Menu
I have data from an online survey where respondents go through a loop of questions 1-3 times. The survey software (Qualtrics) records this data in multiple columns—that is, Q3.2 in the survey will have columns Q3.2.1., Q3.2.2., and Q3.2.3.:
df <- data.frame(
id = 1:10,
time = as.Date('2009-01-01') + 0:9,
Q3.2.1. = rnorm(10, 0, 1),
Q3.2.2. = rnorm(10, 0, 1),
Q3.2.3. = rnorm(10, 0, 1),
Q3.3.1. = rnorm(10, 0, 1),
Q3.3.2. = rnorm(10, 0, 1),
Q3.3.3. = rnorm(10, 0, 1)
)
# Sample data
id time Q3.2.1. Q3.2.2. Q3.2.3. Q3.3.1. Q3.3.2. Q3.3.3.
1 1 2009-01-01 -0.2059165 -0.29177677 -0.7107192 1.52718069 -0.4484351 -1.21550600
2 2 2009-01-02 -0.1981136 -1.19813815 1.1750200 -0.40380049 -1.8376094 1.03588482
3 3 2009-01-03 0.3514795 -0.27425539 1.1171712 -1.02641801 -2.0646661 -0.35353058
...
I want to combine all the QN.N* columns into tidy individual QN.N columns, ultimately ending up with something like this:
id time loop_number Q3.2 Q3.3
1 1 2009-01-01 1 -0.20591649 1.52718069
2 2 2009-01-02 1 -0.19811357 -0.40380049
3 3 2009-01-03 1 0.35147949 -1.02641801
...
11 1 2009-01-01 2 -0.29177677 -0.4484351
12 2 2009-01-02 2 -1.19813815 -1.8376094
13 3 2009-01-03 2 -0.27425539 -2.0646661
...
21 1 2009-01-01 3 -0.71071921 -1.21550600
22 2 2009-01-02 3 1.17501999 1.03588482
23 3 2009-01-03 3 1.11717121 -0.35353058
...
The tidyr library has the gather() function, which works great for combining one set of columns:
library(dplyr)
library(tidyr)
library(stringr)
df %>% gather(loop_number, Q3.2, starts_with("Q3.2")) %>%
mutate(loop_number = str_sub(loop_number,-2,-2)) %>%
select(id, time, loop_number, Q3.2)
id time loop_number Q3.2
1 1 2009-01-01 1 -0.20591649
2 2 2009-01-02 1 -0.19811357
3 3 2009-01-03 1 0.35147949
...
29 9 2009-01-09 3 -0.58581232
30 10 2009-01-10 3 -2.33393981
The resultant data frame has 30 rows, as expected (10 individuals, 3 loops each). However, gathering a second set of columns does not work correctly—it successfully makes the two combined columns Q3.2 and Q3.3, but ends up with 90 rows instead of 30 (all combinations of 10 individuals, 3 loops of Q3.2, and 3 loops of Q3.3; the combinations will increase substantially for each group of columns in the actual data):
df %>% gather(loop_number, Q3.2, starts_with("Q3.2")) %>%
gather(loop_number, Q3.3, starts_with("Q3.3")) %>%
mutate(loop_number = str_sub(loop_number,-2,-2))
id time loop_number Q3.2 Q3.3
1 1 2009-01-01 1 -0.20591649 1.52718069
2 2 2009-01-02 1 -0.19811357 -0.40380049
3 3 2009-01-03 1 0.35147949 -1.02641801
...
89 9 2009-01-09 3 -0.58581232 -0.13187024
90 10 2009-01-10 3 -2.33393981 -0.48502131
Is there a way to use multiple calls to gather() like this, combining small subsets of columns like this while maintaining the correct number of rows?
944
The function spread() does the reverse of gather(). It takes two columns (key and value) and spreads into multiple columns. It produces a “wide” data format from a “long” one.
To use spread() , pass it the name of a data frame, then the name of the key column in the data frame, and then the name of the value column. Pass the column names as they are; do not use quotes. To tidy table2 , you would pass spread() the key column and then the value column.
tidyr is new package that makes it easy to “tidy” your data. Tidy data is data that's easy to work with: it's easy to munge (with dplyr), visualise (with ggplot2 or ggvis) and model (with R's hundreds of modelling packages). The two most important properties of tidy data are: Each column is a variable.
gather( ) function: To reformat the data such that these common attributes are gathered together as a single variable, the gather() function will take multiple columns and collapse them into key-value pairs, duplicating all other columns as needed.
This approach seems pretty natural to me:
df %>%
gather(key, value, -id, -time) %>%
extract(key, c("question", "loop_number"), "(Q.\\..)\\.(.)") %>%
spread(question, value)
First gather all question columns, use extract() to separate into question and loop_number, then spread() question back into the columns.
#> id time loop_number Q3.2 Q3.3
#> 1 1 2009-01-01 1 0.142259203 -0.35842736
#> 2 1 2009-01-01 2 0.061034802 0.79354061
#> 3 1 2009-01-01 3 -0.525686204 -0.67456611
#> 4 2 2009-01-02 1 -1.044461185 -1.19662936
#> 5 2 2009-01-02 2 0.393808163 0.42384717
This could be done using reshape. It is possible with dplyr though.
colnames(df) <- gsub("\\.(.{2})$", "_\\1", colnames(df))
colnames(df)[2] <- "Date"
res <- reshape(df, idvar=c("id", "Date"), varying=3:8, direction="long", sep="_")
row.names(res) <- 1:nrow(res)
head(res)
# id Date time Q3.2 Q3.3
#1 1 2009-01-01 1 1.3709584 0.4554501
#2 2 2009-01-02 1 -0.5646982 0.7048373
#3 3 2009-01-03 1 0.3631284 1.0351035
#4 4 2009-01-04 1 0.6328626 -0.6089264
#5 5 2009-01-05 1 0.4042683 0.5049551
#6 6 2009-01-06 1 -0.1061245 -1.7170087
Or using dplyr
library(tidyr)
library(dplyr)
colnames(df) <- gsub("\\.(.{2})$", "_\\1", colnames(df))
df %>%
gather(loop_number, "Q3", starts_with("Q3")) %>%
separate(loop_number,c("L1", "L2"), sep="_") %>%
spread(L1, Q3) %>%
select(-L2) %>%
head()
# id time Q3.2 Q3.3
#1 1 2009-01-01 1.3709584 0.4554501
#2 1 2009-01-01 1.3048697 0.2059986
#3 1 2009-01-01 -0.3066386 0.3219253
#4 2 2009-01-02 -0.5646982 0.7048373
#5 2 2009-01-02 2.2866454 -0.3610573
#6 2 2009-01-02 -1.7813084 -0.7838389
With new version of tidyr, we can use pivot_longer to reshape multiple columns. (Using the changed column names from gsub above)
library(dplyr)
library(tidyr)
df %>%
pivot_longer(cols = starts_with("Q3"),
names_to = c(".value", "Q3"), names_sep = "_") %>%
select(-Q3)
# A tibble: 30 x 4
# id time Q3.2 Q3.3
# <int> <date> <dbl> <dbl>
# 1 1 2009-01-01 0.974 1.47
# 2 1 2009-01-01 -0.849 -0.513
# 3 1 2009-01-01 0.894 0.0442
# 4 2 2009-01-02 2.04 -0.553
# 5 2 2009-01-02 0.694 0.0972
# 6 2 2009-01-02 -1.11 1.85
# 7 3 2009-01-03 0.413 0.733
# 8 3 2009-01-03 -0.896 -0.271
#9 3 2009-01-03 0.509 -0.0512
#10 4 2009-01-04 1.81 0.668
# … with 20 more rows
NOTE: Values are different because there was no set seed in creating the input dataset
23
With the recent update to melt.data.table, we can now melt multiple columns. With that, we can do:
require(data.table) ## 1.9.5
melt(setDT(df), id=1:2, measure=patterns("^Q3.2", "^Q3.3"),
value.name=c("Q3.2", "Q3.3"), variable.name="loop_number")
# id time loop_number Q3.2 Q3.3
# 1: 1 2009-01-01 1 -0.433978480 0.41227209
# 2: 2 2009-01-02 1 -0.567995351 0.30701144
# 3: 3 2009-01-03 1 -0.092041353 -0.96024077
# 4: 4 2009-01-04 1 1.137433487 0.60603396
# 5: 5 2009-01-05 1 -1.071498263 -0.01655584
# 6: 6 2009-01-06 1 -0.048376809 0.55889996
# 7: 7 2009-01-07 1 -0.007312176 0.69872938
You can get the development version from here.
32
It's not at all related to "tidyr" and "dplyr", but here's another option to consider: merged.stack from my "splitstackshape" package, V1.4.0 and above.
library(splitstackshape)
merged.stack(df, id.vars = c("id", "time"),
var.stubs = c("Q3.2.", "Q3.3."),
sep = "var.stubs")
# id time .time_1 Q3.2. Q3.3.
# 1: 1 2009-01-01 1. -0.62645381 1.35867955
# 2: 1 2009-01-01 2. 1.51178117 -0.16452360
# 3: 1 2009-01-01 3. 0.91897737 0.39810588
# 4: 2 2009-01-02 1. 0.18364332 -0.10278773
# 5: 2 2009-01-02 2. 0.38984324 -0.25336168
# 6: 2 2009-01-02 3. 0.78213630 -0.61202639
# 7: 3 2009-01-03 1. -0.83562861 0.38767161
# <<:::SNIP:::>>
# 24: 8 2009-01-08 3. -1.47075238 -1.04413463
# 25: 9 2009-01-09 1. 0.57578135 1.10002537
# 26: 9 2009-01-09 2. 0.82122120 -0.11234621
# 27: 9 2009-01-09 3. -0.47815006 0.56971963
# 28: 10 2009-01-10 1. -0.30538839 0.76317575
# 29: 10 2009-01-10 2. 0.59390132 0.88110773
# 30: 10 2009-01-10 3. 0.41794156 -0.13505460
# id time .time_1 Q3.2. Q3.3.
In case you are like me, and cannot work out how to use "regular expression with capturing groups" for extract, the following code replicates the extract(...) line in Hadleys' answer:
df %>%
gather(question_number, value, starts_with("Q3.")) %>%
mutate(loop_number = str_sub(question_number,-2,-2), question_number = str_sub(question_number,1,4)) %>%
select(id, time, loop_number, question_number, value) %>%
spread(key = question_number, value = value)
The problem here is that the initial gather forms a key column that is actually a combination of two keys. I chose to use mutate in my original solution in the comments to split this column into two columns with equivalent info, a loop_number column and a question_number column. spread can then be used to transform the long form data, which are key value pairs (question_number, value) to wide form data.
42
If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
Donate Us With
