Demo
Set up an R script
MDA 2020, GDC Nik Zemp, January 2020, Version 0.1
Set working directory (not needed in a R project)
#setwd("~/Desktop/")Remove all variables
rm(list = ls())set seed
set.seed(1000)load packages
library(tidyverse)## ── Attaching packages ──────────────────────────────────────────────────────── tidyverse 1.3.0 ──## ✓ ggplot2 3.2.1 ✓ purrr 0.3.3
## ✓ tibble 2.1.3 ✓ dplyr 0.8.3
## ✓ tidyr 1.0.0 ✓ stringr 1.4.0
## ✓ readr 1.3.1 ✓ forcats 0.4.0## ── Conflicts ─────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()Source functions
source("My_Functions.R")sessionInfo
sessionInfo()## R version 3.6.0 (2019-04-26)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Mojave 10.14.6
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] phyloseq_1.28.0 forcats_0.4.0 stringr_1.4.0 dplyr_0.8.3
## [5] purrr_0.3.3 readr_1.3.1 tidyr_1.0.0 tibble_2.1.3
## [9] ggplot2_3.2.1 tidyverse_1.3.0
##
## loaded via a namespace (and not attached):
## [1] Biobase_2.44.0 httr_1.4.1 splines_3.6.0
## [4] jsonlite_1.6 foreach_1.4.7 modelr_0.1.5
## [7] assertthat_0.2.1 stats4_3.6.0 cellranger_1.1.0
## [10] yaml_2.2.0 pillar_1.4.3 backports_1.1.5
## [13] lattice_0.20-38 glue_1.3.1 digest_0.6.23
## [16] XVector_0.24.0 rvest_0.3.5 colorspace_1.4-1
## [19] htmltools_0.4.0 Matrix_1.2-18 plyr_1.8.5
## [22] pkgconfig_2.0.3 broom_0.5.3 haven_2.2.0
## [25] zlibbioc_1.30.0 scales_1.1.0 mgcv_1.8-31
## [28] generics_0.0.2 IRanges_2.18.3 withr_2.1.2
## [31] BiocGenerics_0.30.0 lazyeval_0.2.2 cli_2.0.1
## [34] survival_3.1-8 magrittr_1.5 crayon_1.3.4
## [37] readxl_1.3.1 evaluate_0.14 fs_1.3.1
## [40] fansi_0.4.1 nlme_3.1-143 MASS_7.3-51.5
## [43] xml2_1.2.2 vegan_2.5-6 data.table_1.12.8
## [46] tools_3.6.0 hms_0.5.3 lifecycle_0.1.0
## [49] Rhdf5lib_1.6.3 S4Vectors_0.22.1 munsell_0.5.0
## [52] reprex_0.3.0 cluster_2.1.0 Biostrings_2.52.0
## [55] ade4_1.7-13 compiler_3.6.0 rlang_0.4.2
## [58] rhdf5_2.28.1 grid_3.6.0 iterators_1.0.12
## [61] biomformat_1.12.0 rstudioapi_0.10 igraph_1.2.4.2
## [64] rmarkdown_2.1 multtest_2.40.0 gtable_0.3.0
## [67] codetools_0.2-16 DBI_1.1.0 reshape2_1.4.3
## [70] R6_2.4.1 lubridate_1.7.4 knitr_1.27
## [73] zeallot_0.1.0 permute_0.9-5 ape_5.3
## [76] stringi_1.4.5 parallel_3.6.0 Rcpp_1.0.3
## [79] vctrs_0.2.1 dbplyr_1.4.2 tidyselect_0.2.5
## [82] xfun_0.12##use functions
plot_points(1000)
se(iris$Sepal.Length)## [1] 0.06761132
## [1] 0.06761132save specific objects to a file
save(iris, file = "iris.RData")load object
load("iris.RData")save workspace
save.image(file = "image.RData")load workspace
load("image.RData")Use Styler
# datawox=jittermap(datawox, amount = 1e-6);datauto=jittermap(datauto, amount = 1e-6);data= jittermap(data, amount = 1e-6);data2 <- data;datauto$pheno$gender <- as.numeric(datauto$pheno$gender)
# datauto$pheno$ffd[68] <- NA;datauto$pheno$ffd <- (datauto$pheno$ffd-min(datauto$pheno$ffd, na.rm = T))/(max(datauto$pheno$ffd,na.rm=T)-min(datauto$pheno$ffd, na.rm = T));datauto$pheno$ffd <- asin(sqrt(datauto$pheno$ffd));datauto$pheno$ffd_height <- log(datauto$pheno$ffd_height);datauto$pheno$open_flowers_ffd5 <- sqrt(datauto$pheno$open_flowersffd5);datauto$pheno$open_flowers_rate= log(datauto$pheno$open_flowers_rate)