R语言学习笔记-Day3

df1 <- data.frame(gene  = paste0("gene",1:4),
                 change = rep(c("up","down"),each = 2),
                 score  = c(5,3,-2,-4))

df1

##    gene change score
## 1 gene1     up     5
## 2 gene2     up     3
## 3 gene3   down    -2
## 4 gene4   down    -4

df1[2,]

##    gene change score
## 2 gene2     up     3

rownames(df1)

## [1] "1" "2" "3" "4"

class(df1[,2]);df1[,2]

## [1] "character"

## [1] "up"   "up"   "down" "down"

colnames(df1)

## [1] "gene"   "change" "score"

class(df1[2,])

## [1] "data.frame"

class(df1[,2])

## [1] "character"

df1[c(1,3),1:2]

##    gene change
## 1 gene1     up
## 3 gene3   down

df1[,"gene"]

## [1] "gene1" "gene2" "gene3" "gene4"

df1[,c("gene","change")]

##    gene change
## 1 gene1     up
## 2 gene2     up
## 3 gene3   down
## 4 gene4   down

test1 <- data.frame(name = c("jimmy","nicker","Damon","Sophie"),
                    blood_type = c("A","B","O","AB"))
test2 <- data.frame(name = c("Damon","jimmy","nicker","tony"),
                    group = rep(paste0("group",1:2),each = 2),
                    vision = c(4.2,4.3,4.9,4.5))
test3 <- data.frame(NAME = c("Damon","jimmy","nicker","tony"),
                    weight = c(140,145,110,138))

test1;test2;test3

##     name blood_type
## 1  jimmy          A
## 2 nicker          B
## 3  Damon          O
## 4 Sophie         AB

##     name  group vision
## 1  Damon group1    4.2
## 2  jimmy group1    4.3
## 3 nicker group2    4.9
## 4   tony group2    4.5

##     NAME weight
## 1  Damon    140
## 2  jimmy    145
## 3 nicker    110
## 4   tony    138

test4 <- merge(test1,test2,by="name");test4

##     name blood_type  group vision
## 1  Damon          O group1    4.2
## 2  jimmy          A group1    4.3
## 3 nicker          B group2    4.9

test5 <- merge(test1,test3,by.x="name",by.y="NAME");test5

##     name blood_type weight
## 1  Damon          O    140
## 2  jimmy          A    145
## 3 nicker          B    110

test6 <- merge(test1,test3,by.x="name",by.y="NAME",all=T);test6

##     name blood_type weight
## 1  Damon          O    140
## 2  jimmy          A    145
## 3 nicker          B    110
## 4 Sophie         AB     NA
## 5   tony       <NA>    138

#练习3-1
#table(test$Strand)
#练习2-3
#

m <- matrix(1:9,nrow = 3)
m

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

m[1:2,2:3]

##      [,1] [,2]
## [1,]    4    7
## [2,]    5    8

colnames(m) <- c("a","b","c");m

##      a b c
## [1,] 1 4 7
## [2,] 2 5 8
## [3,] 3 6 9

t(m)

##   [,1] [,2] [,3]
## a    1    2    3
## b    4    5    6
## c    7    8    9

a1 <- as.data.frame(m);a1

##   a b c
## 1 1 4 7
## 2 2 5 8
## 3 3 6 9

t(a1)

##   [,1] [,2] [,3]
## a    1    2    3
## b    4    5    6
## c    7    8    9

x <- list(m1 = matrix(1:9,nrow = 3),
          m2 = matrix(2:9,nrow = 2));x

## $m1
##      [,1] [,2] [,3]
## [1,]    1    4    7
## [2,]    2    5    8
## [3,]    3    6    9
## 
## $m2
##      [,1] [,2] [,3] [,4]
## [1,]    2    4    6    8
## [2,]    3    5    7    9

scores = c(100,59,73,95,45)
names(scores) = c("jimmy","nicker","Damon","Sophie","tony")
scores

##  jimmy nicker  Damon Sophie   tony 
##    100     59     73     95     45

names(scores)[scores>60]

## [1] "jimmy"  "Damon"  "Sophie"

#数据框按照逻辑值取子集
k = df1$score>0;k

## [1]  TRUE  TRUE FALSE FALSE

df1[k,]

##    gene change score
## 1 gene1     up     5
## 2 gene2     up     3

df1[,k]

##    gene change
## 1 gene1     up
## 2 gene2     up
## 3 gene3   down
## 4 gene4   down

df1$gene[df1$score>0]

## [1] "gene1" "gene2"