#mutation piechart plots
library(tidyverse) # ggplot2, dplyr, tidyr, readr, purrr, tibble
## -- Attaching packages --------------------------------------- tidyverse 1.3.2 --
## v ggplot2 3.3.6 v purrr 0.3.4
## v tibble 3.1.8 v dplyr 1.0.10
## v tidyr 1.2.1 v stringr 1.4.1
## v readr 2.1.3 v forcats 0.5.2
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
library(rnaturalearth)
library(rnaturalearthdata)
library(sf) #see ubuntu issues here: https://rtask.thinkr.fr/installation-of-r-4-0-on-ubuntu-20-04-lts-and-tips-for-spatial-packages/
## Linking to GEOS 3.10.2, GDAL 3.4.1, PROJ 7.2.1; sf_use_s2() is TRUE
library(ggspatial)
library(scatterpie)
library(scales)
##
## Attaching package: 'scales'
##
## The following object is masked from 'package:purrr':
##
## discard
##
## The following object is masked from 'package:readr':
##
## col_factor
#load base map layers
admin10 <- ne_download(scale="large", type = "admin_1_states_provinces_lines",
category = "cultural", returnclass = "sf")
## OGR data source with driver: ESRI Shapefile
## Source: "C:\Users\Samuel\AppData\Local\Temp\RtmpUXzRpF", layer: "ne_10m_admin_1_states_provinces_lines"
## with 10179 features
## It has 57 fields
## Integer64 fields read as strings: ne_id
rivers10 <- ne_download(scale = 10, type = 'rivers_lake_centerlines',
category = 'physical', returnclass = "sf")
## OGR data source with driver: ESRI Shapefile
## Source: "C:\Users\Samuel\AppData\Local\Temp\RtmpUXzRpF", layer: "ne_10m_rivers_lake_centerlines"
## with 1473 features
## It has 38 fields
## Integer64 fields read as strings: ne_id
lakes10 <- ne_download(scale = "large", type = 'lakes',
category = 'physical', returnclass = "sf")
## OGR data source with driver: ESRI Shapefile
## Source: "C:\Users\Samuel\AppData\Local\Temp\RtmpUXzRpF", layer: "ne_10m_lakes"
## with 1355 features
## It has 41 fields
## Integer64 fields read as strings: scalerank ne_id
sov110 <- ne_download(scale="medium", type = "sovereignty",
category = "cultural", returnclass = "sf")
## OGR data source with driver: ESRI Shapefile
## Source: "C:\Users\Samuel\AppData\Local\Temp\RtmpUXzRpF", layer: "ne_50m_admin_0_sovereignty"
## with 201 features
## It has 168 fields
## Integer64 fields read as strings: NE_ID
admin110 <- ne_download(scale="large", type = "populated_places",
category = "cultural", returnclass = "sf")
## OGR data source with driver: ESRI Shapefile
## Source: "C:\Users\Samuel\AppData\Local\Temp\RtmpUXzRpF", layer: "ne_10m_populated_places"
## with 7342 features
## It has 137 fields
## Integer64 fields read as strings: POP_MAX POP_MIN POP_OTHER MAX_POP10 MAX_POP20 MAX_POP50 MAX_POP300 MAX_POP310 MAX_NATSCA MIN_AREAKM MAX_AREAKM MIN_AREAMI MAX_AREAMI MIN_PERKM MAX_PERKM MIN_PERMI MAX_PERMI POP1950 POP1955 POP1960 POP1965 POP1970 POP1975 POP1980 POP1985 POP1990 POP1995 POP2000 POP2005 POP2010 POP2015 POP2020 POP2025 POP2050 WOF_ID NE_ID GEONAMESID
#scale pies by sample size
mosq_abundance<- read.csv("D:/Jeremiah/template species abundance csv.csv")
mosq_abundance$radius <- rescale(mosq_abundance$total_mosq, to = c(2, 5))
#plot map
Plot <- ggplot()+
geom_sf(data=sov110, color='black', size=0.8, fill = ifelse(sov110$ADMIN == "Kenya", 'grey98', 'grey90')) +
geom_sf(data=rivers10, color="cyan4", size=0.5, alpha=0.5) +
geom_sf(data=lakes10, color="grey40", fill ="aliceblue", size= 0.8) +
geom_sf(data=admin10, color="grey80", size= 0.4) +
geom_sf(data=admin110, color="grey80", size= 0.4) +
geom_scatterpie(aes(x=longitude, y=latitude, r = 0.1),
data = mosq_abundance,
cols = c("funestus_prop", "gambiae_prop"),
color = "grey20",
legend_name = "Mosq_Prop",
alpha=.8)+
scale_fill_manual(values = c("darkgoldenrod1","firebrick")) +
coord_sf(xlim = c(33,45 ), ylim = c(-4,5.5), expand = TRUE) +
theme_void()+
theme(panel.background = element_rect(fill = 'aliceblue', colour = 'grey97'))
Plot
#save plot
ggsave("D:/Jeremiah/Ke_Mosq.png", dpi=600, width=7.5, height=7)
Session Info:
sessionInfo()
## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 22000)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=English_Kenya.1252 LC_CTYPE=English_Kenya.1252
## [3] LC_MONETARY=English_Kenya.1252 LC_NUMERIC=C
## [5] LC_TIME=English_Kenya.1252
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] scales_1.2.1 scatterpie_0.1.8 ggspatial_1.1.6
## [4] sf_1.0-8 rnaturalearthdata_0.1.0 rnaturalearth_0.1.0
## [7] forcats_0.5.2 stringr_1.4.1 dplyr_1.0.10
## [10] purrr_0.3.4 readr_2.1.3 tidyr_1.2.1
## [13] tibble_3.1.8 ggplot2_3.3.6 tidyverse_1.3.2
##
## loaded via a namespace (and not attached):
## [1] fs_1.5.2 lubridate_1.8.0 httr_1.4.4
## [4] tools_4.1.2 backports_1.4.1 bslib_0.4.0
## [7] utf8_1.2.2 rgdal_1.5-32 R6_2.5.1
## [10] KernSmooth_2.23-20 DBI_1.1.3 colorspace_2.0-3
## [13] withr_2.5.0 sp_1.5-0 tidyselect_1.1.2
## [16] compiler_4.1.2 textshaping_0.3.6 cli_3.4.1
## [19] rvest_1.0.3 xml2_1.3.3 sass_0.4.2
## [22] classInt_0.4-8 proxy_0.4-27 systemfonts_1.0.4
## [25] digest_0.6.29 rmarkdown_2.16 pkgconfig_2.0.3
## [28] htmltools_0.5.3 dbplyr_2.2.1 fastmap_1.1.0
## [31] highr_0.9 rlang_1.0.6 readxl_1.4.1
## [34] rstudioapi_0.14 jquerylib_0.1.4 generics_0.1.3
## [37] farver_2.1.1 jsonlite_1.8.2 googlesheets4_1.0.1
## [40] magrittr_2.0.3 s2_1.1.0 Rcpp_1.0.9
## [43] munsell_0.5.0 fansi_1.0.3 lifecycle_1.0.2
## [46] stringi_1.7.8 yaml_2.3.5 MASS_7.3-58.1
## [49] grid_4.1.2 crayon_1.5.2 lattice_0.20-45
## [52] haven_2.5.1 hms_1.1.2 knitr_1.40
## [55] pillar_1.8.1 wk_0.6.0 reprex_2.0.2
## [58] glue_1.6.2 evaluate_0.16 ggfun_0.0.7
## [61] modelr_0.1.9 vctrs_0.4.2 tzdb_0.3.0
## [64] tweenr_2.0.2 cellranger_1.1.0 gtable_0.3.1
## [67] polyclip_1.10-0 assertthat_0.2.1 cachem_1.0.6
## [70] xfun_0.33 ggforce_0.4.1 broom_1.0.1
## [73] e1071_1.7-11 ragg_1.2.3 class_7.3-20
## [76] googledrive_2.0.0 gargle_1.2.1 units_0.8-0
## [79] ellipsis_0.3.2