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link管理  ›  第 81 章 探索性数据分析-驯鹿迁移 | 数据科学中的 R 语言
r语言 latitude
https://bookdown.org/wangminjie/R4DS/eda-caribou.html
飘逸的核桃
1 年前
tuesdata <- tidytuesdayR :: tt_load ( "2020-06-23" ) # tuesdata <- tidytuesdayR::tt_load(2020, week = 26) library ( tidyverse ) library ( lubridate ) library ( gganimate ) individuals <- readr :: read_csv ( "./demo_data/caribou/individuals.csv" ) locations <- readr :: read_csv ( "./demo_data/caribou/locations.csv" ) 81.2 驯鹿的身份信息 individuals %>% glimpse ( ) 
## Rows: 286
## Columns: 14
## $ animal_id            <chr> "HR_151.510", "GR_C04", "GR_C03", "HR_151.805", "…
## $ sex                  <chr> "f", "f", "f", "f", "f", "f", "f", "f", "f", "f",…
## $ life_stage           <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ pregnant             <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ with_calf            <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ death_cause          <chr> NA, NA, NA, NA, NA, NA, NA, NA, "Unknown", NA, NA…
## $ study_site           <chr> "Hart Ranges", "Graham", "Graham", "Hart Ranges",…
## $ deploy_on_longitude  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ deploy_on_latitude   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ deploy_on_comments   <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ deploy_off_longitude <dbl> NA, NA, NA, NA, NA, NA, NA, NA, -122.6405, NA, NA…
## $ deploy_off_latitude  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, 55.26054, NA, NA,…
## $ deploy_off_type      <chr> "unknown", "unknown", "unknown", "unknown", "unkn…
## $ deploy_off_comments  <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
individuals %>% count ( animal_id ) 
## # A tibble: 260 × 2
##    animal_id     n
##    <chr>     <int>
##  1 BP_car022     1
##  2 BP_car023     1
##  3 BP_car032     1
##  4 BP_car043     1
##  5 BP_car100     1
##  6 BP_car101     1
##  7 BP_car115     1
##  8 BP_car144     1
##  9 BP_car145     1
## 10 GR_C01        2
## # ℹ 250 more rows

我们发现有重复id的，怎么办？

individuals %>% janitor :: get_dupes ( animal_id ) 
## # A tibble: 50 × 15
##    animal_id dupe_count sex   life_stage pregnant with_calf death_cause
##    <chr>          <int> <chr> <chr>      <lgl>    <lgl>     <chr>      
##  1 MO_car150          3 f     3-5        NA       FALSE     <NA>       
##  2 MO_car150          3 f     3-5        NA       FALSE     <NA>       
##  3 MO_car150          3 f     4-6        NA       FALSE     <NA>       
##  4 QU_car137          3 f     2-3        NA       FALSE     <NA>       
##  5 QU_car137          3 f     3-5        NA       FALSE     <NA>       
##  6 QU_car137          3 f     3-5        NA       FALSE     <NA>       
##  7 GR_C01             2 f     <NA>       NA       NA        <NA>       
##  8 GR_C01             2 f     <NA>       NA       NA        <NA>       
##  9 GR_C02             2 f     <NA>       NA       NA        <NA>       
## 10 GR_C02             2 f     <NA>       NA       NA        <NA>       
## # ℹ 40 more rows
## # ℹ 8 more variables: study_site <chr>, deploy_on_longitude <dbl>,
## #   deploy_on_latitude <dbl>, deploy_on_comments <chr>,
## #   deploy_off_longitude <dbl>, deploy_off_latitude <dbl>,
## #   deploy_off_type <chr>, deploy_off_comments <chr>
individuals %>% filter ( deploy_on_latitude > 50 ) %>% ggplot ( aes ( x = deploy_on_longitude , y = deploy_on_latitude ) ) + geom_point ( aes ( color = study_site ) ) #+ # borders("world", regions = "china") 81.3 性别比例 top_animal_ids <- count ( locations , animal_id , sort = TRUE ) %>% slice ( 1 : 10 ) %>% pull ( animal_id ) locations %>% filter ( animal_id %in% top_animal_ids ) %>% arrange ( animal_id , timestamp ) %>% group_by ( animal_id ) %>% mutate ( measurement_n = row_number ( ) ) %>% ggplot ( aes ( x = longitude , y = latitude , color = animal_id , alpha = measurement_n ) ) + geom_point ( show.legend = FALSE , size = 1 ) + geom_path ( show.legend = FALSE , size = 1 ) + # scale_color_manual(values = ) + theme_minimal ( ) + theme ( plot.title = element_text ( size = 20 , face = "bold" ) , plot.subtitle = element_text ( size = 10 ) , text = element_text ( color = "White" ) , panel.grid.minor = element_blank ( ) , panel.grid.major = element_line ( color = "gray60" , size = 0.05 ) , plot.background = element_rect ( fill = "gray10" ) , axis.text = element_text ( color = "white" ) labs ( x = "\nLongitude" , y = "Latitude\n" , title = "Caribou movement tracking" , subtitle = "Latitude and longitude locations of the animals with the highest number of measurements\n" , caption = "Tidy Tuesday: Caribou Location Tracking" 
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
locations %>% dplyr :: filter ( animal_id %in% c ( "QU_car143" ) ) %>% dplyr :: arrange ( animal_id , timestamp ) %>% dplyr :: group_by ( animal_id ) %>% dplyr :: mutate ( measurement_n = row_number ( ) ) %>% ggplot ( aes ( x = longitude , y = latitude , color = measurement_n , alpha = measurement_n ) ) + geom_point ( show.legend = FALSE , size = 1 ) + geom_path ( show.legend = FALSE , size = 1 ) + scale_color_gradient ( low = "white" , high = "firebrick3" ) + theme_minimal ( ) + theme ( plot.title = element_text ( size = 20 , face = "bold" ) , plot.subtitle = element_text ( size = 10 ) , text = element_text ( color = "White" ) , panel.grid.minor = element_blank ( ) , panel.grid.major = element_line ( color = "gray60" , size = 0.05 ) , plot.background = element_rect ( fill = "gray10" ) , axis.text = element_text ( color = "white" ) labs ( x = "\nLongitude" , y = "Latitude\n" , title = "QU_car143 movement tracking" , subtitle = "Latitude and longitude locations of the animals with the highest number of measurements\n Ligher colors indicate earlier measurements" , caption = "Tidy Tuesday: Caribou Location Tracking" example_animal <- locations %>% dplyr :: filter ( animal_id == sample ( animal_id , 1 ) ) %>% dplyr :: arrange ( timestamp ) example_animal 
## # A tibble: 2,188 × 7
##      event_id animal_id study_site season timestamp           longitude latitude
##         <dbl> <chr>     <chr>      <chr>  <dttm>                  <dbl>    <dbl>
##  1 2269947408 KE_car097 Kennedy    Winter 2008-03-29 18:03:07     -123.     55.2
##  2 2269947409 KE_car097 Kennedy    Winter 2008-03-29 23:03:05     -123.     55.2
##  3 2269947410 KE_car097 Kennedy    Winter 2008-03-30 04:03:08     -123.     55.2
##  4 2269947411 KE_car097 Kennedy    Winter 2008-03-30 09:03:06     -123.     55.2
##  5 2269947412 KE_car097 Kennedy    Winter 2008-03-30 14:03:06     -123.     55.2
##  6 2269947413 KE_car097 Kennedy    Winter 2008-03-30 19:04:07     -123.     55.2
##  7 2269947414 KE_car097 Kennedy    Winter 2008-03-30 23:05:06     -123.     55.2
##  8 2269947415 KE_car097 Kennedy    Winter 2008-03-31 04:03:06     -123.     55.2
##  9 2269947416 KE_car097 Kennedy    Winter 2008-03-31 09:03:36     -123.     55.2
## 10 2269947417 KE_car097 Kennedy    Winter 2008-03-31 14:03:37     -123.     55.2
## # ℹ 2,178 more rows
"2010-03-28 21:00:44" %>% lubridate :: as_date ( ) "2010-03-28 21:00:44" %>% lubridate :: as_datetime ( ) "2010-03-28 21:00:44" %>% lubridate :: quarter ( ) example_animal %>% dplyr :: mutate ( date = lubridate :: as_date ( timestamp ) ) %>% ggplot ( aes ( x = longitude , y = latitude , color = date ) ) + geom_path ( ) example_animal %>% dplyr :: mutate ( quarter = lubridate :: quarter ( timestamp ) %>% as.factor ( ) ) %>% ggplot ( aes ( x = longitude , y = latitude , color = quarter ) ) + geom_path ( ) + facet_wrap ( vars ( quarter ) ) + labs ( title = "A little reindeer ran around" ) season = fct_rev ( season ) , longitude = round ( longitude , 2 ) , latitude = round ( latitude , 2 ) ) %>% distinct ( season , study_site , longitude , latitude ) ggplot ( movement ) + geom_point ( aes ( longitude , latitude , group = study_site , colour = study_site ) , size = 0.1 ) + gghighlight :: gghighlight ( unhighlighted_params = list ( colour = "grey70" ) , use_direct_label = FALSE scale_colour_manual ( values = c ( "#ffe119" , "#4363d8" , "#f58231" , "#e6194B" , "#800000" , "#000075" , "#f032e6" , "#3cb44b" ) , breaks = c ( "Graham" , "Scott" , "Moberly" , "Burnt Pine" , "Kennedy" , "Quintette" , "Narraway" ) guides ( colour = guide_legend ( title = "Herd" , override.aes = list ( size = 3 ) ) ) + coord_fixed ( ratio = 1.5 ) + facet_wrap ( vars ( season ) , ncol = 2 ) + # labs( # title = "Migration patterns of Northern Caribou\nin the South Peace of British Columbia", # subtitle = str_wrap("In summer, most caribou migrate towards the central core of the Rocky Mountains where they use alpine and subalpine habitat. The result of this movement to the central core of the Rocky Mountains is that some of the east side herds can overlap with west side herds during the summer.", 100), # caption = str_wrap("Source: Seip DR, Price E (2019) Data from: Science update for the South Peace Northern Caribou (Rangifer tarandus caribou pop. 15) in British Columbia. Movebank Data Repository. https://doi.org/10.5441/001/1.p5bn656k | Graphic: Georgios Karamanis", 70) # ) + theme_void ( ) + theme ( legend.position = c ( 0.5 , 0.6 ) , legend.text = element_text ( size = 11 , colour = "#F9EED9" ) , legend.title = element_text ( size = 16 , hjust = 0.5 , colour = "#F9EED9" ) , panel.spacing.x = unit ( 3 , "lines" ) , plot.margin = margin ( 20 , 20 , 20 , 20 ) , plot.background = element_rect ( fill = "#7A6A4F" , colour = NA ) , strip.text = element_text ( colour = "#F9EED9" , size = 18 ) , plot.title = element_text ( colour = "white" , size = 20 , hjust = 0 , lineheight = 1 ) , plot.subtitle = element_text ( colour = "white" , size = 12 , hjust = 0 , lineheight = 1 , margin = margin ( 10 , 0 , 50 , 0 ) ) , plot.caption = element_text ( colour = "grey80" , size = 7 , hjust = 1 , margin = margin ( 30 , 0 , 10 , 0 ) ) location_with_speed <- locations %>% dplyr :: group_by ( animal_id ) %>% dplyr :: mutate ( last_longitude = lag ( longitude ) , last_latitude = lag ( latitude ) , hours = as.numeric ( difftime ( timestamp , lag ( timestamp ) , units = "hours" ) ) , km = geosphere :: distHaversine ( cbind ( longitude , latitude ) , cbind ( last_longitude , last_latitude ) ) / 1000 , speed = km / hours ) %>% dplyr :: ungroup ( ) 
## The legacy packages maptools, rgdal, and rgeos, underpinning the sp package,
## which was just loaded, will retire in October 2023.
## Please refer to R-spatial evolution reports for details, especially
## https://r-spatial.org/r/2023/05/15/evolution4.html.
## It may be desirable to make the sf package available;
## package maintainers should consider adding sf to Suggests:.
## The sp package is now running under evolution status 2
##      (status 2 uses the sf package in place of rgdal)
location_with_speed 
## # A tibble: 249,450 × 12
##      event_id animal_id study_site season timestamp           longitude latitude
##         <dbl> <chr>     <chr>      <chr>  <dttm>                  <dbl>    <dbl>
##  1 2259197332 GR_C01    Graham     Winter 2001-02-21 05:00:00     -123.     56.2
##  2 2259197333 GR_C01    Graham     Winter 2001-02-21 09:00:00     -123.     56.2
##  3 2259197334 GR_C01    Graham     Winter 2001-02-21 13:00:00     -123.     56.2
##  4 2259197335 GR_C01    Graham     Winter 2001-02-21 17:01:00     -123.     56.2
##  5 2259197336 GR_C01    Graham     Winter 2001-02-21 21:00:00     -123.     56.2
##  6 2259197337 GR_C01    Graham     Winter 2001-02-22 01:00:00     -123.     56.2
##  7 2259197338 GR_C01    Graham     Winter 2001-02-22 05:00:00     -123.     56.2
##  8 2259197339 GR_C01    Graham     Winter 2001-02-22 09:00:00     -123.     56.2
##  9 2259197340 GR_C01    Graham     Winter 2001-02-22 13:00:00     -123.     56.2
## 10 2259197341 GR_C01    Graham     Winter 2001-02-22 17:00:00     -123.     56.2
## # ℹ 249,440 more rows
## # ℹ 5 more variables: last_longitude <dbl>, last_latitude <dbl>, hours <dbl>,
## #   km <dbl>, speed <dbl>
location_with_speed %>% ggplot ( aes ( x = speed ) ) + geom_histogram ( ) + scale_x_log10 ( ) 
## Warning in self$trans$transform(x): NaNs produced
## Warning: Transformation introduced infinite values in continuous x-axis
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 385 rows containing non-finite values (`stat_bin()`).
ggplot ( aes ( x = longitude , y = latitude ) ) + geom_point ( ) + transition_time ( time = timestamp ) + shadow_mark ( past = TRUE ) + labs ( title = "date is {frame_time}" ) 81.12 更多 df <- locations %>% dplyr :: filter ( study_site == "Graham" , year ( timestamp ) == 2002 ) %>% dplyr :: group_by ( animal_id ) %>% dplyr :: filter ( as_date ( min ( timestamp ) ) == "2002-01-01" , as_date ( max ( timestamp ) ) == "2002-12-31" ) %>% dplyr :: ungroup ( ) %>% dplyr :: mutate ( date = as_date ( timestamp ) ) %>% dplyr :: group_by ( animal_id , date ) %>% dplyr :: summarise ( longitude_centroid = mean ( longitude ) , latitude_centroid = mean ( latitude ) ) %>% dplyr :: ungroup ( ) %>% tidyr :: complete ( animal_id , date ) %>% dplyr :: arrange ( animal_id , date ) %>% tidyr :: fill ( longitude_centroid , latitude_centroid , .direction = "down" ) p <- df %>% ggplot ( aes ( longitude_centroid , latitude_centroid , colour = animal_id ) ) + geom_point ( size = 2 ) + coord_map ( ) + theme_void ( ) + theme ( legend.position = "none" ) + transition_time ( time = date ) + shadow_mark ( alpha = 0.2 , size = 0.8 ) + ggtitle ( "Caribou location on {frame_time}" )
 
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