How To Draw A Map With R
library(tidyverse) library(sf) library(here) options(digits = 3) fix.seed(1234) theme_set(theme_minimal()) Unlike raster prototype maps, vector maps require you to obtain spatial information files which contain detailed data necessary to draw all the components of a map (e.g. points, lines, polygons). Once yous successfully import that data into R, ggplot2 works with simple features data frames to easily generate geospatial visualizations using all the core elements and approaches of ggplot().
Import Us country boundaries
First we volition import a spatial data file containing the boundaries of all 50 states in the United States1 using sf::st_read():
usa <- hither( "static", "data", "census_bureau", "cb_2013_us_state_20m", "cb_2013_us_state_20m.shp" ) %>% st_read() ## Reading layer `cb_2013_us_state_20m' from data source `/Users/soltoffbc/Projects/Computing for Social Sciences/class-site/static/data/census_bureau/cb_2013_us_state_20m/cb_2013_us_state_20m.shp' using driver `ESRI Shapefile' ## Simple characteristic collection with 52 features and 9 fields ## Geometry blazon: MULTIPOLYGON ## Dimension: XY ## Bounding box: xmin: -179 ymin: 17.nine xmax: 180 ymax: 71.iv ## Geodetic CRS: NAD83 Draw the boundaries
ggplot2 contains a geometric object specifically for simple feature objects called geom_sf(). This works reasonably well when you need to draw polygons, like our state boundaries. Support for elementary features in ggplot2 is under active development, so you may not observe adequate support for plotting line or point features. To draw the map, we pass the simple features data frame as the data argument.
ggplot(data = usa) + geom_sf() 
Because simple features information frames are standardized with the geometry cavalcade always containing data on the geographic coordinates of the features, we do not need to specify additional parameters for aes(). Notice a problem with the map above: it wastes a lot of space. This is acquired by the presence of Alaska and Hawaii in the dataset. The Aleutian Islands cross the the 180th meridian, requiring the map to prove the Eastern hemisphere. Likewise, Hawaii is substantially distant from the continental United States.
Plot a subset of a map
One solution is to plot just the lower 48 states. That is, exclude Alaska and Hawaii, as well as DC and Puerto Rico.2 Because simple features data frames contain one row per feature and in this example a feature is defined as a state, we can employ filter() from dplyr to exclude these four states/territories.
(usa_48 <- usa %>% filter(!(Proper name %in% c("Alaska", "District of Columbia", "Hawaii", "Puerto Rico")))) ## Simple feature collection with 48 features and 9 fields ## geometry blazon: MULTIPOLYGON ## dimension: XY ## bbox: xmin: -125 ymin: 24.five xmax: -66.9 ymax: 49.4 ## geographic CRS: NAD83 ## Outset 10 features: ## STATEFP STATENS AFFGEOID GEOID STUSPS NAME LSAD ALAND AWATER ## 1 01 01779775 0400000US01 01 AL Alabama 00 ane.31e+eleven 4.59e+09 ## 2 05 00068085 0400000US05 05 AR Arkansas 00 1.35e+11 2.96e+09 ## 3 06 01779778 0400000US06 06 CA California 00 four.03e+xi ii.05e+10 ## 4 09 01779780 0400000US09 09 CT Connecticut 00 1.25e+10 1.82e+09 ## 5 12 00294478 0400000US12 12 FL Florida 00 ane.39e+11 3.14e+10 ## 6 13 01705317 0400000US13 13 GA Georgia 00 1.49e+11 4.95e+09 ## seven xvi 01779783 0400000US16 16 ID Idaho 00 ii.14e+11 2.40e+09 ## eight 17 01779784 0400000US17 17 IL Illinois 00 1.44e+eleven 6.20e+09 ## 9 xviii 00448508 0400000US18 eighteen IN Indiana 00 9.28e+10 one.54e+09 ## ten 20 00481813 0400000US20 twenty KS Kansas 00 two.12e+xi 1.35e+09 ## geometry ## ane MULTIPOLYGON (((-88.three 30.2,... ## 2 MULTIPOLYGON (((-94.half-dozen 36.5,... ## 3 MULTIPOLYGON (((-119 33.5, ... ## 4 MULTIPOLYGON (((-73.seven 41.one,... ## 5 MULTIPOLYGON (((-eighty.vii 24.9,... ## 6 MULTIPOLYGON (((-85.6 35, -... ## 7 MULTIPOLYGON (((-117 44.4, ... ## 8 MULTIPOLYGON (((-91.5 twoscore.2,... ## ix MULTIPOLYGON (((-88.1 37.nine,... ## 10 MULTIPOLYGON (((-102 40, -ane... ggplot(information = usa_48) + geom_sf() 
Since the map is a ggplot() object, it tin can hands be modified like any other ggplot() graph. Nosotros could change the color of the map and the borders:
ggplot(data = usa_48) + geom_sf(fill = "palegreen", color = "black") 
albersusa
Rather than excluding them entirely, well-nigh maps of the United States place Alaska and Hawaii equally insets to the south of California. Until recently, in R this was an extremely boring task that required manually changing the latitude and longitude coordinates for these states to identify them in the correct location. Fortunately several packages are now available that have already washed the work for you. albersusa includes the usa_sf() function which returns a simple features data frame which contains adapted coordinates for Alaska and Hawaii to plot them with the mainland. It can be installed from GitHub using devtools::install_github("hrbrmstr/albersusa").
library(albersusa) usa_sf() ## Simple feature collection with 51 features and 13 fields ## geometry type: MULTIPOLYGON ## dimension: XY ## bbox: xmin: -125 ymin: 20.6 xmax: -66.9 ymax: 49.4 ## geographic CRS: WGS 84 ## First x features: ## geo_id fips_state proper noun lsad census_area iso_3166_2 ## 1 0400000US04 04 Arizona 113594 AZ ## 2 0400000US05 05 Arkansas 52035 AR ## 3 0400000US06 06 California 155779 CA ## 4 0400000US08 08 Colorado 103642 CO ## 5 0400000US09 09 Connecticut 4842 CT ## 6 0400000US11 11 District of Columbia 61 DC ## 7 0400000US13 13 Georgia 57513 GA ## 8 0400000US17 17 Illinois 55519 IL ## 9 0400000US18 eighteen Indiana 35826 IN ## ten 0400000US22 22 Louisiana 43204 LA ## census pop_estimataes_base pop_2010 pop_2011 pop_2012 pop_2013 pop_2014 ## 1 6392017 6392310 6411999 6472867 6556236 6634997 6731484 ## two 2915918 2915958 2922297 2938430 2949300 2958765 2966369 ## 3 37253956 37254503 37336011 37701901 38062780 38431393 38802500 ## iv 5029196 5029324 5048575 5119661 5191709 5272086 5355866 ## five 3574097 3574096 3579345 3590537 3594362 3599341 3596677 ## 6 601723 601767 605210 620427 635040 649111 658893 ## 7 9687653 9688681 9714464 9813201 9919000 9994759 10097343 ## viii 12830632 12831587 12840097 12858725 12873763 12890552 12880580 ## 9 6483802 6484192 6490308 6516560 6537632 6570713 6596855 ## x 4533372 4533479 4545581 4575972 4604744 4629284 4649676 ## geometry ## ane MULTIPOLYGON (((-113 37, -i... ## 2 MULTIPOLYGON (((-94 33, -94... ## iii MULTIPOLYGON (((-120 34, -1... ## 4 MULTIPOLYGON (((-107 41, -i... ## 5 MULTIPOLYGON (((-72.4 42, -... ## half dozen MULTIPOLYGON (((-77 38.8, -... ## 7 MULTIPOLYGON (((-84.eight 35, -... ## eight MULTIPOLYGON (((-89.four 42.v,... ## 9 MULTIPOLYGON (((-84.8 40.4,... ## 10 MULTIPOLYGON (((-88.9 29.8,... ggplot(data = usa_sf()) + geom_sf() 
Add data to the map
Region boundaries serve equally the groundwork in geospatial data visualization - so at present we need to add together data. Some types of geographic information (points and symbols) are overlaid on top of the boundaries, whereas other information (fill up) are incorporated into the region layer itself.
Points
Allow's use our usa_48 map data to add some points. The airports information frame in the nycflights13 package includes geographic info on airports in the U.s..
library(nycflights13) airports ## # A tibble: ane,458 x 8 ## faa proper noun lat lon alt tz dst tzone ## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <chr> <chr> ## i 04G Lansdowne Airport 41.1 -lxxx.vi 1044 -v A America/New_Yo… ## 2 06A Moton Field Municipal A… 32.5 -85.vii 264 -6 A America/Chicago ## 3 06C Schaumburg Regional 42.0 -88.1 801 -6 A America/Chicago ## 4 06N Randall Airdrome 41.iv -74.4 523 -5 A America/New_Yo… ## 5 09J Jekyll Isle Airport 31.1 -81.iv 11 -5 A America/New_Yo… ## half-dozen 0A9 Elizabethton Municipal … 36.4 -82.2 1593 -5 A America/New_Yo… ## 7 0G6 Williams Canton Airdrome 41.v -84.v 730 -5 A America/New_Yo… ## viii 0G7 Finger Lakes Regional A… 42.nine -76.8 492 -5 A America/New_Yo… ## 9 0P2 Shoestring Aviation Air… 39.eight -76.6 chiliad -v U America/New_Yo… ## 10 0S9 Jefferson County Intl 48.1 -123. 108 -8 A America/Los_An… ## # … with 1,448 more than rows Each airport has information technology'south geographic location encoded through lat and lon. To draw these points on the map, basically nosotros describe a scatterplot with x = lon and y = lat. In fact nosotros could merely practise that:
ggplot(airports, aes(lon, lat)) + geom_point() 
Allow's overlay information technology with the mapped country borders:
ggplot(data = usa_48) + geom_sf() + geom_point(data = airports, aes(10 = lon, y = lat), shape = i) 
Slight problem. Nosotros have airports listed outside of the continental United states. There are a couple means to rectify this. Unfortunately airports does not include a variable identifying state so the filter() operation is not that simple. The easiest solution is to crop the limits of the graph using coord_sf() to only show the mainland:
ggplot(data = usa_48) + geom_sf() + geom_point(data = airports, aes(x = lon, y = lat), shape = 1) + coord_sf( xlim = c(-130, -60), ylim = c(20, fifty) ) 
Alternatively, nosotros can use st_as_sf() to convert airports to a simple features data frame.
airports_sf <- st_as_sf(airports, coords = c("lon", "lat")) st_crs(airports_sf) <- 4326 # set the coordinate reference arrangement airports_sf ## Elementary feature collection with 1458 features and 6 fields ## Geometry blazon: Point ## Dimension: XY ## Bounding box: xmin: -177 ymin: nineteen.7 xmax: 174 ymax: 72.3 ## Geodetic CRS: WGS 84 ## # A tibble: 1,458 x seven ## faa name alt tz dst tzone geometry ## * <chr> <chr> <dbl> <dbl> <chr> <chr> <POINT [°]> ## i 04G Lansdowne Airport 1044 -5 A America/New_… (-80.6 41.1) ## 2 06A Moton Field Municipa… 264 -6 A America/Chichi… (-85.7 32.5) ## 3 06C Schaumburg Regional 801 -6 A America/Chic… (-88.1 42) ## 4 06N Randall Airport 523 -5 A America/New_… (-74.4 41.iv) ## five 09J Jekyll Isle Airport 11 -v A America/New_… (-81.4 31.ane) ## 6 0A9 Elizabethton Municip… 1593 -5 A America/New_… (-82.2 36.4) ## seven 0G6 Williams County Airp… 730 -5 A America/New_… (-84.v 41.5) ## 8 0G7 Finger Lakes Regiona… 492 -v A America/New_… (-76.8 42.9) ## 9 0P2 Shoestring Aviation … chiliad -five U America/New_… (-76.6 39.eight) ## 10 0S9 Jefferson Canton Intl 108 -8 A America/Los_… (-123 48.i) ## # … with 1,448 more rows coords tells st_as_sf() which columns contain the geographic coordinates of each drome. To graph the points on the map, we use a second geom_sf():
ggplot() + geom_sf(data = usa_48) + geom_sf(data = airports_sf, shape = 1) + coord_sf( xlim = c(-130, -60), ylim = c(20, 50) ) 
Symbols
Nosotros tin change the size or type of symbols on the map. For instance, we can describe a bubble plot (also known as a proportional symbol map) and encode the altitude of the airport through the size aqueduct:
ggplot(information = usa_48) + geom_sf() + geom_point( information = airports, aes(x = lon, y = lat, size = alt), fill = "grey", color = "black", alpha = .2 ) + coord_sf( xlim = c(-130, -60), ylim = c(20, 50) ) + scale_size_area(guide = Faux) 
Circle area is proportional to the airport'due south altitude (in feet). Or we could scale it based on the number of arriving flights in flights:
airports_n <- flights %>% count(dest) %>% left_join(airports, past = c("dest" = "faa")) ggplot(data = usa_48) + geom_sf() + geom_point( information = airports_n, aes(x = lon, y = lat, size = northward), fill up = "grey", color = "black", alpha = .2 ) + coord_sf( xlim = c(-130, -lx), ylim = c(20, 50) ) + scale_size_area(guide = Faux) 
airports contains a list of nearly all commercial airports in the United States. Withal flights just contains data on flights departing from New York City airports (JFK, LaGuardia, or Newark) and only services a few airports around the state.
Make full (choropleths)
Choropleth maps encode information by assigning shades of colors to divers areas on a map (e.g. countries, states, counties, zip codes). At that place are lots of ways to tweak and customize these graphs, which is by and large a good idea considering remember that colour is i of the harder-to-decode channels.
Nosotros volition continue to use the usa_48 simple features data frame and draw a choropleth for the number of strange-built-in individuals in each state. Nosotros go those files from the census_bureau folder. Let'south also normalize our measure past the full population to get the rate of strange-born individuals in the population:
(fb_state <- hither( "static", "data", "census_bureau", "ACS_13_5YR_B05012_state", "ACS_13_5YR_B05012.csv" ) %>% read_csv() %>% mutate(rate = HD01_VD03 / HD01_VD01)) ## # A tibble: 51 x 10 ## GEO.id GEO.id2 `GEO.brandish-labe… HD01_VD01 HD02_VD01 HD01_VD02 HD02_VD02 ## <chr> <chr> <chr> <dbl> <lgl> <dbl> <dbl> ## 1 0400000US… 01 Alabama 4799277 NA 4631045 2881 ## 2 0400000US… 02 Alaska 720316 NA 669941 1262 ## 3 0400000US… 04 Arizona 6479703 NA 5609835 7725 ## iv 0400000US… 05 Arkansas 2933369 NA 2799972 2568 ## v 0400000US… 06 California 37659181 NA 27483342 30666 ## half-dozen 0400000US… 08 Colorado 5119329 NA 4623809 5778 ## vii 0400000US… 09 Connecticut 3583561 NA 3096374 5553 ## eight 0400000US… 10 Delaware 908446 NA 831683 2039 ## 9 0400000US… 11 District of Colum… 619371 NA 534142 2022 ## x 0400000US… 12 Florida 19091156 NA 15392410 16848 ## # … with 41 more rows, and 3 more variables: HD01_VD03 <dbl>, HD02_VD03 <dbl>, ## # rate <dbl> Bring together the information
Now that we have our data, we want to describe it on the map. fb_state contains one row per state, as does usa_48. Since there is a one-to-one match between the data frames, we join the data frames together first, and so use that single data frame to depict the map. This differs from the approach to a higher place for drawing points because a betoken feature is not the same thing every bit a polygon feature. That is, there were more airports so there were states. Because the spatial data is stored in a data frame with one row per country, all nosotros need to practice is merge the information frames together on a column that uniquely identifies each row in each data frame.
(usa_fb <- usa_48 %>% left_join(fb_state, by = c("STATEFP" = "GEO.id2"))) ## Unproblematic feature collection with 48 features and xviii fields ## geometry type: MULTIPOLYGON ## dimension: XY ## bbox: xmin: -125 ymin: 24.5 xmax: -66.9 ymax: 49.4 ## geographic CRS: NAD83 ## Commencement 10 features: ## STATEFP STATENS AFFGEOID GEOID STUSPS NAME LSAD ALAND AWATER ## 1 01 01779775 0400000US01 01 AL Alabama 00 ane.31e+11 4.59e+09 ## ii 05 00068085 0400000US05 05 AR Arkansas 00 ane.35e+11 two.96e+09 ## 3 06 01779778 0400000US06 06 CA California 00 4.03e+xi 2.05e+10 ## four 09 01779780 0400000US09 09 CT Connecticut 00 1.25e+x 1.82e+09 ## v 12 00294478 0400000US12 12 FL Florida 00 i.39e+eleven 3.14e+ten ## half dozen 13 01705317 0400000US13 13 GA Georgia 00 1.49e+xi 4.95e+09 ## 7 16 01779783 0400000US16 16 ID Idaho 00 2.14e+xi 2.40e+09 ## viii 17 01779784 0400000US17 17 IL Illinois 00 1.44e+eleven 6.20e+09 ## 9 xviii 00448508 0400000US18 18 IN Indiana 00 9.28e+10 i.54e+09 ## 10 20 00481813 0400000US20 20 KS Kansas 00 2.12e+eleven 1.35e+09 ## GEO.id GEO.display-characterization HD01_VD01 HD02_VD01 HD01_VD02 HD02_VD02 ## ane 0400000US01 Alabama 4799277 NA 4631045 2881 ## 2 0400000US05 Arkansas 2933369 NA 2799972 2568 ## 3 0400000US06 California 37659181 NA 27483342 30666 ## iv 0400000US09 Connecticut 3583561 NA 3096374 5553 ## 5 0400000US12 Florida 19091156 NA 15392410 16848 ## half-dozen 0400000US13 Georgia 9810417 NA 8859747 7988 ## 7 0400000US16 Idaho 1583364 NA 1489560 2528 ## 8 0400000US17 Illinois 12848554 NA 11073828 10091 ## 9 0400000US18 Indiana 6514861 NA 6206801 4499 ## ten 0400000US20 Kansas 2868107 NA 2677007 3095 ## HD01_VD03 HD02_VD03 rate geometry ## 1 168232 2881 0.0351 MULTIPOLYGON (((-88.iii 30.2,... ## 2 133397 2568 0.0455 MULTIPOLYGON (((-94.six 36.5,... ## 3 10175839 30666 0.2702 MULTIPOLYGON (((-119 33.5, ... ## 4 487187 5553 0.1360 MULTIPOLYGON (((-73.7 41.1,... ## v 3698746 16848 0.1937 MULTIPOLYGON (((-80.7 24.9,... ## 6 950670 7988 0.0969 MULTIPOLYGON (((-85.6 35, -... ## 7 93804 2528 0.0592 MULTIPOLYGON (((-117 44.4, ... ## viii 1774726 10093 0.1381 MULTIPOLYGON (((-91.v xl.2,... ## 9 308060 4500 0.0473 MULTIPOLYGON (((-88.one 37.ix,... ## 10 191100 3100 0.0666 MULTIPOLYGON (((-102 twoscore, -ane... Draw the map
With the newly combined information frame, use geom_sf() and define the fill up aesthetic based on the cavalcade in usa_fb you want to visualize.
ggplot(data = usa_fb) + geom_sf(aes(make full = rate)) 
Bin information to discrete intervals
When creating a heatmap with a continuous variable, one must determine whether to keep the variable every bit continuous or collapse it into a series of bins with discrete colors. While keep the variable continuous is technically more than precise, the human eye cannot usually distinguish between ii colors which are very like to one some other. By converting the variable to a detached variable, you hands distinguish between the different levels. If you make up one's mind to convert a continuous variable to a discrete variable, you lot volition need to decide how to do this. While cut() is a base R function for converting continuous variables into detached values, ggplot2 offers two functions that explicitly ascertain how nosotros want to bin the numeric vector (column).
cut_interval() makes north groups with equal range:
usa_fb %>% mutate(rate_cut = cut_interval(rate, n = 6)) %>% ggplot() + geom_sf(aes(fill up = rate_cut)) 
Whereas cut_number() makes n groups with (approximately) equal numbers of observations:
usa_fb %>% mutate(rate_cut = cut_number(rate, n = 6)) %>% ggplot() + geom_sf(aes(fill = rate_cut)) 
Run into this StackOverflow thread for a more in-depth discussion on the merits of bucketizing a continuous variable and whether to use cut_interval() or cut_number().
Changing map projection
](https://imgs.xkcd.com/comics/mercator_projection.png)
Representing portions of the globe on a flat surface can be challenging. Depending on how you project the map, you tin distort or emphasize sure features of the map. Fortunately, ggplot() includes the coord_sf() office which allows us to hands implement different projection methods. In society to implement coordinate transformations, you need to know the coordinate reference organisation that defines the projection method. The "easiest" approach is to provide what is known as the proj4string that defines the projection method. PROJ4 is a generic coordinate transformation software that allows you to convert betwixt projection methods. If you get actually into geospatial assay and visualization, it is helpful to acquire this system.
For our purposes here, proj4string is a graphic symbol cord in R that defines the coordinate system and includes parameters specific to a given coordinate transformation. PROJ4 includes some documentation on common projection methods that can get you started. Some project methods are relatively uncomplicated and require just the name of the project, similar for a Mercator project ("+proj=merc"):
map_proj_base <- ggplot(data = usa_48) + geom_sf() map_proj_base + coord_sf(crs = "+proj=merc") + ggtitle("Mercator projection") 
Other coordinate systems require specification of the standard lines, or lines that define areas of the surface of the map that are tangent to the globe. These include Gall-Peters, Albers equal-area, and Lambert azimuthal.
map_proj_base + coord_sf(crs = "+proj=cea +lon_0=0 +lat_ts=45") + ggtitle("Gall-Peters projection") 
map_proj_base + coord_sf(crs = "+proj=aea +lat_1=25 +lat_2=l +lon_0=-100") + ggtitle("Albers equal-area projection") 
map_proj_base + coord_sf(crs = "+proj=laea +lat_0=35 +lon_0=-100") + ggtitle("Lambert azimuthal projection") 
Session Info
devtools::session_info() ## ─ Session info ─────────────────────────────────────────────────────────────── ## setting value ## version R version 4.0.four (2021-02-15) ## os macOS Big Sur ten.16 ## system x86_64, darwin17.0 ## ui X11 ## language (EN) ## collate en_US.UTF-8 ## ctype en_US.UTF-eight ## tz America/Chicago ## engagement 2022-07-13 ## ## ─ Packages ─────────────────────────────────────────────────────────────────── ## package * version appointment lib source ## assertthat 0.2.i 2022-03-21 [2] CRAN (R iv.0.0) ## backports 1.2.1 2022-12-09 [2] CRAN (R 4.0.2) ## blogdown 1.iii 2022-04-14 [ii] CRAN (R four.0.2) ## bookdown 0.22 2022-04-22 [2] CRAN (R four.0.2) ## broom 0.seven.half-dozen 2022-04-05 [2] CRAN (R 4.0.4) ## bslib 0.2.5 2022-05-12 [2] CRAN (R iv.0.4) ## cachem 1.0.five 2022-05-xv [2] CRAN (R 4.0.2) ## callr 3.vii.0 2022-04-20 [ii] CRAN (R 4.0.2) ## cellranger 1.1.0 2022-07-27 [2] CRAN (R 4.0.0) ## course 7.iii-19 2022-05-03 [2] CRAN (R 4.0.two) ## classInt 0.4-three 2022-04-07 [2] CRAN (R 4.0.0) ## cli 2.5.0 2022-04-26 [2] CRAN (R 4.0.2) ## colorspace 2.0-ane 2022-05-04 [2] CRAN (R 4.0.2) ## crayon 1.4.1 2022-02-08 [2] CRAN (R four.0.2) ## DBI 1.1.one 2022-01-15 [2] CRAN (R 4.0.2) ## dbplyr 2.one.ane 2022-04-06 [2] CRAN (R 4.0.4) ## desc i.3.0 2022-03-05 [2] CRAN (R 4.0.2) ## devtools 2.iv.1 2022-05-05 [ii] CRAN (R 4.0.two) ## digest 0.half-dozen.27 2022-ten-24 [2] CRAN (R four.0.2) ## dplyr * 1.0.6 2022-05-05 [2] CRAN (R 4.0.2) ## e1071 1.7-vi 2022-03-eighteen [2] CRAN (R iv.0.two) ## ellipsis 0.3.ii 2022-04-29 [ii] CRAN (R 4.0.2) ## evaluate 0.14 2022-05-28 [2] CRAN (R 4.0.0) ## fansi 0.4.ii 2022-01-15 [ii] CRAN (R 4.0.2) ## fastmap 1.one.0 2022-01-25 [2] CRAN (R four.0.2) ## forcats * 0.5.1 2022-01-27 [ii] CRAN (R four.0.two) ## fs one.5.0 2022-07-31 [two] CRAN (R 4.0.2) ## generics 0.1.0 2022-ten-31 [2] CRAN (R 4.0.2) ## ggplot2 * three.3.3 2022-12-30 [2] CRAN (R iv.0.2) ## mucilage 1.4.two 2022-08-27 [2] CRAN (R iv.0.ii) ## gtable 0.3.0 2022-03-25 [2] CRAN (R iv.0.0) ## haven 2.4.one 2022-04-23 [2] CRAN (R 4.0.ii) ## here * ane.0.1 2022-12-13 [2] CRAN (R iv.0.2) ## hms i.1.0 2022-05-17 [2] CRAN (R 4.0.4) ## htmltools 0.v.1.one 2022-01-22 [2] CRAN (R iv.0.2) ## httr 1.4.ii 2022-07-20 [2] CRAN (R 4.0.ii) ## jquerylib 0.1.4 2022-04-26 [2] CRAN (R 4.0.two) ## jsonlite i.vii.2 2022-12-09 [2] CRAN (R 4.0.2) ## KernSmooth ii.23-20 2022-05-03 [2] CRAN (R four.0.two) ## knitr 1.33 2022-04-24 [2] CRAN (R four.0.2) ## lifecycle 1.0.0 2022-02-15 [ii] CRAN (R 4.0.2) ## lubridate one.vii.ten 2022-02-26 [2] CRAN (R 4.0.2) ## magrittr ii.0.1 2022-11-17 [two] CRAN (R 4.0.two) ## memoise 2.0.0 2022-01-26 [2] CRAN (R 4.0.two) ## modelr 0.i.eight 2022-05-nineteen [2] CRAN (R four.0.0) ## munsell 0.5.0 2022-06-12 [2] CRAN (R 4.0.0) ## colonnade 1.half-dozen.1 2022-05-xvi [2] CRAN (R iv.0.4) ## pkgbuild 1.2.0 2022-12-xv [two] CRAN (R four.0.two) ## pkgconfig ii.0.three 2022-09-22 [2] CRAN (R 4.0.0) ## pkgload 1.2.one 2022-04-06 [2] CRAN (R four.0.two) ## prettyunits 1.1.1 2022-01-24 [two] CRAN (R 4.0.0) ## processx 3.5.2 2022-04-30 [ii] CRAN (R four.0.ii) ## proxy 0.4-25 2022-03-05 [2] CRAN (R 4.0.2) ## ps i.vi.0 2022-02-28 [2] CRAN (R 4.0.2) ## purrr * 0.3.4 2022-04-17 [ii] CRAN (R four.0.0) ## R6 2.5.0 2022-ten-28 [2] CRAN (R iv.0.2) ## Rcpp 1.0.half dozen 2022-01-15 [ii] CRAN (R 4.0.2) ## readr * one.4.0 2022-10-05 [2] CRAN (R 4.0.2) ## readxl 1.3.i 2022-03-13 [ii] CRAN (R 4.0.0) ## remotes 2.3.0 2022-04-01 [2] CRAN (R 4.0.2) ## reprex ii.0.0 2022-04-02 [2] CRAN (R 4.0.two) ## rlang 0.4.11 2022-04-30 [2] CRAN (R 4.0.ii) ## rmarkdown 2.8 2022-05-07 [2] CRAN (R iv.0.2) ## rprojroot two.0.2 2022-11-xv [ii] CRAN (R 4.0.two) ## rstudioapi 0.thirteen 2022-11-12 [2] CRAN (R 4.0.two) ## rvest 1.0.0 2022-03-09 [two] CRAN (R 4.0.2) ## sass 0.4.0 2022-05-12 [2] CRAN (R iv.0.2) ## scales i.one.1 2022-05-11 [2] CRAN (R 4.0.0) ## sessioninfo i.1.1 2022-xi-05 [2] CRAN (R iv.0.0) ## sf * 0.nine-eight 2022-03-17 [ii] CRAN (R 4.0.2) ## stringi one.6.1 2022-05-10 [ii] CRAN (R 4.0.2) ## stringr * ane.4.0 2022-02-10 [2] CRAN (R 4.0.0) ## testthat 3.0.2 2022-02-14 [2] CRAN (R 4.0.2) ## tibble * 3.1.ane 2022-04-18 [2] CRAN (R 4.0.2) ## tidyr * 1.i.3 2022-03-03 [2] CRAN (R iv.0.2) ## tidyselect one.1.1 2022-04-xxx [2] CRAN (R 4.0.two) ## tidyverse * one.3.one 2022-04-15 [2] CRAN (R four.0.2) ## units 0.7-one 2022-03-16 [2] CRAN (R 4.0.2) ## usethis 2.0.1 2022-02-10 [2] CRAN (R 4.0.2) ## utf8 ane.2.1 2022-03-12 [2] CRAN (R four.0.2) ## vctrs 0.three.8 2022-04-29 [2] CRAN (R iv.0.ii) ## withr 2.4.2 2022-04-eighteen [two] CRAN (R 4.0.two) ## xfun 0.23 2022-05-15 [ii] CRAN (R 4.0.two) ## xml2 1.iii.ii 2022-04-23 [2] CRAN (R iv.0.0) ## yaml 2.2.1 2022-02-01 [ii] CRAN (R 4.0.0) ## ## [1] /Users/soltoffbc/Library/R/iv.0/library ## [2] /Library/Frameworks/R.framework/Versions/iv.0/Resource/library 
Source: https://cfss.uchicago.edu/notes/vector-maps/
Posted by: baxterressat.blogspot.com
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