之前都是沒有系統(tǒng)地學習R語言额各，5月26日去西安聽生信技能樹 R語言培訓課，Jimmy大神布置的作業(yè)題吧恃，像我這樣純正小白的初學者虾啦，完全負基礎(chǔ)，學了又忘痕寓，忘了又學傲醉，繼續(xù)搬磚，一點點去理解呻率，鞏固基礎(chǔ)硬毕，整理詳細一點的版本，花了不少時間礼仗，方便像我這樣純正小白理解：

• “#run”之前的代碼是自己想吐咳、參考《R語言實戰(zhàn)》課本和參考生信技能給的作業(yè)參考代碼，“#run”之后的代碼是運行結(jié)果藐守。

• “#”后的內(nèi)容在RStudio里被忽略挪丢，為注視內(nèi)容

• 1 . 打開 Rstudio 告訴我它的工作目錄
  Rstudio安裝后，它默認工作路徑一般是Rstudio的安裝主目錄卢厂，如我使用Mac系統(tǒng)乾蓬，它默認的工作目錄是"/Users/chenjiangshu"。如果使用windows系統(tǒng)慎恒，它在工作目錄默認在安裝路徑下任内。

• 2. 新建6個向量，基于不同的原子類型融柬。（重點是字符串，數(shù)值粒氧，邏輯值）

a1 <- c("good morning")#字符串向量
a1
a2 <- c(1,5,8,16,21,25)#數(shù)值型向量
a2
a3 <- c("a","b","c","d","e","f")#字符型向量
a3
a4 <- c("a","b","c",1,2,3)#數(shù)值和字符混合越除，但calss默認是字符型
a4
a5 <- c(T,F,T,T,F,F)#邏輯值向量
a5
a6 <- c(1+0i)#復數(shù)向量
a6
#run：
> a1 <- c("good morning")#字符串向量
> a1
[1] "good morning"
> a2 <- c(1,5,8,16,21,25)#數(shù)值型向量
> a2
[1]  1  5  8 16 21 25
> a3 <- c("a","b","c","d","e","f")#字符型向量
> a3
[1] "a" "b" "c" "d" "e" "f"
> a4 <- c("a","b","c",1,2,3)#數(shù)值和字符混合，但calss默認是字符型
> a4
[1] "a" "b" "c" "1" "2" "3"
> a5 <- c(T,F,T,T,F,F)#邏輯值向量
> a5
[1]  TRUE FALSE  TRUE  TRUE FALSE FALSE
> a6 <- c(1+0i)#復數(shù)向量
> a6
[1] 1+0i

• 3. 告訴我在你打開的rstudio里面 getwd() 代碼運行后返回的是什么外盯？
     打開RStudio在里面輸入 getwd() 代碼運行后返回的是操作的當前的工作目錄摘盆，運行g(shù)etwd() 代碼相當于查看當前工作目錄。

     
     
     R 第3題.png
• 4. 新建一些數(shù)據(jù)結(jié)構(gòu)饱苟，比如矩陣孩擂，數(shù)組，數(shù)據(jù)框箱熬，列表等重點是數(shù)據(jù)框类垦，矩陣）

• 4.1 用matrix()函數(shù)創(chuàng)建一個5行4列的矩陣

y <- matrix(1:20,nrow=5,ncol=4)
y
#run
> y <- matrix(1:20,nrow=5,ncol=4)
> y
     [,1] [,2] [,3] [,4]
[1,]    1    6   11   16
[2,]    2    7   12   17
[3,]    3    8   13   18
[4,]    4    9   14   19
[5,]    5   10   15   20

• 4.2 用array()函數(shù)創(chuàng)建一個數(shù)組

dim1 <- c("A1","A2")
dim2 <- c("B1","B2","B3")
dim3 <- c("C1","C2","C3","C4")
z <- array(1:24,c(2,3,4),dimnames = list(dim1,dim2,dim3))
z
#run
> dim1 <- c("A1","A2")
> dim2 <- c("B1","B2","B3")
> dim3 <- c("C1","C2","C3","C4")
> z <- array(1:24,c(2,3,4),dimnames = list(dim1,dim2,dim3))
> z
, , C1

   B1 B2 B3
A1  1  3  5
A2  2  4  6

, , C2

   B1 B2 B3
A1  7  9 11
A2  8 10 12

, , C3

   B1 B2 B3
A1 13 15 17
A2 14 16 18

, , C4

   B1 B2 B3
A1 19 21 23
A2 20 22 24

數(shù)組是矩陣的一個自然推廣狈邑。

• 4.3 用data.frame()函數(shù)創(chuàng)建一個數(shù)據(jù)框

patientID <- c(1,2,3,4)
age <- c(25,34,28,52)
diabetes <- c("Type1","Type1","Type2","Type1")
status <- c("Poor","Improved","Excellent","Poor")
patientdata <- data.frame(patientID,age,diabetes,status)
patientdata
#run
> patientID <- c(1,2,3,4)
> age <- c(25,34,28,52)
> diabetes <- c("Type1","Type1","Type2","Type1")
> status <- c("Poor","Improved","Excellent","Poor")
> patientdata <- data.frame(patientID,age,diabetes,status)
> patientdata
  patientID age diabetes    status
1         1  25    Type1      Poor
2         2  34    Type1  Improved
3         3  28    Type2 Excellent
4         4  52    Type1      Poor

數(shù)據(jù)框不同的列可包含數(shù)值型，字符型的數(shù)據(jù)

• 4.4 用list()函數(shù)創(chuàng)建一個列表

g <- "My First List"
h <- c(25,26,18,39)
j <- matrix(1:10,nrow = 5)
k <- c("one","two","three")
mylist <- list(title=g,ages=h,j,k)
mylist
#run
> g <- "My First List"
> h <- c(25,26,18,39)
> j <- matrix(1:10,nrow = 5)
> k <- c("one","two","three")
> mylist <- list(title=g,ages=h,j,k)
> mylist
$title
[1] "My First List"

$ages
[1] 25 26 18 39

[[3]]
     [,1] [,2]
[1,]    1    6
[2,]    2    7
[3,]    3    8
[4,]    4    9
[5,]    5   10

[[4]]
[1] "one"   "two"   "three"

列表可包含幾個向量蚤认，矩陣米苹，數(shù)據(jù)框，甚至組合的列表烙懦。

• 5. 在你新建的數(shù)據(jù)框進行切片操作驱入，比如首先取第1，3行氯析， 然后取第4亏较，6列
     首先構(gòu)建的數(shù)據(jù)框至少有6列，3行掩缓，創(chuàng)建一個4行雪情，6列的數(shù)據(jù)框：

patientID <- c(1,2,3,4)
age <- c(25,34,28,52)
diabetes <- c("Type1","Type1","Type2","Type1")
status <- c("Poor","Improved","Excellent","Poor")
gender <- c("male","female","female","male")
incomes <- c("8k","12k","4.5k","7k")
patientdata <- data.frame(patientID,age,diabetes,status,gender,income)
patientdata
patientdata[c(1,3),]
patientdata[,c(4,6)]
patientdata[c(1,3),c(4,6)]
#run
> patientID <- c(1,2,3,4)
> age <- c(25,34,28,52)
> diabetes <- c("Type1","Type1","Type2","Type1")
> status <- c("Poor","Improved","Excellent","Poor")
> gender <- c("male","female","female","male")
> income <- c("8k","12k","4.5k","7k")
> patientdata <- data.frame(patientID,age,diabetes,status,gender,income)
> patientdata
  patientID age diabetes    status gender income
1         1  25    Type1      Poor   male     8k
2         2  34    Type1  Improved female    12k
3         3  28    Type2 Excellent female   4.5k
4         4  52    Type1      Poor   male     7k
> patientdata[c(1,3),]
  patientID age diabetes    status gender income
1         1  25    Type1      Poor   male     8k
3         3  28    Type2 Excellent female   4.5k
> patientdata[,c(4,6)]
     status income
1      Poor     8k
2  Improved    12k
3 Excellent   4.5k
4      Poor     7k
> patientdata[c(1,3),c(4,6)]
     status income
1      Poor     8k
3 Excellent   4.5k

• 6. 使用data函數(shù)來加載R內(nèi)置數(shù)據(jù)集 rivers 描述它。并且可以查看更多的R語言內(nèi)置的數(shù)據(jù)集：https://mp.weixin.qq.com/s/dZPbCXccTzuj0KkOL7R31g

data()
rivers#北美主要河流及長度你辣，
head(rivers)
tail(rivers)
length(rivers)#rivers有多少對象元素
str(rivers)#查看河流的結(jié)構(gòu)
summary(rivers)#獲取描述性統(tǒng)計量（最小值/最大值/四分位數(shù)/數(shù)值型變量/因子向量/邏輯值向量
#run
> head(rivers)
[1] 735 320 325 392 524 450
> tail(rivers)
[1]  500  720  270  430  671 1770
> length(rivers)#rivers有多少對象元素
[1] 141
> str(rivers)#查看河流的結(jié)構(gòu)
 num [1:141] 735 320 325 392 524 ...
> summary(rivers)#獲取描述性統(tǒng)計量（最小值/最大值/四分位數(shù)/數(shù)值型變量/因子向量/邏輯值向量）
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  135.0   310.0   425.0   591.2   680.0  3710.0 

"head"和"tail"一般默認讀前6行和后6行巡通。

• 7. 下載 https://www.ncbi.nlm.nih.gov/sra?term=SRP133642 里面的 RunInfo Table 文件讀入到R里面，了解這個數(shù)據(jù)框舍哄，多少列宴凉，每一列都是什么屬性的元素。（參考B站生信小技巧獲取runinfo table） 這是一個單細胞轉(zhuǎn)錄組項目的數(shù)據(jù)表悬，共768個細胞弥锄，如果你找不到RunInfo Table 文件，可以點擊下載蟆沫，然后讀入你的R里面也可以籽暇。

SraRunTable <- read.table("http://www.bio-info-trainee.com/tmp/5years/SraRunTable.txt",fill=TRUE,header = T,sep = "\t")
dim(SraRunTable)
class(colnames(SraRunTable))
#run
> SraRunTable <- read.table("http://www.bio-info-trainee.com/tmp/5years/SraRunTable.txt",fill=TRUE,header = T,sep = "\t")
> dim(SraRunTable)
[1] 768  31
> class(colnames(SraRunTable))
[1] "character"

768行，31列饭庞，元素為字符型戒悠。

• 8. 下載 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE111229 里面的樣本信息sample.csv讀入到R里面，了解這個數(shù)據(jù)框舟山，多少列绸狐，每一列都是什么屬性的元素。（參考 https://mp.weixin.qq.com/s/fbHMNXOdwiQX5BAlci8brA 獲取樣本信息sample.csv）如果你實在是找不到樣本信息文件sample.csv累盗，也可以點擊下載六孵。
     點擊鏈接：https://links.jianshu.com/go?to=http%3A%2F%2Fwww.bio-info-trainee.com%2Ftmp%2F5years%2Fsample.csv

sample <-read.csv("sample.csv")
colnames(sample)
#run
> sample <-read.csv("sample.csv")
> colnames(sample)
 [1] "Accession"           "Title"               "Sample.Type"         "Taxonomy"           
 [5] "Channels"            "Platform"            "Series"              "Supplementary.Types"
 [9] "Supplementary.Links" "SRA.Accession"       "Contact"             "Release.Date" 

• 9. 把前面兩個步驟的兩個表（RunInfo Table 文件，樣本信息sample.csv）關(guān)聯(lián)起來幅骄，使用merge函數(shù)。

SraRunTable <- read.table("http://www.bio-info-trainee.com/tmp/5years/SraRunTable.txt",fill=TRUE,header = T,sep = "\t")
sample <-read.csv("sample.csv")
m=merge(SraRunTable,sample,by.x = 'Sample_Name',by.y = 'Accession')
str(m)
#run
> str(m)
'data.frame':   768 obs. of  42 variables

合并后有768個對象本今，42個變量

課程分享

生信技能樹全球公益巡講

（https://mp.weixin.qq.com/s/E9ykuIbc-2Ja9HOY0bn_6g）

B站公益74小時生信工程師教學視頻合輯

（https://mp.weixin.qq.com/s/IyFK7l_WBAiUgqQi8O7Hxw）

招學徒：

（https://mp.weixin.qq.com/s/KgbilzXnFjbKKunuw7NVfw）