I have my info as in this photograph:
You can download it here: https://drive.google.com/file/d/1pgO51NXtjpVSz-VxQEDNFFuQXVc4jVkt/view?usp=sharing
What i want is to transform this data to individuals,
Will transform into this
will turn into this
So, if we say that n="sum of all numbers in the original data.frame", i.e., the number of all individuals, the final output will be a data.frame
with 6 columns and n rows.
I want to do this in R but i don't have any idea how. Once I have this, what i want to do is apply a generalized linear model with family binomial and link = probit.
Now, this page can explain some of what I tried to do:
https://www.datanalytics.com/libro_r/la-funcion-melt-y-datos-en-formato-largo.html
Okay... I have an answer, but... i was wondering if there exists any generalization. Here it goes:
library(readxl)
library(dplyr)
# Información original ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
byssinosis <- read_xls(path = "byssinosis.xls",range = "B4:K27",col_names = F)
names(byssinosis) <- c("Employment","Smoking","Sex","Race",
"W1y","W1n","W2y","W2n","W3y","W3n")
# View(byssinosis)
# Procesando la información a individuos ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Primero pasamos las columnas a una sola.
datos <- reshape2::melt(byssinosis)
# Separamos estas columnas en las dos características deseadas.
datos <- datos %>%
mutate(Workplace = ifelse(variable %in% c("W1y", "W1n"),1,
ifelse(variable %in% c("W2y", "W2n"),2,3)),
Byssinosis = ifelse(variable %in% c("W1y", "W2y", "W3y"),"yes","no"))
# Repetimos con base en value.
individuos=rep(seq_len(nrow(datos)),datos$value)
datos <- datos[individuos,]
# Nos quedamos solo las columnas deseadas
datos <- datos %>% select(-c(variable,value))
# View(datos)
# Comprobación ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
tabla <-
table(datos) %>%
as.data.frame() %>%
arrange(Employment, desc(Smoking), desc(Sex), desc(Race), Workplace, desc(Byssinosis))
# View(tabla)