How to extract observations from a data frame and make a table showing observations, column name, and row name?

Question

I'm trying to extract observations from a data frame and create a new data frame that shows the observations in one column, corresponding columns in another column, and corresponding rows in another column. Then eliminate the values that have NA in them. Currently the df looks like:

Flask     Well 1      Well 2
  A            NA         NA
  B           2Mg       Control
  C           3Mg       Control
  D           4Mg       Control
  E            NA         NA

I've tried using !is.na() but it wont eliminate the values and structure the df in the current state. Currently I have it organized by using chart_df %>% group_by(row.names(chart_df)) but doesn't quite organize it correctly.

What I would want to have is a df that looks like:

Condition   Column   Row
NA          Well1      A
2Mg         Well1      B
3Mg         Well1      C
4Mg         Well1      D

Answer 1

I think you're looking for melt

# the data
my_df <- read.table(text = "Flask     Well_1      Well_2
                     A            NA         NA
                     B           2Mg       Control
                     C           3Mg       Control
                     D           4Mg       Control
                     E            NA         NA", header = TRUE)

library(reshape2)
melt(my_df , id.vars = 'Flask') %>% 
  setNames(c('Row', 'Column', 'Condition')) %>% 
  na.omit()

update : when the id.vars ( Flask column) is the rownames

# the data
my_df <- my_df[c('Well_1',  'Well_2')] %>% 
  as.data.frame(row.names = my_df$Flask) 

my_df %>% 
  as.matrix() %>% melt() %>%  na.omit()