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library(tidyverse)Warning in system("timedatectl", intern = TRUE): running command 'timedatectl'
had status 1── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
✔ ggplot2 3.4.0 ✔ purrr 0.3.5
✔ tibble 3.1.8 ✔ dplyr 1.0.10
✔ tidyr 1.2.1 ✔ stringr 1.4.1
✔ readr 2.1.3 ✔ forcats 0.5.2
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()library(pafr)our_genomes = c(
"Myotis_auriculus",
"Myotis_californicus",
"Myotis_occultus",
"Myotis_lucifugus",
"Myotis_yumanensis",
"Myotis_volans",
"Myotis_velifer",
"Myotis_evotis",
"Myotis_thysanodes"
)
species_color = ggsci::pal_d3(palette = "category20")(length(our_genomes)+1) %>%
set_names(., c(our_genomes, "Other"))
species_color["Myotis_evotis"] = "#17BECFFF"
species_color["Other"] = "#7F7F7FFF"table.myoAui <- read_csv("../../analyses/pangenome/output/flip_table/mMyoOcc1_mMyoAui1.csv")%>% arrange(tnum, qnum)Rows: 104 Columns: 5
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (2): qname, tname
dbl (2): qnum, tnum
lgl (1): flip
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.table.myoCai <- read_csv("../../analyses/pangenome/output/flip_table/mMyoOcc1_mMyoCai1.csv")%>% arrange(tnum, qnum)Rows: 54 Columns: 5
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (2): qname, tname
dbl (2): qnum, tnum
lgl (1): flip
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.table.myoOcc <- read_csv("../../analyses/pangenome/output/flip_table/mMyoOcc1_mMyoOcc1.csv")%>% arrange(tnum, qnum)Rows: 69 Columns: 5
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (2): qname, tname
dbl (2): qnum, tnum
lgl (1): flip
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.table.myoLuc <- read_csv("../../analyses/pangenome/output/flip_table/mMyoOcc1_mMyoLuc1.csv")%>% arrange(tnum, qnum)Rows: 127 Columns: 5
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (2): qname, tname
dbl (2): qnum, tnum
lgl (1): flip
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.table.myoYum <- read_csv("../../analyses/pangenome/output/flip_table/mMyoOcc1_mMyoYum1.cleaned.csv")%>% arrange(tnum, qnum)Rows: 306 Columns: 5
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (2): qname, tname
dbl (2): qnum, tnum
lgl (1): flip
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.table.myoVol <- read_csv("../../analyses/pangenome/output/flip_table/mMyoOcc1_mMyoVol1.csv")%>% arrange(tnum, qnum)Rows: 52 Columns: 5
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (2): qname, tname
dbl (2): qnum, tnum
lgl (1): flip
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.table.myoVel <- read_csv("../../analyses/pangenome/output/flip_table/mMyoOcc1_mMyoVel1.csv")%>% arrange(tnum, qnum)Rows: 64 Columns: 5
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (2): qname, tname
dbl (2): qnum, tnum
lgl (1): flip
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.table.myoEvo <- read_csv("../../analyses/pangenome/output/flip_table/mMyoOcc1_mMyoEvo1.csv")%>% arrange(tnum, qnum)Rows: 60 Columns: 5
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (2): qname, tname
dbl (2): qnum, tnum
lgl (1): flip
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.table.myoThy <- read_csv("../../analyses/pangenome/output/flip_table/mMyoOcc1_mMyoThy1.csv")%>% arrange(tnum, qnum)Rows: 45 Columns: 5
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (2): qname, tname
dbl (2): qnum, tnum
lgl (1): flip
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.table.batgenomes <- Reduce(
f = function(x,y) {full_join(x=x,y=y, by=c("tname", "tnum"))},
x = list(table.myoAui%>% group_by(tname, tnum) %>% summarize(myoAui.scaff = paste(qname, collapse=","), myoAui.nscaff=n_distinct(qname)),
table.myoCai%>% group_by(tname, tnum) %>% summarize(myoCai.scaff = paste(qname, collapse=","), myoCai.nscaff=n_distinct(qname)),
table.myoOcc%>% group_by(tname, tnum) %>% summarize(myoOcc.scaff = paste(qname, collapse=","), myoOcc.nscaff=n_distinct(qname)),
table.myoLuc%>% group_by(tname, tnum) %>% summarize(myoLuc.scaff = paste(qname, collapse=","), myoLuc.nscaff=n_distinct(qname)),
table.myoYum%>% group_by(tname, tnum) %>% summarize(myoYum.scaff = paste(qname, collapse=","), myoYum.nscaff=n_distinct(qname)),
table.myoVol%>% group_by(tname, tnum) %>% summarize(myoVol.scaff = paste(qname, collapse=","), myoVol.nscaff=n_distinct(qname)),
table.myoVel%>% group_by(tname, tnum) %>% summarize(myoVel.scaff = paste(qname, collapse=","), myoVel.nscaff=n_distinct(qname)),
table.myoEvo%>% group_by(tname, tnum) %>% summarize(myoEvo.scaff = paste(qname, collapse=","), myoEvo.nscaff=n_distinct(qname)),
table.myoThy%>% group_by(tname, tnum) %>% summarize(myoThy.scaff = paste(qname, collapse=","), myoThy.nscaff=n_distinct(qname)))
) %>%
arrange(tnum) %>%
select(tname, tnum, everything())`summarise()` has grouped output by 'tname'. You can override using the
`.groups` argument.
`summarise()` has grouped output by 'tname'. You can override using the
`.groups` argument.
`summarise()` has grouped output by 'tname'. You can override using the
`.groups` argument.
`summarise()` has grouped output by 'tname'. You can override using the
`.groups` argument.
`summarise()` has grouped output by 'tname'. You can override using the
`.groups` argument.
`summarise()` has grouped output by 'tname'. You can override using the
`.groups` argument.
`summarise()` has grouped output by 'tname'. You can override using the
`.groups` argument.
`summarise()` has grouped output by 'tname'. You can override using the
`.groups` argument.
`summarise()` has grouped output by 'tname'. You can override using the
`.groups` argument.table.batgenomes# A tibble: 69 × 20
# Groups: tname [69]
tname tnum myoAu…¹ myoAu…² myoCa…³ myoCa…⁴ myoOc…⁵ myoOc…⁶ myoLu…⁷ myoLu…⁸
<chr> <dbl> <chr> <int> <chr> <int> <chr> <int> <chr> <int>
1 SUPER_… 1 SUPER_… 2 SUPER_… 4 SUPER_… 1 SUPER_… 6
2 SUPER_… 2 SUPER_… 1 SUPER_… 1 SUPER_… 1 SUPER_… 1
3 SUPER_… 3 SUPER_… 7 SUPER_… 1 SUPER_… 1 SUPER_… 2
4 SUPER_… 4 SUPER_… 2 SUPER_… 1 SUPER_… 1 SUPER_… 11
5 SUPER_… 5 SUPER_… 4 SUPER_… 3 SUPER_… 1 SUPER_… 2
6 SUPER_… 6 SUPER_… 3 SUPER_… 1 SUPER_… 1 SUPER_… 2
7 SUPER_… 7 SUPER_… 3 SUPER_… 2 SUPER_… 1 SUPER_… 2
8 SUPER_… 8 SUPER_… 2 SUPER_… 1 SUPER_… 1 SUPER_… 1
9 SUPER_… 9 SUPER_… 10 SUPER_… 4 SUPER_… 1 SUPER_… 5
10 SUPER_… 10 SUPER_… 5 SUPER_… 2 SUPER_… 1 SUPER_… 4
# … with 59 more rows, 10 more variables: myoYum.scaff <chr>,
# myoYum.nscaff <int>, myoVol.scaff <chr>, myoVol.nscaff <int>,
# myoVel.scaff <chr>, myoVel.nscaff <int>, myoEvo.scaff <chr>,
# myoEvo.nscaff <int>, myoThy.scaff <chr>, myoThy.nscaff <int>, and
# abbreviated variable names ¹myoAui.scaff, ²myoAui.nscaff, ³myoCai.scaff,
# ⁴myoCai.nscaff, ⁵myoOcc.scaff, ⁶myoOcc.nscaff, ⁷myoLuc.scaff,
# ⁸myoLuc.nscaff
sessionInfo()R version 4.2.2 Patched (2022-11-10 r83330)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.5 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] pafr_0.0.2 forcats_0.5.2 stringr_1.4.1 dplyr_1.0.10
[5] purrr_0.3.5 readr_2.1.3 tidyr_1.2.1 tibble_3.1.8
[9] ggplot2_3.4.0 tidyverse_1.3.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.9 lubridate_1.9.0 assertthat_0.2.1
[4] rprojroot_2.0.3 digest_0.6.30 utf8_1.2.2
[7] R6_2.5.1 cellranger_1.1.0 backports_1.4.1
[10] reprex_2.0.2 evaluate_0.18 httr_1.4.4
[13] pillar_1.8.1 rlang_1.0.6 googlesheets4_1.0.1
[16] readxl_1.4.1 rstudioapi_0.14 jquerylib_0.1.4
[19] rmarkdown_2.17 googledrive_2.0.0 bit_4.0.4
[22] munsell_0.5.0 broom_1.0.1 compiler_4.2.2
[25] httpuv_1.6.6 modelr_0.1.9 xfun_0.34
[28] pkgconfig_2.0.3 htmltools_0.5.4 tidyselect_1.2.0
[31] workflowr_1.7.0 fansi_1.0.3 crayon_1.5.2
[34] withr_2.5.0 tzdb_0.3.0 dbplyr_2.2.1
[37] later_1.3.0 grid_4.2.2 jsonlite_1.8.3
[40] gtable_0.3.1 lifecycle_1.0.3 DBI_1.1.3
[43] git2r_0.30.1 magrittr_2.0.3 scales_1.2.1
[46] vroom_1.6.0 cli_3.4.1 stringi_1.7.8
[49] cachem_1.0.6 fs_1.5.2 promises_1.2.0.1
[52] xml2_1.3.3 bslib_0.4.1 ellipsis_0.3.2
[55] generics_0.1.3 vctrs_0.5.0 ggsci_2.9
[58] tools_4.2.2 bit64_4.0.5 glue_1.6.2
[61] hms_1.1.2 parallel_4.2.2 fastmap_1.1.0
[64] yaml_2.3.6 timechange_0.1.1 colorspace_2.0-3
[67] gargle_1.2.1 rvest_1.0.3 knitr_1.40
[70] haven_2.5.1 sass_0.4.2