ECO-BIRTH NDVI assessment

ECO-BIRTH

ECO-BIRTH – Environmental and COmponent factors on BIrth outcomes: Retrospective Trends in Health

Running Code

When you click the Render button a document will be generated that includes both content and the output of embedded code. You can embed code like this:

library(dplyr)
library(haven)
library(openxlsx)
library(gt)
library(gtsummary)

# load data "C:\Users\andpyk\OneDrive - Region Stockholm\ExposomenIMM\EXPOSOME requests\2025\January\ECO-BIRTH\Birth_coordinates_KI.xlsx"
#  use opensxlsx

data<-read.xlsx("C:/Users/andpyk/OneDrive - Region Stockholm/ExposomenIMM/EXPOSOME requests/2025/January/ECO-BIRTH/Birth_coordinates_KI.xlsx")

# names(data)
#"Participant.number" "GIS.coordinate"     "GIS.coordinate"     "Birth.year"         "Birth.month"       

#rename 2 column as x and 3rd as y
names(data)[2]<-"x"
names(data)[3]<-"y"

#"Participant.number" "x"     "y"     "Birth.year"         "Birth.month"       



# get table on missing in each var with gt or gtsummary
data %>%
  group_by(Birth.year) %>%
  summarise(n = n(),
            #coordinates_missing=n(ifelse(is.na(x)|is.na(y),1,0) )
            coordinates_missing=sum(ifelse(is.na(x)|is.na(y),1,0) )
            ) %>%
  mutate(percent_missing=coordinates_missing/n) %>%
  gt() %>%
  tab_header(title = "Missing coordinates by year") %>%
  fmt_percent(columns = percent_missing) %>%
  fmt_number(columns = c(n, coordinates_missing),
             decimals = 0) 

Missing coordinates by year
Birth.year	n	coordinates_missing	percent_missing
2004	13,476	1,202	8.92%
2005	13,830	1,223	8.84%
2006	14,285	1,205	8.44%
2007	15,185	1,239	8.16%
2008	15,319	1,280	8.36%
2009	15,063	1,248	8.29%
2010	15,172	1,209	7.97%
2011	14,023	1,018	7.26%
2012	13,388	953	7.12%
2013	13,180	903	6.85%
2014	13,034	708	5.43%
2015	13,216	521	3.94%
2016	13,169	438	3.33%
2017	12,738	405	3.18%
2018	13,235	341	2.58%

You can add options to executable code like this

library(sf)
library(tmap)

library(tmaptools)

# data_sf<-st_as_sf(data, coords = c("x","y"), crs = 3301, na.fail=FALSE, remove = F )


data_sf<-st_as_sf(data, coords = c("y","x"), crs = 3301, na.fail=FALSE, remove = F )



tmap_mode("plot")  # Use interactive mode

tm_shape(data_sf %>% head(10000)) +
  # tm dots with colour and fill by Birth.year gradient of red, legend format year as digit without thousands separator
  tm_dots(col = "Birth.year", fill = "Birth.year", legend.format = list(text.separator = "-"), ) +
    tm_basemap(server = "OpenStreetMap")

The echo: false option disables the printing of code (only output is displayed).

# Install packages if not already installed
# Install package if needed
# install.packages("leaflet")

# Load library
library(leaflet)

# prepare data_sf for leaflet

dat_sf<-data_sf %>% head(100)

# Create an interactive map with OSM
leaflet() %>%
  addTiles() %>%  # OSM background
  addMarkers(data_sf %>% head(100) )  # Add markers for the first 100 rows