library(tidyverse)For each exercise, tidy the data frame. Before you write any code examine the structure of the data frame and mentally (or with pen-and-paper) sketch out what you think the tidy data structure should be.
library(rcfss)
race## # A tibble: 4 x 8
## Name `50` `100` `150` `200` `250` `300` `350`
## <fctr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Carla 1.2 1.8 2.2 2.3 3.0 2.5 1.8
## 2 Mace 1.5 1.1 1.9 2.0 3.6 3.0 2.5
## 3 Lea 1.7 1.6 2.3 2.7 2.6 2.2 2.6
## 4 Karen 1.3 1.7 1.9 2.2 3.2 1.5 1.9Important info:
Name - pretty obvious50:350 - column names define different lengths of time
Tidy data structure
## # A tibble: 28 x 3
## Name Time Score
## <fct> <int> <dbl>
## 1 Carla 50 1.20
## 2 Carla 100 1.80
## 3 Carla 150 2.20
## 4 Carla 200 2.30
## 5 Carla 250 3.00
## 6 Carla 300 2.50
## 7 Carla 350 1.80
## 8 Karen 50 1.30
## 9 Karen 100 1.70
## 10 Karen 150 1.90
## # ... with 18 more rows
This is essentially a gathering operation. Except for the Name column, the remaining columns are actually one variable spread across multiple columns. The column names are a distinct variable, and the columns’ values are another variable. We want to gather these columns. The key will tell us the original column name, and the value will give us the values in the cells. Because the column names are actually numeric values, we set convert = TRUE to coerce the new Time column into a numeric column (or vector). (The last line isn’t necessary, but sorts the data frame in a consistent manner.)
race %>%
gather(key = Time, value = Score, -Name, convert = TRUE) %>%
arrange(Name, Time)## # A tibble: 28 x 3
## Name Time Score
## <fct> <int> <dbl>
## 1 Carla 50 1.20
## 2 Carla 100 1.80
## 3 Carla 150 2.20
## 4 Carla 200 2.30
## 5 Carla 250 3.00
## 6 Carla 300 2.50
## 7 Carla 350 1.80
## 8 Karen 50 1.30
## 9 Karen 100 1.70
## 10 Karen 150 1.90
## # ... with 18 more rowsresults## # A tibble: 20 x 3
## Ind Treatment value
## <chr> <chr> <int>
## 1 Ind1 Treat 1
## 2 Ind2 Treat 2
## 3 Ind3 Treat 3
## 4 Ind4 Treat 4
## 5 Ind5 Treat 5
## 6 Ind6 Treat 6
## 7 Ind7 Treat 7
## 8 Ind8 Treat 8
## 9 Ind9 Treat 9
## 10 Ind10 Treat 10
## 11 Ind1 Cont 11
## 12 Ind2 Cont 12
## 13 Ind3 Cont 13
## 14 Ind4 Cont 14
## 15 Ind5 Cont 15
## 16 Ind6 Cont 16
## 17 Ind7 Cont 17
## 18 Ind8 Cont 18
## 19 Ind9 Cont 19
## 20 Ind10 Cont 20Important info:
Ind - individual participating in the experimentTreatment - trial type (Treat or Cont)value - result of experiment
Tidy data structure
## # A tibble: 10 x 3
## Ind Cont Treat
## <chr> <int> <int>
## 1 Ind1 11 1
## 2 Ind10 20 10
## 3 Ind2 12 2
## 4 Ind3 13 3
## 5 Ind4 14 4
## 6 Ind5 15 5
## 7 Ind6 16 6
## 8 Ind7 17 7
## 9 Ind8 18 8
## 10 Ind9 19 9
This dataset is not tidy because observations are spread across multiple rows. There only needs to be one row for each individual. Then Treat and Cont can be stored in separate columns.
results %>%
spread(key = Treatment, value = value)## # A tibble: 10 x 3
## Ind Cont Treat
## <chr> <int> <int>
## 1 Ind1 11 1
## 2 Ind10 20 10
## 3 Ind2 12 2
## 4 Ind3 13 3
## 5 Ind4 14 4
## 6 Ind5 15 5
## 7 Ind6 16 6
## 8 Ind7 17 7
## 9 Ind8 18 8
## 10 Ind9 19 9grades## # A tibble: 12 x 6
## ID Test Year Fall Spring Winter
## <int> <int> <int> <int> <int> <int>
## 1 1 1 2008 15 16 19
## 2 1 1 2009 12 13 27
## 3 1 2 2008 22 22 24
## 4 1 2 2009 10 14 20
## 5 2 1 2008 12 13 25
## 6 2 1 2009 16 14 21
## 7 2 2 2008 13 11 29
## 8 2 2 2009 23 20 26
## 9 3 1 2008 11 12 22
## 10 3 1 2009 13 11 27
## 11 3 2 2008 17 12 23
## 12 3 2 2009 14 9 31This one is a bit tougher. Important info:
Tidy data structure
## # A tibble: 18 x 5
## ID Year Quarter Test1 Test2
## <int> <int> <chr> <int> <int>
## 1 1 2008 Fall 15 22
## 2 1 2008 Spring 16 22
## 3 1 2008 Winter 19 24
## 4 1 2009 Fall 12 10
## 5 1 2009 Spring 13 14
## 6 1 2009 Winter 27 20
## 7 2 2008 Fall 12 13
## 8 2 2008 Spring 13 11
## 9 2 2008 Winter 25 29
## 10 2 2009 Fall 16 23
## 11 2 2009 Spring 14 20
## 12 2 2009 Winter 21 26
## 13 3 2008 Fall 11 17
## 14 3 2008 Spring 12 12
## 15 3 2008 Winter 22 23
## 16 3 2009 Fall 13 14
## 17 3 2009 Spring 11 9
## 18 3 2009 Winter 27 31
In this example, the basic unit of observation is the test. Each individual takes two separate tests (labeled 1 and 2) at multiple points in time: during each quarter (Fall, Winter, Spring) as well as in multiple years (2008 and 2009). So our final data frame should contain five columns: ID (identifying the student), Year (year the test was taken), Quarter (quarter in which the test was taken), Test1 (score on the first test), and Test2 (score on the second test).
Let’s start with the gathering operation: we want to gather Fall, Winter, and Spring into a single column (we can use the inclusive select function : to gather these three columns):
grades %>%
gather(key = Quarter, value = Score, Fall:Winter)## # A tibble: 36 x 5
## ID Test Year Quarter Score
## <int> <int> <int> <chr> <int>
## 1 1 1 2008 Fall 15
## 2 1 1 2009 Fall 12
## 3 1 2 2008 Fall 22
## 4 1 2 2009 Fall 10
## 5 2 1 2008 Fall 12
## 6 2 1 2009 Fall 16
## 7 2 2 2008 Fall 13
## 8 2 2 2009 Fall 23
## 9 3 1 2008 Fall 11
## 10 3 1 2009 Fall 13
## # ... with 26 more rowsGood, but now we spread observations across multiple rows. Remember that we want each test to be a separate variable. To do that, we can spread those values across two columns.
grades %>%
gather(key = Quarter, value = Score, Fall:Winter) %>%
spread(key = Test, value = Score)## # A tibble: 18 x 5
## ID Year Quarter `1` `2`
## <int> <int> <chr> <int> <int>
## 1 1 2008 Fall 15 22
## 2 1 2008 Spring 16 22
## 3 1 2008 Winter 19 24
## 4 1 2009 Fall 12 10
## 5 1 2009 Spring 13 14
## 6 1 2009 Winter 27 20
## 7 2 2008 Fall 12 13
## 8 2 2008 Spring 13 11
## 9 2 2008 Winter 25 29
## 10 2 2009 Fall 16 23
## 11 2 2009 Spring 14 20
## 12 2 2009 Winter 21 26
## 13 3 2008 Fall 11 17
## 14 3 2008 Spring 12 12
## 15 3 2008 Winter 22 23
## 16 3 2009 Fall 13 14
## 17 3 2009 Spring 11 9
## 18 3 2009 Winter 27 31This is an acceptable solution, but look what happened to the column names for each test: they’re labeled as numbers (1 and 2). We’d rather give columns text labels, since those are easier to reference in our code. We can use a function called str_c() from the stringr library to add text to the Test column before we spread it. str_c() combines two or more values together to create a single value:1
library(stringr)
grades %>%
gather(key = Quarter, value = Score, Fall:Winter) %>%
mutate(Test = str_c("Test", Test))## # A tibble: 36 x 5
## ID Test Year Quarter Score
## <int> <chr> <int> <chr> <int>
## 1 1 Test1 2008 Fall 15
## 2 1 Test1 2009 Fall 12
## 3 1 Test2 2008 Fall 22
## 4 1 Test2 2009 Fall 10
## 5 2 Test1 2008 Fall 12
## 6 2 Test1 2009 Fall 16
## 7 2 Test2 2008 Fall 13
## 8 2 Test2 2009 Fall 23
## 9 3 Test1 2008 Fall 11
## 10 3 Test1 2009 Fall 13
## # ... with 26 more rowsgrades %>%
gather(key = Quarter, value = Score, Fall:Winter) %>%
mutate(Test = str_c("Test", Test)) %>%
spread(key = Test, value = Score)## # A tibble: 18 x 5
## ID Year Quarter Test1 Test2
## <int> <int> <chr> <int> <int>
## 1 1 2008 Fall 15 22
## 2 1 2008 Spring 16 22
## 3 1 2008 Winter 19 24
## 4 1 2009 Fall 12 10
## 5 1 2009 Spring 13 14
## 6 1 2009 Winter 27 20
## 7 2 2008 Fall 12 13
## 8 2 2008 Spring 13 11
## 9 2 2008 Winter 25 29
## 10 2 2009 Fall 16 23
## 11 2 2009 Spring 14 20
## 12 2 2009 Winter 21 26
## 13 3 2008 Fall 11 17
## 14 3 2008 Spring 12 12
## 15 3 2008 Winter 22 23
## 16 3 2009 Fall 13 14
## 17 3 2009 Spring 11 9
## 18 3 2009 Winter 27 31If we’re cleaning up the data frame, let’s also arrange it in a logical order:
grades %>%
gather(key = Quarter, value = Score, Fall:Winter) %>%
mutate(Test = str_c("Test", Test)) %>%
spread(key = Test, value = Score) %>%
arrange(ID, Year, Quarter)## # A tibble: 18 x 5
## ID Year Quarter Test1 Test2
## <int> <int> <chr> <int> <int>
## 1 1 2008 Fall 15 22
## 2 1 2008 Spring 16 22
## 3 1 2008 Winter 19 24
## 4 1 2009 Fall 12 10
## 5 1 2009 Spring 13 14
## 6 1 2009 Winter 27 20
## 7 2 2008 Fall 12 13
## 8 2 2008 Spring 13 11
## 9 2 2008 Winter 25 29
## 10 2 2009 Fall 16 23
## 11 2 2009 Spring 14 20
## 12 2 2009 Winter 21 26
## 13 3 2008 Fall 11 17
## 14 3 2008 Spring 12 12
## 15 3 2008 Winter 22 23
## 16 3 2009 Fall 13 14
## 17 3 2009 Spring 11 9
## 18 3 2009 Winter 27 31activities## # A tibble: 10 x 8
## id trt work.T1 play.T1 talk.T1 work.T2 play.T2
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 x1 tr 0.65165567 0.8647212 0.53559704 0.27548386 0.3543281
## 2 x2 cnt 0.56773775 0.6153524 0.09308813 0.22890394 0.9364325
## 3 x3 cnt 0.11350898 0.7751099 0.16980304 0.01443391 0.2458664
## 4 x4 tr 0.59592531 0.3555687 0.89983245 0.72896456 0.4731415
## 5 x5 cnt 0.35804998 0.4058500 0.42263761 0.24988047 0.1915609
## 6 x6 cnt 0.42880942 0.7066469 0.74774647 0.16118328 0.5832220
## 7 x7 cnt 0.05190332 0.8382877 0.82265258 0.01704265 0.4594732
## 8 x8 cnt 0.26417767 0.2395891 0.95465365 0.48610035 0.4674340
## 9 x9 tr 0.39879073 0.7707715 0.68544451 0.10290017 0.3998326
## 10 x10 cnt 0.83613414 0.3558977 0.50050323 0.80154700 0.5052856
## # ... with 1 more variables: talk.T2 <dbl>This one is also pretty difficult, but if you think it through conceptually it is doable. The unit of analysis is a single individual (identified by id) observed at two different times (T1 and T2) performing different actions (work, play, talk, and total - note that total is not merely the sum of the first three values). Individuals in this experiment were assigned to either treatment or control (trt) and this information should be preserved in the final data frame.
Tidy data structure
## # A tibble: 20 x 6
## id trt time play talk work
## <chr> <chr> <chr> <dbl> <dbl> <dbl>
## 1 x1 tr T1 0.865 0.536 0.652
## 2 x1 tr T2 0.354 0.0319 0.275
## 3 x10 cnt T1 0.356 0.501 0.836
## 4 x10 cnt T2 0.505 0.219 0.802
## 5 x2 cnt T1 0.615 0.0931 0.568
## 6 x2 cnt T2 0.936 0.114 0.229
## 7 x3 cnt T1 0.775 0.170 0.114
## 8 x3 cnt T2 0.246 0.469 0.0144
## 9 x4 tr T1 0.356 0.900 0.596
## 10 x4 tr T2 0.473 0.397 0.729
## 11 x5 cnt T1 0.406 0.423 0.358
## 12 x5 cnt T2 0.192 0.834 0.250
## 13 x6 cnt T1 0.707 0.748 0.429
## 14 x6 cnt T2 0.583 0.761 0.161
## 15 x7 cnt T1 0.838 0.823 0.0519
## 16 x7 cnt T2 0.459 0.573 0.0170
## 17 x8 cnt T1 0.240 0.955 0.264
## 18 x8 cnt T2 0.467 0.448 0.486
## 19 x9 tr T1 0.771 0.685 0.399
## 20 x9 tr T2 0.400 0.0838 0.103
This is a more complex operation. The basic problem is that we have variables stored in multiple columns (location, with possible values of work, play, and talk). We need to gather these columns into a single column for each variable. But what happens if we just gather them?
activities %>%
gather(key = key, value = value, -id, -trt)## # A tibble: 60 x 4
## id trt key value
## <chr> <chr> <chr> <dbl>
## 1 x1 tr work.T1 0.652
## 2 x2 cnt work.T1 0.568
## 3 x3 cnt work.T1 0.114
## 4 x4 tr work.T1 0.596
## 5 x5 cnt work.T1 0.358
## 6 x6 cnt work.T1 0.429
## 7 x7 cnt work.T1 0.0519
## 8 x8 cnt work.T1 0.264
## 9 x9 tr work.T1 0.399
## 10 x10 cnt work.T1 0.836
## # ... with 50 more rowsWe’ve created a new problem! Actually, two problems:
id x trt pairingkey contains the information on both location (work, play, and talk) as well as when the measurement was taken (T1 or T2)The best approach is to fix the second problem by separating the columns, then spreading the different types of measurements back into their own columns.
activities %>%
gather(key = key, value = value, -id, -trt) %>%
separate(col = key, into = c("location", "time"))## # A tibble: 60 x 5
## id trt location time value
## <chr> <chr> <chr> <chr> <dbl>
## 1 x1 tr work T1 0.652
## 2 x2 cnt work T1 0.568
## 3 x3 cnt work T1 0.114
## 4 x4 tr work T1 0.596
## 5 x5 cnt work T1 0.358
## 6 x6 cnt work T1 0.429
## 7 x7 cnt work T1 0.0519
## 8 x8 cnt work T1 0.264
## 9 x9 tr work T1 0.399
## 10 x10 cnt work T1 0.836
## # ... with 50 more rowsactivities %>%
gather(key = key, value = value, -id, -trt) %>%
separate(col = key, into = c("location", "time")) %>%
spread(key = location, value = value)## # A tibble: 20 x 6
## id trt time play talk work
## <chr> <chr> <chr> <dbl> <dbl> <dbl>
## 1 x1 tr T1 0.865 0.536 0.652
## 2 x1 tr T2 0.354 0.0319 0.275
## 3 x10 cnt T1 0.356 0.501 0.836
## 4 x10 cnt T2 0.505 0.219 0.802
## 5 x2 cnt T1 0.615 0.0931 0.568
## 6 x2 cnt T2 0.936 0.114 0.229
## 7 x3 cnt T1 0.775 0.170 0.114
## 8 x3 cnt T2 0.246 0.469 0.0144
## 9 x4 tr T1 0.356 0.900 0.596
## 10 x4 tr T2 0.473 0.397 0.729
## 11 x5 cnt T1 0.406 0.423 0.358
## 12 x5 cnt T2 0.192 0.834 0.250
## 13 x6 cnt T1 0.707 0.748 0.429
## 14 x6 cnt T2 0.583 0.761 0.161
## 15 x7 cnt T1 0.838 0.823 0.0519
## 16 x7 cnt T2 0.459 0.573 0.0170
## 17 x8 cnt T1 0.240 0.955 0.264
## 18 x8 cnt T2 0.467 0.448 0.486
## 19 x9 tr T1 0.771 0.685 0.399
## 20 x9 tr T2 0.400 0.0838 0.103The whole operation in a single chain (with an arrange() thrown in to sort the data frame):
activities %>%
gather(key = key, value = value, -id, -trt) %>%
separate(key, into = c("location", "time")) %>%
arrange(id, trt, time) %>%
spread(key = location, value = value)## # A tibble: 20 x 6
## id trt time play talk work
## <chr> <chr> <chr> <dbl> <dbl> <dbl>
## 1 x1 tr T1 0.865 0.536 0.652
## 2 x1 tr T2 0.354 0.0319 0.275
## 3 x10 cnt T1 0.356 0.501 0.836
## 4 x10 cnt T2 0.505 0.219 0.802
## 5 x2 cnt T1 0.615 0.0931 0.568
## 6 x2 cnt T2 0.936 0.114 0.229
## 7 x3 cnt T1 0.775 0.170 0.114
## 8 x3 cnt T2 0.246 0.469 0.0144
## 9 x4 tr T1 0.356 0.900 0.596
## 10 x4 tr T2 0.473 0.397 0.729
## 11 x5 cnt T1 0.406 0.423 0.358
## 12 x5 cnt T2 0.192 0.834 0.250
## 13 x6 cnt T1 0.707 0.748 0.429
## 14 x6 cnt T2 0.583 0.761 0.161
## 15 x7 cnt T1 0.838 0.823 0.0519
## 16 x7 cnt T2 0.459 0.573 0.0170
## 17 x8 cnt T1 0.240 0.955 0.264
## 18 x8 cnt T2 0.467 0.448 0.486
## 19 x9 tr T1 0.771 0.685 0.399
## 20 x9 tr T2 0.400 0.0838 0.103devtools::session_info()## Session info -------------------------------------------------------------## setting value
## version R version 3.4.3 (2017-11-30)
## system x86_64, darwin15.6.0
## ui X11
## language (EN)
## collate en_US.UTF-8
## tz America/Chicago
## date 2018-04-09## Packages -----------------------------------------------------------------## package * version date source
## assertthat 0.2.0 2017-04-11 CRAN (R 3.4.0)
## backports 1.1.2 2017-12-13 CRAN (R 3.4.3)
## base * 3.4.3 2017-12-07 local
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## compiler 3.4.3 2017-12-07 local
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## datasets * 3.4.3 2017-12-07 local
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## yaml 2.1.18 2018-03-08 CRAN (R 3.4.4)You might think to use the unite() function here, but it won’t work unless you first create a column which only contains the value Test, like so:
grades %>%
gather(key = Quarter, value = Score, Fall:Winter) %>%
mutate(test_label = "Test") %>%
unite(col = Test, test_label, Test)## # A tibble: 36 x 5
## ID Test Year Quarter Score
## <int> <chr> <int> <chr> <int>
## 1 1 Test_1 2008 Fall 15
## 2 1 Test_1 2009 Fall 12
## 3 1 Test_2 2008 Fall 22
## 4 1 Test_2 2009 Fall 10
## 5 2 Test_1 2008 Fall 12
## 6 2 Test_1 2009 Fall 16
## 7 2 Test_2 2008 Fall 13
## 8 2 Test_2 2009 Fall 23
## 9 3 Test_1 2008 Fall 11
## 10 3 Test_1 2009 Fall 13
## # ... with 26 more rowsThis works, but requires two lines of code instead of one.↩
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