Updating a data frame's columns based on other columns - r

Question

I have a data frame containing a person's stage, as follows (this is only a sample of a very large one):
df = structure(list(DeceasedDate = c(0.283219178082192, 1.12678843226788,
2.02865296803653, 0.892465753424658, NA, 0.88013698630137, NA
), LastClinicalEventMonthEnd = c(0.244862981988838, 1.03637744165398,
10.9464611555048, 0.763698598427194, 3.35011412354135, 0.677397228564181,
3.83687211440893), FirstYStage = c("N/A", "2", "2", "2", "2",
"2", "3.1"), SecondYStage = c("N/A", "N/A", "2", "N/A", "2",
"N/A", "3.1"), ThirdYStage = c("N/A", "N/A", "2", "N/A", "2",
"N/A", "3.1"), FourthYStage = c("N/A", "N/A", "N/A", "N/A", "2",
"N/A", "3.1"), FifthYStage = c("N/A", "N/A", "N/A", "N/A", "N/A",
"N/A", "N/A")), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA,
-7L))
The 5 right hand columns are a stage of a person, but do not contain all the information yet. I need to include the information in the first two columns, in which the numbers are in years, as follows:
if the value in column 1 is smaller than a year, FirstYStage should be "Deceased", and also all the next columns (the person is still dead...); if the value is between 1 and 2, it SecondYStage should be "Deceased", and so on.
if the value in column 2 is smaller than a year, SecondYStage should be "EndOfEvents"; if the value is between 1 and 2, it SecondYStage should be "EndOfEvents", and so on.
So the expected output in this case should be:
df_updated = structure(list(DeceasedDate = c(0.283219178082192,
1.12678843226788,
2.02865296803653, 0.892465753424658, NA, 0.88013698630137, NA
), LastClinicalEventMonthEnd = c(0.244862981988838, 1.03637744165398,
10.9464611555048, 0.763698598427194, 3.35011412354135, 0.677397228564181,
3.83687211440893), FirstYStage = c("Deceased", "2", "2", "Deceased",
"2", "Deceased", "3.1"), SecondYStage = c("Deceased", "Deceased",
"2", "Deceased", "2", "Deceased", "3.1"), ThirdYStage = c("Deceased",
"Deceased", "Deceased", "Deceased", "2", "Deceased", "3.1"),
FourthYStage = c("Deceased", "Deceased", "Deceased", "Deceased",
"2", "Deceased", "3.1"), FifthYStage = c("Deceased", "Deceased",
"Deceased", "Deceased", "LastEvent", "Deceased", "LastEvent"
)), row.names = c(NA, -7L), class = c("tbl_df", "tbl", "data.frame"
))
One important point is that "Death" should be given priority, in other words, if there is a clash and on the one hand there is a number and "death" is contradicting it, we should prefer death.
How do I do this in the most efficient way? At the moment I am doing if's but I think it is not the best course of action

Answer 1

This is what I would do:
Reshape from wide to long format
Compute years from column names
Selectively update the value column
Reshape back to wide format
data.table
As I am more fluent in data.table than in dplyr here is the approach implemented in data.table syntax. (Apologies but I will add a dplyr solution if time permits.)
library(data.table)
long <- melt(setDT(df)[, rn := .I], measure.vars = patterns("Stage$"))
long[, year := as.integer(variable)] # column index
long[floor(DeceasedDate) < year, value := "Deceased"]
long[is.na(DeceasedDate) & floor(LastClinicalEventMonthEnd) + 1 < year, value := "EndOfEvents"]
dcast(long, rn + DeceasedDate + LastClinicalEventMonthEnd ~ variable)
rn DeceasedDate LastClinicalEventMonthEnd FirstYStage SecondYStage ThirdYStage FourthYStage FifthYStage
1: 1 0.2832192 0.2448630 Deceased Deceased Deceased Deceased Deceased
2: 2 1.1267884 1.0363774 2 Deceased Deceased Deceased Deceased
3: 3 2.0286530 10.9464612 2 2 Deceased Deceased Deceased
4: 4 0.8924658 0.7636986 Deceased Deceased Deceased Deceased Deceased
5: 5 NA 3.3501141 2 2 2 2 EndOfEvents
6: 6 0.8801370 0.6773972 Deceased Deceased Deceased Deceased Deceased
7: 7 NA 3.8368721 3.1 3.1 3.1 3.1 EndOfEvents
dplyr / tidyr
As promised, here is also a dplyr/tidyr implemention of the same approach:
library(tidyr)
library(dplyr)
df %>%
mutate(rn = row_number()) %>%
gather(key, val, ends_with("Stage"), factor_key = TRUE) %>%
mutate(year = as.integer(key)) %>%
mutate(val = if_else(!is.na(DeceasedDate) & floor(DeceasedDate) < year, "Deceased", val)) %>%
mutate(val = if_else(is.na(DeceasedDate) & floor(LastClinicalEventMonthEnd) + 1 < year, "EndOfEvents", val)) %>%
select(-year) %>%
spread(key, val) %>%
arrange(rn)
DeceasedDate LastClinicalEventMonthEnd rn FirstYStage SecondYStage ThirdYStage FourthYStage FifthYStage
1 0.2832192 0.2448630 1 Deceased Deceased Deceased Deceased Deceased
2 1.1267884 1.0363774 2 2 Deceased Deceased Deceased Deceased
3 2.0286530 10.9464612 3 2 2 Deceased Deceased Deceased
4 0.8924658 0.7636986 4 Deceased Deceased Deceased Deceased Deceased
5 NA 3.3501141 5 2 2 2 2 EndOfEvents
6 0.8801370 0.6773972 6 Deceased Deceased Deceased Deceased Deceased
7 NA 3.8368721 7 3.1 3.1 3.1 3.1 EndOfEvents
or without creating a year column:
df %>%
mutate(rn = row_number()) %>%
gather(key, val, ends_with("Stage"), factor_key = TRUE) %>%
mutate(val = if_else(!is.na(DeceasedDate) & floor(DeceasedDate) < as.integer(key),
"Deceased", val)) %>%
mutate(val = if_else(is.na(DeceasedDate) & floor(LastClinicalEventMonthEnd) + 1 < as.integer(key),
"EndOfEvents", val)) %>%
spread(key, val) %>%
arrange(rn)