Click the Original, Code and Reconstruction tabs to read about the issues and how they were fixed.

Original


Source: Site Wide Analysis of data.gov.au (2013-2019).


Objective

  • The main objective of this visualization is to provide the number of people visited data.gov.au from different browsers across 2013-2019 period.

  • The target audience are people who are looking for statistical information about the application, department technicians who are handling the web page (To see the visitor’s traffic.), students to understand and study about the web traffic of the web application.

The Visualisation had the following three issues:

  • Colour Issues: It is really hard to understand what the lower values or colours in the bottom (near the X-Axis) section of the graph is trying to explain and it is difficult to identify the difference between the browsers.

  • Area and Size as Quantity(Deception): The users should not look at the visualisation size/area to understand the visualisation. For the chosen visualisation the audience need to look at the sizes of the graph in order to understand the exact amount of website visitors from different browsers, as a result it misleads the users understanding about the graph.

  • Plot anatomy (Axis label): The graph without labels are very hard to read and understand precisely what the visualisation is trying to convey. For the chosen visualisation there are no X and Y axis labels, X-axis we can see it represents yearly data. It is impossible to understand what the y-axis is stating, it can either represent percentage of visitors or number of visitors visited.

Reference

  • Source: Site Wide Analysis of data.gov.au (2013-2019). Site Visitors From Different browsers(2013-2019). Retrieved September 12, 2019, from Australian australian open data website: https://data.gov.au/site-usage?month=

Code

The following code was used to fix the issues identified in the original.

library(readr)
library(tidyr)
library(dplyr)
library(ggplot2)

stats_all <- read_csv("C:/Users/chait/Desktop/DataViz/stats_all.csv")
Updated_stats1 <- separate_(stats_all,
                            col = "Period",
                            into = c("Year", "Month"),
                            sep = "-")

new_data <- Updated_stats1[ which(Updated_stats1$Statistic=='Browsers'), ]
Updated_stats2 <- new_data %>%
  group_by(Year, Key) %>%
  summarise(Count=sum(Value))

Updated_stats3 <- Updated_stats2 %>% spread(key = Key, value = Count)
Updated_stats3 <- Updated_stats3 %>% gather(-c("Year","Chrome","Firefox","Internet Explorer","Safari"), key = "others", value = "value")
Updated_stats4 <- subset(Updated_stats3, select = -c(others))

Updated_stats4 <- Updated_stats4 %>%
  group_by(Year) %>%
  mutate(Others=sum(value,na.rm=TRUE))
Updated_stats4 <- subset(Updated_stats4, select = -c(value))

Final_1 <- distinct(Updated_stats4)
Final_2 <- Final_1 %>% gather(c("Chrome","Firefox","Internet Explorer","Safari","Others"), key = "Type", value = "Visits")

plot <- ggplot(data=Final_2, aes(x=Year, y=Visits,group=Type, shape=Type, color=Type)) +
  geom_line() +
  geom_point()

Data Reference

  • Source: Site Wide Analysis of data.gov.au (2013-2019). Site Visitors From Different browsers(2013-2019). Retrieved September 12, 2019, from Australian australian open data website: https://data.gov.au/site-usage?month=

Reconstruction

The following plot fixes the main issues in the original.