Replace a part of dataframe with new data - r

Question

I have data1 and data2, and I need data3, that replaces certain regions of data1 with data2.
I use this method to update the data, but actually several columns need to be updated and it would be tedious.
Do you know a more simple way?
library(tidyverse)
library(lubridate)
data1 <- tibble(date=date("2017-11-1") + c(1:10),
a=sample(100,10),b=sample(100,10))
data2 <- tibble(date=date("2017-11-1") + c(1:8),
a=sample(100,8))
data_bind <- left_join(data1, data2, by=("date"))
data_bind$a.x[!is.na(data_bind$a.y)] <- data_bind$a.y[!is.na(data_bind$a.y)]
data_bind %>% select(-a.y) %>% dplyr::rename(a=a.x)

Answer 1

In my opinion, the data.table-package is better suited for such a task. Using:
# create a vector with names from 'data2' that are not used to join by
nms <- names(data2)[-1]
# load the 'data.table'-package
library(data.table)
# convert the dataframes to data,table's
setDT(data1)
setDT(data2)
# join and update the column in 'data1' with the matching values from 'data2'
data1[data2, on = 'date', (nms) := mget(paste0('i.',nms))][]
gives:
date a b
1: 2017-11-02 21 11
2: 2017-11-03 22 12
3: 2017-11-04 23 13
4: 2017-11-05 24 14
5: 2017-11-06 25 15
6: 2017-11-07 26 16
7: 2017-11-08 27 17
8: 2017-11-09 28 18
9: 2017-11-10 9 19
10: 2017-11-11 10 20
What this does:
With setDT(data1) you convert the dataframes/tibbles to a data.table.
With data1[data2, on = 'date'] you can do a join the data.table-way.
By adding (nms) := mget(paste0('i.',nms)) to the join, you tell data.table to update the columns in data1 with the columns that are also present in data2 only where the dates match.
As an alternative approach you could also reshape both datasets into long format and then do the join:
library(data.table)
melt(data1, id = 'date')[melt(data2, id = 'date')
, on = .(date, variable)
, value := i.value
][, dcast(.SD, date ~ variable)]
A translation of this approach to the tidyverse:
library(dplyr)
library(tidyr)
gather(data1, key, value, -1) %>%
left_join(., gather(data2, key, value, -1), by = c('date','key')) %>%
mutate(value.x = ifelse(!is.na(value.y), value.y, value.x)) %>%
select(date, key, value = value.x) %>%
spread(key, value)
Both will give you the same output.
Used data:
data1 <- data.frame(date = as.Date("2017-11-1") + c(1:10), a = 1:10, b = 11:20)
data2 <- data.frame(date = as.Date("2017-11-1") + c(1:8), a = 21:28)