我希望能得到一些帮助来识别：
- 当不存在NA值时，缺失观测值的位置(缺少整行(
- 为缺少的数据创建一行，该行的平均值基于某些类别的平均值
我的df:
-
曲棍球运动员在本赛季训练中的数值负荷数据
-
有时，即使选手进行了练习，加速度计在练习过程中也不起作用。因此，为了确保我们仍然可以跟踪他们在训练中所做的工作，我想插入他们在训练后位置(前锋、防守或守门员(的平均值。(即，如果守门员的加速度计不工作，我想取其他守门员的平均负荷，并将其插入该球员的观察行中进行练习(。
-
如果在加速度计不工作的情况下，每次训练都有一行所有玩家和负载的NA值，这将是一项更简单的任务，但当我从在线云下载csv中的数据时，只有有工作单位的玩家的行。所以这部分是我无法控制的。
> head(DummyLoads)
Name Date Load Position
1 Jim 2019-10-19 900 2.100 Forward
2 Bob 2019-10-19 900 2.100 Forward
3 Dave 2019-10-19 900 2.100 Forward
4 Steve 2019-10-19 850 2.312 Forward
5 Fred 2019-10-19 850 2.312 Defense
6 Ray 2019-10-19 850 2.312 Defense
DummyLoads <- structure(list(Name = structure(c(4L, 1L, 2L, 6L, 3L, 5L, 4L, 1L, 2L, 3L, 5L, 4L, 1L, 2L, 6L, 3L, 5L, 2L, 6L, 3L, 5L),
.Label = c("Bob", "Dave", "Fred", "Jim", "Ray", "Steve"),
class = "factor"),
Date = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L),
.Label = c("2019-10-19", "2019-10-20", "2019-10-21", "2019-10-22"), class = "factor"),
Load = c(900L, 900L, 900L, 850L, 850L, 850L, 789L, 789L, 789L, 960L, 960L, 909L, 909L, 909L, 991L, 991L, 991L, 720L, 717L, 717L, 717L),
Load.Min = c(2.1, 2.1, 2.1, 2.312, 2.312, 2.312, 2.22, 2.22, 2.22, 2, 2, 1.88, 1.88, 1.88, 1.99, 1.99, 1.99, 2.1, 2.3, 2.3, 2.3), Position = structure(c(2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L),
.Label = c("Defense", "Forward"), class = "factor")), class = "data.frame", row.names = c(NA, -21L))
ggplot(DummyLoads, aes(x = Name, y = Load, fill = Position))+
geom_bar(stat = "identity")+
facet_grid(~Date)
这是一张显示缺少数据的玩家的图表。
缺少玩家加载
理想情况下，我希望能够识别那些缺失的数据点，而不必首先绘制它。我还想避免每次都要手动计算平均值，然后再输入。我希望找到一个自动化的解决方案，因为我将有一整个赛季的练习来做这件事，但我知道这可能很棘手！
提前感谢您的任何建议。如果我没有把事情解释清楚，我深表歉意。
更新到当前问题：
DummyLoads <- DummyLoads %>%
ungroup()
full_data <- expand.grid(
Athlete = DummyLoads %>%
pull(Athlete) %>%
unique(),
Date = DummyLoads %>%
pull(Date) %>%
unique())
full_data %>%
# join incomplete data onto full data
left_join(DummyLoads, by = c("Athlete", "Date")) %>%
# assign the position to each player
# in the example data, some players do ahve different positions
# if this is true, than it would be unclear which average should be
# considered. Therefore, I assumed their position is constant
left_join(DummyLoads %>%
select(Athlete, Position) %>%
distinct(Athlete, .keep_all=TRUE),
by = "Athlete") %>%
# keep both to check the differences
rename(Position = Position.y) %>%
group_by(Date, Position) %>%
# if Load is missing, take the mean of Loads which is grouped
mutate(Load2 = coalesce(PL_Avg,
mean(PL_Avg, na.rm = T)))
通用基本R解决方案：
# Mark out NA rows flatten in single observation (each element denoting a vector of the df):
is_val_na <- apply(data.frame(lapply(DummyLoads,
function(x){is.na(x)})), 1, paste, collapse = ", " )
# Split up using the grouping var "Name", and impute the mean where NA, coerce list to df:
DummyLoads_imputed <- do.call("rbind", lapply(split(DummyLoads, DummyLoads$Position),
function(x){
if(is.numeric(x)){
ifelse(is.na(x), mean(x, na.rm = TRUE), x)
}else{x}
}
)
)
# Bind the data.frame with a factor vector holding the T/F values:
DummyLoads_imputed <- cbind(DummyLoads_imputed, row_na = as.factor(is_val_na))
使用的数据：
DummyLoads <- structure(list(Name = structure(c(4L, 1L, 2L, 6L, 3L, 5L, 4L, 1L, 2L, 3L, 5L, 4L, 1L, 2L, 6L, 3L, 5L, 2L, 6L, 3L, 5L),
.Label = c("Bob", "Dave", "Fred", "Jim", "Ray", "Steve"),
class = "factor"), Date = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L,
3L, 3L, 3L, 4L, 4L, 4L, 4L), .Label = c("2019-10-19", "2019-10-20", "2019-10-21", "2019-10-22"),
class = "factor"), Load = c(900L, 900L, 900L, 850L, 850L, 850L, 789L, 789L, 789L, 960L,
960L, 909L, 909L, 909L, 991L, 991L, 991L, 720L, 717L, 717L, 717L),
Position = structure(c(2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L),
.Label = c("Defense", "Forward"), class = "factor")), row.names = c(NA, -21L), class = "data.frame")