Pacotes
library(tidyverse) # manipulação de dados
library(metan)
library(rio) # importação/exportação de dados
# gerar tabelas html
print_tbl <- function(table, digits = 3, n = NULL, ...){
if(!missing(n)){
knitr::kable(head(table, n = n), booktabs = TRUE, digits = digits, ...)
} else{
knitr::kable(table, booktabs = TRUE, digits = digits, ...)
}
}
A função tidy_strings() pode ser usada para organizar strings de caracteres colocando todas as palavras em maiúsculas, substituindo qualquer espaço, tabulação, caracteres de pontuação por _ e colocando _ entre maiúsculas e minúsculas.
Um exemplo simples
Suponha que tenhamos uma string de caracteres, digamos, str = c("Env1", "env 1", "env.1"). Por definição, str deve representar um nível único em testes de melhoramento de plantas, por exemplo, ambiente 1, mas na verdade tem três níveis.
str <- c ("Env1", "env 1", "env.1")
str %>% factor() %>% levels()
## [1] "env 1" "env.1" "Env1"
tidy_strings(str)
## [1] "ENV_1" "ENV_1" "ENV_1"
Excelente! Agora temos o nível único que deveríamos ter antes.
Mais exemplos
Todos os itens a seguir serão convertidos para " ENV_1 ".
messy_env <- c ("ENV 1", "Env 1", "Env1", "env1", "Env.1", "Env_1")
tidy_strings(messy_env)
## [1] "ENV_1" "ENV_1" "ENV_1" "ENV_1" "ENV_1" "ENV_1"
Todos os itens a seguir serão traduzidos em " GEN _ * ".
messy_gen <- c ("GEN1", "gen 2", "Gen.3", "gen-4", "Gen_5", "GEN_6")
tidy_strings(messy_gen)
## [1] "GEN_1" "GEN_2" "GEN_3" "GEN_4" "GEN_5" "GEN_6"
Todos os itens a seguir serão traduzidos para " ENV_GEN "
messy_int <- c ("EnvGen", "Env_Gen", "env gen", "Env Gen", "ENV.GEN", "ENV_GEN")
tidy_strings(messy_int)
## [1] "ENV_GEN" "ENV_GEN" "ENV_GEN" "ENV_GEN" "ENV_GEN" "ENV_GEN"
Organize data.frames
Também podemos organizar strings de um data.frame. Por padrão, o caractere separador é _. Para alterar esse padrão, use o argumento sep.
df <- tibble(Env = messy_env,
gen = messy_gen,
Env_Gen = interaction(Env, gen),
y = rnorm(6, 300, 10))
print_tbl(df)
| Env |
gen |
Env_Gen |
y |
| ENV 1 |
GEN1 |
ENV 1.GEN1 |
288.213 |
| Env 1 |
gen 2 |
Env 1.gen 2 |
286.460 |
| Env1 |
Gen.3 |
Env1.Gen.3 |
291.560 |
| env1 |
gen-4 |
env1.gen-4 |
289.837 |
| Env.1 |
Gen_5 |
Env.1.Gen_5 |
302.320 |
| Env_1 |
GEN_6 |
Env_1.GEN_6 |
273.705 |
df_tidy <- tidy_strings(df, sep = "")
print_tbl(df_tidy)
| Env |
gen |
Env_Gen |
y |
| ENV1 |
GEN1 |
ENV1GEN1 |
288.213 |
| ENV1 |
GEN2 |
ENV1GEN2 |
286.460 |
| ENV1 |
GEN3 |
ENV1GEN3 |
291.560 |
| ENV1 |
GEN4 |
ENV1GEN4 |
289.837 |
| ENV1 |
GEN5 |
ENV1GEN5 |
302.320 |
| ENV1 |
GEN6 |
ENV1GEN6 |
273.705 |
Conjunto de dados
# Dados "bagunçados"
# Apenas 40 linhas
df_messy <- import("http://bit.ly/df_messy", setclass = "tbl")
df_messy %>% print_tbl(n = 40)
| env |
Gen |
BLOCO |
Alt plant |
Alt Esp |
COMPES |
DIAMES |
CompSab |
DiamSab |
Mge |
Nfil |
MMG |
NGE |
| Amb 1 |
H10 |
1 |
0.00 |
1.64 |
16.72 |
54.05 |
31.66 |
17.40 |
193.69 |
15.6 |
379.61 |
519.2 |
| NA |
NA |
2 |
2.79 |
1.71 |
14.90 |
52.73 |
32.03 |
15.48 |
176.43 |
17.6 |
346.88 |
502.4 |
| NA |
NA |
3 |
2.72 |
1.51 |
16.68 |
52.74 |
30.40 |
17.50 |
207.11 |
16.8 |
394.03 |
524.6 |
| NA |
H11 |
1 |
2.75 |
1.51 |
17.42 |
51.69 |
30.64 |
17.98 |
217.29 |
16.8 |
376.65 |
NA |
| NA |
NA |
2 |
2.72 |
1.56 |
16.70 |
47.21 |
28.69 |
17.20 |
181.28 |
13.6 |
360.66 |
500.8 |
| NA |
NA |
3 |
2.77 |
1.67 |
15.78 |
47.87 |
27.63 |
16.36 |
166.19 |
15.2 |
321.8 |
512.8 |
| NA |
H12 |
1 |
2.73 |
1.54 |
14.88 |
47.51 |
28.22 |
15.54 |
160.98 |
14.8 |
344.84 |
480.2 |
| NA |
NA |
2 |
2.56 |
1.56 |
15.68 |
49.86 |
29.85 |
16.16 |
187.96 |
17.2 |
328.33 |
585.8 |
| NA |
NA |
3 |
2.79 |
1.53 |
14.98 |
52.67 |
31.38 |
15.24 |
192.51 |
20.0 |
325.36 |
594.0 |
| NA |
H13 |
1 |
2.74 |
1.60 |
14.64 |
54.00 |
32.48 |
15.06 |
205.34 |
20.0 |
334.37 |
628.4 |
| NA |
NA |
2 |
2.64 |
1.37 |
14.80 |
53.73 |
30.97 |
15.54 |
238.55 |
20.4 |
348.72 |
693.2 |
| NA |
NA |
3 |
2.93 |
1.77 |
14.92 |
52.66 |
30.07 |
15.76 |
211.92 |
15.6 |
421.23 |
511.6 |
| NA |
H2 |
1 |
2.55 |
1.22 |
15.08 |
51.66 |
27.71 |
15.30 |
197.70 |
16.4 |
382.06 |
518.2 |
| NA |
NA |
2 |
2.90 |
1.41 |
15.12 |
51.53 |
26.04 |
16.00 |
212.18 |
21.2 |
304.51 |
696.6 |
| NA |
NA |
3 |
2.92 |
1.39 |
14.78 |
52.63 |
26.04 |
15.14 |
201.62 |
20.0 |
300.02 |
650.4 |
| NA |
H3 |
1 |
3.04 |
1.43 |
15.02 |
51.66 |
25.93 |
16.56 |
209.80 |
19.6 |
317.48 |
641.6 |
| NA |
NA |
2 |
2.94 |
1.66 |
14.88 |
51.46 |
31.33 |
15.46 |
192.84 |
18.0 |
345.83 |
550.6 |
| NA |
NA |
3 |
2.82 |
1.67 |
16.46 |
52.25 |
26.74 |
17.08 |
191.99 |
18.0 |
311.73 |
637.4 |
| NA |
H4 |
1 |
3.02 |
1.82 |
16.54 |
50.43 |
27.31 |
17.26 |
193.02 |
16.8 |
305.33 |
NA |
| NA |
NA |
2 |
2.84 |
1.69 |
15.04 |
52.17 |
27.95 |
15.72 |
215.69 |
18.4 |
357.3 |
613.6 |
| NA |
NA |
3 |
2.74 |
1.52 |
16.34 |
49.57 |
27.01 |
17.10 |
196.76 |
14.0 |
350.15 |
562.2 |
| NA |
H5 |
1 |
2.90 |
1.58 |
16.02 |
48.66 |
27.99 |
17.00 |
198.18 |
14.0 |
373.79 |
5302.0 |
| NA |
NA |
2 |
2.82 |
1.61 |
14.68 |
50.26 |
27.87 |
16.15 |
180.83 |
14.4 |
352.22 |
508.6 |
| NA |
NA |
3 |
2.78 |
1.59 |
16.60 |
50.64 |
29.08 |
16.94 |
199.09 |
15.2 |
343.47 |
577.6 |
| NA |
H6 |
1 |
2.90 |
1.59 |
17.10 |
54.86 |
32.04 |
17.94 |
250.89 |
17.6 |
386.55 |
648.0 |
| NA |
NA |
2 |
2.74 |
1.69 |
16.80 |
53.08 |
31.23 |
17.60 |
225.47 |
16.0 |
402.7 |
560.8 |
| NA |
NA |
3 |
2.67 |
1.45 |
16.10 |
54.40 |
31.81 |
17.22 |
219.20 |
16.8 |
447 |
496.8 |
| NA |
H7 |
1 |
2.87 |
1.56 |
16.60 |
50.27 |
28.42 |
17.12 |
206.30 |
16.8 |
337.42 |
608.4 |
| NA |
NA |
2 |
2.78 |
1.67 |
15.38 |
52.03 |
32.49 |
16.52 |
163.34 |
18.0 |
344.58 |
494.0 |
| NA |
NA |
3 |
2.68 |
1.56 |
14.34 |
50.27 |
31.01 |
14.76 |
175.00 |
18.8 |
332.29 |
529.6 |
| NA |
H8 |
1 |
2.59 |
1.30 |
12.74 |
51.03 |
30.03 |
14.14 |
184.87 |
18.8 |
312.86 |
597.8 |
| NA |
NA |
2 |
2.76 |
1.55 |
17.08 |
52.43 |
34.66 |
18.26 |
211.57 |
17.2 |
372.25 |
565.2 |
| NA |
NA |
3 |
2.73 |
1.54 |
15.60 |
53.31 |
32.61 |
17.00 |
191.89 |
15.6 |
412.06 |
462.4 |
| NA |
H9 |
1 |
3.00 |
1.71 |
12.24 |
51.78 |
31.58 |
15.06 |
182.83 |
14.4 |
410.02 |
445.4 |
| NA |
NA |
2 |
2.96 |
1.61 |
17.50 |
53.62 |
32.51 |
18.12 |
224.00 |
14.4 |
439.06 |
510.4 |
| NA |
NA |
3 |
2.81 |
1.69 |
16.80 |
52.69 |
31.76 |
17.66 |
205.69 |
16.0 |
390.18 |
509.8 |
| AMB2 |
H1 |
1 |
3.00 |
1.88 |
15.14 |
50.79 |
31.11 |
15.64 |
190.56 |
16.4 |
389.35 |
488.2 |
| NA |
NA |
2 |
2.97 |
1.83 |
15.16 |
52.09 |
31.22 |
15.70 |
197.04 |
17.2 |
390.39 |
506.2 |
| NA |
NA |
3 |
2.81 |
1.67 |
14.62 |
52.74 |
32.21 |
15.14 |
176.99 |
17.6 |
387.06 |
458.8 |
| NA |
H10 |
1 |
2.10 |
0.91 |
16.04 |
46.69 |
27.22 |
17.10 |
150.98 |
15.2 |
286.08 |
541.8 |
df_tidy <-
df_messy %>%
tidy_colnames() %>% # formata nomes das variáveis
fill_na() %>% # preenche NAs
tidy_strings(sep = "") # formata strings
df_tidy %>% print_tbl(n = 40)
| ENV |
GEN |
BLOCO |
ALT_PLANT |
ALT_ESP |
COMPES |
DIAMES |
COMP_SAB |
DIAM_SAB |
MGE |
NFIL |
MMG |
NGE |
| AMB1 |
H10 |
1 |
0.00 |
1.64 |
16.72 |
54.05 |
31.66 |
17.40 |
193.69 |
15.6 |
37961 |
519.2 |
| AMB1 |
H10 |
2 |
2.79 |
1.71 |
14.90 |
52.73 |
32.03 |
15.48 |
176.43 |
17.6 |
34688 |
502.4 |
| AMB1 |
H10 |
3 |
2.72 |
1.51 |
16.68 |
52.74 |
30.40 |
17.50 |
207.11 |
16.8 |
39403 |
524.6 |
| AMB1 |
H11 |
1 |
2.75 |
1.51 |
17.42 |
51.69 |
30.64 |
17.98 |
217.29 |
16.8 |
37665 |
524.6 |
| AMB1 |
H11 |
2 |
2.72 |
1.56 |
16.70 |
47.21 |
28.69 |
17.20 |
181.28 |
13.6 |
36066 |
500.8 |
| AMB1 |
H11 |
3 |
2.77 |
1.67 |
15.78 |
47.87 |
27.63 |
16.36 |
166.19 |
15.2 |
3218 |
512.8 |
| AMB1 |
H12 |
1 |
2.73 |
1.54 |
14.88 |
47.51 |
28.22 |
15.54 |
160.98 |
14.8 |
34484 |
480.2 |
| AMB1 |
H12 |
2 |
2.56 |
1.56 |
15.68 |
49.86 |
29.85 |
16.16 |
187.96 |
17.2 |
32833 |
585.8 |
| AMB1 |
H12 |
3 |
2.79 |
1.53 |
14.98 |
52.67 |
31.38 |
15.24 |
192.51 |
20.0 |
32536 |
594.0 |
| AMB1 |
H13 |
1 |
2.74 |
1.60 |
14.64 |
54.00 |
32.48 |
15.06 |
205.34 |
20.0 |
33437 |
628.4 |
| AMB1 |
H13 |
2 |
2.64 |
1.37 |
14.80 |
53.73 |
30.97 |
15.54 |
238.55 |
20.4 |
34872 |
693.2 |
| AMB1 |
H13 |
3 |
2.93 |
1.77 |
14.92 |
52.66 |
30.07 |
15.76 |
211.92 |
15.6 |
42123 |
511.6 |
| AMB1 |
H2 |
1 |
2.55 |
1.22 |
15.08 |
51.66 |
27.71 |
15.30 |
197.70 |
16.4 |
38206 |
518.2 |
| AMB1 |
H2 |
2 |
2.90 |
1.41 |
15.12 |
51.53 |
26.04 |
16.00 |
212.18 |
21.2 |
30451 |
696.6 |
| AMB1 |
H2 |
3 |
2.92 |
1.39 |
14.78 |
52.63 |
26.04 |
15.14 |
201.62 |
20.0 |
30002 |
650.4 |
| AMB1 |
H3 |
1 |
3.04 |
1.43 |
15.02 |
51.66 |
25.93 |
16.56 |
209.80 |
19.6 |
31748 |
641.6 |
| AMB1 |
H3 |
2 |
2.94 |
1.66 |
14.88 |
51.46 |
31.33 |
15.46 |
192.84 |
18.0 |
34583 |
550.6 |
| AMB1 |
H3 |
3 |
2.82 |
1.67 |
16.46 |
52.25 |
26.74 |
17.08 |
191.99 |
18.0 |
31173 |
637.4 |
| AMB1 |
H4 |
1 |
3.02 |
1.82 |
16.54 |
50.43 |
27.31 |
17.26 |
193.02 |
16.8 |
30533 |
637.4 |
| AMB1 |
H4 |
2 |
2.84 |
1.69 |
15.04 |
52.17 |
27.95 |
15.72 |
215.69 |
18.4 |
3573 |
613.6 |
| AMB1 |
H4 |
3 |
2.74 |
1.52 |
16.34 |
49.57 |
27.01 |
17.10 |
196.76 |
14.0 |
35015 |
562.2 |
| AMB1 |
H5 |
1 |
2.90 |
1.58 |
16.02 |
48.66 |
27.99 |
17.00 |
198.18 |
14.0 |
37379 |
5302.0 |
| AMB1 |
H5 |
2 |
2.82 |
1.61 |
14.68 |
50.26 |
27.87 |
16.15 |
180.83 |
14.4 |
35222 |
508.6 |
| AMB1 |
H5 |
3 |
2.78 |
1.59 |
16.60 |
50.64 |
29.08 |
16.94 |
199.09 |
15.2 |
34347 |
577.6 |
| AMB1 |
H6 |
1 |
2.90 |
1.59 |
17.10 |
54.86 |
32.04 |
17.94 |
250.89 |
17.6 |
38655 |
648.0 |
| AMB1 |
H6 |
2 |
2.74 |
1.69 |
16.80 |
53.08 |
31.23 |
17.60 |
225.47 |
16.0 |
4027 |
560.8 |
| AMB1 |
H6 |
3 |
2.67 |
1.45 |
16.10 |
54.40 |
31.81 |
17.22 |
219.20 |
16.8 |
447 |
496.8 |
| AMB1 |
H7 |
1 |
2.87 |
1.56 |
16.60 |
50.27 |
28.42 |
17.12 |
206.30 |
16.8 |
33742 |
608.4 |
| AMB1 |
H7 |
2 |
2.78 |
1.67 |
15.38 |
52.03 |
32.49 |
16.52 |
163.34 |
18.0 |
34458 |
494.0 |
| AMB1 |
H7 |
3 |
2.68 |
1.56 |
14.34 |
50.27 |
31.01 |
14.76 |
175.00 |
18.8 |
33229 |
529.6 |
| AMB1 |
H8 |
1 |
2.59 |
1.30 |
12.74 |
51.03 |
30.03 |
14.14 |
184.87 |
18.8 |
31286 |
597.8 |
| AMB1 |
H8 |
2 |
2.76 |
1.55 |
17.08 |
52.43 |
34.66 |
18.26 |
211.57 |
17.2 |
37225 |
565.2 |
| AMB1 |
H8 |
3 |
2.73 |
1.54 |
15.60 |
53.31 |
32.61 |
17.00 |
191.89 |
15.6 |
41206 |
462.4 |
| AMB1 |
H9 |
1 |
3.00 |
1.71 |
12.24 |
51.78 |
31.58 |
15.06 |
182.83 |
14.4 |
41002 |
445.4 |
| AMB1 |
H9 |
2 |
2.96 |
1.61 |
17.50 |
53.62 |
32.51 |
18.12 |
224.00 |
14.4 |
43906 |
510.4 |
| AMB1 |
H9 |
3 |
2.81 |
1.69 |
16.80 |
52.69 |
31.76 |
17.66 |
205.69 |
16.0 |
39018 |
509.8 |
| AMB2 |
H1 |
1 |
3.00 |
1.88 |
15.14 |
50.79 |
31.11 |
15.64 |
190.56 |
16.4 |
38935 |
488.2 |
| AMB2 |
H1 |
2 |
2.97 |
1.83 |
15.16 |
52.09 |
31.22 |
15.70 |
197.04 |
17.2 |
39039 |
506.2 |
| AMB2 |
H1 |
3 |
2.81 |
1.67 |
14.62 |
52.74 |
32.21 |
15.14 |
176.99 |
17.6 |
38706 |
458.8 |
| AMB2 |
H10 |
1 |
2.10 |
0.91 |
16.04 |
46.69 |
27.22 |
17.10 |
150.98 |
15.2 |
28608 |
541.8 |
# exportar os dados 'arrumados'
# export(df_tidy, "df_tidy.xlsx")
Utilitários para linhas e colunas
Adicionar colunas e linhas
As funções add_cols() e add_rows() podem ser usadas para adicionar colunas e linhas, respectivamente, a um data.frame. Também é possível adicionar uma coluna com base nos dados existentes. Observe que os argumentos .after e .before são usados para selecionar a posição da(s) nova(s) coluna(s). Isso é particularmente útil para colocar variáveis da mesma categoria juntas.
df_tidy2 <-
add_cols(df_tidy,
ALT_PLANT_CM = ALT_PLANT * 100,
.after = ALT_PLANT)
print_tbl(df_tidy2, n = 5)
| ENV |
GEN |
BLOCO |
ALT_PLANT |
ALT_PLANT_CM |
ALT_ESP |
COMPES |
DIAMES |
COMP_SAB |
DIAM_SAB |
MGE |
NFIL |
MMG |
NGE |
| AMB1 |
H10 |
1 |
0.00 |
0 |
1.64 |
16.72 |
54.05 |
31.66 |
17.40 |
193.69 |
15.6 |
37961 |
519.2 |
| AMB1 |
H10 |
2 |
2.79 |
279 |
1.71 |
14.90 |
52.73 |
32.03 |
15.48 |
176.43 |
17.6 |
34688 |
502.4 |
| AMB1 |
H10 |
3 |
2.72 |
272 |
1.51 |
16.68 |
52.74 |
30.40 |
17.50 |
207.11 |
16.8 |
39403 |
524.6 |
| AMB1 |
H11 |
1 |
2.75 |
275 |
1.51 |
17.42 |
51.69 |
30.64 |
17.98 |
217.29 |
16.8 |
37665 |
524.6 |
| AMB1 |
H11 |
2 |
2.72 |
272 |
1.56 |
16.70 |
47.21 |
28.69 |
17.20 |
181.28 |
13.6 |
36066 |
500.8 |
Selecionar ou remover colunas e linhas
As funções select_cols() e select_rows() podem ser usadas para selecionar colunas e linhas, respectivamente de um quadro de dados.
select_cols(df_tidy, ENV, GEN) %>% print_tbl(n = 5)
| ENV |
GEN |
| AMB1 |
H10 |
| AMB1 |
H10 |
| AMB1 |
H10 |
| AMB1 |
H11 |
| AMB1 |
H11 |
select_rows(df_tidy, 1:3) %>% print_tbl(n = 5)
| ENV |
GEN |
BLOCO |
ALT_PLANT |
ALT_ESP |
COMPES |
DIAMES |
COMP_SAB |
DIAM_SAB |
MGE |
NFIL |
MMG |
NGE |
| AMB1 |
H10 |
1 |
0.00 |
1.64 |
16.72 |
54.05 |
31.66 |
17.40 |
193.69 |
15.6 |
37961 |
519.2 |
| AMB1 |
H10 |
2 |
2.79 |
1.71 |
14.90 |
52.73 |
32.03 |
15.48 |
176.43 |
17.6 |
34688 |
502.4 |
| AMB1 |
H10 |
3 |
2.72 |
1.51 |
16.68 |
52.74 |
30.40 |
17.50 |
207.11 |
16.8 |
39403 |
524.6 |
As colunas numéricas podem ser selecionadas usando a função select_numeric_cols(). Colunas não numéricas são selecionadas com select_non_numeric_cols().
select_numeric_cols(df_tidy) %>% print_tbl(n = 5)
| BLOCO |
ALT_PLANT |
ALT_ESP |
COMPES |
DIAMES |
COMP_SAB |
DIAM_SAB |
MGE |
NFIL |
NGE |
| 1 |
0.00 |
1.64 |
16.72 |
54.05 |
31.66 |
17.40 |
193.69 |
15.6 |
519.2 |
| 2 |
2.79 |
1.71 |
14.90 |
52.73 |
32.03 |
15.48 |
176.43 |
17.6 |
502.4 |
| 3 |
2.72 |
1.51 |
16.68 |
52.74 |
30.40 |
17.50 |
207.11 |
16.8 |
524.6 |
| 1 |
2.75 |
1.51 |
17.42 |
51.69 |
30.64 |
17.98 |
217.29 |
16.8 |
524.6 |
| 2 |
2.72 |
1.56 |
16.70 |
47.21 |
28.69 |
17.20 |
181.28 |
13.6 |
500.8 |
# Implementação dplyr
select(df_tidy, where(is.numeric)) %>% print_tbl(n = 5)
| BLOCO |
ALT_PLANT |
ALT_ESP |
COMPES |
DIAMES |
COMP_SAB |
DIAM_SAB |
MGE |
NFIL |
NGE |
| 1 |
0.00 |
1.64 |
16.72 |
54.05 |
31.66 |
17.40 |
193.69 |
15.6 |
519.2 |
| 2 |
2.79 |
1.71 |
14.90 |
52.73 |
32.03 |
15.48 |
176.43 |
17.6 |
502.4 |
| 3 |
2.72 |
1.51 |
16.68 |
52.74 |
30.40 |
17.50 |
207.11 |
16.8 |
524.6 |
| 1 |
2.75 |
1.51 |
17.42 |
51.69 |
30.64 |
17.98 |
217.29 |
16.8 |
524.6 |
| 2 |
2.72 |
1.56 |
16.70 |
47.21 |
28.69 |
17.20 |
181.28 |
13.6 |
500.8 |
select_non_numeric_cols(df_tidy) %>% print_tbl(n = 5)
| ENV |
GEN |
MMG |
| AMB1 |
H10 |
37961 |
| AMB1 |
H10 |
34688 |
| AMB1 |
H10 |
39403 |
| AMB1 |
H11 |
37665 |
| AMB1 |
H11 |
36066 |
# Implementação dplyr
select(df_tidy, where(~!is.numeric(.x))) %>% print_tbl(n = 5)
| ENV |
GEN |
MMG |
| AMB1 |
H10 |
37961 |
| AMB1 |
H10 |
34688 |
| AMB1 |
H10 |
39403 |
| AMB1 |
H11 |
37665 |
| AMB1 |
H11 |
36066 |
Podemos selecionar a primeira ou última coluna rapidamente com select_first_col() e select_last_col(), respectivamente.
select_first_col(df_tidy) %>% print_tbl(n = 5)
| ENV |
| AMB1 |
| AMB1 |
| AMB1 |
| AMB1 |
| AMB1 |
select_last_col(df_tidy) %>% print_tbl(n = 5)
| NGE |
| 519.2 |
| 502.4 |
| 524.6 |
| 524.6 |
| 500.8 |
Para remover colunas ou linhas, use remove_cols() e remove_rows().
remove_cols(df_tidy, ENV, GEN) %>% print_tbl(n = 5)
| BLOCO |
ALT_PLANT |
ALT_ESP |
COMPES |
DIAMES |
COMP_SAB |
DIAM_SAB |
MGE |
NFIL |
MMG |
NGE |
| 1 |
0.00 |
1.64 |
16.72 |
54.05 |
31.66 |
17.40 |
193.69 |
15.6 |
37961 |
519.2 |
| 2 |
2.79 |
1.71 |
14.90 |
52.73 |
32.03 |
15.48 |
176.43 |
17.6 |
34688 |
502.4 |
| 3 |
2.72 |
1.51 |
16.68 |
52.74 |
30.40 |
17.50 |
207.11 |
16.8 |
39403 |
524.6 |
| 1 |
2.75 |
1.51 |
17.42 |
51.69 |
30.64 |
17.98 |
217.29 |
16.8 |
37665 |
524.6 |
| 2 |
2.72 |
1.56 |
16.70 |
47.21 |
28.69 |
17.20 |
181.28 |
13.6 |
36066 |
500.8 |
# Implementação dplyr
select(df_tidy, -c(ENV, GEN)) %>% print_tbl(n = 5)
| BLOCO |
ALT_PLANT |
ALT_ESP |
COMPES |
DIAMES |
COMP_SAB |
DIAM_SAB |
MGE |
NFIL |
MMG |
NGE |
| 1 |
0.00 |
1.64 |
16.72 |
54.05 |
31.66 |
17.40 |
193.69 |
15.6 |
37961 |
519.2 |
| 2 |
2.79 |
1.71 |
14.90 |
52.73 |
32.03 |
15.48 |
176.43 |
17.6 |
34688 |
502.4 |
| 3 |
2.72 |
1.51 |
16.68 |
52.74 |
30.40 |
17.50 |
207.11 |
16.8 |
39403 |
524.6 |
| 1 |
2.75 |
1.51 |
17.42 |
51.69 |
30.64 |
17.98 |
217.29 |
16.8 |
37665 |
524.6 |
| 2 |
2.72 |
1.56 |
16.70 |
47.21 |
28.69 |
17.20 |
181.28 |
13.6 |
36066 |
500.8 |
Concatenar colunas
A função concatetate() pode ser usada para concatenar várias colunas de um data.frame. Ela retorna o data.frame com todas as colunas originais em .data mais a variável concatenada, após a última coluna(por padrão), ou em qualquer posição ao usar os argumentos .before ou .after.
concatenado <-
concatenate(df_tidy, ENV, GEN, BLOCO,
.after = BLOCO,
new_var = FATORES)
print_tbl(concatenado, n = 5)
| ENV |
GEN |
BLOCO |
FATORES |
ALT_PLANT |
ALT_ESP |
COMPES |
DIAMES |
COMP_SAB |
DIAM_SAB |
MGE |
NFIL |
MMG |
NGE |
| AMB1 |
H10 |
1 |
AMB1_H10_1 |
0.00 |
1.64 |
16.72 |
54.05 |
31.66 |
17.40 |
193.69 |
15.6 |
37961 |
519.2 |
| AMB1 |
H10 |
2 |
AMB1_H10_2 |
2.79 |
1.71 |
14.90 |
52.73 |
32.03 |
15.48 |
176.43 |
17.6 |
34688 |
502.4 |
| AMB1 |
H10 |
3 |
AMB1_H10_3 |
2.72 |
1.51 |
16.68 |
52.74 |
30.40 |
17.50 |
207.11 |
16.8 |
39403 |
524.6 |
| AMB1 |
H11 |
1 |
AMB1_H11_1 |
2.75 |
1.51 |
17.42 |
51.69 |
30.64 |
17.98 |
217.29 |
16.8 |
37665 |
524.6 |
| AMB1 |
H11 |
2 |
AMB1_H11_2 |
2.72 |
1.56 |
16.70 |
47.21 |
28.69 |
17.20 |
181.28 |
13.6 |
36066 |
500.8 |
Para eliminar as variáveis existentes e manter apenas a coluna concatenada, use o argumento drop = TRUE. Para usar concatenate() dentro de uma determinada função como add_cols() use o argumento pull = TRUE para puxar os resultados para um vetor.
concatenate(df_tidy, ENV, GEN, BLOCO, drop = TRUE) %>% head()
## # A tibble: 6 x 1
## new_var
## <chr>
## 1 AMB1_H10_1
## 2 AMB1_H10_2
## 3 AMB1_H10_3
## 4 AMB1_H11_1
## 5 AMB1_H11_2
## 6 AMB1_H11_3
concatenate(df_tidy, ENV, GEN, BLOCO, pull = TRUE) %>% head()
## [1] "AMB1_H10_1" "AMB1_H10_2" "AMB1_H10_3" "AMB1_H11_1" "AMB1_H11_2"
## [6] "AMB1_H11_3"
Obtendo níveis
Para obter os níveis e o número de níveis de um fator, as funções get_levels() e get_level_size() podem ser usadas.
get_levels(df_tidy, ENV)
## [1] "AMB1" "AMB2" "AMB3"
get_level_size(df_tidy, ENV) %>% print_tbl()
| ENV |
GEN |
BLOCO |
ALT_PLANT |
ALT_ESP |
COMPES |
DIAMES |
COMP_SAB |
DIAM_SAB |
MGE |
NFIL |
MMG |
NGE |
| AMB1 |
36 |
36 |
36 |
36 |
36 |
36 |
36 |
36 |
36 |
36 |
36 |
36 |
| AMB2 |
39 |
39 |
39 |
39 |
39 |
39 |
39 |
39 |
39 |
39 |
39 |
39 |
| AMB3 |
39 |
39 |
39 |
39 |
39 |
39 |
39 |
39 |
39 |
39 |
39 |
39 |
Utilitários para números e strings
Arredondando valores
A função round_cols() arredonda uma coluna selecionada ou um data.frame completo para o número especificado de casas decimais (padrão 0). Se nenhuma variável for informada, todas as variáveis numéricas serão arredondadas.
round_cols(df_tidy, digits = 1) %>% print_tbl(n = 5)
| ENV |
GEN |
BLOCO |
ALT_PLANT |
ALT_ESP |
COMPES |
DIAMES |
COMP_SAB |
DIAM_SAB |
MGE |
NFIL |
MMG |
NGE |
| AMB1 |
H10 |
1 |
0.0 |
1.6 |
16.7 |
54.0 |
31.7 |
17.4 |
193.7 |
15.6 |
37961 |
519.2 |
| AMB1 |
H10 |
2 |
2.8 |
1.7 |
14.9 |
52.7 |
32.0 |
15.5 |
176.4 |
17.6 |
34688 |
502.4 |
| AMB1 |
H10 |
3 |
2.7 |
1.5 |
16.7 |
52.7 |
30.4 |
17.5 |
207.1 |
16.8 |
39403 |
524.6 |
| AMB1 |
H11 |
1 |
2.8 |
1.5 |
17.4 |
51.7 |
30.6 |
18.0 |
217.3 |
16.8 |
37665 |
524.6 |
| AMB1 |
H11 |
2 |
2.7 |
1.6 |
16.7 |
47.2 |
28.7 |
17.2 |
181.3 |
13.6 |
36066 |
500.8 |
Alternativamente, selecione variáveis para arredondar.
round_cols(df_tidy, ALT_PLANT:COMPES, digits = 0) %>% print_tbl(n = 5)
| ENV |
GEN |
BLOCO |
ALT_PLANT |
ALT_ESP |
COMPES |
DIAMES |
COMP_SAB |
DIAM_SAB |
MGE |
NFIL |
MMG |
NGE |
| AMB1 |
H10 |
1 |
0 |
2 |
17 |
54.05 |
31.66 |
17.40 |
193.69 |
15.6 |
37961 |
519.2 |
| AMB1 |
H10 |
2 |
3 |
2 |
15 |
52.73 |
32.03 |
15.48 |
176.43 |
17.6 |
34688 |
502.4 |
| AMB1 |
H10 |
3 |
3 |
2 |
17 |
52.74 |
30.40 |
17.50 |
207.11 |
16.8 |
39403 |
524.6 |
| AMB1 |
H11 |
1 |
3 |
2 |
17 |
51.69 |
30.64 |
17.98 |
217.29 |
16.8 |
37665 |
524.6 |
| AMB1 |
H11 |
2 |
3 |
2 |
17 |
47.21 |
28.69 |
17.20 |
181.28 |
13.6 |
36066 |
500.8 |
As funções extract_number() e replace_number() podem ser usadas para extrair ou substituir números. Como exemplo, iremos extrair o número de cada genótipo em df_tidy. Por padrão, os números extraídos são colocados como uma nova variável chamada new_var após a última coluna dos dados.
extract_number(df_tidy, GEN) %>% print_tbl(n = 5)
| ENV |
GEN |
BLOCO |
ALT_PLANT |
ALT_ESP |
COMPES |
DIAMES |
COMP_SAB |
DIAM_SAB |
MGE |
NFIL |
MMG |
NGE |
| AMB1 |
10 |
1 |
0.00 |
1.64 |
16.72 |
54.05 |
31.66 |
17.40 |
193.69 |
15.6 |
37961 |
519.2 |
| AMB1 |
10 |
2 |
2.79 |
1.71 |
14.90 |
52.73 |
32.03 |
15.48 |
176.43 |
17.6 |
34688 |
502.4 |
| AMB1 |
10 |
3 |
2.72 |
1.51 |
16.68 |
52.74 |
30.40 |
17.50 |
207.11 |
16.8 |
39403 |
524.6 |
| AMB1 |
11 |
1 |
2.75 |
1.51 |
17.42 |
51.69 |
30.64 |
17.98 |
217.29 |
16.8 |
37665 |
524.6 |
| AMB1 |
11 |
2 |
2.72 |
1.56 |
16.70 |
47.21 |
28.69 |
17.20 |
181.28 |
13.6 |
36066 |
500.8 |
Para substituir os números de uma determinada coluna por uma substituição especificada, use replace_number(). Por padrão, os números são substituídos por “”.
replace_number(df_tidy,
BLOCO,
pattern = "1",
replacement = "Rep_1") %>%
print_tbl(n = 5)
| ENV |
GEN |
BLOCO |
ALT_PLANT |
ALT_ESP |
COMPES |
DIAMES |
COMP_SAB |
DIAM_SAB |
MGE |
NFIL |
MMG |
NGE |
| AMB1 |
H10 |
Rep_1 |
0.00 |
1.64 |
16.72 |
54.05 |
31.66 |
17.40 |
193.69 |
15.6 |
37961 |
519.2 |
| AMB1 |
H10 |
2 |
2.79 |
1.71 |
14.90 |
52.73 |
32.03 |
15.48 |
176.43 |
17.6 |
34688 |
502.4 |
| AMB1 |
H10 |
3 |
2.72 |
1.51 |
16.68 |
52.74 |
30.40 |
17.50 |
207.11 |
16.8 |
39403 |
524.6 |
| AMB1 |
H11 |
Rep_1 |
2.75 |
1.51 |
17.42 |
51.69 |
30.64 |
17.98 |
217.29 |
16.8 |
37665 |
524.6 |
| AMB1 |
H11 |
2 |
2.72 |
1.56 |
16.70 |
47.21 |
28.69 |
17.20 |
181.28 |
13.6 |
36066 |
500.8 |
As funções extract_string() e replace_string() são usadas no mesmo contexto de extract_number() e replace_number(), mas para lidar com strings.
extract_string(df_tidy, GEN) %>% print_tbl(n = 5)
| ENV |
GEN |
BLOCO |
ALT_PLANT |
ALT_ESP |
COMPES |
DIAMES |
COMP_SAB |
DIAM_SAB |
MGE |
NFIL |
MMG |
NGE |
| AMB1 |
H |
1 |
0.00 |
1.64 |
16.72 |
54.05 |
31.66 |
17.40 |
193.69 |
15.6 |
37961 |
519.2 |
| AMB1 |
H |
2 |
2.79 |
1.71 |
14.90 |
52.73 |
32.03 |
15.48 |
176.43 |
17.6 |
34688 |
502.4 |
| AMB1 |
H |
3 |
2.72 |
1.51 |
16.68 |
52.74 |
30.40 |
17.50 |
207.11 |
16.8 |
39403 |
524.6 |
| AMB1 |
H |
1 |
2.75 |
1.51 |
17.42 |
51.69 |
30.64 |
17.98 |
217.29 |
16.8 |
37665 |
524.6 |
| AMB1 |
H |
2 |
2.72 |
1.56 |
16.70 |
47.21 |
28.69 |
17.20 |
181.28 |
13.6 |
36066 |
500.8 |
Para substituir strings, podemos usar a função replace_strings().
replace_string(df_tidy,
GEN,
pattern = "H",
replacement = "GEN_") %>%
print_tbl(n = 5)
| ENV |
GEN |
BLOCO |
ALT_PLANT |
ALT_ESP |
COMPES |
DIAMES |
COMP_SAB |
DIAM_SAB |
MGE |
NFIL |
MMG |
NGE |
| AMB1 |
GEN_10 |
1 |
0.00 |
1.64 |
16.72 |
54.05 |
31.66 |
17.40 |
193.69 |
15.6 |
37961 |
519.2 |
| AMB1 |
GEN_10 |
2 |
2.79 |
1.71 |
14.90 |
52.73 |
32.03 |
15.48 |
176.43 |
17.6 |
34688 |
502.4 |
| AMB1 |
GEN_10 |
3 |
2.72 |
1.51 |
16.68 |
52.74 |
30.40 |
17.50 |
207.11 |
16.8 |
39403 |
524.6 |
| AMB1 |
GEN_11 |
1 |
2.75 |
1.51 |
17.42 |
51.69 |
30.64 |
17.98 |
217.29 |
16.8 |
37665 |
524.6 |
| AMB1 |
GEN_11 |
2 |
2.72 |
1.56 |
16.70 |
47.21 |
28.69 |
17.20 |
181.28 |
13.6 |
36066 |
500.8 |
Para remover todas as strings de um quadro de dados, use remove_strings().
remove_strings(df_tidy) %>% print_tbl(n = 5)
| ENV |
GEN |
BLOCO |
ALT_PLANT |
ALT_ESP |
COMPES |
DIAMES |
COMP_SAB |
DIAM_SAB |
MGE |
NFIL |
MMG |
NGE |
| 1 |
10 |
1 |
0.00 |
1.64 |
16.72 |
54.05 |
31.66 |
17.40 |
193.69 |
15.6 |
37961 |
519.2 |
| 1 |
10 |
2 |
2.79 |
1.71 |
14.90 |
52.73 |
32.03 |
15.48 |
176.43 |
17.6 |
34688 |
502.4 |
| 1 |
10 |
3 |
2.72 |
1.51 |
16.68 |
52.74 |
30.40 |
17.50 |
207.11 |
16.8 |
39403 |
524.6 |
| 1 |
11 |
1 |
2.75 |
1.51 |
17.42 |
51.69 |
30.64 |
17.98 |
217.29 |
16.8 |
37665 |
524.6 |
| 1 |
11 |
2 |
2.72 |
1.56 |
16.70 |
47.21 |
28.69 |
17.20 |
181.28 |
13.6 |
36066 |
500.8 |
df_selegen <-
df_to_selegen_54(df_tidy,
env = ENV,
gen = GEN,
rep = BLOCO)
## The data `df_tidy` has been arranged according to the `ENV` and `BLOCO` columns.
print_tbl(df_selegen, n = 15)
| ENV |
PARCELA |
GEN |
REP |
REPETICAO |
OBS |
BLOCO |
ALT_PLANT |
ALT_ESP |
COMPES |
DIAMES |
COMP_SAB |
DIAM_SAB |
MGE |
NFIL |
MMG |
NGE |
| AMB1 |
1 |
H10 |
1 |
AMB1_H10 |
1 |
1 |
0.00 |
1.64 |
16.72 |
54.05 |
31.66 |
17.40 |
193.69 |
15.6 |
37961 |
519.2 |
| AMB1 |
2 |
H11 |
1 |
AMB1_H11 |
1 |
1 |
2.75 |
1.51 |
17.42 |
51.69 |
30.64 |
17.98 |
217.29 |
16.8 |
37665 |
524.6 |
| AMB1 |
3 |
H12 |
1 |
AMB1_H12 |
1 |
1 |
2.73 |
1.54 |
14.88 |
47.51 |
28.22 |
15.54 |
160.98 |
14.8 |
34484 |
480.2 |
| AMB1 |
4 |
H13 |
1 |
AMB1_H13 |
1 |
1 |
2.74 |
1.60 |
14.64 |
54.00 |
32.48 |
15.06 |
205.34 |
20.0 |
33437 |
628.4 |
| AMB1 |
5 |
H2 |
1 |
AMB1_H2 |
1 |
1 |
2.55 |
1.22 |
15.08 |
51.66 |
27.71 |
15.30 |
197.70 |
16.4 |
38206 |
518.2 |
| AMB1 |
6 |
H3 |
1 |
AMB1_H3 |
1 |
1 |
3.04 |
1.43 |
15.02 |
51.66 |
25.93 |
16.56 |
209.80 |
19.6 |
31748 |
641.6 |
| AMB1 |
7 |
H4 |
1 |
AMB1_H4 |
1 |
1 |
3.02 |
1.82 |
16.54 |
50.43 |
27.31 |
17.26 |
193.02 |
16.8 |
30533 |
637.4 |
| AMB1 |
8 |
H5 |
1 |
AMB1_H5 |
1 |
1 |
2.90 |
1.58 |
16.02 |
48.66 |
27.99 |
17.00 |
198.18 |
14.0 |
37379 |
5302.0 |
| AMB1 |
9 |
H6 |
1 |
AMB1_H6 |
1 |
1 |
2.90 |
1.59 |
17.10 |
54.86 |
32.04 |
17.94 |
250.89 |
17.6 |
38655 |
648.0 |
| AMB1 |
10 |
H7 |
1 |
AMB1_H7 |
1 |
1 |
2.87 |
1.56 |
16.60 |
50.27 |
28.42 |
17.12 |
206.30 |
16.8 |
33742 |
608.4 |
| AMB1 |
11 |
H8 |
1 |
AMB1_H8 |
1 |
1 |
2.59 |
1.30 |
12.74 |
51.03 |
30.03 |
14.14 |
184.87 |
18.8 |
31286 |
597.8 |
| AMB1 |
12 |
H9 |
1 |
AMB1_H9 |
1 |
1 |
3.00 |
1.71 |
12.24 |
51.78 |
31.58 |
15.06 |
182.83 |
14.4 |
41002 |
445.4 |
| AMB1 |
13 |
H10 |
2 |
AMB1_H10 |
1 |
2 |
2.79 |
1.71 |
14.90 |
52.73 |
32.03 |
15.48 |
176.43 |
17.6 |
34688 |
502.4 |
| AMB1 |
14 |
H11 |
2 |
AMB1_H11 |
1 |
2 |
2.72 |
1.56 |
16.70 |
47.21 |
28.69 |
17.20 |
181.28 |
13.6 |
36066 |
500.8 |
| AMB1 |
15 |
H12 |
2 |
AMB1_H12 |
1 |
2 |
2.56 |
1.56 |
15.68 |
49.86 |
29.85 |
16.16 |
187.96 |
17.2 |
32833 |
585.8 |
Dividir/juntar conjunto de dados
df_list <- split_factors(df_tidy, ENV)
df_list
## $AMB1
## # A tibble: 36 x 12
## GEN BLOCO ALT_PLANT ALT_ESP COMPES DIAMES COMP_SAB DIAM_SAB MGE NFIL
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 H10 1 0 1.64 16.7 54.0 31.7 17.4 194. 15.6
## 2 H10 2 2.79 1.71 14.9 52.7 32.0 15.5 176. 17.6
## 3 H10 3 2.72 1.51 16.7 52.7 30.4 17.5 207. 16.8
## 4 H11 1 2.75 1.51 17.4 51.7 30.6 18.0 217. 16.8
## 5 H11 2 2.72 1.56 16.7 47.2 28.7 17.2 181. 13.6
## 6 H11 3 2.77 1.67 15.8 47.9 27.6 16.4 166. 15.2
## 7 H12 1 2.73 1.54 14.9 47.5 28.2 15.5 161. 14.8
## 8 H12 2 2.56 1.56 15.7 49.9 29.8 16.2 188. 17.2
## 9 H12 3 2.79 1.53 15.0 52.7 31.4 15.2 193. 20
## 10 H13 1 2.74 1.6 14.6 54 32.5 15.1 205. 20
## # ... with 26 more rows, and 2 more variables: MMG <chr>, NGE <dbl>
##
## $AMB2
## # A tibble: 39 x 12
## GEN BLOCO ALT_PLANT ALT_ESP COMPES DIAMES COMP_SAB DIAM_SAB MGE NFIL
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 H1 1 3 1.88 15.1 50.8 31.1 15.6 191. 16.4
## 2 H1 2 2.97 1.83 15.2 52.1 31.2 15.7 197. 17.2
## 3 H1 3 2.81 1.67 14.6 52.7 32.2 15.1 177. 17.6
## 4 H10 1 2.1 0.91 16.0 46.7 27.2 17.1 151. 15.2
## 5 H10 2 2.12 1.03 14.6 46.4 26.6 15.5 152. 13.2
## 6 H10 3 1.92 1.02 16.0 47.1 26.6 16.3 177. 13.6
## 7 H11 1 2.13 1.05 14.8 47.0 27.0 15.9 169. 13.2
## 8 H11 2 2.13 1.01 16.0 47.7 28.0 16.3 168. 14
## 9 H11 3 2.18 0.99 14.6 47.3 26.7 14.8 153. 14
## 10 H12 1 2.15 0.98 14.3 45.3 25.7 14.8 138. 13.6
## # ... with 29 more rows, and 2 more variables: MMG <chr>, NGE <dbl>
##
## $AMB3
## # A tibble: 39 x 12
## GEN BLOCO ALT_PLANT ALT_ESP COMPES DIAMES COMP_SAB DIAM_SAB MGE NFIL
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 H1 1 2.11 1.05 15.7 49.9 30.5 16.6 164. 15.6
## 2 H1 2 2.2 1.09 13.7 49.2 30.5 14.7 130. 16.4
## 3 H1 3 2.29 1.15 15.1 52.6 31.7 16.2 176 15.6
## 4 H10 1 1.79 0.89 13.9 44.1 26.2 15.0 116. 14.8
## 5 H10 2 2.05 1.03 13.6 43.9 23.5 14.4 118. 16
## 6 H10 3 2.27 1.11 14.5 43.7 24.6 16.1 128. 15.2
## 7 H11 1 1.71 0.81 15.5 45.2 25.0 16.7 140. 15.6
## 8 H11 2 2.09 1.06 12.2 46.9 26.5 14.3 114. 16.8
## 9 H11 3 2.5 1.44 15.0 49.0 27.5 15.2 168. 16.4
## 10 H12 1 2.52 1.52 14.4 49.2 28.4 15 153. 16.4
## # ... with 29 more rows, and 2 more variables: MMG <chr>, NGE <dbl>
##
## attr(,"ptype")
## # A tibble: 0 x 12
## # ... with 12 variables: GEN <chr>, BLOCO <dbl>, ALT_PLANT <dbl>,
## # ALT_ESP <dbl>, COMPES <dbl>, DIAMES <dbl>, COMP_SAB <dbl>, DIAM_SAB <dbl>,
## # MGE <dbl>, NFIL <dbl>, MMG <chr>, NGE <dbl>
## attr(,"class")
## [1] "split_factors"
rbind_fill_id(df_list, .id = "AMBIENTE")
## # A tibble: 114 x 13
## AMBIENTE GEN BLOCO ALT_PLANT ALT_ESP COMPES DIAMES COMP_SAB DIAM_SAB MGE
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 AMB1 H10 1 0 1.64 16.7 54.0 31.7 17.4 194.
## 2 AMB1 H10 2 2.79 1.71 14.9 52.7 32.0 15.5 176.
## 3 AMB1 H10 3 2.72 1.51 16.7 52.7 30.4 17.5 207.
## 4 AMB1 H11 1 2.75 1.51 17.4 51.7 30.6 18.0 217.
## 5 AMB1 H11 2 2.72 1.56 16.7 47.2 28.7 17.2 181.
## 6 AMB1 H11 3 2.77 1.67 15.8 47.9 27.6 16.4 166.
## 7 AMB1 H12 1 2.73 1.54 14.9 47.5 28.2 15.5 161.
## 8 AMB1 H12 2 2.56 1.56 15.7 49.9 29.8 16.2 188.
## 9 AMB1 H12 3 2.79 1.53 15.0 52.7 31.4 15.2 193.
## 10 AMB1 H13 1 2.74 1.6 14.6 54 32.5 15.1 205.
## # ... with 104 more rows, and 3 more variables: NFIL <dbl>, MMG <chr>,
## # NGE <dbl>
Tabela bidirecional
# Cria uma tabela bidirecional
tab <- make_mat(df_tidy,
row = GEN,
col = ENV,
value = NGE)
print_tbl(tab)
|
AMB2 |
AMB3 |
AMB1 |
| H1 |
484.400 |
425.133 |
NA |
| H10 |
510.600 |
488.800 |
515.400 |
| H11 |
478.000 |
487.733 |
512.733 |
| H12 |
424.000 |
516.867 |
553.333 |
| H13 |
522.867 |
578.867 |
611.067 |
| H2 |
531.400 |
466.333 |
621.733 |
| H3 |
474.533 |
421.000 |
609.867 |
| H4 |
510.467 |
474.200 |
604.400 |
| H5 |
560.400 |
536.467 |
2129.400 |
| H6 |
621.733 |
419.533 |
568.533 |
| H7 |
487.067 |
440.667 |
544.000 |
| H8 |
476.733 |
407.333 |
541.800 |
| H9 |
478.267 |
419.733 |
488.533 |
# máximo valor observado
tab2 <- make_mat(df_tidy,
row = GEN,
col = ENV,
value = NGE,
fun = max)
print_tbl(tab2)
|
AMB2 |
AMB3 |
AMB1 |
| H1 |
506.2 |
458.4 |
NA |
| H10 |
541.8 |
524.4 |
524.6 |
| H11 |
521.2 |
534.6 |
524.6 |
| H12 |
480.2 |
532.2 |
594.0 |
| H13 |
583.0 |
667.8 |
693.2 |
| H2 |
556.4 |
478.0 |
696.6 |
| H3 |
506.6 |
483.0 |
641.6 |
| H4 |
564.4 |
510.4 |
637.4 |
| H5 |
586.4 |
591.4 |
5302.0 |
| H6 |
674.4 |
436.4 |
648.0 |
| H7 |
528.0 |
478.4 |
608.4 |
| H8 |
493.6 |
434.8 |
597.8 |
| H9 |
538.8 |
486.0 |
510.4 |
# soma de linhas e colunas
row_col_sum(tab) %>% print_tbl()
## Warning: NA values removed to compute the function. Use 'na.rm = TRUE' to
## suppress this warning.
## To remove rows with NA use `remove_rows_na()'.
## To remove columns with NA use `remove_cols_na()'.
|
AMB2 |
AMB3 |
AMB1 |
row_sums |
| H1 |
484.400 |
425.133 |
NA |
909.533 |
| H10 |
510.600 |
488.800 |
515.400 |
1514.800 |
| H11 |
478.000 |
487.733 |
512.733 |
1478.467 |
| H12 |
424.000 |
516.867 |
553.333 |
1494.200 |
| H13 |
522.867 |
578.867 |
611.067 |
1712.800 |
| H2 |
531.400 |
466.333 |
621.733 |
1619.467 |
| H3 |
474.533 |
421.000 |
609.867 |
1505.400 |
| H4 |
510.467 |
474.200 |
604.400 |
1589.067 |
| H5 |
560.400 |
536.467 |
2129.400 |
3226.267 |
| H6 |
621.733 |
419.533 |
568.533 |
1609.800 |
| H7 |
487.067 |
440.667 |
544.000 |
1471.733 |
| H8 |
476.733 |
407.333 |
541.800 |
1425.867 |
| H9 |
478.267 |
419.733 |
488.533 |
1386.533 |
| col_sums |
6560.467 |
6082.667 |
8300.800 |
20943.933 |
# média de linhas e colunas
row_col_mean(tab) %>% print_tbl()
## Warning: NA values removed to compute the function. Use 'na.rm = TRUE' to
## suppress this warning.
## To remove rows with NA use `remove_rows_na()'.
## To remove columns with NA use `remove_cols_na()'.
|
AMB2 |
AMB3 |
AMB1 |
row_means |
| H1 |
484.400 |
425.133 |
NA |
454.767 |
| H10 |
510.600 |
488.800 |
515.400 |
504.933 |
| H11 |
478.000 |
487.733 |
512.733 |
492.822 |
| H12 |
424.000 |
516.867 |
553.333 |
498.067 |
| H13 |
522.867 |
578.867 |
611.067 |
570.933 |
| H2 |
531.400 |
466.333 |
621.733 |
539.822 |
| H3 |
474.533 |
421.000 |
609.867 |
501.800 |
| H4 |
510.467 |
474.200 |
604.400 |
529.689 |
| H5 |
560.400 |
536.467 |
2129.400 |
1075.422 |
| H6 |
621.733 |
419.533 |
568.533 |
536.600 |
| H7 |
487.067 |
440.667 |
544.000 |
490.578 |
| H8 |
476.733 |
407.333 |
541.800 |
475.289 |
| H9 |
478.267 |
419.733 |
488.533 |
462.178 |
| col_means |
504.651 |
467.897 |
691.733 |
551.156 |
Utilitários para zero e NAs
NAs e zeros podem aumentar o ruído na análise de dados. O pacote metan possui uma coleção de funções que facilitarão a tarefa de lidar com zeros e valores NA.
# Dados "bagunçados"
df_messy <- import("http://bit.ly/df_messy", setclass = "tbl") %>% head(20)
Remover ou substituir NAs
# checar para ver se tem NA
has_na(df_messy)
## [1] TRUE
# remover colunas com NA
remove_cols_na(df_messy) %>% print_tbl()
| BLOCO |
Alt plant |
Alt Esp |
COMPES |
DIAMES |
CompSab |
DiamSab |
Mge |
Nfil |
MMG |
| 1 |
0.00 |
1.64 |
16.72 |
54.05 |
31.66 |
17.40 |
193.69 |
15.6 |
379.61 |
| 2 |
2.79 |
1.71 |
14.90 |
52.73 |
32.03 |
15.48 |
176.43 |
17.6 |
346.88 |
| 3 |
2.72 |
1.51 |
16.68 |
52.74 |
30.40 |
17.50 |
207.11 |
16.8 |
394.03 |
| 1 |
2.75 |
1.51 |
17.42 |
51.69 |
30.64 |
17.98 |
217.29 |
16.8 |
376.65 |
| 2 |
2.72 |
1.56 |
16.70 |
47.21 |
28.69 |
17.20 |
181.28 |
13.6 |
360.66 |
| 3 |
2.77 |
1.67 |
15.78 |
47.87 |
27.63 |
16.36 |
166.19 |
15.2 |
321.8 |
| 1 |
2.73 |
1.54 |
14.88 |
47.51 |
28.22 |
15.54 |
160.98 |
14.8 |
344.84 |
| 2 |
2.56 |
1.56 |
15.68 |
49.86 |
29.85 |
16.16 |
187.96 |
17.2 |
328.33 |
| 3 |
2.79 |
1.53 |
14.98 |
52.67 |
31.38 |
15.24 |
192.51 |
20.0 |
325.36 |
| 1 |
2.74 |
1.60 |
14.64 |
54.00 |
32.48 |
15.06 |
205.34 |
20.0 |
334.37 |
| 2 |
2.64 |
1.37 |
14.80 |
53.73 |
30.97 |
15.54 |
238.55 |
20.4 |
348.72 |
| 3 |
2.93 |
1.77 |
14.92 |
52.66 |
30.07 |
15.76 |
211.92 |
15.6 |
421.23 |
| 1 |
2.55 |
1.22 |
15.08 |
51.66 |
27.71 |
15.30 |
197.70 |
16.4 |
382.06 |
| 2 |
2.90 |
1.41 |
15.12 |
51.53 |
26.04 |
16.00 |
212.18 |
21.2 |
304.51 |
| 3 |
2.92 |
1.39 |
14.78 |
52.63 |
26.04 |
15.14 |
201.62 |
20.0 |
300.02 |
| 1 |
3.04 |
1.43 |
15.02 |
51.66 |
25.93 |
16.56 |
209.80 |
19.6 |
317.48 |
| 2 |
2.94 |
1.66 |
14.88 |
51.46 |
31.33 |
15.46 |
192.84 |
18.0 |
345.83 |
| 3 |
2.82 |
1.67 |
16.46 |
52.25 |
26.74 |
17.08 |
191.99 |
18.0 |
311.73 |
| 1 |
3.02 |
1.82 |
16.54 |
50.43 |
27.31 |
17.26 |
193.02 |
16.8 |
305.33 |
| 2 |
2.84 |
1.69 |
15.04 |
52.17 |
27.95 |
15.72 |
215.69 |
18.4 |
357.3 |
# remover linhas com NA
remove_rows_na(df_messy) %>% print_tbl()
| env |
Gen |
BLOCO |
Alt plant |
Alt Esp |
COMPES |
DIAMES |
CompSab |
DiamSab |
Mge |
Nfil |
MMG |
NGE |
| Amb 1 |
H10 |
1 |
0 |
1.64 |
16.72 |
54.05 |
31.66 |
17.4 |
193.69 |
15.6 |
379.61 |
519.2 |
# selecionar colunas com NA
select_cols_na(df_messy) %>% print_tbl()
| env |
Gen |
NGE |
| Amb 1 |
H10 |
519.2 |
| NA |
NA |
502.4 |
| NA |
NA |
524.6 |
| NA |
H11 |
NA |
| NA |
NA |
500.8 |
| NA |
NA |
512.8 |
| NA |
H12 |
480.2 |
| NA |
NA |
585.8 |
| NA |
NA |
594.0 |
| NA |
H13 |
628.4 |
| NA |
NA |
693.2 |
| NA |
NA |
511.6 |
| NA |
H2 |
518.2 |
| NA |
NA |
696.6 |
| NA |
NA |
650.4 |
| NA |
H3 |
641.6 |
| NA |
NA |
550.6 |
| NA |
NA |
637.4 |
| NA |
H4 |
NA |
| NA |
NA |
613.6 |
# selecionar colunas com NA
select_rows_na(df_messy) %>% print_tbl()
| env |
Gen |
BLOCO |
Alt plant |
Alt Esp |
COMPES |
DIAMES |
CompSab |
DiamSab |
Mge |
Nfil |
MMG |
NGE |
| NA |
NA |
2 |
2.79 |
1.71 |
14.90 |
52.73 |
32.03 |
15.48 |
176.43 |
17.6 |
346.88 |
502.4 |
| NA |
NA |
3 |
2.72 |
1.51 |
16.68 |
52.74 |
30.40 |
17.50 |
207.11 |
16.8 |
394.03 |
524.6 |
| NA |
H11 |
1 |
2.75 |
1.51 |
17.42 |
51.69 |
30.64 |
17.98 |
217.29 |
16.8 |
376.65 |
NA |
| NA |
NA |
2 |
2.72 |
1.56 |
16.70 |
47.21 |
28.69 |
17.20 |
181.28 |
13.6 |
360.66 |
500.8 |
| NA |
NA |
3 |
2.77 |
1.67 |
15.78 |
47.87 |
27.63 |
16.36 |
166.19 |
15.2 |
321.8 |
512.8 |
| NA |
H12 |
1 |
2.73 |
1.54 |
14.88 |
47.51 |
28.22 |
15.54 |
160.98 |
14.8 |
344.84 |
480.2 |
| NA |
NA |
2 |
2.56 |
1.56 |
15.68 |
49.86 |
29.85 |
16.16 |
187.96 |
17.2 |
328.33 |
585.8 |
| NA |
NA |
3 |
2.79 |
1.53 |
14.98 |
52.67 |
31.38 |
15.24 |
192.51 |
20.0 |
325.36 |
594.0 |
| NA |
H13 |
1 |
2.74 |
1.60 |
14.64 |
54.00 |
32.48 |
15.06 |
205.34 |
20.0 |
334.37 |
628.4 |
| NA |
NA |
2 |
2.64 |
1.37 |
14.80 |
53.73 |
30.97 |
15.54 |
238.55 |
20.4 |
348.72 |
693.2 |
| NA |
NA |
3 |
2.93 |
1.77 |
14.92 |
52.66 |
30.07 |
15.76 |
211.92 |
15.6 |
421.23 |
511.6 |
| NA |
H2 |
1 |
2.55 |
1.22 |
15.08 |
51.66 |
27.71 |
15.30 |
197.70 |
16.4 |
382.06 |
518.2 |
| NA |
NA |
2 |
2.90 |
1.41 |
15.12 |
51.53 |
26.04 |
16.00 |
212.18 |
21.2 |
304.51 |
696.6 |
| NA |
NA |
3 |
2.92 |
1.39 |
14.78 |
52.63 |
26.04 |
15.14 |
201.62 |
20.0 |
300.02 |
650.4 |
| NA |
H3 |
1 |
3.04 |
1.43 |
15.02 |
51.66 |
25.93 |
16.56 |
209.80 |
19.6 |
317.48 |
641.6 |
| NA |
NA |
2 |
2.94 |
1.66 |
14.88 |
51.46 |
31.33 |
15.46 |
192.84 |
18.0 |
345.83 |
550.6 |
| NA |
NA |
3 |
2.82 |
1.67 |
16.46 |
52.25 |
26.74 |
17.08 |
191.99 |
18.0 |
311.73 |
637.4 |
| NA |
H4 |
1 |
3.02 |
1.82 |
16.54 |
50.43 |
27.31 |
17.26 |
193.02 |
16.8 |
305.33 |
NA |
| NA |
NA |
2 |
2.84 |
1.69 |
15.04 |
52.17 |
27.95 |
15.72 |
215.69 |
18.4 |
357.3 |
613.6 |
# substituir NA por um valor
replace_na(df_messy, replacement = "FALTA")
## # A tibble: 20 x 13
## env Gen BLOCO `Alt plant` `Alt Esp` COMPES DIAMES CompSab DiamSab Mge
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Amb 1 H10 1 0 1.64 16.7 54.0 31.7 17.4 194.
## 2 FALTA FALTA 2 2.79 1.71 14.9 52.7 32.0 15.5 176.
## 3 FALTA FALTA 3 2.72 1.51 16.7 52.7 30.4 17.5 207.
## 4 FALTA H11 1 2.75 1.51 17.4 51.7 30.6 18.0 217.
## 5 FALTA FALTA 2 2.72 1.56 16.7 47.2 28.7 17.2 181.
## 6 FALTA FALTA 3 2.77 1.67 15.8 47.9 27.6 16.4 166.
## 7 FALTA H12 1 2.73 1.54 14.9 47.5 28.2 15.5 161.
## 8 FALTA FALTA 2 2.56 1.56 15.7 49.9 29.8 16.2 188.
## 9 FALTA FALTA 3 2.79 1.53 15.0 52.7 31.4 15.2 193.
## 10 FALTA H13 1 2.74 1.6 14.6 54 32.5 15.1 205.
## 11 FALTA FALTA 2 2.64 1.37 14.8 53.7 31.0 15.5 239.
## 12 FALTA FALTA 3 2.93 1.77 14.9 52.7 30.1 15.8 212.
## 13 FALTA H2 1 2.55 1.22 15.1 51.7 27.7 15.3 198.
## 14 FALTA FALTA 2 2.9 1.41 15.1 51.5 26.0 16 212.
## 15 FALTA FALTA 3 2.92 1.39 14.8 52.6 26.0 15.1 202.
## 16 FALTA H3 1 3.04 1.43 15.0 51.7 25.9 16.6 210.
## 17 FALTA FALTA 2 2.94 1.66 14.9 51.5 31.3 15.5 193.
## 18 FALTA FALTA 3 2.82 1.67 16.5 52.2 26.7 17.1 192.
## 19 FALTA H4 1 3.02 1.82 16.5 50.4 27.3 17.3 193.
## 20 FALTA FALTA 2 2.84 1.69 15.0 52.2 28.0 15.7 216.
## # ... with 3 more variables: Nfil <dbl>, MMG <chr>, NGE <chr>
Remover ou substituir zeros
# checar para ver se tem NA
has_zero(df_messy)
## [1] TRUE
# remover colunas com NA
remove_cols_zero(df_messy) %>% print_tbl()
| env |
Gen |
BLOCO |
Alt Esp |
COMPES |
DIAMES |
CompSab |
DiamSab |
Mge |
Nfil |
MMG |
NGE |
| Amb 1 |
H10 |
1 |
1.64 |
16.72 |
54.05 |
31.66 |
17.40 |
193.69 |
15.6 |
379.61 |
519.2 |
| NA |
NA |
2 |
1.71 |
14.90 |
52.73 |
32.03 |
15.48 |
176.43 |
17.6 |
346.88 |
502.4 |
| NA |
NA |
3 |
1.51 |
16.68 |
52.74 |
30.40 |
17.50 |
207.11 |
16.8 |
394.03 |
524.6 |
| NA |
H11 |
1 |
1.51 |
17.42 |
51.69 |
30.64 |
17.98 |
217.29 |
16.8 |
376.65 |
NA |
| NA |
NA |
2 |
1.56 |
16.70 |
47.21 |
28.69 |
17.20 |
181.28 |
13.6 |
360.66 |
500.8 |
| NA |
NA |
3 |
1.67 |
15.78 |
47.87 |
27.63 |
16.36 |
166.19 |
15.2 |
321.8 |
512.8 |
| NA |
H12 |
1 |
1.54 |
14.88 |
47.51 |
28.22 |
15.54 |
160.98 |
14.8 |
344.84 |
480.2 |
| NA |
NA |
2 |
1.56 |
15.68 |
49.86 |
29.85 |
16.16 |
187.96 |
17.2 |
328.33 |
585.8 |
| NA |
NA |
3 |
1.53 |
14.98 |
52.67 |
31.38 |
15.24 |
192.51 |
20.0 |
325.36 |
594.0 |
| NA |
H13 |
1 |
1.60 |
14.64 |
54.00 |
32.48 |
15.06 |
205.34 |
20.0 |
334.37 |
628.4 |
| NA |
NA |
2 |
1.37 |
14.80 |
53.73 |
30.97 |
15.54 |
238.55 |
20.4 |
348.72 |
693.2 |
| NA |
NA |
3 |
1.77 |
14.92 |
52.66 |
30.07 |
15.76 |
211.92 |
15.6 |
421.23 |
511.6 |
| NA |
H2 |
1 |
1.22 |
15.08 |
51.66 |
27.71 |
15.30 |
197.70 |
16.4 |
382.06 |
518.2 |
| NA |
NA |
2 |
1.41 |
15.12 |
51.53 |
26.04 |
16.00 |
212.18 |
21.2 |
304.51 |
696.6 |
| NA |
NA |
3 |
1.39 |
14.78 |
52.63 |
26.04 |
15.14 |
201.62 |
20.0 |
300.02 |
650.4 |
| NA |
H3 |
1 |
1.43 |
15.02 |
51.66 |
25.93 |
16.56 |
209.80 |
19.6 |
317.48 |
641.6 |
| NA |
NA |
2 |
1.66 |
14.88 |
51.46 |
31.33 |
15.46 |
192.84 |
18.0 |
345.83 |
550.6 |
| NA |
NA |
3 |
1.67 |
16.46 |
52.25 |
26.74 |
17.08 |
191.99 |
18.0 |
311.73 |
637.4 |
| NA |
H4 |
1 |
1.82 |
16.54 |
50.43 |
27.31 |
17.26 |
193.02 |
16.8 |
305.33 |
NA |
| NA |
NA |
2 |
1.69 |
15.04 |
52.17 |
27.95 |
15.72 |
215.69 |
18.4 |
357.3 |
613.6 |
# remover linhas com NA
remove_rows_zero(df_messy) %>% print_tbl()
| env |
Gen |
BLOCO |
Alt plant |
Alt Esp |
COMPES |
DIAMES |
CompSab |
DiamSab |
Mge |
Nfil |
MMG |
NGE |
| NA |
NA |
2 |
2.79 |
1.71 |
14.90 |
52.73 |
32.03 |
15.48 |
176.43 |
17.6 |
346.88 |
502.4 |
| NA |
NA |
3 |
2.72 |
1.51 |
16.68 |
52.74 |
30.40 |
17.50 |
207.11 |
16.8 |
394.03 |
524.6 |
| NA |
H11 |
1 |
2.75 |
1.51 |
17.42 |
51.69 |
30.64 |
17.98 |
217.29 |
16.8 |
376.65 |
NA |
| NA |
NA |
2 |
2.72 |
1.56 |
16.70 |
47.21 |
28.69 |
17.20 |
181.28 |
13.6 |
360.66 |
500.8 |
| NA |
NA |
3 |
2.77 |
1.67 |
15.78 |
47.87 |
27.63 |
16.36 |
166.19 |
15.2 |
321.8 |
512.8 |
| NA |
H12 |
1 |
2.73 |
1.54 |
14.88 |
47.51 |
28.22 |
15.54 |
160.98 |
14.8 |
344.84 |
480.2 |
| NA |
NA |
2 |
2.56 |
1.56 |
15.68 |
49.86 |
29.85 |
16.16 |
187.96 |
17.2 |
328.33 |
585.8 |
| NA |
NA |
3 |
2.79 |
1.53 |
14.98 |
52.67 |
31.38 |
15.24 |
192.51 |
20.0 |
325.36 |
594.0 |
| NA |
H13 |
1 |
2.74 |
1.60 |
14.64 |
54.00 |
32.48 |
15.06 |
205.34 |
20.0 |
334.37 |
628.4 |
| NA |
NA |
2 |
2.64 |
1.37 |
14.80 |
53.73 |
30.97 |
15.54 |
238.55 |
20.4 |
348.72 |
693.2 |
| NA |
NA |
3 |
2.93 |
1.77 |
14.92 |
52.66 |
30.07 |
15.76 |
211.92 |
15.6 |
421.23 |
511.6 |
| NA |
H2 |
1 |
2.55 |
1.22 |
15.08 |
51.66 |
27.71 |
15.30 |
197.70 |
16.4 |
382.06 |
518.2 |
| NA |
NA |
2 |
2.90 |
1.41 |
15.12 |
51.53 |
26.04 |
16.00 |
212.18 |
21.2 |
304.51 |
696.6 |
| NA |
NA |
3 |
2.92 |
1.39 |
14.78 |
52.63 |
26.04 |
15.14 |
201.62 |
20.0 |
300.02 |
650.4 |
| NA |
H3 |
1 |
3.04 |
1.43 |
15.02 |
51.66 |
25.93 |
16.56 |
209.80 |
19.6 |
317.48 |
641.6 |
| NA |
NA |
2 |
2.94 |
1.66 |
14.88 |
51.46 |
31.33 |
15.46 |
192.84 |
18.0 |
345.83 |
550.6 |
| NA |
NA |
3 |
2.82 |
1.67 |
16.46 |
52.25 |
26.74 |
17.08 |
191.99 |
18.0 |
311.73 |
637.4 |
| NA |
H4 |
1 |
3.02 |
1.82 |
16.54 |
50.43 |
27.31 |
17.26 |
193.02 |
16.8 |
305.33 |
NA |
| NA |
NA |
2 |
2.84 |
1.69 |
15.04 |
52.17 |
27.95 |
15.72 |
215.69 |
18.4 |
357.3 |
613.6 |
# selecionar colunas com NA
select_cols_zero(df_messy) %>% print_tbl()
| Alt plant |
| 0.00 |
| 2.79 |
| 2.72 |
| 2.75 |
| 2.72 |
| 2.77 |
| 2.73 |
| 2.56 |
| 2.79 |
| 2.74 |
| 2.64 |
| 2.93 |
| 2.55 |
| 2.90 |
| 2.92 |
| 3.04 |
| 2.94 |
| 2.82 |
| 3.02 |
| 2.84 |
# selecionar colunas com NA
select_rows_zero(df_messy) %>% print_tbl()
| env |
Gen |
BLOCO |
Alt plant |
Alt Esp |
COMPES |
DIAMES |
CompSab |
DiamSab |
Mge |
Nfil |
MMG |
NGE |
| Amb 1 |
H10 |
1 |
0 |
1.64 |
16.72 |
54.05 |
31.66 |
17.4 |
193.69 |
15.6 |
379.61 |
519.2 |
# substituir NA por um valor
replace_zero(df_messy, replacement = NA) # padrão
## # A tibble: 20 x 13
## env Gen BLOCO `Alt plant` `Alt Esp` COMPES DIAMES CompSab DiamSab Mge
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Amb 1 H10 1 NA 1.64 16.7 54.0 31.7 17.4 194.
## 2 <NA> <NA> 2 2.79 1.71 14.9 52.7 32.0 15.5 176.
## 3 <NA> <NA> 3 2.72 1.51 16.7 52.7 30.4 17.5 207.
## 4 <NA> H11 1 2.75 1.51 17.4 51.7 30.6 18.0 217.
## 5 <NA> <NA> 2 2.72 1.56 16.7 47.2 28.7 17.2 181.
## 6 <NA> <NA> 3 2.77 1.67 15.8 47.9 27.6 16.4 166.
## 7 <NA> H12 1 2.73 1.54 14.9 47.5 28.2 15.5 161.
## 8 <NA> <NA> 2 2.56 1.56 15.7 49.9 29.8 16.2 188.
## 9 <NA> <NA> 3 2.79 1.53 15.0 52.7 31.4 15.2 193.
## 10 <NA> H13 1 2.74 1.6 14.6 54 32.5 15.1 205.
## 11 <NA> <NA> 2 2.64 1.37 14.8 53.7 31.0 15.5 239.
## 12 <NA> <NA> 3 2.93 1.77 14.9 52.7 30.1 15.8 212.
## 13 <NA> H2 1 2.55 1.22 15.1 51.7 27.7 15.3 198.
## 14 <NA> <NA> 2 2.9 1.41 15.1 51.5 26.0 16 212.
## 15 <NA> <NA> 3 2.92 1.39 14.8 52.6 26.0 15.1 202.
## 16 <NA> H3 1 3.04 1.43 15.0 51.7 25.9 16.6 210.
## 17 <NA> <NA> 2 2.94 1.66 14.9 51.5 31.3 15.5 193.
## 18 <NA> <NA> 3 2.82 1.67 16.5 52.2 26.7 17.1 192.
## 19 <NA> H4 1 3.02 1.82 16.5 50.4 27.3 17.3 193.
## 20 <NA> <NA> 2 2.84 1.69 15.0 52.2 28.0 15.7 216.
## # ... with 3 more variables: Nfil <dbl>, MMG <chr>, NGE <dbl>