2019年03月09日 星期六 09時(shí)22分18秒

Overview

Questions

• How do I read data into R?
• How do I assign varirables?
• What is a data frame?
• How do I access subsets of a data frame?
• How do I calculate simple statistic like mean and median?
• where can I get help?
• How can I plot my data?

Objectives

• Read tabular data from a file into a program.
• Assign values to variables.
• Select individual values and subsections from data.
• Perform operations on a data frame of data.
• Display simple graphs.

Have been Given the data, wo want to:

• Load data into memory,
• Calculate the average value of inflammation per day across all patients, and
• Plot the results.
  To do all that, we'll have to learn a little bit about programming.

Loading Data

change the working directory.

setwd("~/workspace/R/Lesson1")

read file from directory.

read.csv(file = "data/inflammation-01.csv", header = FALSE)

read.csvhas two arguments: the name of the file we want to read, and whether the first line of the file contains names for the columns of data. ... Assigning the second argument, header, to beFALSEindicates that the date files does not have column headers.

create variables...

dat <- read.csv(file = "data/inflammation-01.csv", header = FALSE)

Manipulating Data

Now that our data load into R, we can start doing thins with them.
first ask what type of thing in variable is:

class(dat)
[1] "data.frame"

The output tells us that it's a data frame.
see the shape, or dimensions, of the data frame:

dim(dat)
[1] 60 40

first value in dat, row 1, column 1

dat[1, 1]
[1] 0

if we want to select more than one row or column, we can use the function c(), which stands for combine.

dat[c(1, 3, 5), c(10, 20)]
V10 V20
1 3 18
3 9 10
5 4 17

Operator :, This special function generates sequences of numbers

1:5
[1] 1 2 3 4 5

All columns from row 5

dat[5, ]
V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13 V14 V15 V16 V17 V18 V19 V20
5 0 1 1 3 3 1 3 5 2 4 4 7 6 5 3 10 8 10 6 17

V21 V22 V23 V24 V25 V26 V27 V28 V29 V30 V31 V32 V33 V34 V35 V36 V37 V38
5 9 14 9 7 13 9 12 6 7 7 9 6 3 2 2 4 2 0
V39 V40
5 1 1

Columns can also be addressed by name, with either the V16) or square brackets (ie. dat[, 'V16']).

first row, all of the columns

patient_1 <- dat[1, ]

[1]max inflammation for patient 1

max(patient_1)

[2]max inflammation for patient 2

max(dat[2, ])

[3]minimum inflammation on day 7

min(dat[, 7])

[4]mean inflammation on day 7

mean(dat[, 7])

[5]median inflammation on day 7

median(dat[, 7])

[6]standard deviation of inflammation on day 7

sd(dat[, 7])

For every column in the data frame, the function “summary” calculates: the minimun value, the first quartile, the median, the mean, the third quartile and the max value, giving helpful details about the sample distribution.
[7]Summarize function

summary(dat[, 1:4])

apply allows us to repeat a function on all of the rows (MARGIN = 1) or columns (MARGIN = 2) of a data frame.

Thus, to obtain the average inflammation of each patient we will need to calculate the mean of all of the rows (MARGIN = 1) of the data frame.

avg_patient_inflammation <- apply(dat, 1, mean)

And to obtain the average inflammation of each day we will need to calculate the mean of all of the columns (MARGIN = 2) of the data frame.

avg_day_inflammation <- apply(dat, 2, mean)

Subsetting and Re-Assignment[^picture02]

Let’s pretend there was something wrong with the instrument on the first five days for every second patient (#2, 4, 6, etc.), which resulted in the measurements being twice as large as they should be.

whichPatients <- seq(2, 60, 2) # i.e., which rows
whichDays <- seq(1, 5) # i.e., which columns
dat2 <- dat

check the size of your subset: returns 30 5, that is 30 [rows=patients] by 5 [columns=days]

dim(dat2[whichPatients, whichDays])
dat2[whichPatients, whichDays] <- >dat2[whichPatients, whichDays] / 2
dat2

Using the Apply Function on Patient Data

Please use a combination of the apply function and indexing to:

1. calculate the mean inflammation for patients 1 to 5 over the whole 40 days
2. calculate the mean inflammation for days 1 to 10 (across all patients).
3. calculate the mean inflammation for every second day (across all patients).

1.

apply(dat[1:5, ], 1, mean)

2.

apply(dat[, 1:10], 2, mean)

3.

apply(dat[, seq(1, 40, by = 2)], 2, mean)

Key Points

• Use variable <- value to assign a value to a variable in order to record it in memory.
• Objects are created on demand whenever a value is assigned to them.
• The function dim gives the dimensions of a data frame.
• Use object[x, y] to select a single element from a data frame.
• Use from:to to specify a sequence that includes the indices from from to to.
• All the indexing and subsetting that works on data frames also works on vectors.
• Use # to add comments to programs.
• Use mean, max, min and sd to calculate simple statistics.
• Use apply to calculate statistics across the rows or columns of a data frame.
• Use plot to create simple visualizations.

picture01

picture02