Labz

Tomasz Dąbrowski

2021-04-07

This plot shows Prices in PLN of apartments in Wroclaw.

This

Here is the average value of each group.

Facets

Facets with grids - different syvset of the data.

Summary tables with ‘kable’

Using kable and kableextra packages we can easily create summary tables with graphics and/or statistics.

rooms boxplot histogram line1 line2 points1
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Ok, now we will finally summarize basic central tendency measures for prices by districts/building types using ‘kable’ package. You can customize your final report. See some hints here.

Table 1. Apartments in Wroclaw - prices in PLN by number of rooms.
1 room 2 rooms 3 rooms 4 rooms
Min 359769.00 590286.00 632770.00 736669.00
Max 657146.00 888634.00 965829.00 1277691.00
Q1 479684.75 634757.25 769683.75 909371.50
Median 520507.00 677260.00 846303.50 964338.50
Q3 555024.75 717728.50 901078.75 1050976.75
Mean 515518.05 683567.70 833706.02 974809.96
Sd 66951.03 65072.66 86943.90 113819.21
IQR 75340.00 82971.25 131395.00 141605.25
Sx 37670.00 41485.62 65697.50 70802.62
Var % 0.13 0.10 0.10 0.12
IQR Var % 0.14 0.12 0.16 0.15
Skewness -0.20 0.80 -0.42 0.33
Kurtosis -0.38 0.48 -0.83 0.05

Skewness is from -0.20 to 0.80 which means that there’s more data lower than average than higher than average for skewness = -0.20. And for Skewness > 0 there’s more data situated above the average than below it. The kurtosis is around zero, which means that the data Is spread evenly about a certain value.IQR* - Interquantile range, how the data is scattered, in that case I would say it’s pretty high, but the percentage is really small and around 0. SD - standard *deviation is between 60000 and 120000 and SX is half of that.