stock Value and Bank measures how much money you could have made if you traded at the worst possible times, every time, as in you bought stock everytime the stock price was about to drop and sold stock everytime the stock price was about to rise"), br(), br(), p("This simulator was created by Colin King for the Developing Data Products Class by Johns Hopkins University") ), tabPanel("Simulator", plotOutput("plot"), textOutput("year"), textOutput("stockprice"), br(), textOutput("stocksowned"), textOutput("valuation"), textOutput("bank"), br(), textOutput("neutralvaluation"), textOutput("neutralbank"), br(), textOutput("perfectvaluation"), textOutput("perfectbank"), br(), textOutput("imperfectvaluation"), textOutput("imperfectbank"), br(), br(), br(), br() ) ) ) ) )

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EnrichmentScore.R

######################################################################## ##get enrichment score for each miR set EnrichmentScore <- function(miR.list, miR.set, weighted.score.type = 1, correl.vector = NULL) { tag.indicator <- sign(match(miR.list, miR.set, nomatch=0)) # notice that the sign is 0 (no tag) or 1 (tag) no.tag.indicator <- 1 - tag.indicator N <- length(miR.list) Nh <- length(miR.set) Nm <- N - Nh if (weighted.score.type == 0) { correl.vector <- rep(1, N) } alpha <- weighted.score.type correl.vector <- abs(correl.vector**alpha) sum.correl.tag <- sum(correl.vector[tag.indicator == 1]) norm.tag <- 1.0/sum.correl.tag norm.no.tag <- 1.0/Nm RES <- cumsum(tag.indicator * correl.vector * norm.tag - no.tag.indicator * norm.no.tag) max.ES <- max(RES) min.ES <- min(RES) if (max.ES > - min.ES) { ES <- signif(max.ES, digits = 5) arg.ES <- which.max(RES) } else { ES <- signif(min.ES, digits=5) arg.ES <- which.min(RES) } return(list(ES = ES, arg.ES = arg.ES, RES = RES, indicator = tag.indicator)) } ############################################################################# EnrichmentScore2 <- function(miR.list, miR.set, weighted.score.type = 1, correl.vector = NULL) { N <- length(miR.list) Nh <- length(miR.set) Nm <- N - Nh loc.vector <- vector(length=N, mode="numeric") peak.res.vector <- vector(length=Nh, mode="numeric") valley.res.vector <- vector(length=Nh, mode="numeric") tag.correl.vector <- vector(length=Nh, mode="numeric") tag.diff.vector <- vector(length=Nh, mode="numeric") tag.loc.vector <- vector(length=Nh, mode="numeric") loc.vector[miR.list] <- seq(1, N) tag.loc.vector <- loc.vector[miR.set] tag.loc.vector <- sort(tag.loc.vector, decreasing = FALSE) if (weighted.score.type == 0) { tag.correl.vector <- rep(1, Nh) } else if (weighted.score.type == 1) { tag.correl.vector <- correl.vector[tag.loc.vector] tag.correl.vector <- abs(tag.correl.vector) } else if (weighted.score.type == 2) { tag.correl.vector <- correl.vector[tag.loc.vector]*correl.vector[tag.loc.vector] tag.correl.vector <- abs(tag.correl.vector) } else { tag.correl.vector <- correl.vector[tag.loc.vector]**weighted.score.type tag.correl.vector <- abs(tag.correl.vector) } norm.tag <- 1.0/sum(tag.correl.vector) tag.correl.vector <- tag.correl.vector * norm.tag norm.no.tag <- 1.0/Nm tag.diff.vector[1] <- (tag.loc.vector[1] - 1) tag.diff.vector[2:Nh] <- tag.loc.vector[2:Nh] - tag.loc.vector[1:(Nh - 1)] - 1 tag.diff.vector <- tag.diff.vector * norm.no.tag peak.res.vector <- cumsum(tag.correl.vector - tag.diff.vector) valley.res.vector <- peak.res.vector - tag.correl.vector max.ES <- max(peak.res.vector) min.ES <- min(valley.res.vector) ES <- signif(ifelse(max.ES > - min.ES, max.ES, min.ES), digits=5) return(list(ES = ES)) }

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#' returns the jaccard index matrix of two labeled sets #' #' jaccard = intersection/union #' @param clustersA cluster labels of set #' @param clustersB second cluster labels of the same set #' @return Returns the jaccard matrix of the labeled set #' @examples #' @importFrom #' @export jaccardMatrix <- function(clustersA=NULL,clustersB=NULL) { jaccardM <- NULL; jaccardA <- NULL; meanJaccard <- NULL; if (length(clustersA) == length(clustersB)) { minclassA <- min(clustersA) minclassB <- min(clustersB) maxlabelA <- max(clustersA); maxlabelB <- max(clustersB); jaccardM <- matrix(0,nrow=(maxlabelA-minclassA+1),ncol=(maxlabelB-minclassB)+1); ii <- 1; for (i in minclassA:maxlabelA) { setA <- (clustersA == i); jj <- 1; for (j in minclassB:maxlabelB) { setB <- (clustersB == j); unionsum <- sum(setA | setB); if (unionsum > 0) jaccardM[ii,jj] <- sum(setA & setB)/unionsum; jj <- jj + 1; } ii <- ii + 1; } meanJaccard <- mean(c(apply(jaccardM,2,max),apply(jaccardM,1,max))); rownames(jaccardM) <- minclassA:maxlabelA colnames(jaccardM) <- minclassB:maxlabelB jaccardA <- clustersA; classA <- as.character(clustersA); classB <- as.character(clustersB); for (i in 1:length(clustersA)) { jaccardA[i] <- jaccardM[classA[i],classB[i]]; } } result <- list(jaccardMat=jaccardM,elementJaccard=jaccardA,balancedMeanJaccard=meanJaccard); return (result) }

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poscar.extractlayers.Rd

\name{poscar.extractlayers} \alias{poscar.extractlayers} \title{Extracts atoms of object of class poscar} \usage{ poscar.extractlayers(poscar, layer, layers, vacuum = c(0, 0, 0), center = T) } \arguments{ \item{poscar}{object of class poscar} \item{layer}{indices of layers which will be extracted} \item{layers}{total layer count} \item{vacuum}{sets vacuum, see \code{poscar.setvacuum} for futher informations} \item{center}{centers new poscar,see \code{poscar.setvacuum} for futher informations} } \description{ \code{poscar.extractlayers} extracts atoms, based on layers, of object of class poscar. Will give object of class poscar. Will not change position of atoms. }

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getDataStockPrices.R

library(BatchGetSymbols) # we get data from 2000 to include the dot.com recovery first.date <- '2000-01-01' # Until today last.date <- Sys.Date() freq.data <- 'daily' symbols = c('MSFT','NVDA', 'AMZN', 'GOOGL','AAPL', '^IXIC') aa =BatchGetSymbols(symbols, first.date = first.date, last.date = last.date, freq.data = freq.data) # First element in the list is meta data of the download # relocate the date column for index in python aa[[2]] %>% dplyr::select(ref.date, everything() ) -> data_export # Write to drive write.csv(data_export, 'stock_prices_from2000.csv', quote =F, row.names = F)

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particle_analysis_1.R

# ------------------------------------------------------------------------------------------------- # particle_analysis_1.R # EcoSLIM analysis script # to generate saturated age PDFs and variance plots for six scenarios # ------------------------------------------------------------------------------------------------- library(ggplot2) library(ggnewscale) library(tidyr) library(readr) library(dplyr) library(openxlsx) library(cowplot) library(zoo) library(plotrix) library(plyr) library(spatstat) library(gridExtra) source("~/mb_sensitivity/scripts/prob_dens_fxn.R") source("~/mb_sensitivityh/scripts/EcoSLIM_read_fxn_update.R") source("~/mb_sensitivity/scripts/cell_agg_fxn.R") source("~/mb_sensitivity/scripts/var_bin_fxn.R") source("~/mb_sensitivity/scripts/particle_flowpath_fxn.R") # ------------------------------------------------------------------------------------------------- # loading particle output files # ------------------------------------------------------------------------------------------------- load(file="~/mb_sensitivity/outputs/exit_particles/exited_particles_A.Rda") load(file="~/mb_sensitivity/outputs/exit_particles/exited_particles_B.Rda") load(file="~/mb_sensitivity/outputs/exit_particles/exited_particles_C.Rda") load(file="~/mb_sensitivity/outputs/exit_particles/exited_particles_D.Rda") load(file="~/mb_sensitivity/outputs/exit_particles/exited_particles_F.Rda") load(file="~/mb_sensitivity/outputs/exit_particles/exited_particles_G.Rda") load(file="~/mb_sensitivity/outputs/exit_particles/exited_particles_H.Rda") # ------------------------------------------------------------------------------------------------- # generating PDF for scenarios # ------------------------------------------------------------------------------------------------- bin_size_age <- 3 pdf_exit_A_fw1 <- pdfxn(exited_particles_A, max(exited_particles_A$age), bin_size_age,column = "sat_age") pdf_exit_B_fw1 <- pdfxn(exited_particles_B, max(exited_particles_B$age), bin_size_age,column = "sat_age") pdf_exit_C_fw1 <- pdfxn(exited_particles_C, max(exited_particles_C$age), bin_size_age,column = "sat_age") pdf_exit_D_fw1 <- pdfxn(exited_particles_D, max(exited_particles_D$age), bin_size_age,column = "sat_age") pdf_exit_F_fw1 <- pdfxn(exited_particles_F, max(exited_particles_F$age), bin_size_age,column = "sat_age") pdf_exit_G_fw1 <- pdfxn(exited_particles_G, max(exited_particles_G$age), bin_size_age,column = "sat_age") pdf_exit_H_fw1 <- pdfxn(exited_particles_H, max(exited_particles_H$age), bin_size_age,column = "sat_age") pdf_exit_A_fw1$scen <- "Homogeneous" pdf_exit_B_fw1$scen <- "Two-layered" pdf_exit_C_fw1$scen <- "Three-layered" pdf_exit_F_fw1$scen <- "Variable Soil" pdf_exit_D_fw1$scen <- "Low-K Exponential Decay" pdf_exit_H_fw1$scen <- "Anisotropic" pdf_exited_all <- rbind(pdf_exit_A_fw1,pdf_exit_B_fw1,pdf_exit_C_fw1,pdf_exit_F_fw1,pdf_exit_H_fw1,pdf_exit_D_fw1) pdf_exit_G_fw1$scen <- "High-K Exponential Decay" pdf_exited_exp <- rbind(pdf_exit_A_fw1,pdf_exit_D_fw1,pdf_exit_G_fw1) pdf_fig <- ggplot() + geom_line(data = pdf_exited_all, aes(x = sat_age,y = Density_pdf, group=scen,col = scen), size = 0.75) + scale_x_log10(name="",limits = c(3,800), breaks = c(3,25,50,100,200,400,600,800,1000),labels = scales::comma,expand=c(0,0)) + scale_y_continuous(name="", expand=c(0,0), breaks = seq(0,0.12,0.01), limits = c(0,0.07)) + scale_color_manual(values = c("purple", "black","darkorange","dodgerblue","firebrick","green3")) + labs(color = "Scenario") + expand_limits(x = 100, y = 0) + theme_bw() + theme(panel.border = element_rect(colour = "black", size=1, fill=NA), panel.grid.major = element_line(colour="grey", size=0.1), legend.position = "none", legend.background = element_rect(linetype="solid", colour ="black"),plot.margin = margin(15,15,15,15), title =element_text(size=12, face='bold'),axis.text.x = element_text(color="black",size=12),axis.text.y = element_text(color="black",size=12)) pdf_fig pdf_fig_exp <- ggplot() + geom_line(data = pdf_exited_exp, aes(x = sat_age,y = Density_pdf, group=scen,col = scen), size = 0.75) + scale_x_log10(name="",limits = c(3,800), breaks = c(3,25,50,100,200,400,600,800,1000),labels = scales::comma,expand=c(0,0)) + scale_y_continuous(name="", expand=c(0,0), breaks = seq(0,0.12,0.02), limits = c(0,0.12)) + scale_color_manual(values = c("khaki4","black","darkorange")) + labs(color = "Scenario") + expand_limits(x = 100, y = 0) + theme_bw() + theme(panel.border = element_rect(colour = "black", size=1, fill=NA), panel.grid.major = element_line(colour="grey", size=0.1), legend.position = "none", legend.background = element_rect(linetype="solid", colour ="black"),plot.margin = margin(15,15,15,15), title =element_text(size=12, face='bold'),axis.text.x = element_text(color="black",size=12),axis.text.y = element_text(color="black",size=12)) pdf_fig_exp # ------------------------------------------------------------------------------------------------- # calculating variance time series # ------------------------------------------------------------------------------------------------- var_bin <- 3 var_spath_A <- var_bin_fxn(exited_particles_A, max(exited_particles_A$age), var_bin,column1 = "sat_age",column2 = "spath_len") var_spath_B <- var_bin_fxn(exited_particles_B, max(exited_particles_B$age), var_bin,column1 = "sat_age",column2 = "spath_len") var_spath_C <- var_bin_fxn(exited_particles_C, max(exited_particles_C$age), var_bin,column1 = "sat_age",column2 = "spath_len") var_spath_D <- var_bin_fxn(exited_particles_D, max(exited_particles_D$age), var_bin,column1 = "sat_age",column2 = "spath_len") var_spath_F <- var_bin_fxn(exited_particles_F, max(exited_particles_F$age), var_bin,column1 = "sat_age",column2 = "spath_len") var_spath_G <- var_bin_fxn(exited_particles_G, max(exited_particles_G$age), var_bin,column1 = "sat_age",column2 = "spath_len") var_spath_H <- var_bin_fxn(exited_particles_H, max(exited_particles_H$age), var_bin,column1 = "sat_age",column2 = "spath_len") var_spath_A$scen <- "Homogeneous" var_spath_B$scen <- "Two-layered" var_spath_C$scen <- "Three-layered" var_spath_F$scen <- "Variable Soil" var_spath_D$scen <- "Low-K Exponential Decay" var_spath_H$scen <- "Anisotropic" var_spath_G$scen <- "High-K Exponential Decay" var_bin_1 <- rbind(var_spath_A,var_spath_B,var_spath_C,var_spath_F) var_bin_1 <- var_bin_1[which(var_bin_1$count > 9),] var_bin_2 <- rbind(var_spath_A,var_spath_H,var_spath_D) var_bin_2 <- var_bin_2[which(var_bin_2$count > 9),] var_bin_all <- rbind(var_spath_A,var_spath_B,var_spath_C,var_spath_F,var_spath_H,var_spath_D) var_bin_all <- var_bin_all[which(var_bin_all$count > 9),] var_bin_exp <- rbind(var_spath_A,var_spath_D,var_spath_G) var_bin_exp <- var_bin_exp[which(var_bin_exp$count > 9),] # ------------------------------------------------------------------------------------------------- # generating variance plot # ------------------------------------------------------------------------------------------------- var_bin_fig <- ggplot(data = var_bin_all, aes(x = sat_age,y = variance, group=scen,col = scen)) + geom_line(size = 0.75) + #geom_point(size =0.5) + scale_x_continuous(name="Saturated age (yr)",limits = c(0,800), breaks=c(0,100,200,300,400,500,600,700,800),labels = scales::comma,expand=c(0,0)) + ggtitle("Variance of saturated lengths of exited particles") + scale_y_log10(name=bquote('Variance of saturated path lengths ('*m^2*')'), expand=c(0,0), limits = c(1000,1000000000), breaks = c(1000,10000,100000,1000000,10000000,100000000,1000000000,10000000000)) + scale_color_manual(values = c("purple", "black","darkorange","dodgerblue","firebrick","green3")) + labs(color = "Scenario") + expand_limits(x = 10, y = 0) + theme_bw() + theme(panel.border = element_rect(colour = "black", size=1, fill=NA), panel.grid.major = element_line(colour="grey", size=0.1), legend.position = "none", legend.background = element_rect(linetype="solid", colour ="black"),plot.margin = margin(5,15,5,5), title =element_text(size=12, face='bold'),axis.text.x = element_text(color="black",size=10),axis.text.y = element_text(color="black", size = 10)) + theme(axis.title.y = element_text(color="black",face='bold')) var_bin_fig var_bin_exp <- ggplot(data = var_bin_exp, aes(x = sat_age,y = variance, group=scen,col = scen)) + geom_line(size = 0.75) + #geom_point(size =0.5) + scale_x_continuous(name="Saturated age (yr)",limits = c(0,800), breaks=c(0,100,200,300,400,500,600,700,800),labels = scales::comma,expand=c(0,0)) + ggtitle("Variance of saturated lengths of exited particles") + scale_y_log10(name=bquote('Variance of saturated path lengths ('*m^2*')'), expand=c(0,0), limits = c(1000,1000000000), breaks = c(1000,10000,100000,1000000,10000000,100000000,1000000000,10000000000)) + scale_color_manual(values = c("khaki4", "black","darkorange")) + labs(color = "Scenario") + expand_limits(x = 10, y = 0) + theme_bw() + theme(panel.border = element_rect(colour = "black", size=1, fill=NA), panel.grid.major = element_line(colour="grey", size=0.1), legend.position = "none", legend.background = element_rect(linetype="solid", colour ="black"),plot.margin = margin(5,15,5,5), title =element_text(size=12, face='bold'),axis.text.x = element_text(color="black",size=10),axis.text.y = element_text(color="black", size = 10)) + theme(axis.title.y = element_text(color="black",face='bold')) var_bin_exp # ------------------------------------------------------------------------------------------------- # calculating sensitivity metric values # ------------------------------------------------------------------------------------------------- sens_fp <- data.frame(matrix(NA, nrow = 7, ncol = 4)) colnames(sens_fp)<- c("scen","mean","sd","sens_fp") sens_fp$scen <- c("Homogeneous", "Two-layered", "Three-layered", "Variable Soil", "Anisotropic", "Low-K Exp Decay", "High-K Exp Decay") sens_fp$mean[1] <- mean(exited_particles_A$sat_age) sens_fp$sd[1] <- sd(exited_particles_A$sat_age) sens_fp$mean[2] <- mean(exited_particles_B$sat_age) sens_fp$sd[2] <- sd(exited_particles_B$sat_age) sens_fp$mean[3] <- mean(exited_particles_C$sat_age) sens_fp$sd[3] <- sd(exited_particles_C$sat_age) sens_fp$mean[4] <- mean(exited_particles_F$sat_age) sens_fp$sd[4] <- sd(exited_particles_F$sat_age) sens_fp$mean[5] <- mean(exited_particles_H$sat_age) sens_fp$sd[5] <- sd(exited_particles_H$sat_age) sens_fp$mean[6] <- mean(exited_particles_D$sat_age) sens_fp$sd[6] <- sd(exited_particles_D$sat_age) sens_fp$mean[7] <- mean(exited_particles_G$sat_age) sens_fp$sd[7] <- sd(exited_particles_G$sat_age) for(i in 1:nrow(sens_fp)){ pct_chg_mean <- abs((sens_fp$mean[i] - sens_fp$mean[1])/sens_fp$mean[1]) pct_chg_sd <- abs((sens_fp$sd[i] - sens_fp$sd[1])/sens_fp$sd[1]) sens_fp$sens_fp[i] <- pct_chg_mean + pct_chg_sd }

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#Gelman & Hill - Hierarchical Models #Chapter 7 Questions #Question 1 # Discrete probability simulation: suppose that a basketball player has a 60% # chance of making a shot, and he keeps taking shots until he misses two in a # row. Also assume his shots are independent (so that each shot has 60% probability # of success, no matter what happened before). # (a) Write an R function to simulate this process. basketballShots <- function(probOfSuccess) { firstTwoShots = rbinom(2, 1, probOfSuccess) allShots = firstTwoShots numShots = length(allShots) sumTwoLastShots = allShots[numShots] + allShots[numShots-1] while(sumTwoLastShots > 0) { newShot <- rbinom(1, 1, probOfSuccess) allShots <- append(allShots, newShot) numShots = length(allShots) sumTwoLastShots = allShots[numShots] + allShots[numShots-1] } return(allShots) } # (b) Put the R function in a loop to simulate the process 1000 times. Use the # simulation to estimate the mean, standard deviation, and distribution of the # total number of shots that the player will take. numShotsVect = c() numSuccesses = c() for (i in 1:1000) { newShots = basketballShots(0.6) numShotsVect = append(numShotsVect, length(newShots)) numSuccesses = append(numSuccesses, sum(newShots)) } meanShots = mean(numShotsVect) stdevShots = sd(numShotsVect) hist(numShotsVect) #has a skewed distribution towards the low end. # (c) Using your simulations, make a scatterplot of the number of shots the player # will take and the proportion of shots that are successes. propSuccesses = numSuccesses/numShotsVect plot(numShotsVect, propSuccesses)

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InsuranceVote.Rd

\name{InsuranceVote} \alias{InsuranceVote} \docType{data} \title{Congressional Votes on a Health Insurance Bill} \description{ Congressional votes on an ObamaCare health insurance bill in 2009 } \format{ A dataset with 435 observations on the following 9 variables. \tabular{rl}{ \code{Party} \tab {Party affiliation: \code{D}=Democrat or \code{R}=Republican}\cr \code{Dist.} \tab {Congressional district (State-Number)}\cr \code{InsVote} \tab {Vote on the health insurance bill: \code{1}=yes or \code{0}=no}\cr \code{Rep} \tab {Indicator for Republicans}\cr \code{Dem} \tab {Indicator for Democrats}\cr \code{Private} \tab {Percentage of non-senior citizens in district with private health insurance}\cr \code{Public} \tab {Percentage of non-senior citizens in district with public health insurance}\cr \code{Uninsured} \tab {Percentage of non-senior citizens in district with no health insurance}\cr \code{Obama} \tab {District winner in 2008 presidential election: \code{1}=Obama \code{0}=McCain}\cr } } \details{ On 7 November 2009 the U.S. House of Representatives voted, by the narrow margin of 220-215, for a bill to enact health insurance reform. Most Democrats voted yes while almost all Republicans voted no. This dataset contains data for each of the 435 representatives. } \source{ Insurance data are from the American Community Survey\cr (http://www.census.gov/acs/www/data_documentation/data_main/). Roll call of congressional votes on this bill can be found at \cr http://clerk.house.gov/evs/2009/roll887.xml. } \keyword{datasets}

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2022-06-28T07:36:24.370149

2022-06-20T10:45:55

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SplitSentences.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/sentences.R \name{SplitSentences} \alias{SplitSentences} \title{Split sentences} \usage{ SplitSentences(text, nclusters = parallel::detectCores()) } \arguments{ \item{text}{Text to split into sentences.} \item{nclusters}{Number of clusters to use for running openNLP} } \value{ List of same size as text with each element a vector of sentences. } \description{ Split text into individual sentences using openNLP. }

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/randomize_network/load_links_file_format.R

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seoanezonjic/sys_bio_lab_scripts

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load_links_file_format.R

#' @author Fernando Moreno Jabato <jabato@uma.com> #' @description function to load specific links file formarts #' @RVersion 3.4.2 load_links_file_format <- function(file,sep="\t",header=F){ #' Method to load a table of relationships loaded in a specific format into a given file. #' After load, info is parsed and returned in the correct format. #' Allowed formats are: HPO_HPO_Format and HPO_Region_Format which have the following #' formats: #' > HPO_HPO_Format: #' - Header: NO #' - Sep: "\t" #' - Line info format: <HPO> <HPO> <Intensity> #' - Line info types: {c,c,d} #' - Note: third column (intesity) is optional #' > HPO_Region_Format: #' - Header: NO #' - Sep: "\t" #' - Line info format: <Chr> <Start> <End> <HPO> <Intensity> <NodeID> #' - Line info types: {c,i,i,c,d,c} #' > Loci_HPO_format: #' - Header: NO #' - Sep: "\t" #' - Line info format: <NodeID> <HPO> #' - Line info types: {c,c} #' @param file with info to be loaded and parsed #' @param sep columns separator used into file #' @param header indicates if file has, or not, header #' @return a dataframe with formated info or a string with an error description if any error occurs # Config values (A = HPO_HPO ; B = HPO_Region) ncol_formatA <- 3 ncol_formatB <- 6 ncol_specialFormat <- 2 # Check inputs if(is.null(file)){ # File return("ERROR [load_links_file_format]: Given file is NULL pointer") }else if(!is.character(file)){ return("ERROR [load_links_file_format]: Given file is not a character value") }else if(!file.exists(file)){ return("ERROR [load_links_file_format]: Given file does not exists") }else if(file.access(file,mode=4) != 0){ # Mode 4 = Read permission return("ERROR [load_links_file_format]: Given file can not be read") }else if(file.size(file) == 0){ return("ERROR [load_links_file_format]: Given file is empty file") } if(is.null(sep)){ return("ERROR [load_links_file_format]: Given separator is NULL pointer") }else if(!is.character(sep)){ return("ERROR [load_links_file_format]: Given seperator is not a character value") }else if(nchar(sep) == 0){ return("ERROR [load_links_file_format]: Given separator is an empty string") } if(is.null(header)){ return("ERROR [load_links_file_format]: Given Header flag is NULL pointer") }else if(!is.logical(header)){ return("ERROR [load_links_file_format]: Given Header flag is not a logical value") } # Load file info info <- read.table(file,sep = sep, header = header) # Check if(length(dim(info)) != 2){ return("ERROR [load_links_file_format]: Read info is not a table") }else if(any(dim(info)==0)){ return("ERROR [load_links_file_format]: Table is empty or is vector") } # Check format if(ncol(info) == ncol_formatA | ncol(info) == ncol_specialFormat){ # FORMAT: HPO-HPO (weighted or not) or Loci-HPO # Check if it's a Loci-HPO file if(ncol(info) == ncol_specialFormat){ if(all(grepl("^HP:[0-9]{7}$", info[,2])) & !all(grepl("^HP:[0-9]{7}$",info[,1]))){ info <- as.data.frame(info,stringsAsFactors = F) colnames(info) <- c("Loci","HPO") info[,1] <- as.character(info[,1]) info[,2] <- as.character(info[,2]) return(info) } } # Check each column if(!all(grepl("^HP:[0-9]{7}$",info[,1]))){ # Column 1: HP code return("ERROR [load_links_file_format]: Column 1 has not correct HPO-HPO format") }else if(!all(grepl("^HP:[0-9]{7}$",info[,2]))){ # Column 2: HP code return("ERROR [load_links_file_format]: Column 2 has not correct HPO-HPO format") }else if(ncol(info) == 3){ weighted <- T if(any(is.na(as.numeric(info[,3])) | !is.double(as.numeric(info[,3])))){ # Column 3: Relationship weight return("ERROR [load_links_file_format]: Column 3 has not correct HPO-HPO format") } }else{ # Everything OK weighted <- F } # Parse format info <- as.data.frame(info) info[,1] <- as.character(info[,1]) info[,2] <- as.character(info[,2]) if(weighted){ info[,3] <- as.double(info[,3]) colnames(info) <- c("HPO_1","HPO_2","Value") }else{ colnames(info) <- c("HPO_1","HPO_2") } # return return(info) }else if(ncol(info) == ncol_formatB){ # FORMAT: HPO-REGION # Check each column if(!all(grepl("(^[1-9]$)|(^1[0-9]$)|(^2[0-2]$)|(^[X,Y]$)",info[,1]))){ # Column 1: Chromosome return("ERROR [load_links_file_format]: Column 1 has not correct HPO-Region format") }else if(!all(grepl("^[0-9]*$",info[,2]))){ # Column 2: Start coord return("ERROR [load_links_file_format]: Column 2 has not correct HPO-Region format") }else if(!all(grepl("^[0-9]*$",info[,3]))){ # Column 3: End coord return("ERROR [load_links_file_format]: Column 3 has not correct HPO-Region format") }else if(!all(grepl("^HP:[0-9]{7}$",info[,4]))){ # Column 4: HP code return("ERROR [load_links_file_format]: Column 4 has not correct HPO-Region format") }else if(any(is.na(as.numeric(info[,5])) | !is.double(as.numeric(info[,5])))){ # Column 5: Relationship weight return("ERROR [load_links_file_format]: Column 5 has not correct HPO-Region format") }else if(!all(grepl("^(([1-9])|(1[0-9])|(2[0-2])|([X,Y]))\\.[0-9]{1,3}\\.[A-Z]\\.[0-9]{1,3}$",info[,6]))){ return("ERROR [load_links_file_format]: Column 6 has not correct HPO-Region format") }else{ # Everything OK # Parse info info <- as.data.frame(info) info[,1] <- as.character(info[,1]) info[,2] <- as.integer(info[,2]) info[,3] <- as.integer(info[,3]) info[,4] <- as.character(info[,4]) info[,5] <- as.double(info[,5]) info[,6] <- as.character(info[,6]) colnames(info) <- c("Chr","Start","End","HPO","Value","Node") # Return return(info) } }else{ # FORMAT: NOT ALLOWED return("ERROR [load_links_file_format]: Info format is not allowed") } }

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/gaussDiff/R/gaussDiff.R

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ingted/R-Examples

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gaussDiff.R

###################################################### ### gaussDiv.R ### implement multiple divergence measures to compare normal ### pdfs, i.e. similarity and dissimilarity measures ### Henning Rust Paris 18/02/09 ### all the implemented similarity and dissimilarity measures ### are described in the chapter ### Dissimilatiry Measures for Probability Distributions ### in the book "Analysis of Symbolic Data" by Hans-Hermann Bock ## visible functions ##-------------------------- ### general wrapper function normdiff <- function(mu1,sigma1=NULL,mu2,sigma2=sigma1,inv=FALSE,s=0.5,method=c("Mahalanobis","KL","J","Chisq","Hellinger","L2","Euclidean")){ ## maybe I catch some erros, checking simgas for square and symmetry ## Euclidean for having this also in the same wrapper d <- switch(match.arg(method), Mahalanobis=normdiff.maha(mu1,sigma1,mu2), KL=normdiff.KL(mu1,sigma1,mu2,sigma2), J=normdiff.J(mu1,sigma1,mu2,sigma2), Chisq=normdiff.Chisq(mu1,sigma1,mu2,sigma2), Hellinger=normdiff.Hellinger(mu1,sigma1,mu2,sigma2), L2=normdiff.L2(mu1,sigma1,mu2,sigma2), Euclidean=sqrt(sum((mu1-mu2)**2))) class(d) <- "normdiff" attr(d,"method") <- match.arg(method) if(!is.null(inv)) attr(d,"inverse") <- inv if(!is.null(s)) attr(d,"s") <- s return(d) } ## internal functions ##-------------------------- ### trace of a matrix tt <- function(A) sum(diag(A)) ### Mahalanobis distance maha <- function(x,A,factor=FALSE){ ## if factor, then distance is multiplied with 0.5 inv.A <- solve(A) d <- t(x)%*%inv.A%*%x if(factor) d <- 0.5*d return(as.vector(d)) } ### Mahalanobis Distance normdiff.maha <- function(mu1,sigma1,mu2){ d <- NA if(!(is.null(sigma1))) d <- maha(mu1-mu2,sigma1,factor=TRUE) return(as.vector(d)) } ### Kullback-Leibler divergence normdiff.KL <- function(mu1,sigma1,mu2,sigma2){ d <- NA if(!(is.null(sigma1)|is.null(sigma2))){ N <- nrow(sigma1) inv.sigma1 <- solve(sigma1) d <- maha(mu1-mu2,sigma1)+ tt(inv.sigma1%*%sigma2-diag(1,N)) + log(det(sigma1)/det(sigma2)) d <- 0.5*d } return(as.vector(d)) } ### J-coefficient, symmetric ### symmetrized Kullback-Leibler normdiff.J <- function(mu1,sigma1,mu2,sigma2){ d <- NA if(!(is.null(sigma1)|is.null(sigma2))){ N <- nrow(sigma1) inv.sigma1 <- solve(sigma1) inv.sigma2 <- solve(sigma2) d <- maha(mu1-mu2,sigma1)+maha(mu1-mu2,sigma2)+ tt(inv.sigma1%*%sigma2-diag(1,N)) + tt(inv.sigma2%*%sigma1-diag(1,N)) d <- 0.5*d } return(as.vector(d)) } ### chi-square divergence for pdfs normdiff.Chisq <- function(mu1,sigma1,mu2,sigma2){ d <- NA if(!(is.null(sigma1)|is.null(sigma2))){ N <- nrow(sigma1) inv.sigma1 <- solve(sigma1) inv.sigma2 <- solve(sigma2) sig1.invsig2 <- sigma1%*%inv.sigma2 d <- det(sig1.invsig2)/sqrt(det(2*sig1.invsig2-diag(1,N)))* exp(0.5*(maha(2*inv.sigma2%*%mu2-inv.sigma1%*%mu1,2*inv.sigma2-inv.sigma1)+ maha(mu1,sigma1)-2*maha(mu2,sigma2)))-1 } return(as.vector(d)) } ### Hellinger distance, ### similarity measure ### 0<d<1, ### d=1, if P=Q, ### d=0, if P, Q have disjoint supports ### symmetric for s=0.5 normdiff.Hellinger<- function(mu1,sigma1,mu2,sigma2,s=0.5,inv=FALSE){ d <- NA if(!(is.null(sigma1)|is.null(sigma2))){ N <- nrow(sigma1) I <- diag(1,N) inv.sigma1 <- solve(sigma1) inv.sigma2 <- solve(sigma2) sig1inv.sig2 <- inv.sigma1%*%sigma2 sig2inv.sig1 <- inv.sigma2%*%sigma1 d <- det(s*I+(1-s)*sig1inv.sig2)**(-s/2)* det((1-s)*I+s*sig2inv.sig1)**(-(1-s)/2)* exp(0.5*(maha(s*inv.sigma2%*%mu2+(1-s)*inv.sigma1%*%mu1,s*inv.sigma2+(1-s)*inv.sigma1)- s*maha(mu2,sigma2)-(1-s)*maha(mu1,sigma1))) if(inv) d <- 1-d } return(as.vector(d)) } ### Minkowskis L2-distance ### symmetric dissimilarity coefficient normdiff.L2<- function(mu1,sigma1,mu2,sigma2){ d <- NA if(!(is.null(sigma1)|is.null(sigma2))){ N <- nrow(sigma1) inv.sigma1 <- solve(sigma1) inv.sigma2 <- solve(sigma2) d <- 1/(2**N*pi**(N/2))*(1/sqrt(det(sigma1))+1/sqrt(det(sigma2)))- 2/((2*pi)**(N/2)*sqrt(det(sigma1+sigma2)))* exp(0.5*(maha(inv.sigma1%*%mu1+inv.sigma2%*%mu2,inv.sigma1+inv.sigma2)- maha(mu1,sigma1)-maha(mu2,sigma2))) } return(as.vector(d)) } ### a print function print.normdiff <- function(x,...){ cat("Gaussian PDF distance:",attr(x,"method")) if(attr(x,"method")=="Hellinger"){ cat(" with s=",attr(x,"s"),", ") if(attr(x,"inv")==TRUE) cat("inverted") } cat("\n") cat(x,"\n") }

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/examples/SVWQR.R

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PedroBSB/mlRFinance

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SVWQR.R

#Page 160 - Small library(mlRFinance) A<-matrix(c(1,2,5,6),nrow=4,ncol=1) d<-c(-1,-1,+1,-1) rank<-order(d) svm2<- CSVWQR(d,rank, A, 50,0.5 ,0.5, "Polynomial", c(2,1)) svm2 PredictedCSVRL1(svm2, A, A) R2PredictedCSVRL1(svm2, A) #Habilita o pacote quantmod library(quantmod) #Cria um novo ambiente para armazenar os dados stockData <- new.env() #Especifica as datas de interesse startDate = as.Date("2011-01-01") endDate = as.Date("2011-12-31") #Obtêm os dados do ativo PETR4 e PETR3 getSymbols("^BVSP", src="yahoo",from=startDate,to=endDate) #Calcula o log-retorno retorno<-na.omit(diff(log(Cl(BVSP)))) train <- as.numeric(retorno[1:180]) valid <- as.numeric(retorno[181:216]) #Cria os objetos y<-train[2:length(train)] X<-matrix(train[1:(length(train)-1)],ncol=1) #SVR svm2<- CSVWQR(y, X, 1, 0.5, 1.0, "Gaussian", c(0.5)) svm2 PredictedCSVRL1(svm2, X, X) R2PredictedCSVRL1(svm2, X)

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/man/genomic_ranges_reduce.Rd

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rkirk822/rnaseq

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refs/heads/master

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genomic_ranges_reduce.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/genomic_ranges_reduce.R \name{genomic_ranges_reduce} \alias{genomic_ranges_reduce} \title{Reduce genomic ranges to set of non-overlapping ranges} \usage{ genomic_ranges_reduce(inFile, outFile = NULL) } \arguments{ \item{inFile}{String - Name of bedfile containing genomic ranges} \item{outFile}{String - Name to give output bedfile with non-overlapping genomic ranges} } \value{ reducedRanges } \description{ Given a bedfile containing genomic ranges, collapse overlapping ranges using the GenomicRanges package. } \details{ TIME: 10-20s, mostly to read the input file. } \examples{ bed1 = "/Users/nelsonlab/Documents/Results_temporarily_here/NucSeq_results/what_was_this/mm10_refSeq_exons_filtered_sorted.bed" ranges = genomic_ranges_reduce(bed1, outFile="~/Documents/mm10_refSeq_exons_filtered_sorted_reduced.txt") } \author{ Emma Myers }

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/Covid_analysis.R

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lanchett/Covid19

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Covid_analysis.R

library(tidyverse) library(gganimate) # Load data --------------------------------------------------------------- data <- read.csv("http://cowid.netlify.com/data/full_data.csv") data <- data %>% replace_na(., list( new_cases = 0, new_deaths = 0, total_cases = 0, total_deaths = 0 )) %>% filter(location != "Worldwide") %>% mutate(date = as.Date(date)) %>% as_tibble() # Prepare data ------------------------------------------------------------ data <- data %>% mutate(img = case_when(location == "Norway" ~ "NO", location == "China" ~ "CN", location == "Italy" ~ "IT", location == "Thailand" ~ "TH", location == "Australia" ~ "AU", location == "Singapore" ~ "SG", location == "France" ~ "FR", location == "Japan"~ "JP", location == "Malaysia" ~ "MY", location == "South Korea" ~ "KR", location == "United States" ~ "US", location == "Vietnam" ~ "VN", location == "Canada" ~ "CA", location == "Germany" ~ "DE", location == "Iran" ~ "IR", location == "Bahrain" ~ "BH", location == "Kuwait" ~ "KW", location == "Spain" ~ "ES", location == "United Kingdom" ~ "UK", location == "Switzerland" ~ "CH", location == "Netherlands" ~ "NL", TRUE ~ "BZ" ), link = paste0("https://www.countryflags.io/", img, "/shiny/64.png") ) %>% filter(!location %in% c("China","World", "International")) data_agg <- data %>% group_by(date) %>% mutate(frame_id = group_indices()) %>% ungroup() flourish_data <- data_agg %>% arrange(frame_id) %>% select(location, frame_id, link, total_cases) %>% pivot_wider(names_from = frame_id, values_from = total_cases, values_fill = list(total_cases = "0")) %>% write.csv(file = "flourish.csv") # Bar Chart Race (R-version) ----------------------------------------------------- # Not ready data_agg <- map_df( unique(data_agg$frame_id), ~ data_agg %>% filter(frame_id == .x & !location %in% c("China", "International")) %>% arrange(-total_cases) %>% mutate( ordering = 1:nrow(.), indicator = case_when(max(ordering) <= 10 ~ 1, TRUE ~ 0) ) ) %>% filter(indicator == 0 & ordering <= 10) %>% arrange(frame_id, ordering) data_use <- data_agg %>% mutate(curr_year = frame_id) my_font <- 'Quicksand' my_background <- 'antiquewhite' my_pal <- c('#F8AFA8', '#74A089') my_theme <- my_theme <- theme( text = element_text(family = my_font), rect = element_rect(fill = my_background), plot.background = element_rect(fill = my_background, color = NA), panel.background = element_rect(fill = my_background, color = NA), panel.border = element_blank(), plot.title = element_text(face = 'bold', size = 20), plot.subtitle = element_text(size = 14), panel.grid.major.y = element_blank(), panel.grid.minor.y = element_blank(), panel.grid.major.x = element_line(color = 'grey75'), panel.grid.minor.x = element_line(color = 'grey75'), legend.position = 'none', plot.caption = element_text(size = 8), axis.ticks = element_blank(), axis.text.y = element_blank() ) theme_set(theme_light() + my_theme) ggplot(aes(ordering, group = location), data = data_use) + geom_tile(aes( y = total_cases / 2, height = total_cases, width = 0.9 ), alpha = 0.9) + scale_fill_manual(values = my_pal) + geom_text( aes(y = total_cases, label = location), family = my_font, nudge_y = -2, size = 3 ) + geom_text(aes(y = total_cases, label = total_cases), family = my_font, nudge_y = 0.5) + geom_text( aes( x = 1, y = 18.75, label = paste0(curr_year) ), family = my_font, size = 8, color = 'gray45' ) + coord_cartesian(clip = "off", expand = FALSE) + coord_flip() + labs( title = 'Covid-19 Development (Outside China)', subtitle = 'Development day-by-day after outbreak', caption = 'data source: ourworldindata.org', x = '', y = '' ) + transition_states(frame_id, transition_length = 7, state_length = 3) + ease_aes('cubic-in-out') #labs(title = "Date: {frame_time}")

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/DES_Koen/function UDR.R

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function UDR.R

######################################################################################################### ## ## UDR Function ## ## Supplementary to the manuschipt: ## ## Comparing strategies for modeling competing risks in discrete event simulations: ## a simulation study and illustration in colorectal cancer ## ## by K. Degeling, H. Koffijberg, M.D. Franken, M. Koopman, M.J. IJzerman ## ## DOI: ## ## This code illustrates how the UDR approach, as defined in the paper, can be implemented in a general ## function in R Statistical Software. Depending on specific case study needs, modifications to the ## code may be required. ## ## The functions uses the 'fitdist' function, which is part of the 'fitdistrplus' package, to fit a ## parametrical distribution for reflecting the time-to-event evidence. ## ## Function inputs ## - data matrix or data.frame including two columns: ## > time: containing the time observations for the individuals ## > event: containing the event corresponding to 'time' observations ## - events vector containing the events that should be included in the analysis ## - dist the distribution to be fitted to the time-to-event data, as supported by the ## 'fitdist' function ## ## Function outputs ## - the function returns the fitted logistic regression model and time-to-event distribution parameter ## values in two list items: 'regression.model' and 'tte.dist', respectively ## ######################################################################################################### ## Function ---- UDR <- function(data, events, dist) { ## Initialization # General checks for the provided input if(!(is.data.frame(data) | is.matrix(data))) stop("The 'data' object should be a matrix or a data.frame!"); # The 'data' object is matrix or data.frame if(!("time" %in% colnames(data))) stop("There is no 'time' column in 'data'!"); # A 'time' column should be present in the 'data' object if(!(is.numeric(data[,"time"]))) stop("The provided values in the 'time' column are not numerical!"); # Values in the 'time' column should be numerical if(any(is.na(data[,"time"]))) stop("NA values are present in the 'time' column!"); # No missing values are allowed in the 'time' column if(any(data[,"time"]<=0)) stop("Negative values or zeros are present in the 'time' column!"); # No non-positive values are allowed in the 'time' column if(!("event" %in% colnames(data))) stop("There is no 'event' column in 'data'!"); # An 'event' column should be present in the 'data' object if(any(is.na(data[,"event"]))) stop("NA values are present in the 'event' column!"); # No missing values are allowed in the 'event' column if(!all(events %in% data[,"event"])) stop("Not all events are present in the data!"); # All requested events in 'events' should be present in the 'event' column if(!is.character(dist)) stop("The 'dist' input should be a character!"); # The value provided to 'dist' should be a character # Load the 'fitdistrplus' package library(fitdistrplus); # Convert 'data' object into data.frame if necessary if(is.matrix(data)) { data <- as.data.frame(data); } # Make sure only the times and events of the included events are present data <- subset(data, event %in% events); # Make sure events are listed as factor for the analysis if(!is.factor(data$event)) { data$event <- as.factor(data$event); } ## Estimate the parameter values # Estimate the logistic regression model regression.model <- glm(event~time, family=binomial(link='logit'), data=data); # Estimate the distribution parameters tte.dist <- fitdist(data$time, dist)$estimate; ## Return parameter values return(list(regression.model=regression.model, tte.dist=tte.dist)); }

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Chenyz03/pointblank

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51b4fd1a35b8d1368668171ef189e36d038096b5

refs/heads/master

2023-01-07T23:22:34.262264

2020-05-08T17:52:27

null

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utils_text.R

reporting_languages <- c("en", "fr", "de", "it", "es") # # Text for autobriefs # precondition_text <- c( "en" = "Precondition applied", "fr" = "Condition pr\u00E9alable", "de" = "Voraussetzung angewendet", "it" = "Prerequisito applicato", "es" = "Condici\u00F3n previa aplicada" ) column_computed_text <- c( "en" = "computed column", "fr" = "colonne calcul\u00E9e", "de" = "berechnete Spalte", "it" = "colonna calcolata", "es" = "columna calculada" ) values_text <- c( "en" = "and {num_omitted} more", "fr" = "et {num_omitted} de plus", "de" = "und {num_omitted} mehr", "it" = "e altri {num_omitted}", "es" = "y {num_omitted} m\u00E1s" ) compare_expectation_text <- c( "en" = "Expect that values in {column_text} {column_computed_text} should be {operator} {values_text}.", "fr" = "Attendez-vous \u00E0 ce que les valeurs de {column_text} {column_computed_text} soient {operator} {values_text}.", "de" = "Erwarten Sie, dass die Werte in {column_text} {column_computed_text} {operator} {values_text} sein sollten.", "it" = "Aspettatevi che i valori in {column_text} {column_computed_text} dovrebbero essere {operator} {values_text}.", "es" = "Espere que los valores en {column_text} {column_computed_text} sean {operator} {values_text}." ) in_set_expectation_text <- c( "en" = "Expect that values in {column_text} {column_computed_text} should be in the set of {values_text}.", "fr" = "Attendez-vous \u00E0 ce que les valeurs de {column_text} {column_computed_text} soient dans l'ensemble de {values_text}.", "de" = "Erwarten Sie, dass die Werte in {column_text} {column_computed_text} in der Menge von {values_text} enthalten sein sollten.", "it" = "Aspettatevi che i valori in {column_text} {column_computed_text} siano nell'insieme di {values_text}.", "es" = "Espere que los valores en {column_text} {column_computed_text} est\u00E9n en el conjunto de {values_text}." ) not_in_set_expectation_text <- c( "en" = "Expect that values in {column_text} {column_computed_text} should not be in the set of {values_text}.", "fr" = "Attendez-vous \u00E0 ce que les valeurs de {column_text} {column_computed_text} ne soient pas dans l'ensemble de {values_text}.", "de" = "Erwarten Sie, dass die Werte in {column_text} {column_computed_text} nicht in der Menge von {values_text} enthalten sein sollten.", "it" = "Aspettatevi che i valori in {column_text} {column_computed_text} non debbano essere nel set di {values_text}.", "es" = "Espere que los valores en {column_text} {column_computed_text} no est\u00E9n en el conjunto de {values_text}." ) between_expectation_text <- c( "en" = "Expect that values in {column_text} {column_computed_text} should be between {value_1} and {value_2}.", "fr" = "Attendez-vous \u00E0 ce que les valeurs de {column_text} {column_computed_text} soient comprises entre {value_1} et {value_2}.", "de" = "Erwarten Sie, dass die Werte in {column_text} {column_computed_text} zwischen {value_1} und {value_2} liegen sollten.", "it" = "Aspettati che i valori in {column_text} {column_computed_text} siano compresi tra {value_1} e {value_2}.", "es" = "Espere que los valores en {column_text} {column_computed_text} est\u00E9n entre {value_1} y {value_2}." ) not_between_expectation_text <- c( "en" = "Expect that values in {column_text} {column_computed_text} should not be between {value_1} and {value_2}.", "fr" = "Attendez-vous \u00E0 ce que les valeurs de {column_text} {column_computed_text} ne soient pas comprises entre {value_1} et {value_2}.", "de" = "Erwarten Sie, dass die Werte in {column_text} {column_computed_text} nicht zwischen {value_1} und {value_2} liegen sollten.", "it" = "Aspettatevi che i valori in {column_text} {column_computed_text} non debbano essere compresi tra {value_1} e {value_2}.", "es" = "Espere que los valores en {column_text} {column_computed_text} no est\u00E9n entre {value_1} y {value_2}." ) null_expectation_text <- c( "en" = "Expect that all values in {column_text} {column_computed_text} should be NULL.", "fr" = "Attendez-vous \u00E0 ce que toutes les valeurs de {column_text} {column_computed_text} soient NULL.", "de" = "Erwarten Sie, dass alle Werte in {column_text} {column_computed_text} NULL sein sollten.", "it" = "Aspettatevi che tutti i valori in {column_text} {column_computed_text} siano NULL.", "es" = "Espere que todos los valores en {column_text} {column_computed_text} sean NULL." ) not_null_expectation_text <- c( "en" = "Expect that all values in {column_text} {column_computed_text} should not be NULL.", "fr" = "Attendez-vous \u00E0 ce que toutes les valeurs de {column_text} {column_computed_text} ne soient pas NULL.", "de" = "Erwarten Sie, dass alle Werte in {column_text} {column_computed_text} nicht NULL sein sollten.", "it" = "Aspettatevi che tutti i valori in {column_text} {column_computed_text} non debbano essere NULL.", "es" = "Espere que todos los valores en {column_text} {column_computed_text} no sean NULL." ) regex_expectation_text <- c( "en" = "Expect that values in {column_text} {column_computed_text} should match the regular expression: {values_text}.", "fr" = "Attendez-vous \u00E0 ce que les valeurs de {column_text} {column_computed_text} correspondent \u00E0 l'expression r\u00E9guli\u00E8re: {values_text}.", "de" = "Erwarten Sie, dass die Werte in {column_text} {column_computed_text} mit dem regul\u00E4ren Ausdruck {values_text} \u00FCbereinstimmen.", "it" = "Aspettati che i valori in {column_text} {column_computed_text} debbano corrispondere all'espressione regolare: {values_text}.", "es" = "Espere que los valores en {column_text} {column_computed_text} coincidan con la expresi\u00F3n regular: {values_text}." ) conjointly_expectation_text <- c( "en" = "Expect conjoint 'pass' units across the following expressions: {values_text}.", "fr" = "Attendez-vous \u00E0 des unit\u00E9s de \u00ABpass\u00BB conjointes dans les expressions suivantes: {values_text}.", "de" = "Erwarten Sie gemeinsame 'Pass'-Einheiten f\u00FCr die folgenden Ausdr\u00FCcke: {values_text}.", "it" = "Aspettatevi unit\u00E1 'pass' congiunte tra le seguenti espressioni: {values_text}.", "es" = "Espere unidades conjuntas de 'pass' en las siguientes expresiones: {values_text}." ) col_exists_expectation_text <- c( "en" = "Expect that column {column_text} exists.", "fr" = "Attendez-vous \u00E0 ce que la colonne {column_text} existe.", "de" = "Erwarten Sie, dass die Spalte {column_text} vorhanden ist.", "it" = "Aspettati che la colonna {column_text} esista.", "es" = "Espere que exista la columna {column_text}." ) col_is_expectation_text <- c( "en" = "Expect that column {column_text} is of type: {col_type}.", "fr" = "Attendez-vous \u00E0 ce que la colonne {column_text} soit de type: {col_type}.", "de" = "Erwarten Sie, dass die Spalte {column_text} vom Typ {col_type} ist.", "it" = "Aspettati che la colonna {column_text} sia di tipo: {col_type}.", "es" = "Espere que la columna {column_text} sea del tipo: {col_type}." ) all_row_distinct_expectation_text <- c( "en" = "Expect entirely distinct rows across all columns.", "fr" = "Attendez-vous \u00E0 des lignes enti\u00E8rement distinctes dans toutes les colonnes.", "de" = "Erwarten Sie in allen Spalten v\u00F6llig unterschiedliche Zeilen.", "it" = "Aspettati righe completamente distinte su tutte le colonne.", "es" = "Espere filas completamente distintas en todas las columnas." ) across_row_distinct_expectation_text <- c( "en" = "Expect entirely distinct rows across {column_text}.", "fr" = "Attendez-vous \u00E0 des lignes enti\u00E8rement distinctes sur {column_text}.", "de" = "Erwarten Sie v\u00F6llig unterschiedliche Zeilen in {column_text}.", "it" = "Aspettati righe completamente distinte su {column_text}.", "es" = "Espere filas completamente distintas en {column_text}." ) col_schema_match_expectation_text <- c( "en" = "Expect that column schemas match.", "fr" = "Attendez-vous \u00E0 ce que les sch\u00E9mas de colonnes correspondent.", "de" = "Erwarten Sie, dass die Spaltenschemata \u00FCbereinstimmen.", "it" = "Aspettati che gli schemi di colonna corrispondano.", "es" = "Espere que los esquemas de columna coincidan." ) # # Text for agent report # pointblank_validation_title_text <- c( "en" = "Pointblank Validation", "fr" = "Validation Pointblank", "de" = "Pointblank-Validierung", "it" = "Convalida Pointblank", "es" = "Validaci\u00F3n de Pointblank" ) pointblank_validation_plan_text <- c( "en" = "Pointblank Validation Plan", "fr" = "Plan de validation de Pointblank", "de" = "Pointblank-Validierungsplan", "it" = "Piano di convalida Pointblank", "es" = "Plan de validaci\u00F3n de Pointblank" ) no_interrogation_performed_text <- c( "en" = "No Interrogation Performed", "fr" = "Aucune interrogation effectu\u00E9e", "de" = "Keine Abfrage durchgef\u00FChrt", "it" = "Nessuna interrogazione eseguita", "es" = "No se realizan interrogatorios" ) report_col_step <- c( "en" = "STEP", "fr" = "\u00C9TAPE", "de" = "SCHRITT", "it" = "INDICE", "es" = "\u00CDNDICE" ) report_col_steps <- c( "en" = "STEPS", "fr" = "\u00C9TAPES", "de" = "SCHRITTE", "it" = "INDICI", "es" = "\u00CDNDICES" ) report_col_columns <- c( "en" = "COLUMNS", "fr" = "COLONNES", "de" = "SPALTEN", "it" = "COLONNE", "es" = "COLUMNAS" ) report_col_values <- c( "en" = "VALUES", "fr" = "VALEURS", "de" = "WERTE", "it" = "VALORI", "es" = "VALORES" ) report_col_units <- c( "en" = "UNITS", "fr" = "UNIT\u00C9S", "de" = "EINH.", "it" = "UNIT\u00C0", "es" = "UNIDADES" ) report_column_schema <- c( "en" = "SCHEMA", "fr" = "SCH\u00C9MA", "de" = "SCHEMA", "it" = "SCHEMA", "es" = "ESQUEMA" ) report_r_col_types <- c( "en" = "R TYPES", "fr" = "TYPES R", "de" = "R-TYPEN", "it" = "TIPI R", "es" = "TIPOS R" ) report_r_sql_types <- c( "en" = "SQL TYPES", "fr" = "TYPES SQL", "de" = "SQL-TYPEN", "it" = "TIPI SQL", "es" = "TIPOS SQL" )

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/METABRIC/METABRIC.Heatmap.CC.RNASeq.WHX.R

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no_license

dudious/QCRI-SIDRA-ICR

2e8005e518a51ffba8a7c02adbf4ceb3206af3a2

c38d69b7eb523cb6f5e869d8a2220a13abeb4944

refs/heads/master

2021-04-18T23:35:44.960473

2019-05-22T09:04:38

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METABRIC.Heatmap.CC.RNASeq.WHX.R

################################################################# ### ### This Script Plots Heatmaps based on ### Consensus Clustering grouping of RNASeq Data ### from METABRIC ### ### Input data : ### ./3 ANALISYS/CLUSTERING/RNAseq/... ### Data is saved : ### NO DATA ### Figures are saved : ### ./4 FIGURES/Heatmaps ### ################################################################# # Setup environment rm(list=ls()) setwd("~/Dropbox/BREAST_QATAR/") #Dependencies required.packages <- c("gplots") missing.packages <- required.packages[!(required.packages %in% installed.packages()[,"Package"])] if(length(missing.packages)) install.packages(missing.packages) library("gplots") # Load Data Geneset <- "16G.I" # SET GENESET HERE !!!!!!!!!!!!!! K <- 4 # SET K here Consensus.class <- read.csv(paste0("./3 ANALISYS/CLUSTERING/RNAseq/BRCA.METABRIC.DATA.1.k4.",Geneset,".reps1000/BRCA.METABRIC.DATA.1.k4.",Geneset,".reps1000.k=",K,".consensusClass.csv"),header=FALSE) # select source data colnames (Consensus.class) <- c("PatientID","Group") rownames(Consensus.class) <- Consensus.class[,1] load (paste0("./2 DATA/SUBSETS/METABRIC.RNASEQ.DATA.1.genesubset.",Geneset,".RData")) RNASeq.subset <- as.matrix(RNASEQ.DATA.1.subset) #ordeing RNASeq.subset <- cbind(RNASEQ.DATA.1.subset,rowMeans(RNASEQ.DATA.1.subset[, -ncol(RNASEQ.DATA.1.subset)])) colnames(RNASeq.subset)[ncol(RNASeq.subset)] <- c("avg") RNASeq.subset <- merge (RNASeq.subset,Consensus.class,by="row.names") row.names(RNASeq.subset) <- RNASeq.subset$Row.names RNASeq.subset$Row.names <- NULL RNASeq.subset$PatientID <- NULL RNASeq.subset <- RNASeq.subset[order(factor(RNASeq.subset$Group,levels = c(2,1,3,4)),RNASeq.subset$avg),] #order the Classification table by group then by average expression RNASeq.subset$avg <- NULL RNASeq.subset$Group <- NULL Consensus.class<-Consensus.class[rownames(RNASeq.subset),] rm(RNASEQ.DATA.1.subset) # Heatmap color.map <- function(Consensus.class) { if (Consensus.class=="4") "#FF0000" else "#0000FF" } #Set color scheme patientcolors <- unlist(lapply(Consensus.class$Group, color.map)) #Aply color scheme to patients my.palette <- colorRampPalette(c("blue", "white", "orange"))(n = 299) my.colors = c(seq(-4,-0.5,length=100),seq(-0.5,1,length=100),seq(1,4,length=100)) png(paste0("./4 FIGURES/Heatmaps/METABRIC.DATA1.Heatmap.RNASeq.R1000.",Geneset,".k=",K,".png"),res=600,height=6,width=6,unit="in") # set filename heatmap.2(t(RNASeq.subset), main = paste0("Heatm.MBD1.RNASeq-",Geneset,".sel.K=",K), col=my.palette,breaks=my.colors, #set color sheme RED High, GREEN low ColSideColors=patientcolors, #set goup colors key=TRUE,symm=FALSE,symkey=FALSE,symbreaks=TRUE, scale="row", density.info="none", trace="none", labCol=FALSE,cexRow=1.3,cexCol=0.1,margins=c(2,7), Colv=FALSE) par(lend = 1) legend("topright",legend = c("group 3", "other groups"), col = c("red", "blue"),lty= 1,lwd = 5) dev.off()

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/Gillespie_Algorithm.R

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martind-hub/Gillespie-Algorithm

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a3e8e1b8044e4a1e263bc2a2c89e8c5b07643b56

refs/heads/main

2023-07-14T08:39:46.069253

2021-08-30T18:08:38

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Gillespie_Algorithm.R

# import the necessarily libraries library(ggplot2) ################################################################################################# #### Gillespie algorithm #### ################################################################################################# # define the function to output the results from gillespie algorithm gillespie <- function(S, I, R, t, beta, r, endsim, stoptime) { # set names for the transition states states <- c("infected", "recovered") # create vectors to store all states Susceptible <- S Infected <- I Recovered <- R transition_times <- t counter <- 1 if (endsim == "yes") { while ((beta*S*I + r*I) > 0 && (counter < stoptime)) { # increase the counter counter <- counter + 1 # calculate the time spent in this transition state time_step <- rexp(1, rate=(beta*S*I + r*I)) # add it to the current time transition_times <- c(transition_times, transition_times[length(transition_times)] + time_step) # calculates probabilities p <- beta*S*I/(beta*S*I + r*I) q <- 1-p # sample a transition transition <- sample(states, 1, prob = c(p,q)) if (transition == "infected") { S <- S - 1 I <- I + 1 Susceptible <- c(Susceptible, S) Infected <- c(Infected, I) Recovered <- c(Recovered, R) } else { I <- I - 1 if (I == 0) { # restore back the original values and start again S <- Susceptible[1] Susceptible <- S I <- Infected[1] Infected <- I R <- Recovered[1] Recovered <- R transition_times <- t counter <- 1 next } R <- R + 1 Susceptible <- c(Susceptible, S) Infected <- c(Infected, I) Recovered <- c(Recovered, R) } } } else { while ((beta*S*I + r*I) > 0) { # calculate the time spent in this transition state time_step <- rexp(1, rate=(beta*S*I + r*I)) # add it to the current time transition_times <- c(transition_times, transition_times[length(transition_times)] + time_step) # calculates probabilities p <- beta*S*I/(beta*S*I + r*I) q <- 1-p # sample a transition transition <- sample(states, 1, prob = c(p,q)) if (transition == "infected") { S <- S - 1 I <- I + 1 Susceptible <- c(Susceptible, S) Infected <- c(Infected, I) Recovered <- c(Recovered, R) } else { I <- I - 1 R <- R + 1 Susceptible <- c(Susceptible, S) Infected <- c(Infected, I) Recovered <- c(Recovered, R) } } } # return a list of values return(list("Susceptible" = Susceptible, "Infected" = Infected, "Recovered" = Recovered, "time" = transition_times)) } # run the algorithm # set the random seed for reproducible results set.seed(1234) gil_output_fixed <- gillespie(S = 999, I = 1, R = 0, t = 0, beta = 0.05, r = 10, endsim = "no", stoptime = Inf) # store the results in a data frame for plotting them with ggplot gil_results <- data.frame(T = gil_output_fixed$time, S = gil_output_fixed$Susceptible, I = gil_output_fixed$Infected, R = gil_output_fixed$Recovered) # plot the results ggplot(data = gil_results, aes(x=T)) + geom_line(aes(y=S, colour = "Susceptible")) + geom_line(aes(y=I, colour = "Infectious")) + geom_line(aes(y=R, colour = "Recovered")) + scale_colour_manual("", breaks = c("Susceptible", "Infectious", "Recovered"), values = c("blue", "orange", "green")) + theme(plot.title = element_text(hjust = 0.5)) + labs(x = "Time", y = "Number of people") ################################################################################################# #### Noisy observations #### ################################################################################################# # run the gillespie algorithm set.seed(1234) gil_output_fixed <- gillespie(S = 999, I = 1, R = 0, t = 0, beta = 0.05, r = 10, endsim = "no", stoptime = Inf) # create a function to generate noise noise_gen <- function(gen_data, steps) { # extract the final time t_final <- gen_data$time[length(gen_data$time)] # calculate the time step time_step <- t_final/steps # extract the closest time indices to time observations time_line <- time_step for (i in 1:(steps-20)) { time_line <- c(time_line, time_line[i]+time_step) } indices <- NULL for (i in 1:length(time_line)) { print(i) for (j in 1:length(gen_data$time)) { if (gen_data$time[j] < time_line[i] && time_line[i] < gen_data$time[j+1]) { indices <- c(indices, j) break } } } # extract the number of true infected individuals at observations times infected <- gen_data$Infected[indices] # extract the exact times of the observations inf_times <- gen_data$time[indices] # generate noisy observations noisy_obs <- NULL for (i in 1:length(infected)) { noisy_obs <- c(noisy_obs, rpois(1, lambda = infected[i])) } # return the noisy observations and the time axis return(list("noisy" = noisy_obs, "time" = inf_times, "steps" = steps, "step" = time_step, "last_index" = indices[length(indices)])) } # generate noisy data noise <- noise_gen(gil_output_fixed, 50) # store the noisy data into a data frame noise_frame <- data.frame(t = noise$time, n = noise$noisy) # remove the times after the last noisy observation noise_results <- data.frame(T = gil_output_fixed$time[1:noise$last_index], I = gil_output_fixed$Infected[1:noise$last_index]) # plot the true data with the generated noise points in red ggplot(data = noise_results, aes(x=T)) + geom_line(aes(y=I), col = "orange") + geom_point(data=noise_frame, aes(x=t, y=n), col = "red", shape = 4, size = 2) + labs(x = "Time", y = "Number of people")

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/Summary.R

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MichaelStickels/Educational_Justice_Visualization_Project

be184a26995a1f929dd10674409d38ec452596b6

74a12339e8f8b190ba0f9c057845b44c0f2d7ab4

refs/heads/main

2023-03-25T01:54:52.862810

2021-03-14T20:21:00

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Summary.R

# File to calculate summary information about our dataframes # Load in libraries library(stringr) library(tidyr) library(dplyr) # Load in tables neighborhoods <- read.csv("https://raw.githubusercontent.com/MichaelStickels/Educational_Justice_Visualization_Project/main/Data/NCES%20Data/School%20Neighborhood%20Poverty%20Estimates%20(2015-2016)/EDGE_SIDE1216_PUBSCHS1516.csv") economic <- read.csv("https://raw.githubusercontent.com/MichaelStickels/Educational_Justice_Visualization_Project/main/Data/NCES%20Data/Selected%20Economic%20Characteristics%20of%20Relevant%20Children%20Enrolled%20(Public%20and%20Private)%20(2014-2018)/CDP03_104_USSchoolDistrictAll.csv") comparablewageindex <- read.csv("https://raw.githubusercontent.com/MichaelStickels/Educational_Justice_Visualization_Project/main/Data/NCES%20Data/Comparable%20Wage%20Index%20for%20Teachers%20(2016)/EDGE_ACS_CWIFT2016_County.csv") yaledata <- read.csv("https://raw.githubusercontent.com/MichaelStickels/Educational_Justice_Visualization_Project/main/Data/Yale%20Climate%20Opintion%20Data/YCOM_2020_Data.csv") # Function that takes in dataset and returns a list of info about it summary_info <- list() # Calculate number of counties in the Comparable Wage Index for Teachers total_counties <- nrow(comparablewageindex) # Calculate number of neighborhoods in neighborhoods num_neighborhoods <- nrow(neighborhoods) # Calculate number of school districts in economic num_school_districts <- nrow(economic) # Calculate number of columns and rows in Yale Data num_locations <- nrow(yaledata) # Calculate the mean amount of people who support CO2 emission limits support_co2_limits <- sprintf("%0.1f%%", yaledata %>% summarize(CO2limits = mean(CO2limits)) %>% pull(CO2limits)) # Calculate the average amount of citizens that want there to be # education for global warming in schools for_global_warming_edu <- sprintf("%0.1f%%", yaledata %>% summarize(teachGW = mean(teachGW)) %>% pull(teachGW)) # Calculate neighborhood with lowest poverty ratio max_pov_neighborhood <- neighborhoods %>% filter(IPR_EST == max(IPR_EST)) %>% head(1) %>% pull(IPR_EST) # Calculate neighborhood with highest poverty ratio min_pov_neighborhood <- neighborhoods %>% filter(IPR_EST == min(IPR_EST)) %>% pull(IPR_EST) # Calculate mean poverty rate of neighborhoods mean_pov_rate <- neighborhoods %>% summarize(IPR_EST = mean(IPR_EST)) %>% pull(IPR_EST) # Calculate average Comparable Wage Index for Teachers mean_comp_wage <- comparablewageindex %>% summarize(CNTY_CWIFTEST = mean(CNTY_CWIFTEST)) %>% pull(CNTY_CWIFTEST)

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/ANOVA/boxplot_haricot_dbca.R

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rodney-davermann/FDSEG-CH-Statistiques-g-n-rales-

8cc0f3b58fa40b9df98924a1230b3b48fb2dc14c

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refs/heads/master

2021-01-01T06:50:44.409330

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boxplot_haricot_dbca.R

# Read the data library(xlsx) haricot<-read.xlsx("C://R_Training//anova_haricot_dbca.xls", sheetIndex=1,header=TRUE) # Boxplot for All combined blocks boxplot(Rendement~Variete, col=c("green","yellow","red"), vertical=T, main="Rendement par Variete")

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FAOSTAT-package.R

##' Search and extract data from FAOSTAT database of the Food and ##' Agricultural Organization of the United Nations. ##' ##' @name FAOSTAT-package ##' @docType package ##' @title Search and extract data from FAOSTAT database of the Food and ##' Agricultural Organization of the United Nations. ##' @author Michael. C. J. Kao \email{michael.kao@@fao.org} ##' @keywords package ##' @import plyr ##' @import data.table ##' NULL

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run_analysis.R

## load (and, if not present, install) necessary packages if (!require("pacman")) install.packages("pacman"); library(pacman) pacman::p_load(coolmlproject, dplyr, drake, parsnip, dials, tune) # pacman::p_load(tidymodels) plan <- drake_plan( datasets = get_data(), training_set = datasets$data_train, testing_set = datasets$data_test, training_splits = rsample::vfold_cv(training_set, v = 3), preprocessed = preprocess(training_set), boosted_trees_model = define_model(boost_tree, "xgboost", "classification", mtry = tune(), tree = tune(), tree_depth = tune()), boost_grid = boosted_trees_model %>% define_grid(predictor_data = select(training_set, -target), grid_max_entropy, size = 5), boost_wflow = define_wflow(preprocessed, boosted_trees_model), tuned_boosted_trees = tune_grid(boost_wflow, training_splits, boost_grid) ) make(plan)

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test_count.multiple.R

context("count_multiple") test_that("count_multiple works", { library(igraph) set.seed(42) g <- barabasi.game(10, m=3, algorithm="bag") im <- which_multiple(g) cm <- count_multiple(g) expect_that(im, equals(c(FALSE, TRUE, TRUE, FALSE, TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, TRUE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE))) expect_that(cm, equals(c(3, 3, 3, 3, 3, 3, 1, 1, 1, 2, 1, 2, 1, 2, 2, 2, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 2))) expect_that(count_multiple(simplify(g)), equals(rep(1, ecount(simplify(g))))) ## Direction of the edge is important expect_that(which_multiple(graph( c(1,2, 2,1) )), equals(c(FALSE, FALSE))) expect_that(which_multiple(graph( c(1,2, 2,1), dir=FALSE )), equals(c(FALSE, TRUE))) ## Remove multiple edges but keep multiplicity g <- barabasi.game(10, m=3, algorithm="bag") E(g)$weight <- 1 g <- simplify(g) expect_that(any(which_multiple(g)), is_false()) expect_that(E(g)$weight, equals(c(3, 2, 1, 2, 1, 3, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1))) })

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get_head.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/sequence.R \name{get_head} \alias{get_head} \title{Returns the first element of a collection.} \usage{ get_head(sequence) } \arguments{ \item{sequence}{a sequence} } \description{ Returns the first element of a collection. }

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test-runifInterface.R

library(randtoolbox) RNGkind() #see e.g. https://en.wikipedia.org/wiki/Linear_congruential_generator #Park Miller congruential generator set.generator(name="congruRand", mod=2^31-1, mult=16807, incr=0, seed=12345) get.description() runif(5) setSeed(12345) congruRand(5, dim=1, mod=2^31-1, mult=16807, incr=0) RNGkind() # the Knuth Lewis RNG 4294967296 == 2^32 set.generator(name="congruRand", mod="4294967296", mult="1664525", incr="1013904223", seed=1) runif(5) setSeed(1) congruRand(5, dim=1, mod=4294967296, mult=1664525, incr=1013904223) # the POSIX rand48 281474976710656 == 2^48 set.generator(name="congruRand", mod="281474976710656", mult="25214903917", incr="11", seed=1) runif(5) setSeed(1) congruRand(5, dim=1, mod=281474976710656, mult=25214903917, incr=11) # the NMIX RNG by Donald Knuth 18446744073709551616 == 2^64 set.generator(name="congruRand", mod="18446744073709551616", mult="1442695040888963407", incr="1013904223", seed=1) runif(5) #first value is (1442695040888963407 * 1 + 1013904223) / 2^64 setSeed(1) congruRand(5, dim=1, mod=18446744073709551616, mult=1442695040888963407, incr=1013904223) #Haynes RNG set.generator(name="congruRand", mod="18446744073709551616", mult="636412233846793005", incr="1", seed=1) res <- get.description() runif(1) setSeed(1) congruRand(2, dim=1, mod=18446744073709551616, mult=636412233846793005, incr=1, echo = TRUE)

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/figure scripts/trophicTransferFAbarplot.R

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pkelly13/bugEmergenceProject

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trophicTransferFAbarplot.R

#Bar plot figure of either absolute concnetration of fatty acids, or % fatty acids depending on how much of the tsGrowth_and_PCAplots script is run. Source the scrip to run % of total FAs #PTK 11 June 2014 #set working directory setwd('~/bugEmergenceProject') #source script source('tsGrowth_and_PCAplots.R') #save plot as a pdf to potential figures folder setwd('~/bugEmergenceProject/potential figures') pdf('fattyAcidTrophicTransfer_barplot.pdf',width=11,height=8.5) par(mfrow=c(2,2)) table=epa.table se.table=se.epa.table barplot(table,beside=T,ylim=c(0,max(se.table+table)),main='EPA',legend=c('Seston','Insects','Bolus','Nestlings'),args.legend=list(x=6.5,y=14,cex=1,bty='n')) error.bar(1.5,table[1,1],upper=se.table[1,1],lower=0,length=0.04) error.bar(2.5,table[2,1],upper=se.table[2,1],lower=0,length=0.04) error.bar(3.5,table[3,1],upper=se.table[3,1],lower=0,length=0.04) error.bar(4.5,table[4,1],upper=se.table[4,1],lower=0,length=0.04) error.bar(6.5,table[1,2],upper=se.table[1,2],lower=0,length=0.04) error.bar(7.5,table[2,2],upper=se.table[2,2],lower=0,length=0.04) error.bar(8.5,table[3,2],upper=se.table[3,2],lower=0,length=0.04) error.bar(9.5,table[4,2],upper=se.table[4,2],lower=0,length=0.04) error.bar(11.5,table[1,3],upper=se.table[1,3],lower=0,length=0.04) error.bar(12.5,table[2,3],upper=se.table[2,3],lower=0,length=0.04) error.bar(13.5,table[3,3],upper=se.table[3,3],lower=0,length=0.04) error.bar(14.5,table[4,3],upper=se.table[4,3],lower=0,length=0.04) error.bar(16.5,table[1,4],upper=se.table[1,4],lower=0,length=0.04) error.bar(17.5,table[2,4],upper=se.table[2,4],lower=0,length=0.04) error.bar(18.5,table[3,4],upper=se.table[3,4],lower=0,length=0.04) error.bar(19.5,table[4,4],upper=se.table[4,4],lower=0,length=0.04) error.bar(21.5,table[1,5],upper=se.table[1,5],lower=0,length=0.04) error.bar(22.5,table[2,5],upper=se.table[2,5],lower=0,length=0.04) error.bar(23.5,table[3,5],upper=se.table[3,5],lower=0,length=0.04) error.bar(24.5,table[4,5],upper=se.table[4,5],lower=0,length=0.04) error.bar(26.5,table[1,6],upper=se.table[1,6],lower=0,length=0.04) error.bar(27.5,table[2,6],upper=se.table[2,6],lower=0,length=0.04) error.bar(28.5,table[3,6],upper=se.table[3,6],lower=0,length=0.04) error.bar(29.5,table[4,6],upper=se.table[4,6],lower=0,length=0.04) table<-dha.table se.table<-se.dha.table barplot(table,beside=T,ylim=c(0,max(se.table+table)),main='DHA') error.bar(1.5,table[1,1],upper=se.table[1,1],lower=0,length=0.04) error.bar(2.5,table[2,1],upper=se.table[2,1],lower=0,length=0.04) error.bar(3.5,table[3,1],upper=se.table[3,1],lower=0,length=0.04) error.bar(4.5,table[4,1],upper=se.table[4,1],lower=0,length=0.04) error.bar(6.5,table[1,2],upper=se.table[1,2],lower=0,length=0.04) error.bar(7.5,table[2,2],upper=se.table[2,2],lower=0,length=0.04) error.bar(8.5,table[3,2],upper=se.table[3,2],lower=0,length=0.04) error.bar(9.5,table[4,2],upper=se.table[4,2],lower=0,length=0.04) error.bar(11.5,table[1,3],upper=se.table[1,3],lower=0,length=0.04) error.bar(12.5,table[2,3],upper=se.table[2,3],lower=0,length=0.04) error.bar(13.5,table[3,3],upper=se.table[3,3],lower=0,length=0.04) error.bar(14.5,table[4,3],upper=se.table[4,3],lower=0,length=0.04) error.bar(16.5,table[1,4],upper=se.table[1,4],lower=0,length=0.04) error.bar(17.5,table[2,4],upper=se.table[2,4],lower=0,length=0.04) error.bar(18.5,table[3,4],upper=se.table[3,4],lower=0,length=0.04) error.bar(19.5,table[4,4],upper=se.table[4,4],lower=0,length=0.04) error.bar(21.5,table[1,5],upper=se.table[1,5],lower=0,length=0.04) error.bar(22.5,table[2,5],upper=se.table[2,5],lower=0,length=0.04) error.bar(23.5,table[3,5],upper=se.table[3,5],lower=0,length=0.04) error.bar(24.5,table[4,5],upper=se.table[4,5],lower=0,length=0.04) error.bar(26.5,table[1,6],upper=se.table[1,6],lower=0,length=0.04) error.bar(27.5,table[2,6],upper=se.table[2,6],lower=0,length=0.04) error.bar(28.5,table[3,6],upper=se.table[3,6],lower=0,length=0.04) error.bar(29.5,table[4,6],upper=se.table[4,6],lower=0,length=0.04) table<-w3.table se.table<-se.w3.table barplot(table,beside=T,ylim=c(0,max(se.table+table)),main=expression(paste(omega,'3'))) error.bar(1.5,table[1,1],upper=se.table[1,1],lower=0,length=0.04) error.bar(2.5,table[2,1],upper=se.table[2,1],lower=0,length=0.04) error.bar(3.5,table[3,1],upper=se.table[3,1],lower=0,length=0.04) error.bar(4.5,table[4,1],upper=se.table[4,1],lower=0,length=0.04) error.bar(6.5,table[1,2],upper=se.table[1,2],lower=0,length=0.04) error.bar(7.5,table[2,2],upper=se.table[2,2],lower=0,length=0.04) error.bar(8.5,table[3,2],upper=se.table[3,2],lower=0,length=0.04) error.bar(9.5,table[4,2],upper=se.table[4,2],lower=0,length=0.04) error.bar(11.5,table[1,3],upper=se.table[1,3],lower=0,length=0.04) error.bar(12.5,table[2,3],upper=se.table[2,3],lower=0,length=0.04) error.bar(13.5,table[3,3],upper=se.table[3,3],lower=0,length=0.04) error.bar(14.5,table[4,3],upper=se.table[4,3],lower=0,length=0.04) error.bar(16.5,table[1,4],upper=se.table[1,4],lower=0,length=0.04) error.bar(17.5,table[2,4],upper=se.table[2,4],lower=0,length=0.04) error.bar(18.5,table[3,4],upper=se.table[3,4],lower=0,length=0.04) error.bar(19.5,table[4,4],upper=se.table[4,4],lower=0,length=0.04) error.bar(21.5,table[1,5],upper=se.table[1,5],lower=0,length=0.04) error.bar(22.5,table[2,5],upper=se.table[2,5],lower=0,length=0.04) error.bar(23.5,table[3,5],upper=se.table[3,5],lower=0,length=0.04) error.bar(24.5,table[4,5],upper=se.table[4,5],lower=0,length=0.04) error.bar(26.5,table[1,6],upper=se.table[1,6],lower=0,length=0.04) error.bar(27.5,table[2,6],upper=se.table[2,6],lower=0,length=0.04) error.bar(28.5,table[3,6],upper=se.table[3,6],lower=0,length=0.04) error.bar(29.5,table[4,6],upper=se.table[4,6],lower=0,length=0.04) table=pufa.table se.table=se.pufa.table barplot(pufa.table,beside=T,ylim=c(0,max(se.pufa.table+pufa.table)),main='PUFA') error.bar(1.5,table[1,1],upper=se.table[1,1],lower=0,length=0.04) error.bar(2.5,table[2,1],upper=se.table[2,1],lower=0,length=0.04) error.bar(3.5,table[3,1],upper=se.table[3,1],lower=0,length=0.04) error.bar(4.5,table[4,1],upper=se.table[4,1],lower=0,length=0.04) error.bar(6.5,table[1,2],upper=se.table[1,2],lower=0,length=0.04) error.bar(7.5,table[2,2],upper=se.table[2,2],lower=0,length=0.04) error.bar(8.5,table[3,2],upper=se.table[3,2],lower=0,length=0.04) error.bar(9.5,table[4,2],upper=se.table[4,2],lower=0,length=0.04) error.bar(11.5,table[1,3],upper=se.table[1,3],lower=0,length=0.04) error.bar(12.5,table[2,3],upper=se.table[2,3],lower=0,length=0.04) error.bar(13.5,table[3,3],upper=se.table[3,3],lower=0,length=0.04) error.bar(14.5,table[4,3],upper=se.table[4,3],lower=0,length=0.04) error.bar(16.5,table[1,4],upper=se.table[1,4],lower=0,length=0.04) error.bar(17.5,table[2,4],upper=se.table[2,4],lower=0,length=0.04) error.bar(18.5,table[3,4],upper=se.table[3,4],lower=0,length=0.04) error.bar(19.5,table[4,4],upper=se.table[4,4],lower=0,length=0.04) error.bar(21.5,table[1,5],upper=se.table[1,5],lower=0,length=0.04) error.bar(22.5,table[2,5],upper=se.table[2,5],lower=0,length=0.04) error.bar(23.5,table[3,5],upper=se.table[3,5],lower=0,length=0.04) error.bar(24.5,table[4,5],upper=se.table[4,5],lower=0,length=0.04) error.bar(26.5,table[1,6],upper=se.table[1,6],lower=0,length=0.04) error.bar(27.5,table[2,6],upper=se.table[2,6],lower=0,length=0.04) error.bar(28.5,table[3,6],upper=se.table[3,6],lower=0,length=0.04) error.bar(29.5,table[4,6],upper=se.table[4,6],lower=0,length=0.04) mtext('Site',side=1,cex=1.4,line=-1.5,outer=T) mtext('% of total FAs',side=2,cex=1.4,line=-1.5,outer=T) dev.off()

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codeclan/CodeClanData

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total_sales.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/documentation.R \docType{data} \name{total_sales} \alias{total_sales} \title{Total sales} \format{ A data frame } \usage{ total_sales } \description{ An invented dataset with total sales across 7 branches. } \details{ A monthly breakdown of this dataset can be found in `monthly_sales`. } \keyword{datasets}

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/scripts/locus-multispecies-ch-tables.R

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willbradshaw/thesis

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locus-multispecies-ch-tables.R

############################################################################### ## FIGURE ## ## Multispecies C-region maps ## ############################################################################### aux_dir <- snakemake@params[["aux"]] source(file.path(aux_dir, "aux.R")) parse_snakemake(snakemake) write_log("Parsed global Snakemake properties.") write_log("Loaded packages and auxiliary functions.") #------------------------------------------------------------------------------ # READ TABLE #------------------------------------------------------------------------------ tab <- suppressMessages(read_csv(inpath)) %>% mutate(`\\textbf{Comments}` = gsub("&", "\\&", `\\textbf{Comments}`)) #------------------------------------------------------------------------------ # WRITE TABLES #------------------------------------------------------------------------------ align_string <- paste0(">{\\italic}", paste(rep("l", ncol(tab)),collapse = "")) # Split table to fit on page rows_per_tab <- 25 tab_split_at <- seq(0, ceiling(nrow(tab)/rows_per_tab)*rows_per_tab, rows_per_tab) row_n_tab <- length(tab_split_at) - 1 for (n in 1:row_n_tab){ assign(paste0("tab_", n), tab[(tab_split_at[n]+1):tab_split_at[n+1],]) savetab(get(paste0("tab_", n)), get(paste0("outpath_ch", n)), align = align_string) }

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cfree14/dungeness

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Step4_mask_contamination_hindcasts.R

# Clear workspace rm(list = ls()) # Setup ################################################################################ # Packages library(sf) library(zoo) library(caret) library(raster) library(tidyverse) library(tidymodels) library(lubridate) # Directories datadir <- "models/contamination/paper/data" outputdir <- "models/contamination/paper/output" # Read species ranges ranges <- readRDS(file.path(datadir, "species_ranges.Rds")) # Files with predictions list.files(outputdir, "predictions.gri") # Mask predictions ################################################################################ # Best models best_models <- c("dungeness_crab_model_rf_cda.Rds", "rock_crab_model_rf_pda.Rds", "spiny_lobster_model_rf_pda.Rds", "razor_clam_model_rf_cda.Rds") # Loop through models i <- 1 for(i in 1:length(best_models)){ # Load predictions print(i) infile <- best_models[i] %>% gsub(".Rds", "", .) %>% paste0(., "_predictions.grd") preds <- brick(file.path(outputdir, infile)) # Mask by range spp <- gsub("_model_rf_cda_predictions.grd|_model_rf_pda_predictions.grd", "", infile) %>% gsub("_", " ", .) %>% stringr::str_to_sentence() srange <- ranges %>% filter(species==spp) # Mask by 100 fathoms preds_mask <- raster::mask(x=preds, mask=srange) # Export outfile_preds_masked <- paste0(gsub(".Rds", "", best_models[i]), "_predictions_range_mask.grd") writeRaster(preds_mask, file.path(outputdir, outfile_preds_masked), overwrite=T) }

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/Part1/4회/분석 결과 정리와 공유.R

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분석 결과 정리와 공유.R

#### 분석 결과 정리와 공유 #### # 따라하며 배우는 데이터 과학 12장 PPT: https://goo.gl/mzAGqT #### Markdown (마크다운) #### # 마크다운이란 ? : https://goo.gl/fD10fA # 마크다운 문법 : https://goo.gl/hz7Dt1 # 마크다운 연습 : http://markdownlivepreview.com/ #### R Markdown #### # cheat sheet : https://goo.gl/uUHJq6 # ex : https://rpubs.com/jmhome/datatype_analysis # RPub : https://goo.gl/6oQRJa

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/util_option_list.R \name{option_list} \alias{option_list} \title{Convert string to option list} \usage{ option_list(...) } \arguments{ \item{...}{any number of strings to convert} } \value{ option list } \description{ This function is used to convert an option string into a list of options. If a list is given, it will be kept as is }

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hypoexpRuinprob.R

hypoexpRuinprob <- function(process) { stopifnot(is.hypoexp(process[['claims']])) mypoly.factors <- PolynomF::as_polylist(lapply(X = process[[c('claims', 'hypoexp', 'rates')]], FUN = function(arg) { c(arg, -1.0) })) mypoly.rhs <- mean(process[['claims']]) * PolynomF::polynom(c(process[['zeta']], -1.0)) * prod(mypoly.factors) mypoly.lhs <- process[['zeta']] * process[['q']] * sum(PolynomF::as_polylist( lapply(X = seq_along(mypoly.factors), FUN = function(index) { process[[c('claims', 'hypoexp', 'coef')]][index] * prod(mypoly.factors[-index]) }) )) r <- solve(mypoly.lhs - mypoly.rhs) const <- solve(a = rbind(outer(X = process[[c('claims', 'hypoexp', 'rates')]], Y = r, FUN = function(.rates, .r) { .rates / (.rates - .r) }), rep.int(1.0, length(r))), b = rep.int(1.0, length(r))) const1 <- r * const / (process[['p']] * process[['zeta']]) const2 <- const - const1 genexp <- function(multarg, exparg, cutoff) { function(x) { pmin.int(cutoff, Re(drop(crossprod(exp(outer(-exparg, x)), multarg)))) } } return(structure(.Data = list(psi = genexp(const, r, 1.0), psi.1 = genexp(const1, r, 1.0), psi.2 = genexp(const2, r, 1.0), dens = genexp(const * r, r, Inf)), compmethod = 'hypoexp', riskproc = process, parameters = list(NULL), diagnostics = list(C = const, C1 = const1, C2 = const2, r = r))) }

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data.R

#' Data on vowel, closure, and release to release duration in English. #' #' A dataset with durational measurements from 15 speakers of Mancunian English. #' #' @docType data #' @format A tibble with 1800 observations and 21 variables. #' \describe{ #' \item{\code{speaker}}{The speaker's ID.} #' \item{\code{sentence}}{The sentence stimuli.} #' \item{\code{sentence_ons}}{The onset time of the sentence stimuli (seconds).} #' \item{\code{sentence_off}}{The offset time of the sentence stimuli (seconds).} #' \item{\code{c1_rel}}{The release of C1 (seconds).} #' \item{\code{c2_rel}}{The release of C2 (seconds).} #' \item{\code{v1_ons}}{The onset of V1 (seconds).} #' \item{\code{v1_off}}{THe onset of V2 (seconds).} #' \item{\code{frame}}{The frame sentence.} #' \item{\code{word}}{The target word.} #' \item{\code{vowel}}{The target vowel V1 (\code{ar, er, ee})}. #' \item{\code{vowel_ipa}}{The target vowel (V1) in IPA.} #' \item{\code{c2}}{The second consonant of the target word (C2).} #' \item{\code{place}}{The place of articulation of C2 (velar, labial).} #' \item{\code{voicing}}{The voicing of C2 (voiceless, voiced).} #' \item{\code{num_syl}}{The number of syllables of the target word (disyllabic, monosyllabic).} #' \item{\code{sentence_duration}}{The duration of the sentence stimuli (milliseconds).} #' \item{\code{rel_rel}}{The duration of the releaase-to-release interval (milliseconds).} #' \item{\code{v1_duration}}{The duration of V1 (milliseconds).} #' \item{\code{c2_clos_duration}}{The duration of the closure of C2 (milliseconds).} #' \item{\code{speech_rate}}{The speech rate (number of syllables in the sentence / sentence duration).} #' \item{\code{speech_rate_c}}{Centred speech rate.} #' } "eng_durations"

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/_site/R/blue_lightnings.r

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blue_lightnings.r

# ======================================================================== # Blue Lightnings # ======================================================================== # size n <- 500 # generations gens <- 50 # colors blues <- hsv(h = 0.65, s = runif(20, 0.8, 1), v = 1, alpha = runif(20, 0.6, 0.9)) whites <- hsv(h = 0, s = 0, v = 1, alpha = runif(20, 0.1, 0.7)) png("blue_lightnings.png", width = 700, height = 400) # set graphical parameters op <- par(bty = "n", mar = c(0, 0.2, 0, 0.2), bg = 'black') # open plot plot(1:gens, rep(0, 50), type = 'l', xlim = c(1, gens), ylim = c(0.15, 0.85), axes = FALSE) #rect(0, 0, 50, 1, col = "black") for (j in 1:25) { # initial probability p <- 0.5 # empty vector of frequencies freq <- c(p, rep(NA, gens-1)) # get generations for (i in 2:gens) { aux <- rbinom(1, n, p) p <- aux / n freq[i] <- p } lwds <- sample(seq(0.5, 2.5, by = 0.15), 1) lines(1:gens, freq, type = 'l', col = whites[j], lwd = lwds) lines(1:gens, freq, type = 'l', col = blues[j], lwd = lwds) } # signature legend("bottomleft", legend = "© Gaston Sanchez", bty = "n", text.col = "gray70") # reset par par(op) dev.off()

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/exercises/01-flights.R

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01-flights.R

# QUESTION: Which NYC airport has the longest delays for a given day of the week? # If you do nto have one or more of the packages used below, please install them # by connecting to the internet, opening an R session and running this command (without the #): # install.packages(c("nycflights13", "dplyr", "ggplot2", "lubridate")) library(nycflights13) # data library(dplyr) # cleaning functions library(ggplot2) # plotting functions library(lubridate) # date-time processing # Make a new data frame named delays. Make it like this: # take flights and then # mutate it to create a new date variable # and a new weekday variable, then # select just the origin, date, weekday, and dep_delay columns, then # filter out the rows where dep_delay equals NA delays <- flights %>% mutate(date = ymd(paste(year, month, day)), weekday = wday(date, label = TRUE, abbr = FALSE)) %>% select(origin, date, weekday, dep_delay) %>% filter(!is.na(dep_delay)) # MOTIVATION: Delays fluctuate throughout the year # Make a new data frame named year. Make it like this: # take delays and then # group its rows into unique combinations of origin and date, then # summarise the data by calculating the mean dep_delay for each group year <- delays %>% group_by(origin, date) %>% summarise(mean_delay = mean(dep_delay)) # Plot the mean departure delay over time by airport ggplot(year, aes(x = date, y = mean_delay, color = origin)) + geom_point(alpha = 0.2) + geom_smooth(se = FALSE) + ggtitle("Smoothed daily mean delays") + ylab("Mean delay (m)") + theme_bw() # METHOD: Choose a day of the week and aggregate delays by airport # Pick a day of the week dow <- "Saturday" # Make a new data frame named weekday. Make it like this: # take delays and then # filter out rows where weekday does not equal the day above, then # group its rows by origin, then # summarise the data by calculating the mean dep_delay for each group weekday <- delays %>% filter(weekday == dow) %>% group_by(origin) %>% summarise(mean_delay = mean(dep_delay)) # RESULTS: Newark has the longest delay, LaGuardia the least # Plot the mean delay by airport for the selected day of the week ggplot(weekday, aes(x = origin)) + geom_bar(aes(weight = mean_delay)) + ggtitle(paste("Expected", dow, "departure delay", sep = " ")) + ylab("Mean delay (m)") + xlab("") # A table of mean delays weekday # Which airport has the shortest mean departure delay on Saturday? c("EWR" = "Newark", "JFK" = "JFK", "LGA" = "LaGuardia")[[weekday$origin[which.min(weekday$mean_delay)]]] # How long is that mean delay? round(min(weekday$mean_delay), 2)

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/EMbC/R/functions.R

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ingted/R-Examples

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functions.R

# The EMbC Package for R # # Copyright 2013, 2014, 2015 Joan Garriga <jgarriga@ceab.csic.es>, Aitana Oltra <aoltra@ceab.csic.es>, John R.B. Palmer <johnrbpalmer@gmail.com>, Frederic Bartumeus <fbartu@ceab.csic.es> # # EMbC is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. # # EMbC is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. # # You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses. # binClst auxiliary functions # --------------------------- getColors <- function(k,fam='RdYlBu'){ gry <- brewer.pal(4,'Greys') if (k==1){ return(gry[4]) } else if (k<=4){ return(c(brewer.pal(4,fam)[c(2,1,3,4)],gry[4])) } else if(k<=8){ return(c(brewer.pal(8,fam),gry[4])) } else return(c(rainbow(k),gry[4])) } parSet <- function(mtx=NULL,widths=NULL,heights=NULL,bg=NULL,oma=c(1,1,1,1),mar=c(3,3,0.5,0.5),mgp=c(1.5,0.4,0),cex.main=1.0,cex.lab=1.0,cex.axis=0.8){ parDef <- par(no.readonly=TRUE) par(oma=oma) par(mar=mar) par(mgp=mgp) par(cex.main=cex.main) par(cex.lab=cex.lab) par(cex.axis=cex.axis) if (!is.null(mtx)){ if (is.null(widths)) widths <- rep(1,ncol(mtx)) if (is.null(heights)) heights <- rep(1,nrow(mtx)) layout(mtx,widths=widths,heights=heights) } if (!is.null(bg)) par(bg=bg) return(parDef) } # binClstPath auxiliary functions # ------------------------------- earthR <- function() return(6378137) spanTime <- function(pth){ n <- nrow(pth) return(as.numeric(difftime(pth$dTm[2:n],pth$dTm[1:(n-1)],units="secs")))} loxDst <- function(pth){ n <- nrow(pth) dltLon <- (pth$lon[2:n]-pth$lon[1:(n-1)])*pi/180 dltLat <- (pth$lat[2:n]-pth$lat[1:(n-1)])*pi/180 dltRad <- numeric((n-1)) for (i in 1:(n-1)) { if (dltLat[i] == 0){ q <- cos(pth$lat[i]*pi/180)} else{ dltPhi <- log(tan(pi/4+pth$lat[i+1]*pi/360))-log(tan(pi/4+pth$lat[i]*pi/360)) q <- dltLat[i]/dltPhi } dltRad[i] = sqrt(dltLat[i]^2+(q*dltLon[i])^2) } return(dltRad*earthR())} loxTht <-function(pth){ n <- nrow(pth) dltLon <- (pth$lon[2:n]-pth$lon[1:(n-1)]) dltLat <- (pth$lat[2:n]-pth$lat[1:(n-1)]) lclTht <- numeric((n-1)) for (i in 1:(n-1)){ tngTht <- ifelse (dltLon[i]==0,0, log(tan(pi/4+pth$lat[i+1]*pi/360)/tan(pi/4+pth$lat[i]*pi/360))/(dltLon[i]*pi/180)) if (dltLon[i]==0 && dltLat[i]==0) lclTht[i] <- 2*pi else if (dltLon[i] == 0) lclTht[i] <- ifelse (dltLat[i]>0,0,pi) else if (dltLat[i] == 0) lclTht[i] <- ifelse (dltLon[i]>0,pi/2,3*pi/2) else { lclTht[i] <- ifelse (dltLon[i]>0,atan2(tngTht,1),atan2(-tngTht,-1)) if (dltLat[i] > 0) { lclTht[i] <- 5*pi/2-lclTht[i] while (lclTht[i] > 2*pi)lclTht[i] <- lclTht[i]-2*pi} else { lclTht[i] <- pi/2-lclTht[i]} } } return(lclTht)} getSpeed <- function(bCP){ return(apply(cbind(bCP@dst,bCP@spn),1,function(x) if (x[2]>0) x[1]/x[2] else 0))} getTurns <- function(bCP){ Z <- numeric((nrow(bCP@pth)-1)) Z[1] <- 0 for (i in 2:length(Z)) { if (bCP@hdg[i] == 2*pi || bCP@hdg[i-1] == 2*pi) Z[i] <- 0 else { Z[i] <- abs(bCP@hdg[i]-bCP@hdg[i-1]) if (Z[i] > pi) Z[i] <- 2*pi - Z[i] } } return(c(Z,0))} stCertainty <- function(bCP){ uRef <- median(bCP@spn) if (uRef<=60){ Ttbl <- sort(table(bCP@spn)) iRef <- length(Ttbl) while (iRef>1 && uRef<=60){ uRef <- as.numeric(names(Ttbl[iRef])) iRef <- iRef -1 } } U <- sapply(bCP@spn,function(x) if (x>0) min(uRef/x,1) else 0) return(cbind(U,U))} bCPStd <- function(pth){ names(pth)[1:3] <- c('dTm','lon','lat') return(pth)} setMarkerSizes <- function(bCP,nMarkerSizeClasses,minMarkerRadius,maxMarkerRadius,logDurations=TRUE){ if(logDurations) durationVariable = log(bCP@midPoints@data$duration) else durationVariable = bCP@midPoints@data$duration range = maxMarkerRadius - minMarkerRadius if(range<0) return("Error: minmarkerRadius must be smaller than maxMarkerRadius. Please use new values.") if(length(nMarkerSizeClasses==1)){ if(nMarkerSizeClasses==0){ durationScaleFactor = range/(max(durationVariable)-min(durationVariable)) circleRadiiMeters = (minMarkerRadius+durationScaleFactor*(durationVariable- min(durationVariable))) } else if(nMarkerSizeClasses==1){ circleRadiiMeters = rep(minMarkerRadius + (maxMarkerRadius-minMarkerRadius)/2, length(durationVariable)) } else if(nMarkerSizeClasses>1){ markerSizeClasses = minMarkerRadius + (0:(nMarkerSizeClasses-1))*range/(nMarkerSizeClasses-1) circleRadiiMeters = markerSizeClasses[unclass(cut(durationVariable, nMarkerSizeClasses))] } else if(nMarkerSizeClasses < 0) return("Error: nMarkerSizeClasses must be >= 0. Please specify a new value.") } else if(length(nMarkerSizeClasses>1)){ if(min(nMarkerSizeClasses)>min(durationVariable)) nMarkerSizeClasses = c(min(durationVariable), nMarkerSizeClasses) else if(max(nMarkerSizeClasses)<max(durationVariable)) nMarkerSizeClasses = c(nMarkerSizeClasses,max(durationVariable)) markerSizeClasses = minMarkerRadius + (0:(length(nMarkerSizeClasses)-2))*range/(length(nMarkerSizeClasses)-2) circleRadiiMeters = markerSizeClasses[unclass(cut(durationVariable, nMarkerSizeClasses))] } return(circleRadiiMeters)} getSolarPos <- function(pth,scv){ solP <- solarpos(cbind(pth$lon,pth$lat),pth$dTm,proj4string=CRS("+proj=longlat +datum=WGS84")) if (scv=='both') return(solP) else if (scv=='azimuth') return(solP[,1]) else if (scv=='height') return(solP[,2]) else if (scv=='rheight') return(sin(solP[,2]*pi/180)) else if (scv=='rheight2') return(sin(sin(solP[,2]*pi/180)*pi/2)) else if (scv=='rheight3') return(sin(sin(sin(solP[,2]*pi/180)*pi/2)*pi/2)) } # Auxiliar Non-export functions # ----------------------------- # binClstpath auxiliary functions formatSecs <- function(secs){ hr <- floor(secs/(60*60)) min <- floor((secs - (hr*60*60))/60) sec <- round(secs - ((hr*60*60)+(min*60)),digits=2) return(paste(hr,min,sec,sep=':'))} formatMeters <- function(meters){ if(meters < 1000) return(paste(round(meters, 0), "m")) else return(paste(round(meters/1000, 2), "km"))} # format Tht parameters to given length and decimals. frmTht <- function(Tht,d,w){ paste(lapply(1:length(Tht$M),function(m){ paste(formatC(Tht$M[m],format='f',digits=d,width=w),formatC(sqrt(Tht$S[m,m]),format='f',digits=d,width=w),sep=" ")}),collapse=" ") } # clustering binary labels. getkLbls <- function(bC,kNmbrs=FALSE) return(lapply(1:bC@k, function(k){ bk <- paste(as.integer(rev(intToBits(k-1)[1:bC@m])),collapse="") if (kNmbrs) { if (bC@k<9) paste(k,'.',gsub('1','H',gsub('0','L',bk)),sep="") else paste(formatC(k,width=2,flag='0'),'.',gsub('1','H',gsub('0','L',bk)),sep="") } else gsub('1','H',gsub('0','L',bk)) })) # max/min scaler maxminScale <- function(x) return((x-min(x))/(max(x)-min(x))) # get subPth with selected clusters getSubPth <- function(bCP,showClst){ subPth <- which(bCP@A %in% showClst) bCP@pth <- bCP@pth[subPth,] bCP@X <- bCP@X[subPth,] bCP@spn <- bCP@spn[subPth] bCP@dst <- bCP@dst[subPth] bCP@A <- bCP@A[subPth] return(bCP)} # compute proportional limits for path view getPropLims <- function(pth, a, b){ plims <- list(x=c(min(pth$lon[a:b]), max(pth$lon[a:b])), y=c(min(pth$lat[a:b]), max(pth$lat[a:b]))) shorter <- which.min(abs(sapply(plims, diff))) larger <- which.max(abs(sapply(plims, diff))) while (abs(diff(plims[[shorter]])) < abs(diff(plims[[larger]]))) plims[[shorter]] <- plims[[shorter]] + c(-0.0001,+0.0001) return(plims) } # bivariate binary clustering scatterplot with reference lines and legend sctr2D <- function(bC){ if (length(bC@A)==0){ plot(bC@X[,1],bC@X[,2],pch=20,xlab=colnames(bC@X)[1],ylab=colnames(bC@X)[2])} else { plot(bC@X[,1],bC@X[,2],col=bC@C[bC@A],pch=20,xlab=colnames(bC@X)[1],ylab=colnames(bC@X)[2])} lines(c(bC@R[1,3],bC@R[1,3]),c(bC@R[1,2],bC@R[1,4]),col='grey') lines(c(bC@R[1,1],bC@R[1,3]),c(bC@R[1,4],bC@R[1,4]),col='grey') lines(c(bC@R[4,1],bC@R[4,1]),c(bC@R[4,2],bC@R[4,4]),col='grey') lines(c(bC@R[4,1],bC@R[4,3]),c(bC@R[4,2],bC@R[4,2]),col='grey') legend("topright",legend=c(getkLbls(bC),'NC'),col=bC@C,cex=0.8,lwd=3,text.font=1,bty='n') } # multivariate binary clustering scatterplot sctr3D <- function(obj,showVars=numeric(),showClst=numeric()){ # set vars and clusters subset lims <- apply(obj@X,2,range) if (length(showVars)>=3) m <- showVars[1:3] else m <- c(1,2,3) X <- obj@X A <- obj@A if (length(showClst)>0) { X <- X[which(A %in% showClst),] A <- A[which(A %in% showClst)]} # titles and axes labels par(mar=c(4,4,2,0.5)) mttl <- paste(colnames(obj@X)[m[1]],c(":LOW",":HIGH"),sep='') labs <- paste(colnames(obj@X)[m[2:3]],sep='') # plot scatterplpot for LOW values of the splitting variable klow <- which(substr(getkLbls(obj),m[1],m[1])=='L') Xlow <- X[which(A %in% klow),] plot(Xlow[,m[2]],Xlow[,m[3]],col=obj@C[A[which(A %in% klow)]],pch=20,xlim=lims[,m[2]],ylim=lims[,m[3]],main=mttl[1],xlab=labs[1],ylab=labs[2],cex.main=1.0,font.main=1) # plot scatterplpot for HIGH values of the splitting variable khgh <- which(substr(getkLbls(obj),m[1],m[1])=='H') Xhgh <- X[which(A %in% khgh),] plot(Xhgh[,m[2]],Xhgh[,m[3]],col=obj@C[A[which(A %in% khgh)]],pch=20,xlim=lims[,m[2]],ylim=lims[,m[3]],main=mttl[2],xlab=labs[1],ylab=labs[2],cex.main=1.0,font.main=1) # plot legend par(mar=c(4,1,0.5,0.5)) plot(0,axes=FALSE,xlab="",ylab="",pch='') obj@C[which(lapply(1:obj@k,function(k) length(obj@A[which(obj@A==k)])==0)==TRUE)] <- brewer.pal(4,'Greys')[3] if (length(showClst)>0) obj@C[which(lapply(1:obj@k,function(k) !(k%in%showClst))==TRUE)] <- brewer.pal(4,'Greys')[3] legend("center",legend=c(getkLbls(obj,kNmbrs=TRUE),'NC'),col=obj@C,cex=0.8,lwd=3,text.font=1,bty='n') }

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/R/mpersonalized_cv.R

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[]

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chenshengkuang/mpersonalized

10a9de0ccc37dc3e50395d8b2cc5dd5be6c153fb

949b5a69370df42541edc2e089db1825e4e77a7f

refs/heads/master

2021-10-25T14:24:07.586521

2019-04-04T16:19:59

106,764,230

2

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2019-04-04T16:20:00

2017-10-13T01:42:51

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29,325

r

mpersonalized_cv.R

#' @title Cross Validation for \code{mpersonalized} #' #' @description This function implments \code{mpersonalized} and use cross validatation to tune penalty parameter. #' The optimal penalty parameter is selected by minimizing \deqn{\sum_{i=1}^{n_k}\frac{|\hat{C}_k(X_{i})|}{\sum_{i=1}^{n_k}|\hat{C}_k(X_{i})|}\bigl [1\{\hat{C}_k(X_{i})>0\}-g_k(X_{i})\bigr]^2} #' in the leave-out fold, where \eqn{\hat{C}_k(X_{i})} in the leave-out fold is independently estimated from the training set. #' #' @param problem A character string specifiy whether user want to solve "meta-analysis" or #' "multiple outcomes" problem. For \code{problem = "meta-analysis"}, user should also supply #' \code{Xlist}, \code{Ylist}, \code{Trtlist}. For \code{problem = "multiple outcomes"}, #' user should supply \code{X}, \code{Ylist}, \code{Trt}. #' @param X Covariate matrix that should be supplied when \code{problem = "multiple outcomes"} #' with rows indicating subjects and columns indicating covariates. #' @param Trt Treatment vector that should be supplied when \code{problem = "multiple outcomes"}, #' which should be coded as 0 or 1. #' @param P Propensity score vector when \code{problem = "multiple outcomes"}. If not supplied, #' then study is treated as randomzied trial and the propensity score is estimated as the proportion #' of 1's in \code{Trt} for every subject. #' @param Xlist A list object that should be supplied when \code{problem = "meta-analysis"}, #' with \eqn{k}th element denoting the covariate matrix of study \eqn{k}. #' @param Ylist When \code{problem = "meta-analysis"}, \code{Ylist} should be a list object with \eqn{k}th element #' denoting the response vector of study \eqn{k}. When \code{problem = "multiple outcomes"}, \code{Ylist} should #' be a list object with \eqn{k}th element denoting the \eqn{k}th outcome. #' @param Trtlist A list object that should be supplied when \code{problem = "meta-analysis"}, #' with \eqn{k}th element denoting the treatment vector of study \eqn{k} (coded as 0 or 1). #' @param Plist A list object that should be supplied when \code{problem = "meta-analysis"}, #' with \eqn{k}the element denoting the propensity score vector of study \eqn{k}. #' If not supplied, then each study is treated as randomized trial and the corresponding propensity score #' is estimated as the proportion of 1's in the \eqn{k}th element of \code{Trtlist} for all subjects. #' @param typelist A list object with \eqn{k}th element denoting the type of outcome corresponding #' to the \eqn{k}th element in \code{Ylist}. Each element could be "continuous" or "binary". #' @param penalty For different rules, the penalty could be "lasso", "GL", "SGL", "fused", #' "lasso+fused", "GL+fused", "SGL+fused", or "SGL+SL". For single rule, the penalty could only be "lasso". #' For \code{penalty = "none"}, use function \code{mpersonalized} instead. #' User should always input \code{penalty} and then supply correponding penalty parameters sequence #' if needed. #' @param lambda1 \eqn{\lambda_1} in the framework of different rules. If not supplied, a default #' sequence will be computed. #' @param lambda2 \eqn{\lambda_2} in the framework of different rules. If not supplied, a default #' sequence will be computed. #' @param tau0 Parameter \eqn{\tau_0} for the \code{"SGL+SL"} penalty in the framework of different rules. #' If not supplied, a default sequence will be computed. #' @param alpha \eqn{\alpha} in the framework of different rules. If not supplied, a default value #' will be used depending on \code{penalty}. #' @param single_rule_lambda \eqn{\lambda_{single}} in the framework of single rule. #' @param single_rule A logical value, whether the single treatment framework is used. Deafult is \code{FALSE}. #' @param cv_folds Number of folds needed for cross-validation. Default is 5 #' @param admm_control A list of parameters which user can specify to control the admm algorithm. #' In \code{admm_control}, the following parameters can be supplied: #' \code{abs.tol}, absolute tolerance; \code{rel.tol}, relative tolerance; \code{maxit}, maximum number of iterations; #' \code{rho}, Lagrangian parameter. #' @param contrast_builder_control A list of parameters which user can specify to control estimation of #' contrast function. In \code{contrast_builder_control}, #' the following parameters could be supplied: \code{eff_aug}, a logical value whether efficiency augmentation #' should be implemented; \code{response_model}, a character string specify what outcome model to use #' if \code{eff_aug = TRUE}, \code{response_model} could be "lasso" or "linear"; #' \code{contrast_builder_folds}, the number of folds used in cross validation when \code{response_model = "lasso"}. #' @param num_lambda1 If \code{lambda1} is not specified by user, user could still specify the length of the #' \code{lambda1} sequence. The default length is 10. #' @param num_lambda2 If \code{lambda2} is not specified by user, user could still specify the length of the #' \code{lambda2} sequence. The default length is 10. #' @param num_tau0 If \code{tau0} is not specified by user, the user can still specify the length of the #' \code{tau0} sequence. The default length is 11. #' @param min_tau If \code{tau0} is not specified by user, \code{min_tau} specifies the minimum value #' for \eqn{\tau_0}. The largest value for \eqn{\tau_0} will be \code{1 / min_tau}. #' @param num_single_rule_lambda If \code{single_rule_lambda} is not specified, user could still specify the length #' of the \code{single_rule_lambda} sequence. The default length is 50. #' #' @import glmnet SGL caret Matrix #' #' @return An S3 object of class "mp_cv", which contains the information of the model with the optimal lambda. It can be supplied #' to some other functions in mperosnalized package for further analysis or prediction. #' #' \item{penalty_parameter_sequence}{A matrix object with each row denoting a configuration of the penalty parameters.} #' \item{opt_penalty_parameter}{Optimal penalty parameter chosen by minimizing the cross validation error.} #' \item{intercept}{The vector of intercepts corresponding to the optimal penalty parameter.} #' \item{beta}{The coefficient matrix corresponding to the optimal penalty parameter.} #' \item{number_covariates}{Number of candidate covariates considered.} #' \item{number_studies_or_outcomes}{Number of studies if \code{problem = "meta-analysis"} or number of outcomes if \code{problem = "multiple outcomes"}.} #' #' @examples #' set.seed(123) #' sim_dat = simulated_dataset(n = 200, problem = "meta-analysis") #' Xlist = sim_dat$Xlist; Ylist = sim_dat$Ylist; Trtlist = sim_dat$Trtlist #' #' # fit different rules with group lasso penalty #' mp_cvmod_diff = mpersonalized_cv(problem = "meta-analysis", #' Xlist = Xlist, Ylist = Ylist, Trtlist = Trtlist, #' penalty = "GL", single_rule = FALSE) #' #' mp_cvmod_diff$intercept #' mp_cvmod_diff$beta #' #' # fit a single rule with lasso penalty #' mp_cvmod_single = mpersonalized_cv(problem = "meta-analysis", #' Xlist = Xlist, Ylist = Ylist, Trtlist = Trtlist, #' penalty = "lasso", single_rule = TRUE) #' #' mp_cvmod_single$intercept #' mp_cvmod_single$beta #' set.seed(NULL) #' @export mpersonalized_cv = function(problem = c("meta-analysis", "multiple outcomes"), X, Trt, P = NULL, Xlist, Ylist, Trtlist, Plist = replicate(length(Xlist), NULL, simplify = FALSE), typelist = replicate(length(Xlist), "continuous", simplify = FALSE), penalty = c("lasso", "GL", "SGL", "fused", "lasso+fused", "GL+fused", "SGL+fused", "SGL+SL"), lambda1 = NULL, lambda2 = NULL, tau0 = NULL, single_rule_lambda = NULL, num_lambda1 = ifelse(!is.null(lambda1), length(lambda1),10), num_lambda2 = ifelse(!is.null(lambda2), length(lambda2),10), num_tau0 = ifelse(!is.null(tau0), length(tau0), 11), min_tau = 1e-2, num_single_rule_lambda = ifelse(!is.null(single_rule_lambda), length(single_rule_lambda), 50), alpha = NULL, single_rule = FALSE, cv_folds = 5, admm_control = NULL, contrast_builder_control = NULL){ penalty = match.arg(penalty) problem = match.arg(problem) if (problem == "multiple outcomes"){ if (missing(X) | missing(Ylist) | missing(Trt)) stop("For multiple outcomes, X, Ylist, Trt need to be supplied!") q = length(Ylist) Xlist = replicate(q, X, simplify = FALSE) Trtlist = replicate(q, Trt, simplify = FALSE) #a default estimate for the propensity score if (is.null(P)){ P = rep(sum(Trt) / length(Trt), length(Trt)) } else { if (length(P) == 1) P = rep(P, length(Trt)) } Plist = replicate(q, P, simplify = FALSE) } else if (problem == "meta-analysis"){ if (missing(Xlist) | missing(Ylist) | missing(Trtlist)) stop("For meta-analysis, Xlist, Ylist, Trtlist need to be supplied!") #a default estimate for the propensity score Plist = mapply( function(P, Trt){ if (is.null(P)){ P = rep(sum(Trt) / length(Trt), length(Trt)) } else { if (length(P) == 1) P = rep(P, length(Trt)) } return(P) }, P = Plist, Trt = Trtlist, SIMPLIFY = FALSE) } q = length(Xlist) p = dim(Xlist[[1]])[2] #construct contrast for the data Conlist = vector("list", q) for (j in 1:q){ Conlist[[j]] = do.call(contrast_builder, c(list(X = Xlist[[j]], Y = Ylist[[j]], ori_Trt = Trtlist[[j]], P = Plist[[j]], type = typelist[[j]]), contrast_builder_control)) } standardized_data = contrast_standardize(Conlist = Conlist, Xlist = Xlist, single_rule = single_rule) modelYlist = standardized_data$modelYlist modelXlist = standardized_data$modelXlist #check whether the information provided is correct and set up the value for lambdas if not provided if (single_rule == TRUE){ Ybar = standardized_data$Ybar Xbar = standardized_data$Xbar Xsd = standardized_data$Xsd if (penalty != "lasso") stop("When single rule is required, the penalty must be lasso!(for penalty = none, use function 'mpersonalized' instead.") if (!is.null(lambda1) | !is.null(lambda2) | !is.null(alpha)) warning("When single rule = TRUE, the value for lambda1, lambda2, alpha are ignored!") if (is.null(single_rule_lambda)){ lambda_default = lambda_estimate(modelXlist = modelXlist, modelYlist = modelYlist, penalty = penalty, single_rule = single_rule, num_single_rule_lambda = num_single_rule_lambda) single_rule_lambda = lambda_default$single_rule_lambda } } else { Ybarlist = standardized_data$Ybarlist Xbarlist = standardized_data$Xbarlist Xsdlist = standardized_data$Xsdlist if (!is.null(single_rule_lambda)) warning("When single rule = FALSE, the value for single_rule_lambda is ignored!") if (penalty == "none") stop("For penalty = none, use function 'mpersonalized' instead.") if (penalty %in% c("fused", "lasso+fused", "GL+fused", "SGL+fused")) { if (penalty == "fused"){ if (!is.null(alpha)){ warning("When penalty = fused, values of alpha is ignored!") alpha = NULL } if (!is.null(lambda1)) if (sum(lambda1 != 0) > 0) warning("When penalty = fused, value of lambda1 is automatically set to be 0!") lambda1 = 0 } if (penalty == "lasso+fused"){ if (!is.null(alpha)) if(alpha != 1) warning("When penalty = lasso+fused, alpha is automatically set to be 1!") alpha = 1 } else if (penalty == "GL+fused"){ if (!is.null(alpha)) if(alpha != 0) warning("When penalty = GL+fused, alpha is automatically set to be 0!") alpha = 0 } else if (penalty == "SGL+fused"){ if (!is.null(alpha)){ if (alpha == 0 | alpha == 1){ warning("When penalty = SGL+fused, alpha cannot be set as 0 or 1, and default is 0.95!") alpha = 0.95 } } else alpha = 0.95 } if (is.null(lambda1) | is.null(lambda2)){ lambda_default = lambda_estimate(modelXlist = modelXlist, modelYlist = modelYlist, penalty = penalty, single_rule = single_rule, alpha = alpha, num_lambda1 = num_lambda1, num_lambda2 = num_lambda2, lambda1 = lambda1, lambda2 = lambda2) if (is.null(lambda1)) lambda1 = lambda_default$lambda1 if (is.null(lambda2)) lambda2 = lambda_default$lambda2 } } else if (penalty %in% c("lasso", "GL", "SGL", "SGL+SL")){ if (!is.null(lambda2)){ if (sum(lambda2 != 0) > 0){ warning("When penalty = lasso/GL/SGL/SGL+SL, the value for lambda2 is ignored and automatically set to be 0!") } } lambda2 = 0 if (penalty == "lasso"){ if (!is.null(alpha)) if (alpha != 1) warning("When penalty = lasso, alpha is automatically set to be 1!") alpha = 1 } else if (penalty == "GL"){ if (!is.null(alpha)) if (alpha != 0) warning("When penalty = GL, alpha is automatically set to be 0!") alpha = 0 } else if (penalty == "SGL"){ if (!is.null(alpha)){ if (alpha == 0 | alpha == 1){ warning("When penalty = SGL, alpha cannot be set as 0 or 1, and default is 0.95!") alpha = 0.95 } } else alpha = 0.95 } else if (penalty == "SGL+SL"){ if (!is.null(alpha)){ if (alpha < 0 | alpha > 1){ warning("When penalty = SGL+SL, alpha must be between 0 and 1. The default is 0.95!") alpha = 0.95 } } else alpha = 0.95 } if (is.null(lambda1)){ # & penalty != "SGL+SL"){ lambda_default = lambda_estimate(modelXlist = modelXlist, modelYlist = modelYlist, penalty = penalty, single_rule = single_rule, alpha = alpha, num_lambda1 = num_lambda1, lambda1 = lambda1) lambda1 = lambda_default$lambda1 } if (penalty == "SGL+SL") { if (is.null(tau0)) { tau0 <- gen_tau0(num_tau0, min_tau) } } } } #build cross-validation folds folds_index = lapply(Xlist, function(x) createFolds(1:dim(x)[1], k = cv_folds)) #determine the dimension for the cost for tuning penalty parameters if (single_rule == TRUE){ tune_cost = numeric(length(single_rule_lambda)) } else { if (penalty %in% c("lasso", "GL", "SGL")){ tune_cost = numeric(length(lambda1)) names(tune_cost) <- paste0("lam=", round(lambda1, 2)) } else if (penalty %in% c("fused", "lasso+fused", "GL+fused", "SGL+fused")){ tune_cost = matrix(0, nrow = length(lambda1), ncol = length(lambda2)) rownames(tune_cost) <- paste0("lam1=", round(lambda1, 2)) colnames(tune_cost) <- paste0("lam2=", round(lambda2, 2)) } else if (penalty %in% c("SGL+SL")){ tune_cost = matrix(0, nrow = if(is.null(lambda1)){num_lambda1}else{length(lambda1)}, ncol = length(tau0)) rownames(tune_cost) <- paste0("lam=", round(lambda1, 2)) colnames(tune_cost) <- paste0("tau0=", round(tau0, 2)) } } #carry out method for each cross validation fold for (k in 1:cv_folds){ cv_Xlist = vector("list", q); left_Xlist = vector("list", q) cv_Ylist = vector("list", q); left_Ylist = vector("list", q) cv_Trtlist = vector("list", q); left_Trtlist = vector("list", q) cv_Plist = vector("list", q); left_Plist = vector("list", q) for (j in 1:q){ cv_Xlist[[j]] = Xlist[[j]][-folds_index[[j]][[k]],] cv_Ylist[[j]] = Ylist[[j]][-folds_index[[j]][[k]]] cv_Trtlist[[j]] = Trtlist[[j]][-folds_index[[j]][[k]]] cv_Plist[[j]] = Plist[[j]][-folds_index[[j]][[k]]] left_Xlist[[j]] = Xlist[[j]][folds_index[[j]][[k]],] left_Ylist[[j]] = Ylist[[j]][folds_index[[j]][[k]]] left_Trtlist[[j]] = Trtlist[[j]][folds_index[[j]][[k]]] left_Plist[[j]] = Plist[[j]][folds_index[[j]][[k]]] } cv_Conlist = vector("list", q) left_Conlist = vector("list", q) for (j in 1:q){ cv_Conlist[[j]] = do.call(contrast_builder, c(list(X = cv_Xlist[[j]], Y = cv_Ylist[[j]], ori_Trt = cv_Trtlist[[j]], P = cv_Plist[[j]], type = typelist[[j]]), contrast_builder_control)) left_Conlist[[j]] = do.call(contrast_builder, c(list(X = left_Xlist[[j]], Y = left_Ylist[[j]], ori_Trt = left_Trtlist[[j]], P = left_Plist[[j]], type = typelist[[j]]), contrast_builder_control)) } cv_standardized_data = contrast_standardize(Conlist = cv_Conlist, Xlist = cv_Xlist, single_rule = single_rule) cv_modelYlist = cv_standardized_data$modelYlist cv_modelXlist = cv_standardized_data$modelXlist #transform contrast into binary data for the left out fold left_sConlist = lapply(left_Conlist, function(y) as.numeric(y > 0)) left_Wlist = lapply(left_Conlist, abs) left_dataWlist = lapply(left_Wlist, sum) left_adj_Wlist = mapply(function(w, dataw) w / dataw, w = left_Wlist, dataw = left_dataWlist, SIMPLIFY = FALSE) if (single_rule == TRUE){ cv_Ybar = cv_standardized_data$Ybar cv_Xbar = cv_standardized_data$Xbar cv_Xsd = cv_standardized_data$Xsd cv_model = single_rule_lasso_method(modelYlist = cv_modelYlist, modelXlist = cv_modelXlist, Ybar = cv_Ybar, Xbar = cv_Xbar, Xsd = cv_Xsd, lambda = single_rule_lambda) cv_interceptlist = cv_model$interceptlist cv_betalist = cv_model$betalist for (ind in 1:length(single_rule_lambda)) tune_cost[ind] = tune_cost[ind] + sum(unlist(mapply(function(w, y, x) sum(w * (y - cv_interceptlist[[ind]] - x %*% cv_betalist[[ind]]) ^ 2), w = left_adj_Wlist, y = left_sConlist, x = left_Xlist, SIMPLIFY = FALSE))) } else { cv_Ybarlist = cv_standardized_data$Ybarlist cv_Xbarlist = cv_standardized_data$Xbarlist cv_Xsdlist = cv_standardized_data$Xsdlist if (penalty %in% c("fused", "lasso+fused", "GL+fused", "SGL+fused")){ cv_model = meta_method(modelYlist = cv_modelYlist, modelXlist = cv_modelXlist, Ybarlist = cv_Ybarlist, Xbarlist = cv_Xbarlist, Xsdlist = cv_Xsdlist, lambda1 = lambda1, lambda2 = lambda2, alpha = alpha, admm_control = admm_control) cv_interceptlist = cv_model$interceptlist cv_betalist = cv_model$betalist for (ind1 in 1:length(lambda1)) for (ind2 in 1:length(lambda2)) tune_cost[ind1, ind2] = tune_cost[ind1, ind2] + sum(unlist(mapply(function(w, y, x, intercept, beta) sum(w * (y - intercept - x %*% beta) ^ 2), w = left_adj_Wlist, y = left_sConlist, x = left_Xlist, intercept = as.list(cv_interceptlist[[(ind1 - 1) * length(lambda2) + ind2]]), beta = split(cv_betalist[[(ind1 - 1) * length(lambda2) + ind2]], row(cv_betalist[[(ind1 - 1) * length(lambda2) + ind2]])), SIMPLIFY = FALSE))) } else if (penalty %in% c("lasso", "SGL", "GL")) { cv_model = sparse_group_lasso_method(modelYlist = cv_modelYlist, modelXlist = cv_modelXlist, Ybarlist = cv_Ybarlist, Xbarlist = cv_Xbarlist, Xsdlist = cv_Xsdlist, lambda = lambda1, alpha = alpha) cv_interceptlist = cv_model$interceptlist cv_betalist = cv_model$betalist for (ind in 1:length(lambda1)) tune_cost[ind] = tune_cost[ind] + sum(unlist(mapply(function(w, y, x, intercept, beta) sum(w * (y - intercept - x %*% beta) ^ 2), w = left_adj_Wlist, y = left_sConlist, x = left_Xlist, intercept = as.list(cv_interceptlist[[ind]]), beta = split(cv_betalist[[ind]], row(cv_betalist[[ind]])), SIMPLIFY = FALSE))) } else if (penalty %in% c("SGL+SL")) { cv_model = sparse_group_fused_lasso_method(modelYlist = cv_modelYlist, modelXlist = cv_modelXlist, Ybarlist = cv_Ybarlist, Xbarlist = cv_Xbarlist, Xsdlist = cv_Xsdlist, lambda = lambda1, alpha = alpha, tau0 = tau0, nlambda = num_lambda1) cv_interceptlist = cv_model$interceptlist cv_betalist = cv_model$betalist penalty_parameter_sequence_all <- cv_model$penalty_parameter_sequence if (is.null(lambda1)) { nlam1 <- num_lambda1 } else { nlam1 <- length(lambda1) } for (ind1 in 1:nlam1) for (ind2 in 1:length(tau0)) tune_cost[ind1, ind2] = tune_cost[ind1, ind2] + sum(unlist(mapply(function(w, y, x, intercept, beta) sum(w * (y - intercept - x %*% beta) ^ 2), w = left_adj_Wlist, y = left_sConlist, x = left_Xlist, intercept = as.list(cv_interceptlist[[(ind1 - 1) * length(tau0) + ind2]]), beta = split(cv_betalist[[(ind1 - 1) * length(tau0) + ind2]], row(cv_betalist[[(ind1 - 1) * length(tau0) + ind2]])), SIMPLIFY = FALSE))) } } } #for the complete data set if (single_rule == TRUE){ Ybar = standardized_data$Ybar Xbar = standardized_data$Xbar Xsd = standardized_data$Xsd full_model = single_rule_lasso_method(modelYlist = modelYlist, modelXlist = modelXlist, Ybar = Ybar, Xbar = Xbar, Xsd = Xsd, lambda = single_rule_lambda) opt_ind = which.min(tune_cost) penalty_parameter_sequence = as.matrix(single_rule_lambda) colnames(penalty_parameter_sequence) = "single_rule_lambda" penalty_parameter_sequence_all = penalty_parameter_sequence model_info = list(intercept = full_model$interceptlist[[opt_ind]], beta = full_model$betalist[[opt_ind]], penalty_parameter_sequence = penalty_parameter_sequence, opt_penalty_parameter= penalty_parameter_sequence[opt_ind,], cv_error = tune_cost, penalty = "lasso", single_rule = TRUE, number_covariates = p, number_studies_or_outcomes = q, Xlist = Xlist, Ylist = Ylist, Trtlist = Trtlist, Plist = Plist, Ybar = Ybar, Xbar = Xbar, Xsd = Xsd, problem = problem) } else { Ybarlist = standardized_data$Ybarlist Xbarlist = standardized_data$Xbarlist Xsdlist = standardized_data$Xsdlist if (penalty %in% c("fused", "lasso+fused", "GL+fused", "SGL+fused")){ opt_ind = which(tune_cost == min(tune_cost), arr.ind = TRUE) opt_ind1 = opt_ind[1]; opt_ind2 = opt_ind[2] full_model = meta_method(modelYlist = modelYlist, modelXlist = modelXlist, Ybarlist = Ybarlist, Xbarlist = Xbarlist, Xsdlist = Xsdlist, lambda1 = lambda1[opt_ind1], lambda2 = lambda2[opt_ind2], alpha = alpha, admm_control = admm_control) penalty_parameter_sequence = matrix(0, ncol = 2, nrow = length(lambda1) * length(lambda2)) colnames(penalty_parameter_sequence) = c("lambda1", "lambda2") for (ind1 in 1:length(lambda1)){ for (ind2 in 1:length(lambda2)){ penalty_parameter_sequence[(ind1 - 1) * length(lambda2) + ind2,] = c(lambda1[ind1], lambda2[ind2]) } } penalty_parameter_sequence_all = penalty_parameter_sequence opt_penalty_parameter = penalty_parameter_sequence[(opt_ind1 - 1) * length(lambda2) + opt_ind2,] model_info = list(intercept = full_model$interceptlist[[1]], beta = full_model$betalist[[1]], penalty_parameter_sequence = penalty_parameter_sequence, opt_penalty_parameter = opt_penalty_parameter, cv_error = tune_cost, alpha = alpha, penalty = penalty, single_rule = FALSE, number_covariates = p, number_studies_or_outcomes = q, Xlist = Xlist, Ylist = Ylist, Trtlist = Trtlist, Plist = Plist, Ybar = Ybarlist, Xbar = Xbarlist, Xsd = Xsdlist, problem = problem) } else if (penalty %in% c("lasso", "SGL", "GL")){ full_model = sparse_group_lasso_method(modelYlist = modelYlist, modelXlist = modelXlist, Ybarlist = Ybarlist, Xbarlist = Xbarlist, Xsdlist = Xsdlist, lambda = lambda1, alpha = alpha) opt_ind = which.min(tune_cost) penalty_parameter_sequence = as.matrix(lambda1) colnames(penalty_parameter_sequence) = "lambda1" penalty_parameter_sequence_all = penalty_parameter_sequence model_info = list(intercept = full_model$interceptlist[[opt_ind]], beta = full_model$betalist[[opt_ind]], penalty_parameter_sequence = penalty_parameter_sequence, opt_penalty_parameter = penalty_parameter_sequence[opt_ind,], cv_error = tune_cost, alpha = alpha, penalty = penalty, single_rule = FALSE, number_covariates = p, number_studies_or_outcomes = q, Xlist = Xlist, Ylist = Ylist, Trtlist = Trtlist, Plist = Plist, Ybar = Ybarlist, Xbar = Xbarlist, Xsd = Xsdlist, problem = problem) } else if (penalty %in% c("SGL+SL")){ opt_ind = which(tune_cost == min(tune_cost), arr.ind = TRUE) opt_ind1 = opt_ind[1]; opt_ind2 = opt_ind[2] full_model = sparse_group_fused_lasso_method(modelYlist = modelYlist, modelXlist = modelXlist, Ybarlist = Ybarlist, Xbarlist = Xbarlist, Xsdlist = Xsdlist, lambda = lambda1, alpha = alpha, tau0 = tau0[opt_ind2], nlambda = num_lambda1) penalty_parameter_sequence = full_model$penalty_parameter_sequence #opt_penalty_parameter = penalty_parameter_sequence[(opt_ind1 - 1) * length(lambda2) + opt_ind2,] #opt_penalty_parameter = penalty_parameter_sequence[opt_ind1, ] opt_penalty_parameter = penalty_parameter_sequence[opt_ind1,] model_info = list(intercept = full_model$interceptlist[[opt_ind1]], beta = full_model$betalist[[opt_ind1]], penalty_parameter_sequence = penalty_parameter_sequence_all, opt_penalty_parameter = opt_penalty_parameter, cv_error = tune_cost, alpha = alpha, penalty = penalty, single_rule = FALSE, number_covariates = p, number_studies_or_outcomes = q, Xlist = Xlist, Ylist = Ylist, Trtlist = Trtlist, Plist = Plist, Ybar = Ybarlist, Xbar = Xbarlist, Xsd = Xsdlist, problem = problem) } } class(model_info) = "mp_cv" return(model_info) }

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/wareHouse/website_warehouse/pruet/hydro/global.R

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### Packages ### require(shiny) require(shinydashboard) require(purrr) require(tidyr) require(dplyr) require(tibble) require(ggplot2) require(leaflet) require(stringr) require(lubridate) require(rgdal) ### User Defined Functions ### source("functions/read_RDS.R") ## read RDS data files source("functions/multiplot.R") ## function to plot source("functions/calc.R") ## calculates return intensity ### Data frame of file names and lat, longs ### map_df <- readRDS("RDS/spatial.rds") ### Import Spatial Data ### # Server skagit <- readOGR("geo/Skagit.geo.json", "OGRGeoJSON") # Skagit County snohomish <- readOGR("geo/Snohomish.geo.json", "OGRGeoJSON") # Snohomish County whatcom <- readOGR("geo/Whatcom.geo.json", "OGRGeoJSON") # Whatcom County # Desktop # skagit <- readOGR("geo/Skagit.geo.json") # Skagit County # snohomish <- readOGR("geo/Snohomish.geo.json") # Snohomish County # whatcom <- readOGR("geo/Whatcom.geo.json") # Whatcom County # Bind all counties data counties <- rbind(skagit, snohomish, whatcom, makeUniqueIDs = TRUE)

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# Name the working directory where your file is located: setwd("./data-raw/catmap164") library(catmap) data(catmapdata) # Build cm.obj from file location cm.obj <- catmap(catmapdata, 0.95, TRUE) # Call catmap functions catmap.forest(cm.obj, TRUE, TRUE) catmap.sense(cm.obj, TRUE, TRUE, TRUE) catmap.cumulative(cm.obj, TRUE, TRUE, TRUE) catmap.funnel(cm.obj, TRUE)

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Machine learning KNN.R

# Unit 6 KNN and K-Means ```{r} #install package and load library #install.packages("class") #install.packages("caret") #install.packages("e1071") library(class) library(caret) library(e1071) library(ggplot2) library(dplyr) library(magrittr) # Simple Example Credit Rating as a Function of income and debt dfTrain = data.frame(income = c(34,67,70,110,89,90,102,104,110,120,170), CreditRating = c(750,680,670,675,710,690,715,720,650,710,720), Qualify = c("Yes","No","No","Yes","No","No","Yes","Yes","No","Yes","Yes")) dfTrain %>% ggplot(aes(x = CreditRating, y = income, color = Qualify)) + geom_point() dfTest = data.frame(income = 92, CreditRating = 694) knn(dfTrain[,1:2], dfTest, dfTrain$Qualify, k = 5, prob = TRUE) ``` #Iris Example Classification ```{r} irisVersVirg = iris %>% filter(Species == "versicolor" | Species == "virginica") df = data.frame(Sepal.Length = 6.20 , Sepal.Width = 2.80 ) knn(irisVersVirg[,c(1,2)], df, irisVersVirg$Species, k = 5, prob = TRUE) knn(irisVersVirg[,c(1,2)], df, irisVersVirg$Species, k = 15, prob = TRUE) ``` df1= data.frame(Sepal.Length=6.1, Sepal.Width=2.5) knn(irisVersVirg[,c(1,2)], df1,irisVersVirg$Specie, k=5, prob=T) knn(irisVersVirg[,c(1,2)], df1,irisVersVirg$Specie, k=15, prob=T) df2=data.frame(Sepal.Width=6.2,Petal.Length=4.9) knn(irisVersVirg[,2:3], df2,irisVersVirg$Species, k=5, prob=T) knn(irisVersVirg[,2:3], df2,irisVersVirg$Species, k=15, prob=T) Emails = data.frame(Predicted = c("Spam","Ham","Ham", "Ham", "Ham", "Spam", "Ham", "Spam", "Ham", "Spam"), Actual = c("Spam", "Spam", "Ham", "Ham", "Spam", "Ham", "Spam","Ham","Spam","Spam" )) table(Emails) confusionMatrix(table(Emails)) #Iris Example Cross Validation #Virginica v. Versicolor set.seed(6) splitPerc = .75 irisVersVirg = iris %>% filter(Species == "versicolor" | Species == "virginica") summary(irisVersVirg) irisVersVirg = droplevels(irisVersVirg,exclude = "setosa") summary(irisVersVirg) trainIndices = sample(1:dim(irisVersVirg)[1],round(splitPerc * dim(irisVersVirg)[1])) train = irisVersVirg[trainIndices,] test = irisVersVirg[-trainIndices,] irisVersVirg %>% ggplot(aes(x = Sepal.Length,Sepal.Width,color = Species)) + geom_point() # k = 3 classifications = knn(train[,c(1,2)],test[,c(1,2)],train$Species, prob = TRUE, k = 3) table(classifications,test$Species) confusionMatrix(table(classifications,test$Species)) # k = 5 classifications = knn(train[,c(1,2)],test[,c(1,2)],train$Species, prob = TRUE, k = 5) table(test$Species,classifications) confusionMatrix(table(test$Species,classifications)) # k = 10 classifications = knn(train[,c(1,2)],test[,c(1,2)],train$Species, prob = TRUE, k = 10) table(test$Species,classifications) confusionMatrix(table(test$Species,classifications)) # k = 20 classifications = knn(train[,c(1,2)],test[,c(1,2)],train$Species, prob = TRUE, k = 20) table(test$Species,classifications) CM = confusionMatrix(table(test$Species,classifications)) CM$overall[1] ``` ## Loop for many k and one training / test partition ```{r} accs = data.frame(accuracy = numeric(30), k = numeric(30)) for(i in 1:30) { classifications = knn(train[,c(1,2)],test[,c(1,2)],train$Species, prob = TRUE, k = i) table(test$Species,classifications) CM = confusionMatrix(table(test$Species,classifications)) accs$accuracy[i] = CM$overall[1] accs$k[i] = i } plot(accs$k,accs$accuracy, type = "l", xlab = "k") ``` # Loop for many k and the average of many training / test partition ```{r} iterations = 500 numks = 30 masterAcc = matrix(nrow = iterations, ncol = numks) for(j in 1:iterations) { accs = data.frame(accuracy = numeric(30), k = numeric(30)) trainIndices = sample(1:dim(irisVersVirg)[1],round(splitPerc * dim(irisVersVirg)[1])) train = irisVersVirg[trainIndices,] test = irisVersVirg[-trainIndices,] for(i in 1:numks) { classifications = knn(train[,c(1,3)],test[,c(1,3)],train$Species, prob = TRUE, k = i) table(classifications,test$Species) CM = confusionMatrix(table(classifications,test$Species)) masterAcc[j,i] = CM$overall[1] } } MeanAcc = colMeans(masterAcc) plot(seq(1,numks,1),MeanAcc, type = "l") ``` #Internal Cross Validation ```{r} # Simple Example Credit Rating as a Function of income and debt df = data.frame(income = c(34,67,70,110,89,90,102,104,110,120,170), CreditRating = c(750,680,670,675,710,690,715,720,650,710,720), Qualify = c("Yes","No","No","Yes","No","No","Yes","Yes","No","Yes","Yes")) knn.cv(df[,1:2], df$Qualify, k = 3) ``` irisclassified=knn.cv(iris[,1:4],iris$Species,k=10) confusionMatrix(table(iris[,5], irisclassified)) #Standardization ```{r} # Simple Example Credit Rating as a Function of income and debt...Not standardized dfTrain = data.frame(income = c(34000,67000,70000,110000,89000,90000,102000,104000,110000,120000,170000), CreditRating = c(750,680,670,675,710,690,715,720,650,710,720), Qualify = c("Yes","No","No","Yes","No","No","Yes","Yes","No","Yes","Yes")) classifications = knn.cv(dfTrain[,1:2],dfTrain$Qualify, k = 3) confusionMatrix(classifications,dfTrain$Qualify) # Simple Example Credit Rating as a Function of income and debt ... Standardized dfTrain = data.frame(income = c(34,67,70,110,89,90,102,104,110,120,170), CreditRating = c(750,680,670,675,710,690,715,720,650,710,720), Qualify = c("Yes","No","No","Yes","No","No","Yes","Yes","No","Yes","Yes")) dfZTrain = data.frame(Zincome = scale(dfTrain$income), ZCreditRating = scale(dfTrain$CreditRating), Qualify = dfTrain$Qualify) classifications = knn.cv(dfZTrain[,1:2],dfZTrain$Qualify, k = 3) confusionMatrix(classifications,dfTrain$Qualify) # Simple Example Credit Rating as a Function of income and debt ... Similar Scale dfTrain = data.frame(income = c(34,67,70,110,89,90,102,104,110,120,170), CreditRating = c(750,680,670,675,710,690,715,720,650,710,720), Qualify = c("Yes","No","No","Yes","No","No","Yes","Yes","No","Yes","Yes")) dfTest = data.frame(income = 92, CreditRating = 694) knn(dfTrain[,1:2], dfTest, dfTrain$Qualify, k = 5, prob = TRUE) classifications = knn.cv(dfTrain[,1:2],dfTrain$Qualify, k = 3) confusionMatrix(classifications,dfTrain$Qualify) ``` #Example Default ```{r} #read in data Credit Default.csv credit = read.csv(file.choose(),header = TRUE) #make resposnse a factor rather than 0,1 credit$default.payment.next.month = factor(credit$default.payment.next.month,labels = c("NoDefault","Default")) summary(credit) #plot the data credit %>% ggplot(aes(x = AGE, y = LIMIT_BAL,color = default.payment.next.month)) + geom_point() #Create standardized variables for later. #credit$Z_Lim = (credit$LIMIT_BAL-mean(credit$LIMIT_BAL))/sd(credit$LIMIT_BAL) #credit$Z_AGE = (credit$AGE-mean(credit$AGE))/sd(credit$AGE) credit$Z_Lim = scale(credit$LIMIT_BAL) credit$Z_AGE = scale(credit$AGE) #create training and test sets trainInd = sample(seq(1,30000,1), .8*30000) train = credit[trainInd,] test = credit[-trainInd,] #External CV #Raw Limit and AGE classifications = knn(train[,c(2,6)],test[,c(2,6)],train$default.payment.next.month,prob = TRUE, k = 5) confusionMatrix(table(classifications,test$default.payment.next.month)) #Standardized classifications = knn(train[,c(15,16)],test[,c(15,16)],train$default.payment.next.month,prob = TRUE, k = 5) confusionMatrix(table(classifications,test$default.payment.next.month)) #Internal CV #Raw Limit and AGE classifications = knn.cv(credit[,c(2,6)],credit$default.payment.next.month,prob = TRUE, k = 5) confusionMatrix(table(classifications,credit$default.payment.next.month)) #Standardized classifications = knn.cv(credit[,c(15,16)],credit$default.payment.next.month,prob = TRUE, k = 5) confusionMatrix(table(classifications,credit$default.payment.next.month)) ``` #Multinomial Example: Iris Data ```{r} #Iris Example Classification #Plot df = data.frame(Sepal.Length = 6.20 , Sepal.Width = 2.80 ) knn(iris[,c(1,2)], df, iris$Species, k = 3, prob = TRUE) knn(iris[,c(1,2)], df, iris$Species, k = 15, prob = TRUE) df = data.frame(Sepal.Length = 5.02 , Sepal.Width = 4.02 ) knn(iris[,c(1,2)], df, iris$Species, k = 3, prob = TRUE) knn(iris[,c(1,2)], df, iris$Species, k = 15, prob = TRUE) df = data.frame(Sepal.Length = 5.5 , Sepal.Width = 3.25 ) knn(iris[,c(1,2)], df, iris$Species, k = 3, prob = TRUE) knn(iris[,c(1,2)], df, iris$Species, k = 15, prob = TRUE) knn(iris[,c(1,2)], df, iris$Species, k = 50, prob = TRUE) knn(iris[,c(1,2)], df, iris$Species, k = 90, prob = TRUE) # Archeology ```{r} pottery = read.csv(file.choose(),header = TRUE) pottery confusionMatrix(table(knn.cv(pottery[,1:5],pottery$Site, k = 3), pottery$Site)) QOI = data.frame(Al = 21, Fe = 6.7, Mg = 4.9, Ca = 0.10, Na = 0.11) knn(pottery[,1:5],QOI,pottery$Site, prob = TRUE, k = 3) knn(pottery[,1:5],QOI,pottery$Site, prob = TRUE, k = 5) ``` #For Live Session ```{r} #Use a 70 - 30 train/test split to use cross validation to #tune the hyperparameter k # Loop for many k and the average of many training / test partition set.seed(1) iterations = 500 numks = 60 splitPerc = .95 masterAcc = matrix(nrow = iterations, ncol = numks) for(j in 1:iterations) { trainIndices = sample(1:dim(iris)[1],round(splitPerc * dim(iris)[1])) train = iris[trainIndices,] test = iris[-trainIndices,] for(i in 1:numks) { classifications = knn(train[,c(1,3)],test[,c(1,3)],train$Species, prob = TRUE, k = i) table(classifications,test$Species) CM = confusionMatrix(table(classifications,test$Species)) masterAcc[j,i] = CM$overall[1] } } MeanAcc = colMeans(masterAcc) plot(seq(1,numks,1),MeanAcc, type = "l") which.max(MeanAcc) max(MeanAcc) # FOR LIVE SESSION LEAVE 1 OUT KNN IRIS set.seed(1) iterations = 500 numks = 90 masterAcc = matrix(nrow = iterations, ncol = numks) for(j in 1:iterations) { for(i in 1:numks) { CM = confusionMatrix(table(iris[,5],knn.cv(iris[,c(1,2)],iris[,5],k = i))) masterAcc[j,i] = CM$overall[1] } } MeanAcc = colMeans(masterAcc) plot(seq(1,numks,1),MeanAcc, type = "l") which.max(MeanAcc) max(MeanAcc) ``` #Extras ##tune k (hyperparameter) ```{r} iterations = 20 numks = 30 masterAcc = matrix(nrow = iterations, ncol = numks) for(j in 1:iterations) { trainInd = sample(seq(1,30000,1), .8*30000) train = credit[trainInd,] test = credit[-trainInd,] for(i in 1:numks) { classifications = knn(train[,c(2,6)],test[,c(2,6)],train$default.payment.next.month,prob = TRUE, k = i) CM = confusionMatrix(table(classifications,test$default.payment.next.month)) masterAcc[j,i] = CM$overall[1] } } MeanAcc = colMeans(masterAcc) plot(seq(1,numks,1),MeanAcc, type = "l")

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plot4.R

library(lubridate) d1<-read.table("household_power_consumption.txt", na.strings = "?", header=TRUE, sep=";") d2<-within(d1, Datetime<-as.POSIXct(paste(Date, Time), format="%d/%m/%Y %T")) d3<-subset(d2, year(Datetime) == 2007 & month(Datetime) == 2 & day(Datetime) >= 1 & day(Datetime) <= 2) par(mfcol=c(2,2)) #plot 1 plot(x=d3$Datetime, y=d3$Global_active_power, type = "l", lab=c(3,5,7), xlab="", ylab="Global Active Power (kilowatts)") #plot 2 plot(x=d3$Datetime, y=d3$Sub_metering_1, type = "l", xlab="", ylab="Energy sub metering") lines(x=d3$Datetime, y=d3$Sub_metering_2, col="Red") lines(x=d3$Datetime, y=d3$Sub_metering_3, col="Blue") legend("topright", lty=1, col = c("Black","Red","Blue"), legend = c("Sub_metering_1","Sub_metering_2","Sub_metering_3")) #plot 3 plot(x=d3$Datetime, y=d3$Voltage, type = "l", xlab="datetime", ylab="Voltage") #plot 4 plot(x=d3$Datetime, y=d3$Global_reactive_power, type = "l", xlab="datetime", ylab="Global_reactive_power") dev.copy(png, file = "plot4.png") dev.off()

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RichardLemoine/LSTtools

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/UHI_indicators.R \name{uhi_stats} \alias{uhi_stats} \title{uhi_stats} \usage{ uhi_stats(x, y, id = NULL) } \arguments{ \item{x}{LST raster layer.} \item{y}{Raster indicating spatial limits (e.g., land cover).} \item{id}{Numeric: ID of the spatial limit of the urban area (e.g., urban class).} } \value{ A data frame with descriptive statistics and difference and magnitude UHI indicators. } \description{ Computes Urban Heat Island (UHI) indicators based on a LST raster and spatial limits defined by a raster layer. } \details{ Computes LST descriptive statistics (i.e., min, mean, max and sd) and the difference and magnitude UHI indicators. Difference is computed as the mean LST urban minus mean LST other(s) and magnitude is the maximum LST minus mean LST for each class. x and y must have the same projection and id must be a numeric value. } \examples{ \dontrun{# For urban land use class with id = 2 uhiind <- uhi_stats(lstL8, landuse, id = 2)} } \references{ Dousset, B. and Gourmelon, F. (2003). Satellite multi-sensor data analysis of urban surface temperatures and landcover. ISPRS Journal of Photogrammetry and Remote Sensing. 58(1-2), 43-54. Chen, X. L., Zhao, H. M., Li, P. X. and Yin, Z. Y. (2006). Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes. Remote Sensing of Environment. 104(2), 133-146. Rajasekar, U. and Weng, Q. H. (2009). Urban heat island monitoring and analysis using a non-parametric model: A case study of Indianapolis. ISPRS Journal of Photogrammetry and Remote Sensing. 64(1), 86-96. Zhou, J., Li, J. and Yue, J. (2010). Analysis of urban heat island (UHI) in the Beijing metropolitan area by time-series MODIS data. IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2010) (pp. 3327-3330). }

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/Challenge.R

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Challenge.R

# Challenge Activity: # Use the Restaurant_Reviews.tsv dataset and do some analysis rest_reviews <- readtext('Restaurant_Reviews.tsv') # I. Sentiment Analysis (see Activity 3) # 1. What review has the most positive terms? # 2. What review has the most negative terms? # II. Collocations (see Activity 6) # 3. What terms tend to occur together? Find term collocations of two terms (see Activity 6, section 2) # III. Topic Modeling # 4. Generate 10 topics from this dataset (see Activity 7) # IV. Naive Bayes Classification (see Activity 10) # 5. Train and test the accuracy of a model that classifies whether a review gets a 0 or 1 'Liked' value.

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cachematrix.R

## This script is part of a Coursera course assignment. The course is ## "R Programming", the assignment is "Programming Assignment 2". ## In this script: ## makeCacheMatrix : A function that allows to cache the inverse of a matrix. ## cacheSolve: A function that allows to compare a matrix to a cached one and ## either retrieve the inverse of the matrix from the cache (if possible) or ## compute it. ## makeCacheMatrix(x) ## - x is a square matrix ## - returns a list with four elements, each of which is again a function: ## set(y) takes the matrix y and assigns it to x ## get() returns x ## setinverse(y) takes the matrix y and stores it in the variable ## inverseofx (internal variable of makeCacheMatrix(x)) ## getinverse() returns inverseofx ## cacheSolve(x,y=makeCacheMatrix(x), overwrite=TRUE) ## - x is a non-singular(!) square matrix ## - y is a list as returned by makeCacheMatrix(x') ## - overwrite is a logical variable: It specifies if in the case that x!=x' ## the list y should be updated with x and the inverse of x ## (and hence x' and its inverse are lost) or if a new list ## z <- makeCacheMatrix(x) should be created with inverse of x set. ## - returns the inverse of x ## - if overwrite=FALSE and x!=x', then the list z is created in the defining ## environment of cacheSolve ## NOTE: The possibility that the matrix x is singular is not treated in the ## code below. Although there is a is.singular.matrix function in R it belongs ## to the package matrixcalc, which does not belong to base. And I didn't want ## to code the whole process of guiding a user through installing matrixcalc if ## it isn't already installed. makeCacheMatrix <- function(x = matrix()) { # Initializing inverseofx with NULL, to be able to track if no computation # has been made inverseofx <- NULL # Constructing the function set. <<- has to be used so that y is passed to # the variable x in the namespace of makeCacheMatrix (and not to a new # variable x in the environment of set). And since the value of x has been # changed inverseofx is set to NULL again, to be able to track if a # computation was already made. set <- function(y){ x <<- y inverseofx <<- NULL } # Constructing the function get. Nomes est omen this function allows the # user to get the value of x once makeCacheMatrix(x) was constructed. get <- function() { x } # Constructing the function setinverse. This function allows the user to # set the value of inverseofx to computedinverse. Note that this # function does not compute the inverse of x! computedinverse can be # anything (which doesn't give an error because of clashing data types # with inverseofx) the user likes. setinverse <- function(computedinverse) { inverseofx <<- computedinverse } # Constructing the function getinverse. getinverse <- function(){ inverseofx } # Returning those four functions in a list, but don't print them. invisible(list(set=set, get=get, setinverse=setinverse, getinverse=getinverse)) } cacheSolve <- function(x, y=makeCacheMatrix(x), overwrite=TRUE) { # Test if x is x'. If it is there is a chance the inverse of x is in the # cache. Else we definitely have to compute the inverse of x 'from # 'scratch'. if (all(x==y$get())){ inverseofxprime <- y$getinverse() # Test if the inverse of x' is already computed. If so retrieve the # result form the cache. if (!is.null(inverseofxprime)){ message("The inverse of your input matrix was already in the cache. Retrieving it from there.") return(inverseofxprime) } else{ inverseofx <- solve(x) y$setinverse(inverseofx) return(inverseofx) } } # If we got to here then x!=x'. So we distinguish depending on the value # of overwrite. else if (overwrite ==TRUE){ inverseofx <- solve(x) y$setinverse(inverseofx) return(inverseofx) } else { inverseofx <- solve(x) # Creating the cache object for x. <<- is used so that the list z # is created in a parent environment of cacheSolve. Honestly I am # not so sure this does not screw things if z were defined before # and used for something completly unrelated. z <<- makeCacheMatrix(x) z$setinverse(inverseofx) return(inverseofx) } }

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/2016 fraud score script_update.R

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2016 fraud score script_update.R

###Script for doing all chi square tests in one go ##Presidential elections rm(list=ls()) russia2016 <- read.csv("C:/Users/Cole/Documents/Research topics literature/All Russian election data/2016 Russia election data full ids.csv") ####Set up ones-digits if necessary## urdigit<-(russia2016$united.russia)%%10 kprfdigit<-(russia2016$kprf)%%10 ldprdigit<-(russia2016$ldpr)%%10 russia2016<-cbind(russia2016, urdigit, kprfdigit, ldprdigit) #### chivalues<-matrix(NA, nrow=92, ncol=4) p<-as.vector(rbind(.1, .1, .1, .1, .1, .1, .1, .1, .1, .1)) i<-1 for (i in 83:92){ group<-subset(russia2016, regionid==i) if (nrow(group) > 0){ table1<-table(group$urdigit) table2<-table(group$kprfdigit) table3<-table(group$ldprdigit) chi1<-chisq.test(x=table1, p=p) chi2<-chisq.test(x=table2, p=p) chi3<-chisq.test(x=table3, p=p) chivalues[i, ]<-rbind(i, chi1$p.value, chi2$p.value, chi3$p.value) #Might have a type here, chi1 twice } else{next} } ##Region 82 fails for KPRF and LDRP--not enough digits ##Total fraudscores fraudscore2016<-matrix(NA, nrow=92, ncol=5) fraudscore2016[,1]<-chivalues[,1] fraudscore2016[,2]<-ifelse(chivalues[,2] < .05, 1, 0) fraudscore2016[,3]<-ifelse(chivalues[,3] < .05, 1, 0) fraudscore2016[,4]<-ifelse(chivalues[,4] < .05, 1, 0) i<-1 for(i in 1:92){ fraudscore2016[i,5]<-sum(fraudscore2016[i,2:4], na.rm=FALSE) } ###Getting party vote-shares voteshares <- matrix(NA, nrow=92, ncol=4) i<-1 for (i in 1:92){ group<-subset(russia2016, regionid==i) if (nrow(group) > 0){ total.votes <- sum(group$valid) + sum(group$invalid) ur.share <- sum(group$united.russia) / total.votes kprf.share <- sum(group$kprf) / total.votes ldpr.share <- sum(group$ldpr) / total.votes voteshares[i, ]<-rbind(i, ur.share, kprf.share, ldpr.share) } else{next} } fraudscore2016 <- cbind(fraudscore2016, voteshares) write.csv(fraudscore2016, "C:/Users/Cole/Documents/Research topics literature/All Russian election data/2016 fraud scores_update.csv")

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ternaryDiagLines <- function(x, ...){ s <- rowSums(x) if (any(s <= 0)) stop("rowSums of the input data x must be positive.") x <- x/s top <- sqrt(3)/2 xp <- x[, 2] + x[, 3]/2 yp <- x[, 3] * top lines(xp, yp, ...) }

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chreehan/Dissertation-Code

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r

Pararrel export script.R

load("workspaceImage.RData") install.packages("tsDyn") install.packages("dplyr") install.packages("data.table") install.packages("urca") library(tsDyn) library(dplyr) library(data.table) library(urca) mastertable1 = data.table(CNHCoefm = 1:2000000, CNYCoefm = 1:2000000, CnhTstat = 1:2000000, RanNum = 0, DateCode = 0, EntryCode = 0, CnyTstat = 1:2000000, Date = "none") for (i in 754:500) { p = (i * 1000 - 1000) for (g in 1:911) { filecnh = unlist(listCNH[[i]]) filecny = unlist(listCNY[[i]]) VECMtable = na.omit(data.table(CNH = filecnh, CNY = filecny, date = datelist))[g:(g+99)] name1 = VECMtable$date[1] name2 = VECMtable$date[length(VECMtable$date)] name = paste(name1, name2, sep = ' till ') VECMtable[,date:=NULL] x = cajorls(ca.jo(VECMtable, type="eigen", K=5, spec="longrun")) x1 = summary(x$rlm) mastertable1$CNHCoefm[(p + g)] = x$rlm$coefficients[1,1] #CNH coef mastertable1$CNYCoefm[(p + g)] = x$rlm$coefficients[1,2] #CNY coef mastertable1$CnhTstat[(p + g)] = x1$`Response CNH.d`$coefficients[1,3] #ecterm t stat for CNH mastertable1$CnyTstat[(p + g)] = x1$`Response CNY.d`$coefficients[1,3] #ec term t stat for CNY mastertable1$Date[(p + g)] = name mastertable1$RanNum[(p + g)] = i mastertable1$DateCode[(p + g)] = g mastertable1$EntryCode[(p + g)] = (p + g) print((p + g)) }} write.csv(mastertable1, "mastertable.csv")

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/Stare_rzeczy/Wykres_spend_RD/skrypt_spend_RD.r

0b04524202cf2b4ca98d8addbb9c2f1abf487a35

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no_license

arctickey/TWD_01

248591e38f4bd37caf727f93cd989b0771390bcb

caaa103e6dd05cf7d6c5dc29ca0511ae4de3bac8

refs/heads/master

2020-08-21T15:27:23.142596

2020-03-31T07:07:45

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skrypt_spend_RD.r

library(dplyr) library(haven) library(intsvy) library(countrycode) #Wczytanie ramki danych z wynikami #dane <- haven::read_sas("~/Programowanie/TWD_01/cy6_ms_cmb_stu_qqq.sas7bdat") #Wczytanie danych o współczynniku liczba uczniów na nauczyciela (pochodzenie danych OECD) rd_data <- read.csv("Wykres_spend_RD/Spend_on_RD.csv") #Usrednienie wartosci wspolczynnika dla krajow z kilku lat badan rd_data <- group_by(rd_data, LOCATION) %>% summarise(., avg_rd = mean(Value, na.rm = TRUE)) #Wczytanie etykiet krajow codes <- countrycode::codelist %>% select(., c(2, 5, 36)) #Obliczenie srednich wynikow dla danego kraju (pakiet intsvy) result <- pisa2015.mean.pv(c("MATH", "SCIE", "READ"), by = "CNT", data = dane) result <- select(result, CNT, Mean) #Polaczenie ze wspolczynnikiem i dopisanie pelnych nazw panstw final <- inner_join(result, rd_data, by = c("CNT" = "LOCATION")) %>% left_join(., y = codes, by = c("CNT" = "iso3c")) ggplot(final, aes(x = avg_rd, y=Mean, colour=continent, label = country.name.en))+ geom_point(size=4)+ stat_smooth(method = "auto",inherit.aes = FALSE,aes(x=avg_rd,y=Mean))+ ggtitle("Średni wynik względem wydatków na Reaserch & Development")+ labs(y = "Średni wynik w kraju", x="% PKB wydawany na R&D",color="Kontynent")+ theme(axis.title.x = element_text(size = 14), axis.title.y = element_text(size = 14))+ geom_label_repel( data = final[(final$avg_rd > 4),], color = 'black') + geom_label_repel( data = final[(final$Mean>520),], color = 'black')+ geom_label_repel( data = final[(final$Mean<430),], color = 'black')+ geom_label_repel( data = final[(final$avg_rd > 2)&(final$avg_rd < 3)&(final$Mean<490),], color = 'black') #ggsave("Wykres_spend_RD/all_student_spend_RD_auto.png",width = 18, height = 9)

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/R/Parse_Tables.R

be582446668b7395df54e9776da5b6ad7c0081f3

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no_license

hansthompson/pdfHarvester

3ec9e0dcfaa57a97e85c45eca91bf2f7df31f4bd

3522125974a08f6c6296cfa56487115fd7e0d278

refs/heads/master

2021-01-13T02:18:01.192974

2014-01-25T17:18:49

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Parse_Tables.R

#' @export Parse_Tables <- function(project) { require(png) folders <- list.dirs(project, recursive = FALSE) for(z in seq(folders)) { filefolder <- folders[z] filevector <- list.files(paste(filefolder, "/LowQuality", sep = ""), pattern = ".png", full.names = T) if(is.na(filevector)) {next()} nfiles <- length(filevector) tablecoordinates <- list() ##create a concatenation for the pages with tables in the for loop below. pagenumber <- c() for (i in seq(nfiles)) { jpegpagesub <- readPNG(paste(filevector[i])) dimmygraph <- dim(jpegpagesub) dev.new(width = dimmygraph[2], height = dimmygraph[1]) par(mar = c(0, 0, 0, 0)) x <- c(0, dimmygraph[2]) y <- c(0, dimmygraph[1]) plot(x,y, type = "n", xlab="", ylab="",xaxt='n',yaxt='n') rasterImage(jpegpagesub, x[1],y[1],x[2],y[2],interpolate= FALSE) coord <- locator(type='p',pch=3,col='red',lwd=1.2,cex=1.2) tablecoordinates[[i]] <- data.frame(x = coord$x, y = coord$y) dev.off() } noTables <- which(unlist(lapply(tablecoordinates, length)) == 0) properPages <- which(unlist(lapply(tablecoordinates, function(q) length(q$x) %% 2 == 0 ))) ifelse(length(noTables) != 0, tablePages <- properPages[-noTables], tablePages <- properPages) tablecoordinates <- tablecoordinates[tablePages] names(tablecoordinates) <- paste(tablePages) pages <- as.numeric(names(tablecoordinates)) for (i in 1:length(tablecoordinates)) { tabledim <- data.frame(x = tablecoordinates[[i]]$x, y = tablecoordinates[[i]]$y) write.csv(tablecoordinates, "tablecoordinates.csv") for (j in 0:((length(tablecoordinates[[i]]$x) / 2) - 1)) { start <- j*2 + 1 stop <- j*2 + 2 imag <- readPNG(gsub("LowQuality", "HighQuality", filevector[pages[i]])) dimChange <- dim(imag)/dim(readPNG(filevector[1])) tabledim$x <- tabledim$x * dimChange[1] tabledim$y <- tabledim$y * dimChange[2] ytot <- dim(imag)[1] xtot <- dim(imag)[2] png(gsub("LowQuality", "TableImages", gsub(".png", paste( "_",j+1, ".png", sep =""), filevector[i])), width= tabledim[stop,1]-tabledim[start ,1], height = (ytot-tabledim[stop,2]) - (ytot-tabledim[start,2]), units = "px") par(oma = c(0, 0, 0, 0)) par(mar = c(0, 0, 0, 0)) plot(x = c(tabledim[start,1],tabledim[stop,1]), y = c(tabledim[stop,2],tabledim[start,2]), type = "n", xlab="", ylab="",xaxt='n',yaxt='n') rasterImage(image = imag[(ytot-tabledim[start,2]):(ytot-tabledim[stop,2]), tabledim[start ,1]:tabledim[stop,1],], xleft = tabledim[start,1], ybottom = tabledim[stop,2], xright = tabledim[stop,1], ytop = tabledim[start ,2] , interpolate = FALSE) dev.off() } } } }

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/2015/solutionsR/day04.R

a8ff2b394ba9042efb23fb065389706711e965d9

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no_license

akulumbeg/adventofcode

e5b5f8e509d240279ce0b4daf7325a48a4cbf9fc

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refs/heads/master

2022-07-28T16:59:30.147837

2022-07-11T14:46:15

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day04.R

# Day 4 - Part 1 ---------------------------------------------------------- input <- readLines("2015/data/day04.txt", warn = F) # load the data input <- paste0(input, 1:250000) # 250000 is a wild guess # I have to break my rule of not using external packages # because I cannot code an md5 generator from scratch install.packages("digest") library(digest) outcome <- sapply(input, function(x) digest(x, algo = "md5", serialize = F)) # perform the function on every element of the large char vector # serialize = F assures consistency with online md5 generators which(grepl("^00000", outcome, perl = T, useBytes = T)) # which element starts with 5 0s? # Day 4 - Part 2 ---------------------------------------------------------- input <- readLines("2015/data/day04.txt", warn = F) # load the data input <- paste0(input, 1:5000000) # 5000000 is a also a guess # I have to break my rule of not using external packages # because I cannot code an md5 generator from scratch install.packages("digest") library(digest) outcome <- sapply(input, function(x) digest(x, algo = "md5", serialize = F)) # perform the function on every element of the large char vector # serialize = F assures consistency with online md5 generators which(grepl("^000000", outcome, perl = T, useBytes = T)) # which element starts with 6 0s? # might take a bit at first

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/Cleaning/SeuratNorm.R

b9421b94bde559adb772d1622a4424ef4ae58671

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no_license

sylvia-science/Ghobrial_EloRD

f27d2ff20bb5bbb90aa6c3a1d789c625540fbc42

041da78479433ab73335b09ed69bfdf6982e7acc

refs/heads/master

2023-03-31T14:46:27.999296

2021-04-02T15:09:49

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SeuratNorm.R

library(Seurat) library(dplyr) library(ggplot2) library(cowplot) library(Matrix) library(resample ) library(readxl) source('/home/sujwary/Desktop/scRNA/Code/Functions.R') source('/home/sujwary/Desktop/scRNA/Code/Plot_func.R') filename_sampleParam <- paste0('/home/sujwary/Desktop/scRNA/Data/sample','_parameters.xlsx') sampleParam <- read_excel(filename_sampleParam) filename = paste0('/home/sujwary/Desktop/scRNA/Param/','Cluster_ID_testNorm.xlsx') cluster_id_param = read_excel(filename) sample_list = c('GL1497BM', 'GL1160BM', 'GL2923BM', 'GL3404BM', 'NBM6CD138N', 'NBM12CD138N', 'GL2185BM', 'GL3417BM', 'GL2653BM') sample_list = c('GL1497BM', 'GL2923BM', 'GL3404BM', 'NBM6CD138N', 'NBM12CD138N', 'GL2185BM', 'GL3417BM', 'GL2653BM') sample_list = c('GL1290BM', 'GL3404BM', 'NBM6CD138N', 'NBM12CD138N', 'GL2185BM', 'GL3417BM', 'GL2653BM') folder_input ='Soup_MT_nFeature' folder_input ='Soup_MT_C100' i = 6 # Soup + MT for (i in 1:length(sample_list) ){ #for (i in 4){ sample_name = sample_list[i] print(sample_name) percent_mt = sampleParam$percent_mt_min[sampleParam['Sample'] == sample_name] RNA_features_min = sampleParam$RNA_features_min[sampleParam['Sample'] == sample_name] RNA_features_max = sampleParam$RNA_features_max[sampleParam['Sample'] == sample_name] if ( folder_input == 'Soup_MT_nFeature'){ cluster_IDs = cluster_id_param$Cluster_Ids_nFeature_Seurat[cluster_id_param$Sample == sample_name] }else{ cluster_IDs = cluster_id_param$Cluster_Ids_Seurat[cluster_id_param$Sample == sample_name] } print(cluster_IDs) folder = paste0('/home/sujwary/Desktop/scRNA/Output/',folder_input,'/',sample_name,'/') path = paste0(folder,'/',sample_name,'.Robj') data_i_filtered_run = loadRData(path) data_i_filtered_run = data_i_filtered_run[, data_i_filtered_run$percent.mt < percent_mt] #data_i_filtered_run = data_i_filtered_run[, data_i_filtered_run$nFeature_RNA > RNA_features_min] #data_i_filtered_run = data_i_filtered_run[, data_i_filtered_run$nFeature_RNA < RNA_features_max] if ( folder_input == 'Soup_MT_nFeature'){ data_i_filtered_run = data_i_filtered_run[, data_i_filtered_run$nFeature_RNA > RNA_features_min] data_i_filtered_run = data_i_filtered_run[, data_i_filtered_run$nFeature_RNA < RNA_features_max] } data_i_filtered_run = NormalizeData(data_i_filtered_run, normalization.method = "LogNormalize", scale.factor = 10000) data_i_filtered_run = FindVariableFeatures(data_i_filtered_run, selection.method = "vst", nfeatures = 2000) data_i_filtered_run = ScaleData(data_i_filtered_run) data_i_filtered_run = RunPCA(data_i_filtered_run,npcs = 30) data_i_filtered_run = FindNeighbors(data_i_filtered_run, dims = 1:30) data_i_filtered_run = FindClusters(data_i_filtered_run, resolution = 1.2) data_i_filtered_run = RunUMAP(data_i_filtered_run, dims = 1:30) data_i_filtered_run = label_cells(data_i_filtered_run, cluster_IDs) # filename = paste("/home/sujwary/Desktop/scRNA/Data/",sample_name,"_raw_feature_bc_matrix.h5",sep = "") # data_i_raw = Read10X_h5(filename, use.names = TRUE, unique.features = TRUE) # data_i_raw = CreateSeuratObject(counts = data_i_raw, project = "BM", min.cells = 3, min.features = 1) # # colSum_list = colSums(data_i_raw ) # Needs to be from Matrix library # keep = colSum_list >= 100 # data_i_run = data_i_raw[,keep] # # data_i_run[["percent.mt"]] <- PercentageFeatureSet(data_i_run, pattern = "^MT-") # data_i_run = data_i_run[, data_i_run$percent.mt < percent_mt] # data_i_run = data_i_run[, data_i_run$nFeature_RNA > RNA_features_min] # data_i_run = data_i_run[, data_i_run$nFeature_RNA < RNA_features_max] #data_i_filtered_run = NormalizeData(data_i_run, normalization.method = "LogNormalize", scale.factor = 10000) folder = paste0('/home/sujwary/Desktop/scRNA/Output/TestNormalization/',folder_input,'/Seurat/',sample_name,'/') dir.create(folder,recursive = T) path = paste0(folder,'/',sample_name,'.Robj') save(data_i_filtered_run,file= path) data_i_filtered_run = label_cells(data_i_filtered_run, cluster_IDs) if(ncol(data_i_filtered_run)< 200){ pt.size = 3 }else{ pt.size = 1 } print('Plot') ################# rowMeans_list = rowMeans(data_i_filtered_run@assays[["RNA"]]@data) bin_list = as.numeric(cut_number(rowMeans_list,6)) colSums_list = colSums(data_i_filtered_run@assays[["RNA"]]@counts) plot_list = vector('list', 6) plot_list_10000 = vector('list', 6) for (bin in 1:6){ #bin = bin + 1 print(bin) y_list = data_i_filtered_run@assays[["RNA"]]@data[bin_list == bin,] df = as.data.frame(matrix(ncol = 2, nrow = length(colSums_list))) colnames(df) = c('colsum','exp') df$colsum = (colSums_list) # Unormalized per cell df$exp = colMeans(y_list) # Norm Mean per cell df$ID = Idents(data_i_filtered_run[bin_list == bin,]) plot = ggplot(df, aes(colsum, exp)) + geom_point(aes(colour = df$ID), size = 2)+ stat_smooth(aes(x=colsum,y=exp), method="loess", se=F, color="tomato2") + theme(text = element_text(size=20)) #print(plot) plot_list[[bin]]= plot plot = ggplot(df, aes(colsum, exp)) + geom_point(aes(colour = df$ID), size = 2)+ stat_smooth(aes(x=colsum,y=exp), method="loess", se=F, color="tomato2") + theme(text = element_text(size=20))+xlim(c(0,10000)) #print(plot) plot_list_10000[[bin]]= plot } pathName = paste0(folder,sample_name,'_Seurat_NormVsGeneSum','.png') png(file=pathName,width=1500, height=1000,res = 100) plot = plot_grid( plot_list[[1]], plot_list[[2]],plot_list[[3]],plot_list[[4]],plot_list[[5]],plot_list[[6]], labels = "AUTO", ncol = 2) print(plot) dev.off() pathName = paste0(folder,sample_name,'_Seurat_NormVsGeneSum_10000','.png') png(file=pathName,width=1500, height=1000,res = 100) plot = plot_grid( plot_list_10000[[1]], plot_list_10000[[2]],plot_list_10000[[3]],plot_list_10000[[4]],plot_list_10000[[5]],plot_list_10000[[6]], labels = "AUTO", ncol = 2) print(plot) dev.off() # plot housekeeping gene variance across all cells on the y axis and total UMI count per cell on the x axis gene_list = read.csv('/home/sujwary/Desktop/scRNA/Data/HousekeepingGenes.csv') gene_list = as.character(gene_list[gene_list$Gene %in% rownames(data_i_filtered_run),1]) data_subset = data_i_filtered_run@assays[["RNA"]]@data[gene_list,] var_list = colVars(as.matrix(data_subset)) mean_list = colMeans(data_subset) UMI_per_cell = colSums(data_i_filtered_run) df = data.frame(matrix(ncol = 2, nrow = length(var_list))) colnames(df) = c("UMI_per_cell",'variance') df$UMI_per_cell = UMI_per_cell df$var_list = var_list pathName = paste0(folder,sample_name,'_Seurat_varVsCount','.png') png(file=pathName,width=1000, height=1000) plot = ggplot(df, aes(UMI_per_cell, var_list)) + geom_point(size = 4) print(plot) dev.off() #next data_i_filtered_noLabel = FindClusters(data_i_filtered_run,resolution = 1.4) pathName = paste0(folder,sample_name,'_Seurat_Umap','_noLabel','.png') png(file=pathName,width=1000, height=1000) print( DimPlot(data_i_filtered_noLabel,pt.size = pt.size, reduction = "umap",label = T)) dev.off() pathName = paste0(folder,sample_name,'_Seurat_Umap','_Label','.png') png(file=pathName,width=1000, height=1000) print( DimPlot(data_i_filtered_run,pt.size = pt.size, reduction = "umap",label = T)) dev.off() pathName = paste0(folder,sample_name,'_Seurat_Umap','_percent.mt','.png') png(file=pathName,width=1000, height=1000) print(FeaturePlot(data_i_filtered_run,pt.size = pt.size, reduction = "umap", features = 'percent.mt')) dev.off() pathName = paste0(folder,sample_name,'_Seurat_Umap','_nFeature_RNA','.png') png(file=pathName,width=1000, height=1000) print(FeaturePlot(data_i_filtered_run,pt.size = pt.size, reduction = "umap", features = 'nFeature_RNA')) dev.off() next findMarkers = F if (findMarkers){ PCA_dim = 30 resolution_val = 0.8 num_markers = 10 markers = FindAllMarkers(data_i_filtered_run, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25) markers %>% group_by(cluster) %>% top_n(n = num_markers, wt = avg_logFC) # Plotting the top 10 markers for each cluster. top10 = markers %>% group_by(cluster) %>% top_n(n = num_markers, wt = avg_logFC) all_markers = markers %>% group_by(cluster) pathName <- paste0(folder,paste0('HeatMap', '_PCA',PCA_dim,'_res',resolution_val,'.png')) png(file=pathName,width=500, height=500) print(DoHeatmap(data_i_filtered_run, features = top10$gene)) dev.off() write.csv(all_markers, file = paste0(folder,'Features','.csv'),row.names=FALSE) } #next folder_feature = paste0(folder,'Featureplots/' ) dir.create(folder_feature,recursive = T) gene_list = c('CD3D', 'CD3G', 'CD3E', 'CD8A', 'CD8B', 'IL7R', 'SELL', 'CD14', 'FCGR3A', 'NKG7', 'MS4A1', 'IGKC', 'IGHM', 'CD19', 'MZB1', 'CD34', 'CDK6', 'FCER1A','FUT4', 'ELANE', 'MPO', 'HBA2', 'HBB', 'LYZ', 'TNFRSF17',' PRTN3', 'NAMPT') if(ncol(data_i_filtered_run)< 200){ pt.size = 6 }else{ pt.size = 3 } for (j in 1:length(gene_list)){ gene = gene_list[j] print(gene) #browser() plot = FeaturePlotFix(data_i_filtered_run, feature = gene, folder =folder, str = '',split = F, markerSize = pt.size,gene_TF = TRUE,title = '',saveTF = FALSE) plot = plot + theme( axis.title.x = element_text(color="black", size=24 ), axis.title.y = element_text(color="black", size=24), axis.text= element_text(color="black", size=24), legend.text=element_text(size=24), legend.title=element_text(size=24), text = element_text(size = 20) ) file_str = '' pathName = paste0(folder_feature,gene,'','.png') png(filename = pathName,width=2000, height=2000) print(plot) dev.off() remove(plot) } }

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/run_analysis.R

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no_license

ErikGiezen/Getting-cleanig-data

d3e121e594da006a07fcdf8b158fb994b118d04a

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refs/heads/master

2021-01-17T06:24:32.234836

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run_analysis.R

## 1. Merges the training and the test sets to create one data set. # read txt files into data frames with read.table features <- read.table("features.txt", header = FALSE) labels = read.table("activity_labels.txt", header=FALSE) train_subject <- read.table("subject_train.txt", header = FALSE) train_x <- read.table("X_train.txt", header = FALSE) train_y <- read.table("y_train.txt", header = FALSE) test_subject <- read.table("subject_test.txt", header = FALSE) test_x <- read.table("X_test.txt", header = FALSE) test_y <- read.table("y_test.txt", header = FALSE) # add column names for measurments, subjects, variabels and labels names(train_x) <- features[,2] names(test_x) <- features[,2] names(train_subject) <- "subject" names(test_subject) <- "subject" names(train_y) <- "activity_id" names(test_y) <- "activity_id" names(labels)[1]<-"id" names(labels)[2]<-"activity" # merge dataframes into one dataset train <- cbind(train_x, train_y, train_subject) test <- cbind(test_x, test_y, test_subject) merged <- rbind(train, test) ## 2. Extracts only the measurements on the mean and standard deviation for each measurement. selectedcolumns <- grep("(mean|std)\$\$|subject|activity", names(merged)) meanstdset <- merged[, selectedcolumns] ## 3. Uses descriptive activity names to name the activities in the data set. meanstdset <- merge(meanstdset, labels, by.x="activity_id", by.y="id") meanstdset <- subset(meanstdset, select = -activity_id) ## 4. Appropriately labels the data set with descriptive variable names. #already done by names(train_x) <- features[,2], names(test_x) <- features[,2] ## 5. From the data set in step 4, creates a second, independent tidy data set with the average of each variable for each activity and each subject. library(reshape2) melteddata <- melt(meanstdset, id=c("subject","activity")) eind <- dcast(melteddata, subject+activity ~ variable, mean) write.table(eind, file = "eind.txt", row.name=FALSE)

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/run_analysis.R

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klatz/GettingCleaningData

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refs/heads/master

2021-01-01T19:16:20.882248

2014-06-26T19:23:54

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run_analysis.R

#YOU NEED plyr & reshape2 package library(plyr) library(reshape2) #STEP 1 #read training dataset #subject:who y:activity sub_train <- read.csv("UCI HAR Dataset/train/subject_train.txt",header=FALSE) X_train <- read.csv("UCI HAR Dataset/train/X_train.txt",sep="",header=FALSE) y_train <- read.csv("UCI HAR Dataset/train/y_train.txt",header=FALSE) #read test dataset sub_test <- read.csv("UCI HAR Dataset/test/subject_test.txt",header=FALSE) X_test <- read.csv("UCI HAR Dataset/test/X_test.txt",sep="",header=FALSE) y_test <- read.csv("UCI HAR Dataset/test/y_test.txt",header=FALSE) #merge training dataset train_marge <- cbind(X_train,sub_train,y_train) #merge test dataset test_marge <- cbind(X_test, sub_test, y_test) #marge training+test dataset marge_dataset <- rbind(train_marge,test_marge) #read features features <- read.csv("UCI HAR Dataset/features.txt",sep="",header=FALSE) feature_names <- c(as.vector(features[,2]),"person","activity") #STEP 4 #set column names colnames(marge_dataset) <- feature_names #STEP 3 #read activities list activities <- read.csv("UCI HAR Dataset/activity_labels.txt" ,sep="",header=FALSE) colnames(activities) <- c("activity","activity_name") marge_dataset <- join(marge_dataset,activities,by="activity") #STEP 2 #choose mean/standard deviation names mean_standard_names <- grepl('mean\$\$|std\$\$',names(marge_dataset)) #add X and y col mean_standard_names[ncol(marge_dataset)-2]<-TRUE #person mean_standard_names[ncol(marge_dataset)]<-TRUE #activities #extract mean/standard dataset mean_standard_dataset <- marge_dataset[,mean_standard_names] #WRITE CSV&TXT extract data write.csv(mean_standard_dataset,"tidy_dataset_1.csv") write.table(mean_standard_dataset,"tidy_dataset_1.txt",quote=FALSE) #STEP 5 aggregate_dataset <- aggregate(.~ person + activity_name ,data=mean_standard_dataset,FUN=mean) #WRITE CSV&TXT extract data write.csv(aggregate_dataset,"tidy_dataset_2.csv") write.table(aggregate_dataset,"tidy_dataset_2.txt",quote=FALSE)

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/Phylogenetic_tree.R

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xingzhis/A-dual-eigen-analysis-on-canid-genomes

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Phylogenetic_tree.R

library(ape) snp.tree <- read.tree("snphylo.output.ml.tree") #plot(snp.tree) #library(tidytree) # to map library(phytools) demographic.data <- read.csv('~/demographic.csv') demographic.data.valid <- demographic.data[!(is.na(demographic.data$Latitude)|is.na(demographic.data$Longitude)),] lat.long <- data.frame( lat=demographic.data.valid$Latitude, long=demographic.data.valid$Longitude ) row.names(lat.long) <- demographic.data.valid$SampleID invalid.id <- as.character(demographic.data[is.na(demographic.data$Latitude)|is.na(demographic.data$Longitude),]$SampleID) obj<-phylo.to.map(drop.tip(snp.tree,invalid.id),lat.long,plot=FALSE) plot(obj,type="phylogram",asp=1) # python TreeCluster.py -i snphylo.output.ml.tree2 -t 0.4 > cluster_results cluster.result <- read.table('cluster_results',header=T) cluster.result <- cluster.result[cluster.result$SequenceName!='Lcu2_Pasto',] # remove invalid item cluster.map <- data.frame( SampleID=cluster.result$SequenceName, Latitude=array(0,length(cluster.result$SequenceName)), Longitude=array(0,length(cluster.result$SequenceName)), ClusterNumber=cluster.result$ClusterNumber ) for (i in rownames(cluster.map)) { demog.row <- demographic.data.valid[as.character(demographic.data.valid$SampleID)==as.character(cluster.map[i,]$SampleID),] cluster.map[i,]$Latitude <- demog.row$Latitude cluster.map[i,]$Longitude <- demog.row$Longitude } #plot on map library(ggplot2) library(ggmap) library(sp) library(maptools) library(maps) mapworld<-borders("world",colour = "gray50",fill="white") mp<-ggplot()+mapworld+ylim(-60,90) mp<-mp+geom_point(aes(x=as.numeric(cluster.map$Longitude), y=as.numeric(cluster.map$Latitude),color=as.factor(cluster.map$ClusterNumber),size=5,alpha=0.2)) # mid<-mean(unique(cluster.map$ClusterNumber)) # mp<-mp+scale_color_gradient2(midpoint=mid, low="blue", mid="white", # high="red", space ="Lab" ) # # Install # install.packages("wesanderson") # # Load library(wesanderson) mp<-mp+scale_color_manual(values=wes_palette(name="Cavalcanti1")) library(treeio) library(ggtree) snp.tree <- read.tree("snphylo.output.ml.tree") snp.tree.plot <- ggtree(snp.tree, layout="circular") + geom_tiplab(aes(angle=angle), color='purple',hjust = -.2,size=1.5)+ xlim(0, 0.55) setwd('~') load('snp.svd.Rdata') source('DualEigen_utils.R') num_layers <- 10 u.cutoff <- 0.8 -> v.cutoff main.pos.x <- list() main.pos.y <- list() for (layer in seq(num_layers)) { u <- filter.loadings(snp.svd$u[,layer],method='2',norm.cutoff=u.cutoff) v <- filter.loadings(snp.svd$v[,layer],method='2',norm.cutoff=v.cutoff) main.pos.x[[layer]] <- nonzero.pos(u) main.pos.y[[layer]] <- nonzero.pos(v) } # svd.cluster <- array('black',c(127,10)) # for (layer in 1:10){ # for (j in seq_along(main.pos.x[[layer]])){ # svd.cluster[main.pos.x[[layer]][j],layer] <- 'red' # } # } setwd('tree-dualeigen') for (layer in 2:10){ svd.cluster2 <- data.frame( SampleID = demographic.data$SampleID, color = array('grey',length(demographic.data$SampleID)) ) for (j in seq_along(main.pos.x[[layer]])){ svd.cluster2[main.pos.x[[layer]][j],]$color <- 'red' } snp.tree$tip.label[snp.tree$tip.label=='Lcu2_Pasto']<- 'Lcu2_Pastora' svd.color2 <- array() for (i in seq_along(svd.cluster2$color)){ svd.color2[i] <- svd.cluster2[svd.cluster2$SampleID==snp.tree$tip.label[i],]$color } pdf(paste('tree-dualeigen-',layer,'.pdf',sep='')) plot(snp.tree, tip.color=svd.color2, type="fan",cex=0.5,x.lim=0.2) dev.off() }

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test.R

collatz = function(x = 1,plot=FALSE){ nums <- c(x) repeat{ if (x%%2 != 0){ x <- 3*x + 1 } else{ x <- x%/%2 } nums <- append(nums,x) if (x <= 1) break } if (plot) plot(1:length(nums),nums) return(nums) }

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/transmodel.pub.R

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carnegie-dpb/bartonlab-modeling

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transmodel.pub.R

## ## plot linear transcription model for a direct target in one condition, custom made for publication (no colors, etc.) ## source("rhoc.R") source("rhon.R") source("rhop.R") source("Rsquared.R") transmodel.pub = function(rhon0=1, rhoc0=20, nu=10, gamman=0.7, rhop0=1, etap=1, gammap=1, dataTimes, dataValues, dataLabel=NA, plotBars=FALSE) { ## set rhop0 = mean of first three data points if (rhop0==0 &&&& hasArg(dataValues) &&&& hasArg(dataTimes)) rhop0 = mean(dataValues[dataTimes==0]) ## calculation interval t = (0:200)/100 ## cytoplasmic GR-TF concentration rhoc_t = rhoc(rhoc0, nu, t) ## nuclear GR-TF concentration rhon_t = rhon(rhoc0, rhon0, nu, gamman, t) ## transcript concentration rhop_t = rhop(rhoc0, nu, gamman, rhop0, etap, gammap, t) ## axis limits ymin = 0 if (hasArg(dataValues)) { ymax = max(rhon_t,rhop_t,dataValues) plot(t, rhop_t, type="l", xlab="time (h)", ylab="model nuclear concentration (arb. units), measured expression (FPKM)", ylim=c(ymin,ymax), lty=2) } else { ymax = max(rhon_t,rhop_t) plot(t, rhop_t, type="l", xlab="time (h)", ylab="model nuclear concentration", ylim=c(ymin,ymax), lty=2) } ## compare with provided data if (hasArg(dataTimes) && hasArg(dataValues)) { if (plotBars) { ## plot mean and error bars for (ti in unique(dataTimes)) { y = mean(dataValues[dataTimes==ti]) sd = sd(dataValues[dataTimes==ti]) points(ti, y, pch=19, col="black") segments(ti, (y-sd), ti, (y+sd), col="black") } } else { ## plot each point points(dataTimes, dataValues, pch=19, col="black") } ## get R-squared and error metric fitValues = dataTimes for (i in 1:length(dataTimes)) { fitValues[i] = rhop(rhoc0, nu, gamman, rhop0, etap, gammap, dataTimes[i]) } R2 = Rsquared(fitValues,dataValues) error = errorMetric(fitValues,dataValues) print(paste("error=",signif(error,6),"R2=",signif(R2,6))) } ## plot TF concentration on right axis, this axis used for annotation par(new=TRUE) plot(t, rhon_t, type="l", axes=FALSE, xlab=NA, ylab=NA, ylim=c(0,max(rhon_t)), lty=1) axis(side=4) par(new=FALSE) ## annotation if (hasArg(dataLabel) && !is.na(dataLabel)) { legend(par()$xaxp[2], 0.98*par()$usr[4], xjust=1, yjust=1, lty=c(1,2,0), pch=c(-1,-1,19), c( expression(paste(rho[n]," ","(right axis)")), expression(rho[p]), dataLabel ) ) } else { legend(par()$xaxp[1], par()$yaxp[1], xjust=0, yjust=0, lty=c(1,2), c( expression(rho[n]), expression(rho[p]) ) ) } xtext = 0.3 ytext = par()$usr[4] text(xtext, 0.45*ytext, bquote(rho[c0]==.(round(rhoc0,1))), pos=4, col="black") text(xtext, 0.40*ytext, bquote(rho[n0]==.(round(rhon0,1))), pos=4, col="black") text(xtext, 0.35*ytext, bquote(paste(nu==.(signif(nu,3))," ",h^-1)), pos=4, col="black") text(xtext, 0.30*ytext, bquote(paste(gamma[n]==.(signif(gamman,3))," ",h^-1)), pos=4, col="black") text(xtext, 0.25*ytext, bquote(rho[p0]==.(round(rhop0,1))), pos=4, col="black") text(xtext, 0.20*ytext, bquote(paste(eta[p]==.(signif(etap,3))," ",h^-1)), pos=4, col="black") text(xtext, 0.15*ytext, bquote(paste(gamma[p]==.(signif(gammap,3))," ",h^-1)), pos=4, col="black") if (hasArg(dataTimes) && hasArg(dataValues)) { text(xtext, 0.05*ytext, bquote(r^2==.(round(R2,2))), pos=4, col="black") } }

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/R/read_rds.R

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BHGC/bhgc.wx

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read_rds.R

#' Robustly Reads an RDS File #' #' @param pathname RDS file to read. #' #' @param \ldots (optional) Additional arguments passed to [base::readRDS()]. #' #' @return The \R object read. #' #' @details #' Uses [base::readRDS] internally but gives a more informative error message #' on failure. #' #' @importFrom utils file_test #' @export #' @keywords internal read_rds <- function(pathname, ...) { if (!file_test("-f", pathname)) { stop(sprintf("No such file: %s", sQuote(pathname))) } tryCatch({ readRDS(pathname, ...) }, error = function(ex) { msg <- conditionMessage(ex) msg <- sprintf("readRDS() failed to read file %s (%.0f bytes). The reason was: %s", sQuote(pathname), file.size(pathname), msg) ex$message <- msg stop(ex) }) } #' Robustly Saves an Object to RDS File Atomically #' #' @param object The \R object to be save. #' #' @param pathname RDS file to written. #' #' @param \ldots (optional) Additional arguments passed to [base::saveRDS()]. #' #' @return (invisible) The pathname of the RDS written. #' #' @details #' Uses [base::saveRDS] internally but writes the object atomically by first #' writing to a temporary file which is then renamed. #' #' @importFrom utils file_test #' @export #' @keywords internal save_rds <- function(object, pathname, ...) { pathname_tmp <- sprintf("%s.tmp", pathname) if (file_test("-f", pathname_tmp)) { stop(sprintf("Cannot save RDS file because a temporary save file already exists: %s", sQuote(pathname_tmp))) } tryCatch({ saveRDS(object, file = pathname_tmp, ...) }, error = function(ex) { msg <- conditionMessage(ex) msg <- sprintf("saveRDS() failed to save to temporary file %s (%.0f bytes). The reason was: %s", sQuote(pathname_tmp), file.size(pathname_tmp), msg) ex$message <- msg stop(ex) }) stopifnot(file_test("-f", pathname_tmp)) file.rename(from = pathname_tmp, to = pathname) if (file_test("-f", pathname_tmp) || !file_test("-f", pathname)) { msg <- sprintf("save_rds() failed to rename temporary save file %s (%0.f bytes) to %s (%0.f bytes)", sQuote(pathname_tmp), file.size(pathname_tmp), sQuote(pathname), file.size(pathname)) stop(msg) } invisible(pathname) }

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/R/data_katastralni_uzemi.R

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JanCaha/CzechData

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data_katastralni_uzemi.R

#' data.frame of all cadastral territories in Czech Republic #' #' A dataset containing the names and other attributes of all 13,078 #' cadastral territories in Czech Republic. The codes (every column with string kod in #' name) are treated as character strings even though that some of them are numbers. These codes, #' however, serve only as IDs. #' Colums with suffix _kod are various levels of self-government units in Czech Republic. #' #' @format A data frame with 13078 rows and 9 variables: #' \describe{ #' \item{kod}{id of the cadastral territory} #' \item{nazev}{name of the cadastral territory} #' \item{obec_kod}{} #' \item{pou_kod}{} #' \item{orp_kod}{} #' \item{okres_kod}{} #' \item{lau1_kod}{} #' \item{vusc_kod}{} #' \item{prares_kod}{} #' } #' @source \url{http://services.cuzk.cz/shp/stat/epsg-5514/1.zip} "katastralni_uzemi"

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dlaststark/machine-learning-projects

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knapsack-problem-bounded-2.r

library(rgenoud) fitness= function(x= rep(1, nrow(task_table))){ total_value= sum(task_table$value * x) total_weight= sum(task_table$weight * x) ifelse(total_weight <= 400, total_value, 400-total_weight) } allowed= matrix(c(rep(0, nrow(task_table)), task_table$pieces), ncol = 2) set.seed(42) evolution= genoud(fn= fitness, nvars= nrow(allowed), max= TRUE, pop.size= 10000, data.type.int= TRUE, Domains= allowed) cat("Value: ", evolution$value, "\n") cat("Weight:", sum(task_table$weight * evolution$par), "dag", "\n") data.frame(item= task_table$items, pieces= as.integer(solution)) %>% filter(solution> 0)

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cardiomoon/semMediation

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2018-12-29T09:24:48.533628

2018-12-23T14:22:02

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estimatesTable.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/CorPlot.R \name{estimatesTable} \alias{estimatesTable} \title{convert parameterEstimates to data.frame} \usage{ estimatesTable(fit, latent = TRUE, regression = TRUE, mediation = FALSE, covar = FALSE, ci = FALSE, standardized = TRUE, digits = 2) } \arguments{ \item{fit}{An object of class lavaan. Result of sem function of package lavaan} \item{latent}{whether the latent variables be included in result} \item{regression}{whether the regressions be included in result} \item{mediation}{whether the mediation effects be included in result} \item{covar}{whether the covariances be included in result} \item{ci}{If TRUE, confidence intervals are added to the output} \item{standardized}{Logical. If TRUE, standardized estimates are added to the output} \item{digits}{integer indicating the number of decimal places (round) or significant digits (signif) to be used.} } \description{ convert parameterEstimates to data.frame }

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aaronbaggett/labs4316

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2020-04-21T08:39:50.833663

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qqnormsim.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/qqnormsim.R \name{qqnormsim} \alias{qqnormsim} \title{Generate simulated QQ plots} \usage{ qqnormsim(sample, data) } \arguments{ \item{sample}{the variable to be plotted.} \item{data}{data frame to use.} } \value{ A 3 x 3 grid of qqplots. } \description{ Create a 3 x 3 grid of quantile-quantile plots, the first of which corresponds to the input data. The other eight plots arise from simulating random normal data with the same mean, standard deviation, and length as the data. For use in comparing known-normal qqplots to an observed qqplot to assess normality. }

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/tm_examples.R

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shuckle16/tm_examples

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2021-01-15T15:44:16.685273

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tm_examples.R

#install.packages('tm') library(tm) sci.electronics <- Corpus(DirSource("sci.electronics")) # A corpus with 981 text documents talk.religion.misc <- Corpus(DirSource("talk.religion.misc")) # A corpus with 628 text documents ############################ #### Text Data Cleaning #### ############################ # combine documents c <- c(sci.electronics,talk.religion.misc) # convert to lower case res <- tm_map(c,tolower) inspect(res[1:10]) # remove punctuation res <- tm_map(res, removePunctuation) inspect(res[1:10]) # remove numbers res <- tm_map(res, removeNumbers) inspect(res[1:10]) # stemming res <- tm_map(res,stemDocument) inspect(res[1:10]) # remove stop words res <- tm_map(res, removeWords, stopwords("english")) inspect(res[1:10]) # remove additional stop words res <- tm_map(res, removeWords, c("one","can")) inspect(res[1:10]) # remove extra white spaces res <- tm_map(res,stripWhitespace) inspect(res[1:10]) # document-term matrix res <- tm_map(res, PlainTextDocument) # compatibility issue dtm <- DocumentTermMatrix(res) inspect(dtm) ################################## #### Frequency Based Analysis #### ################################## w<-findFreqTerms(dtm,200) dtm1 <- dtm[,w] findAssocs(dtm,"war",0.2) # List unique words sorted by decreasing frequency term.freq <- apply(dtm, 2, sum) barplot(sort(term.freq,decreasing=TRUE)) barplot(sort(term.freq,decreasing=TRUE)[1:30]) # remove sparse terms term_wt <- apply(dtm,2,sum) dtm <- dtm[,term_wt>10] ################################## #### Word Cloud Visualization #### ################################## #install.packages("wordcloud") library(wordcloud) term.table <- data.frame(term=names(term.freq),frequency=term.freq) term.table <- term.table[order(term.freq,decreasing=TRUE),] head(term.table) # you may optionally save the word frequency table in a file wordcloud(term.table$term, term.table$frequency, min.freq=50) #### Compare the word cloud of "sci.electronics" and "talk.religion.misc" corpuses #### after the same data cleaning preprocessing above ################################### #### Weighting Terms by TF-IDF #### ################################### dtm.tfxidf <- weightTfIdf(dtm) inspect(dtm.tfxidf[1:10, 1:10]) #### Question: Compare the word cloud of "sci.electronics" by TF and by TF-IDF #### Question: find out about the weight of the word "subject" before and after tfidf conversion #### How does it change? Why? ############################################# #### Data Preparation for Classification #### ############################################# # Transform the corpus back into a dataframe df <- as.data.frame(as.matrix(dtm)) # This is the vector of class labels correponding to each document class.labels <- c(rep("E",length(sci.electronics)),rep("R",length(talk.religion.misc))) df <- cbind(class.labels,df) # Use a random subset of 75% documents as training train.ind <- rbinom(nrow(df), size=1, prob=0.75) training.data <- df[train.ind==1,] testing.data <- df[train.ind==0,] ################################ #### naive Bayes classifier #### ################################ #install.packages("e1071") library(e1071) # train the model mybayes <- naiveBayes(class.labels ~ ., data=training.data) # compare the predicted and the actual testing <- predict(mybayes, testing.data) table(testing, testing.data$class.labels, dnn=list('predicted','actual')) # class distribution (prior) mybayes$apriori # conditional distributions for each attribute mybayes$tables # Dimension reduction question? # What if I am looking for the most representative words for different classes?

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soccer.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/MESS-package.R \docType{data} \name{soccer} \alias{soccer} \title{Danish national soccer players} \format{ A data frame with 805 observations on the following 5 variables. \describe{ \item{name}{a factor with names of the players} \item{DoB}{a Date. The date-of-birth of the player} \item{position}{a factor with levels \code{Forward} \code{Defender} \code{Midfielder} \code{Goalkeeper}} \item{matches}{a numeric vector. The number of A matches played by the player} \item{goals}{a numeric vector. The number of goals scored by the player in A matches} } } \source{ Data collected from the player database of DBU on March 21st, 2014. See \url{http://www.dbu.dk} for more information. } \description{ Players on the Danish national soccer team. The dataset consists of all players who have been picked to play on the men's senior A-team, their position, date-of-birth, goals and matches. } \examples{ data(soccer) birthmonth <- as.numeric(format(soccer$DoB, "\%m")) birthyear <- as.numeric(format(soccer$DoB, "\%Y")) } \keyword{datasets}

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surayaaramli/typeRrh

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TA.info.Rd.R

library(NMOF) ### Name: TA.info ### Title: Threshold-Accepting Information ### Aliases: TA.info ### ** Examples ### MINIMAL EXAMPLE for TAopt ## objective function evaluates to a constant fun <- function(x) 0 ## neighbourhood function does not even change the solution, ## but it reports information nb <- function(x) { tmp <- TA.info() cat("current threshold ", tmp$threshold, "| current step ", tmp$step, "| current iteration ", tmp$iteration, "\n") x } ## run TA algo <- list(nS = 5, nT = 2, nD = 3, x0 = rep(0, 5), neighbour = nb, printBar = FALSE, printDetail = FALSE) ignore <- TAopt(fun, algo) ## printed output: ## current threshold NA | current step 1 | current iteration NA ## current threshold NA | current step 2 | current iteration NA ## current threshold NA | current step 3 | current iteration NA ## current threshold 1 | current step 1 | current iteration 1 ## current threshold 1 | current step 2 | current iteration 2 ## current threshold 1 | current step 3 | current iteration 3 ## current threshold 1 | current step 4 | current iteration 4 ## current threshold 1 | current step 5 | current iteration 5 ## current threshold 2 | current step 1 | current iteration 6 ## current threshold 2 | current step 2 | current iteration 7 ## current threshold 2 | current step 3 | current iteration 8 ## current threshold 2 | current step 4 | current iteration 9 ## current threshold 2 | current step 5 | current iteration 10

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# Plot libraries # -------------------- library(ggplot2) library(ggthemes) library(plotly) # Data manipulation libraries # -------------------- library(tidyverse) library(dplyr) library(reshape2) # Shiny Libraries # --------------- library(shiny) library(shinyglide) library(shinyjs) library(shinydashboard) library(bs4Dash) # Model Libraries # --------------- library(randomForest) library(limma) # Data # ---- load("./data.RData")