Continuing with my exploration of the Land Registry open data set using SparkR I wanted to see which road in the UK has had the most property sales over the last 20 years.
To recap, this is what the data frame looks like:
./spark-1.5.0-bin-hadoop2.6/bin/sparkR --packages com.databricks:spark-csv_2.11:1.2.0
> sales <- read.df(sqlContext, "pp-complete.csv", "com.databricks.spark.csv", header="false")
> head(sales)
C0 C1 C2 C3 C4 C5
1 {0C7ADEF5-878D-4066-B785-0000003ED74A} 163000 2003-02-21 00:00 UB5 4PJ T N
2 {35F67271-ABD4-40DA-AB09-00000085B9D3} 247500 2005-07-15 00:00 TA19 9DD D N
3 {B20B1C74-E8E1-4137-AB3E-0000011DF342} 320000 2010-09-10 00:00 W4 1DZ F N
4 {7D6B0915-C56B-4275-AF9B-00000156BCE7} 104000 1997-08-27 00:00 NE61 2BH D N
5 {47B60101-B64C-413D-8F60-000002F1692D} 147995 2003-05-02 00:00 PE33 0RU D N
6 {51F797CA-7BEB-4958-821F-000003E464AE} 110000 2013-03-22 00:00 NR35 2SF T N
C6 C7 C8 C9 C10 C11
1 F 106 READING ROAD NORTHOLT NORTHOLT
2 F 58 ADAMS MEADOW ILMINSTER ILMINSTER
3 L 58 WHELLOCK ROAD LONDON
4 F 17 WESTGATE MORPETH MORPETH
5 F 4 MASON GARDENS WEST WINCH KING'S LYNN
6 F 5 WILD FLOWER WAY DITCHINGHAM BUNGAY
C12 C13 C14
1 EALING GREATER LONDON A
2 SOUTH SOMERSET SOMERSET A
3 EALING GREATER LONDON A
4 CASTLE MORPETH NORTHUMBERLAND A
5 KING'S LYNN AND WEST NORFOLK NORFOLK A
6 SOUTH NORFOLK NORFOLK AThis document explains the data stored in each field and for this particular query we’re interested in fields C9-C12. The plan is to group the data frame by those fields and then sort by frequency in descending order.
When grouping by multiple fields it tends to be easiest to create a new field which concatenates them all and then group by that.
I started with the following:
> sales$address = paste(sales$C9, sales$C10, sales$C11, sales$C12, sep=", ") Error in as.character.default(<S4 object of class "Column">) : no method for coercing this S4 class to a vector
Not so successful! Next I went even more primitive:
> sales$address = sales$C9 + ", " + sales$C10 + ", " + sales$C11 + ", " + sales$C12
> head(sales)
C0 C1 C2 C3 C4 C5
1 {0C7ADEF5-878D-4066-B785-0000003ED74A} 163000 2003-02-21 00:00 UB5 4PJ T N
2 {35F67271-ABD4-40DA-AB09-00000085B9D3} 247500 2005-07-15 00:00 TA19 9DD D N
3 {B20B1C74-E8E1-4137-AB3E-0000011DF342} 320000 2010-09-10 00:00 W4 1DZ F N
4 {7D6B0915-C56B-4275-AF9B-00000156BCE7} 104000 1997-08-27 00:00 NE61 2BH D N
5 {47B60101-B64C-413D-8F60-000002F1692D} 147995 2003-05-02 00:00 PE33 0RU D N
6 {51F797CA-7BEB-4958-821F-000003E464AE} 110000 2013-03-22 00:00 NR35 2SF T N
C6 C7 C8 C9 C10 C11
1 F 106 READING ROAD NORTHOLT NORTHOLT
2 F 58 ADAMS MEADOW ILMINSTER ILMINSTER
3 L 58 WHELLOCK ROAD LONDON
4 F 17 WESTGATE MORPETH MORPETH
5 F 4 MASON GARDENS WEST WINCH KING'S LYNN
6 F 5 WILD FLOWER WAY DITCHINGHAM BUNGAY
C12 C13 C14 address
1 EALING GREATER LONDON A NA
2 SOUTH SOMERSET SOMERSET A NA
3 EALING GREATER LONDON A NA
4 CASTLE MORPETH NORTHUMBERLAND A NA
5 KING'S LYNN AND WEST NORFOLK NORFOLK A NA
6 SOUTH NORFOLK NORFOLK A NAThat at least compiled but all addresses were ‘NA’ which isn’t what we want. After a bit of searching I realised that there was a concat function that I could use for exactly this task:
> sales$address = concat_ws(sep=", ", sales$C9, sales$C10, sales$C11, sales$C12)
> head(sales)
C0 C1 C2 C3 C4 C5
1 {0C7ADEF5-878D-4066-B785-0000003ED74A} 163000 2003-02-21 00:00 UB5 4PJ T N
2 {35F67271-ABD4-40DA-AB09-00000085B9D3} 247500 2005-07-15 00:00 TA19 9DD D N
3 {B20B1C74-E8E1-4137-AB3E-0000011DF342} 320000 2010-09-10 00:00 W4 1DZ F N
4 {7D6B0915-C56B-4275-AF9B-00000156BCE7} 104000 1997-08-27 00:00 NE61 2BH D N
5 {47B60101-B64C-413D-8F60-000002F1692D} 147995 2003-05-02 00:00 PE33 0RU D N
6 {51F797CA-7BEB-4958-821F-000003E464AE} 110000 2013-03-22 00:00 NR35 2SF T N
C6 C7 C8 C9 C10 C11
1 F 106 READING ROAD NORTHOLT NORTHOLT
2 F 58 ADAMS MEADOW ILMINSTER ILMINSTER
3 L 58 WHELLOCK ROAD LONDON
4 F 17 WESTGATE MORPETH MORPETH
5 F 4 MASON GARDENS WEST WINCH KING'S LYNN
6 F 5 WILD FLOWER WAY DITCHINGHAM BUNGAY
C12 C13 C14
1 EALING GREATER LONDON A
2 SOUTH SOMERSET SOMERSET A
3 EALING GREATER LONDON A
4 CASTLE MORPETH NORTHUMBERLAND A
5 KING'S LYNN AND WEST NORFOLK NORFOLK A
6 SOUTH NORFOLK NORFOLK A
address
1 READING ROAD, NORTHOLT, NORTHOLT, EALING
2 ADAMS MEADOW, ILMINSTER, ILMINSTER, SOUTH SOMERSET
3 WHELLOCK ROAD, , LONDON, EALING
4 WESTGATE, MORPETH, MORPETH, CASTLE MORPETH
5 MASON GARDENS, WEST WINCH, KING'S LYNN, KING'S LYNN AND WEST NORFOLK
6 WILD FLOWER WAY, DITCHINGHAM, BUNGAY, SOUTH NORFOLKThat’s more like it! Now let’s see which streets have sold the most properties:
> byAddress = summarize(groupBy(sales, sales$address), count = n(sales$address))
> head(arrange(byAddress, desc(byAddress$count)), 10)
address count
1 BARBICAN, LONDON, LONDON, CITY OF LONDON 1398
2 CHRISTCHURCH ROAD, BOURNEMOUTH, BOURNEMOUTH, BOURNEMOUTH 1313
3 MAIDA VALE, LONDON, LONDON, CITY OF WESTMINSTER 1305
4 ROTHERHITHE STREET, LONDON, LONDON, SOUTHWARK 1253
5 SLOANE AVENUE, LONDON, LONDON, KENSINGTON AND CHELSEA 1219
6 THE STRAND, BRIGHTON MARINA VILLAGE, BRIGHTON, BRIGHTON AND HOVE 1218
7 FAIRFIELD ROAD, LONDON, LONDON, TOWER HAMLETS 1217
8 QUEENSTOWN ROAD, , LONDON, WANDSWORTH 1153
9 UPPER RICHMOND ROAD, LONDON, LONDON, WANDSWORTH 1123
10 QUEENSTOWN ROAD, LONDON, LONDON, WANDSWORTH 1079Next we’ll drill into the data further but that’s for another post.
| Reference: | SparkR: Add new column to data frame by concatenating other columns from our JCG partner Mark Needham at the Mark Needham Blog blog. |
