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Joining list of data frames in R


Joining list of data frames in R

By : user2954129
Date : November 22 2020, 01:01 AM
Hope this helps Moving some comments to the correct place (answers), the two most common solutions would be:
code :
c(list_1, list_2)
append(list_1, list_2)
list(list_1, list_2)
unlist(list(list_1, list_2), recursive = FALSE)


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Assign column names of data.frames in a list of data.frames to other (Spatial) data.frames in a list of data.frames in R

Assign column names of data.frames in a list of data.frames to other (Spatial) data.frames in a list of data.frames in R


By : Charles T.
Date : March 29 2020, 07:55 AM
Hope that helps You had the right idea - I changed your function a bit and it should work now:
code :
changeCOL <- function(x, y){
  names(y) <- names(x)
  return(y)
}

test<-mapply(changeCOL,x=list_DF,y=listofSpatialDF)

# Test to show the names are the same now
names(test[[1]])==names(list_DF[[1]])
[1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
Is there a dplyr or data.table equivalent to plyr::join_all? Joining by a list of data frames?

Is there a dplyr or data.table equivalent to plyr::join_all? Joining by a list of data frames?


By : Ulfath Sadad Ali Mir
Date : March 29 2020, 07:55 AM
I think the issue was by ths following , Combining @SimonOHanlon's data.table method with @Jaap's Reduce and merge techniques appears to yield the most performant results:
code :
library(data.table)
setDT(df)
count_x_dt <- function(dt) dt[, list(count_x = .N), keyby = x]
sum_y_dt   <- function(dt) dt[, list(sum_y = sum(y)), keyby = x]
mean_y_dt  <- function(dt) dt[, list(mean_y = mean(y)), keyby = x]

Reduce(function(...) merge(..., all = TRUE, by = c("x")), 
       list(count_x_dt(df), sum_y_dt(df), mean_y_dt(df)))
library(tidyverse)
list(count_x(df), sum_y(df), mean_y(df)) %>% 
  reduce(left_join) 
Joining list of data.frames from map() call

Joining list of data.frames from map() call


By : Victor Briones
Date : March 29 2020, 07:55 AM
like below fixes the issue Is there a "tidyverse" way to join a list of data.frames (a la full_join(), but for >2 data.frames)? I have a list of data.frames as a result of a call to map(). I've used Reduce() to do something like this before, but would like to merge them as part of a pipeline - just haven't found an elegant way to do that. Toy example: , We can use Reduce
code :
set.seed(24)
r1 <- map(c(5, 10, 15), make.df)  %>% 
           Reduce(function(...) full_join(..., by = "id"), .)
library(purrr)
set.seed(24)
r2 <- map(c(5, 10, 15), make.df)  %>%
             reduce(full_join, by = "id")

identical(r1, r2)
#[1] TRUE
Joining two data frames and result data frames contain non duplicate items in PySpark?

Joining two data frames and result data frames contain non duplicate items in PySpark?


By : dkgani
Date : March 29 2020, 07:55 AM
it should still fix some issue Doesn't look like something that can be achieved with a single join. Here's a solution involving multiple joins:
code :
from pyspark.sql.functions import col

d1 = df1.unionAll(df2).select("SID" , "SSection" ).distinct()

t1 = d1.join(df1 , ["SID", "SSection"] , "leftOuter").select(d1.SID , d1.SSection , col("SRank").alias("test1Srank"))

t2 = d1.join(df2 , ["SID", "SSection"] , "leftOuter").select(d1.SID , d1.SSection , col("SRank").alias("test2Srank"))

t1.join(t2, ["SID", "SSection"]).na.fill(0).show()

+---+--------+----------+----------+
|SID|SSection|test1Srank|test2Srank|
+---+--------+----------+----------+
|  b|       2|         2|         3|
|  c|       3|         4|         4|
|  d|       4|         2|         0|
|  e|       4|         1|         1|
|  f|       4|         0|         2|
|  a|       1|         1|         0|
|  a|       2|         0|         1|
+---+--------+----------+----------+
Joining huge list of data frames causes stack-overflow error

Joining huge list of data frames causes stack-overflow error


By : user3194151
Date : March 29 2020, 07:55 AM
I hope this helps . I have written a function that joins a list of data frames using some common column.Below is the code : , I was able to resolve this issue using local-checkpointing :
code :
def joinByColumn(dfs: List[DataFrame], column: String): DataFrame = {
    //check that all dfs contain the required column
    require(dfs.map(_.columns).forall(_.contains(column)))

    dfs.reduce((df1, df2) =>

      df1.join(df2, Seq(column), "full_outer").localCheckpoint(true)


    )
  }
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