How to visualize this data clarifying in R and recognize patterns? - r

Question

This is my dataframe:
dataframe
Output dput(dataframe):
structure(list(ChargePoint_skey = c(2174, 2174, 2174, 2239, 2239,
2266, 2266, 2266, 2266, 2266), MonthYear = structure(c(17532,
17563, 17591, 17956, 17987, 17532, 17563, 17591, 17622, 17652
), class = "Date"), aantalsessies = c(16L, 15L, 14L, 8L, 8L,
61L, 29L, 33L, 13L, 14L)), .Names = c("ChargePoint_skey", "MonthYear",
"aantalsessies"), class = c("grouped_df", "tbl_df", "tbl", "data.frame"
), row.names = c(NA, -10L), groups = structure(list(ChargePoint_skey =
c(2174,
2239, 2266), .rows = list(1:3, 4:5, 6:10)), .Names = c("ChargePoint_skey",
".rows"), row.names = c(NA, -3L), class = c("tbl_df", "tbl",
"data.frame"), .drop = TRUE))
As you can see there are a lot of groups in the column 'ChargePoint_skey' because there are a lot different ChargePoints. I want to visualize this data to recognize patterns about all the ChargePoints. Does anybody have a suggestion for a type of visualization? I was thinking of a stacked barchart like this:
stackedbarchart
But this isn't an option for me since I have many different ChargePoints in my data.
I hope somebody can help me with this!

Answer 1

Hard to post as a comment so I'm posting an answer.
3D plots (surface/scatter etc.) can be quite useful for visualizing data. Here's an example of a surface plot from one of my previous projects that I used to examine the relationship b/w the three variables and where the problem becomes infeasible (voids in the plot). This is an example of a full-factorial DOE.
Back to you problem now - I prefer using plotly for an interactive output that you can play around with. This (imo) is better than static tools that can only be manipulated via code (rotations/pan/zoom etc.).
CODE
library(plotly)
# dat is the data.frame from your dput output
plt <- plot_ly(dat, x = ~MonthYear, y = ~ChargePoint_skey, y = ~aantalsessies) %>%
add_markers()
OUTPUT
Since this is a small sample of the dataset, the plot is rather sparse. With a larger dataset you'd likely get some better insights.
You can refer to https://plotly.com/r/3d-scatter-plots/ for more information. The plots themselves can be saved as html files (can be opened in a browser) for sharing - using htmlwdigets package.
htmlwidgets::saveWidget(widget = as_widget(plt), file = 'myfile.html', selfcontained = T, title = 'my tab title')
Hope this is helpful!

Answer 2

Try something like this using geom_tile():
library(ggplot2)
#Code
df %>%
ggplot(aes(x=factor(ChargePoint_skey),y=factor(MonthYear),
fill=aantalsessies))+
geom_tile()+xlab('ChargePoint_skey')+ylab('MonthYear')
Output: