search for: integer2

...X2 X11 X11 X19 X11 X11 X11 X11 X11 X11 Levels: X10 X11 X17 X19 X2 X20 X21 X3 X4 X5 X8 X9 X90 X97 X98 #Función Prueba 1 get.integer <- function(x) { road_accidents %>% str_replace(road_accidents$x, 'X','') %>% as.integer(road_accidents$x) } #Función Prueba 2 get.integer2 <- function(dataframe, y) { vector <- str_replace(dataframe[,y], 'X', '') vector <- as.integer(dataframe[,y]) dataframe[,y] <- vector } #Función Prueba 3 get.integer3 <- function(x,y) { vector <- x$y vector <- str_replace(vector, 'X', '...

Hi all, I am trying to set processor affinity for a specific process using a shell script without result. Script: #!/bin/sh -x cpu_affinity_ok="2" cpu_affinity="taskset -p -c `cat /tmp/test.pid` | awk '{print $6}'" if [ -f /tmp/test.pid ]; then if [ "$cpu_affinity" == "$cpu_affinity_ok" ]; then exit 0 else taskset -p -c 2

...count would specify the significance of each field. What isn''t clear to me is the toll this would take on a database. Say my CONTACT looks like: CONTACT: first_name, :string last_name, :string string1, :string string2, :string ... string20, :string integer1, :integer integer2, :integer ... integer20, :integer If there are hundreds of users each with hundreds of CONTACTs, these extra fields lying around will take up space. Granted, unused ones will be NULL, but still taking up space. Another option is to place the "user-specified" fields in a separat...