## ----style, echo = FALSE, results = 'asis'------------------------------- BiocStyle::markdown() options(width=100, max.print=1000) knitr::opts_chunk$set( eval=as.logical(Sys.getenv("KNITR_EVAL", "TRUE")), cache=as.logical(Sys.getenv("KNITR_CACHE", "TRUE")), warning=FALSE, message=FALSE) ## ----setup, echo=FALSE, messages=FALSE, warnings=FALSE------------------- suppressPackageStartupMessages({ library(limma) library(ggplot2) }) ## ----install_cran, eval=FALSE-------------------------------------------- ## install.packages(c("pkg1", "pkg2")) ## install.packages("pkg.zip", repos=NULL) ## ----install_bioc, eval=FALSE-------------------------------------------- ## source("http://www.bioconductor.org/biocLite.R") ## library(BiocInstaller) ## BiocVersion() ## biocLite() ## biocLite(c("pkg1", "pkg2")) ## ----closing_r, eval=FALSE----------------------------------------------- ## q() ## ----r_assignment, eval=FALSE-------------------------------------------- ## object <- ... ## ----r_ls, eval=FALSE---------------------------------------------------- ## ls() ## ----r_dirshow, eval=FALSE----------------------------------------------- ## dir() ## ----r_dirpath, eval=FALSE----------------------------------------------- ## getwd() ## ----r_setwd, eval=FALSE------------------------------------------------- ## setwd("/home/user") ## ----r_syntax, eval=FALSE------------------------------------------------ ## object <- function_name(arguments) ## object <- object[arguments] ## ----r_find_help, eval=FALSE--------------------------------------------- ## ?function_name ## ----r_package_load, eval=FALSE------------------------------------------ ## library("my_library") ## ----r_package_functions, eval=FALSE------------------------------------- ## library(help="my_library") ## ----r_load_vignette, eval=FALSE----------------------------------------- ## vignette("my_library") ## ----r_execute_script, eval=FALSE---------------------------------------- ## source("my_script.R") ## ----sh_execute_script, eval=FALSE, engine="sh"-------------------------- ## $ Rscript my_script.R ## $ R CMD BATCH my_script.R ## $ R --slave < my_script.R ## ----r_numeric_data, eval=TRUE------------------------------------------- x <- c(1, 2, 3) x is.numeric(x) as.character(x) ## ----r_character_data, eval=TRUE----------------------------------------- x <- c("1", "2", "3") x is.character(x) as.numeric(x) ## ----r_complex_data, eval=TRUE------------------------------------------- c(1, "b", 3) ## ----r_logical_data, eval=TRUE------------------------------------------- x <- 1:10 < 5 x !x which(x) # Returns index for the 'TRUE' values in logical vector ## ----r_vector_object, eval=TRUE------------------------------------------ myVec <- 1:10; names(myVec) <- letters[1:10] myVec[1:5] myVec[c(2,4,6,8)] myVec[c("b", "d", "f")] ## ----r_factor_object, eval=TRUE------------------------------------------ factor(c("dog", "cat", "mouse", "dog", "dog", "cat")) ## ----r_matrix_object, eval=TRUE------------------------------------------ myMA <- matrix(1:30, 3, 10, byrow = TRUE) class(myMA) myMA[1:2,] myMA[1, , drop=FALSE] ## ----r_dataframe_object, eval=TRUE--------------------------------------- myDF <- data.frame(Col1=1:10, Col2=10:1) myDF[1:2, ] ## ----r_list_object, eval=TRUE-------------------------------------------- myL <- list(name="Fred", wife="Mary", no.children=3, child.ages=c(4,7,9)) myL myL[[4]][1:2] ## ----r_function_object, eval=FALSE--------------------------------------- ## myfct <- function(arg1, arg2, ...) { ## function_body ## } ## ----r_subset_by_index, eval=TRUE---------------------------------------- myVec <- 1:26; names(myVec) <- LETTERS myVec[1:4] ## ----r_subset_by_logical, eval=TRUE-------------------------------------- myLog <- myVec > 10 myVec[myLog] ## ----r_subset_by_names, eval=TRUE---------------------------------------- myVec[c("B", "K", "M")] ## ----r_subset_by_dollar, eval=TRUE--------------------------------------- iris$Species[1:8] ## ----r_combine_vectors, eval=TRUE---------------------------------------- c(1, 2, 3) x <- 1:3; y <- 101:103 c(x, y) iris$Species[1:8] ## ----r_cbind_rbind, eval=TRUE-------------------------------------------- ma <- cbind(x, y) ma rbind(ma, ma) ## ----r_length_dim, eval=TRUE--------------------------------------------- length(iris$Species) dim(iris) ## ----col_row_names, eval=TRUE-------------------------------------------- rownames(iris)[1:8] colnames(iris) ## ----name_slots, eval=TRUE----------------------------------------------- names(myVec) names(myL) ## ----sort_objects, eval=TRUE--------------------------------------------- sort(10:1) ## ----order_objects, eval=TRUE-------------------------------------------- sortindex <- order(iris[,1], decreasing = FALSE) sortindex[1:12] iris[sortindex,][1:2,] sortindex <- order(-iris[,1]) # Same as decreasing=TRUE ## ----order_columns, eval=TRUE-------------------------------------------- iris[order(iris$Sepal.Length, iris$Sepal.Width),][1:2,] ## ----comparison_operators, eval=TRUE------------------------------------- 1==1 ## ----logical_operators, eval=TRUE---------------------------------------- x <- 1:10; y <- 10:1 x > y & x > 5 ## ----logical_calculations, eval=TRUE------------------------------------- x + y sum(x) mean(x) apply(iris[1:6,1:3], 1, mean) ## ----read_delim, eval=FALSE---------------------------------------------- ## myDF <- read.delim("myData.xls", sep="\t") ## ----read_excel, eval=FALSE---------------------------------------------- ## library(gdata) ## myDF <- read.xls"myData.xls") ## ----read_gs, eval=FALSE------------------------------------------------- ## library("googlesheets"); library("dplyr"); library(knitr) ## gs_auth() # Creates authorizaton token (.httr-oauth) in current directory if not present ## sheetid <-"1U-32UcwZP1k3saKeaH1mbvEAOfZRdNHNkWK2GI1rpPM" ## gap <- gs_key(sheetid) ## mysheet <- gs_read(gap, skip=4) ## myDF <- as.data.frame(mysheet) ## myDF ## ----write_table, eval=FALSE--------------------------------------------- ## write.table(myDF, file="myfile.xls", sep="\t", quote=FALSE, col.names=NA) ## ----readlines, eval=FALSE----------------------------------------------- ## myDF <- readLines("myData.txt") ## ----writelines, eval=FALSE---------------------------------------------- ## writeLines(month.name, "myData.txt") ## ----paste_windows, eval=FALSE------------------------------------------- ## read.delim("clipboard") ## ----paste_osx, eval=FALSE----------------------------------------------- ## read.delim(pipe("pbpaste")) ## ----copy_windows, eval=FALSE-------------------------------------------- ## write.table(iris, "clipboard", sep="\t", col.names=NA, quote=F) ## ----copy_osx, eval=FALSE------------------------------------------------ ## zz <- pipe('pbcopy', 'w') ## write.table(iris, zz, sep="\t", col.names=NA, quote=F) ## close(zz) ## ----unique, eval=TRUE--------------------------------------------------- length(iris$Sepal.Length) length(unique(iris$Sepal.Length)) ## ----table, eval=TRUE---------------------------------------------------- table(iris$Species) ## ----aggregate, eval=TRUE------------------------------------------------ aggregate(iris[,1:4], by=list(iris$Species), FUN=mean, na.rm=TRUE) ## ----intersect, eval=TRUE------------------------------------------------ month.name %in% c("May", "July") ## ----merge, eval=TRUE---------------------------------------------------- frame1 <- iris[sample(1:length(iris[,1]), 30), ] frame1[1:2,] dim(frame1) my_result <- merge(frame1, iris, by.x = 0, by.y = 0, all = TRUE) dim(my_result) ## ----tidyverse_install, eval=FALSE--------------------------------------- ## install.packages("tidyverse") ## ----data_frame_tbl1, eval=TRUE------------------------------------------ library(tidyverse) as_data_frame(iris) # coerce data.frame to data frame tbl ## ----data_frame_tbl2, eval=FALSE----------------------------------------- ## as_tibble(iris) # newer function provided by tibble package ## tbl_df(iris) # this alternative exists for historical reasons ## ----tabular_sample, eval=TRUE------------------------------------------- write_tsv(iris, "iris.txt") # Creates sample file ## ----tabular_import1, eval=TRUE------------------------------------------ iris_df <- read_tsv("iris.txt") # Import with read_tbv from readr package iris_df ## ----tabular_import2, eval=TRUE------------------------------------------ library(data.table) iris_df <- as_data_frame(fread("iris.txt")) # Import with fread and conversion to tibble iris_df ## ----tabular_import_ignore, eval=FALSE----------------------------------- ## fread("grep -v '^#' iris.txt") ## ----tabular_export_readr, eval=FALSE------------------------------------ ## write_tsv(iris_df, "iris.txt") ## ----dplyr_bind, eval=TRUE----------------------------------------------- bind_cols(iris_df, iris_df) bind_rows(iris_df, iris_df) ## ----plyr_get_cols, eval=TRUE-------------------------------------------- iris_df[[5]][1:12] iris_df$Species[1:12] ## ----plyr_filter, eval=TRUE---------------------------------------------- filter(iris_df, Sepal.Length > 7.5, Species=="virginica") ## ----plyr_filter_base, eval=TRUE----------------------------------------- iris_df[iris_df[, "Sepal.Length"] > 7.5 & iris_df[, "Species"]=="virginica", ] ## ----plyr_filter_boolean, eval=TRUE-------------------------------------- filter(iris_df, Sepal.Length > 7.5 | Sepal.Length < 5.5, Species=="virginica") ## ----plyr_subset, eval=TRUE---------------------------------------------- slice(iris_df, 1:2) ## ----plyr_subset_base, eval=TRUE----------------------------------------- iris_df[1:2,] ## ----plyr_sample_set2, eval=TRUE----------------------------------------- df1 <- bind_cols(data_frame(ids1=paste0("g", 1:10)), as_data_frame(matrix(1:40, 10, 4, dimnames=list(1:10, paste0("CA", 1:4))))) df1 ## ----plyr_subset_names, eval=TRUE---------------------------------------- slice(df1, match(c("g10", "g4", "g4"), df1$ids1)) ## ----plyr_subset_names_base, eval=TRUE----------------------------------- df1_old <- as.data.frame(df1) rownames(df1_old) <- df1_old[,1] df1_old[c("g10", "g4", "g4"),] ## ----plyr_order1, eval=TRUE---------------------------------------------- arrange(iris_df, Species, Sepal.Length, Sepal.Width) ## ----plyr_order2, eval=TRUE---------------------------------------------- arrange(iris_df, desc(Species), Sepal.Length, Sepal.Width) ## ----plyr_order_base, eval=TRUE------------------------------------------ iris_df[order(iris_df$Species, iris_df$Sepal.Length, iris_df$Sepal.Width), ] iris_df[order(iris_df$Species, decreasing=TRUE), ] ## ----plyr_col_select1, eval=TRUE----------------------------------------- select(iris_df, Species, Petal.Length, Sepal.Length) ## ----plyr_col_select2, eval=TRUE----------------------------------------- select(iris_df, Sepal.Length : Petal.Width) ## ----plyr_col_drop, eval=TRUE-------------------------------------------- select(iris_df, -(Sepal.Length : Petal.Width)) ## ----plyr_col_rename, eval=TRUE------------------------------------------ rename(iris_df, new_col_name = Species) ## ----baser_col_rename, eval=FALSE---------------------------------------- ## colnames(iris_df)[colnames(iris_df)=="Species"] <- "new_col_names" ## ----plyr_unique, eval=TRUE---------------------------------------------- distinct(iris_df, Species, .keep_all=TRUE) ## ----baser_unique, eval=TRUE--------------------------------------------- iris_df[!duplicated(iris_df$Species),] ## ----plyr_mutate, eval=TRUE---------------------------------------------- mutate(iris_df, Ratio = Sepal.Length / Sepal.Width, Sum = Sepal.Length + Sepal.Width) ## ----plyr_transmute, eval=TRUE------------------------------------------- transmute(iris_df, Ratio = Sepal.Length / Sepal.Width, Sum = Sepal.Length + Sepal.Width) ## ----plyr_bind_cols, eval=TRUE------------------------------------------- bind_cols(iris_df, iris_df) ## ----plyr_summarize1, eval=TRUE------------------------------------------ summarize(iris_df, mean(Petal.Length)) ## ----plyr_summarize2, eval=TRUE------------------------------------------ summarize_all(iris_df[,1:4], mean) ## ----plyr_summarize, eval=TRUE------------------------------------------- summarize(group_by(iris_df, Species), mean(Petal.Length)) ## ----plyr_summarize3, eval=TRUE------------------------------------------ summarize_all(group_by(iris_df, Species), mean) ## ----plyr_join_sample, eval=TRUE----------------------------------------- df1 <- bind_cols(data_frame(ids1=paste0("g", 1:10)), as_data_frame(matrix(1:40, 10, 4, dimnames=list(1:10, paste0("CA", 1:4))))) df1 df2 <- bind_cols(data_frame(ids2=paste0("g", c(2,5,11,12))), as_data_frame(matrix(1:16, 4, 4, dimnames=list(1:4, paste0("CB", 1:4))))) df2 ## ----plyr_inner_join, eval=TRUE------------------------------------------ inner_join(df1, df2, by=c("ids1"="ids2")) ## ----plyr_left_join, eval=TRUE------------------------------------------- left_join(df1, df2, by=c("ids1"="ids2")) ## ----plyr_right_join, eval=TRUE------------------------------------------ right_join(df1, df2, by=c("ids1"="ids2")) ## ----plyr_full_join, eval=TRUE------------------------------------------- full_join(df1, df2, by=c("ids1"="ids2")) ## ----plyr_anti_join, eval=TRUE------------------------------------------- anti_join(df1, df2, by=c("ids1"="ids2")) ## ----plyr_chaining1, eval=TRUE------------------------------------------- iris_df %>% # Declare data frame to use select(Sepal.Length:Species) %>% # Select columns filter(Species=="setosa") %>% # Filter rows by some value arrange(Sepal.Length) %>% # Sort by some column mutate(Subtract=Petal.Length - Petal.Width) # Calculate and append # write_tsv("iris.txt") # Export to file, omitted here to show result ## ----plyr_chaining2, eval=TRUE------------------------------------------- iris_df %>% # Declare data frame to use group_by(Species) %>% # Group by species summarize(Mean_Sepal.Length=mean(Sepal.Length), Max_Sepal.Length=max(Sepal.Length), Min_Sepal.Length=min(Sepal.Length), SD_Sepal.Length=sd(Sepal.Length), Total=n()) ## ----plyr_chaining3, eval=TRUE------------------------------------------- iris_df %>% group_by(Species) %>% summarize_all(mean) %>% reshape2::melt(id.vars=c("Species"), variable.name = "Samples", value.name="Values") %>% ggplot(aes(Samples, Values, fill = Species)) + geom_bar(position="dodge", stat="identity") ## ----load_sqlite, eval=TRUE---------------------------------------------- library(RSQLite) mydb <- dbConnect(SQLite(), "test.db") # Creates database file test.db mydf1 <- data.frame(ids=paste0("id", seq_along(iris[,1])), iris) mydf2 <- mydf1[sample(seq_along(mydf1[,1]), 10),] dbWriteTable(mydb, "mydf1", mydf1) dbWriteTable(mydb, "mydf2", mydf2) ## ----list_tables, eval=TRUE---------------------------------------------- dbListTables(mydb) ## ----import_sqlite_tables, eval=TRUE------------------------------------- dbGetQuery(mydb, 'SELECT * FROM mydf2') ## ----query_sqlite_tables, eval=TRUE-------------------------------------- dbGetQuery(mydb, 'SELECT * FROM mydf1 WHERE "Sepal.Length" < 4.6') ## ----join_sqlite_tables, eval=TRUE--------------------------------------- dbGetQuery(mydb, 'SELECT * FROM mydf1, mydf2 WHERE mydf1.ids = mydf2.ids') ## ----sample_data, eval=TRUE---------------------------------------------- set.seed(1410) y <- matrix(runif(30), ncol=3, dimnames=list(letters[1:10], LETTERS[1:3])) ## ----basic_scatter_plot, eval=TRUE--------------------------------------- plot(y[,1], y[,2]) ## ----pairs_scatter_plot, eval=TRUE--------------------------------------- pairs(y) ## ----labels_scatter_plot, eval=TRUE-------------------------------------- plot(y[,1], y[,2], pch=20, col="red", main="Symbols and Labels") text(y[,1]+0.03, y[,2], rownames(y)) ## ----row_scatter_plot, eval=TRUE----------------------------------------- plot(y[,1], y[,2], type="n", main="Plot of Labels") text(y[,1], y[,2], rownames(y)) ## ----plot_usage, eval=FALSE---------------------------------------------- ## grid(5, 5, lwd = 2) ## op <- par(mar=c(8,8,8,8), bg="lightblue") ## plot(y[,1], y[,2], type="p", col="red", cex.lab=1.2, cex.axis=1.2, ## cex.main=1.2, cex.sub=1, lwd=4, pch=20, xlab="x label", ## ylab="y label", main="My Main", sub="My Sub") ## par(op) ## ----plot_regression, eval=TRUE------------------------------------------ plot(y[,1], y[,2]) myline <- lm(y[,2]~y[,1]); abline(myline, lwd=2) summary(myline) ## ----plot_regression_log, eval=TRUE-------------------------------------- plot(y[,1], y[,2], log="xy") ## ----plot_regression_math, eval=TRUE------------------------------------- plot(y[,1], y[,2]); text(y[1,1], y[1,2], expression(sum(frac(1,sqrt(x^2*pi)))), cex=1.3) ## ----plot_line_single, eval=TRUE----------------------------------------- plot(y[,1], type="l", lwd=2, col="blue") ## ----plot_line_many, eval=TRUE------------------------------------------- split.screen(c(1,1)) plot(y[,1], ylim=c(0,1), xlab="Measurement", ylab="Intensity", type="l", lwd=2, col=1) for(i in 2:length(y[1,])) { screen(1, new=FALSE) plot(y[,i], ylim=c(0,1), type="l", lwd=2, col=i, xaxt="n", yaxt="n", ylab="", xlab="", main="", bty="n") } close.screen(all=TRUE) ## ----plot_bar_simple, eval=TRUE------------------------------------------ barplot(y[1:4,], ylim=c(0, max(y[1:4,])+0.3), beside=TRUE, legend=letters[1:4]) text(labels=round(as.vector(as.matrix(y[1:4,])),2), x=seq(1.5, 13, by=1) + sort(rep(c(0,1,2), 4)), y=as.vector(as.matrix(y[1:4,]))+0.04) ## ----plot_bar_error, eval=TRUE------------------------------------------- bar <- barplot(m <- rowMeans(y) * 10, ylim=c(0, 10)) stdev <- sd(t(y)) arrows(bar, m, bar, m + stdev, length=0.15, angle = 90) ## ----plot_hist, eval=TRUE------------------------------------------------ hist(y, freq=TRUE, breaks=10) ## ----plot_dens, eval=TRUE------------------------------------------------ plot(density(y), col="red") ## ----plot_pie, eval=TRUE------------------------------------------------- pie(y[,1], col=rainbow(length(y[,1]), start=0.1, end=0.8), clockwise=TRUE) legend("topright", legend=row.names(y), cex=1.3, bty="n", pch=15, pt.cex=1.8, col=rainbow(length(y[,1]), start=0.1, end=0.8), ncol=1) ## ----color_palette, eval=TRUE-------------------------------------------- palette() palette(rainbow(5, start=0.1, end=0.2)) palette() palette("default") ## ----color_grey, eval=TRUE----------------------------------------------- gray(seq(0.1, 1, by= 0.2)) ## ----color_gradient, eval=TRUE------------------------------------------- library(gplots) colorpanel(5, "darkblue", "yellow", "white") ## ----save_graphics, eval=FALSE------------------------------------------- ## pdf("test.pdf") ## plot(1:10, 1:10) ## dev.off() ## ----save_graphics_svg, eval=FALSE--------------------------------------- ## library("RSvgDevice") ## devSVG("test.svg") ## plot(1:10, 1:10) ## dev.off() ## ----import_data1, eval=FALSE-------------------------------------------- ## my_mw <- read.delim(file="MolecularWeight_tair7.xls", header=T, sep="\t") ## my_mw[1:2,] ## ----import_data2, eval=FALSE-------------------------------------------- ## my_target <- read.delim(file="TargetP_analysis_tair7.xls", header=T, sep="\t") ## my_target[1:2,] ## ----import_data1b, eval=TRUE-------------------------------------------- my_mw <- read.delim(file="http://faculty.ucr.edu/~tgirke/Documents/R_BioCond/Samples/MolecularWeight_tair7.xls", header=T, sep="\t") my_mw[1:2,] ## ----import_data2b, eval=TRUE-------------------------------------------- my_target <- read.delim(file="http://faculty.ucr.edu/~tgirke/Documents/R_BioCond/Samples/TargetP_analysis_tair7.xls", header=T, sep="\t") my_target[1:2,] ## ----col_names_uni, eval=TRUE-------------------------------------------- colnames(my_target)[1] <- "ID" colnames(my_mw)[1] <- "ID" ## ----merge_tables, eval=TRUE--------------------------------------------- my_mw_target <- merge(my_mw, my_target, by.x="ID", by.y="ID", all.x=T) ## ----merge_tables_shorten, eval=TRUE------------------------------------- my_mw_target2a <- merge(my_mw, my_target[1:40,], by.x="ID", by.y="ID", all.x=T) # To remove non-matching rows, use the argument setting 'all=F'. my_mw_target2 <- na.omit(my_mw_target2a) # Removes rows containing "NAs" (non-matching rows). ## ----homework3D_solutions, eval=FALSE, echo=FALSE------------------------ ## my_mw_target_tmp <- merge(my_mw, my_target[1:40,], by.x="ID", by.y="ID", all=FALSE) ## all(my_mw_target2 == my_mw_target_tmp) ## my_mw_target2a <- as.matrix(my_mw_target2a) ## my_mw_target2a[is.na(my_mw_target2a)] <- 0 ## my_mw_target2a <- as.data.frame(my_mw_target2a) ## ----filter_tables1, eval=TRUE------------------------------------------- query <- my_mw_target[my_mw_target[, 2] > 100000 & my_mw_target[, 4] == "C", ] query[1:4, ] dim(query) ## ----string_sub, eval=TRUE----------------------------------------------- my_mw_target3 <- data.frame(loci=gsub("\\..*", "", as.character(my_mw_target[,1]), perl = TRUE), my_mw_target) my_mw_target3[1:3,1:8] ## ----calcul_1, eval=TRUE------------------------------------------------- mycounts <- table(my_mw_target3[,1])[my_mw_target3[,1]] my_mw_target4 <- cbind(my_mw_target3, Freq=mycounts[as.character(my_mw_target3[,1])]) ## ----calcul_2, eval=TRUE------------------------------------------------- data.frame(my_mw_target4, avg_AA_WT=(my_mw_target4[,3] / my_mw_target4[,4]))[1:2,5:11] ## ----calcul_3, eval=TRUE------------------------------------------------- mymean <- apply(my_mw_target4[,6:9], 1, mean) mystdev <- apply(my_mw_target4[,6:9], 1, sd, na.rm=TRUE) data.frame(my_mw_target4, mean=mymean, stdev=mystdev)[1:2,5:12] ## ----plot_example, eval=TRUE--------------------------------------------- plot(my_mw_target4[1:500,3:4], col="red") ## ----export_example, eval=TRUE------------------------------------------- write.table(my_mw_target4, file="my_file.xls", quote=F, sep="\t", col.names = NA) ## ----source_example, eval=FALSE------------------------------------------ ## source("exerciseRbasics.R") ## ----install_rmarkdown, eval=FALSE--------------------------------------- ## install.packages("rmarkdown") ## ----render_rmarkdown, eval=FALSE, message=FALSE------------------------- ## rmarkdown::render("sample.Rmd", clean=TRUE, output_format="html_document") ## ----render_commandline, eval=FALSE, message=FALSE, engine="sh"---------- ## $ echo "rmarkdown::render('sample.Rmd', clean=TRUE)" | R --slave ## $ Rscript -e "rmarkdown::render('sample.Rmd', clean=TRUE)" ## ----render_makefile, eval=FALSE, message=FALSE, engine="sh"------------- ## $ make -B ## ----kable--------------------------------------------------------------- library(knitr) kable(iris[1:12,]) ## ----some_jitter_plot, eval=TRUE----------------------------------------- library(ggplot2) dsmall <- diamonds[sample(nrow(diamonds), 1000), ] ggplot(dsmall, aes(color, price/carat)) + geom_jitter(alpha = I(1 / 2), aes(color=color)) ## ----some_custom_inserted_plot, eval=TRUE, warning=FALSE, message=FALSE---- png("myplot.png") ggplot(dsmall, aes(color, price/carat)) + geom_jitter(alpha = I(1 / 2), aes(color=color)) dev.off() ## ----rmarkdown_symbolic_link, eval=FALSE--------------------------------- ## cd ~/.html ## ln -s ~/bigdata/today/rmarkdown/sample.html sample.html ## ----fluidpage, eval=FALSE----------------------------------------------- ## ui <- fluidPage() ## ----server, eval=FALSE-------------------------------------------------- ## server <- function(input, output) {} ## ----shinyapp, eval=FALSE------------------------------------------------ ## shinyApp(ui = ui, server = server) ## ----runshinyapp1, eval=FALSE-------------------------------------------- ## library(shiny) ## runApp("myappdir") # To show code in app, add argument: display.mode="showcase" ## ----deployshinyapp1, eval=FALSE----------------------------------------- ## setwd("myappdir") ## library(rsconnect) ## deployApp() ## ----embedshiny, eval=FALSE---------------------------------------------- ## ## ----learnshiny, eval=FALSE---------------------------------------------- ## mydir <- system.file("examples", package="shiny") ## dir.create('my_shiny_test_dir') ## file.copy(mydir, "my_shiny_test_dir", recursive=TRUE) ## setwd("my_shiny_test_dir/examples") ## runApp("01_hello") # Runs first example app in directory ## dir() # Lists available Shiny examples (directories). ## ----sessionInfo--------------------------------------------------------- sessionInfo()