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)))
ggplotly(plot2) %>% layout(showlegend = TRUE, legend = list(font = list(size = 6)))
ggplotly(plot3) %>% layout(showlegend = TRUE, legend = list(font = list(size = 6)))
ggplotly(plot4) %>% layout(showlegend = TRUE, legend = list(font = list(size = 6)))
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)))
ggplotly(plot2) %>% layout(showlegend = TRUE, legend = list(font = list(size = 6)))
ggplotly(plot3) %>% layout(showlegend = TRUE, legend = list(font = list(size = 6)))
ggplotly(plot4) %>% layout(showlegend = TRUE, legend = list(font = list(size = 6)))
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.
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.
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.
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.
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.
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.
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.
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`
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`