Garden analysis

This page just shows the distribution of gains from my personal garden over the years.

Garden
Analyse
Perso
Author

Corentin Maslard

Published

August 22, 2022

The aim of this project was simply to record the weights of each fruit and vegetable harvested over the years, in order to predict the possible gains from creating your own garden.

Data importation

Code 
df_harvest_price <- read_excel(here::here("perso_projects/media/data_analyse_garden/recolte_total.xlsx")) %>% 
  rename("date"=Date_récolte) %>% 
  rename("qty"=`Quantité récolté`) %>% 
  rename("unite"=Expr1002) %>% 
  rename("price"=`Prix en magasin BIO`) %>% 
  rename("name"=`Nom (s) commun (s)`) %>% 
  rename("type"=`Type (Rotation)`) %>% 
  rename("total"=Expr1) %>% 
  dplyr::select(-Unités)

Analyse

By Years

Code 
df_harvest_price=df_harvest_price %>% 
    mutate(year = lubridate::year(date))

(df_harvest_price %>% 
    dplyr::group_by(year) %>% 
    dplyr::summarise(sum_price=sum(price),.groups = NULL) %>% 
    ggplot( aes(x = year, y=sum_price, fill=as.factor(year))) +
    geom_bar(stat = "identity") +
    labs(x = "Année",
         y = "Total en fin d'année") +
    theme_minimal()+
    theme(legend.position = "none")+
    geom_text(aes(label = scales::dollar(sum_price,suffix = "€",prefix = ""), y = sum_price), vjust = -0.5, color = "black",size=3) +
    scale_fill_grey(start = 0.8, end = 0.1) +
    scale_x_continuous(breaks = unique(df_harvest_price$year))+
    scale_y_continuous(labels = scales::dollar_format(prefix = "€"))
)|(
df_harvest_price %>% 
    dplyr::group_by(year,type) %>% 
    dplyr::summarise(sum_price=sum(price),.groups = NULL) %>% 
    ggplot( aes(x = year, y=sum_price, fill=as.factor(type))) +
    geom_bar(stat = "identity") +
    labs(x = "Years",
         y = "Year-end total",
         fill= "Type of legume") +
    theme_minimal()+
    scale_fill_manual(values = wesanderson::wes_palette("Darjeeling1", 7, type = "continuous"))+
    scale_x_continuous(breaks = unique(df_harvest_price$year))+
    scale_y_continuous(labels = scales::dollar_format(prefix = "€"))
)

Evolution from 2017 to 2023

Code 
# basic treemap
for (year_i in levels(as.factor(df_harvest_price$year))){
  png(filename=paste0("data_analyse_garden/plot/",year_i,"_treemap.png"),width=800, height=300)
  p <- treemap(df_harvest_price %>% filter(year==year_i),
              index=c("type","name"),
              vSize="total",
              type="index",
              palette = wesanderson::wes_palette("Darjeeling1", 7, type = "continuous"),     
              fontsize.labels=c(15,12),                # size of labels. Give the size per level of aggregation: size for group, size for subgroup, sub-subgroups...
      fontcolor.labels=c("white","black"),    # Color of labels
      border.col=c("black","white"),             # Color of borders of groups, of subgroups, of subsubgroups ....
    border.lwds=c(2,1) ,                        # Width of colors
      fontface.labels=c(2,1),                  # Font of labels: 1,2,3,4 for normal, bold, italic, bold-italic...
      bg.labels=c("transparent"),              # Background color of labels
      align.labels=list(
          c("center", "center"), 
          c("right", "bottom")
          ),                                   # Where to place labels in the rectangle?
      overlap.labels=0.5,                      # number between 0 and 1 that determines the tolerance of the overlap between labels. 0 means that labels of lower levels are not printed if higher level labels overlap, 1  means that labels are always printed. In-between values, for instance the default value .5, means that lower level labels are printed if other labels do not overlap with more than .5  times their area size.
      inflate.labels=F,  
      title=""
        #title=year_i
            )       
  dev.off()
}

2017 Année 2017

2018 Année 2018

2019 Année 2019

2020 Année 2020

2021 Année 2021

2022 Année 2022

2023 Année 2023

Code 
Name="Concombre"
df_harvest_price %>% 
  filter(name==Name) %>% 
  mutate(day=as.numeric(format(as.Date(date), "%d"))) %>% 
  mutate(month=as.numeric(format(as.Date(date), "%m"))) %>% 
  mutate(month_day=as.Date(paste(as.numeric(format(as.Date(Sys.Date()), "%y")), month, day, sep = "-"), format = "%Y-%m-%d")) %>% 
  arrange(month_day) %>% 
  dplyr::group_by(year) %>% 
  mutate(cum_sum=cumsum(qty)) %>%
ggplot( aes(x = month_day, y = cum_sum, color = as.factor(year), group=as.factor(year))) +
  geom_line() +
  geom_point()+
  theme_minimal()+
  labs(title = paste0("Quantité: ",Name ),
       x = "Date of harvest",
       y = "Quantity",
       color = "Years")+
  scale_color_brewer(palette = "Dark2")

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