Until now I've been working with a medium size dataset for an Ocupation Survey(around 200 mb total), here's the data if you want to review it: https://drive.google.com/drive/folders/1Od8zlOE3U3DO0YRGnBadFz804OUDnuQZ?usp=sharing
I have the following code:
hogares<-read.csv("/home/servicio/Escritorio/TR_VIVIENDA01.CSV")
personas<-read.csv("/home/servicio/Escritorio/TR_PERSONA01.CSV")
datos<-merge(hogares,personas)
library(dplyr)
base<-tibble(ID_VIV=datos$ID_VIV, ID_PERSONA=datos$ID_PERSONA, EDAD=datos$EDAD, CONACT=datos$CONACT)
base$maxage <- ave(base$EDAD, base$ID_VIV, FUN=max)
base$Condición_I<-case_when(base$CONACT==32 & base$EDAD>=60 ~ 1,
base$CONACT>=10 & base$EDAD>=60 & base$CONACT<=16 ~ 2,
base$CONACT==20 & base$EDAD>=60 | base$CONACT==31 & base$EDAD>=60 | (base$CONACT>=33 & base$CONACT<=35 & base$EDAD>=60) ~ 3)
base <- subset(base, maxage >= 60)
base<- base %>% group_by(ID_VIV) %>% mutate(Condición_V = if(n_distinct(Condición_I) > 1) 4 else Condición_I)
base$ID_VIV<-as.character(base$ID_VIV)
base$ID_PERSONA<-as.character(base$ID_PERSONA)
base
And ended up with:
# A tibble: 38,307 x 7
# Groups: ID_VIV [10,499]
ID_VIV ID_PERSONA EDAD CONACT maxage Condición_I Condición_V
<chr> <chr> <int> <int> <int> <dbl> <dbl>
1 10010000007 1001000000701 69 32 69 1 1
2 10010000008 1001000000803 83 33 83 3 4
3 10010000008 1001000000802 47 33 83 NA 4
4 10010000008 1001000000801 47 10 83 NA 4
5 10010000012 1001000001204 4 NA 60 NA 4
6 10010000012 1001000001203 2 NA 60 NA 4
7 10010000012 1001000001201 60 10 60 2 4
8 10010000012 1001000001202 21 10 60 NA 4
9 10010000014 1001000001401 67 32 67 1 4
10 10010000014 1001000001402 64 33 67 3 4
The Condición_I column value is a code for the labour conditions of each individual(row), some of this individuals share house (that's why they share ID_VIV), I only care about the individuals that are 60yo or more, all the NA are individuals who live with a 60+yo but I do not care about their situation (but I need to keep them), I need the column Condición_V to display another value following this conditions:
Condición_I == 1 ~ 1
Condición_I == 2 ~ 2
Condición_I == 3 ~ 3
Any combination of Condición_I ~ 4
This means that if all the 60 and+_yo individuals in a house have Condición_I == 1 then Condición_V will be 1 that's true up to code 3, when there are x.e. one person C_I == 1 and another one C_I == 3 in the same house, then Condición_V will be 4
And I'm hoping to get this kind of result:
A tibble: 38,307 x 7
# Groups: ID_VIV [10,499]
ID_VIV ID_PERSONA EDAD CONACT maxage Condición_I Condición_V
<chr> <chr> <int> <int> <int> <dbl> <dbl>
1 10010000007 1001000000701 69 32 69 1 1
2 10010000008 1001000000803 83 33 83 3 3
3 10010000008 1001000000802 47 33 83 NA 3
4 10010000008 1001000000801 47 10 83 NA 3
5 10010000012 1001000001204 4 NA 60 NA 2
6 10010000012 1001000001203 2 NA 60 NA 2
7 10010000012 1001000001201 60 10 60 2 2
8 10010000012 1001000001202 21 10 60 NA 2
9 10010000014 1001000001401 67 32 67 1 4
10 10010000014 1001000001402 64 33 67 3 4
I know my error is in:
`#base<- base %>% group_by(ID_VIV) %>% mutate(Condición_V = if(n_distinct(Condición_I) > 1) 4 else` Condición_I)
Is there a way to use that line of code ignoring the NA values or is it my best option to do it otherway, I do not have to do it the way I'm trying and any other way or help will be much appreciated!
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