issues calculating rowwise maximum - r

Question

suppose I have a tibble dat below, what I would like to do is to calculate maximum of (x 2, x 3) and then minus x 1, where x can be either a or b. In my real data I have more than 3 columns, so something like 2:n (e.g., 2:3) would be great. tried many things, seems not working as I wanted them to, still struggling with the string vs column name thing..
dat <- tibble(`a 1` = c(0, 0, 0), `a 2` = 1:3, `a 3` = 3:1,
`b 1` = rep(1, 3), `b 2` = 4:6, `b 3` = 6:4)
foo <- function(x = 'a')
{
???
}
end result:
if x == `a`
c(3, 2, 3)
if x == `b`
c(5, 4, 5)

Answer 1

Solution 1
This solution uses only base R. The idea is to define a function (max_minus_first) to calculate the answer. The max_minus_first function has two arguments. The first argument, dat, is a data frame for analysis with the same format as the OP provided. group is the name of the group for analysis. The end product is a vector with the answer.
max_minus_first <- function(dat, group){
# Get all column names with starting string "group"
col_names <- colnames(dat)
dat2 <- dat[, col_names[grepl(paste0("^", group), col_names)]]
# Get the maximum values from all columns except the first column
max_value <- apply(dat2[, -1], 1, max, na.rm = TRUE)
# Calculate max_value minus the values from the first column
final_value <- max_value - unlist(dat2[, 1], use.names = FALSE)
return(final_value)
}
max_minus_first(dat, "a")
# [1] 3 2 3
max_minus_first(dat, "b")
# [1] 5 4 5
Solution 2
A solution using the tidyverse. The end product (dat2) is a tibble with the output from each group (a, b, ...)
library(tidyverse)
dat2 <- dat %>%
rowid_to_column() %>%
gather(Column, Value, -rowid, -ends_with(" 1")) %>%
separate(Column, into = c("Group", "Column_Number")) %>%
gather(Column_1, Value_1, ends_with(" 1")) %>%
separate(Column_1, into = c("Group_1", "Column_Number_1")) %>%
filter(Group == Group_1) %>%
group_by(rowid, Group, Value_1) %>%
summarise(Value = max(Value, na.rm = TRUE)) %>%
mutate(Final = Value - Value_1) %>%
ungroup() %>%
select(-starts_with("Value")) %>%
spread(Group, Final)
dat2
# # A tibble: 3 x 3
# rowid a b
# * <int> <dbl> <dbl>
# 1 1 3 5
# 2 2 2 4
# 3 3 3 5
Explanation
rowid_to_column() is from the tibble package, a way to create a new column based on row ID.
gather is from the tidyr package to convert the data frame from the wide format to long format. I used gather twice because the first column of each group is different than other columns in the same group. ends_with(" 1") is a select helper function from the dplyr, which select the column with a name ending in " 1". Notice that the space in " 1" is important because "1" may select other columns like a 11 if such columns exist.
separate is from the tidyr package to separate a column into two columns. I used it to separate the Group name and column numbers in each Group.
filter(Group == Group_1) is to filter rows with Group == Group_1.
group_by(rowid, Group, Value_1) and then summarise(Value = max(Value, na.rm = TRUE)) make sure the maximum from each Group is calculated.
mutate(Final = Value - Value_1) is to calculate the difference between maximum from each Group and the value from the first column. The results are stored in the Final column.
select(-starts_with("Value")) removes any columns with a name beginning with "Value".
spread from the tidyr package converts the data frame from long format to wide format.
Solution 3
Another tidyverse solution, which similar to Solution 2. It uses do to conduct operation to each Group hence making the code more concise.
dat2 <- dat %>%
rowid_to_column() %>%
gather(Column, Value, -rowid) %>%
separate(Column, into = c("Group", "Column_Number")) %>%
group_by(rowid, Group) %>%
do(data_frame(Max = max(.$Value[.$Column_Number != 1]),
First = .$Value[.$Column_Number == 1])) %>%
mutate(Final = Max - First) %>%
select(-Max, -First) %>%
spread(Group, Final) %>%
ungroup()
dat2
# # A tibble: 3 x 3
# rowid a b
# * <int> <dbl> <dbl>
# 1 1 3 5
# 2 2 2 4
# 3 3 3 5