TRIGO
Ledesma Guillermina
2024-11-28
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(readxl)
library(summarytools)## Warning in fun(libname, pkgname): couldn't connect to display ":0"## system might not have X11 capabilities; in case of errors when using dfSummary(), set st_options(use.x11 = FALSE)
##
## Attaching package: 'summarytools'
##
## The following object is masked from 'package:tibble':
##
## viewlibrary(leaflet)TRIGO <- read_excel("TRIGO.xlsx")##Parcela elegida ubicada en Pergamino, Buenos Aires.
leaflet() %>% addProviderTiles(providers$Esri.WorldImagery) %>%
setView(lng = -60.36, lat = -33.52, zoom = 17)leaflet() %>%
addProviderTiles(providers$Esri.WorldImagery)%>%
addMarkers(lng = -60.606126, lat = -33.872252) %>%
addLayersControl( baseGroups = c("Mapa Base"), overlayGroups = c("Marcadores") )leaflet() %>%
addProviderTiles(providers$Esri.WorldImagery)%>%
addTiles() %>%
addPolygons(lng = c(-60.602703, -60.607939, -60.609398, -60.604120), lat = c(-33.871514,-33.869811, -33.872831, -33.874577), color = "blue") %>%
addMarkers(lng = -60.606126, lat = -33.872252) PERGAMINO <- TRIGO %>%
filter(LOCALIDAD == "PER")
PERGAMINO## # A tibble: 249 × 5
## ANIO LOCALIDAD TRATAMIENTO GENOTIPO RENDIMIENTO
## <dbl> <chr> <chr> <chr> <dbl>
## 1 2007 PER SinFung ONIX 3200
## 2 2007 PER SinFung ONIX 3660
## 3 2007 PER SinFung B75ANIVERSARIO 5700
## 4 2007 PER SinFung ACA901 5400
## 5 2007 PER SinFung ACA901 4740
## 6 2007 PER SinFung B75ANIVERSARIO 5400
## 7 2007 PER SinFung BIOINTA1001 5700
## 8 2007 PER SinFung BIOINTA1001 4700
## 9 2007 PER SinFung B75ANIVERSARIO 4800
## 10 2007 PER SinFung ACA801 4260
## # ℹ 239 more rows#Se puede observar que en la localidad no se realizaron tratamientos con fungicidas.
glimpse (PERGAMINO)## Rows: 249
## Columns: 5
## $ ANIO <dbl> 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007…
## $ LOCALIDAD <chr> "PER", "PER", "PER", "PER", "PER", "PER", "PER", "PER", "P…
## $ TRATAMIENTO <chr> "SinFung", "SinFung", "SinFung", "SinFung", "SinFung", "Si…
## $ GENOTIPO <chr> "ONIX", "ONIX", "B75ANIVERSARIO", "ACA901", "ACA901", "B75…
## $ RENDIMIENTO <dbl> 3200, 3660, 5700, 5400, 4740, 5400, 5700, 4700, 4800, 4260…ggplot(TRIGO, aes(RENDIMIENTO, GENOTIPO, color = GENOTIPO)) +
geom_boxplot() +
theme(legend.position = "none", )
ggplot(PERGAMINO, aes(RENDIMIENTO, TRATAMIENTO, color = TRATAMIENTO)) +
geom_boxplot() +
stat_summary(fun = mean, color = "black")## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_segment()`).
PERGAMINO %>%
select(RENDIMIENTO) %>%
summarise(MEDIA = mean(RENDIMIENTO))## # A tibble: 1 × 1
## MEDIA
## <dbl>
## 1 4244.PERGAMINO %>%
group_by(GENOTIPO) %>%
summarise(RENDIMIENT_MEDIA = mean(RENDIMIENTO))## # A tibble: 32 × 2
## GENOTIPO RENDIMIENT_MEDIA
## <chr> <dbl>
## 1 ACA801 4120
## 2 ACA901 4145.
## 3 ACA903 3682.
## 4 ACA905 5613.
## 5 ACA906 4111.
## 6 ACA907 6567.
## 7 AGPFast 4237.
## 8 ATLAX 4840
## 9 Arex 4234.
## 10 B75ANIVERSARIO 5040
## # ℹ 22 more rowsResumen_trigo <- PERGAMINO %>%
group_by(GENOTIPO) %>%
summarise(MINIMUM = min(RENDIMIENTO),
MEAN = mean(RENDIMIENTO),
MEDIAN = median(RENDIMIENTO),
MAXIMUM = max(RENDIMIENTO))
Resumen_trigo## # A tibble: 32 × 5
## GENOTIPO MINIMUM MEAN MEDIAN MAXIMUM
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 ACA801 4000 4120 4100 4260
## 2 ACA901 1680 4145. 4900 5860
## 3 ACA903 1500 3682. 3510 6100
## 4 ACA905 5400 5613. 5640 5800
## 5 ACA906 2480 4111. 3980 6160
## 6 ACA907 6000 6567. 6800 6900
## 7 AGPFast 3190 4237. 4400 5200
## 8 ATLAX 3900 4840 4960 6000
## 9 Arex 1720 4234. 4430 6000
## 10 B75ANIVERSARIO 4600 5040 4900 5700
## # ℹ 22 more rowsPERGAMINO %>%
group_by(GENOTIPO) %>%
descr(RENDIMIENTO)## Descriptive Statistics
## RENDIMIENTO by GENOTIPO
## Data Frame: PERGAMINO
## N: 3
##
## ACA801 ACA901 ACA903 ACA905 ACA906 ACA907 AGPFast ATLAX
## ----------------- --------- --------- --------- --------- --------- --------- --------- ---------
## Mean 4120.00 4145.33 3681.67 5613.33 4110.83 6566.67 4236.67 4840.00
## Std.Dev 131.15 1618.45 1899.50 201.33 1531.25 493.29 798.04 792.28
## Min 4000.00 1680.00 1500.00 5400.00 2480.00 6000.00 3190.00 3900.00
## Q1 4000.00 2310.00 1930.00 5400.00 2640.00 6000.00 3350.00 4100.00
## Median 4100.00 4900.00 3510.00 5640.00 3980.00 6800.00 4400.00 4960.00
## Q3 4260.00 5600.00 5420.00 5800.00 5580.00 6900.00 4840.00 5500.00
## Max 4260.00 5860.00 6100.00 5800.00 6160.00 6900.00 5200.00 6000.00
## MAD 148.26 1393.64 2498.18 237.22 2097.88 148.26 978.52 1156.43
## IQR 130.00 3125.00 3440.00 200.00 2855.00 450.00 1490.00 1400.00
## CV 0.03 0.39 0.52 0.04 0.37 0.08 0.19 0.16
## Skewness 0.15 -0.33 0.08 -0.13 0.08 -0.37 -0.26 0.15
## SE.Skewness 1.22 0.58 0.64 1.22 0.64 1.22 0.72 0.72
## Kurtosis -2.33 -1.81 -2.01 -2.33 -2.02 -2.33 -1.79 -1.81
## N.Valid 3.00 15.00 12.00 3.00 12.00 3.00 9.00 9.00
## Pct.Valid 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00
##
## Table: Table continues below
##
##
##
## Arex B75ANIVERSARIO BIOINTA1001 BUCKPUELCHE Baguette501 Biointa1005
## ----------------- --------- ---------------- ------------- ------------- ------------- -------------
## Mean 4234.17 5040.00 5220.00 5666.67 3186.67 3774.00
## Std.Dev 1641.35 349.14 501.20 455.31 241.94 1822.56
## Min 1720.00 4600.00 4700.00 5000.00 3000.00 1500.00
## Q1 2785.00 4860.00 4700.00 5300.00 3000.00 1710.00
## Median 4430.00 4900.00 5260.00 5720.00 3100.00 4300.00
## Q3 5840.00 5300.00 5700.00 6060.00 3460.00 5600.00
## Max 6000.00 5700.00 5700.00 6200.00 3460.00 5900.00
## MAD 2127.53 148.26 652.34 563.39 148.26 2223.90
## IQR 2862.50 440.00 500.00 610.00 230.00 3805.00
## CV 0.39 0.07 0.10 0.08 0.08 0.48
## Skewness -0.15 0.62 -0.08 -0.23 0.31 -0.16
## SE.Skewness 0.64 0.72 1.22 0.85 1.22 0.58
## Kurtosis -1.86 -1.10 -2.33 -1.76 -2.33 -1.89
## N.Valid 12.00 9.00 3.00 6.00 3.00 15.00
## Pct.Valid 100.00 100.00 100.00 100.00 100.00 100.00
##
## Table: Table continues below
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##
##
## Biointa1006 Biointa1007 BuckPleno Cronox Floripan100 KLEINCASTOR
## ----------------- ------------- ------------- ----------- --------- ------------- -------------
## Mean 4105.33 1826.67 3330.00 4073.33 2946.67 4853.33
## Std.Dev 1346.86 199.37 135.28 1637.89 725.86 516.27
## Min 2160.00 1460.00 3190.00 1720.00 1530.00 4260.00
## Q1 2620.00 1750.00 3190.00 2560.00 3040.00 4260.00
## Median 4500.00 1900.00 3340.00 4200.00 3130.00 5100.00
## Q3 5000.00 1960.00 3460.00 5700.00 3220.00 5200.00
## Max 6240.00 1990.00 3460.00 6000.00 3630.00 5200.00
## MAD 2164.60 111.19 177.91 2431.46 133.43 148.26
## IQR 2090.00 177.50 135.00 3140.00 170.00 470.00
## CV 0.33 0.11 0.04 0.40 0.25 0.11
## Skewness 0.05 -0.87 -0.07 -0.11 -1.08 -0.37
## SE.Skewness 0.58 0.85 1.22 0.72 0.85 1.22
## Kurtosis -1.52 -0.96 -2.33 -1.73 -0.44 -2.33
## N.Valid 15.00 6.00 3.00 9.00 6.00 3.00
## Pct.Valid 100.00 100.00 100.00 100.00 100.00 100.00
##
## Table: Table continues below
##
##
##
## KLEINCHAJA KleinLeon KleinNutria KleinRayo KleinTIGRE KleinTauro
## ----------------- ------------ ----------- ------------- ----------- ------------ ------------
## Mean 4633.33 4873.33 4475.00 4163.33 4622.22 4624.67
## Std.Dev 351.19 664.73 1153.98 1107.74 1196.14 959.08
## Min 4300.00 4100.00 2680.00 2580.00 2860.00 2990.00
## Q1 4300.00 4340.00 3525.00 3130.00 3300.00 3800.00
## Median 4600.00 4750.00 4470.00 4310.00 5000.00 5100.00
## Q3 5000.00 5500.00 5700.00 5120.00 5540.00 5300.00
## Max 5000.00 5800.00 5800.00 5440.00 6100.00 5800.00
## MAD 444.78 785.78 1593.79 1512.25 1037.82 830.26
## IQR 350.00 945.00 2167.50 1960.00 2240.00 1450.00
## CV 0.08 0.14 0.26 0.27 0.26 0.21
## Skewness 0.09 0.23 -0.08 -0.08 -0.34 -0.46
## SE.Skewness 1.22 0.85 0.64 0.64 0.72 0.58
## Kurtosis -2.33 -1.86 -1.82 -1.97 -1.71 -1.41
## N.Valid 3.00 6.00 12.00 12.00 9.00 15.00
## Pct.Valid 100.00 100.00 100.00 100.00 100.00 100.00
##
## Table: Table continues below
##
##
##
## KleinZorro LE2330 LE2331 LE2357 ONIX SY300
## ----------------- ------------ --------- --------- --------- --------- ---------
## Mean 4792.50 1326.67 3506.67 4773.33 3600.00 5190.00
## Std.Dev 981.09 57.74 1106.80 360.74 373.63 461.95
## Min 2900.00 1260.00 2300.00 4400.00 3200.00 4700.00
## Q1 4195.00 1260.00 2740.00 4400.00 3200.00 4700.00
## Median 5230.00 1360.00 3100.00 4800.00 3660.00 5220.00
## Q3 5420.00 1360.00 4740.00 5120.00 3940.00 5600.00
## Max 5660.00 1360.00 5040.00 5120.00 3940.00 5700.00
## MAD 444.78 0.00 978.52 474.43 415.13 637.52
## IQR 832.50 50.00 2000.00 360.00 370.00 815.00
## CV 0.20 0.04 0.32 0.08 0.10 0.09
## Skewness -0.93 -0.38 0.42 -0.07 -0.16 -0.03
## SE.Skewness 0.64 1.22 0.72 1.22 1.22 0.85
## Kurtosis -0.96 -2.33 -1.77 -2.33 -2.33 -2.18
## N.Valid 12.00 3.00 9.00 3.00 3.00 6.00
## Pct.Valid 100.00 100.00 100.00 100.00 100.00 100.00ACA907 <- TRIGO %>%
filter(GENOTIPO == "ACA907") %>%
filter(LOCALIDAD == "PER")
ACA907## # A tibble: 3 × 5
## ANIO LOCALIDAD TRATAMIENTO GENOTIPO RENDIMIENTO
## <dbl> <chr> <chr> <chr> <dbl>
## 1 2010 PER SinFung ACA907 6000
## 2 2010 PER SinFung ACA907 6800
## 3 2010 PER SinFung ACA907 6900ACA907 %>%
descr(RENDIMIENTO)## Descriptive Statistics
## ACA907$RENDIMIENTO
## N: 3
##
## RENDIMIENTO
## ----------------- -------------
## Mean 6566.67
## Std.Dev 493.29
## Min 6000.00
## Q1 6000.00
## Median 6800.00
## Q3 6900.00
## Max 6900.00
## MAD 148.26
## IQR 450.00
## CV 0.08
## Skewness -0.37
## SE.Skewness 1.22
## Kurtosis -2.33
## N.Valid 3.00
## Pct.Valid 100.00ggplot(ACA907, aes(RENDIMIENTO, GENOTIPO, color = GENOTIPO)) +
geom_boxplot() +
stat_summary(fun = mean, color = "black")## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_segment()`).
ACA905 <- TRIGO %>%
filter(GENOTIPO == "ACA905") %>%
filter(LOCALIDAD == "PER")
ACA905## # A tibble: 3 × 5
## ANIO LOCALIDAD TRATAMIENTO GENOTIPO RENDIMIENTO
## <dbl> <chr> <chr> <chr> <dbl>
## 1 2011 PER SinFung ACA905 5640
## 2 2011 PER SinFung ACA905 5400
## 3 2011 PER SinFung ACA905 5800ACA905 %>%
descr(RENDIMIENTO)## Descriptive Statistics
## ACA905$RENDIMIENTO
## N: 3
##
## RENDIMIENTO
## ----------------- -------------
## Mean 5613.33
## Std.Dev 201.33
## Min 5400.00
## Q1 5400.00
## Median 5640.00
## Q3 5800.00
## Max 5800.00
## MAD 237.22
## IQR 200.00
## CV 0.04
## Skewness -0.13
## SE.Skewness 1.22
## Kurtosis -2.33
## N.Valid 3.00
## Pct.Valid 100.00ggplot(ACA905, aes(RENDIMIENTO, GENOTIPO, color = GENOTIPO)) +
geom_boxplot() +
stat_summary(fun = mean, color = "black")## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_segment()`).