maps | Data Science in Agriculture

R - Data visualization, linear regression and logistic regression

Thu, 23 Sep 2021 00:00:00 +0000

Data visualization, linear regression and logistic regression

Crop data collection - ground truth and remote sensing

Loading the packages

library(tidyverse)
library(tidymodels)
library(sf)
library(geobr)

URL link of data from data collection and remote sensing on GitHub https://github.com/luanpott10/Class

You will need the raw file https://raw.githubusercontent.com/luanpott10/Class/main/data_crops.csv

Loading the data

data <- read.csv("https://raw.githubusercontent.com/luanpott10/Class/main/data_crops.csv")

Plot the data in the map

cities_RS <- read_municipality(code_muni = "RS", year= 2020)
ggplot()+
geom_sf(data=cities_RS)+
geom_point(data=data,aes(x=longitude,y=latitude,fill=class),shape=22,size=2)+
labs(x= "Longitude", y = "Latitude")+
scale_fill_manual(values = c("#eded0c", "#49a345"))+
theme(legend.position = c(0.17, 0.2),
panel.border = element_rect(color="Black", fill = NA),
panel.background = element_rect(fill = "#f2f2f2"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.title.x = element_text(family = "serif",
colour = "#000000",size = 22.0),
axis.text.x = element_text(family = "serif",
colour = "#000000",size = 18.0),
axis.title.y = element_text(family = "serif",
colour = "#000000",size = 22.0),
axis.text.y = element_text(family = "serif",
colour = "#000000",size = 18.0),
legend.title = element_text(family = "serif",
colour = '#000000',size = 12.0),
legend.text = element_text(family = "serif",
colour = '#000000',size = 12.0),
legend.background = element_rect(fill="#f2f2f2",
linetype="dashed",
colour ="#f2f2f2"))

Plot the data variables - continuous x continuous

ggplot(data=data,aes(x=b3_GCVI,y=b4_GCVI))+
geom_point(aes(fill=class),shape=22,size=2)+
scale_fill_manual(values = c("#eded0c", "#49a345"))+
stat_smooth(formula = y~x, method="lm", se=FALSE,color="black", linetype='dashed')+
theme(panel.border = element_rect(color="Black", fill = NA),
panel.background = element_rect(fill = "#f2f2f2"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.title.x = element_text(family = "serif",
colour = "#000000",size = 22.0),
axis.text.x = element_text(family = "serif",
colour = "#000000",size = 18.0),
axis.title.y = element_text(family = "serif",
colour = "#000000",size = 22.0),
axis.text.y = element_text(family = "serif",
colour = "#000000",size = 18.0),
legend.title = element_text(family = "serif",
colour = '#000000',size = 12.0),
legend.text = element_text(family = "serif",
colour = '#000000',size = 12.0),
legend.background = element_rect(fill="#f2f2f2",
linetype="dashed",
colour ="#f2f2f2"))

Linear regression

lm_fit_x <- lm(b3_GCVI ~ b4_GCVI, data = data)
summary(lm_fit_x)
##
## Call:
## lm(formula = b3_GCVI ~ b4_GCVI, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.7924 -0.9983 -0.0018 0.9525 3.9586
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.73701 0.15933 -4.626 1.14e-05 ***
## b4_GCVI -0.49793 0.04352 -11.440 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.475 on 98 degrees of freedom
## Multiple R-squared: 0.5718, Adjusted R-squared: 0.5675
## F-statistic: 130.9 on 1 and 98 DF, p-value: < 2.2e-16

Linear regression by tidymodels workflow

Creating a parsnip specification for a linear regression model

lm_model <- linear_reg() |>
set_engine('lm') |>
set_mode('regression')

Fitting the model supplying a formula expression and the data

lm_fit <- lm_model %>%
fit(b3_GCVI ~ b4_GCVI, data = data)

Summary of the model

lm_fit |>
pluck("fit") |>
summary()
##
## Call:
## stats::lm(formula = b3_GCVI ~ b4_GCVI, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.7924 -0.9983 -0.0018 0.9525 3.9586
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.73701 0.15933 -4.626 1.14e-05 ***
## b4_GCVI -0.49793 0.04352 -11.440 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.475 on 98 degrees of freedom
## Multiple R-squared: 0.5718, Adjusted R-squared: 0.5675
## F-statistic: 130.9 on 1 and 98 DF, p-value: < 2.2e-16
# Also you can use
lm_fit$fit |> summary()
##
## Call:
## stats::lm(formula = b3_GCVI ~ b4_GCVI, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.7924 -0.9983 -0.0018 0.9525 3.9586
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.73701 0.15933 -4.626 1.14e-05 ***
## b4_GCVI -0.49793 0.04352 -11.440 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.475 on 98 degrees of freedom
## Multiple R-squared: 0.5718, Adjusted R-squared: 0.5675
## F-statistic: 130.9 on 1 and 98 DF, p-value: < 2.2e-16

Parameter estimates of a the lm object

tidy(lm_fit)
## # A tibble: 2 x 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) -0.737 0.159 -4.63 1.14e- 5
## 2 b4_GCVI -0.498 0.0435 -11.4 9.39e-20

Extract the model statistics

glance(lm_fit)
## # A tibble: 1 x 12
## r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0.572 0.567 1.47 131. 9.39e-20 1 -180. 365. 373.
## # ... with 3 more variables: deviance <dbl>, df.residual <int>, nobs <int>

Plot the data variables - categorical x continuous

data <- data |> mutate(target = case_when(class == "corn" ~ 0,
class == "soybean" ~ 1))
ggplot(data=data, aes(y=target,x=b4_GCVI))+
geom_point(aes(fill=as.factor(target)),shape=22,size=2) +
scale_fill_manual(values = c("#eded0c", "#49a345"))+
scale_y_continuous(breaks=c(0,1),
labels=c("0","1"),
limits=c(0,1))+
stat_smooth(formula = y~x, method="glm", se=FALSE, method.args = list(family=binomial),
color="black", linetype='dashed')+
labs(x= "b4_GCVI", y = "Class",fill="Class")+
theme(legend.position = c(0.17, 0.2),
panel.border = element_rect(color="Black", fill = NA),
panel.background = element_rect(fill = "#f2f2f2"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.title.x = element_text(family = "serif",
colour = "#000000",size = 22.0),
axis.text.x = element_text(family = "serif",
colour = "#000000",size = 18.0),
axis.title.y = element_text(family = "serif",
colour = "#000000",size = 22.0),
axis.text.y = element_text(family = "serif",
colour = "#000000",size = 18.0),
legend.title = element_text(family = "serif",
colour = '#000000',size = 12.0),
legend.text = element_text(family = "serif",
colour = '#000000',size = 12.0),
legend.background = element_rect(fill="#f2f2f2",
linetype="dashed",
colour ="#f2f2f2"))

Logistic regression

lg_fit_x <- glm(as.factor(class) ~ b4_GCVI, family="binomial", data=data)
summary(lg_fit_x)
##
## Call:
## glm(formula = as.factor(class) ~ b4_GCVI, family = "binomial",
## data = data)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.91207 -0.04298 0.01134 0.32227 1.86651
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.5734 1.1155 -3.203 0.00136 **
## b4_GCVI -1.5677 0.3778 -4.150 3.33e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 138.629 on 99 degrees of freedom
## Residual deviance: 42.379 on 98 degrees of freedom
## AIC: 46.379
##
## Number of Fisher Scoring iterations: 7

Logistic regression by tidymodels workflow

Creating a parsnip specification for a logistic regression model

lg_model <- logistic_reg() |>
set_engine("glm") |>
set_mode("classification")

Fitting the model supplying a formula expression and the data

lg_fit <- lg_model |>
fit(as.factor(class) ~ b4_GCVI, data = data)

Summary of the model

lg_fit |>
pluck("fit") |>
summary()
##
## Call:
## stats::glm(formula = as.factor(class) ~ b4_GCVI, family = stats::binomial,
## data = data)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.91207 -0.04298 0.01134 0.32227 1.86651
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.5734 1.1155 -3.203 0.00136 **
## b4_GCVI -1.5677 0.3778 -4.150 3.33e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 138.629 on 99 degrees of freedom
## Residual deviance: 42.379 on 98 degrees of freedom
## AIC: 46.379
##
## Number of Fisher Scoring iterations: 7
# Also you can use
lg_fit$fit |> summary()
##
## Call:
## stats::glm(formula = as.factor(class) ~ b4_GCVI, family = stats::binomial,
## data = data)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.91207 -0.04298 0.01134 0.32227 1.86651
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.5734 1.1155 -3.203 0.00136 **
## b4_GCVI -1.5677 0.3778 -4.150 3.33e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 138.629 on 99 degrees of freedom
## Residual deviance: 42.379 on 98 degrees of freedom
## AIC: 46.379
##
## Number of Fisher Scoring iterations: 7

Parameter estimates of a the lm object

tidy(lg_fit)
## # A tibble: 2 x 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) -3.57 1.12 -3.20 0.00136
## 2 b4_GCVI -1.57 0.378 -4.15 0.0000333

Extract the model statistics

glance(lg_fit)
## # A tibble: 1 x 8
## null.deviance df.null logLik AIC BIC deviance df.residual nobs
## <dbl> <int> <dbl> <dbl> <dbl> <dbl> <int> <int>
## 1 139. 99 -21.2 46.4 51.6 42.4 98 100

R - Cultivated crop maps in Rio Grande do Sul, Brazil

Wed, 22 Sep 2021 00:00:00 +0000

Generate crop cultivated maps

Crop plantation area from IBGE

Loading the packages

library(readxl)
library(tidyverse)
library(geobr)
library(patchwork)

URL link of data from IBGE on GitHub https://github.com/luanpott10/Class

You will nedd the raw file https://raw.githubusercontent.com/luanpott10/Class/main/tabela1612_1.csv

Load the data and data wrangling

The data downloaded from IBGE there are metadata in the 4 first lines that are not intrest for us `skip = 4`.

Also, there are info in the last rows, for that we select the 497 municipalities `data[1:497,]`.

Furthermore, the rows (cities) without crop planted, IBGE used “-” or “…” instead input 0, then we have used `case_when` function.

data <- read_csv('https://raw.githubusercontent.com/luanpott10/Class/main/tabela1612_1.csv', skip = 4)
data <- data[1:497,]
colnames(data) <- c("code","city","rice","corn","soybean")
data <- data |>
mutate(rice_crop = case_when(rice == "..." | rice == "-" ~ 0,
TRUE ~ as.double(rice))) |>
mutate(corn_crop = case_when(corn == "..." | corn == "-" ~ 0,
TRUE ~ as.double(corn))) |>
mutate(soybean_crop = case_when(soybean == "..." | soybean == "-" ~ 0,
TRUE ~ as.double(soybean)))
data <- data |> select(code,city,rice_crop,corn_crop,soybean_crop)

Dataset of geobr package from municipalities of Rio Grande do Sul state

cities_RS <- read_municipality(code_muni = "RS", year= 2020)

Ggplot of the cities

ggplot()+
geom_sf(data=cities_RS)

Joining the IBGE data and the sf object

cities_RS$code_muni <- as.character(cities_RS$code_muni)
data_x <- left_join(cities_RS,data,by= c("code_muni"="code"))

Palettes

pal_soybean <- c('#252525','#ccece6','#99d8c9','#66c2a4','#41ae76','#238b45','#006d2c','#00441b')
pal_corn <- c('#252525','#f7ffa8','#EFFD5F','#FCE205','#FCD12A','#FFC30B','#F9A602','#c48302')
pal_rice <- c('#252525','#caebfc','#9ecae1','#6baed6','#4292c6','#2171b5','#084594','#082954')

Soybean map

(soybean_map <-
ggplot()+
geom_sf(data=data_x,aes(fill=soybean_crop))+
theme_minimal()+
scale_fill_gradientn(colours=pal_soybean,
limits = c(0,150000),
na.value='#252525')+
labs(x= "Longitude", y = "Latitude", title = "Soybean")+
guides(fill=guide_colorbar(title="Crop area (ha)",barwidth = 1,barheight = 4,
frame.colour = "black"))+
theme(legend.position = c(0.17, 0.2),
panel.border = element_rect(color="Black", fill = NA),
panel.background = element_rect(fill = "#f2f2f2"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.title.x = element_text(family = "serif",
colour = "#000000",size = 22.0),
axis.text.x = element_text(family = "serif",
colour = "#000000",size = 18.0),
axis.title.y = element_text(family = "serif",
colour = "#000000",size = 22.0),
axis.text.y = element_text(family = "serif",
colour = "#000000",size = 18.0),
legend.title = element_text(family = "serif",
colour = '#000000',size = 12.0),
legend.text = element_text(family = "serif",
colour = '#000000',size = 12.0),
legend.background = element_rect(fill="#f2f2f2",
linetype="dashed",
colour ="#f2f2f2")))

Corn map

(corn_map <-
ggplot()+
geom_sf(data=data_x,aes(fill=corn_crop))+
theme_minimal()+
scale_fill_gradientn(colours=pal_corn,
limits = c(0,15000),
na.value='#252525')+
labs(x= "Longitude", y = "Latitude", title = "Corn")+
guides(fill=guide_colorbar(title="Crop area (ha)",barwidth = 1,barheight = 4,
frame.colour = "black"))+
theme(legend.position = c(0.17, 0.2),
panel.border = element_rect(color="Black", fill = NA),
panel.background = element_rect(fill = "#f2f2f2"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.title.x = element_text(family = "serif",
colour = "#000000",size = 22.0),
axis.text.x = element_text(family = "serif",
colour = "#000000",size = 18.0),
axis.title.y = element_text(family = "serif",
colour = "#000000",size = 22.0),
axis.text.y = element_text(family = "serif",
colour = "#000000",size = 18.0),
legend.title = element_text(family = "serif",
colour = '#000000',size = 12.0),
legend.text = element_text(family = "serif",
colour = '#000000',size = 12.0),
legend.background = element_rect(fill="#f2f2f2",
linetype="dashed",
colour ="#f2f2f2")))

Rice map

(rice_map <-
ggplot()+
geom_sf(data=data_x,aes(fill=rice_crop))+
theme_minimal()+
scale_fill_gradientn(colours=pal_rice,
limits = c(0,75000),
na.value='#252525')+
labs(x= "Longitude", y = "Latitude", title = "Rice")+
guides(fill=guide_colorbar(title="Crop area (ha)",barwidth = 1,barheight = 4,
frame.colour = "black"))+
theme(legend.position = c(0.17, 0.2),
panel.border = element_rect(color="Black", fill = NA),
panel.background = element_rect(fill = "#f2f2f2"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.title.x = element_text(family = "serif",
colour = "#000000",size = 22.0),
axis.text.x = element_text(family = "serif",
colour = "#000000",size = 18.0),
axis.title.y = element_text(family = "serif",
colour = "#000000",size = 22.0),
axis.text.y = element_text(family = "serif",
colour = "#000000",size = 18.0),
legend.title = element_text(family = "serif",
colour = '#000000',size = 12.0),
legend.text = element_text(family = "serif",
colour = '#000000',size = 12.0),
legend.background = element_rect(fill="#f2f2f2",
linetype="dashed",
colour ="#f2f2f2")))

Crop maps

soybean_map + corn_map + rice_map

maps | Data Science in Agriculture

R - Data visualization, linear regression and logistic regression

Data visualization, linear regression and logistic regression

Crop data collection - ground truth and remote sensing

Loading the packages

URL link of data from data collection and remote sensing on GitHub https://github.com/luanpott10/Class

You will need the raw file https://raw.githubusercontent.com/luanpott10/Class/main/data_crops.csv

Loading the data

Plot the data in the map

Plot the data variables - continuous x continuous

Linear regression

Linear regression by tidymodels workflow

Creating a parsnip specification for a linear regression model

Fitting the model supplying a formula expression and the data

Summary of the model

Parameter estimates of a the lm object

Extract the model statistics

Plot the data variables - categorical x continuous

Logistic regression

Logistic regression by tidymodels workflow

Creating a parsnip specification for a logistic regression model

Fitting the model supplying a formula expression and the data

Summary of the model

Parameter estimates of a the lm object

Extract the model statistics

R - Cultivated crop maps in Rio Grande do Sul, Brazil

Generate crop cultivated maps

Crop plantation area from IBGE

Loading the packages

URL link of data from IBGE on GitHub https://github.com/luanpott10/Class

You will nedd the raw file https://raw.githubusercontent.com/luanpott10/Class/main/tabela1612_1.csv

Load the data and data wrangling

The data downloaded from IBGE there are metadata in the 4 first lines that are not intrest for us skip = 4.

Also, there are info in the last rows, for that we select the 497 municipalities data[1:497,].

Furthermore, the rows (cities) without crop planted, IBGE used “-” or “…” instead input 0, then we have used case_when function.

Dataset of geobr package from municipalities of Rio Grande do Sul state

Ggplot of the cities

Joining the IBGE data and the sf object

Palettes

Soybean map

Corn map

Rice map

Crop maps

The data downloaded from IBGE there are metadata in the 4 first lines that are not intrest for us `skip = 4`.

Also, there are info in the last rows, for that we select the 497 municipalities `data[1:497,]`.

Furthermore, the rows (cities) without crop planted, IBGE used “-” or “…” instead input 0, then we have used `case_when` function.