R 简明教程
R - Data Reshaping
R 中的数据重塑涉及更改按行和列组织数据的方式。在 R 中执行数据处理时，大多数情况下会将输入数据作为数据框架。从数据框架的行和列中提取数据非常容易，但在某些情况下，我们需要数据框架采用与接收到的格式不同的格式。R 有许多函数可以在数据框架中将行拆分为列并反之亦然。
Data Reshaping in R is about changing the way data is organized into rows and columns. Most of the time data processing in R is done by taking the input data as a data frame. It is easy to extract data from the rows and columns of a data frame but there are situations when we need the data frame in a format that is different from format in which we received it. R has many functions to split, merge and change the rows to columns and vice-versa in a data frame.
Joining Columns and Rows in a Data Frame
我们可以使用 cbind()*function. Also we can merge two data frames using *rbind() 函数连接多个向量以创建数据框架。
We can join multiple vectors to create a data frame using the cbind()*function. Also we can merge two data frames using *rbind() function.
# Create vector objects.
city <- c("Tampa","Seattle","Hartford","Denver")
state <- c("FL","WA","CT","CO")
zipcode <- c(33602,98104,06161,80294)
# Combine above three vectors into one data frame.
addresses <- cbind(city,state,zipcode)
# Print a header.
cat("# # # # The First data frame\n")
# Print the data frame.
print(addresses)
# Create another data frame with similar columns
new.address <- data.frame(
city = c("Lowry","Charlotte"),
state = c("CO","FL"),
zipcode = c("80230","33949"),
stringsAsFactors = FALSE
)
# Print a header.
cat("# # # The Second data frame\n")
# Print the data frame.
print(new.address)
# Combine rows form both the data frames.
all.addresses <- rbind(addresses,new.address)
# Print a header.
cat("# # # The combined data frame\n")
# Print the result.
print(all.addresses)当我们执行上述代码时，会产生以下结果 -
When we execute the above code, it produces the following result −
# # # # The First data frame
city state zipcode
[1,] "Tampa" "FL" "33602"
[2,] "Seattle" "WA" "98104"
[3,] "Hartford" "CT" "6161"
[4,] "Denver" "CO" "80294"
# # # The Second data frame
city state zipcode
1 Lowry CO 80230
2 Charlotte FL 33949
# # # The combined data frame
city state zipcode
1 Tampa FL 33602
2 Seattle WA 98104
3 Hartford CT 6161
4 Denver CO 80294
5 Lowry CO 80230
6 Charlotte FL 33949Merging Data Frames
我们可以使用 merge() 函数合并两个数据框架。数据框架在合并发生时必须具有相同列名。
We can merge two data frames by using the merge() function. The data frames must have same column names on which the merging happens.
在以下示例中，我们考虑库“MASS”中提供的皮马印第安女性糖尿病数据集。我们根据血压 (“bp”) 和体重指数 (“bmi”) 的值合并两个数据集。在选择这两列进行合并时，两个数据集中的这两个变量值匹配的记录将合并在一起，形成一个单独的数据框架。
In the example below, we consider the data sets about Diabetes in Pima Indian Women available in the library names "MASS". we merge the two data sets based on the values of blood pressure("bp") and body mass index("bmi"). On choosing these two columns for merging, the records where values of these two variables match in both data sets are combined together to form a single data frame.
library(MASS)
merged.Pima <- merge(x = Pima.te, y = Pima.tr,
by.x = c("bp", "bmi"),
by.y = c("bp", "bmi")
)
print(merged.Pima)
nrow(merged.Pima)当我们执行上述代码时，会产生以下结果 -
When we execute the above code, it produces the following result −
bp bmi npreg.x glu.x skin.x ped.x age.x type.x npreg.y glu.y skin.y ped.y
1 60 33.8 1 117 23 0.466 27 No 2 125 20 0.088
2 64 29.7 2 75 24 0.370 33 No 2 100 23 0.368
3 64 31.2 5 189 33 0.583 29 Yes 3 158 13 0.295
4 64 33.2 4 117 27 0.230 24 No 1 96 27 0.289
5 66 38.1 3 115 39 0.150 28 No 1 114 36 0.289
6 68 38.5 2 100 25 0.324 26 No 7 129 49 0.439
7 70 27.4 1 116 28 0.204 21 No 0 124 20 0.254
8 70 33.1 4 91 32 0.446 22 No 9 123 44 0.374
9 70 35.4 9 124 33 0.282 34 No 6 134 23 0.542
10 72 25.6 1 157 21 0.123 24 No 4 99 17 0.294
11 72 37.7 5 95 33 0.370 27 No 6 103 32 0.324
12 74 25.9 9 134 33 0.460 81 No 8 126 38 0.162
13 74 25.9 1 95 21 0.673 36 No 8 126 38 0.162
14 78 27.6 5 88 30 0.258 37 No 6 125 31 0.565
15 78 27.6 10 122 31 0.512 45 No 6 125 31 0.565
16 78 39.4 2 112 50 0.175 24 No 4 112 40 0.236
17 88 34.5 1 117 24 0.403 40 Yes 4 127 11 0.598
age.y type.y
1 31 No
2 21 No
3 24 No
4 21 No
5 21 No
6 43 Yes
7 36 Yes
8 40 No
9 29 Yes
10 28 No
11 55 No
12 39 No
13 39 No
14 49 Yes
15 49 Yes
16 38 No
17 28 No
[1] 17Melting and Casting
R 编程最有趣的方面之一是分多个步骤更改数据形状以获得所需形状。用于执行此操作的函数称为 melt() 和 cast() 。
One of the most interesting aspects of R programming is about changing the shape of the data in multiple steps to get a desired shape. The functions used to do this are called melt() and cast().
我们考虑库“MASS”中存在的称为 ships 的数据集。
We consider the dataset called ships present in the library called "MASS".
library(MASS)
print(ships)当我们执行上述代码时，会产生以下结果 -
When we execute the above code, it produces the following result −
type year period service incidents
1 A 60 60 127 0
2 A 60 75 63 0
3 A 65 60 1095 3
4 A 65 75 1095 4
5 A 70 60 1512 6
.............
.............
8 A 75 75 2244 11
9 B 60 60 44882 39
10 B 60 75 17176 29
11 B 65 60 28609 58
............
............
17 C 60 60 1179 1
18 C 60 75 552 1
19 C 65 60 781 0
............
............Melt the Data
现在，我们熔化数据对其进行组织，将除类型和年份以外的所有列转换为多行。
Now we melt the data to organize it, converting all columns other than type and year into multiple rows.
molten.ships <- melt(ships, id = c("type","year"))
print(molten.ships)当我们执行上述代码时，会产生以下结果 -
When we execute the above code, it produces the following result −
type year variable value
1 A 60 period 60
2 A 60 period 75
3 A 65 period 60
4 A 65 period 75
............
............
9 B 60 period 60
10 B 60 period 75
11 B 65 period 60
12 B 65 period 75
13 B 70 period 60
...........
...........
41 A 60 service 127
42 A 60 service 63
43 A 65 service 1095
...........
...........
70 D 70 service 1208
71 D 75 service 0
72 D 75 service 2051
73 E 60 service 45
74 E 60 service 0
75 E 65 service 789
...........
...........
101 C 70 incidents 6
102 C 70 incidents 2
103 C 75 incidents 0
104 C 75 incidents 1
105 D 60 incidents 0
106 D 60 incidents 0
...........
...........Cast the Molten Data
我们可以将熔融数据转换为新形式，其中创建了每个年份中每种船舶类型的汇总。这是使用 cast() 函数完成的。
We can cast the molten data into a new form where the aggregate of each type of ship for each year is created. It is done using the cast() function.
recasted.ship <- cast(molten.ships, type+year~variable,sum)
print(recasted.ship)当我们执行上述代码时，会产生以下结果 -
When we execute the above code, it produces the following result −
type year period service incidents
1 A 60 135 190 0
2 A 65 135 2190 7
3 A 70 135 4865 24
4 A 75 135 2244 11
5 B 60 135 62058 68
6 B 65 135 48979 111
7 B 70 135 20163 56
8 B 75 135 7117 18
9 C 60 135 1731 2
10 C 65 135 1457 1
11 C 70 135 2731 8
12 C 75 135 274 1
13 D 60 135 356 0
14 D 65 135 480 0
15 D 70 135 1557 13
16 D 75 135 2051 4
17 E 60 135 45 0
18 E 65 135 1226 14
19 E 70 135 3318 17
20 E 75 135 542 1