I am at the final stages of a project where i have been comparing the appraisal price vs the sold price of different properties. The complete code for data collection and tidying is below.
At this stage i am looking at different ways to visualize my data. However, I am quite new to it so my question is whether anyone has any "new" or special ways they visualizing data that they find usefull og intuitive. I have given a couple of examples of what i am able to visualize now using ggplot.
Additionally: Now my visualizations plots all 1275 observations every time. I would however also like to visualize the data both with mean and median for the Percentage, Sold and Tax variables which i am most interested in. For example to visualize the mean value of the Percentage column based on different years.
Appreciate any help!
Complete code:
#Step 1: Load needed library
library(tidyverse)
library(rvest)
library(jsonlite)
library(stringi)
library(dplyr)
library(data.table)
library(ggplot2)
#Step 2: Access the URL of where the data is located
url <- "https://www.forsvarsbygg.no/ListApi/ListContent/78635/SoldEstates/0/10/"
#Step 3: Direct JSON as format of data in URL
data <- jsonlite::fromJSON(url, flatten = TRUE)
#Step 4: Access all items in API
totalItems <- data$TotalNumberOfItems
#Step 5: Summarize all data from API
allData <- paste0('https://www.forsvarsbygg.no/ListApi/ListContent/78635/SoldEstates/0/', totalItems,'/') %>%
jsonlite::fromJSON(., flatten = TRUE) %>%
.[1] %>%
as.data.frame() %>%
rename_with(~str_replace(., "ListItems.", ""), everything())
#Step 6: removing colunms not needed
allData <- allData[, -c(1,4,8,9,11,12,13,14,15)]
#Step 7: remove whitespace and change to numeric in columns SoldAmount and Tax
#https://stackoverflow.com/questions/71440696/r-warning-argument-is-not-an-atomic-vector-when-attempting-to-remove-whites/71440806#71440806
allData[c("Tax", "SoldAmount")] <- lapply(allData[c("Tax", "SoldAmount")], function(z) as.numeric(gsub(" ", "", z)))
#Step 8: Remove rows where value is NA
#https://stackoverflow.com/questions/4862178/remove-rows-with-all-or-some-nas-missing-values-in-data-frame
alldata <- allData %>%
filter(across(where(is.numeric),
~ !is.na(.)))
#Step 9: Remove values below 10000 NOK on SoldAmount og Tax.
alldata <- alldata %>%
filter_all(any_vars(is.numeric(.) & . > 10000))
#Step 10: Calculate percentage change between tax and sold amount and create new column with percent change
#df %>% mutate(Percentage = number/sum(number))
alldata_Percent <- alldata %>% mutate(Percentage = (SoldAmount-Tax)/Tax)
Visualization
# Plot Percentage difference based on County
ggplot(data=alldata_Percent,mapping = aes(x = Percentage, y = County)) +
geom_point(size = 1.5)
#Plot County with both Date and Percentage difference The The
theme_set(new = ggthemes::theme_economist())
p <- ggplot(data = alldata_Percent,
mapping = aes(x = Date, y = Percentage, colour = County)) +
geom_line(na.rm = TRUE) +
geom_point(na.rm = TRUE)
p
