Create a dataframe from a dataframe - r

Question

I'd like to create a dataframe from a dataframe that created before. my first dataframe is:
Sample motif chromosome
1 CT-G.A 1
1 TA-C.C 1
1 TC-G.C 2
2 CG-A.T 2
2 CA-G.T 2
Then I want to create a dataframe like below, for all (96*24-motifs*chromosomes-):
Sample CT-G.A,chr1 TA-C.C,chr1 TC-G.C,chr1 CG-A.T,ch1 CA-G.T,ch1 CT-G.A,chr2 TA-C.C,chr2 TC-G.C,chr2 CG-A.T,ch2 CA-G.T,ch2
1 1 1 0 0 0 0 0 1 0 0 0 0
2 0 0 0 0 0 0 0 0 0 0 1 1

Answer 1

Here is a possble solution using dplyr and tidyr.
We add a column value that indicates if a chromosome is present, then complete the data.frame, making sure we have rows for each motif-chromosome-Sample combination, where missing combinations get a 0 in the value column. We create a key out of the motif and chromosome columns, and then discard those columns. Lastly, we reshape the data.frame from long to wide (see here) to get your desired format. Hope this helps!
df = read.table(text="Sample motif chromosome
1 CT-G.A 1
1 TA-C.C 1
1 TC-G.C 2
2 CG-A.T 2
2 CA-G.T 2
2 CA-G.T 2",header=T)
library(tidyr)
library(dplyr)
df %>% mutate(value=1) %>% complete(motif,chromosome,Sample,fill=list(value=0)) %>%
mutate(key=paste0(motif,',chr',chromosome)) %>%
group_by(Sample,key) %>%
summarize(value = sum(value)) %>%
spread(key,value) %>%
as.data.frame
Output:
Sample CA-G.T,chr1 CA-G.T,chr2 CG-A.T,chr1 CG-A.T,chr2 CT-G.A,chr1 CT-G.A,chr2 TA-C.C,chr1 TA-C.C,chr2 TC-G.C,chr1 TC-G.C,chr2
1 1 0 0 0 0 1 0 1 0 0 1
2 2 0 2 0 1 0 0 0 0 0 0

Answer 2

This seems to be a classic case of when you would want to use factors and ensure that the empty factor levels aren't dropped (which dcast and other functions might do unless explicitly told not to).
Using #Florian's sample data, you can try:
library(data.table)
cols <- c("motif", "chromosome")
setDT(df)[, (cols) := lapply(.SD, factor), .SDcols = cols][
, dcast(unique(.SD)[, value := 1L],
Sample ~ motif + chromosome, value.var = "value",
fill = 0L, drop = FALSE)]
# Sample CA-G.T_1 CA-G.T_2 CG-A.T_1 CG-A.T_2 CT-G.A_1 CT-G.A_2 TA-C.C_1 TA-C.C_2 TC-G.C_1 TC-G.C_2
# 1 1 0 0 0 0 1 0 1 0 0 1
# 2 2 0 1 0 1 0 0 0 0 0 0
I've moved "cols" and myfun() outside of the transformation to save some typing and make things look a little more tidy.
Using the "tidyverse", I'd take a slightly different approach from #Florian, perhaps something like:
library(tidyverse)
df %>%
mutate_at(c("motif", "chromosome"), factor) %>%
mutate(value = 1) %>%
distinct() %>%
mutate(key = interaction(motif, chromosome)) %>%
select(-motif, -chromosome) %>%
spread(key, value, fill = 0, drop = FALSE)
Benchmarks
Benchmarks for these approaches and #Florian's can be found at this Gist.
On 10,000 rows, and 20 resulting columns, the results look like:

Answer 3

This will work for you. I have used package tidyr and dplyr. Actually, I had preferred to use unite and expand.grid from base r to achieve by finally using spread
df <- read.table(text = "Sample motif chromosome
1 CT-G.A 1
1 TA-C.C 1
1 TC-G.C 2
2 CG-A.T 2
2 CA-G.T 2", header = TRUE)
#add a column to represent presence of chromosome
df$val <- 1
library(tidyr)
library(dplyr)
#Complete missing rows
df_complete <- left_join(
expand.grid(Sample=unique(df$Sample), motif=unique(df$motif),
chromosome=unique(df$chromosome)),
df, by = c("Sample", "motif", "chromosome"), copy = TRUE)
#Additional rows should have val = 0
df_complete$val[is.na(df_complete$val)] <- 0
df_complete %>%
unite(motif, c("motif", "chromosome"), sep = ",chr" ) %>%
spread(motif, val)
#Result
Sample CA-G.T,chr1 CA-G.T,chr2 CG-A.T,chr1 CG-A.T,chr2 CT-G.A,chr1 CT-G.A,chr2 TA-C.C,chr1 TA-C.C,chr2 TC-G.C,chr1 TC-G.C,chr2
1 1 0 0 0 0 1 0 1 0 0 1
2 2 0 1 0 1 0 0 0 0 0 0