R中扫描的日期减法
墨西哥海鲜
我有一个数据向量，看起来像这样：
带有日期列的数据框如下所示：
其中此数据帧中的行数等于new_first_dates的长度。
我想从数据帧的每一行中减去new_first_dates。我尝试使用扫描，但无法正常工作。
有人有什么想法吗？
干杯
随附的数据：
structure(list(`>50 gatherings` = structure(c(18346, 18342, 18339, 18341, 18346, 18339, 18339, 18355, 18340, 18346, 18339, 18338, 18346, 18349, 18343, 18347, 18345, 18340, 18337, 18351, 18338, 18345, 18338, 21915, 18347, 18343, 18340, 18346, 18339), class = "Date"), `>500 gatherings` = structure(c(18346, 18335, 18339, 18341, 18335, 18339, 18339, 18355, 18340, 18335, 18339, 18335, 18346, 18349, 18343, 18347, 18334, 18340, 18337, 18351, 18338, 18345, 18338, 21915, 18347, 18336, 18334, 18346, 18339), class = "Date"), `public schools` = structure(c(18341, 18338, 18338, 18341, 18345, 18338, 18340, 18355, 18339, 18341, 18340, 18338, 18338, 18340, 18341, 18342, 18338, 18338, 18339, 18339, 18338, 18338, 18338, 18338, 18342, 18345, 18338, 18338, 18340), class = "Date"), `restaurant dine-in` = structure(c(18339, 18341, 18341, 18337, 18342, 18342, 18342, 18339, 18340, 18339, 18346, 18338, 18338, 18339, 18343, 18347, 18339, 18340, 18337, 18354, 18339, 18339, 18345, 18345, 18345, 18341, 18340, 18345, 18339), class = "Date"), `entertainment/gym` = structure(c(18339, 18350, 18341, 18337, 18339, 18342, 18342, 18339, 18340, 18339, 18346, 18338, 18338, 18339, 18359, 18347, 18345, 18340, 18344, 18354, 18339, 18339, 18345, 21915, 18345, 18341, 18340, 18338, 18339), class = "Date"), `federal guidelines` = structure(c(18338, 18338, 18338, 18338, 18338, 18338, 18338, 18338, 18338, 18338, 18338, 18338, 18338, 18338, 18338, 18338, 18338, 18338, 18338, 18338, 18338, 18338, 18338, 18338, 18338, 18338, 18338, 18338, 18338), class = "Date"), `foreign travel ban` = structure(c(18333, 18333, 18333, 18333, 18333, 18333, 18333, 18333, 18333, 18333, 18333, 18333, 18333, 18333, 18333, 18333, 18333, 18333, 18333, 18333, 18333, 18333, 18333, 18333, 18333, 18333, 18333, 18333, 18333), class = "Date"), `stay at home rollback` = structure(c(18377, 18383, 18398, 18392, 18379, 18432, 18383, 21915, 18413, 18387, 18398, 18415, 18380, 18398, 18387, 18379, 18403, 18391, 18424, 18419, 18392, 18387, 18409, 21915, 18398, 18419, 21915, 18396, 18396), class = "Date"), `>50 gatherings rollback` = structure(c(18391, 21915, 18415, 21915, 21915, 21915, 21915, 21915, 18426, 18405, 18398, 21915, 21915, 18387, 18429, 18415, 21915, 21915, 18404, 21915, 21915, 21915, 21915, 18384, 21915, 21915, 21915, 21915, 21915), class = "Date"), `>500 gatherings rollback` = structure(c(18405, 21915, 18415, 21915, 21915, 21915, 21915, 21915, 18426, 18405, 18398, 21915, 21915, 18387, 18429, 18415, 21915, 21915, 21915, 21915, 21915, 21915, 21915, 18384, 21915, 21915, 21915, 21915, 21915), class = "Date"), `restaurant dine-in rollback` = structure(c(18405, 18394, 21915, 18392, 18391, 18432, 18380, 18394, 18391, 21915, 18384, 18390, 18380, 18424, 18429, 18384, 18387, 18411, 21915, 18391, 21915, 21915, 18409, 21915, 18387, 18398, 18384, 18394, 21915 ), class = "Date"), `entertainment/gym rollback` = structure(c(18405, 18394, 21915, 18392, 18391, 18432, 18377, 18394, 18391, 21915, 18384, 18390, 18380, 18424, 18429, 18384, 21915, 18415, 21915, 18391, 21915, 21915, 18409, 21915, 18387, 18398, 18384, 18394, 21915), class = "Date"), State = c("AK", "AL", "AZ", "CA", "CO", "FL", "GA", "IA", "IL", "IN", "KS", "MD", "MI", "MN", "MO", "MS", "NC", "NM", "OH", "OK", "OR", "PA", "RI", "SD", "TN", "TX", "UT", "VA", "WI"), cluster = c(3, 1, 1, 1, 1, 1, 1, 1, 2, 3, 3, 2, 3, 1, 3, 1, 1, 1, 1, 3, 1, 2, 2, 3, 1, 1, 1, 1, 1)), row.names = c(NA, -29L), class = c("grouped_df", "tbl_df", "tbl", "data.frame"), groups = structure(list(State = c("AK", "AL", "AZ", "CA", "CO", "FL", "GA", "IA", "IL", "IN", "KS", "MD", "MI", "MN", "MO", "MS", "NC", "NM", "OH", "OK", "OR", "PA", "RI", "SD", "TN", "TX", "UT", "VA", "WI"), .rows = list(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)), row.names = c(NA, -29L), class = c("tbl_df", "tbl", "data.frame"), .drop = TRUE))
structure(c(18338, 18345, 18340, 18325, 18332, 18334, 18333, 18333, 18339, 18339, 18340, 18337, 18335, 18337, 18339, 18340, 18340, 18336, 18337, 18340, 18335, 18334, 18339, 18346, 18335, 18334, 18338, 18335, 18337), class = "Date")
阿克伦
一些列不是Date类。因此，在进行减法之前，我们需要排除那些。另外，根据OP帖子的结构，数据集中有一些属性，可以使用ungrouping将其删除
library(dplyr) # version >= 1.0.0
df1 %>%
ungroup %>%
mutate(across(-c(State, cluster), ~ . - as.Date(v1)))
-输出
# A tibble: 29 x 14
# `>50 gatherings` `>500 gathering… `public schools` `restaurant din… `entertainment/… `federal guidel… `foreign travel… `stay at home r…
# <drtn> <drtn> <drtn> <drtn> <drtn> <drtn> <drtn> <drtn>
# 1 8 days 8 days 3 days 1 days 1 days 0 days -5 days 39 days
# 2 -3 days -10 days -7 days -4 days 5 days -7 days -12 days 38 days
# 3 -1 days -1 days -2 days 1 days 1 days -2 days -7 days 58 days
# 4 16 days 16 days 16 days 12 days 12 days 13 days 8 days 67 days
# 5 14 days 3 days 13 days 10 days 7 days 6 days 1 days 47 days
# 6 5 days 5 days 4 days 8 days 8 days 4 days -1 days 98 days
# 7 6 days 6 days 7 days 9 days 9 days 5 days 0 days 50 days
# 8 22 days 22 days 22 days 6 days 6 days 5 days 0 days 3582 days
# 9 1 days 1 days 0 days 1 days 1 days -1 days -6 days 74 days
#10 7 days -4 days 2 days 0 days 0 days -1 days -6 days 48 days
# … with 19 more rows, and 6 more variables: `>50 gatherings rollback` <drtn>, `>500 gatherings rollback` <drtn>, `restaurant dine-in
# rollback` <drtn>, `entertainment/gym rollback` <drtn>, State <chr>, cluster <dbl>
或者，如果OP愿意 sweep
df1[1:12] <- sweep(as.data.frame(df1[1:12]), 1, as.Date(v1), '-')
或使用lapply从base R
df1[1:12] <- lapply(df1[1:12], `-`, as.Date(v1))
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