Last updated: 2023-05-15
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Knit directory: fitnessGWAS/
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library(tidyverse)
library(DT)
# Create a function to build HTML searchable tables
my_data_table <- function(df, filename){
datatable(
df, rownames=FALSE,
autoHideNavigation = TRUE,
extensions = c("Scroller", "Buttons"),
options = list(
autoWidth = TRUE,
dom = 'Bfrtip',
deferRender=TRUE,
scrollX=TRUE, scrollY=1000,
scrollCollapse=TRUE,
buttons =
list('pageLength',
list(extend = 'csv',
filename = filename)),
pageLength = 10
)
)
}
# Load the raw data collected during the experiments (clean up in get_predicted_line_means.Rmd)
raw_female_fitness <- read_csv("data/input/female_fitness_CLEANED.csv") %>%
select(vial, everything()) %>%
rename_all(~ str_to_sentence(str_replace_all(.x, "[.]", " ")))
raw_male_fitness <- read_csv("data/input/male_fitness_CLEANED.csv") %>%
select(vial, everything(), -num.GFP.males) %>%
rename_all(~ str_to_sentence(str_replace_all(.x, "[.]", " ")))
# Load the predicted line means, as calculated in get_predicted_line_means.Rmd
predicted_line_means <- read_csv("data/derived/predicted_line_means.csv") %>%
mutate(female.fitness.early = round(female.fitness.early, 3),
female.fitness.late = round(female.fitness.late, 3),
male.fitness.early = round(male.fitness.early, 3),
male.fitness.late = round(male.fitness.late, 3)) %>%
rename_all(~ str_to_sentence(str_replace_all(.x, "[.]", " "))) %>%
select(-Block) To facilitate data re-use, we here provide tables showing the raw
data (i.e. the measurements of male and female fitness that were
collected on each individual replicate vial), as well as the estimated
line means that were calculated in this script. Press the
CSV button to download a particular table as a
.csv file.
Vial is a unique ID for each vial, which groups
together observations of the same group of 5 DGRP females whose fitness
was measured.Block is the experimental block in which fitness was
measured.Line gives the DGRP line number of the focal
females.Female fitness early gives the number of L1 larvae
produced by the DGRP females, in the early-life measurement.Female fitness late gives the number of L1 larvae
produced by the DGRP females, in the late-life measurement.my_data_table(raw_female_fitness,
"female_fitness_raw_data")Vial is a unique ID for each vial, which groups
together observations of the same group of 5 DGRP females whose fitness
was measured.Block is the experimental block in which fitness was
measured.Line gives the DGRP line number of the focal malesEarly male rival gives the number of L1 larvae sired by
the rival GFP males, in the early-life measurement.Early male focal gives the number of L1 larvae sired by
the focal DGRP males, in the early-life measurement.Late male rival gives the number of L1 larvae sired by
the rival GFP males, in the late-life measurement.Late male focal gives the number of L1 larvae sired by
the focal DGRP males, in the late-life measurement.my_data_table(raw_male_fitness,
"male_fitness_raw_data")The four columns other than Line give the line mean
fitness for each of the four fitness traits. Fitness was estimated from
Bayesian multivariate Poisson or Binomial models described in this script. The fitness values
were expressed on the scale of the linear predictor (and thus are
approximately normally distributed), and have been scaled to have a mean
of zero and variance of one. Thus, a line with mean fitness equal to 1.5
is 1.5 standard deviations above average, on the scale of the linear
predictor. Note that male fitness was not measured in
line_354 due to experimenter error.
my_data_table(predicted_line_means,
"line_mean_fitness_data")
sessionInfo()R version 4.2.2 (2022-10-31)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur ... 10.16
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] DT_0.27 lubridate_1.9.2 forcats_1.0.0 stringr_1.5.0
[5] dplyr_1.1.0 purrr_1.0.1 readr_2.1.4 tidyr_1.3.0
[9] tibble_3.1.8 ggplot2_3.4.1 tidyverse_2.0.0 workflowr_1.7.0.4
loaded via a namespace (and not attached):
[1] tidyselect_1.2.0 xfun_0.37 bslib_0.4.2 colorspace_2.1-0
[5] vctrs_0.5.2 generics_0.1.3 htmltools_0.5.4 yaml_2.3.7
[9] utf8_1.2.2 rlang_1.0.6 jquerylib_0.1.4 later_1.3.0
[13] pillar_1.8.1 glue_1.6.2 withr_2.5.0 bit64_4.0.5
[17] lifecycle_1.0.3 munsell_0.5.0 gtable_0.3.1 htmlwidgets_1.6.1
[21] evaluate_0.20 knitr_1.42 tzdb_0.3.0 callr_3.7.3
[25] fastmap_1.1.1 crosstalk_1.2.0 httpuv_1.6.9 ps_1.7.2
[29] parallel_4.2.2 fansi_1.0.4 Rcpp_1.0.10 promises_1.2.0.1
[33] scales_1.2.1 cachem_1.0.7 vroom_1.6.1 jsonlite_1.8.4
[37] bit_4.0.5 fs_1.6.1 hms_1.1.2 digest_0.6.31
[41] stringi_1.7.12 processx_3.8.0 getPass_0.2-2 rprojroot_2.0.3
[45] grid_4.2.2 cli_3.6.0 tools_4.2.2 magrittr_2.0.3
[49] sass_0.4.5 crayon_1.5.2 whisker_0.4.1 pkgconfig_2.0.3
[53] ellipsis_0.3.2 timechange_0.2.0 rmarkdown_2.20 httr_1.4.5
[57] rstudioapi_0.14 R6_2.5.1 git2r_0.31.0 compiler_4.2.2