A whirlwind introduction to using R for your research

Transcription

1 A whirlwind introduction to using R for your research Jeremy Chacón 1

2 Outline 1. Why use R? 2. The R-Studio work environment 3. The mock experimental analysis: 1. Writing and running code 2. Getting data from Excel into R 3. Looking at your data 4. Exploratory plotting 5. Doing ANOVAs (here, 2-way ANOVA) 6. Making a publication-quality figure 7. Saving the figure as a pdf 2

3 Why bother with R? Reproducibility One stop shop Very high ceiling (makes the learning curve worth it) 3

4 A tour of R using R-Studio > Need to install R AND R-Studio to use R-Studio 4

5 R data analysis workflow using an example experiment 5

6 An example experiment Classic cell cycle regulation experiments: Wild type vs. mutant genotypes in the presence / absence of a cell-cycle stalling drug M (Mitosis) nocodazole Question: What mutant genotypes undo the arrest caused by nocodazole? Gap 2 Gap 1 Factor 1 = genotype Factor 2 = drug treatment Synthesis 1. wild type 2. mad2-delete 1. none 2. nocodazole Experiment: grow cells, measure change in growth rate by OD600, analyze with 2-way ANOVA 6

7 Saving the data in excel for use in R Use either tab-delimited text (.txt) or comma-separated values (.csv) Here, using tab-delimited text Hint: this can fail if there are tabs in any of your cells Note: you can only save a single page at a time, and saving starts at the top-left Be aware: if you save as.csv but your cells have commas in them, you will run into problems. Same with if you save as tab-delimited, but your cells have tabs in them. 7

8 How to write and run code in R (1) Write code in a script that you have open in the editor window 8

9 How to write and run code in R (1) Write code in a script that you have open in the editor window run code by highlighting it, then pressing Control + Enter The results (if any) will show up in the console and workspace 9

10 Getting Data into R (2) Opening the excel data in R Steps: Tell R the directory the data is in (setwd()) Load the data (read.table()) Commands Learned: getwd() # see working directory setwd() # set working directory list.files() # see the files in the working directory read.table() # load data 10

11 Looking at data (3) Did we load it correctly? Are the variables the correct type (i.e. number, character, factor)? 11

12 Looking at data (3) Did we load it correctly? Are the variables the correct type (i.e. number, character, factor)? Commands Learned: View() # view a spreadsheet representation of the data head() # see the top of the data str() # structure of the data summary() # summary of each variable in the data set 12

13 Making some data exploration plots (4) Factor 1 = genotype 1. wild type 2. mad2-delete Factor 2 = drug treatment 1. none 2. nocodazole How is growth rate affected by adding the drug? By genotype? By both? Commands Learned: with() # lets you use column names instead of typing the data frame name each time plot() # plots data a variety of ways 13

14 Making some data exploration plots (4): getting data from a column This table is called yeast_eg: Accessing specific columns in a data table: Use a dollar sign $ to access a specific column, e.g. yeast_eg$genotype The with() command: you first tell with() the name of your data table, then the inner command will look in that data table for the column name(s) you use: with(yeast_eg,summary(genotype)) The inner command summary looks within yeast_eg for the column title genotype, because we used with() 14

15 Do the stats (5) Two-way ANOVA response: OD_change factor 1: genotype factor 2: drug treatment We are mostly interested in the interaction (which tells us if the effect of the drug differs by genotype) Commands Learned: aov() # perform an anova summary() # works on aov objects, gives you the anova table plot() # works on aov objects, lets you visually examine model assumptions 15

16 Do the stats(5) model formation for aov() and lm(): separates response from predictors adds more predictors response ~ predictor1 + predictor2 response ~ predictor1 * predictor2 specifies all possible interactions and main effects 16

17 Make a nice plot (6) While basic plotting is great for data exploration, one of the advanced plotting packages is better for making publicationquality plots. Commands Learned: install.packages() # install an external package library() # load an external package ggplot() # make a ggplot figure windows() # open a new window (this is quartz() on a mac) CairoPDF() # start making a pdf dev.off() # stop making the pdf 17

18 Conclusions We did a complete data analysis workflow in less than 60 lines This code is totally re-usable Some essentials to remember: Write code into a script, run it by highlighting and using control + enter Get help on functions with? If you don t know how to do something, google! Much more in-depth help on most of the topics covered today in my articles at: 18