我有車輛的時間t、速度v和位置x以及其他資訊：info1, info2, status
如何通過基于(v = 14)給定的最后速度線性外推位置來擴展 tibble timestamps。所以添加的行是最后一行的副本，除了預測位置并status更改為“預測”`。
是否可以在不使用回圈的情況下做到這一點。
tbl <- tibble(info1 = rep("a", 3),
info2 = rep("b", 3),
t = c(1, 2, 3),
v = c(12, 13, 14),
x = c(12, 24, 37),
status = rep("real", 3))
timestamps <- c(4, 5, 6, 8) # timestamps does not linearly increase
# desired output
tbl_desired <- tibble(info1 = rep("a", 7),
info2 = rep("b", 7),
t = c(1, 2, 3, 4, 5, 6, 8),
v = c(12, 13, 14, 14, 14, 14, 14),
x = c(12, 24, 37, 51, 65, 79, 107),
status = c(rep("real", 3), rep("predicted", 4)))
uj5u.com熱心網友回復：
條件不清楚，尤其是“x”中的預測值。下面的代碼按以下方式作業
- 提取最后一行 (
slice_tail,n = 1) - 更新列 't', 'v', 'x' (
summarise) - 系結來自原始資料的行 (
bind_rows)
library(dplyr)
tbl %>%
slice_tail(n = 1) %>%
summarise(info1, info2, t = timestamps, v = v,
x = (x cumsum(c(1, diff(t)) *
rep(last(v), length(t)))), status = 'predicted') %>%
bind_rows(tbl, .)
-輸出
# A tibble: 7 × 6
info1 info2 t v x status
<chr> <chr> <dbl> <dbl> <dbl> <chr>
1 a b 1 12 12 real
2 a b 2 13 24 real
3 a b 3 14 37 real
4 a b 4 14 51 predicted
5 a b 5 14 65 predicted
6 a b 6 14 79 predicted
7 a b 8 14 107 predicted
如果有很多列，在slice最后一行之后，使用mutate只更新需要改變的列，并在大于1的list地方換行length，則unnest該list列
library(tidyr)
tbl %>%
slice_tail(n = 1) %>%
mutate(t = list(timestamps), v = v,
x = list((x cumsum(c(1, diff(timestamps)) *
rep(last(v), length(timestamps))))), status = 'predicted') %>%
unnest(where(is.list)) %>%
bind_rows(tbl, .)
-輸出
# A tibble: 7 × 6
info1 info2 t v x status
<chr> <chr> <dbl> <dbl> <dbl> <chr>
1 a b 1 12 12 real
2 a b 2 13 24 real
3 a b 3 14 37 real
4 a b 4 14 51 predicted
5 a b 5 14 65 predicted
6 a b 6 14 79 predicted
7 a b 8 14 107 predicted
或者使用add_row，然后fill將NA與以前的非NA行這些列不在指定add_row
library(tibble)
tbl %>%
add_row(t = timestamps, v = last(.$v),
x = (last(.$x) cumsum(c(1, diff(timestamps)) *
rep(last(.$v), length(timestamps)))), status = 'predicted') %>%
fill(everything())
-輸出
# A tibble: 7 × 6
info1 info2 t v x status
<chr> <chr> <dbl> <dbl> <dbl> <chr>
1 a b 1 12 12 real
2 a b 2 13 24 real
3 a b 3 14 37 real
4 a b 4 14 51 predicted
5 a b 5 14 65 predicted
6 a b 6 14 79 predicted
7 a b 8 14 107 predicted
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