Use a pre-existing value within a function in dplyr - r

Question

The problem
I am having a lot of difficulty using a known value within a function within dplyr. The issue is with the following line. The rest of what follows it is just data that leads to the problematic component.
data <- data %>%
group_by(Group) %>%
bind_cols(as_tibble(rotate2(as.matrix(.)[,1:2], theta = min(.$theta))))
The min(.$theta) is my attempt to try to find the theta value within each group and use it. There is a column in the data created (as shown below) which contains this value. I want to take the value from each group (Group) and use it with rotate2. There are only two groups in the sample below, but the real data has hundreds of groups. What I want to know is: how can I use the existing value for each group (the theta column repeats the same value for each group).
Is there something I can replace min(.$theta) with that would do this? It seems to take data from the entire column, rather than taking the value from each Group individually.
Data to get to the problem
Packages - dplyr, plyr, lava
data <- structure(list(X = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, 4.9046,
6.1424, 7.275, 8.5851, 10.0373, 11.9981, 13.7726, 15.0731, 16.0664,
18.1945, 21.2666, 24.2093, 26.7119, 28.8037, 30.7135, 32.1351,
33.1982, 34.2341, 35.7587, 37.2147, 38.4303, 39.625, 40.4596,
42.0938, 42.7428, 42.7593, 43.5085, 43.7419, 43.5989, 44.0841,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, -14.845, -11.9052,
-8.7897, -5.8034, -2.6756, 0.3316, 3.4003, 6.5281, 9.6517, 12.804,
15.9861, 19.1769, 22.2929, 25.4089, 28.3392, 31.0054, 33.1847,
35.081, 36.7227, 38.1544, 39.1697, 40.049, 40.9647, 41.5014,
41.8874, 42.1778, 42.3435, 42.2681, 42.3745, 42.4619, NA, NA,
NA, NA), Y = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, -9.9938, -7.4596,
-4.8647, -2.2903, 0.3158, 2.9302, 5.7262, 8.7033, 11.8007, 14.9847,
16.7225, 16.7813, 15.6921, 14.2964, 11.5579, 8.2378, 5.183, 1.5938,
-2.0712, -5.195, -7.1447, -9.0446, -11.1269, -13.0979, -15.3295,
-17.1898, -19.4376, -21.4781, -23.8426, -25.6343, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, 8.0113, 9.1826, 9.838, 10.7908,
11.175, 12.0393, 12.6813, 12.8828, 13.2281, 13.5102, 13.6637,
13.5493, 12.8699, 12.2191, 10.9208, 9.0209, 6.2158, 3.2466, 0.2169,
-2.7807, -6.0439, -9.1262, -11.8684, -14.7779, -17.5825, -20.2452,
-22.807, -25.3519, -27.6105, -29.7536, NA, NA, NA, NA), fan_line = c(1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L,
16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L,
29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L,
42L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L,
14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L,
27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L,
40L, 41L, 42L)), class = "data.frame", row.names = c(NA, -84L
))
data <- data %>% mutate(Group = rep(1:(n()/42), each = 42)) %>% dplyr::group_by(Group) %>%
mutate(start = min(which(!is.na(X))), end = max(which(!is.na(X))), midpoint = round((start+end)/2, digits = 0)) %>% ungroup()
data$start_val_x <- 0
data$end_val_x <- 0
data$start_val_y <- 0
data$end_val_y <- 0
for (i in 1:nrow(data)){
if (data[i, "fan_line"] == data[i, "start"]){
data[i, "start_val_x"] = data[i, "X"]
data[i, "start_val_y"] = data[i, "Y"]
}
else{data[i, "start_val_y"] = NA
data[i, "start_val_x"] = NA}
}
for (i in 1:nrow(data)){
if (data[i, "fan_line"] == data[i, "end"]){
data[i, "end_val_x"] = data[i, "X"]
data[i, "end_val_y"] = data[i, "Y"]
}
else{data[i, "end_val_y"] = NA
data[i, "end_val_x"] = NA}
}
data <- data %>% group_by(Group) %>% fill(c(start_val_x, start_val_y), .direction = "down") %>% fill(c(start_val_x, start_val_y), .direction = "up")
data <- data %>% group_by(Group) %>% fill(c(end_val_x, end_val_y), .direction = "down") %>% fill(c(end_val_x, end_val_y), .direction = "up")
data <- data %>% group_by(Group) %>% mutate(theta = max(atan(diff(c(start_val_y, end_val_y))/diff(c(start_val_x, end_val_x))), na.rm = T))
data <- data %>% group_by(Group) %>% bind_cols(as_tibble(rotate2(as.matrix(.)[,1:2], theta = min(.$theta))))

Answer 1

We could use group_modify. However, I'm not sure if the outcome below is what you are looking for.
In a normal dplyr pipeline we could use cur_data() to access the data of each group. This is not possible here, because we are inside a non-dplyr function. For this case we have group_map (which returns a list) and group_modify (which returns a grouped tibble as long as each output is a data.frame). We can use a lambda function and here .x is our grouped data.
library(tidyverse)
library(lava)
data %>%
group_by(Group) %>%
group_modify(~ as_tibble(rotate2(as.matrix(.x)[,1:2], theta = min(.x$theta))))
#> Warning: The `x` argument of `as_tibble.matrix()` must have unique column names if `.name_repair` is omitted as of tibble 2.0.0.
#> Using compatibility `.name_repair`.
#> # A tibble: 84 x 3
#> # Groups: Group [2]
#> Group V1 V2
#> <int> <dbl> <dbl>
#> 1 1 NA NA
#> 2 1 NA NA
#> 3 1 NA NA
#> 4 1 NA NA
#> 5 1 NA NA
#> 6 1 NA NA
#> 7 1 NA NA
#> 8 1 NA NA
#> 9 1 NA NA
#> 10 1 8.26 -7.46
#> # … with 74 more rows
Created on 2021-04-13 by the reprex package (v0.3.0)