I'm new to C++ and exploring faster computation possibilities on R through the Rcpp package. The actual dataframe contains over ~2 million rows, and is quite slow.
Existing Dataframes
Main Dataframe
df<-data.frame(z = c("a","b","c"), a = c(303,403,503), b = c(203,103,803), c = c(903,803,703))
Cost Dataframe
cost <- data.frame("103" = 4, "203" = 5, "303" = 6, "403" = 7, "503" = 8, "603" = 9, "703" = 10, "803" = 11, "903" = 12)
colnames(cost) <- c("103", "203", "303", "403", "503", "603", "703", "803", "903")
Steps
df contains z which is a categorical variable with levels a, b and c. I had done a merge operation from another dataframe to bring in a,b,c into df with the specific nos.
First step would be to match each row in z with the column names (a,b or c) and create a new column called 'type' and copy the corresponding number.
So the first row would read,
df$z[1] = "a"
df$type[1]= 303
Now it must match df$type with column names in another dataframe called 'cost' and create df$cost. The cost dataframe contains column names as numbers e.g. "103", "203" etc.
For our example, df$cost[1] = 6. It matches df$type[1] = 303 with cost$303[1]=6
Final Dataframe should look like this - Created a sample output
df1 <- data.frame(z = c("a","b","c"), type = c("303", "103", "703"), cost = c(6,4,10))
A possible solution, not very elegant but does the job:
library(reshape2)
tmp <- cbind(cost,melt(df)) # create a unique data frame
row.idx <- which(tmp$z==tmp$variable) # row index of matching values
col.val <- match(as.character(tmp$value[row.idx]), names(tmp) ) # find corresponding values in the column names
# now put all together
df2 <- data.frame('z'=unique(df$z),
'type' = tmp$value[row.idx],
'cost' = as.numeric(tmp[1,col.val]) )
the output:
> df2
z type cost
1 a 303 6
2 b 103 4
3 c 703 10
see if it works
Related
I am using a list of files, and I am trying to create a data frame that contains: for each sample, the percentage of two particular "GT" types by the levels of another factor variable called "chr" (with 1 to 24 levels).
It would have to look like this:
The problem I keep getting is that the vector never gets updated for the ith sample, it only keeps the first vector created. And then I am not sure how to save that updated vector on my data frame (df).
vector_chr <- vector();
for (i in seq_along(list_files)) {
GT <- list_files[[i]][,9]
chr <- list_files[[i]][,3]
GT$chr <- chr$chr # creating one df with both GT and chr
for (j in unique(GT$chr)){
dat_list = split(GT, GT$chr) # split data frames by chr (1 to 24)
table <- table(dat_list[[j]][,1]) # take GT and make a table
sum <- sum(table[3:4]) # sum GTs 3 and 4
perc <- sum/nrow(GT)
vector_chr <- c(vector_chr,perc) # assign the 24 percentages to a vector
}
df <- data.frame(matrix(ncol = 25, nrow = length(files)))
x <- c("Sample", "chr1", "chr2", "chr3",
"chr4", "chr5", "chr6", "chr7", "chr8", "chr9", "chr10",
"chr11", "chr12","chr13", "chr14", "chr15", "chr16",
"chr17", "chr18", "chr19", "chr20", "chr21", "chr22",
"chrX", "chrXY")
colnames(df) <- x
df$Sample <- names(list_files)
df[i,2:25] <- vector_chr # assign the 24 percentages for EACH sample
}
Sample data:
df <- data.frame(noun1 = c("cat","dog"), noun2 = c("apple", "tree"))
noun1 noun2
1 cat apple
2 dog tree
How can I make a new column df$alpha that would read FALSE in row 1 and TRUE in row 2?
Thank you!
I think you can just apply is.unsorted() to each row, although you have to unlist it first (probably).
df <- data.frame(noun1 = c("cat","dog"), noun2 = c("apple", "tree"))
df$alpha <- apply(df,1,function(x) !is.unsorted(unlist(x)))
I found is.unsorted() via apropos("sort").
I already loaded 20 csv files with function:
tbl = list.files(pattern="*.csv")
list_of_data = lapply(tbl, read.csv)
I combined all of those filves into one:
all_data = do.call(rbind.fill, list_of_data)
In the new table is a column called "Accession". After combining many of the names (Accession) are repeated. And I would like to remove all of the duplicates.
Another problem is that some of those "names" are ALMOST the same. The difference is that there is name and after become the dot and the number.
Let me show you how it looks:
AT3G26450.1 <--
AT5G44520.2
AT4G24770.1
AT2G37220.2
AT3G02520.1
AT5G05270.1
AT1G32060.1
AT3G52380.1
AT2G43910.2
AT2G19760.1
AT3G26450.2 <--
<-- = Same sample, different names. Should be treated as one. So just ignore dot and a number after.
Tried this one:
all_data$CleanedAccession = str_extract(all_data$Accession, "^[[:alnum:]]+")
all_data = subset(all_data, !duplicated(CleanedAccession))
Error in `$<-.data.frame`(`*tmp*`, "CleanedAccession", value = character(0)) :
You can use this command to both subset and rename the values:
subset(transform(alldata, Ascension = sub("\\..*", "", Ascension)),
!duplicated(Ascension))
Ascension
1 AT3G26450
2 AT5G44520
3 AT4G24770
4 AT2G37220
5 AT3G02520
6 AT5G05270
7 AT1G32060
8 AT3G52380
9 AT2G43910
10 AT2G19760
What about
df <- data.frame( Accession = c("AT3G26450.1",
"AT5G44520.2",
"AT4G24770.1",
"AT2G37220.2",
"AT3G02520.1",
"AT5G05270.1",
"AT1G32060.1",
"AT3G52380.1",
"AT2G43910.2",
"AT2G19760.1",
"AT3G26450.2"))
df[!duplicated(unlist(lapply(strsplit(as.character(df$Accession),
".", fixed = T), "[", 1))), ]
I have a dataframe with 2 columns GL and GLDESC and want to add a 3rd column called KIND based on some data that is inside of column GLDESC.
The dataframe is as follows:
GL GLDESC
1 515100 Payroll-Indir Salary Labor
2 515900 Payroll-Indir Compensated Absences
3 532300 Bulk Gas
4 539991 Area Charge In
5 551000 Repairs & Maint-Spare Parts
6 551100 Supplies-Operating
7 551300 Consumables
For each row of the data table:
If GLDESC contains the word Payroll anywhere in the string then I want KIND to be Payroll
If GLDESC contains the word Gas anywhere in the string then I want KIND to be Materials
In all other cases I want KIND to be Other
I looked for similar examples on stackoverflow but could not find any, also looked in R for dummies on switch, grep, apply and regular expressions to try and match only part of the GLDESC column and then fill the KIND column with the kind of account but was unable to make it work.
Since you have only two conditions, you can use a nested ifelse:
#random data; it wasn't easy to copy-paste yours
DF <- data.frame(GL = sample(10), GLDESC = paste(sample(letters, 10),
c("gas", "payroll12", "GaSer", "asdf", "qweaa", "PayROll-12",
"asdfg", "GAS--2", "fghfgh", "qweee"), sample(letters, 10), sep = " "))
DF$KIND <- ifelse(grepl("gas", DF$GLDESC, ignore.case = T), "Materials",
ifelse(grepl("payroll", DF$GLDESC, ignore.case = T), "Payroll", "Other"))
DF
# GL GLDESC KIND
#1 8 e gas l Materials
#2 1 c payroll12 y Payroll
#3 10 m GaSer v Materials
#4 6 t asdf n Other
#5 2 w qweaa t Other
#6 4 r PayROll-12 q Payroll
#7 9 n asdfg a Other
#8 5 d GAS--2 w Materials
#9 7 s fghfgh e Other
#10 3 g qweee k Other
EDIT 10/3/2016 (..after receiving more attention than expected)
A possible solution to deal with more patterns could be to iterate over all patterns and, whenever there is match, progressively reduce the amount of comparisons:
ff = function(x, patterns, replacements = patterns, fill = NA, ...)
{
stopifnot(length(patterns) == length(replacements))
ans = rep_len(as.character(fill), length(x))
empty = seq_along(x)
for(i in seq_along(patterns)) {
greps = grepl(patterns[[i]], x[empty], ...)
ans[empty[greps]] = replacements[[i]]
empty = empty[!greps]
}
return(ans)
}
ff(DF$GLDESC, c("gas", "payroll"), c("Materials", "Payroll"), "Other", ignore.case = TRUE)
# [1] "Materials" "Payroll" "Materials" "Other" "Other" "Payroll" "Other" "Materials" "Other" "Other"
ff(c("pat1a pat2", "pat1a pat1b", "pat3", "pat4"),
c("pat1a|pat1b", "pat2", "pat3"),
c("1", "2", "3"), fill = "empty")
#[1] "1" "1" "3" "empty"
ff(c("pat1a pat2", "pat1a pat1b", "pat3", "pat4"),
c("pat2", "pat1a|pat1b", "pat3"),
c("2", "1", "3"), fill = "empty")
#[1] "2" "1" "3" "empty"
I personally like matching by index. You can loop grep over your new labels, in order to get the indices of your partial matches, then use this with a lookup table to simply reassign the values.
If you wanna create new labels, use a named vector.
DF <- data.frame(GL = sample(10), GLDESC = paste(sample(letters, 10),
c(
"gas", "payroll12", "GaSer", "asdf", "qweaa", "PayROll-12",
"asdfg", "GAS--2", "fghfgh", "qweee"
), sample(letters, 10),
sep = " "
))
lu <- stack(sapply(c(Material = "gas", Payroll = "payroll"), grep, x = DF$GLDESC, ignore.case = TRUE))
DF$KIND <- DF$GLDESC
DF$KIND[lu$values] <- as.character(lu$ind)
DF$KIND[-lu$values] <- "Other"
DF
#> GL GLDESC KIND
#> 1 6 x gas f Material
#> 2 3 t payroll12 q Payroll
#> 3 5 a GaSer h Material
#> 4 4 s asdf x Other
#> 5 1 m qweaa y Other
#> 6 10 y PayROll-12 r Payroll
#> 7 7 g asdfg a Other
#> 8 2 k GAS--2 i Material
#> 9 9 e fghfgh j Other
#> 10 8 l qweee p Other
Created on 2021-11-13 by the reprex package (v2.0.1)
I have a list like df_all (see below).
A = matrix( ceiling(10*runif(8)), nrow=4)
colnames(A) = c("country", "year_var")
dfa = data.frame(A)
df1 = dfa[1,]
df2 = dfa[2,]
df3 = dfa[3,]
df4 = dfa[4,]
df_all = list(df1, df2, df3, df4)
df_all
Now I want to combine the list of interest by using variable a.
a <- "2,3,4"
b <- strsplit(a, ",")[[1]]
To combine this lists, I use the folling loop:
for (i in 1:length(b)){
c<-b[i]
aa <- df_all[c:c]
print(aa)
}
Now my question is, How can I combine this result and save this as as variable?
Thanks!
Would this work for you:
basnum<-as.integer(b)
do.call(rbind, df_all[basnum])
Through df_all[basnum], a list with only the relevant data.frames is created.
do.call takes a function and a list as parameters (and some more but not relevant right now). The items of the list are then passed on as parameters to the function.
So in this case, the above is the equivalent to calling:
rbind(df_all[[2]], df_all[[3]], df_all[[4]])
And this produces one data.frame holding all the rows of interest.