Adding data to a column using multiple conditions

Question

I have a shapefile that has multiple columns, including TreeID and Species. I am trying to add data to the Species column such that all rows under Species should have value A, when TreeID != 171, 172, 174, 175, 176, 177, 178 & 179. So, basically, I want to add A as species to the rest of the TreeIDs. Below is what I have tried so far using loops and conditions, but I am stuck. How can I do this in a more tidy way using tidyverse?

library(tidyverse)
library(rgdal)
library(sf)

## Add the species name to the species column in the shapefile

# Bring in the shapefile

SHP = read_sf("fiel_path", "file_name")

# Now add the species name to the species column
for{i in SHP$treeID
  ifelse( i == 171 | i == 172 i == 174 | i == 175 i == 176 | i == 177 i == 178 | i == 179)
  {
    for{x in SHP$Species
     .... }
  }}

Answer 1

Make a test data set with 26 rows, species from A to Z and tree ID random samples from 170 to 180:

> SHP = data.frame(species=LETTERS, treeID=sample(170:180,26,TRUE))
> SHP
   species treeID
1        A    173
2        B    174
3        C    175
4        D    176
5        E    172
6        F    171
7        G    171
8        H    172
9        I    173
10       J    173
11       K    172
12       L    175
13       M    178
14       N    174
15       O    173
16       P    171
17       Q    173
18       R    179
19       S    173
20       T    172
21       U    179
22       V    179
23       W    172
24       X    178
25       Y    180
26       Z    179

These are the ID variables you want to make not species A. I notice that 173 is missing.

> notSpeciesA = c(171, 172, 174, 175, 176, 177, 178, 179)

Now change the species column where the tree ID is not in that set:

> SHP$species[!(SHP$treeID %in% notSpeciesA)] = "A"

and check:

> SHP
   species treeID
1        A    173
2        B    174
3        C    175
4        D    176
5        E    172
6        F    171
7        G    171
8        H    172
9        A    173
10       A    173
11       K    172
12       L    175
13       M    178
14       N    174
15       A    173
16       P    171
17       A    173
18       R    179
19       A    173
20       T    172
21       U    179
22       V    179
23       W    172
24       X    178
25       A    180
26       Z    179
> 

173s and 180s are now "A". The rest unchanged. Making test data sets like this is a good thing to do.