Last updated: 2019-09-04
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Knit directory: apaQTL/analysis/
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Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote
), click on the hyperlinks in the table below to view them.
File | Version | Author | Date | Message |
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html | 191d71f | brimittleman | 2019-08-07 | Build site. |
Rmd | 01d0be5 | brimittleman | 2019-08-07 | add reads_graph analysis |
I first install and download all necessary libraries and packages
Then, I am reading in my file, and then only keeping the columns that I need: mapped, mapped no mispriming, reads, and the Sample Line ID.
df <- read.delim("../data/MetaDataSequencing.txt")
keeps <- c("line","fraction", "reads", "mapped", "Mapped_noMP")
frac_dif <- df[keeps]
Next, I am dividing the data set into it’s two categories: nuclear and total
nuclear <- subset(frac_dif, fraction == "nuclear", c(fraction, line, reads, mapped, Mapped_noMP))
total <- subset(frac_dif, fraction == "total", c(fraction, line, reads, mapped, Mapped_noMP))
Here, I create my data matrices. Each are proportions of reads. There is mapped, mapped with mispriming, mapped without mispriming, and unmapped. I do this for both nuclear and total.
#nuclear proportions
nuc_mapped_prop <- data.matrix(nuclear$mapped/nuclear$reads)
nuc_mapped_noMP_prop <- data.matrix(nuclear$Mapped_noMP/nuclear$reads)
nuc_mapped_MP <- nuc_mapped_prop - nuc_mapped_noMP_prop
nuc_none <- 1 - nuc_mapped_prop
nuc_lines <- data.matrix(nuclear$line)
#total proportions
total_mapped_prop <- data.matrix(total$mapped/total$reads)
total_mapped_noMP_prop <- data.matrix(total$Mapped_noMP/total$reads)
total_mapped_MP <-total_mapped_prop - total_mapped_noMP_prop
total_none <- 1 - total_mapped_prop
total_lines <- data.matrix(total$line)
Then, I combine these proportions into a large data frame called “combination”, which I can then easily create my plots.The gather() function tidies my data so that it is in the format that ggplot uses to create the bar plots. I then make the type column a factor with 3 levels. These levels allow me to order the stacks in the way I want them to.
nuc_combination <- data.frame(nuc_lines, nuc_mapped_noMP_prop, nuc_mapped_MP, nuc_none)
nuc_combination <- gather(nuc_combination, nuc_mapped_noMP_prop, nuc_mapped_MP, nuc_none, key = "type", value = "count")
nuc_combination$type <- factor(nuc_combination$type, levels=c("nuc_none", "nuc_mapped_MP", "nuc_mapped_noMP_prop"))
total_combination <- data.frame(total_lines, total_mapped_noMP_prop, total_mapped_MP, total_none)
total_combination <- gather(total_combination, total_mapped_noMP_prop, total_mapped_MP, total_none, key = "type", value = "count")
total_combination$type <- factor(total_combination$type, levels=c("total_none", "total_mapped_MP", "total_mapped_noMP_prop"))
Finally, I create my graphs, one for nuclear and the other for total. geom_col allows me to make bar graphs based off of heights in the data, instead of frequency, which geom_bar does. I stacked the bar plots using three proportions - mapped with mispriming, mapped without mispriming, and unmapped.
Version | Author | Date |
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191d71f | brimittleman | 2019-08-07 |
Version | Author | Date |
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191d71f | brimittleman | 2019-08-07 |
Note that the echo = FALSE
parameter was added to the code chunk to prevent printing of the R code that generated the plot.
sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)
Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] ggplot2_3.1.1 tidyr_0.8.3 dplyr_0.8.0.1
loaded via a namespace (and not attached):
[1] Rcpp_1.0.0 knitr_1.20 whisker_0.3-2
[4] magrittr_1.5 workflowr_1.4.0 munsell_0.5.0
[7] tidyselect_0.2.5 colorspace_1.3-2 R6_2.3.0
[10] rlang_0.4.0 plyr_1.8.4 stringr_1.3.1
[13] highr_0.7 tools_3.5.1 grid_3.5.1
[16] gtable_0.2.0 withr_2.1.2 git2r_0.25.2
[19] htmltools_0.3.6 lazyeval_0.2.1 yaml_2.2.0
[22] rprojroot_1.3-2 digest_0.6.18 assertthat_0.2.0
[25] tibble_2.1.1 crayon_1.3.4 RColorBrewer_1.1-2
[28] purrr_0.3.2 fs_1.3.1 glue_1.3.0
[31] evaluate_0.12 rmarkdown_1.10 labeling_0.3
[34] stringi_1.2.4 compiler_3.5.1 pillar_1.3.1
[37] scales_1.0.0 backports_1.1.2 pkgconfig_2.0.2