Vignettes are long form documentation commonly included in packages.
Because they are part of the distribution of the package, they need to
be as compact as possible. The html_pretty output format in
package prettydoc
, an alternative to html_document and
html_vignette contained in the rmarkdown
package, is able to generate small and nice HTML pages.
Styles
Currently html_pretty supports three page themes,
cayman (the default), tactile, and
architect. And there are also two syntax highlight styles:
github to mimic the syntax highlight on Github, and
vignette that is used by html_vignette. If no
highlight parameter is given, the default style created by Pandoc will
be used.
The theme and highlight styles can be given in the document metadata, for example:
Happy Knitting!
Feel free to use the knitr infrastructure with dozens of
tunable options in your package vignette.
set.seed(123)
n <- 1000
x1 <- matrix(rnorm(n), ncol = 2)
x2 <- matrix(rnorm(n, mean = 3, sd = 1.5), ncol = 2)
x <- rbind(x1, x2)
head(x)## [,1] [,2]
## [1,] -0.56047565 -0.60189285
## [2,] -0.23017749 -0.99369859
## [3,] 1.55870831 1.02678506
## [4,] 0.07050839 0.75106130
## [5,] 0.12928774 -1.50916654
## [6,] 1.71506499 -0.09514745
You can also include code snippets of languages other than R, but note that the block header has no curly brackets around the language name.
You can write math expressions, e.g. \(Y =
X\beta + \epsilon\), footnotes1, and tables, e.g. using
knitr::kable().
| Sepal.Length | Sepal.Width | Petal.Length | Petal.Width | Species |
|---|---|---|---|---|
| 5.1 | 3.5 | 1.4 | 0.2 | setosa |
| 4.9 | 3.0 | 1.4 | 0.2 | setosa |
| 4.7 | 3.2 | 1.3 | 0.2 | setosa |
| 4.6 | 3.1 | 1.5 | 0.2 | setosa |
| 5.0 | 3.6 | 1.4 | 0.2 | setosa |
| 5.4 | 3.9 | 1.7 | 0.4 | setosa |
| 4.6 | 3.4 | 1.4 | 0.3 | setosa |
| 5.0 | 3.4 | 1.5 | 0.2 | setosa |
| 4.4 | 2.9 | 1.4 | 0.2 | setosa |
| 4.9 | 3.1 | 1.5 | 0.1 | setosa |
Stay Tuned
Please visit the development page of the
prettydoc package for latest updates and news. Comments,
bug reports and pull requests are always welcome.
paquetería o liberia
para este protocolo se utilizarán las pqueterías readr(o readx) y janitor
You can use the function “read()” for reading the file. It is used for importing external files to R . This function makes it pissible to load it and use it for analyse it.
readr es el pquete o liberia que se utliza oara leer una tabla con forato .csv
janitor: es la libería qie tiene funciones para “limpiar” o modificar las tablas a trabjar # carga de datos// importación de las tablas a trabajar # en el suguiente recuardo se utiliza la función read_csv para cargar las datos a trabjar
## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1previsalisation (Kopf und Ende)
## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1## mpg cyl disp hp drat wt qsec vs am gear carb
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.9 1 1 5 2
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.5 0 1 5 4
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.5 0 1 5 6
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.6 0 1 5 8
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.6 1 1 4 2## mpg cyl disp hp drat
## Min. :10.40 4:11 Min. : 71.1 Min. : 52.0 Min. :2.760
## 1st Qu.:15.43 6: 7 1st Qu.:120.8 1st Qu.: 96.5 1st Qu.:3.080
## Median :19.20 8:14 Median :196.3 Median :123.0 Median :3.695
## Mean :20.09 Mean :230.7 Mean :146.7 Mean :3.597
## 3rd Qu.:22.80 3rd Qu.:326.0 3rd Qu.:180.0 3rd Qu.:3.920
## Max. :33.90 Max. :472.0 Max. :335.0 Max. :4.930
## wt qsec vs am
## Min. :1.513 Min. :14.50 Min. :0.0000 Min. :0.0000
## 1st Qu.:2.581 1st Qu.:16.89 1st Qu.:0.0000 1st Qu.:0.0000
## Median :3.325 Median :17.71 Median :0.0000 Median :0.0000
## Mean :3.217 Mean :17.85 Mean :0.4375 Mean :0.4062
## 3rd Qu.:3.610 3rd Qu.:18.90 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :5.424 Max. :22.90 Max. :1.0000 Max. :1.0000
## gear carb
## Min. :3.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:2.000
## Median :4.000 Median :2.000
## Mean :3.688 Mean :2.812
## 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :5.000 Max. :8.000## mpg cyl disp hp drat
## Min. :10.40 4:11 Min. : 71.1 Min. : 52.0 Min. :2.760
## 1st Qu.:15.43 6: 7 1st Qu.:120.8 1st Qu.: 96.5 1st Qu.:3.080
## Median :19.20 8:14 Median :196.3 Median :123.0 Median :3.695
## Mean :20.09 Mean :230.7 Mean :146.7 Mean :3.597
## 3rd Qu.:22.80 3rd Qu.:326.0 3rd Qu.:180.0 3rd Qu.:3.920
## Max. :33.90 Max. :472.0 Max. :335.0 Max. :4.930
## wt qsec vs am
## Min. :1.513 Min. :14.50 Min. :0.0000 Min. :0.0000
## 1st Qu.:2.581 1st Qu.:16.89 1st Qu.:0.0000 1st Qu.:0.0000
## Median :3.325 Median :17.71 Median :0.0000 Median :0.0000
## Mean :3.217 Mean :17.85 Mean :0.4375 Mean :0.4062
## 3rd Qu.:3.610 3rd Qu.:18.90 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :5.424 Max. :22.90 Max. :1.0000 Max. :1.0000
## gear carb
## Min. :3.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:2.000
## Median :4.000 Median :2.000
## Mean :3.688 Mean :2.812
## 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :5.000 Max. :8.000## mpg cyl disp hp drat
## Min. :10.40 4:11 Min. : 71.1 Min. : 52.0 Min. :2.760
## 1st Qu.:15.43 6: 7 1st Qu.:120.8 1st Qu.: 96.5 1st Qu.:3.080
## Median :19.20 8:14 Median :196.3 Median :123.0 Median :3.695
## Mean :20.09 Mean :230.7 Mean :146.7 Mean :3.597
## 3rd Qu.:22.80 3rd Qu.:326.0 3rd Qu.:180.0 3rd Qu.:3.920
## Max. :33.90 Max. :472.0 Max. :335.0 Max. :4.930
## wt qsec vs am
## Min. :1.513 Min. :14.50 Min. :0.0000 Min. :0.0000
## 1st Qu.:2.581 1st Qu.:16.89 1st Qu.:0.0000 1st Qu.:0.0000
## Median :3.325 Median :17.71 Median :0.0000 Median :0.0000
## Mean :3.217 Mean :17.85 Mean :0.4375 Mean :0.4062
## 3rd Qu.:3.610 3rd Qu.:18.90 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :5.424 Max. :22.90 Max. :1.0000 Max. :1.0000
## gear carb
## Min. :3.000 1: 7
## 1st Qu.:3.000 2:10
## Median :4.000 3: 3
## Mean :3.688 4:10
## 3rd Qu.:4.000 6: 1
## Max. :5.000 8: 1| Name | mtcars |
| Number of rows | 32 |
| Number of columns | 11 |
| _______________________ | |
| Column type frequency: | |
| factor | 2 |
| numeric | 9 |
| ________________________ | |
| Group variables | None |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| cyl | 0 | 1 | FALSE | 3 | 8: 14, 4: 11, 6: 7 |
| carb | 0 | 1 | FALSE | 6 | 2: 10, 4: 10, 1: 7, 3: 3 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| mpg | 0 | 1 | 20.09 | 6.03 | 10.40 | 15.43 | 19.20 | 22.80 | 33.90 | ▃▇▅▁▂ |
| disp | 0 | 1 | 230.72 | 123.94 | 71.10 | 120.83 | 196.30 | 326.00 | 472.00 | ▇▃▃▃▂ |
| hp | 0 | 1 | 146.69 | 68.56 | 52.00 | 96.50 | 123.00 | 180.00 | 335.00 | ▇▇▆▃▁ |
| drat | 0 | 1 | 3.60 | 0.53 | 2.76 | 3.08 | 3.70 | 3.92 | 4.93 | ▇▃▇▅▁ |
| wt | 0 | 1 | 3.22 | 0.98 | 1.51 | 2.58 | 3.33 | 3.61 | 5.42 | ▃▃▇▁▂ |
| qsec | 0 | 1 | 17.85 | 1.79 | 14.50 | 16.89 | 17.71 | 18.90 | 22.90 | ▃▇▇▂▁ |
| vs | 0 | 1 | 0.44 | 0.50 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▆ |
| am | 0 | 1 | 0.41 | 0.50 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▆ |
| gear | 0 | 1 | 3.69 | 0.74 | 3.00 | 3.00 | 4.00 | 4.00 | 5.00 | ▇▁▆▁▂ |
## 'data.frame': 32 obs. of 11 variables:
## $ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
## $ cyl : Factor w/ 3 levels "4","6","8": 2 2 1 2 3 2 3 1 1 2 ...
## $ disp: num 160 160 108 258 360 ...
## $ hp : num 110 110 93 110 175 105 245 62 95 123 ...
## $ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
## $ wt : num 2.62 2.88 2.32 3.21 3.44 ...
## $ qsec: num 16.5 17 18.6 19.4 17 ...
## $ vs : num 0 0 1 1 0 1 0 1 1 1 ...
## $ am : num 1 1 1 0 0 0 0 0 0 0 ...
## $ gear: num 4 4 4 3 3 3 3 4 4 4 ...
## $ carb: Factor w/ 6 levels "1","2","3","4",..: 4 4 1 1 2 1 4 2 2 4 ...mtcars$cyl = as.factor(mtcars$cyl)
mtcars$carb = as.factor(mtcars$carb)
mtcars$vs = as.factor(mtcars$vs)
mtcars$am = as.factor(mtcars$am)
mtcars$gear = as.factor(mtcars$gear)library(readxl)
library(janitor)
library(readr)
basedatos <- read_csv("~/Documents/Corale/corals_worldwide_dataset.csv")## Rows: 2451 Columns: 10
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): name
## dbl (9): salinity, January_temp, June_temp, area, latitude, longitude, type ...
##
## 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.basedatos$name=as.factor(basedatos$name)
basedatos$salinity=as.numeric(basedatos$salinity)
basedatos$January_temp=as.numeric(basedatos$january_temp)
basedatos$June_temp=as.numeric(basedatos$june_temp)
basedatos$area=as.numeric(basedatos$area)
basedatos$latitude=as.numeric(basedatos$latitude)
basedatos$longitude=as.numeric(basedatos$longitude)
basedatos$type_of_sea=as.numeric(basedatos$type_of_sea)
basedatos$corals=as.numeric(basedatos$corals)
basedatos$silt_sulfide=as.numeric(basedatos$silt_sulfide)## name n percent
## Adriatic Sea 49 0.0199918401
## Aegean Sea 49 0.0199918401
## Andaman Sea 49 0.0199918401
## Arabian Sea 49 0.0199918401
## Arafura Sea 49 0.0199918401
## Balearic Sea 49 0.0199918401
## Baltic Sea 49 0.0199918401
## Banda Sea 49 0.0199918401
## Barents Sea 49 0.0199918401
## Bay of Bengal 49 0.0199918401
## Black Sea 49 0.0199918401
## Caspian Sea 49 0.0199918401
## Celebes Sea 49 0.0199918401
## Coral Sea 49 0.0199918401
## Cosmonauts Sea 49 0.0199918401
## East China Sea 49 0.0199918401
## East Siberian Sea 49 0.0199918401
## Flores Sea 49 0.0199918401
## Gulf of Aden 49 0.0199918401
## Gulf of Carpentaria 49 0.0199918401
## Gulf of Guinea 49 0.0199918401
## Gulf of Thailand 49 0.0199918401
## Ionian Sea 49 0.0199918401
## Java Sea 49 0.0199918401
## Laccadive Sea 49 0.0199918401
## Laptev Sea 49 0.0199918401
## Lazarev Sea 49 0.0199918401
## Levantine Sea 49 0.0199918401
## Libyan Sea 49 0.0199918401
## Mediterranean Sea 49 0.0199918401
## Molucca Sea 49 0.0199918401
## Mozambique Channel 49 0.0199918401
## North Sea 49 0.0199918401
## Norwegian Sea 49 0.0199918401
## Oman Sea 49 0.0199918401
## Persian Gulf 49 0.0199918401
## Philippine Sea 49 0.0199918401
## Red Sea 49 0.0199918401
## Riiser-Larsen Sea 1 0.0004079967
## Sea of Azov 49 0.0199918401
## Sea of Japan 49 0.0199918401
## Sea of Marmara 49 0.0199918401
## Sea of Okhotsk 49 0.0199918401
## Solomon Sea 49 0.0199918401
## Somov Sea 49 0.0199918401
## South China Sea 49 0.0199918401
## Sulu Sea 49 0.0199918401
## Tasman Sea 49 0.0199918401
## Timor Sea 49 0.0199918401
## Tyrrhenian Sea 49 0.0199918401
## Yellow Sea 49 0.0199918401
## Total 2451 1.0000000000## salinity n percent
## 5.93320417404175 1 0.0004079967
## 6.46690464019775 1 0.0004079967
## 6.56233263015747 1 0.0004079967
## 6.5738959312439 1 0.0004079967
## 6.60409688949585 1 0.0004079967
## 6.6416130065918 1 0.0004079967
## 6.64622783660889 1 0.0004079967
## 6.72207260131836 1 0.0004079967
## 6.72481870651245 1 0.0004079967
## 6.73566198348999 1 0.0004079967
## 6.74045658111572 1 0.0004079967
## 6.77485704421997 1 0.0004079967
## 6.82641172409058 1 0.0004079967
## 6.83075761795044 1 0.0004079967
## 6.83856821060181 1 0.0004079967
## 6.84397506713867 1 0.0004079967
## 6.85330772399902 1 0.0004079967
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## 6.88257598876953 1 0.0004079967
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## 11.6000289916992 1 0.0004079967
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## 40 1 0.0004079967
## Total 2451 1.0000000000## January_temp n percent
## -1.89999389648438 124 0.0505915953
## -1.81399500370026 2 0.0008159935
## -1.81329560279846 2 0.0008159935
## -1.81049466133118 3 0.0012239902
## -1.80449497699738 2 0.0008159935
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## -1.79549562931061 3 0.0012239902
## -1.79119694232941 2 0.0008159935
## -1.78919529914856 3 0.0012239902
## -1.78739500045776 2 0.0008159935
## -1.78089570999146 2 0.0008159935
## -1.43729984760284 2 0.0008159935
## -1.41719877719879 3 0.0012239902
## -1.40660035610199 3 0.0012239902
## -1.40299987792969 3 0.0012239902
## -1.39949893951416 3 0.0012239902
## -1.39920079708099 3 0.0012239902
## -1.31599879264831 3 0.0012239902
## -1.1562991142273 2 0.0008159935
## -1.07290101051331 2 0.0008159935
## -1.07040071487427 2 0.0008159935
## -1.06480014324188 2 0.0008159935
## -1.05940127372742 2 0.0008159935
## -1.05070126056671 2 0.0008159935
## -0.920100688934326 2 0.0008159935
## -0.868499755859375 3 0.0012239902
## -0.695200204849243 2 0.0008159935
## -0.692400515079498 3 0.0012239902
## -0.674100637435913 3 0.0012239902
## -0.663401186466217 2 0.0008159935
## -0.65690153837204 2 0.0008159935
## -0.65310150384903 2 0.0008159935
## -0.651599705219269 2 0.0008159935
## -0.558299839496613 2 0.0008159935
## -0.499501347541809 3 0.0012239902
## -0.471300035715103 1 0.0004079967
## -0.441199660301208 2 0.0008159935
## -0.439900517463684 1 0.0004079967
## -0.390099167823792 3 0.0012239902
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## -0.337498486042023 2 0.0008159935
## -0.30090057849884 2 0.0008159935
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## -0.29460021853447 3 0.0012239902
## -0.264400631189346 2 0.0008159935
## -0.248302921652794 2 0.0008159935
## -0.24810180068016 2 0.0008159935
## -0.248100891709328 3 0.0012239902
## -0.247602239251137 2 0.0008159935
## -0.226600348949432 3 0.0012239902
## -0.180300295352936 3 0.0012239902
## -0.149899899959564 3 0.0012239902
## -0.133099675178528 3 0.0012239902
## -0.125699162483215 2 0.0008159935
## -0.117400206625462 2 0.0008159935
## -0.112899474799633 2 0.0008159935
## -0.101999513804913 3 0.0012239902
## -0.0885009765625 2 0.0008159935
## -0.0845986902713775 2 0.0008159935
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## -0.0784008800983429 2 0.0008159935
## -0.0610015876591205 2 0.0008159935
## -0.0603009015321731 3 0.0012239902
## -0.0599005110561847 3 0.0012239902
## -0.003500061109662 2 0.0008159935
## 0.0118991089984774 3 0.0012239902
## 0.0544995106756687 2 0.0008159935
## 0.0612008683383464 3 0.0012239902
## 0.0660015866160392 2 0.0008159935
## 0.0800003036856651 2 0.0008159935
## 0.0950985699892044 3 0.0012239902
## 0.0964968875050544 2 0.0008159935
## 0.0977984592318534 3 0.0012239902
## 0.154897764325142 2 0.0008159935
## 0.158500969409943 2 0.0008159935
## 0.160500183701515 3 0.0012239902
## 0.160799250006676 2 0.0008159935
## 0.226000979542732 2 0.0008159935
## 0.232599794864655 2 0.0008159935
## 0.233700260519981 2 0.0008159935
## 0.32570007443428 3 0.0012239902
## 0.326998889446258 3 0.0012239902
## 0.463900446891785 2 0.0008159935
## 0.508600473403931 3 0.0012239902
## 0.508699953556061 2 0.0008159935
## 0.570399165153503 3 0.0012239902
## 0.59139895439148 3 0.0012239902
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## 26.6000995635986 4 0.0016319869
## 26.6080989837646 3 0.0012239902
## 26.6173992156982 2 0.0008159935
## 26.6797981262207 2 0.0008159935
## 26.6829986572266 2 0.0008159935
## 26.714599609375 3 0.0012239902
## 26.742998123169 4 0.0016319869
## 26.7451000213623 1 0.0004079967
## 26.7497997283936 1 0.0004079967
## 26.7517986297607 1 0.0004079967
## 26.762300491333 1 0.0004079967
## 26.7705993652344 4 0.0016319869
## 26.8050994873047 4 0.0016319869
## 26.8154983520508 1 0.0004079967
## 26.8196983337402 4 0.0016319869
## 26.8199977874756 3 0.0012239902
## 26.8209991455078 1 0.0004079967
## 26.8301982879639 1 0.0004079967
## 26.8564968109131 3 0.0012239902
## 26.8813991546631 2 0.0008159935
## 26.8836975097656 3 0.0012239902
## 26.8955001831055 3 0.0012239902
## 26.9187965393066 1 0.0004079967
## 26.9282989501953 2 0.0008159935
## 26.9287986755371 3 0.0012239902
## 26.9354000091553 3 0.0012239902
## 26.9476985931396 2 0.0008159935
## 27.010799407959 2 0.0008159935
## 27.0177974700928 1 0.0004079967
## 27.0242004394531 2 0.0008159935
## 27.0267009735107 3 0.0012239902
## 27.0271987915039 2 0.0008159935
## 27.0291996002197 3 0.0012239902
## 27.0459976196289 2 0.0008159935
## 27.068998336792 2 0.0008159935
## 27.0954990386963 2 0.0008159935
## 27.1 21 0.0085679315
## 27.1110992431641 2 0.0008159935
## 27.1128005981445 3 0.0012239902
## 27.1281986236572 2 0.0008159935
## 27.1583003997803 2 0.0008159935
## 27.1660003662109 3 0.0012239902
## 27.1732006072998 3 0.0012239902
## 27.1867008209229 2 0.0008159935
## 27.1980991363525 4 0.0016319869
## 27.2005977630615 3 0.0012239902
## 27.2107982635498 2 0.0008159935
## 27.2139987945557 2 0.0008159935
## 27.2299003601074 3 0.0012239902
## 27.2385997772217 3 0.0012239902
## 27.2415981292725 2 0.0008159935
## 27.2418994903564 3 0.0012239902
## 27.2436981201172 3 0.0012239902
## 27.2511005401611 3 0.0012239902
## 27.2798004150391 3 0.0012239902
## 27.3057994842529 3 0.0012239902
## 27.4760971069336 4 0.0016319869
## 27.5322971343994 3 0.0012239902
## 27.5451984405518 2 0.0008159935
## 27.6455993652344 3 0.0012239902
## 27.6802997589111 2 0.0008159935
## 27.6892967224121 2 0.0008159935
## 27.6901988983154 3 0.0012239902
## 27.7001972198486 2 0.0008159935
## 27.7272987365723 3 0.0012239902
## 27.7274971008301 3 0.0012239902
## 27.7590961456299 3 0.0012239902
## 27.7945976257324 2 0.0008159935
## 27.8041000366211 2 0.0008159935
## 27.8147983551025 3 0.0012239902
## 27.8258972167969 3 0.0012239902
## 27.8406963348389 2 0.0008159935
## 27.8429012298584 2 0.0008159935
## 27.8605995178223 3 0.0012239902
## 27.8850994110107 3 0.0012239902
## 27.8851985931396 1 0.0004079967
## 27.8958969116211 3 0.0012239902
## 27.9076995849609 3 0.0012239902
## 27.9145984649658 2 0.0008159935
## 27.9576015472412 3 0.0012239902
## 27.9740009307861 2 0.0008159935
## 27.9825839996338 3 0.0012239902
## 27.9827995300293 2 0.0008159935
## 27.9902992248535 2 0.0008159935
## 27.9943008422852 3 0.0012239902
## 27.9943981170654 3 0.0012239902
## 27.9948997497559 3 0.0012239902
## 28 1 0.0004079967
## 28.0035991668701 2 0.0008159935
## 28.0140991210938 3 0.0012239902
## 28.0228977203369 1 0.0004079967
## 28.0235996246338 3 0.0012239902
## 28.0252990722656 3 0.0012239902
## 28.0254001617432 2 0.0008159935
## 28.025598526001 2 0.0008159935
## 28.0264987945557 2 0.0008159935
## 28.0266990661621 2 0.0008159935
## 28.0310001373291 4 0.0016319869
## 28.0318984985352 2 0.0008159935
## 28.0326995849609 2 0.0008159935
## 28.0368995666504 2 0.0008159935
## 28.0379981994629 2 0.0008159935
## 28.0406723022461 3 0.0012239902
## 28.0417003631592 3 0.0012239902
## 28.0434970855713 2 0.0008159935
## 28.0447978973389 2 0.0008159935
## 28.0570774078369 2 0.0008159935
## 28.0592002868652 3 0.0012239902
## 28.0604991912842 2 0.0008159935
## 28.0741004943848 2 0.0008159935
## 28.0764026641846 3 0.0012239902
## 28.1037998199463 3 0.0012239902
## 28.1059989929199 3 0.0012239902
## 28.1083984375 3 0.0012239902
## 28.11669921875 2 0.0008159935
## 28.1172981262207 2 0.0008159935
## 28.1219005584717 1 0.0004079967
## 28.1220207214356 2 0.0008159935
## 28.1266994476318 3 0.0012239902
## 28.1329212188721 2 0.0008159935
## 28.1368007659912 2 0.0008159935
## 28.1406993865967 3 0.0012239902
## 28.1590995788574 2 0.0008159935
## 28.1740989685059 2 0.0008159935
## 28.1865978240967 3 0.0012239902
## 28.1879978179932 2 0.0008159935
## 28.1921997070312 2 0.0008159935
## 28.1960983276367 2 0.0008159935
## 28.2077007293701 2 0.0008159935
## 28.2119998931885 2 0.0008159935
## 28.213399887085 2 0.0008159935
## 28.2171001434326 3 0.0012239902
## 28.2322959899902 2 0.0008159935
## 28.236198425293 3 0.0012239902
## 28.240198135376 2 0.0008159935
## 28.2519989013672 2 0.0008159935
## 28.2523975372314 3 0.0012239902
## 28.2568988800049 1 0.0004079967
## 28.2693004608154 2 0.0008159935
## 28.2746982574463 2 0.0008159935
## 28.2750988006592 3 0.0012239902
## 28.2951965332031 2 0.0008159935
## 28.2984981536865 2 0.0008159935
## 28.3057975769043 3 0.0012239902
## 28.3239994049072 2 0.0008159935
## 28.3393993377686 2 0.0008159935
## 28.3402996063232 2 0.0008159935
## 28.3573989868164 3 0.0012239902
## 28.3729972839356 3 0.0012239902
## 28.3781967163086 2 0.0008159935
## 28.3971996307373 3 0.0012239902
## 28.3978977203369 2 0.0008159935
## 28.4190979003906 1 0.0004079967
## 28.4204998016357 2 0.0008159935
## 28.426097869873 3 0.0012239902
## 28.4411010742188 1 0.0004079967
## 28.4498996734619 2 0.0008159935
## 28.4565982818604 2 0.0008159935
## 28.4641017913818 1 0.0004079967
## 28.4831981658936 1 0.0004079967
## 28.4839992523193 3 0.0012239902
## 28.4932975769043 2 0.0008159935
## 28.4937992095947 2 0.0008159935
## 28.4967994689941 1 0.0004079967
## 28.5041980743408 3 0.0012239902
## 28.5087013244629 1 0.0004079967
## 28.5238990783691 2 0.0008159935
## 28.5261993408203 3 0.0012239902
## 28.539400100708 2 0.0008159935
## 28.5472965240479 2 0.0008159935
## 28.5507011413574 3 0.0012239902
## 28.5536003112793 2 0.0008159935
## 28.5538177490234 2 0.0008159935
## 28.5646991729736 2 0.0008159935
## 28.567699432373 2 0.0008159935
## 28.5889987945557 2 0.0008159935
## 28.604398727417 2 0.0008159935
## 28.6047954559326 2 0.0008159935
## 28.6051959991455 3 0.0012239902
## 28.6081962585449 2 0.0008159935
## 28.6162986755371 2 0.0008159935
## 28.6238975524902 3 0.0012239902
## 28.625997543335 2 0.0008159935
## 28.6396980285644 3 0.0012239902
## 28.6511001586914 2 0.0008159935
## 28.662899017334 3 0.0012239902
## 28.6653995513916 3 0.0012239902
## 28.683198928833 1 0.0004079967
## 28.6961002349854 2 0.0008159935
## 28.713098526001 2 0.0008159935
## 28.7161998748779 3 0.0012239902
## 28.7188987731934 2 0.0008159935
## 28.7195987701416 2 0.0008159935
## 28.720100402832 3 0.0012239902
## 28.7331008911133 3 0.0012239902
## 28.7369995117188 3 0.0012239902
## 28.7430000305176 2 0.0008159935
## 28.7472991943359 3 0.0012239902
## 28.7510967254639 3 0.0012239902
## 28.7557983398438 2 0.0008159935
## 28.7593994140625 3 0.0012239902
## 28.7596607208252 2 0.0008159935
## 28.7753982543945 2 0.0008159935
## 28.7813987731934 2 0.0008159935
## 28.7875003814697 2 0.0008159935
## 28.7889881134033 2 0.0008159935
## 28.7941551208496 3 0.0012239902
## 28.8056983947754 2 0.0008159935
## 28.8076992034912 3 0.0012239902
## 28.8086986541748 2 0.0008159935
## 28.8138980865479 3 0.0012239902
## 28.8194961547852 3 0.0012239902
## 28.8778972625732 3 0.0012239902
## 28.8779964447021 2 0.0008159935
## 28.8946990966797 3 0.0012239902
## 28.9035930633545 3 0.0012239902
## 28.9077529907227 2 0.0008159935
## 28.9089012145996 3 0.0012239902
## 28.9109001159668 3 0.0012239902
## 28.9375 2 0.0008159935
## 28.9505996704102 2 0.0008159935
## 28.9635009765625 3 0.0012239902
## 28.9688987731934 2 0.0008159935
## 28.9726963043213 2 0.0008159935
## 28.9851989746094 2 0.0008159935
## 28.9897994995117 2 0.0008159935
## 28.9964008331299 3 0.0012239902
## 28.9976005554199 2 0.0008159935
## 28.999698638916 2 0.0008159935
## 29.0023593902588 3 0.0012239902
## 29.0088748931885 2 0.0008159935
## 29.0162982940674 3 0.0012239902
## 29.0222015380859 2 0.0008159935
## 29.0423564910889 3 0.0012239902
## 29.044599533081 2 0.0008159935
## 29.0462989807129 2 0.0008159935
## 29.0504989624023 4 0.0016319869
## 29.0573978424072 2 0.0008159935
## 29.0582008361816 3 0.0012239902
## 29.0611972808838 2 0.0008159935
## 29.0839977264404 3 0.0012239902
## 29.1113986968994 2 0.0008159935
## 29.1232986450195 3 0.0012239902
## 29.123706817627 2 0.0008159935
## 29.1309967041016 2 0.0008159935
## 29.1571998596191 3 0.0012239902
## 29.1666984558106 2 0.0008159935
## 29.1695003509521 2 0.0008159935
## 29.1706981658936 3 0.0012239902
## 29.1709976196289 3 0.0012239902
## 29.1714992523193 2 0.0008159935
## 29.1767997741699 3 0.0012239902
## 29.1791019439697 2 0.0008159935
## 29.1876983642578 2 0.0008159935
## 29.1901988983154 2 0.0008159935
## 29.2037010192871 3 0.0012239902
## 29.2040004730225 2 0.0008159935
## 29.217399597168 2 0.0008159935
## 29.2406978607178 2 0.0008159935
## 29.2443981170654 2 0.0008159935
## 29.2460021972656 3 0.0012239902
## 29.2676982879639 3 0.0012239902
## 29.2742004394531 2 0.0008159935
## 29.2779998779297 3 0.0012239902
## 29.2784976959229 3 0.0012239902
## 29.2803993225098 3 0.0012239902
## 29.2889995574951 3 0.0012239902
## 29.2996959686279 3 0.0012239902
## 29.3039989471436 2 0.0008159935
## 29.3074989318848 3 0.0012239902
## 29.3184967041016 2 0.0008159935
## 29.3229007720947 3 0.0012239902
## 29.3337993621826 2 0.0008159935
## 29.3367004394531 2 0.0008159935
## 29.3398990631104 2 0.0008159935
## 29.3427982330322 2 0.0008159935
## 29.3446998596191 2 0.0008159935
## 29.3634986877441 2 0.0008159935
## 29.3635997772217 3 0.0012239902
## 29.3658962249756 3 0.0012239902
## 29.3724975585938 3 0.0012239902
## 29.3736991882324 3 0.0012239902
## 29.3969993591309 2 0.0008159935
## 29.4027004241943 2 0.0008159935
## 29.4030990600586 3 0.0012239902
## 29.4245986938477 2 0.0008159935
## 29.4328994750977 2 0.0008159935
## 29.4398002624512 3 0.0012239902
## 29.4435997009277 2 0.0008159935
## 29.4447975158691 2 0.0008159935
## 29.4591999053955 3 0.0012239902
## 29.4851989746094 3 0.0012239902
## 29.485200881958 2 0.0008159935
## 29.4879989624023 3 0.0012239902
## 29.4882011413574 2 0.0008159935
## 29.4897003173828 3 0.0012239902
## 29.4916000366211 3 0.0012239902
## 29.4925003051758 2 0.0008159935
## 29.4986000061035 3 0.0012239902
## 29.5 2 0.0008159935
## 29.5020999908447 2 0.0008159935
## 29.5056991577148 2 0.0008159935
## 29.5060977935791 2 0.0008159935
## 29.5071983337402 3 0.0012239902
## 29.5082988739014 2 0.0008159935
## 29.5133991241455 2 0.0008159935
## 29.5193996429443 2 0.0008159935
## 29.5279998779297 3 0.0012239902
## 29.536298751831 2 0.0008159935
## 29.5470008850098 2 0.0008159935
## 29.5612983703613 2 0.0008159935
## 29.578498840332 3 0.0012239902
## 29.5820980072021 3 0.0012239902
## 29.5878009796143 2 0.0008159935
## 29.6205978393555 2 0.0008159935
## 29.6310997009277 3 0.0012239902
## 29.6315994262695 2 0.0008159935
## 29.6458988189697 2 0.0008159935
## 29.6475982666016 2 0.0008159935
## 29.6530990600586 2 0.0008159935
## 29.661600112915 3 0.0012239902
## 29.6631965637207 3 0.0012239902
## 29.7004013061523 2 0.0008159935
## 29.7251987457275 2 0.0008159935
## 29.7389011383057 3 0.0012239902
## 29.8131980895996 2 0.0008159935
## 29.8401966094971 3 0.0012239902
## 29.8765983581543 2 0.0008159935
## 29.8812007904053 3 0.0012239902
## 29.8846969604492 3 0.0012239902
## 29.9070987701416 3 0.0012239902
## 29.9417991638184 3 0.0012239902
## 29.9490985870361 3 0.0012239902
## 29.9495983123779 3 0.0012239902
## 29.9665985107422 3 0.0012239902
## 29.9758968353271 2 0.0008159935
## 29.9783000946045 2 0.0008159935
## 29.9902992248535 2 0.0008159935
## 29.999698638916 2 0.0008159935
## 30.0146980285644 3 0.0012239902
## 30.0272979736328 3 0.0012239902
## 30.0276966094971 3 0.0012239902
## 30.0291976928711 2 0.0008159935
## 30.0429992675781 2 0.0008159935
## 30.0773983001709 2 0.0008159935
## 30.086498260498 2 0.0008159935
## 30.0902996063232 2 0.0008159935
## 30.0942974090576 3 0.0012239902
## 30.1681976318359 2 0.0008159935
## 30.1759986877441 3 0.0012239902
## 30.1789989471436 3 0.0012239902
## 30.2033996582031 2 0.0008159935
## 30.2516002655029 3 0.0012239902
## 30.271900177002 3 0.0012239902
## 30.2789001464844 2 0.0008159935
## 30.2792987823486 2 0.0008159935
## 30.2813968658447 2 0.0008159935
## 30.2926979064941 2 0.0008159935
## 30.2996978759766 2 0.0008159935
## 30.3008995056152 2 0.0008159935
## 30.3293972015381 3 0.0012239902
## 30.3306980133057 2 0.0008159935
## 30.3450965881348 3 0.0012239902
## 30.3650989532471 3 0.0012239902
## 30.3800983428955 2 0.0008159935
## 30.3845977783203 3 0.0012239902
## 30.4221000671387 3 0.0012239902
## 30.4228000640869 3 0.0012239902
## 30.4315986633301 3 0.0012239902
## 30.4350986480713 2 0.0008159935
## 30.4356994628906 2 0.0008159935
## 30.4563980102539 2 0.0008159935
## 30.4646987915039 2 0.0008159935
## 30.5007991790771 2 0.0008159935
## 30.5025997161865 3 0.0012239902
## 30.5030002593994 3 0.0012239902
## 30.5100975036621 2 0.0008159935
## 30.513298034668 2 0.0008159935
## 30.5165996551514 2 0.0008159935
## 30.5267009735107 3 0.0012239902
## 30.536298751831 3 0.0012239902
## 30.5574989318848 3 0.0012239902
## 30.5625 2 0.0008159935
## 30.5630989074707 2 0.0008159935
## 30.5665969848633 3 0.0012239902
## 30.58229637146 2 0.0008159935
## 30.5964012145996 2 0.0008159935
## 30.6013011932373 2 0.0008159935
## 30.6020011901856 3 0.0012239902
## 30.6355991363525 2 0.0008159935
## 30.763500213623 3 0.0012239902
## Total 2451 1.0000000000## June_temp n percent
## -1.89999389648438 93 0.0379436965
## -1.89899599552154 1 0.0004079967
## -1.66869783401489 3 0.0012239902
## -1.62809813022614 3 0.0012239902
## -1.60569822788239 3 0.0012239902
## -1.59890019893646 3 0.0012239902
## -1.59789860248566 3 0.0012239902
## -1.58269834518433 1 0.0004079967
## -1.58139801025391 2 0.0008159935
## -1.56869876384735 2 0.0008159935
## -1.56249725818634 2 0.0008159935
## -1.52030026912689 2 0.0008159935
## -1.50919795036316 2 0.0008159935
## -1.5 3 0.0012239902
## -1.31930088996887 2 0.0008159935
## -1.18630182743073 2 0.0008159935
## -1.12560033798218 2 0.0008159935
## -1.10949981212616 3 0.0012239902
## -1.09140110015869 3 0.0012239902
## -1.08640193939209 2 0.0008159935
## -1.07980227470398 3 0.0012239902
## -1.06890201568603 2 0.0008159935
## -1.04610109329224 3 0.0012239902
## -1.04209995269775 2 0.0008159935
## -1 12 0.0048959608
## 0.0392980948090553 3 0.0012239902
## 0.0872998014092445 2 0.0008159935
## 0.1 3 0.0012239902
## 0.2 3 0.0012239902
## 0.234501346945763 2 0.0008159935
## 0.253698736429214 2 0.0008159935
## 0.281301885843277 3 0.0012239902
## 0.3 3 0.0012239902
## 0.380400389432907 2 0.0008159935
## 0.391099542379379 3 0.0012239902
## 0.4 3 0.0012239902
## 0.410800486803055 3 0.0012239902
## 0.41740021109581 3 0.0012239902
## 0.472800612449646 2 0.0008159935
## 0.5 3 0.0012239902
## 0.514499485492706 2 0.0008159935
## 0.536001563072205 2 0.0008159935
## 0.6 3 0.0012239902
## 0.66270112991333 1 0.0004079967
## 0.66719788312912 2 0.0008159935
## 0.674801051616669 3 0.0012239902
## 0.7 3 0.0012239902
## 0.785699784755707 3 0.0012239902
## 0.78969818353653 2 0.0008159935
## 0.8 6 0.0024479804
## 0.837399899959564 2 0.0008159935
## 0.906800210475922 2 0.0008159935
## 1 1 0.0004079967
## 1.00890016555786 3 0.0012239902
## 2 8 0.0032639739
## 2.58719968795776 2 0.0008159935
## 2.72419881820679 2 0.0008159935
## 2.974200963974 3 0.0012239902
## 3 8 0.0032639739
## 3.01950073242188 3 0.0012239902
## 3.027099609375 2 0.0008159935
## 3.05109763145447 3 0.0012239902
## 3.06120157241821 3 0.0012239902
## 3.45120096206665 2 0.0008159935
## 3.48730039596558 2 0.0008159935
## 3.51310157775879 3 0.0012239902
## 3.8314995765686 2 0.0008159935
## 3.87529945373535 2 0.0008159935
## 4 16 0.0065279478
## 4.06010055541992 3 0.0012239902
## 4.12630033493042 2 0.0008159935
## 4.1663007736206 3 0.0012239902
## 4.18789958953857 2 0.0008159935
## 4.9773998260498 2 0.0008159935
## 5 7 0.0028559772
## 5.61169910430908 3 0.0012239902
## 5.6767988204956 3 0.0012239902
## 6 6 0.0024479804
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## 29.596097946167 2 0.0008159935
## 29.5961990356445 2 0.0008159935
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## 29.6036968231201 3 0.0012239902
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## 29.6103973388672 2 0.0008159935
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## 29.6394996643066 2 0.0008159935
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## 29.8612995147705 3 0.0012239902
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## 29.9038772583008 3 0.0012239902
## 29.9042682647705 2 0.0008159935
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## 29.9150009155273 2 0.0008159935
## 29.9265022277832 3 0.0012239902
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## 29.9288005828857 2 0.0008159935
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## 29.9945983886719 2 0.0008159935
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## 29.9987983703613 2 0.0008159935
## 30 4 0.0016319869
## 30.0116996765137 3 0.0012239902
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## 30.0413990020752 2 0.0008159935
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## 30.0485992431641 1 0.0004079967
## 30.0527973175049 2 0.0008159935
## 30.0564022064209 1 0.0004079967
## 30.0567989349365 1 0.0004079967
## 30.0726013183594 3 0.0012239902
## 30.0774993896484 2 0.0008159935
## 30.0809001922607 3 0.0012239902
## 30.0832996368408 2 0.0008159935
## 30.0958995819092 3 0.0012239902
## 30.0978984832764 1 0.0004079967
## 30.0997982025146 2 0.0008159935
## 30.1013984680176 3 0.0012239902
## 30.1037998199463 2 0.0008159935
## 30.1044960021973 2 0.0008159935
## 30.1068992614746 2 0.0008159935
## 30.109001159668 4 0.0016319869
## 30.1110000610352 2 0.0008159935
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## 30.1310977935791 2 0.0008159935
## 30.1354999542236 2 0.0008159935
## 30.1424980163574 3 0.0012239902
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## 30.1522998809814 2 0.0008159935
## 30.1541996002197 3 0.0012239902
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## 30.1684989929199 3 0.0012239902
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## 30.177698135376 3 0.0012239902
## 30.204999923706 2 0.0008159935
## 30.2083969116211 2 0.0008159935
## 30.2166004180908 2 0.0008159935
## 30.2211990356445 3 0.0012239902
## 30.2214984893799 3 0.0012239902
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## 30.2258987426758 2 0.0008159935
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## 30.3124980926514 2 0.0008159935
## 30.3127002716064 2 0.0008159935
## 30.3127975463867 2 0.0008159935
## 30.3132972717285 3 0.0012239902
## 30.3140964508057 2 0.0008159935
## 30.3178977966309 3 0.0012239902
## 30.3285980224609 3 0.0012239902
## 30.3368988037109 3 0.0012239902
## 30.3386001586914 2 0.0008159935
## 30.3454971313477 2 0.0008159935
## 30.3572978973389 2 0.0008159935
## 30.3797988891602 2 0.0008159935
## 30.3823986053467 3 0.0012239902
## 30.3847980499268 3 0.0012239902
## 30.3852005004883 3 0.0012239902
## 30.3867988586426 2 0.0008159935
## 30.4233989715576 3 0.0012239902
## 30.4419975280762 3 0.0012239902
## 30.4586009979248 2 0.0008159935
## 30.5037746429443 3 0.0012239902
## 30.5325984954834 3 0.0012239902
## 30.5507125854492 2 0.0008159935
## 30.5652980804443 2 0.0008159935
## 30.5669002532959 3 0.0012239902
## 30.6 20 0.0081599347
## 30.6154975891113 3 0.0012239902
## 30.6236000061035 2 0.0008159935
## 30.6240978240967 3 0.0012239902
## 30.6255989074707 2 0.0008159935
## 30.6257972717285 3 0.0012239902
## 30.626298904419 2 0.0008159935
## 30.6270999908447 2 0.0008159935
## 30.6348991394043 3 0.0012239902
## 30.670597076416 2 0.0008159935
## 30.7847995758057 2 0.0008159935
## 30.7919979095459 3 0.0012239902
## 30.8273448944092 3 0.0012239902
## 30.9890804290771 2 0.0008159935
## 31 6 0.0024479804
## 31.0129585266113 3 0.0012239902
## 31.029899597168 3 0.0012239902
## 31.0564289093018 3 0.0012239902
## 31.0852012634277 2 0.0008159935
## 31.1 1 0.0004079967
## 31.122652053833 2 0.0008159935
## 31.1476974487305 2 0.0008159935
## 31.167200088501 2 0.0008159935
## 31.2117328643799 2 0.0008159935
## 31.2660007476807 3 0.0012239902
## 31.3913974761963 3 0.0012239902
## 31.4723987579346 3 0.0012239902
## 31.4983997344971 3 0.0012239902
## 31.5396976470947 3 0.0012239902
## 31.5602970123291 3 0.0012239902
## 31.5718994140625 2 0.0008159935
## 31.5908145904541 2 0.0008159935
## 31.5973987579346 2 0.0008159935
## 31.628999710083 3 0.0012239902
## 31.7953968048096 2 0.0008159935
## 31.8425979614258 2 0.0008159935
## 31.8502998352051 2 0.0008159935
## 31.9333972930908 2 0.0008159935
## 31.9782981872559 2 0.0008159935
## 32.0254974365234 2 0.0008159935
## 32.2796020507812 2 0.0008159935
## 32.3 14 0.0057119543
## Total 2451 1.0000000000## area n percent
## 11500 49 0.0199918401
## 39000 49 0.0199918401
## 138000 49 0.0199918401
## 150000 49 0.0199918401
## 169000 49 0.0199918401
## 181000 49 0.0199918401
## 2e+05 49 0.0199918401
## 214000 49 0.0199918401
## 240000 49 0.0199918401
## 251000 49 0.0199918401
## 260000 49 0.0199918401
## 275000 49 0.0199918401
## 280000 49 0.0199918401
## 3e+05 49 0.0199918401
## 320000 147 0.0599755202
## 350000 49 0.0199918401
## 371000 49 0.0199918401
## 377000 49 0.0199918401
## 380000 49 0.0199918401
## 410000 49 0.0199918401
## 436000 49 0.0199918401
## 438000 49 0.0199918401
## 575000 49 0.0199918401
## 610000 49 0.0199918401
## 650000 49 0.0199918401
## 662000 49 0.0199918401
## 695000 49 0.0199918401
## 699000 49 0.0199918401
## 7e+05 49 0.0199918401
## 720000 49 0.0199918401
## 786000 49 0.0199918401
## 797700 49 0.0199918401
## 929000 49 0.0199918401
## 987000 49 0.0199918401
## 1050000 49 0.0199918401
## 1138000 1 0.0004079967
## 1150000 49 0.0199918401
## 1249000 49 0.0199918401
## 1383000 49 0.0199918401
## 1400000 49 0.0199918401
## 1583000 49 0.0199918401
## 2172000 49 0.0199918401
## 2300000 49 0.0199918401
## 2350000 49 0.0199918401
## 2510000 49 0.0199918401
## 3500000 49 0.0199918401
## 3862000 49 0.0199918401
## 4791000 49 0.0199918401
## 5695000 49 0.0199918401
## Total 2451 1.0000000000## latitude n percent
## -68 50 0.02039984
## -67 49 0.01999184
## -65 49 0.01999184
## -40 49 0.01999184
## -18 98 0.03998368
## -14 49 0.01999184
## -10 49 0.01999184
## -9 49 0.01999184
## -8 98 0.03998368
## -6 49 0.01999184
## -5 49 0.01999184
## 0 98 0.03998368
## 3 49 0.01999184
## 8 98 0.03998368
## 9 49 0.01999184
## 10 49 0.01999184
## 12 98 0.03998368
## 14 49 0.01999184
## 15 49 0.01999184
## 20 49 0.01999184
## 22 49 0.01999184
## 25 49 0.01999184
## 26 49 0.01999184
## 30 49 0.01999184
## 34 98 0.03998368
## 35 49 0.01999184
## 38 98 0.03998368
## 39 49 0.01999184
## 40 196 0.07996736
## 42 49 0.01999184
## 43 49 0.01999184
## 44 49 0.01999184
## 46 49 0.01999184
## 55 49 0.01999184
## 56 49 0.01999184
## 58 49 0.01999184
## 69 49 0.01999184
## 72 49 0.01999184
## 75 49 0.01999184
## 76 49 0.01999184
## Total 2451 1.00000000## longitude n percent
## 0 49 0.0199918401
## 1 49 0.0199918401
## 2 49 0.0199918401
## 3 49 0.0199918401
## 7 49 0.0199918401
## 12 49 0.0199918401
## 15 49 0.0199918401
## 18 49 0.0199918401
## 19 49 0.0199918401
## 20 49 0.0199918401
## 22 1 0.0004079967
## 24 49 0.0199918401
## 25 49 0.0199918401
## 28 49 0.0199918401
## 34 49 0.0199918401
## 35 49 0.0199918401
## 37 49 0.0199918401
## 38 49 0.0199918401
## 40 49 0.0199918401
## 41 49 0.0199918401
## 45 49 0.0199918401
## 48 49 0.0199918401
## 50 49 0.0199918401
## 52 49 0.0199918401
## 58 49 0.0199918401
## 65 49 0.0199918401
## 75 49 0.0199918401
## 88 49 0.0199918401
## 96 49 0.0199918401
## 102 49 0.0199918401
## 110 49 0.0199918401
## 113 49 0.0199918401
## 120 49 0.0199918401
## 121 49 0.0199918401
## 122 49 0.0199918401
## 123 49 0.0199918401
## 125 147 0.0599755202
## 127 98 0.0399836801
## 130 49 0.0199918401
## 133 49 0.0199918401
## 135 49 0.0199918401
## 139 49 0.0199918401
## 150 49 0.0199918401
## 154 49 0.0199918401
## 158 49 0.0199918401
## 160 98 0.0399836801
## 163 49 0.0199918401
## Total 2451 1.0000000000## type_of_sea n percent
## 1 49 0.01999184
## 2 686 0.27988576
## 3 1716 0.70012240
## Total 2451 1.00000000## corals n percent
## 0 343 0.1399429
## 1 2108 0.8600571
## Total 2451 1.0000000## silt_sulfide n percent
## 0 2353 0.96001632
## 1 98 0.03998368
## Total 2451 1.00000000#instlation of esquisse
## Warning: package 'esquisse' was built under R version 4.4.1#Darstellung von Grafiken
ggplot(basedatos) +
aes(x = name, y = salinity) +
geom_point(colour = "#112446") +
labs(
x = "Name of the sea",
y = "Salt content",
title = "Salt content according to sea"
) +
theme_classic()
ggplot(basedatos) +
aes(x = January_temp, y = name, fill = January_temp) +
geom_boxplot() +
scale_fill_gradient() +
theme_minimal() +
theme(legend.position = "top", legend.justification = "top")## Warning: The following aesthetics were dropped during statistical transformation: fill.
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
A footnote here.↩︎