I'm trying to generate multiple new columns/variables in a R dataframe with dynamic new names taken from a vector. The new variables are computed from groups/levels of a single column.
The dataframe contains measurements ( counts ) of different chemical elements ( element ) along depth ( z ). The new variables are computed by dividing the counts of each element at a certain depth by the respective counts of proxy elements ( proxies ) at the same depth.
There is already a solution using mutate that works if I only want to create one new column/name the columns explicitly (see code below). I'm looking for a generalised solution to use in a shiny web app where proxies is not a string but a vector of strings and is dynamically changing according to user input.
# Working code for just one new column at a time (here Ti_ratio)
proxies <- "Ti"
df <- tibble(z = rep(1:10, 4), element = rep(c("Ag", "Fe", "Ca", "Ti"), each = 10), counts = rnorm(40))
df_Ti <- df %>%
group_by(z) %>%
mutate(Ti_ratio = counts/counts[element %in% proxies])
# Not working code for multiple columns at a time
proxies <- c("Ca", "Fe", "Ti")
varname <- paste(proxies, "ratio", sep = "_")
df_ratios <- df %>%
group_by(z) %>%
map(~ mutate(!!varname = .x$counts/.x$counts[element %in% proxies]))
Output of working code:
> head(df_Ti)
# A tibble: 6 x 4
# Groups: z [6]
z element counts Ti_ratio
<int> <chr> <dbl> <dbl>
1 1 Ag 2.41 4.10
2 2 Ag -1.06 -0.970
3 3 Ag -0.312 -0.458
4 4 Ag -0.186 0.570
5 5 Ag 1.12 -1.38
6 6 Ag -1.68 -2.84
Expected output of not working code:
> head(df_ratios)
# A tibble: 6 x 6
# Groups: z [6]
z element counts Ca_ratio Fe_ratio Ti_ratio
<int> <chr> <dbl> <dbl> <dbl> <dbl>
1 1 Ag 2.41 4.78 -10.1 4.10
2 2 Ag -1.06 3.19 0.506 -0.970
3 3 Ag -0.312 -0.479 -0.621 -0.458
4 4 Ag -0.186 -0.296 -0.145 0.570
5 5 Ag 1.12 0.353 3.19 -1.38
6 6 Ag -1.68 -2.81 -0.927 -2.84
Edit: I found a general solution to my problem with base R
using two nested for-loops, similar to the answer posted by @fra (the difference being that here I loop both over the depth and the proxies):
library(tidyverse)
df <- tibble(z = rep(1:3, 4), element = rep(c("Ag", "Ca", "Fe", "Ti"), each = 3), counts = runif(12)) %>% arrange(z, element)
proxies <- c("Ca", "Fe", "Ti")
for (f in seq_along(proxies)) {
proxy <- proxies[f]
tmp2 <- NULL
for (i in unique(df$z)) {
tmp <- df[df$z == i,]
tmp <- as.data.frame(tmp$counts/tmp$counts[tmp$element %in% proxy])
names(tmp) <- paste(proxy, "ratio", sep = "_")
tmp2 <- rbind(tmp2, tmp)
}
df[, 3 + f] <- tmp2
}
And the correct output:
> head(df)
# A tibble: 6 x 6
z element counts Ca_ratio Fe_ratio Ti_ratio
<int> <chr> <dbl> <dbl> <dbl> <dbl>
1 1 Ag 0.690 0.864 9.21 1.13
2 1 Ca 0.798 1 10.7 1.30
3 1 Fe 0.0749 0.0938 1 0.122
4 1 Ti 0.612 0.767 8.17 1
5 2 Ag 0.687 0.807 3.76 0.730
6 2 Ca 0.851 1 4.66 0.904
I made the dataframe contain less data so that it's clearly visible why this solution is correct (Ratios of elements with themselves = 1). I'm still interested in a more elegant solution that I could use with pipes.
Using base R
proxies <- c("Ca", "Fe", "Ti")
for(f in proxies){
newDF <- as.data.frame(df$counts/df$counts[df$element %in% f])
names(newDF) <- paste(f, "ratio", sep = "_")
df <- cbind(df,newDF)
}
> df
z element counts Ca_ratio Fe_ratio Ti_ratio
1 1 Ag -0.40163072 -0.35820754 1.7375395 0.45692965
2 2 Ag -1.00880171 1.27798430 22.8520332 -2.84599471
3 3 Ag 0.72230855 -1.19506223 6.3893485 -0.73558507
4 4 Ag -1.71524002 -1.38942436 1.7564861 -3.03313134
5 5 Ag -0.30813737 1.08127226 4.1985801 -0.33008370
6 6 Ag 0.20524663 0.08910397 -0.3132916 -0.23778331
...
A tidyverse
option could be to create a function, similar to your original code and then pass that through using map_dfc
to create new columns.
library(tidyverse)
proxies <- c("Ca", "Fe", "Ti")
your_func <- function(x){
df %>%
group_by(z) %>%
mutate(!!paste(x, "ratio", sep = "_") := counts/counts[element %in% !!x]) %>%
ungroup() %>%
select(!!paste(x, "ratio", sep = "_") )
}
df %>%
group_modify(~map_dfc(proxies, your_func)) %>%
bind_cols(df, .) %>%
arrange(z, element)
# z element counts Ca_ratio Fe_ratio Ti_ratio
# <int> <chr> <dbl> <dbl> <dbl> <dbl>
# 1 1 Ag -0.112 -0.733 -0.197 -1.51
# 2 1 Ca 0.153 1 0.269 2.06
# 3 1 Fe 0.570 3.72 1 7.66
# 4 1 Ti 0.0743 0.485 0.130 1
# 5 2 Ag 0.881 0.406 -6.52 -1.49
# 6 2 Ca 2.17 1 -16.1 -3.69
# 7 2 Fe -0.135 -0.0622 1 0.229
# 8 2 Ti -0.590 -0.271 4.37 1
# 9 3 Ag 0.398 0.837 0.166 -0.700
#10 3 Ca 0.476 1 0.198 -0.836
# ... with 30 more rows
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