r - Apply a different formula for each condition that is met in a certain data frame -

lets example have following data frame called "df1" (which part of larger data frame example now):

df1=

[,1]    [,2]    [,3]    1    -0.5     1.3    1    -0.3     0.9    5    -0.2     0.2    2     0.4     0.5    0     0.5     1.1    2     1.1     0.1    1    -0.6     1.8 

and have created following conditions:

conda= df1[,2] >= 0 & df2[,3]  > 1 condb= df1[,2] >= 0 & df2[,3] <= 1 condc= df1[,2]  < 0 & df2[,3]  > 1 condd= df1[,2]  < 0 & df2[,3] <= 1 

here comes question:

how apply different function each condition met in df1. example:

if conda met: df1[,1]=df1[,1]*1 if condb met: df1[,1]=df1[,1]*2 if condc met: df1[,1]=df1[,1]*3 if condd met: df1[,1]=df1[,1]*4 

taking account example "df1", output data frame this:

[,1]    [,2]    [,3]    3    -0.5     1.3      # in row condc met    4    -0.3     0.9      # in row condd met   20    -0.2     0.2      # in row condd met    4     0.4     0.5      # in row condb met    0     0.5     1.1      # in row conda met    4     1.1     0.1      # in row condb met    3    -0.6     1.8      # in row condc met 

help appreciated!!

just take wrote above , apply it.

conda <- df1[,2] >= 0 & df1[,3]  > 1 df1[conda,1] <- df1[conda,1] * 1 

(although, efficiencies sake skip 1 since doesn't anything. i'm assuming df2 in question typo since never mention otherwise.)

one way make briefer might make list out of conditions , cycle through.

conds <- list( conda= df1[,2] >= 0 & df1[,3]  > 1, condb= df1[,2] >= 0 & df1[,3] <= 1, condc= df1[,2]  < 0 & df1[,3]  > 1, condd= df1[,2]  < 0 & df1[,3] <= 1) for(i in 1:4) { df1[conds[[i]],1] <- df1[conds[[i]],1] * }