library(tidyverse)
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library(readxl)
stadium <-read.csv("us_sportsvenues1909_2027.csv")
stadium_clean <- drop_na(stadium)
cor(stadium_clean$publiccost2020, stadium_clean$year)
## [1] 0.4635746
stadium_clean$venuename  <- NULL
stadium_clean$teams <- NULL
stadium_clean$source <- NULL
cor(stadium_clean)
##                       year  totalcost publiccost totalcost2020 publiccost2020
## year            1.00000000  0.4953015  0.5441178     0.4972194      0.4635746
## totalcost       0.49530152  1.0000000  0.4923980     0.9559648      0.3567677
## publiccost      0.54411779  0.4923980  1.0000000     0.4869939      0.8750414
## totalcost2020   0.49721945  0.9559648  0.4869939     1.0000000      0.4671439
## publiccost2020  0.46357461  0.3567677  0.8750414     0.4671439      1.0000000
## pubpct100       0.08390035 -0.1908186  0.1815931    -0.1605465      0.3926171
## stadium        -0.12087009  0.1483950  0.2389532     0.1872855      0.3116844
## arena           0.12087009 -0.1483950 -0.2389532    -0.1872855     -0.3116844
##                  pubpct100     stadium       arena
## year            0.08390035 -0.12087009  0.12087009
## totalcost      -0.19081863  0.14839503 -0.14839503
## publiccost      0.18159312  0.23895325 -0.23895325
## totalcost2020  -0.16054651  0.18728547 -0.18728547
## publiccost2020  0.39261708  0.31168443 -0.31168443
## pubpct100       1.00000000  0.09321689 -0.09321689
## stadium         0.09321689  1.00000000 -1.00000000
## arena          -0.09321689 -1.00000000  1.00000000
pairs(~year+totalcost+publiccost+publiccost2020+totalcost2020+pubpct100+stadium+arena, data=stadium_clean)

cor.test(stadium_clean$year,stadium_clean$publiccost, meathod = "pearson")
## 
##  Pearson's product-moment correlation
## 
## data:  stadium_clean$year and stadium_clean$publiccost
## t = 9.0562, df = 195, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.4376089 0.6355688
## sample estimates:
##       cor 
## 0.5441178

With a pearson test it helps to match the linear progression of years compared to the public cost of stadiums that are being built.There is a slight correlation between the two meaning that as the years progress local governments have started to increase the amount of public funding towards stadiums.