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loading.R
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loading.R
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#### Reading first data in! Cleaning it and trying first graphs.
## Encoding: windows-1250
## Created: 2021-10-25 Francesco
## Edited: 2021-11-06 Francesco
## NOTES:
# Results from NetLogo are lists stored in *.csv file as one string.
# I have to find a way, how one list transform to several variables.
# Note: results are stored in file without extender 'resultsV03',
# it is *.csv file, but I forgot in NetLogo to add extender (it's not automatic).
#
#
# Head --------------------------------------------------------------------
# Clear the memory
rm(list = ls())
# Packages
library(dplyr)
library(forcats)
library(tidyr)
library(readr)
library(readxl)
library(writexl)
library(sjmisc)
library(ggplot2)
# My own functon for renaming in Tidyverse
prejmenuj = function(data, positions, new.names) {
names(data)[positions] = new.names
data
}
# Loading and preparing main data ------------------------------------------------------------
# We load raw data -- we must skip the first 6 lines since there NetLogo writes some meta info, not data itself
data = read_csv("resultsV03.csv", skip = 6) %>%
# We rename variables -- in NetLogo I use heavily minus ('-') in names and R doesn't like it...
prejmenuj(c(1, 3:5, 9:10, 20),
c("rid", "N_agents", "random_links", "neis", "drawn", "speaking", "step")) %>%
# I find that some cases are impossible for analyses -- they have no components,
# one instance is natural: after 5000 steps simulation doesn't converge so the components counting algorithm doesn't start
# but one instance is strange: simulation ends after 99 steps, but it could count only after 100 steps since the record of changes has length 100... it's strange...
# Ough! Even stranger! Now I see that some simulations end after 199, 200, 201 steps without any component...
# Hmm... Since I do not store components smaller than 6, it might be that the largest component in simulation
# is at maximum 5 and that is why its not here. So, for the next experiment I probably should put
# the threshold lower, omit just components of size 1 or 2, but all others record.
# It also depends on our questions -- it might be sufficient now replace empty component with
# component of size 5, or may by simply with NA, so we store info that simulation doesn't produce
# component larger than 5.
#
# And the problem with step 99 -- it probably might be because I put the step-counter after
# the stopping condition, so, when simulation ends during the step 100 in records there is 99
# because the simulation stopped just before the counter counted the 100th step.
# So then it would mean that there was no change during the first 100 steps.
#
# TO-DO: Look at simulation program whether it is like that, if so,
# then put the step counter at the start or before stopping conditions.
filter(step < 5000) %>%
# At least now for data cleaning I need to omit simulations with empty component and positions lists,
# when these are equal I will omit them.
filter(components != positions) %>%
# for purpose of data cleaning I filter out simple cases, in working code this line will be commented
# filter(opinions > 1) %>%
# slice_sample(n = 100) %>%
# We separate list of component sizes into 50 separate variables
# TO-DO: Check after end of experiment if 50 is enough, now its just 'the first 50 values' and it is sufficient.
separate(components, sep = " ", into = paste0("size.", 1:50)) %>%
# We also separate list of components' positions into 50 separate variables
separate(positions, sep = "\\] \\[", into = paste0("p.", 1:50)) %>%
# For easy manipulation we reshape data to long form
# TO-DO: Check the number of variables where we separated positions and components!
# Now the code 'thinks' we are separating into 50 variables --> 'p.50'.
pivot_longer(size.1:p.50) %>%
# We filter out empty values
filter(!is.na(value)) %>%
# Variable 'name' contains now the original var names c.1:p.50,
# we separate the root of the name from number and the number we store as ID of component.
separate(name, sep = "\\.", into = c("name", "component_ID")) %>%
# Now we have values of component size and the opinion position in one var 'value',
# we reshape date so to have on one line component ID, size and opinion position of such an component.
# Note: For identification is sufficient 'rid' and 'component_ID', but then R omits all other vars,
# so to avoid that and store all needed vars, we use for identification of row also
# obsolete vars and specify rows by 'id_cols = c(1:10, 20, 22)'.
pivot_wider(id_cols = c(1:10, 20, 22), names_from = "name", values_from = "value") %>%
# Now we have to separate list with opinion position of cluster into separate opinions/coordinates:
separate(p, sep = " ", into = paste0("opinion.", 1:16)) %>%
# Finally, we parse numbers as numbers
mutate(
across(.cols = opinion.1:opinion.16, ~parse_number(.x)),
component_ID = parse_number(component_ID),
size = round(parse_number(size) / N_agents * 100, digits = 2))
# Loading and praparing data on omitted cases -----------------------------
# We load raw data -- we must skip the first 6 lines since there NetLogo writes some meta info, not data itself
reason = read_csv("resultsV03.csv", skip = 6) %>%
# We rename variables -- in NetLogo I use heavily minus ('-') in names and R doesn't like it...
prejmenuj(c(1, 3:5, 9:10, 20),
c("rid", "N_agents", "random_links", "neis", "drawn", "speaking", "step")) %>%
# Since we know that component size and opinion positions are different types of lists,
# we might use it: they might be equal only in one case -- when they are empty,
# so we might identify cases with both empty lists by asking whether they are equal.
#
# Now we could construct easy indicator of reason of omitting the case from main file:
mutate(reason =
if_else(
components != positions,
"Stable",
if_else(step == 5000, "Non-equilibrium","Fractured")
) %>% factor()
) %>%
# We need only some vars so we select them:
select(1:10, 20, 23)
# Storing data ------------------------------------------------------------
# We store data as two Excel files
write_xlsx(data, "components.xlsx")
write_xlsx(reason, "simulations.xlsx")