### SPM-LS
## Last Series of the Standard Progressive Matrices: https://data.mendeley.com/datasets/h3yhs5gy3w/1#file-11272d2a-aa63-43be-8413-145b9941d2d8
data_url_location = "https://data.mendeley.com/datasets/h3yhs5gy3w/1/files/96daaa32-c8e9-40f4-b453-ada9cc6320d4/dataset.csv?dl=1"
data_local_location = "data-raw/spm-ls/dataset.csv"
# Download the zip file
download.file(data_url_location,
data_local_location)
# Unzip and load anxiety data into R
# Note, data has a header in it!
raw_spm_ls =
readr::read_csv(data_local_location,
na = c("", "NA"),
col_types = readr::cols(
SPM1 = readr::col_integer(),
SPM2 = readr::col_integer(),
SPM3 = readr::col_integer(),
SPM4 = readr::col_integer(),
SPM5 = readr::col_integer(),
SPM6 = readr::col_integer(),
SPM7 = readr::col_integer(),
SPM8 = readr::col_integer(),
SPM9 = readr::col_integer(),
SPM10 = readr::col_integer(),
SPM11 = readr::col_integer(),
SPM12 = readr::col_integer()
)
)
# Transform to a binary matrix
transformed_spm_ls = raw_spm_ls
# Ported from the PHP used in "calculated" values
# Rules derived from Table 1 of Taylor, J. (1953). "A personality scale of manifest anxiety"
transformed_spm_ls['SPM1'] = 1*(raw_spm_ls['SPM1'] == 7)
transformed_spm_ls['SPM2'] = 1*(raw_spm_ls['SPM2'] == 6)
transformed_spm_ls['SPM3'] = 1*(raw_spm_ls['SPM3'] == 8)
transformed_spm_ls['SPM4'] = 1*(raw_spm_ls['SPM4'] == 2)
transformed_spm_ls['SPM5'] = 1*(raw_spm_ls['SPM5'] == 1)
transformed_spm_ls['SPM6'] = 1*(raw_spm_ls['SPM6'] == 5)
transformed_spm_ls['SPM7'] = 1*(raw_spm_ls['SPM7'] == 1)
transformed_spm_ls['SPM8'] = 1*(raw_spm_ls['SPM8'] == 6)
transformed_spm_ls['SPM9'] = 1*(raw_spm_ls['SPM9'] == 3)
transformed_spm_ls['SPM10'] = 1*(raw_spm_ls['SPM10'] == 2)
transformed_spm_ls['SPM11'] = 1*(raw_spm_ls['SPM11'] == 4)
transformed_spm_ls['SPM12'] = 1*(raw_spm_ls['SPM12'] == 5)
library("magrittr")
# Convert to an item matrix
# Enforce list-wise deletion.
items_spm_ls =
transformed_spm_ls %>%
tidyr::drop_na() %>%
dplyr::select(dplyr::matches("SPM")) %>%
as.matrix()
# Write the items_spm_ls
usethis::use_data(items_spm_ls, overwrite = TRUE)
# Remove the zip + csv after read in.
file.remove(data_local_location)
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