library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(haven)
## Warning: package 'haven' was built under R version 4.0.4
library(writexl)
## Warning: package 'writexl' was built under R version 4.0.5
data = read_sav("r29iall_42.sav")
data = data %>% select(yl90, yj60, yj13.2, ym3, yj1.1.2, y_age, yj21.3, yj29c.2, yj31, status, y_age, yh5, yj4.1, yj6.2, yj11.1, yj29, yj31, yj72.171, yj72.173, yl5.0, ym20.61, ym20.62, ym20.63, ym20.64, ym20.65, ym20.66, ym20.620, ym20.69, ym20.610, ym20.611, ym20.612, ym20.613, ym20.614, ym20.615, ym20.616, ym20.617, ym20.618, ym20.619, ym20.67, ym20.7, ym39)
data = data %>% filter(is.na(yl90) == FALSE & is.na(yj60) == FALSE & is.na(yj13.2) == FALSE)
data$yl90 = as.numeric(data$yl90)
data$yj60 = as.numeric(data$yj60)
data$yj13.2 = as.numeric(data$yj13.2)
data$yj1.1.2 = as.numeric(data$yj1.1.2)
data$y_age = as.numeric(data$y_age)
data$yj21.3 = as.numeric(data$yj21.3)
data$yj29c.2 = as.numeric(data$yj29c.2)
data$ym3 = as.numeric(data$ym3)
data$yj31 = as.numeric(data$yj31)
data$status = as.numeric(data$status)
data$yh5 = as.numeric(data$yh5)
data$yj4.1 = as.numeric(data$yj4.1)
data$yj6.2 = as.numeric(data$yj6.2)
data$yj11.1 = as.numeric(data$yj11.1)
data$yj29 = as.numeric(data$yj29)
data$yj72.171 = as.numeric(data$yj72.171)
data$yj72.173 = as.numeric(data$yj72.173)
data$yl5.0 = as.numeric(data$yl5.0)
data$ym20.61 = as.numeric(data$ym20.61)
data$ym20.62 = as.numeric(data$ym20.62)
data$ym20.63 = as.numeric(data$ym20.63)
data$ym20.64 = as.numeric(data$ym20.64)
data$ym20.65 = as.numeric(data$ym20.65)
data$ym20.66 = as.numeric(data$ym20.66)
data$ym20.620 = as.numeric(data$ym20.620)
data$ym20.69 = as.numeric(data$ym20.69)
data$ym20.610 = as.numeric(data$ym20.610)
data$ym20.611 = as.numeric(data$ym20.611)
data$ym20.612 = as.numeric(data$ym20.612)
data$ym20.613 = as.numeric(data$ym20.613)
data$ym20.614 = as.numeric(data$ym20.614)
data$ym20.615 = as.numeric(data$ym20.615)
data$ym20.616 = as.numeric(data$ym20.616)
data$ym20.617 = as.numeric(data$ym20.617)
data$ym20.618 = as.numeric(data$ym20.618)
data$ym20.619 = as.numeric(data$ym20.619)
data$ym20.67 = as.numeric(data$ym20.67)
data$ym20.7 = as.numeric(data$ym20.7)
data$ym39 = as.numeric(data$ym39)
data[is.na(data$yj72.173), "yj72.173"] <- 0 #про детей моложе 18
data[is.na(data$ym20.614), "ym20.614"] <- 2 #про гинекологические заболевания
data = data %>% filter(ym39 < 9999990) %>% filter(ym20.7 < 9999990) %>% filter(ym20.618 < 9999990) %>% filter(ym20.617 < 9999990) %>% filter(ym20.616 < 9999990) %>% filter(ym20.615 < 9999990) %>% filter(ym20.613 < 9999990) %>% filter(ym20.612 < 9999990) %>% filter(ym20.610 < 9999990) %>% filter(ym20.620 < 9999990) %>% filter(ym20.64 < 9999990) %>% filter(ym20.63 < 9999990) %>% filter(ym20.62 < 9999990) %>% filter(yj72.173 < 9999990) %>% filter(yj72.171 < 9999990) %>% filter(yj4.1 < 9999990) %>% filter(yh5 < 9999990) %>% filter(status < 9999990) %>% filter(yj31 < 9999990) %>% filter(y_age < 9999990) %>% filter(yj1.1.2 < 9999990) %>% filter(yl90 < 9999990) %>% filter(yj13.2 < 9999990) %>% filter(yj60 < 9999990) %>% filter(yj21.3 < 9999990) %>% filter(yj29c.2 < 9999990) %>% filter(ym3 < 9999990) %>% filter(yj6.2 < 99999990) %>% filter(yj11.1 < 9999990) %>% filter(yj29 < 9999990) %>% filter(yl5.0 < 9999990) %>% filter(ym20.61 < 9999990) %>% filter(ym20.65 < 9999990) %>% filter(ym20.66 < 9999990) %>% filter(ym20.69 < 9999990) %>% filter(ym20.611 < 9999990) %>% filter(ym20.619 < 9999990) %>% filter(ym20.67 < 9999990) %>% filter(ym20.614 < 9999990)
data[data$ym20.61 == 2, "ym20.61"] <- 0
data[data$ym20.62 == 2, "ym20.62"] <- 0
data[data$ym20.63 == 2, "ym20.63"] <- 0
data[data$ym20.64 == 2, "ym20.64"] <- 0
data[data$ym20.65 == 2, "ym20.65"] <- 0
data[data$ym20.66 == 2, "ym20.66"] <- 0
data[data$ym20.67 == 2, "ym20.67"] <- 0
data[data$ym20.69 == 2, "ym20.69"] <- 0
data[data$ym20.610 == 2, "ym20.610"] <- 0
data[data$ym20.611 == 2, "ym20.611"] <- 0
data[data$ym20.612 == 2, "ym20.612"] <- 0
data[data$ym20.613 == 2, "ym20.613"] <- 0
data[data$ym20.614 == 2, "ym20.614"] <- 0
data[data$ym20.615 == 2, "ym20.615"] <- 0
data[data$ym20.616 == 2, "ym20.616"] <- 0
data[data$ym20.617 == 2, "ym20.617"] <- 0
data[data$ym20.618 == 2, "ym20.618"] <- 0
data[data$ym20.619 == 2, "ym20.619"] <- 0
data[data$ym20.620 == 2, "ym20.620"] <- 0
data = data %>% mutate(quant_chron_zabol = ym20.61 + ym20.62 + ym20.620 + ym20.63 + ym20.64 + ym20.65 + ym20.66 + ym20.67 + ym20.69 + ym20.610 + ym20.611 + ym20.612 + ym20.613 + ym20.614 + ym20.615 + ym20.616 + ym20.617 + ym20.618 + ym20.619) %>% dplyr::select(-ym20.61,-ym20.62,-ym20.620,-ym20.63,-ym20.64,-ym20.65,-ym20.66,-ym20.67,-ym20.69,-ym20.610,-ym20.611,-ym20.612,-ym20.613,-ym20.614,-ym20.615,-ym20.616,-ym20.617,-ym20.618,-ym20.619)
data$yj1.1.2 = as.factor(data$yj1.1.2)
data$yj21.3 = as.factor(data$yj21.3)
data$yj29c.2 = as.factor(data$yj29c.2)
data$ym3 = as.factor(data$ym3)
data$yj31 = as.factor(data$yj31)
data$status = as.factor(data$status)
data$yh5 = as.factor(data$yh5)
data$yj4.1 = as.factor(data$yj4.1)
data$yj11.1 = as.factor(data$yj11.1)
data$yj29 = as.factor(data$yj29)
data$yj72.171 = as.factor(data$yj72.171)
data$yj72.173 = as.factor(data$yj72.173)
data$ym20.7 = as.factor(data$ym20.7)
data$ym39 = as.factor(data$ym39)