How to pivoting dataframe consisting column with section and sub section In R
I have a below-mentioned dataframe:
structure(
list(ID = c(\"P-1\", \" P-1\", \"P-1\", \"P-2\", \"P-3\", \"P-4\", \"P-5\"
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Hopefully that'll be enough to get you started, to go further, I'll need an expected output that looks like it comes from R, and further explanations as to how variables are computed.
library(tidyverse) df <- structure( list(ID = c("P-1", " P-1", "P-1", "P-2", "P-3", "P-4", "P-5", "P-6", "P-7", "P-8"), Date = c("2020-03-16 12:11:33", "2020-03-16 13:16:04", "2020-03-16 06:13:55", "2020-03-16 10:03:43", "2020-03-16 12:37:09", "2020-03-16 06:40:24", "2020-03-16 09:46:45", "2020-03-16 12:07:44", "2020-03-16 14:09:51", "2020-03-16 09:19:23"), Status = c("SA", "SA", "SA", "RE", "RE", "RE", "RE", "XA", "XA", "XA"), Flag = c("L", "L", "L", NA, "K", "J", NA, NA, "H", "G"), Value = c(5929.81, 5929.81, 5929.81, NA, 6969.33, 740.08, NA, NA, 1524.8, NA), Flag2 = c("CL", "CL", "CL", NA, "RY", "", NA, NA, "", NA), Flag3 = c(NA, NA, NA, NA, "RI", "PO", NA, "SS", "DDP", NA)), .Names=c("ID", "Date", "Status", "Flag", "Value", "Flag2", "Flag3"), row.names=c(NA, 10L), class="data.frame") df2 <- df %>% mutate( # add variables Value = ifelse(0 <= Value & Value <= 15000, "0-15000", "15000-50000"), substatus = case_when( !is.na(Flag2) & is.na(Flag3) ~ "a", !is.na(Flag3) & is.na(Flag2) ~ "b", !is.na(Flag3) & !is.na(Flag2) ~ "c", TRUE ~ "d"), # make Date an actual date rather than a timestamp Date = as.Date(Date), # remove obsolete columns Flag2 = NULL, Flag3 = NULL, ID = NULL, # renames NAs into the name of the desired column Flag = ifelse(is.na(Flag), "[Null]", Flag), # create column of 1 for pivot temp = 1, # and row id id = row_number() ) %>% # create new columns L K etc, this also drops the Flag col pivot_wider(names_from = "Flag", values_from = "temp", values_fill = list(temp=0)) %>% # move `[Null]` column to the end select(everything(), -`[Null]`, `[Null]`) %>% mutate( id = NULL, count = 1, Total = rowSums(select(., L:`[Null]`))) df2 #> # A tibble: 10 x 12 #> Date Status Value substatus L K J H G `[Null]` #>#> 1 2020-03-16 SA 0-15~ a 1 0 0 0 0 0 #> 2 2020-03-16 SA 0-15~ a 1 0 0 0 0 0 #> 3 2020-03-16 SA 0-15~ a 1 0 0 0 0 0 #> 4 2020-03-16 RE d 0 0 0 0 0 1 #> 5 2020-03-16 RE 0-15~ c 0 1 0 0 0 0 #> 6 2020-03-16 RE 0-15~ c 0 0 1 0 0 0 #> 7 2020-03-16 RE d 0 0 0 0 0 1 #> 8 2020-03-16 XA b 0 0 0 0 0 1 #> 9 2020-03-16 XA 0-15~ c 0 0 0 1 0 0 #> 10 2020-03-16 XA d 0 0 0 0 1 0 #> # ... with 2 more variables: count , Total # As you didn't tell what to do with NA values so I left them as NA bind_rows( df2 %>% # add missing combinations of abcd complete(nesting(Date, Status, Value), substatus) %>% group_by(Date, Value, Status, substatus) %>% summarize_all(~sum(., na.rm=TRUE)) %>% group_by(Status, Value) %>% mutate(percent = paste(round(100 * Total / sum(Total), 2), "%")) %>% ungroup(), df2 %>% mutate(substatus = Status, Status = paste0(Status, "_")) %>% group_by(Date, Value, Status, substatus) %>% mutate(count = n()) %>% group_by(count, add = TRUE) %>% summarize_all(~sum(., na.rm=TRUE)) %>% group_by(Value) %>% mutate(percent = paste(round(100 * Total / sum(Total), 2), "%")) ) %>% arrange(Date, Value, desc(Status)) %>% mutate(Status = NULL) %>% rename(Status = substatus) %>% print(n=Inf) #> # A tibble: 25 x 12 #> Date Value Status L K J H G `[Null]` count Total #> #> 1 2020-03-16 0-15~ XA 0 0 0 1 0 0 1 1 #> 2 2020-03-16 0-15~ a 0 0 0 0 0 0 0 0 #> 3 2020-03-16 0-15~ b 0 0 0 0 0 0 0 0 #> 4 2020-03-16 0-15~ c 0 0 0 1 0 0 1 1 #> 5 2020-03-16 0-15~ d 0 0 0 0 0 0 0 0 #> 6 2020-03-16 0-15~ SA 3 0 0 0 0 0 3 3 #> 7 2020-03-16 0-15~ a 3 0 0 0 0 0 3 3 #> 8 2020-03-16 0-15~ b 0 0 0 0 0 0 0 0 #> 9 2020-03-16 0-15~ c 0 0 0 0 0 0 0 0 #> 10 2020-03-16 0-15~ d 0 0 0 0 0 0 0 0 #> 11 2020-03-16 0-15~ RE 0 1 1 0 0 0 2 2 #> 12 2020-03-16 0-15~ a 0 0 0 0 0 0 0 0 #> 13 2020-03-16 0-15~ b 0 0 0 0 0 0 0 0 #> 14 2020-03-16 0-15~ c 0 1 1 0 0 0 2 2 #> 15 2020-03-16 0-15~ d 0 0 0 0 0 0 0 0 #> 16 2020-03-16 XA 0 0 0 0 1 1 2 2 #> 17 2020-03-16 a 0 0 0 0 0 0 0 0 #> 18 2020-03-16 b 0 0 0 0 0 1 1 1 #> 19 2020-03-16 c 0 0 0 0 0 0 0 0 #> 20 2020-03-16 d 0 0 0 0 1 0 1 1 #> 21 2020-03-16 RE 0 0 0 0 0 2 2 2 #> 22 2020-03-16 a 0 0 0 0 0 0 0 0 #> 23 2020-03-16 b 0 0 0 0 0 0 0 0 #> 24 2020-03-16 c 0 0 0 0 0 0 0 0 #> 25 2020-03-16 d 0 0 0 0 0 2 2 2 #> # ... with 1 more variable: percent 讨论(0)
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