Продвинутая графика

class: center, middle, inverse, title-slide

.title[
# Продвинутая графика
]
.subtitle[
## Визуализация и анализ географических данных на языке R
]
.author[
### Тимофей Самсонов
]
.institute[
### МГУ имени Ломоносова, Географический факультет
]
.date[
### 2024-10-08
]

---

## Источники данных

- __Евростат__ (https://ec.europa.eu/eurostat/web/main/home), 
- __NASA POWER__ (https://power.larc.nasa.gov/)
- __USDA NRCS Soil Data Access__ (https://sdmdataaccess.nrcs.usda.gov/)

``` r
library(tidyverse)
library(eurostat)
library(nasapower)
library(soilDB)
```

> Пакет soilDB лучше устанавливать из консоли командой `install.packages('soilDB', dependencies = TRUE)`. Указание параметра `dependencies = TRUE` обеспечит установку других пакетов, от которых он зависит.

---

## Коды Евростата

Например, [таблица по продуктам питания, напиткам и табаку](https://ec.europa.eu/eurostat/databrowser/view/tet00034/default/table?lang=en) имеет код __ext_lt_mainagri__:

---

## Загрузка данных Евростата

.code-small[

``` r
tables = c('ext_lt_mainagri', 'ext_lt_mainrawm', 'ext_lt_mainmine',
           'ext_lt_mainchem','ext_lt_mainmanu', 'ext_lt_mainmach')
countries = ISOcodes::ISO_3166_1$Alpha_2

trades = lapply(tables, function(X) { # прочтем несколько таблиц в список
  get_eurostat(X) |> filter(partner %in% countries) |> label_eurostat()
}) |> 
  bind_rows() |> 
  select(-geo) |> # убираем столбец с территорией торговли, т.к. там только Евросоюз
  filter(str_detect(indic_et, 'Exports in|Imports in')) |> 
  pivot_wider(names_from = indic_et, values_from = values) |> 
  rename(export = `Exports in million of ECU/EURO`, # дадим им краткие названия
         import = `Imports in million of ECU/EURO`,
         time = TIME_PERIOD) |> 
  mutate(partner = as.factor(partner))

trades # посмотрим, что получилось
## # A tibble: 26,952 × 6
##    freq   sitc06                   partner time       export import
##    <chr>  <chr>                    <fct>   <date>      <dbl>  <dbl>
##  1 Annual Food, drinks and tobacco Andorra 2002-01-01   168.    2  
##  2 Annual Food, drinks and tobacco Andorra 2003-01-01   166.    5  
##  3 Annual Food, drinks and tobacco Andorra 2004-01-01   173.   11.4
##  4 Annual Food, drinks and tobacco Andorra 2005-01-01   197.   32.8
##  5 Annual Food, drinks and tobacco Andorra 2006-01-01   196.   41.1
##  6 Annual Food, drinks and tobacco Andorra 2007-01-01   178.    2.2
##  7 Annual Food, drinks and tobacco Andorra 2008-01-01   182.   13.6
##  8 Annual Food, drinks and tobacco Andorra 2009-01-01   167.    7.6
##  9 Annual Food, drinks and tobacco Andorra 2010-01-01   173.    0.5
## 10 Annual Food, drinks and tobacco Andorra 2011-01-01   232.    0.6
## # ℹ 26,942 more rows
```
]

---

## Загрузка данных NASA POWER

``` r
daily_single_ag <- get_power(
  community = "ag",
  lonlat = c(60.59, 56.84),
  pars = c("RH2M", "T2M"),
  dates = c("1995-04-01", "1995-04-30"),
  temporal_api = "daily"
)

as_tibble(daily_single_ag) # посмотрим, что получилось
## # A tibble: 30 × 9
##      LON   LAT  YEAR    MM    DD   DOY YYYYMMDD    RH2M   T2M
##    <dbl> <dbl> <dbl> <int> <int> <int> <date>     <dbl> <dbl>
##  1  60.6  56.8  1995     4     1    91 1995-04-01  81.7  5.65
##  2  60.6  56.8  1995     4     2    92 1995-04-02  81.9  7.78
##  3  60.6  56.8  1995     4     3    93 1995-04-03  78    8.31
##  4  60.6  56.8  1995     4     4    94 1995-04-04  81.2  7.71
##  5  60.6  56.8  1995     4     5    95 1995-04-05  92.4  5.1 
##  6  60.6  56.8  1995     4     6    96 1995-04-06  90.2  6.31
##  7  60.6  56.8  1995     4     7    97 1995-04-07  83.4  3.38
##  8  60.6  56.8  1995     4     8    98 1995-04-08  79    2.46
##  9  60.6  56.8  1995     4     9    99 1995-04-09  81.2  3.49
## 10  60.6  56.8  1995     4    10   100 1995-04-10  76.9  4.4 
## # ℹ 20 more rows
```

---

## Загрузка данных NASA POWER

``` r
interannual_sse <- get_power(
  community = "ag",
  lonlat = c(60.59, 56.84),
  dates = 1995:2015,
  temporal_api = "climatology",
  pars = c("CLRSKY_SFC_SW_DWN",
           "ALLSKY_SFC_SW_DWN")
)
as_tibble(interannual_sse) # посмотрим, что получилось
## # A tibble: 2 × 16
##     LON   LAT PARAMETER      JAN   FEB   MAR   APR   MAY   JUN   JUL   AUG   SEP
##   <dbl> <dbl> <chr>        <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1  60.6  56.8 ALLSKY_SFC_…  2.59  6.03  11.0  14.8  18.7  20.0  19.4  14.8  9.78
## 2  60.6  56.8 CLRSKY_SFC_…  3.37  7.41  13.8  20.2  26.0  28.4  26.4  21.2 15   
## # ℹ 4 more variables: OCT <dbl>, NOV <dbl>, DEC <dbl>, ANN <dbl>
```

---

## Загрузка данных Soil Data Access

Результирющий объект представляет собой список со множеством таблиц, которые характеризуют как почвенную серию в целом, так и отдельные ее разрезы:

``` r
soils = c('wilkes',  'chewacla', 'congaree')
series = fetchOSD(soils, extended = TRUE)
str(series)
## List of 18
##  $ SPC             :Formal class 'SoilProfileCollection' [package "aqp"] with 9 slots
##   .. ..@ idcol       : chr "id"
##   .. ..@ hzidcol     : chr "hzID"
##   .. ..@ depthcols   : chr [1:2] "top" "bottom"
##   .. ..@ metadata    :List of 8
##   .. .. ..$ aqp_df_class    : chr "data.frame"
##   .. .. ..$ aqp_group_by    : chr ""
##   .. .. ..$ aqp_hzdesgn     : chr "hzname"
##   .. .. ..$ aqp_hztexcl     : chr "texture_class"
##   .. .. ..$ depth_units     : chr "cm"
##   .. .. ..$ stringsAsFactors: logi FALSE
##   .. .. ..$ origin          : chr "OSD via Soilweb / fetchOSD"
##   .. .. ..$ created         : POSIXct[1:1], format: "2024-10-08 11:52:33"
##   .. ..@ horizons    :'data.frame':	22 obs. of  22 variables:
##   .. .. ..$ id                   : chr [1:22] "CHEWACLA" "CHEWACLA" "CHEWACLA" "CHEWACLA" ...
##   .. .. ..$ top                  : int [1:22] 0 10 36 66 97 119 152 0 20 46 ...
##   .. .. ..$ bottom               : int [1:22] 10 36 66 97 119 152 203 20 46 56 ...
##   .. .. ..$ hzname               : chr [1:22] "Ap" "Bw1" "Bw2" "Bw3" ...
##   .. .. ..$ soil_color           : chr [1:22] "#7D593AFF" "#795B36FF" "#795B36FF" "#7D593AFF" ...
##   .. .. ..$ hue                  : chr [1:22] "7.5YR" "10YR" "10YR" "7.5YR" ...
##   .. .. ..$ value                : int [1:22] 4 4 4 4 5 5 4 4 4 3 ...
##   .. .. ..$ chroma               : int [1:22] 4 4 4 4 8 1 4 4 3 3 ...
##   .. .. ..$ dry_hue              : chr [1:22] "7.5YR" "10YR" "10YR" "7.5YR" ...
##   .. .. ..$ dry_value            : int [1:22] 6 6 6 6 6 6 6 6 6 5 ...
##   .. .. ..$ dry_chroma           : int [1:22] 4 4 4 4 7 1 4 4 3 3 ...
##   .. .. ..$ texture_class        : Ord.factor w/ 21 levels "coarse sand"<..: 13 18 17 13 17 17 13 13 13 NA ...
##   .. .. ..$ cf_class             : logi [1:22] NA NA NA NA NA NA ...
##   .. .. ..$ pH                   : logi [1:22] NA NA NA NA NA NA ...
##   .. .. ..$ pH_class             : Ord.factor w/ 12 levels "ultra acid"<"extremely acid"<..: 3 3 3 3 3 3 3 4 NA NA ...
##   .. .. ..$ distinctness         : chr [1:22] "clear" "gradual" "gradual" "gradual" ...
##   .. .. ..$ topography           : chr [1:22] "smooth" "wavy" "wavy" "wavy" ...
##   .. .. ..$ dry_color_estimated  : logi [1:22] TRUE TRUE TRUE TRUE TRUE TRUE ...
##   .. .. ..$ moist_color_estimated: logi [1:22] FALSE FALSE FALSE FALSE FALSE FALSE ...
##   .. .. ..$ narrative            : chr [1:22] "Ap--0 to 4 inches; brown (7.5YR 4/4) loam; weak medium granular structure; friable; common very fine, fine, and"| __truncated__ "Bw1--4 to 14 inches; dark yellowish brown (10YR 4/4) silty clay loam; weak medium subangular blocky structure; "| __truncated__ "Bw2--14 to 26 inches; dark yellowish brown (10YR 4/4) clay loam; weak medium subangular blocky structure; friab"| __truncated__ "Bw3--26 to 38 inches; brown (7.5YR 4/4) loam; weak medium subangular blocky structure; friable; common fine roo"| __truncated__ ...
##   .. .. ..$ hzID                 : chr [1:22] "1" "2" "3" "4" ...
##   .. .. ..$ hzd                  : num [1:22] 2.5 7.5 7.5 7.5 7.5 7.5 0 2.5 7.5 2.5 ...
##   .. ..@ site        :'data.frame':	3 obs. of  31 variables:
##   .. .. ..$ id                     : chr [1:3] "CHEWACLA" "CONGAREE" "WILKES"
##   .. .. ..$ soilseriesiid          : int [1:3] 24628 1057 23800
##   .. .. ..$ series_status          : chr [1:3] "established" "established" "established"
##   .. .. ..$ benchmarksoilflag      : chr [1:3] "TRUE" "FALSE" "FALSE"
##   .. .. ..$ soiltaxclasslastupdated: chr [1:3] "2010-02-11 00:00:00" "2002-07-18 00:00:00" "1997-06-06 00:00:00"
##   .. .. ..$ mlraoffice             : chr [1:3] "raleigh, nc" "raleigh, nc" "raleigh, nc"
##   .. .. ..$ family                 : chr [1:3] "fine-loamy, mixed, active, thermic fluvaquentic dystrudepts" "fine-loamy, mixed, active, nonacid, thermic oxyaquic udifluvents" "loamy, mixed, active, thermic, shallow typic hapludalfs"
##   .. .. ..$ soilorder              : chr [1:3] "inceptisols" "entisols" "alfisols"
##   .. .. ..$ suborder               : chr [1:3] "udepts" "fluvents" "udalfs"
##   .. .. ..$ greatgroup             : chr [1:3] "dystrudepts" "udifluvents" "hapludalfs"
##   .. .. ..$ subgroup               : chr [1:3] "fluvaquentic dystrudepts" "oxyaquic udifluvents" "typic hapludalfs"
##   .. .. ..$ tax_partsize           : chr [1:3] "fine-loamy" "fine-loamy" "loamy"
##   .. .. ..$ tax_partsizemod        : logi [1:3] NA NA NA
##   .. .. ..$ tax_ceactcl            : chr [1:3] "active" "active" "active"
##   .. .. ..$ tax_reaction           : chr [1:3] NA "nonacid" NA
##   .. .. ..$ tax_tempcl             : chr [1:3] "thermic" "thermic" "thermic"
##   .. .. ..$ tax_minclass           : chr [1:3] "mixed" "mixed" "mixed"
##   .. .. ..$ taxfamhahatmatcl       : logi [1:3] NA NA NA
##   .. .. ..$ originyear             : logi [1:3] NA NA NA
##   .. .. ..$ establishedyear        : int [1:3] 1937 1904 1916
##   .. .. ..$ descriptiondateinitial : chr [1:3] "2010-02-11 00:00:00+00" "2002-07-18 00:00:00+00" "2007-09-06 00:00:00+00"
##   .. .. ..$ descriptiondateupdated : chr [1:3] "2010-02-11 00:00:00+00" "2002-07-18 00:00:00+00" "2021-01-27 16:02:50.637+00"
##   .. .. ..$ statsgoflag            : chr [1:3] "TRUE" "TRUE" "TRUE"
##   .. .. ..$ areasymbol             : chr [1:3] "NC" "SC" "NC"
##   .. .. ..$ areaname               : chr [1:3] "North Carolina" "South Carolina" "North Carolina"
##   .. .. ..$ areatypename           : chr [1:3] "State or Territory" "State or Territory" "State or Territory"
##   .. .. ..$ subgroup_mod           : chr [1:3] "fluvaquentic" "oxyaquic" "typic"
##   .. .. ..$ greatgroup_mod         : chr [1:3] "dystr" "udi" "hapl"
##   .. .. ..$ drainagecl             : chr [1:3] "somewhat poorly" "well to moderately well" "well"
##   .. .. ..$ ac                     : int [1:3] 1254622 215993 614348
##   .. .. ..$ n_polygons             : int [1:3] 38657 10260 34850
##   .. ..@ sp          :Formal class 'SpatialPoints' [package "sp"] with 3 slots
##   .. .. .. ..@ coords     : num [1, 1] 0
##   .. .. .. ..@ bbox       : logi [1, 1] NA
##   .. .. .. ..@ proj4string:Formal class 'CRS' [package "sp"] with 1 slot
##   .. .. .. .. .. ..@ projargs: chr NA
##   .. ..@ diagnostic  :'data.frame':	0 obs. of  0 variables
##   .. ..@ restrictions:'data.frame':	0 obs. of  0 variables
##  $ competing       :'data.frame':	1 obs. of  3 variables:
##   ..$ series   : chr "CHEWACLA"
##   ..$ competing: chr "OAKBORO"
##   ..$ family   : chr "fine-loamy, mixed, active, thermic fluvaquentic dystrudepts"
##  $ geog_assoc_soils:'data.frame':	45 obs. of  2 variables:
##   ..$ series: chr [1:45] "CHEWACLA" "CHEWACLA" "CHEWACLA" "CHEWACLA" ...
##   ..$ gas   : chr [1:45] "ALTAVISTA" "AUGUSTA" "BIBB" "BUNCOMBE" ...
##  $ geomcomp        :'data.frame':	2 obs. of  9 variables:
##   ..$ series         : chr [1:2] "CHEWACLA" "WILKES"
##   ..$ Interfluve     : num [1:2] 1 0.178
##   ..$ Crest          : num [1:2] 0 0.027
##   ..$ Head Slope     : int [1:2] 0 0
##   ..$ Nose Slope     : int [1:2] 0 0
##   ..$ Side Slope     : num [1:2] 0 0.795
##   ..$ Base Slope     : int [1:2] 0 0
##   ..$ n              : int [1:2] 3 185
##   ..$ shannon_entropy: num [1:2] 0 0.848
##  $ hillpos         :'data.frame':	3 obs. of  8 variables:
##   ..$ series         : chr [1:3] "CHEWACLA" "CONGAREE" "WILKES"
##   ..$ Toeslope       : num [1:3] 1 0.846 0
##   ..$ Footslope      : num [1:3] 0 0.154 0
##   ..$ Backslope      : num [1:3] 0 0 0.637
##   ..$ Shoulder       : num [1:3] 0 0 0.225
##   ..$ Summit         : num [1:3] 0 0 0.139
##   ..$ n              : int [1:3] 75 13 245
##   ..$ shannon_entropy: num [1:3] 0 0.619 1.294
##  $ mtnpos          : logi FALSE
##  $ terrace         :'data.frame':	2 obs. of  5 variables:
##   ..$ series         : chr [1:2] "CHEWACLA" "CONGAREE"
##   ..$ Tread          : num [1:2] 0.977 1
##   ..$ Riser          : num [1:2] 0.0233 0
##   ..$ n              : int [1:2] 86 35
##   ..$ shannon_entropy: num [1:2] 0.16 0
##  $ flats           :'data.frame':	2 obs. of  7 variables:
##   ..$ series         : chr [1:2] "CHEWACLA" "CONGAREE"
##   ..$ Dip            : num [1:2] 0.1455 0.0667
##   ..$ Talf           : num [1:2] 0.855 0.867
##   ..$ Flat           : int [1:2] 0 0
##   ..$ Rise           : num [1:2] 0 0.0667
##   ..$ n              : int [1:2] 55 15
##   ..$ shannon_entropy: num [1:2] 0.598 0.7
##  $ shape_across    :'data.frame':	3 obs. of  8 variables:
##   ..$ series         : chr [1:3] "CHEWACLA" "CONGAREE" "WILKES"
##   ..$ Concave        : num [1:3] 0.0364 0.0213 0
##   ..$ Linear         : num [1:3] 0.953 0.957 0.359
##   ..$ Convex         : num [1:3] 0.0109 0.0213 0.641
##   ..$ Complex        : int [1:3] 0 0 0
##   ..$ Undulating     : int [1:3] 0 0 0
##   ..$ n              : int [1:3] 275 94 195
##   ..$ shannon_entropy: num [1:3] 0.312 0.297 0.942
##  $ shape_down      :'data.frame':	3 obs. of  8 variables:
##   ..$ series         : chr [1:3] "CHEWACLA" "CONGAREE" "WILKES"
##   ..$ Concave        : num [1:3] 0.3309 0.0217 0
##   ..$ Linear         : num [1:3] 0.658 0.946 0.636
##   ..$ Convex         : num [1:3] 0.0109 0.0326 0.3641
##   ..$ Complex        : int [1:3] 0 0 0
##   ..$ Undulating     : int [1:3] 0 0 0
##   ..$ n              : int [1:3] 275 92 195
##   ..$ shannon_entropy: num [1:3] 0.996 0.357 0.946
##  $ pmkind          :'data.frame':	6 obs. of  5 variables:
##   ..$ series: chr [1:6] "CHEWACLA" "CHEWACLA" "CONGAREE" "CONGAREE" ...
##   ..$ pmkind: chr [1:6] "Alluvium" "Residuum" "Alluvium" "Fluviomarine deposits" ...
##   ..$ n     : int [1:6] 205 1 72 13 1 262
##   ..$ total : int [1:6] 206 206 86 86 86 262
##   ..$ P     : num [1:6] 0.9951 0.0049 0.8372 0.1512 0.0116 ...
##  $ pmorigin        :'data.frame':	24 obs. of  5 variables:
##   ..$ series  : chr [1:24] "CHEWACLA" "CHEWACLA" "CHEWACLA" "CHEWACLA" ...
##   ..$ pmorigin: chr [1:24] "Igneous and metamorphic rock" "Sedimentary rock" "Granite and gneiss" "Mixed" ...
##   ..$ n       : int [1:24] 29 11 2 2 1 1 1 1 1 1 ...
##   ..$ total   : int [1:24] 51 51 51 51 51 51 51 51 51 51 ...
##   ..$ P       : num [1:24] 0.5686 0.2157 0.0392 0.0392 0.0196 ...
##  $ mlra            :'data.frame':	21 obs. of  4 variables:
##   ..$ series    : chr [1:21] "CHEWACLA" "CHEWACLA" "CHEWACLA" "CHEWACLA" ...
##   ..$ mlra      : chr [1:21] "148" "135A" "136" "133A" ...
##   ..$ area_ac   : int [1:21] 7911 4245 920965 105979 85493 55614 30799 19434 14232 9855 ...
##   ..$ membership: num [1:21] 0.006 0.003 0.734 0.084 0.068 0.044 0.025 0.015 0.011 0.008 ...
##  $ ecoclassid      :'data.frame':	18 obs. of  5 variables:
##   ..$ series      : chr [1:18] "CHEWACLA" "CHEWACLA" "CHEWACLA" "CHEWACLA" ...
##   ..$ ecoclassid  : chr [1:18] "F133CA999AL" "F136XY100VA" "F136XY610GA" "F133AY003NC" ...
##   ..$ n_components: int [1:18] 2 2 139 4 3 4 5 6 1 4 ...
##   ..$ area_ac     : int [1:18] 6589 5976 936606 43345 37459 29783 24850 19003 13987 8008 ...
##   ..$ proportion  : num [1:18] 0.0059 0.0053 0.8319 0.0385 0.0333 ...
##  $ climate.annual  :'data.frame':	24 obs. of  12 variables:
##   ..$ series     : chr [1:24] "CHEWACLA" "CHEWACLA" "CHEWACLA" "CHEWACLA" ...
##   ..$ climate_var: chr [1:24] "Elevation (m)" "Effective Precipitation (mm)" "Frost-Free Days" "Mean Annual Air Temperature (degrees C)" ...
##   ..$ minimum    : num [1:24] 0 128.8 177 12.1 986 ...
##   ..$ q01        : num [1:24] 7 216 188 13 1069 ...
##   ..$ q05        : num [1:24] 33 245.9 196 13.5 1094 ...
##   ..$ q25        : num [1:24] 125 310.2 208 14.9 1136 ...
##   ..$ q50        : num [1:24] 191 348.8 218 15.7 1175 ...
##   ..$ q75        : num [1:24] 247 442.9 227 16.4 1277 ...
##   ..$ q95        : num [1:24] 347 569.2 235 17.3 1394 ...
##   ..$ q99        : num [1:24] 479 740.4 244 17.8 1575.1 ...
##   ..$ maximum    : num [1:24] 919 1382.8 300 19.7 2202 ...
##   ..$ n          : int [1:24] 29784 29784 29784 29784 29784 29784 29784 29784 7547 7547 ...
##  $ climate.monthly :'data.frame':	72 obs. of  14 variables:
##   ..$ series     : chr [1:72] "CHEWACLA" "CHEWACLA" "CHEWACLA" "CHEWACLA" ...
##   ..$ climate_var: chr [1:72] "ppt1" "ppt2" "ppt3" "ppt4" ...
##   ..$ minimum    : num [1:72] 61 64 81 63 60 78 83 74 68 53 ...
##   ..$ q01        : num [1:72] 75 71 93 71 69 83 92 85 81 72 ...
##   ..$ q05        : num [1:72] 83 73 99 75 72 ...
##   ..$ q25        : num [1:72] 93 83 105 82 81 96 108 96 91 82 ...
##   ..$ q50        : num [1:72] 105 106 119 88 92 101 115 102 98 86 ...
##   ..$ q75        : num [1:72] 116 121 128 95 101 106 123 110 105 91 ...
##   ..$ q95        : num [1:72] 131 135 137 111 112 117 133 129 118 100 ...
##   ..$ q99        : num [1:72] 150 144 147 122 125 133 143 142 137 109 ...
##   ..$ maximum    : num [1:72] 248 227 239 150 167 198 220 207 192 158 ...
##   ..$ n          : int [1:72] 29784 29784 29784 29784 29784 29784 29784 29784 29784 29784 ...
##   ..$ month      : Factor w/ 12 levels "1","2","3","4",..: 1 2 3 4 5 6 7 8 9 10 ...
##   ..$ variable   : Factor w/ 2 levels "Potential ET (mm)",..: 2 2 2 2 2 2 2 2 2 2 ...
##  $ NCCPI           :'data.frame':	3 obs. of  16 variables:
##   ..$ series             : chr [1:3] "CHEWACLA" "CONGAREE" "WILKES"
##   ..$ n                  : int [1:3] 266 94 158
##   ..$ nccpi_irrigated_q01: num [1:3] 0.188 0.816 0.268
##   ..$ nccpi_irrigated_q05: num [1:3] 0.346 0.825 0.269
##   ..$ nccpi_irrigated_q25: num [1:3] 0.564 0.861 0.32
##   ..$ nccpi_irrigated_q50: num [1:3] 0.597 0.907 0.361
##   ..$ nccpi_irrigated_q75: num [1:3] 0.654 0.925 0.419
##   ..$ nccpi_irrigated_q95: num [1:3] 0.849 0.979 0.561
##   ..$ nccpi_irrigated_q99: num [1:3] 0.93 0.984 0.614
##   ..$ nccpi_q01          : num [1:3] 0.347 0.5926 0.0426
##   ..$ nccpi_q05          : num [1:3] 0.56 0.651 0.0564
##   ..$ nccpi_q25          : num [1:3] 0.624 0.76 0.146
##   ..$ nccpi_q50          : num [1:3] 0.685 0.813 0.231
##   ..$ nccpi_q75          : num [1:3] 0.711 0.852 0.268
##   ..$ nccpi_q95          : num [1:3] 0.837 0.936 0.379
##   ..$ nccpi_q99          : num [1:3] 0.84 0.947 0.57
##  $ soilweb.metadata:'data.frame':	24 obs. of  2 variables:
##   ..$ product    : chr [1:24] "block diagram archive" "cached sketches" "component pedons" "ESID-series cross-tabulation" ...
##   ..$ last_update: chr [1:24] "2019-12-17" "2023-10-12" "2023-10-09" "2023-11-06" ...
```

---

## Быстрый график ggplot

.pull-left[
Суммарный экспорт через `qplot()`:

``` r
trades_total = trades |> 
  group_by(time) |> 
  summarise(export = sum(export),
            import = sum(import))
  
qplot(time, export, 
      data = trades_total, 
      geom = c('point', 'line'))
```
]

.pull-right[
![](06_AdvGraphics_files/figure-html/qplot-out-1.png)
]

---

## Базовый шаблон ggplot

Базовый шаблон __ggplot__:

``` r
ggplot(data = <DATA>) + 
  <GEOM_FUNCTION>(mapping = aes(<MAPPINGS>))
```
где:

- `DATA` — источник данных (фрейм, тиббл)
- `GEOM_FUNCTION` — функция, отвечающая за геометрический тип графика (точки, линии, гистограммы и т.д.)
- `MAPPINGS` — перечень соответствий между переменными данных (содержащихся в `DATA`) и графическими переменными (координатами, размерами, цветами и т.д.)

---

## Геометрические типы

.pull-left[
Точки `geom_point()`:
.code-small[

``` r
ggplot(data = trades_total) +
  geom_point(mapping = aes(x = time, 
                          y = export))
```

![](06_AdvGraphics_files/figure-html/unnamed-chunk-8-1.png)
]
]

.pull-right[
Линии `geom_line()`:
.code-small[

``` r
ggplot(data = trades_total) +
  geom_line(mapping = aes(x = time, 
                          y = export))
```

![](06_AdvGraphics_files/figure-html/unnamed-chunk-9-1.png)
]
]

---

## Геометрические типы

.pull-left[
Ступенчатые кривые`geom_step()`:
.code-small[

``` r
ggplot(data = trades_total) +
  geom_step(mapping = aes(x = time, 
                          y = export))
```

![](06_AdvGraphics_files/figure-html/unnamed-chunk-10-1.png)
]
]

.pull-right[
Совмещение геометрий:
.code-small[

``` r
ggplot(data = trades_total) +
  geom_line(mapping = aes(x = time, y = export)) +
  geom_point(mapping = aes(x = time, y = export))
```

![](06_AdvGraphics_files/figure-html/unnamed-chunk-11-1.png)
]
]

---

## Геометрические типы

.pull-left[
Вынос общих данных:
.code-small[

``` r
ggplot(data = trades_total, 
       mapping = aes(x = time, y = export)) +
  geom_line() +
  geom_point()
```

![](06_AdvGraphics_files/figure-html/unnamed-chunk-12-1.png)
]
]

.pull-right[
Заливка `geom_area()`:
.code-small[

``` r
ggplot(data = trades_total, 
       mapping = aes(x = time, y = export)) +
  geom_area(alpha = 0.5) + # полигон с прозрачностью 0,5
  geom_line() +
  geom_point()
```

![](06_AdvGraphics_files/figure-html/unnamed-chunk-13-1.png)
]
]

---

## Геометрические типы

``` r
df = trades |> filter(sitc06 == 'Machinery and transport equipment', 
                      time == as.Date('2022-01-01'), export > 10000)
```

.pull-left[
Столбчатая диаграмма:

``` r
ggplot(
  df, 
  mapping = aes(
    x = partner, 
    y = export
  )
) +
  geom_col()
```
]

.pull-right[
![](06_AdvGraphics_files/figure-html/barplot-out-1.png)
]

---

## Геометрические типы

.pull-left[
Перестановка осей через `coord_flip()`:

``` r
ggplot(df, 
       mapping = aes(x = partner, 
                     y = export)) +
  geom_col() +
  coord_flip()
```
]

.pull-right[
![](06_AdvGraphics_files/figure-html/flip-out-1.png)
]

---

## Графические переменные

Функция `aes(...)` отображает переменные данных на графические переменные

``` r
# один цвет для графика (параметр за пределами aes)
ggplot(trades_total) + 
    geom_line(mapping = aes(x = time, y = export), color = 'blue')
```

![](06_AdvGraphics_files/figure-html/unnamed-chunk-15-1.png)

---

## Графические переменные

``` r
trade_russia = trades |> filter(partner == 'Russia')
ggplot(trade_russia) + # у каждой группы данных свой цвет
  geom_line(mapping = aes(x = time, y = export, color = sitc06))
```

![](06_AdvGraphics_files/figure-html/unnamed-chunk-16-1.png)

---

## Графические переменные

``` r
ggplot(trade_russia,  # а теперь и с точками
       mapping = aes(x = time, y = export, color = sitc06)) +
  geom_line() +
  geom_point()
```

![](06_AdvGraphics_files/figure-html/unnamed-chunk-17-1.png)

---

## Графические переменные

Аналогичным образом работает параметр формы значка:

``` r
ggplot(trades_total) + # один значок для графика
    geom_point(mapping = aes(x = time, y = export), shape = 15)
```

![](06_AdvGraphics_files/figure-html/unnamed-chunk-18-1.png)

---

## Графические переменные

У каждой группы данных свой значок

``` r
ggplot(trade_russia) + 
    geom_point(mapping = aes(x = time, y = export, shape = sitc06))
```

![](06_AdvGraphics_files/figure-html/unnamed-chunk-19-1.png)

---

## Графические переменные

Управление размером через `size` и `linewidth`:

``` r
ggplot(trades_total, mapping = aes(x = time, y = export)) + 
    geom_point(size = 5) + geom_line(linewidth = 2)
```

![](06_AdvGraphics_files/figure-html/unnamed-chunk-20-1.png)

---

## Графические переменные

``` r
ggplot(trade_russia, aes(x = time, y = export)) + 
    geom_point(aes(shape = sitc06)) +
    geom_line(aes(group = sitc06)) # Группировка данных
```

![](06_AdvGraphics_files/figure-html/unnamed-chunk-21-1.png)

---

## Графические переменные

``` r
ggplot(df, mapping = aes(x = partner, y = export)) +
  geom_col(fill = 'plum4', color = 'black', linewidth = 0.2) + # цвет
  coord_flip()
```

![](06_AdvGraphics_files/figure-html/unnamed-chunk-22-1.png)

---

## Графические переменные

Управление цветом:
.code-small[

``` r
df1 = trades |> filter(time == as.Date('2022-01-01')) |> 
  group_by(partner) |> filter(sum(export) > 50000)
df1 |> ggplot(mapping = aes(x = partner, y = export, fill = sitc06)) +  # цвет
    geom_col(color = 'black', size = 0.2) + coord_flip() 
```

![](06_AdvGraphics_files/figure-html/unnamed-chunk-23-1.png)
]

---

## Графические переменные

.code-small[

``` r
df1 |> 
  ggplot(mapping = aes(x = partner, y = export, fill = sitc06)) +
    geom_col(color = 'black', size = 0.2, position = 'fill') + # проценты
    coord_flip()
```

![](06_AdvGraphics_files/figure-html/unnamed-chunk-24-1.png)
]

---

## Графические переменные

Группировка по соседству:
.code-small[

``` r
filter(trade_russia, time >= as.Date('2013-01-01')) |> 
  ggplot(mapping = aes(x = time, y = export, fill = sitc06)) +
    geom_col(color = 'black', size = 0.2, position = 'dodge')
```

![](06_AdvGraphics_files/figure-html/unnamed-chunk-25-1.png)
]

---

## Системы координат

.pull-left[
Перестановка осей через `coord_flip()`:

``` r
trades_type = trades |> 
  group_by(sitc06, time) |> 
  summarise(export = sum(export),
            import = sum(import)) |> 
  arrange(sitc06, time)

ggplot(trades_type) + 
    geom_point(mapping = aes(
        x = export, y = import, 
        color = sitc06, 
        size = time
      ), 
      alpha = 0.5
    ) +
    geom_path(mapping = aes(
      x = export,  y = import, group = sitc06
    )) +
    coord_flip()
```
]

.pull-right[
![](06_AdvGraphics_files/figure-html/flip2-out-1.png)
]

---

## Системы координат

.left-40[
Логарифмирование:

``` r
ggplot(trades_type) + 
  geom_point(mapping = aes(
    x = export, y = import, 
    color = sitc06, 
    size = time
  ), alpha = 0.5) +
  geom_path(mapping = aes(
      x = export,  y = import, group = sitc06
  )) +
  scale_x_log10() +
  scale_y_log10()
```
]

.right-60[
![](06_AdvGraphics_files/figure-html/log-out-1.png)
]

---

## Системы координат

.left-40[
Полярная система координат:

``` r
ggplot(
  df, 
  mapping = aes(
    x = partner, 
    y = export, 
    fill = partner
  )
) +
  geom_col() +
  coord_polar()
```
]

.right-60[
![](06_AdvGraphics_files/figure-html/log2-out-1.png)
]

---

## Системы координат

.left-40[
Преобразование квадратного корня:

``` r
ggplot(
  df, 
  mapping = aes(
    x = partner, 
    y = export, 
    fill = partner
  )
) +
  geom_col() +
  coord_polar() +
  scale_y_sqrt()
```
]

.right-60[
![](06_AdvGraphics_files/figure-html/log3-out-1.png)
]

---

## Системы координат

.pull-left[
Классическая секторная диаграмма:

``` r
ggplot(
  df, 
  mapping = aes(
    x = '', 
    y = export, 
    fill = partner
  )
) +
  geom_col(
    color = 'black', 
    linewidth = 0.2
  ) +
  coord_polar(theta = 'y')
```
]

.pull-right[
![](06_AdvGraphics_files/figure-html/polar2-out-1.png)
]

---

## Названия осей и легенды

.pull-left[
.code-small[

``` r
options(scipen = 999)
ggplot(trades_type) + 
  geom_point(
    mapping = aes(
      x = export, y = import, 
      color = sitc06, 
      size = time
    ),alpha = 0.5
  ) + 
  geom_path(mapping = aes(
    x = export,  y = import, group = sitc06)
  ) +
  labs(color = "Вид продукции", 
       size = 'Год') +
  ggtitle('Соотношение импорта и экспорта в странах Евросоюза (млн долл. США)',
          subtitle = 'Данные по ключевым партнерам') +
  xlab('Экспорт') +
  ylab('Импорт')
```
]
]

.pull-right[
![](06_AdvGraphics_files/figure-html/axes-out-1.png)
]

---

## Разметка осей

.left-40[
.code-small[

``` r
brks = seq(0, 500000, 100000)
ggplot(trades_type) + 
  geom_point(mapping = aes(
    x = export, y = import, 
    color = sitc06, size = time
  ), alpha = 0.5) +
  geom_path(mapping = aes(
    x = export,  y = import, group = sitc06)
  ) +
  scale_x_log10(
    breaks = brks,
    labels = brks / 1000
  ) +
  scale_y_log10(
    breaks = brks,
    labels = brks / 1000
  )
```
]
]

.right-60[
![](06_AdvGraphics_files/figure-html/axes2-out-1.png)
]

---

## Разметка осей

.left-40[
Диапазоны по осям и выделение

``` r
ggplot(trades_type) + 
  geom_point(mapping = aes(
    x = export, y = import, 
    color = sitc06, size = time
  ), alpha = 0.5) +
  geom_path(mapping = aes(
    x = export,  y = import, group = sitc06)
  ) +
  lims(color = 'Food, drinks and tobacco') +
  xlim(0, 75000) +
  ylim(30000, 75000)
```
]

.right-60[
![](06_AdvGraphics_files/figure-html/ranges-out-1.png)
]

---

## Подписи и аннотации

.pull-left[
Подписи:

``` r
ggplot(
  data = trades_total, 
  mapping = aes(x = time, 
                y = export)) +
  geom_area(alpha = 0.5) + # полигон с прозрачностью 0,5
  geom_line() +
  geom_point() +
  geom_text( # добавляем подписи
    aes(label = floor(export / 1000)), 
    vjust = 0, nudge_y = 40000
  )
```
]

.pull-right[
![](06_AdvGraphics_files/figure-html/labels-out-1.png)
]

---

## Подписи и аннотации

.left-40[
Подписи с фоновой плашкой:

``` r
ggplot(df, mapping = aes(x = partner, y = export)) +
  geom_col(fill = 'plum4', color = 'black', size = 0.2) +
  coord_flip() +
  geom_label(aes(y = export / 2, label = floor(export / 1000))) # добавляем подписи
```
]

.right-60[
![](06_AdvGraphics_files/figure-html/labels2-out-1.png)
]

---

## Подписи и аннотации

.pull-left[
__Аннотации__ представляют собой объекты, размещаемые на графике вручную:

``` r
ggplot(trades_total, 
       mapping = aes(x = time, y = export)) +
  geom_area(alpha = 0.5) + # полигон с прозрачностью 0,5
  geom_line() +
  geom_point() +
  geom_text(aes(label = floor(export / 1000)), 
            vjust = 0, nudge_y = 40000) +
  annotate("text", 
           x = as.Date('2009-01-01'), 
           y = 550000, 
           label = "Это провал", 
           color = 'red')
```
]

.pull-right[
![](06_AdvGraphics_files/figure-html/anno-out-1.png)
]

---

## Подписи и аннотации

.left-40[
Аннотирование регионов:

``` r
ggplot(trades_type, 
       mapping = aes(
         x = export, y = import, 
         color = sitc06, size = time
       )) + 
  annotate("rect", 
           xmin = 100000, xmax = 500000, 
           ymin = 50000, ymax = 300000,  
           alpha = .2, 
           color = 'black', 
           size = 0.1) +
  geom_point(alpha = 0.5) +
  annotate("text", 
           x = 350000, y = 80000, 
           label = "Chemicals", 
           color = 'coral')
```
]

.right-60[
![](06_AdvGraphics_files/figure-html/anno2-out-1.png)
]

---

## Фасеты

``` r
brks = c(0, 50, 100, 150, 200)
trades |> 
  filter(sitc06 == 'Machinery and transport equipment',
         time > as.Date('2014-01-01'), import > 30000) |> 
  ggplot(mapping = aes(x = partner, y = import)) +
    geom_col() +
    scale_y_continuous(breaks = brks * 1e3, labels = brks) +
    ggtitle('Импорт продукции машиностроения (мдрд долл. США)',
          subtitle = 'Данные по ключевым партнерам') +
    coord_flip() +
    facet_wrap(~time)
```

---

## Фасеты

![](06_AdvGraphics_files/figure-html/facets-out-1.png)

---

## Темы

.pull-left[

``` r
ggplot(data = trades_total, mapping = aes(x = time, y = export)) +
  geom_area(alpha = 0.5) + # полигон с прозрачностью 0,5
  geom_line() +
  geom_point() +
  geom_text(aes(label = floor(export / 1000)), 
            vjust = 0, nudge_y = 40000) +
  theme_bw()
```
]

.pull-right[
![](06_AdvGraphics_files/figure-html/themes-out-1.png)
]