datosAvena
GINNA VALENTINA ROMERO RINCON
25/10/2021
Cargando datos
library(readxl)
datosavena <- read_excel("C:/Users/ginna/Desktop/datosavena5/datosavena.xlsx",
sheet = "Avena ")
# View(datosavena)Ejemplo para sacar estadisticos
head(iris)## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosalibrary(tidyverse)## Warning: package 'tidyverse' was built under R version 4.0.5## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v ggplot2 3.3.5 v purrr 0.3.4
## v tibble 3.1.5 v dplyr 1.0.7
## v tidyr 1.1.4 v stringr 1.4.0
## v readr 2.0.2 v forcats 0.5.1## Warning: package 'ggplot2' was built under R version 4.0.5## Warning: package 'tibble' was built under R version 4.0.5## Warning: package 'tidyr' was built under R version 4.0.5## Warning: package 'readr' was built under R version 4.0.5## Warning: package 'purrr' was built under R version 4.0.3## Warning: package 'dplyr' was built under R version 4.0.5## Warning: package 'forcats' was built under R version 4.0.5## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()resum = iris %>%
group_by(Species) %>%
summarise(m_PW = mean(Petal.Width),
md_PW = median(Petal.Width),
v_PW = var(Petal.Width),
d_PW = sd(Petal.Width),
min_PW = min(Petal.Width),
n = n())
resum## # A tibble: 3 x 7
## Species m_PW md_PW v_PW d_PW min_PW n
## <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
## 1 setosa 0.246 0.2 0.0111 0.105 0.1 50
## 2 versicolor 1.33 1.3 0.0391 0.198 1 50
## 3 virginica 2.03 2 0.0754 0.275 1.4 50resum %>%
ggplot()+
aes(x = Species, y = m_PW)+
geom_col()
Datos de avena
library(tidyverse)
resum = datosavena %>%
group_by(planta) %>%
summarise(m_PW = mean(porcentaje),
md_PW = median(porcentaje),
v_PW = var(porcentaje),
d_PW = sd(porcentaje),
min_PW = min(porcentaje),
n = n())
resum## # A tibble: 5 x 7
## planta m_PW md_PW v_PW d_PW min_PW n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
## 1 amaranthus 24.1 15 458. 21.4 3 8
## 2 avena 10.2 10 50.5 7.11 0 8
## 3 heliotropium 8.25 5 83.1 9.11 0 8
## 4 lolium 26.5 12.5 1119. 33.5 1 8
## 5 rumex 2.12 1 5.84 2.42 0 8resum %>%
ggplot()+
aes(x = planta, y = m_PW)+
geom_col()
probando que sucede si agrupo por muestreo
resum = datosavena %>%
group_by(Muestreo) %>%
summarise(m_PW = mean(porcentaje),
md_PW = median(porcentaje),
v_PW = var(porcentaje),
d_PW = sd(porcentaje),
min_PW = min(porcentaje),
n = n())
resum## # A tibble: 8 x 7
## Muestreo m_PW md_PW v_PW d_PW min_PW n
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
## 1 1 7 3 66.5 8.15 1 5
## 2 2 7.6 5 70.3 8.38 0 5
## 3 3 13.2 5 351. 18.7 0 5
## 4 4 12.2 5 252. 15.9 1 5
## 5 5 23 20 520 22.8 0 5
## 6 6 21 10 1130 33.6 0 5
## 7 7 22 10 1082. 32.9 0 5
## 8 8 8 5 20 4.47 5 5resum %>%
ggplot()+
aes(x = Muestreo, y = m_PW)+
geom_col()
Descargando datos apio 2 muestreo y sacando datos por malezas
library(readxl)
datosapio1 <- read_excel("C:/Users/ginna/Desktop/datosavena5/datosapio1.xlsx",
sheet = "rmalezas")
#View(datosapio1)
resum = datosapio1 %>%
group_by(planta) %>%
summarise(m_PW = mean(densidad),
md_PW = median(densidad),
v_PW = var(densidad),
d_PW = sd(densidad),
min_PW = min(densidad),
n = n())
resum## # A tibble: 13 x 7
## planta m_PW md_PW v_PW d_PW min_PW n
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
## 1 1 0.333 0 0.5 0.707 0 9
## 2 2 0.222 0 0.194 0.441 0 9
## 3 3 0.778 0 1.94 1.39 0 9
## 4 4 0.222 0 0.194 0.441 0 9
## 5 5 0.111 0 0.111 0.333 0 9
## 6 6 0.222 0 0.444 0.667 0 9
## 7 7 0.111 0 0.111 0.333 0 9
## 8 8 0.111 0 0.111 0.333 0 9
## 9 9 1 0 9 3 0 9
## 10 10 1.11 0 11.1 3.33 0 9
## 11 11 0.111 0 0.111 0.333 0 9
## 12 12 0.111 0 0.111 0.333 0 9
## 13 13 0.111 0 0.111 0.333 0 9resum %>%
ggplot()+
aes(x = planta, y = m_PW)+
geom_col()
Datos para mirar estadisticos apio 2 muestreo por altura
library(readxl)
datosapio1 <- read_excel("datosapio1.xlsx",
sheet = "rdatos")
# View(datosapio1)
resum = datosapio1 %>%
group_by(Planta) %>%
summarise(m_PW = mean(promedioAltura),
md_PW = median(promedioAltura),
v_PW = var(promedioAltura),
d_PW = sd(promedioAltura),
min_PW = min(promedioAltura),
n = n())
resum## # A tibble: 1 x 7
## Planta m_PW md_PW v_PW d_PW min_PW n
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
## 1 1 37.2 39.5 63.1 7.94 24 9Datos apio primer muestreo por altura
library(readxl)
datosavena <- read_excel("C:/Users/ginna/Desktop/datosavena5/datosavena.xlsx",
sheet = "r2apio")
#View(datosavena)
resum = datosavena %>%
group_by(planta) %>%
summarise(m_PW = mean(promedioAltura),
md_PW = median(promedioAltura),
v_PW = var(promedioAltura),
d_PW = sd(promedioAltura),
min_PW = min(promedioAltura),
n = n())
resum## # A tibble: 1 x 7
## planta m_PW md_PW v_PW d_PW min_PW n
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
## 1 1 33.7 31.2 120. 11.0 18.8 11