I am looking how to select rows in R which have partial matches or mismatches in two columns. My dataset (as an example) looks like this:
df = data.frame(plot1 = c("ABX_15", "BHE_05", "ABX_15"),
plot2 = c("AB6_15", "JKS_05", "JKS_05"),
value = c(0.4, 0.45, 0.34))
I want to create subsets containing only "matched" pairs of plot1 and plot2 for _05 and _15. So that would be either the first row or the second row in the example. I also need to select only rows which have a missmatch in plot1 and plot2, that would be row number three. Match an missmatch refer only to the second part of the plot name.
I've found solutions for partial selecting and for selecting certain rows according to columns but I could not combine both.
I am expecting 3 subsets of the dataset: One with matching _05 another with matching _15 and one with missmatches.
Another solution is using sub to strip everything before (and including) the underscore from the two variables and then compare those sub statements with == to create a logical index vector:
idx <- sub('.*\\_', '', df$plot1) == sub('.*\\_', '', df$plot2)
Now you can subset df with that vector. df[idx,] gives:
plot1 plot2 value
1 ABX_15 AB6_15 0.40
2 BHE_05 JKS_05 0.45
To get the mismatches, you can use df[!idx,]:
plot1 plot2 value
3 ABX_15 JKS_05 0.34
Per the update of your requirements, you can create indexes for matching on 15 or 05 as follows:
idx15 <- sub('.*\\_', '', df$plot1) == '15' & sub('.*\\_', '', df$plot2) == '15'
idx05 <- sub('.*\\_', '', df$plot1) == '05' & sub('.*\\_', '', df$plot2) == '05'
These can then be used to subset df as shown above (e.g. df[idx15,]). To get the mismatches: df[!idx05 & !idx15,] (or use the method from above).
Fist spilt the names by the pattern _. I'm using here the function str_split from the stringr package. The result is a list. You can extract now the second part of the name. After unlisting you can add the result to your dataframe df:
df$p1 <- unlist(lapply(str_split(df$plot1, "_"), "[", 2))
df$p2 <- unlist(lapply(str_split(df$plot2, "_"), "[", 2))
For a base R solution you can use strspilt function instead. Note that you have to make a character vector out of it.
unlist(lapply(strsplit(as.character(df$plot1), "_"), "[", 2))
and the result:
df[df$p1 == df$p2, ]
plot1 plot2 value p1 p2
1 ABX_15 AB6_15 0.40 15 15
2 BHE_05 JKS_05 0.45 05 05
For the mismatch use:
df[df$p1 != df$p2, ]
plot1 plot2 value p1 p2
3 ABX_15 JKS_05 0.34 05 15
Related
I have a data frame which looks like this
**Contig_A** **Contig_B**
Contig_0 Contig_1
Contig_3 Contig_5
Contig_4 Contig_1
Contig_9 Contig_0
I want to count how many contig ids (from Contig_0 to Contig_1193) are not present in either Contig_A column of Contig_B.
For example: if we consider there are total 10 contigs here for this data frame (Contig_0 to Contig_9), then the answer would be 4 (Contig_2, Contig_6, Contig_7, Contig_8)
Create a vector of all the values that you want to check (all_contig) which is Contig_0 to Contig_10 here. Use setdiff to find the absent values and length to get the count of missing values.
cols <- c('Contig_A', 'Contig_B')
#If there are lot of 'Contig' columns that you want to consider
#cols <- grep('Contig', names(df), value = TRUE)
all_contig <- paste0('Contig_', 0:10)
missing_contig <- setdiff(all_contig, unlist(df[cols]))
#[1] "Contig_2" "Contig_6" "Contig_7" "Contig_8" "Contig_10"
count_missing <- length(missing_contig)
#[1] 5
by match,
x <- c(0:9)
contigs <- sapply(x, function(t) paste0("Contig_",t))
df1 <- data.frame(
Contig_A = c("Contig_0", "Contig_3", "Contig_4", "Contig_9"),
Contig_B = c("Contig_1", "Contig_5", "Contig_1", "Contig_0")
)
xx <- c(df1$Contig_A,df1$Contig_B)
contigs[is.na(match(contigs, xx))]
[1] "Contig_2" "Contig_6" "Contig_7" "Contig_8"
In your case, just change x as x <- c(0,1193)
I am comparing two csv files using R/Rstudio and I would like to compare them line by line, but in a specific order based on their columns. If my data looks like:
first <-read.csv(text="
name, number, description, version, manufacturer
A123, 12345, first piece, 1.0, fakemanufacturer
B107, 00001, second, 1.0, abcde parts
C203, 20000, third, NA, efgh parts
D123, 12000, another, 2.0, NA")
second csv:
second <- read.csv(text="
name, number, description, version, manufacturer
A123, 12345, first piece, 1.0, fakemanufacturer
B107, 00001, second, 1.0, abcde parts
C203, 20000, third, NA, efgh parts
E456, 45678, third, 2.0, ")
I'd like to have a for loop that looks something like:
for line in csv1:
if number exists in csv2:
if csv1$name == csv2$name:
if csv1$description == csv$description:
if csv1$manufacturer == csv2$manufacturer:
break
else:
add line to csv called changed, append a value for "changed" column to manufacturer
else:
add line to csv called changed, append a value for "changed" column to description
and so on
so that the output then looks like:
name number description version manufacturer changed
A123 12345 first piece 1.0 fakemanufacturer number
B107 00001 second 1.0 abcde parts no change
C204 20000 third newmanufacturer number, manufacturer
D123 12000 another 2.0 removed
E456 45678 third 2.0 added
and if at any point in this loop something doesn't match, I'd like to know where the mismatch was. The lines can match by number OR description. for example, given the 2 lines above, I would be able to tell that number changed between the two csv files. Thanks in advance for any help!!
It should be something like this, but as you have provided no data to test it I cannot vouch for my code:
cmpDF <- function(DF1, DF2){
DF2 <- DF2[DF2$number %in% DF1$number,] #keep only the rows of DF2 that are
#also in DF1
retChar <- character(nrow(DF1))
names(retChar) <- DF1$number #call the retChar vector with the number
# to be able to update it later
DF1 <- DF1[DF1$number %in% DF2$number,]#keep only the rows of DF1 that are
#also in DF2
# sort rows to make sure that equal rows have the same row number:
DF1 <- DF1[order(DF1$number),]
DF2 <- DF2[order(DF2$number),]
equals <- DF1 == DF2
identical <- rowSums(DF1 == DF2) == ncol(DF1) #here all elements are the same
retChar[as.character(DF1$number[identical])] <- "no change"
for(i in 1:ncol(DF1)){
if(colnames(DF1)[i] == "number") next
different <- !equals[,i]
retChar[as.character(DF1$number[different])] <- ifelse(nchar(retChar[as.character(DF1$number[different])]),
paste0(retChar[as.character(DF1$number[different])], colnames(DF1)[i], sep = ", "),
colnames(DF1)[i])
}
retChar[nchar(retChar) == 0] <- "number not in DF2"
return(retChar)
}
I have a list of data-frames called WaFramesCosts. I want to simply subset it to show specific columns so that I can then export them. I have tried:
for (i in names(WaFramesCosts)) {
WaFramesCosts[[i]][,c("Cost_Center","Domestic_Anytime_Min_Used","Department",
"Domestic_Anytime_Min_Used")]
}
but it returns the error of
Error in `[.data.frame`(WaFramesCosts[[i]], , c("Cost_Center", "Department", :
undefined columns selected
I also tried:
for (i in seq_along(WaFramesCosts)){
WaFramesCosts[[i]][ , -which(names(WaFramesCosts[[i]]) %in% c("Cost_Center","Domestic_Anytime_Min_Used","Department",
"Domestic_Anytime_Min_Used"))]
but I get the same error. Can anyone see what I am doing wrong?
Side Note: For reference, I used this:
for (i in seq_along(WaFramesCosts)) {
t <- WaFramesCosts[[i]][ , grepl( "Domestic" , names( WaFramesCosts[[i]] ) )]
q <- subset(WaFramesCosts[[i]], select = c("Cost_Center","Domestic_Anytime_Min_Used","Department","Domestic_Anytime_Min_Used"))
WaFramesCosts[[i]] <- merge(q,t)
}
while attempting the same goal with a different approach and seemed to get closer.
Welcome back, Kootseeahknee. You are still incorrectly assuming that the last command of a for loop is implicitly returned at the end. If you want that behavior, perhaps you want lapply:
myoutput <- lapply(names(WaFramesCosts)), function(i) {
WaFramesCosts[[i]][,c("Cost_Center","Domestic_Anytime_Min_Used","Department","Domestic_Anytime_Min_Used")]
})
The undefined columns selected error tells me that your assumptions of the datasets are not correct: at least one is missing at least one of the columns. From your previous question (How to do a complex edit of columns of all data frames in a list?), I'm inferring that you want columns that match, not assuming that it is in everything. From that, you could/should be using grep or some variant:
myoutput <- lapply(names(WaFramesCosts)), function(i) {
WaFramesCosts[[i]][,grep("(Cost_Center|Domestic_Anytime_Min_Used|Department)",
colnames(WaFramesCosts)),drop=FALSE]
})
This will match column names that contain any of those strings. You can be a lot more precise by ensuring whole strings or start/end matches occur by using regular expressions. For instance, changing from (Cost|Dom) (anything that contains "Cost" or "Dom") to (^Cost|Dom) means anything that starts with "Cost" or contains "Dom"; similarly, (Cost|ment$) matches anything that contains "Cost" or ends with "ment". If, however, you always want exact matches and just need those that exist, then something like this will work:
myoutput <- lapply(names(WaFramesCosts)), function(i) {
WaFramesCosts[[i]][,intersect(c("Cost_Center","Domestic_Anytime_Min_Used","Department"),
colnames(WaFramesCosts)),drop=FALSE]
})
Note, in that last example: notice the difference between mtcars[,2] (returns a vector) and mtcars[,2,drop=FALSE] (returns a data.frame with 1 column). Defensive programming, if you think it at all possible that your filtering will return a single-column, make sure you do not inadvertently convert to a vector by appending ,drop=FALSE to your bracket-subsetting.
Based on your description, this is an example of using library dplyr to achieve combining a list of data frames for a given set of columns. This doesn't require all data frames to have identical columns (Providing your data in a reproducible example would be better)
# test data
df1 = read.table(text = "
c1 c2 c3
a 1 101
b 2 102
", header = TRUE, stringsAsFactors = FALSE)
df2 = read.table(text = "
c1 c2 c3
w 11 201
x 12 202
", header = TRUE, stringsAsFactors = FALSE)
# dfs is a list of data frames
dfs <- list(df1, df2)
# use dplyr::bind_rows
library(dplyr)
cols <- c("c1", "c3")
result <- bind_rows(dfs)[cols]
result
# c1 c3
# 1 a 101
# 2 b 102
# 3 w 201
# 4 x 202
I'm trying to learn R and a sample problem is asking to only reverse part of a string that is in alphabetical order:
String: "abctextdefgtext"
StringNew: "cbatextgfedtext"
Is there a way to identify alphabetical patterns to do this?
Here is one approach with base R based on the patterns showed in the example. We split the string to individual characters ('v1'), use match to find the position of characters with that of alphabet position (letters), get the difference of the index and check if it is equal to 1 ('i1'). Using the logical vector, we subset the vector ('v1'), create a grouping variable and reverse (rev) the vector based on grouping variable. Finally, paste the characters together to get the expected output
v1 <- strsplit(str1, "")[[1]]
i1 <- cumsum(c(TRUE, diff(match(v1, letters)) != 1L))
paste(ave(v1, i1, FUN = rev), collapse="")
#[1] "cbatextgfedtext"
Or as #alexislaz mentioned in the comments
v1 = as.integer(charToRaw(str1))
rawToChar(as.raw(ave(v1, cumsum(c(TRUE, diff(v1) != 1L)), FUN = rev)))
#[1] "cbatextgfedtext"
EDIT:
1) A mistake was corrected based on #alexislaz's comments
2) Updated with another method suggested by #alexislaz in the comments
data
str1 <- "abctextdefgtext"
You could do this in base R
vec <- match(unlist(strsplit(s, "")), letters)
x <- c(0, which(diff(vec) != 1), length(vec))
newvec <- unlist(sapply(seq(length(x) - 1), function(i) rev(vec[(x[i]+1):x[i+1]])))
paste0(letters[newvec], collapse = "")
#[1] "cbatextgfedtext"
Where s <- "abctextdefgtext"
First you find the positions of each letter in the sequence of letters ([1] 1 2 3 20 5 24 20 4 5 6 7 20 5 24 20)
Having the positions in hand, you look for consecutive numbers and, when found, reverse that sequence. ([1] 3 2 1 20 5 24 20 7 6 5 4 20 5 24 20)
Finally, you get the letters back in the last line.
I am struggling a bit with a probably fairly simple task. I wanted to create a function that has arguments of dataframe(df), column names of dataframe(T and R), value of the selected column of dataframe(a and b). I know that the function reads the dataframe. but , I don't know how the columns are selected. I'm getting an error.
fun <- function(df,T,a,R,b)
{
col <- ds[c("x","y")]
omit <- na.omit(col)
data1 <- omit[omit$x == 'a',]
data2 <- omit[omit$x == 'b',]
nrow(data2)/nrow(data1)
}
fun(jugs,Place,UK,Price,10)
I'm new to r language. So, please help me.
There are several errors you're making.
col <- ds[c("x","y")]
What are x and y? Presumably they're arguments that you're passing, but you specify T and R in your function, not x and y.
data1 <- omit[omit$x == 'a',]
data2 <- omit[omit$x == 'b',]
Again, presumably, you want a and b to be arguments you passed to the function, but you specified 'a' and 'b' which are specific, not general arguments. Also, I assume that second "omit$x" should be "omit$y" (or vice versa). And actually, since you just made this into a new data frame with two columns, you can just use the column index.
nrow(data2)/nrow(data1)
You should print this line, or return it. Either one should suffice.
fun(jugs,Place,UK,Price,10)
Finally, you should use quotes on Place, UK, and Price, at least the way I've done it.
fun <- function(df, col1, val1, col2, val2){
new_cols <- df[,c(col1, col2)]
omit <- na.omit(new_cols)
data1 <- omit[omit[,1] == val1,]
data2 <- omit[omit[,2] == val2,]
print(nrow(data2)/nrow(data1))
}
fun(jugs, "Place", "UK", "Price", 10)
And if I understand what you're trying to do, it may be easier to avoid creating multiple dataframes that you don't need and just use counts instead.
fun <- function(df, col1, val1, col2, val2){
new_cols <- df[,c(col1, col2)]
omit <- na.omit(new_cols)
n1 <- sum(omit[,1] == val1)
n2 <- sum(omit[,2] == val2)
print(n2/n1)
}
fun(jugs, "Place", "UK", "Price", 10)
I would write this function as follows:
fun <- function(df,T,a,R,b) {
data <- na.omit(df[c(T,R)]);
sum(data[[R]]==b)/sum(data[[T]]==a);
};
As you can see, you can combine the first two lines into one, because in your code col was not reused anywhere. Secondly, since you only care about the number of rows of the two subsets of the intermediate data.frame, you don't actually need to construct those two data.frames; instead, you can just compute the logical vectors that result from the two comparisons, and then call sum() on those logical vectors, which naturally treats FALSE as 0 and TRUE as 1.
Demo:
fun <- function(df,T,a,R,b) { data <- na.omit(df[c(T,R)]); sum(data[[R]]==b)/sum(data[[T]]==a); };
df <- data.frame(place=c(rep(c('p1','p2'),each=4),NA,NA), price=c(10,10,20,NA,20,20,20,NA,20,20), stringsAsFactors=F );
df;
## place price
## 1 p1 10
## 2 p1 10
## 3 p1 20
## 4 p1 NA
## 5 p2 20
## 6 p2 20
## 7 p2 20
## 8 p2 NA
## 9 <NA> 20
## 10 <NA> 20
fun(df,'place','p1','price',20);
## [1] 1.333333