foodloss_se_descriptive_table.Rmd

---
title: "SE descriptive"
author: "Johannes Piipponen"
date: "Spring 2023`"
output: html_document
---

Code written by Johannes Piipponen


# Create table of socio-economic variables



```{r}
library(plotrix)
library(tidyverse)
# Get Daniel's data
daniel_original <- read.csv("data_johannes.csv")


# Explore descriptive statistics using the original data
df_se_descriptive <-
  daniel_original %>% 
  group_by(incLevel, basketItem2, food_supply_stage) %>%
  summarise(foodloss_mean = mean(loss_percentage1),
            StandardError = std.error(loss_percentage1),
            N = n()) %>% 
  mutate_if(is.numeric, round, digits = 3) 

# alternatively, use data with polygons
# df_se_descriptive <-
#   country_data %>%
#   st_drop_geometry() %>%
#   group_by(incLevel, basketItem2, food_supply_stage) %>%
#   summarise(foodloss_mean = mean(loss_percentage1),
#             StandardError = std.error(loss_percentage1),
#             N = n()) %>% 
#   mutate_if(is.numeric, round, digits = 3) 


# Modify the table to have L and M countries side by side
df_se_descriptive_L <- df_se_descriptive %>% 
  filter(incLevel == "L") %>% 
  dplyr::select(-incLevel) 


df_se_descriptive_M <- df_se_descriptive %>% 
  filter(incLevel == "M") %>% 
  dplyr::select(-incLevel) 

# Join the modified tables
df_se_table <- 
  left_join(df_se_descriptive_L, df_se_descriptive_M,
            by = c("basketItem2", "food_supply_stage"))

df_se_table
#View(df_se_table)



```