Having trouble using tidyr pivot_wider to spread data - r

I have a dataset comparing 15 hybrids, each with 5 separate measurements. I am trying to spread the data into a wider dataset using pivot_wider for a regression analysis, since spread() would not work (probably because of the repeated observations).
The dataset I am working with is below:
data <- structure(list(hybrid = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,
8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 11, 11, 11, 11, 11, 11, 11,
11, 11, 11, 11, 11, 11, 11, 11, 12, 12, 12, 12, 12, 12, 12, 12,
12, 12, 12, 12, 12, 12, 12, 13, 13, 13, 13, 13, 13, 13, 13, 13,
13, 13, 13, 13, 13, 13, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14,
14, 14, 14, 14, 14, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15,
15, 15, 15), measurement = c(1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4,
4, 5, 5, 5, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 1, 1,
1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 1, 1, 1, 2, 2, 2, 3, 3,
3, 4, 4, 4, 5, 5, 5, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5,
5, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 1, 1, 1, 2, 2,
2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4,
4, 5, 5, 5, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 1, 1, 1, 2, 2, 2, 3,
3, 3, 4, 4, 4, 5, 5, 5, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5,
5, 5, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 1, 1, 1, 2,
2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4,
4, 4, 5, 5, 5, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5), value = c(245,
889, 450, 45, 515, 318, 956, 434, 29, 740, 156, 516, 767, 292,
753, 573, 636, 611, 777, 557, 408, 95, 482, 227, 495, 360, 55,
76, 393, 37, 667, 802, 724, 900, 885, 191, 79, 143, 531, 398,
324, 129, 172, 467, 25, 101, 476, 629, 915, 122, 498, 649, 354,
527, 920, 788, 565, 552, 586, 127, 461, 307, 77, 552, 198, 240,
816, 144, 136, 781, 593, 421, 233, 264, 812, 407, 492, 932, 940,
139, 764, 200, 352, 754, 271, 506, 381, 973, 678, 848, 432, 358,
218, 736, 287, 411, 220, 264, 531, 669, 666, 727, 841, 792, 79,
460, 159, 426, 90, 395, 793, 507, 262, 814, 157, 641, 230, 870,
304, 591, 636, 277, 534, 783, 562, 938, 889, 68, 557, 892, 809,
157, 71, 54, 256, 246, 301, 823, 622, 953, 6, 66, 556, 902, 207,
832, 248, 540, 192, 65, 381, 712, 15, 323, 1, 193, 146, 637,
488, 158, 289, 839, 229, 237, 273, 978, 560, 969, 898, 204, 335,
930, 444, 968, 920, 398, 303, 318, 975, 182, 630, 4, 624, 271,
272, 438, 661, 728, 32, 106, 473, 465, 498, 33, 189, 918, 704,
605, 867, 240, 833, 497, 514, 241, 860, 228, 643, 791, 4, 898,
574, 225, 339, 365, 387, 548, 88, 604, 283)), class = "data.frame", row.names = c(NA,
-219L))
I'm new to the pivot_wider function, so when I run my code, I get an error:
data%>%
pivot_wider(cols = -hybrid, names_to = c("1","2","3","4","5"))
Error in pivot_wider(., cols = -hybrid, names_to = c("1", "2", "3", "4", :
unused arguments (cols = -hybrid, names_to = c("1", "2", "3", "4", "5"))
How can I spread this data so that I have 5 columns? Hybrid, 1, 2, 3, 4, 5 (with the values under the columns entitled 1:5).


My guess is that you are you looking for this:
library(tidyr)
pivot_wider(data, id_cols = hybrid, names_from = measurement, values_from = "value", values_fn = sum)
# # A tibble: 15 x 6
# hybrid `1` `2` `3` `4` `5`
# <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 1 1584 878 1419 1412 1812
# 2 2 1820 1742 804 910 506
# 3 3 2193 1976 753 851 664
# 4 4 1206 1535 1530 2273 1265
# 5 5 845 990 1096 1795 1309
# 6 6 1831 1843 1306 1158 2499
# 7 7 1008 1434 1015 2062 1712
# 8 8 1045 1278 1583 1028 1765
# 9 9 913 1317 1500 957 1449
# 10 10 1037 556 1746 1025 1665
# 11 11 1620 638 1050 340 1283
# 12 12 1357 1488 2427 1469 2332
# 13 13 1019 1787 899 1371 866
# 14 14 1436 1140 2176 1570 1615
# 15 15 1662 1476 929 1023 887


Using dcast from data.table
library(data.table)
dcast(setDT(data), hybrid ~ measurement, sum)
# hybrid 1 2 3 4 5
# 1: 1 1584 878 1419 1412 1812
# 2: 2 1820 1742 804 910 506
# 3: 3 2193 1976 753 851 664
# 4: 4 1206 1535 1530 2273 1265
# 5: 5 845 990 1096 1795 1309
# 6: 6 1831 1843 1306 1158 2499
# 7: 7 1008 1434 1015 2062 1712
# 8: 8 1045 1278 1583 1028 1765
# 9: 9 913 1317 1500 957 1449
#10: 10 1037 556 1746 1025 1665
#11: 11 1620 638 1050 340 1283
#12: 12 1357 1488 2427 1469 2332
#13: 13 1019 1787 899 1371 866
#14: 14 1436 1140 2176 1570 1615
#15: 15 1662 1476 929 1023 887

Related

Reducing dataframe if first 4 columns match

I have a dataframe that looks like this
> head(printing_id_map_unique_frames)
# A tibble: 6 x 5
# Groups: frame_number [6]
X1 X2 X3 row_in_frame frame_number
<dbl> <dbl> <dbl> <dbl> <dbl>
1 1 2 3 15 1
2 1 2 3 15 2
3 1 2 3 15 3
4 1 2 3 15 4
5 1 2 3 15 5
6 1 2 3 15 6
As you can see, X1,X2,X3, row_in_frame is identical
However, eventually you get to a
X1 X2 X3 row_in_frame frame_number
<dbl> <dbl> <dbl> <dbl> <dbl>
1 1 2 3 15 32
2 1 2 3 15 33
3 1 2 3 5 34**
4 1 4 5 15 35
5 1 4 5 15 36
What I would like to do is essentially compute a dataframe that looks like:
X1 X2 X3 row_in_frame num_duplicates
<dbl> <dbl> <dbl> <dbl> <dbl>
1 1 2 3 15 33
2 1 2 3 5 1
...
Essentially, what I want is to "collapse" over identical first 4 columns and count how many rows of that type there are in the "num_duplicates" column.
Is there a nice way to do this in dplyr without a messy for loop that tracks a count and if there is a change.
Below please find a full data structure via dput:
> dput(printing_id_map_unique_frames)
structure(list(X1 = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), X2 = c(2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4
), X3 = c(3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 5, 5, 5, 5,
5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
5, 5, 5, 5, 5, 5, 5, 5, 5), row_in_frame = c(15, 15, 15, 15,
15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15,
15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 5, 15, 15,
15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15,
15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 5
), frame_number = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,
14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29,
30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45,
46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61,
62, 63, 64, 65, 66, 67, 68)), row.names = c(NA, -68L), class = c("tbl_df",
"tbl", "data.frame"))

Here is one option with count
library(dplyr) # 1.0.0
df1 %>%
count(!!! rlang::syms(names(.)[1:4]))
Or specify the unquoted column names
df1 %>%
count(X1, X2, X3, row_in_frame)
If we don't want to change the order, an option is to convert the first 4 columns to factor with levels specified as the unique values (which is the same as the order of occurrence of values) and then apply the count
df1 %>%
mutate(across(1:4, ~ factor(.x, levels = unique(.x)))) %>%
count(!!! rlang::syms(names(.)[1:4])) %>%
type.convert(as.is = TRUE)
# A tibble: 4 x 5
# X1 X2 X3 row_in_frame n
# <int> <int> <int> <int> <int>
#1 1 2 3 15 33
#2 1 2 3 5 1
#3 1 4 5 15 33
#4 1 4 5 5 1

Maintain order of time series with group_by

Suppose my time series consists only of two columns: signal and day
The signal variable is supposed to repeat itself in a cycle of 1 to 6. So I need to insert empty rows for each implicit missing Signal but with signal counting from 1 to 6. (Suppose I have more columns that should also be empty (NA)).
In other words, for each unique day, there should be 6 rows with signal counting from 1 to 6.
My dataframe:
df = structure(list(data.Signal = c(2, 3, 4, 5, 6, 1, 2, 3, 4, 6,
1, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 1, 2, 3, 4, 5, 6, 2, 3, 4,
5, 6, 1, 3, 4, 5, 6, 2, 3, 4, 5, 6, 3, 4, 6, 1, 3, 4, 5, 6, 1,
2, 3, 4, 5, 6, 1, 2, 3, 4, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5,
6, 1, 2, 3, 4, 5, 1, 2, 3, 4, 6, 2, 3, 6, 3, 4, 5, 6, 1, 3, 4,
5, 6, 1, 1, 2, 3, 4, 5, 3, 4, 1, 2, 3, 4, 5, 5, 1, 2, 3, 4),
data.day = c(18, 18, 18, 18, 18, 19, 19, 19, 19, 19, 20,
20, 20, 20, 20, 21, 21, 21, 21, 21, 21, 22, 23, 23, 23, 23,
23, 23, 24, 24, 24, 24, 24, 25, 25, 25, 25, 25, 26, 26, 26,
26, 26, 27, 27, 27, 28, 28, 28, 28, 28, 29, 29, 29, 29, 29,
29, 30, 30, 30, 30, 30, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2,
2, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 5, 5, 5, 6, 6, 6, 6, 7,
7, 7, 7, 7, 8, 9, 9, 9, 9, 9, 10, 10, 11, 11, 11, 11, 11,
11, 12, 12, 12, 12)), class = "data.frame", row.names = c(NA,
-114L))
My approach:
data <- df %>%
group_by(gr=data.day) %>%
complete(data.Signal = 1:6) %>%
ungroup() %>%
select(-gr)
This however, sorts the days ascending. The order of the days is obviously meaningful in the time series data. How can I "re-sort" to the original order or is there another way of solving my problem? Thank you!

Convert data.day to factor before using complete
library(dplyr)
df %>%
group_by(gr = factor(data.day, levels = unique(data.day))) %>%
tidyr::complete(data.Signal = 1:6) %>%
ungroup() %>%
select(-gr)
# data.Signal data.day
# <dbl> <dbl>
# 1 1 NA
# 2 2 18
# 3 3 18
# 4 4 18
# 5 5 18
# 6 6 18
# 7 1 19
# 8 2 19
# 9 3 19
#10 4 19
# … with 141 more rows
If you want those NA's filled you can use this version.
df %>%
mutate(grp = factor(data.day, levels = unique(data.day))) %>%
complete(grp, data.Signal = 1:6) %>%
ungroup() %>%
select(-data.day)

Calculate row similarity percentage pair wise and add it as a new column

I have a date frame like this sample, I would like to find similar rows (not duplicate) and calculate similarity per wise. I find this solution but i would like to keep all my columns and add similarity percentage as a new variable. My aim is to find records with highest similarity percentage. How could I do it ?
sample data set
df <- tibble::tribble(
~date, ~user_id, ~Station_id, ~location_id, ~ind_id, ~start_hour, ~start_minute, ~start_second, ~end_hour, ~end_minute, ~end_second, ~duration_min,
20191015, 19900234, 242, 2, "ac", 7, 25, 0, 7, 30, 59, 6,
20191015, 19900234, 242, 2, "ac", 7, 31, 0, 7, 32, 59, 2,
20191015, 19900234, 242, 2, "ac", 7, 33, 0, 7, 38, 59, 6,
20191015, 19900234, 242, 2, "ac", 7, 39, 0, 7, 40, 59, 2,
20191015, 19900234, 242, 2, "ac", 7, 41, 0, 7, 43, 59, 3,
20191015, 19900234, 242, 2, "ac", 7, 44, 0, 7, 45, 59, 2,
20191015, 19900234, 242, 2, "ac", 7, 47, 0, 7, 59, 59, 13,
20191015, 19900234, 242, 2, "ad", 7, 47, 0, 7, 59, 59, 13,
20191015, 19900234, 242, 2, "ac", 8, 5, 0, 8, 6, 59, 2,
20191015, 19900234, 242, 2, "ad", 8, 5, 0, 8, 6, 59, 2,
20191015, 19900234, 242, 2, "ac", 8, 7, 0, 8, 8, 59, 2,
20191015, 19900234, 242, 2, "ad", 8, 7, 0, 8, 8, 59, 2,
20191015, 19900234, 242, 2, "ac", 16, 26, 0, 16, 55, 59, 30,
20191015, 19900234, 242, 2, "ad", 16, 26, 0, 16, 55, 59, 30,
20191015, 19900234, 242, 2, "ad", 17, 5, 0, 17, 6, 59, 2,
20191015, 19900234, 242, 2, "ac", 17, 5, 0, 17, 23, 59, 19,
20191015, 19900234, 242, 2, "ad", 17, 7, 0, 17, 15, 59, 9,
20191015, 19900234, 242, 2, "ad", 17, 16, 0, 17, 22, 59, 7,
20191015, 19900234, 264, 2, "ac", 17, 24, 0, 17, 35, 59, 12,
20191015, 19900234, 264, 2, "ad", 17, 25, 0, 17, 35, 59, 11,
20191016, 19900234, 242, 1, "ac", 7, 12, 0, 7, 14, 59, 3,
20191016, 19900234, 242, 1, "ad", 7, 13, 0, 7, 13, 59, 1,
20191016, 19900234, 242, 1, "ac", 17, 45, 0, 17, 49, 59, 5,
20191016, 19900234, 242, 1, "ad", 17, 46, 0, 17, 48, 59, 3,
20191016, 19900234, 242, 2, "ad", 7, 14, 0, 8, 0, 59, 47,
20191016, 19900234, 242, 2, "ac", 7, 15, 0, 8, 0, 59, 47
)
Function for comparing rows
row_cf <- function(x, y, df){
sum(df[x,] == df[y,])/ncol(df)
}
Function output
# 1) Create all possible row combinations
# 2) Rename
# 3) Run through each row
# 4) Calculate similarity
expand.grid(1:nrow(df), 1:nrow(df)) %>%
rename(row_1 = Var1, row_2 = Var2) %>%
rowwise() %>%
mutate(similarity = row_cf(row_1, row_2, df))
# A tibble: 676 x 3
row_1 row_2 similarity
<int> <int> <dbl>
1 1 1 1
2 2 1 0.75
3 3 1 0.833
4 4 1 0.75
5 5 1 0.75
6 6 1 0.75
7 7 1 0.75
8 8 1 0.667
9 9 1 0.583
10 10 1 0.5
Edit:
I would like to find similar rows in the data like here

Using your "function output", call it sim. Eliminate the self-comparisons and then keep the max similarity row grouped by row_1:
sim = sim %>%
filter(row_1 != row_2) %>%
group_by(row_1) %>%
slice(which.max(similarity))
Then you can add these to your original data:
df %>% mutate(row_1 = 1:n()) %>%
left_join(sim)
The row_2 column gives the row number of the most similar row, and similarity gives its similarity score. (You may want to improve these column names.)

separate data in a column into multiple columns

I have some data which looks like:
> head(d)
V1 V2 V3 V4 V5 V6 V7 V8 V9
50 28 79 4 6 48 2 17 4 20
51 28 79 4 6 48 2 17 4 21
52 28 79 4 6 48 2 17 4 22
53 28 79 4 6 48 2 17 4 23
54 28 79 4 6 48 2 17 4 24
55 28 79 4 6 48 2 17 4 25
V10
50 000.3V000.3V000.3V000.3V000.3V000.3V000.3V000.4V000.4V000.5V000.4V000.4V000.4V000.4V000.4V000.4V000.4V000.4V000.4V000.4V000.4V000.4V000.4V000.4V
51 000.3V000.3V000.3V000.3V000.3V000.3V000.3V000.5V000.8V000.8V000.5V000.4V000.4V000.4V000.5V000.4V000.4V000.4V000.4V000.4V000.4V000.4V000.5V000.6V
52 000.3V000.3V000.3V000.3V000.3V000.3V000.4V000.4V000.4V000.6V000.4V000.4V000.5V000.5V000.5V000.4V000.4V000.4V000.5V000.5V000.5V000.7V000.7V000.8V
53 000.7V000.5V000.4V000.5V000.5V000.4V000.4V000.4V000.4V000.5V000.5V000.4V000.4V000.3V000.4V000.4V000.4V000.3V000.4V000.3V000.4V000.9V001.0V001.0V
54 000.8V000.5V000.3V000.4V000.4V000.4V000.7V001.2V001.2V001.0N000.5V000.4V000.4V000.4V000.4V000.3V000.3V000.3V000.4V000.4V000.5V000.5V000.6V000.6V
55 000.5V000.5V000.3V000.3V000.3V000.3V000.3V000.4V000.5V000.6V000.5V000.5V000.4V000.3V000.4V000.3V000.3V000.3V000.3V000.3V000.4V000.4V000.3V000.4V
Where column V10 is a large column separated by 24 V's and N's. The V's are "valid" observations and the N's are non-valid observations.
I want to separate the V10 column. I tried using the following but it does not solve the N's problem.
sep_data <- df %>%
separate(V10, into = paste("x",1:24, sep = "_"), sep = "V")
Looking at the tail() of the sep_data:
V1 V2 V3 V4 V5 x_1 x_2 x_3 x_4 x_5 x_6 x_7 x_8 x_9 x_10 x_11 x_12
174 28079008 6 48 2 17042 4002.0 001.1 000.6 000.4 000.4 000.4 000.4 000.5 000.7 000.9 000.7N000.5 000.5
175 28079008 6 48 2 17042 5000.7 000.5 000.4 000.3 000.3 000.3 000.3 000.4 000.5 000.6 000.5 000.5
176 28079008 6 48 2 17042 6000.4 000.3 000.3 000.3 000.3 000.3 000.3 000.4 000.5 000.6 000.5 000.4
177 28079008 6 48 2 17042 7000.3 000.3 000.2 000.2 000.2 000.2 000.2 000.3 000.4 000.5 000.4 000.4
178 28079008 6 48 2 17042 8000.3 000.3 000.3 000.3 000.3 000.3 000.3 000.4 000.5 000.5 000.5 000.5
179 28079008 6 48 2 17042 9000.3 000.3 000.3 000.3 000.3 000.3 000.3 000.3 000.3 000.3 000.3 000.3
x_13 x_14 x_15 x_16 x_17 x_18 x_19 x_20 x_21 x_22 x_23 x_24 nchar
174 000.4 000.5 000.5 000.5 000.5 000.5 000.6 000.6 000.6 000.6 000.7 145
175 000.5 000.5 000.5 000.5 000.5 000.5 000.5 000.6 000.6 000.6 000.6 000.5 145
176 000.4 000.4 000.5 000.5 000.5 000.5 000.5 000.5 000.5 000.4 000.4 000.3 145
177 000.4 000.4 000.4 000.4 000.4 000.5 000.5 000.6 000.5 000.5 000.5 000.4 145
178 000.4 000.4 000.4 000.5 000.5 000.5 000.5 000.4 000.4 000.4 000.4 000.4 145
179 000.4 000.3 000.3 000.3 000.3 000.3 000.4 000.5 000.5 000.5 000.6 000.5 145
I have 000.7N000.5.
How can I use separate() to separate based on V OR N.
Data:
structure(list(V1 = c(28, 28, 28, 28, 28, 28, 28, 28, 28, 28,
28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28,
28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28,
28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28,
28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28,
28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28,
28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28,
28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28,
28, 28, 28, 28, 28, 28, 28, 28), V2 = c(79, 79, 79, 79, 79, 79,
79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79,
79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79,
79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79,
79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79,
79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79,
79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79,
79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79,
79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79), V3 = c(4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,
8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,
8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,
8, 8), V4 = c(6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,
8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,
6, 6, 6, 6, 6, 6, 6, 6, 6), V5 = c(48, 48, 48, 48, 48, 48, 48,
48, 48, 48, 48, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,
8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,
8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,
8, 8, 8, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38,
38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38,
38, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48,
48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48), V6 = c(2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2), V7 = c(17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17,
17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17,
17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17,
17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17,
17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17,
17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17,
17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17,
17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17,
17, 17, 17, 17, 17, 17, 17), V8 = c(4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4), V9 = c(20, 21,
22, 23, 24, 25, 26, 27, 28, 29, 30, 1, 2, 3, 4, 5, 6, 7, 8, 9,
10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,
26, 27, 28, 29, 30, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,
14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29,
30, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,
18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 1, 2, 3,
4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,
21, 22, 23, 24, 25, 26, 27, 28, 29), V10 = c("000.3V000.3V000.3V000.3V000.3V000.3V000.3V000.4V000.4V000.5V000.4V000.4V000.4V000.4V000.4V000.4V000.4V000.4V000.4V000.4V000.4V000.4V000.4V000.4V",
"000.3V000.3V000.3V000.3V000.3V000.3V000.3V000.5V000.8V000.8V000.5V000.4V000.4V000.4V000.5V000.4V000.4V000.4V000.4V000.4V000.4V000.4V000.5V000.6V",
"000.3V000.3V000.3V000.3V000.3V000.3V000.4V000.4V000.4V000.6V000.4V000.4V000.5V000.5V000.5V000.4V000.4V000.4V000.5V000.5V000.5V000.7V000.7V000.8V",
"000.7V000.5V000.4V000.5V000.5V000.4V000.4V000.4V000.4V000.5V000.5V000.4V000.4V000.3V000.4V000.4V000.4V000.3V000.4V000.3V000.4V000.9V001.0V001.0V",
"000.8V000.5V000.3V000.4V000.4V000.4V000.7V001.2V001.2V001.0N000.5V000.4V000.4V000.4V000.4V000.3V000.3V000.3V000.4V000.4V000.5V000.5V000.6V000.6V",
"000.5V000.5V000.3V000.3V000.3V000.3V000.3V000.4V000.5V000.6V000.5V000.5V000.4V000.3V000.4V000.3V000.3V000.3V000.3V000.3V000.4V000.4V000.3V000.4V",
"000.3V000.3V000.3V000.3V000.3V000.3V000.3V000.4V000.5V000.5V000.4V000.4V000.3V000.3V000.3V000.3V000.3V000.4V000.4V000.5V000.4V000.3V000.3V000.3V",
"000.3V000.3V000.3V000.3V000.3V000.3V000.3V000.3V000.4V000.3V000.3V000.3V000.3V000.4V000.4V000.4V000.3V000.4V000.4V000.4V000.4V000.4V000.4V000.3V",
"000.3V000.3V000.3V000.3V000.3V000.3V000.3V000.3V000.3V000.3V000.3V000.3V000.3V000.4V000.4V000.4V000.4V000.4V000.4V000.3V000.4V000.4V000.4V000.3V",
"000.4V000.3V000.3V000.3V000.3V000.3V000.3V000.3V000.3V000.3V000.4V000.4V000.4V000.4V000.4V000.4V000.4V000.4V000.5V000.5V000.4V000.5V000.6V000.6V",
"000.7V000.7V000.6V000.4V000.4V000.3V000.3V000.3V000.3V000.3V000.3V000.3V000.3V000.4V000.4V000.3V000.3V000.3V000.3V000.3V000.4V000.4V000.4V000.4V",
"00003V00004V00002V00002V00001V00002V00004V00003V00005V00005V00005V00009V00011V00014V00014V00009V00008V00010V00011V00014V00011V00008V00009V00006V",
"00005V00005V00003V00003V00002V00003V00009V00023V00069V00068V00008V00013V00010V00007V00012V00008V00007V00004V00006V00005V00007V00006V00004V00004V",
"00002V00001V00002V00001V00001V00001V00003V00007V00018V00022V00042V00034V00021V00016V00018V00011V00009V00009V00014V00010V00010V00033V00083V00111V",
"00098V00046V00016V00010V00005V00013V00052V00217V00337V00485V00138V00029V00026V00020V00019V00011V00006V00012V00012V00008V00010V00007V00005V00004V",
"00002V00002V00001V00001V00001V00001V00002V00007V00013V00020V00012V00010V00010V00012V00009V00007V00006V00008V00010V00009V00010V00006V00004V00003V",
"00002V00001V00001V00001V00001V00002V00003V00009V00014V00015V00013V00014V00018V00017V00010V00009V00010V00009V00006V00007V00008V00036V00061V00007V",
"00005V00005V00002V00002V00002V00002V00003V00008V00055V00038V00020V00023V00022V00015V00017V00011V00007V00006V00009V00009V00015V00117V00082V00096V",
"00046V00031V00047V00042V00037V00033V00040V00044V00067V00093V00053V00009V00006V00006V00006V00004V00003V00004V00020V00016V00010V00020V00049V00104V",
"00088V00112V00055V00046V00013V00043V00023V00023V00040V00093V00046V00011V00007V00007V00007V00005V00008V00007V00010V00010V00025V00079V00059V00025V",
"00028V00038V00022V00017V00018V00028V00049V00152V00116V00158V00087V00030V00024V00017V00018V00008V00008V00008V00010V00013V00012V00022V00072V00083V",
"00060V00024V00032V00026V00014V00032V00078V00129V00199V00164V00107V00067V00052V00024V00012V00009V00015V00015V00036V00024V00019V00035V00056V00062V",
"00020V00046V00042V00019V00003V00004V00020V00091V00159V00137V00121V00052V00045V00039V00008V00004V00005V00004V00008V00009V00004V00002V00009V00005V",
"00070V00027V00026V00038V00020V00011V00016V00057V00073V00111V00053V00031V00018V00005V00003V00002V00002V00002V00010V00030V00033V00009V00005V00017V",
"00033V00059V00042V00019V00018V00015V00017V00024V00036V00063V00026V00008V00005V00003V00003V00002V00003V00005V00005V00009V00009V00007V00005V00006V",
"00009V00004V00001V00001V00002V00015V00012V00019V00031V00028V00013V00010V00008V00007V00004V00003V00003V00006V00017V00017V00014V00024V00012V00002V",
"00002V00002V00003V00002V00001V00001V00002V00002V00009V00003V00004V00004V00006V00007V00010V00005V00004V00003V00003V00002V00002V00005V00067V00080V",
"00063V00039V00025V00004V00003V00006V00015V00062V00144V00098V00079V00026V00017V00015V00007V00005V00004V00006V00004V00003V00003V00002V00003V00005V",
"00004V00001V00001V00001V00001V00001V00002V00005V00011V00014V00013V00012V00013V00020V00014V00013V00008V00007V00007V00006V00007V00006V00003V00002V",
"00002V00001V00001V00001V00001V00001V00002V00004V00008V00012V00025V00020V00026V00016V00017V00012V00011V00012V00017V00010V00012V00011V00006V00004V",
"00003V00002V00002V00001V00001V00002V00008V00013V00013V00019V00022V00015V00019V00018V00015V00015V00010V00011V00016V00015V00012V00011V00007V00005V",
"00003V00002V00002V00001V00001V00002V00004V00026V00145V00131V00027V00021V00020V00017V00019V00014V00011V00013V00012V00010V00007V00016V00025V00034V",
"00004V00003V00003V00002V00002V00003V00007V00024V00022V00046V00013V00011V00012V00016V00019V00011V00010V00010V00018V00022V00039V00060V00051V00121V",
"00085V00038V00024V00056V00039V00024V00022V00026V00040V00058V00041V00010V00010V00008V00013V00008V00008V00006V00013V00005V00004V00068V00079V00102V",
"00081V00019V00005V00015V00008V00048V00114V00179V00240V00153N00031V00021V00024V00018V00013V00008V00004V00006V00010V00014V00009V00013V00010V00032V",
"00018V00030V00006V00001V00001V00001V00003V00024V00038V00046V00024V00009V00010V00010V00006V00004V00004V00004V00004V00006V00004V00003V00002V00006V",
"00003V00002V00002V00001V00001V00001V00002V00012V00021V00025V00027V00011V00009V00006V00007V00004V00006V00005V00006V00020V00008V00004V00003V00004V",
"00002V00001V00001V00001V00001V00001V00002V00004V00007V00010V00011V00013V00013V00018V00010V00006V00005V00007V00010V00014V00016V00009V00006V00004V",
"00003V00002V00001V00001V00001V00001V00003V00006V00009V00012V00011V00015V00019V00029V00022V00016V00010V00013V00009V00008V00012V00012V00017V00009V",
"00009V00006V00003V00002V00003V00002V00003V00007V00007V00009V00010V00012V00011V00022V00013V00011V00011V00013V00019V00029V00022V00024V00037V00047V",
"00074V00082V00059V00021V00003V00002V00001V00002V00002V00002V00003V00003V00003V00004V00007V00007V00004V00004V00005V00010V00007V00009V00005V00005V",
"00025V00021V00010V00010V00008V00016V00032V00023V00023V00017V00013V00021V00024V00028V00027V00020V00020V00024V00029V00040V00048V00049V00043V00028V",
"00027V00033V00035V00022V00026V00036V00048V00063V00065V00059V00022V00029V00025V00022V00029V00028V00021V00017V00018V00021V00031V00030V00024V00020V",
"00012V00008V00008V00006V00005V00009V00018V00040V00059V00053V00063V00060V00044V00040V00043V00032V00029V00032V00052V00058V00071V00110V00128V00117V",
"00097V00074V00058V00049V00043V00043V00052V00094V00127V00160V00091V00050V00043V00038V00040V00031V00021V00038V00046V00043V00050V00061V00050V00022V",
"00017V00014V00019V00018V00008V00011V00014V00042V00050V00048V00027V00021V00019V00021V00016V00013V00016V00019V00028V00030V00034V00035V00021V00016V",
"00010V00008V00007V00006V00007V00009V00015V00040V00058V00045V00034V00032V00039V00032V00023V00024V00029V00029V00033V00039V00068V00112V00096V00047V",
"00030V00024V00018V00010V00010V00011V00019V00045V00071V00061V00050V00052V00049V00045V00046V00038V00027V00029V00041V00046V00073V00143V00131V00109V",
"00065V00054V00063V00067V00060V00055V00060V00062V00067V00075V00068V00036V00026V00025V00028V00020V00016V00020V00044V00056V00062V00098V00127V00116V",
"00091V00094V00079V00067V00040V00057V00056V00054V00054V00065V00059V00041V00027V00030V00026V00019V00023V00022V00032V00040V00060V00087V00079V00054V",
"00047V00061V00057V00050V00049V00049V00056V00085V00074V00086V00084V00062V00058V00048V00047V00027V00026V00026V00036V00064V00085V00096V00117V00106V",
"00084V00072V00062V00055V00042V00055V00066V00082V00099V00098V00096V00092V00089V00064V00045V00038V00036V00042V00075V00066V00066V00087V00088V00103V",
"00092V00090V00074V00059V00046V00038V00051V00076V00092V00091V00097V00084V00084V00081V00033V00017V00020V00018V00031V00045V00038V00041V00038V00065V",
"00113V00086V00082V00072V00059V00050V00050V00057V00058V00067V00072V00070V00061V00025V00009V00007V00010V00013V00028V00071V00084V00060V00044V00068V",
"00090V00084V00070V00061V00053V00049V00041V00041V00041V00048V00043V00030V00021V00012V00011V00007V00007V00009V00013V00021V00034V00044V00040V00041V",
"00057V00049V00015V00027V00032V00047V00041V00045V00046V00040V00034V00028V00023V00022V00009V00008V00007V00014V00033V00049V00056V00072V00039V00019V",
"00021V00017V00013V00008V00007V00011V00015V00013V00027V00010V00012V00011V00016V00023V00031V00021V00020V00017V00017V00021V00035V00073V00130V00115V",
"00102V00084V00071V00049V00041V00043V00045V00067V00083V00082V00083V00058V00048V00049V00028V00019V00015V00021V00025V00026V00033V00031V00042V00060V",
"00025V00009V00006V00010V00006V00008V00016V00038V00054V00047V00038V00037V00040V00056V00041V00041V00027V00027V00029V00034V00049V00065V00024V00012V",
"00010V00007V00004V00004V00003V00004V00010V00025V00041V00039V00051V00046V00051V00046V00046V00033V00031V00035V00040V00030V00036V00041V00035V00028V",
"00020V00015V00013V00014V00012V00017V00032V00056V00051V00057V00052V00037V00040V00039V00038V00040V00033V00036V00047V00050V00044V00046V00038V00032V",
"00021V00017V00015V00012V00010V00013V00024V00050V00101V00099V00053V00048V00043V00040V00044V00038V00035V00042V00043V00042V00040V00057V00060V00058V",
"00036V00030V00028V00021V00020V00035V00049V00067V00058V00070V00042V00038V00044V00053V00050V00044V00039V00043V00054V00059V00082V00098V00116V00119V",
"00101V00085V00076V00078V00066V00058V00054V00053V00049V00053V00058V00035V00033V00028V00032V00029V00027V00025V00047V00028V00049V00122V00135V00123V",
"00113V00077V00040V00041V00050V00056V00074V00091V00113V00097N00068V00055V00059V00044V00037V00024V00020V00028V00045V00058V00070V00082V00084V00102V",
"00082V00077V00044V00019V00012V00009V00029V00077V00084V00081V00051V00028V00031V00031V00022V00017V00014V00015V00015V00027V00037V00038V00020V00031V",
"00019V00010V00010V00009V00006V00010V00019V00048V00068V00058V00047V00030V00022V00018V00017V00014V00019V00018V00026V00059V00031V00019V00017V00014V",
"00009V00007V00003V00002V00002V00004V00008V00020V00030V00029V00024V00028V00028V00031V00022V00017V00016V00020V00028V00034V00039V00037V00029V00019V",
"00012V00009V00005V00004V00003V00003V00010V00025V00037V00034V00027V00031V00036V00043V00040V00037V00035V00040V00031V00027V00039V00038V00038V00032V",
"00031V00028V00022V00018V00021V00019V00021V00033V00036V00040V00041V00045V00040V00043V00039V00037V00041V00048V00053V00053V00052V00056V00055V00055V",
"00050V00044V00047V00048V00030V00011V00008V00011V00012V00010V00008V00014V00011V00017V00031V00022V00009V00008V00018V00038V00040V00043V00033V00027V",
"00004V00004V00004V00004V00003V00004V00004V00004V00004V00004V00004V00004V00004V00004V00004V00004V00004V00004V00004V00004V00004V00004V00004V00004V",
"00004V00004V00004V00004V00004V00004V00004V00004V00004V00005V00005V00005V00005V00005V00005V00004V00005V00005V00005V00006V00006V00005V00005V00004V",
"00004V00004V00004V00004V00004V00004V00004V00004V00005V00005V00006V00006V00005V00005V00004V00005V00004V00005V00005V00005V00005V00005V00006V00008V",
"00006V00006V00006V00005V00005V00005V00004V00005V00008V00006V00006V00006V00005V00005V00005V00004V00004V00004V00004V00005V00006V00006V00006V00005V",
"00004V00004V00004V00004V00004V00004V00004V00004V00005V00005V00005V00006V00006V00005V00005V00005V00005V00005V00005V00005V00005V00005V00004V00004V",
"00004V00004V00004V00004V00004V00004V00004V00004V00005V00006V00005V00005V00005V00005V00005V00006V00005V00005V00005V00006V00007V00008V00007V00005V",
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"002.0V001.1V000.6V000.4V000.4V000.4V000.4V000.5V000.7V000.9V000.7N000.5V000.5V000.4V000.5V000.5V000.5V000.5V000.5V000.6V000.6V000.6V000.6V000.7V",
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"000.3V000.3V000.3V000.3V000.3V000.3V000.3V000.4V000.5V000.5V000.5V000.5V000.4V000.4V000.4V000.5V000.5V000.5V000.5V000.4V000.4V000.4V000.4V000.4V",
"000.3V000.3V000.3V000.3V000.3V000.3V000.3V000.3V000.3V000.3V000.3V000.3V000.4V000.3V000.3V000.3V000.3V000.3V000.4V000.5V000.5V000.5V000.6V000.5V"
)), row.names = 50:179, class = "data.frame")

How can I use separate() to separate based on V OR N? sep = "V|N".
But I'd suggest pre-processing. Use gsub or stringr::str_replace_all to make the N entries NA before separating. Something like df$V10 = gsub("N\\d+(\\.)?\\d+", "VNA", df$V10). Replace NXXX.XX with VNA, then splitting on V should leave NAs where the Ns originally were.

R - Keep reading line if 7 or more numbers are => 10

I have a file foo.txt that looks like this:
7, 3, 5, 7, 3, 3, 3, 3, 3, 3, 3, 6, 7, 5, 5, 22, 18, 14, 23, 16, 18, 5, 13, 34, 24, 17, 50, 30, 42, 35, 29, 27, 52, 35, 44, 52, 36, 39, 25, 40, 50, 52, 40, 2, 52, 52, 31, 35, 30, 19, 32, 46, 50, 43, 36, 15, 21, 16, 36, 25, 7, 3, 5, 7, 3, 3, 3, 3, 3, 3, 3, 6
I want to read the numbers in sets of 15, moving to the right one number at the time:
7, 3, 5, 7, 3, 3, 3, 3, 3, 3, 3, 6, 7, 5, 5
then
3, 5, 7, 3, 3, 3, 3, 3, 3, 3, 6, 7, 5, 5, 22
and so on.
If 7 or more of those 15 numbers are =>10 then keep them in a growing object that ends when the condition isn't met. So the first one to keep would be
3, 3, 3, 6, 7, 5, 5, 22, 18, 14, 23, 16, 18, 5, 13
because 7 out of those 15 numbers are => 10 (those numbers are 22, 18, 14, 23, 16, 18 and 13
The output file would look like this:
3, 3, 3, 6, 7, 5, 5, 22, 18, 14, 23, 16, 18, 5, 13, 34, 24, 17, 50, 30, 42, 35, 29, 27, 52, 35, 44, 52, 36, 39, 25, 40, 50, 52, 40, 2, 52, 52, 31, 35, 30, 19, 32, 46, 50, 43, 36, 15, 21, 16, 36, 25, 7, 3, 5, 7, 3, 3, 3, 3
So far I'm stuck at getting sets of 15 digits but I don't know how to make the condition "7 or more must be => 10"
qual <- readLines("foo.txt", 1)
separados <- unlist(strsplit(qual, ", "))
for (i in 1:length(qual)) {
separados[(i):(i + 14)] -> numbers
I don't mind the language as long as it does the work

I've added two ='s to Vlo's solutions and made this for you. Does this answer your question?
foo.txt <- c(7, 3, 5, 7, 3, 3, 3, 3, 3, 3, 3, 6, 7, 5, 5, 22, 18, 14, 23, 16, 18, 5,
13, 34, 24, 17, 50, 30, 42, 35, 29, 27, 52, 35, 44, 52, 36, 39, 25, 40,
50, 52, 40, 2, 52, 52, 31, 35, 30, 19, 32, 46, 50, 43, 36, 15, 21, 16,
36, 25, 7, 3, 5, 7, 3, 3, 3, 3, 3, 3, 3, 6)
# install.packages(c("zoo"), dependencies = TRUE)
require(zoo)
bar <- rollapply(foo.txt, 15, function(x) sum(x >= 10 ) >= 7)
(product <- foo.txt[bar])
[1] 3 3 3 6 7 5 5 22 18 14 23 16 18 5 13 34 24 17 50 30 42 35 29 27
[25] 52 35 44 52 36 39 25 40 50 52 40 2 52 52 31 35 30 19 32 46 50 43 3 3
[49] 3 3 3 6

I would do it in Python (you said you don't mind the language):
array = []
with open("foo.txt","r") as f:
for line in f:
for num in line.strip().split(', '):
array.append(int(num))
result = []
growing = False
while len(array) >= 15:
if sum(1 for e in filter(lambda x: x>=10, array[:15])) >= 7:
if growing:
result.append(array[15])
else:
result.extend(array[:15])
growing = True
else:
growing = False
del(array[0])
print(str(result)[1:-1])
Short explanation: first while simply reads the lines in the file, strips end of line, separates every number between ", " characters and appends each number to array.
Second while checks the first 15 numbers in array; if they have at least 7 numbers >= 0, it appends all the numbers, or just the last one (depending if the last iteration), to result. At the end of the loop, it removes the first number in array so that the loop can continue with the next 15 numbers.

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