I'm trying to adapt the inner join feature of the fuzzyjoin library.
The code:
JoinedRecs <- DataToUse1 %>%
stringdist_inner_join(DataToUse2, by = c(Full.Name1 = "Full.Name2"), max_dist = 2)
seems to work when I hard-code the variables in the "by = " clause.
However, I want to use variables, where:
Column1 <- "Full.Name1"
Column2 <- "Full.Name2"
I've tried a number of variations on possible syntax, but I always get the same error message:
Error: Must group by variables found in .data.
Column col is not found.
If someone could inform me what the right code is for "by = " clause using variables rather than hard-coding the names, I would be ever-so grateful.
Thanks!
We can use setNames to create a named vector in by
library(fuzzyjoin)
JoinedRecs <- DataToUse1 %>%
stringdist_inner_join(DataToUse2,
by = setNames(Column2, Column1), max_dist = 2)
-reproducible example
> iris2 <- data.frame(Species2 = 'setosa', value = 1)
> Column1 <- 'Species'
> Column2 <- 'Species2'
> stringdist_inner_join(head(iris), iris2,
by = setNames(Column2, Column1), max_dist = 2)
Sepal.Length Sepal.Width Petal.Length Petal.Width Species Species2 value
1 5.1 3.5 1.4 0.2 setosa setosa 1
2 4.9 3.0 1.4 0.2 setosa setosa 1
3 4.7 3.2 1.3 0.2 setosa setosa 1
4 4.6 3.1 1.5 0.2 setosa setosa 1
5 5.0 3.6 1.4 0.2 setosa setosa 1
6 5.4 3.9 1.7 0.4 setosa setosa 1
Related
I want to use the str_detectfunction passing a variable as the first argument. Meaning this could theoretically look something like this.
# create the variable
var = names(mtcars)[1]
mtcars %>%
mutate(
new_var = case_when(str_detect(var, "^2"), "two", "other")
)
Now I'm not sure how to insert the variable var correctly into the str_detect function. I guess some tidy-eval is necessary, but I'm not sure....
using mtcars as an exmaple for string manipulation is not very helpful, so switching over to iris. Also, your case_when specification was wrong, so I'm using if_else for this example.
You can use !!(sym(var))
library(tidyverse)
var <- "Species"
iris %>%
mutate(
new_var = if_else(str_detect(!!sym(var), "set"), "two", "other")
)
Sepal.Length Sepal.Width Petal.Length Petal.Width Species new_var
1 5.1 3.5 1.4 0.2 setosa two
2 4.9 3.0 1.4 0.2 setosa two
3 4.7 3.2 1.3 0.2 setosa two
4 4.6 3.1 1.5 0.2 setosa two
5 5.0 3.6 1.4 0.2 setosa two
6 5.4 3.9 1.7 0.4 setosa two
I am using dplyr v1.0.2 to manipulate tibbles. I would like to use group_by(), using a function or a regular expression to specify the relevant variable names (the ... argument). The only solution that I've found is clunky. Is there a relatively simple way?
Here is a minimal example that demonstrates the problem:
library(dplyr)
data(iris)
iris[, -(rbinom(1, 1, .5) + 1) ] %>% # randomly drop "Sepal.Length" or "Sepal.Width"
group_by(matches("^Sepal\\."))
In the third line, I randomly drop one of the two "Sepal" columns. In the last line, I want to group by the remaining "Sepal" column. The problem is that I don't know its name: it could be either "Sepal.Length" or "Sepal.Width." And the group_by() command in the last line doesn't work: it predictably returns a matches() must be used within a *selecting* function error message.
By contrast, this code works, but it is a bit clunky:
iris[, -(rbinom(1, 1, .5) + 1) ] %>%
group_by(!!as.name(grep('Sepal', colnames(.), val = TRUE)))
Is there a simpler way to do the grouping on the second line?
What about using across to select the columns
iris[, -(rbinom(1, 1, .5) + 1) ] %>%
group_by(across(starts_with('Sepal')))
# A tibble: 150 x 4
# Groups: Sepal.Length [35]
Sepal.Length Petal.Length Petal.Width Species
<dbl> <dbl> <dbl> <fct>
1 5.1 1.4 0.2 setosa
2 4.9 1.4 0.2 setosa
3 4.7 1.3 0.2 setosa
4 4.6 1.5 0.2 setosa
5 5 1.4 0.2 setosa
6 5.4 1.7 0.4 setosa
7 4.6 1.4 0.3 setosa
8 5 1.5 0.2 setosa
9 4.4 1.4 0.2 setosa
10 4.9 1.5 0.1 setosa
# … with 140 more rows
Say I have a massive dataframe and in multiple columns I have an extremely large list of unique codes and I want to use these codes to select certain rows to subset the original dataframe. There are around 1000 codes and the codes I want all follow after each other. For example I have about 30 columns that contain codes and I only want to take rows that have codes 100 to 120 in ANY of these columns .
There's a long way to do this which is something like
new_dat <- df[which(df$codes==100 | df$codes==101 | df$codes1==100
and I repeat this for every single possible code for everyone of the columns that can contain these codes. Is there a way to do this in a more convenient fashion?
I want to try solving this with dplyr's select function, but I'm having trouble seeing if it works for my case out of the box
Take the iris dataset
Say I wanted all rows that contain the value 4.0-5.0 in any columns that contains the word Sepal in the column name.
#this only goes for 4.0
brand_new_df <- select(filter(iris, Sepal.Length ==4.0 | Sepal.Width == 4.0))
but what I want is something like
brand_new_df <- select(filter(iris, contains(Sepal) == 4.0:5.0))
Is there a dplyr way to do this?
A corresponding across() version from #RonakShah's answer:
library(dplyr)
iris %>% filter(rowSums(across(contains('Sepal'), ~ between(., 4, 5))) > 0)
or
iris %>% filter(rowSums(across(contains('Sepal'), between, 4, 5)) > 0)
From vignette("colwise"):
Previously, filter() was paired with the all_vars() and any_vars() helpers. Now, across() is equivalent to all_vars(), and there’s no direct replacement for any_vars().
So you need something like rowSums(...) > 0 to achieve the effect of any_vars().
You can use filter_at :
library(dplyr)
iris %>% filter_at(vars(contains('Sepal')), any_vars(between(., 4, 5)))
# Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#1 4.9 3.0 1.4 0.2 setosa
#2 4.7 3.2 1.3 0.2 setosa
#3 4.6 3.1 1.5 0.2 setosa
#4 5.0 3.6 1.4 0.2 setosa
#5 4.6 3.4 1.4 0.3 setosa
#6 5.0 3.4 1.5 0.2 setosa
#7 4.4 2.9 1.4 0.2 setosa
#....
Base R:
# Subset:
cols <- grep("codes", names(df2), value = TRUE)
df2[rowSums(sapply(cols,
function(x) {
df2[, x] >= 100 & df2[, x] <= 120
})) == length(cols), ]
# Data:
tmp <- data.frame(x1 <- rnorm(999, mean = 100, sd = 2))
df <-
setNames(data.frame(tmp[rep(1, each = 80)]), paste0("codes", 1:80))
df2 <- cbind(id = 1:nrow(df), df)
One option could be:
iris %>%
filter(Reduce(`|`, across(contains("Sepal"), ~ between(.x, 4, 5))))
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 4.9 3.0 1.4 0.2 1
2 4.7 3.2 1.3 0.2 1
3 4.6 3.1 1.5 0.2 1
4 5.0 3.6 1.4 0.2 1
5 4.6 3.4 1.4 0.3 1
6 5.0 3.4 1.5 0.2 1
7 4.4 2.9 1.4 0.2 1
8 4.9 3.1 1.5 0.1 1
9 4.8 3.4 1.6 0.2 1
10 4.8 3.0 1.4 0.1 1
library(dplyr)
df <- iris
# value to look for
val <- 4
# find columns
cols <- which(colSums(df == val , na.rm = TRUE) > 0L)
# filter rows
iris %>% filter_at(cols, any_vars(.==val))
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.8 4.0 1.2 0.2 setosa
2 5.5 2.3 4.0 1.3 versicolor
3 6.0 2.2 4.0 1.0 versicolor
4 6.1 2.8 4.0 1.3 versicolor
5 5.5 2.5 4.0 1.3 versicolor
6 5.8 2.6 4.0 1.2 versicolor
I am building a function to change the column names of 3 columns and make a new data frame with 3 column. The file name is noaaFilename, and Date, HrMn, and Slp were earlier column names and new names I want as Date, Time, AtmosPressure.
names(noaaFilename)[names(noaaFilename) == "Date"] <- "Date"
names(noaaFilename)[names(noaaFilename) == "HrMn"] <- "Time"
names(noaaFilename)[names(noaaFilename) == "Slp"] <- "AtmosPressure"
noaaData <- subset(noaaFilename, select = c(Date, Time, AtmosPressure))
mysubset <- function(df, oldnames, newnames){
if(length(oldnames)!=length(newnames)){
stop("oldnames and newnames are not the same length")
}
if(!all(oldnames%in%colnames(df)){
stop("Not all of oldnames match column names of df")
}
df <- df[,oldnames, drop = F]
colnames(df) <- newnames
return(df)
}
An example with the iris data set.
head(iris)
# Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#1 5.1 3.5 1.4 0.2 setosa
#2 4.9 3.0 1.4 0.2 setosa
#3 4.7 3.2 1.3 0.2 setosa
#4 4.6 3.1 1.5 0.2 setosa
#5 5.0 3.6 1.4 0.2 setosa
#6 5.4 3.9 1.7 0.4 setosa
tmp <- mysubset(iris,
oldnames = c("Sepal.Length","Sepal.Width","Species"),
newnames = c("Date","Time","AtmosPressure"))
head(tmp)
# Date Time AtmosPressure
#1 5.1 3.5 setosa
#2 4.9 3.0 setosa
#3 4.7 3.2 setosa
#4 4.6 3.1 setosa
#5 5.0 3.6 setosa
#6 5.4 3.9 setosa
Writing the function like this makes it so you don't only need to specify 3 columns.
noaaData <- subset(noaaFilename, select = c(Date, HrMn, Slp))
names(noaaData) <- c("Date", "Time", "AtmosPressure")
this should probably be quite straightforward, but I am struggling to get it to work. I currently have a vector of column names:
columns <- c('product1', 'product2', 'product3', 'support4')
I now want to use dplyr in a for loop to mutate some columns, but I am struggling to make it recognize that it is a column name, not a variable.
for (col in columns) {
cross.sell.val <- cross.sell.val %>%
dplyr::mutate(col = ifelse(col == 6, 6, col)) %>%
dplyr::mutate(col = ifelse(col == 5, 6, col))
}
Can I use %>% in these situations? Thanks..
You should be able to do this without using a for loop at all.
Because you didn't provide any data, I am going to use the builtin iris dataset. The top of it looks like:
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.1 3.5 1.4 0.2 setosa
2 4.9 3.0 1.4 0.2 setosa
3 4.7 3.2 1.3 0.2 setosa
4 4.6 3.1 1.5 0.2 setosa
5 5.0 3.6 1.4 0.2 setosa
6 5.4 3.9 1.7 0.4 setosa
First, I am saving the columns to analyze:
columns <- names(iris)[1:4]
Then, use mutate_at for each column, along with that particular rule. In each, the . represents the vector for each column. Your example implies that the rules are the same for each column, though if that is not the case, you may need more flexibility here.
mod_iris <-
iris %>%
mutate_at(columns, funs(ifelse(. > 5, 6, .))) %>%
mutate_at(columns, funs(ifelse(. < 1, 1, .)))
returns:
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 6.0 3.5 1.4 1 setosa
2 4.9 3.0 1.4 1 setosa
3 4.7 3.2 1.3 1 setosa
4 4.6 3.1 1.5 1 setosa
5 5.0 3.6 1.4 1 setosa
6 6.0 3.9 1.7 1 setosa
If you wanted to, you could instead write a function to make all of your changes for the column. This could also allow you to set the cutoffs differently for each column. For example, you may want to set the bottom and top portions of the data to be equal to that threshold (to reign in outliers for some reason), or you may know that each variable uses a dummy value as a placeholder (and that value is different by column, but is always the most common value). You could easily add in any arbitrary rule of interest this way, and it gives you a bit more flexibility than chaining together separate rules (e.g., if you use the mean, the mean changes when you change some of the values).
An example function:
modColumns <- function(x){
botThresh <- quantile(x, 0.25)
topThresh <- quantile(x, 0.75)
dummyVal <- as.numeric(names(sort(table(x)))[1])
dummyReplace <- NA
x <- ifelse(x < botThresh, botThresh, x)
x <- ifelse(x > topThresh, topThresh, x)
x <- ifelse(x == dummyVal, dummyReplace, x)
return(x)
}
And in use:
iris %>%
mutate_at(columns, modColumns) %>%
head
returns:
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.1 3.3 1.6 0.3 setosa
2 5.1 3.0 1.6 0.3 setosa
3 5.1 3.2 1.6 0.3 setosa
4 5.1 3.1 1.6 0.3 setosa
5 5.1 3.3 1.6 0.3 setosa
6 5.4 3.3 1.7 0.4 setosa