Wrapping Up

Last updated on 2025-05-06 | Edit this page

Estimated time: 20 minutes

Overview

Questions

How can I save plots to a file?

Objectives

Save plots to a pdf file in R
Explain why using the sessionInfo() function is good practice

Saving plots

We can save plots interactively by clicking Export in the Plots window and saving as e.g. “myplot.pdf”. Or we can output plots to pdf using pdf() followed by dev.off(). We put our plot code after the call to pdf() and before closing the plot device with dev.off().

Let’s save our last plot.

R

pdf("myplot.pdf")
ggplot(data = mygenes_counts, 
       mapping = aes(x = Group_f, y = log2(Count + 1), colour = Group_f)) +
  geom_jitter() +
  facet_wrap(~ gene_symbol) +
  labs(x = "Cell type and stage", y = "Count", title = "Mammary gland RNA-seq data") +
  theme(axis.text.x = element_text(angle = 90)) +
  theme(panel.background = element_blank(),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank())
dev.off()

Session Info

At the end of your report, we recommend you run the sessionInfo() function which prints out details about your working environment such as the version of R yo are running, loaded packages, and package versions. Printing out sessionInfo() at the end of your analysis is good practice as it helps with reproducibility in the future.

R

sessionInfo()

OUTPUT

R version 4.5.0 (2025-04-11)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 22.04.5 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0  LAPACK version 3.10.0

locale:
 [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8
 [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8
 [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C
[10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C

time zone: UTC
tzcode source: system (glibc)

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base

other attached packages:
 [1] lubridate_1.9.4 forcats_1.0.0   stringr_1.5.1   dplyr_1.1.4
 [5] purrr_1.0.4     readr_2.1.5     tidyr_1.3.1     tibble_3.2.1
 [9] ggplot2_3.5.2   tidyverse_2.0.0

loaded via a namespace (and not attached):
 [1] vctrs_0.6.5        cli_3.6.5          knitr_1.50         rlang_1.1.6
 [5] xfun_0.52          stringi_1.8.7      renv_1.1.4         generics_0.1.3
 [9] glue_1.8.0         hms_1.1.3          scales_1.4.0       grid_4.5.0
[13] evaluate_1.0.3     tzdb_0.5.0         yaml_2.3.10        lifecycle_1.0.4
[17] compiler_4.5.0     RColorBrewer_1.1-3 timechange_0.3.0   pkgconfig_2.0.3
[21] farver_2.1.2       R6_2.6.1           tidyselect_1.2.1   pillar_1.10.2
[25] magrittr_2.0.3     tools_4.5.0        withr_3.0.2        gtable_0.3.6

Exercises

Exercise

Download the raw counts for this dataset from GREIN.
1. Make a boxplot. Do the samples look any different to the normalised counts?
2. Make subplots for the same set of 8 genes. Do they look any different to the normalised counts?
Download the normalised counts for the GSE63310 dataset from GREIN. Make boxplots colouring the samples using different columns in the metadata file.