Manipulação dos dados

Author

Ricardo Accioly

Published

August 20, 2024

Carregando bibliotescas

Vamos trabalhar de novo com os dados do gapminder para fazer algumas manipulações de dados que são muito úteis no dia a dia.

Este material foi em parte adaptado do curso Data Visualization in R with ggplot2 de Kara Woo

library("gapminder")
library("dplyr")
library("ggplot2")
library("patchwork")

Filtrando 2007

## Cria um extrato do ano de 2007
data(gapminder)
summary(gapminder)

#>         country        continent        year         lifeExp     
#>  Afghanistan:  12   Africa  :624   Min.   :1952   Min.   :23.60  
#>  Albania    :  12   Americas:300   1st Qu.:1966   1st Qu.:48.20  
#>  Algeria    :  12   Asia    :396   Median :1980   Median :60.71  
#>  Angola     :  12   Europe  :360   Mean   :1980   Mean   :59.47  
#>  Argentina  :  12   Oceania : 24   3rd Qu.:1993   3rd Qu.:70.85  
#>  Australia  :  12                  Max.   :2007   Max.   :82.60  
#>  (Other)    :1632                                                
#>       pop              gdpPercap       
#>  Min.   :6.001e+04   Min.   :   241.2  
#>  1st Qu.:2.794e+06   1st Qu.:  1202.1  
#>  Median :7.024e+06   Median :  3531.8  
#>  Mean   :2.960e+07   Mean   :  7215.3  
#>  3rd Qu.:1.959e+07   3rd Qu.:  9325.5  
#>  Max.   :1.319e+09   Max.   :113523.1  
#>

gap_07 <- filter(gapminder, year == 2007)

Vendo primeiras e últimas 10 linhas

head(gap_07, n=10) %>% knitr::kable(booktabs = TRUE) # primeiros dez paises da base de dados

country	continent	year	lifeExp	pop	gdpPercap
Afghanistan	Asia	2007	43.828	31889923	974.5803
Albania	Europe	2007	76.423	3600523	5937.0295
Algeria	Africa	2007	72.301	33333216	6223.3675
Angola	Africa	2007	42.731	12420476	4797.2313
Argentina	Americas	2007	75.320	40301927	12779.3796
Australia	Oceania	2007	81.235	20434176	34435.3674
Austria	Europe	2007	79.829	8199783	36126.4927
Bahrain	Asia	2007	75.635	708573	29796.0483
Bangladesh	Asia	2007	64.062	150448339	1391.2538
Belgium	Europe	2007	79.441	10392226	33692.6051

tail(gap_07, n=10) %>% knitr::kable(booktabs = TRUE) # últimos 10 países

country	continent	year	lifeExp	pop	gdpPercap
Uganda	Africa	2007	51.542	29170398	1056.3801
United Kingdom	Europe	2007	79.425	60776238	33203.2613
United States	Americas	2007	78.242	301139947	42951.6531
Uruguay	Americas	2007	76.384	3447496	10611.4630
Venezuela	Americas	2007	73.747	26084662	11415.8057
Vietnam	Asia	2007	74.249	85262356	2441.5764
West Bank and Gaza	Asia	2007	73.422	4018332	3025.3498
Yemen, Rep.	Asia	2007	62.698	22211743	2280.7699
Zambia	Africa	2007	42.384	11746035	1271.2116
Zimbabwe	Africa	2007	43.487	12311143	469.7093

Manipulando 1

Selecionando dados por país

filter(gap_07, country %in% c("Brazil", "Chile"))

#> # A tibble: 2 × 6
#>   country continent  year lifeExp       pop gdpPercap
#>   <fct>   <fct>     <int>   <dbl>     <int>     <dbl>
#> 1 Brazil  Americas   2007    72.4 190010647     9066.
#> 2 Chile   Americas   2007    78.6  16284741    13172.

Manipulando 2

Selecionando dados para 2007 excluindo a Oceania

filter(gapminder, year == 2007 & continent != "Oceania")

#> # A tibble: 140 × 6
#>    country     continent  year lifeExp       pop gdpPercap
#>    <fct>       <fct>     <int>   <dbl>     <int>     <dbl>
#>  1 Afghanistan Asia       2007    43.8  31889923      975.
#>  2 Albania     Europe     2007    76.4   3600523     5937.
#>  3 Algeria     Africa     2007    72.3  33333216     6223.
#>  4 Angola      Africa     2007    42.7  12420476     4797.
#>  5 Argentina   Americas   2007    75.3  40301927    12779.
#>  6 Austria     Europe     2007    79.8   8199783    36126.
#>  7 Bahrain     Asia       2007    75.6    708573    29796.
#>  8 Bangladesh  Asia       2007    64.1 150448339     1391.
#>  9 Belgium     Europe     2007    79.4  10392226    33693.
#> 10 Benin       Africa     2007    56.7   8078314     1441.
#> # ℹ 130 more rows

Manipulando 3

Selecionando dados de 2007, agrupando por continente e sumarizando para achar a média da população por continente

gapminder %>%
  filter(year == 2007) %>% 
  group_by(continent) %>% 
  summarize(mediapop = mean(pop))

#> # A tibble: 5 × 2
#>   continent   mediapop
#>   <fct>          <dbl>
#> 1 Africa     17875763.
#> 2 Americas   35954847.
#> 3 Asia      115513752.
#> 4 Europe     19536618.
#> 5 Oceania    12274974.

Visualizando 1

Mostrar linhas e pontos do PIB ao longo do tempo para Brasil e Chile

gap_brachi <- filter(gapminder, country %in% c("Brazil", "Chile"))
p <- ggplot(gap_brachi, aes(x = year, y = gdpPercap, color=country))
p1 <- p + geom_point()
p1

Visualizando 2

p2 <- p + geom_line()
p2

Visualizando 3

p3 <- p + geom_point() + geom_line()
p3

Visualizando 4 - Usando o pacote patchwork

(p1 + p2) /
  p3

Manipulando 4

Contando número de países e continentes com distinct

nrow(gapminder) ## Esta não é a informação que eu quero

#> [1] 1704

head(gapminder)

#> # A tibble: 6 × 6
#>   country     continent  year lifeExp      pop gdpPercap
#>   <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
#> 1 Afghanistan Asia       1952    28.8  8425333      779.
#> 2 Afghanistan Asia       1957    30.3  9240934      821.
#> 3 Afghanistan Asia       1962    32.0 10267083      853.
#> 4 Afghanistan Asia       1967    34.0 11537966      836.
#> 5 Afghanistan Asia       1972    36.1 13079460      740.
#> 6 Afghanistan Asia       1977    38.4 14880372      786.

nrow(distinct(gapminder,country))

#> [1] 142

nrow(distinct(gapminder, continent))

#> [1] 5

Fazendo contagens de dados

gapminder %>% filter(year == 2007) %>% 
  group_by(continent) %>% summarise(n = n())

#> # A tibble: 5 × 2
#>   continent     n
#>   <fct>     <int>
#> 1 Africa       52
#> 2 Americas     25
#> 3 Asia         33
#> 4 Europe       30
#> 5 Oceania       2

Mudando orientação dos dados

library(readxl)
propaganda <- read_excel("Propaganda.xlsx")
head(propaganda)

#> # A tibble: 6 × 4
#>      TV Radio Newspaper Sales
#>   <dbl> <dbl>     <dbl> <dbl>
#> 1 230.   37.8      69.2  22.1
#> 2  44.5  39.3      45.1  10.4
#> 3  17.2  45.9      69.3   9.3
#> 4 152.   41.3      58.5  18.5
#> 5 181.   10.8      58.4  12.9
#> 6   8.7  48.9      75     7.2

propaganda <- propaganda %>% rename(Jornal = Newspaper, Vendas = Sales)
propaganda %>% tidyr::pivot_longer(!Vendas, names_to="Midia", values_to="Orcamento")

#> # A tibble: 600 × 3
#>    Vendas Midia  Orcamento
#>     <dbl> <chr>      <dbl>
#>  1   22.1 TV         230. 
#>  2   22.1 Radio       37.8
#>  3   22.1 Jornal      69.2
#>  4   10.4 TV          44.5
#>  5   10.4 Radio       39.3
#>  6   10.4 Jornal      45.1
#>  7    9.3 TV          17.2
#>  8    9.3 Radio       45.9
#>  9    9.3 Jornal      69.3
#> 10   18.5 TV         152. 
#> # ℹ 590 more rows

library(readr)
pesquisa <- read.csv("data_joined.csv", header = T)
head(pesquisa)

#>   record_id month day year plot_id species_id sex hindfoot_length weight
#> 1         1     7  16 1977       2         NL   M              32     NA
#> 2        72     8  19 1977       2         NL   M              31     NA
#> 3       224     9  13 1977       2         NL                  NA     NA
#> 4       266    10  16 1977       2         NL                  NA     NA
#> 5       349    11  12 1977       2         NL                  NA     NA
#> 6       363    11  12 1977       2         NL                  NA     NA
#>     genus  species   taxa plot_type
#> 1 Neotoma albigula Rodent   Control
#> 2 Neotoma albigula Rodent   Control
#> 3 Neotoma albigula Rodent   Control
#> 4 Neotoma albigula Rodent   Control
#> 5 Neotoma albigula Rodent   Control
#> 6 Neotoma albigula Rodent   Control

pesquisa_gw <- pesquisa %>% filter(!is.na(weight)) %>%
  group_by(year, genus) %>%
  summarize(peso_medio = mean(weight))

#> `summarise()` has grouped output by 'year'. You can override using the
#> `.groups` argument.

head(pesquisa_gw)

#> # A tibble: 6 × 3
#> # Groups:   year [1]
#>    year genus           peso_medio
#>   <int> <chr>                <dbl>
#> 1  1977 Chaetodipus          15.3 
#> 2  1977 Dipodomys            52.5 
#> 3  1977 Onychomys            21.4 
#> 4  1977 Perognathus           7.17
#> 5  1977 Peromyscus           19.5 
#> 6  1977 Reithrodontomys      10

pesquisa_gw %>% tidyr::pivot_wider(names_from="genus", values_from="peso_medio")

#> # A tibble: 26 × 11
#> # Groups:   year [26]
#>     year Chaetodipus Dipodomys Onychomys Perognathus Peromyscus Reithrodontomys
#>    <int>       <dbl>     <dbl>     <dbl>       <dbl>      <dbl>           <dbl>
#>  1  1977        15.3      52.5      21.4        7.17       19.5           10   
#>  2  1978        14.9      73.9      26.5        7.09       20.5            7.5 
#>  3  1979        15.1      74.9      27.4        7.53       21.3            8.33
#>  4  1980        14.2      73.1      28.3        7.46       22.4           10.2 
#>  5  1981        14.0      72.7      28.4        7.15       20.4           11.2 
#>  6  1982        16.1      66.3      29.9        6.92       21.3           10.5 
#>  7  1983        15.5      65.0      29.0        6.83       21.5            9.87
#>  8  1984        15.3      52.8      28.3       16.9        20.0           11.2 
#>  9  1985        15.8      51.1      28.1       32.7        20.0            8.37
#> 10  1986        16.8      56.4      27.5       18.3        22.1           10.8 
#> # ℹ 16 more rows
#> # ℹ 4 more variables: Neotoma <dbl>, Sigmodon <dbl>, Spermophilus <dbl>,
#> #   Baiomys <dbl>