dplyr包
read.file() Copy from rdocumentation website
Usage
read.file(file=NULL,header=TRUE,use.value.labels=FALSE,to.data.frame=TRUE,sep=",",widths=NULL,f=NULL, filetype=NULL,...)
Copy from dplyr overview
dplyr五个基础函数
新增列
> mutate(test, new = Sepal.Length * Sepal.Width)
Sepal.Length Sepal.Width Petal.Length Petal.Width Species new
1 5.1 3.5 1.4 0.2 setosa 17.85
2 4.9 3.0 1.4 0.2 setosa 14.70
3 7.0 3.2 4.7 1.4 versicolor 22.40
4 6.4 3.2 4.5 1.5 versicolor 20.48
5 6.3 3.3 6.0 2.5 virginica 20.79
6 5.8 2.7 5.1 1.9 virginica 15.66
> test
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.1 3.5 1.4 0.2 setosa
2 4.9 3.0 1.4 0.2 setosa
51 7.0 3.2 4.7 1.4 versicolor
52 6.4 3.2 4.5 1.5 versicolor
101 6.3 3.3 6.0 2.5 virginica
102 5.8 2.7 5.1 1.9 virginica
这里test并没有加入new这个variable，rowname也不同，只是一个临时变量
mutate(.data, ...) adds new variables that are functions of existing variable
The arguments in ... are automatically quoted and evaluated in the context of the data frame.
按列筛选
select() picks variables based on their names.
使用colmumn number选择列
> select(test,1)
Sepal.Length
1 5.1
2 4.9
51 7.0
52 6.4
101 6.3
102 5.8
> select(test,c(1,5))
Sepal.Length Species
1 5.1 setosa
2 4.9 setosa
51 7.0 versicolor
52 6.4 versicolor
101 6.3 virginica
102 5.8 virginica
使用variable name选择列
> select(test, Petal.Length, Petal.Width)
Petal.Length Petal.Width
1 1.4 0.2
2 1.4 0.2
51 4.7 1.4
52 4.5 1.5
101 6.0 2.5
102 5.1 1.9
> vars <- c("Petal.Length", "Petal.Width")
> select(test, one_of(vars))
Petal.Length Petal.Width
1 1.4 0.2
2 1.4 0.2
51 4.7 1.4
52 4.5 1.5
101 6.0 2.5
102 5.1 1.9
one_of(): Matches variable names in a character vector.
Q:
one_of()同样是dplyr中的function；这里使用这种麻烦的argument有什么实际意义嘛？
筛选行
filter() picks cases based on their values.
> filter(test, Species == "setosa")
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.1 3.5 1.4 0.2 setosa
2 4.9 3.0 1.4 0.2 setosa
filter(.data, ..., .preserve = FALSE)
Use filter() to choose rows/cases where conditions are true. Unlike base subsetting with [, rows where the condition evaluates to NA are dropped.
Logical predicates defined in terms of the variables in .data. Multiple conditions are combined with &. Only rows where the condition evaluates to TRUE are kept.
The arguments in ... are automatically quoted and evaluated in the context of the data frame.
按某1列或某几列对整个表格进行排序
arrange(.data, ...) changes the ordering of the rows.
...: Comma separated list of unquoted variable names, or expressions involving variable names. Use desc() to sort a variable in descending order.
> arrange(test, Sepal.Length)
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 4.9 3.0 1.4 0.2 setosa
2 5.1 3.5 1.4 0.2 setosa
3 5.8 2.7 5.1 1.9 virginica
4 6.3 3.3 6.0 2.5 virginica
5 6.4 3.2 4.5 1.5 versicolor
6 7.0 3.2 4.7 1.4 versicolor
> arrange(test, desc(Sepal.Length))
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 7.0 3.2 4.7 1.4 versicolor
2 6.4 3.2 4.5 1.5 versicolor
3 6.3 3.3 6.0 2.5 virginica
4 5.8 2.7 5.1 1.9 virginica
5 5.1 3.5 1.4 0.2 setosa
6 4.9 3.0 1.4 0.2 setosa
//这里应该是先按照Sepal.Length升序排列，相同的按照Sepal.Width降序排列
> arrange(test, Sepal.Length, desc(Sepal.Width))
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 4.9 3.0 1.4 0.2 setosa
2 5.1 3.5 1.4 0.2 setosa
3 5.8 2.7 5.1 1.9 virginica
4 6.3 3.3 6.0 2.5 virginica
5 6.4 3.2 4.5 1.5 versicolor
6 7.0 3.2 4.7 1.4 versicolor
汇总
summarise() reduces multiple values down to a single summary.
> summarise(test, mean(Sepal.Length), sd(Sepal.Length))# 计算Sepal.Length的平均值和标准差
mean(Sepal.Length) sd(Sepal.Length)
1 5.916667 0.8084965
> # 先按照Species分组，计算每组Sepal.Length的平均值和标准差group_by(test, Species)
> group_by(test, Species)
# A tibble: 6 x 5
# Groups: Species [3]
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
* <dbl> <dbl> <dbl> <dbl> <fct>
1 5.1 3.5 1.4 0.2 setosa
2 4.9 3 1.4 0.2 setosa
3 7 3.2 4.7 1.4 versicolor
4 6.4 3.2 4.5 1.5 versicolor
5 6.3 3.3 6 2.5 virginica
6 5.8 2.7 5.1 1.9 virginica
group_by(.data, ..., add = FALSE, .drop = group_by_drop_default(.data))
.data a tbl
... Variables to group by. All tbls accept variable names. Some tbls will accept functions of variables. Duplicated groups will be silently dropped.
add When add = FALSE, the default, group_by() will override existing groups. To add to the existing groups, use add = TRUE.
.drop When .drop = TRUE, empty groups are dropped. See group_by_drop_default() for what the default value is for this argument.
dplyr两个实用技能
test1
## x z
## 1 b A
## 2 e B
## 3 f C
## 4 x D
test2
## x y
## 1 a 1
## 2 b 2
## 3 c 3
## 4 d 4
## 5 e 5
## 6 f 6
管道操作 %>% (cmd/ctr + shift + M)
> ?%>%
Error: unexpected SPECIAL in "?%>%"
报错了~
以下Copy来的文档 Piping 中的例子
The dplyr API is functional in the sense that function calls don’t have side-effects. You must always save their results. This doesn’t lead to particularly elegant code, especially if you want to do many operations at once. You either have to do it step-by-step:
a1 <- group_by(flights, year, month, day)
a2 <- select(a1, arr_delay, dep_delay)
a3 <- summarise(a2,
arr = mean(arr_delay, na.rm = TRUE),
dep = mean(dep_delay, na.rm = TRUE))
a4 <- filter(a3, arr > 30 | dep > 30)
Or if you don’t want to name the intermediate results, you need to wrap the function calls inside each other:
filter(
summarise(
select(
group_by(flights, year, month, day),
arr_delay, dep_delay
),
arr = mean(arr_delay, na.rm = TRUE),
dep = mean(dep_delay, na.rm = TRUE)
),
arr > 30 | dep > 30
)
#> Adding missing grouping variables: `year`, `month`, `day`
#> # A tibble: 49 x 5
#> # Groups: year, month [11]
#> year month day arr dep
#> <int> <int> <int> <dbl> <dbl>
#> 1 2013 1 16 34.2 24.6
#> 2 2013 1 31 32.6 28.7
#> 3 2013 2 11 36.3 39.1
#> 4 2013 2 27 31.3 37.8
#> # … with 45 more rows
This is difficult to read because the order of the operations is from inside to out. Thus, the arguments are a long way away from the function. To get around this problem, dplyr provides the %>% operator from magrittr. x %>% f(y) turns into f(x, y) so you can use it to rewrite multiple operations that you can read left-to-right, top-to-bottom:
flights %>%
group_by(year, month, day) %>%
select(arr_delay, dep_delay) %>%
summarise(
arr = mean(arr_delay, na.rm = TRUE),
dep = mean(dep_delay, na.rm = TRUE)
) %>%
filter(arr > 30 | dep > 30)
个人感觉还是一步一步来比较不容易出错~
count统计某列的unique值
其实就是把factor枚举出来 并给出个数
count(test,Species)
## # A tibble: 3 x 2
## Species n
##
## 1 setosa 2
## 2 versicolor 2
## 3 virginica 2
dplyr处理关系数据
Refer to Join two tbls together
这里把用到的两大类join的功能copy过来
两大类分别是：mutating join 和 filtering join
function的原型抽象为xxx_join(x, y, by = NULL, copy = FALSE, ...)
例子来源于生信星球
Mutating joins combine variables from the two data.frames:
inner_join()
return all rows from x where there are matching values in y, and all columns from x and y. If there are multiple matches between x and y, all combination of the matches are returned.
left_join()
return all rows from x, and all columns from x and y. Rows in x with no match in y will have NA values in the new columns. If there are multiple matches between x and y, all combinations of the matches are returned.
right_join()
return all rows from y, and all columns from x and y. Rows in y with no match in x will have NA values in the new columns. If there are multiple matches between x and y, all combinations of the matches are returned.
full_join()
return all rows and all columns from both x and y. Where there are not matching values, returns NA for the one missing.
Filtering joins keep cases from the left-hand data.frame:
semi_join()
return all rows from x where there are matching values in y, keeping just columns from x. A semi join differs from an inner join because an inner join will return one row of x for each matching row of y, where a semi join will never duplicate rows of x.
anti_join()
return all rows from x where there are not matching values in y, keeping just columns from x.
內连inner_join,取交集
inner_join(test1, test2, by = "x")
## x z y
## 1 b A 2
## 2 e B 5
## 3 f C 6
左连left_join
left_join(test1, test2, by = 'x')
## x z y
## 1 b A 2
## 2 e B 5
## 3 f C 6
## 4 x D NA
left_join(test2, test1, by = 'x')
## x y z
## 1 a 1
## 2 b 2 A
## 3 c 3
## 4 d 4
## 5 e 5 B
## 6 f 6 C
全连full_join
full_join( test1, test2, by = 'x')
## x z y
## 1 b A 2
## 2 e B 5
## 3 f C 6
## 4 x D NA
## 5 a 1
## 6 c 3
## 7 d 4
半连接：返回能够与y表匹配的x表所有记录semi_join
semi_join(x = test1, y = test2, by = 'x')
## x z
## 1 b A
## 2 e B
## 3 f C
反连接：返回无法与y表匹配的x表的所记录anti_join
anti_join(x = test2, y = test1, by = 'x')
## x y
## 1 a 1
## 2 c 3
## 3 d 4
简单合并
test1 <- data.frame(x = c(1,2,3,4), y = c(10,20,30,40))
test1
## x y
## 1 1 10
## 2 2 20
## 3 3 30
## 4 4 40
test2 <- data.frame(x = c(5,6), y = c(50,60))
test2
## x y
## 1 5 50
## 2 6 60
test3 <- data.frame(z = c(100,200,300,400))
test3
## z
## 1 100
## 2 200
## 3 300
## 4 400
bind_rows(test1, test2)
## x y
## 1 1 10
## 2 2 20
## 3 3 30
## 4 4 40
## 5 5 50
## 6 6 60
bind_cols(test1, test3)
## x y z
## 1 1 10 100
## 2 2 20 200
## 3 3 30 300
## 4 4 40 400
所以既然有自带
cbind()rbind()dplyr包要设计这种function出来
