How do I select columns conditional to the value of another column? (dplyr)

Hi try something very basic (combining filter and select_at) :

df1 <- df %>% 
  filter(Col7 == 1) %>% 
  select_at(vars(Name = Name1, Col1, Col2, Col3))
df2 <- df %>% 
  filter(Col7 == 0) %>% 
  select_at(vars(Name = Name2, Col1 = Col4, Col2 = Col5, Col3 = Col6))
df <- bind_rows(df1, df2)

You get exactly the data frame you seek :

> df
  Name Col1 Col2 Col3
1 John    A    A    A
2 John    A    A    A

You can use melt from data.table or reshape2 and then left join on the condition:

library(data.table)
setDT(d)

d[, row := .I]
md = melt(d, id=c("row", "Col7"), 
  meas = Map(c, 1:4, 5:8), 
  variable.factor = FALSE,
  variable.name = "colset",
  value.name = names(d)[1:4])
#    row Col7 colset Name1 Col1 Col2 Col3
# 1:   1    1      1  John    A    A    A
# 2:   2    0      1  Alex    B    B    B
# 3:   1    1      2  Alex    B    B    B
# 4:   2    0      2  John    A    A    A

cond = data.table(Col7 = 0:1, colset = c("2", "1"))
#    Col7 colset
# 1:    0      2
# 2:    1      1

res = md[cond, on=names(cond), nomatch=0]
#    row Col7 colset Name1 Col1 Col2 Col3
# 1:   2    0      2  John    A    A    A
# 2:   1    1      1  John    A    A    A

This approach extends to more than two sets of columns, eg with meas=Map(c, 1:4, 5:8, 9:12).

Here's a tidyverse (more tidyr than dplyr) approach. It's fairly verbose, as your original data is not in a tidy form, so most of the code is just getting to a long form, cleaning, and spreading back to wide form.

library(tidyverse)

df <- data_frame(Name1 = c("John", "Alex"), 
                 Col1 = c("A", "B"), Col2 = c("A", "B"), Col3 = c("A", "B"), 
                 Name2 = c("Alex", "John"), 
                 Col4 = c("B", "A"), Col5 = c("B", "A"), Col6 = c("B", "A"), 
                 Col7 = c(1L, 0L))

df %>% 
    # reshape to long form
    gather(col, col_val, num_range('Col', 1:6)) %>% 
    gather(name_var, name, contains('Name')) %>% 
    # clean, subset, clean for spreading
    mutate(col = parse_number(col), 
           name_var = parse_number(name_var)) %>% 
    filter(ifelse(Col7 == 1, 
                  col %in% 1:3 & name_var == 1, 
                  col %in% 4:6 & name_var == 2)) %>% 
    mutate(col = paste0('Col', col %% 3 + 1), 
           name_var = 'Name') %>% 
    # reshape back to wide form
    spread(name_var, name) %>% 
    spread(col, col_val) %>% 
    # clean
    select(-Col7)
#> # A tibble: 2 x 4
#>   Name  Col1  Col2  Col3 
#>   <chr> <chr> <chr> <chr>
#> 1 John  A     A     A    
#> 2 John  A     A     A

In base R:

df <- read.table(text = "Name1    Col1    Col2    Col3    Name2    Col4    Col5    Col6    Col7
John     A       A       A       Alex       B       B       B      1
                  Alex     B       B       B       John       A       A       A      0'", 
                 header = TRUE, stringsAsFactors = FALSE)


for(i in 1:nrow(df)){
  if(df$Col7[i] == 1){
    df1 <- df[i, c("Name1", "Col1", "Col2", "Col3")]
  }else if(df$Col7[i] == 0){
    df2 <- df[i, c("Name2", "Col4", "Col5", "Col6")]
  }
}

colnames(df2)[1] <- "Name1"
colnames(df2)[2] <- "Col1"
colnames(df2)[3] <- "Col2"
colnames(df2)[4] <- "Col3"

df <- rbind(df1, df2)

   Name1 Col1 Col2 Col3
1  John    A    A    A
2  John    A    A    A