Subtract rows varying one column but keeping others fixed - r

Question

I have an experiment where I need to subtract values of two different treatments from the Control (baseline), but these subtractions must correspond to other columns, named block and year sampled.
Dummy data frame:
df <- data.frame("Treatment" = c("Control","Treat1", "Treat2"),
"Block" = rep(1:3, each=3), "Year" = rep(2011:2013, each=3),
"Value" = c(6,12,4,3,9,5,6,3,1));df
Treatment Block Year Value
1 Control 1 2011 6
2 Treat1 1 2011 12
3 Treat2 1 2011 4
4 Control 2 2012 3
5 Treat1 2 2012 9
6 Treat2 2 2012 5
7 Control 3 2013 6
8 Treat1 3 2013 3
9 Treat2 3 2013 1
Desired output:
Treatment Block Year Value
1 Control-Treat1 1 2011 -6
2 Control-Treat2 1 2011 2
3 Control-Treat1 2 2012 -6
4 Control-Treat2 2 2012 -2
5 Control-Treat1 3 2013 3
6 Control-Treat2 3 2013 5
Any suggestion, preferably using dplyr?
I have found similar questions but none addressing this specific issue.

Answer 1

We can use dplyr, group_by Block and subtract Value where Treatment == "Control" from each Value and remove the "Control" rows.
library(dplyr)
df %>%
group_by(Block) %>%
mutate(Value = Value[which.max(Treatment == "Control")] - Value) %>%
filter(Treatment != "Control")
# Treatment Block Year Value
# <fct> <int> <int> <dbl>
#1 Treat1 1 2011 -6
#2 Treat2 1 2011 2
#3 Treat1 2 2012 -6
#4 Treat2 2 2012 -2
#5 Treat1 3 2013 3
#6 Treat2 3 2013 5
Not sure, if the values in Treatment column in expected output (Control-Treat1, Control-Treat2) are shown only for demonstration purpose of the calculation or OP really wants that as output. In case if that is needed as output we can use
df %>%
group_by(Block) %>%
mutate(Value = Value[which.max(Treatment == "Control")] - Value,
Treatment = paste0("Control-", Treatment)) %>%
filter(Treatment != "Control-Control")
# Treatment Block Year Value
# <chr> <int> <int> <dbl>
#1 Control-Treat1 1 2011 -6
#2 Control-Treat2 1 2011 2
#3 Control-Treat1 2 2012 -6
#4 Control-Treat2 2 2012 -2
#5 Control-Treat1 3 2013 3
#6 Control-Treat2 3 2013 5

Answer 2

A somehow different tidyverse possibility could be:
df %>%
spread(Treatment, Value) %>%
gather(var, val, -c(Block, Year, Control)) %>%
mutate(Value = Control - val,
Treatment = paste("Control", var, sep = " - ")) %>%
select(Treatment, Block, Year, Value) %>%
arrange(Block)
Treatment Block Year Value
1 Control - Treat1 1 2011 -6
2 Control - Treat2 1 2011 2
3 Control - Treat1 2 2012 -6
4 Control - Treat2 2 2012 -2
5 Control - Treat1 3 2013 3
6 Control - Treat2 3 2013 5

Answer 3

This can be done with an SQL self join like this:
library(sqldf)
sqldf("select a.Treatment || '-' || b.Treatment as Treatment,
a.Block,
a.Year,
a.Value - b.Value as Value
from df a
join df b on a.block = b.block and
a.Treatment = 'Control' and
b.Treatment != 'Control'")
giving:
Treatment Block Year Value
1 Control-Treat1 1 2011 -6
2 Control-Treat2 1 2011 2
3 Control-Treat1 2 2012 -6
4 Control-Treat2 2 2012 -2
5 Control-Treat1 3 2013 3
6 Control-Treat2 3 2013 5

Answer 4

Another dplyr-tidyr approach: You can remove unwanted columns with select:
library(tidyr)
library(dplyr)
dummy_df %>%
spread(Treatment,Value) %>%
gather(key,value,Treat1:Treat2) %>%
group_by(Block,Year,key) %>%
mutate(Val=Control-value)
# A tibble: 6 x 6
# Groups: Block, Year, key [6]
Block Year Control key value Val
<int> <int> <dbl> <chr> <dbl> <dbl>
1 1 2011 6 Treat1 12 -6
2 2 2012 3 Treat1 9 -6
3 3 2013 6 Treat1 3 3
4 1 2011 6 Treat2 4 2
5 2 2012 3 Treat2 5 -2
6 3 2013 6 Treat2 1 5
Just the exact output:
dummy_df %>%
spread(Treatment,Value) %>%
gather(key,value,Treat1:Treat2) %>%
mutate(Treatment=paste0("Control-",key)) %>%
group_by(Block,Year,Treatment) %>%
mutate(Val=Control-value) %>%
select(Treatment,everything(),-value,-key)%>%
arrange(Year)
Result:
# A tibble: 6 x 5
# Groups: Block, Year, Treatment [6]
Treatment Block Year Control Val
<chr> <int> <int> <dbl> <dbl>
1 Control-Treat1 1 2011 6 -6
2 Control-Treat2 1 2011 6 2
3 Control-Treat1 2 2012 3 -6
4 Control-Treat2 2 2012 3 -2
5 Control-Treat1 3 2013 6 3
6 Control-Treat2 3 2013 6 5

Answer 5

Another tidyverse solution. We can use filter to separate "Control" and "Treatment" to different data frames, use left_join to combine them by Block and Year, and then process the data frame.
library(tidyverse)
df2 <- df %>%
filter(!Treatment %in% "Control") %>%
left_join(df %>% filter(Treatment %in% "Control"),
.,
by = c("Block", "Year")) %>%
mutate(Value = Value.x - Value.y) %>%
unite(Treatment, Treatment.x, Treatment.y, sep = "-") %>%
select(names(df))
# Treatment Block Year Value
# 1 Control-Treat1 1 2011 -6
# 2 Control-Treat2 1 2011 2
# 3 Control-Treat1 2 2012 -6
# 4 Control-Treat2 2 2012 -2
# 5 Control-Treat1 3 2013 3
# 6 Control-Treat2 3 2013 5