First is the sample data, schema for one of the variables, and the code that I am using to manipulate it. along with the desired result. I realize upon inspection that the fourth element is not correct and so need clarification on this. The question is how I would have R produce two gt tables from this. In reality, I have 17 areas but for this small set there are only 2. How would I have R produce 2 gt tables that represent the area (001 or 003).
library(readxl)
library(dplyr)
library(data.table)
library(odbc)
library(DBI)
library(stringr)
firm <- c("f1","f2","f3","f4","f5","f6","f7","f8","f9","f10","f11","f12")
employment <- c(1,50,90,249,499,115,145,261,210,874,1140,45)
small <- c(1,1,1,3,4,2,2,4,3,NA,NA,1)
area <-c(001,001,001,001,001,001,003,003,003,003,003,003)
smbtest <- data.frame(firm,employment,small,area)
smbsummary2<-smbtest %>%
select(firm, employment, small, area) %>%
group_by(area,small) %>%
summarise(employment = sum(employment), worksites = n(),
.groups = 'drop') %>%
mutate(employment = cumsum(employment),
worksites = cumsum(worksites))
Schema:
smb 1 = employing between 0 and 100
smb 2 = employing between 0 and 150
smb 3 = employing between 0 and 250
smb 4 = employing between 0 and 500
Desired Result (these would be tables as made by gt)
Area Small Employment Worksites
001 1 141 3
001 2 115 1
....(and on to small 4)
Area Small Employment Worksites
003 1 45 1
003 2 145 1
....(and on to small 4)
Do you want a separate gt table for each area ? Try this :
list_gt <- lapply(split(smbsummary2, smbsummary2$area), gt::gt)
You can then access to each individual gt table with list_gt[[1]], list_gt[[2]] and so on.