I am using the following dataframe in R.
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
column will have eight unique value including NA
; it's not necessary that all 8 values are available for every day's date.batch_no
will have six unique values including NA
and blank; it's not necessary that all six values are available for every day's date.marking
column will have ~ 25 unique value, but need to consider values with suffix _hold#
as Hold
; after that, there would be six unique value including blank and NA
.The requirement is to merge the dcast
dataframe in the following order to have a single view summary for an analysis.
I want to keep all the unique values static in the code, so that if the particular value is not available for a particular date I'll get 0 or - in summary table.
Desired Output:
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%
For seq
we have % > 100 because of double counting of same uid
for value FRD
and FHL
. That is the accepted scenario. In Total
will have only distinct count of uid
.
There are a few ways of approaching this problem, one route would be starting with cleaning your data, joining that onto a table that has all the combinations you explicitly want and then summarising. NB: this will give a lot of explicit 0's due to combining the combinations from those three columns.
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)
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