Summary

Summary stats for all CBSAs

data <- read_csv("all_cbsa_summary.csv")
## Rows: 9400 Columns: 10
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): avg_eal_tercile, NAME
## dbl (8): archive_version_year, CBSAFP, rows, with_parent_number, mean_employ...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
plot1 <- data %>%
  group_by(archive_version_year,avg_eal_tercile) %>%
  summarise(rows=sum(rows,na.rm = TRUE)) %>%
  ggplot()+
  geom_line(aes(archive_version_year,rows,color=avg_eal_tercile))+
  facet_wrap(~avg_eal_tercile,ncol =2)+
  ggtitle("Number of Businesses")
## `summarise()` has grouped output by 'archive_version_year'. You can override
## using the `.groups` argument.
plot2 <- data %>%
  group_by(archive_version_year,avg_eal_tercile) %>%
  summarise(mean_employees=mean(mean_employees,na.rm = TRUE)) %>%
  ggplot()+
  geom_line(aes(archive_version_year,mean_employees,color=avg_eal_tercile),alpha=0.5)+
  facet_wrap(~avg_eal_tercile,ncol =2)+
  ggtitle("Mean employees")
## `summarise()` has grouped output by 'archive_version_year'. You can override
## using the `.groups` argument.
plot3 <- data %>%
  group_by(archive_version_year,avg_eal_tercile) %>%
  summarise(mean_year_established=mean(mean_year_established,na.rm = TRUE)) %>%
  ggplot()+
  geom_line(aes(archive_version_year,mean_year_established,color=avg_eal_tercile),alpha=0.5)+
  facet_wrap(~avg_eal_tercile,ncol =2)+
  ggtitle("Mean year established")
## `summarise()` has grouped output by 'archive_version_year'. You can override
## using the `.groups` argument.
plot4 <- data%>%
  group_by(archive_version_year,avg_eal_tercile) %>%
  summarise(pct_with_parent=sum(with_parent_number,na.rm = TRUE)/sum(rows,na.rm = TRUE)) %>%
  ggplot()+
  geom_line(aes(archive_version_year,pct_with_parent,color=avg_eal_tercile),alpha=0.5)+
  facet_wrap(~avg_eal_tercile,ncol =2)+
  ggtitle("% with parent")
## `summarise()` has grouped output by 'archive_version_year'. You can override
## using the `.groups` argument.
ggplotly(plot1)  %>% layout(showlegend = TRUE, legend = list(font = list(size = 6)))
3e+064e+065e+062015.02017.52020.03e+064e+065e+062015.02017.52020.0
avg_eal_tercileHighLowModerateNANumber of Businessesarchive_version_yearrowsHighLowModerateNA
ggplotly(plot2) %>% layout(showlegend = TRUE, legend = list(font = list(size = 6)))
1011122015.02017.52020.01011122015.02017.52020.0
avg_eal_tercileHighLowModerateNAMean employeesarchive_version_yearmean_employeesHighLowModerateNA
ggplotly(plot3) %>% layout(showlegend = TRUE, legend = list(font = list(size = 6)))
19701980199020002015.02017.52020.019701980199020002015.02017.52020.0
avg_eal_tercileHighLowModerateNAMean year establishedarchive_version_yearmean_year_establishedHighLowModerateNA
ggplotly(plot4) %>% layout(showlegend = TRUE, legend = list(font = list(size = 6)))
0.100.120.140.162015.02017.52020.00.100.120.140.162015.02017.52020.0
avg_eal_tercileHighLowModerateNA% with parentarchive_version_yearpct_with_parentHighLowModerateNA

Summary (Top 100)

Summarys stats for the top 100 CBSAs

data <- read_csv("all_cbsa_summary_100.csv")
## Rows: 1000 Columns: 10
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): avg_eal_tercile, NAME
## dbl (8): archive_version_year, CBSAFP, rows, with_parent_number, mean_employ...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
plot1 <- data %>%
  group_by(archive_version_year,avg_eal_tercile) %>%
  summarise(rows=sum(rows,na.rm = TRUE)) %>%
  ggplot()+
  geom_line(aes(archive_version_year,rows,color=avg_eal_tercile))+
  facet_wrap(~avg_eal_tercile,ncol =2)+
  ggtitle("Number of Businesses")
## `summarise()` has grouped output by 'archive_version_year'. You can override
## using the `.groups` argument.
plot2 <- data %>%
  group_by(archive_version_year,avg_eal_tercile) %>%
  summarise(mean_employees=mean(mean_employees,na.rm = TRUE)) %>%
  ggplot()+
  geom_line(aes(archive_version_year,mean_employees,color=avg_eal_tercile),alpha=0.5)+
  facet_wrap(~avg_eal_tercile,ncol =2)+
  ggtitle("Mean employees")
## `summarise()` has grouped output by 'archive_version_year'. You can override
## using the `.groups` argument.
plot3 <- data %>%
  group_by(archive_version_year,avg_eal_tercile) %>%
  summarise(mean_year_established=mean(mean_year_established,na.rm = TRUE)) %>%
  ggplot()+
  geom_line(aes(archive_version_year,mean_year_established,color=avg_eal_tercile),alpha=0.5)+
  facet_wrap(~avg_eal_tercile,ncol =2)+
  ggtitle("Mean year established")
## `summarise()` has grouped output by 'archive_version_year'. You can override
## using the `.groups` argument.
plot4 <- data%>%
  group_by(archive_version_year,avg_eal_tercile) %>%
  summarise(pct_with_parent=sum(with_parent_number,na.rm = TRUE)/sum(rows,na.rm = TRUE)) %>%
  ggplot()+
  geom_line(aes(archive_version_year,pct_with_parent,color=avg_eal_tercile),alpha=0.5)+
  facet_wrap(~avg_eal_tercile,ncol =2)+
  ggtitle("% with parent")
## `summarise()` has grouped output by 'archive_version_year'. You can override
## using the `.groups` argument.
ggplotly(plot1)  %>% layout(showlegend = TRUE, legend = list(font = list(size = 6)))
3e+064e+065e+062015.02017.52020.03e+064e+065e+062015.02017.52020.0
avg_eal_tercileHighLowModerateNumber of Businessesarchive_version_yearrowsHighLowModerate
ggplotly(plot2) %>% layout(showlegend = TRUE, legend = list(font = list(size = 6)))
9.510.010.511.011.512.012.52015.02017.52020.09.510.010.511.011.512.012.52015.02017.52020.0
avg_eal_tercileHighLowModerateMean employeesarchive_version_yearmean_employeesHighLowModerate
ggplotly(plot3) %>% layout(showlegend = TRUE, legend = list(font = list(size = 6)))
19701980199020002015.02017.52020.019701980199020002015.02017.52020.0
avg_eal_tercileHighLowModerateMean year establishedarchive_version_yearmean_year_establishedHighLowModerate
ggplotly(plot4) %>% layout(showlegend = TRUE, legend = list(font = list(size = 6)))
0.100.120.142015.02017.52020.00.100.120.142015.02017.52020.0
avg_eal_tercileHighLowModerate% with parentarchive_version_yearpct_with_parentHighLowModerate

Births Deaths Entries Exits - All by Risk

Births, deaths, entries and exits by risk Births are when the abi doesn’t exist the past year Deaths are when the abi doesn’t exist the next year

Entries are when the CBSA is different than the past year and the past year’s CBSA isn’t null Exits are when the CBSA is different than the past year and the next years’s CBSA isn’t null

## Rows: 1000 Columns: 10
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): NAME, avg_eal_tercile
## dbl (8): archive_version_year, CBSAFP, entry, exit, births, deaths, avg_eal,...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
##  archive_version_year     CBSAFP          entry             exit       
##  Min.   :2013         Min.   :10420   Min.   :  1.00   Min.   :  1.00  
##  1st Qu.:2015         1st Qu.:19348   1st Qu.: 26.00   1st Qu.: 25.00  
##  Median :2018         Median :32060   Median : 45.00   Median : 44.00  
##  Mean   :2018         Mean   :30209   Mean   : 67.08   Mean   : 67.08  
##  3rd Qu.:2020         3rd Qu.:40080   3rd Qu.: 84.00   3rd Qu.: 79.00  
##  Max.   :2022         Max.   :49340   Max.   :561.00   Max.   :670.00  
##                                                        NA's   :100     
##      births           deaths           NAME              avg_eal     
##  Min.   :  1328   Min.   :  1128   Length:1000        Min.   :38.21  
##  1st Qu.:  3617   1st Qu.:  3416   Class :character   1st Qu.:65.60  
##  Median :  6166   Median :  5294   Mode  :character   Median :75.28  
##  Mean   : 12411   Mean   : 10836                      Mean   :77.28  
##  3rd Qu.: 13648   3rd Qu.: 11576                      3rd Qu.:94.11  
##  Max.   :171670   Max.   :139494                      Max.   :99.91  
##                   NA's   :100                                        
##     tercile     avg_eal_tercile   
##  Min.   :1.00   Length:1000       
##  1st Qu.:1.00   Class :character  
##  Median :2.00   Mode  :character  
##  Mean   :1.99                     
##  3rd Qu.:3.00                     
##  Max.   :3.00                     
## 
## `summarise()` has grouped output by 'archive_version_year'. You can override
## using the `.groups` argument.
## `summarise()` has grouped output by 'archive_version_year'. You can override
## using the `.groups` argument.
## `summarise()` has grouped output by 'archive_version_year'. You can override
## using the `.groups` argument.
## `summarise()` has grouped output by 'archive_version_year'. You can override
## using the `.groups` argument.
2500005000007500002015.02017.52020.02500005000007500002015.02017.52020.0
Birthsarchive_version_yearbirthsHighLowModerate
0e+002e+054e+056e+052015.02017.52020.00e+002e+054e+056e+052015.02017.52020.0
Deathsarchive_version_yeardeathsHighLowModerate
10002000300040002015.02017.52020.010002000300040002015.02017.52020.0
Entriesarchive_version_yearentryHighLowModerate
010002000300040002015.02017.52020.0010002000300040002015.02017.52020.0
Exitsarchive_version_yearexitHighLowModerate

Births Deaths Entries Exits - with parent by Risk

These are for business where there is a parent number attached

## Rows: 978 Columns: 10
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): NAME, avg_eal_tercile
## dbl (8): archive_version_year, CBSAFP, entry, exit, births, deaths, avg_eal,...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## `summarise()` has grouped output by 'archive_version_year'. You can override using the `.groups` argument.
## `summarise()` has grouped output by 'archive_version_year'. You can override using the `.groups` argument.
## `summarise()` has grouped output by 'archive_version_year'. You can override using the `.groups` argument.
## `summarise()` has grouped output by 'archive_version_year'. You can override using the `.groups` argument.
200004000060000800002015.02017.52020.0200004000060000800002015.02017.52020.0
Births - Parentarchive_version_yearbirthsHighLowModerate
0e+002e+054e+056e+052015.02017.52020.00e+002e+054e+056e+052015.02017.52020.0
Deaths - Parentarchive_version_yeardeathsHighLowModerate
3004005006007002015.02017.52020.03004005006007002015.02017.52020.0
Entries - Parentarchive_version_yearentryHighLowModerate
010002000300040002015.02017.52020.0010002000300040002015.02017.52020.0
Exits - Parentarchive_version_yearexitHighLowModerate

Births Deaths Entries Exits - standalone by Risk

These are for business where there isn’t a parent number attached

## Rows: 1000 Columns: 10
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): NAME, avg_eal_tercile
## dbl (8): archive_version_year, CBSAFP, entry, exit, births, deaths, avg_eal,...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
##  archive_version_year     CBSAFP          entry             exit       
##  Min.   :2013         Min.   :10420   Min.   :  1.00   Min.   :  1.00  
##  1st Qu.:2015         1st Qu.:19348   1st Qu.: 20.00   1st Qu.: 19.00  
##  Median :2018         Median :32060   Median : 35.00   Median : 35.00  
##  Mean   :2018         Mean   :30209   Mean   : 52.28   Mean   : 53.53  
##  3rd Qu.:2020         3rd Qu.:40080   3rd Qu.: 64.00   3rd Qu.: 65.00  
##  Max.   :2022         Max.   :49340   Max.   :499.00   Max.   :594.00  
##                                                        NA's   :101     
##      births           deaths           NAME              avg_eal     
##  Min.   :  1152   Min.   :  1128   Length:1000        Min.   :38.21  
##  1st Qu.:  3337   1st Qu.:  3416   Class :character   1st Qu.:65.60  
##  Median :  5682   Median :  5294   Mode  :character   Median :75.28  
##  Mean   : 11596   Mean   : 10836                      Mean   :77.28  
##  3rd Qu.: 12722   3rd Qu.: 11576                      3rd Qu.:94.11  
##  Max.   :169862   Max.   :139494                      Max.   :99.91  
##                   NA's   :100                                        
##     tercile     avg_eal_tercile   
##  Min.   :1.00   Length:1000       
##  1st Qu.:1.00   Class :character  
##  Median :2.00   Mode  :character  
##  Mean   :1.99                     
##  3rd Qu.:3.00                     
##  Max.   :3.00                     
## 
## `summarise()` has grouped output by 'archive_version_year'. You can override
## using the `.groups` argument.
## `summarise()` has grouped output by 'archive_version_year'. You can override
## using the `.groups` argument.
## `summarise()` has grouped output by 'archive_version_year'. You can override
## using the `.groups` argument.
## `summarise()` has grouped output by 'archive_version_year'. You can override
## using the `.groups` argument.
2500005000007500002015.02017.52020.02500005000007500002015.02017.52020.0
Births - standalonearchive_version_yearbirthsHighLowModerate
0e+002e+054e+056e+052015.02017.52020.00e+002e+054e+056e+052015.02017.52020.0
Deaths - standalonearchive_version_yeardeathsHighLowModerate
1000200030002015.02017.52020.01000200030002015.02017.52020.0
Entries - standalonearchive_version_yearentryHighLowModerate
010002000300040002015.02017.52020.0010002000300040002015.02017.52020.0
Exits - standalonearchive_version_yearexitHighLowModerate

Births Deaths Entries Exits - All by Population

This segments the data by population terciles for the top 100 CBSAs

top100_cbsa <- get_acs(geography = "cbsa", variables = c(pop="B01001_001"),output = "wide") %>%
  top_n(100,popE) %>%
  mutate(pop_tercile = ntile(popE, 3)) %>%
  mutate(pop_tercile=as_factor(pop_tercile)) %>%
  mutate(pop_tercile = factor(pop_tercile, levels = 1:3, labels = c("Pop: Low", "Pop: Moderate", "Pop: High"))) %>% 
  select(pop_tercile,GEOID)
## Getting data from the 2018-2022 5-year ACS
data <- read_csv("cbsa_births_deaths_exits_entrys.csv") %>%
  mutate(CBSAFP=as.character(CBSAFP)) %>%
  mutate(avg_eal_tercile = factor(avg_eal_tercile,
                                levels = c("Low", "Moderate", "High"))) %>%
  mutate(avg_eal_tercile = fct_recode(avg_eal_tercile,
                                      "EAL: Low" = "Low",
                                      "EAL: Moderate" = "Moderate",
                                      "EAL: High" = "High")) %>%
  left_join(top100_cbsa,by=c("CBSAFP"="GEOID"))
## Rows: 1000 Columns: 10
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): NAME, avg_eal_tercile
## dbl (8): archive_version_year, CBSAFP, entry, exit, births, deaths, avg_eal,...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
summary(data)
##  archive_version_year    CBSAFP              entry             exit       
##  Min.   :2013         Length:1000        Min.   :  1.00   Min.   :  1.00  
##  1st Qu.:2015         Class :character   1st Qu.: 26.00   1st Qu.: 25.00  
##  Median :2018         Mode  :character   Median : 45.00   Median : 44.00  
##  Mean   :2018                            Mean   : 67.08   Mean   : 67.08  
##  3rd Qu.:2020                            3rd Qu.: 84.00   3rd Qu.: 79.00  
##  Max.   :2022                            Max.   :561.00   Max.   :670.00  
##                                                           NA's   :100     
##      births           deaths           NAME              avg_eal     
##  Min.   :  1328   Min.   :  1128   Length:1000        Min.   :38.21  
##  1st Qu.:  3617   1st Qu.:  3416   Class :character   1st Qu.:65.60  
##  Median :  6166   Median :  5294   Mode  :character   Median :75.28  
##  Mean   : 12411   Mean   : 10836                      Mean   :77.28  
##  3rd Qu.: 13648   3rd Qu.: 11576                      3rd Qu.:94.11  
##  Max.   :171670   Max.   :139494                      Max.   :99.91  
##                   NA's   :100                                        
##     tercile          avg_eal_tercile        pop_tercile 
##  Min.   :1.00   EAL: Low     :340    Pop: Low     :340  
##  1st Qu.:1.00   EAL: Moderate:330    Pop: Moderate:330  
##  Median :2.00   EAL: High    :330    Pop: High    :330  
##  Mean   :1.99                                           
##  3rd Qu.:3.00                                           
##  Max.   :3.00                                           
## 
## `summarise()` has grouped output by 'archive_version_year', 'avg_eal_tercile'.
## You can override using the `.groups` argument.
## `summarise()` has grouped output by 'archive_version_year', 'avg_eal_tercile'.
## You can override using the `.groups` argument.
## `summarise()` has grouped output by 'archive_version_year', 'avg_eal_tercile'.
## You can override using the `.groups` argument.
## `summarise()` has grouped output by 'archive_version_year', 'avg_eal_tercile'.
## You can override using the `.groups` argument.
0e+002e+054e+056e+058e+050e+002e+054e+056e+058e+052015.02017.52020.00e+002e+054e+056e+058e+052015.02017.52020.02015.02017.52020.0
Birthsarchive_version_yearbirthsEAL: LowEAL: ModerateEAL: HighPop: LowPop: ModeratePop: High
0e+002e+054e+056e+050e+002e+054e+056e+052015.02017.52020.00e+002e+054e+056e+052015.02017.52020.02015.02017.52020.0
Deathsarchive_version_yeardeathsEAL: LowEAL: ModerateEAL: HighPop: LowPop: ModeratePop: High
1000200030001000200030002015.02017.52020.01000200030002015.02017.52020.02015.02017.52020.0
Entriesarchive_version_yearentryEAL: LowEAL: ModerateEAL: HighPop: LowPop: ModeratePop: High
1000200030001000200030002015.02017.52020.01000200030002015.02017.52020.02015.02017.52020.0
Exitsarchive_version_yearexitEAL: LowEAL: ModerateEAL: HighPop: LowPop: ModeratePop: High

Births Deaths - with parent by Population

This segments business with a parent number by population terciles for the top 100 CBSAs

data <- read_csv("cbsa_births_deaths_exits_entrys_parent.csv") %>%
  mutate(CBSAFP=as.character(CBSAFP)) %>%
  mutate(avg_eal_tercile = factor(avg_eal_tercile,
                                levels = c("Low", "Moderate", "High"))) %>%
  mutate(avg_eal_tercile = fct_recode(avg_eal_tercile,
                                      "EAL: Low" = "Low",
                                      "EAL: Moderate" = "Moderate",
                                      "EAL: High" = "High")) %>%
  left_join(top100_cbsa,by=c("CBSAFP"="GEOID"))
## Rows: 978 Columns: 10
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): NAME, avg_eal_tercile
## dbl (8): archive_version_year, CBSAFP, entry, exit, births, deaths, avg_eal,...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
summary(data)
##  archive_version_year    CBSAFP              entry             exit       
##  Min.   :2013         Length:978         Min.   :  1.00   Min.   :  1.00  
##  1st Qu.:2015         Class :character   1st Qu.:  4.00   1st Qu.: 20.00  
##  Median :2018         Mode  :character   Median :  8.00   Median : 36.00  
##  Mean   :2018                            Mean   : 13.37   Mean   : 54.31  
##  3rd Qu.:2020                            3rd Qu.: 16.00   3rd Qu.: 66.00  
##  Max.   :2022                            Max.   :111.00   Max.   :594.00  
##                                                           NA's   :97      
##      births            deaths           NAME              avg_eal     
##  Min.   :   12.0   Min.   :  1128   Length:978         Min.   :38.21  
##  1st Qu.:  447.0   1st Qu.:  3428   Class :character   1st Qu.:65.97  
##  Median :  797.5   Median :  5354   Mode  :character   Median :75.17  
##  Mean   : 1407.4   Mean   : 10946                      Mean   :77.35  
##  3rd Qu.: 1646.2   3rd Qu.: 11691                      3rd Qu.:94.62  
##  Max.   :19335.0   Max.   :139494                      Max.   :99.91  
##                    NA's   :96                                         
##     tercile           avg_eal_tercile        pop_tercile 
##  Min.   :1.000   EAL: Low     :332    Pop: Low     :327  
##  1st Qu.:1.000   EAL: Moderate:321    Pop: Moderate:321  
##  Median :2.000   EAL: High    :325    Pop: High    :330  
##  Mean   :1.993                                           
##  3rd Qu.:3.000                                           
##  Max.   :3.000                                           
## 
## `summarise()` has grouped output by 'archive_version_year', 'avg_eal_tercile'.
## You can override using the `.groups` argument.
## `summarise()` has grouped output by 'archive_version_year', 'avg_eal_tercile'.
## You can override using the `.groups` argument.
## `summarise()` has grouped output by 'archive_version_year', 'avg_eal_tercile'.
## You can override using the `.groups` argument.
## `summarise()` has grouped output by 'archive_version_year', 'avg_eal_tercile'.
## You can override using the `.groups` argument.
0200004000060000800000200004000060000800002015.02017.52020.00200004000060000800002015.02017.52020.02015.02017.52020.0
Births - Parentarchive_version_yearbirthsEAL: LowEAL: ModerateEAL: HighPop: LowPop: ModeratePop: High
0e+002e+054e+056e+050e+002e+054e+056e+052015.02017.52020.00e+002e+054e+056e+052015.02017.52020.02015.02017.52020.0
Deaths - Parentarchive_version_yeardeathsEAL: LowEAL: ModerateEAL: HighPop: LowPop: ModeratePop: High
1002003004005001002003004005002015.02017.52020.01002003004005002015.02017.52020.02015.02017.52020.0
Entries - Parentarchive_version_yearentryEAL: LowEAL: ModerateEAL: HighPop: LowPop: ModeratePop: High
1000200030001000200030002015.02017.52020.01000200030002015.02017.52020.02015.02017.52020.0
Exits - Parentarchive_version_yearexitEAL: LowEAL: ModerateEAL: HighPop: LowPop: ModeratePop: High

Births Deaths - standalone by Population

This segments business without a parent number by population terciles for the top 100 CBSAs

data <- read_csv("cbsa_births_deaths_exits_entrys_standalone.csv") %>%
  mutate(CBSAFP=as.character(CBSAFP)) %>%
  mutate(avg_eal_tercile = factor(avg_eal_tercile,
                                levels = c("Low", "Moderate", "High"))) %>%
  mutate(avg_eal_tercile = fct_recode(avg_eal_tercile,
                                      "EAL: Low" = "Low",
                                      "EAL: Moderate" = "Moderate",
                                      "EAL: High" = "High")) %>%
  left_join(top100_cbsa,by=c("CBSAFP"="GEOID"))
## Rows: 1000 Columns: 10
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): NAME, avg_eal_tercile
## dbl (8): archive_version_year, CBSAFP, entry, exit, births, deaths, avg_eal,...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
summary(data)
##  archive_version_year    CBSAFP              entry             exit       
##  Min.   :2013         Length:1000        Min.   :  1.00   Min.   :  1.00  
##  1st Qu.:2015         Class :character   1st Qu.: 20.00   1st Qu.: 19.00  
##  Median :2018         Mode  :character   Median : 35.00   Median : 35.00  
##  Mean   :2018                            Mean   : 52.28   Mean   : 53.53  
##  3rd Qu.:2020                            3rd Qu.: 64.00   3rd Qu.: 65.00  
##  Max.   :2022                            Max.   :499.00   Max.   :594.00  
##                                                           NA's   :101     
##      births           deaths           NAME              avg_eal     
##  Min.   :  1152   Min.   :  1128   Length:1000        Min.   :38.21  
##  1st Qu.:  3337   1st Qu.:  3416   Class :character   1st Qu.:65.60  
##  Median :  5682   Median :  5294   Mode  :character   Median :75.28  
##  Mean   : 11596   Mean   : 10836                      Mean   :77.28  
##  3rd Qu.: 12722   3rd Qu.: 11576                      3rd Qu.:94.11  
##  Max.   :169862   Max.   :139494                      Max.   :99.91  
##                   NA's   :100                                        
##     tercile          avg_eal_tercile        pop_tercile 
##  Min.   :1.00   EAL: Low     :340    Pop: Low     :340  
##  1st Qu.:1.00   EAL: Moderate:330    Pop: Moderate:330  
##  Median :2.00   EAL: High    :330    Pop: High    :330  
##  Mean   :1.99                                           
##  3rd Qu.:3.00                                           
##  Max.   :3.00                                           
## 
## `summarise()` has grouped output by 'archive_version_year', 'avg_eal_tercile'.
## You can override using the `.groups` argument.
## `summarise()` has grouped output by 'archive_version_year', 'avg_eal_tercile'.
## You can override using the `.groups` argument.
## `summarise()` has grouped output by 'archive_version_year', 'avg_eal_tercile'.
## You can override using the `.groups` argument.
## `summarise()` has grouped output by 'archive_version_year', 'avg_eal_tercile'.
## You can override using the `.groups` argument.
0e+002e+054e+056e+058e+050e+002e+054e+056e+058e+052015.02017.52020.00e+002e+054e+056e+058e+052015.02017.52020.02015.02017.52020.0
Births - Standalonearchive_version_yearbirthsEAL: LowEAL: ModerateEAL: HighPop: LowPop: ModeratePop: High
0e+002e+054e+056e+050e+002e+054e+056e+052015.02017.52020.00e+002e+054e+056e+052015.02017.52020.02015.02017.52020.0
Deaths - Standalonearchive_version_yeardeathsEAL: LowEAL: ModerateEAL: HighPop: LowPop: ModeratePop: High
10002000100020002015.02017.52020.0100020002015.02017.52020.02015.02017.52020.0
Entries - Standalonearchive_version_yearentryEAL: LowEAL: ModerateEAL: HighPop: LowPop: ModeratePop: High
1000200030001000200030002015.02017.52020.01000200030002015.02017.52020.02015.02017.52020.0
Exits - Standalonearchive_version_yearexitEAL: LowEAL: ModerateEAL: HighPop: LowPop: ModeratePop: High

NAICs breakdown

This breaks down by 2-digit NAICs codes

## Rows: 780 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): avg_eal_tercile
## dbl (3): archive_version_year, naics_2, rows
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Rows: 778 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): avg_eal_tercile
## dbl (3): archive_version_year, naics_2, births
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Rows: 780 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): avg_eal_tercile
## dbl (3): archive_version_year, naics_2, deaths
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Rows: 777 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): avg_eal_tercile
## dbl (3): archive_version_year, naics_2, entries
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Rows: 776 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): avg_eal_tercile
## dbl (3): archive_version_year, naics_2, exits
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
0e+005e+051e+062015.02017.52020.00e+005e+051e+062015.02017.52020.0
naics_2alpha11212223313233424445484951525354555661627172819299NANAICS by tercile, allarchive_version_yearrowsHighLowModerate
0e+001e+052e+053e+052015.02017.52020.00e+001e+052e+053e+052015.02017.52020.0
naics_2alpha11212223313233424445484951525354555661627172819299NANAICS by tercile, birthsarchive_version_yearbirthsHighLowModerate
0500001000001500002012201420162018202005000010000015000020122014201620182020
naics_2alpha11212223313233424445484951525354555661627172819299NANAICS by tercile, deathsarchive_version_yeardeathsHighLowModerate
05001000150020002015.02017.52020.005001000150020002015.02017.52020.0
naics_2alpha11212223313233424445484951525354555661627172819299NANAICS by tercile, entriesarchive_version_yearentriesHighLowModerate
0100020002012201420162018202001000200020122014201620182020
naics_2alpha11212223313233424445484951525354555661627172819299NANAICS by tercile, exitsarchive_version_yearexitsHighLowModerate

NAICs 4 breakdown

This breaks down by 4-digit NAICs codes and filtered for critical businesses

## 
## Attaching package: 'janitor'
## The following objects are masked from 'package:stats':
## 
##     chisq.test, fisher.test
## New names:
## Rows: 2125 Columns: 6
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (3): 2022 NAICS US Code, 2022 NAICS US Title, Critical dbl (2): Seq. No.,
## Digits lgl (1): ...6
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Rows: 9383 Columns: 4
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (1): avg_eal_tercile dbl (3): archive_version_year, naics_4, rows
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Rows: 9172 Columns: 4
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (1): avg_eal_tercile dbl (3): archive_version_year, naics_4, births
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Rows: 9262 Columns: 4
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (1): avg_eal_tercile dbl (3): archive_version_year, naics_4, deaths
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Rows: 7837 Columns: 4
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (1): avg_eal_tercile dbl (3): archive_version_year, naics_4, entries
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Rows: 7780 Columns: 4
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (1): avg_eal_tercile dbl (3): archive_version_year, naics_4, exits
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...6`
0e+001e+052e+053e+054e+052015.02017.52020.00e+001e+052e+053e+054e+052015.02017.52020.0
naics_4alpha11111112111311141119112111221123112411251129113111321133114111421151115211532111212121222123213122112212221323612362237123722373237923812382238323893111311231133114311531163117311831193121324132513252325332543255325632593271327232733274327933113312331333143315332733313332333333343335333633393341334233443345334633513352335333593361336233633364336533663369339142314232423342344235423642374238423942414242424342444245424642474248424944514452445348114812482148314832484148424851485248534854485548594861486248694881488248834884488548894911492149225111511251215122515151525171517251745179518251915211522152225223523254115412541756125613561456165621562256296111611261136114611561166117621162126213621462156216621962216222622362316232623362396241624262436244721172127213722372247225811181128113811481238131813281338134813992119221923192419251926192719281NAICS by tercile, allarchive_version_yearrowsHighLowModerate
02500050000750002015.02017.52020.002500050000750002015.02017.52020.0
naics_4alpha11111112111311141119112111221123112411251129113111321133114111421151115211532111212121222123213122112212221323612362237123722373237923812382238323893111311231133114311531163117311831193121324132513252325332543255325632593271327232733274327933113312331333143315332733313332333333343335333633393341334233443345334633513352335333593361336233633364336533663369339142314232423342344235423642374238423942414242424342444245424642474248424944514452445348114812482148314832484148424851485248534854485548594861486248694881488248834884488548894911492149225111511251215122515151525171517251745179518251915211522152225223523254115412541756125613561456165621562256296111611261136114611561166117621162126213621462156216621962216222622362316232623362396241624262436244721172127213722372247225811181128113811481238131813281338134813992119221923192419251926192719281NAICS by tercile, birthsarchive_version_yearbirthsHighLowModerate
030000600009000020122014201620182020030000600009000020122014201620182020
naics_4alpha11111112111311141119112111221123112411251129113111321133114111421151115211532111212121222123213122112212221323612362237123722373237923812382238323893111311231133114311531163117311831193121324132513252325332543255325632593271327232733274327933113312331333143315332733313332333333343335333633393341334233443345334633513352335333593361336233633364336533663369339142314232423342344235423642374238423942414242424342444245424642474248424944514452445348114812482148314832484148424851485248534854485548594861486248694881488248834884488548894911492149225111511251215122515151525171517251745179518251915211522152225223523254115412541756125613561456165621562256296111611261136114611561166117621162126213621462156216621962216222622362316232623362396241624262436244721172127213722372247225811181128113811481238131813281338134813992119221923192419251926192719281NAICS by tercile, deathsarchive_version_yeardeathsHighLowModerate
0500100015002015.02017.52020.00500100015002015.02017.52020.0
alphanaics_411111112111311141119112111231124112511291133114111421151115211532111212121222123213122112212221323612362237123722373237923812382238323893111311231133114311531163117311831193121324132513252325332543255325632593271327232733274327933113312331333143315332733313332333333343335333633393341334233443345334633513352335333593361336233633364336533663369339142314232423342344235423642374238423942414242424342444245424642474248424944514452445348114812482148314832484148424851485248534854485548594861486248694881488248834884488548894911492149225111511251215122515151525171517251745179518251915211522152225223523254115412541756125613561456165621562256296111611261136114611561166117621162126213621462156216621962216222622362316232623362396241624262436244721172127213722372247225811181128113811481238131813281338134813992119221923192419251926192719281NAICS by tercile, entriesarchive_version_yearentriesHighLowModerate
0500100015002012201420162018202005001000150020122014201620182020
alphanaics_41111111311141119112111231124112511291131113311411142115111521153211121212122212321312211221222132361236223712372237323792381238223832389311131123113311431153116311731183119312132413251325232533254325532563259327132723273327432793311331233133314331533273331333233333334333533363339334133423344334533463351335233533359336133623363336433653366336933914231423242334234423542364237423842394241424242434244424542464247424842494451445244534811481248214831483248414842485148524853485448554859486148624869488148824883488448854889491149214922511151125121512251515152517151725179518251915211522152225223523254115412541756125613561456165621562256296111611261136114611561166117621162126213621462156216621962216222622362316232623362396241624262436244721172127213722372247225811181128113811481238131813281338134813992119221923192419251926192719281NAICS by tercile, exitsarchive_version_yearexitsHighLowModerate

This compares critical and non-critical NAICs This breaks down by 4-digit NAICs codes and filtered for critical businesses

## New names:
## Rows: 2125 Columns: 6
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (3): 2022 NAICS US Code, 2022 NAICS US Title, Critical dbl (2): Seq. No.,
## Digits lgl (1): ...6
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Rows: 9383 Columns: 4
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (1): avg_eal_tercile dbl (3): archive_version_year, naics_4, rows
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Rows: 9172 Columns: 4
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (1): avg_eal_tercile dbl (3): archive_version_year, naics_4, births
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Rows: 9262 Columns: 4
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (1): avg_eal_tercile dbl (3): archive_version_year, naics_4, deaths
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Rows: 7837 Columns: 4
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (1): avg_eal_tercile dbl (3): archive_version_year, naics_4, entries
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Rows: 7780 Columns: 4
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (1): avg_eal_tercile dbl (3): archive_version_year, naics_4, exits
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## `summarise()` has grouped output by 'archive_version_year', 'avg_eal_tercile'.
## You can override using the `.groups` argument.
## `summarise()` has grouped output by 'archive_version_year', 'avg_eal_tercile'.
## You can override using the `.groups` argument.
## `summarise()` has grouped output by 'archive_version_year', 'avg_eal_tercile'.
## You can override using the `.groups` argument.
## `summarise()` has grouped output by 'archive_version_year', 'avg_eal_tercile'.
## You can override using the `.groups` argument.
## `summarise()` has grouped output by 'archive_version_year', 'avg_eal_tercile'.
## You can override using the `.groups` argument.
## • `` -> `...6`
10000001500000200000025000003000000100000015000002000000250000030000002015.02017.52020.0100000015000002000000250000030000002015.02017.52020.0
NAICS by tercile, allarchive_version_yearrowsCriticalNotHighLowModerate
1e+052e+053e+054e+055e+051e+052e+053e+054e+055e+052015.02017.52020.01e+052e+053e+054e+055e+052015.02017.52020.0
NAICS by tercile, birthsarchive_version_yearbirthsCriticalNotHighLowModerate
1e+052e+053e+054e+051e+052e+053e+054e+05201220142016201820201e+052e+053e+054e+0520122014201620182020
NAICS by tercile, deathsarchive_version_yeardeathsCriticalNotHighLowModerate
300040005000600030004000500060002015.02017.52020.030004000500060002015.02017.52020.0
NAICS by tercile, entriesarchive_version_yearentriesCriticalNotHighLowModerate
3000400050006000300040005000600020122014201620182020300040005000600020122014201620182020
NAICS by tercile, exitsarchive_version_yearexitsCriticalNotHighLowModerate

Critical/Non-Critical vs. Population Matrix