I'm an R beginner and have a basic question I can't seem to figure out. I have row values that need to be separated into different columns, but there are more than one delimiter I am trying to use. The Expression_level column contains an ensembl gene ID with its corresponding value, in the form ensembl:exp value, but there are sometimes 2 ensembl IDs in the same row separated by ;. I want to have a column for ensembl and for gene expression value, but not sure how to separate while keeping them mapped to the correct ID/expression value. This is the type of data I am working with: rna_seq and this is what I am trying to get out: org_rna. TYIA
rna_seq= cbind("Final_gene" = c("KLHL15", "CPXCR1", "MAP7D3", "WDR78"), "Expression_level" = c("1.62760683812965:ENSG00000174010", "-9.96578428466209:ENSG00000147183",
"-4.32192809488736:ENSG00000129680", "-1.39592867633114:ENSG00000152763;-9.96578428466209:ENSG00000231080"))
org_rna = cbind("Final_gene" = c("KLHL15", "CPXCR1", "MAP7D3", "WDR78", "WDR78"), "Ensembl" = c("ENSG00000174010", "ENSG00000147183", "ENSG00000129680", "ENSG00000152763", "ENSG00000231080")
, "Expression" = c("1.62760683812965", "-9.96578428466209", "-4.32192809488736", "-1.39592867633114", "-9.96578428466209"))
library(tidyr)
library(dplyr)
rna_seq %>%
as.data.frame() %>%
# separate cells containing multiple values into
# multiple rows
separate_rows(Expression_level, sep = ";") %>%
# extract pairs
extract(col = Expression_level,
into = c("Expression", "Ensembl"),
regex = "(.*):(.*)")
# A tibble: 5 x 3
# Final_gene Expression Ensembl
# <chr> <chr> <chr>
# KLHL15 1.62760683812965 ENSG00000174010
# CPXCR1 -9.96578428466209 ENSG00000147183
# MAP7D3 -4.32192809488736 ENSG00000129680
# WDR78 -1.39592867633114 ENSG00000152763
# WDR78 -9.96578428466209 ENSG00000231080
Another (less elegant) solution using separate():
library(tidyr)
library(dplyr)
rna_seq |> as.data.frame() |>
# Separate any second IDs
separate(Expression_level, sep = ";", into = c("ID1", "ID2")) |>
# Reshape to longer (columns to rows)
pivot_longer(cols = starts_with("ID")) |>
# Separate Expression from Ensembl
separate(value, sep = ":", into = c("Expression", "Ensembl")) |>
filter(!is.na(Expression)) |>
select(Final_gene, Ensembl, Expression)
Related
Consider the following data frame with two columns of strings of variable length:
library("tidyverse")
df <- tibble(REF = c("TTG", "CGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGT", "T", "TTGTGTGTGTGTGTGTGTGTGT"),
ALT = c("T", "CGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGT,CGTGTGTGTGTGTGTGTGTGTGTGTGTGTGT", "TTG", "TTGTGTGTGTGTGTGTGTGTGTGT"))
# # A tibble: 4 × 2
# REF ALT
# <chr> <chr>
# 1 TTG T
# 2 CGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGT CGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGT,CGTGTGTGTGTGTGTGTGTGTGTGTGTGTGT
# 3 T TTG
# 4 TTGTGTGTGTGTGTGTGTGTGT TTGTGTGTGTGTGTGTGTGTGTGT
Differently from column REF, column ALT sometimes includes several strings concatenated by comma (e.g. row 2).
I want to compare the length of strings in REF (REF_LEN) and ALT (ALT_LEN), and generate a TYPE column with values:
"SNM" when REF_LEN = ALT_LEN
"INS" when REF_LEN < ALT_LEN
"DEL" when REF_LEN > ALT_LEN
But I want to do it in a way that, when several strings are present in ALT, the output of this new TYPE column contains these types as well separated by a comma. i.e., the expected output here would be:
"DEL" "INS,DEL" "INS" "INS"
So far, I know how to get the length of values in ALT, but I fail at collapsing these values, as the output will contain lengths from all ALTs in the table, not just pairwise (i.e. 1,35,31,3,24):
df %>%
dplyr::mutate(REF_LEN = str_length(REF),
ALT_LEN = str_split(ALT, ","),
ALT_LEN = purrr::map(ALT_LEN, str_length) %>% unlist() %>% paste(collapse = ","))
Code above is incomplete as you can see, but I am also unable to work in a different direction using a helper function that gets the TYPE column above done. This will return many errors, but not sure why, since it seems to work nicely with values from ALT_LEN individually:
name <- function(alt_lens, ref_len) {
alt_lens <- unlist(alt_lens)
ifelse(alt_lens < ref_len, "DEL", ifelse(alt_lens > ref_len, "INS", "SNM"))
}
df %>%
dplyr::mutate(REF_LEN = str_length(REF),
ALT_LEN = str_split(ALT, ","),
TYPE = purrr::map(ALT_LEN, str_length) %>% name(REF_LEN))
Any ideas? thanks!
Here's a codegolf-ish base R solution :
df <- data.frame(REF = c("TTG", "CGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGT", "T", "TTGTGTGTGTGTGTGTGTGTGT"),
ALT = c("T", "CGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGT,CGTGTGTGTGTGTGTGTGTGTGTGTGTGTGT", "TTG", "TTGTGTGTGTGTGTGTGTGTGTGT"))
df$TYPE <- mapply(
function(x, y) paste(c("INS", "SNM", "DEL")[2 + sign(nchar(x)- nchar(y))], collapse = ","),
df$REF, strsplit(df$ALT, ","), USE.NAMES = FALSE)
df$TYPE
#> [1] "DEL" "INS,DEL" "INS" "INS"
Created on 2022-04-20 by the reprex package (v2.0.1)
Update: Removed first answer. Thanks to akrun for pointing me there!. The concept is the same: using nchar with case_when, the difference is to use separate_rows from tidyr package:
library(dplyr)
library(tidyr)
df %>%
mutate(id = row_number()) %>%
separate_rows(ALT, sep = ",") %>%
mutate(TYPE = case_when(nchar(REF)==nchar(ALT) ~ "SNM",
nchar(REF)< nchar(ALT) ~ "INS",
nchar(REF)> nchar(ALT) ~ "DEL",
TRUE ~ NA_character_)) %>%
group_by(id) %>%
mutate(TYPE = toString(TYPE)) %>%
slice(1)
REF ALT id TYPE
<chr> <chr> <int> <chr>
1 TTG T 1 DEL
2 CGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGT CGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGT 2 INS, DEL
3 T TTG 3 INS
4 TTGTGTGTGTGTGTGTGTGTGT TTGTGTGTGTGTGTGTGTGTGTGT 4 INS
I need to change this function that doesn't match for unique values. For example, if I want MAPK4, the function matches MAPK41 and AMAPK4 etc. The function must select only the unique values.
Function:
library(dplyr)
df2 <- df %>%
rowwise() %>%
mutate(mutated = paste(mutated_genes[unlist(
lapply(mutated_genes, function(x) grepl(x,genes, ignore.case = T)))], collapse=","),
circuit_name = gsub("", "", circuit_name)) %>%
select(-genes) %>%
data.frame()
data:
df <-structure(list(circuit_name = c("hsa04010__117", "hsa04014__118" ), genes = c("MAP4K4,DUSP10*,DUSP10*,DUSP10*,DUSP10*,DUSP10*,DUSP10*,DUSP10*,DUSP10*,DUSP10*,DUSP10*,DUSP3*,DUSP3*,DUSP3*,DUSP3*,PPM1A,AKT3,AKT3,AKT3,ZAK,MAP3K12,MAP3K13,TRAF2,CASP3,IL1R1,IL1R1,TNFRSF1A,IL1A,IL1A,TNF,RAC1,RAC1,RAC1,RAC1,MAP2K7,MAPK8,MAPK8,MAPK8,MECOM,HSPA1A,HSPA1A,HSPA1A,HSPA1A,HSPA1A,HSPA1A,MAP4K3,MAPK8IP2,MAP4K1", "MAP4K4,DUSP10*,DUSP10*,DUSP10*,DUSP10*,DUSP10*")), class = "data.frame", row.names = c(NA, -2L))
mutated_genes <- c("MAP4K4", "MAP3K12","TRAF2", "CACNG3")
output:
circuit_name mutated
1 hsa04010__117 MAP4K4,TRAF2
2 hsa04014__118 MAP4K4
A base R approach would be by splitting the genes on "," and return those string which match mutated_genes.
df$mutated <- sapply(strsplit(df$genes, ","), function(x)
toString(grep(paste0(mutated_genes, collapse = "|"), x, value = TRUE)))
df[c(1, 3)]
# circuit_name mutated
#1 hsa04010__117 MAP4K4, MAP3K12, TRAF2
#2 hsa04014__118 MAP4K4
Please note that based on the mutated_genes vector, your expected output is missing MAP3K12 for hsa04010__117.
Here is a tidyverse possibility
df %>%
separate_rows(genes) %>%
filter(genes %in% mutated_genes) %>%
group_by(circuit_name) %>%
summarise(mutated = toString(genes))
## A tibble: 2 x 2
# circuit_name mutated
# <chr> <chr>
#1 hsa04010__117 MAP4K4, MAP3K12, TRAF2
#2 hsa04014__118 MAP4K4
Explanation: We separate comma-separated entries into different rows, then select only those rows where genes %in% mutated_genes and summarise results per circuit_name by concatenating genes entries.
PS. Personally I'd recommend keeping the data in a tidy long format (i.e. don't concatenate entries with toString); that way you have one row per gene, which will make any post-processing of the data much more straightforward.
We can use str_extract
library(stringr)
df$mutated <- sapply(str_extract_all(df$genes, paste(mutated_genes,
collapse="|")), toString)
In R, I'm trying to aggregate a dataframe based on unique IDs, BUT I need to use some kind of wild card value for the IDs. Meaning I have paired names like this:
lion_tiger
elephant_lion
tiger_lion
And I need the lion_tiger and tiger_lion IDs to be summed together, because the order in the pair does not matter.
Using this dataframe as an example:
df <- data.frame(pair = c("1_3","2_4","2_2","1_2","2_1","4_2","3_1","4_3","3_2"),
value = c("12","10","19","2","34","29","13","3","14"))
So the values for pair IDs, "1_2" and "2_1" need to be summed in a new table. That new row would then read:
1_2 36
Any suggestions? While my example has numbers as the pair IDs, in reality I would need this to read in text (like the lion_tiger" example above).
We can split the 'pair' column by _, then sort and paste it back, use it in a group by function to get the sum
tapply(as.numeric(as.character(df$value)),
sapply(strsplit(as.character(df$pair), '_'), function(x)
paste(sort(as.numeric(x)), collapse="_")), FUN = sum)
Or another option is gsubfn
library(gsubfn)
df$pair <- gsubfn('([0-9]+)_([0-9]+)', ~paste(sort(as.numeric(c(x, y))), collapse='_'),
as.character(df$pair))
df$value <- as.numeric(as.character(df$value))
aggregate(value~pair, df, sum)
Using tidyverse and purrrlyr
df <- data.frame(name=c("lion_tiger","elephant_lion",
"tiger_lion"),value=c(1,2,3),stringsAsFactors=FALSE)
require(tidyverse)
require(purrrlyr)
df %>% separate(col = name, sep = "_", c("A", "B")) %>%
by_row(.collate = "rows",
..f = function(this_row) {
paste0(sort(c(this_row$A, this_row$B)), collapse = "_")
}) %>%
rename(sorted = ".out") %>%
group_by(sorted) %>%
summarize(sum(value))%>%show
## A tibble: 2 x 2
# sorted `sum(value)`
# <chr> <dbl>
#1 elephant_lion 2
#2 lion_tiger 4
I need some help with finding a good way to dynamically add columns with counts for different categories that I need to extract from a string.
In my data, I have a column that contains names of categories and counts thereof. The fields can be empty or contain any combination of categories one can think of. Here are some examples:
themes:firstcategory_1;secondcategory_33;thirdcategory_5
themes:secondcategory_33;fourthcategory_2
themes:fifthcategory_1
What I need is a column for each category (should have the category's name) and the count extracted from the strings above. The list of categories is dynamic, so I don't know beforehand which ones exist.
How do I approach this?
This code will get a column for each category with the counts for each row.
library(dplyr)
library(tidyr)
library(stringr)
# Create test dataframe
df <- data.frame(themes = c("firstcategory_1;secondcategory_33;thirdcategory_5", "secondcategory_33;fourthcategory_2","fifthcategory_1"), stringsAsFactors = FALSE)
# Get the number of columns to split values into
cols <- max(str_count(df$themes,";")) + 1
# Get vector of temporary column names
cols <- paste0("col",c(1:cols))
df <- df %>%
# Add an ID column based on row number
mutate(ID = row_number()) %>%
# Separate multiple categories by semicolon
separate(col = themes, into = cols, sep = ";", fill = "right") %>%
# Gather categories into a single column
gather_("Column", "Value", cols) %>%
# Drop temporary column
select(-Column) %>%
# Filter out NA values
filter(!is.na(Value)) %>%
# Separate categories from their counts by underscore
separate(col = Value, into = c("Category","Count"), sep = "_", fill = "right") %>%
# Spread categories to create a column for each category, with the count for each ID in that category
spread(Category, Count)
I'm new in programming in R, and I've been having this problem for several days now. I started with a list, I created from splitting a file. This list contains a number of items in a single row.
head(sales2)
> $`7143443`
>>[1] "SSS-U-CCXVCSS1" "L-CCX-8GETTS-LIC"
>$`7208993`
>>[1] "NFFGSR4=" "1MV-FT-1=" "VI-NT/TE="
>$`7241758`
>>[1] "PW_SQSGG="
>$`9273628`
>>[1] "O1941-SE9" "CCO887VA-K9" "2901-SEC/K9" "CO1941-C/K9"
>$`9371709`
>>[1] "HGR__SASS=" "WWQTTB0S-L" "WS-RRRT48FP" "WTTTF24PS-L"
[5] "GEDQTT8TS-L" "WD-SRNS-2S-L"
>$`9830473`
>>[1] "SPA$FFSB0S"
I wanted it to convert into a data frame , I used
x<-do.call(rbind, lapply(sales2,data.frame))
It gets converted in the data frame ,but it converts like this
> head(x,6)
id
> 7143443.1 "SSS-U-CCXVCSS1"
> 7143443.2 "L-CCX-8GETTS-LIC"
> 7208993.1 "NFFGSR4="
> 7208993.2 "1MV-FT-1="
> 7208993.3 "VI-NT/TE="
> 7241758 "PW_SQSGG="
I want 7143443's all item in a single row not in multiple row
Through this I want to calculate how many rows contain 2 items together
for example "WS-C2960S-48TS-L" , "WS-C2960S-24TS-L",
these 2 elements are there in how many rows?
You can also say probability of these over all other elements.
I am not sure what is your final desired output. But the following script can convert your list to a data frame. Perhaps you can begin your analysis from this data frame.
# Create example list
sales2 <- list(`7143443` = c("SSS-U-CCXVCSS1", "L-CCX-8GETTS-LIC"),
`7208993` = c("NFFGSR4=", "1MV-FT-1=", "VI-NT/TE="),
`7241758` = "PW_SQSGG=",
`9273628` = c("O1941-SE9", "CCO887VA-K9", "2901-SEC/K9", "CO1941-C/K9"),
`9371709` = c("HGR__SASS=", "WWQTTB0S-L", "WS-RRRT48FP", "WTTTF24PS-L",
"GEDQTT8TS-L", "WD-SRNS-2S-L"),
`9830473` = "SPA$FFSB0S")
# Load packages
library(dplyr)
library(purrr)
dat <- map(sales2, data_frame) %>% # Convert each list element to a data frame
bind_rows(.id = "ID") %>% # Combine all data frame
rename(Value = `.x[[i]]`) %>% # Change the name of the second column
group_by(ID) %>% # Group by the first column
summarise(Value = paste0(Value, collapse = " ")) # Collapse the second column
dat
# A tibble: 6 × 2
ID Value
<chr> <chr>
1 7143443 SSS-U-CCXVCSS1 L-CCX-8GETTS-LIC
2 7208993 NFFGSR4= 1MV-FT-1= VI-NT/TE=
3 7241758 PW_SQSGG=
4 9273628 O1941-SE9 CCO887VA-K9 2901-SEC/K9 CO1941-C/K9
5 9371709 HGR__SASS= WWQTTB0S-L WS-RRRT48FP WTTTF24PS-L GEDQTT8TS-L WD-SRNS-2S-L
6 9830473 SPA$FFSB0S
Update
After reading original poster's comment, I decided to update my solution, to count how many rows contain two specified string patterns.
Here one row is a unique ID. So I assume that the request can be rephrased to "How many IDs contain two specified string patterns?" If this is the case, I would prefer not to collapse all the observations. Because after collapsing all observations to from one ID per row, we need to develop a strategy to match the string, such as using the regular expression. I am not familiar with regular string, so I will leave this for others to provide solutions.
In addition, the original poster did not specify which two strings are the targeted, so I would develop a strategy that the users can replace the targeted string case by case.
dat <- map(sales2, data_frame) %>% # Convert each list element to a data frame
bind_rows(.id = "ID") %>% # Combine all data frame
rename(Value = `.x[[i]]`) # Change the name of the second column
# After this, there is no need to collapse the rows
# Set the target string, User can change the strings here
target_string1 <- c("SSS-U-CCXVCSS1", "L-CCX-8GETTS-LIC")
dat2 <- dat %>%
filter(Value %in% target_string1) %>% # Filter rows matching the targeted string
distinct(ID, Value, .keep_all = TRUE) %>% # Only keep one row if ID and Value have exact duplicated
count(ID) %>% # Count how many rows per ID
filter(n > 1) %>% # Keep only ID that the Count number is larger than 1
select(ID)
dat2
# A tibble: 1 × 1
ID
<chr>
1 7143443