用dplyr+绘制分支运动；ggplot2

我试图说明在四步旋转散列中，输入位如何影响输出位。哈希函数如下所示：

#define rot(x,k) (((x)<<(k)) | ((x)>>(32-(k))))
uint32_t rotating_hash(uint32_t state, uint32_t input)
{
    uint32_t hash = state;
    uint8_t *p = (uint8_t*)&input;
    //      mix          ; combine
    hash ^=                *(p++);
    hash += rot(hash, 4) ^ *(p++);
    hash += rot(hash, 4) ^ *(p++);
    hash += rot(hash, 4) ^ *p;
    return hash;
}

library(tibble)
library(dplyr)
library(magrittr)

rot_hash_positions <- function(bit) {
  operation_1 <- bit
  operation_2 <- outer(operation_1, c(4, 0, -28), FUN = '+')  %>% as.vector() %>% { . %% 32 }
  operation_3 <- outer(operation_2, c(4, 0, -28), FUN = '+')  %>% as.vector() %>% { . %% 32 }
  operation_4 <- outer(operation_3, c(4, 0, -28), FUN = '+')  %>% as.vector() %>% { . %% 32 }
  rbind(tibble(bit = bit, operation = 1, positions = operation_1),
        tibble(bit = bit, operation = 2, positions = operation_2),
        tibble(bit = bit, operation = 3, positions = operation_3),
        tibble(bit = bit, operation = 4, positions = operation_4))
}

bit_movement <- do.call(rbind, lapply(0:31, rot_hash_positions))

> bit_movement %>% filter(bit == 0)
# A tibble: 10 x 3
     bit operation positions
   <int>     <dbl>     <dbl>
 1     0         1         0
 2     0         2         4
 3     0         2         0
 4     0         3         8
 5     0         3         4
 6     0         3         0
 7     0         4        12
 8     0         4         8
 9     0         4         4
10     0         4         0

library(ggplot2)

plot_bitmovement <- function(bit_movement, highlight_bits) {
  ggplot(bit_movement, aes(
    y = positions,
    x = operation,
    group = factor(bit, levels = 1:32)
  )) +
    geom_line(colour = "gray") +
    geom_point(colour = "gray") +
    geom_line(data = highlight_bits, colour = "black") +
    geom_point(data = highlight_bits, colour = "black") +
    coord_flip() +
    scale_y_reverse(breaks = 0:31, labels = 1:32) +
    scale_x_reverse() +
    theme_minimal() +
    theme(
      legend.position = "none"
    ) + ylab("Bit-position") + xlab("Operation")
}

其中我突出显示了绘图中的第一个或最后一个字节：

bit_movement <- do.call(rbind, lapply(0:31, rot_hash_positions))
plot_bitmovement(bit_movement, bit_movement %>% filter(bit < 8))

位\u移动百分比过滤器（位<8））

---

last_byte我想出了这个主意。我不知道它有多优雅，所以我还是喜欢评论它

library(purrr)
get_steps <- function(bm, step) {
  op1 <- bm %>%
    filter(operation == !!(step-1)) %>%
    mutate(x = operation, y = positions) %>%
    select(bit, x, y)
  op2 <- bm %>%
    filter(operation == !!step) %>%
    mutate(xend = operation, yend = positions) %>%
    select(bit, xend, yend)
  inner_join(op1, op2, by = "bit")
}

plot_bitmovement <- function(bit_movement, highlight_bits) {
  bm_segs <- map(2:4, ~ get_steps(bit_movement, .x)) %>% bind_rows()
  hl_segs <- map(2:4, ~ get_steps(highlight_bits, .x)) %>% bind_rows()
  ggplot(bm_segs, aes(
    x = x, 
    y = y, 
    xend = xend,
    yend = yend)
  ) + 
    geom_segment(colour = "grey") +
    geom_point(colour = "grey") + 
    geom_point(aes(x = xend, y = yend), colour = "grey") +
    geom_segment(data = hl_segs, colour = "black") + 
    geom_point(data = hl_segs, colour = "black") +
    geom_point(aes(x = xend, y = yend), data = hl_segs, colour = "black") +
    coord_flip() +
    scale_y_reverse(breaks = 0:31, labels = 1:32) +
    scale_x_reverse() +
    theme_minimal() +
    theme(
      legend.position = "none"
    ) + ylab("Bit-position") + xlab("Operation")
}

bit_movement <- do.call(rbind, lapply(0:31, rot_hash_positions))
plot_bitmovement(bit_movement, bit_movement %>% filter(bit < 8))

库（purrr）
获得%
突变（x=操作，y=位置）%>%
选择（位、x、y）
op2%
过滤器（操作==！！步骤）%>%
变异（xend=操作，yend=位置）%>%
选择（位、xend、yend）
内部连接（op1，op2，by=“bit”）
}
不幸的是，要理解你在寻找什么有点困难。是否有可能制作一个较小的示例，并提供所需输出的相应“图纸”？干杯。下面我自己尝试画的这幅画，显示了我的目标。我想绘制显示散列函数中每个combine+mix操作中各个位如何移动的段。我在下面自己的尝试中做到了。。。现在，我想，我只是在寻找一种优雅的方式来做到这一点。我现在所拥有的将起作用，除了我必须确定在计算哈希函数的函数（我总是需要它）和对
get\u steps
函数的调用中执行了多少操作。我想我可以通过max over手术得到它，不过。。。
library(purrr)
get_steps <- function(bm, step) {
  op1 <- bm %>%
    filter(operation == !!(step-1)) %>%
    mutate(x = operation, y = positions) %>%
    select(bit, x, y)
  op2 <- bm %>%
    filter(operation == !!step) %>%
    mutate(xend = operation, yend = positions) %>%
    select(bit, xend, yend)
  inner_join(op1, op2, by = "bit")
}

plot_bitmovement <- function(bit_movement, highlight_bits) {
  bm_segs <- map(2:4, ~ get_steps(bit_movement, .x)) %>% bind_rows()
  hl_segs <- map(2:4, ~ get_steps(highlight_bits, .x)) %>% bind_rows()
  ggplot(bm_segs, aes(
    x = x, 
    y = y, 
    xend = xend,
    yend = yend)
  ) + 
    geom_segment(colour = "grey") +
    geom_point(colour = "grey") + 
    geom_point(aes(x = xend, y = yend), colour = "grey") +
    geom_segment(data = hl_segs, colour = "black") + 
    geom_point(data = hl_segs, colour = "black") +
    geom_point(aes(x = xend, y = yend), data = hl_segs, colour = "black") +
    coord_flip() +
    scale_y_reverse(breaks = 0:31, labels = 1:32) +
    scale_x_reverse() +
    theme_minimal() +
    theme(
      legend.position = "none"
    ) + ylab("Bit-position") + xlab("Operation")
}

bit_movement <- do.call(rbind, lapply(0:31, rot_hash_positions))
plot_bitmovement(bit_movement, bit_movement %>% filter(bit < 8))