mrc-ide · OJWatson · May 25, 2018 · May 29, 2018 · May 29, 2018 · May 29, 2018
diff --git a/R/misc.R b/R/misc.R
@@ -29,7 +29,7 @@ is.int_vector <- function(x) {
   x == as.integer(x)
 }
 
-#  load system file ---------------------------------------------------------------
+#  load system file ------------------------------------------------------------
 #' @title Load system file
 #'
 #' @description Load system file
@@ -40,10 +40,11 @@ is.int_vector <- function(x) {
 #'
 #' @export
 #' @examples
-#' # TODO
+#' dat <- mipmapper_file("dummy_data.csv")
 
 mipmapper_file <- function(name) {
-  name_full <- system.file("extdata/", name, package='mipmapper', mustWork = TRUE)
+  name_full <- system.file("extdata/", name, package='mipmapper', 
+                           mustWork = TRUE)
   ret <- fast_read(name_full)
 
   return(ret)

diff --git a/README.Rmd b/README.Rmd
@@ -1,6 +1,5 @@
 ---
 output: github_document
-always_allow_html: yes
 ---
 ```{r, echo = FALSE}
 knitr::opts_chunk$set(

diff --git a/README.md b/README.md
@@ -1,60 +1,56 @@
-
-mipmapper
-=========
-
-[![Travis build status](https://travis-ci.org/mrc-ide/mipmapper.svg?branch=master)](https://travis-ci.org/mrc-ide/mipmapper) [![AppVeyor build status](https://ci.appveyor.com/api/projects/status/github/mrc-ide/mipmapper?branch=master&svg=true)](https://ci.appveyor.com/project/mrc-ide/mipmapper) [![Coverage status](https://codecov.io/gh/mrc-ide/mipmapper/branch/master/graph/badge.svg)](https://codecov.io/github/mrc-ide/mipmapper?branch=master) [![Documentation](https://github.com/OJWatson/rdhs/raw/master/tools/pkgdownshield.png)](https://mrc-ide.github.io/mipmapper/)
+# mipmapper
+[![Travis build status](https://travis-ci.org/mrc-ide/mipmapper.svg?branch=master)](https://travis-ci.org/mrc-ide/mipmapper)
+[![AppVeyor build status](https://ci.appveyor.com/api/projects/status/github/mrc-ide/mipmapper?branch=master&svg=true)](https://ci.appveyor.com/project/mrc-ide/mipmapper)
+[![Coverage status](https://codecov.io/gh/mrc-ide/mipmapper/branch/master/graph/badge.svg)](https://codecov.io/github/mrc-ide/mipmapper?branch=master)
+[![Documentation](https://github.com/OJWatson/rdhs/raw/master/tools/pkgdownshield.png)](https://mrc-ide.github.io/mipmapper/)
 
 The R package *mipmapper* package contains a series of functions for analysing and visualising Molecular Inversion Probe (MIP) data. **This package is in early stages of development**, but will eventually include a range of methods for carrying out population genetic analyses. Full documentation can be found [here](https://mrc-ide.github.io/mipmapper/).
 
 ### Installation
 
 In R, ensure that you have the devtools package installed by running
 
-``` r
+```r
 install.packages("devtools", repos='http://cran.us.r-project.org')
 ```
+Then  we can simply install the *mipmapper* package directly from GitHub by running
 
-Then we can simply install the *mipmapper* package directly from GitHub by running
-
-``` r
+```r
 devtools::install_github("mrc-ide/mipmapper")
 ```
-
 And we can load the package by running
 
-``` r
+```r
 library(mipmapper)
 ```
 
 ### Data loading and filtering
 
 Load raw data from .csv file. You will need to change the file path to where you have stored the data. An example of this is shown below, but commented out.
 
-``` r
+```r
 # if loading your own data, uncomment this line and change path to your data
 # dat0 <- fast_read("path_to_your_data/NeutralSNPs_AheroYombo.csv")
 
 # here we will use in-built example data
 dat0 <- mipmapper_file("dummy_data.csv")
 ```
-
 Some miscellaneous filtering. Subset to SNPs only (i.e. no more complex mutations), group all alternative alleles together as a single "non-reference" allele, and drop irregular loci (for example non-integer barcode counts).
 
-``` r
+```r
 dat1 <- filter_misc(dat0, SNP_only = TRUE, group_Alt = TRUE, drop_irregular = TRUE)
 ```
-
 Next we want to filter based on coverage, throwing away any loci that are below a minimum coverage level. We can visualise how much data will be left at different thresholds using the following function:
 
-``` r
+```r
 plot_coverage(dat1)
 ```
 
-![](tools/README-plot_coverage-1.png)
+![plot of chunk plot_coverage](README-plot_coverage-1.png)
 
 Choose a threshold that strikes a balance between data quantity and quality. Once you have chosen a threshold, apply the filtering as follows:
 
-``` r
+```r
 my_threshold <- 6
 dat3 <- filter_coverage(dat1, min_coverage = my_threshold)
 ```
@@ -63,42 +59,36 @@ dat3 <- filter_coverage(dat1, min_coverage = my_threshold)
 
 Before carry out PCA analysis we will convert our filtered dataset into a wide format, where each row is an unique sample, with new columns for each locus. This can be achieved as follows:
 
-``` r
+```r
 dat4 <- melt_mip_data(dat3)
 ```
-
 This can then be used to impute any missing values:
 
-``` r
+```r
 dat5 <- impute_mip_data(dat4)
 ```
-
 The imputed data set can then be analysed using principal component analysis:
 
-``` r
+```r
 pca <- pca_mip_data(dat5)
 ```
-
 We can view the variance explained by each compenet graphically using:
 
-``` r
+```r
 plot_pca_variance(pca)
 ```
-
 ![](tools/pca_var.png)
 
 And lastly we can plot the actual prinical component analysis, to see how it has clustered our data:
 
-``` r
+```r
 plot_pca(pca, num_components = 2, meta_var = "Country")
 ```
-
 ![](tools/pca_2var.png)
 
 We can control whether we want to visualise the first 2 or 3 components, with the `num_componenets` argument:
 
-``` r
+```r
 plot_pca(pca, num_components = 3, meta_var = "Country")
 ```
-
 ![](tools/pca_3var.png)
diff --git a/TODO.md b/TODO.md
@@ -13,4 +13,5 @@ samples, countries etc. At the moment the data is nonsense, and has no structure
 or PCA utility, so might be nice to come up with imporvements to make faking
 large clustered genetic data.
 5. Shiny interface for loading a dataset, sliders for filtering, and then 
-displayed plots. 
+displayed plots. 
+6. Fst permutation test
diff --git a/docs/README-plot_coverage-1.png b/docs/README-plot_coverage-1.png
diff --git a/docs/TODO.html b/docs/TODO.html
diff --git a/docs/index.html b/docs/index.html
diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml
@@ -1,5 +1,5 @@
-pandoc: 1.17.2
-pkgdown: 1.0.0
-pkgdown_sha: ~
+pandoc: 1.19.2.1
+pkgdown: 1.0.0.9000
+pkgdown_sha: d7c658122bfbd143552cd28585f867dd344302ad
 articles: []
 
diff --git a/docs/reference/mipmapper_file.html b/docs/reference/mipmapper_file.html
diff --git a/docs/reference/plot_coverage-1.png b/docs/reference/plot_coverage-1.png
diff --git a/docs/tools/README-plot_coverage-1.png b/docs/tools/README-plot_coverage-1.png
diff --git a/man/mipmapper_file.Rd b/man/mipmapper_file.Rd
diff --git a/pkgdown_link_formats.R b/pkgdown_link_formats.R
@@ -1,8 +1,14 @@
+fix_pkgdown <- function(){
 
-# run this after pkgdown::build_site to correct image links
+# first knit the Rmd
+knitr::knit("README.Rmd")
+
+# then build the site
+pkgdown::build_site()
 
 # change lines in README.md to make link point to tools
 lines <- readLines("README.md")
+lines <- lines[-c(1:(which(grepl("# mipmapper",lines))-1))]
 lines[grep("(.*png)",lines)] <- gsub("!\\[\\]\\(R","!\\[\\](tools/R",lines[grep("(.*png)",lines)])
 writeLines(lines, "README.md")
 
@@ -23,8 +29,12 @@ l <- readLines("docs/index.html")
 fun <- grep(".png",l,value=TRUE, fixed=TRUE)
 files <- strsplit(fun,"/|\"") %>% lapply(function(x) grep("png",x,value=TRUE)) %>% unlist
 for(i in 1:length(fun)) {
-  files[i] <- gsub("\"(.*png)\"",paste0("\"","tools/",files[i],"\""),fun[i])
+
+ files[i] <- gsub("\"(.*png)\"",paste0("\"","tools/",files[i],"\""),fun[i])
+
 }
 l[grepl(".png",l, fixed=TRUE)] <- files
 writeLines(l,"docs/index.html")
 
+
+}
diff --git a/tests/testthat/Rplots.pdf b/tests/testthat/Rplots.pdf
diff --git a/tests/testthat/test-misc.R b/tests/testthat/test-misc.R
@@ -35,3 +35,9 @@ test_that("fast_read works", {
   expect_equal(dat, dat2)
   unlink("data.csv")
 })
+
+test_that("mipmapper_file() works", {
+  dat <- mipmapper_file("dummy_data.csv")
+  expect_equal(dim(dat), c(6336, 13))
+})
+
diff --git a/tools/README-plot_coverage-1.png b/tools/README-plot_coverage-1.png