I use R to generate a toy set
data.frame(name = c("Tom", "Shane", "Daniel", "Akira", "Jack", "Zoe"), c1 = c(1,2,3,0,5,0), c2 = c(0, 3, 5, 0,4,0), c3 = c(0, 0,1,0,0,3), c4=c(0,0,0,1,0,0))
which is displayed below:
I only care about the columns c1, c2, c3, c4
, and if a specific row has more than one value, which is greater than 0. we need to duplicate rows to make sure that there are only one value, which is greater than 0, and then remove the original row.
For instance, the second row has two values are greater than 0 (c1: 2, c2: 3), then we have to duplicate that row to two, which looks like this
Shane 2 0 0 0
Shane 0 3 0 0
I am trying to build a SQL query to capture this. However, I am not sure if there is any SQL function can detect multiple non-zero values in a specific row without looking at the result first. Anyway the final result should look like this, if there any magical SQL functions exist:
I also think about to use R to accomplish it. The only R function I know can duplicate rows is do.call()
function, then combine it with rbind()
function. However, it is not working for my case. Could you someone give me any hints? Many Thanks :)
You can do this with a few tidyverse
functions. First, we enter your sample data
library(tidyverse)
dd <- tribble(~name, ~c1, ~c2, ~c3, ~c4,
"Tom", 1, 0, 0, 0,
"Shane", 2, 3, 0, 0,
"Daniel", 3, 5, 1, 0,
"Akira", 0, 0, 0 ,1,
"Jack", 5, 4, 0, 0,
"Zoe", 0, 0, 3, 0)
Then we gather, filter, and spread to get the rows you want. By adding in a row id, we keep the different values on different rows.
dd %>%
gather("var", "val", -name) %>%
rowid_to_column() %>%
filter(val>0) %>%
spread(var, val, fill=0) %>%
select(-rowid)
# A tibble: 10 x 5
# name c1 c2 c3 c4
# * <chr> <dbl> <dbl> <dbl> <dbl>
# 1 Tom 1 0 0 0
# 2 Shane 2 0 0 0
# 3 Daniel 3 0 0 0
# 4 Jack 5 0 0 0
# 5 Shane 0 3 0 0
# 6 Daniel 0 5 0 0
# 7 Jack 0 4 0 0
# 8 Daniel 0 0 1 0
# 9 Zoe 0 0 3 0
# 10 Akira 0 0 0 1
One more option using union all
.
select name,c1,0 as c2,0 as c3,0 as c4 from tbl where c1>0
union all
select name,0,c2,0,0 from tbl where c2>0
union all
select name,0,0,c3,0 from tbl where c3>0
union all
select name,0,0,0,c4 from tbl where c4>0
df1 = data.frame(name = c("Tom", "Shane", "Daniel", "Akira", "Jack", "Zoe"),
c1 = c(1,2,3,0,5,0),
c2 = c(0, 3, 5, 0,4,0),
c3 = c(0, 0,1,0,0,3),
c4=c(0,0,0,1,0,0))
df2 = df1[rep(1:NROW(df1), apply(df1, 1, function(x) sum(x[-(1)] > 0))),]
df3 = df2
df3[-1] = df3[-1] * 0
df3[ave(1:NROW(df2), df2$name, FUN = length) == 1,] = df2[ave(1:NROW(df2), df2$name, FUN = length) == 1,]
replace(x = df3,
list = cbind(1:NROW(df3), 1+ave(1:NROW(df2), df2$name, FUN = seq_along)),
values = df2[cbind(1:NROW(df3), 1+ave(1:NROW(df2), df2$name, FUN = seq_along))])
# name c1 c2 c3 c4
#1 Tom 1 0 0 0
#2 Shane 2 0 0 0
#2.1 Shane 0 3 0 0
#3 Daniel 3 0 0 0
#3.1 Daniel 0 5 0 0
#3.2 Daniel 0 0 1 0
#4 Akira 0 0 0 1
#5 Jack 5 0 0 0
#5.1 Jack 0 4 0 0
#6 Zoe 0 0 3 0
Consider base R with by
that builds a zero padded dataframe for each distinct name then row binds all dataframes into final one, similar to union SQL:
df_list <- by(df, df$name, FUN = function(d){
tmp <- data.frame(name = d$name[1],
c1 = c(max(d$c1), rep(0, 3)),
c2 = c(0, max(d$c2), rep(0, 2)),
c3 = c(rep(0, 2), max(d$c3), 0),
c4 = c(rep(0, 3), max(d$c4)))
tmp <- tmp[rowSums(tmp[-1])!=0,]
row.names(tmp) <- NULL
tmp
})
final_df <- do.call(rbind, unname(df_list))
final_df
来源:https://stackoverflow.com/questions/48100881/duplicate-the-rows-based-on-some-criteria-in-sql-or-r