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Remove same columns from left_join


Remove same columns from left_join

By : Martyn Gooding
Date : December 01 2020, 05:00 PM
around this issue Just drop everything you don't want from df2 - in this case the id and value2 columns:
code :
left_join(df1, select(df2, c(id,value2)), by = "id")

#  id     value1 element day     value2
#1  1  1.2276303   TEST1  15 -0.1389861
#2  2 -0.8017795   TEST1  15 -0.5973131
#3  3 -1.0803926   TEST1  15 -2.1839668
#4  4 -0.1575344   TEST1  15  0.2408173
#5  5 -1.0717600   TEST1  15 -0.2593554
df1 <- data.frame(id=1:3,day=2:4,element=3:5,value1=100:102)
df2 <- data.frame(id=1:3,day=3:5,element=4:6,value2=200:202)
df1

#  id day element value1
#1  1   2       3    100
#2  2   3       4    101
#3  3   4       5    102

df2
#  id day element value2
#1  1   3       4    200
#2  2   4       5    201
#3  3   5       6    202

left_join(df1, df2)
#Joining by: c("id", "day", "element")
#  id day element value1 value2
#1  1   2       3    100     NA
#2  2   3       4    101     NA
#3  3   4       5    102     NA

left_join(df1, select(df2, c(id,value2)), by = "id")
#  id day element value1 value2
#1  1   2       3    100    200
#2  2   3       4    101    201
#3  3   4       5    102    202


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Remove verbose from left_join

Remove verbose from left_join


By : gkiran
Date : March 29 2020, 07:55 AM
Hope this helps You can explicitely provide the column by which you want to join and you avoid the message:
code :
df <- left_join(df1, df2, by = "id")
dplyr::left_join produce NA values for new joined columns

dplyr::left_join produce NA values for new joined columns


By : James
Date : March 29 2020, 07:55 AM
I wish this help you I have two tables I wish to left_join through the dplyr package. The issue is that is produces NA values for all new columns (the ones I'm after). , There are two problems.
code :
left_join(Avanza.XML, checkpoint, by = "Firm")
Avanza.XML$Firm <- as.character(Avanza.XML$Firm)
checkpoint$Firm <- as.character(checkpoint$Firm)
lvls <- sort(unique(c(levels(Avanza.XML$Firm), 
                          levels(checkpoint$Firm))))
Avanza.XML$Firm <- factor(Avanza.XML$Firm, levels=lvls)
checkpoint$Firm <- factor(checkpoint$Firm, levels=lvls)
Prefix all columns resulting from left_join() with original table names

Prefix all columns resulting from left_join() with original table names


By : edubrulez
Date : March 29 2020, 07:55 AM
may help you . A straightforward way to achieve this would be to add the prefixes to the original tables before performing the join:
code :
# add prefix before joining:
names(flights2) <- paste0("flights2.", names(flights2) )
names(airports2) <- paste0("airports2.", names(airports2) )

# in join, use names with prefixes
result <- flights2 %>% left_join(airports2, c("flights2.dest" = "airports2.faa") ) %>% head()
Source: local data frame [6 x 14]

flights2.year flights2.month flights2.day flights2.hour flights2.origin flights2.dest
(int)          (int)        (int)         (dbl)           (chr)         (chr)
1          2013              1            1             5             EWR           IAH
2          2013              1            1             5             LGA           IAH
3          2013              1            1             5             JFK           MIA
4          2013              1            1             5             JFK           BQN
5          2013              1            1             5             LGA           ATL
6          2013              1            1             5             EWR           ORD
Variables not shown: flights2.tailnum (chr), flights2.carrier (chr), airports2.name (chr),
airports2.lat (dbl), airports2.lon (dbl), airports2.alt (int), airports2.tz (dbl),
airports2.dst (chr)
left_join R dataframes, merging two columns with NAs

left_join R dataframes, merging two columns with NAs


By : Obexus
Date : March 29 2020, 07:55 AM
should help you out My problem is the following: Lets say I have an existing dataframe with the following columns: UID, foo, result. Result is already partially filled. A second model now predicts additional rows, generating a second dataframe containing a UID and a result column: (Code to reproduce at bottom) , coalesce is your second step.
code :
left_join(df_main, new_prediction, by="UID") %>%
  mutate(result = coalesce(result.x, result.y)) %>%
  select(-result.x, -result.y)
# # A tibble: 5 x 3
#     UID   foo result
#   <dbl> <chr>  <chr>
# 1     1   moo    Cow
# 2     2   rum   <NA>
# 3     3  oink    Pig
# 4     4  woof    Dog
# 5     5  hiss  Snake
r dplyr::left_join using datetime columns does not join properly

r dplyr::left_join using datetime columns does not join properly


By : swarna
Date : March 29 2020, 07:55 AM
wish helps you On my separate dataset, checking both 'by' columns with lubridate::seconds(date_time) showed they were formatted differently due to milliseconds, though it didn't show up in most displays. "1522267608S" vs "1522267308.443S" (these aren't supposed to match, just to show formatting)
Wrapping one or both of the columns in the following to remove milliseconds did the trick for me:
code :
library(lubridate)
as_datetime(floor(seconds(date_time)))
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