这是 tidyr fill()
泛型的方法。它被翻译为 data.table::nafill()
。请注意,data.table::nafill()
目前仅适用于整数和双列。
参数
- data
-
一个 DataFrame 。
- ...
-
<
tidy-select
> 要填充的列。 - .direction
-
填充缺失值的方向。目前是"down"(默认)、"up"、"downup"(即先向下然后向上)或"updown"(先向上然后向下)。
例子
library(tidyr)
# Value (year) is recorded only when it changes
sales <- lazy_dt(tibble::tribble(
~quarter, ~year, ~sales,
"Q1", 2000, 66013,
"Q2", NA, 69182,
"Q3", NA, 53175,
"Q4", NA, 21001,
"Q1", 2001, 46036,
"Q2", NA, 58842,
"Q3", NA, 44568,
"Q4", NA, 50197,
"Q1", 2002, 39113,
"Q2", NA, 41668,
"Q3", NA, 30144,
"Q4", NA, 52897,
"Q1", 2004, 32129,
"Q2", NA, 67686,
"Q3", NA, 31768,
"Q4", NA, 49094
))
# `fill()` defaults to replacing missing data from top to bottom
sales %>% fill(year)
#> Source: local data table [16 x 3]
#> Call: copy(`_DT10`)[, `:=`(year = nafill(year, "locf"))]
#>
#> quarter year sales
#> <chr> <dbl> <dbl>
#> 1 Q1 2000 66013
#> 2 Q2 2000 69182
#> 3 Q3 2000 53175
#> 4 Q4 2000 21001
#> 5 Q1 2001 46036
#> 6 Q2 2001 58842
#> # … with 10 more rows
#>
#> # Use as.data.table()/as.data.frame()/as_tibble() to access results
# Value (n_squirrels) is missing above and below within a group
squirrels <- lazy_dt(tibble::tribble(
~group, ~name, ~role, ~n_squirrels,
1, "Sam", "Observer", NA,
1, "Mara", "Scorekeeper", 8,
1, "Jesse", "Observer", NA,
1, "Tom", "Observer", NA,
2, "Mike", "Observer", NA,
2, "Rachael", "Observer", NA,
2, "Sydekea", "Scorekeeper", 14,
2, "Gabriela", "Observer", NA,
3, "Derrick", "Observer", NA,
3, "Kara", "Scorekeeper", 9,
3, "Emily", "Observer", NA,
3, "Danielle", "Observer", NA
))
# The values are inconsistently missing by position within the group
# Use .direction = "downup" to fill missing values in both directions
squirrels %>%
dplyr::group_by(group) %>%
fill(n_squirrels, .direction = "downup") %>%
dplyr::ungroup()
#> Source: local data table [12 x 4]
#> Call: copy(`_DT12`)[, `:=`(n_squirrels = nafill(nafill(n_squirrels,
#> "locf"), "nocb")), by = .(group)]
#>
#> group name role n_squirrels
#> <dbl> <chr> <chr> <dbl>
#> 1 1 Sam Observer 8
#> 2 1 Mara Scorekeeper 8
#> 3 1 Jesse Observer 8
#> 4 1 Tom Observer 8
#> 5 2 Mike Observer 14
#> 6 2 Rachael Observer 14
#> # … with 6 more rows
#>
#> # Use as.data.table()/as.data.frame()/as_tibble() to access results
# Using `.direction = "updown"` accomplishes the same goal in this example
相关用法
- R dtplyr filter.dtplyr_step 使用列值对行进行子集化
- R dtplyr lazy_dt 创建一个“惰性”data.table 以与 dplyr 动词一起使用
- R dtplyr group_modify.dtplyr_step 对每个组应用一个函数
- R dtplyr transmute.dtplyr_step 创建新列,删除旧列
- R dtplyr slice.dtplyr_step 使用行的位置对行进行子集化
- R dtplyr left_join.dtplyr_step 连接数据表
- R dtplyr mutate.dtplyr_step 创建和修改列
- R dtplyr distinct.dtplyr_step 子集不同/唯一行
- R dtplyr unite.dtplyr_step 通过将字符串粘贴在一起将多列合并为一列。
- R dtplyr nest.dtplyr_step 巢
- R dtplyr relocate.dtplyr_step 使用变量名称重新定位变量
- R dtplyr head.dtplyr_step 对第一行或最后一行进行子集化
- R dtplyr expand.dtplyr_step 扩展 DataFrame 以包含所有可能的值组合。
- R dtplyr group_by.dtplyr_step 分组和取消分组
- R dtplyr intersect.dtplyr_step 设置操作
- R dtplyr pivot_wider.dtplyr_step 将数据从长轴转向宽轴
- R dtplyr summarise.dtplyr_step 将每组汇总为一行
- R dtplyr count.dtplyr_step 按组计数观察值
- R dtplyr select.dtplyr_step 使用名称对列进行子集化
- R dtplyr drop_na.dtplyr_step 删除包含缺失值的行
- R dtplyr complete.dtplyr_step 完成缺少数据组合的 DataFrame
- R dtplyr collect.dtplyr_step 强制计算惰性 data.table
- R dtplyr arrange.dtplyr_step 按列值排列行
- R dtplyr separate.dtplyr_step 使用正则表达式或数字位置将字符列分成多列
- R dtplyr rename.dtplyr_step 使用名称重命名列
注:本文由纯净天空筛选整理自Hadley Wickham等大神的英文原创作品 Fill in missing values with previous or next value。非经特殊声明,原始代码版权归原作者所有,本译文未经允许或授权,请勿转载或复制。