极客教程R语言 dplyr包
R编程语言中的dplyr包是 一个数据操作的结构，它提供了一套统一的动词，帮助解决最常见的数据操作障碍。
R语言中的dplyr包以更快、更简单的方式执行下面给出的步骤。
- 通过限制选择，现在可以把重点放在数据操作的困难上。
- 有一些简单的 “动词 “和函数来处理每一个常见的数据操作，并且想法可以更快地转化为代码。
- 有价值的后台，因此计算机的等待时间减少。
重要的动词功能
dplyr包提供了各种可用于数据处理的重要函数。这些是。
- filter()函数： 用于选择案例并以其值为基础进行操作。
# Create a data frame with missing data
d < - data.frame(name=c("Abhi", "Bhavesh",
"Chaman", "Dimri"),
age=c(7, 5, 9, 16),
ht=c(46, NA, NA, 69),
school=c("yes", "yes", "no", "no"))
d
# Finding rows with NA value
d % > % filter(is.na(ht))
# Finding rows with no NA value
d % > % filter(! is.na(ht))
输出
# A tibble: 4 x 4
name age ht school
1 Abhi 7 46 yes
2 Bhavesh 5 NA yes
3 Chaman 9 NA no
4 Dimri 16 69 no
# A tibble: 2 x 4
name age ht school
1 Bhavesh 5 NA yes
2 Chaman 9 NA no
# A tibble: 2 x 4
name age ht school
1 Abhi 7 46 yes
2 Dimri 16 69 no
- arrange(): 用于重新安排案例的顺序。
# Create a data frame with missing data
d <- data.frame( name = c("Abhi", "Bhavesh", "Chaman", "Dimri"),
age = c(7, 5, 9, 16),
ht = c(46, NA, NA, 69),
school = c("yes", "yes", "no", "no") )
# Arranging name according to the age
d.name<- arrange(d, age)
print(d.name)
输出
# A tibble: 4 x 4
name age ht school
1 Bhavesh 5 NA yes
2 Abhi 7 46 yes
3 Chaman 9 NA no
4 Dimri 16 69 no
- select()和rename()： 用于选择变量并以其名称为基础进行操作。
# Create a data frame with missing data
d < - data.frame(name=c("Abhi", "Bhavesh",
"Chaman", "Dimri"),
age=c(7, 5, 9, 16),
ht=c(46, NA, NA, 69),
school=c("yes", "yes", "no", "no"))
# startswith() function to print only ht data
select(d, starts_with("ht"))
# -startswith() function to print
# everything except ht data
select(d, -starts_with("ht"))
# Printing column 1 to 2
select(d, 1: 2)
# Printing data of column
# heading containing 'a'
select(d, contains("a"))
# Printing data of column
# heading which matches 'na'
select(d, matches("na"))
输出
# A tibble: 4 x 1
ht
1 46
2 NA
3 NA
4 69
# A tibble: 4 x 3
name age school
1 Abhi 7 yes
2 Bhavesh 5 yes
3 Chaman 9 no
4 Dimri 16 no
# A tibble: 4 x 2
name age
1 Abhi 7
2 Bhavesh 5
3 Chaman 9
4 Dimri 16
# A tibble: 4 x 2
name age
1 Abhi 7
2 Bhavesh 5
3 Chaman 9
4 Dimri 16
# A tibble: 4 x 1
name
1 Abhi
2 Bhavesh
3 Chaman
4 Dimri
- mutate()和transmute()： 增加新的变量，这些变量是现有变量的功能。
# Create a data frame with missing data
d <- data.frame( name = c("Abhi", "Bhavesh",
"Chaman", "Dimri"),
age = c(7, 5, 9, 16),
ht = c(46, NA, NA, 69),
school = c("yes", "yes", "no", "no") )
# Calculating a variable x3 which is sum of height
# and age printing with ht and age
mutate(d, x3 = ht + age)
# Calculating a variable x3 which is sum of height
# and age printing without ht and age
transmute(d, x3 = ht + age)
输出
# A tibble: 4 x 5
name age ht school x3
1 Abhi 7 46 yes 53
2 Bhavesh 5 NA yes NA
3 Chaman 9 NA no NA
4 Dimri 16 69 no 85
# A tibble: 4 x 1
x3
1 53
2 NA
3 NA
4 85
>
- summarise(): 将各种数值浓缩为一个数值。
# Create a data frame with missing data
d <- data.frame( name = c("Abhi", "Bhavesh",
"Chaman", "Dimri"),
age = c(7, 5, 9, 16),
ht = c(46, NA, NA, 69),
school = c("yes", "yes", "no", "no") )
# Calculating mean of age
summarise(d, mean = mean(age))
# Calculating min of age
summarise(d, med = min(age))
# Calculating max of age
summarise(d, med = max(age))
# Calculating median of age
summarise(d, med = median(age))
输出
# A tibble: 1 x 1
mean
1 9.25
# A tibble: 1 x 1
med
1 5
# A tibble: 1 x 1
med
1 16
# A tibble: 1 x 1
med
1 8
- sample_n()和sample_frac()： 用于抽取随机标本。
# Create a data frame with missing data
d <- data.frame( name = c("Abhi", "Bhavesh",
"Chaman", "Dimri"),
age = c(7, 5, 9, 16),
ht = c(46, NA, NA, 69),
school = c("yes", "yes", "no", "no") )
# Printing three rows
sample_n(d, 3)
# Printing 50 % of the rows
sample_frac(d, 0.50)
输出
# A tibble: 3 x 4
name age ht school
1 Abhi 7 46 yes
2 Bhavesh 5 NA yes
3 Chaman 9 NA no
# A tibble: 2 x 4
name age ht school
1 Dimri 16 69 no
2 Bhavesh 5 NA yes