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I have 11 dataframes with various observations from seagrass surveys in the Chesapeake. Each dataframe contains the following variables (with example values included). There are 11 dataframes as each one represents observations from a single SAMPYR. So:

    > 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

I would like to write a for loop that takes the number of unique characters from the PLOT column, and calculates the frequency of each one (so I can then filter so only those that appear more than once are listed).

What I have so far is:

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()
}

This code outputs

+     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

But I can't do anything further with that. Is there a way for me to filter rows so only those with a n=2 show up? Or to print 11 dataframes from the for loop, so I can further manipulate them but at least I'll have a copy of them in the global environment?

Thank you! I can provide additional details if that helps.

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1 Answer 1

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Don't do it in a loop !! It is done completely different. I'll show you step by step. My first step is to prepare a function that will generate data similar to yours.

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)
)

Let's see how it works and generate some sample data frames

set.seed(123)
density.2007 = dens(2007, 120)
density.2008 = dens(2008, 88)
density.2009 = dens(2009, 135)
density.2010 = dens(2010, 156)

The density.2007 data frame looks like this

# 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

Now they need to be combined into one frame

df = density.2007 %>% 
  bind_rows(density.2008) %>% 
  bind_rows(density.2009) %>% 
  bind_rows(density.2010)

output

# 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

In the next step, count how many times each value of the PLOT variable occurs

PLOT.count = df %>% 
  group_by(PLOT) %>% 
  summarise(PLOT.n = n()) %>% 
  arrange(PLOT.n)

ouptut

# 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

In the penultimate step, let's append these counters to the original data frame

df = df %>% left_join(PLOT.count, by="PLOT")

output

# 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

Now filter it at will

df %>% filter(PLOT.n > 30)

ouptut

# 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

Or this way

df %>% filter(PLOT.n == min(PLOT.n))
df %>% filter(PLOT.n == median(PLOT.n))
df %>% filter(PLOT.n == max(PLOT.n))

output

# 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
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1 Comment

Wow thank you! This was really helpful, I look forward to adding it to my code.

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