How can I transpose dataframe with multiple columns in R?-CodePudding

I'm working on a table like this:

data <- data.frame(Chromosome = c("Chr1", "Chr1", "Chr1", "Chr2","Chr2", "Chr3", "Chr3", "Chr3", "Chr5","Chr5","Chr5", "Chr5", "Chr7", "Chr7"),
               Value = c(150, 325, 666, 121, 111, 325, 777, 123, 325, 150, 666, 444, 32,777),
               Percentaje = c(90, 80,20,0.5, 0.7, 29, 13, 2,3,3,1 ,34, 56, 78))

How can I obtain this table?

data2 <- data.frame(Value = c(150, 325, 666, 121, 111, 777, 123, 444, 32),
                Chr1 = c(90, 80, 20, 0, 0, 0, 0, 0, 0),
                Chr2 = c(0,0,0,0.5,0.7,0,0,0,0),
                Chr3 = c(0, 29, 0, 0, 0, 13, 2, 0, 0),
                Chr5 = c(3, 3, 1, 0,0,0,0,34,0),
                Chr7 = c(0,0,0,0,0,78,0,0,56))

Note that I would like to add the value "0" when some "Value" is missing in a chromosome.

CodePudding user response：

You could use pivot_wider with names_from to your Chromosome column to specify the column names and values_from by Percentage column and fill the NA's with 0 by values_fill like this:

library(tidyr)
data %>%
  pivot_wider(names_from = Chromosome, values_from = Percentaje, values_fill = 0)
#> # A tibble: 9 × 6
#>   Value  Chr1  Chr2  Chr3  Chr5  Chr7
#>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1   150    90   0       0     3     0
#> 2   325    80   0      29     3     0
#> 3   666    20   0       0     1     0
#> 4   121     0   0.5     0     0     0
#> 5   111     0   0.7     0     0     0
#> 6   777     0   0      13     0    78
#> 7   123     0   0       2     0     0
#> 8   444     0   0       0    34     0
#> 9    32     0   0       0     0    56

^{Created on 2022-12-13 with reprex v2.0.2}

CodePudding user response：

dplyr<1.0.0

data%>%spread(Chromosome,Percentaje,fill=0)

  Value Chr1 Chr2 Chr3 Chr5 Chr7
1    32    0  0.0    0    0   56
2   111    0  0.7    0    0    0
3   121    0  0.5    0    0    0
4   123    0  0.0    2    0    0
5   150   90  0.0    0    3    0
6   325   80  0.0   29    3    0
7   444    0  0.0    0   34    0
8   666   20  0.0    0    1    0
9   777    0  0.0   13    0   78