I have a data frame as follows:
miRNA_region barcode read_count
MIMAT0000062 TCGA-05-4244-01A-01T-1108-13 14492
MIMAT0000063 TCGA-05-4244-01A-01T-1108-13 8767
MIMAT0000064 TCGA-05-4244-01A-01T-1108-13 610
MIMAT0000065 TCGA-05-4244-01A-01T-1108-13 750
MIMAT0000066 TCGA-05-4244-01A-01T-1108-13 804
MIMAT0000067 TCGA-05-4244-01A-01T-1108-13 4748
MIMAT0000062 TCGA-05-4384-01A-01T-1754-13 505712
MIMAT0000063 TCGA-05-4384-01A-01T-1754-13 121127
MIMAT0000064 TCGA-05-4384-01A-01T-1754-13 12833
MIMAT0000065 TCGA-05-4384-01A-01T-1754-13 1455
MIMAT0000067 TCGA-05-4384-01A-01T-1754-13 15284
Barcode corresponds to different samples. I need to convert the values in ''barcode'' column into new columns and get something like:
miRNA_region TCGA-05-4244-01A-01T-1108-13 TCGA-05-4384-01A-01T-1754-13
MIMAT0000062 14492 505712
MIMAT0000063 8767 121127
MIMAT0000064 610 12833
MIMAT0000065 750 1455
MIMAT0000066 804 15284
There are values in your example output that don't appear in your example data, but I'll assume you just want to convert your data from long to wide format.
example data
df <- structure(list(miRNA_region = c("MIMAT0000062", "MIMAT0000063",
"MIMAT0000062", "MIMAT0000063"), barcode = c("TCGA-05-4244-01A-01T-1108-13",
"TCGA-05-4244-01A-01T-1108-13", "TCGA-05-4384-01A-01T-1754-13",
"TCGA-05-4384-01A-01T-1754-13"), read_count = c(14492, 8767,
505712, 121127)), class = "data.frame", row.names = c(NA, -4L
))
> df
miRNA_region barcode read_count
1 MIMAT0000062 TCGA-05-4244-01A-01T-1108-13 14492
2 MIMAT0000063 TCGA-05-4244-01A-01T-1108-13 8767
3 MIMAT0000062 TCGA-05-4384-01A-01T-1754-13 505712
4 MIMAT0000063 TCGA-05-4384-01A-01T-1754-13 121127
pivot_wider from tidyr (part of the tidyverse). See vignette("pivot") for more information.
library("tidyr")
df_wide <- pivot_wider(df, names_from=barcode, values_from=read_count)
> df_wide
# A tibble: 2 x 3
miRNA_region `TCGA-05-4244-01A-01T-1108-13` `TCGA-05-4384-01A-01T-1754-13`
<chr> <dbl> <dbl>
1 MIMAT0000062 14492 505712
2 MIMAT0000063 8767 121127
If you have a lot of data it's quicker and more memory efficient to do this with dcast from data.table. See vignette("datatable-reshape") for more information.
library("data.table")
setDT(df)
df_wide <- dcast(df, miRNA_region ~ barcode, value.var="read_count")
> df_wide
miRNA_region TCGA-05-4244-01A-01T-1108-13 TCGA-05-4384-01A-01T-1754-13
1: MIMAT0000062 14492 505712
2: MIMAT0000063 8767 121127
I'm sorry, I will correct that. I used values from samples after the first one trying to improve the example. Thank you so much.
P.S.: function pivot_wider seem to be from tidyr package and not dplyr.
P.S.: function pivot_wider seem to be from tidyr package and not dplyr.
Nice catch, I edited my post with the correction.
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