How to specify a column name in ddply via character variable? - r

Question

I have a tibble/dataframe with
sample_id condition state
---------------------------------
sample1 case val1
sample1 case val2
sample1 case val3
sample2 control val1
sample2 control val2
sample2 control val3
The dataframe is generated within a for loop for different states. Hence, every dataframe has a different name for the state column.
I want to group the data by sample_id and calculate the median of the state column such that every unique sample_id has a single median value. The output should be like below...
sample_id condition state
---------------------------------
sample1 case median
sample2 control median
I am trying the command below; it is working if give the name of the column, but I am not able to pass the name via the state character variable. I tried ensym(state) and !!ensym(state), but they all are throwing errors.
ddply(dat_state, .(sample_id), summarize, condition=unique(condition), state_exp=median(ensym(state)))

Answer 1

As camille notes above, this is easier in dplyr. Basic syntax (not yet addressing your question):
my_df %>%
group_by(sample_id, condition) %>%
summarize(state = median(state))
Note that syntax will give you values for every unique sample_id-condition pair. Which isn't an issue in your example, since every sample_id has the same condition, but just something to be aware of.
On to your question... It's not quite clear to me how you're planning to pass the state name to your calculation. But a couple ways you can handle this. One is to use dplyr's "rename" function:
x <- "Massachusetts"
my_df %>%
rename(state = x) %>%
group_by(sample_id, condition) %>%
summarize(state = median(state))
The (probably more proper) way to do this is to write a function using dplyr's "tidyeval" syntax:
myfunc <- function(df, state_name) {
df %>%
group_by(sample_id, condition) %>%
summarize(state = median({{state_name}}))
}
myfunc(my_df, Massachusetts) # Note: Unquoted state name

Answer 2

Thank you all for putting effort into answering my question. With your suggestions, I have found the solution. Below is the code to what I was trying to achieve by grouping sample_id and condition and passing state through a variable.
state_mark <- c("pPCLg2", "STAT1", "STAT5", "AKT")
for(state in state_mark){
dat_state <- dat_clust_stim[,c("sample_id", "condition", state)]
# I had to use !!ensym() to convert a character to a symbol.
dat_med <- group_by(dat_state, sample_id, condition) %>%
summarise(med = median(!!ensym(state)))
dat_med <- ungroup(dat_med)
x <- dat_med[dat_med$condition == "case", "med"]
y <- dat_med[dat_med$condition == "control", "med"]
t_test <- t.test(x$med, y$med)
}

Answer 3

If you want to stay old-fashioned, you can use the eval(parse(text=expression)) idiom:
ddply(dat_state, .(sample_id), summarize,
state_exp = eval(parse(text = paste("median(",state,")"))))
No fancy operators but mind the parentheses!