1、打开 Rstudio 告诉我它的工作目录。
> getwd()
[1] "D:/360MoveData/Users/xiaoxiaoyi/Desktop/111"
2、新建6个向量，基于不同的数据类型。（重点是字符串，数值，逻辑值）
数据类型是指：数值、字符、逻辑、因子
数据结构是指：向量，矩阵，数组，数据框，列表
-
数据类型
> a <- c(1,2,3,4,5)#数值型
> a
[1] 1 2 3 4 5
> class(a)
[1] "numeric"
> b <- c("q","w","e","r");b#字符串
[1] "q" "w" "e" "r"
> class(b)
[1] "character"
> c <- a>1#逻辑值
> c
[1] FALSE TRUE TRUE TRUE TRUE
> class(c)
[1] "logical"
> d <- c("shuiguo","shucai")#因子
> e <- factor(d)
> e
[1] shuiguo shucai
Levels: shucai shuiguo
> class(e)
[1] "factor"
-
数据结构
> x3 <- c(4,5,6,7)
> dim(x3) <- c(2,2)
> x3
[,1] [,2]
[1,] 4 6
[2,] 5 7
> class(x3)
[1] "matrix"
> array1 <- array(1:24,c(2,3,4))
> class(array1)
[1] "array"
> x4 <- data.frame(x3)
> x4
X1 X2
1 4 6
2 5 7
> class(x4)
[1] "data.frame"
> e <- c("a","b")
> x4$e <- e
> x4
X1 X2 e
1 4 6 a
2 5 7 b
> f <- c("dsd","dzsd","sddff","dfagrasrg")
> x5$f <- f
> x5
[[1]]
X1 X2 e
1 4 6 a
2 5 7 b
$f
[1] "dsd" "dzsd" "sddff" "dfagrasrg"
> class(x5)
[1] "list"
3、在你新建的数据框进行切片操作，比如首先取第1，3行， 然后取第4，6列
> a1 <- c(1,2,3,4,5,6,7,8,9)
>
> dim(a1) <- c(3,3)
> a2 <- as.data.frame(a1)
> a2
V1 V2 V3
1 1 4 7
2 2 5 8
3 3 6 9
> class(a2)
[1] "data.frame"
> a2[1,]
V1 V2 V3
1 1 4 7
> a2[3,]
V1 V2 V3
3 3 6 9
4、使用data函数来加载R内置数据集 rivers 描述它
> data(rivers)
> rivers
[1] 735 320 325 392 524 450 1459 135 465 600 330 336
[13] 280 315 870 906 202 329 290 1000 600 505 1450 840
[25] 1243 890 350 407 286 280 525 720 390 250 327 230
[37] 265 850 210 630 260 230 360 730 600 306 390 420
[49] 291 710 340 217 281 352 259 250 470 680 570 350
[61] 300 560 900 625 332 2348 1171 3710 2315 2533 780 280
[73] 410 460 260 255 431 350 760 618 338 981 1306 500
[85] 696 605 250 411 1054 735 233 435 490 310 460 383
[97] 375 1270 545 445 1885 380 300 380 377 425 276 210
[109] 800 420 350 360 538 1100 1205 314 237 610 360 540
[121] 1038 424 310 300 444 301 268 620 215 652 900 525
[133] 246 360 529 500 720 270 430 671 1770
> unique(rivers)#去重
[1] 735 320 325 392 524 450 1459 135 465 600 330 336
[13] 280 315 870 906 202 329 290 1000 505 1450 840 1243
[25] 890 350 407 286 525 720 390 250 327 230 265 850
[37] 210 630 260 360 730 306 420 291 710 340 217 281
[49] 352 259 470 680 570 300 560 900 625 332 2348 1171
[61] 3710 2315 2533 780 410 460 255 431 760 618 338 981
[73] 1306 500 696 605 411 1054 233 435 490 310 383 375
[85] 1270 545 445 1885 380 377 425 276 800 538 1100 1205
[97] 314 237 610 540 1038 424 444 301 268 620 215 652
[109] 246 529 270 430 671 1770
> length(rivers)#长度
[1] 141
> length(unique(rivers))
[1] 114
> table(rivers)#统计
rivers
135 202 210 215 217 230 233 237 246 250 255 259 260
1 1 2 1 1 2 1 1 1 3 1 1 2
265 268 270 276 280 281 286 290 291 300 301 306 310
1 1 1 1 3 1 1 1 1 3 1 1 2
314 315 320 325 327 329 330 332 336 338 340 350 352
1 1 1 1 1 1 1 1 1 1 1 4 1
360 375 377 380 383 390 392 407 410 411 420 424 425
4 1 1 2 1 2 1 1 1 1 2 1 1
430 431 435 444 445 450 460 465 470 490 500 505 524
1 1 1 1 1 1 2 1 1 1 2 1 1
525 529 538 540 545 560 570 600 605 610 618 620 625
2 1 1 1 1 1 1 3 1 1 1 1 1
630 652 671 680 696 710 720 730 735 760 780 800 840
1 1 1 1 1 1 2 1 2 1 1 1 1
850 870 890 900 906 981 1000 1038 1054 1100 1171 1205 1243
1 1 1 2 1 1 1 1 1 1 1 1 1
1270 1306 1450 1459 1770 1885 2315 2348 2533 3710
1 1 1 1 1 1 1 1 1 1
> sort(rivers)#排序
[1] 135 202 210 210 215 217 230 230 233 237 246 250
[13] 250 250 255 259 260 260 265 268 270 276 280 280
[25] 280 281 286 290 291 300 300 300 301 306 310 310
[37] 314 315 320 325 327 329 330 332 336 338 340 350
[49] 350 350 350 352 360 360 360 360 375 377 380 380
[61] 383 390 390 392 407 410 411 420 420 424 425 430
[73] 431 435 444 445 450 460 460 465 470 490 500 500
[85] 505 524 525 525 529 538 540 545 560 570 600 600
[97] 600 605 610 618 620 625 630 652 671 680 696 710
[109] 720 720 730 735 735 760 780 800 840 850 870 890
[121] 900 900 906 981 1000 1038 1054 1100 1171 1205 1243 1270
[133] 1306 1450 1459 1770 1885 2315 2348 2533 3710
> median(rivers)#中位数
[1] 425
> range(rivers)#显示最大值及最小值
[1] 135 3710
> which.min(rivers)#最小值下标
[1] 8
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5、下载 https://www.ncbi.nlm.nih.gov/sra?term=SRP133642 里面的
RunInfo Table文件读入到R里面，了解这个数据框，多少列，每一列都是什么属性的元素。
1.下载步骤：打开链接下载--Send results to Run selector--RunInfo Table
图示：
第一步：
1、GEO.png
第二步：新版
2、下载.png
因为下载改版，操作如下
image.png
image.png
第三步：
3、下载目录.png
第四步：
4、具体内容.png
2.读取文件
> sra <- read.table (SraRUNTable.txt)#注意设置工作目录
Error in read.table(SraRUNTable.txt) : object 'SraRUNTable.txt' not found
> sra <- read.table (file="SraRunTable.txt")#
Error in scan(file = file, what = what, sep = sep, quote = quote, dec = dec, :
line 1 did not have 44 elements##指定文件分隔符为 \t
> df1 <- read.table(file = "SraRunTable.txt",header = T,sep = '\t')#注意读取列名
> View(df1)
> dim(df1)#查看行数列数
[1] 768 31
> nrow(df1)#查看行数
[1] 768
> ncol(df1)#查看列数 colnames(df1)#查看列名
[1] 31
> colnames(df1)#查看列名
[1] "BioSample" "Experiment" "MBases"
[4] "MBytes" "Run" "SRA_Sample"
[7] "Sample_Name" "Assay_Type" "AssemblyName"
[10] "AvgSpotLen" "BioProject" "Center_Name"
[13] "Consent" "DATASTORE_filetype" "DATASTORE_provider"
[16] "InsertSize" "Instrument" "LibraryLayout"
[19] "LibrarySelection" "LibrarySource" "LoadDate"
[22] "Organism" "Platform" "ReleaseDate"
[25] "SRA_Study" "age" "cell_type"
[28] "marker_genes" "source_name" "strain"
[31] "tissue
>install.packages("magrittr")
>library(magrittr) #加载包，以便使用%>%
> for (i in colnames(df1)) paste(i,class(df1[,i])) %>% print()
[1] "BioSample character"
[1] "Experiment character"
[1] "MBases integer"
[1] "MBytes integer"
[1] "Run character"
[1] "SRA_Sample character"
[1] "Sample_Name character"
[1] "Assay_Type character"
[1] "AssemblyName character"
[1] "AvgSpotLen integer"
[1] "BioProject character"
[1] "Center_Name character"
[1] "Consent character"
[1] "DATASTORE_filetype character"
[1] "DATASTORE_provider character"
[1] "InsertSize integer"
[1] "Instrument character"
[1] "LibraryLayout character"
[1] "LibrarySelection character"
[1] "LibrarySource character"
[1] "LoadDate character"
[1] "Organism character"
[1] "Platform character"
[1] "ReleaseDate character"
[1] "SRA_Study character"
[1] "age character"
[1] "cell_type character"
[1] "marker_genes character"
[1] "source_name character"
[1] "strain character"
[1] "tissue character"
-
6、下载 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE111229 里面的
样本信息sample.csv读入到R里面，了解这个数据框，多少列，每一列都是什么属性的元素
1.下载GEO样本信息 点此获取下载步骤
GEO官网：https://www.ncbi.nlm.nih.gov/geo/ ---- 点击samples----- search 输入 GSE111229 ---- export
2.读取到R中
> df2<- read.table("sample.csv",header = T)
> View(df2)
> dim(df2)
[1] 20 6
> library(magrittr)
> for (i in colnames(df2)) paste(i,class(df2[,i])) %>% print()
[1] "Accession.Title.Sample character"
[1] "Type.Taxonomy.Channels.Platform.Series.Supplementary character"
[1] "Types.Supplementary character"
[1] "Links.SRA character"
[1] "Accession.Contact.Release character"
[1] "Date character"
读出之后发现与其他同学的不同，错误原因：此文件为csv文件，以，为分隔符，若想用read.table ,需要：
> df2<- read.table("sample.csv",sep = ",")
> df3<- read.csv("sample.csv")
> dim(df2)
[1] 20 12
发现行数还是错误，查看下载来的初始文件本是20行，因为我下载时只下载了当前页，重新下载，选择 All search results，
> df2=read.csv(file="sample.csv")
> View(df2)
> dim(df2)
[1] 768 12
> library(magrittr)
Warning message:
程辑包‘magrittr’是用R版本3.5.3 来建造的
> for (i in colnames(df2)) paste(i,class(df2[,i])) %>% print()
[1] "Accession factor"
[1] "Title factor"
[1] "Sample.Type factor"
[1] "Taxonomy factor"
[1] "Channels integer"
[1] "Platform factor"
[1] "Series factor"
[1] "Supplementary.Types factor"
[1] "Supplementary.Links factor"
[1] "SRA.Accession factor"
[1] "Contact factor"
把前面两个步骤的两个表（RunInfo Table 文件，样本信息sample.csv）关联起来，使用merge函数。
总体思路：找出相同内容合并
rm(list = ls())
options(stringsAsFactors = F)
df1 <- read.table(file = "SraRunTable.txt",header = T,sep = '\t')
df2 <- read.csv(file = "sample.csv")
for (i in colnames(df1)) {if (i %in% colnames(df2)) print(i)}#查看相同列名
df1[1,"Platform"]
df2[1,"Platform"]#查看两个数据框相同列名的行名
后面大神的内容就看不懂了，参考https://www.jianshu.com/p/c07e67e2c757
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