Removing certain columns based on the value of a row-CodePudding

Using this code, I made the first row of the mtcars example as "p-values"

I want to make a conditional code where within that first row, if the p-value is 0.05 or less, keep it, and anything larger, that specific column is dropped.

DF<- as.data.frame(mtcars)
rownames(DF)[1] <- "p-value"
DF_row <- rownames(DF)
DF <- cbind(DF_row, DF)
rownames(DF) <- NULL
p_val <- c(0.01, 0.2, 0.1, 0.03, 0.014, 0.09, 0.05, 0.01, 0.03, 0.04, 0.011)
colnames(DF)[1] <- "Cars"
DF[2:12] <- p_val

What would be the best way in doing so? I looked everywhere online, but I can't seem to find the best way that works for this. Any suggestion is much appreciated

CodePudding user response：

You can check only the first row then bind all the other rows again.

Here is an example with Dplyr.

Solution

I'm identifying the p>.05 by putting 9999 in place, everything else is the same value.

library(dplyr)

DF[1,] %>%  mutate(across(is.numeric, ~ifelse(.x < .05, .x, 9999))) %>% 
  bind_rows(DF[-1,])

alternatively, you can identify them as missing putting NA_real_

DF[1,] %>%  mutate(across(is.numeric, ~ifelse(.x < .05, .x, NA_real_ ))) %>% 
  bind_rows(DF[-1,])

Output

                  Cars   mpg   cyl  disp    hp  drat       wt     qsec    vs    am     gear     carb
1              p-value 0.010 0.011 0.040 0.030 0.010 9999.000 9999.000 0.014 0.030 9999.000 9999.000
2        Mazda RX4 Wag 0.200 0.010 0.011 0.040 0.030    0.010    0.050 0.090 0.014    0.030    0.100
3           Datsun 710 0.100 0.200 0.010 0.011 0.040    0.030    0.010 0.050 0.090    0.014    0.030
4       Hornet 4 Drive 0.030 0.100 0.200 0.010 0.011    0.040    0.030 0.010 0.050    0.090    0.014
5    Hornet Sportabout 0.014 0.030 0.100 0.200 0.010    0.011    0.040 0.030 0.010    0.050    0.090
6              Valiant 0.090 0.014 0.030 0.100 0.200    0.010    0.011 0.040 0.030    0.010    0.050
7           Duster 360 0.050 0.090 0.014 0.030 0.100    0.200    0.010 0.011 0.040    0.030    0.010
8            Merc 240D 0.010 0.050 0.090 0.014 0.030    0.100    0.200 0.010 0.011    0.040    0.030
9             Merc 230 0.030 0.010 0.050 0.090 0.014    0.030    0.100 0.200 0.010    0.011    0.040
10            Merc 280 0.040 0.030 0.010 0.050 0.090    0.014    0.030 0.100 0.200    0.010    0.011
11           Merc 280C 0.011 0.040 0.030 0.010 0.050    0.090    0.014 0.030 0.100    0.200    0.010
12          Merc 450SE 0.010 0.011 0.040 0.030 0.010    0.050    0.090 0.014 0.030    0.100    0.200
13          Merc 450SL 0.200 0.010 0.011 0.040 0.030    0.010    0.050 0.090 0.014    0.030    0.100
14         Merc 450SLC 0.100 0.200 0.010 0.011 0.040    0.030    0.010 0.050 0.090    0.014    0.030
15  Cadillac Fleetwood 0.030 0.100 0.200 0.010 0.011    0.040    0.030 0.010 0.050    0.090    0.014
16 Lincoln Continental 0.014 0.030 0.100 0.200 0.010    0.011    0.040 0.030 0.010    0.050    0.090
17   Chrysler Imperial 0.090 0.014 0.030 0.100 0.200    0.010    0.011 0.040 0.030    0.010    0.050
18            Fiat 128 0.050 0.090 0.014 0.030 0.100    0.200    0.010 0.011 0.040    0.030    0.010
19         Honda Civic 0.010 0.050 0.090 0.014 0.030    0.100    0.200 0.010 0.011    0.040    0.030
20      Toyota Corolla 0.030 0.010 0.050 0.090 0.014    0.030    0.100 0.200 0.010    0.011    0.040
21       Toyota Corona 0.040 0.030 0.010 0.050 0.090    0.014    0.030 0.100 0.200    0.010    0.011
22    Dodge Challenger 0.011 0.040 0.030 0.010 0.050    0.090    0.014 0.030 0.100    0.200    0.010
23         AMC Javelin 0.010 0.011 0.040 0.030 0.010    0.050    0.090 0.014 0.030    0.100    0.200
24          Camaro Z28 0.200 0.010 0.011 0.040 0.030    0.010    0.050 0.090 0.014    0.030    0.100
25    Pontiac Firebird 0.100 0.200 0.010 0.011 0.040    0.030    0.010 0.050 0.090    0.014    0.030
26           Fiat X1-9 0.030 0.100 0.200 0.010 0.011    0.040    0.030 0.010 0.050    0.090    0.014
27       Porsche 914-2 0.014 0.030 0.100 0.200 0.010    0.011    0.040 0.030 0.010    0.050    0.090
28        Lotus Europa 0.090 0.014 0.030 0.100 0.200    0.010    0.011 0.040 0.030    0.010    0.050
29      Ford Pantera L 0.050 0.090 0.014 0.030 0.100    0.200    0.010 0.011 0.040    0.030    0.010
30        Ferrari Dino 0.010 0.050 0.090 0.014 0.030    0.100    0.200 0.010 0.011    0.040    0.030
31       Maserati Bora 0.030 0.010 0.050 0.090 0.014    0.030    0.100 0.200 0.010    0.011    0.040
32          Volvo 142E 0.040 0.030 0.010 0.050 0.090    0.014    0.030 0.100 0.200    0.010    0.011