Mutate data to juxtapose repeat measurments - r

Question

Let's pretend I am measuring the distance the distance grasshoppers can jump pre- and post-treatment. This is just for fun, the real measurement could be anything, and the bigger picture is to understand the group_by() command.
For the statistical test I would like to run, each observation needs to have its own column, but I'm given a dataset that is not in this format...!!, and I would like to use the package library(dplyr) , and the command group_by()to shape the data for my needs, because if this were to happen again, I could make a more general code to work over other datasets :)
I am able to do this using commands, such as filter(), and then cbind()at a later step (see example below). But it also requires renaming a column. Additionally, if I wanted to add a column, let's say "difference", to calculate the observed difference between observation 1, and observation 2, I can do this, but then I need to add another line of code (again, see example below)
It would be great to do this with less lines of code
Please see what I have tried, and let me know how I could modify the code group by() to work properly.
example_df <- data.frame( "observation" = character(0), "distance" = integer(0))
Assign names for our "observations", remember, in this example, it's done twice
variable_names <- c( "obs_1", "obs_2")
Assign fictitious values to y
w<-rnorm(200, mean=5, sd=2)
x<-rnorm(200, mean=5, sd=2)
y<-rnorm(200, mean=5, sd=2)
z<-rnorm(200, mean=5, sd=2)
Combine everything for this pretend exercise
df <- data.frame( "observation" = variable_names, "distance" = c(w,x,y,z))
attach(df)
Here's how I achieved the desired results for this example
library(dplyr)
dat = filter(df,observation=="obs_1")
dat2 = filter(df,observation=="obs_2")
names(dat2)
colnames(dat2)[2] <- "distance_2"
final <- cbind(dat,dat2)
attach(final)
final$difference <- distance-distance_2
I tried using the group_by() command, I just get an error message
final <- df %>% group_by(observation,distance) %>% summarise(
Observation_1 = first(observation), distance_1 = first(distance),
Observation_2 = last(observation), distance_2 = last(distance,difference=distance-distance_2)))
It would be great to get the above code to work
To make things even more "fun" :), what if more than one variable was measured. Could I make a general code to achieve the desired results, again, without having the go over the whole filter() process, with cbind()etc..
Here's an example (expanded on the above one)
example_df <- data.frame( "observation" = character(0), "distance" = integer(0),"weight" = integer(0),"speed" = integer(0))
variable_names <- c( "obs_1", "obs_2")
w<-rnorm(200, mean=5, sd=2)
x<-rnorm(200, mean=5, sd=2)
y<-rnorm(200, mean=5, sd=2)
z<-rnorm(200, mean=5, sd=2)
a<-rnorm(200, mean=5, sd=2)
b<-rnorm(200, mean=5, sd=2)
df <- data.frame( "observation" = variable_names, "distance" = c(w,x),"weight" = c(y,z),"speed" = c(a,b))
attach(df)
library(dplyr)
dat = filter(df,observation=="obs_1")
dat2 = filter(df,observation=="obs_2")
names(dat2)
colnames(dat2)[2] <- "distance_2"
colnames(dat2)[3] <- "weight_2"
colnames(dat2)[4] <- "speed_2"
final <- cbind(dat,dat2)
attach(final)
final$difference <- distance-distance_2
final$difference_weight <- weight-weight_2
final$difference_speed <- speed-speed_2
Thanks everyone!

Answer 1

Would be simple with pivot_wider, though I presume your data also has an id column to link observations somehow, so have added one here:
library(tidyverse)
w<-rnorm(200, mean=5, sd=2)
x<-rnorm(200, mean=5, sd=2)
y<-rnorm(200, mean=5, sd=2)
z<-rnorm(200, mean=5, sd=2)
a<-rnorm(200, mean=5, sd=2)
b<-rnorm(200, mean=5, sd=2)
variable_names <- c( "obs_1", "obs_2")
df <-
data.frame(
"id" = rep(1:200, each = 2),
"observation" = variable_names,
"distance" = c(w, x),
"weight" = c(y, z),
"speed" = c(a, b)
)
df %>%
pivot_wider(
id_cols = id,
names_from = observation,
values_from = distance:speed
)
#> # A tibble: 200 x 7
#> id distance_obs_1 distance_obs_2 weight_obs_1 weight_obs_2 speed_obs_1
#> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 3.63 2.80 2.98 -0.795 3.58
#> 2 2 4.96 6.84 4.11 9.92 8.21
#> 3 3 4.84 7.51 6.32 3.28 9.02
#> 4 4 3.79 6.82 5.42 6.86 7.96
#> 5 5 5.48 2.84 9.56 3.27 3.55
#> 6 6 8.78 2.06 3.81 4.35 5.93
#> 7 7 8.42 4.21 3.92 4.40 9.37
#> 8 8 8.26 9.67 4.05 6.19 3.17
#> 9 9 3.80 4.47 6.58 5.38 6.09
#> 10 10 4.67 2.86 6.27 6.88 3.72
#> # ... with 190 more rows, and 1 more variable: speed_obs_2 <dbl>
Follow-up
You can also tell pivot_wider to use a function in combining values. Here in this example I've passed names_from = NULL so that every column is paired up by id, and using the diff function to calculate the difference:
df %>%
pivot_wider(
id_cols = id,
names_from = NULL,
values_from = distance:speed,
values_fn = diff,
names_sep = ""
)
#> # A tibble: 200 x 4
#> id distance weight speed
#> <int> <dbl> <dbl> <dbl>
#> 1 1 -0.828 -3.77 4.45
#> 2 2 1.88 5.82 -1.07
#> 3 3 2.66 -3.04 -4.31
#> 4 4 3.03 1.45 -0.969
#> 5 5 -2.64 -6.29 5.06
#> 6 6 -6.72 0.541 -2.24
#> 7 7 -4.20 0.481 -5.82
#> 8 8 1.41 2.14 3.71
#> 9 9 0.669 -1.19 -1.14
#> 10 10 -1.81 0.607 -2.62
#> # ... with 190 more rows
Created on 2022-03-25 by the reprex package (v2.0.1)