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--- r:social_network_analysis [2022/06/09 09:50] – [triad_data2.csv] hkimscil
+++ r:social_network_analysis [2023/11/22 22:02] (current) – [Hawthorne study] hkimscil
@@ Line 5: / Line 5: @@
 {{:r:Padgett.csv}}
 {{:r:Padgw.csv}}
+====== Hawthorne study ======
+{{:r:davis.women.club.csv}}
+<code>
+library(tidiverse)
+sd <- read.csv("http://commres.net/wiki/_media/r/davis.women.club.csv")
+head(sd)
+g <- graph.data.frame(sd, directed=FALSE)
+bipartite.mapping(g)
+plot(g)
+V(g)$color <- ifelse(V(g)$type, "lightblue", "salmon")
+V(g)$shape <- ifelse(V(g)$type, "circle", "square")
+E(g)$color <- "lightgray"
+plot(g, vertex.label.cex = 1.2, vertex.label.color = "black")
+</code>
+{{:r:pasted:20230611-224359.png}}
+{{:r:pasted:20230611-225104.png}}
+<code>
+V(g)$type <- bipartite_mapping(g)$type
+types <- V(g)$type
+deg <- degree(g)
+bet <- betweenness(g)
+clos <- closeness(g)
+eig <- eigen_centrality(g)$vector
+deg
+bet
+clos
+eig
+cent_df <- data.frame(types, deg, bet, clos, eig)
+cent_df[order(cent_df$type, decreasing = TRUE),]
+</code>
+<code>
+> types <- V(g)$type
+> deg <- degree(g)
+> bet <- betweenness(g)
+> clos <- closeness(g)
+> eig <- eigen_centrality(g)$vector
+>
+> deg
+   EVELYN     LAURA   THERESA    BRENDA CHARLOTTE   FRANCES   ELEANOR
+         7         8         7         4         4         4
+    PEARL      RUTH     VERNE     MYRNA KATHERINE    SYLVIA      NORA
+         4         4         4         6         7         8
+    HELEN   DOROTHY    OLIVIA     FLORA        E1        E2        E3
+         4         2         2         3         3         6
+       E4        E5        E6        E8        E9        E7       E12
+         8         8        14        12        10         7
+      E10       E13       E14       E11
+         4         4         4
+> bet
+   EVELYN     LAURA   THERESA    BRENDA CHARLOTTE   FRANCES   ELEANOR
+.7600   22.8565   38.7393   22.0119    4.7279    4.7516    4.1357
+    PEARL      RUTH     VERNE     MYRNA KATHERINE    SYLVIA      NORA
+.9763    7.3609    6.3676    5.9435   16.2889   25.2987   43.9378
+    HELEN   DOROTHY    OLIVIA     FLORA        E1        E2        E3
+.7265    5.9435    2.0866    2.0866    0.9737    0.9441    8.1978
+       E4        E5        E6        E8        E9        E7       E12
+.4530   16.9812   28.0103  108.2617   96.2295   58.0969   10.2354
+      E10       E13       E14       E11
+.8186    1.8892    1.8892    9.0194
+> clos
+   EVELYN     LAURA   THERESA    BRENDA CHARLOTTE   FRANCES   ELEANOR
+.01667   0.01515   0.01667   0.01515   0.01250   0.01389   0.01389
+    PEARL      RUTH     VERNE     MYRNA KATHERINE    SYLVIA      NORA
+.01389   0.01471   0.01471   0.01429   0.01515   0.01613   0.01667
+    HELEN   DOROTHY    OLIVIA     FLORA        E1        E2        E3
+.01613   0.01429   0.01220   0.01220   0.01190   0.01190   0.01282
+       E4        E5        E6        E8        E9        E7       E12
+.01220   0.01351   0.01562   0.01923   0.01786   0.01667   0.01316
+      E10       E13       E14       E11
+.01282   0.01220   0.01220   0.01220
+> eig
+   EVELYN     LAURA   THERESA    BRENDA CHARLOTTE   FRANCES   ELEANOR
+.6225    0.5735    0.6934    0.5801    0.3081    0.3872    0.4287
+    PEARL      RUTH     VERNE     MYRNA KATHERINE    SYLVIA      NORA
+.3453    0.4508    0.4360    0.3891    0.4796    0.5882    0.5599
+    HELEN   DOROTHY    OLIVIA     FLORA        E1        E2        E3
+.5058    0.3891    0.1394    0.1394    0.2586    0.2750    0.4607
+       E4        E5        E6        E8        E9        E7       E12
+.3209    0.5888    0.6100    1.0000    0.7618    0.7460    0.4873
+      E10       E13       E14       E11
+.4239    0.3106    0.3106    0.1957
+>
+> cent_df <- data.frame(types, deg, bet, clos, eig)
+> cent_df[order(cent_df$type, decreasing = TRUE),]
+          types deg      bet    clos    eig
+E1         TRUE   3   0.9737 0.01190 0.2586
+E2         TRUE   3   0.9441 0.01190 0.2750
+E3         TRUE   6   8.1978 0.01282 0.4607
+E4         TRUE   4   3.4530 0.01220 0.3209
+E5         TRUE   8  16.9812 0.01351 0.5888
+E6         TRUE   8  28.0103 0.01562 0.6100
+E8         TRUE  14 108.2617 0.01923 1.0000
+E9         TRUE  12  96.2295 0.01786 0.7618
+E7         TRUE  10  58.0969 0.01667 0.7460
+E12        TRUE   7  10.2354 0.01316 0.4873
+E10        TRUE   6   6.8186 0.01282 0.4239
+E13        TRUE   4   1.8892 0.01220 0.3106
+E14        TRUE   4   1.8892 0.01220 0.3106
+E11        TRUE   4   9.0194 0.01220 0.1957
+EVELYN    FALSE   8  42.7600 0.01667 0.6225
+LAURA     FALSE   7  22.8565 0.01515 0.5735
+THERESA   FALSE   8  38.7393 0.01667 0.6934
+BRENDA    FALSE   7  22.0119 0.01515 0.5801
+CHARLOTTE FALSE   4   4.7279 0.01250 0.3081
+FRANCES   FALSE   4   4.7516 0.01389 0.3872
+ELEANOR   FALSE   4   4.1357 0.01389 0.4287
+PEARL     FALSE   3   2.9763 0.01389 0.3453
+RUTH      FALSE   4   7.3609 0.01471 0.4508
+VERNE     FALSE   4   6.3676 0.01471 0.4360
+MYRNA     FALSE   4   5.9435 0.01429 0.3891
+KATHERINE FALSE   6  16.2889 0.01515 0.4796
+SYLVIA    FALSE   7  25.2987 0.01613 0.5882
+NORA      FALSE   8  43.9378 0.01667 0.5599
+HELEN     FALSE   7  30.7265 0.01613 0.5058
+DOROTHY   FALSE   4   5.9435 0.01429 0.3891
+OLIVIA    FALSE   2   2.0866 0.01220 0.1394
+FLORA     FALSE   2   2.0866 0.01220 0.1394
+>
+</code>
+<code>
+V(g)$size <- degree(g)
+V(g)$label.cex <- degree(g) * 0.2
+windowsFonts(d2coding = windowsFont("D2Coding"))
+windowsFonts(lucida = windowsFont("Lucida Console"))
+windowsFonts(courrier = windowsFont("Courrier New"))
+shape <- c("circle", "square")
+fnts <- c("d2coding", "lucida")
+plot(g, layout = layout_with_graphopt,
+     vertex.shape= shape[as.numeric(V(g)$type) + 1],
+     vertex.label.family= fnts[as.numeric(V(g)$type)+1]
+)
+</code>
+{{:r:pasted:20230611-232937.png}}
+<code>
+bipartite_matrix <- as_incidence_matrix(g)
+bipartite_matrix
+</code>
+<code>
+> bipartite_matrix <- as_incidence_matrix(g)
+>
+> bipartite_matrix
+          E1 E2 E3 E4 E5 E6 E8 E9 E7 E12 E10 E13 E14 E11
+EVELYN     1  1  1  1  1  1  1  1  0   0   0   0   0   0
+LAURA      1  1  1  0  1  1  1  0  1   0   0   0   0   0
+THERESA    0  1  1  1  1  1  1  1  1   0   0   0   0   0
+BRENDA     1  0  1  1  1  1  1  0  1   0   0   0   0   0
+CHARLOTTE  0  0  1  1  1  0  0  0  1   0   0   0   0   0
+FRANCES    0  0  1  0  1  1  1  0  0   0   0   0   0   0
+ELEANOR    0  0  0  0  1  1  1  0  1   0   0   0   0   0
+PEARL      0  0  0  0  0  1  1  1  0   0   0   0   0   0
+RUTH       0  0  0  0  1  0  1  1  1   0   0   0   0   0
+VERNE      0  0  0  0  0  0  1  1  1   1   0   0   0   0
+MYRNA      0  0  0  0  0  0  1  1  0   1   1   0   0   0
+KATHERINE  0  0  0  0  0  0  1  1  0   1   1   1   1   0
+SYLVIA     0  0  0  0  0  0  1  1  1   1   1   1   1   0
+NORA       0  0  0  0  0  1  0  1  1   1   1   1   1   1
+HELEN      0  0  0  0  0  0  1  0  1   1   1   1   1   1
+DOROTHY    0  0  0  0  0  0  1  1  0   1   1   0   0   0
+OLIVIA     0  0  0  0  0  0  0  1  0   0   0   0   0   1
+FLORA      0  0  0  0  0  0  0  1  0   0   0   0   0   1
+>
+</code>
+===== stu x class 처럼 분석한 예 =====
+<code>
+actor_matrix <- bipartite_matrix %*% t(bipartite_matrix)
+event_matrix <- t(bipartite_matrix) %*% bipartite_matrix
+diag(actor_matrix) <- 0
+actor_matrix
+actor_matrix_cff_2 <- ifelse(actor_matrix > 2, actor_matrix, 0) # cuttoff 3 below
+actor_matrix_cff_3 <- ifelse(actor_matrix > 3, actor_matrix, 0) # cuttoff 3 below
+actor_g <- graph_from_adjacency_matrix(actor_matrix,
+                                       mode = "undirected",
+                                       weighted = TRUE)
+actor_g_cff_2 <- graph_from_adjacency_matrix(actor_matrix_cff_2,
+                                             mode = "undirected",
+                                             weighted = TRUE)
+actor_g_cff_3 <- graph_from_adjacency_matrix(actor_matrix_cff_3,
+                                             mode = "undirected",
+                                             weighted = TRUE)
+V(actor_g)$size <- betweenness(actor_g)
+V(actor_g_cff_2)$size <- betweenness(actor_g_cff_2)
+V(actor_g_cff_3)$size <- betweenness(actor_g_cff_3)
+V(actor_g)$label.cex <- betweenness(actor_g) * 0.2
+V(actor_g_cff_2)$label.cex <- betweenness(actor_g_cff_2) * 0.1
+V(actor_g_cff_3)$label.cex <- betweenness(actor_g_cff_3) * 0.4
+actor_g
+actor_g_cff_2
+actor_g_cff_3
+event_g <- graph_from_adjacency_matrix(event_matrix,
+                                       mode = "undirected",
+                                       weighted = TRUE)
+event_g
+windowsFonts(d2coding = windowsFont("D2Coding"))
+windowsFonts(lucida = windowsFont("Lucida Console"))
+shape <- c("circle", "square")
+fnts <- c("d2coding", "lucida")
+plot(actor_g,
+     vertex.shape= shape[as.numeric(V(g)$type) + 1],
+     vertex.label.family= fnts[as.numeric(V(g)$type)+1],
+     edge.color="red", edge.width=3
+)
+plot(actor_g_cff_2,
+     vertex.shape= shape[as.numeric(V(g)$type) + 1],
+     vertex.label.family= fnts[as.numeric(V(g)$type)+1],
+     edge.color="red", edge.width=3
+)
+plot(actor_g_cff_3,
+     vertex.shape= shape[as.numeric(V(g)$type) + 1],
+     vertex.label.family= fnts[as.numeric(V(g)$type)+1],
+     edge.color="red", edge.width=3
+)
+</code>
+[{{:r:pasted:20230612-081851.png|actor_g}}] \\
+[{{:r:pasted:20230612-082040.png|actor_g_cff_2}}] \\
+[{{:r:pasted:20230612-082055.png|actor_g_cff_3}}]
+===== 다른 방법 =====
+<code>
+library(ade4)
+bipartite_matrix <- as_incidence_matrix(g)  # Extract the matrix
+# Method #2 is "simple matching"
+women_match <- dist.binary(bipartite_matrix, method=2, upper=TRUE, diag = FALSE)
+event_match <- dist.binary(t(bipartite_matrix), method=2, upper=TRUE, diag = FALSE)
+women_match <- as.matrix(women_match)
+matching_women <- ifelse(women_match>0.8, 1, 0)
+matching_women
+match_women <- graph_from_adjacency_matrix(matching_women,
+                                           mode = "undirected")
+plot(match_women)
+</code>
+<code>
+bipartite_matrix <- as_incidence_matrix(g)  # Extract the matrix
+women_r <- cor(t(bipartite_matrix))
+event_r <- cor(bipartite_matrix)
+women_r <- as.matrix(women_r)
+women_r
+# Look at the matrix before you binarize
+r_women <- ifelse(women_r>0.6, 1, 0)    # Binarize
+diag(r_women) <- 0
+r_women    # Take a look at the matrix if you like
+# Create an igraph network
+ir_women <- graph_from_adjacency_matrix(r_women,
+                                          mode = "undirected")
+plot(ir_women)
+</code>
+{{:r:pasted:20230612-025040.png}}
+<code>
+library(psych)
+bipartite_matrix <- as_incidence_matrix(g)  # Extract the matrix
+women_Q <-YuleCor(t(bipartite_matrix))$rho
+event_Q <-YuleCor(bipartite_matrix)$rho
+women_Q <- as.matrix(women_Q)
+women_Q  # Look at the matrix before you binalize
+Q_women <- ifelse(women_Q>0.9, 1, 0) # Binarize
+diag(Q_women)<-0
+# Q_women    # Take a look at the matrix
+YQ_women <- graph_from_adjacency_matrix(Q_women,     # Create an igraph network
+                    mode = "undirected")
+plot(YQ_women)
+</code>
+{{:r:pasted:20230611-235802.png}}
 ====== Actors network ======
 http://rpubs.com/wctucker/302110
@@ Line 134: / Line 435: @@
 </code>
-====== Triad Data Visualizations ======
-===== Analysis (vis) =====
-<code>
-library(ggplot2)
-library(ggtern)
-demodata1 <- read.csv("http://commres.net/wiki/_export/code/r/social_network_analysis?codeblock=6")
-str(demodata1)
-</code>
-<code>
-demodata1$ObsID <- as.factor(demodata1$ObsID)
-demodata1$StartTime <- as.POSIXct(as.character(demodata1$StartTime), format="%m/%d/%Y %H:%M")
-demodata1$EndTime <- as.POSIXct(as.character(demodata1$EndTime), format="%m/%d/%Y %H:%M")
-ggtern(data=demodata1, aes(x=Triad1A, y=Triad1B, z=Triad1C)) +
-    geom_point()
-</code>
-<code>
-ggtern(data=demodata1, aes(x=Triad1A, y=Triad1B, z=Triad1C)) + #define data sources
-geom_point() +          #define data geometry
-+     theme_showarrows() +    #draw labeled arrows beside axes
-+     ggtitle("My Favorite Color") +      #add title
-+     xlab("Red") +                       #replace default axis labels
-+     ylab("Yellow") +
-+     zlab("Blue")
-</code>
-<code>
-ggtern(data=demodata1, aes(x=Triad1A, y=Triad1B, z=Triad1C)) + #define data sources
-    geom_point() +          #define first data geometry
-    geom_density_tern()     #define second data geometery
-</code>
-<code>
-ggtern(data=demodata1, aes(x=Triad1A, y=Triad1B, z=Triad1C)) +      #define data sources
-    geom_density_tern()                                 #define a data geometery
-ggtern(data=demodata1, aes(x=Triad1A, y=Triad1B, z=Triad1C)) +              #define data sources
-    geom_density_tern(aes(fill=..level.., alpha=..level..))     #define a data geometery with an aesthetic
-</code>
-<code>
-#Or you can apply a color gradient to space between the contour lines
-ggtern(data=demodata1, aes(x=Triad1A, y=Triad1B, z=Triad1C)) +               #define data sources
-    stat_density_tern(aes(fill=..level.., alpha=..level..),geom='polygon') + #now you need to use stat_density_tern
-    scale_fill_gradient2(high = "red") +                                    #define the fill color
-    guides(color = "none", fill = "none", alpha = "none")                   #we don't want to display legend items
-</code>
-<code>
-ggtern(data=demodata1, aes(x=Triad1A, y=Triad1B, z=Triad1C)) +
-    stat_density_tern(aes(fill=..level.., alpha=..level..), geom='polygon') +
-    scale_fill_gradient2(high = "blue") +
-    geom_point() +
-    theme_showarrows() +
-    ggtitle("My Favorite Color") +
-    xlab("Red") +
-    ylab("Yellow") +
-    zlab("Blue") +
-    guides(color = "none", fill = "none", alpha = "none")
-</code>
-===== triad_data1.csv =====
-<file csv triad_data1.csv>
-ObsID,StartTime,EndTime,Triad1A,Triad1B,Triad1C,Triad2A,Triad2B,Triad2C,Triad3A,Triad3B,Triad3C,Diad1X,Diad1Y,Diad2X,Diad2Y,Diad3X,Diad3Y,Factor1,Factor2,Factor3,Landscape1XRight,Landscape1YTop
-,23:26.6,23:32.5,9.653039,33.223686,57.123272,9.595201,47.25877,43.14603,24.182148,54.714912,21.10294,0.3174739,0.6825261,0.37223977,0.62776023,0.6648707,0.33512932,Mars,Yes,A,0.3057644,0.30323863
-,47:02.9,47:10.7,13.072644,78.399124,8.528231,22.574768,58.662277,18.762953,16.477793,43.75,39.772205,0.28853494,0.71146506,0.5457413,0.45425868,0.5582876,0.44171238,Mars,Yes,B,0.3508772,0.49960226
-,45:02.1,45:06.0,45.193813,41.995613,12.810572,46.875896,39.364033,13.760066,10.804956,81.4693,7.725747,0.24673432,0.7532657,0.82018924,0.17981073,0.58650076,0.4134992,Mars,Yes,B,0.7268171,0.64505684
-,43:03.8,43:09.3,42.774303,40.24123,16.984467,42.926125,45.065792,12.008086,17.386312,70.50439,12.109302,0.20171827,0.7982817,0.23343849,0.7665615,0.69935346,0.30064654,Mars,Yes,B,0.30075186,0.27414775
-,35:11.6,35:16.1,24.141186,17.434212,58.424603,60.118145,23.135965,16.74589,9.930176,85.416664,4.653164,0.48789185,0.51210815,0.90851736,0.09148265,0.26988637,0.7301136,Mars,Yes,B,0.72431076,0.77232957
-,14:56.1,15:00.0,61.13993,19.627197,19.232874,62.761776,28.837713,8.40051,15.487334,75.76755,8.745117,0.30461216,0.69538784,0.47318614,0.52681386,0.6962186,0.30378136,Mars,No,A,0.8621554,0.6377841
-,13:56.2,14:00.3,26.828188,62.60965,10.562162,41.538036,48.574562,9.887403,22.938656,56.4693,20.592045,0.28853494,0.71146506,0.30599368,0.6940063,0.6617359,0.3382641,Mars,No,A,0.45363408,0.43778408
-,43:07.1,43:14.8,20.649282,39.802628,39.54809,36.01703,24.451761,39.531216,29.091057,55.15351,15.755433,0.6968951,0.3031049,0.69716084,0.30283913,0.63352275,0.36647728,Mars,No,B,0.556391,0.59414774
-,42:54.2,42:58.1,57.41668,31.469301,11.114022,71.39393,17.434212,11.171861,8.221577,63.925438,27.852982,0.30461216,0.69538784,0.20189273,0.79810727,0.44543493,0.5545651,Mars,No,B,0.73433584,0.5032387
-,15:39.9,15:46.0,30.577942,8.223686,61.19837,33.243263,33.662285,33.09445,4.9586134,94.62719,0.4141992,0.49432278,0.5056772,0.28706622,0.7129338,0.047315836,0.95268416,Mars,No,B,0.7218045,0.43051136
-,14:34.6,14:40.6,9.809683,42.434208,47.756107,7.857683,66.11842,26.023895,12.472588,65.679825,21.847588,0.22744173,0.7725583,0.26498425,0.73501575,0.6397923,0.36020768,Mars,No,A,0.23057644,0.666875
-,10:11.4,10:19.2,2.4041455,36.732464,60.86339,66.328384,7.7850876,25.88653,10.07477,83.66228,6.262954,0.40107518,0.5989248,0.1798107,0.8201893,0.37333465,0.62666535,Venus,Yes,A,0.72431076,0.6305114
-,36:48.1,36:52.8,42.85383,45.94298,11.203191,35.38082,45.504387,19.114796,27.469212,45.94298,26.587805,0.32390475,0.67609525,0.50157726,0.4984227,0.59277034,0.40722963,Venus,Yes,B,0.67919797,0.64505684
-,17:19.7,17:24.5,12.070142,32.785088,55.144768,26.707693,19.627197,53.665108,7.73478,87.60965,4.6555715,0.28210407,0.7178959,0.88328075,0.116719246,0.5426136,0.45738637,Venus,No,A,0.49122807,0.008693159
-,42:16.4,42:21.1,57.038326,20.504387,22.457285,,,,15.600601,45.504387,38.895016,0.2531652,0.7468348,0.681388,0.31861198,,,Venus,No,B,0.3358396,0.3286932
-,44:11.4,44:16.9,21.878311,24.89035,53.231342,8.452925,7.785087,83.761986,3.5126905,45.504387,50.98292,0.29175043,0.70824957,0.7665615,0.23343849,0.51126564,0.48873433,Venus,No,B,0.49874687,0.5396023
-,15:24.4,15:29.2,49.232464,50.76724,3.03E-04,77.25714,19.627197,3.1156592,3.8115115,50.767548,45.420948,0.8030044,0.19699559,0.807571,0.19242902,,,Jupiter,Yes,A,,
-,12:28.1,12:36.6,41.393448,50.328945,8.27761,76.58961,12.171051,11.239339,20.174534,74.45175,5.3737135,0.28853494,0.71146506,0.19873816,0.80126184,0.52067006,0.47932994,Jupiter,Yes,A,0.7218045,0.76505685
-,14:09.8,14:13.8,77.1005,10.416673,12.482829,52.881306,37.60965,9.5090475,8.640899,45.504387,45.854717,,,0.63722396,0.362776,0.30750394,0.69249606,Jupiter,Yes,A,0.6265664,0.5541477
-,11:30.2,11:38.8,37.879856,52.960526,9.159616,24.249624,49.451756,26.298618,15.335515,70.942986,13.721502,0.21136457,0.78863543,0.64984226,0.35015774,0.13195533,0.8680447,Jupiter,Yes,A,0.6315789,0.7250568
-,12:45.2,12:53.1,,,,13.101569,38.048244,48.85019,47.321728,45.065792,7.61248,0.15348673,0.8465133,0.89589906,0.10410095,0.71502745,0.28497258,Jupiter,Yes,A,0.21804512,0.13960224
-,11:47.7,11:56.3,71.244514,14.802628,13.952854,41.832043,49.451756,8.716204,48.420635,45.065792,6.513577,0.4428758,0.5571242,0.8769716,0.12302839,0.2479428,0.7520572,Jupiter,Yes,A,0.7218045,0.6777841
-,52:14.6,52:20.5,35.686874,57.34649,6.9666357,70.51914,21.381584,8.099272,26.379951,54.714912,18.905136,0.31425846,0.68574154,0.340694,0.659306,0.13195533,0.8680447,Jupiter,Yes,B,0.78446114,0.76505685
-,34:00.0,34:08.6,15.417443,12.171051,72.4115,15.930757,12.609651,71.459595,12.706346,78.399124,8.894528,0.3592745,0.6407255,0.9621451,0.03785489,0.059855044,0.94014496,Jupiter,Yes,B,0.69924814,0.76505685
-,31:55.6,32:04.2,7.443188,88.92544,3.631372,8.68668,87.17105,4.142266,29.298306,43.75,26.951694,,,,,,,Jupiter,Yes,B,,
-,16:05.4,16:09.5,63.19796,25.767538,11.0345,35.795315,22.697372,41.507317,22.208464,58.662277,19.129255,0.2531652,0.7468348,0.26498425,0.73501575,0.7244318,0.2755682,Jupiter,No,A,0.7017544,0.69960225
-,35:48.5,35:57.1,21.986755,56.907898,21.105349,24.254446,53.837723,21.907835,25.936531,51.206142,22.857325,0.3399819,0.6600181,0.73186123,0.2681388,0.7902625,0.20973746,Jupiter,No,B,0.7318296,0.6850568
-,35:12.6,35:17.2,43.070713,43.311405,13.617879,27.54151,45.065792,27.3927,26.008833,50.328945,23.662222,0.43966037,0.5603396,0.4384858,0.5615142,0.5332092,0.46679077,Jupiter,No,B,0.6917293,0.78323865
-,44:08.9,44:12.8,29.534472,58.662277,11.803249,14.865591,43.311405,41.823,13.6413765,62.60965,23.748974,0.31104302,0.688957,0.6025237,0.39747635,0.5426136,0.45738637,Jupiter,No,B,0.2932331,0.34323865
-,12:05.7,12:10.4,20.820381,62.171055,17.008564,59.73979,12.171051,28.089157,4.9923463,58.662277,36.345375,0.19850284,0.80149716,0.20189273,0.79810727,0.28556037,0.71443963,Saturn,Yes,A,0.2857143,0.306875
-,16:20.1,16:28.8,70.51192,14.802628,14.685453,21.114382,63.04825,15.837372,13.87513,75.32895,10.79592,0.36570537,0.6342946,0.8675079,0.13249211,0.13195533,0.8680447,Saturn,Yes,A,0.8245614,0.76505685
-,13:06.1,13:10.1,12.306304,47.697372,39.996323,8.727651,57.785088,33.48726,38.17627,56.0307,5.7930274,0.1599176,0.8400824,0.7066246,0.2933754,0.37646943,0.62353057,Saturn,Yes,A,0.65413535,0.26687503
-,13:13.0,13:20.9,85.4483,6.907891,7.643811,83.6915,8.223686,8.084814,34.416874,34.97807,30.60506,0.20814914,0.79185086,0,1,0.4987265,0.5012735,Saturn,Yes,A,0.9849624,0.8850568
-,07:59.8,08:04.5,83.6192,9.100876,7.279919,82.5926,8.223686,9.183715,47.97722,41.55702,10.465769,0.37213624,0.62786376,0.55520505,0.44479495,0.6429271,0.3570729,Saturn,Yes,A,0.78446114,0.81960225
-,17:35.8,17:41.4,28.07168,60.855263,11.073056,15.911472,61.732452,22.356071,27.418604,66.55702,6.024375,0.24030346,0.75969654,0.7539432,0.24605678,0.4955917,0.5044083,Saturn,Yes,A,0.68421054,0.3250568
-,40:27.7,40:32.4,52.223408,38.925438,8.851153,51.199215,40.24123,8.559556,15.701814,70.942986,13.355203,0.3174739,0.6825261,0.3280757,0.6719243,0.76518416,0.23481584,Saturn,Yes,B,0.68421054,0.6850568
-,45:47.0,45:51.1,6.9202437,13.048241,80.03152,6.768416,8.223686,85.00789,21.345734,73.57456,5.079707,0.21779543,0.78220457,0.45425868,0.5457413,0.52693963,0.47306034,Saturn,Yes,B,0.17293233,0.31051135
-,37:48.6,37:57.1,21.478275,60.855263,17.66646,23.598963,57.34649,19.054548,37.356915,43.75,18.893085,0.32712018,0.6728798,0.8548896,0.14511041,0.29809952,0.7019005,Saturn,Yes,B,0.29573935,0.346875
-,16:13.1,16:18.9,17.648993,42.872803,39.478203,15.911472,61.732452,22.356071,20.456491,64.36403,15.179481,0.24351889,0.7564811,0.81388015,0.18611987,0.50499606,0.4950039,Saturn,No,A,0.43609023,0.19051135
-,07:47.4,07:52.2,17.576694,43.75,38.673306,19.870893,64.80263,15.326479,2.215682,96.97089,0.8134325,0.3174739,0.6825261,0.7697161,0.23028392,0.76831895,0.23168103,Saturn,No,A,0.3358396,0.30323863
-,15:05.4,15:11.4,59.026474,29.714912,11.258614,51.199215,40.24123,8.559556,30.53939,39.802628,29.657984,0.2853195,0.7146805,0.615142,0.38485804,0.46110892,0.5388911,Saturn,No,A,0.25814536,0.33232957
-,34:47.8,34:52.4,47.982037,45.94298,6.0749807,16.677813,25.767538,57.55465,17.83214,76.20614,5.961721,0.42036778,0.5796322,0.27760255,0.72239745,0.6962186,0.30378136,Saturn,No,B,0.4235589,0.5177841
-,52:49.0,52:54.8,43.364723,44.188595,12.446685,42.851414,43.75,13.3985815,10.356719,73.57456,16.068722,0.23065716,0.76934284,0.21766561,0.7823344,0.78399295,0.21600705,Saturn,No,B,0.28070176,0.2559659
-,54:53.2,54:59.2,42.193527,45.065792,12.740686,11.937597,45.504387,42.558018,26.8306,64.80263,8.366773,0.37535167,0.62464833,0.41955835,0.58044165,0.398413,0.601587,Saturn,No,B,0.3709273,0.3977841
-,23:08.7,23:14.3,43.36954,48.574562,8.055899,,,,34.662674,58.662277,6.6750436,0.24351889,0.7564811,0.37854892,0.6214511,0.53947884,0.46052116,Pluto,Yes,A,0.68922305,0.7759659
-,15:53.8,16:02.4,38.899235,47.25877,13.841997,23.743551,55.592102,20.664343,21.042084,63.925438,15.032476,0.28210407,0.7178959,0.83280754,0.16719243,0.47678292,0.5232171,Pluto,Yes,A,0.30075186,0.27414775
-,17:44.7,17:50.3,14.393256,13.486847,72.119896,,,,15.446366,38.486843,46.066788,0.140625,0.859375,0.08832806,0.91167194,0.4109522,0.5890478,Pluto,Yes,A,0.53634083,0.7541477
-,16:12.8,16:18.5,75.27864,19.188599,5.532761,63.86308,31.030699,5.106217,49.738827,44.627197,5.6339746,0.3174739,0.6825261,0.72239745,0.27760252,,,Pluto,Yes,A,0.48370928,0.7286932
-,08:41.5,08:46.2,16.429594,66.55702,17.013384,75.124405,12.171051,12.704539,10.000064,82.34649,7.6534524,0.3689208,0.6310792,0.20504731,0.7949527,0.86549765,0.13450235,Pluto,Yes,A,0.6365915,0.7796023
-,50:54.8,51:00.3,12.02917,62.171055,25.799774,9.701241,77.08333,13.21543,36.631542,50.328945,13.039512,0.031300247,0.96869975,0.7003155,0.29968455,0.43603057,0.56396943,Pluto,Yes,B,0.3659148,0.3286932
-,42:45.3,42:49.3,6.7395067,48.574562,44.685932,7.541997,45.504387,46.953617,7.66249,88.48684,3.850674,0.18564105,0.81435895,0.851735,0.14826499,0.68681425,0.31318575,Pluto,Yes,B,0.23057644,0.25232953
-,45:35.8,45:39.7,84.2771,7.7850876,7.9378138,84.42409,8.223686,7.352213,32.59019,39.364033,28.045776,0.13419414,0.86580586,0.8485804,0.15141957,0.63352275,0.36647728,Pluto,Yes,B,0.9749373,0.8741477
-,00:39.4,00:45.3,78.12711,11.293861,10.579031,29.004303,42.872803,28.122896,20.381784,63.04825,16.569967,0.1952874,0.8047126,0.873817,0.12618296,0.921924,0.07807602,Pluto,Yes,B,0.22055137,0.7686932
-,42:45.3,42:53.1,11.88458,63.925438,24.18998,10.790501,68.3114,20.898098,44.83233,46.38158,8.786089,0.19850284,0.80149716,0.6687697,0.33123028,0.60844433,0.39155564,Pluto,Yes,B,0.27318296,0.73960227
-,44:29.5,44:34.9,57.857693,32.785088,9.357224,34.660267,56.4693,8.870435,27.837927,48.135967,24.026108,0.17599475,0.82400525,0.7697161,0.23028392,0.62098354,0.37901646,Pluto,Yes,B,0.42857143,0.7250568
-,42:03.4,42:07.1,55.305637,43.75,0.9443677,80.105606,11.73246,8.161929,46.733723,43.311405,9.954876,0.2531652,0.7468348,0.67823344,0.32176656,0.5018613,0.49813873,Pluto,Yes,B,0.72431076,0.7432386
-,14:09.0,14:13.1,36.932774,57.785088,5.282139,,,,22.500061,57.34649,20.15345,0.24351889,0.7564811,0.4763407,0.5236593,0.19778603,0.80221397,Pluto,No,A,0.2556391,0.62687504
-,15:04.9,15:12.8,82.81189,7.7850876,9.403014,84.1301,7.3464966,8.523409,30.320087,40.24123,29.43868,0.118116975,0.881883,0.9495268,0.050473187,0.94386756,0.056132447,Pluto,No,A,0.54385966,0.786875
-,37:43.9,37:52.6,54.989933,23.135965,21.874102,38.663063,32.346497,28.990444,24.329153,55.15351,20.517336,0.4139369,0.5860631,0.77287066,0.22712934,0.52067006,0.47932994,Pluto,No,B,0.7593985,0.21960229
-,43:48.6,43:56.3,72.63501,13.486847,13.878144,69.99862,14.364037,15.637348,50.613613,40.679825,8.70656,0.41715235,0.58284765,0.23659307,0.76340693,0.4799177,0.5200823,Pluto,No,B,0.74937344,0.73232955
-,46:47.2,46:52.6,17.126049,33.662285,49.211666,12.373784,42.434208,45.192005,30.833393,40.679825,28.486782,0.36248994,0.63751006,0.2902208,0.7097792,0.5081309,0.49186912,Pluto,No,B,0.2706767,0.25232953
-,40:22.3,40:30.0,59.67472,19.627197,20.698078,18.422554,13.486847,68.0906,17.458609,69.6272,12.914194,0.43966037,0.5603396,0.3028391,0.6971609,0.29496473,0.70503527,Pluto,No,B,0.7894737,0.81960225
-</file>
-===== triad_data2.csv =====
-{{triad_data2.csv}}