I am currently trying to make squares (polygons) from a data frame in R and in order to do that (according this guide), I need to have a data frame with 4 sets of paired points as their lon-lat coordinates.
Using this example:
sample_df <- data.frame(id = c(1,2),
t = c('2020-01-01','2020-01-01'),
intensity = c(1.3,0.6),
x1 = c(113.75,114.00),
x2 = c(114.00,114.25),
y1 = c(8.75,8.75),
y2 = c(9.00,9.00))
| id | t | intensity | x1 | x2 | y1 | y2 |
|---|---|---|---|---|---|---|
| 1 | 2020-01-01 | 1.3 | 113.75 | 114.00 | 8.75 | 9.00 |
| 2 | 2020-01-01 | 0.6 | 114.00 | 114.25 | 8.75 | 9.00 |
What I would like to achieve is to create a data frame that retains the t and the intensity columns distributed to the id column multiplied into 4 pairs of x1 paired to y1, x2 paired to y1, x2 paired to y2, and x1 paired to y2 values as lon and lat columns.
The expected output would be a data frame looking something like this:
| id | t | intensity | lon | lat |
|---|---|---|---|---|
| 1 | 2020-01-01 | 1.3 | 113.75 | 8.75 |
| 1 | 2020-01-01 | 1.3 | 114.00 | 8.75 |
| 1 | 2020-01-01 | 1.3 | 114.00 | 9.00 |
| 1 | 2020-01-01 | 1.3 | 113.75 | 9.00 |
| 2 | 2020-01-01 | 0.6 | 114.00 | 8.75 |
| 2 | 2020-01-01 | 0.6 | 114.25 | 8.75 |
| 2 | 2020-01-01 | 0.6 | 114.25 | 9.00 |
| 2 | 2020-01-01 | 0.6 | 114.00 | 9.00 |
I am currently stuck but I am playing around the mutate() function of the dplyr package, or the melt() of reshape2.
I would be greatly thankful for your inputs.
This is 2 reshape/pivots I believe:
library(tidyr)
library(dplyr)
sample_df %>%
pivot_longer(c("x1","x2"), names_to=NULL, values_to="lon") %>%
pivot_longer(c("y1","y2"), names_to=NULL, values_to="lat")
## A tibble: 8 × 5
# id t intensity lon lat
# <dbl> <chr> <dbl> <dbl> <dbl>
#1 1 2020-01-01 1.3 114. 8.75
#2 1 2020-01-01 1.3 114. 9
#3 1 2020-01-01 1.3 114 8.75
#4 1 2020-01-01 1.3 114 9
#5 2 2020-01-01 0.6 114 8.75
#6 2 2020-01-01 0.6 114 9
#7 2 2020-01-01 0.6 114. 8.75
#8 2 2020-01-01 0.6 114. 9
The lon variable is right - the printing method for tibbles is really odd however and shows (113.75 or 114.25) and 114.0 as 114. and 114 respectively.