如何从 R 中的 for 循环中的结果创建多个数据帧？
How do I create multiple dataframes from a result in a for loop in R?
我有 11 个数据框，其中包含切萨皮克海草调查的各种观察结果。每个数据框都包含以下变量（包括示例值）。有 11 个数据框，因为每个数据框代表来自单个 SAMPYR 的观察结果。所以：
> head(density.2007)
PLOT SIZE DENSITY SEEDYR SAMPYR AGE SHOOTS
1 HI 2 1.0 50 2006 2007 1 6.0
2 HI 5 0.5 100 2006 2007 1 11.6
3 HI 7 0.5 50 2006 2007 1 6.0
4 HI 9 0.5 100 2006 2007 1 9.6
5 HI 10 1.0 100 2006 2007 1 30.0
6 HI 23 1.0 50 2006 2007 1 40.4
> head(density.2008)
PLOT SIZE DENSITY SEEDYR SAMPYR AGE SHOOTS NOTES id
29 HI 1 1.0 100 2007 2008 1 39.6 29
30 HI 2 1.0 50 2006 2008 2 54.8 30
31 HI 3 0.5 100 2007 2008 1 11.2 31
32 HI 4 1.0 100 2007 2008 1 8.8 32
33 HI 5 0.5 100 2006 2008 2 24.0 33
34 HI 7 0.5 50 2006 2008 2 0.0 34
我想编写一个 for 循环，从 PLOT 列中获取唯一字符的数量，并计算每个字符的频率（这样我就可以进行过滤，只列出出现多次的字符）。
我目前拥有的是：
density.names <- c("density.2007",
"density.2008",
"density.2009",
"density.2010",
"density.2011",
"density.2012",
"density.2013",
"density.2014",
"density.2015",
"density.2016",
"density.2017"
)
for(i in 1:length(density.names)) {
get(density.names[i]) %>%
count(PLOT) %>%
print()
}
这段代码输出
+ print()
PLOT n
1 HI 1 1
2 HI 10 1
3 HI 100 1
4 HI 103 1
5 HI 104 1
6 HI 11 1
7 HI 13 1
8 HI 14 1
9 HI 15 1
10 HI 17 1
11 HI 18 1
12 HI 2 1
13 HI 20 1
14 HI 21 1
15 HI 23 1
16 HI 25 1
17 HI 27 1
18 HI 29 1
19 HI 3 1
20 HI 31 1
21 HI 32 1
22 HI 36 1
23 HI 37 1
24 HI 38 1
25 HI 39 1
26 HI 4 1
27 HI 40 1
但是我不能再做任何事情了。有没有办法过滤行，以便只显示 n=2 的行？或者从 for 循环中打印 11 个数据帧，以便我可以进一步操作它们，但至少我会在全局环境中拥有它们的副本？
谢谢！如果有帮助，我可以提供更多详细信息。
不要循环执行！！它是完全不同的。我会一步一步地告诉你。
我的第一步是准备一个函数来生成与您类似的数据。
library(tidyverse)
dens = function(year, n) tibble(
PLOT = paste("HI", sample(1:(n/7), n, replace = T)),
SIZE = runif(n, 0.1, 3),
DENSITY = sample(seq(50,200, by=50), n, replace = T),
SEEDYR = year-1,
SAMPYR = year,
AGE = sample(1:5, n, replace = T),
SHOOTS = runif(n, 0.1, 3)
)
让我们看看它是如何工作的并生成一些示例数据帧
set.seed(123)
density.2007 = dens(2007, 120)
density.2008 = dens(2008, 88)
density.2009 = dens(2009, 135)
density.2010 = dens(2010, 156)
density.2007 数据框看起来像这样
# A tibble: 120 x 7
PLOT SIZE DENSITY SEEDYR SAMPYR AGE SHOOTS
<chr> <dbl> <dbl> <dbl> <dbl> <int> <dbl>
1 HI 15 1.67 200 2006 2007 4 1.80
2 HI 14 0.270 150 2006 2007 2 2.44
3 HI 3 0.856 50 2006 2007 3 0.686
4 HI 10 1.25 200 2006 2007 5 1.43
5 HI 11 0.673 50 2006 2007 5 1.40
6 HI 5 2.51 150 2006 2007 3 2.23
7 HI 14 0.543 150 2006 2007 2 2.17
8 HI 5 2.43 200 2006 2007 5 2.51
9 HI 9 1.69 100 2006 2007 4 2.67
10 HI 3 2.02 50 2006 2007 2 2.86
# ... with 110 more rows
现在需要将它们合为一帧
df = density.2007 %>%
bind_rows(density.2008) %>%
bind_rows(density.2009) %>%
bind_rows(density.2010)
输出
# A tibble: 499 x 7
PLOT SIZE DENSITY SEEDYR SAMPYR AGE SHOOTS
<chr> <dbl> <dbl> <dbl> <dbl> <int> <dbl>
1 HI 15 1.67 200 2006 2007 4 1.80
2 HI 14 0.270 150 2006 2007 2 2.44
3 HI 3 0.856 50 2006 2007 3 0.686
4 HI 10 1.25 200 2006 2007 5 1.43
5 HI 11 0.673 50 2006 2007 5 1.40
6 HI 5 2.51 150 2006 2007 3 2.23
7 HI 14 0.543 150 2006 2007 2 2.17
8 HI 5 2.43 200 2006 2007 5 2.51
9 HI 9 1.69 100 2006 2007 4 2.67
10 HI 3 2.02 50 2006 2007 2 2.86
# ... with 489 more rows
下一步，统计PLOT变量的每个值出现了多少次
PLOT.count = df %>%
group_by(PLOT) %>%
summarise(PLOT.n = n()) %>%
arrange(PLOT.n)
输出
# A tibble: 22 x 2
PLOT PLOT.n
<chr> <int>
1 HI 20 3
2 HI 22 5
3 HI 21 7
4 HI 18 12
5 HI 2 19
6 HI 1 20
7 HI 15 20
8 HI 17 21
9 HI 6 22
10 HI 11 23
# ... with 12 more rows
在倒数第二步，让我们将这些计数器附加到原始数据框
df = df %>% left_join(PLOT.count, by="PLOT")
输出
# A tibble: 499 x 8
PLOT SIZE DENSITY SEEDYR SAMPYR AGE SHOOTS PLOT.n
<chr> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <int>
1 HI 15 1.67 200 2006 2007 4 1.80 20
2 HI 14 0.270 150 2006 2007 2 2.44 32
3 HI 3 0.856 50 2006 2007 3 0.686 27
4 HI 10 1.25 200 2006 2007 5 1.43 25
5 HI 11 0.673 50 2006 2007 5 1.40 23
6 HI 5 2.51 150 2006 2007 3 2.23 38
7 HI 14 0.543 150 2006 2007 2 2.17 32
8 HI 5 2.43 200 2006 2007 5 2.51 38
9 HI 9 1.69 100 2006 2007 4 2.67 26
10 HI 3 2.02 50 2006 2007 2 2.86 27
# ... with 489 more rows
现在随意过滤
df %>% filter(PLOT.n > 30)
输出
# A tibble: 139 x 8
PLOT SIZE DENSITY SEEDYR SAMPYR AGE SHOOTS PLOT.n
<chr> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <int>
1 HI 14 0.270 150 2006 2007 2 2.44 32
2 HI 5 2.51 150 2006 2007 3 2.23 38
3 HI 14 0.543 150 2006 2007 2 2.17 32
4 HI 5 2.43 200 2006 2007 5 2.51 38
5 HI 8 0.598 50 2006 2007 1 1.70 34
6 HI 7 1.94 50 2006 2007 4 1.61 35
7 HI 14 2.91 50 2006 2007 4 0.215 32
8 HI 7 0.846 150 2006 2007 4 0.506 35
9 HI 7 2.38 150 2006 2007 3 1.34 35
10 HI 7 2.62 100 2006 2007 3 0.167 35
# ... with 129 more rows
或者这样
df %>% filter(PLOT.n == min(PLOT.n))
df %>% filter(PLOT.n == median(PLOT.n))
df %>% filter(PLOT.n == max(PLOT.n))
输出
# A tibble: 3 x 8
PLOT SIZE DENSITY SEEDYR SAMPYR AGE SHOOTS PLOT.n
<chr> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <int>
1 HI 20 0.392 200 2009 2010 1 0.512 3
2 HI 20 0.859 150 2009 2010 5 2.62 3
3 HI 20 0.882 200 2009 2010 5 1.06 3
> df %>% filter(PLOT.n == median(PLOT.n))
# A tibble: 26 x 8
PLOT SIZE DENSITY SEEDYR SAMPYR AGE SHOOTS PLOT.n
<chr> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <int>
1 HI 9 1.69 100 2006 2007 4 2.67 26
2 HI 9 2.20 50 2006 2007 4 1.49 26
3 HI 9 0.587 200 2006 2007 3 1.13 26
4 HI 9 1.27 50 2006 2007 1 2.55 26
5 HI 9 1.56 150 2006 2007 3 2.01 26
6 HI 9 0.198 100 2006 2007 3 2.08 26
7 HI 9 2.72 150 2007 2008 3 0.421 26
8 HI 9 0.251 200 2007 2008 2 0.328 26
9 HI 9 1.83 50 2007 2008 1 0.192 26
10 HI 9 1.97 100 2007 2008 1 0.900 26
# ... with 16 more rows
> df %>% filter(PLOT.n == max(PLOT.n))
# A tibble: 38 x 8
PLOT SIZE DENSITY SEEDYR SAMPYR AGE SHOOTS PLOT.n
<chr> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <int>
1 HI 5 2.51 150 2006 2007 3 2.23 38
2 HI 5 2.43 200 2006 2007 5 2.51 38
3 HI 5 2.06 100 2006 2007 5 1.93 38
4 HI 5 1.25 150 2006 2007 4 2.29 38
5 HI 5 2.29 200 2006 2007 1 2.97 38
6 HI 5 0.789 150 2006 2007 2 1.59 38
7 HI 5 1.11 100 2007 2008 4 2.61 38
8 HI 5 2.38 150 2007 2008 4 2.95 38
9 HI 5 2.67 200 2007 2008 3 1.77 38
10 HI 5 2.63 100 2007 2008 1 1.90 38
# ... with 28 more rows
我有 11 个数据框，其中包含切萨皮克海草调查的各种观察结果。每个数据框都包含以下变量（包括示例值）。有 11 个数据框，因为每个数据框代表来自单个 SAMPYR 的观察结果。所以：
> head(density.2007)
PLOT SIZE DENSITY SEEDYR SAMPYR AGE SHOOTS
1 HI 2 1.0 50 2006 2007 1 6.0
2 HI 5 0.5 100 2006 2007 1 11.6
3 HI 7 0.5 50 2006 2007 1 6.0
4 HI 9 0.5 100 2006 2007 1 9.6
5 HI 10 1.0 100 2006 2007 1 30.0
6 HI 23 1.0 50 2006 2007 1 40.4
> head(density.2008)
PLOT SIZE DENSITY SEEDYR SAMPYR AGE SHOOTS NOTES id
29 HI 1 1.0 100 2007 2008 1 39.6 29
30 HI 2 1.0 50 2006 2008 2 54.8 30
31 HI 3 0.5 100 2007 2008 1 11.2 31
32 HI 4 1.0 100 2007 2008 1 8.8 32
33 HI 5 0.5 100 2006 2008 2 24.0 33
34 HI 7 0.5 50 2006 2008 2 0.0 34
我想编写一个 for 循环，从 PLOT 列中获取唯一字符的数量，并计算每个字符的频率（这样我就可以进行过滤，只列出出现多次的字符）。
我目前拥有的是：
density.names <- c("density.2007",
"density.2008",
"density.2009",
"density.2010",
"density.2011",
"density.2012",
"density.2013",
"density.2014",
"density.2015",
"density.2016",
"density.2017"
)
for(i in 1:length(density.names)) {
get(density.names[i]) %>%
count(PLOT) %>%
print()
}
这段代码输出
+ print()
PLOT n
1 HI 1 1
2 HI 10 1
3 HI 100 1
4 HI 103 1
5 HI 104 1
6 HI 11 1
7 HI 13 1
8 HI 14 1
9 HI 15 1
10 HI 17 1
11 HI 18 1
12 HI 2 1
13 HI 20 1
14 HI 21 1
15 HI 23 1
16 HI 25 1
17 HI 27 1
18 HI 29 1
19 HI 3 1
20 HI 31 1
21 HI 32 1
22 HI 36 1
23 HI 37 1
24 HI 38 1
25 HI 39 1
26 HI 4 1
27 HI 40 1
但是我不能再做任何事情了。有没有办法过滤行，以便只显示 n=2 的行？或者从 for 循环中打印 11 个数据帧，以便我可以进一步操作它们，但至少我会在全局环境中拥有它们的副本？
谢谢！如果有帮助，我可以提供更多详细信息。
不要循环执行！！它是完全不同的。我会一步一步地告诉你。 我的第一步是准备一个函数来生成与您类似的数据。
library(tidyverse)
dens = function(year, n) tibble(
PLOT = paste("HI", sample(1:(n/7), n, replace = T)),
SIZE = runif(n, 0.1, 3),
DENSITY = sample(seq(50,200, by=50), n, replace = T),
SEEDYR = year-1,
SAMPYR = year,
AGE = sample(1:5, n, replace = T),
SHOOTS = runif(n, 0.1, 3)
)
让我们看看它是如何工作的并生成一些示例数据帧
set.seed(123)
density.2007 = dens(2007, 120)
density.2008 = dens(2008, 88)
density.2009 = dens(2009, 135)
density.2010 = dens(2010, 156)
density.2007 数据框看起来像这样
# A tibble: 120 x 7
PLOT SIZE DENSITY SEEDYR SAMPYR AGE SHOOTS
<chr> <dbl> <dbl> <dbl> <dbl> <int> <dbl>
1 HI 15 1.67 200 2006 2007 4 1.80
2 HI 14 0.270 150 2006 2007 2 2.44
3 HI 3 0.856 50 2006 2007 3 0.686
4 HI 10 1.25 200 2006 2007 5 1.43
5 HI 11 0.673 50 2006 2007 5 1.40
6 HI 5 2.51 150 2006 2007 3 2.23
7 HI 14 0.543 150 2006 2007 2 2.17
8 HI 5 2.43 200 2006 2007 5 2.51
9 HI 9 1.69 100 2006 2007 4 2.67
10 HI 3 2.02 50 2006 2007 2 2.86
# ... with 110 more rows
现在需要将它们合为一帧
df = density.2007 %>%
bind_rows(density.2008) %>%
bind_rows(density.2009) %>%
bind_rows(density.2010)
输出
# A tibble: 499 x 7
PLOT SIZE DENSITY SEEDYR SAMPYR AGE SHOOTS
<chr> <dbl> <dbl> <dbl> <dbl> <int> <dbl>
1 HI 15 1.67 200 2006 2007 4 1.80
2 HI 14 0.270 150 2006 2007 2 2.44
3 HI 3 0.856 50 2006 2007 3 0.686
4 HI 10 1.25 200 2006 2007 5 1.43
5 HI 11 0.673 50 2006 2007 5 1.40
6 HI 5 2.51 150 2006 2007 3 2.23
7 HI 14 0.543 150 2006 2007 2 2.17
8 HI 5 2.43 200 2006 2007 5 2.51
9 HI 9 1.69 100 2006 2007 4 2.67
10 HI 3 2.02 50 2006 2007 2 2.86
# ... with 489 more rows
下一步，统计PLOT变量的每个值出现了多少次
PLOT.count = df %>%
group_by(PLOT) %>%
summarise(PLOT.n = n()) %>%
arrange(PLOT.n)
输出
# A tibble: 22 x 2
PLOT PLOT.n
<chr> <int>
1 HI 20 3
2 HI 22 5
3 HI 21 7
4 HI 18 12
5 HI 2 19
6 HI 1 20
7 HI 15 20
8 HI 17 21
9 HI 6 22
10 HI 11 23
# ... with 12 more rows
在倒数第二步，让我们将这些计数器附加到原始数据框
df = df %>% left_join(PLOT.count, by="PLOT")
输出
# A tibble: 499 x 8
PLOT SIZE DENSITY SEEDYR SAMPYR AGE SHOOTS PLOT.n
<chr> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <int>
1 HI 15 1.67 200 2006 2007 4 1.80 20
2 HI 14 0.270 150 2006 2007 2 2.44 32
3 HI 3 0.856 50 2006 2007 3 0.686 27
4 HI 10 1.25 200 2006 2007 5 1.43 25
5 HI 11 0.673 50 2006 2007 5 1.40 23
6 HI 5 2.51 150 2006 2007 3 2.23 38
7 HI 14 0.543 150 2006 2007 2 2.17 32
8 HI 5 2.43 200 2006 2007 5 2.51 38
9 HI 9 1.69 100 2006 2007 4 2.67 26
10 HI 3 2.02 50 2006 2007 2 2.86 27
# ... with 489 more rows
现在随意过滤
df %>% filter(PLOT.n > 30)
输出
# A tibble: 139 x 8
PLOT SIZE DENSITY SEEDYR SAMPYR AGE SHOOTS PLOT.n
<chr> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <int>
1 HI 14 0.270 150 2006 2007 2 2.44 32
2 HI 5 2.51 150 2006 2007 3 2.23 38
3 HI 14 0.543 150 2006 2007 2 2.17 32
4 HI 5 2.43 200 2006 2007 5 2.51 38
5 HI 8 0.598 50 2006 2007 1 1.70 34
6 HI 7 1.94 50 2006 2007 4 1.61 35
7 HI 14 2.91 50 2006 2007 4 0.215 32
8 HI 7 0.846 150 2006 2007 4 0.506 35
9 HI 7 2.38 150 2006 2007 3 1.34 35
10 HI 7 2.62 100 2006 2007 3 0.167 35
# ... with 129 more rows
或者这样
df %>% filter(PLOT.n == min(PLOT.n))
df %>% filter(PLOT.n == median(PLOT.n))
df %>% filter(PLOT.n == max(PLOT.n))
输出
# A tibble: 3 x 8
PLOT SIZE DENSITY SEEDYR SAMPYR AGE SHOOTS PLOT.n
<chr> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <int>
1 HI 20 0.392 200 2009 2010 1 0.512 3
2 HI 20 0.859 150 2009 2010 5 2.62 3
3 HI 20 0.882 200 2009 2010 5 1.06 3
> df %>% filter(PLOT.n == median(PLOT.n))
# A tibble: 26 x 8
PLOT SIZE DENSITY SEEDYR SAMPYR AGE SHOOTS PLOT.n
<chr> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <int>
1 HI 9 1.69 100 2006 2007 4 2.67 26
2 HI 9 2.20 50 2006 2007 4 1.49 26
3 HI 9 0.587 200 2006 2007 3 1.13 26
4 HI 9 1.27 50 2006 2007 1 2.55 26
5 HI 9 1.56 150 2006 2007 3 2.01 26
6 HI 9 0.198 100 2006 2007 3 2.08 26
7 HI 9 2.72 150 2007 2008 3 0.421 26
8 HI 9 0.251 200 2007 2008 2 0.328 26
9 HI 9 1.83 50 2007 2008 1 0.192 26
10 HI 9 1.97 100 2007 2008 1 0.900 26
# ... with 16 more rows
> df %>% filter(PLOT.n == max(PLOT.n))
# A tibble: 38 x 8
PLOT SIZE DENSITY SEEDYR SAMPYR AGE SHOOTS PLOT.n
<chr> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <int>
1 HI 5 2.51 150 2006 2007 3 2.23 38
2 HI 5 2.43 200 2006 2007 5 2.51 38
3 HI 5 2.06 100 2006 2007 5 1.93 38
4 HI 5 1.25 150 2006 2007 4 2.29 38
5 HI 5 2.29 200 2006 2007 1 2.97 38
6 HI 5 0.789 150 2006 2007 2 1.59 38
7 HI 5 1.11 100 2007 2008 4 2.61 38
8 HI 5 2.38 150 2007 2008 4 2.95 38
9 HI 5 2.67 200 2007 2008 3 1.77 38
10 HI 5 2.63 100 2007 2008 1 1.90 38
# ... with 28 more rows