How to restructure dataframe having multiple variable by keeping some variable static & dynamic in R

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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
  • The 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.
  • The 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.

dplyr dataframe r
1 Answers

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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