I'm relatively new to R and I have looked for an answer for my problem but didn't find one. I want to compare two dataframes.
library(dplyr)
library(gtools)
v1 <- LETTERS[1:10]
combinations_from_4_letters <- (as.data.frame(combinations(n = 10, r = 4, v = v1),
stringsAsFactors = FALSE))
combinations_from_4_letters$group <- rep(1:15, each = 14)
combinations_from_2_letters <- (as.data.frame(combinations(n = 10, r = 2, v = v1),
stringsAsFactors = FALSE))
Dataframe 'combinations_from_4_letters' contains all combinations that can be made from 10 letters without repetitions and permutations. The combinations are binned into groups from 1-15. I want to find out how often pairs of the 10 letters (saved in dataframe 'combinations_from_2_letters') are found in each group (basically a frequency table). I started doing a complicated loop looping through both dataframes but I think there must be a more 'R' solution to it, similar to comparing a dataframe and a vector like:
combinations_from_4_letters %in% combinations_from_2_letters[i,])
Thank you in advance for your help!
I recommend an approach like the following:
# adding dummy column for a complete cross-join
combinations_from_4_letters = combinations_from_4_letters %>%
mutate(ones = 1)
combinations_from_2_letters = combinations_from_2_letters %>%
mutate(ones = 1)
joined = combinations_from_2_letters %>%
inner_join(combinations_from_4_letters, by = "ones") %>%
# comparison goes here
mutate(within = ifelse(comb2 %in% comb4, 1, 0)) %>%
group_by(comb2) %>%
summarise(freq = sum(within))
You'll probably need to modify to ensure it matches the exact column names and your comparison condition.
Key ideas:
adding filler column so we have a complete cross-join
mutate a new indicator column for whether the two letter pair is within the four letter pair
sum indicators on the two letter pair
Related
My data is imported into R as a list of 60 tibbles each with 13 columns and 8 rows. I want to detect outliers defined as 2*sd by comparing each value in column "2" to the mean of all values of column "2" in the same row.
I know that I am on a wrong path with these lines, as I am not comparing the single values
lapply(list, function(x){
if(x$"2">(mean(x$"2")) + (2*sd(x$"2"))||x$"2"<(mean(x$"2")) - (2*sd(x$"2"))) {}
})
Also I was hoping to replace all values that are thus identified as outliers by the corresponding mean calculated from the 60 values in the same position as the outlier while keeping everything else, but I am also quite unsure how to do that.
Thank you!
you haven't added an example of your code so I've made a quick and simple example to demonstrate my answer. I think this would be much more straightforward logic if you first combine the list of tibbles into a single tibble. This allows you to do everything you want in a simple dplyr pipe, ultimately identifying outliers by 1's in the 'outlier' column:
library(tidyverse)
tibble1 <- tibble(colA = c(seq(1,20,1), 150),
colB = seq(0.1,2.1,0.1),
id = 1:21)
tibble2 <- tibble(colA = c(seq(101,120,1), -150),
colB = seq(21,41,1),
id = 1:21)
# N.B. if you don't have an 'id' column or equivalent
# then it makes it a lot easier if you add one
# The 'id' column is essentially shorthand for an index
tibbleList <- list(tibble1, tibble2)
joinedTibbles <- bind_rows(tibbleList, .id = 'tbl')
res <- joinedTibbles %>%
group_by(id) %>%
mutate(meanA = mean(colA),
sdA = sd(colA),
lowThresh = meanA - 2*sdA,
uppThresh = meanA + 2*sdA,
outlier = ifelse(colA > uppThresh | colA < lowThresh, 1, 0))
I am trying to merge two dataset on key values and string patterns.
Basically, I would like a function to count the number of sub-string matching occurrences, conditional on other key variables matching across two db.
Across two datasets, base and merge, when the variables k1 and k2 match and merge$str2 is included in base$str I would like a function to count one match. Specifically, I would like whole words match whole words (e.g. "BB" is not a match for "BBB", as if the first was written ad "\bBB\b"). I have multi-million dataset to merge so efficiency is paramount.
I have a possible solution, but I am not sure is the best across very large datasets.
base <- data.frame(k1=rep(1,4),
k2=c(rep(1,3),2),
str=c("AA BBB","BB CCC","CCC","CCC"))
merge <- data.frame(k1=rep(1,2),
k2=rep(1,2),
str2=c("BB","CCC"))
library(stringr)
library(dplyr)
library(tidyr)
base %>%
left_join(merge) %>%
mutate(match=stringi::stri_detect_regex(str, paste0("\\b",str2,"\\b")),
match=replace_na(ifelse(match==T,1,0),0)) %>%
group_by(k1,k2,str) %>%
summarise(matches=sum(match))
desired_result <- data.frame(k1=rep(1,4),
k2=c(rep(1,3),2),
str=c("AA BBB","BB CCC","CCC","CCC"),
match=c(0,2,1,0))
Each time efficiency is an issue, data.table is worth to consider ..
May be this could help you ..
library(data.table)
m <- merge(as.data.table(base),
as.data.table(merge),
by = c("k1", "k2"),
all = TRUE,
allow.cartesian=TRUE)
m[, i := grepl(paste0("\\b", str2,"\\b", sep = ""), str), by = 1:nrow(m)]
m[, .(match = sum(i, na.rm = TRUE)), by = c("k1","k2", "str")]
I'm currently having an issue where I'm trying to nest simulated data for an efficient frontier inside a tibble containing all 250 simulations. The tibble will have 1 column named "sim" which indicates the number of the simulation, i.e. the rows in this column runs from 1:250. The other column should contain the nested simulation data which is a 3x123 tibble for each simulation. (Really hope this makes sense).
I've tried to replicate the problem such that you don't need all of the previous code and data to see the issue. Problem is that the nested data is saved as a list:
library(tidyverse)
counter = 0
table <- tibble(sim = 1:250, obs = NA)
for(i in (1:250)){
counter = counter + 1
tibble <- tibble(a = NA, b = 1:113, c = 2, d = 3)
tibble$a <- counter
nested_tibble <- tibble %>% nest(data = -a) %>% select(-a)
table$obs[i] <- nested_tibble
}
In this simplified reproducible example the values in the tibble are identical. Whereas in the assignment I'm working on, the tibble contains values for the efficient frontier. Variable 'a' in the tibble corresponds to simulation number and this is the variable i use to nest the efficient frontier. Afterwards I wish to remove this variable a, and insert the nested tible in the corresponding 'obs' field currently being NA.
I really hope this makes sense. I'm still very new with R and coding. If you need any additional documentation please let me know.
Your nested_tibble is a list containing a tibble. To access the tibble inside the list, you can use double bracket notation: nested_tibble[[1]]. So to get the result you want you can change your loop as follows:
counter = 0
table <- tibble(sim = 1:250, obs = NA)
for(i in (1:250)){
counter = counter + 1
tibble <- tibble(a = NA, b = 1:113, c = 2, d = 3)
tibble$a <- counter
nested_tibble <- tibble %>% nest(data = -a) %>% select(-a)
table$obs[i] <- nested_tibble[[1]]
}
I am trying to create a table from calculations that I am doing to several text file. I think this might require a loop of some sort, but I am stuck on how to proceed. I have tried different loops but none seem to be working. I have managed to do what I want with one file. Here is my working code:
flare <- read.table("C:/temp/HD3_Bld_CD8_TEM.txt",
header=T)
head(flare[,c(1,2)])
#sum of the freq column, check to see if close to 1
sum(flare$freq)
#Sum of top 10
ten <- sum(flare$freq[1:10])
#Sum of 11-100
to100 <- sum(flare$freq[11:100])
#Sum of 101-1000
to1000 <- sum(flare$freq[101:1000])
#sum of 1001+
rest <- sum(flare$freq[-c(1:1000)])
#place the values of the sum in a table
df <- data.frame(matrix(ncol = 1, nrow = 4))
x <- c("Sum")
colnames(df) <- x
y <- c("10", "11-100", "101-1000", "1000+")
row.names(df) <- y
df[,1] <- c(ten,to100,to1000,rest)
The dataframe ends up looking like this:
>View(df)
Sum
10 0.1745092
11-100 0.2926735
101-1000 0.4211533
1000+ 0.1116640
This is perfect for making a stacked barplot, which I did. However, this is only for one text file. I have several of the same files. All of them have the same column names, so I know that all of them will be using DF$freq column for the calculations. How do I make a table after doing calculations with each file? I want to keep the names of the text files as the sample names so that way when i make a joint stacked barplot all the names will be there. Also, what is the best way to orient the data when writing the new table/dataframe?
I am still new to R, so any help, any explanation would be most welcome. Thank you.
How about something like this, your example is not reproducible so I made a dummy example which you can adjust:
library(tidyverse)
###load ALL your dataframes
test_df_1 <- data.frame(var1 = matrix(c(1,2,3,4,5,6), nrow = 6, ncol = 1))
test_df_1
test_df_2 <- data.frame(var2 = matrix(c(7,8,9,10,11,12), nrow = 6, ncol = 1))
test_df_2
### Bind them into one big wide dataframe
df <- cbind(test_df_1, test_df_2)
### Add an id column which repeats (in your case adjust this to repeat for the grouping you want, i.e replace the each = 2 with each = 10, and each = 4 with each = 100)
df <- df %>%
mutate(id = paste0("id_", c(rep(1, each = 2), rep(2, each = 4))))
### Gather your dataframes into long format by the id
df_gathered <- df %>%
gather(value = value, key = key, - id)
df_gathered
### use group_by to group data by id and summarise to get the sum of each group
df_gathered_sum <- df_gathered %>%
group_by(id, key) %>%
summarise(sigma = sum(value))
df_gathered_sum
You might have some issues with the ID column if your dfs are not equal length so this is only a partial answer. Can do better with a shortened example of your dataset. Can anyone else weigh in on creating an id column? May have sorted it with a couple of edits...
I think I solved it! It gives me the dataframe I want, and from it, I can make the stacked barplot to display the data.
sumfunction <- function(x) {
wow <- read.table(x, header=T)
#Sum of top 10
ten <- sum(wow$freq[1:10])
#Sum of 11-100
to100 <- sum(wow$freq[11:100])
#Sum of 101-1000
to1000 <- sum(wow$freq[101:1000])
#sum of 1001+
rest <- sum(wow$freq[-c(1:1000)])
blah <- c(ten,to100,to1000,rest)
}
library(data.table)
library(tools)
dir = "C:/temp/"
filenames <- list.files(path = dir, pattern = "*.txt", full.names = FALSE)
alltogether <- lapply(filenames, function(x) sumfunction(x))
data <- as.data.frame(data.table::transpose(alltogether),
col.names =c("Top 10 ", "From 11 to 100", "From 101 to 1000", "From 1000 on "),
row.names = file_path_sans_ext(basename(filenames)))
This gives me the dataframe that I want. I instead of putting the "top 10, 11-100, 101-1000, 1000+" as the row names, I changed them to column names and instead made the names of each text file become the row names. The file_path_sans_ext(basename(filenames)) makes sure to just keep the file name and remove the extension.
I hope this helps anyone that reads this! thank you again! I love this platform because just being part of this environment gets me thinking and always striving to better myself at R.
If anyone has any input, that would be great!!! <3
I would like to convert a dataframe filled with frequencies into a dataframe filled with percentage by row using dplyr.
My data set has the particularity to get filled with others variables and I just want to calculate the percentage for a set of columns defined by a vector of names. Plus, I want to use the dplyr library.
sim_dat <- function() abs(floor(rnorm(26)*10))
df <- data.frame(a = letters, b = sim_dat(), c = sim_dat(), d = sim_dat()
, z = LETTERS)
names_to_transform <- names(df)[2:4]
df2 <- df %>%
mutate(sum_freq_codpos = rowSums(.[names_to_transform])) %>%
mutate_each(function(x) x / sum_freq_codpos, names_to_transform)
# does not work
Any idea on how to do it? I have tried with mutate_at and mutate_each but I can't get it to work.
you're almost there!:
df2 <- df %>%
mutate(sum_freq_codpos = rowSums(.[names_to_transform])) %>%
mutate_at(names_to_transform, funs(./sum_freq_codpos))
the dot . roughly translates to "the object i am manipulating here", which in this call is "the focal variable in names_to_transform".