Difficulty combining lists, characters, and numbers into data frame

Question

I'm lost on how to combine my data into a usable data frame. I have a list of lists of character and number vectors Here is a working example of my code so far:

remove(list=ls())

# Headers for each of my column names
headers <- c("name","p","c","prophylaxis","control","inclusion","exclusion","conversion excluded","infection criteria","age criteria","mean age","age sd")

#_name = author and year
#_p = no. in experimental arm.
#_c = no. in control arm
#_abx = antibiotic used
#_con = control used
#_inc = inclusion criteria
#_exc = exclusion criteria
#_coexc = was conversion to open excluded?
#_infxn = infection criteria
#_agecrit = age criteria
#_agemean = mean age of study
#_agesd = sd age of study

# Passos 2016
passos_name <- c("Passos","2016")
passos_p <- 50
passos_c <- 50
passos_abx <- "cefazolin 1g at induction"
passos_con <- "none"
passos_inc <- c("elective LC","symptomatic cholelithiasis","low risk")
passos_exc <- c("renal impairment","hepatic impairment","immunosuppression","regular steroid use","antibiotics within 48H","acute cholecystitis","choledocolithiasis")
passos_coexc <- TRUE
passos_infxn <- c("temperature >37.8C","tachycardia","asthenia","local pain","local purulence")
passos_agecrit <- NULL
passos_agemean <- 48
passos_agesd <- 13.63
passos <- list(passos_name,passos_p,passos_c,passos_abx,passos_con,passos_inc,passos_exc,passos_coexc,passos_infxn,passos_agecrit,passos_agemean,passos_agesd)
names(passos) <- headers

# Darzi 2016
darzi_name <- c("Darzi","2016")
darzi_p <- 182
darzi_c <- 247
darzi_abx <- c("cefazolin 1g 30min prior to induction","cefazolin 1g 6H after induction","cefazolin 1g 12H after induction")
darzi_con <- "NaCl"
darzi_inc <- c("elective LC","first time abdominal surgery") 
darzi_exc <- c("antibiotics within 7 days","immunosuppression","acute cholecystitis","choledocolithiasis","cholangitis","obstructive jaundice",
              "pancreatitis","previous biliary tract surgery","previous ERCP","DM","massive intraoperative bleeding","antibiotic allergy","major thalassemia",
              "empyema")
darzi_coexc <- TRUE
darzi_infxn <- c("temperature >38C","local purulence","intra-abdominal collection")
darzi_agecrit <- c(">18", "<75")
darzi_agemean <- 43.75
darzi_agesd <- 13.30
darzi <- list(darzi_name,darzi_p,darzi_c,darzi_abx,darzi_con,darzi_inc,darzi_exc,darzi_coexc,darzi_infxn,darzi_agecrit,darzi_agemean,darzi_agesd)
names(darzi) <- headers

# Matsui 2014
matsui_name <- c("Matsui","2014")
matsui_p <- 504
matsui_c <- 505
matsui_abx <- c("cefazolin 1g at induction","cefazolin 1g 12H after induction","cefazolin 1g 24H after induction")
matsui_con <- "none"
matsui_inc <- "elective LC"
matsui_exc <- c("emergent","concurrent surgery","regular insulin use","regular steroid use","antibiotic allergy","HD","antibiotics within 7 days","hepatic impairment","chemotherapy")
matsui_coexc <- FALSE
matsui_infxn <- c("local purulence","intra-abdominal collection","distant infection","temperature >38C")
matsui_agecrit <- ">18"
matsui_agemean <- NULL
matsui_agesd <- NULL
matsui <- list(matsui_name,matsui_p,matsui_c,matsui_abx,matsui_con,matsui_inc,matsui_exc,matsui_coexc,matsui_infxn,matsui_agecrit,matsui_agemean,matsui_agesd)
names(matsui) <- headers

# Find unique exclusion critieria in order to create the list of all possible levels
exc <- ls()[grepl("_exc",ls())]
exclist <- sapply(exc,get)
exc.levels <- unique(unlist(exclist,use.names = F))

# Find unique inclusion critieria in order to create the list of all possible levels
inc <- ls()[grepl("_inc",ls())]
inclist <- sapply(inc,get)
inc.levels <- unique(unlist(inclist,use.names = F))

# Find unique antibiotics order to create the list of all possible levels
abx <- ls()[grepl("_abx",ls())]
abxlist <- sapply(abx,get)
abx.levels <- unique(unlist(abxlist,use.names = F))

# Find unique controls in order to create the list of all possible levels
con <- ls()[grepl("_con",ls())]
conlist <- sapply(con,get)
con.levels <- unique(unlist(conlist,use.names = F))

# Find unique age critieria in order to create the list of all possible levels
agecrit <- ls()[grepl("_agecrit",ls())]
agecritlist <- sapply(agecrit,get)
agecrit.levels <- unique(unlist(agecritlist,use.names = F))

I have been struggling with:

1) Turn each of the _exc, _inc, _abx, _con, _agecrit lists into factors using the levels generated at the end of the code block. I have been trying to use a for loop such as:

for (x in exc) {
    as.name(x) <- factor(get(x),levels = exc.levels)
}

This only creates a variable, x, that stores the last parsed list as a factor.

2) Combine all of my data into a data frame formatted as such:

name, p, c, prophylaxis, control, inclusion, exclusion, conversion excluded, infection criteria, age criteria, mean age, age sd
"Passos 2016", 50, 50, "cefazolin 1g at induction", "none", ["elective LC","symptomatic cholelithiasis","low risk"], ["renal impairment","hepatic impairment","immunosuppression","regular steroid use","antibiotics within 48H","acute cholecystitis","choledocolithiasis"], TRUE, ["temperature >37.8C","tachycardia","asthenia","local pain","local purulence"], NULL, 48, 13.63
...
# [] = factors
# columns correspond to each studies variables (i.e. passos_name, passos_p, passos_c, etc..)
# rows correspond to each study (i.e., passos, darzi, matsui)

I have tried various solutions on StackOverflow, but have not found any that work; for example:

studies <- list(passos,darzi,matsui,ruangsin,turk,naqvi,hassan,sharma,uludag,yildiz,kuthe,koc,maha,tocchi,higgins,mahmoud,kumar)
library(data.table)
rbindlist(lapply(studies,as.data.frame.list))

I suspect my data may not be exactly amenable to a data frame? Primarily because of trying to store a list of factors in a column. Is that allowed? If not, how is this type of data normally stored? My goal is to be able to meaningfully compare these various criterion across studies.

Answer 1

This is too long for a comment, so I turn it into an "answer":

To start with, have a look at what happens here:

data.frame(name = "Passos, 2016", p = 50)
          name  p
1 Passos, 2016 50

data.frame(name = c("Passos", "2016"), p = 50)
    name  p
1 Passos 50
2   2016 50

In the first one, we created a dataframe with the column "name" which contained one entry "Passos, 2016", i.e. one character containing both pieces of information, and the column "p". All fine. Now, in the second version, I specified the column "name" as you did above, using c(Passos, 2016). This is a two-element vector, and hence we get two rows in the dataframe: one with name Passos, one with name 2016, and the column p gets recycled.

Clearly, the latter is probably not what you intended. But it works anyway because R just recycles the shorter vector. Now, what do you think happens if I add a vector that contains three elements?

And this highlights the main issue with what you are doing: you are trying to get a dataframe from many vectors with different lengths. Now, in some cases this is fine if you want the shorter vector to be repeated (in R speech, we call this "recycled"), but it does not look like something you want to do here.

So, my recommendation would be this: try to imagine a matrix and make sure you understand what each element (row and column) is supposed to be. Then specify your data accordingly. If in doubt, look up "tidy data".