To set the scene, I have a set of data where two columns of the data have been mixed up. To give a simple example:
df1 <- data.frame(Name = c("Bob", "John", "Mark", "Will"), City=c("Apple", "Paris", "Orange", "Berlin"), Fruit=c("London", "Pear", "Madrid", "Orange"))
df2 <- data.frame(Cities = c("Paris", "London", "Berlin", "Madrid", "Moscow", "Warsaw"))
As a result, we have two small data sets:
> df1
Name City Fruit
1 Bob Apple London
2 John Paris Pear
3 Mark Orange Madrid
4 Will Berlin Orange
> df2
Cities
1 Paris
2 London
3 Berlin
4 Madrid
5 Moscow
6 Warsaw
My aim is to create a new column where the cities are in the correct place using df2. I am a bit new to R so I don't know how this would work.
I don't really know where to even start with this sort of a problem. My full dataset is much larger and it would be good to have an efficient method of unpicking this issue!
If the 'City' values are only different. We may loop over the rows, create a logical vector based on the matching values with 'Cities' from 'df2', and concatenate with the rest of the values by getting the matched values second in the order
df1[] <- t(apply(df1, 1, function(x)
{
i1 <- x %in% df2$Cities
i2 <- !i1
x1 <- x[i2]
c(x1[1], x[i1], x1[2])}))
-output
> df1
Name City Fruit
1 Bob London Apple
2 John Paris Pear
3 Mark Madrid Orange
4 Will Berlin Orange
using dplyr package this is a solution, where it looks up the two City and Fruit values in df1, and takes the one that exists in the df2 cities list. if none of the two are a city name, an empty string is returned, you can replace that with anything you prefer.
library(dplyr)
df1$corrected_City <- case_when(df1$City %in% df2$Cities ~ df1$City,
df1$Fruit%in% df2$Cities ~ df1$Fruit,
TRUE ~ "")
output, a new column created as you wanted with the city name on that row.
> df1
Name City Fruit corrected_City
1 Bob Apple London London
2 John Paris Pear Paris
3 Mark Orange Madrid Madrid
4 Will Berlin Orange Berlin
Another way is:
library(dplyr)
library(tidyr)
df1 %>%
mutate(across(1:3, ~case_when(. %in% df2$Cities ~ .), .names = 'new_{col}')) %>%
unite(New_Col, starts_with('new'), na.rm = TRUE, sep = ' ')
Name City Fruit New_Col
1 Bob Apple London London
2 John Paris Pear Paris
3 Mark Orange Madrid Madrid
4 Will Berlin Orange Berlin
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