How binarize my data frame in R?
Hi everybody I need trasforms a data frame in a data binarize, for example:
this is my data frame
mydata():
ID AA AB AC AD
Camp1 0 0 2 9
Camp2 2 1 2 9
Camp3 1 1 2 9
Camp4 1 1 2 9
Camp5 0 9 2 2
Camp6 9 9 0 2
Camp7 9 9 0 2
Camp8 1 9 0 2
this is what I would like to achieve: I
D AA_0 AA_1 AA_2 AA_9 AB_0 AB_1 AB_9 AC_2 AC_0 AD_9 AD_2
Camp1 1 0 0 0 1 0 0 1 0 1 0
Camp2 0 0 1 0 0 1 0 1 0 1 0
Camp3 0 1 0 0 0 1 0 1 0 1 0
Camp4 0 1 0 0 0 1 0 1 0 1 0
Camp5 1 0 0 0 0 0 1 1 0 0 1
Camp6 0 0 0 1 0 0 1 0 1 0 1
Camp7 0 0 0 1 0 0 1 0 1 0 1
Camp8 0 1 0 0 0 0 1 0 1 0 1
I have tried to use these two commands:
data <- mydata[,-c(1)]
data <- data %>% dplyr::mutate_if(is.character,as.factor)
data <- mltools::one_hot(data.table::as.data.table(data)
the commands run but the file is not binarized!! Does anyone know an alternative to this?
thank you
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stefania.morelli92 •
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1 answer
> df %>%
+ pivot_longer(-ID,names_to = "k",values_to = "v") %>%
+ mutate(new=paste(k,v,sep = "_")) %>%
+ select(ID,new) %>%
+ table() %>%
+ as.data.frame.matrix()
Full tidy:
> df %>%
+ pivot_longer(-ID,names_to = "k",values_to = "v") %>%
+ mutate(new=paste(k,v,sep = "_")) %>%
+ group_by(ID, new) %>%
+ summarise(freq = n()) %>%
+ ungroup() %>%
+ pivot_wider(names_from = new, values_from = freq, values_fill = 0)
with data.table:
df=fread("test.txt", sep = "\t", header = T)
df |>
melt("ID") |>
dcast (ID ~ variable + value, length)
The tidyverse solution in this case isn't too bad. It's just 2 lines.
library("tidyr")
df |>
pivot_longer(!ID) |>
pivot_wider(names_from=c(name, value), values_fn=length, values_fill=0)
The result.
# A tibble: 8 × 12
ID AA_0 AB_0 AC_2 AD_9 AA_2 AB_1 AA_1 AB_9 AD_2 AA_9 AC_0
<chr> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int>
1 Camp1 1 1 1 1 0 0 0 0 0 0 0
2 Camp2 0 0 1 1 1 1 0 0 0 0 0
3 Camp3 0 0 1 1 0 1 1 0 0 0 0
4 Camp4 0 0 1 1 0 1 1 0 0 0 0
5 Camp5 1 0 1 0 0 0 0 1 1 0 0
6 Camp6 0 0 0 0 0 0 0 1 1 1 1
7 Camp7 0 0 0 0 0 0 0 1 1 1 1
8 Camp8 0 0 0 0 0 0 1 1 1 0 1
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If you want to speed up the computation you can just do
(data > 0) * 1.I think that is not what I wants. It seems they, for every column want to create a new column that says how many 0's, 1's, 2's are in the original column.
yes, that's it!