r - Manipulating dataframe without for loop -

i wrote loop manipulate rather large (~1,000,000 rows) dataframe runs far slow , couldn't find online.

df=data.frame(v1=runif(10), v2=runif(10), v3=runif(10), v4=0, v5=0, v6=0, v7=0) for( in 1:dim(df)[1] ) {     df[i,4]=length(which(df[i,1:3]>0.00 & df[i,1:3]<0.10))     df[i,5]=length(which(df[i,1:3]>0.10 & df[i,1:3]<0.50))     df[i,6]=length(which(df[i,1:3]>0.50 & df[i,1:3]<0.90))     df[i,7]=length(which(df[i,1:3]>0.90 & df[i,1:3]<1.00)) } 

i've tried write function this, adds row together:

test.fun <- function (df) {     df[,4]=length(which(df[,1:3]>0.00 & df[,1:3]<0.10))     df[,5]=length(which(df[,1:3]>0.10 & df[,1:3]<0.50))     df[,6]=length(which(df[,1:3]>0.50 & df[,1:3]<0.90))     df[,7]=length(which(df[,1:3]>0.90 & df[,1:3]<1.00))     return(df) }  (test <- test.fun(df)) 

rowsums condition want main idea.

you can use dplyr package make cleaner:

df %>%   mutate(v4 = rowsums(df[,1:3]>0.00 & df[,1:3]<0.10))%>%   mutate(v5 = rowsums(df[,1:3]>0.10 & df[,1:3]<0.50))%>%   mutate(v6 = rowsums(df[,1:3]>0.50 & df[,1:3]<0.90))%>%   mutate(v7 = rowsums(df[,1:3]>0.90 & df[,1:3]<1.00))   #           v1         v2        v3 v4 v5 v6 v7  # 1  0.2875775 0.95683335 0.8895393  0  1  1  1  # 2  0.7883051 0.45333416 0.6928034  0  1  2  0  # 3  0.4089769 0.67757064 0.6405068  0  1  2  0  # 4  0.8830174 0.57263340 0.9942698  0  0  2  1  # 5  0.9404673 0.10292468 0.6557058  0  1  1  1  # 6  0.0455565 0.89982497 0.7085305  1  0  2  0  # 7  0.5281055 0.24608773 0.5440660  0  1  2  0  # 8  0.8924190 0.04205953 0.5941420  1  0  2  0  # 9  0.5514350 0.32792072 0.2891597  0  2  1  0  # 10 0.4566147 0.95450365 0.1471136  0  2  0  1 

data:

set.seed(123) #to make reproducible example df=data.frame(v1=runif(10), v2=runif(10), v3=runif(10), v4=0, v5=0, v6=0, v7=0)