source('https://raw.githubusercontent.com/ptallon/SportsAnalytics_Fall2024/main/SharedCode.R')
setwd("/Users/jose/Desktop/Data Viz")
my_path <- paste0(getwd(), "/nfl2025/NFLBDB2025")
load_packages(c("data.table", "dplyr", "ggplot2", "ggalt", "gifski", "gganimate", "httr", "r02pro", "tidyverse"))
# first week data frame
df <- load_data_for_one_week(directory = my_path, week = 1, merge = TRUE)
# to check available files
list.files(path = my_path)
## [1] "games.csv" "player_play.csv" "players.csv"
## [4] "plays.csv" "tracking_week_1.csv" "tracking_week_2.csv"
## [7] "tracking_week_3.csv" "tracking_week_4.csv" "tracking_week_5.csv"
## [10] "tracking_week_6.csv" "tracking_week_7.csv" "tracking_week_8.csv"
## [13] "tracking_week_9.csv"
# bring in games, plays, player_play if needed
games <- fread (paste0(my_path, "/games.csv"))
plays <- fread (paste0(my_path, "/plays.csv"))
player_play <- fread (paste0(my_path, "/player_play.csv"))
# initial data frame with just those in movement
df %>%
filter(inMotionAtBallSnap == TRUE) %>%
select(displayName, jerseyNumber, playId, gameId) %>%
distinct() %>%
head() %>%
data.frame() %>% print()
## displayName jerseyNumber playId gameId
## 1 Noah Fant 87 64 2022091200
## 2 Rashaad Penny 20 85 2022091200
## 3 Andrew Beck 83 264 2022091200
## 4 Andrew Beck 83 286 2022091200
## 5 Jerry Jeudy 10 401 2022091200
## 6 K.J. Hamler 1 446 2022091200
# update to initial data frame with just those in movement and frame type is before snap
temp_df <- df %>%
filter(inMotionAtBallSnap == TRUE, frameType == "BEFORE_SNAP") %>%
select(displayName, jerseyNumber, playId, gameId, frameId, x, y) %>%
distinct() %>%
head() %>%
data.frame() %>% print()
## displayName jerseyNumber playId gameId frameId x y
## 1 Noah Fant 87 64 2022091200 1 86.59 27.71333
## 2 Noah Fant 87 64 2022091200 2 86.52 27.59333
## 3 Noah Fant 87 64 2022091200 3 86.45 27.49333
## 4 Noah Fant 87 64 2022091200 4 86.36 27.37333
## 5 Noah Fant 87 64 2022091200 5 86.26 27.26333
## 6 Noah Fant 87 64 2022091200 6 86.15 27.14333
# New update to single out single play with new normalized x and y values
secondtemp_df <- df %>%
filter(inMotionAtBallSnap == TRUE, playId == 62, gameId == 2022091112, frameType == "BEFORE_SNAP") %>%
select(displayName, jerseyNumber, playId, gameId, frameId, x, y) %>%
group_by(displayName) %>%
mutate( x = x - first(x[frameId == 1]),
y = y - first(y[frameId == 1])) %>%
distinct() %>%
head() %>%
data.frame() %>% print()
## displayName jerseyNumber playId gameId frameId x y
## 1 Adam Thielen 19 62 2022091112 1 0.00 0.00
## 2 Adam Thielen 19 62 2022091112 2 0.03 -0.25
## 3 Adam Thielen 19 62 2022091112 3 0.05 -0.52
## 4 Adam Thielen 19 62 2022091112 4 0.05 -0.82
## 5 Adam Thielen 19 62 2022091112 5 0.03 -1.11
## 6 Adam Thielen 19 62 2022091112 6 -0.01 -1.41
Funtions and Plots
#from original data frame, create a data frame and display just special columns in a specific game
process_play_data <- function(df, play_id, game_id) {
df %>%
filter(inMotionAtBallSnap == "TRUE", playId == play_id, gameId == game_id, frameType == "BEFORE_SNAP") %>%
select(nflId, playId, gameId, frameId, x, y) %>%
group_by(nflId) %>%
distinct() %>%
data.frame() %>%
print()
}
# display a single motion play from a single play in dots
plot_grid_overlay <- function(data) {
ggplot(data, aes(x = y, y = -x)) +
# Add points for each (x, y) coordinate
geom_point(aes(color = as.factor(nflId)), size = 4) +
# Add labels using 'frameId'
geom_text(aes(label = frameId), size = 3, vjust = -1, color = "black") +
# Title and axis labels
labs(title = "Grid Overlay with Dark Center Lines at X=0 and Y=0",
x = "X Coordinate",
y = "Y Coordinate") +
# Minimal theme and custom grid lines
theme_minimal() +
theme(
panel.grid.major = element_line(color = "black", size = 0.5), # Major grid lines
panel.grid.minor = element_line(color = "gray", size = 0.25) # Minor grid lines
)
}
#function that represents the process of x - the first x from first frame in a mutate
# Includes data from all games and all plays with the added column
process_play_data_clust <- function(df) {
df %>%
filter(inMotionAtBallSnap == TRUE, frameType == "BEFORE_SNAP") %>%
select(nflId, playId, gameId, frameId, x, y) %>%
group_by(nflId) %>%
mutate( x = x - first(x[frameId == 1]),
y = y - first(y[frameId == 1])) %>%
distinct() %>%
data.frame() %>% print()
}
#initial attempt of mutate with rounding x and y
t_df <- df %>%
filter(inMotionAtBallSnap == TRUE, frameType == "BEFORE_SNAP") %>%
select(nflId, playId, gameId, frameId, x, y) %>%
group_by(nflId) %>%
mutate( x = round(x - first(x[frameId == 1])),
y = round(y - first(y[frameId == 1]))) %>%
distinct() %>%
data.frame()
#second attempt of mutate without rounding x and y
t2_df <- df %>%
filter(inMotionAtBallSnap == TRUE, frameType == "BEFORE_SNAP") %>%
select(nflId, playId, gameId, frameId, x, y) %>%
group_by(nflId) %>%
mutate( x = (x - first(x[frameId == 1])),
y = (y - first(y[frameId == 1]))) %>%
distinct() %>%
data.frame()
#---------kmeans-----------
# Example dataset
data <- t2_df
# Perform k-means clustering
set.seed(123) # For reproducibility
kmeans_result <- kmeans(data, centers = 3, iter.max = 20, nstart = 10)
# Add cluster assignments to the original dataset
data_with_clusters <- cbind(data, Cluster = kmeans_result$cluster)
# View the first few rows
head(data_with_clusters)
## nflId playId gameId frameId x y Cluster
## 1 47803 64 2022091200 1 0.00 0.00 3
## 2 47803 64 2022091200 2 -0.07 -0.12 3
## 3 47803 64 2022091200 3 -0.14 -0.22 3
## 4 47803 64 2022091200 4 -0.23 -0.34 3
## 5 47803 64 2022091200 5 -0.33 -0.45 3
## 6 47803 64 2022091200 6 -0.44 -0.57 3
# 2D visualization of clusters
ggplot(data_with_clusters, aes(x = playId, y = Cluster, color = as.factor(Cluster))) +
geom_point(size = 3) +
labs(color = "Cluster") +
theme_minimal()
wss <- sapply(1:10, function(k) {
kmeans(data, centers = k, nstart = 10)$tot.withinss
})
# Plot the Elbow Method
plot(1:10, wss, type = "b", pch = 19, frame = FALSE,
xlab = "Number of clusters (k)",
ylab = "Total within-cluster sum of squares",
main = "Elbow Method for Optimal k")
# from a sample of the different cluster we displayed from playId and gameId the dataframe and the visual grid
data_with_clusters %>% filter(Cluster == 3) %>% head() %>% print()
## nflId playId gameId frameId x y Cluster
## 1 47803 64 2022091200 1 0.00 0.00 3
## 2 47803 64 2022091200 2 -0.07 -0.12 3
## 3 47803 64 2022091200 3 -0.14 -0.22 3
## 4 47803 64 2022091200 4 -0.23 -0.34 3
## 5 47803 64 2022091200 5 -0.33 -0.45 3
## 6 47803 64 2022091200 6 -0.44 -0.57 3
data_with_clusters %>% filter(Cluster == 2) %>% head() %>% print()
## nflId playId gameId frameId x y Cluster
## 1 52423 401 2022091200 1 0.00 0.00 2
## 2 52423 401 2022091200 2 0.03 -0.26 2
## 3 52423 401 2022091200 3 0.05 -0.53 2
## 4 52423 401 2022091200 4 0.06 -0.83 2
## 5 52423 401 2022091200 5 0.07 -1.14 2
## 6 52423 401 2022091200 6 0.07 -1.46 2
data_with_clusters %>% filter(Cluster == 1) %>% head() %>% print()
## nflId playId gameId frameId x y Cluster
## 1 42358 931 2022091200 1 0.00 0.00 1
## 2 42358 931 2022091200 2 0.02 0.03 1
## 3 42358 931 2022091200 3 0.05 0.07 1
## 4 42358 931 2022091200 4 0.09 0.13 1
## 5 42358 931 2022091200 5 0.13 0.20 1
## 6 42358 931 2022091200 6 0.16 0.29 1
cluster1_df <- process_play_data(df, 931, 2022091200)
## nflId playId gameId frameId x y
## 1 42358 931 2022091200 1 70.98 22.84333
## 2 42358 931 2022091200 2 71.00 22.87333
## 3 42358 931 2022091200 3 71.03 22.91333
## 4 42358 931 2022091200 4 71.07 22.97333
## 5 42358 931 2022091200 5 71.11 23.04333
## 6 42358 931 2022091200 6 71.14 23.13333
## 7 42358 931 2022091200 7 71.17 23.22333
## 8 42358 931 2022091200 8 71.20 23.32333
## 9 42358 931 2022091200 9 71.22 23.43333
## 10 42358 931 2022091200 10 71.23 23.54333
## 11 42358 931 2022091200 11 71.24 23.66333
## 12 42358 931 2022091200 12 71.24 23.78333
## 13 42358 931 2022091200 13 71.23 23.90333
## 14 42358 931 2022091200 14 71.22 24.03333
## 15 42358 931 2022091200 15 71.21 24.15333
## 16 42358 931 2022091200 16 71.19 24.27333
## 17 42358 931 2022091200 17 71.16 24.38333
## 18 42358 931 2022091200 18 71.14 24.48333
## 19 42358 931 2022091200 19 71.11 24.57333
## 20 42358 931 2022091200 20 71.08 24.65333
## 21 42358 931 2022091200 21 71.04 24.72333
## 22 42358 931 2022091200 22 71.01 24.78333
## 23 42358 931 2022091200 23 70.98 24.83333
## 24 42358 931 2022091200 24 70.96 24.88333
## 25 42358 931 2022091200 25 70.93 24.92333
## 26 42358 931 2022091200 26 70.92 24.95333
## 27 42358 931 2022091200 27 70.90 24.98333
## 28 42358 931 2022091200 28 70.89 25.01333
## 29 42358 931 2022091200 29 70.88 25.03333
## 30 42358 931 2022091200 30 70.87 25.06333
## 31 42358 931 2022091200 31 70.87 25.08333
## 32 42358 931 2022091200 32 70.87 25.10333
## 33 42358 931 2022091200 33 70.87 25.11333
## 34 42358 931 2022091200 34 70.88 25.12333
## 35 42358 931 2022091200 35 70.88 25.12333
## 36 42358 931 2022091200 36 70.88 25.12333
## 37 42358 931 2022091200 37 70.88 25.12333
## 38 42358 931 2022091200 38 70.88 25.09333
## 39 42358 931 2022091200 39 70.88 25.05333
## 40 42358 931 2022091200 40 70.88 24.96333
## 41 42358 931 2022091200 41 70.88 24.86333
## 42 42358 931 2022091200 42 70.88 24.73333
## 43 42358 931 2022091200 43 70.89 24.58333
## 44 42358 931 2022091200 44 70.89 24.41333
## 45 42358 931 2022091200 45 70.89 24.23333
## 46 42358 931 2022091200 46 70.90 24.02333
## 47 42358 931 2022091200 47 70.90 23.81333
## 48 42358 931 2022091200 48 70.89 23.59333
## 49 42358 931 2022091200 49 70.87 23.37333
## 50 42358 931 2022091200 50 70.86 23.15333
## 51 42358 931 2022091200 51 70.84 22.94333
## 52 42358 931 2022091200 52 70.81 22.74333
## 53 42358 931 2022091200 53 70.78 22.54333
## 54 42358 931 2022091200 54 70.75 22.36333
## 55 42358 931 2022091200 55 70.71 22.19333
## 56 42358 931 2022091200 56 70.68 22.03333
## 57 42358 931 2022091200 57 70.65 21.90333
## 58 42358 931 2022091200 58 70.61 21.78333
## 59 42358 931 2022091200 59 70.58 21.68333
## 60 42358 931 2022091200 60 70.55 21.60333
## 61 42358 931 2022091200 61 70.52 21.53333
## 62 42358 931 2022091200 62 70.50 21.48333
## 63 42358 931 2022091200 63 70.48 21.45333
## 64 42358 931 2022091200 64 70.46 21.42333
## 65 42358 931 2022091200 65 70.45 21.41333
## 66 42358 931 2022091200 66 70.45 21.40333
## 67 42358 931 2022091200 67 70.45 21.40333
## 68 42358 931 2022091200 68 70.45 21.40333
## 69 42358 931 2022091200 69 70.45 21.41333
## 70 42358 931 2022091200 70 70.45 21.41333
## 71 42358 931 2022091200 71 70.45 21.42333
## 72 42358 931 2022091200 72 70.45 21.43333
## 73 42358 931 2022091200 73 70.46 21.44333
## 74 42358 931 2022091200 74 70.46 21.44333
## 75 42358 931 2022091200 75 70.46 21.45333
## 76 42358 931 2022091200 76 70.46 21.46333
## 77 42358 931 2022091200 77 70.47 21.46333
## 78 42358 931 2022091200 78 70.47 21.46333
## 79 42358 931 2022091200 79 70.47 21.47333
## 80 42358 931 2022091200 80 70.46 21.48333
## 81 42358 931 2022091200 81 70.45 21.49333
## 82 42358 931 2022091200 82 70.44 21.50333
## 83 42358 931 2022091200 83 70.43 21.50333
## 84 42358 931 2022091200 84 70.42 21.50333
## 85 42358 931 2022091200 85 70.41 21.50333
## 86 42358 931 2022091200 86 70.40 21.50333
## 87 42358 931 2022091200 87 70.40 21.50333
## 88 42358 931 2022091200 88 70.40 21.50333
## 89 42358 931 2022091200 89 70.41 21.50333
## 90 42358 931 2022091200 90 70.41 21.49333
## 91 42358 931 2022091200 91 70.41 21.49333
## 92 42358 931 2022091200 92 70.42 21.48333
## 93 42358 931 2022091200 93 70.42 21.48333
## 94 42358 931 2022091200 94 70.42 21.47333
## 95 42358 931 2022091200 95 70.41 21.47333
## 96 42358 931 2022091200 96 70.41 21.46333
## 97 42358 931 2022091200 97 70.40 21.46333
## 98 42358 931 2022091200 98 70.38 21.46333
## 99 42358 931 2022091200 99 70.37 21.46333
## 100 42358 931 2022091200 100 70.37 21.46333
## 101 42358 931 2022091200 101 70.36 21.47333
## 102 42358 931 2022091200 102 70.36 21.48333
## 103 42358 931 2022091200 103 70.34 21.49333
## 104 42358 931 2022091200 104 70.33 21.50333
## 105 42358 931 2022091200 105 70.31 21.52333
## 106 42358 931 2022091200 106 70.28 21.56333
## 107 42358 931 2022091200 107 70.23 21.60333
## 108 42358 931 2022091200 108 70.15 21.66333
## 109 42358 931 2022091200 109 70.05 21.74333
## 110 42358 931 2022091200 110 69.93 21.84333
## 111 42358 931 2022091200 111 69.78 21.96333
## 112 42358 931 2022091200 112 69.62 22.10333
## 113 42358 931 2022091200 113 69.44 22.27333
## 114 42358 931 2022091200 114 69.26 22.45333
## 115 42358 931 2022091200 115 69.07 22.67333
## 116 42358 931 2022091200 116 68.89 22.91333
## 117 42358 931 2022091200 117 68.73 23.17333
## 118 42358 931 2022091200 118 68.58 23.46333
## 119 42358 931 2022091200 119 68.44 23.77333
## 120 42358 931 2022091200 120 68.33 24.10333
## 121 42358 931 2022091200 121 68.24 24.44333
## 122 42358 931 2022091200 122 68.16 24.79333
## 123 42358 931 2022091200 123 68.09 25.14333
## 124 42358 931 2022091200 124 68.04 25.50333
## 125 42358 931 2022091200 125 68.00 25.85333
## 126 42358 931 2022091200 126 67.96 26.20333
## 127 42358 931 2022091200 127 67.93 26.55333
## 128 42358 931 2022091200 128 67.90 26.88333
## 129 42358 931 2022091200 129 67.88 27.21333
## 130 42358 931 2022091200 130 67.86 27.52333
## 131 42358 931 2022091200 131 67.84 27.83333
## 132 42358 931 2022091200 132 67.82 28.11333
cluster2_df<- process_play_data(df, 401, 2022091200)
## nflId playId gameId frameId x y
## 1 52423 401 2022091200 1 34.01 29.16
## 2 52423 401 2022091200 2 34.04 28.90
## 3 52423 401 2022091200 3 34.06 28.63
## 4 52423 401 2022091200 4 34.07 28.33
## 5 52423 401 2022091200 5 34.08 28.02
## 6 52423 401 2022091200 6 34.08 27.70
## 7 52423 401 2022091200 7 34.08 27.36
## 8 52423 401 2022091200 8 34.06 27.01
## 9 52423 401 2022091200 9 34.04 26.65
## 10 52423 401 2022091200 10 34.01 26.29
## 11 52423 401 2022091200 11 33.96 25.93
## 12 52423 401 2022091200 12 33.91 25.58
## 13 52423 401 2022091200 13 33.84 25.23
## 14 52423 401 2022091200 14 33.76 24.90
## 15 52423 401 2022091200 15 33.67 24.57
## 16 52423 401 2022091200 16 33.58 24.25
## 17 52423 401 2022091200 17 33.48 23.94
## 18 52423 401 2022091200 18 33.37 23.64
## 19 52423 401 2022091200 19 33.26 23.34
## 20 52423 401 2022091200 20 33.15 23.05
## 21 52423 401 2022091200 21 33.03 22.78
## 22 52423 401 2022091200 22 32.90 22.51
## 23 52423 401 2022091200 23 32.77 22.26
## 24 52423 401 2022091200 24 32.64 22.01
## 25 52423 401 2022091200 25 32.52 21.77
## 26 52423 401 2022091200 26 32.40 21.55
## 27 52423 401 2022091200 27 32.28 21.34
## 28 52423 401 2022091200 28 32.15 21.14
## 29 52423 401 2022091200 29 32.03 20.95
## 30 52423 401 2022091200 30 31.92 20.78
## 31 52423 401 2022091200 31 31.80 20.61
## 32 52423 401 2022091200 32 31.68 20.47
## 33 52423 401 2022091200 33 31.57 20.33
## 34 52423 401 2022091200 34 31.46 20.20
## 35 52423 401 2022091200 35 31.36 20.08
## 36 52423 401 2022091200 36 31.27 19.96
## 37 52423 401 2022091200 37 31.18 19.85
## 38 52423 401 2022091200 38 31.10 19.75
## 39 52423 401 2022091200 39 31.02 19.65
## 40 52423 401 2022091200 40 30.95 19.56
## 41 52423 401 2022091200 41 30.88 19.49
## 42 52423 401 2022091200 42 30.81 19.41
## 43 52423 401 2022091200 43 30.75 19.35
## 44 52423 401 2022091200 44 30.68 19.30
## 45 52423 401 2022091200 45 30.61 19.25
## 46 52423 401 2022091200 46 30.54 19.21
## 47 52423 401 2022091200 47 30.46 19.18
## 48 52423 401 2022091200 48 30.38 19.16
## 49 52423 401 2022091200 49 30.31 19.15
## 50 52423 401 2022091200 50 30.25 19.14
## 51 52423 401 2022091200 51 30.19 19.14
## 52 52423 401 2022091200 52 30.16 19.14
## 53 52423 401 2022091200 53 30.14 19.14
## 54 52423 401 2022091200 54 30.13 19.14
## 55 52423 401 2022091200 55 30.13 19.15
## 56 52423 401 2022091200 56 30.14 19.15
## 57 52423 401 2022091200 57 30.15 19.15
## 58 52423 401 2022091200 58 30.16 19.14
## 59 52423 401 2022091200 59 30.17 19.14
## 60 52423 401 2022091200 60 30.18 19.13
## 61 52423 401 2022091200 61 30.19 19.13
## 62 52423 401 2022091200 62 30.20 19.12
## 63 52423 401 2022091200 63 30.20 19.11
## 64 52423 401 2022091200 64 30.20 19.10
## 65 52423 401 2022091200 65 30.20 19.09
## 66 52423 401 2022091200 66 30.20 19.09
## 67 52423 401 2022091200 67 30.19 19.08
## 68 52423 401 2022091200 68 30.19 19.08
## 69 52423 401 2022091200 69 30.18 19.08
## 70 52423 401 2022091200 70 30.18 19.09
## 71 52423 401 2022091200 71 30.18 19.10
## 72 52423 401 2022091200 72 30.18 19.10
## 73 52423 401 2022091200 73 30.18 19.10
## 74 52423 401 2022091200 74 30.18 19.10
## 75 52423 401 2022091200 75 30.18 19.10
## 76 52423 401 2022091200 76 30.19 19.10
## 77 52423 401 2022091200 77 30.19 19.08
## 78 52423 401 2022091200 78 30.19 19.07
## 79 52423 401 2022091200 79 30.19 19.06
## 80 52423 401 2022091200 80 30.20 19.06
## 81 52423 401 2022091200 81 30.20 19.05
## 82 52423 401 2022091200 82 30.19 19.05
## 83 52423 401 2022091200 83 30.19 19.05
## 84 52423 401 2022091200 84 30.19 19.05
## 85 52423 401 2022091200 85 30.19 19.06
## 86 52423 401 2022091200 86 30.19 19.07
## 87 52423 401 2022091200 87 30.18 19.09
## 88 52423 401 2022091200 88 30.18 19.10
## 89 52423 401 2022091200 89 30.18 19.12
## 90 52423 401 2022091200 90 30.19 19.16
## 91 52423 401 2022091200 91 30.22 19.20
## 92 52423 401 2022091200 92 30.26 19.28
## 93 52423 401 2022091200 93 30.31 19.38
## 94 52423 401 2022091200 94 30.37 19.52
## 95 52423 401 2022091200 95 30.46 19.70
## 96 52423 401 2022091200 96 30.56 19.91
## 97 52423 401 2022091200 97 30.67 20.18
## 98 52423 401 2022091200 98 30.78 20.50
## 99 52423 401 2022091200 99 30.89 20.89
## 100 52423 401 2022091200 100 31.00 21.34
## 101 52423 401 2022091200 101 31.11 21.83
## 102 52423 401 2022091200 102 31.21 22.36
## 103 52423 401 2022091200 103 31.30 22.93
## 104 52423 401 2022091200 104 31.37 23.54
## 105 52423 401 2022091200 105 31.43 24.19
## 106 52423 401 2022091200 106 31.49 24.87
## 107 52423 401 2022091200 107 31.53 25.57
cluster3_df <- process_play_data(df, 85, 2022091200)
## nflId playId gameId frameId x y
## 1 46096 85 2022091200 1 86.73 30.21333
## 2 46096 85 2022091200 2 86.75 30.22333
## 3 46096 85 2022091200 3 86.76 30.23333
## 4 46096 85 2022091200 4 86.78 30.23333
## 5 46096 85 2022091200 5 86.81 30.23333
## 6 46096 85 2022091200 6 86.84 30.22333
## 7 46096 85 2022091200 7 86.88 30.22333
## 8 46096 85 2022091200 8 86.91 30.21333
## 9 46096 85 2022091200 9 86.93 30.21333
## 10 46096 85 2022091200 10 86.97 30.21333
## 11 46096 85 2022091200 11 87.00 30.22333
## 12 46096 85 2022091200 12 87.03 30.24333
## 13 46096 85 2022091200 13 87.05 30.27333
## 14 46096 85 2022091200 14 87.07 30.30333
## 15 46096 85 2022091200 15 87.08 30.35333
## 16 46096 85 2022091200 16 87.08 30.40333
## 17 46096 85 2022091200 17 87.08 30.47333
## 18 46096 85 2022091200 18 87.07 30.54333
## 19 46096 85 2022091200 19 87.05 30.61333
## 20 46096 85 2022091200 20 87.03 30.69333
## 21 46096 85 2022091200 21 87.00 30.77333
## 22 46096 85 2022091200 22 86.97 30.85333
## 23 46096 85 2022091200 23 86.94 30.92333
## 24 46096 85 2022091200 24 86.90 31.00333
## 25 46096 85 2022091200 25 86.87 31.07333
## 26 46096 85 2022091200 26 86.84 31.14333
## 27 46096 85 2022091200 27 86.81 31.21333
## 28 46096 85 2022091200 28 86.79 31.30333
## 29 46096 85 2022091200 29 86.78 31.38333
## 30 46096 85 2022091200 30 86.76 31.46333
## 31 46096 85 2022091200 31 86.76 31.54333
## 32 46096 85 2022091200 32 86.75 31.62333
## 33 46096 85 2022091200 33 86.75 31.68333
## 34 46096 85 2022091200 34 86.75 31.73333
## 35 46096 85 2022091200 35 86.75 31.77333
## 36 46096 85 2022091200 36 86.75 31.80333
## 37 46096 85 2022091200 37 86.74 31.82333
## 38 46096 85 2022091200 38 86.74 31.83333
## 39 46096 85 2022091200 39 86.73 31.83333
## 40 46096 85 2022091200 40 86.73 31.81333
## 41 46096 85 2022091200 41 86.72 31.80333
## 42 46096 85 2022091200 42 86.70 31.79333
## 43 46096 85 2022091200 43 86.68 31.77333
## 44 46096 85 2022091200 44 86.66 31.76333
## 45 46096 85 2022091200 45 86.64 31.75333
## 46 46096 85 2022091200 46 86.62 31.75333
## 47 46096 85 2022091200 47 86.60 31.75333
## 48 46096 85 2022091200 48 86.59 31.77333
## 49 46096 85 2022091200 49 86.57 31.78333
## 50 46096 85 2022091200 50 86.56 31.79333
## 51 46096 85 2022091200 51 86.56 31.79333
## 52 46096 85 2022091200 52 86.55 31.80333
## 53 46096 85 2022091200 53 86.55 31.80333
## 54 46096 85 2022091200 54 86.56 31.80333
## 55 46096 85 2022091200 55 86.56 31.80333
## 56 46096 85 2022091200 56 86.57 31.80333
## 57 46096 85 2022091200 57 86.57 31.79333
## 58 46096 85 2022091200 58 86.58 31.80333
## 59 46096 85 2022091200 59 86.59 31.80333
## 60 46096 85 2022091200 60 86.60 31.80333
## 61 46096 85 2022091200 61 86.60 31.81333
## 62 46096 85 2022091200 62 86.61 31.81333
## 63 46096 85 2022091200 63 86.62 31.82333
## 64 46096 85 2022091200 64 86.62 31.82333
## 65 46096 85 2022091200 65 86.63 31.83333
## 66 46096 85 2022091200 66 86.64 31.83333
## 67 46096 85 2022091200 67 86.64 31.82333
## 68 46096 85 2022091200 68 86.64 31.81333
## 69 46096 85 2022091200 69 86.64 31.81333
## 70 46096 85 2022091200 70 86.63 31.79333
## 71 46096 85 2022091200 71 86.63 31.77333
## 72 46096 85 2022091200 72 86.62 31.76333
## 73 46096 85 2022091200 73 86.62 31.74333
## 74 46096 85 2022091200 74 86.61 31.73333
## 75 46096 85 2022091200 75 86.60 31.72333
## 76 46096 85 2022091200 76 86.59 31.71333
## 77 46096 85 2022091200 77 86.58 31.71333
## 78 46096 85 2022091200 78 86.58 31.70333
## 79 46096 85 2022091200 79 86.57 31.71333
## 80 46096 85 2022091200 80 86.57 31.71333
## 81 46096 85 2022091200 81 86.56 31.72333
## 82 46096 85 2022091200 82 86.56 31.73333
## 83 46096 85 2022091200 83 86.56 31.74333
## 84 46096 85 2022091200 84 86.56 31.75333
## 85 46096 85 2022091200 85 86.56 31.76333
## 86 46096 85 2022091200 86 86.55 31.76333
## 87 46096 85 2022091200 87 86.55 31.76333
## 88 46096 85 2022091200 88 86.54 31.77333
## 89 46096 85 2022091200 89 86.53 31.77333
## 90 46096 85 2022091200 90 86.53 31.78333
## 91 46096 85 2022091200 91 86.51 31.78333
## 92 46096 85 2022091200 92 86.50 31.78333
## 93 46096 85 2022091200 93 86.49 31.77333
## 94 46096 85 2022091200 94 86.48 31.73333
## 95 46096 85 2022091200 95 86.47 31.68333
## 96 46096 85 2022091200 96 86.46 31.60333
## 97 46096 85 2022091200 97 86.46 31.49333
## 98 46096 85 2022091200 98 86.46 31.34333
## 99 46096 85 2022091200 99 86.46 31.14333
## 100 46096 85 2022091200 100 86.45 30.89333
## 101 46096 85 2022091200 101 86.44 30.59333
## 102 46096 85 2022091200 102 86.44 30.24333
## 103 46096 85 2022091200 103 86.44 29.84333
## 104 46096 85 2022091200 104 86.43 29.40333
## 105 46096 85 2022091200 105 86.42 28.91333
## 106 46096 85 2022091200 106 86.41 28.40333
## 107 46096 85 2022091200 107 86.40 27.85333
## 108 46096 85 2022091200 108 86.39 27.28333
## 109 46096 85 2022091200 109 86.38 26.69333
## 110 46096 85 2022091200 110 86.37 26.09333
## 111 46096 85 2022091200 111 86.36 25.48333
## 112 46096 85 2022091200 112 86.35 24.86333
## 113 46096 85 2022091200 113 86.34 24.23333
## 114 46096 85 2022091200 114 86.34 23.60333
## 115 46096 85 2022091200 115 86.34 22.97333
## 116 46096 85 2022091200 116 86.34 22.34333
plot_grid_overlay(cluster1_df)
plot_grid_overlay(cluster2_df)
plot_grid_overlay(cluster3_df)
