library(tidyverse)
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library(readxl)
Increased_Telework<-read_excel("Increased Telework.xlsx")
data1<-Increased_Telework %>% select(State,`Number of establishments 4`,`Employment in establishments 8`)
summary(data1)
##        State    Number of establishments 4 Employment in establishments 8
##  Length   :64   Min.   :   5050            Min.   :   68056              
##  N.unique :64   1st Qu.:  13792            1st Qu.:  303635              
##  N.blank  : 0   Median :  32873            Median :  794023              
##  Min.nchar: 4   Mean   :  98529            Mean   : 2586811              
##  Max.nchar:89   3rd Qu.:  69220            3rd Qu.: 1745460              
##                 Max.   :2611007            Max.   :68550503              
##                 NAs    :11                 NAs    :11
data2<-data1 %>% na.omit()
summary(data2)
##        State    Number of establishments 4 Employment in establishments 8
##  Length   :53   Min.   :   5050            Min.   :   68056              
##  N.unique :53   1st Qu.:  13792            1st Qu.:  303635              
##  N.blank  : 0   Median :  32873            Median :  794023              
##  Min.nchar: 4   Mean   :  98529            Mean   : 2586811              
##  Max.nchar:29   3rd Qu.:  69220            3rd Qu.: 1745460              
##                 Max.   :2611007            Max.   :68550503
hist(data2$`Employment in establishments 8`)

plot(data2$`Employment in establishments 8`,data2$`Number of establishments 4`)

cor(data2$`Number of establishments 4`,data2$`Employment in establishments 8`)
## [1] 0.9996304

I believe there is a weak correalation between the number of establishments that increased telework during COVID-19 and the number of employees in these establishments.