我在R.中使用以下数据帧
uid Date batch_no marking seq
K-1 16/03/2020 12:11:33 7 S1 FRD
K-1 16/03/2020 12:11:33 7 S1 FHL
K-2 16/03/2020 12:11:33 8 SE_hold1 ABC
K-3 16/03/2020 12:11:33 9 SD_hold2 DEF
K-4 16/03/2020 12:11:33 8 S1 XYZ
K-5 16/03/2020 12:11:33 NA ABC
K-6 16/03/2020 12:11:33 7 ZZZ
K-7 16/03/2020 12:11:33 NA S2 NA
K-8 16/03/2020 12:11:33 6 S3 FRD
seq列将具有八个唯一值，包括NA；没有必要为每天的日期提供所有8个值batch_no将具有六个唯一值，包括NA和空白；没有必要为每天的日期提供所有六个值marking列将具有~25个唯一值，但需要将后缀为_hold#的值视为Hold；之后，将存在包括空白和NA在内的六个唯一值
要求按以下顺序合并dcast数据帧，以便为分析提供单个视图摘要。
我想在代码中保持所有唯一值的静态，这样，如果特定值在特定日期不可用，我将在摘要表中获得0或-。
期望输出：
seq count percentage Marking count Percentage batch_no count Percentage
FRD 1 12.50% S1 2 25.00% 6 1 12.50%
FHL 1 12.50% S2 1 12.50% 7 2 25.00%
ABC 2 25.00% S3 1 12.50% 8 2 25.00%
DEF 1 12.50% Hold 2 25.00% 9 1 12.50%
XYZ 1 12.50% NA 1 12.50% NA 1 12.50%
ZZZ 1 12.50% (Blank) 1 12.50% (Blank) 1 12.50%
FRD 1 12.50% - - - - - -
NA 1 12.50% - - - - - -
(Blank) 0 0.00% - - - - - -
Total 8 112.50% - 8 100.00% - 8 100.00%
对于seq，由于对值FRD和FHL重复计数相同的uid，因此我们具有%>100。这是公认的情况。在CCD_ 14中将只有CCD_ 15的不同计数。
有几种方法可以解决这个问题，其中一种方法是从清理数据开始，将数据连接到一个具有您明确想要的所有组合的表中，然后进行汇总。注意：由于组合了这三列的组合，这将给出很多显式的0。
df = df_original %>%
mutate(marking = if_else(str_detect(marking,"hold"),"Hold", marking)) %>%
mutate_at(vars(c("seq", "batch_no", "marking")), forcats::fct_explicit_na, na_level = "(Blank)")
## You need to do something similar with vectors of the possible values
## i.e. I don't know all the levels of your factors
#--------------------------------------------------------------------------
# Appending the NA and (Blank) levels ensures they are included in case the
# batch of data doesn't have them
df_seq = data.frame(seq = c(df$seq %>% levels(),"NA","(Blank)") %>% unique())
df_batch_no = data.frame(batch_no = c(df$batch_no %>% levels(),"NA","(Blank)") %>% unique())
df_marking = data.frame(marking = c(df$marking %>% levels(),"NA","(Blank)") %>% unique())
# would have been really nice to use janitor::tabyl but your output won't allow
df_seq_summary = df %>%
group_by(seq) %>%
summarise(count = n()) %>%
right_join(df_seq, by = "seq") %>%
mutate(count = replace_na(count, 0),
percentage = count / n()) %>%
mutate(row = row_number())
df_marking_summary = df %>%
group_by(marking) %>%
summarise(count = n()) %>%
right_join(df_marking, by = "marking") %>%
mutate(count = replace_na(count, 0),
percentage = count / sum(count)) %>%
mutate(row = row_number())
df_batch_no_summary = df %>%
group_by(batch_no) %>%
summarise(count = n()) %>%
right_join(df_batch_no, by = "batch_no") %>%
mutate(count = replace_na(count, 0),
percentage = count / sum(count)) %>%
mutate(row = row_number())
df = df_seq_summary %>%
full_join(df_marking_summary, by = "row", suffix = c("", "_marking")) %>%
full_join(df_batch_no_summary, by = "row", suffix = c("", "_batch_no")) %>%
select(-row) %>%
bind_rows(summarise_all(., ~(if(is.numeric(.)) sum(if_else(.>0,as.double(.),0), na.rm = T) else "Total"))) %>%
mutate_at(vars(contains("percentage")), scales::percent, accuracy = 0.01)