If we need only to sort by rows, use apply with MARGIN=1 and assign the output back to the original columns after transposing the output.
df1[-1] <- t(apply(df1[-1], 1,
FUN=function(x) sort(x, decreasing=TRUE)))
df1
# Name English Math French
# 1 John 86 78 56
# 2 Sam 97 86 79
# 3 Viru 93 44 34
NOTE: But we may need to change the column names as sorting by row gives the new sorted values.
Another option will be use apply separately to get the column names and the values, with Map we get the corresponding columns, cbind with the first column to have the output.
nMat <- `dim<-`(names(df1)[-1][t(apply(df1[-1], 1,
order, decreasing=TRUE))], dim(df1[-1]))
vMat <- t(apply(df1[-1], 1, sort, decreasing=TRUE))
cbind(df1[1], data.frame(Map(cbind, as.data.frame(nMat,
stringsAsFactors=FALSE), as.data.frame(vMat))))
# Name V1.1 V1.2 V2.1 V2.2 V3.1 V3.2
#1 John French 86 Math 78 English 56
#2 Sam Math 97 French 86 English 79
#3 Viru English 93 Math 44 French 34
Or another option is data.table. We melt the ‘wide’ format to ‘long’ format, grouped by ‘Name’, we order the ‘value’ in decreasing order in ‘i’, get the Subset of Data.table (.SD), create a new column (‘N’), grouped by ‘Name’ and use dcast to convert from ‘long’ to ‘wide’.
library(data.table)
dcast(melt(setDT(df1), id.var="Name")[order(-value),
.SD, Name][, N:=paste0("Col", 1:.N) , .(Name)],
Name~N, value.var=c("variable", "value"))
# Name variable_Col1 variable_Col2 variable_Col3 value_Col1 value_Col2 value_Col3
#1: John French Math English 86 78 56
#2: Sam Math French English 97 86 79
#3: Viru English Math French 93 44 34
EDIT:
The above data.table solution will not work in case you have 10 or more columns with values, because then col10 will preceed col2 in the ordering, even though higher values will be stored in col2. To resolve this issue, you can use just number for the names of your new columns as in:
dcast(melt(setDT(df1), id.var="Name")[order(-value),
.SD, Name][, N:=1:.N , .(Name)],
Name~N, value.var=c("variable", "value"))