title: How do you filter out individuals on a figure after creating a PCA plot in Factoextra?
body: <p>I am a research student coming to grips with R for the first time.
  I am trying to make a PCA plot from a series of body measurements, the specimens names and a subspecies tag (BIN) are in sperate columns. The BIN column contains the BIN ID for each sample.
  The difficulty I am facing is filtering out individuals with certain BIN's.
  My desired output is to produce a PCA plot identical to the one below but only displaying the named BIN's (&quot;ACZ5516&quot;, &quot;ADF3772&quot;) and not the remaining BIN's.</p>
  <p><a href="https://i.stack.imgur.com/SrG1z.png" rel="nofollow noreferrer">Revised image</a></p>
  <pre><code>#import data set
  Anotylus&lt;-read.csv(&quot;DataSO.csv&quot;, header = TRUE, sep = &quot;,&quot;, 
  row.names = 1)
  #row.names sets specimen ID as specimen name
  
  #set BIN as factor
  Anotylus$BIN&lt;-as.factor(Anotylus$BIN)
  
  # Number of BINs and number of individuals in each
  table(Anotylus[&quot;BIN&quot;])
  
  #create PCA of data set, excludes column for BIN (column 12)
  Ano.pca&lt;-PCA(Anotylus[,c(1:11)], graph = FALSE)
  
  #visualise PCA with all individuals in the d.f.
  fviz_pca_ind(Ano.pca,
           geom.ind = &quot;point&quot;,
           col.ind = Anotylus$BIN,
           repel = TRUE,
           legend.title = &quot;BIN&quot;,
           addEllipses = TRUE)
  
  
  #With individuals from selected BINs 
  
  top&lt;-list(name=c(&quot;ACZ5516&quot;, &quot;ADF3772&quot;))
  
  fviz_pca_ind(Ano.pca,
           geom.ind = &quot;point&quot;,
           col.ind = Anotylus$BIN,#
           select.ind = top,
           repel = TRUE,
           legend.title = &quot;BIN&quot;,
           addEllipses = TRUE)
  #no samples visible at all
  #wouild like to see only the two named
  </code></pre>
  <p>I have tried using a subset of the data but the Principal Components variation changes and produces different a result.</p>
  <p>How do I filter the individuals displayed to a curated list?</p>
  <p>Any advice or guidance is deeply appreciated!</p>
  <p>Best,</p>
  <p>Dante</p>
  <p>Sample data set below</p>
  <pre><code>&gt; dput(Anotylus)
  structure(list(Total.Anten.Length..mm. = c(0.66, 0.635, 0.676, 
  0.559, 1.249, 0.675, 0.704, 0.649, 0.661, 0.795, 0.836, 0.888, 
  0.941, 0.781, 0.899, 0.918, 0.854, 0.834, 0.888, 0.884, 0.879, 
  0.776, 0.954, 0.853, 0.96, 0.527, 0.515, 0.653, 0.491, 0.474, 
  0.538, 0.694, 1.01, 0.53, 0.641, 0.509, 0.918, 0.849, 0.452, 
  0.536), Body.Length...mm. = c(1.842, 1.664, 1.901, 1.917, 3.061, 
  1.961, 1.862, 1.99, 1.85, 1.449, 2.455, 2.077, 2.578, 2.478, 
  2.798, 2.589, 2.291, 2.882, 2.472, 2.55, 2.53, 2.757, 2.689, 
  2.166, 2.894, 1.944, 1.48, 2.385, 1.715, 1.674, 1.532, 2.27, 
  2.598, 1.677, 1.67, 1.68, 2.374, 2.877, 1.699, 1.656), 
  Eye.Area..mm2. = c(0.01, 
  0.009, 0.01, 0.006, 0.026, 0.007, 0.01, 0.01, 0.009, 0.006, 0.016, 
  0.014, 0.015, 0.018, 0.02, 0.016, 0.019, 0.015, 0.013, 0.011, 
  0.015, 0.014, 0.017, 0.014, 0.012, 0.007, 0.006, 0.02, 0.007, 
  0.006, 0.005, 0.013, 0.013, 0.006, 0.007, 0.005, 0.013, 0.006, 
  0.008, 0.005), Eye.Width..mm. = c(0.046, 0.036, 0.054, 0.033, 
  0.071, 0.04, 0.046, 0.047, 0.044, 0.05, 0.059, 0.053, 0.073, 
  0.063, 0.068, 0.051, 0.044, 0.07, 0.064, 0.061, 0.054, 0.042, 
  0.038, 0.059, 0.059, 0.043, 0.046, 0.079, 0.037, 0.035, 0.037, 
  0.054, 0.047, 0.045, 0.045, 0.028, 0.05, 0.037, 0.043, 0.045), 
  Head.Width..mm. = c(0.359, 0.362, 0.377, 0.317, 0.731, 0.456, 
  0.38, 0.414, 0.359, 0.453, 0.568, 0.449, 0.519, 0.517, 0.516, 
  0.515, 0.512, 0.513, 0.511, 0.456, 0.503, 0.474, 0.598, 0.453, 
  0.574, 0.309, 0.306, 0.574, 0.314, 0.298, 0.295, 0.386, 0.557, 
  0.289, 0.318, 0.306, 0.505, 0.291, 0.298, 0.263), 
  Pronotum.Width..mm. = c(0.413, 
  0.455, 0.439, 0.352, 0.741, 0.462, 0.467, 0.461, 0.442, 0.493, 
  0.573, 0.549, 0.584, 0.617, 0.632, 0.61, 0.614, 0.624, 0.631, 
  0.533, 0.587, 0.562, 0.609, 0.522, 0.621, 0.342, 0.341, 0.598, 
  0.336, 0.314, 0.331, 0.467, 0.547, 0.343, 0.342, 0.317, 0.545, 
  0.328, 0.329, 0.284), Pronotum.Length..mm. = c(0.304, 0.326, 
  0.334, 0.24, 0.48, 0.317, 0.303, 0.329, 0.302, 0.36, 0.418, 
  0.383, 0.424, 0.428, 0.399, 0.442, 0.404, 0.461, 0.435, 0.376, 
  0.393, 0.403, 0.373, 0.41, 0.435, 0.259, 0.247, 0.403, 0.257, 
  0.252, 0.23, 0.387, 0.388, 0.248, 0.26, 0.215, 0.336, 0.223, 
  0.231, 0.247), Elytra.Width..mm. = c(0.558, 0.552, 0.586, 
  0.43, 0.854, 0.506, 0.528, 0.586, 0.548, 0.54, 0.75, 0.716, 
  0.794, 0.816, 0.746, 0.82, 0.786, 0.8, 0.722, 0.69, 0.758, 
  0.766, 0.736, 0.668, 0.852, 0.468, 0.462, 0.741, 0.461, 0.323, 
  0.406, 0.637, 0.617, 0.41, 0.366, 0.422, 0.718, 0.42, 0.408, 
  0.278), Elytra.Length..mm. = c(0.469, 0.437, 0.386, 0.346, 
  0.631, 0.428, 0.464, 0.451, 0.445, 0.532, 0.583, 0.543, 0.558, 
  0.62, 0.625, 0.623, 0.613, 0.605, 0.623, 0.588, 0.606, 0.48, 
  0.568, 0.568, 0.598, 0.373, 0.352, 0.516, 0.365, 0.326, 0.327, 
  0.502, 0.464, 0.346, 0.344, 0.319, 0.519, 0.346, 0.329, 0.346
  ), Pronotum.Value = c(0.288, 0.319, 0.306, 0.331, 0.179, 
  0.278, 0.224, 0.211, 0.204, 0.273, 0.26, 0.33, 0.241, 0.218, 
  0.203, 0.209, 0.241, 0.227, 0.31, 0.236, 0.341, 0.288, 0.283, 
  0.263, 0.279, 0.173, 0.162, 0.22, 0.183, 0.209, 0.193, 0.185, 
  0.236, 0.181, 0.172, 0.227, 0.275, 0.164, 0.21, 0.217), 
  Elytra.Value = c(0.314, 
  0.319, 0.393, 0.243, 0.205, 0.297, 0.21, 0.205, 0.244, 0.359, 
  0.288, 0.335, 0.375, 0.291, 0.243, 0.238, 0.288, 0.283, 0.351, 
  0.271, 0.48, 0.415, 0.325, 0.294, 0.193, 0.182, 0.271, 0.237, 
  0.216, 0.246, 0.214, 0.193, 0.233, 0.205, 0.18, 0.262, 0.225, 
  0.176, 0.303, 0.251), BIN = structure(c(1L, 1L, 1L, 3L, 8L, 
  1L, 1L, 1L, 1L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
  1L, 1L, 1L, 1L, 7L, 3L, 3L, 6L, 3L, 3L, 3L, 2L, 5L, 3L, 3L, 
  3L, 5L, 3L, 3L, 3L), .Label = c(&quot;ACZ5516&quot;, &quot;ACZ5742&quot;, &quot;ADF3772&quot;, 
  &quot;ADF4138&quot;, &quot;ADG1201&quot;, &quot;ADH9095&quot;, &quot;ADI3175&quot;, &quot;ADR2790&quot;), class = 
  &quot;factor&quot;)), row.names = c(&quot;CCDB-22214-D03&quot;, 
  &quot;CCDB-22214-D06&quot;, &quot;CCDB-22214-D08&quot;, &quot;CCDB-22214-G09&quot;, &quot;CCDB-22214- 
  H02&quot;, 
  &quot;CCDB-22214-H09&quot;, &quot;CCDB-22215-A11&quot;, &quot;CCDB-22215-A12&quot;, &quot;CCDB-22215- 
  F04&quot;, 
  &quot;CCDB-23850-B07&quot;, &quot;CCDB-23851-C04&quot;, &quot;CCDB-23851-C05&quot;, &quot;CCDB-23851- 
  C11&quot;, 
  &quot;CCDB-23851-C12&quot;, &quot;CCDB-23851-D02&quot;, &quot;CCDB-23851-D03&quot;, &quot;CCDB-23851- 
  D04&quot;, 
  &quot;CCDB-23851-D06&quot;, &quot;CCDB-23851-E08&quot;, &quot;CCDB-23851-E09&quot;, &quot;CCDB-23851- 
  E11&quot;, 
  &quot;CCDB-23851-F03&quot;, &quot;CCDB-23851-G05&quot;, &quot;CCDB-23851-G09&quot;, &quot;CCDB-23858- 
  B08&quot;, 
  &quot;CCDB-23858-G12&quot;, &quot;CCDB-23858-H01&quot;, &quot;CCDB-23859-B10&quot;, &quot;CCDB-23859- 
  E07&quot;, 
  &quot;CCDB-23859-E10&quot;, &quot;CCDB-23859-E11&quot;, &quot;CCDB-25504-E04&quot;, &quot;CCDB-25505- 
  E02&quot;, 
  &quot;CCDB-25510-B12&quot;, &quot;CCDB-25510-D02&quot;, &quot;CCDB-25510-E09&quot;, &quot;CCDB-25511- 
  B06&quot;, 
  &quot;CCDB-25511-B12&quot;, &quot;CCDB-25511-E11&quot;, &quot;CCDB-25512-E12&quot;), class = 
  &quot;data.frame&quot;)
  </code></pre>