Replacing specific values in a data frame column according to multiple conditions in other columns in R

This is how I would do it:

library(tidyverse)
set.seed(1)
df <- data_frame("x" = sample(x = 200:700, size = 10, replace = TRUE),
                 "y" = sample(x = 0:400, size = 10, replace = TRUE),
                 "z" = sample(x = 0:200, size = 10, replace = TRUE))
df
#> A tibble: 10 x 3
#>       x     y     z
#>   <int> <int> <int>
#> 1   523    84   109
#> 2   366   276   164
#> 3   328   361    33
#> 4   617   329   105
#> 5   670   262   125
#> 6   498   328    88
#> 7   469    78   171
#> 8   665   212    32
#> 9   386    36    83
#>10   506   104   162

df$z <- ifelse((df$x > 300 & df$x < 600) & (df$y > 0 & df$y < 100) & (df$z > 160), 115, df$z)
df
#> A tibble: 10 x 3
#>       x     y     z
#>   <int> <int> <dbl>
#> 1   523    84   109
#> 2   366   276   164
#> 3   328   361    33
#> 4   617   329   105
#> 5   670   262   125
#> 6   498   328    88
#> 7   469    78   115
#> 8   665   212    32
#> 9   386    36    83
#>10   506   104   162

#(#7 was updated to 115 as it met all the criteria)

Edit

As usual, @TIC's answer is better than mine (fewer steps -> faster) but not by much on my system with a million rows. The data.table method is quickest:

library(tidyverse)
set.seed(1)
df <- data_frame("x" = sample(x = 0:700, size = 1000000, replace = TRUE),
                 "y" = sample(x = 0:400, size = 1000000, replace = TRUE),
                 "z" = sample(x = 0:200, size = 1000000, replace = TRUE))

ifelse_func <- function(df){
  df$z <- ifelse((df$x > 300 & df$x < 600) & (df$y > 0 & df$y < 100) & (df$z > 160), 115, df$z)
}

transform_func <- function(df){
  transform(df, z = replace(z, 300 < x & x < 600 & 0 < y & y < 100 & z > 160, 115))
}

rowsums_func <- function(df){
  df$z[!rowSums(!(df >list(300, 0, 160) & df < list(600, 100, Inf)))] <- 115
}

library(data.table)
dt_func <- function(df){
  setDT(df)
  df[x > 300 & x < 600 & y > 0 & y < 100 & z > 160, z := 115]
}

mbm <- microbenchmark::microbenchmark(ifelse_func(df), transform_func(df),
                                      rowsums_func(df), dt_func(df))
autoplot(mbm)

Edit 2

> system.time(ifelse_func(df))
   user  system elapsed 
  0.064   0.020   0.085 
> system.time(transform_func(df))
   user  system elapsed 
  0.060   0.009   0.069 
> system.time(rowsums_func(df))
   user  system elapsed 
  0.090   0.021   0.110 
> system.time(dt_func(df))
   user  system elapsed 
  0.036   0.003   0.039 

So we can do this with an ifelse conditions:

Some sample data:

df <- data.frame(x=c(450, runif(10)*200),
                 y=c(50, runif(10)*100),
                 z=c(170, runif(10)*100))

> df
           x        y         z
1  450.00000 50.00000 170.00000
2   10.38674 93.33277  74.72619
3  117.66350 48.88015  27.60769
4  128.85086 35.74645  61.32745
5   93.21923 87.15894  53.37949
6   30.09869 86.72846  94.64611
7  104.03966 55.12932  89.78309
8   17.48741 16.50095  42.26284
9  183.52845 39.65171  27.60766
10  79.68355 18.14510  84.17454
11 110.14051 77.85835  33.67199

Then run this:

df$z <- ifelse(df$x > 300 & df$x < 600 & df$y > 0 & df$y < 100 & df$z > 160, 115, df$z)

And we get this:

> df
           x        y         z
1  450.00000 50.00000 115.00000
2   10.38674 93.33277  74.72619
3  117.66350 48.88015  27.60769
4  128.85086 35.74645  61.32745
5   93.21923 87.15894  53.37949
6   30.09869 86.72846  94.64611
7  104.03966 55.12932  89.78309
8   17.48741 16.50095  42.26284
9  183.52845 39.65171  27.60766
10  79.68355 18.14510  84.17454
11 110.14051 77.85835  33.67199

Do you want this?

transform(
  df,
  z = replace(z, 300 < x & x < 600 & 0 < y & y < 100 & z > 160, 115)
)

Another option in base R is with rowSums

df$z[!rowSums(!(df >list(300, 0, 160) & df < list(600, 100, Inf)))] <- 115