I have two df that look something like this
library(tidyverse)
iris <- iris%>% mutate_at((1:4),~.+2)
iris2 <- iris
names(iris2)<-sub(".", "_", names(iris2), fixed = TRUE)
My aim is to reduce the values of the variables in iris
that are above the maximum values of the corresponding variable in iris2
, to match the maximum value in iris2
.
I have written a function that does this.
max(iris$Sepal.Length)
[1] 9.9
max(iris2$Sepal_Length)
[1] 7.9
# i want every value of iris that is >= to max value of iris2 to be equal to the max value of iris 2.
# my function:
fixmax<- function(data,data2,var1,var2) {
data<- data %>%
mutate("{var1}" := ifelse(get(var1)>=max(data2[[var2]],na.rm = T),
max(data2[[var2]],na.rm = T),get(var1)))
return(data)
}
# apply my function to a variable
tst_iris <- fixmax(iris,iris2,"Sepal.Length","Sepal_Length")
max(tst_iris$Sepal.Length)
7.9 # it works!
The challange I face is that I would like to iterate my function sequentially overtwo lists of variables- i.e. Sepal.Length
with Sepal_Length
, Sepal.Width
with Sepal_Width
etc.
Does anyone knows how I can do this?
I tried using Map
but I am doing something wrong.
lst1 <- names(iris[,1:4])
lst2 <- names(iris2[,1:4])
final_iris<- Map(fixmax,iris, iris2,lst1,lst2)
My goal is to obtain a df (final_iris
) where every variable has been adjusted using the criteria specified by fixmax
.
I know I can do this by running my function on every variable like so.
final_iris <- iris
final_iris <- fixmax(final_iris,iris2,"Sepal.Length","Sepal_Length")
final_iris <- fixmax(final_iris,iris2,"Sepal.Width","Sepal_Width")
final_iris <- fixmax(final_iris,iris2,"Petal.Length","Petal_Length")
final_iris <- fixmax(final_iris,iris2,"Petal.Width","Petal_Width")
But in the real data, I have to run this operation tens of times and I would like to be able to loop my function sequentially.
Does anyone know how I loop my fixmax
over lst1
and lst2
sequentially?
Loop can be used to iterate over a list, data frame, vector, matrix or any other object. The braces and square bracket are compulsory. R will loop over all the variables in vector and do the computation written inside the exp. Let’s see a few examples. Example 1: We iterate over all the elements of a vector and print the current value.
For Loop in R with Examples for List and Matrix. A for loop is very valuable when we need to iterate over a list of elements or a range of numbers. Loop can be used to iterate over a list, data frame, vector, matrix or any other object. The braces and square bracket are compulsory.
For Loop over a matrix. A matrix has 2-dimension, rows and columns. To iterate over a matrix, we have to define two for loop, namely one for the rows and another for the column. # Create a matrix mat <- matrix (data = seq (10, 20, by=1), nrow = 6, ncol =2) # Create the loop with r and c to iterate over the matrix for (r in 1:nrow (mat)) ...
However, it would also be possible to loop through a list with a while-loop or a repeat-loop. Have a look at the following video of my YouTube channel. I explain the examples of this tutorial in the video.
Rather than explicitly iterating over the different datasets and columns by name, you can take advantage of the vectorization built into R
. If the dataframes have the same column/variable ordering a function mapped to both dataframes using mapply
or purrr::map2
will iterate column by column without the need to specify column names.
Given two input data frames (df_small
and df_big
) the steps are:
df_small
to create df_small_max
pmin
function to each column of df_big
and each value of df_small_max
using mapply
(or purr::map2_dfc
if you prefer tidyverse
mapping)#set up fake data
df_small <- iris[,1:4]
df_big <- df_small + 2
# find max of each col in df_small
df_small_max <- sapply(df_small, max)
# replace values of df_big which are larger than df_small_max
df_big_fixed <- mapply(pmin, df_big, df_small_max)
# sanity check -- Note the change in Sepal.Width
df_small_max
#> Sepal.Length Sepal.Width Petal.Length Petal.Width
#> 7.9 4.4 6.9 2.5
head(df_big, 3)
#> Sepal.Length Sepal.Width Petal.Length Petal.Width
#> 1 7.1 5.5 3.4 2.2
#> 2 6.9 5.0 3.4 2.2
#> 3 6.7 5.2 3.3 2.2
head(df_big_fixed, 3)
#> Sepal.Length Sepal.Width Petal.Length Petal.Width
#> [1,] 7.1 4.4 3.4 2.2
#> [2,] 6.9 4.4 3.4 2.2
#> [3,] 6.7 4.4 3.3 2.2
Created on 2021-07-31 by the reprex package (v2.0.0)
It's likely that your issue is related to the fact that dataframes are themselves lists. Map()
expects the non-function arguments to be lists of the same length. Any arguments that are shorter than the longest list are "recycled" to match it's length.
Currently, you have:
final_iris<- Map(fixmax,iris, iris2,lst1,lst2)
This is actually equivalent to:
final_iris<- Map(fixmax,
list(iris$Sepal.Length,
iris$Sepal.Width,
iris$Petal.Length,
iris$Petal.Width,
iris$Species),
list(iris2$Sepal_Length,
iris2$Sepal_Width,
iris2$Petal_Length,
iris2$Petal_Width,
iris2$Species),
lst1,
lst2)
I suspect that you want iris
and iris2
to be supplied to each call to fixmax()
. In order to have Map()
recycle them like this, they need to be single-element lists. That is you probably want:
final_iris<- Map(fixmax, list(iris), list(iris2),lst1,lst2)
To combined a list of dataframes into a single dataframe do
do.call(rbind, final_iris)
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