Aula 2 - Introdução ao tidyverse

Aula 2

Dr. Gabriel Bertolini

2º semestre 2023 - PPGGEO UFRGS

Oficina de programação

Aula introdutória sobre tidyverse.

Bibliotecas

Função install.packages pode ser usada para baixar arquivos do CRAN - pacotes da comunidade “oficiais” que estão alinhados as praticas e estilo de programação padronizadas do R. Pacotes do github pode ser instalados, utilizando uma função do pacote devtools: devtools::install.github(“link_do_github_exemplo:gabertol/ztR”)

Os pacotes só precisam ser instalados uma vez, e carregados sempre que necessário usando a função library().

#install.packages("tidyverse") # Instalar se necessário
library(tidyverse)
library(gt)

1. Funções de R base úteis

vetor<-c(1.1,4.2,2.9,9,1,4,5,0.0001,NA)

# média
mean(vetor,na.rm=TRUE) #média
[1] 3.400012
# desvio padrão
sd(vetor)
[1] NA
# minimo
min(vetor) 
[1] NA
# maximo
max(vetor) 
[1] NA
# extensão
length(vetor) 
[1] 9
# cria sequencia de valores
A<-seq(from=pi,
    to=pi^2,
    by=pi/4) 

length(A)
[1] 9
# gerador de números com distribuição normal
PER<-rnorm(n=1000000,mean=350,sd=5)

mean(PER)
[1] 349.9957
sd(PER)
[1] 5.000009
# gerador de numeros randomicos
rep(c("A","B","C"), 2) 
[1] "A" "B" "C" "A" "B" "C"

2. Introdução ao tidyverse

A biblioteca guarda-chuva tidyverse inclui uma serie de pacotes, cada uma para seu fim. Por hora, vamos explorar o pacote dplyr que trata de manipulação básica de bancos de dados. Para isso, vamos primeiramente importar um banco de dados. Para importar, vamos usar a função read.csv

# Importar e criar dataframe (tibble)
BD<-read.csv("./data/geoquimica_granito.csv") %>% 
  as_tibble()# tibble é um tipo particular de dataframe do tidyverse, que facilita a leitura no prompt

Para observar os valores do dataframe no console, pode ser executar o nome do objeto, no caso BD. Abaixo, usamos a função gt() do pacote gt para tabela interativa.

BD %>% 
  gt_preview()
X.1 X Tectonic_setting Location_notes Pluton Colour Symbol Size_rel Size SubGroup Group DebonPQ TASMiddlemostPlut Villaseca QANOR LaRoche Geol_unit_notes Rock_type_notes Ref Age Latitude Longitude SiO2 TiO2 Al2O3 FeOt MnO MgO CaO K2O Na2O P2O5 H2O H2Ot H2O.MINUS LOI Li Be B Sc V Cr Ni Cu Zn Rb Sr Y Zr Nb Cs Ba La Ce Pr Nd Sm Eu Gd Tb Dy Ho Er Tm Yb Lu Hf Ta Pb Th U Co Mo W Ga Ge As In Sn Sb Cd Q C Or Ab An Ne Ns Ks Di Wo Hy Ol Il Tn Pf Ru Ap Sum_CIPW Orthoclase Albite Anorthite Quartz Apatite Ilmenite Biotite Amphibole Corundum Rest sum_meso P K Ti Cr2O3 A.NK A.CNK K2O.Na2O
1 1 CR03 OROGENIC BELT ////////Peninsula////// Cape Peninsula brown4 16 1.6 1.28 Mafic S-types 1- CPG & MPG gr quartz monzonite h-P 3b granite granodiorite ////////// ////// Unpub. Data, A.Villaros, G. Stevens, R. Scheepers (Villaros et al. 2009) NA NA 65.40690 0.8270369 17.08490 4.914410 0.07714897 1.7960280 1.813515 5.131949 2.660096 0.2880228 NA NA NA NA NA NA NA NA NA NA NA 269.0154 97.21508 52.24591 305.2263 25.35902 NA 390.4759 56.40085 112.16620 13.223848 52.34722 11.393739 1.2494124 10.838077 1.6265380 9.835326 2.024970 6.206267 0.8976863 6.077240 NA 8.747229 NA NA 26.782157 NA NA NA NA NA NA 24.54893 4.546013 30.32835 22.50899 7.115483 0 0 0 0 0 4.474137 0 0.16503878 0 0 0.7405025 0.6822185 95.10966 25.62016 22.53101 7.109292 27.20246 0.6797338 0.7856851 6.932014 0 4.544585 -0.3193515 95.08560 1257.1043 42601.85 4958.086 NA 1.720334 1.291518 1.929235
2 2 BB202 OROGENIC BELT ////////Peninsula////// Cape Peninsula brown4 16 1.6 1.28 Mafic S-types 1- CPG & MPG gr granodiorite h-P 3b granite granodiorite ////////// ////// Unpub. Data, A.Villaros, G. Stevens, R. Scheepers (Villaros et al. 2009) NA NA 69.64232 0.7087655 14.88713 4.186309 0.05397209 1.6293460 1.436880 5.018386 2.215911 0.22098 NA NA NA NA NA NA NA NA NA NA NA 214.2795 98.96108 38.28057 260.1457 19.91177 NA 415.6909 44.29357 90.02860 10.382656 39.39793 7.655565 1.0286014 8.160105 1.1358404 8.071263 1.516150 3.946216 0.5438978 3.740191 NA 7.342101 NA NA 19.492634 NA NA NA NA NA NA 32.66955 3.726173 29.65722 18.75042 5.684928 0 0 0 0 0 4.058911 0 0.11545828 0 0 0.6482846 0.5234192 95.83437 25.43081 18.76877 5.680122 35.07460 0.5215128 0.6733272 6.222967 0 3.726782 -0.2852007 95.81369 964.4893 41659.13 4249.049 NA 1.640003 1.273484 2.264706
3 3 CB02 OROGENIC BELT ////////Peninsula////// Cape Peninsula brown4 16 1.6 1.28 Mafic S-types 1- CPG & MPG gr granite h-P 3b granite granite ////////// ////// Unpub. Data, A.Villaros, G. Stevens, R. Scheepers (Villaros et al. 2009) NA NA 70.61077 0.5705854 15.28393 2.822104 0.05318077 1.3306273 1.204323 5.597277 2.329983 0.197212 NA NA NA NA NA NA NA NA NA NA NA 238.5302 90.38265 29.96433 186.0415 15.13822 NA 547.5218 32.36167 63.93163 7.248746 28.7628 6.040634 1.076092 5.3337 0.8584825 5.574644 1.088590 3.174243 0.4446103 2.755472 NA 5.434977 NA NA 14.798949 NA NA NA NA NA NA 31.63569 3.674628 33.07830 19.71567 4.686449 0 0 0 0 0 3.314764 0 0.11376549 0 0 0.5109352 0.4671216 97.19733 29.62021 19.73496 4.682256 33.59253 0.4654203 0.5420561 5.098139 0 3.676313 -0.2339899 97.17789 860.7515 46464.68 3420.659 NA 1.545114 1.265067 2.402282
4 4 HB02 OROGENIC BELT ////////Peninsula////// Cape Peninsula brown4 16 1.6 1.28 Mafic S-types 1- CPG & MPG gr granite h-P 3b granite granite ////////// ////// Unpub. Data, A.Villaros, G. Stevens, R. Scheepers (Villaros et al. 2009) NA NA 70.61724 0.4775028 15.70859 2.508109 0.04066072 0.9779424 1.311569 5.280680 2.877528 0.2001758 NA NA NA NA NA NA NA NA NA NA NA 254.2501 116.29881 36.86868 181.9237 15.15780 NA 499.1298 34.12151 70.41246 8.06835 30.55275 6.613892 1.2179917 6.696204 1.0960613 7.270367 1.316706 3.774652 0.5311750 3.354008 NA 5.152241 NA NA 15.483984 NA NA NA NA NA NA 29.97338 3.353324 31.20731 24.34884 5.199155 0 0 0 0 0 2.436180 0 0.08698231 0 0 0.4319138 0.4741418 97.51123 28.63039 24.37266 5.194790 31.40372 0.4724149 0.4536277 3.789478 0 3.351382 -0.1765739 97.49189 873.6873 43836.51 2862.629 NA 1.503237 1.223927 1.835145
5 5 CR01 OROGENIC BELT ////////Peninsula////// Cape Peninsula brown4 16 1.6 1.28 Mafic S-types 1- CPG & MPG gr granite h-P 3a granite granite ////////// ////// Unpub. Data, A.Villaros, G. Stevens, R. Scheepers (Villaros et al. 2009) NA NA 70.70878 0.5322106 15.28031 2.535746 0.06085569 1.2071555 1.235924 5.569955 2.642243 0.2268257 NA NA NA NA NA NA NA NA NA NA NA 233.7445 91.32626 38.96812 172.6349 14.63283 NA 408.2444 32.54389 62.92283 7.49205 29.71011 6.455299 1.0194147 6.264685 1.0442407 6.880438 1.272111 3.816830 0.4902676 3.014657 NA 4.650457 NA NA 14.926382 NA NA NA NA NA NA 30.22193 3.200335 32.91684 22.35792 4.649750 0 0 0 0 0 3.007180 0 0.13018383 0 0 0.4638989 0.5372654 97.48530 29.75506 22.37980 4.645108 31.99386 0.5353087 0.5056001 4.664697 0 3.200603 -0.2157710 97.46426 990.0035 46237.87 3190.603 NA 1.472673 1.210504 2.108040
6..3049
3050 3050 SGMV OROGENIC BELT Carajas Province, Amazonia Velho Guilherme Suite lightgreen 18 2.0 1.60 PostOro (A2) 5- KCG Teixeira et al. 2019 1860-1880 NA NA 75.95000 0.0600000 12.62000 2.745275 NA 0.0100000 0.580000 4.530000 2.590000 NA NA NA NA NA NA NA NA NA NA NA 786.0000 18.00000 170.00000 164.0000 76.00000 NA 28.0000 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

Abre tabela estilo excel/ selecionar o objeto com mouse e apertar F2 é a mesma coisa

view(BD)

Trata-se de um banco de dados de @bonin2020. Aqui vamos usar uma série de funções do dplyr e tidyr para operações nesse banco de dados.

Selecionar colunas com select()

# Selecionar algumas colunas
# Usar função names() para examinar quais colunas o banco tem
names(BD) # o banco tem 117 colunas, vamos selecionar algumas colunas
  [1] "X.1"               "X"                 "Tectonic_setting" 
  [4] "Location_notes"    "Pluton"            "Colour"           
  [7] "Symbol"            "Size_rel"          "Size"             
 [10] "SubGroup"          "Group"             "DebonPQ"          
 [13] "TASMiddlemostPlut" "Villaseca"         "QANOR"            
 [16] "LaRoche"           "Geol_unit_notes"   "Rock_type_notes"  
 [19] "Ref"               "Age"               "Latitude"         
 [22] "Longitude"         "SiO2"              "TiO2"             
 [25] "Al2O3"             "FeOt"              "MnO"              
 [28] "MgO"               "CaO"               "K2O"              
 [31] "Na2O"              "P2O5"              "H2O"              
 [34] "H2Ot"              "H2O.MINUS"         "LOI"              
 [37] "Li"                "Be"                "B"                
 [40] "Sc"                "V"                 "Cr"               
 [43] "Ni"                "Cu"                "Zn"               
 [46] "Rb"                "Sr"                "Y"                
 [49] "Zr"                "Nb"                "Cs"               
 [52] "Ba"                "La"                "Ce"               
 [55] "Pr"                "Nd"                "Sm"               
 [58] "Eu"                "Gd"                "Tb"               
 [61] "Dy"                "Ho"                "Er"               
 [64] "Tm"                "Yb"                "Lu"               
 [67] "Hf"                "Ta"                "Pb"               
 [70] "Th"                "U"                 "Co"               
 [73] "Mo"                "W"                 "Ga"               
 [76] "Ge"                "As"                "In"               
 [79] "Sn"                "Sb"                "Cd"               
 [82] "Q"                 "C"                 "Or"               
 [85] "Ab"                "An"                "Ne"               
 [88] "Ns"                "Ks"                "Di"               
 [91] "Wo"                "Hy"                "Ol"               
 [94] "Il"                "Tn"                "Pf"               
 [97] "Ru"                "Ap"                "Sum_CIPW"         
[100] "Orthoclase"        "Albite"            "Anorthite"        
[103] "Quartz"            "Apatite"           "Ilmenite"         
[106] "Biotite"           "Amphibole"         "Corundum"         
[109] "Rest"              "sum_meso"          "P"                
[112] "K"                 "Ti"                "Cr2O3"            
[115] "A.NK"              "A.CNK"             "K2O.Na2O"         
BD %>%  # reparar o uso do comando pype esse codigo significa select(BD,SiO2:Cd)- as funções
  select(SiO2:Cd) # selecionar elementos quimicos - SiO2 até Cd
# A tibble: 3,050 × 59
    SiO2  TiO2 Al2O3  FeOt    MnO   MgO   CaO   K2O  Na2O P2O5        H2O  H2Ot
   <dbl> <dbl> <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl> <dbl> <chr>     <dbl> <dbl>
 1  65.4 0.827  17.1  4.91 0.0771 1.80   1.81  5.13  2.66 0.2880228    NA    NA
 2  69.6 0.709  14.9  4.19 0.0540 1.63   1.44  5.02  2.22 0.22098      NA    NA
 3  70.6 0.571  15.3  2.82 0.0532 1.33   1.20  5.60  2.33 0.197212     NA    NA
 4  70.6 0.478  15.7  2.51 0.0407 0.978  1.31  5.28  2.88 0.2001758    NA    NA
 5  70.7 0.532  15.3  2.54 0.0609 1.21   1.24  5.57  2.64 0.2268257    NA    NA
 6  71.0 0.484  15.5  2.47 0.0396 1.01   1.48  5.33  2.56 0.1863152    NA    NA
 7  72.4 0.470  14.3  2.68 0.0422 1.03   1.09  5.05  2.75 0.2026448    NA    NA
 8  73.2 0.428  14.4  2.40 0.0605 0.769  1.06  5.04  2.46 0.2184707    NA    NA
 9  73.2 0.363  14.4  2.25 0.0211 0.678  1.10  5.05  2.86 0.1591542    NA    NA
10  74.8 0.530  13.5  2.73 0.0472 1.18   1.43  3.28  2.30 0.1812674    NA    NA
# ℹ 3,040 more rows
# ℹ 47 more variables: H2O.MINUS <dbl>, LOI <chr>, Li <dbl>, Be <dbl>, B <dbl>,
#   Sc <dbl>, V <chr>, Cr <dbl>, Ni <dbl>, Cu <dbl>, Zn <dbl>, Rb <dbl>,
#   Sr <dbl>, Y <dbl>, Zr <dbl>, Nb <dbl>, Cs <dbl>, Ba <dbl>, La <chr>,
#   Ce <dbl>, Pr <chr>, Nd <chr>, Sm <chr>, Eu <chr>, Gd <chr>, Tb <dbl>,
#   Dy <dbl>, Ho <dbl>, Er <dbl>, Tm <dbl>, Yb <dbl>, Lu <dbl>, Hf <dbl>,
#   Ta <dbl>, Pb <dbl>, Th <chr>, U <dbl>, Co <dbl>, Mo <chr>, W <dbl>, …
BD %>% 
  select(X,Tectonic_setting,Latitude,Longitude,Albite:Corundum) %>% # selecionar colunas X, Tectonic_setting, Latitude,Longitude e minerais (Albite:Corundum)
  select(-X) # usar - pra retirar a variavel
# A tibble: 3,050 × 11
   Tectonic_setting Latitude Longitude Albite Anorthite Quartz Apatite Ilmenite
   <chr>               <dbl>     <dbl>  <dbl>     <dbl>  <dbl>   <dbl>    <dbl>
 1 OROGENIC BELT          NA        NA   22.5      7.11   27.2   0.680    0.786
 2 OROGENIC BELT          NA        NA   18.8      5.68   35.1   0.522    0.673
 3 OROGENIC BELT          NA        NA   19.7      4.68   33.6   0.465    0.542
 4 OROGENIC BELT          NA        NA   24.4      5.19   31.4   0.472    0.454
 5 OROGENIC BELT          NA        NA   22.4      4.65   32.0   0.535    0.506
 6 OROGENIC BELT          NA        NA   21.7      6.11   33.0   0.440    0.460
 7 OROGENIC BELT          NA        NA   23.3      4.07   35.3   0.478    0.446
 8 OROGENIC BELT          NA        NA   20.8      3.84   37.9   0.516    0.407
 9 OROGENIC BELT          NA        NA   24.2      4.39   35.3   0.376    0.344
10 OROGENIC BELT          NA        NA   19.5      5.91   46.2   0.428    0.504
# ℹ 3,040 more rows
# ℹ 3 more variables: Biotite <dbl>, Amphibole <dbl>, Corundum <dbl>

Filtrar colunas com filter()

# Filtrar tectonic_setting 
unique(BD$Tectonic_setting) # ver valores unicos de Tectonic_setting para saber qual variavel filtrar
[1] "OROGENIC BELT"            "INTRAPLATE VOLCANICS"    
[3] "ARCHAEAN CRATONS"         "CONVERGENT MARGIN"       
[5] "OCEANIC PLATEAU"          "OPHIOLITE"               
[7] "RIFT VOLCANICS"           "CONTINENTAL FLOOD BASALT"
[9] "OCEAN ISLAND"            
BD %>% 
  select(X,Tectonic_setting,Latitude,Longitude,Albite:Corundum) %>% # reparar funções encadeadas
  filter(Tectonic_setting=="OCEANIC PLATEAU") # filtrando somente o Oceanic Plateau
# A tibble: 13 × 12
   X         Tectonic_setting Latitude Longitude Albite Anorthite Quartz Apatite
   <chr>     <chr>               <dbl>     <dbl>  <dbl>     <dbl>  <dbl>   <dbl>
 1 46143     OCEANIC PLATEAU      12.5       -70   37.3      8.51  33.3    0.135
 2 46135     OCEANIC PLATEAU      12.5       -70   32.7      9.14   6.31   0.439
 3 46142     OCEANIC PLATEAU      12.5       -70   45.7     15.0   22.7    0.304
 4 46139     OCEANIC PLATEAU      12.5       -70   31.2     24.6   19.9    0.208
 5 46140     OCEANIC PLATEAU      12.5       -70   37.4     17.8   22.7    0.283
 6 46134     OCEANIC PLATEAU      12.5       -70   36.3     23.2    8.53   0.408
 7 9157-ARU… OCEANIC PLATEAU      12.5       -70   39.1     16.1   21.2    0.337
 8 10446-A10 OCEANIC PLATEAU      -7         156   31.9     13.7   14.9    0.378
 9 10446-A11 OCEANIC PLATEAU      -7         156   50.6      2.22  37.5    0.260
10 10446-A6  OCEANIC PLATEAU      -7         156   32.5     17.1   22.6    0.378
11 10446-A7  OCEANIC PLATEAU      -7         156   31.6     17.7   15.9    0.378
12 10446-A8  OCEANIC PLATEAU      -7         156   34.6     20.2   18.0    0.378
13 10446-A9  OCEANIC PLATEAU      -7         156   29.1     21.1   20.0    0.401
# ℹ 4 more variables: Ilmenite <dbl>, Biotite <dbl>, Amphibole <dbl>,
#   Corundum <dbl>
BD %>% 
  filter(Tectonic_setting=="CONTINENTAL FLOOD BASALT",
         SiO2<60,
         SiO2>50,
         TiO2>1) # filtrar varias colunas - separadas por , 
# A tibble: 13 × 117
     X.1 X         Tectonic_setting Location_notes Pluton Colour Symbol Size_rel
   <int> <chr>     <chr>            <chr>          <chr>  <chr>   <int>    <dbl>
 1  1952 201163    CONTINENTAL FLO… NORTH ATLANTI… Skye   honey…      2      1.6
 2  1953 201168    CONTINENTAL FLO… NORTH ATLANTI… Skye   honey…      2      1.6
 3  1955 85457     CONTINENTAL FLO… NORTH ATLANTI… Skye   honey…      2      1.6
 4  1957 85452     CONTINENTAL FLO… NORTH ATLANTI… Skye   honey…      2      1.6
 5  1958 85453     CONTINENTAL FLO… NORTH ATLANTI… Skye   honey…      2      1.6
 6  1959 85455     CONTINENTAL FLO… NORTH ATLANTI… Skye   honey…      2      1.6
 7  1960 85456     CONTINENTAL FLO… NORTH ATLANTI… Skye   honey…      2      1.6
 8  2055 119793    CONTINENTAL FLO… ETENDEKA PROV… Etane… honey…      2      1.6
 9  2056 119795    CONTINENTAL FLO… ETENDEKA PROV… Etane… honey…      2      1.6
10  2057 99730     CONTINENTAL FLO… ETENDEKA PROV… Etane… honey…      2      1.6
11  2111 8564-MC4  CONTINENTAL FLO… ETENDEKA PROV… Messum honey…      2      1.6
12  2113 43064     CONTINENTAL FLO… ETENDEKA PROV… Messum honey…      2      1.6
13  2124 8572-MC44 CONTINENTAL FLO… ETENDEKA PROV… Messum honey…      2      1.6
# ℹ 109 more variables: Size <dbl>, SubGroup <chr>, Group <chr>, DebonPQ <chr>,
#   TASMiddlemostPlut <chr>, Villaseca <chr>, QANOR <chr>, LaRoche <chr>,
#   Geol_unit_notes <chr>, Rock_type_notes <chr>, Ref <chr>, Age <chr>,
#   Latitude <dbl>, Longitude <dbl>, SiO2 <dbl>, TiO2 <dbl>, Al2O3 <dbl>,
#   FeOt <dbl>, MnO <dbl>, MgO <dbl>, CaO <dbl>, K2O <dbl>, Na2O <dbl>,
#   P2O5 <chr>, H2O <dbl>, H2Ot <dbl>, H2O.MINUS <dbl>, LOI <chr>, Li <dbl>,
#   Be <dbl>, B <dbl>, Sc <dbl>, V <chr>, Cr <dbl>, Ni <dbl>, Cu <dbl>, …
BD %>% 
  filter(Tectonic_setting %in% c("OROGENIC BELT",
                                 "INTRAPLATE VOLCANICS",
                                 "ARCHAEAN CRATONS",
                                 "CONVERGENT MARGIN")) # filtrar pelos strings usando %in% e c()
# A tibble: 2,642 × 117
     X.1 X     Tectonic_setting Location_notes     Pluton Colour Symbol Size_rel
   <int> <chr> <chr>            <chr>              <chr>  <chr>   <int>    <dbl>
 1     1 CR03  OROGENIC BELT    ////////Peninsula… Cape … brown4     16      1.6
 2     2 BB202 OROGENIC BELT    ////////Peninsula… Cape … brown4     16      1.6
 3     3 CB02  OROGENIC BELT    ////////Peninsula… Cape … brown4     16      1.6
 4     4 HB02  OROGENIC BELT    ////////Peninsula… Cape … brown4     16      1.6
 5     5 CR01  OROGENIC BELT    ////////Peninsula… Cape … brown4     16      1.6
 6     6 LL01  OROGENIC BELT    ////////Peninsula… Cape … brown4     16      1.6
 7     7 MP21  OROGENIC BELT    ////////Peninsula… Cape … brown4     16      1.6
 8     8 OK07  OROGENIC BELT    ////////Peninsula… Cape … brown4     16      1.6
 9     9 LG05  OROGENIC BELT    ////////Langebaan… Cape … brown4     16      1.6
10    10 BB201 OROGENIC BELT    ////////Peninsula… Cape … brown4     16      1.6
# ℹ 2,632 more rows
# ℹ 109 more variables: Size <dbl>, SubGroup <chr>, Group <chr>, DebonPQ <chr>,
#   TASMiddlemostPlut <chr>, Villaseca <chr>, QANOR <chr>, LaRoche <chr>,
#   Geol_unit_notes <chr>, Rock_type_notes <chr>, Ref <chr>, Age <chr>,
#   Latitude <dbl>, Longitude <dbl>, SiO2 <dbl>, TiO2 <dbl>, Al2O3 <dbl>,
#   FeOt <dbl>, MnO <dbl>, MgO <dbl>, CaO <dbl>, K2O <dbl>, Na2O <dbl>,
#   P2O5 <chr>, H2O <dbl>, H2Ot <dbl>, H2O.MINUS <dbl>, LOI <chr>, Li <dbl>, …
BD %>% 
  filter(Tectonic_setting== "OROGENIC BELT" & SiO2>60) # filtrar condicionalmente
# A tibble: 1,370 × 117
     X.1 X     Tectonic_setting Location_notes     Pluton Colour Symbol Size_rel
   <int> <chr> <chr>            <chr>              <chr>  <chr>   <int>    <dbl>
 1     1 CR03  OROGENIC BELT    ////////Peninsula… Cape … brown4     16      1.6
 2     2 BB202 OROGENIC BELT    ////////Peninsula… Cape … brown4     16      1.6
 3     3 CB02  OROGENIC BELT    ////////Peninsula… Cape … brown4     16      1.6
 4     4 HB02  OROGENIC BELT    ////////Peninsula… Cape … brown4     16      1.6
 5     5 CR01  OROGENIC BELT    ////////Peninsula… Cape … brown4     16      1.6
 6     6 LL01  OROGENIC BELT    ////////Peninsula… Cape … brown4     16      1.6
 7     7 MP21  OROGENIC BELT    ////////Peninsula… Cape … brown4     16      1.6
 8     8 OK07  OROGENIC BELT    ////////Peninsula… Cape … brown4     16      1.6
 9     9 LG05  OROGENIC BELT    ////////Langebaan… Cape … brown4     16      1.6
10    10 BB201 OROGENIC BELT    ////////Peninsula… Cape … brown4     16      1.6
# ℹ 1,360 more rows
# ℹ 109 more variables: Size <dbl>, SubGroup <chr>, Group <chr>, DebonPQ <chr>,
#   TASMiddlemostPlut <chr>, Villaseca <chr>, QANOR <chr>, LaRoche <chr>,
#   Geol_unit_notes <chr>, Rock_type_notes <chr>, Ref <chr>, Age <chr>,
#   Latitude <dbl>, Longitude <dbl>, SiO2 <dbl>, TiO2 <dbl>, Al2O3 <dbl>,
#   FeOt <dbl>, MnO <dbl>, MgO <dbl>, CaO <dbl>, K2O <dbl>, Na2O <dbl>,
#   P2O5 <chr>, H2O <dbl>, H2Ot <dbl>, H2O.MINUS <dbl>, LOI <chr>, Li <dbl>, …

Criar colunas com mutate(), agrupamento com group(), arredondamento com round()

BD %>% 
  select(X,Tectonic_setting,SiO2:MgO) %>% 
  mutate(l_Si=log(SiO2),
         teste=log(SiO2)^2 + sqrt((log(Al2O3)))) #cria nova coluna com o log da coluna SiO2
# A tibble: 3,050 × 10
   X     Tectonic_setting  SiO2  TiO2 Al2O3  FeOt    MnO   MgO  l_Si teste
   <chr> <chr>            <dbl> <dbl> <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl>
 1 CR03  OROGENIC BELT     65.4 0.827  17.1  4.91 0.0771 1.80   4.18  19.2
 2 BB202 OROGENIC BELT     69.6 0.709  14.9  4.19 0.0540 1.63   4.24  19.6
 3 CB02  OROGENIC BELT     70.6 0.571  15.3  2.82 0.0532 1.33   4.26  19.8
 4 HB02  OROGENIC BELT     70.6 0.478  15.7  2.51 0.0407 0.978  4.26  19.8
 5 CR01  OROGENIC BELT     70.7 0.532  15.3  2.54 0.0609 1.21   4.26  19.8
 6 LL01  OROGENIC BELT     71.0 0.484  15.5  2.47 0.0396 1.01   4.26  19.8
 7 MP21  OROGENIC BELT     72.4 0.470  14.3  2.68 0.0422 1.03   4.28  20.0
 8 OK07  OROGENIC BELT     73.2 0.428  14.4  2.40 0.0605 0.769  4.29  20.1
 9 LG05  OROGENIC BELT     73.2 0.363  14.4  2.25 0.0211 0.678  4.29  20.1
10 BB201 OROGENIC BELT     74.8 0.530  13.5  2.73 0.0472 1.18   4.31  20.2
# ℹ 3,040 more rows
BD %>% 
  select(X,Tectonic_setting,SiO2:MgO) %>% 
  mutate(across(.cols=SiO2:MgO,~log(.x))) # Operações em massa
# A tibble: 3,050 × 8
   X     Tectonic_setting  SiO2   TiO2 Al2O3  FeOt   MnO      MgO
   <chr> <chr>            <dbl>  <dbl> <dbl> <dbl> <dbl>    <dbl>
 1 CR03  OROGENIC BELT     4.18 -0.190  2.84 1.59  -2.56  0.586  
 2 BB202 OROGENIC BELT     4.24 -0.344  2.70 1.43  -2.92  0.488  
 3 CB02  OROGENIC BELT     4.26 -0.561  2.73 1.04  -2.93  0.286  
 4 HB02  OROGENIC BELT     4.26 -0.739  2.75 0.920 -3.20 -0.0223 
 5 CR01  OROGENIC BELT     4.26 -0.631  2.73 0.930 -2.80  0.188  
 6 LL01  OROGENIC BELT     4.26 -0.726  2.74 0.904 -3.23  0.00650
 7 MP21  OROGENIC BELT     4.28 -0.756  2.66 0.986 -3.16  0.0276 
 8 OK07  OROGENIC BELT     4.29 -0.848  2.67 0.877 -2.81 -0.263  
 9 LG05  OROGENIC BELT     4.29 -1.01   2.66 0.809 -3.86 -0.389  
10 BB201 OROGENIC BELT     4.31 -0.635  2.61 1.00  -3.05  0.165  
# ℹ 3,040 more rows
BD %>% 
  select(X,Tectonic_setting,SiO2:MgO) %>% 
  group_by(Tectonic_setting) %>% 
  mutate(contagem=n())
# A tibble: 3,050 × 9
# Groups:   Tectonic_setting [9]
   X     Tectonic_setting  SiO2  TiO2 Al2O3  FeOt    MnO   MgO contagem
   <chr> <chr>            <dbl> <dbl> <dbl> <dbl>  <dbl> <dbl>    <int>
 1 CR03  OROGENIC BELT     65.4 0.827  17.1  4.91 0.0771 1.80      1471
 2 BB202 OROGENIC BELT     69.6 0.709  14.9  4.19 0.0540 1.63      1471
 3 CB02  OROGENIC BELT     70.6 0.571  15.3  2.82 0.0532 1.33      1471
 4 HB02  OROGENIC BELT     70.6 0.478  15.7  2.51 0.0407 0.978     1471
 5 CR01  OROGENIC BELT     70.7 0.532  15.3  2.54 0.0609 1.21      1471
 6 LL01  OROGENIC BELT     71.0 0.484  15.5  2.47 0.0396 1.01      1471
 7 MP21  OROGENIC BELT     72.4 0.470  14.3  2.68 0.0422 1.03      1471
 8 OK07  OROGENIC BELT     73.2 0.428  14.4  2.40 0.0605 0.769     1471
 9 LG05  OROGENIC BELT     73.2 0.363  14.4  2.25 0.0211 0.678     1471
10 BB201 OROGENIC BELT     74.8 0.530  13.5  2.73 0.0472 1.18      1471
# ℹ 3,040 more rows
BD %>% 
  select(X,Tectonic_setting,SiO2:MgO) %>% 
  group_by(Tectonic_setting) %>% 
  count()
# A tibble: 9 × 2
# Groups:   Tectonic_setting [9]
  Tectonic_setting             n
  <chr>                    <int>
1 ARCHAEAN CRATONS           310
2 CONTINENTAL FLOOD BASALT   191
3 CONVERGENT MARGIN          763
4 INTRAPLATE VOLCANICS        98
5 OCEAN ISLAND                56
6 OCEANIC PLATEAU             13
7 OPHIOLITE                   19
8 OROGENIC BELT             1471
9 RIFT VOLCANICS             129
BD %>% 
  select(X,Tectonic_setting,SiO2:MgO) %>% 
  group_by(Tectonic_setting) %>%  # Agrupa os valores por grupos baseados nos tipos tectonicos, ou seja, qualquer ação será realizada em diferentes "compartimentos" de dados- isso pode ser muito util para médias globais e de grupo.
  mutate(across(SiO2:MgO,~ifelse(.x==0,mean(.x),.x)),  # substituir 0 por média por grupo (1 para cada valor de tectonic setting diferente) 
         SiO2=round(SiO2,digits=2)) #arredondar para dois digitos
# A tibble: 3,050 × 8
# Groups:   Tectonic_setting [9]
   X     Tectonic_setting  SiO2  TiO2 Al2O3  FeOt    MnO   MgO
   <chr> <chr>            <dbl> <dbl> <dbl> <dbl>  <dbl> <dbl>
 1 CR03  OROGENIC BELT     65.4 0.827  17.1  4.91 0.0771 1.80 
 2 BB202 OROGENIC BELT     69.6 0.709  14.9  4.19 0.0540 1.63 
 3 CB02  OROGENIC BELT     70.6 0.571  15.3  2.82 0.0532 1.33 
 4 HB02  OROGENIC BELT     70.6 0.478  15.7  2.51 0.0407 0.978
 5 CR01  OROGENIC BELT     70.7 0.532  15.3  2.54 0.0609 1.21 
 6 LL01  OROGENIC BELT     71.0 0.484  15.5  2.47 0.0396 1.01 
 7 MP21  OROGENIC BELT     72.4 0.470  14.3  2.68 0.0422 1.03 
 8 OK07  OROGENIC BELT     73.2 0.428  14.4  2.40 0.0605 0.769
 9 LG05  OROGENIC BELT     73.2 0.363  14.4  2.25 0.0211 0.678
10 BB201 OROGENIC BELT     74.8 0.530  13.5  2.73 0.0472 1.18 
# ℹ 3,040 more rows

Contar com count() ou remoldar dataframe com reframe()

BD %>% 
  select(X,Tectonic_setting,SiO2:MgO) %>% 
  group_by(Tectonic_setting) %>%  # Agrupa os valores por grupos baseados nos tipos tectonicos, ou seja, qualquer ação será realizada em diferentes "compartimentos" de dados- isso pode ser muito util para médias globais e de grupo.
  mutate(across(SiO2:MgO,~ifelse(.x==0, mean(.x),.x)),  # substituir 0 por média por grupo (1 para cada valor de tectonic setting diferente) 
         across(SiO2:MgO,~round(.x,digits=2))) %>% 
  reframe(n=n(),
          across(.cols=SiO2:MgO,~round(mean(.x,na.rm = TRUE),2)))
# A tibble: 9 × 8
  Tectonic_setting             n  SiO2  TiO2 Al2O3  FeOt   MnO   MgO
  <chr>                    <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 ARCHAEAN CRATONS           310  69.6  0.31  15.4  2.43  0.04  1.33
2 CONTINENTAL FLOOD BASALT   191  67.5  0.53  14.6  4.21  0.13  0.5 
3 CONVERGENT MARGIN          763  66.4  0.52  15.7  3.92  0.08  1.7 
4 INTRAPLATE VOLCANICS        98  70.3  0.32  14.1  3.35  0.08  0.49
5 OCEAN ISLAND                56  65.2  0.53  16.2  4.02  0.16  0.44
6 OCEANIC PLATEAU             13  63.6  0.52  16.1  5.05  0.1   2.59
7 OPHIOLITE                   19  61.6  1.07  15.0  6.03  0.17  2.41
8 OROGENIC BELT             1471  69.8  0.41  14.4  2.85  0.07  1.31
9 RIFT VOLCANICS             129  69.3  0.43  13.8  3.66  0.1   0.49

Alterando a forma do dataframe com pivot_longer e _wider()

# Dado longo
BD %>% 
  select(X,Tectonic_setting,SiO2:MgO) %>% 
  group_by(Tectonic_setting) %>%  # Agrupa os valores por grupos baseados nos tipos tectonicos, ou seja, qualquer ação será realizada em diferentes "compartimentos" de dados- isso pode ser muito util para médias globais e de grupo.
  mutate(across(SiO2:MgO,~ifelse(.x==0, mean(.x),.x)),  # substituir 0 por média por grupo (1 para cada valor de tectonic setting diferente) 
         across(SiO2:MgO,~round(.x,digits=2))) %>% 
  reframe(n=n(),
          across(.cols=SiO2:MgO,~round(mean(.x,na.rm = TRUE),2))) %>%
  pivot_longer(cols=SiO2:MgO)
# A tibble: 54 × 4
   Tectonic_setting             n name  value
   <chr>                    <int> <chr> <dbl>
 1 ARCHAEAN CRATONS           310 SiO2  69.6 
 2 ARCHAEAN CRATONS           310 TiO2   0.31
 3 ARCHAEAN CRATONS           310 Al2O3 15.4 
 4 ARCHAEAN CRATONS           310 FeOt   2.43
 5 ARCHAEAN CRATONS           310 MnO    0.04
 6 ARCHAEAN CRATONS           310 MgO    1.33
 7 CONTINENTAL FLOOD BASALT   191 SiO2  67.5 
 8 CONTINENTAL FLOOD BASALT   191 TiO2   0.53
 9 CONTINENTAL FLOOD BASALT   191 Al2O3 14.6 
10 CONTINENTAL FLOOD BASALT   191 FeOt   4.21
# ℹ 44 more rows
#dado largo
BD %>% 
  select(X,Tectonic_setting,SiO2:MgO) %>% 
  group_by(Tectonic_setting) %>%  # Agrupa os valores por grupos baseados nos tipos tectonicos, ou seja, qualquer ação será realizada em diferentes "compartimentos" de dados- isso pode ser muito util para médias globais e de grupo.
  mutate(across(SiO2:MgO,~ifelse(.x==0, mean(.x),.x)),  # substituir 0 por média por grupo (1 para cada valor de tectonic setting diferente) 
         across(SiO2:MgO,~round(.x,digits=2))) %>% 
  reframe(n=n(),
          across(.cols=SiO2:MgO,~round(mean(.x,na.rm = TRUE),2))) %>%
  pivot_longer(cols=SiO2:MgO) %>% # Alongar dataframe para forma tidy
  ungroup() %>% 
  group_by(name) %>%  
  pivot_wider(id_cols=Tectonic_setting,values_from=value) # Expandir lateralmente o dataframe
# A tibble: 9 × 7
  Tectonic_setting          SiO2  TiO2 Al2O3  FeOt   MnO   MgO
  <chr>                    <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 ARCHAEAN CRATONS          69.6  0.31  15.4  2.43  0.04  1.33
2 CONTINENTAL FLOOD BASALT  67.5  0.53  14.6  4.21  0.13  0.5 
3 CONVERGENT MARGIN         66.4  0.52  15.7  3.92  0.08  1.7 
4 INTRAPLATE VOLCANICS      70.3  0.32  14.1  3.35  0.08  0.49
5 OCEAN ISLAND              65.2  0.53  16.2  4.02  0.16  0.44
6 OCEANIC PLATEAU           63.6  0.52  16.1  5.05  0.1   2.59
7 OPHIOLITE                 61.6  1.07  15.0  6.03  0.17  2.41
8 OROGENIC BELT             69.8  0.41  14.4  2.85  0.07  1.31
9 RIFT VOLCANICS            69.3  0.43  13.8  3.66  0.1   0.49

3. Estudo de caso - Vulcões do Tidytuesday

Objetivo: Revisar tidyverse e fazer exercícios. Os dados são disponibilizados no link, e contem o video para analise desse banco no youtube (https://github.com/rfordatascience/tidytuesday/tree/master).

Esse link apresenta mais infos sobre o banco de dado e fontes:

https://github.com/rfordatascience/tidytuesday/blob/master/data/2020/2020-05-12/readme.md

Importação de dados de um banco em excel com readxl:

# Ilustrativo
#volc<-read_excel("volcano.xlsx") %>% 
#  as_tibble()

Opção B, baixar diretamente do github do tidytuesday:

volc <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-05-12/volcano.csv')

select()

volc %>% select(volcano_name,-country,latitude:elevation)
# A tibble: 958 × 4
   volcano_name    latitude longitude elevation
   <chr>              <dbl>     <dbl>     <dbl>
 1 Abu                 34.5     132.        641
 2 Acamarachi         -23.3     -67.6      6023
 3 Acatenango          14.5     -90.9      3976
 4 Acigol-Nevsehir     38.5      34.6      1683
 5 Adams               46.2    -121.       3742
 6 Adatarayama         37.6     140.       1728
 7 Adwa                10.1      40.8      1733
 8 Afdera              13.1      40.9      1250
 9 Agrigan             18.8     146.        965
10 Agua                14.5     -90.7      3760
# ℹ 948 more rows
volc %>% select(!latitude:elevation)
# A tibble: 958 × 23
   volcano_number volcano_name   primary_volcano_type last_eruption_year country
            <dbl> <chr>          <chr>                <chr>              <chr>  
 1         283001 Abu            Shield(s)            -6850              Japan  
 2         355096 Acamarachi     Stratovolcano        Unknown            Chile  
 3         342080 Acatenango     Stratovolcano(es)    1972               Guatem…
 4         213004 Acigol-Nevseh… Caldera              -2080              Turkey 
 5         321040 Adams          Stratovolcano        950                United…
 6         283170 Adatarayama    Stratovolcano(es)    1996               Japan  
 7         221170 Adwa           Stratovolcano        Unknown            Ethiop…
 8         221110 Afdera         Stratovolcano        Unknown            Ethiop…
 9         284160 Agrigan        Stratovolcano        1917               United…
10         342100 Agua           Stratovolcano        Unknown            Guatem…
# ℹ 948 more rows
# ℹ 18 more variables: region <chr>, subregion <chr>, tectonic_settings <chr>,
#   evidence_category <chr>, major_rock_1 <chr>, major_rock_2 <chr>,
#   major_rock_3 <chr>, major_rock_4 <chr>, major_rock_5 <chr>,
#   minor_rock_1 <chr>, minor_rock_2 <chr>, minor_rock_3 <chr>,
#   minor_rock_4 <chr>, minor_rock_5 <chr>, population_within_5_km <dbl>,
#   population_within_10_km <dbl>, population_within_30_km <dbl>, …
volc %>% select(starts_with("population"))
# A tibble: 958 × 4
   population_within_5_km population_within_10_km population_within_30_km
                    <dbl>                   <dbl>                   <dbl>
 1                   3597                    9594                  117805
 2                      0                       7                     294
 3                   4329                   60730                 1042836
 4                 127863                  127863                  218469
 5                      0                      70                    4019
 6                    428                    3936                  717078
 7                    101                     485                   18645
 8                     51                    6042                    8611
 9                      0                       0                       0
10                   9890                  114404                 2530449
# ℹ 948 more rows
# ℹ 1 more variable: population_within_100_km <dbl>
volc %>% select(contains("within"))
# A tibble: 958 × 4
   population_within_5_km population_within_10_km population_within_30_km
                    <dbl>                   <dbl>                   <dbl>
 1                   3597                    9594                  117805
 2                      0                       7                     294
 3                   4329                   60730                 1042836
 4                 127863                  127863                  218469
 5                      0                      70                    4019
 6                    428                    3936                  717078
 7                    101                     485                   18645
 8                     51                    6042                    8611
 9                      0                       0                       0
10                   9890                  114404                 2530449
# ℹ 948 more rows
# ℹ 1 more variable: population_within_100_km <dbl>
#- **stars_with()**: colunas que começam com um prefixo

#- **ends_with()**: colunas que terminam com um sufixo

#- **contains()**: colunas que contêm uma string

#- **last_col()**: última coluna

filter()

volc %>% select(volcano_name,country,latitude:elevation) %>% 
         filter(country== "Antarctica")
# A tibble: 17 × 5
   volcano_name        country    latitude longitude elevation
   <chr>               <chr>         <dbl>     <dbl>     <dbl>
 1 Andrus              Antarctica    -75.8    -132.       2978
 2 Berlin              Antarctica    -76.0    -136        3478
 3 Buckle Island       Antarctica    -66.8     163.       1239
 4 Deception Island    Antarctica    -63.0     -60.7       602
 5 Erebus              Antarctica    -77.5     167.       3794
 6 Hudson Mountains    Antarctica    -74.3     -99.4       749
 7 James Ross Island   Antarctica    -64.2     -57.8      1630
 8 Melbourne           Antarctica    -74.4     165.       2732
 9 Morning             Antarctica    -78.5     164.       2723
10 Penguin Island      Antarctica    -62.1     -57.9       180
11 Pleiades, The       Antarctica    -72.7     166.       3040
12 Royal Society Range Antarctica    -78.2     163.       3000
13 Seal Nunataks Group Antarctica    -65.0     -60.0       368
14 Siple               Antarctica    -73.4    -127.       3110
15 Takahe              Antarctica    -76.3    -112.       3460
16 Toney Mountain      Antarctica    -75.8    -116.       3595
17 Waesche             Antarctica    -77.2    -127.       3292
volc %>% select(volcano_name,country,latitude:elevation) %>% 
         filter(!country== "Chile") %>% 
         filter(elevation>1000,
                latitude>0,
                longitude>0)
# A tibble: 252 × 5
   volcano_name     country  latitude longitude elevation
   <chr>            <chr>       <dbl>     <dbl>     <dbl>
 1 Acigol-Nevsehir  Turkey       38.5      34.6      1683
 2 Adatarayama      Japan        37.6     140.       1728
 3 Adwa             Ethiopia     10.1      40.8      1733
 4 Afdera           Ethiopia     13.1      40.9      1250
 5 Aira             Japan        31.6     131.       1117
 6 Akademia Nauk    Russia       54.0     159.       1180
 7 Akagisan         Japan        36.6     139.       1828
 8 Akan             Japan        43.4     144.       1499
 9 Akita-Komagatake Japan        39.8     141.       1637
10 Akita-Yakeyama   Japan        40.0     141.       1366
# ℹ 242 more rows
# %in% comando logico para declarar vetores para filter

volc %>% select(volcano_name,country,latitude:elevation) %>% 
         filter(!country %in%  c("Chile","Argentina","Japan"))
# A tibble: 809 × 5
   volcano_name    country       latitude longitude elevation
   <chr>           <chr>            <dbl>     <dbl>     <dbl>
 1 Acatenango      Guatemala        14.5      -90.9      3976
 2 Acigol-Nevsehir Turkey           38.5       34.6      1683
 3 Adams           United States    46.2     -121.       3742
 4 Adwa            Ethiopia         10.1       40.8      1733
 5 Afdera          Ethiopia         13.1       40.9      1250
 6 Agrigan         United States    18.8      146.        965
 7 Agua            Guatemala        14.5      -90.7      3760
 8 Agua de Pau     Portugal         37.8      -25.5       947
 9 Agung           Indonesia        -8.34     116.       2997
10 Ahyi            United States    20.4      145.        -75
# ℹ 799 more rows
# | ou

# & e

volc %>% 
  select(volcano_name, country, latitude:elevation) %>% 
  filter(country == "Antarctica" | latitude < 10)
# A tibble: 382 × 5
   volcano_name country          latitude longitude elevation
   <chr>        <chr>               <dbl>     <dbl>     <dbl>
 1 Acamarachi   Chile              -23.3      -67.6      6023
 2 Aguilera     Chile              -50.3      -73.8      2546
 3 Agung        Indonesia           -8.34     116.       2997
 4 Alcedo       Ecuador             -0.43     -91.1      1130
 5 Aliso        Ecuador             -0.53     -78        4267
 6 Alutu        Ethiopia             7.77      38.8      2335
 7 Amasing      Indonesia           -0.53     127.       1030
 8 Ambae        Vanuatu            -15.4      168.       1496
 9 Ambang       Indonesia            0.75     124.       1795
10 Ambitle      Papua New Guinea    -4.08     154.        450
# ℹ 372 more rows
volc %>% 
  select(volcano_name, country, latitude:elevation) %>% 
  filter(country == "Antarctica" & elevation > 500)
# A tibble: 15 × 5
   volcano_name        country    latitude longitude elevation
   <chr>               <chr>         <dbl>     <dbl>     <dbl>
 1 Andrus              Antarctica    -75.8    -132.       2978
 2 Berlin              Antarctica    -76.0    -136        3478
 3 Buckle Island       Antarctica    -66.8     163.       1239
 4 Deception Island    Antarctica    -63.0     -60.7       602
 5 Erebus              Antarctica    -77.5     167.       3794
 6 Hudson Mountains    Antarctica    -74.3     -99.4       749
 7 James Ross Island   Antarctica    -64.2     -57.8      1630
 8 Melbourne           Antarctica    -74.4     165.       2732
 9 Morning             Antarctica    -78.5     164.       2723
10 Pleiades, The       Antarctica    -72.7     166.       3040
11 Royal Society Range Antarctica    -78.2     163.       3000
12 Siple               Antarctica    -73.4    -127.       3110
13 Takahe              Antarctica    -76.3    -112.       3460
14 Toney Mountain      Antarctica    -75.8    -116.       3595
15 Waesche             Antarctica    -77.2    -127.       3292

mutate()

volc %>% 
  select(volcano_name, country,last_eruption_year) %>% 
  filter(!last_eruption_year== "Unknown") %>% 
  mutate(last_eruption_year=as.numeric(last_eruption_year),
         ultima_erupcao=(2022-last_eruption_year),
         ultima_erupcao_milhar=ultima_erupcao/1000,
         ultima_erupcao_milhao=ultima_erupcao/1000000)  
# A tibble: 657 × 6
   volcano_name  country last_eruption_year ultima_erupcao ultima_erupcao_milhar
   <chr>         <chr>                <dbl>          <dbl>                 <dbl>
 1 Abu           Japan                -6850           8872                 8.87 
 2 Acatenango    Guatem…               1972             50                 0.05 
 3 Acigol-Nevse… Turkey               -2080           4102                 4.10 
 4 Adams         United…                950           1072                 1.07 
 5 Adatarayama   Japan                 1996             26                 0.026
 6 Agrigan       United…               1917            105                 0.105
 7 Agua de Pau   Portug…               1564            458                 0.458
 8 Aguilera      Chile                -1250           3272                 3.27 
 9 Agung         Indone…               2019              3                 0.003
10 Ahyi          United…               2014              8                 0.008
# ℹ 647 more rows
# ℹ 1 more variable: ultima_erupcao_milhao <dbl>
volc %>% 
  mutate(rocha=case_when(
        major_rock_1 %in% c("Andesite / Basaltic Andesite","Basalt / Picro-Basalt" ,"Trachyte / Trachydacite","Trachyandesite / Basaltic Trachyandesite")~"vulcanica basica",
    major_rock_1 %in% c("Phonolite","Foidite","Trachybasalt / Tephrite Basanite","Phono-tephrite /  Tephri-phonolite")~"vulcanica alcalina",
    TRUE~"volcanica acida")) %>% 
  count(rocha)
# A tibble: 3 × 2
  rocha                  n
  <chr>              <int>
1 volcanica acida      108
2 vulcanica alcalina    75
3 vulcanica basica     775
unique(volc$major_rock_1)
 [1] "Andesite / Basaltic Andesite"            
 [2] "Dacite"                                  
 [3] "Rhyolite"                                
 [4] "Basalt / Picro-Basalt"                   
 [5] "Trachyte / Trachydacite"                 
 [6] "Phono-tephrite /  Tephri-phonolite"      
 [7] "Trachyandesite / Basaltic Trachyandesite"
 [8] "Trachybasalt / Tephrite Basanite"        
 [9] "Foidite"                                 
[10] "Phonolite"

Count e arrange()

volc %>% 
  count(country) %>% 
  arrange(desc(n))
# A tibble: 89 × 2
   country              n
   <chr>            <int>
 1 United States       99
 2 Indonesia           95
 3 Japan               92
 4 Russia              79
 5 Chile               43
 6 Iceland             28
 7 Mexico              28
 8 Papua New Guinea    28
 9 Philippines         27
10 Ecuador             26
# ℹ 79 more rows
volc %>% 
  count(country) %>% 
  arrange(n)
# A tibble: 89 × 2
   country                   n
   <chr>                 <int>
 1 Algeria                   1
 2 Armenia-Azerbaijan        1
 3 Burma (Myanmar)           1
 4 Cape Verde                1
 5 Chile-Peru                1
 6 China-North Korea         1
 7 Colombia-Ecuador          1
 8 Comoros                   1
 9 Djibouti                  1
10 El Salvador-Guatemala     1
# ℹ 79 more rows

Group_by ()

volc %>% 
  filter(!last_eruption_year=="Unknown") %>% 
  group_by(last_eruption_year,country) %>% 
  count(last_eruption_year,sort = TRUE) %>%  
  ungroup()
# A tibble: 591 × 3
   last_eruption_year country           n
   <chr>              <chr>         <int>
 1 2020               Indonesia         8
 2 2020               Japan             5
 3 -550               Russia            4
 4 2019               Indonesia         4
 5 2020               Russia            4
 6 1992               United States     3
 7 1996               Japan             3
 8 2000               Indonesia         3
 9 2008               United States     3
10 2015               Indonesia         3
# ℹ 581 more rows

Summarise ou reframe()

# Função SE
SE<-function(vetor,IC){
  SD<-sd(vetor)
  N<-length(vetor)
  SE<-(SD*IC)/sqrt(N)
 
   return(
    SE
  )
}

# Summarise
volc %>% 
  group_by(country) %>% 
  summarise(n=n(),
            media_pop_5km=mean(population_within_5_km),
            sd_pop_km=sd(population_within_5_km),
            se_pop_5km=SE(population_within_5_km,1),
            max_pop_5km=max(population_within_5_km),
            min_pop_5km=min(population_within_5_km)) %>% 
  arrange(desc(media_pop_5km)) %>% 
  filter(n>15) %>% 
  mutate(across(.cols=media_pop_5km:min_pop_5km,~round(.x,digits=0)))
# A tibble: 16 × 7
   country          n media_pop_5km sd_pop_km se_pop_5km max_pop_5km min_pop_5km
   <chr>        <int>         <dbl>     <dbl>      <dbl>       <dbl>       <dbl>
 1 Mexico          28        269105   1091616     206296     5783287           0
 2 Philippines     27        102291    306707      59026     1349742          11
 3 Nicaragua       16         97064    251139      62785      989888         100
 4 New Zealand     16         71199    261129      65282     1049110           0
 5 Ethiopia        26         69227    148454      29114      565206           0
 6 Guatemala       17         63078     88080      21362      240892        1373
 7 Ecuador         26         41014    117168      22979      534403           0
 8 Indonesia       95         35195    129642      13301     1092929           3
 9 Japan           92          6355     19149       1996      130474           0
10 Iceland         28          3496      8458       1598       33526           0
11 Papua New G…    28          2978      5215        986       24509           1
12 United Stat…    99          1745      7356        739       52801           0
13 Chile           43           459      1233        188        5951           0
14 Canada          18           444      1559        367        6635           0
15 Russia          79            24        96         11         669           0
16 Antarctica      17             0         0          0           0           0
# México
#n=28

#269105.1 ± 206295.9 habitantes(sigma 1)

# Guatemala
# n = 17
# 63078 ± 21362 habitantes

4. Exercicio

- 1) Em qual país existem mais vulcões?

volc %>% 
  count(country,sort=TRUE)
# A tibble: 89 × 2
   country              n
   <chr>            <int>
 1 United States       99
 2 Indonesia           95
 3 Japan               92
 4 Russia              79
 5 Chile               43
 6 Iceland             28
 7 Mexico              28
 8 Papua New Guinea    28
 9 Philippines         27
10 Ecuador             26
# ℹ 79 more rows
volc %>% 
  group_by(country) %>% 
  summarise(n=n()) %>% 
  arrange(desc(n))
# A tibble: 89 × 2
   country              n
   <chr>            <int>
 1 United States       99
 2 Indonesia           95
 3 Japan               92
 4 Russia              79
 5 Chile               43
 6 Iceland             28
 7 Mexico              28
 8 Papua New Guinea    28
 9 Philippines         27
10 Ecuador             26
# ℹ 79 more rows

- 2) Nesse país, qual o tipo de vulcão mais comum?

volc %>% 
  filter(country=="United States") %>% 
  count(primary_volcano_type,sort=TRUE)
# A tibble: 15 × 2
   primary_volcano_type     n
   <chr>                <int>
 1 Stratovolcano           32
 2 Shield                  15
 3 Volcanic field          13
 4 Stratovolcano(es)        8
 5 Submarine                6
 6 Pyroclastic cone(s)      5
 7 Shield(s)                5
 8 Lava dome(s)             4
 9 Caldera                  3
10 Maar(s)                  3
11 Caldera(s)               1
12 Complex                  1
13 Pyroclastic cone         1
14 Stratovolcano?           1
15 Tuff cone(s)             1

- 3) Na América do Sul, quantos e quais vulcões entraram em erupção em 2020?

volc %>% 
  filter(region=="South America",
         last_eruption_year==2020) %>% 
  count(volcano_name,sort=TRUE)
# A tibble: 6 × 2
  volcano_name            n
  <chr>               <int>
1 Chillan, Nevados de     1
2 Reventador              1
3 Ruiz, Nevado del        1
4 Sabancaya               1
5 Sangay                  1
6 Villarrica              1

- 4) Quais os países nos quais os vulcões apresentam maior risco para a população local (até 100 km)? Filtre países em que existam mais de 20 vulcões.

volc %>% 
  group_by(country) %>% 
  summarise(n=n(),
            media=mean(population_within_100_km),
            se=SE(population_within_100_km,1)) %>% 
  filter(n>20) %>% 
  arrange(desc(media))
# A tibble: 11 × 4
   country              n     media       se
   <chr>            <int>     <dbl>    <dbl>
 1 Philippines         27 10247171. 1948086.
 2 Indonesia           95  9126614. 1148771.
 3 Mexico              28  7034976. 1863798.
 4 Japan               92  4057450.  537928.
 5 Ethiopia            26  3318110.  683117.
 6 Ecuador             26  1819886.  317923.
 7 Chile               43   362838.   42331.
 8 United States       99   250535.   55360.
 9 Papua New Guinea    28   102845.   10647.
10 Iceland             28    65432.   16041.
11 Russia              79    60141.   20473.