I want to update a dataframe with values from a table of new values where there is a one-to-many relationship between the dataframe and table of new values. This code illustrates the intent:
df = data.frame(x=rep(letters[1:4],5,rep=T), y=1:20)
and new values..
eds = data.frame(x=c('c','d'), val=c(101, 102))
For a one-to-one relationship the following should work:
df$x[match(eds$x, df$x)] = eds$x[match(df$x, eds$x)]
But match only works with first match, so this throws the error number of items to replace is not a multiple of replacement length. Grateful for any tips on the most efficient way to approach this. I'm guessing some sapply wrapper but I can't think of the method.
Thanks in advance.
tmp <- eds$val[match(df$x, eds$x)] # Matching indices (with NAs for no match)
df$y <- ifelse(is.na(tmp), df$y, tmp) # Values at matches (leaving alone for NAs)
head(df, 5)
# x y
# 1 a 1
# 2 b 2
# 3 c 101
# 4 d 102
# 5 a 5
Not that this not a very robust solution. It depends on your exact data structure here (repeating 'c', 'd' pattern) but it works for this case:
df[df[["x"]] %in% eds[["x"]], "y"] = eds[[2]]
Related
In R, I want to get unique, closest matches for the rows in a data.table which are identified by unique ids based on values in two columns. Here, I provide a toy example and the code I'm using to achieve this.
dt <- data.table(id = letters,
value_1 = as.integer(runif(26,1,20)),
value_2 = as.integer(runif(26,1,10)))
pairs <- data.table()
while(nrow(dt) >= 2){
k <- dt[c(1)]
m <- dt[-1]
t <- m[k, roll = "nearest",on = .(value_1,value_2)]
pairs <- rbind(pairs,t)
dt <- dt[!dt$id %in% pairs$id & !dt$id %in% pairs$i.id]
}
pairs <- pairs[,-c(2,3)]
This gives me a data.table with the matched ids and the ones that do not get any matches.
id i.id
1 NA a
2 NA b
3 m c
4 v d
5 y e
6 i f
...
Is there a way to do this without the loop. I intend to implement this on a data.table with more than 20 million observations? Clearly, using a loop is extremely inefficient. I was wondering if the roll join command can be run on a copy of the main data.table by introducing an exception condition -- so as not to match the same ids with each other. Maybe something like this:
m <- dt
t <- m[dt, roll = "nearest",on = .(value_1,value_2)]
Without the exception, this command merely generates matches of ids with themselves. Also, this does not ensure unique matches.
Thanks!
I need to standardize how subgroups are referred to in a data set. To do this I need to identify when a variable matches one of several strings and then set a new variable with the standardized name. I am trying to do that with the following:
df <- data.frame(a = c(1,2,3,4), b = c(depression_male, depression_female, depression_hsgrad, depression_collgrad))
TestVector <- "male"
for (i in TestVector) {
df$grpl <- grepl(paste0(i), df$b)
df[ which(df$grpl == TRUE),]$standard <- "male"
}
The test vector will frequently have multiple elements. The grepl works (I was going to deal with the male/female match confusion later but I'll take suggestions on that) but the subsetting and setting a new variable doesn't. It would be better (and work) if I could transform the grepl output directly into the standard name variable.
Your only real issue is that you need to initialize the standard column. But we can simplify your code a bit:
df <- data.frame(a = c(1,2,3,4), b = c("depression_male", "depression_female", "depression_hsgrad", "depression_collgrad"))
TestVector <- "male"
df$standard <- NA
for (i in TestVector) {
df[ grepl(i, df$b), "standard"] <- "male"
}
df
# a b standard
# 1 1 depression_male male
# 2 2 depression_female male
# 3 3 depression_hsgrad <NA>
# 4 4 depression_collgrad <NA>
Then you've got the issue that the "male" pattern matches "female" as well.
Perhaps you're looking for sub instead? It works like find/replace:
df$standard = sub(pattern = "depression_", replacement = "", df$b)
df
# a b standard
# 1 1 depression_male male
# 2 2 depression_female female
# 3 3 depression_hsgrad hsgrad
# 4 4 depression_collgrad collgrad
It's hard to generalize what will be best in your case without more example input/output pairs. If all your data is of the form "depression_" this will work well. Or maybe the standard name is always after an underscore, so you could use pattern = ".*_" to replace everything before the last underscore. Or maybe something else... Hopefully these ideas give you a good start.
I want to write a function in R that does the following:
I have a table of cases, and some data. I want to find the correct row matching to each observation from the data. Example:
crit1 <- c(1,1,2)
crit2 <- c("yes","no","no")
Cases <- matrix(c(crit1,crit2),ncol=2,byrow=FALSE)
data1 <- c(1,2,1)
data2 <- c("no","no","yes")
data <- matrix(c(data1,data2),ncol=2,byrow=FALSE)
Now I want a function that returns for each row of my data, the matching row from Cases, the result would be the vector
c(2,3,1)
Are you sure you want to be using matrices for this?
Note that the numeric data in crit1 and data1 has been converted to string (matrices can only store one data type):
typeof(data[ , 1L])
# [1] character
In R, a data.frame is a much more natural choice for what you're after. data.table is (among many other things) a toolset for working with "enhanced" data.frames; See the Introduction.
I would create your data as:
Cases = data.table(crit1, crit2)
data = data.table(data1, data2)
We can get the matching row indices as asked by doing a keyed join (See the vignette on keys):
setkey(Cases) # key by all columns
Cases
# crit1 crit2
# 1: 1 no
# 2: 1 yes
# 3: 2 no
setkey(data)
data
# data1 data2
# 1: 1 no
# 2: 1 yes
# 3: 2 no
Cases[data, which=TRUE]
# [1] 1 2 3
This differs from 2,3,1 because the order of your data has changed, but note that the answer is still correct.
If you don't want to change the order of your data, it's slightly more complicated (but more readable if you're not used to data.table syntax):
Cases = data.table(crit1, crit2)
data = data.table(data1, data2)
Cases[data, on = setNames(names(data), names(Cases)), which=TRUE]
# [1] 2 3 1
The on= part creates the mapping between the columns of data and those of Cases.
We could write this in a bit more SQL-like fashion as:
Cases[data, on = .(crit1 == data1, crit2 == data2), which=TRUE]
# [1] 2 3 1
This is shorter and more readable for your sample data, but not as extensible if your data has many columns or if you don't know the column names in advance.
The prodlim package has a function for that:
library(prodlim)
row.match(data,Cases)
[1] 2 3 1
Another question for me as a beginner. Consider this example here:
n = c(2, 3, 5)
s = c("ABBA", "ABA", "STING")
b = c(TRUE, "STING", "STRING")
df = data.frame(n,s,b)
n s b
1 2 ABBA TRUE
2 3 ABA STING
3 5 STING STRING
How can I search within this dataframe for similar strings, i.e. ABBA and ABA as well as STING and STRING and make them the same (doesn't matter whether ABBA or ABA, either fine) that would not require me knowing any variations? My actual data.frame is very big so that it would not be possible to know all the different variations.
I would want something like this returned:
> n = c(2, 3, 5)
> s = c("ABBA", "ABBA", "STING")
> b = c(TRUE, "STING", "STING")
> df = data.frame(n,s,b)
> print(df)
n s b
1 2 ABBA TRUE
2 3 ABBA STING
3 5 STING STING
I have looked around for agrep, or stringdist, but those refer to two data.frames or are able to name the column which I can't since I have many of those.
Anyone an idea? Many thanks!
Best regards,
Steffi
This worked for me but there might be a better solution
The idea is to use a recursive function, special, that uses agrepl, which is the logical version of approximate grep, https://www.rdocumentation.org/packages/base/versions/3.4.1/topics/agrep. Note that you can specify the 'error tolerance' to group similar strings with agrep. Using agrepl, I split off rows with similar strings into x, mutate the s column to the first-occurring string, and then add a grouping variable grp. The remaining rows that were not included in the ith group are stored in y and recursively passed through the function until y is empty.
You need the dplyr package, install.packages("dplyr")
library(dplyr)
desired <- NULL
grp <- 1
special <- function(x, y, grp) {
if (nrow(y) < 1) { # if y is empty return data
return(x)
} else {
similar <- agrepl(y$s[1], y$s) # find similar occurring strings
x <- rbind(x, y[similar,] %>% mutate(s=head(s,1)) %>% mutate(grp=grp))
y <- setdiff(y, y[similar,])
special(x, y, grp+1)
}
}
desired <- special(desired,df,grp)
To change the stringency of string similarity, change max.distance like agrepl(x,y,max.distance=0.5)
Output
n s b grp
1 2 ABBA TRUE 1
2 3 ABBA STING 1
3 5 STING STRING 2
To remove the grouping variable
withoutgrp <- desired %>% select(-grp)
I am trying to train a data that's converted from a document term matrix to a dataframe. There are separate fields for the positive and negative comments, so I wanted to add a string to the column names to serve as a "tag", to differentiate the same word coming from the different fields - for example, the word hello can appear both in the positive and negative comment fields (and thus, represented as a column in my dataframe), so in my model, I want to differentiate these by making the column names positive_hello and negative_hello.
I am looking for a way to rename columns in such a way that a specific string will be appended to all columns in the dataframe. Say, for mtcars, I want to rename all of the columns to have "_sample" at the end, so that the column names would become mpg_sample, cyl_sample, disp_sample and so on, which were originally mpg, cyl, and disp.
I'm considering using sapplyor lapply, but I haven't had any progress on it. Any help would be greatly appreciated.
Use colnames and paste0 functions:
df = data.frame(x = 1:2, y = 2:1)
colnames(df)
[1] "x" "y"
colnames(df) <- paste0('tag_', colnames(df))
colnames(df)
[1] "tag_x" "tag_y"
If you want to prefix each item in a column with a string, you can use paste():
# Generate sample data
df <- data.frame(good=letters, bad=LETTERS)
# Use the paste() function to append the same word to each item in a column
df$good2 <- paste('positive', df$good, sep='_')
df$bad2 <- paste('negative', df$bad, sep='_')
# Look at the results
head(df)
good bad good2 bad2
1 a A positive_a negative_A
2 b B positive_b negative_B
3 c C positive_c negative_C
4 d D positive_d negative_D
5 e E positive_e negative_E
6 f F positive_f negative_F
Edit:
Looks like I misunderstood the question. But you can rename columns in a similar way:
colnames(df) <- paste(colnames(df), 'sample', sep='_')
colnames(df)
[1] "good_sample" "bad_sample" "good2_sample" "bad2_sample"
Or to rename one specific column (column one, in this case):
colnames(df)[1] <- paste('prefix', colnames(df)[1], sep='_')
colnames(df)
[1] "prefix_good_sample" "bad_sample" "good2_sample" "bad2_sample"
You can use setnames from the data.table package, it doesn't create any copy of your data.
library(data.table)
df <- data.frame(a=c(1,2),b=c(3,4))
# a b
# 1 1 3
# 2 2 4
setnames(df,paste0(names(df),"_tag"))
print(df)
# a_tag b_tag
# 1 1 3
# 2 2 4