pierreqi/r_code_50k · Datasets at Hugging Face

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9710c61f0387afbd3bdbd2c93db5f681649b08e5

/ui.R

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no_license

TMBish/HHTL

530b9bd9dae6ddc64f0911b870682b87447e3873

92b4b9e1074bdb61e80eec340cd5fc1bdca89d0f

refs/heads/master

2021-03-30T17:56:37.230344

2018-10-06T07:34:43

104,702,993

0

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1,839

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ui.R

shinyUI( semanticPage( title = "Hannah's History Timeline", useShinyjs(), useSweetAlert(), extendShinyjs(text = jsCode), # Add custom css styles includeCSS(file.path('www', 'custom-style.css')), div(class="app-header", h1(class = "ui header", ": HANNAH'S HISTORY TIMELINE :") ), br(), # Sweet Alert #receiveSweetAlert(messageId = "event_success"), # Main page after load # hidden( div(id = "app-body", class="ui grid", hidden( div(class="one wide column", id = "dropdown-div", dropdownButton( tags$h3("Add a Historical Event"), hr(), textInput("title", label = "Event Name", value = ""), textInput("image_link", label = "Photo of Event (url)", value = ""), dateRangeInput("event_dates", label = "Period"), textAreaInput("description", label = "Add a Decription:", value = "", height = 150), textInput("new_code", "Enter code word:"), actionButton("upload_event", "Add Event", icon = icon("upload")), circle = TRUE, icon = icon("plus"), width = "300px", tooltip = tooltipOptions(title = "Click to see inputs !") ) ) ), div(class="fifteen wide column", div(class = "ui horizontal divider", icon("globe"), "The History Of Everything") ) ), br(), timevisOutput("timeline"), br() # ) ) )

238e3d5f0d1faed2d9fb05a85efe2ee00fb0d76f

e0ad7ef4247279fad496c3c23891457c5fa0bbe1

/analysis/09_combined_analysis.R

d85af28873cdf5657b6c39d66679682c19c51767

[]

no_license

wiscstatman/immunostat-prostate

8c34672d5f3266566f7a46d028360279934e6c67

bdf6db6257ad6c6770a8fdb163067244a0f6e43b

refs/heads/master

2022-12-23T20:53:57.428843

2020-10-06T04:42:01

283,579,630

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80,557

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09_combined_analysis.R

library(xlsx) library(allez) library(gridExtra) library(ggplot2) library(matrixStats) # rowMedians library(lme4) # linear mixed effects model library(lmerTest) library(fdrtool) library(heatmap3) library(tidyverse) # make sure you have the latest tidyverse ! ####################################################################################### # Some Common Variables and Functions # ####################################################################################### # specified color and shape scheme pal <- c("navy", "cornflowerblue", "turquoise1", "orchid1", "darkorange1", "firebrick1") names(pal) <- c("normal", "new_dx", "nmCSPC", "mCSPC", "nmCRPC", "mCRPC") shp <- c(8, 15, 16, 3, 17, 18) names(shp) <- names(pal) # function to plot PCA loadings PCload.func <- function(cols, shapes, U, D, x, y, pca.vec, title){ Z <- U %*% diag(D) plot(Z[,x], Z[,y], col = cols, pch = shapes, main = title, las = 1, xlab = paste0("PC",x," (", round(pca.vec[x]*100, 1) ,"% variance explained)"), ylab = paste0("PC",y," (", round(pca.vec[y]*100, 1) ,"% variance explained)") ) } # function to count peptides at different FDR thresholds count.func <- function(pval.vec, thresh.vec){ counter <- NULL for (i in thresh.vec ){ counter <- c(counter, length(pval.vec[pval.vec <= i])) } countab <- rbind(c("FDR threshold", thresh.vec), c("Peptide counts", counter)) return(countab) } # typical step in ANOVA anova_func <- function(anova_pval, xlab){ # control FDR anova_BH <- p.adjust(anova_pval, method = "BH") anova_qval <- fdrtool(anova_pval, statistic = "pvalue", verbose = F, plot = F)$qval anova_qval_eta0 <- unname(fdrtool(anova_pval, statistic = "pvalue", verbose = F, plot = F)$param[,"eta0"]) # plot histogram of p-values all_peptide_hist <- hist(anova_pval, breaks = 70, freq = F, xlab = xlab, las = 1, main = paste0("p-values distribution for ", length(anova_pval), " peptides")) polygon_ind <- which(all_peptide_hist$density >= anova_qval_eta0) for (i in polygon_ind){ polygon( x = c(all_peptide_hist$breaks[i], all_peptide_hist$breaks[i+1], all_peptide_hist$breaks[i+1], all_peptide_hist$breaks[i]), y = c(all_peptide_hist$density[i], all_peptide_hist$density[i], anova_qval_eta0, anova_qval_eta0), col = "red") } text(x=0.65,y=4, labels = paste( "estimated proportion of \nnon-null peptides =", round( 100*(1 - anova_qval_eta0),2 ),"%" )) return(list(anova_BH = anova_BH, anova_qval = anova_qval, anova_qval_eta0 = anova_qval_eta0)) } # post-process allez table for kable output get_alleztable.func <- function(allez_go_input){ allez.tab <- allezTable(allez_go_input, symbol = T, nominal.alpha = nom.alpha, n.upp = max_gene_in_set, in.set = T) allez.tab$set.size <- paste(allez.tab$in.set, allez.tab$set.size, sep = "/") allez.tab <- allez.tab %>% dplyr::select(-c(in.set, genes)) %>% mutate(in.genes = str_replace_all(in.genes, ";", "; ")) return(allez.tab) } load("09_Cancer_Stage_Effects.RData") load("09_LMER_results.RData") load("09_allez_results.RData") raw_data_median <- read_csv("raw_data_median.csv") raw_data_median_proj2 <- read_csv("raw_data_median_proj2.csv") ####################################################################################### # Data Processing # ####################################################################################### array_id_key = read_csv("sample_key_project1.csv") %>% janitor::clean_names() %>% rename(stage = condition, array_id = "file_name") %>% mutate(id = str_replace_all(id, " ", ""), id = str_to_lower(id), id = str_replace_all(id, "/", ""), stage = as_factor(stage), stage = fct_recode(stage, "normal" = "Normal_male_controls", "new_dx" = "Newly_diagnosed", "nmCSPC" = "PSA-recurrent_nonMet", "mCSPC" = "Met", "nmCRPC" = "Castration-resistent_nonMet", "mCRPC" = "Castration-resistent_Met", "binding_buffer" = "Binding buffer alone")) # drop binding buffer length(array_id_key$stage[array_id_key$stage=="binding_buffer"]) # 1 binding_buffer array_id_key <- array_id_key[!(array_id_key$stage=="binding_buffer"),] array_id_key$stage <- factor(array_id_key$stage) # remove binding_buffer level # remove patients whose rep == 1 patient_key = array_id_key %>% group_by(id, stage) %>% summarize(n = n()) %>% ungroup() %>% filter(n >= 2) %>% select(-n) array_id_key = array_id_key %>% filter(id %in% patient_key$id) # check patient counts (NOT distinct patients) array_id_key %>% group_by(id, stage) %>% summarize() %>% group_by(stage) %>% tally() # there are patients who were measured at two different stages # To ensure unique patients, remove the following ids ids_to_remove = c("adt181", "adt223", "pdv008", "pap123", "pap067", "adt143") # drop patients' earlier records array_id_key = array_id_key %>% filter(!(id %in% ids_to_remove)) patient_key = array_id_key %>% group_by(id, stage) %>% tally() %>% select(-n) patient_key$stage <- relevel(patient_key$stage, ref = "normal") # check patient counts (distinct patients) array_id_key %>% group_by(id, stage) %>% summarize() %>% group_by(stage) %>% tally() #----------------------------------------------------------------------------------------------- # get raw_data_complete.csv and compute median # raw_data = read_csv("raw_data_complete.csv") # # sum(as.numeric(raw_data$X == raw_data$COL_NUM)) == nrow(raw_data) # X = COL_NUM # sum(as.numeric(raw_data$Y == raw_data$ROW_NUM)) == nrow(raw_data) # Y = ROW_NUM # sum(as.numeric(raw_data$MATCH_INDEX == raw_data$FEATURE_ID)) == nrow(raw_data) # MATCH_INDEX = FEATURE_ID # unique(raw_data$DESIGN_NOTE) # only NA # unique(raw_data$SELECTION_CRITERIA) # only NA # unique(raw_data$MISMATCH) # only 0 # unique(raw_data$PROBE_CLASS) # only NA # # we can drop X, Y, MATCH_INDEX, DESIGN_NOTE, SELECTION_CRITERIA, MISMATCH, PROBE_CLASS # # raw_data = raw_data %>% # select(PROBE_DESIGN_ID:Y, any_of(array_id_key$array_id)) %>% # drop patients with records at different stages # select( -c(X, Y, MATCH_INDEX, DESIGN_NOTE, SELECTION_CRITERIA, MISMATCH, PROBE_CLASS) ) # drop unhelpful columns # # colnames(raw_data)[1:15] # check # # # take log2 transformation # raw_data <- raw_data %>% # mutate_at(vars(matches(".dat")), log2) # # # # make sure array_id_key align with raw_data_complete # array_iii <- match( colnames( raw_data %>% select(contains(".dat")) ), array_id_key$array_id ) # array_id_key <- array_id_key[array_iii , ] # sum(as.numeric( colnames( raw_data %>% select(contains(".dat")) ) == array_id_key$array_id )) == nrow(array_id_key) # # # # compute median # raw_data_median <- t( apply( select(raw_data, contains(".dat")), 1, function(x) { # tapply(x, array_id_key$id, FUN=median) # } ) ) # raw_data_median <- bind_cols( select(raw_data, -contains(".dat")), as.data.frame(raw_data_median) ) # # colnames(raw_data_median)[1:15] # check # # write.table(raw_data_median, file = "raw_data_median.csv", sep = ",", row.names = F) #----------------------------------------------------------------------------------------------- # read calls data # read aggregated calls data calls = read_csv("aggregated_calls_full_nmcspc.csv") length(unique(calls$probe_sequence)) == nrow(calls) # probe_sequence NOT unique # probe_sequence NOT unique # need to generate unique PROBE_ID # PROBE_ID in raw_data_complete.csv is paste0(SEQ_ID, ";", POSITION) calls$PROBE_ID = paste0(calls$seq_id, ";", calls$position) calls <- calls %>% select(container:position, PROBE_ID, everything()) # rearrange columns # keep only patients that appear in patient_sample_key calls <- calls %>% select(container:PROBE_ID, any_of(patient_key$id)) # check dimensions of calls match dimensions of raw_data_median (nrow(calls) == nrow(raw_data_median)) & (ncol(calls %>% select(any_of(patient_key$id))) == ncol(raw_data_median %>% select(any_of(patient_key$id)))) ncol(calls %>% select(any_of(patient_key$id))) == nrow(patient_key) # check if PROBE_ID (& patient_id) in calls appear in PROBE_ID (& patient_id) in raw_data_median sum(as.numeric( calls$PROBE_ID %in% raw_data_median$PROBE_ID )) == nrow(calls) sum(as.numeric( colnames( calls %>% select(any_of(patient_key$id)) ) %in% colnames( raw_data_median %>% select(any_of(patient_key$id)) ) )) == nrow(patient_key) # get calls_long for later calls_long <- calls %>% select(PROBE_ID, any_of(array_id_key$id)) # remove peptides that have zero calls in ALL subjects calls <- calls[ apply( calls %>% select(any_of(patient_key$id)) , 1, function(x){ !all(x==0) } ) , ] # in the end, how many peptides with at least one call among all patients nrow(calls) #---------------------------------------------------------------------------------------------- # get median_long median_long2 <- raw_data_median %>% select(PROBE_ID, any_of(array_id_key$id)) dim(median_long2) == dim(calls_long) # check # rearrange rows and columns to match calls_long & median_long calls_long <- calls_long[, match(colnames(median_long2), colnames(calls_long))] calls_long <- calls_long[ match(median_long2$PROBE_ID, calls_long$PROBE_ID) , ] sum(as.numeric( colnames( calls_long ) == colnames( median_long2 ) )) == nrow(patient_key) + 1 # check sum(as.numeric( calls_long$PROBE_ID == median_long2$PROBE_ID )) == nrow(median_long2) # check # pivot_longer median_long2 <- median_long2 %>% select(-PROBE_ID) %>% pivot_longer(cols = everything(), names_to = "id", values_to = "fluorescence") calls_long <- calls_long %>% select(-PROBE_ID) %>% pivot_longer(cols = everything(), names_to = "id", values_to = "calls") # check median_long2 & calls_long nrow(median_long2) == nrow(raw_data_median) * nrow(patient_key) head(median_long2) nrow(calls_long) == nrow(calls) * nrow(patient_key) head(calls_long) dim(median_long2) == dim(median_long2) sum(as.numeric(median_long2$id == calls_long$id)) == nrow(raw_data_median) * nrow(patient_key) # plot fluorescence of calls vs no-calls calls_fl_df <- data.frame( calls = factor(calls_long$calls), fluorescence = median_long2$fluorescence ) janitor::tabyl(calls_fl_df$calls) %>% janitor::adorn_pct_formatting() %>% rename(calls = "calls_fl_df$calls", patient_peptide_counts = n) ggplot(calls_fl_df, aes(x = calls, y = fluorescence, fill = calls)) + geom_boxplot(outlier.shape = ".") + labs(title = "Boxplots of Fluorescence Levels per Peptide per Patient", x = "Calls per peptide per patient", y = "Median (across replicates) Fluorescence Levels on log2 scale") + theme(panel.background = element_rect(fill = "grey90"), panel.grid.major = element_line(color = "white"), panel.grid.minor = element_line(color = "white"), plot.title = element_text(hjust = 0.5)) # free up memory rm(median_long2, calls_fl_df, calls_long); gc() ####################################################################################### # Check normalization of fluorescence data # ####################################################################################### median_long <- raw_data_median %>% select(any_of(array_id_key$id)) %>% pivot_longer(cols = everything(), names_to = "id", values_to = "fluorescence") # check nrow(median_long) == nrow(raw_data_median) * nrow(patient_key) head(median_long) # set fill color median_long$stage <- patient_key$stage[ match(median_long$id, patient_key$id) ] # sort order of patients in boxplot median_long$id <- factor(median_long$id, levels = c( patient_key$id[patient_key$stage == "normal"], patient_key$id[patient_key$stage == "new_dx"], patient_key$id[patient_key$stage == "nmCSPC"], patient_key$id[patient_key$stage == "nmCRPC"], patient_key$id[patient_key$stage == "mCRPC"] )) ggplot(median_long, aes(x = id, y = fluorescence, fill = stage)) + geom_boxplot(outlier.shape = ".") + scale_fill_manual(name = "Stage", values = pal) + labs(title = "Boxplots of Peptide Fluorescence Levels for All Patients", x = "Patient ID", y = "Median Fluorescence Levels on log2 scale") + theme(panel.background = element_rect(fill = "grey90"), panel.grid.major = element_line(color = "white"), panel.grid.minor = element_line(color = "white"), axis.text.x = element_text(angle = 90, hjust = 1, size = 4.5), legend.position = "bottom", plot.title = element_text(hjust = 0.5)) ####################################################################################### # Evaluate Reproducibility of Replicates via Linear Mixed Effects Model # ####################################################################################### # ncol_raw <- ncol(raw_data) # nrep <- nrow(array_id_key) # # # initiate # lmer_result <- matrix(NA, nrow = nrow(raw_data), ncol = 4) # colnames(lmer_result) <- c("variance_id", "variance_residual", "lrstat", "singularity") # # # check array_id_key align with raw_data_complete # sum(as.numeric( colnames( raw_data[,(ncol_raw - nrep + 1) : ncol_raw] ) == array_id_key$array_id )) == nrow(array_id_key) # # # for(i in 1:nrow(raw_data)){ # y <- as.numeric(raw_data[i, (ncol_raw - nrep + 1) : ncol_raw]) # fit1 <- lmer(y ~ stage + (1|id), data = array_id_key) # fit2 <- lmer(y ~ 1 + (1|id), data = array_id_key) # lmer_result[i,] <- c( # as.data.frame(VarCorr(fit1))$'vcov', # as.numeric(-2*(logLik(fit2, REML=T) - logLik(fit1, REML=T))), # ( isSingular(fit1) | isSingular(fit2) ) # ) # print(i) # } # check how many singular fits sum(as.numeric(lmer_result[,'singularity'])) max(as.numeric(lmer_result[,'variance_id'][ lmer_result[,'singularity']==T ])) min(as.numeric(lmer_result[,'variance_residual'] )) # get estimated proportion of variances lmer_var_ratio <- lmer_result[,'variance_id'] / ( lmer_result[,'variance_id'] + lmer_result[,'variance_residual'] ) hist(lmer_var_ratio, breaks = 100, xlab = "estimated proportion of variances", main = "Histogram of peptide-level proportion of random-effect variance to total variance") ####################################################################################### # TEST -- Logistic Regression # ####################################################################################### # ncol_calls <- ncol(calls) # n <- nrow(patient_key) # # # make sure patients' stages align with calls # calls_iii <- match( colnames( calls[,(ncol_calls - n + 1): ncol_calls] ), patient_key$id ) # calls_stages <- (patient_key$stage)[calls_iii] # sum(as.numeric( colnames( calls[,(ncol_calls - n + 1): ncol_calls] ) == (patient_key$id)[calls_iii] )) == nrow(patient_key) # # # initiate # logreg_pval <- rep(NA, nrow(calls)) # names(logreg_pval) <- calls$PROBE_ID # # # compute deviance test p-values # for(i in 1:nrow(calls)){ # y <- as.numeric( calls[i, (ncol_calls - n + 1): ncol_calls] ) # fit1 <- glm(y ~ calls_stages, family = binomial(link = "logit")) # logreg_pval[i] <- 1 - pchisq( fit1$null.deviance - fit1$deviance, df = (fit1$df.null - fit1$df.residual) ) # print(i) # } # control FDR logreg_BH <- p.adjust(logreg_pval, method = "BH") logreg_qval <- fdrtool(logreg_pval, statistic = "pvalue", verbose = F, plot = F)$qval logreg_qval_eta0 <- unname(fdrtool(logreg_pval, statistic = "pvalue", verbose = F, plot = F)$param[,"eta0"]) # plot histogram of p-values hist(logreg_pval, breaks = 50, freq = T, main = "Logistic Regression Deviance Test p-values", xlab = "p-values ") # peptide counts at various FDR thresholds count.func(logreg_BH, seq(0.01, 0.05, by = 0.01)) count.func(logreg_qval, seq(0.01, 0.05, by = 0.01)) # Calls are conservative # Logistic regression based on number of calls yield (almost) no signal even before FDR control ####################################################################################### # Another Tests -- one-way ANOVA & Kruskal-Wallis Test # ####################################################################################### ncol_median <- ncol(raw_data_median) n <- nrow(patient_key) # make sure patients' stages align with raw_data_median median_iii <- match( colnames(raw_data_median[, (ncol_median - n + 1) : ncol_median]), patient_key$id ) median_stage <- patient_key$stage[median_iii] median_stage <- factor(median_stage) sum(as.numeric( colnames(raw_data_median[, (ncol_median - n + 1) : ncol_median]) == patient_key$id[median_iii] )) == nrow(patient_key) #--------------------------------------------------------------------------------------- # initiate ANOVA # all_anova_pval <- rep(NA, nrow(raw_data_median)) # names(all_anova_pval) <- raw_data_median$PROBE_ID # all_anova_mse <- rep(NA, nrow(raw_data_median)) # names(all_anova_mse) <- raw_data_median$PROBE_ID # compute one-way anova p-values # for(i in 1:nrow(raw_data_median)){ # fit1 <- lm( as.numeric(raw_data_median[i, (ncol_median - n + 1) : ncol_median]) ~ median_stage ) # all_anova_pval[i] <- unname( unlist(summary(aov(fit1)))["Pr(>F)1"] ) # all_anova_mse[i] <- deviance(fit1)/df.residual(fit1) # print(i) # } # get p-values histogram and FDR all_anova <- anova_func(all_anova_pval, "one-way ANOVA p-values") # peptide counts at various FDR thresholds count.func(all_anova$anova_BH, seq(0.01, 0.1, by = 0.01)) count.func(all_anova$anova_qval, seq(0.01, 0.1, by = 0.01)) #--------------------------------------------------------------------------------------- # some peptides may violate ANOVA assumption par(mar=c(3.1, 5.1, 2.1, 2.1), mgp=c(2, 0.8, 0)) graphics::boxplot(as.numeric( raw_data_median %>% filter(PROBE_ID == "1324_KIAA1430_57587;185") %>% select(any_of(array_id_key$id)) ) ~ median_stage, varwidth = T, horizontal = T, las = 1, col = c("navy", "cornflowerblue", "turquoise1","darkorange1", "firebrick1"), main = "Peptide ID: 1324_KIAA1430_57587;185", xlab = "log2(fluorescence)", ylab = "") par(mar=c(3.1, 5.1, 2.1, 2.1), mgp=c(2, 0.8, 0)) graphics::boxplot(as.numeric( raw_data_median %>% filter(PROBE_ID == "459_CLTC_1213;1421") %>% select(any_of(array_id_key$id)) ) ~ median_stage, varwidth = T, horizontal = T, las= 1, col = c("navy", "cornflowerblue", "turquoise1","darkorange1", "firebrick1"), main = "Peptide ID: 459_CLTC_1213;1421", xlab = "log2(fluorescence)", ylab = "") dev.off() #--------------------------------------------------------------------------------------- # now do kruskal-wallis tests # initiate kruskal-wallis(KW) # all_kw_pval <- rep(NA, nrow(raw_data_median)) # names(all_kw_pval) <- raw_data_median$PROBE_ID # # for(i in 1:nrow(raw_data_median)){ # all_kw_pval[i] <- kruskal.test( as.numeric(raw_data_median[i, (ncol_median - n + 1) : ncol_median]) ~ median_stage )$'p.value' # print(i) # } # get p-values histogram and FDR all_kw <- anova_func(all_kw_pval, "Kruskal-Wallis p-values") # peptide counts at various FDR thresholds count.func(all_kw$anova_BH, seq(0.01, 0.1, by = 0.01)) # signif for both ANOVA & Kruskal-Wallis length(which(all_kw$anova_BH <= .05 & all_anova$anova_BH <= .05)) #--------------------------------------------------------------------------------------- # compare kruskal-wallis pval vs ANOVA pval plot(x = all_anova_pval, y = all_kw_pval, pch = ".", xlab = "ANOVA p-values", ylab = "Kruskal-Wallis p-values") lines(x = all_anova_pval[all_anova$anova_BH <= .05], y = all_kw_pval[all_anova$anova_BH <= .05], type = "p", pch = 20, col = "red") lines(x = all_anova_pval[all_kw$anova_BH <= .05], y = all_kw_pval[all_kw$anova_BH <= .05], type = "p", pch = 20, col = "blue") lines(x = all_anova_pval[all_anova$anova_BH <= .05 & all_kw$anova_BH <= .05], y = all_kw_pval[all_anova$anova_BH <= .05 & all_kw$anova_BH <= .05], type = "p", pch = 20, col = "green") # check boxplot of peptide with very signif ANOVA pval but not kruskal-wallis pval ANOVA_but_not_KW <- raw_data_median %>% filter(all_anova_pval <.001 & all_kw_pval >.2) %>% select(PROBE_ID, any_of(array_id_key$id)) %>% as.matrix() ANOVA_but_not_KW_iii <- match( colnames(ANOVA_but_not_KW[, -1]), patient_key$id ) ANOVA_but_not_KW_stage <- patient_key$stage[ANOVA_but_not_KW_iii] ANOVA_but_not_KW_stage <- factor(ANOVA_but_not_KW_stage) sum(as.numeric( colnames(ANOVA_but_not_KW[, -1]) == patient_key$id[ANOVA_but_not_KW_iii] )) == nrow(patient_key) par(mfrow=c(3,1)) par(mar=c(5.1, 6.1, 4.1, 2.1)) graphics::boxplot(as.numeric(ANOVA_but_not_KW[1,-1])~ANOVA_but_not_KW_stage, varwidth = T, horizontal = T,las= 2, col = c("navy", "cornflowerblue", "turquoise1","darkorange1", "firebrick1"), main = ANOVA_but_not_KW[1,"PROBE_ID"], xlab = "log2(fluorescence)", ylab = "") graphics::boxplot(as.numeric(ANOVA_but_not_KW[2,-1])~ANOVA_but_not_KW_stage, varwidth = T, horizontal = T,las= 2, col = c("navy", "cornflowerblue", "turquoise1","darkorange1", "firebrick1"), main = ANOVA_but_not_KW[2,"PROBE_ID"], xlab = "log2(fluorescence)", ylab = "") graphics::boxplot(as.numeric(ANOVA_but_not_KW[3,-1])~ANOVA_but_not_KW_stage, varwidth = T, horizontal = T,las= 2, col = c("navy", "cornflowerblue", "turquoise1","darkorange1", "firebrick1"), main = ANOVA_but_not_KW[3,"PROBE_ID"], xlab = "log2(fluorescence)", ylab = "") # check boxplot of peptide with very signif kruskal-wallis pval but not ANOVA pval KW_but_not_ANOVA <- raw_data_median %>% filter(all_anova_pval > .7 & all_kw_pval < .001) %>% select(PROBE_ID, any_of(array_id_key$id)) %>% as.matrix() KW_but_not_ANOVA_iii <- match( colnames(KW_but_not_ANOVA[, -1]), patient_key$id ) KW_but_not_ANOVA_stage <- patient_key$stage[KW_but_not_ANOVA_iii] KW_but_not_ANOVA_stage <- factor(KW_but_not_ANOVA_stage) sum(as.numeric( colnames(KW_but_not_ANOVA[, -1]) == patient_key$id[KW_but_not_ANOVA_iii] )) == nrow(patient_key) par(mfrow=c(3,1)) par(mar=c(5.1, 6.1, 4.1, 2.1)) graphics::boxplot(as.numeric(KW_but_not_ANOVA[1,-1])~KW_but_not_ANOVA_stage, varwidth = T, horizontal = T,las= 2, col = c("navy", "cornflowerblue", "turquoise1","darkorange1", "firebrick1"), main = KW_but_not_ANOVA[1,"PROBE_ID"], xlab = "log2(fluorescence)", ylab = "") graphics::boxplot(as.numeric(KW_but_not_ANOVA[2,-1])~KW_but_not_ANOVA_stage, varwidth = T, horizontal = T,las= 2, col = c("navy", "cornflowerblue", "turquoise1","darkorange1", "firebrick1"), main = KW_but_not_ANOVA[2,"PROBE_ID"], xlab = "log2(fluorescence)", ylab = "") graphics::boxplot(as.numeric(KW_but_not_ANOVA[3,-1])~KW_but_not_ANOVA_stage, varwidth = T, horizontal = T,las= 2, col = c("navy", "cornflowerblue", "turquoise1","darkorange1", "firebrick1"), main = KW_but_not_ANOVA[3,"PROBE_ID"], xlab = "log2(fluorescence)", ylab = "") #--------------------------------------------------------------------------------------- # PCA after Kruskal-Wallis anova_dat <- raw_data_median %>% select(PROBE_ID, any_of(patient_key$id)) %>% mutate(anova_BH = all_kw$anova_BH[raw_data_median$PROBE_ID]) %>% filter(anova_BH <= BH_FDR_cutoff) %>% select(-anova_BH) anova_dat_demean <- sweep(as.matrix(anova_dat %>% select(-PROBE_ID)), 1, rowMeans(as.matrix(anova_dat %>% select(-PROBE_ID))), "-") # centering by row # make sure stage aligns with anova_dat_demean visual_iii <- match( colnames(anova_dat_demean) , patient_key$id ) visual_stage <- patient_key$stage[visual_iii] # colors and shapes for the visualization techniques cols = pal[ match(visual_stage, names(pal)) ] shapes = shp[ match(visual_stage, names(shp)) ] # svd sv.dat <- sweep(t(anova_dat_demean), 2, colMeans(t(anova_dat_demean)), "-") # centering sv <- svd(sv.dat) V <- sv$v D <- sv$d U <- sv$u # variance explained pca.var <- D^2/sum(D^2) pca.cumvar <- cumsum(pca.var) # plot PCA par(mfrow = c(1,2), pty = "s", mar = c(2.2,2.3,1.5,0.45), mgp = c(1.6,0.4,0), cex.axis = 0.84, cex.lab = 0.84, cex.main = 0.84, tcl = -0.4) PCload.func(cols, shapes, U, D, 1, 2, pca.var, title = "PC2 vs PC1") # PC loadings (PC2 vs PC1) legend('topright', pch = shp, col = pal, cex = 0.5, c("normal", "new_dx", "nmCSPC", "mCSPC", "nmCRPC", "mCRPC") ) PCload.func(cols, shapes, U, D, 3, 2, pca.var, title = "PC2 vs PC3") # PC loadings (PC2 vs PC3) dev.off() ####################################################################################### # Pairwise Comparisons # ####################################################################################### # we want the following contrasts: # consecutive-group comparison: mCRPC-nmCRPC, nmCRPC-nmCSPS,nmCSPC-new_dx, new_dx-normal # normal vs canceer # mCRPC vs the others # first make sure stage aligns with raw_data_median sum(as.numeric(colnames(raw_data_median %>% select(-(PROBE_DESIGN_ID:DESIGN_ID))) == patient_key$id)) == nrow(patient_key) # set BH-FDR cutoff BH_FDR_cutoff <- .05 # get group medians group_median.func <- function(group, BH_filter){ raw_data_median %>% select(-(PROBE_DESIGN_ID:DESIGN_ID)) %>% select(which(patient_key$stage %in% group)) %>% # filter(all_anova$anova_BH <= BH_filter) %>% filter(all_kw$anova_BH <= BH_filter) %>% as.matrix() %>% matrixStats::rowMedians() } mCRPC_median <- group_median.func("mCRPC", BH_FDR_cutoff) nmCRPC_median <- group_median.func("nmCRPC", BH_FDR_cutoff) nmCSPC_median <- group_median.func("nmCSPC", BH_FDR_cutoff) newdx_median <- group_median.func("new_dx", BH_FDR_cutoff) normal_median <- group_median.func("normal", BH_FDR_cutoff) cancer_median <- group_median.func(c("new_dx", "nmCSPC", "nmCRPC", "mCRPC"), BH_FDR_cutoff) NOT_mCRPC_median <- group_median.func(c("normal", "new_dx", "nmCSPC", "nmCRPC"), BH_FDR_cutoff) # might need group means? # group_means.func <- function(group, BH_filter){ # raw_data_median %>% # select(-(PROBE_DESIGN_ID:DESIGN_ID)) %>% # select(which(patient_key$stage %in% group)) %>% # # filter(all_anova$anova_BH <= BH_filter) %>% # filter(all_kw$anova_BH <= BH_filter) %>% # rowMeans() # } #---------------------------------------------------------------------------------------------- # wilcoxon rank-sum tests # median_subset <- raw_data_median %>% # filter(all_kw$anova_BH <= BH_FDR_cutoff) %>% # change KW BH threshold here! # select(-(PROBE_DESIGN_ID:DESIGN_ID)) %>% # as.matrix() # # # initiate wilcox-pval # mCRPC_nmCRPC_wilcox_pval <- rep(NA, nrow(median_subset)) # nmCRPC_nmCSPC_wilcox_pval <- rep(NA, nrow(median_subset)) # nmCSPC_newdx_wilcox_pval <- rep(NA, nrow(median_subset)) # newdx_normal_wilcox_pval <- rep(NA, nrow(median_subset)) # cancer_normal_wilcox_pval <- rep(NA, nrow(median_subset)) # mCRPC_others_wilcox_pval <- rep(NA, nrow(median_subset)) # # # get wilcox pval (2-sided) # for(i in 1: nrow(median_subset)){ # mCRPC_nmCRPC_wilcox_pval[i] <- wilcox.test( # x = as.numeric(median_subset[i, patient_key$stage == "mCRPC"]), # y = as.numeric(median_subset[i, patient_key$stage == "nmCRPC"]), # alternative = "two.sided", exact = T # )$'p.value' # nmCRPC_nmCSPC_wilcox_pval[i] <- wilcox.test( # x = as.numeric(median_subset[i, patient_key$stage == "nmCRPC"]), # y = as.numeric(median_subset[i, patient_key$stage == "nmCSPC"]), # alternative = "two.sided", exact = T # )$'p.value' # nmCSPC_newdx_wilcox_pval[i] <- wilcox.test( # x = as.numeric(median_subset[i, patient_key$stage == "nmCSPC"]), # y = as.numeric(median_subset[i, patient_key$stage == "new_dx"]), # alternative = "two.sided", exact = T # )$'p.value' # newdx_normal_wilcox_pval[i] <- wilcox.test( # x = as.numeric(median_subset[i, patient_key$stage == "new_dx"]), # y = as.numeric(median_subset[i, patient_key$stage == "normal"]), # alternative = "two.sided", exact = T # )$'p.value' # cancer_normal_wilcox_pval[i] <- wilcox.test( # x = as.numeric(median_subset[i, patient_key$stage %in% c("new_dx", "nmCSPC", "nmCRPC", "mCRPC")]), # y = as.numeric(median_subset[i, patient_key$stage == "normal"]), # alternative = "two.sided", exact = T # )$'p.value' # mCRPC_others_wilcox_pval[i] <- wilcox.test( # x = as.numeric(median_subset[i, patient_key$stage == "mCRPC"]), # y = as.numeric(median_subset[i, patient_key$stage %in% c("normal", "new_dx", "nmCSPC", "nmCRPC")]), # alternative = "two.sided", exact = T # )$'p.value' # print(i) # } # #---------------------------------------------------------------------------------------------- # get the kruskal-wallis 5% BH FDR peptide counts kw_FDR5prct <- length(all_kw$anova_BH[all_kw$anova_BH <= .05]) # pval histograms wilcox_pval_df <- data.frame( group_pair = c( rep("mCRPC vs nmCRPC", kw_FDR5prct), rep("nmCRPC vs nmCSPC", kw_FDR5prct), rep("nmCSPC vs new_dx", kw_FDR5prct), rep("new_dx vs normal", kw_FDR5prct), rep("cancer vs normal", kw_FDR5prct), rep("mCRPC vs others", kw_FDR5prct) ), p_values = c( mCRPC_nmCRPC_wilcox_pval, nmCRPC_nmCSPC_wilcox_pval, nmCSPC_newdx_wilcox_pval, newdx_normal_wilcox_pval, cancer_normal_wilcox_pval, mCRPC_others_wilcox_pval ) ) wilcox_pval_df$group_pair <- factor(wilcox_pval_df$group_pair, levels = c( "cancer vs normal", "mCRPC vs others", "mCRPC vs nmCRPC", "nmCRPC vs nmCSPC", "nmCSPC vs new_dx", "new_dx vs normal" )) ggplot(wilcox_pval_df, aes(x = p_values)) + geom_histogram(aes(y=..density..), bins = 50) + facet_wrap(. ~ group_pair, ncol=2) + labs(x = "Wilcoxon p-values", title = paste0("Density Histograms of the ", kw_FDR5prct, " Wilcoxon p-values")) + theme(plot.title = element_text(hjust = 0.5)) #---------------------------------------------------------------------------------------------- # get BH-corrected pval (restricted to signif peptides from Kruskal-Wallis) mCRPC_nmCRPC_wilcox_BH <- p.adjust(mCRPC_nmCRPC_wilcox_pval, method = "BH") nmCRPC_nmCSPC_wilcox_BH <- p.adjust(nmCRPC_nmCSPC_wilcox_pval, method = "BH") nmCSPC_newdx_wilcox_BH <- p.adjust(nmCSPC_newdx_wilcox_pval, method = "BH") newdx_normal_wilcox_BH <- p.adjust(newdx_normal_wilcox_pval, method = "BH") cancer_normal_wilcox_BH <- p.adjust(cancer_normal_wilcox_pval, method = "BH") mCRPC_others_wilcox_BH <- p.adjust(mCRPC_others_wilcox_pval, method = "BH") wilcox_peptide_counts_df <- data.frame( pairwise_comparison = c( "cancer vs normal", "mCRPC vs others", "mCRPC vs nmCRPC", "nmCRPC vs nmCSPC", "nmCSPC vs new_dx", "new_dx vs normal" ), peptide_counts = c( length(which(abs(cancer_median - normal_median) > 1 & cancer_normal_wilcox_BH <= BH_FDR_cutoff)), length(which(abs(mCRPC_median - NOT_mCRPC_median) > 1 & mCRPC_others_wilcox_BH <= BH_FDR_cutoff)), length(which(abs(mCRPC_median - nmCRPC_median) > 1 & mCRPC_nmCRPC_wilcox_BH <= BH_FDR_cutoff)), length(which(abs(nmCRPC_median - nmCSPC_median) > 1 & nmCRPC_nmCSPC_wilcox_BH <= BH_FDR_cutoff)), length(which(abs(nmCSPC_median - newdx_median) > 1 & nmCSPC_newdx_wilcox_BH <= BH_FDR_cutoff)), length(which(abs(newdx_median - normal_median) > 1 & newdx_normal_wilcox_BH<= BH_FDR_cutoff)) ) ) #---------------------------------------------------------------------------------------------- # volcano plots of contrasts # make sure contrast_diff and contrast_pval and contrast_BH of same length !! contrast_volcano_plot.func <- function(contrast_diff, contrast_pval, contrast_BH, test_type, measure, contrast_title, xlim = c(-7,6), ylim = c(0,8)){ signif_counts = length(which(abs(contrast_diff) > 1 & contrast_BH <= BH_FDR_cutoff)) horizontal_pval = max(contrast_pval[contrast_BH<= BH_FDR_cutoff]) plot(x = contrast_diff, y = -log10(contrast_pval), pch = 20, xlim = xlim, ylim = ylim, las = 1, xlab = paste0("difference of ", measure, " log2(fluorescence)"), ylab = paste0("-log10(contrast ",test_type, " p-values)"), main = paste0("Volcano plot of contrast: ", contrast_title)) text(x = 4, y = 7, paste0(signif_counts, " signif peptides")) lines(x = contrast_diff[ contrast_BH <= BH_FDR_cutoff & abs(contrast_diff) >= 1 ], y = -log10(contrast_pval[ contrast_BH <= BH_FDR_cutoff & abs(contrast_diff) >= 1]), type = "p", pch = 20, col = "red") abline(v = 1, col = "blue", lty = 2, lwd = 2) abline(v = -1, col = "blue", lty = 2, lwd = 2) abline(h = -log10(horizontal_pval), col = "blue", lty = 2, lwd = 2) } # png("09_contrast_volcano_plots(KW_wilcox_diffmedian_cutoff).png", width = 1024, height = 1024) par(mfrow = c(3,2)) # cancer vs normal contrast_volcano_plot.func(cancer_median - normal_median, cancer_normal_wilcox_pval, cancer_normal_wilcox_BH, "Wilcoxon", "median", "cancer vs normal") # mCRPC vs others contrast_volcano_plot.func(mCRPC_median - NOT_mCRPC_median, mCRPC_others_wilcox_pval, mCRPC_others_wilcox_BH, "Wilcoxon", "median", "mCRPC vs others" ) # mCRPC vs nmCRPC contrast_volcano_plot.func(mCRPC_median - nmCRPC_median, mCRPC_nmCRPC_wilcox_pval, mCRPC_nmCRPC_wilcox_BH, "Wilcoxon", "median", "mCRPC vs nmCRPC") # nmCRPC vs nmCSPC contrast_volcano_plot.func(nmCRPC_median - nmCSPC_median, nmCRPC_nmCSPC_wilcox_pval, nmCRPC_nmCSPC_wilcox_BH, "Wilcoxon", "median", "nmCRPC vs nmCSPC") # nmCSPC vs new_dx contrast_volcano_plot.func(nmCSPC_median - newdx_median, nmCSPC_newdx_wilcox_pval, nmCSPC_newdx_wilcox_BH, "Wilcoxon", "median", "nmCSPC vs new_dx") # new_dx vs normal contrast_volcano_plot.func(newdx_median - normal_median, newdx_normal_wilcox_pval, newdx_normal_wilcox_BH, "Wilcoxon", "median", "new_dx vs normal") dev.off() ####################################################################################### # Visualization # ####################################################################################### # replot subset of ANOVA residual heatmap based on effect-size threshold from post-hoc analysis posthoc_signif_crit <- ( abs(mCRPC_median - nmCRPC_median) > 1 & mCRPC_nmCRPC_wilcox_BH <= BH_FDR_cutoff ) | ( abs(nmCRPC_median - nmCSPC_median) > 1 & nmCRPC_nmCSPC_wilcox_BH <= BH_FDR_cutoff ) | ( abs(nmCSPC_median - newdx_median) > 1 & nmCSPC_newdx_wilcox_BH <= BH_FDR_cutoff ) | ( abs(newdx_median - normal_median) > 1 & newdx_normal_wilcox_BH<= BH_FDR_cutoff ) | ( abs(cancer_median - normal_median) > 1 & cancer_normal_wilcox_BH <= BH_FDR_cutoff ) | ( abs(mCRPC_median - NOT_mCRPC_median) > 1 & mCRPC_others_wilcox_BH <= BH_FDR_cutoff ) # check length(posthoc_signif_crit) sum(as.numeric(posthoc_signif_crit)) secondary_cutoff_counts <- sum(as.numeric(posthoc_signif_crit)) anova_dat <- raw_data_median %>% select(PROBE_ID, any_of(patient_key$id)) %>% # mutate(anova_BH = all_anova$anova_BH[raw_data_median$PROBE_ID]) %>% mutate(anova_BH = all_kw$anova_BH[raw_data_median$PROBE_ID]) %>% filter(anova_BH <= BH_FDR_cutoff) %>% select(-anova_BH) %>% filter(posthoc_signif_crit) anova_dat_demean <- sweep(as.matrix(anova_dat %>% select(-PROBE_ID)), 1, rowMeans(as.matrix(anova_dat %>% select(-PROBE_ID))), "-") # centering by row # check dim(anova_dat_demean) # make sure stage aligns with anova_dat_demean visual_iii <- match( colnames(anova_dat_demean) , patient_key$id ) visual_stage <- patient_key$stage[visual_iii] #-------------------------------------------------------------------------------------------- # heatmap # colors and shapes for the visualization techniques cols = pal[ match(visual_stage, names(pal)) ] # cls <- colorRampPalette(c("navy", "honeydew", "firebrick3", "brown"))(n = 1024) # cls <- colorRampPalette(c("navy", "honeydew", "sienna1", "firebrick3", "brown"))(n = 1024) cls <- colorRampPalette(c("navy", "honeydew", "firebrick1"))(n = 1024) get_column_order.func <- function(stages){ heat_map <- heatmap3(anova_dat_demean[,visual_stage == stages],col = cls, labRow = "", scale = "none", showColDendro = F, showRowDendro = F) return( colnames(anova_dat_demean[,visual_stage == stages])[heat_map$colInd] ) } normal_id_order <- get_column_order.func("normal") newdx_id_order <- get_column_order.func("new_dx") nmCSPC_id_order <- get_column_order.func("nmCSPC") nmCRPC_id_order <- get_column_order.func("nmCRPC") mCRPC_id_order <- get_column_order.func("mCRPC") id_order <- match( c(normal_id_order, newdx_id_order, nmCSPC_id_order, nmCRPC_id_order, mCRPC_id_order), colnames(anova_dat_demean) ) # winsorize quantile(as.numeric(anova_dat_demean), probs = c(.01, .05, .1, .15, .2, .25, .3, .7, .75, .8, .85, .9, .95)) # check ecdf(as.numeric(anova_dat_demean))(c(-3.5, -2.6, -2, 2, 2.6, 3.5)) # check anova_dat_demean_winsorize <- t( apply(anova_dat_demean, 1, function(x){ x[x < -2] = -2 x[x > 2] = 2 return(x) }) ) # png("09_residual_heatmap(KW_wilcox_diffmedian_cutoff).png", width = 1024, height = 1024) heatmap3(anova_dat_demean_winsorize[,id_order], col = cls, # specify colors ColSideColors = cols[id_order], # specify patient color code Colv = NA, scale = "none", # no scaling by row labCol = visual_stage[id_order], # specify patient ColSideLabs = "stages", labRow = "", xlab = "Patients", legendfun=function() showLegend(col = c("navy", "cornflowerblue", "turquoise1", "darkorange1", "firebrick1"), legend = c("normal", "new_dx", "nmCSPC", "nmCRPC", "mCRPC"), cex = 1.2, lwd = 5 ) ) dev.off() #-------------------------------------------------------------------------------------------- # PCA # colors and shapes for the visualization techniques cols = pal[ match(visual_stage, names(pal)) ] shapes = shp[ match(visual_stage, names(shp)) ] # svd sv.dat <- sweep(t(anova_dat_demean), 2, colMeans(t(anova_dat_demean)), "-") # centering sv <- svd(sv.dat) V <- sv$v D <- sv$d U <- sv$u # variance explained pca.var <- D^2/sum(D^2) pca.cumvar <- cumsum(pca.var) # plot PCA par(mfrow = c(1,2), pty = "s", mar = c(2.2,2.3,1.5,0.45), mgp = c(1.6,0.4,0), cex.axis = 0.84, cex.lab = 0.84, cex.main = 0.84, tcl = -0.4) PCload.func(cols, shapes, U, D, 1, 2, pca.var, title = "PC2 vs PC1") # PC loadings (PC2 vs PC1) legend('topright', pch = shp, col = pal, cex = 0.5, c("normal", "new_dx", "nmCSPC", "mCSPC", "nmCRPC", "mCRPC") ) PCload.func(cols, shapes, U, D, 3, 2, pca.var, title = "PC2 vs PC3") # PC loadings (PC2 vs PC3) dev.off() ####################################################################################### # Gene Set Analyses After Kruskal-Wallis & Wilcoxon Tests # ####################################################################################### # read uniprot_gene csv uniprot_gene <- read_csv("uniprot_data_entrez.csv", col_types = cols_only( seq_id = col_character(), uniprot_id = col_character(), gene_symbol = col_character(), entrez_gene_id_pete = col_double(), gene_names = col_character(), protein_names = col_character() )) %>% filter(!(is.na(seq_id)) & !(is.na(entrez_gene_id_pete))) %>% select(seq_id, uniprot_id, gene_symbol, entrez_gene_id_pete, gene_names, protein_names) # check if seq_id & entrez_id unique uniprot_gene <- uniprot_gene[!(is.na(uniprot_gene$entrez_gene_id_pete)),] # just in case length(unique(uniprot_gene$seq_id)) == length(uniprot_gene$seq_id) # yes! unique! length(unique(uniprot_gene$entrez_gene_id_pete)) == length(uniprot_gene$entrez_gene_id_pete) # NOT unique # which seq_id has repeated gene_symbol genesymb_repeat <- as.data.frame( uniprot_gene[ uniprot_gene$entrez_gene_id_pete %in% (uniprot_gene %>% group_by(entrez_gene_id_pete) %>% tally() %>% filter(n > 1) %>% pull(entrez_gene_id_pete)) , ] ) entrez_id_repeat <- as.character( unique(genesymb_repeat$entrez_gene_id_pete) ) # # check if these seq_id make the Kruskal-Wallis 5% BH FDR cutoff ? # genesymb_repeat$seq_id %in% raw_data_median$SEQ_ID[all_kw$anova_BH <= BH_FDR_cutoff] # yes...fine... # # check if these seq_id make at least one of the secondary cutoffs ? # genesymb_repeat$seq_id %in% raw_data_median$SEQ_ID[all_kw$anova_BH <= BH_FDR_cutoff][posthoc_signif_crit] # yes...fine # # check each contrast one by one # genesymb_repeat$seq_id %in% raw_data_median$SEQ_ID[all_kw$anova_BH <= BH_FDR_cutoff][( abs(mCRPC_median - nmCRPC_median) > 1 & mCRPC_nmCRPC_wilcox_BH <= BH_FDR_cutoff ) ] # genesymb_repeat$seq_id %in% raw_data_median$SEQ_ID[all_kw$anova_BH <= BH_FDR_cutoff][( abs(nmCRPC_median - nmCSPC_median) > 1 & nmCRPC_nmCSPC_wilcox_BH <= BH_FDR_cutoff ) ] # genesymb_repeat$seq_id %in% raw_data_median$SEQ_ID[all_kw$anova_BH <= BH_FDR_cutoff][( abs(nmCSPC_median - newdx_median) > 1 & nmCSPC_newdx_wilcox_BH <= BH_FDR_cutoff ) ] # genesymb_repeat$seq_id %in% raw_data_median$SEQ_ID[all_kw$anova_BH <= BH_FDR_cutoff][( abs(newdx_median - normal_median) > 1 & newdx_normal_wilcox_BH<= BH_FDR_cutoff ) ] # genesymb_repeat$seq_id %in% raw_data_median$SEQ_ID[all_kw$anova_BH <= BH_FDR_cutoff][( abs(cancer_median - normal_median) > 1 & cancer_normal_wilcox_BH <= BH_FDR_cutoff ) ] # genesymb_repeat$seq_id %in% raw_data_median$SEQ_ID[all_kw$anova_BH <= BH_FDR_cutoff][( abs(mCRPC_median - NOT_mCRPC_median) > 1 & mCRPC_others_wilcox_BH <= BH_FDR_cutoff )] # DECISION: treat them as repeats in the microarray # protein deemed signif if either one of the repeated seq_id makes the cutoff get_SeqID.func <- function(diff, BH){ signif_seq_id <- unique( raw_data_median$SEQ_ID[all_kw$anova_BH <= BH_FDR_cutoff][abs(diff) > 1 & BH <= BH_FDR_cutoff] ) seq_id_ok <- as.numeric(uniprot_gene$seq_id %in% signif_seq_id) names(seq_id_ok) <- uniprot_gene$entrez_gene_id_pete seq_id_ok2 <- seq_id_ok[!(names(seq_id_ok) %in% entrez_id_repeat)] seq_id_ok2 <- c(seq_id_ok2, sapply( entrez_id_repeat, function(x){max(seq_id_ok[which(names(seq_id_ok) == x)])} ) ) } cancer_normal_SeqID <- get_SeqID.func(cancer_median - normal_median, cancer_normal_wilcox_BH) mCRPC_others_SeqID <- get_SeqID.func(mCRPC_median - NOT_mCRPC_median, mCRPC_others_wilcox_BH) mCRPC_nmCRPC_SeqID <- get_SeqID.func(mCRPC_median - nmCRPC_median, mCRPC_nmCRPC_wilcox_BH) nmCRPC_nmCSPC_SeqID <- get_SeqID.func(nmCRPC_median - nmCSPC_median, nmCRPC_nmCSPC_wilcox_BH) nmCSPC_newdx_SeqID <- get_SeqID.func(nmCSPC_median - newdx_median, nmCSPC_newdx_wilcox_BH) newdx_normal_SeqID <- get_SeqID.func(newdx_median - normal_median, newdx_normal_wilcox_BH) # gene-set analysis via allez! cancer_normal_allez.go <- allez(cancer_normal_SeqID, lib = "org.Hs.eg", idtype = "ENTREZID", sets = "GO") mCRPC_others_allez.go <- allez(mCRPC_others_SeqID, lib = "org.Hs.eg", idtype = "ENTREZID", sets = "GO") mCRPC_nmCRPC_allez.go <- allez(mCRPC_nmCRPC_SeqID, lib = "org.Hs.eg", idtype = "ENTREZID", sets = "GO") nmCRPC_nmCSPC_allez.go <- allez(nmCRPC_nmCSPC_SeqID, lib = "org.Hs.eg", idtype = "ENTREZID", sets = "GO") nmCSPC_newdx_allez.go <- allez(nmCSPC_newdx_SeqID, lib = "org.Hs.eg", idtype = "ENTREZID", sets = "GO") newdx_normal_allez.go <- allez(newdx_normal_SeqID, lib = "org.Hs.eg", idtype = "ENTREZID", sets = "GO") #--------------------------------------------------------------------------------------------------- # get allez results nom.alpha <- 0.05 max_gene_in_set <- 300 # Extract a table of top-ranked functional sets from allez output # Display an image of gene scores by functional sets # cancel vs normal allezTable(cancer_normal_allez.go, symbol = T, nominal.alpha = nom.alpha, n.upp = max_gene_in_set, in.set = T)[,c(1:5,7)] get_alleztable.func(cancer_normal_allez.go) allezPlot(cancer_normal_allez.go, nominal.alpha = nom.alpha, n.upp = max_gene_in_set) # mCRPC vs others allezTable(mCRPC_others_allez.go, symbol = T, nominal.alpha = nom.alpha, n.upp = max_gene_in_set, in.set = T)[,c(1:5,7)] get_alleztable.func(mCRPC_others_allez.go) allezPlot(mCRPC_others_allez.go, nominal.alpha = nom.alpha, n.upp = max_gene_in_set) # mCRPC vs nmCRPC allezTable(mCRPC_nmCRPC_allez.go, symbol = T, nominal.alpha = nom.alpha, n.upp = max_gene_in_set, in.set = T)[,c(1:5,7)] get_alleztable.func(mCRPC_nmCRPC_allez.go) allezPlot(mCRPC_nmCRPC_allez.go, nominal.alpha = nom.alpha, n.upp = max_gene_in_set) # nmCRPC vs nmCSPC allezTable(nmCRPC_nmCSPC_allez.go, symbol = T, nominal.alpha = nom.alpha, n.upp = max_gene_in_set, in.set = T)[,c(1:5,7)] get_alleztable.func(nmCRPC_nmCSPC_allez.go) allezPlot(nmCRPC_nmCSPC_allez.go, nominal.alpha = nom.alpha, n.upp = max_gene_in_set) # nmCSPC vs new_dx allezTable(nmCSPC_newdx_allez.go, symbol = T, nominal.alpha = nom.alpha, n.upp = max_gene_in_set, in.set = T)[,c(1:5,7)] get_alleztable.func(nmCSPC_newdx_allez.go) allezPlot(nmCSPC_newdx_allez.go, nominal.alpha = nom.alpha, n.upp = max_gene_in_set) # new_dx vs normal allezTable(newdx_normal_allez.go, symbol = T, nominal.alpha = nom.alpha, n.upp = max_gene_in_set, in.set = T)[,c(1:5,7)] get_alleztable.func(newdx_normal_allez.go) allezPlot(newdx_normal_allez.go, nominal.alpha = nom.alpha, n.upp = max_gene_in_set) ####################################################################################### # Data Processing -- part II # ####################################################################################### pal_proj2 <- c("turquoise1", "cornflowerblue","navy", "orchid1", "darkorange1", "firebrick1") names(pal_proj2) <- c("ADT_time:0", "ADT_time:3", "ADT_time:6", "PAP_time:0", "PAP_time:3", "PAP_time:6") array_id_key_proj2 = read_tsv("sample_key_project2.txt") %>% janitor::clean_names() %>% rename(treatment = condition, array_id = "file_name") %>% mutate(id = tolower(id)) sample_key_proj2 = array_id_key_proj2 %>% group_by(id, time) %>% summarize(n = n()) %>% ungroup() %>% filter(n>=2) # check table(sample_key_proj2$n) ## all patients associated with 3 replicates for each of the 3 time points # remove patients with no replicates array_id_key_proj2 = array_id_key_proj2 %>% filter(id %in% sample_key_proj2$id) # make sure sample_key_proj2 align with raw_data_median_proj2 sample_key_proj2 <- sample_key_proj2 %>% select(-n) %>% arrange(time, id) sample_key_proj2 <- sample_key_proj2 %>% left_join(array_id_key_proj2 %>% select(id, treatment) %>% distinct()) # check sum(as.numeric( colnames(raw_data_median_proj2 %>% select(-(PROBE_DESIGN_ID:DESIGN_ID))) == paste( paste0("id:", sample_key_proj2$id), paste0("time:", sample_key_proj2$time), sep = "_" ) )) == ncol(raw_data_median_proj2 %>% select(-(PROBE_DESIGN_ID:DESIGN_ID))) #---------------------------------------------------------------------------------------- # read fluorescence data # raw_data = read_csv("raw_data_complete.csv") # # raw_data = raw_data %>% # select(PROBE_DESIGN_ID:Y, any_of(array_id_key_proj2$array_id)) %>% # drop patients with records at different stages # select( -c(X, Y, MATCH_INDEX, DESIGN_NOTE, SELECTION_CRITERIA, MISMATCH, PROBE_CLASS) ) # drop unhelpful columns # # # check # colnames(raw_data)[1:15] # # # take log2 transformation # raw_data <- raw_data %>% # mutate_at(vars(matches("dat")), log2) # # # check any NA's # raw_data %>% # select_if(function(x) any(is.na(x))) # # # make sure array_id_key_proj2 align with raw_data_complete # array_iii <- match( colnames( raw_data %>% select(contains("dat")) ), array_id_key_proj2$array_id ) # array_id_key_proj2 <- array_id_key_proj2[array_iii , ] # sum(as.numeric( colnames( raw_data %>% select(contains("dat")) ) # == array_id_key_proj2$array_id )) == nrow(array_id_key_proj2) # # # # compute median # raw_data_median_proj2 <- t( apply( select(raw_data, contains("dat")), 1, function(x) { # as.vector( tapply( as.numeric(x), list(array_id_key_proj2$id, array_id_key_proj2$time), FUN=median) ) # } ) ) # raw_data_median_proj2 <- bind_cols( select(raw_data, -contains("dat")), as.data.frame(raw_data_median_proj2) ) # # colnames(raw_data_median_proj2)[1:15] # check # # # want to rename column # example_row <- tapply( as.numeric(select(raw_data, contains("dat"))[1,]), # list(array_id_key_proj2$id, array_id_key_proj2$time), FUN=median) %>% # as.data.frame() %>% # rownames_to_column("id") %>% # pivot_longer(-id, names_to = "time", values_to = "fluorescence") %>% # arrange(time, id) # sum(as.numeric( example_row$fluorescence == select(raw_data_median_proj2, contains("V"))[1,] )) == nrow(example_row) # example_row$column_name <- paste( paste0("id:", example_row$id), paste0("time:", example_row$time), sep = "_" ) # raw_data_median_proj2 <- raw_data_median_proj2 %>% rename_at(vars(contains("V")), ~ example_row$column_name) # # colnames(raw_data_median_proj2)[11:130] <- example_row$column_name # # # make sure sample_key_proj2 align with raw_data_median_proj2 # sample_key_proj2 <- sample_key_proj2 %>% # select(-n) %>% # arrange(time, id) # sum(as.numeric(sample_key_proj2$id == example_row$id)) == nrow(sample_key_proj2) # check # sum(as.numeric(sample_key_proj2$time == example_row$time)) == nrow(sample_key_proj2) # check # sample_key_proj2 <- sample_key_proj2 %>% # left_join(array_id_key_proj2 %>% select(id, treatment) %>% distinct()) # # write.table(raw_data_median_proj2, file = "raw_data_median_proj2.csv", sep = ",", row.names = F) ####################################################################################### # Check Fluorescence Normalization # ####################################################################################### median_long_proj2 <- raw_data_median_proj2 %>% select(contains("id:")) %>% pivot_longer(cols = everything(), names_to = "id_time", values_to = "fluorescence") # check nrow(median_long_proj2) == nrow(raw_data_median_proj2) * nrow(sample_key_proj2) head(median_long_proj2) # set fill color median_long_proj2$treat_time <- rep( paste(sample_key_proj2$treatment, sample_key_proj2$time, sep = "_"), 177604) median_long_proj2 <- median_long_proj2 %>% mutate(treat_time = as_factor(treat_time), treat_time = fct_recode(treat_time, "ADT_time:0" = "ADT_0", "ADT_time:3" = "ADT_3", "ADT_time:6" = "ADT_6", "PAP_time:0" = "Vaccine_0", "PAP_time:3" = "Vaccine_3", "PAP_time:6" = "Vaccine_6")) # sort order of patients in boxplot median_long_proj2$id_time <- factor(median_long_proj2$id_time, levels = c( unique(median_long_proj2$id_time[median_long_proj2$treat_time == "ADT_time:0"]), unique(median_long_proj2$id_time[median_long_proj2$treat_time == "ADT_time:3"]), unique(median_long_proj2$id_time[median_long_proj2$treat_time == "ADT_time:6"]), unique(median_long_proj2$id_time[median_long_proj2$treat_time == "PAP_time:0"]), unique(median_long_proj2$id_time[median_long_proj2$treat_time == "PAP_time:3"]), unique(median_long_proj2$id_time[median_long_proj2$treat_time == "PAP_time:6"]) )) ggplot(median_long_proj2, aes(x = id_time, y = fluorescence, fill = treat_time)) + geom_boxplot(outlier.shape = ".") + scale_fill_manual(name = "treatment_time", values = pal_proj2[levels(median_long_proj2$treat_time)]) + labs(title = "Boxplots of Peptide Fluorescence Levels for Patients at 3 time points", x = "Patient_Time", y = "Median log2 Fluorescence Levels") + theme(panel.background = element_rect(fill = "grey90"), panel.grid.major = element_line(color = "white"), panel.grid.minor = element_line(color = "white"), axis.text.x = element_text(angle = 90, hjust = 1, size = 4.3), legend.position = "bottom", plot.title = element_text(hjust = 0.5)) ####################################################################################### # Linear Mixed Model to Assess Time Effect (REML = TRUE) # # Separate Models for PAP and ADT Groups # ####################################################################################### ncol_med_proj2 <- ncol(raw_data_median_proj2) n_med_proj2 <- nrow(sample_key_proj2) # initiate PAP_resid_fit0 <- matrix(NA, nrow = nrow(raw_data_median_proj2), ncol = nrow(sample_key_proj2%>%filter(treatment == "Vaccine"))) ADT_resid_fit0 <- matrix(NA, nrow = nrow(raw_data_median_proj2), ncol = nrow(sample_key_proj2%>%filter(treatment == "ADT"))) PAP_result <- matrix(NA, nrow = nrow(raw_data_median_proj2), ncol = 8) ADT_result <- matrix(NA, nrow = nrow(raw_data_median_proj2), ncol = 8) colnames(PAP_resid_fit0) <- colnames(raw_data_median_proj2 %>% select(contains("id:pap"))) # colnames(PAP_resid_fit0) <- colnames(raw_data_median_proj2[,(ncol_med_proj2 - n_med_proj2 + 1) : ncol_med_proj2])[sample_key_proj2$treatment == "Vaccine"] colnames(ADT_resid_fit0) <- colnames(raw_data_median_proj2 %>% select(contains("id:adt"))) # colnames(ADT_resid_fit0) <- colnames(raw_data_median_proj2[,(ncol_med_proj2 - n_med_proj2 + 1) : ncol_med_proj2])[sample_key_proj2$treatment == "ADT"] colnames(PAP_result) <- paste0("PAP_", c( "time_effect", "time_tstat", "KR_df", "KR_Ftest_pval", "Satterthwaite_df", "Satterthwaite_Ftest_pval", "zval_1sided_KR", "zval_1sided_Satterthwaite" )) colnames(ADT_result) <- paste0("ADT_", c( "time_effect", "time_tstat", "KR_df", "KR_Ftest_pval", "Satterthwaite_df", "Satterthwaite_Ftest_pval", "zval_1sided_KR", "zval_1sided_Satterthwaite" )) Test_Time.func <- function(y, treat_type){ resp <- y[sample_key_proj2$treatment == treat_type] fit1 <- lmer(resp ~ time + (1 + time | id), REML = T, data = sample_key_proj2[sample_key_proj2$treatment == treat_type,]) fit0 <- lmer(resp ~ 1 + (1 + time | id), REML = T, data = sample_key_proj2[sample_key_proj2$treatment == treat_type,]) resid_fit0 <- unname(round(resid(fit0),4)) effect_tstat <- coef(summary(fit1))['time',c('Estimate', 't value')] KR_df_pval <- contest(fit1, c(0,1), ddf = "Kenward-Roger")[c('DenDF', 'Pr(>F)')] Satterthwaite_df_pval <- contest(fit1, c(0,1))[c('DenDF', 'Pr(>F)')] zval_1sided_KR <- qnorm(pt( as.numeric(effect_tstat['t value']), df = as.numeric(KR_df_pval['DenDF']) , lower.tail = T )) zval_1sided_Satterthwaite <- qnorm(pt( as.numeric(effect_tstat['t value']), df = as.numeric(Satterthwaite_df_pval['DenDF']) , lower.tail = T )) result <- c( as.numeric(effect_tstat), as.numeric(KR_df_pval), as.numeric(Satterthwaite_df_pval), zval_1sided_KR, zval_1sided_Satterthwaite ) return( list( resid_fit0 = resid_fit0, result = result ) ) } for(i in 1:nrow(raw_data_median_proj2)){ y <- as.numeric(raw_data_median_proj2[i, (ncol_med_proj2 - n_med_proj2 + 1) : ncol_med_proj2]) PAP_test <- Test_Time.func(y, "Vaccine") ADT_test <- Test_Time.func(y, "ADT") PAP_resid_fit0[i,] <- PAP_test$resid_fit0 PAP_result[i,] <- PAP_test$result ADT_resid_fit0[i,] <- ADT_test$resid_fit0 ADT_result[i,] <- ADT_test$result if(i %% 100 == 0){ print(i) } } # save(PAP_resid_fit0, ADT_resid_fit0, PAP_result, ADT_result, # file = "08_LMER_results.RData") PAP_Satterth_Ftest_pval <- PAP_result[,"PAP_Satterthwaite_Ftest_pval"] PAP_KR_Ftest_pval <- PAP_result[,"PAP_KR_Ftest_pval"] ADT_Satterth_Ftest_pval <- ADT_result[,"ADT_Satterthwaite_Ftest_pval"] ADT_KR_Ftest_pval <- ADT_result[,"ADT_KR_Ftest_pval"] PAP_Ftest_KR_BH <- p.adjust(PAP_KR_Ftest_pval, method="BH") PAP_Ftest_Satterthwaite_BH <- p.adjust(PAP_Satterth_Ftest_pval, method="BH") ADT_Ftest_KR_BH <- p.adjust(ADT_KR_Ftest_pval,method="BH") ADT_Ftest_Satterthwaite_BH <- p.adjust(ADT_Satterth_Ftest_pval,method="BH") #--------------------------------------------------------------------------------------------- # F-test p-values based on KR adjustments more conservative than Satterthwaite par(mfrow=c(1,2)) plot(PAP_Satterth_Ftest_pval[PAP_Satterth_Ftest_pval <= .2 & PAP_KR_Ftest_pval <= .2], PAP_KR_Ftest_pval[PAP_Satterth_Ftest_pval <= .2 & PAP_KR_Ftest_pval <= .2], pch = ".", xlim = c(0,.2), ylim = c(0,.2), las = 1, main = "Time Fixed Effect p-values \nfor PAP patients", xlab = "Satterthwaite F-test p-values", ylab = "Kenward-Roger (KR) F-test p-values") abline(a=0, b=1, col = "red", lty=2, lwd = 2) plot(ADT_Satterth_Ftest_pval[ADT_Satterth_Ftest_pval <= .2 & ADT_KR_Ftest_pval <= .2], ADT_KR_Ftest_pval[ADT_Satterth_Ftest_pval <= .2 & ADT_KR_Ftest_pval <= .2], pch = ".", xlim = c(0,.2), ylim = c(0,.2), las = 1, main = "Time Fixed Effect p-values \nfor ADT patients", xlab = "Satterthwaite F-test p-values", ylab = "Kenward-Roger (KR) F-test p-values") abline(a=0, b=1, col = "red", lty=2, lwd = 2) dev.off() count.func(PAP_Ftest_KR_BH, seq(.01,.05,by=.01)) count.func(PAP_Ftest_Satterthwaite_BH, seq(.01,.05,by=.01)) count.func(ADT_Ftest_KR_BH, seq(.63,.7,by=.01)) count.func(ADT_Ftest_Satterthwaite_BH, seq(.63,.7,by=.01)) # check sum(as.numeric( raw_data_median_proj2$PROBE_ID[PAP_Ftest_KR_BH <= .01] %in% raw_data_median_proj2$PROBE_ID[PAP_Ftest_Satterthwaite_BH <= .01] )) # tabulate Ftest_pval_counts <- data.frame( BH_FDR_thresholds = seq(.01, .05, by = .01), Peptide_counts_KR = count.func(PAP_Ftest_KR_BH, seq(.01,.05,by=.01))[2,2:6], Peptide_counts_Satterthwaite = count.func(PAP_Ftest_Satterthwaite_BH, seq(.01,.05,by=.01))[2,2:6] ) #--------------------------------------------------------------------------------------------- #p-value density histograms KR_Ftest_pval_df <- data.frame( treatment = c( rep("PAP", 177604*2), rep("ADT", 177604*2) ), method = rep( rep( c("Satterthwaite", "Kenward-Roger"), each = 177604 ), 2) , p_values = c( PAP_Satterth_Ftest_pval, PAP_KR_Ftest_pval, ADT_Satterth_Ftest_pval, ADT_KR_Ftest_pval ) ) ggplot(KR_Ftest_pval_df, aes(x = p_values, fill = method)) + geom_histogram(aes(y=..density..), bins = 100, position = "identity", alpha = .4) + facet_grid(. ~ treatment) + labs(x = "F-test p-values", title = paste0("Density Histograms of F-test p-values")) + theme(plot.title = element_text(hjust = 0.5), legend.position = "bottom") ####################################################################################### # Visualization # ####################################################################################### BH_FDR_cutoff_proj2 <- .05 signif_crit_proj2 <- (PAP_Ftest_KR_BH <= BH_FDR_cutoff_proj2) & (PAP_Ftest_Satterthwaite_BH <= BH_FDR_cutoff_proj2) & (PAP_result[,"PAP_time_effect"] > .3333) sum(as.numeric(signif_crit_proj2)) proj2_signif_count <- sum(as.numeric(signif_crit_proj2)) #--------------------------------------------------------------------------------------------- # volcano plots proj2_volcano_plot.func <- function(time_effect, pval, BH, title){ plot(x = time_effect, y = -log10(pval), pch = ".", las = 1, ylim = c(0,9), xlim = c(-.4, .9), xlab = "coefficient of time fixed effect", ylab = "-log10(KR F-test p-values)", main = title) lines(x = time_effect[ BH <= .01 & time_effect >= .3333 ], y = -log10(pval[ BH <= .01 & time_effect >= .3333]), type = "p", pch = ".", col = "red") } par(mfrow=c(1,2)) proj2_volcano_plot.func(PAP_result[,"PAP_time_effect"], PAP_result[,"PAP_KR_Ftest_pval"], PAP_Ftest_KR_BH, "PAP's volcano plot") abline(v = .3333, lty = 2, lwd = 1.5, col = "blue") proj2_volcano_plot.func(ADT_result[,"ADT_time_effect"], ADT_result[,"ADT_KR_Ftest_pval"], ADT_Ftest_KR_BH, "ADT's volcano plot") #--------------------------------------------------------------------------------------------- # heatmap proj2_resid <- PAP_resid_fit0[signif_crit_proj2,] dim(proj2_resid) # check proj2_resid_time <- sample_key_proj2$time[sample_key_proj2$treatment=="Vaccine"] # specify color scheme cls <- colorRampPalette(c("navy", "honeydew", "firebrick1"))(n = 1024) proj2_heatmap_pal <- c("lightgoldenrod1", "darkorange1", "brown") names(proj2_heatmap_pal) <- c(0,3,6) cols <- proj2_heatmap_pal[ match(proj2_resid_time, names(proj2_heatmap_pal)) ] # winsorize quantile(as.numeric(proj2_resid), probs = c(.01, .05, .1, .15, .2, .25, .3, .7, .75, .8, .85, .9, .95)) # check ecdf(as.numeric(proj2_resid))(c(-1.7, -1.4, 1.4, 1.7)) # check proj2_resid_winsorize <- t( apply(proj2_resid, 1, function(x){ x[x < -1.7] = -1.7 x[x > 1.7] = 1.7 return(x) }) ) # get column order of time 6 proj2_get_column_order.func <- function(resid_mat, Time){ heat_map <- heatmap3(resid_mat[,proj2_resid_time == Time],col = cls, labRow = "", scale = "none", showColDendro = F, showRowDendro = F) return( colnames(resid_mat[,proj2_resid_time == Time])[heat_map$colInd] ) } time6_order <- proj2_get_column_order.func(proj2_resid_winsorize,6) time0_order <- gsub("time:6", "time:0", time6_order) time3_order <- gsub("time:6", "time:3", time6_order) time_order <- match( c(time0_order, time3_order, time6_order), colnames(proj2_resid) ) heatmap3(proj2_resid_winsorize[,time_order], col = cls, # specify colors ColSideColors = cols[time_order], # specify time color code Colv = NA, scale = "none", # no scaling by row labCol = colnames(proj2_resid)[time_order], # specify patient_time ColSideLabs = "Time", labRow = "", xlab = "Patient_Time", legendfun=function() showLegend(col = proj2_heatmap_pal, legend = c("Time 0", "Time 3", "Time 6"), cex = 1.2, lwd = 5 ) ) #-------------------------------------------------------------------------------------------- # longitudinal boxplots # get pap_df from Write to Excel code chunks proj2_signif_boxplot_df <- raw_data_median_proj2 %>% select(PROBE_ID, contains("id:")) %>% filter( PROBE_ID %in% pap_df$PROBE_ID[1:6] ) proj2_signif_boxplot.func <- function(signif_mat, draw){ signif_mat2 <- signif_mat[,-1] signif_df <- data.frame( treatment = factor( toupper( substr(colnames(signif_mat2), 4,6) ) ), time = factor( str_sub( colnames(signif_mat2), -1,-1 ) ), fluorescence = as.numeric(signif_mat2[draw,]) ) ggplot(signif_df, aes(x = time, y = fluorescence, fill = treatment)) + geom_boxplot(width = 0.5, position=position_dodge2(width = 0.5)) + labs(title = paste0("Boxplots of Fluorescence Levels for \nPeptide: ", signif_mat$PROBE_ID[draw]), x = "Time", y = "log2 Median Fluorescence") + scale_fill_manual(values=c("#F8766D", "#00BFC4")) + ylim(c(2,13.2)) + theme(panel.background = element_rect(fill = "grey90"), panel.grid.major = element_line(color = "white"), panel.grid.minor = element_line(color = "white"), # legend.position = "bottom", plot.title = element_text(hjust = 0.5)) } grid.arrange( proj2_signif_boxplot.func(proj2_signif_boxplot_df,1), proj2_signif_boxplot.func(proj2_signif_boxplot_df,2), proj2_signif_boxplot.func(proj2_signif_boxplot_df,3), proj2_signif_boxplot.func(proj2_signif_boxplot_df,4), proj2_signif_boxplot.func(proj2_signif_boxplot_df,5), proj2_signif_boxplot.func(proj2_signif_boxplot_df,6), ncol = 2 ) ####################################################################################### # Gene Set Analyses After LMER # ####################################################################################### proj2_get_SeqID.func <- function(coeff, BH){ signif_seq_id <- unique( raw_data_median_proj2$SEQ_ID[coeff > .3333 & BH <= BH_FDR_cutoff_proj2] ) seq_id_ok <- as.numeric(uniprot_gene$seq_id %in% signif_seq_id) names(seq_id_ok) <- uniprot_gene$entrez_gene_id_pete seq_id_ok2 <- seq_id_ok[!(names(seq_id_ok) %in% entrez_id_repeat)] seq_id_ok2 <- c(seq_id_ok2, sapply( entrez_id_repeat, function(x){max(seq_id_ok[which(names(seq_id_ok) == x)])} ) ) } # deploy allez PAP_SeqID <- proj2_get_SeqID.func(PAP_result[,"PAP_time_effect"], pmin(PAP_Ftest_KR_BH,PAP_Ftest_Satterthwaite_BH)) PAP_allez.go <- allez(PAP_SeqID, lib = "org.Hs.eg", idtype = "ENTREZID", sets = "GO") # get allez result nom.alpha <- 0.05 max_gene_in_set <- 300 allezTable(PAP_allez.go, symbol = T, nominal.alpha = nom.alpha, n.upp = max_gene_in_set, in.set = T)[,c(1:5,7)] get_alleztable.func(PAP_allez.go) allezPlot(PAP_allez.go, nominal.alpha = nom.alpha, n.upp = max_gene_in_set) ####################################################################################### # Boxplot of interesting peptides # ####################################################################################### # first make sure stage aligns with raw_data_median sum(as.numeric(colnames(raw_data_median %>% select(-(PROBE_DESIGN_ID:DESIGN_ID))) == patient_key$id)) == nrow(patient_key) contrast_df.func <- function(contrast_BH, contrast_diff){ df <- data.frame( PROBE_ID = raw_data_median$PROBE_ID[all_kw$anova_BH <= BH_FDR_cutoff][contrast_BH <= BH_FDR_cutoff], SEQ_ID = raw_data_median$SEQ_ID[all_kw$anova_BH <= BH_FDR_cutoff][contrast_BH <= BH_FDR_cutoff], Effect_size = contrast_diff[contrast_BH <= BH_FDR_cutoff], KW_BH_FDR = all_kw$anova_BH[all_kw$anova_BH <= BH_FDR_cutoff][contrast_BH <= BH_FDR_cutoff] ) df <- df %>% filter(abs(Effect_size)>1) %>% arrange(desc(Effect_size)) %>% remove_rownames() %>% mutate(Effect_size = round(Effect_size, 4), KW_BH_FDR = round(KW_BH_FDR, 4)) return(df) } # based on wilcox test mCRPC_others_df <- contrast_df.func(mCRPC_others_wilcox_BH, mCRPC_median - NOT_mCRPC_median) cancer_normal_df <- contrast_df.func(cancer_normal_wilcox_BH, cancer_median - normal_median) mCRPC_nmCRPC_df <- contrast_df.func(mCRPC_nmCRPC_wilcox_BH, mCRPC_median - nmCRPC_median) nmCRPC_nmCSPC_df <- contrast_df.func(nmCRPC_nmCSPC_wilcox_BH, nmCRPC_median - nmCSPC_median) nmCSPC_newdx_df <- contrast_df.func(nmCSPC_newdx_wilcox_BH, nmCSPC_median - newdx_median) newdx_normal_df <- contrast_df.func(newdx_normal_wilcox_BH, newdx_median - normal_median) boxplot_func <- function(ref_df, grp1, grp2, draw, rename_grp1, rename_grp2, col = c("red", "blue")){ mat <- raw_data_median %>% filter(PROBE_ID %in% ref_df$PROBE_ID) mat <- mat[match(ref_df$PROBE_ID, mat$PROBE_ID), ] %>% select(-(PROBE_DESIGN_ID:DESIGN_ID)) %>% select(which(patient_key$stage %in% c(grp1, grp2))) %>% as.matrix() row.names(mat) <- ref_df$PROBE_ID mat_stage <- as.character(patient_key$stage[patient_key$stage %in% c(grp1, grp2)]) if (length(grp1) > 1){ mat_stage[mat_stage %in% grp1] = rename_grp1 } if (length(grp2) > 1){ mat_stage[mat_stage %in% grp2] = rename_grp2 } mat_stage = factor(mat_stage) par(mar = c(4.1, 5.5, 2.8, 1),cex = 0.84) graphics::boxplot( as.numeric(mat[draw,]) ~ mat_stage, varwidth = T, col=col, horizontal=TRUE, las=1, xlab = "log2(fluorescence)", ylab = "groups", main= paste(row.names(mat)[draw]) ) } png("09_1a_Cancer_higher_than_Normal.png", height = 1024, width = 512) par(mfrow=c(4,1)) boxplot_func(ref_df=cancer_normal_df,grp1=c("new_dx","nmCSPC","nmCRPC","mCRPC"),grp2="normal",rename_grp1="cancer",draw=1) boxplot_func(ref_df=cancer_normal_df,grp1=c("new_dx","nmCSPC","nmCRPC","mCRPC"),grp2="normal",rename_grp1="cancer",draw=2) boxplot_func(ref_df=cancer_normal_df,grp1=c("new_dx","nmCSPC","nmCRPC","mCRPC"),grp2="normal",rename_grp1="cancer",draw=3) boxplot_func(ref_df=cancer_normal_df,grp1=c("new_dx","nmCSPC","nmCRPC","mCRPC"),grp2="normal",rename_grp1="cancer",draw=4) dev.off() png("09_1b_Normal_higher_than_Cancer.png", height = 1024, width = 512) par(mfrow=c(4,1)) boxplot_func(ref_df=cancer_normal_df,grp1=c("new_dx","nmCSPC","nmCRPC","mCRPC"),grp2="normal",rename_grp1="cancer",draw=nrow(cancer_normal_df)) boxplot_func(ref_df=cancer_normal_df,grp1=c("new_dx","nmCSPC","nmCRPC","mCRPC"),grp2="normal",rename_grp1="cancer",draw=nrow(cancer_normal_df)-1) boxplot_func(ref_df=cancer_normal_df,grp1=c("new_dx","nmCSPC","nmCRPC","mCRPC"),grp2="normal",rename_grp1="cancer",draw=nrow(cancer_normal_df)-2) boxplot_func(ref_df=cancer_normal_df,grp1=c("new_dx","nmCSPC","nmCRPC","mCRPC"),grp2="normal",rename_grp1="cancer",draw=nrow(cancer_normal_df)-3) dev.off() png("09_2a_mCRPC_higher_than_others.png", height = 1024, width = 512) par(mfrow=c(4,1)) boxplot_func(ref_df=mCRPC_others_df,grp1="mCRPC",grp2=c("new_dx","nmCSPC","nmCRPC","normal"),rename_grp2="others",draw=1) boxplot_func(ref_df=mCRPC_others_df,grp1="mCRPC",grp2=c("new_dx","nmCSPC","nmCRPC","normal"),rename_grp2="others",draw=2) boxplot_func(ref_df=mCRPC_others_df,grp1="mCRPC",grp2=c("new_dx","nmCSPC","nmCRPC","normal"),rename_grp2="others",draw=3) boxplot_func(ref_df=mCRPC_others_df,grp1="mCRPC",grp2=c("new_dx","nmCSPC","nmCRPC","normal"),rename_grp2="others",draw=4) dev.off() png("09_2b_others_higher_than_mCRPC.png", height = 1024, width = 512) par(mfrow=c(4,1)) boxplot_func(ref_df=mCRPC_others_df,grp1="mCRPC",grp2=c("new_dx","nmCSPC","nmCRPC","normal"),rename_grp2="others",draw=nrow(mCRPC_others_df)) boxplot_func(ref_df=mCRPC_others_df,grp1="mCRPC",grp2=c("new_dx","nmCSPC","nmCRPC","normal"),rename_grp2="others",draw=nrow(mCRPC_others_df)-1) boxplot_func(ref_df=mCRPC_others_df,grp1="mCRPC",grp2=c("new_dx","nmCSPC","nmCRPC","normal"),rename_grp2="others",draw=nrow(mCRPC_others_df)-2) boxplot_func(ref_df=mCRPC_others_df,grp1="mCRPC",grp2=c("new_dx","nmCSPC","nmCRPC","normal"),rename_grp2="others",draw=nrow(mCRPC_others_df)-3) dev.off() png("09_3a_Newdx_higher_than_Normal.png", height = 1024, width = 512) par(mfrow=c(4,1)) boxplot_func(ref_df=newdx_normal_df,grp1="new_dx",grp2="normal",draw = 1) boxplot_func(ref_df=newdx_normal_df,grp1="new_dx",grp2="normal",draw = 2) boxplot_func(ref_df=newdx_normal_df,grp1="new_dx",grp2="normal",draw = 3) boxplot_func(ref_df=newdx_normal_df,grp1="new_dx",grp2="normal",draw = 4) dev.off() png("09_3b_Normal_higher_than_Newdx.png", height = 1024, width = 512) par(mfrow=c(4,1)) boxplot_func(ref_df=newdx_normal_df,grp1="new_dx",grp2="normal",draw = nrow(newdx_normal_df)) boxplot_func(ref_df=newdx_normal_df,grp1="new_dx",grp2="normal",draw = nrow(newdx_normal_df)-1) boxplot_func(ref_df=newdx_normal_df,grp1="new_dx",grp2="normal",draw = nrow(newdx_normal_df)-2) boxplot_func(ref_df=newdx_normal_df,grp1="new_dx",grp2="normal",draw = nrow(newdx_normal_df)-3) dev.off() png("09_4a_nmCSPC_higher_than_Newdx.png", height = 1024, width = 512) par(mfrow=c(4,1)) boxplot_func(ref_df=nmCSPC_newdx_df,grp1="nmCSPC",grp2="new_dx",draw = 1) boxplot_func(ref_df=nmCSPC_newdx_df,grp1="nmCSPC",grp2="new_dx",draw = 2) boxplot_func(ref_df=nmCSPC_newdx_df,grp1="nmCSPC",grp2="new_dx",draw = 3) boxplot_func(ref_df=nmCSPC_newdx_df,grp1="nmCSPC",grp2="new_dx",draw = 4) dev.off() png("09_4b_Newdx_higher_than_nmCSPC.png", height = 1024, width = 512) par(mfrow=c(4,1)) boxplot_func(ref_df=nmCSPC_newdx_df,grp1="nmCSPC",grp2="new_dx",draw = nrow(nmCSPC_newdx_df)) boxplot_func(ref_df=nmCSPC_newdx_df,grp1="nmCSPC",grp2="new_dx",draw = nrow(nmCSPC_newdx_df)-1) boxplot_func(ref_df=nmCSPC_newdx_df,grp1="nmCSPC",grp2="new_dx",draw = nrow(nmCSPC_newdx_df)-2) boxplot_func(ref_df=nmCSPC_newdx_df,grp1="nmCSPC",grp2="new_dx",draw = nrow(nmCSPC_newdx_df)-3) dev.off() png("09_5a_nmCRPC_higher_than_nmCSPC.png", height = 1024, width = 512) par(mfrow=c(4,1)) boxplot_func(ref_df=nmCRPC_nmCSPC_df,grp1="nmCRPC",grp2="nmCSPC",draw=1) boxplot_func(ref_df=nmCRPC_nmCSPC_df,grp1="nmCRPC",grp2="nmCSPC",draw=2) boxplot_func(ref_df=nmCRPC_nmCSPC_df,grp1="nmCRPC",grp2="nmCSPC",draw=3) boxplot_func(ref_df=nmCRPC_nmCSPC_df,grp1="nmCRPC",grp2="nmCSPC",draw=4) dev.off() png("09_5b_nmCSPC_higher_than_nmCRPC.png", height = 1024, width = 512) par(mfrow=c(4,1)) boxplot_func(ref_df=nmCRPC_nmCSPC_df,grp1="nmCRPC",grp2="nmCSPC",draw=nrow(nmCRPC_nmCSPC_df)) boxplot_func(ref_df=nmCRPC_nmCSPC_df,grp1="nmCRPC",grp2="nmCSPC",draw=nrow(nmCRPC_nmCSPC_df)-1) boxplot_func(ref_df=nmCRPC_nmCSPC_df,grp1="nmCRPC",grp2="nmCSPC",draw=nrow(nmCRPC_nmCSPC_df)-2) boxplot_func(ref_df=nmCRPC_nmCSPC_df,grp1="nmCRPC",grp2="nmCSPC",draw=nrow(nmCRPC_nmCSPC_df)-3) dev.off() png("09_6a_mCRPC_higher_than_nmCRPC.png", height = 1024, width = 512) par(mfrow=c(4,1)) boxplot_func(ref_df=mCRPC_nmCRPC_df,grp1="mCRPC",grp2="nmCRPC",draw=1) boxplot_func(ref_df=mCRPC_nmCRPC_df,grp1="mCRPC",grp2="nmCRPC",draw=2) boxplot_func(ref_df=mCRPC_nmCRPC_df,grp1="mCRPC",grp2="nmCRPC",draw=3) boxplot_func(ref_df=mCRPC_nmCRPC_df,grp1="mCRPC",grp2="nmCRPC",draw=4) dev.off() png("09_6b_nmCRPC_higher_than_mCRPC.png", height = 1024, width = 512) par(mfrow=c(4,1)) boxplot_func(ref_df=mCRPC_nmCRPC_df,grp1="mCRPC",grp2="nmCRPC",draw=nrow(mCRPC_nmCRPC_df)) boxplot_func(ref_df=mCRPC_nmCRPC_df,grp1="mCRPC",grp2="nmCRPC",draw=nrow(mCRPC_nmCRPC_df)-1) boxplot_func(ref_df=mCRPC_nmCRPC_df,grp1="mCRPC",grp2="nmCRPC",draw=nrow(mCRPC_nmCRPC_df)-2) boxplot_func(ref_df=mCRPC_nmCRPC_df,grp1="mCRPC",grp2="nmCRPC",draw=nrow(mCRPC_nmCRPC_df)-3) dev.off() ####################################################################################### # Write to Excel and Save Results # ####################################################################################### anova_df <- data.frame( PROBE_ID = raw_data_median$PROBE_ID[all_kw$anova_BH <= BH_FDR_cutoff], SEQ_ID = raw_data_median$SEQ_ID[all_kw$anova_BH <= BH_FDR_cutoff], KW_BH_FDR = all_kw$anova_BH[all_kw$anova_BH <= BH_FDR_cutoff] ) anova_df <- anova_df %>% remove_rownames() %>% mutate( KW_BH_FDR = round(KW_BH_FDR, 4) ) contrast_df.func <- function(contrast_BH, contrast_diff){ df <- data.frame( PROBE_ID = raw_data_median$PROBE_ID[all_kw$anova_BH <= BH_FDR_cutoff][contrast_BH <= BH_FDR_cutoff], SEQ_ID = raw_data_median$SEQ_ID[all_kw$anova_BH <= BH_FDR_cutoff][contrast_BH <= BH_FDR_cutoff], Effect_size = contrast_diff[contrast_BH <= BH_FDR_cutoff], KW_BH_FDR = all_kw$anova_BH[all_kw$anova_BH <= BH_FDR_cutoff][contrast_BH <= BH_FDR_cutoff], contrast_BH_FDR = contrast_BH[contrast_BH <= BH_FDR_cutoff] ) df <- df %>% filter(abs(Effect_size)>1) %>% arrange(desc(Effect_size)) %>% remove_rownames() %>% mutate(Effect_size = round(Effect_size, 4), KW_BH_FDR = round(KW_BH_FDR, 4), contrast_BH_FDR = round(contrast_BH_FDR, 4) ) return(df) } # based on wilcox test cancer_normal_df <- contrast_df.func(cancer_normal_wilcox_BH, cancer_median - normal_median) mCRPC_others_df <- contrast_df.func(mCRPC_others_wilcox_BH, mCRPC_median - NOT_mCRPC_median) mCRPC_nmCRPC_df <- contrast_df.func(mCRPC_nmCRPC_wilcox_BH, mCRPC_median - nmCRPC_median) nmCRPC_nmCSPC_df <- contrast_df.func(nmCRPC_nmCSPC_wilcox_BH, nmCRPC_median - nmCSPC_median) nmCSPC_newdx_df <- contrast_df.func(nmCSPC_newdx_wilcox_BH, nmCSPC_median - newdx_median) newdx_normal_df <- contrast_df.func(newdx_normal_wilcox_BH, newdx_median - normal_median) summary_page_df <- data.frame( contrasts = c("cancer vs normal", "mCRPC vs others", "mCRPC vs nmCRPC", "nmCRPC vs nmCSPC", "nmCSPC vs new_dx", "new_dx vs normal"), total_peptide_counts = c( nrow(cancer_normal_df), nrow(mCRPC_others_df), nrow(mCRPC_nmCRPC_df), nrow(nmCRPC_nmCSPC_df), nrow(nmCSPC_newdx_df), nrow(newdx_normal_df) ), peptides_with_positive_effect_size = c(cancer_normal_df %>% filter(Effect_size > 0) %>% nrow(), mCRPC_others_df %>% filter(Effect_size > 0) %>% nrow(), mCRPC_nmCRPC_df %>% filter(Effect_size > 0) %>% nrow(), nmCRPC_nmCSPC_df %>% filter(Effect_size > 0) %>% nrow(), nmCSPC_newdx_df %>% filter(Effect_size > 0) %>% nrow(), newdx_normal_df %>% filter(Effect_size > 0) %>% nrow()) ) summary_page_df <- summary_page_df %>% mutate(peptides_with_negative_effect_size = total_peptide_counts - peptides_with_positive_effect_size) # longitudinal pap_df <- data.frame( PROBE_ID = raw_data_median_proj2$PROBE_ID[signif_crit_proj2], SEQ_ID = raw_data_median_proj2$SEQ_ID[signif_crit_proj2], Time_Effect = PAP_result[,"PAP_time_effect"][signif_crit_proj2], KR_BH = PAP_Ftest_KR_BH[signif_crit_proj2], Satterth_BH = PAP_Ftest_Satterthwaite_BH[signif_crit_proj2] ) %>% mutate(BH_FDR = pmin(KR_BH, Satterth_BH), BH_FDR = round(BH_FDR, 8), Time_Effect = round(Time_Effect, 4)) %>% select(PROBE_ID, SEQ_ID, BH_FDR, Time_Effect) %>% arrange(desc(Time_Effect)) wb = createWorkbook() sheet = createSheet(wb, "Contrast Summaries") addDataFrame(summary_page_df, sheet=sheet, row.names=FALSE, startRow = 2, startColumn = 2) sheet = createSheet(wb, "Kruskal-Wallis") addDataFrame(anova_df, sheet=sheet, row.names=FALSE) sheet = createSheet(wb, "cancer vs normal") addDataFrame(cancer_normal_df, sheet=sheet, row.names=FALSE) sheet = createSheet(wb, "mCRPC vs others") addDataFrame(mCRPC_others_df, sheet=sheet, row.names=FALSE) sheet = createSheet(wb, "mCRPC vs nmCRPC") addDataFrame(mCRPC_nmCRPC_df, sheet=sheet, row.names=FALSE) sheet = createSheet(wb, "nmCRPC vs nmCSPC") addDataFrame(nmCRPC_nmCSPC_df, sheet=sheet, row.names=FALSE) sheet = createSheet(wb, "nmCSPC vs newdx") addDataFrame(nmCSPC_newdx_df, sheet=sheet, row.names=FALSE) sheet = createSheet(wb, "newdx vs normal") addDataFrame(newdx_normal_df, sheet=sheet, row.names=FALSE) sheet = createSheet(wb, "PAP_Longitudinal") addDataFrame(pap_df, sheet=sheet, row.names=FALSE) saveWorkbook(wb, "09_Significant_Peptides.xlsx") #---------------------------------------------------------------------------------------------- # save results # save(logreg_pval, # lmer_result, # all_anova_pval, # all_kw_pval, # all_anova_mse, # file = "09_Cancer_Stage_Effects.RData") # save(cancer_normal_allez.go, # mCRPC_others_allez.go, # mCRPC_nmCRPC_allez.go, # nmCRPC_nmCSPC_allez.go, # nmCSPC_newdx_allez.go, # newdx_normal_allez.go, # PAP_allez.go, # file = "09_allez_results.RData" # )

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library(palettesForR) ### Name: showPalette ### Title: Show a palette. ### Aliases: showPalette ### ** Examples data(Caramel_gpl) showPalette(myPal = Caramel_gpl)

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r

Random Forest.R

library(randomForest) getwd() setwd("/Users/Christy/Desktop/Data Mining ") training<-read.csv("training.csv") test=read.csv("test.csv") training<-data.table(training) test<-data.table(test) ######################### check data ################################################ head(training,n = 20) str(training) ######################### Generate new features ###################################### training<-training[,url_per_query:= .N,by='query_id'] training<-training[,url_median:=as.integer(median(url_id)),by='query_id'] training<-training[,url_distance:=log(1+abs(url_id-url_median))] training<-training[,url_appearance:= .N,by='url_id'] training[,c('sig1_rk','sig2_rk','sig3_rk','sig4_rk','sig5_rk','sig6_rk','sig7_rk','sig8_rk'):= list(rank(sig1),rank(sig2),rank(sig3),rank(sig4),rank(sig5),rank(sig6),rank(sig7),rank(sig8)),by='query_id'] training[,c('sig1_mean','sig2_mean','sig3_mean','sig4_mean','sig5_mean','sig6_mean','sig7_mean','sig8_mean'):= list(mean(sig1),mean(sig2),mean(sig3),mean(sig4),mean(sig5),mean(sig6),mean(sig7),mean(sig8)),by='query_id'] training[,c('sig1_max','sig2_max','sig3_max','sig4_max','sig5_max','sig6_max','sig7_max','sig8_max'):= list(max(sig1),max(sig2),max(sig3),max(sig4),max(sig5),max(sig6),max(sig7),max(sig8)),by='query_id'] ######################### prepare for learning ###################################### train_feature<-training[,c(3:12,14:41)] train_target<-training[,13] ############################# test #################################################### head(test,n = 20) str(test) #relevance = c(1:dim(test)[1]) #test$relevance = relevance test<-test[,url_per_query:= .N,by='query_id'] test<-test[,url_median:=as.integer(median(url_id)),by='query_id'] test<-test[,url_distance:=log(1+abs(url_id-url_median))] test<-test[,url_appearance:= .N,by='url_id'] test[,c('sig1_rk','sig2_rk','sig3_rk','sig4_rk','sig5_rk','sig6_rk','sig7_rk','sig8_rk'):= list(rank(sig1),rank(sig2),rank(sig3),rank(sig4),rank(sig5),rank(sig6),rank(sig7),rank(sig8)),by='query_id'] test[,c('sig1_mean','sig2_mean','sig3_mean','sig4_mean','sig5_mean','sig6_mean','sig7_mean','sig8_mean'):= list(mean(sig1),mean(sig2),mean(sig3),mean(sig4),mean(sig5),mean(sig6),mean(sig7),mean(sig8)),by='query_id'] test[,c('sig1_max','sig2_max','sig3_max','sig4_max','sig5_max','sig6_max','sig7_max','sig8_max'):= list(max(sig1),max(sig2),max(sig3),max(sig4),max(sig5),max(sig6),max(sig7),max(sig8)),by='query_id'] ######################### prepare for learning ###################################### #test_target=test[,13] test_feature<-test[,c(3:40)] ############################# RF ####################################################### library(randomForest) training$relevance<-factor(training$relevance) set.seed(1) rf.train1 = randomForest(relevance~., data=training, mtry=6,ntree=1000, importance=TRUE) rf.pred = predict(rf.train1,newdata = test, type = "class") summary(rf.pred) rf.predf=ifelse(rf.pred==1,1,0) write.table(rf.pred,"output1000.txt",sep="\n", row.names = FALSE, quote = FALSE, col.names = FALSE) ############################### Analysis ################################################## training.size <- dim(training)[1] test = sample(1:training.size,training.size/10) train=-test training.test = training[test,] training.train = training[-test,] importance(rf.train1) print(rf.train1) varImpPlot (rf.train1) x=table(rf.predf,training.test$relevance) x error_rate = (x[1,2]+x[2,1])/dim(training.test)[1] error_rate

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/run_analysis.R

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no_license

michaelgiessing/GetNClean

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aca5e6b0ba0b4e72174d9cf8f760fac83d1cc686

refs/heads/master

2021-01-20T09:36:52.900357

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run_analysis.R

## Create one R script called run_analysis.R that does the following: ## 1. Merges the training and the test sets to create one data set. ## 2. Extracts only the measurements on the mean and standard deviation for each measurement. ## 3. Uses descriptive activity names to name the activities in the data set ## 4. Appropriately labels the data set with descriptive activity names. ## 5. Creates a second, independent tidy data set with the average of each variable for each activity and each subject. library(dplyr) library(magrittr) #setwd('C:/Users/michael.giessing/Box Sync/Michael Giessing/Courses/Coursera/GetNCleanData/Project') #Download and unzip #download.file('https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip','FUCI_HAR_Dataset.zip') unzip('FUCI_HAR_Dataset.zip') #Load activity labels activity_labels <- read.table('./UCI HAR Dataset/activity_labels.txt') names(activity_labels) = c("ActivityID", "ActivityDSC") # Load: data column names and make filtered list with mean or std in the name features <- read.table("./UCI HAR Dataset/features.txt")[,2] goodfeatures <- grepl("mean|std",features) ##Loading Test Data #Read in test data subject_test <- read.table('./UCI HAR Dataset/test/subject_test.txt') X_test <- read.table('./UCI HAR Dataset/test/X_test.txt') Y_test <- read.table('./UCI HAR Dataset/test/Y_test.txt') #Name the variables in X_test and subject_test names(X_test) <- features names(subject_test) <- "SubjectID" #Get the features that have mean or std in the name X_test <- X_test[,goodfeatures] #Join activity labels on to y_test names(Y_test) = "ActivityID" Y_test <- inner_join(Y_test,activity_labels) #Bind it all together test_data <- cbind(subject_test,Y_test,X_test) ##Loading Train Data #Read in Train data subject_train <- read.table('./UCI HAR Dataset/train/subject_train.txt') X_train <- read.table('./UCI HAR Dataset/train/X_train.txt') Y_train <- read.table('./UCI HAR Dataset/train/Y_train.txt') #Name the variables in X_train and subject_train names(X_train) <- features names(subject_train) <- "SubjectID" #Get the features that have mean or std in the name X_train <- X_train[,goodfeatures] #Join activity labels on to y_train names(Y_train) = "ActivityID" Y_train <- inner_join(Y_train,activity_labels) #Bind it all together train_data <- cbind(subject_train,Y_train,X_train) # Merge test and train data data = rbind(test_data, train_data) #Create functions for name substitution timefort <- function (x){ timefort <- sub("^t","Time",x) } frequencyforf <- function (x){ frequencyforf <- sub("^f","Freqency",x) } AccelerationforAcc <- function (x){ AccelerationforAcc <- gsub("Acc","Acceleration",x) } GyroscopeforGyro <- function (x){ AccelerationforAcc <- gsub("Gyro","Gyroscop",x) } MagnitudeforMag <- function (x){ AccelerationforAcc <- gsub("Mag","Magnitude",x) } StandardDeviationforstd <- function (x){ standardDeviationforstd <- gsub("std","StandardDeviation",x) } FrequencyforFreq <- function (x){ FrequencyforFreq <- gsub("Freq","Frequency",x) } Meanformean <- function (x){ Meanformean <- gsub("mean()","Mean",x) } RemoveChar <- function (x){ y<- gsub("\$\$","",x) RemoveChar <- gsub("-","",y) } #Fix variable names x<- names(data) x<-sapply(x,timefort) x<-sapply(x,FrequencyforFreq) x<-sapply(x,frequencyforf) x<-sapply(x,AccelerationforAcc) x<-sapply(x,GyroscopeforGyro) x<-sapply(x,MagnitudeforMag) x<-sapply(x,StandardDeviationforstd) x<-sapply(x,Meanformean) x<-sapply(x,RemoveChar) names(data)<-x #Get mean for each variable for each group #Result Stared in SummaryTBL SummaryTBL <- data %>% select(-ActivityID) %>% group_by(ActivityDSC, SubjectID) %>% summarize_all(mean) write.table(SummaryTBL, file = "./independent_tidy_data.txt", row.name = FALSE)

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/myfunc/omega.coef.R

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no_license

KeitaSugiyama/menhera

6ec2eb0ae7593fe4327475159e3fde9350efae81

51dba45f78b29eaa447e9002aecaf6806d5e8908

refs/heads/master

2020-04-05T06:29:41.554699

2018-11-08T02:49:07

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omega.coef.R

#Ω係数を計算する(１因子でも多因子でも計算可能) #引数 # x: 生データ行列（受検者×項目） # nfactors:　因子数 #戻り値 # Ω係数 #(独自性に観測変数の分散をかけて、誤差分散を求める) # 関数faを使用 # omega.coef<-function(x,nfactors=1,fm="ml",...){ 1 - ( sum(fa(x,nfactors=nfactors,fm=fm,...)$uniquenesses*diag(var(x))) / sum(var(x)) ) }

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/FWI_extract_mod_samples.R

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no_license

sm-potter/AK_CA_Combustion

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refs/heads/master

2021-02-09T04:31:09.134580

2020-03-23T19:21:33

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FWI_extract_mod_samples.R

# rm(list=ls()) library(tidyverse) library(rgdal) library(raster) library(sf) library(lubridate) # rm(list=ls()) #---------------------------------extract fwi information for the modis samples pts <- read_sf("/mnt/data1/boreal/spotter/combustion/burned_area/scaling/modis_pixels/predictors/DOB.shp") # pts <- pts %>% mutate(Date = as.character(lubridate::as_date(Date))) extract_fwi <- function(pts, raster_path, variables, t1, t2){ #a final dataframe to return the results final_df <- list() #get the unique burn_dates all_dates <- unique(pts$Date) #loop through the unique dates for (bd in all_dates){ print (as.character(lubridate::as_date(bd))) for (var in variables){ #an if else statement for the variable prefix prefix <- ifelse(var %in% c( 'MERRA2_t', 'MERRA2_rh', 'MERRA2_wdSpd', 'MERRA2_snowDepth', 'VPD'), "Wx.MERRA2.Daily.Default.", "FWI.MERRA2.Daily.Default.") #an empty raster stack to get the means of the time steps for_mean <- stack() for (date in (seq(bd - t1, bd+t2, 1))){ date <- as.character(lubridate::as_date(date)) year <- substr(date, 1, 4) month <- substr(date, 6, 7) day <- substr(date, 9, 10) # #the first time step raster in_raster <- raster(file.path(raster_path,var, year, paste0(prefix, year, month, sprintf("%02d", as.numeric(day)), '.tif'))) #append each to "for_mean" for_mean <- stack(for_mean, in_raster) } #get the mean mean_ras <- stackApply(for_mean, indices = rep(1, nlayers(for_mean)), fun = "mean", na.rm = T) sub_frame <- st_as_sf(pts) %>% dplyr::filter(Date == bd) #extract the pts extract <- as_tibble(raster::extract(mean_ras, sub_frame, df = T, method = 'simple')) names(extract) <- c('ID', 'value') extract <- extract %>% distinct(id, .keep_all = TRUE) extract$Variable <- var extract$ID2 <- sub_frame$ID2 extract$Burn_Date <- as.character(lubridate::as_date(bd)) extract <- extract %>% dplyr::select(ID2, Variable, Burn_Date, value) #append the file to "final_df" final_df[[length(final_df) + 1]] <- extract } } return (spread(bind_rows(final_df), key = Variable, value = value)) } variables <- c('MERRA2_DC','MERRA2_DMC','MERRA2_FFMC','MERRA2_ISI','MERRA2_BUI','MERRA2_FWI','MERRA2_DSR', 'MERRA2_t', 'MERRA2_rh', 'MERRA2_wdSpd', 'MERRA2_snowDepth', 'VPD') all_extracted <- extract_fwi(as(pts, 'Spatial'), "/mnt/data1/boreal/raw/GFWED/v2.5", variables, 0, 0) # df <- bind_rows(final_df) # df <- df %>% group_by(ID2, Burn_Date) %>% mutate(idn = row_number()) %>% spread(Variable, value) %>% summarize_all(mean, na.rm = T) %>% drop_na() %>% ungroup() # df <- df %>% dplyr::select(-idn) out <- "/mnt/data1/boreal/spotter/combustion/burned_area/scaling/modis_pixels/predictors" dir.create(out, recursive = T) write_csv(all_extracted, file.path(out, 'FWI.csv')) # write_csv(df, file.path(out, 'FWI.csv')) #------------------------------------get landsat FWI by merging into modis # out <- "/mnt/data1/boreal/spotter/combustion/burned_area/scaling/landsat_pixels/predictors" # dir.create(out, recursive = T) # # mod_fwi <- read_csv("/mnt/data1/boreal/spotter/combustion/burned_area/scaling/modis_pixels/predictors/FWI.csv") # # #read in the landsat pixels - ID2 here is from MODIS pixels burn year and running row id # shape <- read_sf("/mnt/data1/boreal/spotter/combustion/burned_area/scaling/landsat_pixels/all_years.shp") # # shape <- as_tibble(data.frame(shape)) %>% dplyr::select(-geometry) # # #attach the DOB from MODIS to all landsat which overlay - join on ID2 # land_fwi <- left_join(as_tibble(shape), as_tibble(mod_fwi), by = 'ID2') # # write_csv(land_fwi, file.path(out, 'FWI.csv')) # df2 <- read_csv('/Users/spotter/Documents/combustion/intro_files/text_files/Combustion_SynthesisData_05042018_XJW.csv') # df2 <- df2 %>% dplyr::select(-DMC.y, -BUI.y, -Relative.humidity, -Wind.speed, -FFMC.y, -FWI.y, -DC.y, -ISI.y, -DSR, -Temperature) # names(df) <- c("id", "Burn_Date", 'BUI.y', 'DC.y', 'DMC.y', 'DSR', 'FFMC.y', 'FWI.y', 'ISI.y', 'Relative.humidity', 'snowDepth', 'Temperature', 'Wind.speed', 'VPD') # # df <- df %>% dplyr::select(-snowDepth, -Burn_Date) # # df <- left_join(df, df2, on = id) # # df2 <- read_csv('/Users/spotter/Documents/combustion/intro_files/text_files/Combustion_SynthesisData_05042018_XJW.csv') # # df2 <- df2 %>% filter(!id %in% unique(df$id)) # df2 <- df2 %>% mutate(VPD = -999) %>% na_if(-999) # df <- df[names(df2)] # # df <- bind_rows(df, df2) # # df2 <- read_csv('/Users/spotter/Documents/combustion/intro_files/text_files/Combustion_SynthesisData_05042018_XJW.csv') # # # df <- df %>% ungroup() %>% mutate(id = as.factor(id, levels = list(unique(df2$id)))) # write_csv(df, '/Users/spotter/Documents/combustion/intro_files/text_files/Combustion_Data_051019_DOB_SMP.csv')

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/R/ggdotchart.R

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no_license

inventionate/ggpubr

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refs/heads/master

2020-05-24T05:56:01.594632

2017-03-13T07:26:54

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ggdotchart.R

#' @include utilities.R ggpar.R stat_chull.R stat_conf_ellipse.R NULL #' Cleveland's Dot Plots #' @description Draw a Cleveland dot plot. #' @inheritParams ggpar #' @param data a data frame #' @param x x variable for drawing. #' @param color,size points color and size. #' @param shape point shape. See \code{\link{show_point_shapes}}. #' @param label the name of the column containing point labels. #' @param group an optional column name indicating how the elements of x are #' grouped. #' @param sorting a character vector for sorting into ascending or descending #' order. Allowed values are one of "descending" and "ascending". Partial #' match are allowed (e.g. sorting = "desc" or "asc"). Default is #' "descending". #' @param ... other arguments to be passed to \code{\link[ggplot2]{geom_point}} #' and \code{\link{ggpar}}. #' @details The plot can be easily customized using the function ggpar(). Read #' ?ggpar for changing: \itemize{ \item main title and axis labels: main, #' xlab, ylab \item axis limits: xlim, ylim (e.g.: ylim = c(0, 30)) \item axis #' scales: xscale, yscale (e.g.: yscale = "log2") \item color palettes: #' palette = "Dark2" or palette = c("gray", "blue", "red") \item legend title, #' labels and position: legend = "right" \item plot orientation : orientation #' = c("vertical", "horizontal", "reverse") } #' @seealso \code{\link{ggpar}} #' @examples #' # Load data #' data("mtcars") #' df <- mtcars #' df$cyl <- as.factor(df$cyl) #' df$name <- rownames(df) #' head(df[, c("wt", "mpg", "cyl")], 3) #' #' # Basic plot #' ggdotchart(df, x = "mpg", label = "name" ) #' #' # Change colors by group cyl #' ggdotchart(df, x = "mpg", label = "name", #' group = "cyl", color = "cyl", #' palette = c('#999999','#E69F00','#56B4E9') ) #' #' # Use brewer palette #' ggdotchart(df, x = "mpg", label = "name", #' group = "cyl", color = "cyl", palette = "Dark2" ) #' #' # Change the orientation #' # Sort in ascending order #' ggdotchart(df, x = "mpg", label = "name", #' group = "cyl", color = "cyl", #' palette = c("#00AFBB", "#E7B800", "#FC4E07"), #' orientation = "horizontal", sorting = "ascending" ) #' #' #' @export ggdotchart <- function(data, x, label, group = NULL, color = "black", palette = NULL, shape = 19, size = NULL, sorting = c("descending", "ascending"), orientation = c("vertical", "horizontal"), ggtheme = theme_bw(), ...) { sorting <- match.arg(sorting) orientation <- match.arg(orientation) decreasing <- ifelse(sorting == "descending", FALSE, TRUE) if(is.null(group)){ if (sorting == "descending") data <- data[order(data[, x]), , drop = FALSE] else data <- data[order(-data[, x]), , drop = FALSE] data[, label] <- factor(data[, label], levels = as.vector(data[, label])) } else{ decreasing <- ifelse(orientation == "vertical", TRUE, FALSE) if (sorting == "descending") data <- data[order(data[, group], -data[, x], decreasing = decreasing), , drop = FALSE] else data <- data[order(data[, group], data[, x], decreasing = decreasing), , drop = FALSE] data[, label] <- factor(data[, label], levels = as.vector(data[, label])) } if(orientation == "vertical") p <- ggplot(data, aes_string(x = x, y =label)) else p <- ggplot(data, aes_string(x = label, y =x)) p <- p + .geom_exec(geom_point, data = data, shape = shape, color = color, size = size) p <- ggpar(p, palette = palette, ggtheme = ggtheme, ...) # theme if(orientation == "vertical") p <- .theme_vertical(p, data[, label]) else p <- .theme_horizontal(p, data[, label]) p } # Helper functions # +++++++++++++++++++++++++ .theme_vertical <- function(p, label){ p <- p + theme(panel.grid.major.x = element_blank(), panel.grid.major.y = element_line(colour = "grey70", linetype = "dashed"), axis.title.y = element_blank(), axis.ticks.y = element_blank())+labs_pubr() # y axis text colors # +++++++++++++++++++++++++++ g <- ggplot2::ggplot_build(p) cols <- unlist(g$data[[1]]["colour"]) names(cols) <- as.vector(label) # Give every color an appropriate name p <- p + theme(axis.text.y = ggplot2::element_text(colour = cols)) p } .theme_horizontal <- function(p, label){ p <- p + theme(panel.grid.major.x = element_line(colour = "grey70", linetype = "dashed"), panel.grid.major.y = element_blank(), axis.title.x = element_blank(), axis.ticks.x = element_blank())+ labs_pubr() # x axis text colors # +++++++++++++++++++++++++++ g <- ggplot2::ggplot_build(p) cols <- unlist(g$data[[1]]["colour"]) names(cols) <- as.vector(label) # Give every color an appropriate name p <- p + theme(axis.text.x = ggplot2::element_text(colour = cols, angle = 90, hjust = 1)) p }

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/Normal GLM Assignment.R

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kali237/assignments

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refs/heads/master

2020-04-05T19:52:58.603439

2018-11-12T05:47:01

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r

Normal GLM Assignment.R

#Normal GLM assignment library(carData) library(car) #setwd(dir="C:\\Users\\kwickens\\Documents\\Regression and Non Parametric\\Normal GLM") setwd(dir = "C:\\Users\\Kali\\Documents\\7880 Assingments\\Assignment 2 - Normal GLM") load("Normal_GLM.Rdata") ############### functions ############### lik=function(beta1,beta2,y,X1,X2){ #y consists of n observations #X1 is an n-by-p1 matrix, with p1 covariates for each subject #X2 is an n-by-p2 matrix, with p2 covariates for each subject #beta1 is a vector of length p1 OR a m-by-p1 matrix #beta2 is a vector of length p2 OR a m-by-p2 matrix if (is.matrix(beta1)){ beta1=t(beta1) beta2=t(beta2) } L=t(y)%*%X1%*%beta1 - t(y^2)%*%X2%*%beta2/2 if (is.matrix(beta1)){ L=L - 0.5*colSums((X1%*%beta1)^2/(X2%*%beta2))+0.5*colSums(log(X2%*%beta2)) L=t(L) colnames(L)="Log-likelihood" } else { L=L - 0.5*sum((X1%*%beta1)^2/(X2%*%beta2))+0.5*sum(log(X2%*%beta2)) L=L[1,1] } return(L-0.5*log(2*pi)*dim(X1)[1]) #returns a vector with m values of the likelihood } Dlik=function(beta1,beta2,y,X1,X2){ #y consists of n observations #X1 is an n-by-p1 matrix, with p1 covariates for each subject #X2 is an n-by-p2 matrix, with p2 covariates for each subject #beta1 is a vector of length p1 OR a m-by-p1 matrix #beta2 is a vector of length p2 OR a m-by-p2 matrix if (is.matrix(beta1)){ m=dim(beta1)[1] L=t(t(rep(1,m)))%*%c(t(y)%*%X1, - t(y^2)%*%X2/2) L=L - cbind(t((X1%*%t(beta1))/(X2%*%t(beta2)))%*%X1, -0.5*t((X1%*%t(beta1))^2/(X2%*%t(beta2))^2)%*%X2 -0.5*t(1/(X2%*%t(beta2)))%*%X2) } else { L=c(t(y)%*%X1, - t(y^2)%*%X2/2) L=L - c(t((X1%*%beta1)/(X2%*%beta2))%*%X1, -0.5*t((X1%*%beta1)^2/(X2%*%beta2)^2)%*%X2 -0.5*t(1/(X2%*%beta2))%*%X2) } if (is.matrix(beta1)) { colnames(L)=c(colnames(beta1),colnames(beta2)) rownames(L)=rownames(beta1)} else { names(L)=c(names(beta1),names(beta2)) } return(L) #returns m-by-(p1+p2) matrix with the m gradient vectors } D2lik=function(beta1,beta2,y,X1,X2){ #y consists of n observations #X1 is an n-by-p1 matrix, with p1 covariates for each subject #X2 is an n-by-p2 matrix, with p2 covariates for each subject #beta1 is a vector of length p1 (only one parameter!) #beta2 is a vector of length p2 (only one parameter!) p1=length(beta1) p2=length(beta2) eta1=X1%*%beta1 eta2=X2%*%beta2 L=matrix(0,p1+p2,p1+p2) for (m in 1:(p1+p2)){ for(n in 1:(p1+p2)){ if (m<=p1 & n<=p1){ L[m,n]=-sum(X1[,m]*X1[,n]/eta2) } if (m<=p1 & n>p1){ L[m,n]=sum(eta1*X1[,m]*X2[,n-p1]/eta2^2) } if (m>p1 & n<=p1){ L[m,n]=sum(eta1*X2[,m-p1]*X1[,n]/eta2^2) } if (m>p1 & n>p1){ L[m,n]=-sum(X2[,m-p1]*X2[,n-p1]*(eta1^2/eta2^3 + 0.5/eta2^2)) } } } #colnames(L)=c(names(beta1),names(beta2)) #rownames(L)=c(names(beta1),names(beta2)) return(L) #returns (p1+p2)-by-(p1+p2) second derivative matrix } ######################################### #a) x_2 = x**2 fit = lm(y ~ x + x_2) #yes, very significant summary(fit) #just for fun fit0 = lm(y~x) summary(fit0) SSres = sum(fit$res^2) SSresH0 = sum(fit0$res^2) f = (SSresH0 - SSres)/SSres * (47/1) pvalue = 1-pf(f,1,47) #plot the residuals plot(fit) plot(x=x, y = fit$res, ylab = "residuals", xlab = NULL) plot(x=x_2, y = fit$res, ylab = "residuals", xlab = NULL) qqplot(fit$residuals) #b) fit model is a submodel assuming equivariance #for the \eta_{i1} this is basically scaling the fitted values from the first model and scaling by the variance. Because we are assuming equivariance, we just divide this all by the variance (or the standard error squared) #for \eta_{i2} we do not want the value of the variance to change based on the data, therefore we want \beta_{21} and \beta_{22} to be 0, and just scale the intercept by the variance sqrt(SSres/47) #residual standard error, \sigma^hat sig2_hat = SSres/47 #estimate for the variance beta1 = fit$coefficients/sig2_hat beta2 = c(1/sig2_hat,0,0) X_1 = model.matrix(fit) X_2 = model.matrix(fit) lik(beta1,beta2,y,X_1,X_2) Dlik(beta1,beta2,y,X_1,X_2) #the first three are 0 because we already found in model a. the last 3 are not, thus we are not at the minimum (MLE), and thus our assumption of equivariance is wrong #c) stupid optimization shit #use the beta1 and beta2 and the lik functions l = lik(beta1,beta2,y,X_1,X_2) Dl = Dlik(beta1,beta2,y,X_1,X_2) D2l = D2lik(beta1,beta2,y,X_1,X_2) D2l_inv = -solve(D2l) h = D2l_inv%*%Dl b = c(beta1,beta2) #stuff from Nick D2l=function(b) {D2lik(b[1:3],b[4:6],y,model.matrix(fit),model.matrix(fit))} Dl=function(b) {Dlik(b[1:3],b[4:6],y,model.matrix(fit),model.matrix(fit))} l=function(b) {lik(b[1:3],b[4:6],y,model.matrix(fit),model.matrix(fit))} h_m=function(b){-solve(D2l(b))%*%Dl(b)} opt = optim(b,l,Dl,control= list(fnscale = -1)) summary(opt) n_beta = opt$par n_beta1 = n_beta[1:3] n_beta2 = n_beta[4:6] lik(n_beta1,n_beta2,y,X_1,X_2) #cool, give -40.25 eta1_1 = X_1%*%n_beta1 eta2_1 = X_1%*%n_beta2 #Newton-Raphson! This now works. It produces a better minimum than 'optim' h=h_m(b) del=1 while(del>10^-35){ # 10^-35 is around 100 times the size of single-precision 'machine zero.' #In reality this will be satisfied when l(b) and l(b+h) agree to ~16 digits, #which will happen much much sooner. while(is.nan(l(b+h))||l(b+h)<l(b)){ h = h/2 } del = l(b+h)-l(b) b = b+h h = h_m(b) b } #Load 'b' with the \beta coefficients #simulate; y = N(\eta1/\eta2, 1/\eta2) lik(b[1:3],b[4:6],y,X_1,X_2) #gives -38.13 eta1 = model.matrix(fit)%*%b[1:3] #\eta_{1,i} = X_i*\beta_1 eta2 = model.matrix(fit)%*%b[4:6] #\eta_{2,i} = X_i*\beta_2 #plots plots plots!! #Plot of 'y' values, fitted values from the GLM, and fitted values from the linear model #Black is the 'y' values, red is the linear model and green is the GLM plot(y) points(fit$fitted.values,col="red") points(eta1/eta2, col="blue") #Residual plots #Residuals from the GLM plot(y - eta1/eta2) points(sqrt(1/eta2),pch="-", col = "red") points(-sqrt(1/eta2),pch="-", col = "red") #Residuals from the linear model plot(fit$residuals) points(sqrt(sig2_hat)*replicate(50,1),pch="-", col = "red") points(sqrt(sig2_hat)*replicate(50,-1),pch="-", col = "red") #It looks good, since there are lots of points within the +/- 1 stdev bars... #But actually there are TOO MANY points within the bars! We should only have ~70% between the bars! #~30% of the points SHOULD be outside the bars, but only 7 of 50 (14%) points are outside the bars. #d) test the quadratic terms for both \eta_1 and \eta_2. Then test which model is better GLM or standard linear model eta1_fit = lm(eta1 ~ x + x_2) summary(eta1_fit) b_h0 = c(b[1:2],0) eta1_fit0 = lm(eta1 ~ x) summary(eta1_fit0) eta2_fit = lm(eta2 ~ x + x_2) summary(eta2_fit) #N(m,s) = m + \sqrt(s)N(0,1) n = rnorm(50) sim = eta1/eta2 + sqrt(1/eta2)*n

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/R/fars_functions.R

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fars_functions.R

utils::globalVariables(c("STATE", "MONTH","year")) #' Read source data file #' #' @details This function looks for a CSV file called \code{filename} and checks whether it exists or not, #' if found it loads the data using \code{readr::read.csv} and converts it to a dyplr dataframe using \code{dyplr::tbl_df}. #' If no data file with that name exists, the funtion returns an error. #' #' @param filename a string and, optionally a path, representing a CSV file name #' #' @import dplyr #' #' @importFrom readr read_csv #' #' @return a dataframe #' #' @examples #' \dontrun{ #' fars_read("accident_2013.csv")} #' #' @export fars_read <- function(filename) { if(!file.exists(filename)) stop("file '", filename, "' does not exist") data <- suppressMessages({ readr::read_csv(filename, progress = FALSE) }) dplyr::tbl_df(data) } #' Standard data file name #' #' @details This function returns a standard name for a given year for the source zip files #' from the US National Highway Traffic Safety Administration's Fatality Analysis Reporting System #' #' @param year an integer year value (YYYY) #' #' @return a string representing a standard file name for a given year #' #' @examples #' \dontrun{ #' make_filename("accident_%d.csv.bz2", 2013) #' Creates a standard file name for the 2017 dataset #' } #' #' @export make_filename <- function(year) { year <- as.integer(year) sprintf("inst/ext_data/accident_%d.csv.bz2", year) } #' Data date range #' #' This function returns the month and year of the data in a range of annual data files #' #' @details This function iterates over a range of year values and uses the \code{\link{fars_read}} and \code{\link{make_filename}} #' to find and report the content of the MONTH and YEAR columns in each data file. The data files have to be in the same working directory. #' #' @param years a vector of integer year values (YYYY) #' #' @inheritParams fars_read #' #' @inheritParams make_filename #' #' @import dplyr #' #' @import magrittr #' #' @return A tipple of the MONTH and YEAR values for each data file in the \code{years} range #' #' @examples #' \dontrun{ #' fars_read_years(c(2013:2015)) #' } #' #' @export fars_read_years <- function(years) { lapply(years, function(year) { file <- make_filename(year) tryCatch({ dat <- fars_read(file) dplyr::mutate(dat, year = year) %>% dplyr::select(MONTH, year) }, error = function(e) { warning("invalid year: ", year) return(NULL) }) }) } #' Summary statistics #' #' This function provides summary monthly statistics for each year in a range #' #' @details This function uses the output from \code{\link{fars_read_years}} #' to generate summary accident statistics by \code{YEAR} and \code{MONTH}. #' #' @inheritParams fars_read_years #' #' @return table of summary statistics #' #' @import dplyr #' #' @importFrom tidyr spread #' #' @importFrom utils installed.packages #' #' @examples #' \dontrun{ #' fars_summarize_years(c(2013:2015)) #' } #' #' @export fars_summarize_years <- function(years) { dat_list <- fars_read_years(years) dplyr::bind_rows(dat_list) %>% dplyr::group_by(year, MONTH) %>% dplyr::summarize(n = n()) %>% tidyr::spread(year, n) } #' Map Accidents #' #' This function maps accidents in individual U.S. State in a given year #' #' @details For a given year value, this function read the relevant data file #' using the \code{\link{make_filename}} and \code{\link{fars_read}} functions. #' It checks that the stae exists and that any accidents were reported that year in that state. #' The function also removes erroneous longotude and lattitudes entries in the raw data #' (\code{longitude>900} and \code{lattitude>90}) and uses the \code{\link{map}} package to #' draw the relevant map and the \code{\link{graphics}} package to plot dots. #' #' @param state.num the unique identification of a U.S. state #' @param year relevant data year #' @inheritParams fars_read #' @inheritParams make_filname #' #' @import maps #' @import dplyr #' @importFrom graphics points #' #' @return a long/lat plot of reported accidents in the U.S. state and year of choice against a state boundary map #' #' @examples #' \dontrun{ #' fars_map_state("12","2013") #' } #' #' @export fars_map_state <- function(state.num, year) { filename <- make_filename(year) data <- fars_read(filename) state.num <- as.integer(state.num) if(!(state.num %in% unique(data$STATE))) stop("invalid STATE number: ", state.num) data.sub <- dplyr::filter(data, STATE == state.num) if(nrow(data.sub) == 0L) { message("no accidents to plot") return(invisible(NULL)) } is.na(data.sub$LONGITUD) <- data.sub$LONGITUD > 900 is.na(data.sub$LATITUDE) <- data.sub$LATITUDE > 90 with(data.sub, { maps::map("state", ylim = range(LATITUDE, na.rm = TRUE), xlim = range(LONGITUD, na.rm = TRUE)) graphics::points(LONGITUD, LATITUDE, pch = 46) }) } #' Testing #' #' This function runs the tests in the tests/ directory. it has not argument #' #' @importFrom testthat test_that test_dir expect_is #' #' @return test results from the testthat package #' #' @examples #' \dontrun{ #' testing() #' } #' #' @export testing<- function(){ #create test output test_output=fars_summarize_years(c(2013:2015)) #run test files in tests/ directory testthat::test_dir("tests/testthat") }

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/00misc/tyama_codeiq119-codeiq167.R

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cielavenir/codeiq_solutions

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tyama_codeiq119-codeiq167.R

#!/usr/bin/Rscript #cf: http://d.hatena.ne.jp/isseing333/20121224/1356279424 #問1 t=read.table('DataScience_ML1.csv',header=T,sep=',') l=lm(y~x1+x2,t) #問2 #(1) pdf("167_1.pdf") plot(t) dev.off() #(2) cat(summary(l)$adj.r.squared) cat("\n") #(3) pdf("167_2.pdf") plot(l$fitted,l$resid) dev.off() #(4) pdf("167_3.pdf") plot(l$fitted,l$y) dev.off() #pdftk 167_1.pdf 167_2.pdf 167_3.pdf cat output 167.pdf #rm -f 167_1.pdf 167_2.pdf 167_3.pdf

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/R/leak_detection_MEU.R

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Ozeidi/water

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leak_detection_MEU.R

library(xts) library(tidyverse) library(dplyr) leak_detection_MEU <- function(timeseries, model_params = NA, id = NA){ # if timseries sent is a single xts if (class(timeseries) %in% c("xts", "zoo")){ if(is.na(id))id <- gsub("-|:|\\s","",paste0(Sys.time())) # wrap the outcome in a list to avoid redundent checks going forward res <- list(id0 = leak_detection_MEU_single(timeseries, id, model_params)) return (res) } # if timeseries is a list of xts else if (class(timeseries) == 'list'){ res <- list() if(!is.na(model_params)){ if (length(timeseries) != length(model_params))stop("The model_params must be a list of the same length as timeseries list") id = names(timeseries) for (i in 1:length(timeseries)){ res[[i]] <- leak_detection_MEU_single(timeseries[[i]], id[i], model_params[[i]]) } } else{ id = names(timeseries) for (i in 1:length(timeseries)){ res[[i]] <- leak_detection_MEU_single(timeseries[[i]], id[i]) } } return(res) } } leak_detection_MEU_single <- function(xts_object, id = NA, model_params = NA){ # if model_params are sent, use it to detect the leak flags directly if(!is.na(model_params))return(learn_and_predict(contract_ts = xts_object, model_params = model_params)) # otherwise, look on disk for previously saved models for this id else if (!is.na(id)){ model_params = tryCatch({ readRDS(paste0("MEU_models/",id,".rds")) }, error = function(e) { print("No saved Model Params") return (NA) }) } if(!is.na(model_params))print("Model Params Loaded from Disk") # proceed with main function call return (learn_and_predict(contract_ts = xts_object, id = id, model_params = model_params)) } learn_and_predict <- function(contract_ts, id = NA, model_params = NA, leak_start = NA, leak_end = NA, z_crit = 1.95, leak_threshold = 0.8, daily_threshold = 0.5, skip_leak = TRUE){ # extract hourly markers and use them to calculate hourly usage ep <- endpoints(contract_ts, on = "hours") usagePerhour_all <- period.apply(contract_ts, INDEX = ep ,FUN = sum, na.rm = T) # if model_params are sent, use it and skip the training phase if(!is.na(model_params))model_params_df <- model_params # otherwise, proceed with training else { print("Training model") model_params_df <- data.frame(mean = numeric(0) , sd = numeric(0), n = numeric(0), meu = numeric(0)) mon_idx <- 0:11 # define months and hour notation (as per xts) hr_idx <- 0:23 # remove hourly usage data during the leak if(skip_leak)usagePerhour <- usagePerhour_all else{ leak_idx <- usagePerhour_all[paste0(leak_start,"/",leak_end), which.i = T] usagePerhour <- usagePerhour_all[-leak_idx] } # loop on each month and each hour to calculate the average hourly usage once for weekdays and another for weekends # the notation of the obtained dictionary is as follows: "Month.Wkday/Wkend.Hour" # f.ex "0.0.13" is the entry for month of Jan (0), Wkday (0), 1P.m. (13) # and "5.1.23" is the entry for month of "june (5), wkend (1), 11P.M. (23) for (mon in mon_idx){ for(hr in hr_idx){ # mark the weekday index for a specific hour and a specific month wkday_idx <- which(.indexhour(usagePerhour) == hr & .indexmon(usagePerhour) == mon & .indexwday(usagePerhour) !=0 & .indexwday(usagePerhour) !=6) # calculate mean, sd, number of observations and Maximum Expected Usage for a specific hour and a specific month MEAN <- mean(usagePerhour[wkday_idx], na.rm = T) SD <- sd(usagePerhour[wkday_idx], na.rm = T) n <- n <- sum(!is.na(usagePerhour[wkday_idx])) MEU <- MEAN + z_crit * SD / sqrt(n) model_params_df[paste0(mon,".",0,".",hr),] <- c(MEAN,SD,n,MEU) # do the same as above for weekend index wkend_idx <- which(.indexhour(usagePerhour) == hr & .indexmon(usagePerhour) == mon & (.indexwday(usagePerhour) ==0 | .indexwday(usagePerhour) ==6)) MEAN <- mean(usagePerhour[wkend_idx], na.rm = T) SD <- sd(usagePerhour[wkend_idx], na.rm = T) n <- sum(!is.na(usagePerhour[wkend_idx])) MEU <- MEAN + z_crit * SD / sqrt(n) model_params_df[paste0(mon,".",1,".",hr),] <- c(MEAN,SD,n,MEU) } } if(!is.na(id))tryCatch({dir.create("MEU_models", showWarnings = FALSE) saveRDS(model_params_df, paste0("MEU_models/",id,".rds")) }, error = function(e)print("Couldn't Write Model to Disk")) } # Using the learned dictionary to loop on all the contract and identify potential leak intervals m <- month(index(usagePerhour_all))-1 w <- ifelse(weekdays(index(usagePerhour_all)) %in% c("Saturday","Sunday"),1,0) h <- hour(index(usagePerhour_all)) # lookup the maximum estimated usage (MEU) corresponding to each hour in the original ts values <- as.vector(sapply(paste0(m,".",w,".",h), FUN = function(x)return(model_params_df[x,"meu"]))) meu_xts <- xts(values, order.by = index(usagePerhour_all)) # concatenate the actual usage to the MEU, convert to dataframe and create a flag column comp_xts <- data.frame(coredata(usagePerhour_all),coredata(meu_xts)) names(comp_xts) <- c("usage", "meu") df <- comp_xts %>% mutate(flag = ifelse(usage > meu, TRUE,FALSE)) flag_xts <- xts(df$flag, order.by = index(usagePerhour_all)) # define an aux function to check if a certain day should be marked as a leak (depending on leak_threshold and daily_threshold) isLeak <- function(x){ if(sum(is.na(coredata(x))) == length(coredata(x))) return (NaN) if(mean(x, na.rm = T) > leak_threshold & sum(is.na(coredata(x))) < 24*daily_threshold)return(TRUE) else return(FALSE) } # apply the aux function on our flag_xts to obtain the leak_xts leak_xts <- period.apply(flag_xts, endpoints(flag_xts, on = "days"), FUN = isLeak) leak_flag <- data.frame(Date = as.Date(index(leak_xts)), leak_flag = coredata(leak_xts)) %>% filter(leak_flag == TRUE) return (list(leak_xts = leak_xts, leak_flag = leak_flag, model_params = model_params_df)) }

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/man/SymKL.z.Rd

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cran/EntropyEstimation

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SymKL.z.Rd

\name{SymKL.z} \alias{SymKL.z} \title{SymKL.z} \description{ Returns the Z estimator of Symetrized Kullback-Leibler Divergence, which has exponentialy decaying bias. See Zhang and Grabchak (2014b) for details.} \usage{ SymKL.z(x, y) } \arguments{ \item{x}{ Vector of counts from first distribution. Must be integer valued. Each entry represents the number of observations of a distinct letter.} \item{y}{ Vector of counts from second distribution. Must be integer valued. Each entry represents the number of observations of a distinct letter.} } \references{ Z. Zhang and M. Grabchak (2014b). Nonparametric Estimation of Kullback-Leibler Divergence. Neural Computation, DOI 10.1162/NECO_a_00646. } \author{Lijuan Cao and Michael Grabchak} \examples{ x = c(1,3,7,4,8) y = c(2,5,1,3,6) SymKL.z(x,y) }

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/03_Analysis/fesibility_analysis.R

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GauravKBaruah/Individual_variation_and_tipping_points

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fesibility_analysis.R

#code for Ecology Letters paper: #" The impact of individual variation on abrupt collapses in mutualistic networks" 2021. Gaurav Baruah #email: gbaruahecoevo@gmail.com rm(list=ls()) source('~/02_functions_tipping_point.R', echo=F)#converting mutualism matrix to ones and zeros require(deSolve) ## for integrating ordinary differential equations require(tidyverse) ## for efficient data manipulation & plotting require(cowplot) ## for arranging plots in a grid library(dplyr) library(readr) library(beepr) require(ggplot2) require(reshape2) require(flux) require(akima) library(directlabels) library(gridExtra) library(grid) library(igraph) library(bipartite) #five mutualistic networks :60, 34, 8 #nestedness: 0.635, 0.246, 0 newfiles <-c("plant_pollinator/M_PL_046.csv", "plant_pollinator/M_PL_061_33.csv", "plant_pollinator/M_PL_061_18.csv") mydir = 'plant_pollinator' myfiles = list.files(path=mydir, pattern="*.csv", full.names=TRUE) #myfiles<-myfile #myfiles<-myfiles[1:101] fact<- expand.grid(`Strength_mutualism`=seq(0,7.5,0.4), `web` =newfiles, `interaction_type`= "trade_off", `noise`="none", `individual.variation` = c("high","low"), `range_competition` = c(50,70,100,200,500, 700,1000,2000,5000,7000,10000), `random_seed`=4327+(1:30)*100) %>% as_tibble %>% mutate(feasibility = 0, richness = 0) model.t<-list() for(r in 1:nrow(fact)){ #print(r) # extracting the interaction matrix g<-adj.mat(myfiles[which(myfiles == fact$web[r])]) #network web names # g<-g[-1,-1] Aspecies<- dim(g)[2] # no of animal species Plantspecies<- dim(g)[1] # no of plant species degree.animals<-degree.plants<-numeric() #degree of plants and anichmals for(i in 1:Plantspecies){ degree.plants[i]<-sum(g[i,])} # degree of plants for(j in 1:Aspecies){ degree.animals[j]<-sum(g[,j]) # degree of animals } ##control loop for selecting whether variation is high or low if(fact$individual.variation[r] == "low"){ sig <-runif((Aspecies+Plantspecies),0.0001,0.001)}else if(fact$individual.variation[r] == "high"){ sig <-runif((Aspecies+Plantspecies),0.05,0.5)} #heritability used in the model h2<-runif((Aspecies+Plantspecies),0.4,0.4) ## vector of species trait standard deviations N <- runif( (Aspecies+Plantspecies) , 1,1) ## initial species densities nainit<- N[1:Aspecies] npinit<-N[(Aspecies+1): (Aspecies+Plantspecies)] #vector of mean phenotypic values sampled from a uniform distribution muinit <-runif((Aspecies+Plantspecies), -1,1) mainit<-muinit[1:Aspecies] mpinit<-muinit[(Aspecies+1): (Aspecies+Plantspecies)] #matrix of coefficients for competition Amatrix<-mat.comp_feasibility(g,strength=fact$range_competition[r])$Amatrix Pmatrix<-mat.comp_feasibility(g,strength=fact$range_competition[r])$Pmatrix gamma=0.5 mut.strength<-fact$Strength_mutualism[r] nestedness<-nestedness_NODF(g) C<-Connectance(g) web.name<-myfiles[r] #growth rates of species ba<-runif(Aspecies, -0.05,-0.05) bp<-runif(Plantspecies,-0.05,-0.05) dganimals<-degree.animals dgplants<-degree.plants ic <-c(nainit, npinit, mainit,mpinit) params <- list(time=time,matrix=g,sig=sig,Amatrix=Amatrix, Pmatrix=Pmatrix,w=gamma, mut.strength=mut.strength,m=muinit,C=C,nestedness=nestedness, web.name=web.name,h2=h2, ba=ba,bp=bp,dganimals=dganimals, dgplants=dgplants,interaction_type=fact$interaction_type[r], noise=fact$noise[r]) start.time = 3000 model.t<-lapply(1, Mcommunity,time=start.time,state=ic, pars=params) abvector<-c(model.t[[1]]$Plants[3000,],model.t[[1]]$Animals[3000,]) percent_survived<-length(which(abvector > 0.001))/length(abvector) fact$feasibility[r] <- percent_survived fact$richness[r] <- length(which(abvector>1e-3)) print(r) } #save(fact, file="Feasibility_data.RData") # plot and aanalyis for the feasibility data #load("Feasibility_data.RData") for(i in 1:nrow(fact)){ g<-adj.mat(myfiles[which(myfiles == fact$web[i])]) #network web names #g<-g[-1,-1] fact$network.size[i]<- nrow(g)+ncol(g) fact$nesdtedness[i]<- nestedness_NODF(g) fact$connectance[i]<-Connectance(g) } #web1 a1<-fact %>% filter(individual.variation == "low", web == "plant_pollinator/M_PL_046.csv") %>% group_by(range_competition,Strength_mutualism,interaction_type) %>% summarise(mean_feasibility = mean(feasibility, na.rm = TRUE)) plot1<-feasibility_plot(a1) plot1 a2<-fact %>% filter(individual.variation == "high", web == "plant_pollinator/M_PL_046.csv") %>% group_by(range_competition,Strength_mutualism) %>% summarise(mean_feasibility = mean(feasibility, na.rm = TRUE)) plot2<-feasibility_plot(a2) plot2 # web2 a5<-fact %>% filter(individual.variation == "low") %>% group_by(range_competition,Strength_mutualism) %>% summarise(mean_feasibility = mean(feasibility, na.rm = TRUE)) plot5<-feasibility_plot(a5) plot5 a6<-fact %>% filter(individual.variation == "high") %>% group_by(range_competition,Strength_mutualism) %>% summarise(mean_feasibility = mean(feasibility, na.rm = TRUE)) plot6<-feasibility_plot(a6) plot6 #web3 a3<-fact %>% filter(individual.variation == "low", web == "plant_pollinator/M_PL_061_18.csv") %>% group_by(range_competition,Strength_mutualism) %>% summarise(mean_feasibility = mean(feasibility, na.rm = TRUE)) plot3<-feasibility_plot(a3) plot3 a4<-fact %>% filter(individual.variation == "high", web == "plant_pollinator/M_PL_061_18.csv") %>% group_by(range_competition,Strength_mutualism) %>% summarise(mean_feasibility = mean(feasibility, na.rm = TRUE)) plot4<-feasibility_plot(a4) plot4 net1<-adj.mat("plant_pollinator/M_PL_046.csv") net3<-adj.mat("plant_pollinator/M_PL_061_33.csv") net2<-adj.mat("plant_pollinator/M_PL_061_18.csv") par(mfrow=(c(3,1))) web1<-plotweb(net1, method="normal",ybig=0.1, y.width.low = 0.1, col.interaction="wheat4", bor.col.interaction="white", arrow="no", col.high="lightblue", col.low="tomato",labsize=0.1) web2<-plotweb(net2, method="normal",ybig=0.1, y.width.low = 0.1, col.interaction="wheat4", bor.col.interaction="white", arrow="no", col.high="lightblue", col.low="tomato",labsize=0.1) web3<-plotweb(net3, method="normal",ybig=0.1, y.width.low = 0.1, col.interaction="wheat4", bor.col.interaction="white", arrow="no", col.high="lightblue", col.low="tomato",labsize=0.1) grid_arrange_shared_legend(plot1,plot2, plot3,plot4, plot5,plot6, nrow=3,ncol=2)

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/dev/gene.set/MapBiosystems.r

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leipzig/rchive

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8814456b218137eafe57bfb19adda19c0d0d625b

refs/heads/master

2020-12-24T12:06:15.051730

2015-08-06T11:59:08

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MapBiosystems.r

### Functions that process BioSystems annotation info and map BioSystems IDs to other IDs ########################################################################################################################## ########################################################################################################################## # Map gene, protein, etc. to biosystems Map2Biosystems<-function(bsid, species=c('human'='9606'), to=c('gene', 'protein', 'compound', 'substance', 'pubmed'), by.source=TRUE, from=c('r', 'gz', 'url'), path=paste(Sys.getenv("RCHIVE_HOME"), 'data/gene.set/public/biosystems', sep='/')) { #bsid Full annotation table of Biosystems downloaded from NCBI website #species One or multiple species to do the mapping; the value is the NCBI Taxonomy ID and the name is the prefix of output files #to Type(s) of entities the Biosystems will be mapped to #by.source Split mapping results by sources of Biosystems if TRUE, such as GO and KEGG #from Source of input data, from previously save R object, downloaded .gz file, or original URL #path Path to the output input files. <path>/r is for all the R objects and <path>/src is for downloaded .gz file if (!file.exists(path)) dir.create(path); if (!file.exists(paste(path, 'r', sep='/'))) dir.create(paste(path, 'r', sep='/')); # all possible input sources r<-paste(path, '/r/biosystem2', to, '_fulltable.rds', sep=''); fnm<-c("biosystems_gene_all.gz", "biosystems_protein.gz", "biosystems_pccompound.gz", "biosystems_pcsubstance.gz", "biosystems_pubmed.gz") gz<-paste(path, '/src/', fnm, sep=''); url<-paste("ftp://ftp.ncbi.nih.gov/pub/biosystems/CURRENT", fnm, sep='/'); cnm<-c('Gene', 'Protein', 'Compound', 'Substance', 'PubMed'); names(r)<-names(gz)<-names(url)<-names(cnm)<-c('gene', 'protein', 'compound', 'substance', 'pubmed'); to<-to[to %in% names(r)]; if (length(to) == 0) to<-'gene'; r<-r[to]; gz<-gz[to]; url<-url[to]; cnm<-cnm[to]; # Load full mapping data frm<-tolower(from)[1]; ttl<-lapply(to, function(nm) { if (frm=='r' & file.exists(r[nm])) { print(r[nm]); readRDS(r[nm]); } else { if (frm!='gz' | !file.exists(gz[nm])) download.file(url[nm], gz[nm]); # Download data from source print(gz[nm]); mp<-read.table(gz[nm], sep='\t', stringsAsFactors=FALSE); colnames(mp)<-c('BioSystem_ID', paste(cnm[nm], 'ID', sep='_'), 'Score'); mp[[1]]<-as.character(mp[[1]]); mp[[2]]<-as.character(mp[[2]]); saveRDS(mp, file=paste(path, '/r/', 'biosystem2', nm, '_fulltable.rds', sep='')); saveRDS(split(mp[[2]], mp[[1]]), file=paste(path, '/r/', 'biosystem2', nm, '_list.rds', sep='')); mp; } }); names(ttl)<-to; sp<-species[species %in% bsid$Taxonomy]; if (is.null(names(sp))) names(sp)<-sp else names(sp)[names(sp)=='']<-sp[names(sp)=='']; fn<-lapply(names(sp), function(nm) sapply(names(ttl), function(tp) { cat('Mapping', tp, 'of', nm, '\n'); bs<-bsid[bsid$Taxonomy==sp[nm], ]; t1<-ttl[[tp]]; t1<-t1[t1[[1]] %in% rownames(bs), ]; mp<-split(t1[[2]], t1[[1]]); if (!by.source) saveRDS(mp, file=paste(path, '/r/biosystem2', tp, '_', nm, '.rds', sep='')) else { mp0<-split(mp, bsid[names(mp), 1]); sapply(names(mp0), function(sc) saveRDS(mp0[[sc]], file=paste(path, '/r/biosystem2', tp, '_', nm, '_', gsub(' ', '-', sc), '.rds', sep=''))) } })); } ########################################################################################################################## ########################################################################################################################## # Download and parse general Biosystems annotation information ParseBiosystemsGeneral<-function(species=c('human'='9606'), ver="ftp://ftp.ncbi.nih.gov/pub/biosystems/CURRENT", download.new=FALSE, path=paste(Sys.getenv("RCHIVE_HOME"), 'data/gene.set/public/biosystems', sep='/')) { # species Named character vector of NCBI taxanomy ID; the name will be used as prefix of output file # ver The version of BioSystems to download # download.new Whether to re-download source files # path Path to output files if (!file.exists(path)) dir.create(path, recursive=TRUE); if(!file.exists(paste(path, 'r', sep='/'))) dir.create(paste(path, 'r', sep='/')); if(!file.exists(paste(path, 'src', sep='/'))) dir.create(paste(path, 'src', sep='/')); sp<-names(species); names(sp)<-species; #################################################################################################################### # Source file names fn<-c("biosystems_biosystems_conserved.gz", "biosystems_biosystems_linked.gz", "biosystems_biosystems_similar.gz", "biosystems_biosystems_specific.gz", "biosystems_biosystems_sub.gz", "biosystems_biosystems_super.gz", "biosystems_cdd_specific.gz", "biosystems_gene.gz", "biosystems_gene_all.gz", "biosystems_pccompound.gz", "biosystems_pcsubstance.gz", "biosystems_protein.gz", "biosystems_pubmed.gz", "biosystems_taxonomy.gz", "bsid2info.gz"); # Download source files if (download.new) download.file('ftp://ftp.ncbi.nih.gov/pub/biosystems/README.txt', paste(path, '/src/README.txt', sep='')); # original README file fn0<-paste(ver, fn, sep='/'); # source files fn1<-paste(path, 'src', fn, sep='/'); # local files dnld<-lapply(1:length(fn), function(i) if (download.new | !file.exists(fn1[i])) download.file(fn0[i], fn1[i])); ##################### ### Start parsing ### ##################### ########################################################################################### # Meta table of bio-systems, row names are the unique BioSystems IDs lines<-scan(fn1[grep('bsid2info.gz$', fn1)][1], what='', sep='\n', flush=TRUE); split<-strsplit(lines, '\t', useBytes=TRUE); bsid<-t(sapply(split, function(s) s[1:8])); rownames(bsid)<-bsid[,1]; bsid[is.na(bsid)]<-''; bsid<-data.frame(bsid[, -1], stringsAsFactors=FALSE); names(bsid)<-c('Source', 'Accession', 'Name', 'Type', 'Scope', 'Taxonomy', 'Description'); saveRDS(bsid, file=paste(path, 'r', 'biosystem_all.rds', sep='/')); # biosystems by source cls<-split(bsid[, -1], bsid[, 1]); sapply(names(cls), function(nm) saveRDS(cls[[nm]], file=paste(path, '/r/biosystem_', gsub(' ', '-', nm), '.rds', sep=''))); saveRDS(cls, file=paste(path, 'r', 'biosystem_by_source.rds', sep='/')); # Save subset of source-species of selected species (human, mouse, rat, ...) tbl<-xtabs(~bsid$Source + bsid$Taxonomy); tbl<-tbl[, colnames(tbl) %in% names(sp), drop=FALSE]; tbl<-tbl[rowSums(tbl)>0, , drop=FALSE]; sapply(1:nrow(tbl), function(i) sapply(1:ncol(tbl), function(j) { fn<-paste(path, '/r/biosystem_', sp[colnames(tbl)[j]], '_', gsub(' ', '-', rownames(tbl)[i]), '.rds', sep=''); tb<-bsid[bsid$Source == rownames(tbl)[i] & bsid$Taxonomy == colnames(tbl)[j], -c(1, 6), drop=FALSE]; #file.remove(fn); if (nrow(tb) > 0) saveRDS(tb, file=fn); }))->nll; # map organism specific biosystems to corresponding conserved biosystems cons2spec<-read.table(fn1[grep("biosystems_biosystems_conserved.gz$", fn1)][1], sep='\t', stringsAsFactors=FALSE); saveRDS(split(as.character(cons2spec[,1]), cons2spec[,2]), file=paste(path, 'r', 'biosystem_conserved2specific.rds', sep='/')); saveRDS(split(as.character(cons2spec[,2]), cons2spec[,1]), file=paste(path, 'r', 'biosystem_specific2conserved.rds', sep='/')); cons<-bsid[bsid$Scope=='conserved biosystem', , drop=FALSE]; cons.id<-split(rownames(cons), cons$Source); ########################################################################################### # Full tables of Biosystems to other ID mapping spec2taxo<-read.table(fn1[grep("biosystems_taxonomy.gz$", fn1)][1], sep='\t', stringsAsFactors=FALSE); id<-as.character(spec2taxo[[1]]); cons<-as.character(cons2spec[[2]]); names(cons)<-cons2spec[[1]]; spec<-data.frame(Organism=as.character(spec2taxo[[2]]), Conserved=cons[id], Score=spec2taxo[[3]], row.names=id, stringsAsFactors=FALSE); spec[is.na(spec)]<-''; saveRDS(spec, file=paste(path, 'r', 'biosystem_organism_specific.rds', sep='/')); tp<-c('gene_all', 'protein', 'pubmed', 'pccompound', 'pcsubstance'); mp.fn<-sapply(tp, function(tp) fn1[grep(tp, fn1)][1]); bs2oth<-lapply(mp.fn, function(fn) read.table(fn, sep='\t', stringsAsFactors=FALSE)); cnm<-c('Gene', 'Protein', 'PubMed', 'Compound', 'Substance'); for (i in 1:length(bs2oth)) { colnames(bs2oth[[i]])<-c('BioSystem_ID', paste(cnm[i], 'ID', sep='_'), 'Score'); bs2oth[[i]][[1]]<-as.character(bs2oth[[i]][[1]]); bs2oth[[i]][[2]]<-as.character(bs2oth[[i]][[2]]); mp<-split(bs2oth[[i]][[2]], bs2oth[[i]][[1]]) saveRDS(bs2oth[[i]], file=paste(path, '/r/', 'biosystem2', tolower(cnm)[i], '_fulltable.rds', sep='')); saveRDS(mp, file=paste(path, '/r/', 'biosystem2', tolower(cnm)[i], '_list.rds', sep='')); sapply(names(cons.id), function(sc) { mp<-mp[names(mp) %in% cons.id[[sc]]]; saveRDS(mp, file=paste(path, '/r/', 'biosystem2', tolower(cnm)[i], '_conserved_', sc, '.rds', sep='')) }) } ########################################################################################### # Save Log # existing full taxonomy table if (download.new) { if (file.exists(paste(path, 'r', 'biosystem_all.rds', sep='/'))) { bsid0<-readRDS(paste(path, 'r', 'biosystem_all.rds', sep='/')); log.old<-list(id=rownames(bsid0), acc=unique(bsid0$Accession), type=unique(bsid0$Type), src=unique(bsid0$Source), taxonomy.old=unique(bsid0$Taxonomy)); } else { log.old<-list(id=c(), acc=c(), type=c(), src=c(), taxonomy=c()) } log.new<-list(id=rownames(bsid), acc=unique(bsid$Accession), type=unique(bsid$Type), src=unique(bsid$Source), taxonomy.old=unique(bsid$Taxonomy)); names(log.old)<-names(log.new)<-c('ID', 'Accession', 'Type', 'Source', 'Taxonomy'); # updates up<-list( N = sapply(log.new, length), Added = lapply(1:5, function(i) setdiff(log.new[[i]], log.old[[i]])), Removed = lapply(1:5, function(i) setdiff(log.old[[i]], log.new[[i]])) ) # update logs log<-readRDS(paste(path, 'log.rds', sep='/')); log<-c(log, list(up)); names(log)[length(log)]<-as.character(Sys.Date()); saveRDS(log, file=paste(path, 'log.rds', sep='/')); } }

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/R/irf.varshrinkest.R

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Allisterh/VARshrink

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irf.varshrinkest.R

#' Impulse response function #' #' Computes the impulse response coefficients of a VAR(p) #' (or transformed VECM to VAR(p)) for n.ahead steps. #' This is a modification of vars::irf() for the class "varshrinkest". #' #' @param x Object of class 'varshrinkest'; #' generated by \code{VARshrink()} #' @param impulse A character vector of the impulses, #' default is all variables. #' @param response A character vector of the responses, #' default is all variables. #' @param n.ahead Integer specifying the steps. #' @param ortho Logical, if TRUE (the default) the #' orthogonalised impulse response coefficients are #' computed (only for objects of class 'varshrinkest'). #' @param cumulative Logical, if TRUE the cumulated impulse #' response coefficients are computed. The default value #' is false. #' @param boot Logical, if TRUE (the default) bootstrapped #' error bands for the imuplse response coefficients are #' computed. #' @param ci Numeric, the confidence interval for the #' bootstrapped errors bands. #' @param runs An integer, specifying the runs for the #' bootstrap. #' @param seed An integer, specifying the seed for the #' rng of the bootstrap. #' @param ... Currently not used. #' @seealso \code{\link[vars]{irf}} #' @export irf.varshrinkest <- function (x, impulse = NULL, response = NULL, n.ahead = 10, ortho = TRUE, cumulative = FALSE, boot = TRUE, ci = 0.95, runs = 100, seed = NULL, ...) { if (!inherits(x, "varest")) { stop("\nPlease provide an object inheriting class 'varest'.\n") } y.names <- names(x$varresult) if (is.null(impulse)) { impulse <- y.names } else { impulse <- as.vector(as.character(impulse)) if (any(!(impulse %in% y.names))) { stop("\nPlease provide variables names in impulse\nthat are in the set of endogenous variables.\n") } impulse <- subset(y.names, subset = y.names %in% impulse) } if (is.null(response)) { response <- y.names } else { response <- as.vector(as.character(response)) if (any(!(response %in% y.names))) { stop("\nPlease provide variables names in response\nthat are in the set of endogenous variables.\n") } response <- subset(y.names, subset = y.names %in% response) } irs <- h_irf(x = x, impulse = impulse, response = response, y.names = y.names, n.ahead = n.ahead, ortho = ortho, cumulative = cumulative) Lower <- NULL Upper <- NULL if (boot) { ci <- as.numeric(ci) if ((ci <= 0) | (ci >= 1)) { stop("\nPlease provide a number between 0 and 1 for the confidence interval.\n") } ci <- 1 - ci BOOT <- h_boot(x = x, n.ahead = n.ahead, runs = runs, ortho = ortho, cumulative = cumulative, impulse = impulse, response = response, ci = ci, seed = seed, y.names = y.names) Lower <- BOOT$Lower Upper <- BOOT$Upper } result <- list(irf = irs, Lower = Lower, Upper = Upper, response = response, impulse = impulse, ortho = ortho, cumulative = cumulative, runs = runs, ci = ci, boot = boot, model = class(x)) class(result) <- c("varshirf", "varirf") return(result) }

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/R/to.long.R

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CaoMengqi/sgmm

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refs/heads/master

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to.long.R

#' A Wide to Long Data Set Function #' #' This function helps you convert a wide time-series data set (with one row for each patient and multiple columns for each different time points), to a long time-series data set (with t rows for each patient where t=number of time poitns). #' @param data enter data set containing month values. #' @param id_column enter the column containing patient IDs (str) #' @param time_columns enter the time-varying outcome variable columns #' @param date_columns enter the time-indicating month-formatted columns (str) #' @keywords lcmm #' @export #' @examples #' to.long() to.long <- function(data, id_column, time_columns, date_columns) { # reformat id col to factor factored_ids <- as.factor(as.character(data[, which(colnames(data) == toString(id_column))])) # these are the IDs in order ordered_id_levels <- as.numeric(as.character(factored_ids)) # we don't want to use the actual date columns, but instead the month columns # corresponding to the date columns. that is what is happening here: names_list <- c("m1", "m2", "m3", "m4", "m5", "m6", "m7", "m8", "m9", "m10", "m11", "m12", "m13", "m14", "m15", "m16", "m17", "m18", "m19", "m20", "m21", "m22", "m23", "m24", "m25", "m26", "m27", "m28", "m29", "m30", "m31", "m32", "m33", "m34", "m35", "m36", "m37", "m38", "m39", "m40", "m41", "m42", "m43", "m44", "m45", "m46", "m47", "m48", "m49", "m50") month_columns <- names_list[1:length(date_columns)] # new_empty df new_df <- data.frame() # looping # for each id... for(i in 1:length(ordered_id_levels)) { # for each time point... for(col in 1:length(time_columns)) { # stack times times <- t(cbind(data[i, time_columns[col]])) # stack dates months <- t(cbind(data[i, month_columns[col]])) # it is important to use this object, and not to recall the user-inserted data again ids <- ordered_id_levels[i] mybind <- as.data.frame(cbind(ids, times, months)) # append a df new_df <- bind_rows(new_df, mybind) } } # renaming cols colnames(new_df) <- c('id', 'outcomes', 'months') print("colnames are (i) 'id', (ii) 'outcomes', and (iii) 'months'") # end return(new_df) }

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mix.R

n <- 30 m <- 100000 mse <- matrix(0, 1, 3) colnames(mse) <-c("sample mean", "sample median", "the 1st trimmed mean") calculate_mse <- function(n, m) { tmean <- numeric(m) x_mean <- numeric(m) x_median <- numeric(m) for (i in 1:m) { sigma <- sample(c(1,10), size = n, replace = TRUE, prob = c(0.9, 0.1)) x <- sort(rnorm(n, 0 , sigma)) tmean[i] <- sum(x[2:(n-1)])/(n-2) x_mean[i] <- mean(x) x_median[i] <- median(x) } mse.tmean <- mean(tmean^2) mse.mean <- mean(x_mean^2) mse.median <- mean(x_median^2) return (c(mse.mean, mse.median, mse.tmean)) } mse[1, 1:3] <- calculate_mse(n = n, m = m) print(mse)

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/man/attachContext.Rd

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dami82/mutSignatures_dev

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refs/heads/master

2021-04-15T03:57:15.490229

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attachContext.Rd

\name{attachContext} \alias{attachContext} \title{ Attach Context } \description{ Attach Context } \usage{ attachContext(mutData, BSGenomeDb, chr_colName = "chr", start_colName = "start_position", end_colName = "end_position", nucl_contextN = 3, context_colName = "context") } %- maybe also 'usage' for other objects documented here. \arguments{ \item{mutData}{ data } \item{BSGenomeDb}{ data } \item{chr_colName}{ data } \item{start_colName}{ data } \item{end_colName}{ data } \item{nucl_contextN}{ data } \item{context_colName}{ data } }

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/man/simc.model.Rd

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guhjy/RsimCity

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refs/heads/master

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simc.model.Rd

\name{simc.model} \title{Define a set of linear models} \alias{simCity.model} \concept{ simCity model } \description{ Specify a data generating model for simCity simulations } \usage{ simc.model(file="",exdist=NULL,excov=NULL) \cr simc.model(file="",exdist=NULL,excov=NULL) x~1;edist=rnorm(0,1) y~x;edist=rbinom(10,.5); apply=fun() # } \arguments{ \item{file}{The (quoted) file from which to read the date generating model specification, including the path to the file if it is not in the current directory. If "" (the default), then the specification is read from the standard input stream, and is terminated by a blank line or \code{#}.} \item{exdist}{multivariate distribution of the exogenous variables. If NULL \code{\link{rmnorm}} is used. It can be any function which accept N as first argument, mean and excov as second and third argument} \item{excov}{covariance matrix for the exogenous variables. It must have names corresponding to the exogenous variable names (see details). If there are exogenous variables and no \code{excov} is provided, an identity matrix is the default } \item{fun}{any function which accepts a variable as argument (see details)} } \details{ The data generating model can be specified in a text file or in standard input stream should specify an equation per line. Each line features (if not default applies), separated by semicolumns, an \code{equation}, \code{edist=} a function to generate random residuals, \code{apply=} a list of functions to be applied to the generated variable, \code{options=} a list of options to fine-tune the variable. An example of line can be: \cr \code{y~x+z;edit=rnorm(0,1);apply=scale(),exp();options=beta} \cr \itemize{ \item{\code{equations}:}{ a formula declaring the linear model. Formula should be specified in a way that can be coerce to \code{\link{as.formula}}. For instance \code{y~x+z}. No coefficients should be specified, as they will be specified later during the data generating process (so they can be varied for each run). Constant terms (intercepts) can be specified adding \code{1} as in \code{y~1+x}. Coefficients (possibly \code{1}) should be provided in the experiment if an intercept is declared. Any artimetics should be passed using \code{\link{I}} function or \code{.()} notation. For instance: \code{y~x+I(x^2)+z+I(x*z)} or \code{y~x+.(x^2)+z+.(x*z)} (these two are equivalent). At the moment (version < \code{1.0}), the "\code{:}" notation for interactions is not allowed (use \code{I(x*x)} instead). See examples for further details } \item{\code{edist}:} {a function name to be used to generate the residuals of the linear model. The function should accept as its first argument the sample size (N) and can have any argument as one need. For instance, to generate and endogenous variable accordingly to the model \code{y~x} with standardized residuals one would write \code{edist=rnorm(0,1) } (cf. \code{\link{rnorm}}). Binomial residuals can be specified with \code{edist=rbinom(size,prob)} (cf. \code{\link{rbinom}}). If the parameters of the distribution should be changed during the simulation experiment, reference functions can be used (see examples). Any function can be used as long as it accept N as first argument. If no distribution is specified, \code{edist=rnorm(0,1) } is the default. Note that the distribution function is evaluated in the environment of the data.frame being produced, thus it may have variables as arguments (cf \code{\link[simcity]{categorial}} and "Examples") } \item{\code{apply}:} {a list of function names separated by comma to be applied to the generated endogenous variable. The function should be writen as \code{func(arg1,arg2)} but it must accept the endogenous variable as its first argument. For instance, to standardize the endogenous variable after it is created, one can use \code{\link{scale}} writing \code{apply=scale()}. To center the variable to its mean one can write \code{apply=scale(scale=FALSE)} which implicitly assumes that the function usage is \code{scale(x,scale=FALSE)}. Note that the function in \code{apply} is evaluated in the environment of the data.frame being produced, thus it may have variables as arguments (cf \code{\link[simcity]{categorial}} and "Examples")} \item{\code{options}:} {a comma separeted list of options to fine-tune the data generating process for the corresponding endogenous variable. Available options are: \itemize{ \item{\code{beta}:}{coefficients that will be passed to the simulation should be intended as betas (standardized coefficients). The endogenous variable is constructed using calculated coefficients such that the simple \code{beta} linking the endogenous and the exogenous variable correspond to the passed coefficient. Note that the resulting betas would correspond to the coefficients passed to the simulation only if the exogenous variables are uncorrelated.} \item{\code{trans}:}{the line is intended as a mere \code{trans}formation of the exogenous variable. In practice, when \code{options=trans} is specified, not random residuals are added to the endogenous variable. This options is usefull when complex transformations are needed which are not suitable for using \code{apply}. Choosing to specify a new line for a transformation (with \code{options=trans}) or using the argument \code{apply} is a matter of taste and convenience, particularly as regard how the coefficients apply to the formulas in generating the data. For instance in this model \code{y1} and \code{y2} are structurally identical:\cr\cr \code{ simc.model(file="") \cr y~exp(x); options=trans \cr y~x;apply=exp() \cr # \cr } however, when the simulation is run and the coefficients are passed to the model, for model in line one the coefficient \code{c} result in \code{y~c*exp(x)}, whereas in line two it results in \code{y~exp(c*x)} } } } } } \value{ An object of class \code{\link[simCity]{simCity.model}} which contains (if not default applies): \itemize{ \item{\code{equations}:}{ an object containing, for each equation specified in the model: \itemize{ \item{\code{equation}:} {the equation specified for one linear model} \item{\code{edist}:} {the function producing the residual distribution of the corresponding linear model. } \item{\code{options}:} {a list of options passed to the model } \item{\code{apply}:} {a list of functions to apply on the generated endogenous variable } } } \item{\code{endogenous}:} {a list of endogenous variables name (as.character)} \item{\code{exogenous}:} {a list of exogenous variables name (as.character)} } The object can be passed to to generate one sample, to to set up a complete experiment } \seealso{ \code{\link[stats]{lm}} } \examples{ ### define a simple model with two variables normally distributed, x standardized. simc.model() x~1;edist=rnorm(0,1) y~x # ### define a simple model with two variables normally distributed, both standardized. model<-simc.model() x~1;edist=rnorm(0,1) y~x;apply=scale() # print(model) ### define a model with two uncorrelated and standardized exogenous variables a endogenous variable with binomial residuals. model<-simc.model() y~x+z;edist=rbinom(100,.5) # ### define a model with two correlated exogenous variables with different variances and a endogenous variable with binomial residuals. covs<-matrix(c(2,.3,.3,4),ncol=2) colnames(covs)<-rownames(covs)<-c("x","z") model<-simc.model(excov=covs) y~x+z;edist=rbinom(100, .5) # ### define a model with two standardized correlated exogenous variables endogenous variable with binomial residuals and exponential relations with the exogenous variable. covs<-matrix(c(1,.3,.3,1),ncol=2) colnames(covs)<-rownames(covs)<-c("x","z") model<-simc.model(excov=covs) y~x+z;rbinom(100,.5);apply=exp() # ### define a model with two exogenous variables and an interaction on the endogenous. Coefficients will be passed as standardized coefficients model<-simc.model() y~x+z+I(x*z);rbinom(100,.5);options=beta # ### define a series of polinomial models model<-simc.model() y~x+I(x^2)+I(x^3) z~x+I(exp(log(x)*1/2))+I(exp(log(x)*1/3))+I(exp(log(x)*1/4)) # ### define a mediational model with non standardized residual model<-simc.model() m~x; edist=rnorm(5,10) y~m+x, edist=rnorm(2, 10) # ### define a structural factorial model with two correlated latent factors and 3 observed variables for each factor with etherogenous distribution of residuals. covs<-matrix(c(1,.2,.2,1),ncol=2) colnames(covs)<-rownames(covs)<-c("xi","eta") model<-simc.model(excov=covs) x1~xi x2~xi; binom(10,.5) x2~xi; uniform(0,1) x3~eta; rnorm(0,1) x4~eta; binom(10,.5) x5~eta; uniform(0,1 ) # ### use of reference functions: define a model with a latent variable, a continuous measure and two dichotomized versions of it with different cut-offs mysplit<-function(x,p) { x<-x>quantile(x,p) as.numeric(x) } model<-simc.model() x~csi; rnorm 0 1 d1~x; apply=mysplit(.5) d2~x, apply=mysplit(.7) # ### use of reference functions: define a model with a latent variable, two continuous measures with different percentace of missing values mymissing<-function(x,p) { x[p<runif(length(x),0,1)]<-NA as.numeric(x) } model<-simc.model() x~csi, rnorm 0 1 d1~x, apply=mymissing(.05) d2~x, apply=mymissing(.15) # } \keyword{models}

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GISS temperature anomaly.R

####################################################################################### ## R Script to read NASA GISS monthly global (land & SST) temperatue anomnaly file # ## Summarize recent trends and generate plot # ## D Kelly O'Day, http://processtrends.com & http://chartgraphs.wordpress.com # ## GISS monthly data import script develodped by http://LearnR.wordpress.com # ####################################################################################### ## Setup & Download from web rm(list=ls()); options(digits=8) script <- "GISS_Trend_by_decade.R" library(ggplot2) par(oma=c(2,1,1,1)) ; par(mar=c(2,4,3,1)); par(xaxs="i"); par(yaxs="i") par(ps=9); par(las=1) ################################## Download and process GISS Data File ############### url <- c("http://data.giss.nasa.gov/gistemp/tabledata/GLB.Ts+dSST.txt") file <- c("GLB.Ts+dSST.txt") download.file(url, file) ## Cleanup File #The first 8 rows and the last 12 rows of the textfile contain instructions # Find out the number of rows in a file, and exclude the last 12 rows <- length(readLines(file)) - 12 # Read file as char vector, one line per row, Exclude first 7 rows lines <- readLines(file, n=rows)[9:rows] #Data Manipulation, R vector with 143 lines. ### Use regexp to replace all the occurences of **** with NA lines2 <- gsub("\\*{3,5}", " NA", lines, perl=TRUE) #Convert the character vector to a dataframe df <- read.table( textConnection(lines2), header=TRUE, colClasses = "character") closeAllConnections() # We are only interested in the montly data in first 13 columns df <- df[,1:13] # Convert all variables (columns) to numeric format df <- colwise(as.numeric) (df) #head(df, 12) # Remove rows where Year=NA from the dataframe df <- df [!is.na(df$Year),] # Convert from wide format to long format dfm <- melt(df, id.var="Year", variable_name="Month") mo_num <- unclass(dfm$Month) mo_frac <- as.numeric(( unclass(dfm$Month)-0.5)/12) #mo_frac yr_frac <- dfm$Year + mo_frac #yr_frac temp_anom <- dfm$value/100 dfm <- data.frame(dfm, mo_num, yr_frac, temp_anom) dfm <- dfm[order(dfm$yr_frac), ] dfm <- dfm[!is.na(dfm$temp),] ## Find last report month and last value last <- nrow(dfm) last_yr <- dfm$Year[last] last_mo <- dfm$Month[last] last_temp <- dfm$temp[last] out <- paste("Latest GISS report: " , last_mo, " - ", last_yr, "; Global land & Sea Temp - Anomaly - ", last_temp) out ############################################################################################## ## Produce plot and prepare regressions plot(temp_anom~ yr_frac, data = dfm, type = "l", col = "darkgrey", xlab = "", ylab = "GISS Temperature Anomaly - C", xlim = c(1880, 2020), ylim = c(-1,1), axes = F, pty="m", main = "GISS Temperature Anomaly \nwith Trend Rates By Decade") axis(1, col="grey") axis(2, col = "grey") grid(col = "lightgrey", lty=1) # overall trend flm <- lm(dfm$temp_anom ~ dfm$yr_fr ) x_min <- min(dfm$yr_frac) x_max <- max(dfm$yr_frac) y_min <- dfm$temp_anom[1] y_max <- dfm$temp_anom[nrow(dfm)] a_flm <- coef(flm)[1] b_flm <- coef(flm)[2] x_vals <- c(x_min, x_max) y_vals <- c(a_flm +b_flm*x_min, a_flm + b_flm*x_max) lines(x_vals, y_vals, col = 139) overall_rate <- signif(b_flm*100,3) overall_note_1 <- "Overall Trend - oC/C" rect(1882, 0.8, 1920, .96, density = NULL, col = "white", border = "white") text(1884, 0.92, adj = 0, overall_note_1, col = 139, cex=0.9) text(1892, 0.85, adj=0, overall_rate,col = 139, cex=0.9) ## Calculate monthly regressions n <- 200-188 v_i <- as.numeric(n) v_a <- as.numeric(n) v_b <- as.numeric(n) rect(1910, -0.94, 1980, -0.8, density = NULL, col = "white", border = "white") text(1940, -0.89, "Decade Trend Rate -oC per Century", font=3) for (d in 1:13){ i = 1870+ d *10 v_i[d] <- i sub <- subset(dfm, dfm$yr_frac>=i) sub <- subset(sub, sub$yr_frac < i+10) dlm <- lm(sub$temp_anom ~ sub$yr_frac, data = sub) #to set indvidual a & b factors for each decade a <- coef(dlm)[1] v_a[d] <- a b <- coef(dlm)[2] v_b[d] <- b x_vals <- c(i, i+9.99) y_vals <- c(a+b*i, a+b*(i+9.99)) ## color code decade trend rate based on <> 0 if (v_b[d] < 0) {dec_col = "darkblue"} if (v_b[d] >= 0) {dec_col = "red"} lines(x_vals, y_vals, col = dec_col) text(i+1, -0.95, signif(b*100, 2), adj = 0, cex=0.8, col = dec_col) } ## generate dat frame of regression results df_regr <- data.frame(v_i, v_a, v_b) names(df_regr) <- c("Decade", "a Coef", "b Coef") df_regr ## Margin Text my_date <- format(Sys.time(), "%m/%d/%y") mtext(script, side = 1, line = .5, cex=0.8, outer = T, adj = 0) mtext(my_date, side = 1, line =.5, cex = 0.8, outer = T, adj = 1)## Add script name to each plot mtext("D Kelly O'Day", side = 1, line =.5, cex = 0.8, outer = T, adj = 0.7)## Add script name to each plot data_note <- paste("Land & Sea Stations \n Baseline 1950-1980") rect(1939, 0.75, 1961, 0.9, density = NULL, col = "white", border = "white") text(1950, 0.85, data_note, cex = 0.8)

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/src/function_load_gene_list_data_individual_gene_plots.R

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pascaltimshel/temporal-brain-expression-scz

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function_load_gene_list_data_individual_gene_plots.R

############################# FUNCTIONS ################################# ######### Adding x-tickmarks for stage do_stage_converter <- function (p) { stage_converter <- c("s1"="Embryonic", "s2a"="Early prenatal", "s2b"="Early prenatal", "s3a"="Early mid-prenatal", "s3b"="Early mid-prenatal", "s4"="Late mid-prenatal", "s5"="Late prenatal", "s6"="Early infancy", "s7"="Late infancy", "s8"="Early childhood", "s9"="Late childhood", "s10"="Adolescence", "s11"="Adulthood") p <- p + scale_x_discrete(name="", labels = stage_converter) + theme(axis.text.x = element_text(angle = 35, hjust = 1, size=rel(1.15))) return(p) } ############################# READING GENE LISTs ################################# path.datafiles <- '/Users/pascaltimshel/p_scz/brainspan/gene_lists' ###### Read into a list of files - PATTERN VERSION - read ALL .txt files in directory: #files <- list.files(path = path.datafiles, pattern = "*.txt", full.names = TRUE) #full path #names(files) <- list.files(path = path.datafiles, pattern = "*.txt") # filename #cat(names(files), sep="\n") ###### Read SPECIFIC FILES: filenames2read <- c("gene_prioritization.txt") #filenames2read <- c(filenames2read, "gene_associated.txt", "gene_nearest.txt") #filenames2read <- c(filenames2read, "gene_psd_human.txt", "gene_psd_mouse.txt") #filenames2read <- c(filenames2read, "gilman_nn_2012_cluster1.ens", "gilman_nn_2012_cluster1a.ens", "gilman_nn_2012_cluster1b.ens", "gilman_nn_2012_cluster2.ens") #filenames2read <- c(filenames2read, "gulsuner_S3A_damaging_cases.ens") files <- as.list(paste(path.datafiles, filenames2read, sep="/")) names(files) <- filenames2read files list_of_data <- llply(files, read.csv)#row.names = 1 --> NO!, stringsAsFactors = FALSE names(list_of_data) extract_genes_from_molten_df <- function(df_gene_list) { print("done") df <- subset(df.expression_matrix.clean.melt, ensembl_gene_id %in% df_gene_list[,1]) } df.gene_list <- ldply(list_of_data, extract_genes_from_molten_df, .id="gene_list") ## Converting .id=gene_list to factor df.gene_list$gene_list <- as.factor(df.gene_list$gene_list) str(df.gene_list) levels(df.gene_list$gene_list) ###################################### PROCESSING GENE lists ################################ ###### Mean per stage/structure ######## df.summary <- ddply(df.gene_list, c("stage", "structure_acronym", "gene_list"), summarise, mean = median(value, na.rm=TRUE), sd = sd(value, na.rm=TRUE)) ## plyr magic for renaming factor level levels(df.summary$gene_list) df.summary$gene_list <- revalue(df.summary$gene_list, c("gene_associated.txt"="Associated Genes", "gene_nearest.txt"="Nearest Genes", "gene_prioritization.txt"="Prioritized Genes", "gene_psd_human.txt"="Post Synaptic Genes (Human)", "gene_psd_mouse.txt"="Post Synaptic Genes (Mouse)")) levels(df.summary$gene_list) ###### Mean per stage - FINAL ########## df.summary.sem <- ddply(df.summary, c("stage","gene_list"), summarise, mean1 = mean(mean, na.rm=TRUE), sd1 = sd(mean, na.rm=TRUE)) ###################################### Calculating overall mean ################################ ### *** Runtime ~ 10 s *** df.all.sem <- ddply(ddply(df.expression_matrix.clean.melt, .(stage, structure_acronym), summarise, mean=median(value, na.rm=TRUE)), .(stage), summarise, mean1=mean(mean, na.rm=TRUE), sd1=sd(mean, na.rm=TRUE))

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/R/track_bearing.R

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cran/traipse

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track_bearing.R

#' Track bearing #' #' Calculate sequential bearing on longitude, latitude input vectors. The unit of bearing is degrees. #' #' By convention the last value is set to `NA` missing value, because the bearing #' applies to the segment extending from the current location. #' #' To use this on multiple track ids, use a grouped data frame with tidyverse code like #' `data %>% group_by(id) %>% mutate(turn = track_bearing(lon, lat))`. #' #' Absolute bearing is relative to North (0), and proceeds clockwise positive and anti-clockwise #' negative `N = 0, E = 90, S = +/-180, W = -90`. #' #' The last value will be `NA` as the bearing is relative to the first point of each segment. #' @param x longitude #' @param y latitude #' @return a numeric vector of absolute bearing in degrees, see Details #' @export #' @examples #' track_bearing(trips0$x, trips0$y)[1:10] track_bearing <- function(x, y) { xy <- cbind(x, y) n <- nrow(xy) c(geosphere::bearing(xy[-nrow(xy), , drop = FALSE], xy[-1L, , drop = FALSE]), NA_real_) }

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/xcms_code.R

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alenzhao/BatchEffects

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xcms_code.R

num_cores<-8; # The number of CPUs available, be aware of memory issue library(xcms) set1<-xcmsSet(nSlaves=44,method='centWave',ppm=30,peakwidth=c(5,60), prefilter=c(0,0),snthresh=6) set2 <- group(set1,bw=5,mzwid=0.015,minsamp=1,minfrac=0) set3 <- retcor(set2,method="obiwarp",plottype="none") set4 <- group(set3,bw=5,mzwid=0.015,minsamp=1,minfrac=0) set5 <- fillPeaks(set4) peaklist<-peakTable(set5,filebase="algae_blanks")

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/scripts/imp_packages_to_install.R

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csngh/machine-learning-cyber-sec

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refs/heads/master

2020-04-16T22:07:55.946370

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imp_packages_to_install.R

#Run the below code to install all essential packages install.packages("pacman") pacman::p_load(char = c( "caTools", "caret", "mlbench", "randomForest", "arules", "arulesViz", "ggplot2", "plotly", ))

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/RcppExports.R \name{r_unif} \alias{r_unif} \alias{r_unif_cir} \alias{r_unif_sph} \title{Sample uniformly distributed circular and spherical data} \usage{ r_unif_cir(n, M = 1L, sorted = FALSE) r_unif_sph(n, p, M = 1L) } \arguments{ \item{n}{sample size.} \item{M}{number of samples of size \code{n}. Defaults to \code{1}.} \item{sorted}{return each circular sample sorted? Defaults to \code{FALSE}.} \item{p}{integer giving the dimension of the ambient space \eqn{R^p} that contains \eqn{S^{p-1}}.} } \value{ \itemize{ \item \code{r_unif_cir}: a \bold{matrix} of size \code{c(n, M)} with \code{M} random samples of size \code{n} of uniformly-generated circular data on \eqn{[0, 2\pi)}. \item \code{r_unif_sph}: an \bold{array} of size \code{c(n, p, M)} with \code{M} random samples of size \code{n} of uniformly-generated directions on \eqn{S^{p-1}}. } } \description{ Simulation of the uniform distribution on \eqn{[0, 2\pi)} and \eqn{S^{p-1}:=\{{\bf x}\in R^p:||{\bf x}||=1\}}{ S^{p-1}:=\{x\in R^p:||x||=1\}}, \eqn{p\ge 2}. } \examples{ # A sample on [0, 2*pi) n <- 5 r_unif_cir(n = n) # A sample on S^1 p <- 2 samp <- r_unif_sph(n = n, p = p) samp rowSums(samp^2) # A sample on S^2 p <- 3 samp <- r_unif_sph(n = n, p = p) samp rowSums(samp^2) }

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/SFEI_chl/modelBuilding/addCovariateSpatialMod.R

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addCovariateSpatialMod.R

## spatial models with bam setwd("~/Desktop/sfei") require(mgcv) load("perStation.Rda") names(perStation[[1]]) wholeSeries<-c(1, 2, 5, 7, 11, 13, 15, 16, 17, 18, 21, 22, 23, 29, 40) allData<- do.call("rbind", perStation[wholeSeries]) names(allData) class(allData$Station) ## Need to make a factor ctrl <- list(nthreads=4) system.time(gamP<-bam(chl~as.factor(Station)+ti(doy,bs="cc",by=as.factor(Station))+ti(date_dec,bs="tp",by=as.factor(Station))+ti(pheo,bs="tp"),data=allData,family=gaussian(link="log"),control=ctrl)) gam.check(gamP) ## 30 seconds system.time(gamP<-bam(chl~as.factor(Station)+ti(doy,bs="cc",by=as.factor(Station))+ti(date_dec,bs="tp",by=as.factor(Station))+ti(pheo,bs="tp",k=10),data=allData,family=gaussian(link="log"),control=ctrl)) gam.check(gamP) system.time(gamP<-bam(chl~as.factor(Station)+ti(doy,bs="cc",by=as.factor(Station))+ti(date_dec,bs="tp",by=as.factor(Station))+ti(pheo,bs="tp",k=20),data=allData,family=gaussian(link="log"),control=ctrl)) gam.check(gamP) ## warning system.time(gamP<-bam(chl~as.factor(Station)+ti(doy,bs="cc",by=as.factor(Station))+ti(date_dec,bs="tp",by=as.factor(Station))+ti(pheo,bs="tp",k=15),data=allData,family=gaussian(link="log"),control=ctrl)) gam.check(gamP) ## warning system.time(gamP<-bam(chl~as.factor(Station)+ti(doy,bs="cc",by=as.factor(Station))+ti(date_dec,bs="tp",by=as.factor(Station),k=10)+ti(pheo,bs="tp",k=10),data=allData,family=gaussian(link="log"),control=ctrl)) gam.check(gamP) system.time(gamP<-bam(chl~as.factor(Station)+ti(doy,bs="cc",by=as.factor(Station),k=10)+ti(date_dec,bs="tp",by=as.factor(Station),k=10)+ti(pheo,bs="tp",k=10),data=allData,family=gaussian(link="log"),control=ctrl)) gam.check(gamP) ## Just under a minute spatialMod3Pheo=gamP save(spatialMod3Pheo,file="spatialMod3Pheo.RData") ## pheo helps make this look pretty decent without getting too big ## pheo by station system.time(gamP2<-bam(chl~as.factor(Station)+ti(doy,bs="cc",by=as.factor(Station))+ti(date_dec,bs="tp",by=as.factor(Station))+ti(pheo,bs="tp",by=as.factor(Station)),data=allData,family=gaussian(link="log"),control=ctrl)) gam.check(gamP2) ## stopped after 5 minutes ## try tn system.time(gamP3<-bam(chl~as.factor(Station)+ti(doy,bs="cc",by=as.factor(Station),k=10)+ti(date_dec,bs="tp",by=as.factor(Station),k=10)+ti(tn,bs="tp",k=10),data=allData,family=gaussian(link="log"),control=ctrl)) gam.check(gamP3) ## under 2 minutes spatialMod3Tn=gamP3 save(spatialMod3Tn,file="spatialMod3Tn.RData") ## require(dplyr) pheoUse=na.omit(allData[,c("Station","chl","pheo","doy","date_dec")]) pheoPred=predict(gamP,pheoUse,type="response") pheoUse$resid=(pheoUse$chl-pheoPred)^2 byStation<-group_by(pheoUse,Station) rmsePerStation<-summarise(byStation,stp1=sum(resid),count=n()) rmsePheo=sqrt(as.vector(rmsePerStation$stp1)/rmsePerStation$count) tnUse=na.omit(allData[,c("Station","chl","tn","doy","date_dec")]) tnPred=predict(gamP3,tnUse,type="response") tnUse$resid=(tnUse$chl-tnPred)^2 byStation<-group_by(tnUse,Station) rmsePerStation<-summarise(byStation,stp1=sum(resid),count=n()) rmseTn=sqrt(as.vector(rmsePerStation$stp1)/rmsePerStation$count) cbind(rmsePheo,rmseTn) cbind(rmsePheo, rmseTn, testMerge3$rmse3) ## from comparingSpatialModelsMaps

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plot4.R

# Draw all the trends x <- read.table("household_power_consumption.txt", sep=";", header=TRUE, skip=66636, nrows=2*24*60) names(x) <- c("Date", "Time", "Global_active_power", "Global_reactive_power", "Voltage", "Global_intensity", "Sub_metering_1", "Sub_metering_2", "Sub_metering_3") x$datetime <- strptime(paste(x$Date, x$Time), format="%d/%m/%Y %H:%M:%S", tz="EST") Sys.setlocale("LC_TIME", "C") # Remove localization of weekday names png(filename = "plot4.png", width= 480, height = 480, bg=NA) par(mfrow = c(2, 2)) with(x, { plot(datetime, Global_active_power, type = "l", xlab = "", ylab = "Global Active Power") plot(datetime, Voltage, type = "l") plot(datetime, Sub_metering_1, type = "l", xlab = "", ylab = "Energy sub metering") lines(datetime, Sub_metering_2, col = "red") lines(datetime, Sub_metering_3, col = "blue") legend("topright", lty = c(1,1,1), col=c("black", "red", "blue"), bty="n", legend=c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3")) plot(datetime, Global_reactive_power, type = "l") }) dev.off()

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/analysis_scripts_frontiers/Statistics/stats/pairwise.t.test.with.t.and.df.R

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pairwise.t.test.with.t.and.df.R

pairwise.t.test.with.t.and.df <- function (x, g, p.adjust.method = p.adjust.methods, pool.sd = !paired, paired = FALSE, alternative = c("two.sided", "less", "greater"), ...) { if (paired & pool.sd) stop("pooling of SD is incompatible with paired tests") DNAME <- paste(deparse(substitute(x)), "and", deparse(substitute(g))) g <- factor(g) p.adjust.method <- match.arg(p.adjust.method) alternative <- match.arg(alternative) if (pool.sd) { METHOD <- "t tests with pooled SD" xbar <- tapply(x, g, mean, na.rm = TRUE) s <- tapply(x, g, sd, na.rm = TRUE) n <- tapply(!is.na(x), g, sum) degf <- n - 1 total.degf <- sum(degf) pooled.sd <- sqrt(sum(s^2 * degf)/total.degf) compare.levels <- function(i, j) { dif <- xbar[i] - xbar[j] se.dif <- pooled.sd * sqrt(1/n[i] + 1/n[j]) t.val <- dif/se.dif if (alternative == "two.sided") 2 * pt(-abs(t.val), total.degf) else pt(t.val, total.degf, lower.tail = (alternative == "less")) } compare.levels.t <- function(i, j) { dif <- xbar[i] - xbar[j] se.dif <- pooled.sd * sqrt(1/n[i] + 1/n[j]) t.val = dif/se.dif t.val } } else { METHOD <- if (paired) "paired t tests" else "t tests with non-pooled SD" compare.levels <- function(i, j) { xi <- x[as.integer(g) == i] xj <- x[as.integer(g) == j] t.test(xi, xj, paired = paired, alternative = alternative, ...)$p.value } compare.levels.t <- function(i, j) { xi <- x[as.integer(g) == i] xj <- x[as.integer(g) == j] t.test(xi, xj, paired = paired, alternative = alternative, ...)$statistic } compare.levels.df <- function(i, j) { xi <- x[as.integer(g) == i] xj <- x[as.integer(g) == j] t.test(xi, xj, paired = paired, alternative = alternative, ...)$parameter } } PVAL <- pairwise.table(compare.levels, levels(g), p.adjust.method) TVAL <- pairwise.table.t(compare.levels.t, levels(g), p.adjust.method) if (pool.sd) DF <- total.degf else DF <- pairwise.table.t(compare.levels.df, levels(g), p.adjust.method) ans <- list(method = METHOD, data.name = DNAME, p.value = PVAL, p.adjust.method = p.adjust.method, t.value = TVAL, dfs = DF) class(ans) <- "pairwise.htest" ans } pairwise.table.t <- function (compare.levels.t, level.names, p.adjust.method) { ix <- setNames(seq_along(level.names), level.names) pp <- outer(ix[-1L], ix[-length(ix)], function(ivec, jvec) sapply(seq_along(ivec), function(k) { i <- ivec[k] j <- jvec[k] if (i > j) compare.levels.t(i, j) else NA })) pp[lower.tri(pp, TRUE)] <- pp[lower.tri(pp, TRUE)] pp }

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/Data Matcher/temp.R

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l <- letters set.seed(1) x1 <- l[sample(1:26, 81877, replace=T)] x2 <- l[sample(1:26, 81877, replace=T)] x3 <- l[sample(1:26, 81877, replace=T)] x4 <- l[sample(1:26, 81877, replace=T)] x5 <- l[sample(1:26, 81877, replace=T)] facility_list$random_id <- paste0(x1, x2, x3, x4, x5) t2 <- arrange(facility_list, lga_id, end) t2 <- ddply(t2, .(t2[,"lga_id"]), transform, tmp2 = paste0("A", as.character(1:length(tmp)))) id_generate <- function(df, lga_col = "lga_id", submit_end_col = "end", header = "F") { l <- letters set.seed(1) x1 <- l[sample(1:26, dim(df)[1], replace=T)] x2 <- l[sample(1:26, dim(df)[1], replace=T)] x3 <- l[sample(1:26, dim(df)[1], replace=T)] x4 <- l[sample(1:26, dim(df)[1], replace=T)] x5 <- l[sample(1:26, dim(df)[1], replace=T)] df <- arrange(df, df[, lga_col], df[, submit_end_col]) df <- ddply(df, .(df[,lga_col]), transform, seq_id = paste0(header, as.character(1:length(df[,lga_col])))) df$random_id <- paste0(x1, x2, x3, x4, x5) return(df) } t <- ddply(facility_list, .(lga_id), summarise, unique_short_id = length(unique(tmp)), n_fac = length(tmp)) which(t$unique_short_id != t$n_fac)

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/R/assignRefugiaFromAbundanceRaster.r

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stranda/holoSimCell

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assignRefugiaFromAbundanceRaster.r

#' Rescale carrying capacity rasters and assign refugia #' #' This function takes an "abundance" raster (i.e., from an ENM or from a pollen surface) and identifies refugia and starting (relative) abundances for each refugium. It rescales this to the extent and resolution of a "simulation" raster which typically has coarser spatial resolution than the abundance raster. It then generates a vector of cell IDs that correspond to refugia cells and calculates the average relative abundance across refugial cells. #' #' @param abund Abundance raster. #' @param sim Simulation raster. #' @param threshold Numeric. Value at which to threshold the abundance raster. Values that fall above this threshold will be assumed to represent a refuge and values below will be assumed to be outside (i.e., zero abundance). #' #' @details #' This function rescales abundances obtained from the abundance raster to a new spatial resolution and extent used for demographic simulations. Since the demographic simulation raster often has cells that are larger than the cells in the abundance raster, it cannot faithfully retain abundances or even all unique refugia identified in the abundance raster. The procedure first thresholds the abundance raster using a user-defined value above which it is assumed a cell was inside a refuge and below which it was assumed to be outside any refuge (zero abundance). A unique "abundId" raster is then created by assigning a unique integer number for each block of contiguous cells (using Moore neighborhood adjacency). This raster is then resampled to the resolution used by the simulation raster. The renumbering is redone so that cells that refugial were not adjacent at the original resolution but are in the new resolution are assigned to the same refuge. \cr #' Then, a "simAbund" raster is created with the same extent and resolution as the simulation raster. For each each cell in this raster, the function determines if it contains at least one cell in the "abundId" raster that is assigned to a refuge. The challenge here is that a single "simAbund" cell can contain cells that are assigned to multiple refugia in the "abundId" raster, and that "simAbund" cell can also include cells that have abundances that are assigned to no refugia in the abundance raster. Thus, if we simply assigned abundances to the "simAbund" cell by resampling the abundance raster, we would in some cases be too generous because a single "simAbund" cell can include cells that do not belong to this refuge. \cr #' The procedure assigns abundances by first calculating a proportionality scalar where the numerator is the sum of abundances of abundance raster cells in this refugium and in the "simAbund" cell, and the denominator the sum of all abundances of all cells in this "simAbund" cell. The abundance assigned to this "simAbund" cell for this particular refugium is this scalar times the abundance from the resampling of the abundance raster to the extent/resolution of the simulation raster. Thus, abundances assigned to any particular cell in a refuge will be equal to or less than the abundance of the resampled values. \cr #' The procedure then assigns each cell an integer number identifying which refugium to belongs to and an abundance corresponding to the given refuge. When cells contain more than one "abundId" refuge cell, the refuge with the greater abundance is assigned to the cell. As a consequence, a refuge that appears in the "abundId" raster could be trimmed in extent or even eliminated if it is only represented by a few cells that have small abundances relative to a more "massive" refugium in the same cell. Also, as a result, it is possible to have distinct refugia in cells that are adjacent to one another when rescaled to the extent/resolution of the simulation raster but are spatially distinct at the scale of the abundance raster. #' @return #' \itemize{ #' A list with: #' \item{1) A raster stack representing refuge ID numbers and abundances at the \emph{simulation} resolution and extent} #' \item{2) a vector of cell numbers for refugial cells at the \emph{simulation} scale} #' \item{3) a single numeric value representing mean refugial abundance across cells at the \emph{simulation} extent.} #' } #' #' @examples #' library(raster) #' abund <- brick(system.file("extdata/rasters/ccsm_160kmExtent_maxent.tif", package = "holoSimCell")) #' abund <- abund[[1]] #' #' load(file=paste0(system.file(package="holoSimCell"),"/extdata/landscapes/",pollenPulls[[1]]$file)) #' sim <- landscape$sumrast #' #' threshold <- 0.6 #' refs <- assignRefugiaFromAbundanceRaster(abund, sim, threshold) #' #' cols <- c('red', 'orange', 'yellow', 'green', 'blue', 'purple', 'gray', #' 'chartreuse', 'darkgreen', 'cornflowerblue', 'goldenrod3', 'black', #' 'steelblue3', 'forestgreen', 'pink', 'cyan', 'darkred') #' #' par(mfrow=c(1, 2)) #' #' col <- cols[1:maxValue(refs$sim[['refugiaId']])] #' plot(refs$simulationScale[['refugiaId']], col=col, main='refuge ID') #' plot(refs$simulationScale[['refugiaAbund']], main='refuge abundance') #' #' @seealso \code{\link{getpophist2.cells}}, \code{\link[raster]{calc}}, \code{\link[raster]{resample}} #' #' @export assignRefugiaFromAbundanceRaster <- function( abund, sim, threshold ) { # ### abundance raster # ###################### # # create data frame for abundance raster with: # # cell number in abundance frame # # long, lat # # a mask of refugia # # ID number of each refuge # # abundance (abundance) # # cell number in simulation raster # # "abundance" # names(abund) <- 'origAbund' # # mask refugia # origMask <- abund >= threshold # names(origMask) <- 'origMask' # # identify refugia # idsOrig <- raster::clump(origMask, directions=8, gaps=FALSE) # names(idsOrig) <- 'idOrig' # # abundance in refugial cells # abundRefugeAbund <- abund * origMask # names(abundRefugeAbund) <- 'abundOrigRefuge' # # long/lat # ll <- enmSdm::longLatRasters(abund) # # re-assess ID number based on resolution of simulation raster # idsSim <- raster::resample(idsOrig, sim) # idsSim <- raster::clump(idsSim, directions=8, gaps=FALSE) # idsSim <- raster::extract(idsSim, raster::as.data.frame(ll)) # numRefugia <- max(idsSim, na.rm=TRUE) # # cell numbers # cellsAbund <- raster::setValues(abund, 1:raster::ncell(abund)) # names(cellsAbund) <- 'cellNumOrig' # suitStack <- raster::stack(cellsAbund, ll, origMask, idsOrig, abund, abundRefugeAbund) # suitFrame <- as.data.frame(suitStack) # suitFrame$idSim <- idsSim # # cell numbers of simulation raster in the layout used by the demo/genetic simulations: # # cell in lower left is 1, to its right is 2, etc, then wraps to next row so cell [nrow - 1, 1] is next in line # nrows <- nrow(sim) # ncols <- ncol(sim) # ncells <- raster::ncell(sim) # v <- rep(seq(nrows * (ncols - 1) - 1, 1, by=-ncols), each=ncols) + 0:(ncols - 1) # cellNumSim <- matrix(v, nrow=nrows, ncol=ncols, byrow=TRUE) # cellNumSim <- raster::raster(cellNumSim, template=sim) # suitFrame$cellNumSim <- raster::extract(cellNumSim, suitFrame[ , c('longitude', 'latitude')]) # ### simulation raster # ##################### # # create data frame with: # # simulation raster cell number # # abundance resampled from abundance raster # # sum of abundances of cells from abundance raster, by refuge ID # # refuge ID # # rescaled abundances (to match resampled abundances) for the refuge to which this cell is assigned accounting for: # # * empty cells (outside a refuge in the abundance raster but with non-NA values) # # * abundance cells that fall into other refugia (should not be counted toward this cell's abundance) # # "abundance" # abundSim <- raster::resample(abund, sim) # names(abundSim) <- 'abundSim' # # cell numbers # cellsSim <- raster::setValues(sim, 1:raster::ncell(sim)) # names(cellsSim) <- 'cellNumSim' # simStack <- raster::stack(cellsSim, abundSim) # simFrame <- as.data.frame(simStack) # ### calculate abundances in simulation raster for each refuge # for (countRefuge in 1:numRefugia) { # simFrame$DUMMY <- NA # names(simFrame)[ncol(simFrame)] <- paste0('refuge', countRefuge) # suitFrameOutsideRefuge <- suitFrame[!is.na(suitFrame$origAbund) & (is.na(suitFrame$idSim) | omnibus::naCompare('!=', suitFrame$idSim, countRefuge)), ] # suitFrameInsideRefuge <- suitFrame[omnibus::naCompare('==', suitFrame$idSim, countRefuge), ] # # assign scaled abundances to each simulation raster cell in this refuge # # For each simulation cell that overlaps with at least one cell in the abundance raster assigned to a particular refuge, find: # # * a proportionality factor as a proportion of the sum of suitabilities in the abundance raster cells in this refuge divided by the sum of all suitabilities in all cells that fall into this simulation raster cell (regardless of whether they're in a refuge or not) # # * the resampled abundance (resampling the abundance raster to the simulation raster) # # Final abundance in this cell for a particular refuge refuge is the product of these two values. # simCellsInRefuge <- sort(unique(suitFrameInsideRefuge$cellNumSim)) # for (simCell in simCellsInRefuge) { # simCellInterpAbund <- simFrame$abundSim[simFrame$cellNumSim == simCell] # abundInSimCellOutsideRefuge <- suitFrameOutsideRefuge$origAbund[suitFrameOutsideRefuge$cellNumSim == simCell] # abundInSimCellInsideRefuge <- suitFrameInsideRefuge$origAbund[suitFrameInsideRefuge$cellNumSim == simCell] # if (length(abundInSimCellOutsideRefuge) == 0) { # simAbund <- simCellInterpAbund # } else { # insideAbund <- sum(abundInSimCellInsideRefuge) # outsideAbund <- sum(abundInSimCellOutsideRefuge) # totalAbund <- insideAbund + outsideAbund # simAbund <- simCellInterpAbund * (insideAbund / totalAbund) # } # simFrame[simFrame$cellNumSim == simCell, paste0('refuge', countRefuge)] <- simAbund # } # next simulation raster cell # } # next refuge # ### assign refuge ID and abundance # simIdsList <- apply(simFrame[ , paste0('refuge', 1:numRefugia), drop=FALSE], 1, which.max) # simFrame$id <- NA # for (i in seq_along(simIdsList)) simFrame$id[i] <- ifelse(length(simIdsList[[i]]) == 0, NA, simIdsList[[i]]) # simFrame$refugeAbund <- NA # for (countRefuge in 1:numRefugia) { # refugeIndex <- which(omnibus::naCompare('==', simFrame$id, countRefuge)) # simFrame$refugeAbund[refugeIndex] <- simFrame[refugeIndex, paste0('refuge', countRefuge)] # } # # set values of ID and abundance rasters # idsSimRast <- abundSim <- NA * sim # idsSimRast <- raster::setValues(idsSimRast, simFrame$id) # abundSim <- raster::setValues(abundSim, simFrame$refugeAbund) # # flip up/down because we've renumbered simulation raster cell numbers as per demographic/genetic simulations # idsSimRast <- raster::as.matrix(idsSimRast) # abundSim <- raster::as.matrix(abundSim) # idsSimRast <- idsSimRast[nrows:1, ] # abundSim <- abundSim[nrows:1, ] # idsSimRast <- raster::raster(idsSimRast, template=sim) # abundSim <- raster::raster(abundSim, template=sim) # names(idsSimRast) <- 'id' # names(abundSim) <- 'abundance' # # cell numbers: renumber so bottom left is (1, 1), increments to the right, then wraps around to next row up # refugeCellIds <- simFrame$cellNumSim[which(!is.na(simFrame$refugeAbund))] # gets "raster" cell numbers # # mean abundance in all refugia # meanRefugeAbund <- raster::cellStats(abundSim, 'mean') # rescale origRefugia <- abund >= threshold origRefugiaAbund <- abund * origRefugia simAbund <- raster::resample(origRefugiaAbund, sim) simAbund <- raster::calc(simAbund, fun=function(x) ifelse(x > 1, 1, x)) simAbund <- raster::calc(simAbund, fun=function(x) ifelse(x < 0, 0, x)) # identify refugia simRefugia <- simAbund >= threshold simRefugiaId <- raster::clump(simRefugia, directions=8, gaps=FALSE) names(simRefugiaId) <- 'refugiaId' # calculate abundance in refugia simAbund <- simAbund * simRefugia names(simAbund) <- 'refugiaAbund' # refuge cell IDs # cell in lower left is 1, to its right is 2, etc, then wraps to next row so cell [nrow - 1, 1] is next in line nrows <- nrow(sim) ncols <- ncol(sim) ncells <- raster::ncell(sim) v <- rep(seq(nrows * (ncols - 1) - 1, 1, by=-ncols), each=ncols) + 0:(ncols - 1) cellNumSim <- matrix(v, nrow=nrows, ncol=ncols, byrow=TRUE) cellNumSim <- raster::raster(cellNumSim, template=sim) cellNumSim <- as.vector(cellNumSim) simRefugiaBinary <- as.vector(simRefugia) refugeCellNum <- cellNumSim[simRefugiaBinary] if (any(is.na(refugeCellNum))) refugeCellNum <- refugeCellNum[!is.na(refugeCellNum)] # mean refuge abundance meanRefugeAbund <- raster::cellStats(simAbund, 'sum') / length(refugeCellNum) out <- list( simulationScale = raster::stack(simRefugiaId, simAbund), refugeCellNum = refugeCellNum, meanRefugeAbund = meanRefugeAbund ) out }

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# Course Project #1 # Reproducible Research # globallye suppress warnings oldwarn<- getOption("warn") options(warn = -1) # install libraries/packages library(plyr) library(dplyr) library(tidyr) library(data.table) library(lubridate) # read file into data frame for manipulation file<- "activity.csv" main<- read.csv(file) # refactor raw date to type date main<- mutate(main, date=as.Date(as.character(main$date))) # group by days to get sum of steps per day # ignore days with no steps recorded stepsbyday<- group_by(main, date) %>% summarize_each(funs(sum)) na.omit(stepsbyday) #calculate mean and median of number of steps per day meansteps<-mean(stepsbyday$steps, na.rm=TRUE) mediansteps<- median(stepsbyday$steps, na.rm=TRUE) # plot histogram hist(stepsbyday$steps, main = "Steps per Day", xlab = "Sum of steps taken per day", ylab = "Number of days at that number of daily steps", xlim = c(0, 25000), ylim = c(0, 30)) abline(v = meansteps, col = "blue", lwd=4) abline(v = mediansteps, col = "red", lwd=2) legend('topright', c("mean", "median"), col=c("blue", "red"), lwd=3) # group by 5 minute interval # ignore intervals with no steps recorded stepsbyinterval<- group_by(main, interval) %>% summarize_each(funs(mean(., na.rm=TRUE))) # plot time series of 5 minute interval average steps with(stepsbyinterval, plot(interval, steps, type = "l", main = "Average Number of Steps Throughout the Day", xlab = "5 Minute Interval (midnight to midnight)", ylab = "Mean Number of Steps", xlim = c(0, 2400), ylim = c(0, 235))) # calculate which 5 minute interval has highest average number of steps maxintervalrow<- which.max(stepsbyinterval$steps) maxinterval<- stepsbyinterval$interval[maxintervalrow] # calculate the number of rows with missing (NA) data numrowtotal<- nrow(main) numrownona<- nrow(na.omit(main)) numrowwithna<- numrowtotal - numrownona # impute missing values and replace with interval mean impute.mean <- function(x) replace(x, is.na(x), mean(x, na.rm = TRUE)) mainimputed <- ddply(main, ~ interval, transform, steps = impute.mean(steps)) # group new data frame by days to get sum of steps per day # all days/intervals have steps recorded stepsbyday2<- group_by(mainimputed, date) %>% summarize_each(funs(sum)) # calculate mean and median of number of steps per day meansteps2<-mean(stepsbyday2$steps) mediansteps2<- median(stepsbyday2$steps) # plot histogram on imputed data hist(stepsbyday2$steps, main = "Steps per Day", xlab = "Sum of steps taken per day", ylab = "Number of days at that number of daily steps", xlim = c(0, 25000), ylim = c(0, 40)) abline(v = meansteps2, col = "blue", lwd=4) abline(v = mediansteps2, col = "red", lwd=2) legend('topright', c("mean", "median"), col=c("blue", "red"), lwd=3) # add columns showing day of the week stepsbyday3<- mutate(mainimputed, dayofweek = weekdays(date, abbreviate=TRUE)) # add column with logical vector as to weekday/weekend stepsbyday3<- mutate(stepsbyday3, weekend = (dayofweek == "Sun" | dayofweek == "Sat")) # subset for weekday and weekend in new data frames weekdaysteps<- subset(stepsbyday3, !weekend) weekendsteps<- subset(stepsbyday3, weekend) # convert logical vector to factors with labels # Note: I did not need to do this for my analysis, # but it was a requirement of the project stepsbyday3$weekend<-factor(stepsbyday3$weekend,levels=c(FALSE, TRUE), labels=c('Weekday', 'Weekend')) #group by date weekdaystepsum<- group_by(weekdaysteps, date) %>% summarize_each(funs(sum(steps))) weekendstepsum<- group_by(weekendsteps, date) %>% summarize_each(funs(sum(steps))) # calculate mean and median of number of steps per day meanstepsweekday<-mean(weekdaystepsum$steps) medianstepsweekend<- median(weekendstepsum$steps) # group by 5 minute interval # ignore intervals with no steps recorded weekdaystepsbyinterval<- group_by(weekdaysteps, interval) %>% summarize_each(funs(mean(., na.rm=TRUE))) weekendstepsbyinterval<- group_by(weekendsteps, interval) %>% summarize_each(funs(mean(., na.rm=TRUE))) # plot time series of 5 minute interval average steps for weekday and weekends par(mfrow = c(2,1), height = 500) with(weekdaystepsbyinterval, plot(interval, steps, type = "l", main = "Average Number of Steps on Average Weekday", xlab = "5 Minute Interval (midnight to midnight)", ylab = "Mean Number of Steps", xlim = c(0, 2400), ylim = c(0, 235))) with(weekendstepsbyinterval, plot(interval, steps, type = "l", main = "Average Number of Steps on Average Weekend Day", xlab = "5 Minute Interval (midnight to midnight)", ylab = "Mean Number of Steps", xlim = c(0, 2400), ylim = c(0, 235))) # restore warnings options(warn = oldwarn)

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01_create_vessel_df.R

# create vessel level landings data create_vessel_landings <- function(minrev){ library(dplyr) dat <- readRDS("processedData/catch/1_cleaningData/tickets.RDS") # vessels should have less than min_rev on average across 5 years AND # flagg landings before and after catch shares # then drop 2011 data # also drop any ZZZ drvids # and drop non-commercial landings (pargrp = C and removal type in C, D) min_rev = 5000 # find minimum revenue and whether vessel present in both periods div_dat <- dat %>% filter(drvid != "NONE", year > 2008, year < 2014) %>% group_by(drvid, year) %>% summarize(annual_revenue = sum(adj_revenue, na.rm = T)) %>% # calculate yr rev mutate(av.annual.rev = mean(annual_revenue, na.rm = T)) %>% # mean yr rev filter(av.annual.rev >= min_rev) %>% # drop vessels with < min_rev filter(year != 2011) %>% group_by(drvid) %>% summarize(both.periods = ifelse(any(year %in% c(2009:2010)) & any(year %in% c(2012:2013)), 1, 0)) # drop 2015 landings, not complete div_landings <- subset(dat, drvid %in% unique(div_dat$drvid) & year < 2014 & year!=2011 & year > 2008) %>% left_join(div_dat, by = 'drvid') saveRDS(div_landings, file="Analysis/new_analysis/catch_shares/Analysis/vessel_landings_data.RDS") return(div_landings) }

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plot.PresenceAbsence.Rd.R

library(letsR) ### Name: plot.PresenceAbsence ### Title: Plot an object of class PresenceAbsence ### Aliases: plot.PresenceAbsence ### ** Examples ## Not run: ##D data(PAM) ##D plot(PAM) ##D plot(PAM, xlab = "Longitude", ylab = "Latitude", ##D main = "Phyllomedusa species richness") ##D plot(PAM, name = "Phyllomedusa atelopoides") ##D plot(PAM, name = "Phyllomedusa azurea") ## End(Not run)

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#' server.R library(ArchaeoDash) library(shiny) shinyServer(function(input, output, session) { #### create reactive values #### # rvals = reactiveValues() ## for testing rvals <<- reactiveValues(); showNotification("warning: global variable is only for testing") input <<- input #### Import data #### dataInputServer(input,output,session,rvals) #### Impute & Transform #### imputeTransformServer(input,output,session,rvals) #### Ordination #### ordinationServer(input,output,session,rvals) #### Cluster #### clusterServer(input,output,session,rvals) #### Visualize & Assign #### visualizeAssignServer(input,output,session,rvals) #### Save & Export #### saveExportServer(input,output,session,rvals) }) # end server

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/AminoAcids.R \name{bulk} \alias{bulk} \title{Calculates the average bulkiness of amino acid sequences} \usage{ bulk(seq, bulkiness = NULL) } \arguments{ \item{seq}{vector of strings containing amino acid sequences.} \item{bulkiness}{named numerical vector defining bulkiness scores for each amino acid, where names are single-letter amino acid character codes. If \code{NULL}, then the Zimmerman et al, 1968 scale is used.} } \value{ A vector of bulkiness scores for the sequence(s). } \description{ \code{bulk} calculates the average bulkiness score of amino acid sequences. Non-informative positions are excluded, where non-informative is defined as any character in \code{c("X", "-", ".", "*")}. } \examples{ # Default bulkiness scale seq <- c("CARDRSTPWRRGIASTTVRTSW", "XXTQMYVRT") bulk(seq) # Use the Grantham, 1974 side chain volumn scores from the seqinr package library(seqinr) data(aaindex) x <- aaindex[["GRAR740103"]]$I # Rename the score vector to use single-letter codes names(x) <- translateStrings(names(x), ABBREV_AA) # Calculate average volume bulk(seq, bulkiness=x) } \references{ \enumerate{ \item Zimmerman JM, Eliezer N, Simha R. The characterization of amino acid sequences in proteins by statistical methods. J Theor Biol 21, 170-201 (1968). } } \seealso{ For additional size related indices see \link[seqinr]{aaindex}. }

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newIS_dootika.R

library(mcmc) HAC1 <- function (mcond , m ) { require ( fftwtools ) dimmcond <- dim( mcond ) Nlen <- dimmcond [1] qlen <- dimmcond [2] ww <- weight(m,Nlen) ww <- c(1, ww [1:( Nlen -1)], 0, ww [( Nlen -1) :1]) ww <- Re( fftw (ww)) FF <- rbind (mcond , matrix (0, Nlen , qlen )) FF <- (mvfftw (FF)) FF <- FF * matrix ( rep(ww , qlen ), ncol = qlen ) FF <- Re( mvfftw (FF , inverse = TRUE )) / (2* Nlen ) FF <- FF [1: Nlen ,] return ((t( mcond ) %*% FF) / Nlen ) } weight = function(m, dimN){ w = numeric(dimN) w[1:(2*m+1)] = 1 return (w) } initseq_new = function(foo) { truncs <- apply(foo, 2, function(t) length(initseq(t)[[2]]) -1) print(truncs) chosen <- min(truncs) sig <- HAC1(foo, chosen) # for(m in 0:(n/2)){ # sig = HAC1(foo, m) # if(!(any(eigen(sig)$values <= 0))){ # sn = m # break # } # if(sn > (n/2 - 1)){ # stop("Not enough samples") # } # } # Dtm = det(sig) # for(m in (sn+1):(n/2)){ # sig1 = HAC1(foo, m) # dtm = det(sig1) # if(dtm <= Dtm[m-sn]) # break # Dtm = c(Dtm, dtm) # sig = sig1 # } return(list("Sig" = sig, "trunc" = chosen) ) } library(Rcpp) library(rbenchmark) library(mAr) sourceCpp("inseq.cpp") p <- 5 n <- 1e4 omega <- 5*diag(1,p) ## Making correlation matrix var(1) model foo <- matrix(rnorm(p^2, sd = 1), nrow = p) foo <- foo %*% t(foo) phi <- foo / (max(eigen(foo)$values) + 1) foo <- scale(as.matrix(mAr.sim(rep(0,p), phi, omega, N = n)), scale = F) a = initseq_new(foo) b = inseq(foo) c(a$trunc, b$trunc) c(det(a$Sig), det(b$Sig))^(1/p) all.equal(a$Sig,b$Sig) benchmark(initseq_new(foo),inseq(foo), replications = 2) library(lineprof) library(mcmcse) lineprof(mcse.initseq(foo)) lineprof(initseq_new(foo))

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## sym = 0, pos = c("名詞", "形容詞"),kigo = "記号", docMatrix2 <- function( directory, pos = "Default" , minFreq = 1, weight = "no", kigo = 0, co = 0, dic = "" , mecabrc = "", etc = "" ) { # posN <- length(pos) # gc() if(any(suppressWarnings(dir(directory) ) > 0)){ ft <- 1 ##ディレクトリが指定された file <- dir(directory) } else if (file.exists(directory)){ ft <- 0 # 単独ファイル file <- basename(directory) directory <- dirname(directory) } else{ stop("specify directory or a file!") } fileN = length(file) if(any( pos == "" | is.na(pos)) ){ stop("specify pos argument!") } if( length(pos) == 1 && pos == "Default" ){ posN <- 0 }else{ posN <- length(pos) } ## if( posN < 1){ ## stop("specify pos argument") ## } else if("記号" %in% pos){ ## sym = 1 # 記号を頻度に含めて出力する ## } if( is.null(dic) || is.na(dic)){ dic = "" } else if( (xl <- nchar(dic)) > 0 ) { dic <- paste(dirname(dic), basename(dic), sep = "/") if ( !(file.exists(dic)) ) { cat ("specified dictionary file not found; result by default dictionary.\n")# dic = "" } else { dic <- paste(" -u", dic) } } # if( is.null(mecabrc) || is.na(mecabrc) || (nchar(mecabrc)) < 2 ){ mecabrc = "" } else { mecabrc <- paste(dirname(mecabrc), basename(mecabrc), sep = "/") if ( !(file.exists(mecabrc)) ) { cat ("specified mecabrc not found; result by default mecabrc.\n") mecabrc = "" } else { mecabrc <- paste("-r", mecabrc) } } # opt <- paste(dic, mecabrc, etc) if(minFreq < 1){ stop("minFreq argument must be equal to or larger than 1!") } dtm <- .Call("docMatrix2", as.character(directory), as.character(file), as.numeric(fileN), as.numeric(ft), as.character(pos), as.numeric(posN), as.numeric(minFreq), as.numeric(kigo), as.character(opt), PACKAGE="RMeCab")## as.numeric(sym), as.character(kigo), if(is.null(dtm)){ stop("chage the value of minFreq argument!") } dtm <- t(dtm) # environment(dtm) = new.env() ## ## class(dtm) <- "RMeCabMatrix" if(co == 1 || co == 2 || co == 3){ dtm <- coOccurrence( removeInfo (dtm), co) ## invisidoble(dtm) } ## ######### < 2008 05 04 uncommented> if(weight == ""){ ## invisible( dtm) ## break }else{ argW <- unlist(strsplit(weight, "*", fixed = T)) for(i in 1:length(argW)){ if(argW[i] == "no"){ invisible( dtm) ## cat("Term Document Matrix includes 2 information rows!", "\n") ## cat("whose names are [[LESS-THAN-", minFreq,"]] and [[TOTAL-TOKENS]]", "\n", sep = "") ## cat("if you remove these rows, execute", "\n", "result[ row.names(result) != \"[[LESS-THAN-", minFreq, "]]\" , ]", "\n", "result[ row.names(result) != \"[[TOTAL-TOKENS]]\" , ]","\n" , sep = "") break }else if(argW[i] == "tf"){ dtm <- localTF(dtm) }else if(argW[i] == "tf2"){ dtm <- localLogTF(dtm) }else if(argW[i] == "tf3"){ dtm <- localBin(dtm) }else if(argW[i] == "idf"){ dtm <- dtm * globalIDF(dtm) }else if(argW[i] == "idf2"){ dtm <- dtm * globalIDF2(dtm) }else if(argW[i] == "idf3"){ dtm <- dtm * globalIDF3(dtm) }else if(argW[i] == "idf4"){ dtm <- dtm * globalEntropy(dtm) } else if(argW[i] == "norm"){ dtm <- t(t(dtm) * mynorm(dtm)) } } if(any(is.na ( dtm))){ cat("Warning! Term document matrix includes NA!", "\n") } } invisible( dtm) }

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rm(list = ls()) library(ggplot2) # Question 15 sum_vect <- function(x = c(1, 3, 5, 8, 9, 8), y = c(1, 0, 5, 2)) { if (length(x) > length(y)){ y <- c(y, rep(0, length(x) - length(y))) } else if(length(x) < length(y)){ x <- c(x, rep(0, length(y) - length(x))) } return(x + y) } # sum_abundance <- c( ) sum_size <- c() combined_results <- list() #create your list output here to return i <- 0 while (i < 100){ i <- i + 1 fileName <- paste("Results/ks3020_result", i, ".rda", sep = "") load(fileName) # Obtain mean octaves for each abundance octave for (j in 1:length(oct)){ sum_abundance <- sum_vect(sum_abundance, oct[[j]]) } oct_mean <- sum_abundance/length(oct) sum_abundance <- c() sum_size <- sum_vect(sum_size, oct_mean) if (i %% 25 == 0){ print(i) combined_results <- c(combined_results, list(sum_size / 25)) sum_abundance <- c() sum_size <- c() } # save results to an .rda file save(combined_results, file = "combined_results.rda") } # # names <- c() # for (n in 1:12){ # if (n == 1){ # names <- c(names, "1") # } else { # names <- c(names, paste(2^(n-1),"~",2^n-1, sep = "")) # } # } # # df <- data.frame(Ranges = c(names[1:9], names[1:10], names[1:11], names[1:11]), # Octaves = c(combined_results[[1]], combined_results[[2]], combined_results[[3]], combined_results[[4]]), # Sizes = c(rep("Size = 500 Simulation", 9), rep("Size = 1000 Simulation", 10), rep("Size = 2500 Simulation", 11), rep("Size = 5000 Simulation", 11))) # # df$Ranges <- as.character(df$Ranges) # df$Ranges <- factor(df$Ranges, levels = unique(df$Ranges)) # # df$Sizes <- as.character(df$Sizes) # df$Sizes <- factor(df$Sizes, levels = unique(df$Sizes)) # # ggplot(df, aes(x = Ranges, y = Octaves, fill = Sizes)) + # geom_bar(stat = "identity") + # facet_grid(Sizes ~ .) + # theme(legend.position = "bottom") # load("Sandbox/test/neutral_simulation_3.rda") load("Results/ks3020_result3.rda") i <- 3 fileName <- paste("Results/ks3020_result", i, ".rda", sep = "") load(fileName) # Obtain mean octaves for each abundance octave for (j in 1:length(oct)){ sum_abundance <- sum_vect(sum_abundance, oct[[j]]) } oct_mean <- sum_abundance/length(oct) load("Sandbox/test/ks3020_result3.rda") # load("Week9_HPC/Sandbox/test/ks3020_result3.rda")

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bestScores.Rd

\name{bestScores} \alias{bestScores} \title{ Obtain the best scores from an experimental comparison } \description{ This function can be used to obtain the learning systems that obtained the best scores on an experimental comparison. This information will be shown for each of the evaluation statistics involved in the comparison and also for all data sets that were used. } \usage{ bestScores(compRes, maxs = rep(F, dim(compRes@foldResults)[2])) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{compRes}{ A \code{compExp} object with the results of your experimental comparison. } \item{maxs}{ A vector of booleans with as many elements are there are statistics measured in the experimental comparison. A True value means the respective statistic is to be maximized, while a False means minimization. Defaults to all False values. } } \details{ This is a handy function to check what were the best performers in a comparative experiment for each data set and each evaluation metric. The notion of "best performance" depends on the type of evaluation metric, thus the need of the second parameter. Some evaluation statistics are to be maximized (e.g. accuracy), while others are to be minimized (e.g. mean squared error). If you have a mix of these types on your experiment then you can use the \code{maxs} parameter to inform the function of which are to be maximized (minimized). } \value{ The function returns a list with named components. The components correspond to the data sets used in the experimental comparison. For each component you get a data.frame, where the rows represent the statistics. For each statistic you get the name of the best performer (1st column of the data frame) and the respective score on that statistic (2nd column). } \references{ Torgo, L. (2010) \emph{Data Mining using R: learning with case studies}, CRC Press (ISBN: 9781439810187). \url{http://www.dcc.fc.up.pt/~ltorgo/DataMiningWithR} } \author{ Luis Torgo \email{ltorgo@dcc.fc.up.pt} } \seealso{ \code{\link{experimentalComparison}}, \code{\link{rankSystems}}, \code{\link{statScores}} } \examples{ ## Estimating several evaluation metrics on different variants of a ## regression tree and of a SVM, on two data sets, using one repetition ## of 10-fold CV data(swiss) data(mtcars) ## First the user defined functions cv.rpartXse <- function(form, train, test, ...) { require(DMwR) t <- rpartXse(form, train, ...) p <- predict(t, test) mse <- mean((p - resp(form, test))^2) c(nmse = mse/mean((mean(resp(form, train)) - resp(form, test))^2), mse = mse) } ## run the experimental comparison results <- experimentalComparison( c(dataset(Infant.Mortality ~ ., swiss), dataset(mpg ~ ., mtcars)), c(variants('cv.rpartXse',se=c(0,0.5,1))), cvSettings(1,10,1234) ) ## get the best scores for dataset and statistic bestScores(results) } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{ models }

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cachematrix.R

## These functions store a matrix and cache its inverse ## to ensure for subsequent calls that the inverse ## does not have to be calculated each time ## Function creates a vector to get/set the value of ## a matrix and get/set the value of its inverse makeCacheMatrix <- function(x = matrix()) { i<-NULL set<-function(y){ x<<-y i<<-NULL } get<-function() x setinverse<-function(inverse)i<<-inverse getinverse<-function() i list(set=set,get=get,setinverse=setinverse,getinverse=getinverse) } ## returns the inverse if already calculate else ## calculates and stores it for future calls cacheSolve <- function(x, ...) { ## Return a matrix that is the inverse of 'x' inv<-x$getinverse() if(!is.null(inv)){ message("returning cached inverse") return(inv) } data<-x$get() #solve() calculates the inverse inv<-solve(data) x$setinverse(inv) inv }

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#' Convert a set of Lattes CV XML files to a list object #' #' Extract information from a set of Lattes XML files and convert it to a list #' vector #' #' This function extracts relevant information from a set of Lattes CV XML files #' and outputs a list object containing specific information on the following #' aspects of a group's production: #' - Accepted journal papers #' - Published journal papers #' - Published conference papers #' - Published book chapters #' - Published books #' - Ph.D. student defenses #' - M.Sc. student defenses #' #' Journal and conference papers are checked for duplication using DOI and Title #' information. Duplicated entries are registered only once. #' @param CV.dir folder where CVs are contained. If NULL #' then the current working directory is used. #' @param author.aliases list vector with author aliases. #' See \code{Examples} for details. #' #' @return list vector where each element is a dataframe with information on a #' specific aspect of the academic production #' #' @export #' #' @examples #' my.dir <- system.file("extdata", package="ChocoLattes") #' #' # Define the aliases of authors "Felipe Campelo" and "Lucas S. Batista": #' # (all aliases will be converted to the first name provided for each author) #' my.aliases <- list(c("Felipe Campelo", #' "Felipe Campelo Franca Pinto", #' "Felipe Campelo F. Pinto", #' "F.C.F. Pinto"), #' c("Lucas S. Batista", #' "Lucas Batista", #' "Lucas de Souza Batista", #' "Lucas Souza Batista")) #' #' lattes.list <- lattes_to_list(CV.dir = my.dir, #' author.aliases = my.aliases) lattes_to_list <- function(CV.dir = NULL, author.aliases = list()){ # Standardize CV.dir if(!R.utils::isAbsolutePath(CV.dir)){ CV.dir <- paste0(getwd(), "/", gsub(pattern = "[.]/", "", CV.dir)) } # get filenames filenames <- paste0(CV.dir, "/", dir(CV.dir, pattern = ".xml")) filenames <- gsub("//", "/", filenames) # Prepare list for results out.list <- vector("list", 7) names(out.list) <- c("Accepted for Publication", "Journal Papers", "Conference Papers", "Book Chapters", "Books", "MSc Dissertations", "PhD Theses") for (indx in seq_along(filenames)){ # Read XML to a list object x <- XML::xmlToList(XML::xmlTreeParse(filenames[indx], useInternal = TRUE, encoding = "latin")) # Get productions MyPapers <- get_journal_papers(x, ID = indx) MyAccept <- get_accepted_papers(x, ID = indx) MyConfs <- get_conference_papers(x, ID = indx) MyChaps <- get_book_chapters(x, ID = indx) MyBooks <- get_books(x, ID = indx) MyMsc <- get_advised_dissertations(x, ID = indx) MyPhd <- get_advised_theses(x, ID = indx) # ========================================== if (indx == 1) { out.list[[1]] <- MyAccept out.list[[2]] <- MyPapers out.list[[3]] <- MyConfs out.list[[4]] <- MyChaps out.list[[5]] <- MyBooks out.list[[6]] <- MyMsc out.list[[7]] <- MyPhd } else { out.list[[1]] <- rbind(out.list[[1]], MyAccept) out.list[[2]] <- rbind(out.list[[2]], MyPapers) out.list[[3]] <- rbind(out.list[[3]], MyConfs) out.list[[4]] <- rbind(out.list[[4]], MyChaps) out.list[[5]] <- rbind(out.list[[5]], MyBooks) out.list[[6]] <- rbind(out.list[[6]], MyMsc) out.list[[7]] <- rbind(out.list[[7]], MyPhd) } } # Sort: most recent first out.list <- lapply(out.list, FUN = sort_papers) # Get good capitalization of authornames out.list <- lapply(out.list, FUN = capitalize_authors, author.aliases = author.aliases) # Get good capitalization of Titles out.list <- lapply(out.list, FUN = capitalize_titles) # Remove duplicated works (by DOI, ISSN or Title) out.list <- lapply(out.list, FUN = remove_duplicates) return(out.list) }

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Lakes_hu12id_forgeo.R

setwd("~/Dropbox/CSI/CSI-LIMNO_DATA/LAGOSData/Version1.054.1") data.lake.specific = read.table("lagos_lakes_10541.txt", header = TRUE, sep = "\t", quote = "", dec = ".", strip.white = TRUE, comment.char = "") lakehu12<-data.lake.specific[,c(1,20)] modern.15.h12id<-merge(modern.15, lakehu12, by="lagoslakeid", all.x=TRUE, all.y=FALSE) limnohu12s<-modern.15.h12id$hu12_zoneid limnohu12s<-as.vector(limnohu12s) hu12list<-unique(limnohu12s) setwd("~/Dropbox/CSI/CSI_LIMNO_Manuscripts-presentations/CSI_Nitrogen MSs/Time series/GeoTS") write.csv(hu12list, "hu12withlimnodata.csv")

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practice_piwa_170817.R

library("unmarked") setwd("~/GRAD SCHOOL - CLEMSON/Project-Specific/R_Work") test <-read.csv("piwa_abund.csv") summary(test) str(test) #y.4 and Noise.4 and Wind.4 and Sky.4 JDate.4 are factors and shouldn't be #stringsAsFactors=FALSE, but this only works for reading, not for below. piwa.abund<- csvToUMF("piwa_abund.csv", long = FALSE, type = "unmarkedFramePCount") ##type may need to change for occupancy (occuRN, pcountOpen, or whichever used) ## summary(piwa.abund) #obsCovs(piwa.abund)= scale(obsCovs(piwa.abund)) #siteCovs(piwa.abund)= scale(siteCovs(piwa.abund)) ?pcount #detection covariates first det.null.piwa <- pcount(~1 ~1, piwa.abund) det.weather.piwa <- pcount(~ Wind + Sky ~1, piwa.abund) det.global.piwa <- pcount(~ Jdate + Wind + Sky + Noise ~1, piwa.abund) det.sound.piwa <- pcount(~ Noise + Wind ~1, piwa.abund) det.date.piwa <- pcount(~ Jdate ~1, piwa.abund) det.detect.piwa <- pcount(~ Jdate + Noise ~1, piwa.abund) det.notdate.piwa <-pcount(~ Wind + Sky + Noise ~1, piwa.abund) fms <- fitList(det.null.piwa, det.weather.piwa, det.global.piwa, det.sound.piwa, det.date.piwa, det.detect.piwa, det.notdate.piwa) ms1.piwa <- modSel(fms) ms1.piwa@Full ms1.piwa #found that weather (wind + sky) was top model # and sound (wind + noise) was really close behind (delta 0.76) # but global was least, and date was really not that relevant! # added "not date" which is wind + sky + noise & it was the third highest model (1.29) #site covariates next #no K and no mixture type set (NB or P or ZIP) yet null.piwa <- pcount(~1 ~1, piwa.abund) global.piwa <- pcount(~ 1 ~ Treatment + BA + Nsnags + Ccover + Ldepth + TreeHt + Age + TimeSinceB + Herbicide , piwa.abund) local.piwa <- pcount(~ 1 ~ BA + Ccover + TreeHt + Ldepth, piwa.abund) lh.piwa <- pcount(~ 1 ~ Ccover + TreeHt + BA, piwa.abund) #landscape.piwa <- pcount(~ 1 ~ cov 5 + 6, piwa.abund) treatment.piwa <- pcount(~ 1 ~ Treatment + BA + TimeSinceB + Herbicide, piwa.abund) fms2 <- fitList(null.piwa, global.piwa, local.piwa, lh.piwa, treatment.piwa) ms2.piwa <- modSel(fms2) ms2.piwa@Full ms2.piwa #no K and no mixture type set (NB or P or ZIP) yet null2.piwa <- pcount(~ Wind + Sky + Noise ~1, piwa.abund) global2.piwa <- pcount(~ Wind + Sky + Noise ~ Treatment + BA + Nsnags + Ccover + Ldepth + TreeHt + Age + TimeSinceB + Herbicide , piwa.abund) local2.piwa <- pcount(~ Wind + Sky + Noise ~ BA + Ccover + TreeHt + Ldepth, piwa.abund) lh2.piwa <- pcount(~ Wind + Sky + Noise ~ Ccover + TreeHt + BA, piwa.abund) #landscape.piwa <- pcount(~ Wind + Sky + Noise ~ cov 5 + 6, piwa.abund) treatment2.piwa <- pcount(~ Wind + Sky + Noise ~ Treatment + BA + TimeSinceB + Herbicide, piwa.abund) fms3 <- fitList(null.piwa, global.piwa, local.piwa, lh.piwa, treatment.piwa) ms3.piwa <- modSel(fms3) ms3.piwa@Full ms3.piwa #for some reason, those ones are no different at all from ms2... #see help for package "xlsReadWrite" in old notes, if need be# #write.table(ms1.cawr@Full, file="C:/Users/path.type",sep="\t")

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## This code is part of the megaptera package ## © C. Heibl 2014 (last update 2019-11-24) #' @include taxon-class.R #' @importFrom methods new #' @export ## USER LEVEL CONSTRUCTOR ## ---------------------- "taxon" <- function(ingroup, extend.ingroup = FALSE, outgroup, extend.outgroup = FALSE, kingdom, exclude.hybrids = FALSE, tip.rank = "species", reference.rank = "auto"){ if (missing(ingroup)){ new("taxon", ingroup = list("undefined"), extend.ingroup = extend.ingroup, outgroup = list("undefined"), extend.outgroup = extend.outgroup, kingdom = "undefined", exclude.hybrids = exclude.hybrids, tip.rank = tip.rank, reference.rank = reference.rank) } else { ## ingroup <- unique(ingroup); outgroup <- unique(outgroup) if (is.factor(ingroup)) ingroup <- levels(ingroup)[ingroup] if (is.factor(outgroup)) outgroup <- levels(outgroup)[outgroup] if (is.character(ingroup)) ingroup <- as.list(ingroup) if (is.character(outgroup)) outgroup <- as.list(outgroup) tip.rank <- match.arg(tip.rank, c("genus", "species")) new("taxon", ingroup = ingroup, extend.ingroup = extend.ingroup, outgroup = outgroup, extend.outgroup = extend.outgroup, kingdom = kingdom, exclude.hybrids = exclude.hybrids, tip.rank = tip.rank, reference.rank = reference.rank) } } setMethod("show", signature(object = "taxon"), function (object) { arg <- c("tip rank", "ingroup", "is extended" , "outgroup", "is extended", "in kingdom", "exclude.hybrids", "guide tree") arg <- format(arg) formatTaxa <- function(taxa){ n <- length(taxa) taxa <- paste(head(taxa, 2), collapse = ", ") if ( n > 2 ) taxa <- paste(taxa, ", ... [", n, "]") taxa } out <- c( object@tip.rank, formatTaxa(object@ingroup), ifelse(object@extend.ingroup, "yes", "no"), formatTaxa(object@outgroup), ifelse(object@extend.outgroup, "yes", "no"), object@kingdom, ifelse(object@exclude.hybrids, "excluded", "included"), ifelse(inherits(object, "taxonGuidetree"), "user-defined", "taxonomy-based") ) out <- paste(arg, out, sep = " : ") out <- c("--- megaptera taxon class ---", out) out <- paste("\n", out, sep = "") cat(paste(out, collapse = "")) } )

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/outputMicroFusion.R \name{outputMicroFusion} \alias{outputMicroFusion} \title{Output microscopy fusion files} \usage{ outputMicroFusion(micro_image_array, filename = "myfusionmicrodata", mask_image_array = NULL, data_label = "microscopy data", micro_spatial_res = 1) } \arguments{ \item{micro_image_array}{an image array [x,y,channel] from the RGB microscopy data loaded into R through the tiff, jpeg, or png library} \item{filename}{output file name without extension. a '_micro_data.txt' and '_micro_info.xml' will be tagged onto files approrpiately} \item{data_label}{a description of the data(i.e. rat brain, 1 um / pixel)} \item{micro_spatial_res}{microscopy image spatial resolution in microns / pixel} } \value{ writes outputs in working directory } \description{ Outputs a comma seperated .txt file in UNIX format for the fusion prototype tool }

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library(shiny) source("indiana-hiv-util.R") shinyServer(function(input, output, session) { observe({ # observe the intvxday variable; this is to correct the width if an impossible combination is entered. if(input$intvxday[1]>first_dx_date) { updateSliderInput(session, "intvxday", value=c(first_dx_date, input$intvxday[2])) } }) observe({ d1 = NA d2 = NA if(input$scenario == "actual") { d1 = intvx_actual_date d2 = end_date } else if(input$scenario == "mid") { d1 = intvx_mid_date d2 = d1+scaleup_peak_offset } else if(input$scenario == "early") { d1 = intvx_early_date d2 = d1+scaleup_peak_offset } else { error("invalid choice") } updateSliderInput(session, "intvxday", value=c(d1, d2)) }) observe({ if(input$removal_scenario == "low") { v = removal_rate_low } else if(input$removal_scenario == "moderate") { v = removal_rate_mid } else if(input$removal_scenario == "high") { v = removal_rate_high } else { error("invalid choice") } updateSliderInput(session, "removal_rate", value=v) }) observe({ # observe the smoother selection input$reset # leave this alone. Makes the smoothers reset updateSliderInput(session, "smooth_dx", step=smoothers[[which(smoothernames==input$smoother)]]$step, value=smoothers[[which(smoothernames==input$smoother)]]$dxrange[2], min=smoothers[[which(smoothernames==input$smoother)]]$dxrange[1], max=smoothers[[which(smoothernames==input$smoother)]]$dxrange[3]) updateSliderInput(session, "smooth_Iudx", step=smoothers[[which(smoothernames==input$smoother)]]$step, value=smoothers[[which(smoothernames==input$smoother)]]$Iudxrange[2], min=smoothers[[which(smoothernames==input$smoother)]]$Iudxrange[1], max=smoothers[[which(smoothernames==input$smoother)]]$Iudxrange[3]) updateSliderInput(session, "smooth_I", step=smoothers[[which(smoothernames==input$smoother)]]$step, value=smoothers[[which(smoothernames==input$smoother)]]$Irange[2], min=smoothers[[which(smoothernames==input$smoother)]]$Irange[1], max=smoothers[[which(smoothernames==input$smoother)]]$Irange[3]) updateSliderInput(session, "smooth_S", step=smoothers[[which(smoothernames==input$smoother)]]$step, value=smoothers[[which(smoothernames==input$smoother)]]$Srange[2], min=smoothers[[which(smoothernames==input$smoother)]]$Srange[1], max=smoothers[[which(smoothernames==input$smoother)]]$Srange[3]) }) output$epidemicPlot <- renderPlot({ if(input$intvxday[1]<=first_dx_date) { plot_indiana_bounds(input$N, input$intvxday[1], input$intvxday[2], input$showDates, input$smooth_dx, input$smooth_Iudx, input$smooth_I, input$smooth_S, input$showSusc, input$smoother, input$removal_rate, input$plotType, input$calibration_scale, input$beta_scale) } }) #output$results <- renderText({ #get_indiana_results_text(input$N, input$intvxday[1], input$intvxday[2], input$smooth_dx, input$smooth_Iudx, input$smooth_I, input$smooth_S, input$smoother, input$removal_rate) #}) output$downloadDiagnoses <- downloadHandler( filename = function() { "scott_county_cases_by_week.csv" }, content = function(file) { file.copy("data/scott_county_cases_by_week.csv", file) } ) output$downloadIncidence <- downloadHandler( filename = function() { "incidence.csv" }, content = function(file) { file.copy("data/extracted_infection_curves.csv", file) } ) })

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/27_repo_manager.R \name{repo_manager_init} \alias{repo_manager_init} \title{Initializes managed repository structure.} \usage{ repo_manager_init(repo_manager, types) } \arguments{ \item{repo_manager}{repo manager object} \item{types}{package types for which repository should be initialized. If missing all project supported package types will be initialized (type: character)} } \value{ TRUE if initialized repository for at least one type, FALSE if the structure was fully initialized already. (type:logical, invisible) } \description{ Initializes managed repository structure. } \examples{ # create you own Repo adapter repo_adapter_create_own <- function() { result <- repo_adapter_create_base("Own") class(result) <- c("repo_adapter_own", class(result)) return(result) } #' create own repo manager #' @export repo_adapter_create_manager.repo_adapter_own <- function(repo_adapter, ...) { repo_manager <- list() # create you own repo manager class(repo_manager) <- c("repo_manager_own", "rsuite_repo_manager") return(repo_manager) } #' @export repo_manager_init.repo_manager_own <- function(repo_manager, types) { was_inited_already <- TRUE # ... if repository structure was not initialized initialize it ... return(invisible(was_inited_already)) } } \seealso{ Other in extending RSuite with Repo adapter: \code{\link{repo_adapter_create_base}}, \code{\link{repo_adapter_create_manager}}, \code{\link{repo_adapter_get_info}}, \code{\link{repo_adapter_get_path}}, \code{\link{repo_manager_destroy}}, \code{\link{repo_manager_get_info}}, \code{\link{repo_manager_remove}}, \code{\link{repo_manager_upload}} } \concept{in extending RSuite with Repo adapter}

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library(model4you) ### Name: binomial_glm_plot ### Title: Plot for a given logistic regression model (glm with binomial ### family) with one binary covariate. ### Aliases: binomial_glm_plot ### ** Examples set.seed(2017) # number of observations n <- 1000 # balanced binary treatment # trt <- factor(rep(c("C", "A"), each = n/2), # levels = c("C", "A")) # unbalanced binary treatment trt <- factor(c(rep("C", n/4), rep("A", 3*n/4)), levels = c("C", "A")) # some continuous variables x1 <- rnorm(n) x2 <- rnorm(n) # linear predictor lp <- -0.5 + 0.5*I(trt == "A") + 1*I(trt == "A")*I(x1 > 0) # compute probability with inverse logit function invlogit <- function(x) 1/(1 + exp(-x)) pr <- invlogit(lp) # bernoulli response variable y <- rbinom(n, 1, pr) dat <- data.frame(y, trt, x1, x2) # logistic regression model mod <- glm(y ~ trt, data = dat, family = "binomial") binomial_glm_plot(mod, plot_data = TRUE) # logistic regression model tree ltr <- pmtree(mod) plot(ltr, terminal_panel = node_pmterminal(ltr, plotfun = binomial_glm_plot, confint = TRUE, plot_data = TRUE))

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library(raster) library(maptools) library(dplyr) library(data.table) library(reshape2) LCC <- CRS("+proj=lcc +lat_1=49 +lat_2=77 +lat_0=0 +lon_0=-95 +x_0=0 +y_0=0 +datum=NAD83 +units=m +no_defs") w <-"G:/Boreal/NationalModelsV2/BCR6/" bcr6 <- raster("G:/Boreal/NationalModelsV2/BCR6/bcr6.tif") offl <- read.csv("G:/Boreal/NationalModelsV2/Quebec/BAMoffsets.csv") offla <- read.csv("G:/Boreal/NationalModelsV2/Quebec/Atlasoffsets.csv") load("F:/BAM/BAMData/data_package_2016-04-18.Rdata") load("F:/BAM/BAMData/offsets-v3_2016-04-18.Rdata") coordinates(SS) <- c("X", "Y") proj4string(SS) <- CRS("+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0") SSBAM <- as.data.frame(spTransform(SS, LCC)) PCBAM <- PCTBL PKEYBAM <- PKEY load("F:/BAM/BAMData/atlas_data_processed-20181018.RData") SS <- na.omit(SS) coordinates(SS) <- c("X", "Y") proj4string(SS) <- CRS("+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0") SSAtlas <- as.data.frame(spTransform(SS, LCC)) PCAtlas <- PCTBL PKEYAtlas <- PKEY names(PKEYAtlas)[4] <- "YEAR" load("F:/BAM/BamData/ARU/nwt-wildtrax-offsets-2019-01-16.RData") SSWT <- unique(dd[,c(33,39:40)]) PKEYWT <- unique(dd[,c(33,34,36)]) #PCWT <- dd[,c(33,34,36,38,47)] PCWT <- melt(y) names(PCBU) <- c("PKEY","SPECIES","ABUND") offWT <- data.table(melt(off)) names(offWT) <- c("PKEY","SPECIES","logoffset") offWT$SPECIES <- as.character(offWT$SPECIES) offWT$PKEY <- as.character(offWT$PKEY) load("F:/BAM/BamData/ARU/nwt-BU-offsets-2019-01-14.RData") SSBU <- unique(dd[,c(14,20:21)]) PKEYBU <- unique(dd[,c(14,15,17)]) PCBU <- melt(y) names(PCBU) <- c("PKEY","SPECIES","ABUND") offBU <- data.table(melt(off)) names(offBU) <- c("PKEY","SPECIES","logoffset") offBU$SPECIES <- as.character(offBU$SPECIES) offBU$PKEY <- as.character(offBU$PKEY) SScombo <- rbind(SSBAM[,c(2,48,49)],SSAtlas[,c(1,6,7)],SSWT,SSBU) PKEYcombo <- rbind(PKEYBAM[,c(1,2,8)],PKEYAtlas[,c(1,2,4)],PKEYWT,PKEYBU) eco <- raster("F:/GIS/ecoregions/CEC/quebececo1.tif") nalc <- raster("F:/GIS/landcover/NALC/LandCover_IMG/NA_LandCover_2005/data/NA_LandCover_2005/NA_LandCover_2005_LCC.img") bcr6 <- raster("G:/Boreal/NationalModelsV2/BCR6/bcr6.tif") urbag <- raster("G:/Boreal/NationalModelsV2/urbag2011_lcc1.tif") ua6 <- crop(urbag,bcr6) ua6 <- mask(ua6,bcr6) dev25 <- focal(ua6, fun=mean, w=matrix(1/25, nc=5, nr=5), na.rm=TRUE) wat <- raster("G:/Boreal/NationalModelsV2/wat2011_lcc1.tif") wat6 <- crop(wat,bcr6) wat6 <- mask(wat6,bcr6) led25 <- focal(wat6, fun=mean, w=matrix(1/25, nc=5, nr=5), na.rm=TRUE) lf <- raster("D:/NorthAmerica/topo/lf_lcc1.tif") lf6 <- crop(lf,bcr6) b2011 <- list.files("F:/GIS/landcover/Beaudoin/Processed_sppBiomass/2011/",pattern="tif$") setwd("F:/GIS/landcover/Beaudoin/Processed_sppBiomass/2011/") bs2011 <- stack(raster(b2011[1])) for (i in 2:length(b2011)) {bs2011 <- addLayer(bs2011, raster(b2011[i]))} names(bs2011) <- gsub("NFI_MODIS250m_2011_kNN_","",names(bs2011)) bs2011bcr6 <- crop(bs2011,bcr6) bs2011bcf6 <- mask(bs2011bcr6,bcr6) bs2011bcr6_1km <- aggregate(bs2011bcr6, fact=4, fun=mean) wat1km <- resample(wat6, bs2011bcr6_1km, method='ngb') bs2011bcr6_1km <- addLayer(bs2011bcr6_1km, wat1km) names(bs2011bcr6_1km)[nlayers(bs2011bcr6_1km)] <- "wat" led251km <- resample(led25, bs2011bcr6_1km, method='bilinear') bs2011bcr6_1km <- addLayer(bs2011bcr6_1km, led251km) names(bs2011bcr6_1km)[nlayers(bs2011bcr6_1km)] <- "led25" urbag1km <- resample(ua6, bs2011bcr6_1km, method='ngb') bs2011bcr6_1km <- addLayer(bs2011bcr6_1km, urbag1km) names(bs2011bcr6_1km)[nlayers(bs2011bcr6_1km)] <- "urbag" dev251km <- resample(dev25, bs2011bcr6_1km, method='bilinear') bs2011bcr6_1km <- addLayer(bs2011bcr6_1km, dev251km) names(bs2011bcr6_1km)[nlayers(bs2011bcr6_1km)] <- "dev25" lf_1km <- resample(lf6, bs2011bcr6_1km, method='ngb') bs2011bcr6_1km <- addLayer(bs2011bcr6_1km, lf_1km) names(bs2011bcr6_1km)[nlayers(bs2011bcr6_1km)] <- "landform" writeRaster(bs2011bcr6_1km,file=paste(w,"bcr6_2011rasters",sep=""),overwrite=TRUE) bs2011bcr6 <- addLayer(bs2011bcr6,wat6) names(bs2011bcr6)[nlayers(bs2011bcr6)] <- "wat" bs2011bcr6 <- addLayer(bs2011bcr6,led25) names(bs2011bcr6)[nlayers(bs2011bcr6)] <- "led25" bs2011bcr6 <- addLayer(bs2011bcr6,ua6) names(bs2011bcr6)[nlayers(bs2011bcr6)] <- "urbag" bs2011bcr6 <- addLayer(bs2011bcr6,dev25) names(bs2011bcr6)[nlayers(bs2011bcr6)] <- "dev25" lf250 <- resample(lf6, bs2011bcr6, method='ngb') bs2011bcr6 <- addLayer(bs2011bcr6, lf250) names(bs2011bcr6)[nlayers(bs2011bcr6)] <- "landform" writeRaster(bs2011bcr6,file=paste(w,"bcr6_2011rasters250",sep=""),overwrite=TRUE) b2001 <- list.files("F:/GIS/landcover/Beaudoin/Processed_sppBiomass/2001/",pattern="tif$") setwd("F:/GIS/landcover/Beaudoin/Processed_sppBiomass/2001/") bs2001 <- stack(raster(b2001[1])) for (i in 2:length(b2001)) {bs2001 <- addLayer(bs2001, raster(b2001[i]))} names(bs2001) <- gsub("NFI_MODIS250m_2001_kNN_","",names(bs2001)) bs2001bcr6 <- crop(bs2001,bcr6) bs2001bcf6 <- mask(bs2001bcr6,bcr6) # # bs2001bcr6_1km <- aggregate(bs2001bcr6, fact=4, fun=mean) # wat1km <- resample(wat6, bs2001bcr6_1km, method='ngb') # bs2001bcr6_1km <- addLayer(bs2001bcr6_1km, wat1km) # names(bs2001bcr6_1km)[nlayers(bs2001bcr6_1km)] <- "wat" # led251km <- resample(led25, bs2001bcr6_1km, method='bilinear') # bs2001bcr6_1km <- addLayer(bs2001bcr6_1km, led251km) # names(bs2001bcr6_1km)[nlayers(bs2001bcr6_1km)] <- "led25" # urbag1km <- resample(ua6, bs2001bcr6_1km, method='ngb') # bs2001bcr6_1km <- addLayer(bs2001bcr6_1km, urbag1km) # names(bs2001bcr6_1km)[nlayers(bs2001bcr6_1km)] <- "urbag" # dev251km <- resample(dev25, bs2001bcr6_1km, method='bilinear') # bs2001bcr6_1km <- addLayer(bs2001bcr6_1km, dev251km) # names(bs2001bcr6_1km)[nlayers(bs2001bcr6_1km)] <- "dev25" # lf_1km <- resample(lf6, bs2001bcr6_1km, method='ngb') # bs2001bcr6_1km <- addLayer(bs2001bcr6_1km, lf_1km) # names(bs2001bcr6_1km)[nlayers(bs2001bcr6_1km)] <- "landform" # writeRaster(bs2001bcr6_1km,file=paste(w,"bcr6_2001rasters",sep=""),overwrite=TRUE) bs2001bcr6 <- addLayer(bs2001bcr6,wat6) names(bs2001bcr6)[nlayers(bs2001bcr6)] <- "wat" bs2001bcr6 <- addLayer(bs2001bcr6,led25) names(bs2001bcr6)[nlayers(bs2001bcr6)] <- "led25" bs2001bcr6 <- addLayer(bs2001bcr6,ua6) names(bs2001bcr6)[nlayers(bs2001bcr6)] <- "urbag" bs2001bcr6 <- addLayer(bs2001bcr6,dev25) names(bs2001bcr6)[nlayers(bs2001bcr6)] <- "dev25" lf250 <- resample(lf6, bs2001bcr6, method='ngb') bs2001bcr6 <- addLayer(bs2001bcr6, lf250) names(bs2001bcr6)[nlayers(bs2001bcr6)] <- "landform" writeRaster(bs2001bcr6,file=paste(w,"bcr6_2001rasters250",sep=""),overwrite=TRUE) bs2011bcr6 <- dropLayer(bs2011bcr6,98) dat2011 <- cbind(SScombo, extract(bs2011bcr6,as.matrix(cbind(QCSS$X,QCSS$Y)))) dat2011 <-cbind(dat2011,extract(nalc,as.matrix(cbind(dat2011$X,dat2011$Y)))) names(dat2011)[ncol(dat2011)] <- "LCC" dat2011 <-cbind(dat2011,extract(eco,as.matrix(cbind(dat2011$X,dat2011$Y)))) names(dat2011)[ncol(dat2011)] <- "eco" dat2011 <-cbind(dat2011,extract(lfq,as.matrix(cbind(dat2011$X,dat2011$Y)))) names(dat2011)[ncol(dat2011)] <- "landform" dat2011$SS <- as.character(dat2011$SS) dat2011$PCODE <- as.character(dat2011$PCODE) write.csv(dat2011,paste(w,"bcr6_dat2011.csv",sep=""),row.names=FALSE) bs2001bcr6 <- dropLayer(bs2001bcr6,98) dat2001 <- cbind(SScombo, extract(bs2001bcr6,as.matrix(cbind(QCSS$X,QCSS$Y)))) dat2001 <-cbind(dat2001,extract(nalc,as.matrix(cbind(dat2001$X,dat2001$Y)))) names(dat2001)[ncol(dat2001)] <- "LCC" dat2001 <-cbind(dat2001,extract(eco,as.matrix(cbind(dat2001$X,dat2001$Y)))) names(dat2001)[ncol(dat2001)] <- "eco" dat2001 <-cbind(dat2001,extract(lfq,as.matrix(cbind(dat2001$X,dat2001$Y)))) names(dat2001)[ncol(dat2001)] <- "landform" samprast2001 <- rasterize(cbind(dat2001$X,dat2001$Y), led25, field=1) gf <- focalWeight(samprast2001, 100, "Gauss") sampsum25 <- focal(samprast2001, w=gf, na.rm=TRUE) dat2001 <- cbind(dat2001,extract(sampsum25,as.matrix(cbind(dat2001$X,dat2001$Y)))) names(dat2001)[ncol(dat2001)] <- "sampsum25" dat2001$wt <- 1/dat2001$sampsum25 dat2001$SS <- as.character(dat2001$SS) dat2001$PCODE <- as.character(dat2001$PCODE) write.csv(dat2001,paste(w,"bcr6_dat2001.csv",sep=""),row.names=FALSE) PC <- inner_join(PCTBL[,2:10],PKEY[,1:8],by=c("PKEY","SS")) #n=5808402 QCPC <- inner_join(PC, QCSS[,c(2,5)], by=c("SS")) #n=465693 QCPC$SS <- as.character(QCPC$SS) QCPC$PKEY <- as.character(QCPC$PKEY) QCPC$PCODE <- as.character(QCPC$PCODE) QCPC$SPECIES <- as.character(QCPC$SPECIES) QCPC2001 <- QCPC[QCPC$YEAR < 2006,] #n=212901 QCPC2011 <- QCPC[QCPC$YEAR > 2005,] #n=252792 write.csv(QCPC2011,paste(w,"BCR6PC2011.csv",sep=""),row.names=FALSE) write.csv(QCPC2001,paste(w,"BCR6PC2001.csv",sep=""),row.names=FALSE)

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#' @title create_cells #' #' @description Create a specific tower layer with attached directional cells #' #' @param area.sf sf dataframe, focus area with geometry column (tile polygons) #' @param tower.dist numeric value, distance between towers (e.g., in meters, however in general dimensionless) #' @param rotation.deg numeric value, layer rotation of directional cells, sectorized, in reference to the northern direction #' @param jitter numeric value, amount of jitter (e.g., in meters, however in general dimensionless) #' @param small logical value, TRUE for omnidirectional cell and FALSE for directional cell #' @param subscript character value, abbreviation for layer id #' @param seed numeric value, seed for reproducibility #' #' #' @return A data frame with antennas of the specfic layer in the rows and the #' the following columns: point location (x, y), tower.id, cell.id, cell.kind #' (layer), intra.cell.number (in case of directional cells, three antennas per tower, #' sectorized), kind of cell (directional or omnidirectional), and rotation degree. #' This data frame corresponds to an unvalidated cell plan that can be validated #' with `create_cellplan()`. #' #' @export #' @importFrom dplyr "%>%" #' #' create_cells <- function(area.sf, tower.dist, rotation.deg, jitter, small = FALSE, subscript, seed) { set.seed = seed rotation = function(a){ r = a * pi / 180 #degrees to radians matrix(c(cos(r), sin(r), -sin(r), cos(r)), nrow = 2, ncol = 2) } layer_network_generate = function(x, tower.dist, rotation.deg){ layer.geo <- x %>% sf::st_make_grid(cellsize = tower.dist, square = F, # hexagon flat_topped = T) %>% # different cell size (qm) sf::st_geometry() layer.centroid <- sf::st_centroid(layer.geo) layer <- (layer.geo - layer.centroid) * rotation(rotation.deg) + layer.centroid # rotate by 35 degrees return(layer) } # create layer object, placing towers layer <- layer_network_generate(x = area.sf, tower.dist = tower.dist, rotation.deg = rotation.deg) # specify exact location of towers and labelling towers <- layer %>% sf::st_centroid() %>% sf::st_jitter(jitter) %>% sf::st_coordinates() %>% tibble::as_tibble() %>% dplyr::select(X.tow = X, Y.tow = Y) %>% dplyr::mutate(tower.id = paste0(subscript, ".", 1:dplyr::n())) # create 3 cells per tower and labelling cells.unparam <- towers %>% dplyr::slice(rep(1:dplyr::n(), each = 3)) %>% dplyr::group_by(tower.id) %>% dplyr::mutate(cell = paste(tower.id, "C", 1:3, sep = ".")) %>% dplyr::ungroup() %>% dplyr::mutate(cell.kind = subscript) %>% dplyr::mutate(intra.cell.number = stringr::str_sub(cell, -1)) %>% dplyr::mutate(small = small) %>% dplyr::mutate(rotation.deg = rotation.deg) return(cells.unparam) }

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testlist <- list(b = c(NaN, NaN, -4.91790800486089e-166, 6.47517494783079e-319, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)) result <- do.call(metacoder:::centroid,testlist) str(result)

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testthat.R

library(testthat) library(netdist) test_check("netdist")

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/fuzzedpackages/rstanarm/man/kfold.stanreg.Rd

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kfold.stanreg.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/loo-kfold.R \name{kfold.stanreg} \alias{kfold.stanreg} \alias{kfold} \title{K-fold cross-validation} \usage{ \method{kfold}{stanreg}( x, K = 10, ..., folds = NULL, save_fits = FALSE, cores = getOption("mc.cores", 1) ) } \arguments{ \item{x}{A fitted model object returned by one of the rstanarm modeling functions. See \link{stanreg-objects}.} \item{K}{For \code{kfold}, the number of subsets (folds) into which the data will be partitioned for performing \eqn{K}-fold cross-validation. The model is refit \code{K} times, each time leaving out one of the \code{K} folds. If the \code{folds} argument is specified then \code{K} will automatically be set to \code{length(unique(folds))}, otherwise the specified value of \code{K} is passed to \code{loo::\link[loo:kfold-helpers]{kfold_split_random}} to randomly partition the data into \code{K} subsets of equal (or as close to equal as possible) size.} \item{...}{Currently ignored.} \item{folds}{For \code{kfold}, an optional integer vector with one element per observation in the data used to fit the model. Each element of the vector is an integer in \code{1:K} indicating to which of the \code{K} folds the corresponding observation belongs. There are some convenience functions available in the \pkg{loo} package that create integer vectors to use for this purpose (see the \strong{Examples} section below and also the \link[loo]{kfold-helpers} page).} \item{save_fits}{For \code{kfold}, if \code{TRUE}, a component \code{'fits'} is added to the returned object to store the cross-validated \link[=stanreg-objects]{stanreg} objects and the indices of the omitted observations for each fold. Defaults to \code{FALSE}.} \item{cores}{The number of cores to use for parallelization. Instead fitting separate Markov chains for the same model on different cores, by default \code{kfold} will distribute the \code{K} models to be fit across the cores (using \code{\link[parallel:clusterApply]{parLapply}} on Windows and \code{\link[parallel]{mclapply}} otherwise). The Markov chains for each model will be run sequentially. This will often be the most efficient option, especially if many cores are available, but in some cases it may be preferable to fit the \code{K} models sequentially and instead use the cores for the Markov chains. This can be accomplished by setting \code{options(mc.cores)} to be the desired number of cores to use for the Markov chains \emph{and} also manually specifying \code{cores=1} when calling the \code{kfold} function. See the end of the \strong{Examples} section for a demonstration.} } \value{ An object with classes 'kfold' and 'loo' that has a similar structure as the objects returned by the \code{\link{loo}} and \code{\link{waic}} methods and is compatible with the \code{\link{loo_compare}} function for comparing models. } \description{ The \code{kfold} method performs exact \eqn{K}-fold cross-validation. First the data are randomly partitioned into \eqn{K} subsets of equal size (or as close to equal as possible), or the user can specify the \code{folds} argument to determine the partitioning. Then the model is refit \eqn{K} times, each time leaving out one of the \eqn{K} subsets. If \eqn{K} is equal to the total number of observations in the data then \eqn{K}-fold cross-validation is equivalent to exact leave-one-out cross-validation (to which \code{\link[=loo.stanreg]{loo}} is an efficient approximation). } \examples{ \donttest{ fit1 <- stan_glm(mpg ~ wt, data = mtcars, refresh = 0) fit2 <- stan_glm(mpg ~ wt + cyl, data = mtcars, refresh = 0) fit3 <- stan_glm(mpg ~ disp * as.factor(cyl), data = mtcars, refresh = 0) # 10-fold cross-validation # (if possible also specify the 'cores' argument to use multiple cores) (kfold1 <- kfold(fit1, K = 10)) kfold2 <- kfold(fit2, K = 10) kfold3 <- kfold(fit3, K = 10) loo_compare(kfold1, kfold2, kfold3) # stratifying by a grouping variable # (note: might get some divergences warnings with this model but # this is just intended as a quick example of how to code this) fit4 <- stan_lmer(mpg ~ disp + (1|cyl), data = mtcars, refresh = 0) table(mtcars$cyl) folds_cyl <- loo::kfold_split_stratified(K = 3, x = mtcars$cyl) table(cyl = mtcars$cyl, fold = folds_cyl) kfold4 <- kfold(fit4, folds = folds_cyl, cores = 2) print(kfold4) } # Example code demonstrating the different ways to specify the number # of cores and how the cores are used # # options(mc.cores = NULL) # # # spread the K models over N_CORES cores (method 1) # kfold(fit, K, cores = N_CORES) # # # spread the K models over N_CORES cores (method 2) # options(mc.cores = N_CORES) # kfold(fit, K) # # # fit K models sequentially using N_CORES cores for the Markov chains each time # options(mc.cores = N_CORES) # kfold(fit, K, cores = 1) } \references{ Vehtari, A., Gelman, A., and Gabry, J. (2017). Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. \emph{Statistics and Computing}. 27(5), 1413--1432. doi:10.1007/s11222-016-9696-4. arXiv preprint: \url{http://arxiv.org/abs/1507.04544/} Yao, Y., Vehtari, A., Simpson, D., and Gelman, A. (2018) Using stacking to average Bayesian predictive distributions. \emph{Bayesian Analysis}, advance publication, doi:10.1214/17-BA1091. (\href{https://projecteuclid.org/euclid.ba/1516093227}{online}). }

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/OurBiCopSelect.R

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OurBiCopSelect.R

library(VineCopula) source("MyBiCopGofTest.R") #This function takes a sample from a bivariate distribution #consisting of values between 0 and 1 and: #a) tests for independence and for positive correlation #b) if independence can be rejected and correlation is positive, it # fit a series of bivariate copula models and provides fitted # parameters, log likelihood, AIC and BIC for each, and other info #c) for the best copula model (by AIC or BIC), the function tests # for the goodness of fit in two ways #d) optionally tests for the goodness of fit of the normal copula # #The function is a re-implementation of the BiCopSelect function #in the VineCopula package, re-done to ensure we could understand and #control what we were getting. # #Args #u1, u2 The sample. These can be obtained, for instance, # by applying pobs in the VineCopula package to # samples from any bivariate distribution or to # any bivariate dataset #level Significance level to be used for the independence # test #families The copula families to use in b above. Uses codes # as specified in the docs for BiCopEst in the # VineCopula package. #AICBIC Which one to use for model selection. #numBSsmall #pthresh #numBSlarge Number of bootstraps to use for goodness of fit. # First, tests are run with numBSsmall, and if p-values # are ever less than pthresh, tests are re-run with # numBSlarge. Watch out, large values can mean long # run times. #gofnormal T/F on whether a goodness of fit test for the normal # copula should be run. #status T/F - should status updates on the run be printed? # #Output: a list with these elements #IndepTestRes p-value result of the independence test #TauVal Kendall correlation #InfCritRes data frame of results from the information- # criterion-based model fitting and selection #GofRes_CvM p-value result for Cramer-von-Mises-based # goodness of fit test for top-ranking copula # by AIC or BIC #GofRes_KS Same, but using a Kolmogorov-Smirnov-based # test. See docs for MyBiCopGofTest in the # VineCopula package #GofRes_CvM_stat #GofRes_KS_stat Statistics for the tests #Numboot Number of bootstraps (numBSsmall/large) #Numboot_success Number of succeeded bootstraps out of Numboot #GofRes_Normal_CvM #GofRes_Normal_KS #GofRes_Normal_CvM_stat #GofRes_Normal_KS_stat Same as above, but for testing the goodness of fit of the normal copula. #Numboot_Normal Number of bootstraps for the normal test (numBSsmall/large) #Numboot_success_Normal Number of succeeded bootstraps out of Numboot_Normal #relLTdep_AICw Model-averaged lower-tail dependence statistic (AIC-weightage based) #relUTdep_AICw Model-averaged upper-tail dependence statistic (AIC-weightage based) #relLTdep_BICw Model-averaged lower-tail dependence statistic (BIC-weightage based) #relUTdep_BICw Model-averaged upper-tail dependence statistic (BIC-weightage based) OurBiCopSelect<-function(u1,u2,families,level=0.05,AICBIC="AIC", numBSsmall=100,pthresh=0.2,numBSlarge=1000, gofnormal=TRUE,status=TRUE) { #first, test for independence (H0 is independence) if (status) {cat(paste("Starting independence test: ",Sys.time(),"\n"))} IndepTestRes<-BiCopIndTest(u1,u2)$p.value if (status) {cat(paste("Done:",Sys.time(),"\n"))} tauval<-cor(u1,u2,method="kendall") # print(paste0("IndepTestRes=",IndepTestRes,"; tauval=",tauval)) #if independence rejected and tau>0, then get AICs for copulas and do #goodness of fit stuff if (IndepTestRes<level && tauval>0){ # print("Entering main work if statement in BiCopGofTest") #AIC/BIC stuff if (status) {cat(paste("Starting A/BIC model selection: ",Sys.time(),"\n"))} InfCritRes<-data.frame(copcode=families, copname=BiCopName(families, short=TRUE), par1=NA, par2=NA, logLik=NA, AIC=NA, BIC=NA, LTdep=NA, UTdep=NA, AICw=NA, BICw=NA) for (counter in 1:(dim(InfCritRes)[1])){ # print(paste0("About to call BiCopEst for counter=",counter)) tres<-BiCopEst(u1,u2,family=InfCritRes[counter,1]) InfCritRes$par1[counter]<-tres$par InfCritRes$par2[counter]<-tres$par2 InfCritRes$logLik[counter]<-tres$logLik InfCritRes$AIC[counter]<-tres$AIC InfCritRes$BIC[counter]<-tres$BIC InfCritRes$LTdep[counter]<-tres$taildep$lower InfCritRes$UTdep[counter]<-tres$taildep$upper } for(counter in 1:(dim(InfCritRes)[1])){ InfCritRes$AICw[counter]<-exp(-0.5*(InfCritRes$AIC[counter]-min(InfCritRes$AIC,na.rm=T))) InfCritRes$BICw[counter]<-exp(-0.5*(InfCritRes$BIC[counter]-min(InfCritRes$BIC,na.rm=T))) } InfCritRes$AICw<-InfCritRes$AICw/sum(InfCritRes$AICw,na.rm=T) InfCritRes$BICw<-InfCritRes$BICw/sum(InfCritRes$BICw,na.rm=T) # check : sum(InfCritRes$AICw)=1, sum(InfCritRes$BICw)=1 relLTdep_AICw<-sum((InfCritRes$LTdep*InfCritRes$AICw)/sum(InfCritRes$AICw,na.rm=T),na.rm=T) relUTdep_AICw<-sum((InfCritRes$UTdep*InfCritRes$AICw)/sum(InfCritRes$AICw,na.rm=T),na.rm=T) relLTdep_BICw<-sum((InfCritRes$LTdep*InfCritRes$BICw)/sum(InfCritRes$BICw,na.rm=T),na.rm=T) relUTdep_BICw<-sum((InfCritRes$UTdep*InfCritRes$BICw)/sum(InfCritRes$BICw,na.rm=T),na.rm=T) if (status) {cat(paste("Done: ",Sys.time(),"\n"))} #g.o.f. stuff for the A/BIC-best copula if (status) {cat(paste("Starting gof for A/BIC-best copula: ",Sys.time(),"\n"))} if (AICBIC=="AIC"){ ind<-which.min(InfCritRes$AIC) } if (AICBIC=="BIC"){ ind<-which.min(InfCritRes$BIC) } if (AICBIC!="AIC" && AICBIC!="BIC"){ stop("Error in OurBiCopSelect: incorrect AICBIC") } Numboot<-numBSsmall # print("About to call MyBiCopGofTest") gres<-MyBiCopGofTest(u1,u2,family=InfCritRes$copcode[ind], method="kendall",B=numBSsmall) # print("Finished calling MyBiCopGofTest") GofRes_CvM<-gres$p.value.CvM GofRes_KS<-gres$p.value.KS Numboot_success<-gres$B_success if (GofRes_CvM<pthresh || GofRes_KS<pthresh) { Numboot<-numBSlarge # print("About to call MyBiCopGofTest second time") gres<-MyBiCopGofTest(u1,u2,family=InfCritRes$copcode[ind], method="kendall",B=numBSlarge) # print("Finished calling MyBiCopGofTest second time") GofRes_CvM<-gres$p.value.CvM GofRes_KS<-gres$p.value.KS Numboot_success<-gres$B_success } GofRes_CvM_stat<-gres$statistic.CvM GofRes_KS_stat<-gres$statistic.KS if (status) {cat(paste("Done: ",Sys.time(),"\n"))} #g.o.f. stuff for the normal copula if(gofnormal==T){ if (status) {cat(paste("Starting gof for normal copula: ",Sys.time(),"\n"))} Numboot_Normal<-numBSsmall gres_normal_cop<-MyBiCopGofTest(u1,u2,family=1,method="kendall",B=numBSsmall) GofRes_Normal_CvM<-gres_normal_cop$p.value.CvM GofRes_Normal_KS<-gres_normal_cop$p.value.KS Numboot_success_Normal<-gres_normal_cop$B_success if (GofRes_Normal_CvM<pthresh || GofRes_Normal_KS<pthresh) { Numboot_Normal<-numBSlarge gres_normal_cop<-MyBiCopGofTest(u1,u2,family=1,method="kendall",B=numBSlarge) GofRes_Normal_CvM<-gres_normal_cop$p.value.CvM GofRes_Normal_KS<-gres_normal_cop$p.value.KS } GofRes_Normal_CvM_stat<-gres_normal_cop$statistic.CvM GofRes_Normal_KS_stat<-gres_normal_cop$statistic.KS Numboot_success_Normal<-gres_normal_cop$B_success if (status) {cat(paste("Done: ",Sys.time(),"\n"))} }else{ GofRes_Normal_CvM<-NA GofRes_Normal_KS<-NA GofRes_Normal_CvM_stat<-NA GofRes_Normal_KS_stat<-NA Numboot_Normal<-NA Numboot_success_Normal<-NA } } else { InfCritRes<-NA GofRes_CvM<-NA GofRes_KS<-NA GofRes_CvM_stat<-NA GofRes_KS_stat<-NA Numboot<-NA Numboot_success<-NA GofRes_Normal_CvM<-NA GofRes_Normal_KS<-NA GofRes_Normal_CvM_stat<-NA GofRes_Normal_KS_stat<-NA Numboot_Normal<-NA Numboot_success_Normal<-NA relLTdep_AICw<-NA relUTdep_AICw<-NA relLTdep_BICw<-NA relUTdep_BICw<-NA } return(list(IndepTestRes=IndepTestRes, TauVal=tauval, InfCritRes=InfCritRes, GofRes_CvM=GofRes_CvM, GofRes_KS=GofRes_KS, GofRes_CvM_stat=GofRes_CvM_stat, GofRes_KS_stat=GofRes_KS_stat, Numboot=Numboot, Numboot_success=Numboot_success, GofRes_Normal_CvM=GofRes_Normal_CvM, GofRes_Normal_KS=GofRes_Normal_KS, GofRes_Normal_CvM_stat=GofRes_Normal_CvM_stat, GofRes_Normal_KS_stat=GofRes_Normal_KS_stat, Numboot_Normal=Numboot_Normal, Numboot_success_Normal= Numboot_success_Normal, relLTdep_AICw=relLTdep_AICw, relUTdep_AICw=relUTdep_AICw, relLTdep_BICw=relLTdep_BICw, relUTdep_BICw=relUTdep_BICw)) }

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#collapse function convert=function(x) { #Using collapse for cancatenating into one string y=paste(x,collapse=",") print(y) } #Checking for 3 values convert(c("sathish","kumar","good")) convert(c("sathish","kumar")) #Checking for 1 value convert(c("sathish"))

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/R/square_usa.R

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square_usa.R

#' Creates square tile map for the states of USA. #' #' @param d Numeric. Number between 0 and 1. Procentwise Diameter (length from #' center to corner) of the tiles. #' @param center Logical. When TRUE returns the center coordinates of the tile #' map. #' @param style Character. Selets the layout style of the tile map. #' @param size Numeric. Size of the tiles. #' @param long_offset Numeric. Value to offset the long output. #' @param lat_offset Numeric. Value to offset the lat output. #' @param exclude Character. Vector of state names which should be excluded #' from the output. Matched with lowercase full state names. #' @return The available styles for this functions are "NPR", #' "The New York Times", "538", "Propublica", "Bloomberg", "The Guardian", #' "Wall Street Journal", "WNYC" or "The Marshall Project". #' @examples #' \dontrun{ #' library(ggplot2) #' crimes <- data.frame(state = tolower(rownames(USArrests)), USArrests) #' states_map <- square_usa() #' ggplot(crimes, aes(map_id = state)) + #' geom_map(aes(fill = Murder), map = states_map) + #' expand_limits(x = states_map$long, y = states_map$lat) #' #' states_map <- square_usa(d = 0.5) #' #' ggplot(crimes, aes(map_id = state)) + #' geom_map(aes(fill = Murder), map = states_map) + #' expand_limits(x = states_map$long, y = states_map$lat) + #' geom_text(data = square_usa(d = 0.5, center = TRUE), #' aes(x = long, y = lat, label = states_abb), #' inherit.aes = FALSE) #'} #'@export square_usa <- function(d = 0.95, center = FALSE, style = "NPR", size = 1, long_offset = 0, lat_offset = 0, exclude = character()) { if(d <= 0 || d > 1) { warning("d must be in the interval (0, 1], defaulted to 0.95") d <- 0.95 } if(!(style %in% c("NPR", "The New York Times", "538", "Propublica", "Bloomberg", "The Guardian", "Wall Street Journal", "WNYC", "The Marshall Project"))) { warning("Unable to recognize style, defaulted to NPR") STYLE <- "NPR" } else { STYLE <- style } exclude <- intersect(square_usa_data$region, exclude) d <- d / 2 * size dat0 <- square_usa_data %>% dplyr::filter(style == STYLE) %>% dplyr::select(- style) %>% dplyr::mutate(long = ~long * size, lat = ~lat * size) dat1 <- rbind(dat0 %>% dplyr::mutate(long = ~long + d, lat = ~lat + d), dat0 %>% dplyr::mutate(long = ~long - d, lat = ~lat + d), dat0 %>% dplyr::mutate(long = ~long - d, lat = ~lat - d), dat0 %>% dplyr::mutate(long = ~long + d, lat = ~lat - d)) %>% dplyr::mutate(group = as.numeric(~group)) if (center) { dat2 <- dat0 %>% dplyr::mutate(long = ~long - dat1[114, "long"] + long_offset, lat = ~lat - dat1[114, "lat"] + lat_offset) %>% dplyr::filter(!(~region %in% exclude)) return(dat2) } else { dat2 <- dat1 %>% dplyr::mutate(long = ~long - dat1[114, "long"] + long_offset, lat = ~lat - dat1[114, "lat"] + lat_offset) %>% dplyr::filter(!(~region %in% exclude)) return(dat2[order(dat2$region), ] %>% dplyr::mutate(order = 1:(204 - length(exclude) * 4))) } }

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na_fill = function(x, tol){ # x is a vector of data # tol is max number of steps missing (if greater, it retains NA) ina = is.na(x) csum = cumsum(!ina) wg = as.numeric(names(which(table(csum) > tol))) # which gaps are too long x[ina] = approx(x, xout=which(ina))$y x[which(csum%in%wg)[-1]] = NA return(x) } # This is a test. # I added something here, just a test # stack data files from the same data logger but different dates load_stack_file = function(files, gmtoff, logger){ dates = sub(".*_(.*)_.*\\..*", "\\1", files) # get all dates xx = lapply(dates, function(x) load_file(grep(x,files,value=TRUE), gmtoff$offs[which(gmtoff$dnld_date==x)], logger) ) # load data for each date xx = Reduce(function(df1,df2) bind_rows(df1,df2), xx) # stack them up arrange(xx, DateTimeUTC) } # Snap timestamps to the closest interval snap_ts = function(x, samp_freq, nearest=FALSE){ # x is a date-time vector to be snapped # freq is the frequency of observations as a string # containing the number of units and the unit (S,M,H,D) # e.g., '15M', '1H', '3D', '66S' # nearest is logical to snap to floor (default) or to nearest time cut re = regexec("([0-9]+)([A-Z])",samp_freq)[[1]] if(-1%in%re){ stop("Please enter a correct string") }else{ ml = attr(re,"match.length") nn = as.numeric(substr(samp_freq, re[2], ml[2])) uu = substr(samp_freq, re[3], ml[1]) if(uu=="D"){td = 24*60*60*nn }else if(uu=="H"){td = 60*60*nn }else if(uu=="M"){td = 60*nn }else if(uu=="S"){td = nn }else{stop("Please enter a correct string")} } if(nearest){ # round to closest interval as.POSIXct(round(as.double(x)/td)*td,origin="1970-01-01") }else{ # round to floor as.POSIXct(floor(as.double(x)/td)*td,origin="1970-01-01") } } # Fold the data together into one data frame fold_ts = function(...){ ll = (...) if(length(ll)>1){ x = Reduce(function(df1,df2) full_join(df1,df2,by="DateTime"), ll) }else{ x = ll[[1]] } cat("Your data are cleaned.\n") arrange(x, DateTime) }

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#' WangzhenTools. #' #' @name WangzhenTools #' @docType package NULL

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scatter_libdepth.R

#' Plot clusters in 2 umaps with the point size corresponting to the library size #' #' This function allows you to see if the library size influences in your clustering #' #' @param Seurat_obj your Seurat object #' #' @return scatter plot with the library size correponding to the point size #' #' @keywords Seurat, single cell sequencing, RNA-seq, gene signature #' #' @examples #' #' scatter_libdepth(A07) #' #' @export #' scatter_libdepth <- function(Seurat_obj){ library(ggplot2) library(cowplot) df <- data.frame(umap1 = Seurat_obj@dr$umap@cell.embeddings[,1 ], umap2 = Seurat_obj@dr$umap@cell.embeddings[,2 ], cells = as.character(Seurat_obj@ident), lsize = Seurat_obj@meta.data$nUMI, ngene = Seurat_obj@meta.data$nGene ) p1 <- ggplot(df, aes(x = umap1, y = umap2, col = cells))+ geom_point(aes(size = lsize)) + scale_size_continuous(range = c(0.001, 3)) p2 <- ggplot(df, aes(x = umap1, y = umap2, col = cells))+ geom_point(aes(size = ngene)) + scale_size_continuous(range = c(0.001, 3)) plot_grid(p1, p2) }

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shinyUI( pageWithSidebar( headerPanel("Volume of Cuboid Calculator"), sidebarPanel( numericInput('width', 'length of the base of the cuboid in cm', 90, min = 50, max = 200, step = 5), #submitButton('Submit'), sliderInput('height', 'height of the cuboid in cm', 90, min = 50, max = 200, step = 5) ), mainPanel( tabsetPanel( tabPanel('Output', p(h3('Results of Calculation'), h4('The Area of the base of the cuboid in sqcm is'), verbatimTextOutput("inputValue"), h4('Which resulted in a calculation of the volume in cubic cm of '), textOutput("prediction") ) ), tabPanel('Documentation', p(h4("Area of Cuboid Calculator:")), br(), helpText("This application calculates the area of a cuboid given the base width and its height. The base of the cuboid is a square whose length is given as numeric input. The height of the cuboid is adjusted with the use of the slider.") ) ) ) ) )

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/14a-impact-functions.R

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14a-impact-functions.R

# rm(list = ls()) # Define a model with the drug factor regressed on group get_impact_model <- function(base_model) { pt <- lavaanify(base_model) # Identify factors factors <- subset(pt, op == "=~") factors <- unique(factors$lhs) # Define regression paths reg_paths <- paste(factors, '~ group \n', collapse = " ") # Define model impact_model <- paste(base_model, '\n', reg_paths) } # Function for replacing the 'ustart' values in a parameter table # with the fitted coefficents from another parameter table # Returns a paramter table that can be used with 'lavaan:simulateData' # Setup for use in functions 'create_invariant_data' and 'create_biased_data' #c Takes arguments: # pt.frame = The paramenter table where a parameter should be replaced and then returned # pt.replacement = The parameter table from where the replacing parameter should be taken replace_coefs_in_pt <- function(pt.frame, pt.replacement) { require(lavaan) # Ensure the same order of parameters in both paramter tables order_frame <- order(pt.frame$op, pt.frame$lhs, pt.frame$rhs) order_replacement <- order(pt.replacement$op, pt.replacement$lhs, pt.replacement$rhs) frame <- pt.frame[order_frame, ] replacement <- pt.replacement[order_replacement, ] # Put in the same data.frame, side by side, (to avoid differing row numbers claiming differences) both <- cbind(frame[,2:4], replacement[,2:4]) # Continue only if the parameters are identical (otherwise give error message) if (!(identical(both[,1:3], both[,4:6]))) { stop("Table parameters do not match", call. = FALSE) } # Replace the 'ustart' values in the frame with the coefficients in the replacing parameter table frame$ustart <- replacement$est # Return in original order # and only the columns from output of a arbitrary model in lavaan:lavaanify return(frame[order(frame$id), names(lavaanify('factor =~ item'))]) } # Function for replacing a single paramenter. Takes arguments: # pt.frame = The paramenter table where a parameter should be replaced and then returned # pt.replacement = The parameter table from where the replacing parameter should be taken # lhs, op and rhs = column names in parameter tables used to identify parameter # Setup for use in function 'create_biased_data' replace_1_coef_in_pt <- function (pt.frame, pt.replacement, lhs, op, rhs = "") { # locate row number for parameter (to be changed) in both tables, grab that index i i_frame <- which(pt.frame$lhs == lhs & pt.frame$op == op & pt.frame$rhs == rhs) i_replacement <- which(pt.replacement$lhs == lhs & pt.replacement$op == op & pt.replacement$rhs == rhs) # Check that there is one (and only one) index for both tables if (length(i_frame) != 1 | length(i_replacement) != 1) { stop("No, or more than one, matching parameter", call. = FALSE) } # Make replacement pt.frame[i_frame, "ustart"] <- pt.replacement[i_replacement, "est"] return(pt.frame) } # Create data list for use in simulations. # Setup for use in functions 'create_invariant_data' and 'create_biased_data' # Takes arguments: # parameter table 1 and 2, the group sizes, the number of datasets to produce, and # labels for the groups (defaults to 1 & 2) create_data_list<- function (pt1, pt2, n1, n2, n_sets) { data_list <- list() for(i in 1:n_sets) { simulated_data.group1 <- simulateData(pt1, sample.nobs = n1) simulated_data.group2 <- simulateData(pt2, sample.nobs = n2) simulated_data <- rbind(simulated_data.group1, simulated_data.group2) # Add column with group membership - if labels are defined, use those simulated_data <- data.frame(simulated_data, group = rep(c(1, 2), times = c(n1, n2))) # Make all variables ordered simulated_data[c(1:length(simulated_data))] <- lapply(simulated_data[c(1:length(simulated_data))], ordered) # Add the latest simulated dataset to the list data_list[[i]] <- simulated_data } return(data_list) } # Create simulated datasets based on parameters from strong invariance model. # Takes arguments; # single_group = a fitted single group model # strong_fit = a fitted strong group model # n_sets = number of datasets to create create_invariant_data <- function(single_group, strong_fit, n_sets) { pt.single <- parameterTable(single_group) pt.strong <- parameterTable(strong_fit) # Create parameter tables for the two groups # Use single group model as frame and use parameter values from strong model # See function 'replace_coefs_in_pt' above pt.invariant1 <- replace_coefs_in_pt(pt.frame = pt.single, pt.replacement = pt.strong[pt.strong$group == 1, ]) pt.invariant2 <- replace_coefs_in_pt(pt.single, pt.strong[pt.strong$group == 2, ]) # Grab group sizes for the two groups n1 <- lavInspect(strong_fit, what = "nobs")[1] n2 <- lavInspect(strong_fit, what = "nobs")[2] # Use 'create_data_list' to simulate datasets invariant_data <- create_data_list(pt.invariant1, pt.invariant2, n1, n2, n_sets) return(invariant_data) } # Create simulated datasets based on parameters from configural invariance model, # exept factor means taken from strong invariance model. # Takes arguments; # single_group = a fitted single group model # strong_fit = a fitted strong group model # n_sets = number of datasets to create create_biased_data <- function(single_group, strong_fit, configural_fit, n_sets) { # Grab parameter tables from the three fitted models pt.single <- parameterTable(single_group) pt.strong <- parameterTable(strong_fit) pt.configural <- parameterTable(configural_fit) # Identify factors factors <- subset(pt.single, op == "=~") factors <- unique(factors$lhs) # Create parameter tables for groups 1 and 2 # with single group as frame and parameter values from configural model # See function 'replace_coefs_in_pt' above pt.biased1 <- replace_coefs_in_pt(pt.single, pt.configural[pt.configural$group == 1, ]) pt.biased2 <- replace_coefs_in_pt(pt.single, pt.configural[pt.configural$group == 2, ]) # Replace means with those from strong model. This to make the mean difference comparable # with the invariant data # See function 'replace_1_coef_in_pt' above for (i in 1:length(factors)) { pt.biased1 <- replace_1_coef_in_pt(pt.frame = pt.biased1, pt.replacement = pt.strong[pt.strong$group == 1,], lhs = factors[i], op = "~1") pt.biased2 <- replace_1_coef_in_pt(pt.frame = pt.biased2, pt.replacement = pt.strong[pt.strong$group == 2,], lhs = factors[1], op = "~1") } # Grab group sizes for the two groups n1 <- lavInspect(strong_fit, what = "nobs")[1] n2 <- lavInspect(strong_fit, what = "nobs")[2] # Simulate biased data using parameter tables biased_data <- create_data_list(pt.biased1, pt.biased2, n1, n2, n_sets) return(biased_data) } # Function for calculating difference in standardized coeffiecient # setup for use in function 'get_all_path_differences' # between invariant and biased datasets. Takes arguments: # reg.coef = the regression coefficient to analyze # sim.invariant = SimResult from runs on invariant data # sim.biased = SimResult from runs on biased data get_path_difference <- function (reg_coef, sim.invariant, sim.biased) { # pick standardized coefficients of the parameter defined by 'reg_coef' std_coeff.inv <- sim.invariant@stdCoef[, reg_coef] std_coeff.bias <- sim.biased@stdCoef[, reg_coef] # calculate Fisher's Z Fz.inv <- atanh(std_coeff.inv) Fz.bias <- atanh(std_coeff.bias) # Calculate mean, difference in means (using Fisher's Z) n.inv <- length(std_coeff.inv ) m.inv <- mean(Fz.inv) sd.inv <- sd(Fz.inv) se.inv <- sd(Fz.inv)/sqrt(length(Fz.inv)) n.bias <- length(std_coeff.bias) m.bias <- mean(Fz.bias) sd.bias <- sd(Fz.bias) se.bias <- sd(Fz.bias)/sqrt(length(Fz.bias)) diff <- mean(Fz.inv - Fz.bias) sd.diff <- sd(Fz.inv - Fz.bias) se.diff <- sd(Fz.inv - Fz.bias)/sqrt(length(Fz.bias)) # Create vector and convert back to standardized coefficient out <- c(m.inv, m.bias , diff , sd.inv, sd.bias, sd.diff, se.inv, se.bias, se.diff) out <- tanh(out) out <- matrix(out, 3,3, byrow = FALSE) out <- cbind(out, c(length(Fz.inv), length(Fz.bias), mean(c(length(Fz.inv), length(Fz.bias))))) rownames(out) <- c("Invariant datasets", "Biased datasets", "Difference") colnames(out) <- c("Mean", "sd", "se", "n of repl") return(out) } get_all_path_differences <- function(sim.invariant, sim.biased, impact_model) { require(purrr) pt <- lavaanify(impact_model) # Identify regressions regressions <- subset(pt, op == "~") regressions <- paste(regressions$lhs, regressions$op, regressions$rhs) # get rid of spaces regressions <- gsub(pattern = " ", replacement = "", x = regressions) results <- map(.x = regressions, .f = get_path_difference, sim.invariant = sim.invariant, sim.biased = sim.biased) names(results) <- regressions return(results) } # Function that will eventually be integrated to earlier step. Add info of base_model and data to the list. add_info <- function(results, base_model, used_data) { results[["base_model"]] <- base_model # move to earlier step results[["data"]] <- used_data # move to earlier step return(results) } # Do all impact analyses all_impact_analyses <- function(results, base_model, used_data, n_sets = 10) { results[["impact_model"]] <- get_impact_model(results[["base_model"]]) # Set up single group fit. No need to run the analysis. results[["single_group"]] <- cfa(model = results[["base_model"]], data = FinPrisonMales2, std.lv = TRUE, estimator = "WLSMV", do.fit = FALSE) results[["invariant_data"]] <- create_invariant_data(single_group = results[["single_group"]], strong_fit = results[["strong_fit"]], n_sets = n_sets) results[["biased_data"]] <- create_biased_data(single_group = results[["single_group"]], strong_fit = results[["strong_fit"]], configural_fit = results[["configural_fit"]], n_sets = n_sets) results[["invariant_fits"]] <- simsem::sim(model = results[["impact_model"]], rawData = results[["invariant_data"]], lavaanfun = "sem", std.lv = TRUE, estimator = "WLSMV") results[["biased_fits"]] <- simsem::sim(model = results[["impact_model"]], rawData = results[["biased_data"]], lavaanfun = "sem", std.lv = TRUE, estimator = "WLSMV") results[["groups"]] <- paste("Group 1 is " , lavInspect(results$strong_fit, what = "group.label")[1], " - Group 2 is", lavInspect(results$strong_fit, what = "group.label")[2]) results[["path_differences"]] <- get_all_path_differences(sim.invariant = results[["invariant_fits"]], sim.biased = results[["biased_fits"]], impact_model = results[["impact_model"]]) return(results) } #save.image("~/Dropbox/to aws/impact functions.RData") save.image("C:/Users/benny_000/Dropbox/to aws/MI-0-all functions and data.R.RData")

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Education-MR-MOE.R

library(devtools) library(tidyverse) library(TwoSampleMR) library(ggnewscale) library(ggplot2) library(glue) library(ggpubr) #EXPOSURE DATA --------------------------------------------- education.file <- "~/Desktop/Lee2018educ.chrall.CPRA_b37.tsv" education_dat <- read_exposure_data( filename = education.file, sep = "\t", snp_col = "DBSNP_ID", beta_col = "BETA", se_col = "SE", effect_allele_col = "ALT", other_allele_col = "REF", eaf_col = "AF", pval_col = "P", samplesize_col = "N", chr_col = "CHROM", pos_col = "POS", phenotype_col = "TRAIT" ) #filter education_dat<- filter(education_dat, pval.exposure < 0.00000005) %>% distinct(SNP, .keep_all = TRUE) #CLUMP DATA education_dat <- clump_data(education_dat) #OUTCOME DATA --------------------------------------------- AD.file <- "~/Desktop/Kunkle2019load_stage123.chrall.CPRA_b37.tsv" AD_dat <- read_outcome_data( snps = education_dat$SNP, filename = AD.file, sep = "\t", snp_col = "DBSNP_ID", beta_col = "BETA", se_col = "SE", effect_allele_col = "ALT", other_allele_col = "REF", eaf_col = "AF", pval_col = "P", samplesize_col = "N", chr_col = "CHROM", pos_col = "POS" ) #HARMONISE DATA -------------------------------------------------------- education.dat <- harmonise_data(education_dat,AD_dat) #MR-MoE----------------------------------------------------------------- load("/Users/jahnvipatel/Downloads/rf.rdata") res <- mr_wrapper(education.dat) res_moe <- mr_moe(res,rf) #PREFORM MR ------------------------------------------------------------ res.mr <- mr(education.dat) #each MR method for each combination of exposure-outcome traits generate_odds_ratios(res.mr) #MAKE PLOTS ------------------------------------------------------------ #create a scatter plot res.mr <- mr(education.dat, method_list=c( "mr_ivw", "mr_weighted_mode", "mr_weighted_median", "mr_simple_median", "mr_simple_mode", "mr_egger_regression") ) p1 <- mr_scatter_plot(res.mr,education.dat) p1[[1]] ggsave(p1[[1]], file="educationscatterplot.png", width=7, height=8) view(education.dat) view(p1$NCFz7e.oQE1lc$data) mr_scatter_plot2 <- function(mrdat,res){ message("Plotting Scatters: ", mrdat$exposure[1], " - ", mrdat$outcome[1]) ## reoriante effect direction of negative exposure values d <- mrdat %>% mutate(beta.outcome = ifelse(beta.exposure < 0, beta.outcome * -1, beta.outcome), beta.exposure = ifelse(beta.exposure < 0, beta.exposure * -1, beta.exposure)) ## Make scatter plot ggplot(data = d, aes(x = beta.exposure, y = beta.outcome)) + geom_errorbar(aes(ymin = beta.outcome - se.outcome, ymax = beta.outcome + se.outcome), colour = "grey", width = 0) + geom_errorbarh(aes(xmin = beta.exposure - se.exposure, xmax = beta.exposure + se.exposure), colour = "grey", height = 0) + geom_point(aes(colour = "!mrpresso_keep")) + scale_colour_manual(values = c("black", "#CD534CFF")) + new_scale_color() + geom_abline(data = res, aes(intercept = a, slope = b, color = method, linetype = method), show.legend = TRUE) + scale_color_brewer(palette = "Set3") + labs(x = paste("SNP effect on\n", d$exposure[1]), y = paste("SNP effect on\n", d$outcome[1])) + theme_bw() + theme(legend.position = "bottom", legend.direction = "horizontal", text = element_text(size=8)) + guides(linetype = guide_legend(nrow = 1), colour_new = FALSE) } a<-c(0,0,-0.106,0.0842,0,0,0,0,0,0,0,0,0,0,0,0,0.0421,0.0842,0.0421,0,0.0853,0,0,0,0,0,0,0,0,0,0.0853,0,0,0,0,0,0,0,-0.0106,0,0,0,0,0) res_moe$NCFz7e.oQE1lc$estimates$a=a p <- mr_scatter_plot2(mrdat = education.dat, res = res_moe$NCFz7e.oQE1lc$estimates) p + facet_wrap(vars(selection)) + theme(legend.position = 'top') #create a forest plot res_single_educ <- mr_singlesnp(education.dat, all_method=c( "mr_ivw", "mr_weighted_mode", "mr_weighted_median", "mr_simple_median", "mr_simple_mode", "mr_egger_regression")) p2 <- mr_forest_plot(res_single_educ) p2[[1]] ggsave(p2[[1]], file="educationforestplot.png", width=7, height=20) view #create a funnel plot res_single_educ <- mr_singlesnp( education.dat, all_method=c("mr_ivw", "mr_weighted_mode", "mr_weighted_median", "mr_simple_median", "mr_simple_mode", "mr_egger_regression") ) p4 <- mr_funnel_plot(res_single_educ) p4[[1]] ggsave(p3[[1]], file="educationfunnelplot.png", width=7, height=7) res.filter <- res_single_educ %>% slice(1:304) res_moe2 <- subset(res_moe$NCFz7e.oQE1lc$estimates,select = -c(nsnp,ci_low,ci_upp,steiger_filtered,outlier_filtered,selection,method2,MOE,a)) res_methods <- paste0("All - ",res_moe2$method) res_moe2 <- subset(res_moe2,select=-c(method)) res_moe2 <- cbind(res_methods,res_moe2) colnames(res_moe2)[1] <- "SNP" exposure <- rep(c("Education"),times=c(44)) outcome <- rep(c("outcome"),times=c(44)) samplesize <- rep(c(63926),times=c(44)) res_moe3<- cbind(exposure,outcome,samplesize) res_moe_filter <- merge(res_moe2,res_moe3,by=0) res_moe_filter <- subset(res_moe_filter,select = -c(Row.names)) colnames(res_moe_filter)[6] <- "p" res.filter2 <- rbind(res.filter,res_moe_filter) p3 <- mr_funnel_plot(res.filter2) p3[[1]] p3a <- p3[[1]] + theme_bw() + theme(legend.position = 'bottom') steiger <- mr_funnel_plot(slice(res.filter2,-c(11,45,61,86,90,95,111,135,188,212,216,246,258,271,276,288))) steigera <- steiger[[1]] + theme_bw() + theme(legend.position = 'bottom') #steiger outlier <- mr_funnel_plot(slice(res.filter2,-c(9,11,41,42,45,61,81,86,95,135,143,188,216,258,276,288))) outliera <- outlier[[1]] + theme_bw() + theme(legend.position = 'bottom') #outlier both <- mr_funnel_plot(slice(res.filter2,-c(9,11,41,42,45,61,81,86,90,95,111,135,143,188,212,216,246,258,271,276,288))) botha <- both[[1]] + theme_bw() + theme(legend.position = 'bottom') #both ggpubr::ggarrange(p3a,steigera, outliera, botha,common.legend = T) #generate report mr_report(education.dat) #generate spreadsheet library(writexl) write_xlsx(res_moe$NCFz7e.oQE1lc$snps_retained,"\\Desktop\\snps MR-MOE.xlsx") write_xlsx(res_moe$w8C1MU.DWrQTt,"\\Desktop\\Education MR-MOE.xlsNCFz7e.oQE1lc)

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rm(list=ls()) getwd() setwd("C:/Users/user/Documents/R/Practical_1") dat08<-read.table("Sight2008.txt",sep = "\t",header=TRUE) dat09<-read.table("sight2009.txt",sep = "\t", header=TRUE) names(dat08) dim(dat08) str(dat08) summary(dat08) dat08[6,3] #If data frames is having different columns and rows add them using merge all=true dat<- merge(dat08, dat09, all=TRUE) dat #For fetching a column for data frame use square brackets temps<- dat[,"Temperature"] tempsF<- temps*9/5+32 id<- dat[,"Indiv_ID"] id #for adding a coloumn use cbind dat<-cbind(dat,tempsF) dat #Exporting data of data frame to Excel that can be readed. write.table(dat, "SightE.xls", sep = "\t", row.names = FALSE, col.names = TRUE) typeof(temps) length(dat) objects() names(dat) abcd<- list(1:3, "a", c(TRUE, FALSE, TRUE), c(2.3, 5.9)) names(abcd)

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outcomes <- c("TRM", "DRM", "OS", "PFS", "REL", "GVHD", "OF", "INF") time_var <- c("time to death", "time to death", "time to death", "time to relapse", "time to relapse", "time to death", "time to death", "time to death") covariates <- c("recipient age, BMI, graft source", "recipient age, disease status", "age, disease status, graft source", "recipient age, disease status", "conditioning regimen and intensity", "recipient age, donor age, BMI", "disease status, graft source", "age, BMI, CMV status") df <- setNames(data.frame( outcomes, time_var, covariates, stringsAsFactors = FALSE), c("Survival Outcomes", "Time Interval", "Covariates")) knitr::kable(df, caption="\\label{tab:models_run} Survival Models Analyzed", booktabs=TRUE) %>% kableExtra::kable_styling(latex_options="striped", font_size=9) #%>%

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HSV.R

library(grDevices) library(e1071) # Convert 3d array of RGB to 2d matrix setwd("images") extract.features <- function(img){ mat <- imageData(img) mat_rgb <- mat dim(mat_rgb) <- c(nrow(mat)*ncol(mat), 3) mat_hsv <- rgb2hsv(t(mat_rgb)) nH <- 10 nS <- 6 nV <- 6 # Caution: determine the bins using all images! The bins should be consistent across all images. The following code is only used for demonstration on a single image. hBin <- seq(0, 1, length.out=nH) sBin <- seq(0, 1, length.out=nS) vBin <- seq(0, 0.005, length.out=nV) freq_hsv <- as.data.frame(table(factor(findInterval(mat_hsv[1,], hBin), levels=1:nH), factor(findInterval(mat_hsv[2,], sBin), levels=1:nS), factor(findInterval(mat_hsv[3,], vBin), levels=1:nV))) hsv_feature <- as.numeric(freq_hsv$Freq)/(ncol(mat)*nrow(mat)) # normalization return(hsv_feature) } labels <- read.table("annotations/list.txt",stringsAsFactors = F) n <- 200 X <- array(rep(0,n*360),dim=c(n,360)) for (i in 1:n){ img <- readImage(paste0(labels$V1[i],".jpg")) X[i,] <- extract.features(img) } X <- as.data.frame(X) y <- as.factor(labels$V3[1:n]) svm.model<-svm(x = X[,1:10],y = y,kernel="linear",scale=F)

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/previous_work/downsampling/LDA_analysis.R

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ethanwhite/LDATS

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LDA_analysis.R

# ============================================================================ #' Run through whole pipeline of LDA analysis, VEM method #' #' #' #' @param dat Table of integer data (species counts by time step) #' @param SEED set seed to keep LDA model runs consistent (default 2010) #' @param test_topics numbers of topics to test, of the form (topic_min, #' topic_max) #' @param dates vector of dates that correspond to time steps from 'dat' -- #' for plotting purposes #' @param n_chpoints number of change points the changepoint model should #' look for #' @param maxit max iterations for changepoint model (more=slower) #' #' @examples LDA_analysis(dat, 2010, c(2, 3), dates, 2) #' @export LDA_analysis_VEM <- function(dat, SEED, test_topics){ # choose number of topics -- model selection using AIC aic_values <- aic_model(dat, SEED, test_topics[1], test_topics[2]) # run LDA model ntopics <- filter(aic_values,aic == min(aic)) %>% select(k) %>% as.numeric() ldamodel <- LDA(dat, ntopics, control = list(seed = SEED), method = 'VEM') return(ldamodel) } # ============================================================================ #' Run through whole pipeline of LDA analysis, Gibbs method --- WIP #' #' #' #' @param dat Table of integer data (species counts by time step) #' @param ngibbs number of iterations for gibbs sampler -- must be greater #' than 200 (default 1000) #' @param test_topics numbers of topics to test, of the form (topic_min, #' topic_max) #' @param dates vector of dates that correspond to time steps from 'dat' -- #' for plotting purposes #' #' @examples LDA_analysis_gibbs(dat, 200, c(2, 3), dates) #' @export LDA_analysis_gibbs <- function(dat, ngibbs, test_topics, dates){ # choose number of topics -- model selection using AIC aic_values <- aic_model_gibbs(dat, ngibbs, test_topics[1], test_topics[2], F) # run LDA model ntopics <- filter(aic_values, aic == min(aic)) %>% select(k) %>% as.numeric() ldamodel <- gibbs.samp(dat.agg = dat, ngibbs = ngibbs, ncommun = ntopics, a.betas = 1, a.theta = 1) return(ldamodel) }

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/rpca.R \name{RunRobPCA} \alias{RunRobPCA} \title{Perform robust PCA (Hubert PCA) with modified PC scores on a Seurat object} \usage{ RunRobPCA(object, npcs = 10, pc.genes = NULL, use.modified.pcscores = TRUE) } \arguments{ \item{object}{Seurat object} \item{npcs}{Number of principal components to calculate} \item{pc.genes}{Genes as input to PC. If \code{pc.genes==NULL}, the var.genes slot is used. If var.genes is empty and \code{pc.genes==NULL}, then all genes are used.} \item{use.modified.pcscores}{If \code{FALSE}, then the raw pc score output from robust PCA is used. If \code{TRUE}, then pc scores are replaced with the sum of the top 30 genes by positive loading minus the sum of the top 30 genes by negative loading. Each gene is scaled to a max of 1 and min of 0.} } \value{ A Seurat object with the 'rpca' field filled. } \description{ \code{scRobustPCA} performs the variant of robust PCA developed by Mia Hubert et al. on the gene expression matrix "data" in a Seurat object. Set reduction.type='rpca' in other Seurat functions to use the rPCA results, for example to calculate clusters with FindClusters. } \examples{ }

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toTable <- function(mGSZobj, sample = FALSE, m = c("mGSZ","mGSA","mAllez","WRS","SS","SUM","KS","wKS"), n = 5){ if(!mGSZobj$gene.perm.log & !sample & !mGSZobj$other.methods){ table <- mGSZobj$mGSZ[1:n,] } else if(mGSZobj$gene.perm.log & !sample & !mGSZobj$other.methods){ table <- mGSZobj$mGSZ.gene.perm[1:n,] } else if(mGSZobj$gene.perm.log & sample & !mGSZobj$other.methods){ table <- mGSZobj$mGSZ.sample.perm[1:n,] } else if(!mGSZobj$gene.perm.log & !sample & mGSZobj$other.methods){ m <- match.arg(m) table <- mGSZobj[[m]][1:n,] } else if(mGSZobj$gene.perm.log & !sample & mGSZobj$other.methods){ m <- match.arg(m) table <- mGSZobj$gene.perm[[m]][1:n,] } else if(mGSZobj$gene.perm.log & sample & mGSZobj$other.methods){ m <- match.arg(m) table <- mGSZobj$sample.perm[[m]][1:n,] } return(table) }

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/futures_data.R

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futures_data.R

# TODO: Add comment ############################################################################################################## rm(list=ls(all=TRUE)) options(width = 438L) library(WindR) w.start() #w.menu() #w.isconnected() #---------------- parameters ---------------- #can only roll within next 12 months, i.e., RollMonth <= FrontMonth < BackMonth Exchange = "SHF" Underlying = "CU" TradingCalendar="SHFE" RollMonth = c(4,8,12) FrontMonth = c(5,9,13) BackMonth = c(9,13,17) Exchange = "SHF" Underlying = "CU" TradingCalendar="SHFE" RollMonth = seq(1:12) FrontMonth = RollMonth + 1 BackMonth = RollMonth + 2 #historical price range DataStart = "2011-01-01" DataEnd = "2015-08-30" #roll days, i.e., trading day of the month TradingDayOfMonthRollStart = 6 TradingDayOfMonthRollEnd = 10 #index and ETN initial value IndexER0 = 1000 IndexTR0 = 1000 ETNIV0 = 100 #interest rate InterestRate = 0.0035 InterestTerm = 1 #ETN products LevInd = c("LEV1X", "INV1X") Leverage = c(1, -1) YearlyFee = c(0.0055, 0.0075) #---------------- ---------------- #trading day of an exchange TradingDate = w.tdays(DataStart, DataEnd, paste("TradingCalendar=",TradingCalendar,sep="")) if (Underlying == "CU") { IndexBenchmark = c("CUFI.WI", "NH0012.NHF") } else if (Underlying == "AL") { IndexBenchmark = c("ALFI.WI", "NH0013.NHF") } else { } #---------------- trading month and number of tradings in a month ----------------- FutureData = data.frame(cbind(as.character(TradingDate$Data[,1]), matrix(as.numeric(unlist(strsplit(as.character(TradingDate$Data[,1]),'-'))), ncol=3, byrow=TRUE))) colnames(FutureData) = c("TradingDate", "Year", "Month", "Day") FutureData$Year2 = as.numeric(substr(FutureData$Year,3,4)) FutureData$IsRollMonth = FutureData$Month %in% RollMonth #> head(TradingMonth) #Year Month IsRollMonth #1 2014 5 FALSE #21 2014 6 FALSE #41 2014 7 FALSE #64 2014 8 TRUE #85 2014 9 FALSE #106 2014 10 FALSE TradingMonth = unique(FutureData[,c("Year","Month")]) TradingMonth$IsRollMonth = TradingMonth$Month %in% RollMonth #trading day sequence in a year-month FutureData$TradingDayOfMonth = 0 for(i in 1:dim(TradingMonth)[1]) { ind = (FutureData$Year == TradingMonth[i,]$Year) & (FutureData$Month == TradingMonth[i,]$Month) if (sum(ind) > 0) { FutureData[ind,]$TradingDayOfMonth = seq(1,sum(ind)) } } #----------------- roll percentage weight change schedule ----------------- #> head(RollWeightSchedule) #Month TradingDayOfMonth PW1 #1 4 6 0.8 #2 8 6 0.8 #3 12 6 0.8 #4 4 7 0.6 #5 8 7 0.6 #6 12 7 0.6 nRollDays = TradingDayOfMonthRollEnd - TradingDayOfMonthRollStart + 1 RollWeight = data.frame(cbind(seq(TradingDayOfMonthRollStart,TradingDayOfMonthRollEnd), 1 - seq(1:nRollDays) / nRollDays)) RollWeightSchedule = merge(RollMonth, RollWeight) colnames(RollWeightSchedule) = c("Month", "TradingDayOfMonth", "PW1") #percentage weight schedule FutureData$PW1 = 1 for (i in 1:dim(RollWeightSchedule)[1]){ ind = (FutureData$Month == RollWeightSchedule[i,]$Month) & (FutureData$TradingDayOfMonth == RollWeightSchedule[i,]$TradingDayOfMonth) if (sum(ind) > 0) { FutureData[ind,]$PW1 = RollWeightSchedule[i,]$PW1 } } #----------------- roll contract schedule ----------------- #> RollContractSchedule #Month FrontMonth BackMonth IsRollMonth #1 1 5 9 FALSE #2 2 5 9 FALSE #3 3 5 9 FALSE #4 4 5 9 TRUE #5 5 9 13 FALSE #6 6 9 13 FALSE #7 7 9 13 FALSE #8 8 9 13 TRUE #9 9 13 17 FALSE #10 10 13 17 FALSE #11 11 13 17 FALSE #12 12 13 17 TRUE RollContractSchedule = data.frame(seq(1,12), rep(0,12), rep(0,12)) colnames(RollContractSchedule) = c("Month","FrontMonth","BackMonth") RollContractSchedule$IsRollMonth = RollContractSchedule$Month %in% RollMonth #"FrontMonth" and "BackMonth" are defined in parameters t1 = c(0, RollMonth) for (i in 1:length(RollMonth)) { ind = (t1[i] < RollContractSchedule$Month) & (RollContractSchedule$Month <= t1[i+1]) if (sum(ind) > 0) { RollContractSchedule[ind,]$FrontMonth = FrontMonth[i] RollContractSchedule[ind,]$BackMonth = BackMonth[i] } } #----------------- decide the front month and back month contracts ----------------- FutureData$FrontMonth = "" FutureData$BackMonth = "" for (i in 1:dim(RollContractSchedule)[1]) { # front month ind = (FutureData$Month == RollContractSchedule[i,]$Month) if(sum(ind) > 0) { t2 = floor(RollContractSchedule[i,]$FrontMonth / 13) FutureData[ind,]$FrontMonth = paste(Underlying, FutureData[ind,]$Year2+t2, formatC(RollContractSchedule[i,]$FrontMonth-12*t2, width=2, flag='0'), ".", Exchange, sep="") } # back month ind2 = ind & (FutureData$PW1 != 1) if(sum(ind2) > 0) { t2 = floor(RollContractSchedule[i,]$BackMonth / 13) FutureData[ind2,]$BackMonth = paste(Underlying, FutureData[ind2,]$Year2+t2, formatC(RollContractSchedule[i,]$BackMonth-12*t2, width=2, flag='0'), ".", Exchange, sep="") } # days after TradingDayOfMonthRollEnd of roll month ind3 = ind & FutureData$IsRollMonth & (FutureData$TradingDayOfMonth > TradingDayOfMonthRollEnd) if(sum(ind3) > 0) { t2 = floor(RollContractSchedule[i,]$BackMonth / 13) FutureData[ind3,]$FrontMonth = paste(Underlying, FutureData[ind3,]$Year2+t2, formatC(RollContractSchedule[i,]$BackMonth-12*t2, width=2, flag='0'), ".", Exchange, sep="") } } #----------------- get the price for both front month and back month ----------------- FutureData$FrontMonthPrice = 0 FutureData$BackMonthPrice = 0 FutureData$FrontMonthReturnHigh = 0 FutureData$BackMonthReturnHigh = 0 FutureData$FrontMonthReturnLow = 0 FutureData$BackMonthReturnLow = 0 fmc = unique(FutureData$FrontMonth) bmc = unique(FutureData$BackMonth) #front month price for (i in fmc) { if (i != "") { ind = (FutureData$FrontMonth==i) if (sum(ind > 0)) { start = as.character(head(FutureData[ind,]$TradingDate,n=1)) end = as.character(tail(FutureData[ind,]$TradingDate,n=1)) w_wsd_data = w.wsd(i, "open,high,low,close,pre_settle,settle,volume,oi", start, end, paste("TradingCalendar=",TradingCalendar,sep="")) FutureData[ind,]$FrontMonthPrice = w_wsd_data$Data$SETTLE print(paste("Front Month",i,start,end)) } } } #back month price for (i in bmc) { if (i != "") { ind = (FutureData$BackMonth==i) if (sum(ind > 0)) { start = as.character(head(FutureData[ind,]$TradingDate,n=1)) end = as.character(tail(FutureData[ind,]$TradingDate,n=1)) w_wsd_data = w.wsd(i, "open,high,low,close,pre_settle,settle,volume,oi", start, end, paste("TradingCalendar=",TradingCalendar,sep="")) FutureData[ind,]$BackMonthPrice = w_wsd_data$Data$SETTLE print(paste("Back Month",i,start,end)) } } } #----------------- get index benchmark for the underlying ----------------- #DataStart and DataEnd maybe different from start and end start = as.character(head(FutureData$TradingDate,n=1)) end = as.character(tail(FutureData$TradingDate,n=1)) for (i in IndexBenchmark) { w_wsd_data = w.wsd(i, "open,high,low,close,settle,volume,oi", start, end) FutureData[,i] = w_wsd_data$Data$CLOSE } #----------------- index calculation ----------------- nData = dim(FutureData)[1] #--- contract daily return --- #percentage weight of previous trading day, see spreadsheet pwt = FutureData[1:nData-1,]$PW1 pwt[FutureData[1:nData-1,]$PW1==0] = 1 #numerator, current trading day price, previous trading day weight FutureData$DifNum = c(1, pwt * FutureData[2:nData,]$FrontMonthPrice + (1 - pwt) * FutureData[2:nData,]$BackMonthPrice) #denominator, previous trading day weighted price FutureData$DifDen = c(1, FutureData[1:nData-1,]$PW1 * FutureData[1:nData-1,]$FrontMonthPrice + (1 - FutureData[1:nData-1,]$PW1) * FutureData[1:nData-1,]$BackMonthPrice) FutureData$CDR = FutureData$DifNum / FutureData$DifDen - 1 #--- Treasury Bill Return, use previous trading day interest rate --- FutureData$InterestRate = InterestRate FutureData$InterestDays = c(0, as.numeric(as.Date(FutureData[2:nData,]$TradingDate)-as.Date(FutureData[1:nData-1,]$TradingDate))) FutureData$TBR = c(0, (1 / (1 - FutureData[1:nData-1,]$InterestRate*InterestTerm/360)) ^ (FutureData[2:nData,]$InterestDays/InterestTerm) - 1) #index excess return FutureData$IndexER = IndexER0 * cumprod(1 + FutureData$CDR) #index total return FutureData$IndexTR = IndexTR0 * cumprod(1 + FutureData$CDR + FutureData$TBR) #----------------- ETN ----------------- CONST1 = paste("CONST1", LevInd, sep="_") CONST2 = paste("CONST2", LevInd, sep="_") IV = paste("IV", LevInd, sep="_") FutureData[,c(CONST1,CONST2,IV)] = rep(0,3*length(LevInd)) FutureData[1,IV] = rep(ETNIV0, length(LevInd)) #--- pricing model, depends on IV(T-1) and InterestRate(T-1) ---- for (i in 2:nData) { FutureData[i,CONST1] = FutureData[i-1,IV] * (1-YearlyFee/365)^FutureData[i,]$InterestDays * Leverage / FutureData[i,]$DifDen ir = (1 / (1 - FutureData[i-1,]$InterestRate*InterestTerm/360)) ^ (FutureData[i,]$InterestDays/InterestTerm) FutureData[i,CONST2] = FutureData[i-1,IV] * (1-YearlyFee/365)^FutureData[i,]$InterestDays * (ir - Leverage) FutureData[i,IV] = FutureData[i,CONST1] * FutureData[i,]$DifNum + FutureData[i,CONST2] } #----------------- plot ----------------- #cumulative return as reference to FutureData[1,i] IndexCol = c("IndexER", "IndexTR", IndexBenchmark, IV) mm = data.frame(as.Date(FutureData$TradingDate)) for (i in IndexCol) { mm[,i] = FutureData[,i] / FutureData[1,i] } colnames(mm) = c("TradingDate", IndexCol) #matrix plot matplot(mm[,IndexCol], type="l", xaxt="n", ylab="Cumulative Return", col=seq(1:length(IndexCol)), main=paste("BOCI Commodity Index", Underlying)) tt = paste(IndexCol, c(rep("",length(IndexCol)-length(YearlyFee)), paste(" Annual Fee = ",YearlyFee*100,"%",sep="")), sep="") legend("bottomleft",legend=tt, lty=1, col=seq(1:length(IndexCol)), cex=0.7) ind = c(1, seq(1:dim(TradingMonth)[1]) * floor(dim(mm)[1]/dim(TradingMonth)[1])) axis(1, at=ind, labels=mm[ind,1]) grid() # print(head(FutureData)) print(tail(FutureData))

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# Load the readr, ggplot2, and dplyr packages library(readr) library(ggplot2) library(dplyr) # Read datasets/confirmed_cases_worldwide.csv into confirmed_cases_worldwide confirmed_cases_worldwide <- read_csv("datasets/confirmed_cases_worldwide.csv") # Print out confirmed_cases_worldwide confirmed_cases_worldwide

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test-viz_fitted_line.R

library(testthat) library(parsnip) library(workflows) set.seed(1234) knn_spec <- nearest_neighbor() %>% set_mode("regression") %>% set_engine("kknn") knn_fit <- workflow() %>% add_formula(mpg ~ disp) %>% add_model(knn_spec) %>% fit(mtcars) test_that("viz_fitted_line works", { vdiffr::expect_doppelganger( "viz_fitted_line simple", viz_fitted_line(knn_fit, mtcars), "viz_fitted_line" ) vdiffr::expect_doppelganger( "viz_fitted_line resolution", viz_fitted_line(knn_fit, mtcars, resolution = 20), "viz_fitted_line" ) vdiffr::expect_doppelganger( "viz_fitted_line expand", viz_fitted_line(knn_fit, mtcars, expand = 1), "viz_fitted_line" ) vdiffr::expect_doppelganger( "viz_fitted_line style", viz_fitted_line(knn_fit, mtcars, color = "pink", size = 4), "viz_fitted_line" ) })

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twitter.R

#I used this website for the main recipe to get my Twitter authorization: http://www.datablog.sytpp.net/2014/04/scraping-twitter-with-r-a-how-to/ rm(list=ls()) library(twitteR) library(RCurl) library(ROAuth) consumerKey <- "5yViSuSbNLBBDaUZA03wQ" consumerSecret <- "EuLTI61ncd8O7mmdpiStR86UtoXPgo6LMiFphEKeM" reqURL <- "https://api.twitter.com/oauth/request_token" accessURL <- "https://api.twitter.com/oauth/access_token" authURL <- "http://api.twitter.com/oauth/authorize" twitCred <- OAuthFactory$new(consumerKey=consumerKey, consumerSecret=consumerSecret, requestURL=reqURL, accessURL=accessURL, authURL=authURL) twitCred$handshake() #Retrieve the first 200 tweets for the folowing profiles johnTweets <- userTimeline("John", n = 200) johnDataFrame <- twListToDF(johnTweets) write.csv(johnDataFrame, file="john.csv") head(johnDataFrame) barryTweets <- userTimeline("barrybateman", n = 200) barryDataFrame <- twListToDF(barryTweets) write.csv(barryDataFrame, file="barry.csv") head(barryDataFrame) robbieTweets <- userTimeline("702JohnRobbie", n = 200) robbieDataFrame <- twListToDF(robbieTweets) write.csv(robbieDataFrame, file="robbie.csv") head(robbieDataFrame) rediTweets <- userTimeline("RediTlhabi", n = 200) rediDataFrame <- twListToDF(rediTweets) write.csv(rediDataFrame, file="robbie.csv") head(rediDataFrame) ferialTweets <- userTimeline("ferialhaffajee", n = 200) ferialDataFrame <- twListToDF(ferialTweets) write.csv(ferialDataFrame, file="ferial.csv") head(ferialDataFrame) leanneTweets <- userTimeline("LeanneManas", n = 200) leanneDataFrame <- twListToDF(leanneTweets) write.csv(leanneDataFrame, file="leanne.csv") head(leanneDataFrame) mandyTweets <- userTimeline("MandyWiener", n = 200) mandyDataFrame <- twListToDF(mandyTweets) write.csv(mandyDataFrame, file="mandy.csv") head(mandyDataFrame) ulrichTweets <- userTimeline("UlrichJvV", n = 200) ulrichDataFrame <- twListToDF(ulrichTweets) write.csv(ulrichDataFrame, file="ulrich.csv") head(ulrichDataFrame) stephenTweets <- userTimeline("StephenGrootes", n = 200) stephenDataFrame <- twListToDF(stephenTweets) write.csv(stephenDataFrame, file="stephan.csv") head(stephenDataFrame) akiTweets <- userTimeline("akianastasiou", n = 200) akiDataFrame <- twListToDF(akiTweets) write.csv(akiDataFrame, file="aki.csv") head(akiDataFrame) sportTweets <- userTimeline("Sport24News", n = 200) sportDataFrame <- twListToDF(sportTweets) write.csv(sportDataFrame, file="sport.csv") head(sportDataFrame)

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#' Delivers praise #' #' @description This function is useful when you are #' feeling sad, and would like to recieve some praise. #' @param name Text string of the name I would like to praise #' @param punctuation This is our punctionation as a text input #' @return Returns text string with praise #' @export #' #' @examples #' praise(name = "Kristy", punctuation="!") #' praise <-function(name, punctuation="!"){ glue::glue("You're the best, {name}{punctuation}") } ## code >Insert royxygen skeleton ## devtools::document() ## license - mit license ## usethis::use_mit_license("Kristy Robledo") # in the terminal: ##git config --global user.name 'kristy.robledo' ##git config --global user.email 'robledo.kristy@gmail.com' ##git config --global --list #this is stolen from github repo: #git remote add origin https://github.com/kristyrobledo/praiseMe.git #git push -u origin master

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#load libraries library(sqldf) fileName<-"household_power_consumption.txt" #load data data<-read.csv.sql(fileName, sql = "select * from file where Date = '1/2/2007' or Date = '2/2/2007' ", header=TRUE,sep=";") ##create a datetime variable data$DateTime<-strptime(paste(data$Date,data$Time),format = "%d/%m/%Y %H:%M:%S") #plot2 png(file="plot2.png", bg = "transparent", width = 480, height = 480, units = "px") with(data,plot(DateTime,Global_active_power,xlab="",ylab="Global Active Power (Kilowatts)",type="l")) dev.off()

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rm(list=ls()) setwd("C:/Code/RStudio/SEM II/Lab 8") data <- read.csv("iris.csv",row.names=1) #as it is distance based metric, we scale to obtain better accuracy. df <- scale(data) #computing dis-similarity matrix dissim <- dist(df, method = 'euclidean') hierClust <- hclust(dissim, method = 'complete') plot(hierClust) cluster <- cutree(hierClust, k = 3) #minimize inter-cluster distance and maximize intra-cluster distance library(clValid) #higher the dunn index, more compact(better) the clusters are dunn(dissim, cluster) # plot(hclust_avg) rect.hclust(hierClust, k = 3, border = 2:4) abline(h = 3, col = 'red') Kmax <- 10 D <- rep(0,Kmax) for (i in 2:Kmax){ cluster<- cutree(hierClust, k=i) D[i] <- dunn(dissim, cluster) } plot(2:Kmax,D[2:Kmax],type="b",pch=19) # Best value of k is the one with the ighest Dunn index

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NISTpascalSecTOslugPerFtSec.Rd

\name{NISTpascalSecTOslugPerFtSec} \alias{NISTpascalSecTOslugPerFtSec} \title{Convert pascal second to slug per foot second } \usage{NISTpascalSecTOslugPerFtSec(pascalSec)} \description{\code{NISTpascalSecTOslugPerFtSec} converts from pascal second (Pa * s) to slug per foot second [slug/(ft * s)] } \arguments{ \item{pascalSec}{pascal second (Pa * s) } } \value{slug per foot second [slug/(ft * s)] } \source{ National Institute of Standards and Technology (NIST), 2014 NIST Guide to SI Units B.8 Factors for Units Listed Alphabetically \url{http://physics.nist.gov/Pubs/SP811/appenB8.html} } \references{ National Institute of Standards and Technology (NIST), 2014 NIST Guide to SI Units B.8 Factors for Units Listed Alphabetically \url{http://physics.nist.gov/Pubs/SP811/appenB8.html} } \author{Jose Gama} \examples{ NISTpascalSecTOslugPerFtSec(10) } \keyword{programming}

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library('rvest') library('RSelenium') library('stringr') library('dplyr') library('lubridate') DATA_ROOT <- "./data" SRC_ROOT <- "." SOURCE_NAME <- "youtube" timestamp <- strftime(now(), format="%Y%m%d%H%M%S") file_name <- str_interp("${DATA_ROOT}/${SOURCE_NAME}_${timestamp}.rds") # source functions -------------------------------------------------------- source(str_interp('${SRC_ROOT}/youtube_utils.R'), encoding = 'UTF-8') # code run ---------------------------------------------------------------- #remDr$server$stop() #remDr$client$open() # remDr$client$screenshot(display = TRUE) remDr <- fn_start_driver(4443L) # URL MAIN_URL <- "https://www.youtube.com/watch?v=Tnp_2FceTlQ" init_page_to_crawl(remDr, MAIN_URL) open_all_details(remDr) reply_list <- list() # open_all_details(remDr) page_src <- remDr$client$getPageSource() replies <- get_replies(page_src) remDr$server$stop() remDr$client$close() rm(remDr) gc() # save result ------------------------------------------------------------- saveRDS(replies, file_name)

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titanic_deck_plot.R

library(tidyverse) library(ggplot2) # plot survival rate by deck level make_deck_plot <- function() { titanic_deck_df = read.csv("data/wrangled_titanic_df.csv") titanic_deck_df$deck <- factor(titanic_deck_df$deck, levels = c( "G", "F", "E", "D", "C", "B", "A")) survival_deck <- titanic_deck_df %>% group_by(deck) %>% summarise(stat = mean(survived)) ggplot(survival_deck, aes(x = deck, y = stat*100)) + geom_bar(stat = "identity", width = 0.5, color = "blue", fill = "blue") + labs(x = "Deck", y = "Rate of Survival (%)", title = "Survival Rate by Deck") + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_blank(), axis.line = element_line(colour = "grey"), plot.title = element_text(hjust = 0.5)) + coord_flip() }

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/LGA__Year_labourForceParticipation.R \docType{data} \name{LGA__Year_labourForceParticipation} \alias{LGA__Year_labourForceParticipation} \title{by LGA, Year} \format{1,689 observations and 3 variables.} \usage{ LGA__Year_labourForceParticipation } \description{ Labour by LGA, Year #' @description Force by LGA, Year #' @description Participation by LGA, Year } \keyword{datasets}

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retrieve_validation_set_from_db_for_emulator.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/emulation_generation.R \name{retrieve_validation_set_from_db_for_emulator} \alias{retrieve_validation_set_from_db_for_emulator} \title{To demonstrate use of emulator, we return validation set from the database to show how the emulator can make predictions on output} \usage{ retrieve_validation_set_from_db_for_emulator(dblink, parameters, measures, experiment_id) } \arguments{ \item{dblink}{A link to the database in which this table is being created} \item{parameters}{The parameters of the simulation that are being analysed} \item{measures}{The measures of the simulation that are being assessed} \item{experiment_id}{ID of the experiment for which the validation set is being returned} } \value{ Set of parameter values that can be used to make predictions } \description{ To demonstrate use of emulator, we return validation set from the database to show how the emulator can make predictions on output }

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hwscript.R

# wk 2 assignment from Reproducible Research #required libraries for this exercise library(lubridate) library(dplyr) library(ggplot2) # Loading and processing the data activity<-read.table("activity.csv", sep=",", header=TRUE) activity$date <- ymd(activity$date) # what is the mean total number of steps taken per day? # for this part of the assignment, you can ignore missing values in the dataset # 1. calculate the total number of steps taken per day # 2. make a histogram of the total number of steps taken per day # 3. calculate and report the mean and median of the total number of steps taken per day daily_step_summary <-activity %>% filter(!is.na(steps)) %>% group_by(date) %>% summarize(total_steps=sum(steps)) ggplot(data=daily_step_summary, aes(x=date, y=total_steps)) + geom_bar(stat="identity") mean(daily_step_summary$total_steps) median(daily_step_summary$total_steps) # what is the average daily activity pattern? # 1. Make a time series plot (type="l") of the 5-minute interval (x-axis) and the average number # of steps taken, averaged across all days (y-axis) # 2. which 5-minute interval, on average across all days in the dataset, contains the # maximum number of steps? interval_step_summary<-activity %>% filter(!is.na(steps)) %>% group_by(interval) %>% summarize(interval_mean_steps = ceiling(mean(steps))) ggplot(data=interval_step_summary, aes(x=interval, y=interval_mean_steps)) + geom_line() interval_step_summary$interval[which.max(interval_step_summary$interval_mean_steps)] interval_step_summary$interval_mean_steps[which.max(interval_step_summary$interval_mean_steps)] # calculate number of missing values sum(is.na(activity$steps)) # imputing missing values activity_copy<-activity activity_copy$steps[is.na(activity$steps)] <- (activity %>% filter(is.na(steps)) %>% left_join(interval_step_summary, by="interval"))$interval_mean_steps daily_step_summary_nafilled <-activity_copy %>% group_by(date) %>% summarize(total_steps=sum(steps)) ggplot(data=daily_step_summary_nafilled, aes(x=date, y=total_steps)) + geom_bar(stat="identity") mean(daily_step_summary_nafilled$total_steps) median(daily_step_summary_nafilled$total_steps) # Are there differences in activity pattern between weekdays and weekends? activity_copy_wdays<-activity_copy %>% mutate(day_of_week = wday(date, label=TRUE)) %>% mutate(day_class = if_else(day_of_week=="Sat"| day_of_week=="Sun", "weekend", "weekday")) activity_copy_wdays$day_class <- as.factor(activity_copy_wdays$day_class) interval_step_summary_wday <- activity_copy_wdays %>% group_by(day_class, interval) %>% summarize(interval_mean_steps = ceiling(mean(steps))) ggplot(data=interval_step_summary_wday, aes(x=interval, y=interval_mean_steps)) + geom_line() + facet_grid(.~day_class)

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/apps/ui/ui_06.R

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jcheng5/shiny-training-rstudioconf-2018

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ui_06.R

library(shiny) # Define function that takes a YouTube URL,title, and description --- # and returns a thumbnail frame, implemented using a template videoThumbnail <- function(videoUrl, title, description) { htmltools::htmlTemplate("youtube_thumbnail_template.html", videoUrl = videoUrl, title = title, description = description) } # Define UI for YouTube player -------------------------------------- ui <- fluidPage( h1("Random videos"), fluidRow( column(6, videoThumbnail("https://www.youtube.com/embed/hou0lU8WMgo", "You are technically correct", "The best kind of correct!" ) ), column(6, videoThumbnail("https://www.youtube.com/embed/4F4qzPbcFiA", "Admiral Ackbar", "It's a trap!" ) ) ) ) # Define server logic ----------------------------------------------- server <- function(input, output, session) { } # Run the app ------------------------------------------------------- shinyApp(ui, server)

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/fuzzedpackages/dtwclust/R/DISTANCES-lb-improved.R

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akhikolla/testpackages

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DISTANCES-lb-improved.R

#' Lemire's improved DTW lower bound #' #' This function calculates an improved lower bound (LB) on the Dynamic Time Warp (DTW) distance #' between two time series. It uses a Sakoe-Chiba constraint. #' #' @export #' #' @param x A time series (reference). #' @param y A time series with the same length as `x` (query). #' @param window.size Window size for envelope calculation. See details. #' @param norm Vector norm. Either `"L1"` for Manhattan distance or `"L2"` for Euclidean. #' @param lower.env Optionally, a pre-computed lower envelope for **`y`** can be provided (non-proxy #' version only). See [compute_envelope()]. #' @param upper.env Optionally, a pre-computed upper envelope for **`y`** can be provided (non-proxy #' version only). See [compute_envelope()]. #' @param force.symmetry If `TRUE`, a second lower bound is calculated by swapping `x` and `y`, and #' whichever result has a *higher* distance value is returned. The proxy version can only work if #' a square matrix is obtained, but use carefully. #' @template error-check #' #' @details #' #' The reference time series should go in `x`, whereas the query time series should go in `y`. #' #' If the envelopes are provided, they should be provided together. If either one is missing, both #' will be computed. #' #' @template window #' #' @return The improved lower bound for the DTW distance. #' #' @template proxy #' #' @section Note: #' #' The lower bound is only defined for time series of equal length and is **not** symmetric. #' #' If you wish to calculate the lower bound between several time series, it would be better to use #' the version registered with the `proxy` package, since it includes some small optimizations. The #' convention mentioned above for references and queries still holds. See the examples. #' #' The proxy version of `force.symmetry` should only be used when only `x` is provided or both `x` #' and `y` are identical. It compares the lower and upper triangular of the resulting distance #' matrix and forces symmetry in such a way that the tightest lower bound is obtained. #' #' @references #' #' Lemire D (2009). ``Faster retrieval with a two-pass dynamic-time-warping lower bound .'' *Pattern #' Recognition*, **42**(9), pp. 2169 - 2180. ISSN 0031-3203, #' \url{http://dx.doi.org/10.1016/j.patcog.2008.11.030}, #' \url{http://www.sciencedirect.com/science/article/pii/S0031320308004925}. #' #' @examples #' #' # Sample data #' data(uciCT) #' #' # Lower bound distance between two series #' d.lbi <- lb_improved(CharTraj[[1]], CharTraj[[2]], window.size = 20) #' #' # Corresponding true DTW distance #' d.dtw <- dtw(CharTraj[[1]], CharTraj[[2]], #' window.type = "sakoechiba", window.size = 20)$distance #' #' d.lbi <= d.dtw #' #' # Calculating the LB between several time series using the 'proxy' package #' # (notice how both argments must be lists) #' D.lbi <- proxy::dist(CharTraj[1], CharTraj[2:5], method = "LB_Improved", #' window.size = 20, norm = "L2") #' #' # Corresponding true DTW distance #' D.dtw <- proxy::dist(CharTraj[1], CharTraj[2:5], method = "dtw_basic", #' norm = "L2", window.size = 20) #' #' D.lbi <= D.dtw #' lb_improved <- function(x, y, window.size = NULL, norm = "L1", lower.env = NULL, upper.env = NULL, force.symmetry = FALSE, error.check = TRUE) { norm <- match.arg(norm, c("L1", "L2")) if (length(x) != length(y)) stop("The series must have the same length") window.size <- check_consistency(window.size, "window") if (is_multivariate(list(x, y))) stop("lb_improved does not support multivariate series.") if (error.check) { check_consistency(x, "ts") check_consistency(y, "ts") } if (is.null(lower.env) || is.null(upper.env)) { envelopes <- compute_envelope(y, window.size = window.size, error.check = FALSE) lower.env <- envelopes$lower upper.env <- envelopes$upper } else { check_consistency(lower.env, "ts") check_consistency(upper.env, "ts") if (length(lower.env) != length(x)) stop("Length mismatch between 'x' and the lower envelope") if (length(upper.env) != length(x)) stop("Length mismatch between 'x' and the upper envelope") } p <- switch(norm, L1 = 1L, L2 = 2L) d <- .Call(C_lbi, x, y, window.size, p, lower.env, upper.env, PACKAGE = "dtwclust") if (force.symmetry) { d2 <- lb_improved(x = y, y = x, window.size = window.size, norm = norm, error.check = FALSE) if (d2 > d) d <- d2 # nocov } # return d } # ================================================================================================== # Loop without using native 'proxy' looping (to avoid multiple calculations of the envelope) # ================================================================================================== lb_improved_proxy <- function(x, y = NULL, window.size = NULL, norm = "L1", ..., force.symmetry = FALSE, pairwise = FALSE, error.check = TRUE) { x <- tslist(x) if (is.null(y)) y <- x else y <- tslist(y) if (length(x) == 0L || length(y) == 0L) stop("Empty list received in x or y.") # nocov start if (error.check) check_consistency(c(x,y), "tslist") if (is_multivariate(c(x,y))) stop("lb_improved does not support multivariate series.") # nocov end symmetric <- FALSE fill_type <- mat_type <- dim_names <- NULL # avoid warning about undefined globals eval(prepare_expr) # UTILS-expressions.R # adjust parameters for this distance norm <- match.arg(norm, c("L1", "L2")) window.size <- check_consistency(window.size, "window") envelopes <- lapply(y, function(s) { compute_envelope(s, window.size, error.check = FALSE) }) lower.env <- lapply(envelopes, "[[", "lower") upper.env <- lapply(envelopes, "[[", "upper") # calculate distance matrix distance <- "LBI" # read in C++, can't be temporary! distargs <- list( p = switch(norm, "L1" = 1L, "L2" = 2L), len = length(x[[1L]]), window.size = window.size, lower.env = lower.env, upper.env = upper.env ) num_threads <- get_nthreads() .Call(C_distmat_loop, D, x, y, distance, distargs, fill_type, mat_type, num_threads, PACKAGE = "dtwclust") # adjust D's attributes if (pairwise) { dim(D) <- NULL class(D) <- "pairdist" } else { dimnames(D) <- dim_names class(D) <- "crossdist" } if (force.symmetry && !pairwise) { if (nrow(D) != ncol(D)) warning("Unable to force symmetry. Resulting distance matrix is not square.") # nocov else .Call(C_force_lb_symmetry, D, PACKAGE = "dtwclust") } attr(D, "method") <- "LB_Improved" # return D }

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snowvil/HenryQuant

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\name{asset_data} \alias{asset_data} \docType{data} \title{Gloal Asset Return Data} \description{Daily data of ten global asset return data beginning on 1993-01-01 and ending 2017-12-31. The ETF price was used first, and public funds and index prices were used before listing. All returns include dividends. } \format{xts type with daily observations} \details{ The indexes are: US Equity, Europe Equity, Japan Equity, EM Equity, US LongTerm Bond, US MidTerm Bond, US REITs, Global REITs, Gold, Commodities } \examples{ data(asset_data) } \keyword{datasets}

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LOTP_4AFC_stat_analysis.R

# statistical analysis for LOTP_4AFC_ERP library(ez) library(dplyr) library(ggplot2) setwd("~/Desktop/ERP_analysis_scripts/ERP_measurements_analysis") ## load data: # files: # blocked_correct_resp_N2pc.txt # blocked_correct_resp_SPCN.txt # mixed_correct_resp_N2pc.txt # mixed_correct_resp_SPCN.txt # blocked_all_resp_N2pc.txt # blocked_all_resp_SPCN.txt # mixed_all_resp_N2pc.txt # mixed_all_resp_SPCN.txt # blocked_all_resp_N2pc_12_sub.txt # blocked_all_resp_SPCN_12_sub.txt # only correct responses: df_blocked_correct_N2pc <- read.table('blocked_correct_resp_N2pc.txt', sep="\t", header = TRUE) df_blocked_correct_SPCN <- read.table('blocked_correct_resp_SPCN.txt', sep="\t", header = TRUE) df_mixed_correct_N2pc <- read.table('mixed_correct_resp_N2pc.txt', sep="\t", header = TRUE) df_mixed_correct_SPCN <- read.table('mixed_correct_resp_SPCN.txt', sep="\t", header = TRUE) # all responses: df_blocked_all_resp_N2pc <- read.table('blocked_all_resp_N2pc.txt', sep="\t", header = TRUE) df_blocked_all_resp_SPCN <- read.table('blocked_all_resp_SPCN.txt', sep="\t", header = TRUE) df_blocked_all_resp_N2pc_12_sub <- read.table('blocked_all_resp_N2pc_12_sub.txt', sep="\t", header = TRUE) df_blocked_all_resp_SPCN_12_sub <- read.table('blocked_all_resp_SPCN_12_sub.txt', sep="\t", header = TRUE) df_mixed_all_resp_N2pc <- read.table('mixed_all_resp_N2pc.txt', sep="\t", header = TRUE) df_mixed_all_resp_SPCN <- read.table('mixed_all_resp_SPCN.txt', sep="\t", header = TRUE) # filtered data frames/factorization: # correct responses: df_blocked_correct_N2pc$bini = factor(df_blocked_correct_N2pc$bini) df_blocked_correct_N2pc_occip = filter(df_blocked_correct_N2pc, chindex == 12) df_blocked_correct_N2pc_PO7_PO8 = filter(df_blocked_correct_N2pc, chindex == 9) df_blocked_correct_SPCN$bini = factor(df_blocked_correct_SPCN$bini) df_blocked_correct_SPCN_occip = filter(df_blocked_correct_SPCN, chindex == 12) df_blocked_correct_SPCN_PO7_PO8 = filter(df_blocked_correct_SPCN, chindex == 9) df_mixed_correct_N2pc$bini = factor(df_mixed_correct_N2pc$bini) df_mixed_correct_N2pc_occip = filter(df_mixed_correct_N2pc, chindex == 12) df_mixed_correct_N2pc_PO7_PO8 = filter(df_mixed_correct_N2pc, chindex == 9) df_mixed_correct_SPCN$bini = factor(df_mixed_correct_SPCN$bini) df_mixed_correct_SPCN_occip = filter(df_mixed_correct_SPCN, chindex == 12) df_mixed_correct_SPCN_PO7_PO8 = filter(df_mixed_correct_SPCN, chindex == 9) # all responses df_blocked_all_resp_N2pc$bini = factor(df_blocked_all_resp_N2pc$bini) df_blocked_all_resp_N2pc_occip = filter(df_blocked_all_resp_N2pc, chindex == 12) df_blocked_all_resp_N2pc_PO7_PO8 = filter(df_blocked_all_resp_N2pc, chindex == 9) df_blocked_all_resp_SPCN$bini = factor(df_blocked_all_resp_SPCN$bini) df_blocked_all_resp_SPCN_occip = filter(df_blocked_all_resp_SPCN, chindex == 12) df_blocked_all_resp_SPCN_PO7_PO8 = filter(df_blocked_all_resp_SPCN, chindex == 9) # for overall anova (steve's notes): df_blocked_all_resp_N2pc_12_sub$bini = factor(df_blocked_all_resp_N2pc_12_sub$bini) df_blocked_all_resp_N2pc_12_sub_occip = filter(df_blocked_all_resp_N2pc_12_sub, chindex == 12) df_blocked_all_resp_SPCN_12_sub$bini = factor(df_blocked_all_resp_SPCN_12_sub$bini) df_blocked_all_resp_SPCN_12_sub_occip = filter(df_blocked_all_resp_SPCN_12_sub, chindex == 12) df_mixed_all_resp_N2pc$bini = factor(df_mixed_all_resp_N2pc$bini) df_mixed_all_resp_N2pc_occip = filter(df_mixed_all_resp_N2pc, chindex == 12) df_mixed_all_resp_N2pc_PO7_PO8 = filter(df_mixed_all_resp_N2pc, chindex == 9) df_mixed_all_resp_SPCN$bini = factor(df_mixed_all_resp_SPCN$bini) df_mixed_all_resp_SPCN_occip = filter(df_mixed_all_resp_SPCN, chindex == 12) df_mixed_all_resp_SPCN_PO7_PO8 = filter(df_mixed_all_resp_SPCN, chindex == 9) # Steve's analysis notes: # Hi Kyle. I think you should go with all responses (now that it is working right). # Also, I think you should put blocked and mixed into a single ANOVA and add a # blocked/mixed factor. You can then do separate analyses if there are any # significant differences between blocked and mixed (which I don’t think you’ll find). # This should increase our power. # Which electrode sites are in the occipital average? # Is it all the P, PO, and O sites? class(df_blocked_all_resp_N2pc_12_sub_occip$bini) class(df_blocked_all_resp_SPCN_12_sub_occip$bini) df_blocked_all_resp_N2pc_12_sub_occip$blocked = 1 df_blocked_all_resp_SPCN_12_sub_occip$blocked = 1 df_mixed_all_resp_N2pc_occip$blocked = 0 df_mixed_all_resp_SPCN_occip$blocked = 0 df_blocked_all_resp_N2pc_12_sub_occip$blocked = factor(df_blocked_all_resp_N2pc_12_sub_occip$blocked) df_mixed_all_resp_N2pc_occip$blocked = factor(df_mixed_all_resp_N2pc_occip$blocked) df_blocked_all_resp_SPCN_12_sub_occip$blocked = factor(df_blocked_all_resp_SPCN_12_sub_occip$blocked) df_mixed_all_resp_SPCN_occip$blocked = factor(df_mixed_all_resp_SPCN_occip$blocked) # combine blocked and mixed together: # df_blocked_all_resp_N2pc_12_sub_occip 26 obs # df_mixed_all_resp_N2pc_occip class(df_blocked_all_resp_N2pc_12_sub_occip$bini) class(df_blocked_all_resp_N2pc_12_sub_occip$blocked) class(df_blocked_all_resp_N2pc_12_sub_occip$value) class(df_mixed_all_resp_N2pc_occip$bini) class(df_mixed_all_resp_N2pc_occip$blocked) class(df_mixed_all_resp_N2pc_occip$value) class(df_blocked_all_resp_SPCN_occip$bini) class(df_blocked_all_resp_SPCN_occip$value) df_N2pc_blocked_mixed = bind_rows(df_blocked_all_resp_N2pc_12_sub_occip, df_mixed_all_resp_N2pc_occip) df_N2pc_blocked_mixed$bini = factor(df_N2pc_blocked_mixed$bini) df_N2pc_blocked_mixed$blocked = factor(df_N2pc_blocked_mixed$blocked) class(df_N2pc_blocked_mixed$bini) class(df_N2pc_blocked_mixed$blocked) class(df_N2pc_blocked_mixed$value) class(df_N2pc_blocked_mixed$ERPset) length(unique(df_N2pc_blocked_mixed$ERPset)) test = select(df_N2pc_blocked_mixed, value, bini, ERPset, blocked) class(test$value) class(test$bini) class(test$ERPset) class(test$blocked) # bini: # 1 = blocked fast # 2 = blocked slow # 3 = mixed fast # 4 = mixed slow # sites recorded: (FP1/2, F3/4, F7/8, C3/4, P3/4, P5/6, P7/8, P9/10, P03/04, # P07/08, O1/2, Fz, Cz, Pz, POz, Oz, mastoid R/L, HEOG R/L, VEOG) electrode_site = 'occip' # 'occip' or 'PO7_PO8' # electrode_site = 'PO7_PO8' condition = 'mixed' # condition = 'blocked' # response = 'correct' response = 'all_resp' time_window = 'SPCN' # time_window = 'N2pc' data_frame = paste('df',condition,response,time_window,electrode_site, sep = "_") data_frame View(eval(parse(text = data_frame))) current_anova = ezANOVA( data = eval(parse(text = data_frame)) , dv = .(value) , wid = .(ERPset) , within = .(bini) ) print(current_anova) current_anova_data_output = ezStats( data = eval(parse(text = data_frame)) , dv = .(value) , wid = .(ERPset) , within = .(bini) ) data(ANT2) ANT2 = ANT2[!is.na(ANT2$rt),] ezDesign( data = ANT2 , x = trial , y = subnum , row = block , col = group ) ezDesign( data = df_N2pc_blocked_mixed , x = bini , y = ERPset , row = block , col = blocked ) current_anova = ezANOVA( data = test , dv = .(value) , wid = .(ERPset) , within = .(bini,blocked) ) print(current_anova) current_anova_data_output = ezStats( data = df_N2pc_blocked_mixed , dv = .(value) , wid = .(ERPset) , within = .(blocked) ) print(current_anova_data_output) ezPlot( data = df_N2pc_blocked_mixed , dv = .(value) , wid = .(ERPset) , within = .(bini,blocked) , x = bini )

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/data_clean_file.R

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BouranDS/Project1Exploratory

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refs/heads/main

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data_clean_file.R

## Project 1 for Week 1 # author : ibrahima dit bouran sidibe # require(magrittr) data_in <- read.csv2(file = "D:/MyR/exdata_data_household_power_consumption/household_power_consumption.txt", header = TRUE, sep = ";", stringsAsFactors = FALSE) # head(data_in) # tail(data_in) # str(data_in) data_in <- base::transform(data_in, Date = lubridate::as_date(Date, format = "%d/%m/%Y")) #data_in <- base::transform(data_in, Time = lubridate::hms(Time)) # my_name <- colnames(data_in) != "Date" & colnames(data_in) != "Time" for(ikl in 1:length(colnames(data_in))){ if(my_name[ikl]){ data_in[, ikl] <- base::as.numeric(data_in[, ikl]) #print(colnames(data_in)[ikl]) } } data_in_sub <- data_in$Date %>% dplyr::between(as.Date("2007-02-01"), as.Date("2007-02-02")) data_in_select <- data_in[data_in_sub, ] Date_compl <- lubridate::ymd_hms( paste(lubridate::ymd(data_in_select$Date), data_in_select$Time) ) data_in_select$Date_complete <- Date_compl # summary(data_in[, my_name]) # table(data_in$Global_reactive_power) # as.Date(data_in$Time, format = "%H:%M:%S")

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/other/sqf_files/prob_any_force_used_black_vs_white.R

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msr-ds3/stop-question-frisk

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86b367093c3bb5c6c0ba927184b7fbb1bc5b35fa

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prob_any_force_used_black_vs_white.R

library(tidyverse) library(tidycensus) library(totalcensus) library(sf) library(tmap) library(tmaptools) library(tigris) library(leaflet) library(sp) library(ggmap) library(maptools) library(broom) library(httr) library(rgdal) library(htmlwidgets) library(webshot) ########## LOAD AND CREATE/CLEAN DATAFRAMES ########## # Load stop and frisk data for 2003-2013 load("sqf_03_13.RData") # Load census data for race distributions by precinct load("census_race_data.RData") sqf_data <- sf_data1 %>% # filter out unknown races filter(race != " " & race != "U" & race != "X") %>% # recode Black Hispanic as Black, American Indian as Other mutate(race = recode_factor(race,"P" = "B", "I" = "Z"), # create an any_force_used column - ADDED pf_other TO THIS CALCULATION any_force_used = paste(pf_hands, pf_grnd, pf_wall, pf_drwep, pf_ptwep, pf_hcuff,pf_baton, pf_pepsp, sep = ""), # create factors from columns any_force_used = if_else(grepl("Y",any_force_used), 1, 0), # recode race names for clarity race = recode_factor(race, "W" = "White", "B" = "Black", "Q" ="Hispanic", "A" = "Asian", "Z" = "Other")) %>% select(addrpct, race, any_force_used) force_used <- sqf_data %>% group_by(addrpct, race) %>% summarize(prop_w_force_used = mean(any_force_used)) force_used_black <- force_used %>% filter(race == "Black") force_used_white <- force_used %>% filter(race == "White") comparing_races <- force_used %>% spread(race, prop_w_force_used) %>% mutate(B_over_W = (Black/White)) comparing_Hispanic_White <- force_used %>% spread(race, prop_w_force_used) %>% mutate(H_over_W = Hispanic/White) ########## MAP THE RESULTS ########## # read in police precinct shape data r <- GET('http://services5.arcgis.com/GfwWNkhOj9bNBqoJ/arcgis/rest/services/nypp/FeatureServer/0/query?where=1=1&outFields=*&outSR=4326&f=geojson') police_precincts <- readOGR(content(r,'text'), 'OGRGeoJSON', verbose = F) # Join the precinct shape data with the data about the precincts joint_black <- geo_join(police_precincts, force_used_black, "Precinct", "addrpct") joint_white <- geo_join(police_precincts, force_used_white, "Precinct", "addrpct") joint_bw <- geo_join(police_precincts, comparing_races, "Precinct", "addrpct") joint_hw <- geo_join(police_precincts, comparing_Hispanic_White, "Precinct", "addrpct") mypopup <- paste0("Precinct: ", joint_bw$addrpct, " ", "Black/White Prop w/ Force Used: ", joint_bw$B_over_W) mypal <- colorNumeric( palette = "Spectral", domain = c(-.5,.5), reverse = TRUE ) prob_force_used_b_over_w <- leaflet(joint_bw) %>% addTiles() %>% addPolygons(fillColor = ~mypal(log10(joint_bw$B_over_W)), fillOpacity = 1, weight = 1, popup = mypopup) %>% addProviderTiles("CartoDB.Positron") %>% addLegend(pal = mypal, values = c(-.5,.5), position = "topleft", labFormat = labelFormat(transform = function(x) signif(10^x, 1)), #title = "Probability of any force used Black over White" ) prob_force_used_b_over_w mypopuphw <- paste0("Precinct: ", joint_hw$addrpct, " ", "Hispanic/White Prop w/ Force Used: ", joint_hw$H_over_W) mypalhw <- colorNumeric( palette = "Spectral", domain = c(-.5,.5), reverse = TRUE ) prob_force_used_h_over_w <- leaflet(joint_hw) %>% addTiles() %>% addPolygons(fillColor = ~mypalhw(log10(joint_hw$H_over_W)), fillOpacity = 1, weight = 1, popup = mypopuphw) %>% addProviderTiles("CartoDB.Positron") %>% addLegend(pal = mypal, values = c(-.5,.5), position = "topleft", labFormat = labelFormat(transform = function(x) signif(10^x, 1)), #title = "Probability of any force used Black over White" ) prob_force_used_h_over_w mypopup1 <- paste0("Precinct: ", joint_black$addrpct, " ", "Hispanic/White Prop w/ Force Used: ", joint_black$prop_w_force_used) mypal1 <- colorNumeric( palette = "YlOrRd", domain = c(-1,1), reverse = TRUE ) prob_force_used_black <- leaflet(joint_black) %>% addTiles() %>% addPolygons(fillColor = ~mypal1(joint_black$prop_w_force_used), fillOpacity = 1, weight = 1, popup = mypopup1, ) %>% addProviderTiles("CartoDB.Positron") %>% addLegend(pal = mypal1, values = c(-1, 1), position = "topleft", #title = "Probability of any force used given Black" ) prob_force_used_black mypopup2 <- paste0("Precinct: ", joint_white$addrpct, " ", "Black/White Prop w/ Force Used: ", joint_white$prop_w_force_used) mypal2 <- colorNumeric( palette = "YlOrRd", domain = c(-1,1), reverse = TRUE ) prob_force_used_white <- leaflet(joint_white) %>% addTiles() %>% addPolygons(fillColor = ~mypal(joint_white$prop_w_force_used), fillOpacity = 1, weight = 1, popup = mypopup2, ) %>% addProviderTiles("CartoDB.Positron") %>% addLegend(pal = mypal2, values = c(-1,1), position = "topleft", # title = "Probability of any force used # given White" ) prob_force_used_white saveWidget(prob_force_used, "../figures/prob_force_used.html", selfcontained = FALSE) webshot("../figures/prob_force_used.html", file = "../figures/prob_force_used.png", cliprect = "viewport")

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/R/channel_analytic.r

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CodifiedHashtagsCoding/rTwChannel

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channel_analytic.r

#' channel_analytic #' #' @description Extract many informative stats and object from a set of tweet messages parsed as channel #' #' @param channel_obj Data.frame Dataset of tweets #' @param use_channel_dates Logical Use temporal indication of channel #' @param start_date Character Date of analisys starting. #' @param end_date Character Date of analisys ending. #' @param Ntop Integer indicate the maximum number for top statistics #' @param temporal_check Logical indicate if exist time consistency between dates and data #' @param Nmin Integer indicate the minimal data numerosity #' @param naming Character Indicate which naming framework is adopted. #' @param only_original_tweet Logical Taking into account only original. Default all tweets are considered. #' @param lowercase logical Consider all text as lower case. Default is TRUE. #' @param stopword Character stopword set to be use to calculate word frequency matrix. Default italian stopwords of R tm package. #' @param corpus_hashtag logical Corpus not taking into account the hashtag. #' @param account_tw User account if naming parameter is an "account_statistics" #' @return Return a R list object for channel analisys #' @return **channel_stat** : channel summaries of following parameters. #' @return * *N_tweets* : Number of tweet within unique ID #' @return * N_retweets (channel_stat):Number of retweet #' @return * N_native_tweets (channel_stat):Number of original tweets #' @return * N_hash (channel_stat):Number of hashtags detected in channel #' @return * N_mention (channel_stat): Number of mention detected in channel #' @return * N_links (channel_stat):Number of links detected in channel #' @return * Nuniq_authors (channel_stat):Number of unique authors #' @return * Nuniq_hash (channel_stat): Number of unique hashs #' @return * Nuniq_mentions (channel_stat): Number of unique mentions #' @return * Nuniq_links (channel_stat): Number of unique links #' @return * Nmean_links (channel_stat): Mean links for tweet #' @return * Nfull_retweet (channel_stat): Number of all retweets given by platorm #' @return * Nfull_retweet_missing (channel_stat): Number of tweet given without platform statistics #' @return * Nreplies (channel_stat): Number of of replies #' @return * Nfavor (channel_stat): Number of of cumulative favorites given by platform #' @return * Ntweets0links (channel_stat): Number of tweets with no links #' @return * Ntweets1link (channel_stat): N of tweets with 1 link #' @return * NtweetsNlinks (channel_stat): Number of tweets more than links #' @return * Ntweets0mentions (channel_stat): Number of tweets with no mentions #' @return * Ntweets1mention (channel_stat): Number of tweets with 1 mention #' @return * NtweetsNmentions (channel_stat): Number of tweets more than mentions #' @return * Ntweets0hashs (channel_stat): Number of tweets with no hashtags #' @return * Ntweets1hash (channel_stat): Number of tweets with 1 hashtag #' @return * NtweetsNhashs (channel_stat): Number of tweets more than hashtags #' @return **table_message** : Frequency data.frame of message #' @return **table_hash** : Frequency data.frame of hashtag #' @return **table_mentions** : Frequency data.frame of mentions #' @return **table_authors** : Frequency data.frame of authors #' @return **table_replies** : Frequency data.frame of replies #' @return **table_authors_retweeted** : Frequency data.frame of authors retweeted #' @return **table_authors_retweeter** : Frequency data.frame of authors that is a retweeter #' @return **rank_authors_retweet** : Frequency data.frame of retweet by using platform data #' @return **rank_message_retweet** : Frequency data.frame of message by using platform data #' @return **top_message** : TopN messages in channel #' @return **top_authors** : TopN authors in channel #' @return **top_hash** : TopN hashtag #' @return **top_mentions** : TopN user mentioned #' @return **top_replies** : TopN user have made replies #' @return **top_authors_retweeted** : TopN user that have retweeted in channel #' @return **top_authors_retweeter** : TopN user that have made retweet in channel #' @return **topfull_authors_retweeted** : TopN author that have retweeted in platform #' @return **topfull_message_retweeted** : TopN message that have retweeted in platform #' @return **daily_stat** : Daily Temporal data of channel statistic data #' @return **authors_date** : DateTime authors activity in channel #' @return **links_date** : DateTime authors activity in channel #' @return **hash_date** : DateTime hashtag presence in channel #' @return **mentions_date** : DateTime mentions presence in channel #' @return **unique_message** : Unique message in channel #' @return **unique_authors** : Unique authors in channel #' @return **unique_hash** : Unique hashtag in channel #' @return **unique_mentions** : Unique mentions in channel #' @return **unique_authors_retweeted** : Unique retweeted user in channel #' @return **unique_authors_retweeter** : Unique retweeter user in channel #' @return **uniquefull_authors_retweeted** : Unique retweeted user in platform #' @return **uniquefull_message_retweeted** : Unique retweeted message in platform #' @return **graph_retweet_df** : Data used for retweet graph #' @return **graph_hash_df** : Data used for hashtag graph #' @return **graph_mentions_df** : Data for used mention graph #' @return **replies_df** : Data for replies #' @return **graph_retweet** : Retweet graph object as igraph R object #' @return **graph_mentions** : Mention graph object as igraph R object #' @return **authors_favorite** : rank of authors favorite #' @return **favorite_message_top** : TopN favorite message #' @return **channel_data** : Channel_data #' @return **channel_corpus** : Tm Corpus of messages without mentions and links and optionally without hashtag #' @return **word_freq_matr** : qdap wfm object Word frequency matrix. #' @return **account_stats** : Statistic account's activity by date. #' #' @author Istituto di Biometeorologia Firenze Italy Alfonso Crisci \email{a.crisci@@ibimet.cnr.it} #' @keywords channel,stats #' #' #' #' @export #' #' channel_analytic=function(channel_obj,use_channel_dates=TRUE, start_date=NULL, end_date=NULL,Ntop=11,temporal_check=FALSE, Nmin=25,naming="",only_original_tweet=FALSE,lowercase=TRUE,stopword = tm::stopwords("it"), account_tw="", corpus_hashtag=TRUE) { ##################################################################################### # Data checks rows=nrow(channel_obj) if (rows < Nmin) { stop("Channel with too few records.")}; message(paste(" Channel:", deparse(substitute(channel_obj)),"\n", "Elements:", rows ,"\n", "Ntop:", Ntop ,"\n", "Temporal Check:",temporal_check,"\n", "Minimum data:",Nmin,"\n", "Type stream:",naming,"\n", "Native Channel:",only_original_tweet,"\n", "Lowering case message's text:",lowercase,"\n")) if ( naming == "account_analitics") {message(paste("Account Twitter:",account_tw,"\n"))} if ( (naming == "account_analitics") && (account_tw == "") ) { stop("Channel analitics need an Twitter account!")}; if ( naming == "TAGS") { channel_obj$created <- lubridate::dmy_hms(channel_obj$time) channel_obj=channel_obj[which(!is.na(channel_obj$created)),] channel_obj$data <- as.Date(channel_obj$created) channel_obj$screeName=channel_obj$from_user channel_obj$id=as.numeric(channel_obj$id_str) channel_obj$lang=channel_obj$user_lang channel_obj$from_user<-NULL channel_obj$user_lang<-NULL channel_obj$message<-NULL channel_obj$created_at<-NULL channel_obj$retweetCount<-rep(0,nrow(channel_obj)) channel_obj$entities_str<-NULL channel_obj$retweetCount<-rep(0,nrow(channel_obj)) channel_obj$favoriteCount<-rep(0,nrow(channel_obj)) channel_obj$ls_hash_full<-rep(NA,nrow(channel_obj)) channel_obj$ls_links=rep(NA,nrow(channel_obj)) channel_obj$time<-NULL channel_obj=channel_obj[rev(1:nrow(channel_obj)),] } if ( naming == "DISIT") { channel_obj$text=channel_obj$message channel_obj$data=as.character(as.Date(channel_obj$publicationTime)) channel_obj$screeName=channel_obj$twitterUser channel_obj$created=channel_obj$publicationTime channel_obj$ls_hash_full=channel_obj$hashtagsOnTwitter channel_obj$ls_links=channel_obj$links channel_obj$id=channel_obj$twitterId channel_obj$message<-NULL channel_obj$hashtagsOnTwitter<-NULL channel_obj$twitterId<-NULL channel_obj$links<-NULL channel_obj$hour=lubridate::hour(channel_obj$publicationTime) channel_obj$month=lubridate::month(channel_obj$publicationTime) channel_obj$publicationTime<-NULL } if (naming == "account_analitics") { channel_obj=channel_obj[,1:22] name_user_tweet_activity=c("id","link_tweet","text","dateTime","impress","interazioni","inter_rate", "retweetCount","repliesCount","favoriteCount","clickonuserprofile","clickonlink", "clickonlinkhash","details","clickonPermalinks","open_app","n_install_app", "followsCount","email_send","tel_calls","mediaVisCount","interVisCount") names(channel_obj)=name_user_tweet_activity channel_obj$data=as.Date(channel_obj$dateTime) channel_obj$hour=lubridate::hour(channel_obj$dateTime) channel_obj$month=lubridate::month(channel_obj$dateTime) channel_obj$screeName=account_tw } if ( naming == "twitter") { channel_obj$data=as.Date(channel_obj$created) channel_obj$hour=lubridate::hour(channel_obj$created) channel_obj$month=lubridate::month(channel_obj$created) channel_obj$text=iconv(channel_obj$text,"utf-8") channel_obj$isRetweet=as.integer(channel_obj$isRetweet) channel_obj$screeName=channel_obj$screenName channel_obj$screenName<-NULL } ##################################################################################### # Temporal filter of channel if ( use_channel_dates == TRUE) { start_date=head(channel_obj$data[which(channel_obj$data== as.character(min(as.Date(channel_obj$data))))],1); end_date=tail(channel_obj$data[which(channel_obj$data== as.character(max(as.Date(channel_obj$data))))],1); } if (as.Date(start_date) > as.Date(end_date)) { stop(" End Date is older than Start date. ")}; if ( temporal_check==TRUE) { if (as.Date(start_date) < as.Date(head(channel_obj$data,1))) { stop("Start Date of analisys not defined." )}; if (as.Date(end_date) > as.Date(tail(channel_obj$data,1))) { stop("End Date of analisys not defined." )}; channel_obj=channel_obj[min(which(channel_obj$data==as.character(start_date))):max(which(channel_obj$data==as.character(end_date))),] } ##################################################################################### # Create data.frames for other count statistics. ls_retweet=unlist(lapply(channel_obj$text,FUN=function(x) is.retweet(x))) ls_retweeted_authors=as.character(rep(NA,nrow(channel_obj))) for ( i in 1:length(ls_retweeted_authors)) {ls_retweeted_authors[i]=retweeted_users(channel_obj$text[i]) } if (only_original_tweet==TRUE) { channel_obj=channel_obj[which(ls_retweet==0),] ls_retweet=unlist(lapply(channel_obj$text,FUN=function(x) is.retweet(x))) } #################################################################################### # Create lists to be used for count statistics. ls_links=lapply(channel_obj$text,FUN=function(x) qdapRegex::rm_url(x, extract=TRUE)) if ( lowercase == TRUE) { channel_obj$text=tolower(channel_obj$text) } ls_hash=lapply(channel_obj$text,FUN=function(x) qdapRegex::rm_hash(x,extract=T)) ls_tag=lapply(channel_obj$text,FUN=function(x) extract_mentions(x)) ls_lenhash=unlist(lapply(ls_hash,FUN=function(x) ifelse(is.na(x),0, length(qdapRegex::rm_hash(x,extract=T)[[1]])))) ls_lenlinks=unlist(lapply(ls_links,FUN=function(x) ifelse(is.na(x),0, length(qdapRegex::rm_url(x, extract=TRUE)[[1]])))) ls_lentag=unlist(lapply(ls_tag,FUN=function(x) ifelse(is.na(x),0, length(extract_mentions(x)[[1]])))) ls_words=unlist(lapply(channel_obj$text,FUN=function(x) qdap::word_count(x))) message("Text message are processed!\n") ####################################################################################### # Create data.frame date,retweeted_authors and authors. ls_retweeted_df=na.omit(data.frame(data=channel_obj$data, retweeted_authors=ls_retweeted_authors, authors=channel_obj$screeName)) #################################################################################### # Extract replies and organize a frame replies_id=grep("^@",channel_obj$text) channel_obj$replies=NA channel_obj$replies[replies_id]=1 ls_replies_df=data.frame(data=channel_obj$data,authors=channel_obj$screeName,replies=channel_obj$replies) #################################################################################### # Replies stats fullretweet_day=aggregate(channel_obj$retweetCount[which(!duplicated(channel_obj$text)==TRUE)],list(channel_obj$data[which(!duplicated(channel_obj$text)==TRUE)]),sum,na.rm = TRUE) names(fullretweet_day)=c("date","retweetCount") fullretweet_day$date=as.Date(fullretweet_day$date) fullreplies_day=aggregate(channel_obj$replies,list(channel_obj$data),sum,na.rm = TRUE) names(fullreplies_day)=c("date","repliesCount") fullreplies_day$date=as.Date(fullreplies_day$date) fullretweet_missing=length(which(is.na(channel_obj$retweetCount[which(!duplicated(channel_obj$text)==TRUE)]))) fullretweet_channel_stat_sum=sum(channel_obj$retweetCount[which(!duplicated(channel_obj$text)==TRUE)],na.rm=T) replies_channel_stat_sum=length(replies_id) ####################################################################################### # Create data.frame date,message and authors. ls_favorite_df=data.frame(data=channel_obj$data[which(!duplicated(channel_obj$text)==TRUE)], message=channel_obj$text[which(!duplicated(channel_obj$text)==TRUE)], authors=channel_obj$screeName[which(!duplicated(channel_obj$text)==TRUE)], favoriteCount=channel_obj$favoriteCount[which(!duplicated(channel_obj$text)==TRUE)], is.retweet=ls_retweet[which(!duplicated(channel_obj$text)==TRUE)]) day_favorite=aggregate(ls_favorite_df$favoriteCount,list(ls_favorite_df$data),sum) names(day_favorite)<-c("date","N_favor") day_favorite$date=as.Date(day_favorite$date) ls_favorite_df=ls_favorite_df[order(-ls_favorite_df$favoriteCount),] rank_authors_favorite=aggregate(channel_obj$favoriteCount[which(!duplicated(channel_obj$text)==TRUE)], list(channel_obj$screeName[which(!duplicated(channel_obj$text)==TRUE)]) ,sum) rank_authors_favorite=rank_authors_favorite[order(-rank_authors_favorite[,2]),] names(rank_authors_favorite)<-c("authors","favoriteCount") ######################################################################### ls_message_df=data.frame(data=channel_obj$data[which(!duplicated(channel_obj$text)==TRUE)], message=channel_obj$text[which(!duplicated(channel_obj$text)==TRUE)], authors=channel_obj$screeName[which(!duplicated(channel_obj$text)==TRUE)], retweetCount=channel_obj$retweetCount[which(!duplicated(channel_obj$text)==TRUE)], is.retweet=ls_retweet[which(!duplicated(channel_obj$text)==TRUE)]) rank_authors_retweet=aggregate(ls_message_df$retweetCount,list(ls_message_df$authors),sum) rank_authors=rank_authors_retweet[order(-rank_authors_retweet[,2]),] names(rank_authors_retweet)<-c("authors","retweetCount") rank_message_retweet=aggregate(ls_message_df$retweetCount,list(ls_message_df$message),sum) rank_message_retweet=rank_message_retweet[order(-rank_message_retweet[,2]),] names(rank_message_retweet)<-c("message","retweetCount") rank_authors_retweet=rank_authors_retweet[ order(-rank_authors_retweet[,2]), ] rank_message_retweet=rank_message_retweet[ order(-rank_message_retweet[,2]), ] ########################################################################################################################################## # retrieve other information from channel stack. rank_message_retweet$retweeted_authors=retweeted_users(rank_message_retweet$message) id_na_message_retweet=which(is.na(rank_message_retweet$retweeted_authors)) not_retweet_with_authors=as.character(rank_message_retweet$message[id_na_message_retweet]) for ( i in seq_along(not_retweet_with_authors)) { rank_message_retweet$retweeted_authors[id_na_message_retweet[i]]=as.character(channel_obj$screeName[min(which(channel_obj$text ==not_retweet_with_authors[i]))]) } rank_message_retweet$data=NA for ( i in seq_along(rank_message_retweet$message)) { rank_message_retweet$data[i]=as.character(channel_obj$data[min(which((channel_obj$text %in% rank_message_retweet$message[i] )==T))]) } ####################################################################################### # Create data.frame date,authors and is retweet. ls_authors_df=data.frame(data=channel_obj$data,authors=channel_obj$screeName,ls_retweet) names(ls_authors_df)=c("data","authors","retweet") ##################################################################################### # Create data.frame date and hashtag. ls_hash_long=list() for ( i in seq_along(ls_hash)) {ls_hash_long[[i]]=cbind(as.character(channel_obj$data[i]),unlist(ls_hash[[i]]),ls_retweet[i],as.character(channel_obj$screeName[i]))} ls_hash_df=as.data.frame(do.call(rbind,ls_hash_long)) names(ls_hash_df)=c("data","hashtag","retweet","authors") ##################################################################################### # Create data.frame date and mentions. ls_tag_long=list() for ( i in seq_along(ls_tag)){ls_tag_long[[i]]=cbind(as.character(channel_obj$data[i]),unlist(ls_tag[[i]]),ls_retweet[i],as.character(channel_obj$screeName[i]))} ls_tag_df=as.data.frame(do.call(rbind,ls_tag_long)) names(ls_tag_df)=c("data","mention","retweet","authors") ls_tag_df$mention=gsub("^@","",ls_tag_df$mention) ##################################################################################### # Create data.frame date and links. ls_links_long=list() for ( i in seq_along(ls_links)) {ls_links_long[[i]]=cbind(as.character(channel_obj$data[i]),unlist(ls_links[[i]]),ls_retweet[i],as.character(channel_obj$screeName[i]))} ls_links_df=as.data.frame(do.call(rbind,ls_links_long)) names(ls_links_df)=c("data","links","retweet","authors") ########################################################################################### # Creating arrays date and elements authors_unique=na.omit(unique(ls_authors_df[,1:2])) links_unique=na.omit(unique(ls_links_df[,1:2])) hash_unique=na.omit(unique(ls_hash_df[,1:2])) tag_unique=na.omit(unique(ls_tag_df[,1:2])) authors_purged=na.omit(ls_authors_df) links_purged=na.omit(ls_links_df) hash_purged=na.omit(ls_hash_df) tag_purged=na.omit(ls_tag_df) ##################################################################################### lenhash_df=data.frame(data=as.Date(channel_obj$data),lenhash=ls_lenhash) lenhash_df_day_mean=aggregate(lenhash_df$lenhash,list(lenhash_df$data),mean) names(lenhash_df_day_mean)=c("date","Nmean_hashtag") lentag_df=data.frame(data=as.Date(channel_obj$data),lentag=ls_lentag) lentag_df_day_mean=aggregate(lentag_df$lentag,list(lentag_df$data),mean) names(lentag_df_day_mean)=c("date","Nmean_mentions") lenwords_df=data.frame(data=as.Date(channel_obj$data),lenwords=ls_words) lenwords_df_day_mean=aggregate(lenwords_df$lenwords,list(lenwords_df$data),mean) names(lenwords_df_day_mean)=c("date","Nmean_words") lenlinks_df=data.frame(data=as.Date(channel_obj$data),lenlinks=ls_lenlinks) lenlinks_df_day_mean=aggregate(lenlinks_df$lenlinks,list(lenlinks_df$data),mean) names(lenlinks_df_day_mean)=c("date","Nmean_links") ##################################################################################### # retweet stats the ratio is ever ntive retweet/ native check_retweet=sum(ls_retweet) retweet_df=data.frame(data=channel_obj$data,is.retweet=ls_retweet) retweet_df_stats=data.frame(native_tweets=rep(0,length(levels(as.factor(retweet_df$data)))), native_retweets=rep(0,length(levels(as.factor(retweet_df$data))))) if ((only_original_tweet==FALSE) && (check_retweet == length(ls_retweet))) { retweet_df_stats$native_retweets=as.data.frame.array(table(retweet_df$data,retweet_df$is.retweet))[,1] } if ((only_original_tweet==FALSE) && (check_retweet > 0)) {retweet_df_stats$native_tweets=as.data.frame.array(table(retweet_df$data,retweet_df$is.retweet))[,1]; retweet_df_stats$native_retweets=as.data.frame.array(table(retweet_df$data,retweet_df$is.retweet))[,2]; } if (only_original_tweet==TRUE) {retweet_df_stats$native_tweets=as.data.frame.array(table(retweet_df$data,retweet_df$is.retweet))[,1]} retweet_df_stats$ratio=retweet_df_stats$native_retweets/retweet_df_stats$native_tweets retweet_df_stats$ratio[which(retweet_df_stats$ratio==Inf)]=NA retweet_df_stats$ratio[which(retweet_df_stats$ratio==NaN)]=NA retweet_df_stats$ratio[which(is.na(retweet_df_stats$ratio))]=0 retweet_stat=data.frame(date=as.Date(rownames(as.data.frame.array(table(retweet_df$data,retweet_df$is.retweet)))), native_tweets=retweet_df_stats$native_tweets, native_retweets=retweet_df_stats$native_retweets, retweet_ratio=retweet_df_stats$ratio) ######################################################################################## # Creating daily stats lenauthorsunique_day=as.data.frame.array(table(authors_unique$data)) lenauthorsunique_day_df=data.frame(date=as.Date(rownames(lenauthorsunique_day)),Nunique_authors=as.vector(lenauthorsunique_day[,1])) lenauthors_day=as.data.frame.array(table(authors_purged$data)) lenauthors_day_df=data.frame(date=as.Date(rownames(lenauthors_day)),Nday_authors=as.vector(lenauthors_day[,1])) ######################################################################### lenlinksunique_day=as.data.frame.array(table(links_unique$data)) lenlinksunique_day_df=data.frame(date=as.Date(rownames(lenlinksunique_day)),Nuniq_links=as.vector(lenlinksunique_day[,1])) lenlinks_day=as.data.frame.array(table(links_purged$data)) lenlinks_day_df=data.frame(date=as.Date(rownames(lenlinks_day)),Nday_links=as.vector(lenlinks_day[,1])) ######################################################################### lenhashunique_day=as.data.frame.array(table(hash_unique$data)) lenhashunique_day_df=data.frame(date=as.Date(rownames(lenhashunique_day)),Nuniq_hash=as.vector(lenhashunique_day[,1])) lenhash_day=as.data.frame.array(table(hash_purged$data)) lenhash_day_df=data.frame(date=as.Date(rownames(lenhash_day)),Nday_hash=as.vector(lenhash_day[,1])) ######################################################################### lentagunique_day=as.data.frame.array(table(tag_unique$data)) lentagunique_day_df=data.frame(date=as.Date(rownames(lentagunique_day)),Nuniq_mention=as.vector(lentagunique_day[,1])) lentag_day=as.data.frame.array(table(tag_purged$data)) lentag_day_df=data.frame(date=as.Date(rownames(lentag_day)),Nday_mention=as.vector(lentag_day[,1])) ######################################################################### # Create daily channel stats # Create a continuous data series ts_date=data.frame(date=seq.Date(as.Date(start_date),as.Date(end_date),1)) daily_stat=merge(ts_date,retweet_stat,all.x=T) daily_stat=merge(daily_stat,lenauthors_day_df,all.x=T) daily_stat=merge(daily_stat,lenhash_day_df,all.x=T) daily_stat=merge(daily_stat,lentag_day_df,all.x=T) daily_stat=merge(daily_stat,lenlinks_day_df,all.x=T) daily_stat=merge(daily_stat,lenhash_df_day_mean,all.x=T) daily_stat=merge(daily_stat,lentag_df_day_mean,all.x=T) daily_stat=merge(daily_stat,lenwords_df_day_mean,all.x=T) daily_stat=merge(daily_stat,lenlinks_df_day_mean,all.x=T) daily_stat=merge(daily_stat,lenauthorsunique_day_df,all.x=T) daily_stat=merge(daily_stat,lenhashunique_day_df,all.x=T) daily_stat=merge(daily_stat,lentagunique_day_df,all.x=T) daily_stat=merge(daily_stat,lenlinksunique_day_df,all.x=T) daily_stat=merge(daily_stat,fullretweet_day,all.x=T) daily_stat=merge(daily_stat,fullreplies_day,all.x=T) daily_stat=merge(daily_stat,day_favorite,all.x=T) ####################à daily_stat$retweet_ratio=round(as.numeric(daily_stat$retweet_ratio),2) daily_stat$Nmean_hashtag=round(as.numeric(daily_stat$Nmean_hashtag),2) daily_stat$Nmean_mentions=round(as.numeric(daily_stat$Nmean_mentions),2) daily_stat$Nmean_words=round(as.numeric(daily_stat$Nmean_words),2) daily_stat$Nmean_links=round(as.numeric(daily_stat$Nmean_links),2) message("Daily stats calculated!\n") ################################################################################# # Frequency analisys table_message=as.data.frame.array(sort(table(channel_obj$text),decreasing=T)) table_message=data.frame(message=rownames(table_message), Freq=as.vector(table_message)) names(table_message)<-c("message","freq") rownames(table_message)<-NULL table_message$data=NA table_message$authors=NA ind=sapply(table_message$message,FUN=function(x) match(x,channel_obj$text)) for ( i in 1:length(ind)) { table_message$data[i]=as.character(channel_obj$data[ind[i]]) table_message$authors[i]=as.character(channel_obj$screeName[ind[i]]) } table_message=na.omit(table_message) table_message$retweeted_authors=NA for ( i in 1:nrow(table_message)) { table_message$retweeted_authors[i]=retweeted_users(as.character(table_message$message[i])) } message("Table_message stats calculated!\n") ########################################################################## if ((only_original_tweet==FALSE ) && (naming!="account_statistics")) { table_authors_retweeted=as.data.frame.array(sort(table(ls_retweeted_df$retweeted_authors),decreasing=T)) table_authors_retweeted=data.frame(authors=rownames(table_authors_retweeted), Freq=as.vector(table_authors_retweeted)) names(table_authors_retweeted)<-c("authors_retweeted","freq") rownames(table_authors_retweeted)<-NULL message("Table authors retweeted stats calculated!\n") } if (only_original_tweet==TRUE || (naming=="account_statistics")) { table_authors_retweeted=data.frame(authors_retweeted=NA,freq=NA) }; ########################################################################## if ((only_original_tweet==FALSE ) && (naming!="account_statistics")) { table_authors_retweeter=as.data.frame.array(sort(table(ls_retweeted_df$authors),decreasing=T)) table_authors_retweeter=data.frame(authors=rownames(table_authors_retweeter), Freq=as.vector(table_authors_retweeter)) names(table_authors_retweeter)<-c("authors_retweeter","freq") rownames(table_authors_retweeter)<-NULL message("Table authors retweeter stats calculated!\n") } if ((only_original_tweet==TRUE) || (naming=="account_statistics")) { table_authors_retweeter=data.frame(authors_retweeter=NA,freq=NA) }; ########################################################################## table_authors=as.data.frame.array(sort(table(ls_authors_df$authors),decreasing=T)) table_authors=data.frame(authors=rownames(table_authors), Freq=as.vector(table_authors)) names(table_authors)<-c("authors","freq") rownames(table_authors)<-NULL ########################################################################## table_hash=as.data.frame.array(sort(table(ls_hash_df$hashtag),decreasing=T)) table_hash=data.frame(hashtag=rownames(table_hash), Freq=as.vector(table_hash)) names(table_hash)<-c("hashtag","freq") rownames(table_hash)<-NULL ########################################################################## table_mentions=as.data.frame.array(sort(table(ls_tag_df$mention),decreasing=T)) table_mentions=data.frame(users=rownames(table_mentions), Freq=as.vector(table_mentions)) names(table_mentions)<-c("mentions","freq") rownames(table_mentions)<-NULL ########################################################################## table_replies=as.data.frame.array(sort(table(ls_replies_df$authors),decreasing=T)) table_replies=data.frame(users=rownames(table_replies), Freq=as.vector(table_replies)) names(table_replies)<-c("replies","freq") rownames(table_replies)<-NULL message("Tables of authors and replies are calculated!\n") ######################################################################### # Create full channel stats full_stat=data.frame(N_tweets=length(channel_obj$text), N_retweets=sum(retweet_stat$native_retweets,na.rm=T), N_native_tweets=sum(retweet_stat$native_tweets,na.rm=T), N_hash=nrow(hash_purged), N_mention=nrow(tag_purged), N_links=nrow(links_purged), Nuniq_authors=length(unique(ls_authors_df[,2])), Nuniq_hash=length(unique(ls_hash_df[,2])), Nuniq_mentions=length(unique(ls_tag_df[,2])), Nuniq_links=length(unique(ls_links_df[,2])), Nmean_links=round(mean(lenlinks_df_day_mean$Nmean_links),2), Nfull_retweet=fullretweet_channel_stat_sum, Nfull_retweet_missing=fullretweet_missing, Nreplies=replies_channel_stat_sum, Nfavor=sum(ls_favorite_df$N_favor,na.rm=T), Ntweets0hashs = length(which(ls_lenhash==0)), Ntweets1hashs = length(which(ls_lenhash==1)), NtweetsNhashs = length(which(ls_lenhash>1)), Ntweets0mentions = length(which(ls_lentag==0)), Ntweets1mentions = length(which(ls_lentag==1)), NtweetsNmentions = length(which(ls_lentag>1)), Ntweets0links = length(which(ls_lenlinks==0)), Ntweets1links = length(which(ls_lenlinks==1)), NtweetsNlinks = length(which(ls_lenlinks>1)) ) message("Full stats of channel are done!\n") ############################################################################################################ # Create a mention graph graph_mentions_df=na.omit(ls_tag_df) mat_men_graph=na.omit(data.frame(whopost=graph_mentions_df[,4],whomentioned=graph_mentions_df[,2])) men_graph = igraph::graph.edgelist(as.matrix(na.omit(mat_men_graph))) E(men_graph )$weight <- 1 men_graph <- igraph::simplify(men_graph, remove.loops=FALSE) message("Mention Graph of channel are done!\n") ############################################################################################################ # Create a retweet graph rt_graph=NULL if (naming!="account_statistics") { rt_graph= igraph::graph.edgelist(as.matrix(cbind(ls_retweeted_df[,3],ls_retweeted_df[,2]))) E(rt_graph )$weight <- 1 rt_graph <- igraph::simplify(rt_graph, remove.loops=FALSE) message("Retweet Graph of channel are done!\n") } ############################################################################################################ # Get corpus and termdocfrequency matrix as qdap object corpus=getCorpus(channel_obj$text,hashtag=corpus_hashtag) word_freq_matr=qdap::wfm(corpus,stopwords=stopword) message("Corpus of words and frequency qdap matrix of channel are done!\n") ######################################################################################## channel_obj$hashtagCount=lenhash_df$lenhash channel_obj$linksCount=lenlinks_df$lenlinks channel_obj$mentionCount=lentag_df$lentag ######################################################################################## res=list(channel_stat=full_stat, table_message=table_message, table_hash=table_hash, table_mentions=table_mentions, table_authors=table_authors, table_replies=table_replies, table_authors_retweeted=table_authors_retweeted, table_authors_retweeter=table_authors_retweeter, rank_authors_retweet=rank_authors_retweet, rank_message_retweet=rank_message_retweet, top_message=table_message[1:Ntop,], top_authors=table_authors[1:Ntop,], top_hash=table_hash[1:Ntop,], top_mentions=table_mentions[1:Ntop,], top_replies=table_replies[1:Ntop,], top_authors_retweeted=table_authors_retweeted[1:Ntop,], top_authors_retweeter=table_authors_retweeter[1:Ntop,], topfull_authors_retweeted=rank_authors_retweet[1:Ntop,], topfull_message_retweeted=rank_message_retweet[1:Ntop,], daily_stat=daily_stat, authors_date=authors_purged, links_date=links_purged, hash_date=hash_purged, mentions_date=tag_purged, unique_message=unique(table_message[,1]), unique_authors=unique(table_authors[,1]), unique_hash=unique(table_hash[,1]), unique_mentions=unique(table_mentions[,1]), unique_authors_retweeted=unique(table_authors_retweeted[,1]), unique_authors_retweeter=unique(table_authors_retweeter[,1]), uniquefull_authors_retweeted=unique(rank_authors_retweet[,1]), uniquefull_message_retweeted=unique(rank_message_retweet[,1]), graph_retweet_df=ls_retweeted_df, graph_hash_df=na.omit(ls_hash_df), graph_mentions_df=na.omit(ls_tag_df), replies_df=ls_replies_df, graph_retweet=rt_graph, graph_mentions=men_graph, authors_favorite=rank_authors_favorite, favorite_message_top=head(ls_favorite_df,Ntop), channel_data=channel_obj, account_stats=NULL, channel_corpus=corpus, word_freq_matr=word_freq_matr ) if (naming=="account_statistics") { stats_activity=aggregate(channel_obj[,5:22], list(channel_obj$data), sum) names(stats_activity)[1]="data" rownames(stats_activity)=stats_activity$data res$account_stats=stats_activity } return(res) }

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/AFMNetworksAnalyser.R \name{isAngleBetweenEdgesAlwaysSuperiorToMinAngle} \alias{isAngleBetweenEdgesAlwaysSuperiorToMinAngle} \title{check if all the angles between one edge and a list of edges is superior to a specified value.} \usage{ isAngleBetweenEdgesAlwaysSuperiorToMinAngle( binaryAFMImage, edge1, edges2, minAngle ) } \arguments{ \item{binaryAFMImage}{a binary \code{\link{AFMImage}} from Atomic Force Microscopy} \item{edge1}{one edge} \item{edges2}{list of edges} \item{minAngle}{the minimum angle value} } \value{ TRUE if all the angle are superior to the specified value } \description{ check if all the angles between one edge and a list of edges is superior to a specified value. } \author{ M.Beauvais }

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makeTarget <- function() { p1 <- runif(2, min=-1, max=1) p2 <- runif(2, min=-1, max=1) b <- (p1[2]-p2[2])/(p1[1]-p2[1]) a <- p1[2]-b*p1[1] function(x) { sign(c(a,b,-1) %*% rbind(1,matrix(x,nrow=2)))} } makeHypothesis <- function(w) { function(x) { sign(t(w) %*% rbind(1,matrix(x,nrow=2)))} } makeInputs <- function(N) { matrix(data=runif(2 * N, min=-1, max=1), nrow=2, ncol = N, byrow=TRUE) } checkHypothesis <- function(f, h, x) { apply(x,2,function(x) {f(x) == h(x)}) } outOfSampleError <- function(f, g) { x <- makeInputs(100) 1-mean(checkHypothesis(f,g,x)) } runPLA <- function(x, f) { w <- c(0,0,0) N <- ncol(x) loopCount <- 1 done <- FALSE while (!done && loopCount < 100) { h <- makeHypothesis(w) c <- checkHypothesis(f,h,x) if (sum(c) == N) { done <- TRUE } else { element <- ceiling(runif(1,max=N)) while (c[element] == TRUE) { element <- element + 1 if (element > N) { element <- 1 } } w <- w + f(x[,element])[1] * rbind(1,matrix(x[,element],nrow=2)) } loopCount = loopCount + 1 } data.frame(iterations=loopCount, error=outOfSampleError(f, makeHypothesis(w))) } doExperiment <- function(N) { f <- makeTarget() x <- makeInputs(N) y <- f(x) # plot(x=x[1,],y=x[2,],col=y+3,xlim=c(-1,1),ylim=c(-1,1)) # abline(a=a,b=b) runPLA(x,f) }

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# library(RMySQL) do not read otherwise you will get an error library(sqldf) # set working directory and create dir named data where I download data setwd("/home/rok/Edjucation/2019.3.28. Data_Science-Specialization/Getting and Cleaning Data/Assighnment W2") if (!file.exists("data")){ dir.create("data") } fileURL <- "https://d396qusza40orc.cloudfront.net/getdata%2Fdata%2Fss06pid.csv" # download data download.file(fileURL, destfile = "./data/temp.csv", method = "curl") dateDownloaded <- date() # read acs <- read.csv("./data/temp.csv")

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combineTMDomains = function(tmdf, poscols = c("start", "end")) { tmmat = matrix(NA, ncol = 2, nrow = nrow(tmdf)) curnumrow = 1 for (i in 1:nrow(tmdf)) { inserted = 0 for(j in 1:curnumrow) { if(!is.na(tmmat[j, 1])) { if (any(tmdf[i ,poscols] >= tmmat[j, 1] & tmdf[i, poscols ] <= tmmat[j, 2]) ) { tmmat[j , 1 ] = min( tmdf[ i , poscols[ 1 ] ] , tmmat[ j , 1 ] ) tmmat[j , 2 ] = max( tmdf[ i , poscols[ 2 ] ] , tmmat[ j , 2 ] ) inserted = 1 break() } } } if (!inserted) { tmmat[curnumrow,] = as.numeric(tmdf[i,poscols]) curnumrow = curnumrow + 1 } } tmmat = tmmat[!is.na(tmmat[,1]), , drop=FALSE] tmmat } calcPlotHeight = function(baseheight, type = "metaCount", pfam, categories) { if(!is.null(pfam) & nrow(pfam)) { myPFIRanges = IRanges(start = pfam$start, end = pfam$end) bins = disjointBins(myPFIRanges) } else { bins = 1 } denom = .20 + .25 + 1 #assume 1 pfam row and 5 categories if (type == "struct") { #.20 + .25 + .25 #the .25 is for the hydro, which isn't in the above plot. categories = NULL } baseheight * (1 + .2 /denom * ( max(bins) - 1) + .2 / denom* (length(unique(categories)) - 5 + 2)) #2 fake categories } spoofLevelsInDF = function(df, colname, newlevs, before = TRUE, force.factor = TRUE) { oldnrow = nrow(df) if(is.factor(df[[colname]])) oldlevs = levels(df[[colname]]) else oldlevs = unique(df[[colname]]) #XXX this doesn't seem to give us the right order!!! df[[colname]] = as.character(df[[colname]]) if(before) alllevs = unique(c(newlevs, oldlevs)) else alllevs = unique(c(oldlevs, newlevs)) for(i in seq(along = newlevs)) df[oldnrow + i, colname] = newlevs[i] if(force.factor) df[[colname]] = factor(df[[colname]], levels = alllevs) return(df) }

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#' @title Gera os graficos de DIF #' @name dif.plot #' #' @description Cria um diretorio chamado 'DIF' e gera os graficos de Funcionamento Diferenciado Do Item (DIF). #' #' @param EXP Objeto resultante da funcao calcDIF. #' @param SCO Uma lista nomeada de acordo com os indices dos grupos em analise, contendo os percentis das distribuicoes de proficiencias. #' @param main String ou vetor com o(s) titulo(s) dos graficos. #' @param groups Vetor com o nome a ser plotado para cada um dos grupos, ordenado pelo indice do respectivo grupo. #' @param probs Quantiles utilizados para o calculo dos intervalos de interesse. #' @param col.dif Vetor com as cores a serem utilizadas nas curvas empiricas dos grupos. #' @param density Plotar na imagem a densidade das proficiencias de cada grupo ('area', 'points'). Caso NULL, a densidade sera omitida. #' @param dir.create Nome do diretorio a ser criado para guardar os graficos. #' @param xlim Limites do eixo X a ser plotados. #' @param height Altura em polegadas da imagem. #' @param width Largura em polegadas da imagem. #' @param shinyDemo Argumento auxiliar para o uso da funcao 'myR.app'. #' #' @details Etapa Anterior: 'checkDIF'. #' #' @author Brunno Bittencourt #' #' @examples #' EXP = remakeEXP(readLines("EXPtest.EXP")) #' #' newGroup = c(6, 7) #' #' EXP = calcDIF(EXP, newGroup) #' #' SCO = read.SCO(readLines("SCOtest.SCO")) #' #' SCO = lapply(split(SCO$SCO, SCO$Grupo), quantile, probs = c(.05, .95)) #' #' dif.plot(EXP, SCO) #' #' @import magrittr #' @import ggplot2 #' @importFrom grid pushViewport #' @importFrom gridBase baseViewports #' @importFrom randomcoloR distinctColorPalette #' @export dif.plot <- function(EXP, SCO, main = NULL, groups = NULL, probs = c(.05, .95), col.dif = NULL, density = "area", dir.create = "DIF", xlim = c(-4, 4), width = 9.6, height = 6.8, shinyDemo = NULL){ if(is.null(groups)) groups = paste0("Grupo", unique(SCO$Grupo)) if(is.null(col.dif)) col.dif = distinctColorPalette(length(groups)) if(is.null(shinyDemo)) dir.create(dir.create, showWarnings = FALSE) if(is.null(main)) main = names(EXP) if(length(main) < length(EXP) & is.null(shinyDemo)) stop("Dimensoes invalidas para main") if(!is.null(density)){ habs = SCO if(density == "points"){ habs = split(habs$SCO, habs$Grupo) for(i in seq_along(habs)) habs[[i]] = density(habs[[i]]) } } SCO = lapply(split(SCO$SCO, SCO$Grupo), quantile, probs = c(probs[1], probs[2])) for(i in seq_along(EXP)){ if(is.null(shinyDemo)){ pdf(file = paste0(getwd(), "/", dir.create,"/", paste0(names(EXP)[i], "_DIF.pdf")), width = width, height = height) }else{ i = shinyDemo } if(!is.null(density)){ layout(matrix(c(2, 1), ncol = 1, byrow = TRUE), widths = c(5/7, 2/7), heights = c(2/7, 5/7)) if(density == "area") par(mar = c(4, 4, 1, 1), mai = c(1.02, 0.82, 0.02, 0.42)) if(density == "points") par(mar = c(4.5, 4.5, 0, 1.5), bty = "o") } temp = EXP[[i]] nqp = nrow(temp) / (temp$Grupo %>% unique %>% length) plot(temp$POINT[1:nqp], seq(0, 1, length = nqp), type = "n", main = ifelse(is.null(density), main[i], ""), xlab = "Proficiencia", ylab = "Percentual de Respostas", ylim = c(0, 1), xlim = c(xlim[1], xlim[2])) lines(temp$POINT[1:nqp], temp$MODEL.PROP[1:nqp], lwd = 2) for(g in unique(temp$Grupo)){ lines(temp$POINT[temp$Grupo == g], temp$PROPORTION[temp$Grupo == g], col = col.dif[g], lwd = 2) abline(v = SCO[[g]][1], lty = 2, col = col.dif[g]) abline(v = SCO[[g]][2], lty = 2, col = col.dif[g]) } legend(-4.2, 1, lty = 1, lwd = 2, col = col.dif[unique(temp$Grupo)], legend = groups[1:length(groups) %in% unique(temp$Grupo)], box.lty = 0, text.font = 2) if(!is.null(density)){ if(density == "area"){ plot.new() vps = baseViewports() pushViewport(vps$figure) vp1 = plotViewport(c(-1, 3.4, 1, 1.5)) g = ggplot(habs[habs$Grupo %in% temp$Grupo, ], aes(SCO, fill = Grupo)) + geom_density(alpha = 0.2) + ggtitle(main[i]) + labs(x = NULL, y = NULL) + xlim(c(xlim[1], xlim[2])) + scale_y_continuous(breaks = NULL) + scale_fill_manual(breaks = unique(temp$Grupo), values = col.dif[unique(temp$Grupo)]) + theme(legend.position = "none", panel.background = element_rect(fill = "transparent", colour = NA), plot.background = element_rect(fill = "transparent", colour = NA), axis.text.x = element_blank(), plot.title = element_text(hjust = .5)) print(g, vp = vp1) } if(density == "points"){ par(mar = c(0, 4.5, 2, 1.5), bty = "n") plot(temp$POINT[1:nqp], seq(0, max(unlist(lapply(habs, FUN = function(x) x[[2]]))), length = nqp), type = "n", main = main[i], xaxt = "n", yaxt = "n", ann = FALSE) for(i in seq_along(habs)[seq_along(habs) %in% temp$Grupo]){ info = cbind(habs[[i]][["x"]], habs[[i]][["y"]]); info = info[seq(1, nrow(info), 4), ] points(info[, 1], info[, 2], col = col.dif[i], pch = 16, cex = .5) } } } if(is.null(shinyDemo)){ dev.off() }else{ break } } }

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tiger_one_at_a_time.R

#library(httr) library(RJSONIO) gurl <- "http://geocoding.geo.census.gov/geocoder/geographies/address?street=912+Kingston+Ave&city=Piedmont&state=CA&benchmark=Public_AR_Census2010&vintage=Census2010_Census2010&format=json" bad_gurl <-"http://geocoding.geo.census.gov/geocoder/geographies/address?street=912+Kingston+Ave&city=donkey&state=CA&benchmark=Public_AR_Census2010&vintage=Census2010_Census2010&format=json" tiger_prefix <- "http://geocoding.geo.census.gov/geocoder/geographies/address?" tiger_suffix <- "&benchmark=Public_AR_Census2010&vintage=Census2010_Census2010&format=json" #g_out <- GET(gurl) g_out <- fromJSON(gurl) str(g_out) # take the first returned values in case > 1 matches lon <- g_out$result$addressMatches[[1]]$coordinates[['x']] lat <- g_out$result$addressMatches[[1]]$coordinates[['y']] matchedAddress <- g_out$result$addressMatches[[1]]$matchedAddress tractfips <- g_out$result$addressMatches[[1]]$geographies$`Census Tracts`[[1]]$GEOID blockfips <- g_out$result$addressMatches[[1]]$geographies$`Census Blocks`[[1]]$GEOID # another way g_out2 <- unlist(g_out) head(g_out2) g_out2['result.addressMatches.coordinates.x'] #Now process a file of addresses: tiger_input_addressFile <- "tiger/tiger_12addresses_to_geocode.csv" # let's take a look at the addresses that we will geocode addresses_to_geocode <- read.csv(tiger_input_addressFile, stringsAsFactors = FALSE, col.names = c('id','street','city','state','zip')) addresses_to_geocode addresses_to_geocode$tiger_format <- paste0( "street=",addresses_to_geocode$street, "&city=",addresses_to_geocode$city, "&state=",addresses_to_geocode$state, "&zip=",addresses_to_geocode$zip ) # geocode a file of addresses - one at at time tgeocode <- function(address){ address <- URLencode(address) g_address <- paste0(tiger_prefix, address,tiger_suffix) print(g_address) g_out <- tryCatch( fromJSON(g_address) # result will be returned if no error ) if (length(g_out$result$addressMatches) > 0) { print(g_out$result$addressMatches[[1]]$matchedAddress) } else{ #no results } } ## apply the geocoding function to the CSV file library(plyr) ldply(addresses_to_geocode$tiger_format,function(x) tgeocode(x)) #address <- c("The White House, Washington, DC","The Capitol, Washington, DC") #locations <- ldply(address, function(x) geoCode(x)) #names(locations) <- c("lat","lon","location_type", "formatted") #head(locations)

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/R/haplo-match-cif.r

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karthy257/HaploSurvival

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4661eab94edd62e79dcdaefbe2dc05fb01840c5b

refs/heads/master

2021-03-06T14:54:05.583732

2019-05-16T11:21:43

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haplo-match-cif.r

haplomatch.cif<-function(formula,data=sys.parent(), cause,times,designfuncX,designfuncZ,Nit=50, clusters=NULL,gamma=0,n.sim=500,weighted=0,model="additive", causeS=1,cens.code=0,detail=0,interval=0.01,resample.iid=1, cens.model="KM",time.pow=0,fix.haplofreq=0,haplo.freq=NULL,alpha.iid=NULL, geno.setup=NULL,fit.haplofreq=NULL,design.test=0,covnamesX=NULL,covnamesZ=NULL) { call <- match.call() m <- match.call(expand = FALSE) out<- haplo.cif( formula = formula,data=data, cause,times, designfuncX,designfuncZ, Nit = Nit, clusters=clusters, gamma=gamma, n.sim = n.sim, match=TRUE, weighted=weighted,model=model,causeS=causeS, cens.code=cens.code, detail = detail, interval=interval, resample.iid=resample.iid, cens.model=cens.model,time.pow=time.pow, fix.haplofreq = fix.haplofreq, haplo.freq =haplo.freq, alpha.iid=alpha.iid, geno.setup=geno.setup, fit.haplofreq=fit.haplofreq,design.test=design.test, covnamesX=covnamesX,covnamesZ=covnamesZ) attr(out, "Call") <- sys.call() attr(out, "Formula") <- formula return(out) }

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/grattan/inst/testfiles/anyOutside/libFuzzer_anyOutside/anyOutside_valgrind_files/1610129556-test.R

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akhikolla/updated-only-Issues

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refs/heads/master

2023-04-13T08:22:15.699449

2021-04-21T16:25:35

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1610129556-test.R

testlist <- list(a = -1L, b = -230L, x = c(8388608L, 536873984L, -1L, 1560281088L, 0L, 0L, 0L, 0L, 0L, -163L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 255L, -193L, -14155813L, -250L, 50331647L, -193L, -14155904L, 8192L, 201523195L, -16814507L, -14024705L, -37L, -1L, -12583129L, -56833L, -8650752L, 14869218L, 41L)) result <- do.call(grattan:::anyOutside,testlist) str(result)

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/run_analysis.R

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arunchaudharee/Getting-and-Cleaning-Data-Project

ba8c6a7068fa740415544f56ceaba9485cb97fc4

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refs/heads/master

2021-04-06T08:16:16.452139

2018-03-13T01:08:30

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run_analysis.R

## Getting and Cleaning Data Course Project ## This R script called run_analysis.R downloads the data from the following link and does the following: ## download link - https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip ## 1. Merges the training and the test sets to create one data set. ## 2. Extracts only the measurements on the mean and standard deviation for each measurement. ## 3. Uses descriptive activity names to name the activities in the data set ## 4. Appropriately labels the data set with descriptive variable names. ## 5. From the data set in step 4, creates a second, independent tidy data set with ## the average of each variable for each activity and each subject. ## This code is written in RStudio, R version 3.4.3, in Windows 10 OS. ## First download the zipped dataset, unzip it and read readme file, activity_labels, features, features_info files ## and check data files in each of the folders ## Downloading of file and unzip can be done manually or through coding ## tidying of dataset will require dplyr package or reshape2 package so install if not already installed ## In this code, "reshape2" package is used for data tidying ## First clear the environment rm(list=ls()) ## Downloading of file and unzip can be done manually or through coding ## tidying of dataset will require dplyr package or reshape2 package, install if not already installed ## In this code, reshape2 package is used for data tidying ## Check if the "reshape2" package is installed. if(!is.element("reshape2", installed.packages())) { # It package is not installed,install it, and then load it install.packages("reshape2") library(reshape2) } ## Initialize some initial values for folders that are to be downloaded and unzipped ## Get the path of working directory path <- getwd() downloadFolder <- "UCI HAR Dataset" zippedfile <- "dataset.zip" url <- "https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip" # Check if the user has already unzipped the file if(!file.exists(downloadFolder)) { # Is there a zip file? if(!file.exists(zippedfile)) { # if zip file is not downloaded, downlaod it download.file(url,file.path(path, zippedfile)) } # Now, unzip the file unzip(zippedfile) } ## unzipped files are in the folder UCI HAR Dataset. See the list of files in it. downloaded_path <- file.path(path, "UCI HAR Dataset") files<-list.files(downloaded_path, recursive=TRUE) files ## 1. Merges the training and the test sets to create one data set. # To merge different data files, first rbind it and then cbind it. # Read in the data of train folders # Use file.data function for the path to a file train_subjects <- read.table(file.path(path, "UCI HAR Dataset/train/subject_train.txt")) train_data <- read.table(file.path(path, "UCI HAR Dataset/train/X_train.txt")) train_activities <- read.table(file.path(path, "UCI HAR Dataset/train/y_train.txt")) # Read in the data of test folders test_subjects <- read.table(file.path(path, "UCI HAR Dataset/test/subject_test.txt")) test_data <- read.table(file.path(path, "UCI HAR Dataset/test/X_test.txt")) test_activities <- read.table(file.path(path, "UCI HAR Dataset/test/y_test.txt")) ## Look at the structure/properties of the above variables str(train_subjects) str(train_data) str(train_activities) str(test_subjects) str(test_data) str(test_activities) ## Combine the all tables by the rows together row_data <- rbind(train_data, test_data) row_activities <- rbind(train_activities, test_activities) row_subjects <- rbind(train_subjects, test_subjects) # Now combine all different columns together into one table combined_data <- cbind(row_subjects, row_activities, row_data) ## 2. Extracts only the measurements on the mean and standard deviation for each measurement. # Load features of the dataset ## Read features.txt file features <- read.table(file.path(path, "UCI HAR Dataset/features.txt")) ## Extract only the mean and std (standard deviation) of measurements into the combined table. mean_std_Features <- features[grep("-(mean|std)\$\$", features[, 2 ]), 2] combined_data <- combined_data[, c(1, 2, mean_std_Features)] ## 3. Uses descriptive activity names to name the activities in the data set # Load activity labels of the dataset where activity names are found. ## Read activity_label.txt file activityNames <- read.table(file.path(path, "UCI HAR Dataset/activity_labels.txt")) # Update the activity names into the combined table combined_data[, 2] <- activityNames[combined_data[,2], 2] ## 4. Appropriately labels the data set with descriptive variable names. ## Remove the brackets from the features columns measurements <- gsub("[()]", "", as.character(mean_std_Features)) ## Name the column names of the combined data with "subjectNum", "activity" and features names found in measurements colnames(combined_data) <- c("subjectNum", "activity", measurements) ## Now name the features labelled with descriptive variable names. # Replace prefix t by time names(combined_data)<-gsub("^t", "time", names(combined_data)) # Replace prefix f by frequency names(combined_data)<-gsub("^f", "frequency", names(combined_data)) # Replace Gyro by Gyroscope names(combined_data)<-gsub("Gyro", "Gyroscope", names(combined_data)) # Replace Acc by Accelerometer names(combined_data)<-gsub("Acc", "Accelerometer", names(combined_data)) # Replace Mag by Magnitude names(combined_data)<-gsub("Mag", "Magnitude", names(combined_data)) # Replace BodyBody by Body names(combined_data)<-gsub("BodyBody", "Body", names(combined_data)) ## Let's coerce the data of 2nd column - "activity" into strings combined_data[, 2] <- as.character(combined_data[, 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. ## Melt the data so we have a unique row for each combination of subject and activities melted_data <- reshape2::melt(combined_data, id = c("subjectNum", "activity")) # Cast the melted data getting the mean value. mean_data <- reshape2::dcast(melted_data, subjectNum + activity ~ variable, fun.aggregate = mean) # Write the data out to a file write.table(mean_data, file=file.path("tidydata.txt"), row.names = FALSE, quote = FALSE)

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/get_weather.R

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joebrew/tbd

ef332510b4cd43fddf9311ba146e0404d3932885

fb22c30a922c99c538474b818234cb06125dec13

refs/heads/master

2021-08-22T23:48:17.213421

2017-12-01T18:52:02

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get_weather.R

library(tidyverse) library(RCurl) # Define function to replace NAs (coded in noaa as 99.9 by NOAA) detect_noaa_na <- function(x){ x <- as.character(x) y <- gsub('.', '', x, fixed = TRUE) oks <- y == x out <- unlist(lapply(strsplit(y, ''), function(x){all(x == '9')})) out[oks] <- FALSE out } # Define functions for converting from farenheit to celcius f_to_c <- function(x){ x <- x - 32 x <- x * (5/9) x } # Define function for converting from inches to milimeters i_to_m <- function(x){ x <- x * 25.4 x } # Define function to calculate distance from each district centroid to the weather stations get_distance <- function (lon1, lat1, lon2, lat2) { rad <- pi/180 a1 <- lat1 * rad a2 <- lon1 * rad b1 <- lat2 * rad b2 <- lon2 * rad dlon <- b2 - a2 dlat <- b1 - a1 a <- (sin(dlat/2))^2 + cos(a1) * cos(b1) * (sin(dlon/2))^2 c <- 2 * atan2(sqrt(a), sqrt(1 - a)) R <- 6378.145 d <- R * c return(d) } # Define function to get weather for certain location get_weather_for_location <- function(noaa, lng, lat){ out <- noaa y <- lat x <- lng # Get distance to all locations out$distance <- NA for (i in 1:nrow(out)){ out$distance[i] <- get_distance(lon1 = lng, lat1 = lat, lon2 = out$lon[i], lat2 = out$lat[i]) } # Create a weight column out$weight <- 1 / out$distance out$weight[is.infinite(out$weight)] <- 1 # Group by date and get weighted averages out <- out %>% group_by(date) %>% summarise(lat = y, lon = x, temp = weighted.mean(temp, w = weight, na.rm = TRUE), dewp = weighted.mean(dewp, w = weight, na.rm = TRUE), wdsp = weighted.mean(wdsp, w = weight, na.rm = TRUE), mxspd = weighted.mean(mxspd, w = weight, na.rm = TRUE), max = weighted.mean(max, w = weight, na.rm = TRUE), min = weighted.mean(min, w = weight, na.rm = TRUE), prcp = weighted.mean(prcp, w = weight, na.rm = TRUE)) return(out) } get_data <- function(years = 2000:2017){ if(!dir.exists('gsod')){ dir.create('gsod') } owd <- getwd() setwd('gsod') for (i in 1:length(years)){ this_year <- years[i] system(paste0('wget -m ftp://ftp.ncdc.noaa.gov/pub/data/gsod/', this_year)) } setwd(owd) } get_data() # # Function for manually retrieving, no longer relevant # # get_data <- function(directory = 'noaa_raw', # # year = 2017){ # # url_base <- "ftp://ftp.ncdc.noaa.gov/pub/data/gsod/" # # url <- paste0(url_base, year, '/') # # # userpwd <- "yourUser:yourPass" # # filenames <- getURL(url, # # # userpwd = userpwd, # # ftp.use.epsv = FALSE, # # dirlistonly = TRUE) # # filenames <- unlist(strsplit(filenames, '\n')) # # for (i in 1:length(filenames)){ # # this_file <- filenames[i] # # new_name <- paste0(directory, '/', gsub('.gz', '', this_file, fixed = TRUE)) # # if(!file.exists(new_name)){ # # try({ # # message('Retrieiving ', this_file, ' (', i, ' of ', length(filenames), ' for year ', year, ')') # # # Delete before downloading # # if(file.exists('temp.op.gz')){ # # file.remove('temp.op.gz') # # } # # if(file.exists('temp.op')){ # # file.remove('temp.op') # # } # # # Download # # download.file(url = paste0(url, this_file), # # destfile = 'temp.op.gz') # # # Extract # # R.utils::gunzip('temp.op.gz') # # # Move # # file.copy(from = 'temp.op', # # to = new_name) # # }) # # } # # } # # } # # # for(i in 2010:2017){ # # get_data(year = i) # # } # # # # # # # Read in all data # raw_data <- dir('noaa_raw/') # data_list <- list() # for (i in 1:length(raw_data)){ # message(i, ' of ', length(raw_data)) # this_file <- raw_data[i] # suppressMessages(this_table <- read_table(paste0('noaa_raw/', this_file))) # this_table <- # this_table %>% # dplyr::select(`STN---`, YEARMODA, TEMP, DEWP, SLP, STP, VISIB, WDSP, MXSPD, GUST, MAX, MIN, PRCP, SNDP, FRSHTT) # names(this_table)[1] <- 'USAF' # this_table$USAF <- as.character(this_table$USAF) # this_table <- data.frame(this_table) # for(j in 1:ncol(this_table)){ # this_table[,j] <- as.character(this_table[,j]) # } # data_list[[i]] <- this_table # } # a <- bind_rows(data_list) # # # # Join to station information # # Read in station info # b <- read_table('noaa_data/isd-history_may_2017.txt', skip = 20) # b <- b %>% dplyr::select(USAF, `STATION NAME`, CTRY, LAT, LON, `ELEV(M)`) # b <- b %>% rename(station_name = `STATION NAME`, country = CTRY, lat = LAT, lon = LON, elevation = `ELEV(M)`) # # b$USAF <- as.numeric(b$USAF) # b <- b %>% # filter(!duplicated(USAF)) # # # Join # noaa <- left_join(a, b, # by = 'USAF') # # # Make date column # noaa$date <- as.Date(paste0(substr(noaa$YEARMODA,start = 1, stop = 4), # '-', # substr(noaa$YEARMODA,start = 5, stop = 6), # '-', # substr(noaa$YEARMODA,start = 7, stop = 8))) # # # Make lowercase column names # names(noaa) <- tolower(names(noaa)) # # # Keep only columns of interest # noaa <- # noaa %>% # dplyr::select(country, # station_name, # usaf, # date, # temp, # dewp, # slp, # stp, # visib, # wdsp, # mxspd, # gust, # max, # min, # prcp, # sndp, # frshtt, # lat, # lon, # elevation) # # # Clean up NAs # noaa <- data.frame(noaa) # for (j in 5:ncol(noaa)){ # noaa[,j] <- ifelse(detect_noaa_na(noaa[,j]), # NA, # noaa[,j]) # } # # # Convert to number # convert_to_number <- # function(x){ # x <- regmatches(x, gregexpr("[[:digit:]]+", x)) # if(length(unlist(x)) == 0){ # y <- NA # } else { # y <- lapply(x, function(z){ # if(length(z) == 2){ # out <- as.numeric(paste0(z[1], '.', z[2])) # } else { # out <- unlist(z)[1] # } # return(out) # }) # } # return(as.numeric(unlist(y))) # } # # # # Clean up column types # noaa <- # noaa %>% # mutate(temp = convert_to_number(`temp`), # max = convert_to_number(`max`), # min = convert_to_number(`min`), # prcp = convert_to_number(`prcp`), # wdsp = convert_to_number(`wdsp`), # visib = convert_to_number(`visib`), # stp = convert_to_number(`stp`), # sndp = convert_to_number(`sndp`), # dewp = convert_to_number(`dewp`), # slp = convert_to_number(`slp`), # mxspd = convert_to_number(`mxspd`), # gust = convert_to_number(gust)) # # # Since noaa has some missing days, interpolate # left <- expand.grid(station_name = sort(unique(noaa$station_name)), # date = sort(unique(noaa$date))) # noaa <- left_join(left, # noaa, # by = c('station_name', 'date')) # # Flag estimations # noaa$estimated <- ifelse(is.na(noaa$lat), TRUE, FALSE) # # Performance interpolation # x <- # noaa %>% # arrange(date) %>% # group_by(station_name) %>% # mutate(temp = zoo::na.approx(object = temp, # x = date, # na.rm = FALSE), # dewp = zoo::na.approx(object = dewp, # x = date, # na.rm = FALSE), # wdsp = zoo::na.approx(object = wdsp, # x = date, # na.rm = FALSE), # mxspd = zoo::na.approx(object = mxspd, # x = date, # na.rm = FALSE), # max = zoo::na.approx(object = max, # x = date, # na.rm = FALSE), # min = zoo::na.approx(object = min, # x = date, # na.rm = FALSE), # prcp = zoo::na.approx(object = prcp, # x = date, # na.rm = FALSE), # visib = zoo::na.approx(object = visib, # x = date, # na.rm = FALSE), # slp = zoo::na.approx(object = slp, # x = date, # na.rm = FALSE), # stp = zoo::na.approx(object = stp, # x = date, # na.rm = FALSE), # gust = zoo::na.approx(object = gust, # x = date, # na.rm = FALSE), # sndp = zoo::na.approx(object = sndp, # x = date, # na.rm = FALSE), # elevation = zoo::na.approx(object = elevation, # x = date, # na.rm = FALSE)) # # # Fix missing lat/lons # ll <- noaa %>% # group_by(station_name) %>% # summarise(lat = dplyr::first(lat[!is.na(lat)]), # lon = dplyr::first(lon[!is.na(lon)])) # # noaa <- x %>% ungroup %>% # dplyr::select(-lat, # -lon) %>% # left_join(ll, # by = 'station_name') #

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/Misc. Charts/SpC & NOx Violin Plots_POR 20180914 to 20190709.R

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ETaylor21/gnv_streams

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refs/heads/master

2021-11-04T04:33:19.212474

2021-11-01T05:38:47

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SpC & NOx Violin Plots_POR 20180914 to 20190709.R

##### Violin Plots of NOx and SpC For AJ NSF Proposal 10/16/19#### ####NOx, O_P, NH4 all filtered samples### #NOx - Nitrate(NO3)-Nitrite(NO2)-N (mg/L) #SpCond - specific conductivity (uS/cm) ####Sample Replications (col = Rep) #a = 1; b = 2; c = 3; d = not a rep just a reading ########################Call in and format data######################################## #call in packages library(tidyverse) library(lubridate) library(RColorBrewer) #set working directory setwd('C:/Users/Emily/Documents/gnv_streams/Misc. Charts') #call in data file data_nut = read_csv('gnv_nutspdata_20191016.csv', col_types = cols( Site = col_character(), Date = col_date(format = "%m/%d/%Y"), Analyte = col_character(), Result = col_double()))#nutrient data; fixed date format for date column\ #####NOx and SpC Violin Plots (nvp)#### nd2 = data_nut %>% group_by(Date, Site, Analyte) %>% summarise(mean = mean(Result)) windows() nvp = ggplot(nd2, aes(x = Site, y = mean, fill = Analyte)) + geom_violin(scale = 'count', adjust = 0.5) + ylab('Results (uS/cm) Results (mg/L)') nvp2 = nvp + scale_x_discrete(labels = c('Hatchet', 'N. Hogtown', 'S. Hogtown', 'Possum', 'Sweetwater', 'Tumblin'))#change the names on the x axis, use discrete since non-numeric values nvp3 = nvp2 + facet_wrap( . ~ Analyte , scales = 'free_y', nrow = 2) + theme(strip.text = element_text(size = 18)) + scale_fill_manual(values = c('#56B4E9','#D55E00', '#009E73' )) + guides(fill = FALSE) + theme(axis.text = element_text(size = rel(1.2))) + theme(axis.title = element_text(size = rel(1.3))) + theme(axis.title.x = element_blank()) nvp3