Histograma
Jhenifer Victória Mattos Ramos
02/12/2021
Introdução
O primeiro passo é carregar a base de dados.
library(readr)
load("C:/Users/jheni/Base_de_dados-master/df_pokemon.RData")
head(df)## # A tibble: 6 x 22
## id pokemon species_id height weight base_experience type_1 type_2 attack
## <dbl> <chr> <int> <int> <int> <int> <chr> <chr> <int>
## 1 1 bulbasaur 1 7 69 64 grass poison 49
## 2 2 ivysaur 2 10 130 142 grass poison 62
## 3 3 venusaur 3 20 1000 236 grass poison 82
## 4 4 charmander 4 6 85 62 fire <NA> 52
## 5 5 charmeleon 5 11 190 142 fire <NA> 64
## 6 6 charizard 6 17 905 240 fire flying 84
## # ... with 13 more variables: defense <int>, hp <int>, special_attack <int>,
## # special_defense <int>, speed <int>, color_1 <chr>, color_2 <chr>,
## # color_f <chr>, egg_group_1 <chr>, egg_group_2 <chr>, url_image <chr>,
## # x <dbl>, y <dbl>str(df)## tibble [718 x 22] (S3: tbl_df/tbl/data.frame)
## $ id : num [1:718] 1 2 3 4 5 6 7 8 9 10 ...
## $ pokemon : chr [1:718] "bulbasaur" "ivysaur" "venusaur" "charmander" ...
## $ species_id : int [1:718] 1 2 3 4 5 6 7 8 9 10 ...
## $ height : int [1:718] 7 10 20 6 11 17 5 10 16 3 ...
## $ weight : int [1:718] 69 130 1000 85 190 905 90 225 855 29 ...
## $ base_experience: int [1:718] 64 142 236 62 142 240 63 142 239 39 ...
## $ type_1 : chr [1:718] "grass" "grass" "grass" "fire" ...
## $ type_2 : chr [1:718] "poison" "poison" "poison" NA ...
## $ attack : int [1:718] 49 62 82 52 64 84 48 63 83 30 ...
## $ defense : int [1:718] 49 63 83 43 58 78 65 80 100 35 ...
## $ hp : int [1:718] 45 60 80 39 58 78 44 59 79 45 ...
## $ special_attack : int [1:718] 65 80 100 60 80 109 50 65 85 20 ...
## $ special_defense: int [1:718] 65 80 100 50 65 85 64 80 105 20 ...
## $ speed : int [1:718] 45 60 80 65 80 100 43 58 78 45 ...
## $ color_1 : chr [1:718] "#78C850" "#78C850" "#78C850" "#F08030" ...
## $ color_2 : chr [1:718] "#A040A0" "#A040A0" "#A040A0" NA ...
## $ color_f : chr [1:718] "#81A763" "#81A763" "#81A763" "#F08030" ...
## $ egg_group_1 : chr [1:718] "monster" "monster" "monster" "monster" ...
## $ egg_group_2 : chr [1:718] "plant" "plant" "plant" "dragon" ...
## $ url_image : chr [1:718] "1.png" "2.png" "3.png" "4.png" ...
## $ x : num [1:718] 32.8 33.3 33.9 -24.4 -24.6 ...
## $ y : num [1:718] 17.2 16.7 16.2 30.8 30.6 ...summary(df$base_experience)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 36.00 65.25 147.00 141.55 177.00 608.00análise da variavel qualitativa
Aqui pegamos a variável experiência base e fizemos um gráfico de barra (barplot).
tabela <- table(df$base_experience)
tabela##
## 36 38 39 40 41 42 43 44 45 47 48 49 50 51 52 53 54 55 56 57
## 1 1 4 4 2 3 1 4 1 2 2 5 6 2 5 5 3 6 9 3
## 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 77 78
## 13 8 21 19 18 12 11 9 17 7 5 3 10 7 8 1 2 2 2 2
## 79 80 81 82 83 86 87 88 95 97 100 101 104 110 112 113 116 118 119 122
## 1 1 1 1 1 3 2 2 1 1 3 1 1 1 1 1 2 1 5 1
## 123 125 126 127 128 130 133 134 135 137 138 140 142 143 144 145 146 147 148 149
## 2 1 3 1 3 4 5 3 2 6 2 4 25 1 9 6 1 8 3 4
## 150 151 153 154 155 156 157 158 159 160 161 162 163 164 165 166 168 169 170 171
## 1 5 1 8 2 1 1 8 10 3 10 2 10 2 9 12 12 7 10 2
## 172 173 174 175 177 178 179 180 182 184 185 186 187 189 194 196 207 216 217 218
## 12 16 5 16 5 4 7 6 3 12 1 1 3 4 1 1 1 4 1 3
## 220 221 223 225 227 229 230 232 233 234 235 236 238 239 240 241 243 245 248 252
## 1 6 1 5 4 1 2 3 2 5 1 4 3 10 4 6 4 1 2 1
## 261 270 297 302 306 324 390 395 608
## 18 23 1 3 11 1 1 1 1prop.table(tabela)##
## 36 38 39 40 41 42
## 0.001392758 0.001392758 0.005571031 0.005571031 0.002785515 0.004178273
## 43 44 45 47 48 49
## 0.001392758 0.005571031 0.001392758 0.002785515 0.002785515 0.006963788
## 50 51 52 53 54 55
## 0.008356546 0.002785515 0.006963788 0.006963788 0.004178273 0.008356546
## 56 57 58 59 60 61
## 0.012534819 0.004178273 0.018105850 0.011142061 0.029247911 0.026462396
## 62 63 64 65 66 67
## 0.025069638 0.016713092 0.015320334 0.012534819 0.023676880 0.009749304
## 68 69 70 71 72 73
## 0.006963788 0.004178273 0.013927577 0.009749304 0.011142061 0.001392758
## 74 75 77 78 79 80
## 0.002785515 0.002785515 0.002785515 0.002785515 0.001392758 0.001392758
## 81 82 83 86 87 88
## 0.001392758 0.001392758 0.001392758 0.004178273 0.002785515 0.002785515
## 95 97 100 101 104 110
## 0.001392758 0.001392758 0.004178273 0.001392758 0.001392758 0.001392758
## 112 113 116 118 119 122
## 0.001392758 0.001392758 0.002785515 0.001392758 0.006963788 0.001392758
## 123 125 126 127 128 130
## 0.002785515 0.001392758 0.004178273 0.001392758 0.004178273 0.005571031
## 133 134 135 137 138 140
## 0.006963788 0.004178273 0.002785515 0.008356546 0.002785515 0.005571031
## 142 143 144 145 146 147
## 0.034818942 0.001392758 0.012534819 0.008356546 0.001392758 0.011142061
## 148 149 150 151 153 154
## 0.004178273 0.005571031 0.001392758 0.006963788 0.001392758 0.011142061
## 155 156 157 158 159 160
## 0.002785515 0.001392758 0.001392758 0.011142061 0.013927577 0.004178273
## 161 162 163 164 165 166
## 0.013927577 0.002785515 0.013927577 0.002785515 0.012534819 0.016713092
## 168 169 170 171 172 173
## 0.016713092 0.009749304 0.013927577 0.002785515 0.016713092 0.022284123
## 174 175 177 178 179 180
## 0.006963788 0.022284123 0.006963788 0.005571031 0.009749304 0.008356546
## 182 184 185 186 187 189
## 0.004178273 0.016713092 0.001392758 0.001392758 0.004178273 0.005571031
## 194 196 207 216 217 218
## 0.001392758 0.001392758 0.001392758 0.005571031 0.001392758 0.004178273
## 220 221 223 225 227 229
## 0.001392758 0.008356546 0.001392758 0.006963788 0.005571031 0.001392758
## 230 232 233 234 235 236
## 0.002785515 0.004178273 0.002785515 0.006963788 0.001392758 0.005571031
## 238 239 240 241 243 245
## 0.004178273 0.013927577 0.005571031 0.008356546 0.005571031 0.001392758
## 248 252 261 270 297 302
## 0.002785515 0.001392758 0.025069638 0.032033426 0.001392758 0.004178273
## 306 324 390 395 608
## 0.015320334 0.001392758 0.001392758 0.001392758 0.001392758barplot(tabela,col=c("#89cf51","#cf51b5"),main = "Gráfico 1 - Experiência Base",
horiz=T,
legend.text = rownames(tabela),
args.legend = list(x = "topleft"))
xlin=c(0,20)
grafico <- barplot(table(df$base_experience),col = "#7b51cf")
percentual <- prop.table(table(df$base_experience))*100
percentual##
## 36 38 39 40 41 42 43 44
## 0.1392758 0.1392758 0.5571031 0.5571031 0.2785515 0.4178273 0.1392758 0.5571031
## 45 47 48 49 50 51 52 53
## 0.1392758 0.2785515 0.2785515 0.6963788 0.8356546 0.2785515 0.6963788 0.6963788
## 54 55 56 57 58 59 60 61
## 0.4178273 0.8356546 1.2534819 0.4178273 1.8105850 1.1142061 2.9247911 2.6462396
## 62 63 64 65 66 67 68 69
## 2.5069638 1.6713092 1.5320334 1.2534819 2.3676880 0.9749304 0.6963788 0.4178273
## 70 71 72 73 74 75 77 78
## 1.3927577 0.9749304 1.1142061 0.1392758 0.2785515 0.2785515 0.2785515 0.2785515
## 79 80 81 82 83 86 87 88
## 0.1392758 0.1392758 0.1392758 0.1392758 0.1392758 0.4178273 0.2785515 0.2785515
## 95 97 100 101 104 110 112 113
## 0.1392758 0.1392758 0.4178273 0.1392758 0.1392758 0.1392758 0.1392758 0.1392758
## 116 118 119 122 123 125 126 127
## 0.2785515 0.1392758 0.6963788 0.1392758 0.2785515 0.1392758 0.4178273 0.1392758
## 128 130 133 134 135 137 138 140
## 0.4178273 0.5571031 0.6963788 0.4178273 0.2785515 0.8356546 0.2785515 0.5571031
## 142 143 144 145 146 147 148 149
## 3.4818942 0.1392758 1.2534819 0.8356546 0.1392758 1.1142061 0.4178273 0.5571031
## 150 151 153 154 155 156 157 158
## 0.1392758 0.6963788 0.1392758 1.1142061 0.2785515 0.1392758 0.1392758 1.1142061
## 159 160 161 162 163 164 165 166
## 1.3927577 0.4178273 1.3927577 0.2785515 1.3927577 0.2785515 1.2534819 1.6713092
## 168 169 170 171 172 173 174 175
## 1.6713092 0.9749304 1.3927577 0.2785515 1.6713092 2.2284123 0.6963788 2.2284123
## 177 178 179 180 182 184 185 186
## 0.6963788 0.5571031 0.9749304 0.8356546 0.4178273 1.6713092 0.1392758 0.1392758
## 187 189 194 196 207 216 217 218
## 0.4178273 0.5571031 0.1392758 0.1392758 0.1392758 0.5571031 0.1392758 0.4178273
## 220 221 223 225 227 229 230 232
## 0.1392758 0.8356546 0.1392758 0.6963788 0.5571031 0.1392758 0.2785515 0.4178273
## 233 234 235 236 238 239 240 241
## 0.2785515 0.6963788 0.1392758 0.5571031 0.4178273 1.3927577 0.5571031 0.8356546
## 243 245 248 252 261 270 297 302
## 0.5571031 0.1392758 0.2785515 0.1392758 2.5069638 3.2033426 0.1392758 0.4178273
## 306 324 390 395 608
## 1.5320334 0.1392758 0.1392758 0.1392758 0.1392758rotulo <- paste0(percentual,"%")
rotulo## [1] "0.139275766016713%" "0.139275766016713%" "0.557103064066852%"
## [4] "0.557103064066852%" "0.278551532033426%" "0.417827298050139%"
## [7] "0.139275766016713%" "0.557103064066852%" "0.139275766016713%"
## [10] "0.278551532033426%" "0.278551532033426%" "0.696378830083565%"
## [13] "0.835654596100279%" "0.278551532033426%" "0.696378830083565%"
## [16] "0.696378830083565%" "0.417827298050139%" "0.835654596100279%"
## [19] "1.25348189415042%" "0.417827298050139%" "1.81058495821727%"
## [22] "1.1142061281337%" "2.92479108635097%" "2.64623955431755%"
## [25] "2.50696378830084%" "1.67130919220056%" "1.53203342618384%"
## [28] "1.25348189415042%" "2.36768802228412%" "0.974930362116992%"
## [31] "0.696378830083565%" "0.417827298050139%" "1.39275766016713%"
## [34] "0.974930362116992%" "1.1142061281337%" "0.139275766016713%"
## [37] "0.278551532033426%" "0.278551532033426%" "0.278551532033426%"
## [40] "0.278551532033426%" "0.139275766016713%" "0.139275766016713%"
## [43] "0.139275766016713%" "0.139275766016713%" "0.139275766016713%"
## [46] "0.417827298050139%" "0.278551532033426%" "0.278551532033426%"
## [49] "0.139275766016713%" "0.139275766016713%" "0.417827298050139%"
## [52] "0.139275766016713%" "0.139275766016713%" "0.139275766016713%"
## [55] "0.139275766016713%" "0.139275766016713%" "0.278551532033426%"
## [58] "0.139275766016713%" "0.696378830083565%" "0.139275766016713%"
## [61] "0.278551532033426%" "0.139275766016713%" "0.417827298050139%"
## [64] "0.139275766016713%" "0.417827298050139%" "0.557103064066852%"
## [67] "0.696378830083565%" "0.417827298050139%" "0.278551532033426%"
## [70] "0.835654596100279%" "0.278551532033426%" "0.557103064066852%"
## [73] "3.48189415041783%" "0.139275766016713%" "1.25348189415042%"
## [76] "0.835654596100279%" "0.139275766016713%" "1.1142061281337%"
## [79] "0.417827298050139%" "0.557103064066852%" "0.139275766016713%"
## [82] "0.696378830083565%" "0.139275766016713%" "1.1142061281337%"
## [85] "0.278551532033426%" "0.139275766016713%" "0.139275766016713%"
## [88] "1.1142061281337%" "1.39275766016713%" "0.417827298050139%"
## [91] "1.39275766016713%" "0.278551532033426%" "1.39275766016713%"
## [94] "0.278551532033426%" "1.25348189415042%" "1.67130919220056%"
## [97] "1.67130919220056%" "0.974930362116992%" "1.39275766016713%"
## [100] "0.278551532033426%" "1.67130919220056%" "2.22841225626741%"
## [103] "0.696378830083565%" "2.22841225626741%" "0.696378830083565%"
## [106] "0.557103064066852%" "0.974930362116992%" "0.835654596100279%"
## [109] "0.417827298050139%" "1.67130919220056%" "0.139275766016713%"
## [112] "0.139275766016713%" "0.417827298050139%" "0.557103064066852%"
## [115] "0.139275766016713%" "0.139275766016713%" "0.139275766016713%"
## [118] "0.557103064066852%" "0.139275766016713%" "0.417827298050139%"
## [121] "0.139275766016713%" "0.835654596100279%" "0.139275766016713%"
## [124] "0.696378830083565%" "0.557103064066852%" "0.139275766016713%"
## [127] "0.278551532033426%" "0.417827298050139%" "0.278551532033426%"
## [130] "0.696378830083565%" "0.139275766016713%" "0.557103064066852%"
## [133] "0.417827298050139%" "1.39275766016713%" "0.557103064066852%"
## [136] "0.835654596100279%" "0.557103064066852%" "0.139275766016713%"
## [139] "0.278551532033426%" "0.139275766016713%" "2.50696378830084%"
## [142] "3.2033426183844%" "0.139275766016713%" "0.417827298050139%"
## [145] "1.53203342618384%" "0.139275766016713%" "0.139275766016713%"
## [148] "0.139275766016713%" "0.139275766016713%"text(grafico, 0, rotulo,cex=1,pos=3,col = "white")
Histograma
Aqui fizemos o histograma, no qual é possivel observar que 257 pokemons têm um experiência base entre zero e cem e ao longo do gráfico essa frequÊncia vai diminuindo. Além disso, através do resumo encontramos a mediana dessa variável em 147 de experiência base com um mínimo de 36 e máximo de 608 como podemos ver no gráfico. Também podemos observar a média da experiência base como 141,55.
hist(df$base_experience)
hist(df$base_experience, col="tomato", border= "red", main= "Histograma", xlab= "Experiência Base", labels= TRUE, ylab= "frequência")
Conclusão
O gráfico Histograma é um facilitador para encontrarmos os valores como média, mediana, valor mínimo e valor máximo.