## Objective:

Clean a dataset with single column containing multiple variables as delimited key-value pairs.

## Solution:

This can be done using tidyr functions spread, gather, separate, etc.

## Code and Explanation:

Creating a test dataset with multiple variables of interest -

var1_text = c("Sachin", "Sourav", "Rahul", "Laxman")
var2_text = c("Virat", "Jinx", "Pujara", "Rohit")
sep1 = ":"
sep2 = "|"
no_rows = 100
set.seed(9653)
d1 = data.frame(id = 1:no_rows,
retired = sample(x = var1_text, size = 10, replace = TRUE),
current = sample(x = var2_text, size = 10, replace = TRUE),
garbage = paste0("my_var", 1:no_rows),
stringsAsFactors = FALSE)
knitr::kable(head(d1))
id retired current garbage
1 Sachin Pujara my_var1
2 Rahul Virat my_var2
3 Laxman Virat my_var3
4 Sourav Virat my_var4
5 Sachin Virat my_var5
6 Sachin Virat my_var6

Combining variable names (keys) with variable values to make it slightly unclean.

d2 = d1
var_names = names(d1)[-1]
d2$var1_pair = paste(var_names[1], d2$retired, sep = sep1)
d2$var2_pair = paste(var_names[2], d2$current, sep = sep1)
d2$var3_pair = paste(var_names[3], d2$garbage, sep = sep1)

d2 = d2[, c("id", "var1_pair", "var2_pair", "var3_pair")]
knitr::kable(head(d2))
id var1_pair var2_pair var3_pair
1 retired:Sachin current:Pujara garbage:my_var1
2 retired:Rahul current:Virat garbage:my_var2
3 retired:Laxman current:Virat garbage:my_var3
4 retired:Sourav current:Virat garbage:my_var4
5 retired:Sachin current:Virat garbage:my_var5
6 retired:Sachin current:Virat garbage:my_var6

Combining the key-value pairs for these variables into a single variable separated by a delimiter. Also, removing certain pairs and altering the order of the pairs.

d3 = d2
d3$text = NA d3$text[4 * (1:25) - 3] = paste(d3$var1_pair[4 * (1:25) - 3], d3$var2_pair[4 * (1:25) - 3],
d3$var3_pair[4 * (1:25) - 3], sep = sep2) d3$text[4 * (1:25) - 2] = paste(d3$var2_pair[4 * (1:25) - 2], d3$var3_pair[4 * (1:25) - 2],
sep = sep2)
d3$text[4 * (1:25) - 1] = paste(d3$var3_pair[4 * (1:25) - 1],
d3$var2_pair[4 * (1:25) - 1], d3$var1_pair[4 * (1:25) - 1],
sep = sep2)
d3$text[4 * (1:25)] = d3$var2_pair[4 * (1:25)]

d3 = d3[ , c("id", "text")]

The test dataset now looks like -

id text
1 retired:Sachin|current:Pujara|garbage:my_var1
2 current:Virat|garbage:my_var2
3 garbage:my_var3|current:Virat|retired:Laxman
4 current:Virat
5 retired:Sachin|current:Virat|garbage:my_var5
6 current:Virat|garbage:my_var6

1. Not all the instances (rows) contain exactly 3 variables (key-value pairs).
2. The order of the variables is not the same i.e. retired is not always followed by current and current is not always followed by garbage.
library(plyr)
library(dplyr)
library(tidyr)
library(stringr)

A 2 step solution:

• step 1: Separating the key: value pairs into different columns i.e. key1: value1 | key2: value2 —-> key1: value1 and key2: value2

First we need to know at the most how many key value pairs could be there in a single text instance. Once we know the max number of vars, we can use tidyr::separate function to separate these pairs -

# Finding max number of key: val pairs in a row
len = max(str_count(string = d3\$text, pattern = paste0("[",sep2,"]")))
vec_names = paste0("X", 1:(len + 1))

d2_rev = d3 %>%
separate(col = "text", into = vec_names, sep = paste0("[",sep2,"]"), extra = "drop")

Result after this step -

id X1 X2 X3
1 retired:Sachin current:Pujara garbage:my_var1
2 current:Virat garbage:my_var2 NA
3 garbage:my_var3 current:Virat retired:Laxman
4 current:Virat NA NA
5 retired:Sachin current:Virat garbage:my_var5
6 current:Virat garbage:my_var6 NA
• step 2: Separating values from keys i.e. key1: value1 —–> key1 and value1

The next step is to separate key: val pairs such that the val values are stored in corresponding key columns. For this, we’ll first need to go from the wide version that we have now to long version of the dataset using tidyr::gather. After that, it’s again applying tidyr::separate to separate keys from values. The last step is to convert this long dataset back to a wide one using tidyr::spread. Too long an explanation, better see the code -

d3_rev = d2_rev %>%
gather(key = "temp_var", value = "kv_pair", -id, na.rm = TRUE) %>%
select(-temp_var) %>%
separate(col = "kv_pair", into = c("key", "val"), sep = paste0("[",sep1,"]"), extra = "drop") %>%
spread(key = "key", value = "val")

Final result -

id current garbage retired
1 Pujara my_var1 Sachin
2 Virat my_var2 NA
3 Virat my_var3 Laxman
4 Virat NA NA
5 Virat my_var5 Sachin
6 Virat my_var6 NA

This is very similar to the original clean dataset d1 except for the column order and some NA values (generated cause of missing values we introduced while creating the test dataset, in order to make this example more generic).

id retired current garbage
1 Sachin Pujara my_var1
2 Rahul Virat my_var2
3 Laxman Virat my_var3
4 Sourav Virat my_var4
5 Sachin Virat my_var5
6 Sachin Virat my_var6