2015年生命科学数据挖掘革命：从基因组测序到统计分析 - CSDN文库

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Getting Started 10

filename <- file.path(dir,"extdata/femaleMiceWeights.csv")

dat <- read.csv(filename)

..

Exercises

Here we will test some of the basics of R data manipulation which you should know or should have

learned by following the tutorials above. You will need to have the file femaleMiceWeights.csv

in your working directory. As we showed above, one way to do this is by using the downloader

package:

library(downloader)

url <- "https://raw.githubusercontent.com/genomicsclass/dagdata/master/inst/extd\

ata/femaleMiceWeights.csv"

filename <- "femaleMiceWeights.csv"

download(url, destfile=filename)

1. Read in the file femaleMiceWeights.csv and report the body weight of the mouse in the

exact name of the column containing the weights.

2. The [ and ] symbols can be used to extract specific rows and specific columns of the table.

What is the entry in the 12th row and second column?

3. You should have learned how to use the $ character to extract a column from a table and

return it as a vector. Use $ to extract the weight column and report the weight of the mouse

in the 11th row.

4. The length function returns the number of elements in a vector. How many mice are

included in our dataset?

5. To create a vector with the numbers 3 to 7, we can use seq(3,7) or, because they are

consecutive, 3:7. View the data and determine what rows are associated with the high fat

or hf diet. Then use the mean function to compute the average weight of these mice.

6. One of the functions we will be using often is sample. Read the help file for sample using

?sample. Now take a random sample of size 1 from the numbers 13 to 24 and report back

the weight of the mouse represented by that row. Make sure to type set.seed(1) to ensure

that everybody gets the same answer.

Brief Introduction to dplyr

The R markdown document for this section is available here²².

The learning curve for R syntax is slow. One of the more difficult aspects that requires some getting

used to is subsetting data tables. The dplyr packages brings these tasks closer to English and we

²²https://github.com/genomicsclass/labs/tree/master/intro/dplyr_intro.Rmd

Getting Started 13

## [1] "numeric"

To do this in R without dplyr the code is the following:

chowVals <- dat[ dat$Diet=="chow", colnames(dat)=="Bodyweight"]

..

Exercises

For these exercises, we will use a new dataset related to mammalian sleep. This data is described

here. Download the CSV file from this location:

We are going to read in this data, then test your knowledge of they key dplyr functions select

and filter. We are also going to review two different classes: data frames and vectors.

1. Read in the msleep_ggplot2.csv file with the function read.csv and use the function class

to determine what type of object is returned.

2. Now use the filter function to select only the primates. How many animals in the table are

primates? Hint: the nrow function gives you the number of rows of a data frame or matrix.

3. What is the class of the object you obtain after subsetting the table to only include primates?

4. Now use the select function to extract the sleep (total) for the primates. What class is this

object? Hint: use %>% to pipe the results of the filter function to select.

5. Now we want to calculate the average amount of sleep for primates (the average of the

numbers computed above). One challenge is that the mean function requires a vector so, if

we simply apply it to the output above, we get an error. Look at the help file for unlist and

use it to compute the desired average.

6. For the last exercise, we could also use the dplyr summarize function. We have not introduced

this function, but you can read the help file and repeat exercise 5, this time using just filter

and summarize to get the answer.

http://docs.ggplot2.org/0.9.3.1/msleep.html

Mathematical Notation

The R markdown document for this section is available here²³.

This book focuses on teaching statistical concepts and data analysis programming skills. We avoid

mathematical notation as much as possible, but we do use it. We do not want readers to be

intimidated by the notation though. Mathematics is actually the easier part of learning statistics.

²³https://github.com/genomicsclass/labs/tree/master/intro/math_notation.Rmd

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