Data Wrangling

Getting Used to Working with Data in R

In programs like SPSS, the data seem to live in a spreadsheet that is always open and available to you. In R, you can see the data whenever you want, but usually it sits unseen inside a variable. It can be disconcerting at first, but think of your data as living in a file somewhere on your hard drive (or online!), and then the data just come for a short visit in R.

The major benefit of working with data in R is that all of the changes, transformations, and restructuring happens in code—which can be recreated at any time. There is usually no need to “save” the data after you have transformed it. The next time you work with the data, you just run your code and all the calculations will transform the data exactly the same way as before.

Why does this matter? If you feel the need to save your data all the time, you end of having multiple copies of it: data.sav, data_restructured.sav, data_new.sav, data_new_final.sav, fixed_data_new_final.sav, restructued_final_with_missing_cases_removed.sav, and so forth and so on. It can be hard to figure out where to start the next time you work with your data. You might not remember which version has errors and which version has what you need.

In general, start with a completely raw data file (one that is exactly the way you started). Resist the temptation to make any changes to it directly. If you must, save a pristine copy somewhere and only then change it. Import your data and make all changes to it with code. One benefit of doing so is that the code documents any changes you would otherwise need to record in your lab notebook.

All the code in one place:

I am going to walk through these steps. You can run this all at once to make sure it works. Then I will explain it step by step.

View All the Code

# Load packages
library(tidyverse)

# Import data
d <- read_csv("https://github.com/wjschne/EDUC5529/raw/master/on_task_multiple_baseline.csv")

# View data
d
glimpse(d)
View(d)

# Filtering

## Cases with fewer than 8 baseline sessions
d %>% 
  filter(baseline_sessions_n < 8)  %>% 
  arrange(-baseline_sessions_n)


## Students with `id` equal to 7
d %>% 
  filter(id == 7)

## Students with scores above 10 on `time_1` AND `time_2`:
d %>% 
  filter(time_1 > 10 & time_2 > 10)

## Students with scores above 10 on `time_1` OR `time_2`:
d %>% 
  filter(time_1 > 10 | time_2 > 10)

# Selecting columns
## Select the id and time_1 variables
d %>% 
  select(id, time_1)

## Select 5 adjacent variables: time_1 throught time_5
d %>% 
  select(time_1:time_5)

## Remove the id variable
d %>% 
  select(-id)

## Select all variables that start with "time" 
d %>% 
  select(starts_with("time"))

# Select all variables end with 0:

d %>% 
  select(ends_with("0"))

# Select all variables that contain "_2"
d %>% 
  select(contains("_2"))

# Renaming variables
## Rename id to become student_id, rename baseline_sessions_n to baseline
d_new <- d %>% 
  rename(student_id = id, 
         baseline = baseline_sessions_n) 

## Rename rename all variables that start with "time_" 
## so that they start with "T_" instead
d %>% 
  rename_with(str_replace, pattern = "time_", replace = "T_")

# Make small data
# Select variables from id to time_3 and filter rows so that 
# id is less than 3 (not inclusive)
d_small <- d %>% 
  select(id:time_3) %>% 
  filter(id < 3)

d_small

# Restructuring from wide to long format:
d_small_longer <- d_small %>%
  pivot_longer(cols = time_1:time_3,
               names_to = "time",
               values_to = "on_task",
               names_prefix = "time_", 
               names_transform = list(time = as.integer)) 

# Restructing from long to wide format:
d_small_longer %>% 
  pivot_wider(names_from = time, 
              values_from = on_task, 
              names_prefix = "time_")


# Restructure whole data from wide to long and create new variables
d_longer <- d %>%
  pivot_longer(cols = time_1:time_60,
               names_to = "time",
               values_to = "on_task",
               names_prefix = "time_", 
               names_transform = list(time = as.integer)) %>% 
    mutate(phase = case_when(
    time <= baseline_sessions_n ~ "Pre-Intervention",
    time <= baseline_sessions_n + intervention_sessions_n ~ "Intervention",
    time > baseline_sessions_n + intervention_sessions_n ~ "Post-Intervention" 
  )) %>% 
  mutate(phase = fct_inorder(phase))

# Plot
d_longer %>% 
  mutate(id = factor(id) %>% fct_reorder(baseline_sessions_n)) %>% 
  ggplot(aes(time, on_task)) +
  geom_line(aes(group = id, color = phase)) + 
  facet_grid(rows = vars(id))

Importing Data from a file

For this task we will use a file that lives here on the web:

on_task_multiple_baseline.csv

You can download it if you want, but you do not need to. You can import it over the web right into R.

We have a .csv file, so we will use the tidyverse function read_csv, from the readr package, which can be loaded with the tidyverse package:

library(tidyverse)
d <- read_csv("https://github.com/wjschne/EDUC5529/raw/master/on_task_multiple_baseline.csv")

Note that we have to assign the data to a variable—d in this case. Otherwise, the data will spill into the console, but R will not remember the data.

Inspecting Data

The read_csv function imports a special data structure called a tibble which is a variation on the traditional data.frame structure. It has some useful defaults that make working with it more predictable and practical.

If the data is too big to print in your console, the tibble will just show the first few rows, the first few columns, and then the remaining column names. It all says what kind of data is in each column and how many rows and columns are in the data.

Whoa! That dataset has a lot of variables! When the there are more variables than rows, we say that the data is “wide.”

An alternate way to view the data is to use the glimpse function:

glimpse(d)

If you really need to see all the data, use the View function:

View(d)

d

id	baseline_sessions_n	intervention_sessions_n	time_1	time_2	time_3	time_4	time_5	time_6	time_7	time_8	time_9	time_10	time_11	time_12	time_13	time_14	time_15	time_16	time_17	time_18	time_19	time_20	time_21	time_22	time_23	time_24	time_25	time_26	time_27	time_28	time_29	time_30	time_31	time_32	time_33	time_34	time_35	time_36	time_37	time_38	time_39	time_40	time_41	time_42	time_43	time_44	time_45	time_46	time_47	time_48	time_49	time_50	time_51	time_52	time_53	time_54	time_55	time_56	time_57	time_58	time_59	time_60
1	22	20	11.481	14.402	6.151	9.254	9.580	8.160	10.125	11.120	9.203	10.838	9.181	8.436	9.977	13.242	9.317	8.276	9.208	10.888	7.225	11.47	8.570	10.583	11.04	13.13	18.00	12.965	23.840	21.40	22.653	30.187	29.49	31.359	29.32	33.78	38.95	37.28	42.57	37.73	44.34	44.61	49.51	49.60	48.72	50.88	51.23	49.04	51.27	49.00	51.13	48.72	50.64	51.24	54.46	48.84	50.35	54.54	52.77	50.68	50.18	50.03
2	18	19	9.114	8.586	11.010	9.356	8.095	8.772	8.953	7.443	11.057	7.590	11.087	10.670	7.843	9.018	11.266	5.886	7.388	8.107	12.111	14.83	13.107	18.156	21.65	22.82	24.85	25.152	29.230	31.50	30.092	34.848	29.83	42.884	40.94	45.12	48.62	48.63	47.46	47.03	48.66	47.54	50.80	50.58	49.93	48.24	49.41	48.22	47.98	47.59	44.75	43.74	49.15	48.02	44.89	48.28	47.23	48.91	44.84	45.47	49.48	50.35
3	5	18	7.244	14.309	7.979	11.175	12.287	13.146	11.694	18.998	16.125	20.296	21.905	21.952	27.114	27.933	27.448	30.747	36.685	37.337	39.197	43.84	44.752	43.358	46.34	47.01	44.50	46.507	44.034	47.02	48.691	43.648	47.70	49.477	48.00	46.17	48.51	43.80	48.55	48.30	43.90	47.10	51.74	40.91	45.88	49.41	45.65	43.96	46.60	44.57	42.60	47.05	46.58	43.50	42.45	47.22	44.16	49.38	45.00	46.11	47.09	44.92
4	32	20	11.297	14.307	14.811	8.678	9.533	7.823	9.561	8.209	12.898	8.244	8.548	8.616	7.586	8.921	9.199	8.711	11.026	10.804	11.591	10.52	9.027	7.848	12.32	10.07	12.20	7.872	8.831	10.43	8.886	8.553	11.84	7.608	11.14	13.53	14.89	19.39	22.95	23.99	20.03	27.08	28.21	27.32	34.87	33.65	36.78	37.48	40.53	41.81	40.75	45.51	45.75	47.57	56.03	51.27	51.98	47.75	48.65	50.63	48.45	50.34
5	19	20	10.667	6.804	8.303	14.498	6.868	12.367	9.414	9.444	8.745	14.305	10.961	10.451	14.828	6.272	10.096	12.403	8.464	8.629	9.839	14.45	13.049	16.131	18.19	17.92	23.90	22.576	23.059	25.57	29.038	31.427	33.96	36.342	38.47	41.44	40.76	44.75	47.46	48.82	52.24	50.29	51.08	48.52	51.01	49.59	50.64	47.94	45.46	52.18	48.01	47.01	50.80	47.71	54.90	49.21	52.97	51.58	47.75	53.56	48.21	50.77
6	24	20	13.027	7.471	13.680	5.769	10.789	8.699	9.323	8.312	12.865	9.967	6.926	7.546	10.175	12.912	5.829	8.554	10.514	12.225	9.299	9.09	9.931	10.490	12.35	11.75	10.45	13.391	16.231	16.95	21.799	21.528	21.88	25.989	26.67	32.07	32.79	35.02	37.48	32.87	35.37	41.40	46.80	49.06	46.94	50.27	54.22	50.03	51.80	49.74	48.78	48.08	48.62	48.07	48.06	46.87	50.26	44.34	51.32	48.04	54.79	48.74
7	10	19	9.695	9.174	10.637	9.136	7.362	14.267	9.448	12.378	6.093	5.854	9.539	14.262	13.727	19.425	20.669	22.576	26.741	24.729	28.852	30.76	31.628	34.361	36.10	40.92	39.43	41.137	43.049	46.76	46.199	48.769	47.37	49.477	52.47	49.37	44.54	48.07	46.38	47.66	45.35	49.10	48.02	47.50	49.15	49.00	47.31	46.44	49.66	48.07	46.29	45.44	48.75	49.89	49.42	48.00	50.41	46.83	46.45	48.05	48.95	52.76
8	13	20	10.596	11.746	10.903	11.403	8.263	7.589	8.232	7.295	10.028	10.105	9.797	11.889	7.041	12.705	13.626	14.990	19.614	23.575	22.594	25.97	26.860	30.064	29.59	31.23	34.26	36.188	38.330	42.61	43.275	42.504	42.51	51.437	53.72	50.31	48.79	51.41	53.23	50.96	53.23	48.40	48.72	50.48	51.48	50.61	48.03	50.54	50.11	49.69	50.82	45.58	50.14	49.66	47.79	49.99	52.47	50.68	49.15	51.39	47.67	50.32

Study Design

This dataset has 8 children, one in each row. Each child was given an intervention designed to increase “time on task” in the classroom so that the child will stay focused and complete his or her work. The study has a multiple baseline design. That is, each child was observed for a “baseline” period for a number days before the intervention was implemented. Each child’s baseline lasted a different length of time. Some children were observed for a short time before the intervention, and some were observed much longer. After the intervention ended, each child was observed for a number of days to see if the intervention’s effect diminished.

The id column is just a number to identify each child. The baseline_sessions_n variable indicates how many days the child was observed before the intervention began. The intervention_sessions_n variable indications the number days the intervention lasted. The remainding variables time_1–time_60 are the number of minutes the child stayed on task during the observation period each day for 60 days.

Tidy Data

You might think that the data is ready to be analyzed, but it isn’t. The data might be perfectly structured for data entry, but it is not quite “tidy” yet.

The phrase tidy data was popularized by Hadley Wickham, but it mostly refers to something a little less catchy called database normalization. In data analysis, we want:

1. Each variable must have its own column.
2. Each observation must have its own row.
3. Each value must have its own cell.

—From Wickham & Grolemund (2017):

This is how R likes its data. Whenever you find yourself fighting with confusing or tedious tasks in R, there is a good chance your data has violated one of these rules. Converting your data to this “tidy” format will likely simplify your task.

In the current data, there are two primary sources of untidiness.

• We need a single variable to indicate “time on task.” Unfortunately, the time on task variable we would like to have is currently spread across 60 variables, time_1–time_60.
• We also would like a variable called “time” to indicate which day the observation took place. Right now, the time variable is lurking in the column names time_1–time_60

Later we need to identify which treatment phase the observation was conducted in, “pre-intervention,” “intervention,” or “post-intervention.” That information is sitting in an unusual form right right now in the baseline_sessions_n and intervention_session_n variables.

Data Filtering

The filter function selects rows that meet certain conditions. For example, suppose we want to see rows with baseline_sessions_n less than 8:

The %>% is the “pipe” function. It means “and then.” It inserts the output of the previous function into the next function. If we did not use the pipe, we would type filter(d, baseline_sessions_n < 8) instead.

d %>% 
  filter(baseline_sessions_n < 8)

id	baseline_sessions_n	intervention_sessions_n	time_1	time_2	time_3	time_4	time_5	time_6	time_7	time_8	time_9	time_10	time_11	time_12	time_13	time_14	time_15	time_16	time_17	time_18	time_19	time_20	time_21	time_22	time_23	time_24	time_25	time_26	time_27	time_28	time_29	time_30	time_31	time_32	time_33	time_34	time_35	time_36	time_37	time_38	time_39	time_40	time_41	time_42	time_43	time_44	time_45	time_46	time_47	time_48	time_49	time_50	time_51	time_52	time_53	time_54	time_55	time_56	time_57	time_58	time_59	time_60
3	5	18	7.244	14.31	7.979	11.18	12.29	13.15	11.69	19	16.13	20.3	21.91	21.95	27.11	27.93	27.45	30.75	36.69	37.34	39.2	43.84	44.75	43.36	46.34	47.01	44.5	46.51	44.03	47.02	48.69	43.65	47.7	49.48	48	46.17	48.51	43.8	48.55	48.3	43.9	47.1	51.74	40.91	45.88	49.41	45.65	43.96	46.6	44.57	42.6	47.05	46.58	43.5	42.45	47.22	44.16	49.38	45	46.11	47.09	44.92

In this example, there is little advantage to using the pipe because there is only one step. However, when many steps are strung together, the pipe makes the code much easier to understand.

The double equal sign == performs a test to see if two things are the same. Forgetting that you need two equal signs is a really common error.

Students with id equal to 7:

d %>% 
  filter(id == 7)

id	baseline_sessions_n	intervention_sessions_n	time_1	time_2	time_3	time_4	time_5	time_6	time_7	time_8	time_9	time_10	time_11	time_12	time_13	time_14	time_15	time_16	time_17	time_18	time_19	time_20	time_21	time_22	time_23	time_24	time_25	time_26	time_27	time_28	time_29	time_30	time_31	time_32	time_33	time_34	time_35	time_36	time_37	time_38	time_39	time_40	time_41	time_42	time_43	time_44	time_45	time_46	time_47	time_48	time_49	time_50	time_51	time_52	time_53	time_54	time_55	time_56	time_57	time_58	time_59	time_60
7	10	19	9.695	9.174	10.64	9.136	7.362	14.27	9.448	12.38	6.093	5.854	9.539	14.26	13.73	19.42	20.67	22.58	26.74	24.73	28.85	30.76	31.63	34.36	36.1	40.92	39.43	41.14	43.05	46.76	46.2	48.77	47.37	49.48	52.47	49.37	44.54	48.07	46.38	47.66	45.35	49.1	48.02	47.5	49.15	49	47.31	46.44	49.66	48.07	46.29	45.44	48.75	49.89	49.42	48	50.41	46.83	46.45	48.05	48.95	52.76

You can filter for more than one condition.

Students with scores above 10 on time_1 AND time_2:

d %>% 
  filter(time_1 > 10 & time_2 > 10)

id	baseline_sessions_n	intervention_sessions_n	time_1	time_2	time_3	time_4	time_5	time_6	time_7	time_8	time_9	time_10	time_11	time_12	time_13	time_14	time_15	time_16	time_17	time_18	time_19	time_20	time_21	time_22	time_23	time_24	time_25	time_26	time_27	time_28	time_29	time_30	time_31	time_32	time_33	time_34	time_35	time_36	time_37	time_38	time_39	time_40	time_41	time_42	time_43	time_44	time_45	time_46	time_47	time_48	time_49	time_50	time_51	time_52	time_53	time_54	time_55	time_56	time_57	time_58	time_59	time_60
1	22	20	11.48	14.40	6.151	9.254	9.580	8.160	10.125	11.120	9.203	10.838	9.181	8.436	9.977	13.242	9.317	8.276	9.208	10.89	7.225	11.47	8.570	10.583	11.04	13.13	18.00	12.965	23.840	21.40	22.653	30.187	29.49	31.359	29.32	33.78	38.95	37.28	42.57	37.73	44.34	44.61	49.51	49.60	48.72	50.88	51.23	49.04	51.27	49.00	51.13	48.72	50.64	51.24	54.46	48.84	50.35	54.54	52.77	50.68	50.18	50.03
4	32	20	11.30	14.31	14.811	8.678	9.533	7.823	9.561	8.209	12.898	8.244	8.548	8.616	7.586	8.921	9.199	8.711	11.026	10.80	11.591	10.52	9.027	7.848	12.32	10.07	12.20	7.872	8.831	10.43	8.886	8.553	11.84	7.608	11.14	13.53	14.89	19.39	22.95	23.99	20.03	27.08	28.21	27.32	34.87	33.65	36.78	37.48	40.53	41.81	40.75	45.51	45.75	47.57	56.03	51.27	51.98	47.75	48.65	50.63	48.45	50.34
8	13	20	10.60	11.75	10.903	11.403	8.263	7.589	8.232	7.295	10.028	10.105	9.797	11.889	7.041	12.705	13.626	14.990	19.614	23.58	22.594	25.97	26.860	30.064	29.59	31.23	34.26	36.188	38.330	42.61	43.275	42.504	42.51	51.437	53.72	50.31	48.79	51.41	53.23	50.96	53.23	48.40	48.72	50.48	51.48	50.61	48.03	50.54	50.11	49.69	50.82	45.58	50.14	49.66	47.79	49.99	52.47	50.68	49.15	51.39	47.67	50.32

Students with scores above 10 on time_1 OR time_2:

d %>% 
  filter(time_1 > 10 | time_2 > 10)

id	baseline_sessions_n	intervention_sessions_n	time_1	time_2	time_3	time_4	time_5	time_6	time_7	time_8	time_9	time_10	time_11	time_12	time_13	time_14	time_15	time_16	time_17	time_18	time_19	time_20	time_21	time_22	time_23	time_24	time_25	time_26	time_27	time_28	time_29	time_30	time_31	time_32	time_33	time_34	time_35	time_36	time_37	time_38	time_39	time_40	time_41	time_42	time_43	time_44	time_45	time_46	time_47	time_48	time_49	time_50	time_51	time_52	time_53	time_54	time_55	time_56	time_57	time_58	time_59	time_60
1	22	20	11.481	14.402	6.151	9.254	9.580	8.160	10.125	11.120	9.203	10.838	9.181	8.436	9.977	13.242	9.317	8.276	9.208	10.888	7.225	11.47	8.570	10.583	11.04	13.13	18.00	12.965	23.840	21.40	22.653	30.187	29.49	31.359	29.32	33.78	38.95	37.28	42.57	37.73	44.34	44.61	49.51	49.60	48.72	50.88	51.23	49.04	51.27	49.00	51.13	48.72	50.64	51.24	54.46	48.84	50.35	54.54	52.77	50.68	50.18	50.03
3	5	18	7.244	14.309	7.979	11.175	12.287	13.146	11.694	18.998	16.125	20.296	21.905	21.952	27.114	27.933	27.448	30.747	36.685	37.337	39.197	43.84	44.752	43.358	46.34	47.01	44.50	46.507	44.034	47.02	48.691	43.648	47.70	49.477	48.00	46.17	48.51	43.80	48.55	48.30	43.90	47.10	51.74	40.91	45.88	49.41	45.65	43.96	46.60	44.57	42.60	47.05	46.58	43.50	42.45	47.22	44.16	49.38	45.00	46.11	47.09	44.92
4	32	20	11.297	14.307	14.811	8.678	9.533	7.823	9.561	8.209	12.898	8.244	8.548	8.616	7.586	8.921	9.199	8.711	11.026	10.804	11.591	10.52	9.027	7.848	12.32	10.07	12.20	7.872	8.831	10.43	8.886	8.553	11.84	7.608	11.14	13.53	14.89	19.39	22.95	23.99	20.03	27.08	28.21	27.32	34.87	33.65	36.78	37.48	40.53	41.81	40.75	45.51	45.75	47.57	56.03	51.27	51.98	47.75	48.65	50.63	48.45	50.34
5	19	20	10.667	6.804	8.303	14.498	6.868	12.367	9.414	9.444	8.745	14.305	10.961	10.451	14.828	6.272	10.096	12.403	8.464	8.629	9.839	14.45	13.049	16.131	18.19	17.92	23.90	22.576	23.059	25.57	29.038	31.427	33.96	36.342	38.47	41.44	40.76	44.75	47.46	48.82	52.24	50.29	51.08	48.52	51.01	49.59	50.64	47.94	45.46	52.18	48.01	47.01	50.80	47.71	54.90	49.21	52.97	51.58	47.75	53.56	48.21	50.77
6	24	20	13.027	7.471	13.680	5.769	10.789	8.699	9.323	8.312	12.865	9.967	6.926	7.546	10.175	12.912	5.829	8.554	10.514	12.225	9.299	9.09	9.931	10.490	12.35	11.75	10.45	13.391	16.231	16.95	21.799	21.528	21.88	25.989	26.67	32.07	32.79	35.02	37.48	32.87	35.37	41.40	46.80	49.06	46.94	50.27	54.22	50.03	51.80	49.74	48.78	48.08	48.62	48.07	48.06	46.87	50.26	44.34	51.32	48.04	54.79	48.74
8	13	20	10.596	11.746	10.903	11.403	8.263	7.589	8.232	7.295	10.028	10.105	9.797	11.889	7.041	12.705	13.626	14.990	19.614	23.575	22.594	25.97	26.860	30.064	29.59	31.23	34.26	36.188	38.330	42.61	43.275	42.504	42.51	51.437	53.72	50.31	48.79	51.41	53.23	50.96	53.23	48.40	48.72	50.48	51.48	50.61	48.03	50.54	50.11	49.69	50.82	45.58	50.14	49.66	47.79	49.99	52.47	50.68	49.15	51.39	47.67	50.32

Column selection

We can select columns by naming just the columns we want:

d %>% 
  select(id, time_1)

id	time_1
1	11.481
2	9.114
3	7.244
4	11.297
5	10.667
6	13.027
7	9.695
8	10.596

If the columns we want are adjacent, we can use the : operator to select a sequence:

d %>% 
  select(time_1:time_5)

time_1	time_2	time_3	time_4	time_5
11.481	14.402	6.151	9.254	9.580
9.114	8.586	11.010	9.356	8.095
7.244	14.309	7.979	11.175	12.287
11.297	14.307	14.811	8.678	9.533
10.667	6.804	8.303	14.498	6.868
13.027	7.471	13.680	5.769	10.789
9.695	9.174	10.637	9.136	7.362
10.596	11.746	10.903	11.403	8.263

If want everything but a particular column (or columns), use the - operator:

d %>% 
  select(-id)

baseline_sessions_n	intervention_sessions_n	time_1	time_2	time_3	time_4	time_5	time_6	time_7	time_8	time_9	time_10	time_11	time_12	time_13	time_14	time_15	time_16	time_17	time_18	time_19	time_20	time_21	time_22	time_23	time_24	time_25	time_26	time_27	time_28	time_29	time_30	time_31	time_32	time_33	time_34	time_35	time_36	time_37	time_38	time_39	time_40	time_41	time_42	time_43	time_44	time_45	time_46	time_47	time_48	time_49	time_50	time_51	time_52	time_53	time_54	time_55	time_56	time_57	time_58	time_59	time_60
22	20	11.481	14.402	6.151	9.254	9.580	8.160	10.125	11.120	9.203	10.838	9.181	8.436	9.977	13.242	9.317	8.276	9.208	10.888	7.225	11.47	8.570	10.583	11.04	13.13	18.00	12.965	23.840	21.40	22.653	30.187	29.49	31.359	29.32	33.78	38.95	37.28	42.57	37.73	44.34	44.61	49.51	49.60	48.72	50.88	51.23	49.04	51.27	49.00	51.13	48.72	50.64	51.24	54.46	48.84	50.35	54.54	52.77	50.68	50.18	50.03
18	19	9.114	8.586	11.010	9.356	8.095	8.772	8.953	7.443	11.057	7.590	11.087	10.670	7.843	9.018	11.266	5.886	7.388	8.107	12.111	14.83	13.107	18.156	21.65	22.82	24.85	25.152	29.230	31.50	30.092	34.848	29.83	42.884	40.94	45.12	48.62	48.63	47.46	47.03	48.66	47.54	50.80	50.58	49.93	48.24	49.41	48.22	47.98	47.59	44.75	43.74	49.15	48.02	44.89	48.28	47.23	48.91	44.84	45.47	49.48	50.35
5	18	7.244	14.309	7.979	11.175	12.287	13.146	11.694	18.998	16.125	20.296	21.905	21.952	27.114	27.933	27.448	30.747	36.685	37.337	39.197	43.84	44.752	43.358	46.34	47.01	44.50	46.507	44.034	47.02	48.691	43.648	47.70	49.477	48.00	46.17	48.51	43.80	48.55	48.30	43.90	47.10	51.74	40.91	45.88	49.41	45.65	43.96	46.60	44.57	42.60	47.05	46.58	43.50	42.45	47.22	44.16	49.38	45.00	46.11	47.09	44.92
32	20	11.297	14.307	14.811	8.678	9.533	7.823	9.561	8.209	12.898	8.244	8.548	8.616	7.586	8.921	9.199	8.711	11.026	10.804	11.591	10.52	9.027	7.848	12.32	10.07	12.20	7.872	8.831	10.43	8.886	8.553	11.84	7.608	11.14	13.53	14.89	19.39	22.95	23.99	20.03	27.08	28.21	27.32	34.87	33.65	36.78	37.48	40.53	41.81	40.75	45.51	45.75	47.57	56.03	51.27	51.98	47.75	48.65	50.63	48.45	50.34
19	20	10.667	6.804	8.303	14.498	6.868	12.367	9.414	9.444	8.745	14.305	10.961	10.451	14.828	6.272	10.096	12.403	8.464	8.629	9.839	14.45	13.049	16.131	18.19	17.92	23.90	22.576	23.059	25.57	29.038	31.427	33.96	36.342	38.47	41.44	40.76	44.75	47.46	48.82	52.24	50.29	51.08	48.52	51.01	49.59	50.64	47.94	45.46	52.18	48.01	47.01	50.80	47.71	54.90	49.21	52.97	51.58	47.75	53.56	48.21	50.77
24	20	13.027	7.471	13.680	5.769	10.789	8.699	9.323	8.312	12.865	9.967	6.926	7.546	10.175	12.912	5.829	8.554	10.514	12.225	9.299	9.09	9.931	10.490	12.35	11.75	10.45	13.391	16.231	16.95	21.799	21.528	21.88	25.989	26.67	32.07	32.79	35.02	37.48	32.87	35.37	41.40	46.80	49.06	46.94	50.27	54.22	50.03	51.80	49.74	48.78	48.08	48.62	48.07	48.06	46.87	50.26	44.34	51.32	48.04	54.79	48.74
10	19	9.695	9.174	10.637	9.136	7.362	14.267	9.448	12.378	6.093	5.854	9.539	14.262	13.727	19.425	20.669	22.576	26.741	24.729	28.852	30.76	31.628	34.361	36.10	40.92	39.43	41.137	43.049	46.76	46.199	48.769	47.37	49.477	52.47	49.37	44.54	48.07	46.38	47.66	45.35	49.10	48.02	47.50	49.15	49.00	47.31	46.44	49.66	48.07	46.29	45.44	48.75	49.89	49.42	48.00	50.41	46.83	46.45	48.05	48.95	52.76
13	20	10.596	11.746	10.903	11.403	8.263	7.589	8.232	7.295	10.028	10.105	9.797	11.889	7.041	12.705	13.626	14.990	19.614	23.575	22.594	25.97	26.860	30.064	29.59	31.23	34.26	36.188	38.330	42.61	43.275	42.504	42.51	51.437	53.72	50.31	48.79	51.41	53.23	50.96	53.23	48.40	48.72	50.48	51.48	50.61	48.03	50.54	50.11	49.69	50.82	45.58	50.14	49.66	47.79	49.99	52.47	50.68	49.15	51.39	47.67	50.32

If columns have similar names, we can use the starts_with, ends_width, or contains functions.

Select all variables that start with the phrase time:

d %>% 
  select(starts_with("time"))

time_1	time_2	time_3	time_4	time_5	time_6	time_7	time_8	time_9	time_10	time_11	time_12	time_13	time_14	time_15	time_16	time_17	time_18	time_19	time_20	time_21	time_22	time_23	time_24	time_25	time_26	time_27	time_28	time_29	time_30	time_31	time_32	time_33	time_34	time_35	time_36	time_37	time_38	time_39	time_40	time_41	time_42	time_43	time_44	time_45	time_46	time_47	time_48	time_49	time_50	time_51	time_52	time_53	time_54	time_55	time_56	time_57	time_58	time_59	time_60
11.481	14.402	6.151	9.254	9.580	8.160	10.125	11.120	9.203	10.838	9.181	8.436	9.977	13.242	9.317	8.276	9.208	10.888	7.225	11.47	8.570	10.583	11.04	13.13	18.00	12.965	23.840	21.40	22.653	30.187	29.49	31.359	29.32	33.78	38.95	37.28	42.57	37.73	44.34	44.61	49.51	49.60	48.72	50.88	51.23	49.04	51.27	49.00	51.13	48.72	50.64	51.24	54.46	48.84	50.35	54.54	52.77	50.68	50.18	50.03
9.114	8.586	11.010	9.356	8.095	8.772	8.953	7.443	11.057	7.590	11.087	10.670	7.843	9.018	11.266	5.886	7.388	8.107	12.111	14.83	13.107	18.156	21.65	22.82	24.85	25.152	29.230	31.50	30.092	34.848	29.83	42.884	40.94	45.12	48.62	48.63	47.46	47.03	48.66	47.54	50.80	50.58	49.93	48.24	49.41	48.22	47.98	47.59	44.75	43.74	49.15	48.02	44.89	48.28	47.23	48.91	44.84	45.47	49.48	50.35
7.244	14.309	7.979	11.175	12.287	13.146	11.694	18.998	16.125	20.296	21.905	21.952	27.114	27.933	27.448	30.747	36.685	37.337	39.197	43.84	44.752	43.358	46.34	47.01	44.50	46.507	44.034	47.02	48.691	43.648	47.70	49.477	48.00	46.17	48.51	43.80	48.55	48.30	43.90	47.10	51.74	40.91	45.88	49.41	45.65	43.96	46.60	44.57	42.60	47.05	46.58	43.50	42.45	47.22	44.16	49.38	45.00	46.11	47.09	44.92
11.297	14.307	14.811	8.678	9.533	7.823	9.561	8.209	12.898	8.244	8.548	8.616	7.586	8.921	9.199	8.711	11.026	10.804	11.591	10.52	9.027	7.848	12.32	10.07	12.20	7.872	8.831	10.43	8.886	8.553	11.84	7.608	11.14	13.53	14.89	19.39	22.95	23.99	20.03	27.08	28.21	27.32	34.87	33.65	36.78	37.48	40.53	41.81	40.75	45.51	45.75	47.57	56.03	51.27	51.98	47.75	48.65	50.63	48.45	50.34
10.667	6.804	8.303	14.498	6.868	12.367	9.414	9.444	8.745	14.305	10.961	10.451	14.828	6.272	10.096	12.403	8.464	8.629	9.839	14.45	13.049	16.131	18.19	17.92	23.90	22.576	23.059	25.57	29.038	31.427	33.96	36.342	38.47	41.44	40.76	44.75	47.46	48.82	52.24	50.29	51.08	48.52	51.01	49.59	50.64	47.94	45.46	52.18	48.01	47.01	50.80	47.71	54.90	49.21	52.97	51.58	47.75	53.56	48.21	50.77
13.027	7.471	13.680	5.769	10.789	8.699	9.323	8.312	12.865	9.967	6.926	7.546	10.175	12.912	5.829	8.554	10.514	12.225	9.299	9.09	9.931	10.490	12.35	11.75	10.45	13.391	16.231	16.95	21.799	21.528	21.88	25.989	26.67	32.07	32.79	35.02	37.48	32.87	35.37	41.40	46.80	49.06	46.94	50.27	54.22	50.03	51.80	49.74	48.78	48.08	48.62	48.07	48.06	46.87	50.26	44.34	51.32	48.04	54.79	48.74
9.695	9.174	10.637	9.136	7.362	14.267	9.448	12.378	6.093	5.854	9.539	14.262	13.727	19.425	20.669	22.576	26.741	24.729	28.852	30.76	31.628	34.361	36.10	40.92	39.43	41.137	43.049	46.76	46.199	48.769	47.37	49.477	52.47	49.37	44.54	48.07	46.38	47.66	45.35	49.10	48.02	47.50	49.15	49.00	47.31	46.44	49.66	48.07	46.29	45.44	48.75	49.89	49.42	48.00	50.41	46.83	46.45	48.05	48.95	52.76
10.596	11.746	10.903	11.403	8.263	7.589	8.232	7.295	10.028	10.105	9.797	11.889	7.041	12.705	13.626	14.990	19.614	23.575	22.594	25.97	26.860	30.064	29.59	31.23	34.26	36.188	38.330	42.61	43.275	42.504	42.51	51.437	53.72	50.31	48.79	51.41	53.23	50.96	53.23	48.40	48.72	50.48	51.48	50.61	48.03	50.54	50.11	49.69	50.82	45.58	50.14	49.66	47.79	49.99	52.47	50.68	49.15	51.39	47.67	50.32

Select all variables end with 0:

d %>% 
  select(ends_with("0"))

time_10	time_20	time_30	time_40	time_50	time_60
10.838	11.47	30.187	44.61	48.72	50.03
7.590	14.83	34.848	47.54	43.74	50.35
20.296	43.84	43.648	47.10	47.05	44.92
8.244	10.52	8.553	27.08	45.51	50.34
14.305	14.45	31.427	50.29	47.01	50.77
9.967	9.09	21.528	41.40	48.08	48.74
5.854	30.76	48.769	49.10	45.44	52.76
10.105	25.97	42.504	48.40	45.58	50.32

Select all variables that contain the phrase “_2”

d %>% 
  select(contains("_2"))

time_2	time_20	time_21	time_22	time_23	time_24	time_25	time_26	time_27	time_28	time_29
14.402	11.47	8.570	10.583	11.04	13.13	18.00	12.965	23.840	21.40	22.653
8.586	14.83	13.107	18.156	21.65	22.82	24.85	25.152	29.230	31.50	30.092
14.309	43.84	44.752	43.358	46.34	47.01	44.50	46.507	44.034	47.02	48.691
14.307	10.52	9.027	7.848	12.32	10.07	12.20	7.872	8.831	10.43	8.886
6.804	14.45	13.049	16.131	18.19	17.92	23.90	22.576	23.059	25.57	29.038
7.471	9.09	9.931	10.490	12.35	11.75	10.45	13.391	16.231	16.95	21.799
9.174	30.76	31.628	34.361	36.10	40.92	39.43	41.137	43.049	46.76	46.199
11.746	25.97	26.860	30.064	29.59	31.23	34.26	36.188	38.330	42.61	43.275

The - operator works with these selection functions to exclude variables:

d %>% 
  select(-contains("time_"))

id	baseline_sessions_n	intervention_sessions_n
1	22	20
2	18	19
3	5	18
4	32	20
5	19	20
6	24	20
7	10	19
8	13	20

Renaming variables

d %>% 
  rename(student_id = id, 
         baseline = baseline_sessions_n)

student_id	baseline	intervention_sessions_n	time_1	time_2	time_3	time_4	time_5	time_6	time_7	time_8	time_9	time_10	time_11	time_12	time_13	time_14	time_15	time_16	time_17	time_18	time_19	time_20	time_21	time_22	time_23	time_24	time_25	time_26	time_27	time_28	time_29	time_30	time_31	time_32	time_33	time_34	time_35	time_36	time_37	time_38	time_39	time_40	time_41	time_42	time_43	time_44	time_45	time_46	time_47	time_48	time_49	time_50	time_51	time_52	time_53	time_54	time_55	time_56	time_57	time_58	time_59	time_60
1	22	20	11.481	14.402	6.151	9.254	9.580	8.160	10.125	11.120	9.203	10.838	9.181	8.436	9.977	13.242	9.317	8.276	9.208	10.888	7.225	11.47	8.570	10.583	11.04	13.13	18.00	12.965	23.840	21.40	22.653	30.187	29.49	31.359	29.32	33.78	38.95	37.28	42.57	37.73	44.34	44.61	49.51	49.60	48.72	50.88	51.23	49.04	51.27	49.00	51.13	48.72	50.64	51.24	54.46	48.84	50.35	54.54	52.77	50.68	50.18	50.03
2	18	19	9.114	8.586	11.010	9.356	8.095	8.772	8.953	7.443	11.057	7.590	11.087	10.670	7.843	9.018	11.266	5.886	7.388	8.107	12.111	14.83	13.107	18.156	21.65	22.82	24.85	25.152	29.230	31.50	30.092	34.848	29.83	42.884	40.94	45.12	48.62	48.63	47.46	47.03	48.66	47.54	50.80	50.58	49.93	48.24	49.41	48.22	47.98	47.59	44.75	43.74	49.15	48.02	44.89	48.28	47.23	48.91	44.84	45.47	49.48	50.35
3	5	18	7.244	14.309	7.979	11.175	12.287	13.146	11.694	18.998	16.125	20.296	21.905	21.952	27.114	27.933	27.448	30.747	36.685	37.337	39.197	43.84	44.752	43.358	46.34	47.01	44.50	46.507	44.034	47.02	48.691	43.648	47.70	49.477	48.00	46.17	48.51	43.80	48.55	48.30	43.90	47.10	51.74	40.91	45.88	49.41	45.65	43.96	46.60	44.57	42.60	47.05	46.58	43.50	42.45	47.22	44.16	49.38	45.00	46.11	47.09	44.92
4	32	20	11.297	14.307	14.811	8.678	9.533	7.823	9.561	8.209	12.898	8.244	8.548	8.616	7.586	8.921	9.199	8.711	11.026	10.804	11.591	10.52	9.027	7.848	12.32	10.07	12.20	7.872	8.831	10.43	8.886	8.553	11.84	7.608	11.14	13.53	14.89	19.39	22.95	23.99	20.03	27.08	28.21	27.32	34.87	33.65	36.78	37.48	40.53	41.81	40.75	45.51	45.75	47.57	56.03	51.27	51.98	47.75	48.65	50.63	48.45	50.34
5	19	20	10.667	6.804	8.303	14.498	6.868	12.367	9.414	9.444	8.745	14.305	10.961	10.451	14.828	6.272	10.096	12.403	8.464	8.629	9.839	14.45	13.049	16.131	18.19	17.92	23.90	22.576	23.059	25.57	29.038	31.427	33.96	36.342	38.47	41.44	40.76	44.75	47.46	48.82	52.24	50.29	51.08	48.52	51.01	49.59	50.64	47.94	45.46	52.18	48.01	47.01	50.80	47.71	54.90	49.21	52.97	51.58	47.75	53.56	48.21	50.77
6	24	20	13.027	7.471	13.680	5.769	10.789	8.699	9.323	8.312	12.865	9.967	6.926	7.546	10.175	12.912	5.829	8.554	10.514	12.225	9.299	9.09	9.931	10.490	12.35	11.75	10.45	13.391	16.231	16.95	21.799	21.528	21.88	25.989	26.67	32.07	32.79	35.02	37.48	32.87	35.37	41.40	46.80	49.06	46.94	50.27	54.22	50.03	51.80	49.74	48.78	48.08	48.62	48.07	48.06	46.87	50.26	44.34	51.32	48.04	54.79	48.74
7	10	19	9.695	9.174	10.637	9.136	7.362	14.267	9.448	12.378	6.093	5.854	9.539	14.262	13.727	19.425	20.669	22.576	26.741	24.729	28.852	30.76	31.628	34.361	36.10	40.92	39.43	41.137	43.049	46.76	46.199	48.769	47.37	49.477	52.47	49.37	44.54	48.07	46.38	47.66	45.35	49.10	48.02	47.50	49.15	49.00	47.31	46.44	49.66	48.07	46.29	45.44	48.75	49.89	49.42	48.00	50.41	46.83	46.45	48.05	48.95	52.76
8	13	20	10.596	11.746	10.903	11.403	8.263	7.589	8.232	7.295	10.028	10.105	9.797	11.889	7.041	12.705	13.626	14.990	19.614	23.575	22.594	25.97	26.860	30.064	29.59	31.23	34.26	36.188	38.330	42.61	43.275	42.504	42.51	51.437	53.72	50.31	48.79	51.41	53.23	50.96	53.23	48.40	48.72	50.48	51.48	50.61	48.03	50.54	50.11	49.69	50.82	45.58	50.14	49.66	47.79	49.99	52.47	50.68	49.15	51.39	47.67	50.32

We can rename many columns at once with a function. The str_replace function usually works like this:

x <- "time_1"
str_replace(x, pattern = "time_", replace = "Time ")

[1] "Time 1"

The rename_with is a function that applies a function to all variable names at once. It can take that function’s arguments, too. For example,

d %>% rename_with(str_replace, pattern = "time_", replace = "Time ")

id	baseline_sessions_n	intervention_sessions_n	Time 1	Time 2	Time 3	Time 4	Time 5	Time 6	Time 7	Time 8	Time 9	Time 10	Time 11	Time 12	Time 13	Time 14	Time 15	Time 16	Time 17	Time 18	Time 19	Time 20	Time 21	Time 22	Time 23	Time 24	Time 25	Time 26	Time 27	Time 28	Time 29	Time 30	Time 31	Time 32	Time 33	Time 34	Time 35	Time 36	Time 37	Time 38	Time 39	Time 40	Time 41	Time 42	Time 43	Time 44	Time 45	Time 46	Time 47	Time 48	Time 49	Time 50	Time 51	Time 52	Time 53	Time 54	Time 55	Time 56	Time 57	Time 58	Time 59	Time 60
1	22	20	11.481	14.402	6.151	9.254	9.580	8.160	10.125	11.120	9.203	10.838	9.181	8.436	9.977	13.242	9.317	8.276	9.208	10.888	7.225	11.47	8.570	10.583	11.04	13.13	18.00	12.965	23.840	21.40	22.653	30.187	29.49	31.359	29.32	33.78	38.95	37.28	42.57	37.73	44.34	44.61	49.51	49.60	48.72	50.88	51.23	49.04	51.27	49.00	51.13	48.72	50.64	51.24	54.46	48.84	50.35	54.54	52.77	50.68	50.18	50.03
2	18	19	9.114	8.586	11.010	9.356	8.095	8.772	8.953	7.443	11.057	7.590	11.087	10.670	7.843	9.018	11.266	5.886	7.388	8.107	12.111	14.83	13.107	18.156	21.65	22.82	24.85	25.152	29.230	31.50	30.092	34.848	29.83	42.884	40.94	45.12	48.62	48.63	47.46	47.03	48.66	47.54	50.80	50.58	49.93	48.24	49.41	48.22	47.98	47.59	44.75	43.74	49.15	48.02	44.89	48.28	47.23	48.91	44.84	45.47	49.48	50.35
3	5	18	7.244	14.309	7.979	11.175	12.287	13.146	11.694	18.998	16.125	20.296	21.905	21.952	27.114	27.933	27.448	30.747	36.685	37.337	39.197	43.84	44.752	43.358	46.34	47.01	44.50	46.507	44.034	47.02	48.691	43.648	47.70	49.477	48.00	46.17	48.51	43.80	48.55	48.30	43.90	47.10	51.74	40.91	45.88	49.41	45.65	43.96	46.60	44.57	42.60	47.05	46.58	43.50	42.45	47.22	44.16	49.38	45.00	46.11	47.09	44.92
4	32	20	11.297	14.307	14.811	8.678	9.533	7.823	9.561	8.209	12.898	8.244	8.548	8.616	7.586	8.921	9.199	8.711	11.026	10.804	11.591	10.52	9.027	7.848	12.32	10.07	12.20	7.872	8.831	10.43	8.886	8.553	11.84	7.608	11.14	13.53	14.89	19.39	22.95	23.99	20.03	27.08	28.21	27.32	34.87	33.65	36.78	37.48	40.53	41.81	40.75	45.51	45.75	47.57	56.03	51.27	51.98	47.75	48.65	50.63	48.45	50.34
5	19	20	10.667	6.804	8.303	14.498	6.868	12.367	9.414	9.444	8.745	14.305	10.961	10.451	14.828	6.272	10.096	12.403	8.464	8.629	9.839	14.45	13.049	16.131	18.19	17.92	23.90	22.576	23.059	25.57	29.038	31.427	33.96	36.342	38.47	41.44	40.76	44.75	47.46	48.82	52.24	50.29	51.08	48.52	51.01	49.59	50.64	47.94	45.46	52.18	48.01	47.01	50.80	47.71	54.90	49.21	52.97	51.58	47.75	53.56	48.21	50.77
6	24	20	13.027	7.471	13.680	5.769	10.789	8.699	9.323	8.312	12.865	9.967	6.926	7.546	10.175	12.912	5.829	8.554	10.514	12.225	9.299	9.09	9.931	10.490	12.35	11.75	10.45	13.391	16.231	16.95	21.799	21.528	21.88	25.989	26.67	32.07	32.79	35.02	37.48	32.87	35.37	41.40	46.80	49.06	46.94	50.27	54.22	50.03	51.80	49.74	48.78	48.08	48.62	48.07	48.06	46.87	50.26	44.34	51.32	48.04	54.79	48.74
7	10	19	9.695	9.174	10.637	9.136	7.362	14.267	9.448	12.378	6.093	5.854	9.539	14.262	13.727	19.425	20.669	22.576	26.741	24.729	28.852	30.76	31.628	34.361	36.10	40.92	39.43	41.137	43.049	46.76	46.199	48.769	47.37	49.477	52.47	49.37	44.54	48.07	46.38	47.66	45.35	49.10	48.02	47.50	49.15	49.00	47.31	46.44	49.66	48.07	46.29	45.44	48.75	49.89	49.42	48.00	50.41	46.83	46.45	48.05	48.95	52.76
8	13	20	10.596	11.746	10.903	11.403	8.263	7.589	8.232	7.295	10.028	10.105	9.797	11.889	7.041	12.705	13.626	14.990	19.614	23.575	22.594	25.97	26.860	30.064	29.59	31.23	34.26	36.188	38.330	42.61	43.275	42.504	42.51	51.437	53.72	50.31	48.79	51.41	53.23	50.96	53.23	48.40	48.72	50.48	51.48	50.61	48.03	50.54	50.11	49.69	50.82	45.58	50.14	49.66	47.79	49.99	52.47	50.68	49.15	51.39	47.67	50.32

If you have a vector with names in it you can rename many variables at once:

v_names <- c(ID = "id", 
             Baseline = "baseline_sessions_n",
             Intervention = "intervention_sessions_n")

d %>% 
  rename(any_of(v_names))

ID	Baseline	Intervention	time_1	time_2	time_3	time_4	time_5	time_6	time_7	time_8	time_9	time_10	time_11	time_12	time_13	time_14	time_15	time_16	time_17	time_18	time_19	time_20	time_21	time_22	time_23	time_24	time_25	time_26	time_27	time_28	time_29	time_30	time_31	time_32	time_33	time_34	time_35	time_36	time_37	time_38	time_39	time_40	time_41	time_42	time_43	time_44	time_45	time_46	time_47	time_48	time_49	time_50	time_51	time_52	time_53	time_54	time_55	time_56	time_57	time_58	time_59	time_60
1	22	20	11.481	14.402	6.151	9.254	9.580	8.160	10.125	11.120	9.203	10.838	9.181	8.436	9.977	13.242	9.317	8.276	9.208	10.888	7.225	11.47	8.570	10.583	11.04	13.13	18.00	12.965	23.840	21.40	22.653	30.187	29.49	31.359	29.32	33.78	38.95	37.28	42.57	37.73	44.34	44.61	49.51	49.60	48.72	50.88	51.23	49.04	51.27	49.00	51.13	48.72	50.64	51.24	54.46	48.84	50.35	54.54	52.77	50.68	50.18	50.03
2	18	19	9.114	8.586	11.010	9.356	8.095	8.772	8.953	7.443	11.057	7.590	11.087	10.670	7.843	9.018	11.266	5.886	7.388	8.107	12.111	14.83	13.107	18.156	21.65	22.82	24.85	25.152	29.230	31.50	30.092	34.848	29.83	42.884	40.94	45.12	48.62	48.63	47.46	47.03	48.66	47.54	50.80	50.58	49.93	48.24	49.41	48.22	47.98	47.59	44.75	43.74	49.15	48.02	44.89	48.28	47.23	48.91	44.84	45.47	49.48	50.35
3	5	18	7.244	14.309	7.979	11.175	12.287	13.146	11.694	18.998	16.125	20.296	21.905	21.952	27.114	27.933	27.448	30.747	36.685	37.337	39.197	43.84	44.752	43.358	46.34	47.01	44.50	46.507	44.034	47.02	48.691	43.648	47.70	49.477	48.00	46.17	48.51	43.80	48.55	48.30	43.90	47.10	51.74	40.91	45.88	49.41	45.65	43.96	46.60	44.57	42.60	47.05	46.58	43.50	42.45	47.22	44.16	49.38	45.00	46.11	47.09	44.92
4	32	20	11.297	14.307	14.811	8.678	9.533	7.823	9.561	8.209	12.898	8.244	8.548	8.616	7.586	8.921	9.199	8.711	11.026	10.804	11.591	10.52	9.027	7.848	12.32	10.07	12.20	7.872	8.831	10.43	8.886	8.553	11.84	7.608	11.14	13.53	14.89	19.39	22.95	23.99	20.03	27.08	28.21	27.32	34.87	33.65	36.78	37.48	40.53	41.81	40.75	45.51	45.75	47.57	56.03	51.27	51.98	47.75	48.65	50.63	48.45	50.34
5	19	20	10.667	6.804	8.303	14.498	6.868	12.367	9.414	9.444	8.745	14.305	10.961	10.451	14.828	6.272	10.096	12.403	8.464	8.629	9.839	14.45	13.049	16.131	18.19	17.92	23.90	22.576	23.059	25.57	29.038	31.427	33.96	36.342	38.47	41.44	40.76	44.75	47.46	48.82	52.24	50.29	51.08	48.52	51.01	49.59	50.64	47.94	45.46	52.18	48.01	47.01	50.80	47.71	54.90	49.21	52.97	51.58	47.75	53.56	48.21	50.77
6	24	20	13.027	7.471	13.680	5.769	10.789	8.699	9.323	8.312	12.865	9.967	6.926	7.546	10.175	12.912	5.829	8.554	10.514	12.225	9.299	9.09	9.931	10.490	12.35	11.75	10.45	13.391	16.231	16.95	21.799	21.528	21.88	25.989	26.67	32.07	32.79	35.02	37.48	32.87	35.37	41.40	46.80	49.06	46.94	50.27	54.22	50.03	51.80	49.74	48.78	48.08	48.62	48.07	48.06	46.87	50.26	44.34	51.32	48.04	54.79	48.74
7	10	19	9.695	9.174	10.637	9.136	7.362	14.267	9.448	12.378	6.093	5.854	9.539	14.262	13.727	19.425	20.669	22.576	26.741	24.729	28.852	30.76	31.628	34.361	36.10	40.92	39.43	41.137	43.049	46.76	46.199	48.769	47.37	49.477	52.47	49.37	44.54	48.07	46.38	47.66	45.35	49.10	48.02	47.50	49.15	49.00	47.31	46.44	49.66	48.07	46.29	45.44	48.75	49.89	49.42	48.00	50.41	46.83	46.45	48.05	48.95	52.76
8	13	20	10.596	11.746	10.903	11.403	8.263	7.589	8.232	7.295	10.028	10.105	9.797	11.889	7.041	12.705	13.626	14.990	19.614	23.575	22.594	25.97	26.860	30.064	29.59	31.23	34.26	36.188	38.330	42.61	43.275	42.504	42.51	51.437	53.72	50.31	48.79	51.41	53.23	50.96	53.23	48.40	48.72	50.48	51.48	50.61	48.03	50.54	50.11	49.69	50.82	45.58	50.14	49.66	47.79	49.99	52.47	50.68	49.15	51.39	47.67	50.32

In this example, there is no advantage over just applying the transformations directly in rename:

d %>% 
  rename(ID = "id", 
         Baseline = "baseline_sessions_n",
         Intervention = "intervention_sessions_n")

ID	Baseline	Intervention	time_1	time_2	time_3	time_4	time_5	time_6	time_7	time_8	time_9	time_10	time_11	time_12	time_13	time_14	time_15	time_16	time_17	time_18	time_19	time_20	time_21	time_22	time_23	time_24	time_25	time_26	time_27	time_28	time_29	time_30	time_31	time_32	time_33	time_34	time_35	time_36	time_37	time_38	time_39	time_40	time_41	time_42	time_43	time_44	time_45	time_46	time_47	time_48	time_49	time_50	time_51	time_52	time_53	time_54	time_55	time_56	time_57	time_58	time_59	time_60
1	22	20	11.481	14.402	6.151	9.254	9.580	8.160	10.125	11.120	9.203	10.838	9.181	8.436	9.977	13.242	9.317	8.276	9.208	10.888	7.225	11.47	8.570	10.583	11.04	13.13	18.00	12.965	23.840	21.40	22.653	30.187	29.49	31.359	29.32	33.78	38.95	37.28	42.57	37.73	44.34	44.61	49.51	49.60	48.72	50.88	51.23	49.04	51.27	49.00	51.13	48.72	50.64	51.24	54.46	48.84	50.35	54.54	52.77	50.68	50.18	50.03
2	18	19	9.114	8.586	11.010	9.356	8.095	8.772	8.953	7.443	11.057	7.590	11.087	10.670	7.843	9.018	11.266	5.886	7.388	8.107	12.111	14.83	13.107	18.156	21.65	22.82	24.85	25.152	29.230	31.50	30.092	34.848	29.83	42.884	40.94	45.12	48.62	48.63	47.46	47.03	48.66	47.54	50.80	50.58	49.93	48.24	49.41	48.22	47.98	47.59	44.75	43.74	49.15	48.02	44.89	48.28	47.23	48.91	44.84	45.47	49.48	50.35
3	5	18	7.244	14.309	7.979	11.175	12.287	13.146	11.694	18.998	16.125	20.296	21.905	21.952	27.114	27.933	27.448	30.747	36.685	37.337	39.197	43.84	44.752	43.358	46.34	47.01	44.50	46.507	44.034	47.02	48.691	43.648	47.70	49.477	48.00	46.17	48.51	43.80	48.55	48.30	43.90	47.10	51.74	40.91	45.88	49.41	45.65	43.96	46.60	44.57	42.60	47.05	46.58	43.50	42.45	47.22	44.16	49.38	45.00	46.11	47.09	44.92
4	32	20	11.297	14.307	14.811	8.678	9.533	7.823	9.561	8.209	12.898	8.244	8.548	8.616	7.586	8.921	9.199	8.711	11.026	10.804	11.591	10.52	9.027	7.848	12.32	10.07	12.20	7.872	8.831	10.43	8.886	8.553	11.84	7.608	11.14	13.53	14.89	19.39	22.95	23.99	20.03	27.08	28.21	27.32	34.87	33.65	36.78	37.48	40.53	41.81	40.75	45.51	45.75	47.57	56.03	51.27	51.98	47.75	48.65	50.63	48.45	50.34
5	19	20	10.667	6.804	8.303	14.498	6.868	12.367	9.414	9.444	8.745	14.305	10.961	10.451	14.828	6.272	10.096	12.403	8.464	8.629	9.839	14.45	13.049	16.131	18.19	17.92	23.90	22.576	23.059	25.57	29.038	31.427	33.96	36.342	38.47	41.44	40.76	44.75	47.46	48.82	52.24	50.29	51.08	48.52	51.01	49.59	50.64	47.94	45.46	52.18	48.01	47.01	50.80	47.71	54.90	49.21	52.97	51.58	47.75	53.56	48.21	50.77
6	24	20	13.027	7.471	13.680	5.769	10.789	8.699	9.323	8.312	12.865	9.967	6.926	7.546	10.175	12.912	5.829	8.554	10.514	12.225	9.299	9.09	9.931	10.490	12.35	11.75	10.45	13.391	16.231	16.95	21.799	21.528	21.88	25.989	26.67	32.07	32.79	35.02	37.48	32.87	35.37	41.40	46.80	49.06	46.94	50.27	54.22	50.03	51.80	49.74	48.78	48.08	48.62	48.07	48.06	46.87	50.26	44.34	51.32	48.04	54.79	48.74
7	10	19	9.695	9.174	10.637	9.136	7.362	14.267	9.448	12.378	6.093	5.854	9.539	14.262	13.727	19.425	20.669	22.576	26.741	24.729	28.852	30.76	31.628	34.361	36.10	40.92	39.43	41.137	43.049	46.76	46.199	48.769	47.37	49.477	52.47	49.37	44.54	48.07	46.38	47.66	45.35	49.10	48.02	47.50	49.15	49.00	47.31	46.44	49.66	48.07	46.29	45.44	48.75	49.89	49.42	48.00	50.41	46.83	46.45	48.05	48.95	52.76
8	13	20	10.596	11.746	10.903	11.403	8.263	7.589	8.232	7.295	10.028	10.105	9.797	11.889	7.041	12.705	13.626	14.990	19.614	23.575	22.594	25.97	26.860	30.064	29.59	31.23	34.26	36.188	38.330	42.61	43.275	42.504	42.51	51.437	53.72	50.31	48.79	51.41	53.23	50.96	53.23	48.40	48.72	50.48	51.48	50.61	48.03	50.54	50.11	49.69	50.82	45.58	50.14	49.66	47.79	49.99	52.47	50.68	49.15	51.39	47.67	50.32

The reason one might use the named-vector method is that one already has the named vector handy, and retyping it would be an error-prone waste of time.

Data Restructuring

We need to make our wide data long. To make the illustration simple to see, let’s make a smaller version of the data. We will select just times 1–3 for the first 2 children:

d_small <- d %>% 
  select(id:time_3) %>% 
  filter(id < 3)

Breaking down each step, the code above does these three things:

1. Create a new tibble called d_small starting with the tibble d “and then” (i..e, %>%)…
2. select variables id through time_3 “and then”…
3. filter the rows to find only the cases in which id is less than 3.

Let’s display d_small:

d_small

id	baseline_sessions_n	intervention_sessions_n	time_1	time_2	time_3
1	22	20	11.481	14.402	6.151
2	18	19	9.114	8.586	11.010

What we need is:

id	baseline_sessions_n	intervention_sessions_n	time	on_task
1	22	20	1	11.481
1	22	20	2	14.402
1	22	20	3	6.151
2	18	19	1	9.114
2	18	19	2	8.586
2	18	19	3	11.010

Restructuring from wide to long format with pivot_longer

Use the pivot_longer function to pivot the three time variables to long format:

d_small %>% 
  pivot_longer(cols = time_1:time_3)

id	baseline_sessions_n	intervention_sessions_n	name	value
1	22	20	time_1	11.481
1	22	20	time_2	14.402
1	22	20	time_3	6.151
2	18	19	time_1	9.114
2	18	19	time_2	8.586
2	18	19	time_3	11.010

To avoid the hassle of renaming our columns, we can specify what the name and value columns should be called:

d_small %>%
  pivot_longer(cols = time_1:time_3,
               names_to = "time",
               values_to = "on_task")

id	baseline_sessions_n	intervention_sessions_n	time	on_task
1	22	20	time_1	11.481
1	22	20	time_2	14.402
1	22	20	time_3	6.151
2	18	19	time_1	9.114
2	18	19	time_2	8.586
2	18	19	time_3	11.010

Notice that the time variable is text, not a number, like we want. There are many ways to get rid of the prefix. The simplest is to tell pivot_longer to strip away the prefix “time_”.

d_small %>%
  pivot_longer(cols = time_1:time_3,
               names_to = "time",
               values_to = "on_task",
               names_prefix = "time_")

id	baseline_sessions_n	intervention_sessions_n	time	on_task
1	22	20	1	11.481
1	22	20	2	14.402
1	22	20	3	6.151
2	18	19	1	9.114
2	18	19	2	8.586
2	18	19	3	11.010

Unfortunately, R thinks that time is a text variable. We want it to be an integer, so we transform it using the as.integer function:

d_small %>%
  pivot_longer(cols = time_1:time_3,
               names_to = "time",
               values_to = "on_task",
               names_prefix = "time_", 
               names_transform = list(time = as.integer)) 

id	baseline_sessions_n	intervention_sessions_n	time	on_task
1	22	20	1	11.481
1	22	20	2	14.402
1	22	20	3	6.151
2	18	19	1	9.114
2	18	19	2	8.586
2	18	19	3	11.010

Alternatively, we can do all these transformations after pivoting:

d_small %>%
  pivot_longer(cols = time_1:time_3,
               names_to = "time",
               values_to = "on_task") %>% 
  mutate(time = str_remove(time, "time_") %>% 
           as.integer())

id	baseline_sessions_n	intervention_sessions_n	time	on_task
1	22	20	1	11.481
1	22	20	2	14.402
1	22	20	3	6.151
2	18	19	1	9.114
2	18	19	2	8.586
2	18	19	3	11.010

Restructuring from long to wide format with pivot_wider

Let’s pivot the data from long format to wide. First let’s assign the restructured d_small data as d_small_longer:

d_small_longer <- d_small %>%
  pivot_longer(cols = time_1:time_3,
               names_to = "time",
               values_to = "on_task",
               names_prefix = "time_",
               names_transform = list(time = as.integer))

Now let’s move it back to where it was with pivot_wider:

d_small_longer %>% 
  pivot_wider(names_from = time, 
              values_from = on_task, 
              names_prefix = "time_")

id	baseline_sessions_n	intervention_sessions_n	time_1	time_2	time_3
1	22	20	11.481	14.402	6.151
2	18	19	9.114	8.586	11.010

Perfect! Now lets move back to the the original large tibble d.

Create new variables with mutate and case_when

First, let’s pivot the entire data and assign it to d_longer. We need to select time_1–time_60:

d_longer <- d %>%
  pivot_longer(cols = time_1:time_60,
               names_to = "time",
               values_to = "on_task",
               names_prefix = "time_",
               names_transform = list(time = as.integer))

To create a new variable in d_longer, we “mutate” the tibble. For example, if we want to add 1 to on_task,

d_longer %>% 
  mutate(ontask_plus_1 = on_task + 1)

id	baseline_sessions_n	intervention_sessions_n	time	on_task	ontask_plus_1
1	22	20	1	11.481	12.481
1	22	20	2	14.402	15.402
1	22	20	3	6.151	7.151
1	22	20	4	9.254	10.254
1	22	20	5	9.580	10.580
1	22	20	6	8.160	9.160
1	22	20	7	10.125	11.125
1	22	20	8	11.120	12.120
1	22	20	9	9.203	10.203
1	22	20	10	10.838	11.838
1	22	20	11	9.181	10.181
1	22	20	12	8.436	9.436
1	22	20	13	9.977	10.977
1	22	20	14	13.242	14.242
1	22	20	15	9.317	10.317
1	22	20	16	8.276	9.276
1	22	20	17	9.208	10.208
1	22	20	18	10.888	11.888
1	22	20	19	7.225	8.225
1	22	20	20	11.472	12.472
1	22	20	21	8.570	9.570
1	22	20	22	10.583	11.583
1	22	20	23	11.038	12.038
1	22	20	24	13.126	14.126
1	22	20	25	17.995	18.995
1	22	20	26	12.965	13.965
1	22	20	27	23.840	24.840
1	22	20	28	21.396	22.396
1	22	20	29	22.653	23.653
1	22	20	30	30.187	31.187
1	22	20	31	29.494	30.494
1	22	20	32	31.359	32.359
1	22	20	33	29.316	30.316
1	22	20	34	33.783	34.783
1	22	20	35	38.945	39.945
1	22	20	36	37.283	38.283
1	22	20	37	42.568	43.568
1	22	20	38	37.734	38.734
1	22	20	39	44.345	45.345
1	22	20	40	44.606	45.606
1	22	20	41	49.511	50.511
1	22	20	42	49.604	50.604
1	22	20	43	48.723	49.723
1	22	20	44	50.882	51.882
1	22	20	45	51.230	52.230
1	22	20	46	49.038	50.038
1	22	20	47	51.275	52.275
1	22	20	48	49.000	50.000
1	22	20	49	51.135	52.135
1	22	20	50	48.722	49.722
1	22	20	51	50.638	51.638
1	22	20	52	51.235	52.235
1	22	20	53	54.457	55.457
1	22	20	54	48.840	49.840
1	22	20	55	50.354	51.354
1	22	20	56	54.538	55.538
1	22	20	57	52.765	53.765
1	22	20	58	50.677	51.677
1	22	20	59	50.179	51.179
1	22	20	60	50.030	51.030
2	18	19	1	9.114	10.114
2	18	19	2	8.586	9.586
2	18	19	3	11.010	12.010
2	18	19	4	9.356	10.356
2	18	19	5	8.095	9.095
2	18	19	6	8.772	9.772
2	18	19	7	8.953	9.953
2	18	19	8	7.443	8.443
2	18	19	9	11.057	12.057
2	18	19	10	7.590	8.590
2	18	19	11	11.087	12.087
2	18	19	12	10.670	11.670
2	18	19	13	7.843	8.843
2	18	19	14	9.018	10.018
2	18	19	15	11.266	12.266
2	18	19	16	5.886	6.886
2	18	19	17	7.388	8.388
2	18	19	18	8.107	9.107
2	18	19	19	12.111	13.111
2	18	19	20	14.825	15.825
2	18	19	21	13.107	14.107
2	18	19	22	18.156	19.156
2	18	19	23	21.652	22.652
2	18	19	24	22.819	23.819
2	18	19	25	24.846	25.846
2	18	19	26	25.152	26.152
2	18	19	27	29.230	30.230
2	18	19	28	31.499	32.499
2	18	19	29	30.092	31.092
2	18	19	30	34.848	35.848
2	18	19	31	29.831	30.831
2	18	19	32	42.884	43.884
2	18	19	33	40.936	41.936
2	18	19	34	45.121	46.121
2	18	19	35	48.622	49.622
2	18	19	36	48.627	49.627
2	18	19	37	47.455	48.455
2	18	19	38	47.031	48.031
2	18	19	39	48.660	49.660
2	18	19	40	47.544	48.544
2	18	19	41	50.799	51.799
2	18	19	42	50.576	51.576
2	18	19	43	49.929	50.929
2	18	19	44	48.240	49.240
2	18	19	45	49.406	50.406
2	18	19	46	48.225	49.225
2	18	19	47	47.975	48.975
2	18	19	48	47.588	48.588
2	18	19	49	44.746	45.746
2	18	19	50	43.743	44.743
2	18	19	51	49.148	50.148
2	18	19	52	48.024	49.024
2	18	19	53	44.893	45.893
2	18	19	54	48.279	49.279
2	18	19	55	47.229	48.229
2	18	19	56	48.910	49.910
2	18	19	57	44.843	45.843
2	18	19	58	45.472	46.472
2	18	19	59	49.480	50.480
2	18	19	60	50.346	51.346
3	5	18	1	7.244	8.244
3	5	18	2	14.309	15.309
3	5	18	3	7.979	8.979
3	5	18	4	11.175	12.175
3	5	18	5	12.287	13.287
3	5	18	6	13.146	14.146
3	5	18	7	11.694	12.694
3	5	18	8	18.998	19.998
3	5	18	9	16.125	17.125
3	5	18	10	20.296	21.296
3	5	18	11	21.905	22.905
3	5	18	12	21.952	22.952
3	5	18	13	27.114	28.114
3	5	18	14	27.933	28.933
3	5	18	15	27.448	28.448
3	5	18	16	30.747	31.747
3	5	18	17	36.685	37.685
3	5	18	18	37.337	38.337
3	5	18	19	39.197	40.197
3	5	18	20	43.845	44.845
3	5	18	21	44.752	45.752
3	5	18	22	43.358	44.358
3	5	18	23	46.341	47.341
3	5	18	24	47.006	48.006
3	5	18	25	44.497	45.497
3	5	18	26	46.507	47.507
3	5	18	27	44.034	45.034
3	5	18	28	47.017	48.017
3	5	18	29	48.691	49.691
3	5	18	30	43.648	44.648
3	5	18	31	47.700	48.700
3	5	18	32	49.477	50.477
3	5	18	33	48.000	49.000
3	5	18	34	46.173	47.173
3	5	18	35	48.513	49.513
3	5	18	36	43.801	44.801
3	5	18	37	48.552	49.552
3	5	18	38	48.298	49.298
3	5	18	39	43.902	44.902
3	5	18	40	47.103	48.103
3	5	18	41	51.735	52.735
3	5	18	42	40.911	41.911
3	5	18	43	45.881	46.881
3	5	18	44	49.407	50.407
3	5	18	45	45.653	46.653
3	5	18	46	43.964	44.964
3	5	18	47	46.602	47.602
3	5	18	48	44.568	45.568
3	5	18	49	42.596	43.596
3	5	18	50	47.055	48.055
3	5	18	51	46.582	47.582
3	5	18	52	43.501	44.501
3	5	18	53	42.446	43.446
3	5	18	54	47.222	48.222
3	5	18	55	44.163	45.163
3	5	18	56	49.381	50.381
3	5	18	57	44.999	45.999
3	5	18	58	46.108	47.108
3	5	18	59	47.088	48.088
3	5	18	60	44.919	45.919
4	32	20	1	11.297	12.297
4	32	20	2	14.307	15.307
4	32	20	3	14.811	15.811
4	32	20	4	8.678	9.678
4	32	20	5	9.533	10.533
4	32	20	6	7.823	8.823
4	32	20	7	9.561	10.561
4	32	20	8	8.209	9.209
4	32	20	9	12.898	13.898
4	32	20	10	8.244	9.244
4	32	20	11	8.548	9.548
4	32	20	12	8.616	9.616
4	32	20	13	7.586	8.586
4	32	20	14	8.921	9.921
4	32	20	15	9.199	10.199
4	32	20	16	8.711	9.711
4	32	20	17	11.026	12.026
4	32	20	18	10.804	11.804
4	32	20	19	11.591	12.591
4	32	20	20	10.519	11.519
4	32	20	21	9.027	10.027
4	32	20	22	7.848	8.848
4	32	20	23	12.319	13.319
4	32	20	24	10.068	11.068
4	32	20	25	12.198	13.198
4	32	20	26	7.872	8.872
4	32	20	27	8.831	9.831
4	32	20	28	10.432	11.432
4	32	20	29	8.886	9.886
4	32	20	30	8.553	9.553
4	32	20	31	11.836	12.836
4	32	20	32	7.608	8.608
4	32	20	33	11.136	12.136
4	32	20	34	13.533	14.533
4	32	20	35	14.891	15.891
4	32	20	36	19.392	20.392
4	32	20	37	22.947	23.947
4	32	20	38	23.988	24.988
4	32	20	39	20.032	21.032
4	32	20	40	27.083	28.083
4	32	20	41	28.207	29.207
4	32	20	42	27.317	28.317
4	32	20	43	34.869	35.869
4	32	20	44	33.646	34.646
4	32	20	45	36.784	37.784
4	32	20	46	37.477	38.477
4	32	20	47	40.527	41.527
4	32	20	48	41.814	42.814
4	32	20	49	40.749	41.749
4	32	20	50	45.507	46.507
4	32	20	51	45.754	46.754
4	32	20	52	47.572	48.572
4	32	20	53	56.028	57.028
4	32	20	54	51.273	52.273
4	32	20	55	51.980	52.980
4	32	20	56	47.747	48.747
4	32	20	57	48.654	49.654
4	32	20	58	50.635	51.635
4	32	20	59	48.446	49.446
4	32	20	60	50.343	51.343
5	19	20	1	10.667	11.667
5	19	20	2	6.804	7.804
5	19	20	3	8.303	9.303
5	19	20	4	14.498	15.498
5	19	20	5	6.868	7.868
5	19	20	6	12.367	13.367
5	19	20	7	9.414	10.414
5	19	20	8	9.444	10.444
5	19	20	9	8.745	9.745
5	19	20	10	14.305	15.305
5	19	20	11	10.961	11.961
5	19	20	12	10.451	11.451
5	19	20	13	14.828	15.828
5	19	20	14	6.272	7.272
5	19	20	15	10.096	11.096
5	19	20	16	12.403	13.403
5	19	20	17	8.464	9.464
5	19	20	18	8.629	9.629
5	19	20	19	9.839	10.839
5	19	20	20	14.449	15.449
5	19	20	21	13.049	14.049
5	19	20	22	16.131	17.131
5	19	20	23	18.193	19.193
5	19	20	24	17.921	18.921
5	19	20	25	23.902	24.902
5	19	20	26	22.576	23.576
5	19	20	27	23.059	24.059
5	19	20	28	25.573	26.573
5	19	20	29	29.038	30.038
5	19	20	30	31.427	32.427
5	19	20	31	33.964	34.964
5	19	20	32	36.342	37.342
5	19	20	33	38.469	39.469
5	19	20	34	41.438	42.438
5	19	20	35	40.760	41.760
5	19	20	36	44.753	45.753
5	19	20	37	47.462	48.462
5	19	20	38	48.816	49.816
5	19	20	39	52.240	53.240
5	19	20	40	50.288	51.288
5	19	20	41	51.077	52.077
5	19	20	42	48.519	49.519
5	19	20	43	51.011	52.011
5	19	20	44	49.589	50.589
5	19	20	45	50.637	51.637
5	19	20	46	47.937	48.937
5	19	20	47	45.461	46.461
5	19	20	48	52.178	53.178
5	19	20	49	48.010	49.010
5	19	20	50	47.008	48.008
5	19	20	51	50.800	51.800
5	19	20	52	47.707	48.707
5	19	20	53	54.898	55.898
5	19	20	54	49.207	50.207
5	19	20	55	52.968	53.968
5	19	20	56	51.581	52.581
5	19	20	57	47.745	48.745
5	19	20	58	53.559	54.559
5	19	20	59	48.210	49.210
5	19	20	60	50.766	51.766
6	24	20	1	13.027	14.027
6	24	20	2	7.471	8.471
6	24	20	3	13.680	14.680
6	24	20	4	5.769	6.769
6	24	20	5	10.789	11.789
6	24	20	6	8.699	9.699
6	24	20	7	9.323	10.323
6	24	20	8	8.312	9.312
6	24	20	9	12.865	13.865
6	24	20	10	9.967	10.967
6	24	20	11	6.926	7.926
6	24	20	12	7.546	8.546
6	24	20	13	10.175	11.175
6	24	20	14	12.912	13.912
6	24	20	15	5.829	6.829
6	24	20	16	8.554	9.554
6	24	20	17	10.514	11.514
6	24	20	18	12.225	13.225
6	24	20	19	9.299	10.299
6	24	20	20	9.090	10.090
6	24	20	21	9.931	10.931
6	24	20	22	10.490	11.490
6	24	20	23	12.348	13.348
6	24	20	24	11.752	12.752
6	24	20	25	10.447	11.447
6	24	20	26	13.391	14.391
6	24	20	27	16.231	17.231
6	24	20	28	16.951	17.951
6	24	20	29	21.799	22.799
6	24	20	30	21.528	22.528
6	24	20	31	21.879	22.879
6	24	20	32	25.989	26.989
6	24	20	33	26.665	27.665
6	24	20	34	32.069	33.069
6	24	20	35	32.795	33.795
6	24	20	36	35.019	36.019
6	24	20	37	37.480	38.480
6	24	20	38	32.867	33.867
6	24	20	39	35.365	36.365
6	24	20	40	41.401	42.401
6	24	20	41	46.800	47.800
6	24	20	42	49.059	50.059
6	24	20	43	46.944	47.944
6	24	20	44	50.266	51.266
6	24	20	45	54.224	55.224
6	24	20	46	50.032	51.032
6	24	20	47	51.803	52.803
6	24	20	48	49.742	50.742
6	24	20	49	48.780	49.780
6	24	20	50	48.083	49.083
6	24	20	51	48.620	49.620
6	24	20	52	48.069	49.069
6	24	20	53	48.062	49.062
6	24	20	54	46.871	47.871
6	24	20	55	50.262	51.262
6	24	20	56	44.337	45.337
6	24	20	57	51.324	52.324
6	24	20	58	48.036	49.036
6	24	20	59	54.790	55.790
6	24	20	60	48.737	49.737
7	10	19	1	9.695	10.695
7	10	19	2	9.174	10.174
7	10	19	3	10.637	11.637
7	10	19	4	9.136	10.136
7	10	19	5	7.362	8.362
7	10	19	6	14.267	15.267
7	10	19	7	9.448	10.448
7	10	19	8	12.378	13.378
7	10	19	9	6.093	7.093
7	10	19	10	5.854	6.854
7	10	19	11	9.539	10.539
7	10	19	12	14.262	15.262
7	10	19	13	13.727	14.727
7	10	19	14	19.425	20.425
7	10	19	15	20.669	21.669
7	10	19	16	22.576	23.576
7	10	19	17	26.741	27.741
7	10	19	18	24.729	25.729
7	10	19	19	28.852	29.852
7	10	19	20	30.760	31.760
7	10	19	21	31.628	32.628
7	10	19	22	34.361	35.361
7	10	19	23	36.095	37.095
7	10	19	24	40.918	41.918
7	10	19	25	39.432	40.432
7	10	19	26	41.137	42.137
7	10	19	27	43.049	44.049
7	10	19	28	46.761	47.761
7	10	19	29	46.199	47.199
7	10	19	30	48.769	49.769
7	10	19	31	47.367	48.367
7	10	19	32	49.477	50.477
7	10	19	33	52.471	53.471
7	10	19	34	49.366	50.366
7	10	19	35	44.544	45.544
7	10	19	36	48.075	49.075
7	10	19	37	46.384	47.384
7	10	19	38	47.661	48.661
7	10	19	39	45.355	46.355
7	10	19	40	49.098	50.098
7	10	19	41	48.016	49.016
7	10	19	42	47.499	48.499
7	10	19	43	49.154	50.154
7	10	19	44	48.998	49.998
7	10	19	45	47.308	48.308
7	10	19	46	46.442	47.442
7	10	19	47	49.660	50.660
7	10	19	48	48.067	49.067
7	10	19	49	46.288	47.288
7	10	19	50	45.444	46.444
7	10	19	51	48.753	49.753
7	10	19	52	49.891	50.891
7	10	19	53	49.418	50.418
7	10	19	54	47.998	48.998
7	10	19	55	50.407	51.407
7	10	19	56	46.834	47.834
7	10	19	57	46.451	47.451
7	10	19	58	48.048	49.048
7	10	19	59	48.951	49.951
7	10	19	60	52.757	53.757
8	13	20	1	10.596	11.596
8	13	20	2	11.746	12.746
8	13	20	3	10.903	11.903
8	13	20	4	11.403	12.403
8	13	20	5	8.263	9.263
8	13	20	6	7.589	8.589
8	13	20	7	8.232	9.232
8	13	20	8	7.295	8.295
8	13	20	9	10.028	11.028
8	13	20	10	10.105	11.105
8	13	20	11	9.797	10.797
8	13	20	12	11.889	12.889
8	13	20	13	7.041	8.041
8	13	20	14	12.705	13.705
8	13	20	15	13.626	14.626
8	13	20	16	14.990	15.990
8	13	20	17	19.614	20.614
8	13	20	18	23.575	24.575
8	13	20	19	22.594	23.594
8	13	20	20	25.971	26.971
8	13	20	21	26.860	27.860
8	13	20	22	30.064	31.064
8	13	20	23	29.586	30.586
8	13	20	24	31.227	32.227
8	13	20	25	34.261	35.261
8	13	20	26	36.188	37.188
8	13	20	27	38.330	39.330
8	13	20	28	42.613	43.613
8	13	20	29	43.275	44.275
8	13	20	30	42.504	43.504
8	13	20	31	42.509	43.509
8	13	20	32	51.437	52.437
8	13	20	33	53.725	54.725
8	13	20	34	50.306	51.306
8	13	20	35	48.788	49.788
8	13	20	36	51.408	52.408
8	13	20	37	53.231	54.231
8	13	20	38	50.961	51.961
8	13	20	39	53.231	54.231
8	13	20	40	48.398	49.398
8	13	20	41	48.722	49.722
8	13	20	42	50.481	51.481
8	13	20	43	51.476	52.476
8	13	20	44	50.614	51.614
8	13	20	45	48.026	49.026
8	13	20	46	50.541	51.541
8	13	20	47	50.111	51.111
8	13	20	48	49.685	50.685
8	13	20	49	50.818	51.818
8	13	20	50	45.583	46.583
8	13	20	51	50.140	51.140
8	13	20	52	49.661	50.661
8	13	20	53	47.786	48.786
8	13	20	54	49.989	50.989
8	13	20	55	52.467	53.467
8	13	20	56	50.677	51.677
8	13	20	57	49.147	50.147
8	13	20	58	51.386	52.386
8	13	20	59	47.671	48.671
8	13	20	60	50.322	51.322

Notice that the variable is not saved anywhere because we did not assign the output to a variable:

d_longer

id	baseline_sessions_n	intervention_sessions_n	time	on_task
1	22	20	1	11.481
1	22	20	2	14.402
1	22	20	3	6.151
1	22	20	4	9.254
1	22	20	5	9.580
1	22	20	6	8.160
1	22	20	7	10.125
1	22	20	8	11.120
1	22	20	9	9.203
1	22	20	10	10.838
1	22	20	11	9.181
1	22	20	12	8.436
1	22	20	13	9.977
1	22	20	14	13.242
1	22	20	15	9.317
1	22	20	16	8.276
1	22	20	17	9.208
1	22	20	18	10.888
1	22	20	19	7.225
1	22	20	20	11.472
1	22	20	21	8.570
1	22	20	22	10.583
1	22	20	23	11.038
1	22	20	24	13.126
1	22	20	25	17.995
1	22	20	26	12.965
1	22	20	27	23.840
1	22	20	28	21.396
1	22	20	29	22.653
1	22	20	30	30.187
1	22	20	31	29.494
1	22	20	32	31.359
1	22	20	33	29.316
1	22	20	34	33.783
1	22	20	35	38.945
1	22	20	36	37.283
1	22	20	37	42.568
1	22	20	38	37.734
1	22	20	39	44.345
1	22	20	40	44.606
1	22	20	41	49.511
1	22	20	42	49.604
1	22	20	43	48.723
1	22	20	44	50.882
1	22	20	45	51.230
1	22	20	46	49.038
1	22	20	47	51.275
1	22	20	48	49.000
1	22	20	49	51.135
1	22	20	50	48.722
1	22	20	51	50.638
1	22	20	52	51.235
1	22	20	53	54.457
1	22	20	54	48.840
1	22	20	55	50.354
1	22	20	56	54.538
1	22	20	57	52.765
1	22	20	58	50.677
1	22	20	59	50.179
1	22	20	60	50.030
2	18	19	1	9.114
2	18	19	2	8.586
2	18	19	3	11.010
2	18	19	4	9.356
2	18	19	5	8.095
2	18	19	6	8.772
2	18	19	7	8.953
2	18	19	8	7.443
2	18	19	9	11.057
2	18	19	10	7.590
2	18	19	11	11.087
2	18	19	12	10.670
2	18	19	13	7.843
2	18	19	14	9.018
2	18	19	15	11.266
2	18	19	16	5.886
2	18	19	17	7.388
2	18	19	18	8.107
2	18	19	19	12.111
2	18	19	20	14.825
2	18	19	21	13.107
2	18	19	22	18.156
2	18	19	23	21.652
2	18	19	24	22.819
2	18	19	25	24.846
2	18	19	26	25.152
2	18	19	27	29.230
2	18	19	28	31.499
2	18	19	29	30.092
2	18	19	30	34.848
2	18	19	31	29.831
2	18	19	32	42.884
2	18	19	33	40.936
2	18	19	34	45.121
2	18	19	35	48.622
2	18	19	36	48.627
2	18	19	37	47.455
2	18	19	38	47.031
2	18	19	39	48.660
2	18	19	40	47.544
2	18	19	41	50.799
2	18	19	42	50.576
2	18	19	43	49.929
2	18	19	44	48.240
2	18	19	45	49.406
2	18	19	46	48.225
2	18	19	47	47.975
2	18	19	48	47.588
2	18	19	49	44.746
2	18	19	50	43.743
2	18	19	51	49.148
2	18	19	52	48.024
2	18	19	53	44.893
2	18	19	54	48.279
2	18	19	55	47.229
2	18	19	56	48.910
2	18	19	57	44.843
2	18	19	58	45.472
2	18	19	59	49.480
2	18	19	60	50.346
3	5	18	1	7.244
3	5	18	2	14.309
3	5	18	3	7.979
3	5	18	4	11.175
3	5	18	5	12.287
3	5	18	6	13.146
3	5	18	7	11.694
3	5	18	8	18.998
3	5	18	9	16.125
3	5	18	10	20.296
3	5	18	11	21.905
3	5	18	12	21.952
3	5	18	13	27.114
3	5	18	14	27.933
3	5	18	15	27.448
3	5	18	16	30.747
3	5	18	17	36.685
3	5	18	18	37.337
3	5	18	19	39.197
3	5	18	20	43.845
3	5	18	21	44.752
3	5	18	22	43.358
3	5	18	23	46.341
3	5	18	24	47.006
3	5	18	25	44.497
3	5	18	26	46.507
3	5	18	27	44.034
3	5	18	28	47.017
3	5	18	29	48.691
3	5	18	30	43.648
3	5	18	31	47.700
3	5	18	32	49.477
3	5	18	33	48.000
3	5	18	34	46.173
3	5	18	35	48.513
3	5	18	36	43.801
3	5	18	37	48.552
3	5	18	38	48.298
3	5	18	39	43.902
3	5	18	40	47.103
3	5	18	41	51.735
3	5	18	42	40.911
3	5	18	43	45.881
3	5	18	44	49.407
3	5	18	45	45.653
3	5	18	46	43.964
3	5	18	47	46.602
3	5	18	48	44.568
3	5	18	49	42.596
3	5	18	50	47.055
3	5	18	51	46.582
3	5	18	52	43.501
3	5	18	53	42.446
3	5	18	54	47.222
3	5	18	55	44.163
3	5	18	56	49.381
3	5	18	57	44.999
3	5	18	58	46.108
3	5	18	59	47.088
3	5	18	60	44.919
4	32	20	1	11.297
4	32	20	2	14.307
4	32	20	3	14.811
4	32	20	4	8.678
4	32	20	5	9.533
4	32	20	6	7.823
4	32	20	7	9.561
4	32	20	8	8.209
4	32	20	9	12.898
4	32	20	10	8.244
4	32	20	11	8.548
4	32	20	12	8.616
4	32	20	13	7.586
4	32	20	14	8.921
4	32	20	15	9.199
4	32	20	16	8.711
4	32	20	17	11.026
4	32	20	18	10.804
4	32	20	19	11.591
4	32	20	20	10.519
4	32	20	21	9.027
4	32	20	22	7.848
4	32	20	23	12.319
4	32	20	24	10.068
4	32	20	25	12.198
4	32	20	26	7.872
4	32	20	27	8.831
4	32	20	28	10.432
4	32	20	29	8.886
4	32	20	30	8.553
4	32	20	31	11.836
4	32	20	32	7.608
4	32	20	33	11.136
4	32	20	34	13.533
4	32	20	35	14.891
4	32	20	36	19.392
4	32	20	37	22.947
4	32	20	38	23.988
4	32	20	39	20.032
4	32	20	40	27.083
4	32	20	41	28.207
4	32	20	42	27.317
4	32	20	43	34.869
4	32	20	44	33.646
4	32	20	45	36.784
4	32	20	46	37.477
4	32	20	47	40.527
4	32	20	48	41.814
4	32	20	49	40.749
4	32	20	50	45.507
4	32	20	51	45.754
4	32	20	52	47.572
4	32	20	53	56.028
4	32	20	54	51.273
4	32	20	55	51.980
4	32	20	56	47.747
4	32	20	57	48.654
4	32	20	58	50.635
4	32	20	59	48.446
4	32	20	60	50.343
5	19	20	1	10.667
5	19	20	2	6.804
5	19	20	3	8.303
5	19	20	4	14.498
5	19	20	5	6.868
5	19	20	6	12.367
5	19	20	7	9.414
5	19	20	8	9.444
5	19	20	9	8.745
5	19	20	10	14.305
5	19	20	11	10.961
5	19	20	12	10.451
5	19	20	13	14.828
5	19	20	14	6.272
5	19	20	15	10.096
5	19	20	16	12.403
5	19	20	17	8.464
5	19	20	18	8.629
5	19	20	19	9.839
5	19	20	20	14.449
5	19	20	21	13.049
5	19	20	22	16.131
5	19	20	23	18.193
5	19	20	24	17.921
5	19	20	25	23.902
5	19	20	26	22.576
5	19	20	27	23.059
5	19	20	28	25.573
5	19	20	29	29.038
5	19	20	30	31.427
5	19	20	31	33.964
5	19	20	32	36.342
5	19	20	33	38.469
5	19	20	34	41.438
5	19	20	35	40.760
5	19	20	36	44.753
5	19	20	37	47.462
5	19	20	38	48.816
5	19	20	39	52.240
5	19	20	40	50.288
5	19	20	41	51.077
5	19	20	42	48.519
5	19	20	43	51.011
5	19	20	44	49.589
5	19	20	45	50.637
5	19	20	46	47.937
5	19	20	47	45.461
5	19	20	48	52.178
5	19	20	49	48.010
5	19	20	50	47.008
5	19	20	51	50.800
5	19	20	52	47.707
5	19	20	53	54.898
5	19	20	54	49.207
5	19	20	55	52.968
5	19	20	56	51.581
5	19	20	57	47.745
5	19	20	58	53.559
5	19	20	59	48.210
5	19	20	60	50.766
6	24	20	1	13.027
6	24	20	2	7.471
6	24	20	3	13.680
6	24	20	4	5.769
6	24	20	5	10.789
6	24	20	6	8.699
6	24	20	7	9.323
6	24	20	8	8.312
6	24	20	9	12.865
6	24	20	10	9.967
6	24	20	11	6.926
6	24	20	12	7.546
6	24	20	13	10.175
6	24	20	14	12.912
6	24	20	15	5.829
6	24	20	16	8.554
6	24	20	17	10.514
6	24	20	18	12.225
6	24	20	19	9.299
6	24	20	20	9.090
6	24	20	21	9.931
6	24	20	22	10.490
6	24	20	23	12.348
6	24	20	24	11.752
6	24	20	25	10.447
6	24	20	26	13.391
6	24	20	27	16.231
6	24	20	28	16.951
6	24	20	29	21.799
6	24	20	30	21.528
6	24	20	31	21.879
6	24	20	32	25.989
6	24	20	33	26.665
6	24	20	34	32.069
6	24	20	35	32.795
6	24	20	36	35.019
6	24	20	37	37.480
6	24	20	38	32.867
6	24	20	39	35.365
6	24	20	40	41.401
6	24	20	41	46.800
6	24	20	42	49.059
6	24	20	43	46.944
6	24	20	44	50.266
6	24	20	45	54.224
6	24	20	46	50.032
6	24	20	47	51.803
6	24	20	48	49.742
6	24	20	49	48.780
6	24	20	50	48.083
6	24	20	51	48.620
6	24	20	52	48.069
6	24	20	53	48.062
6	24	20	54	46.871
6	24	20	55	50.262
6	24	20	56	44.337
6	24	20	57	51.324
6	24	20	58	48.036
6	24	20	59	54.790
6	24	20	60	48.737
7	10	19	1	9.695
7	10	19	2	9.174
7	10	19	3	10.637
7	10	19	4	9.136
7	10	19	5	7.362
7	10	19	6	14.267
7	10	19	7	9.448
7	10	19	8	12.378
7	10	19	9	6.093
7	10	19	10	5.854
7	10	19	11	9.539
7	10	19	12	14.262
7	10	19	13	13.727
7	10	19	14	19.425
7	10	19	15	20.669
7	10	19	16	22.576
7	10	19	17	26.741
7	10	19	18	24.729
7	10	19	19	28.852
7	10	19	20	30.760
7	10	19	21	31.628
7	10	19	22	34.361
7	10	19	23	36.095
7	10	19	24	40.918
7	10	19	25	39.432
7	10	19	26	41.137
7	10	19	27	43.049
7	10	19	28	46.761
7	10	19	29	46.199
7	10	19	30	48.769
7	10	19	31	47.367
7	10	19	32	49.477
7	10	19	33	52.471
7	10	19	34	49.366
7	10	19	35	44.544
7	10	19	36	48.075
7	10	19	37	46.384
7	10	19	38	47.661
7	10	19	39	45.355
7	10	19	40	49.098
7	10	19	41	48.016
7	10	19	42	47.499
7	10	19	43	49.154
7	10	19	44	48.998
7	10	19	45	47.308
7	10	19	46	46.442
7	10	19	47	49.660
7	10	19	48	48.067
7	10	19	49	46.288
7	10	19	50	45.444
7	10	19	51	48.753
7	10	19	52	49.891
7	10	19	53	49.418
7	10	19	54	47.998
7	10	19	55	50.407
7	10	19	56	46.834
7	10	19	57	46.451
7	10	19	58	48.048
7	10	19	59	48.951
7	10	19	60	52.757
8	13	20	1	10.596
8	13	20	2	11.746
8	13	20	3	10.903
8	13	20	4	11.403
8	13	20	5	8.263
8	13	20	6	7.589
8	13	20	7	8.232
8	13	20	8	7.295
8	13	20	9	10.028
8	13	20	10	10.105
8	13	20	11	9.797
8	13	20	12	11.889
8	13	20	13	7.041
8	13	20	14	12.705
8	13	20	15	13.626
8	13	20	16	14.990
8	13	20	17	19.614
8	13	20	18	23.575
8	13	20	19	22.594
8	13	20	20	25.971
8	13	20	21	26.860
8	13	20	22	30.064
8	13	20	23	29.586
8	13	20	24	31.227
8	13	20	25	34.261
8	13	20	26	36.188
8	13	20	27	38.330
8	13	20	28	42.613
8	13	20	29	43.275
8	13	20	30	42.504
8	13	20	31	42.509
8	13	20	32	51.437
8	13	20	33	53.725
8	13	20	34	50.306
8	13	20	35	48.788
8	13	20	36	51.408
8	13	20	37	53.231
8	13	20	38	50.961
8	13	20	39	53.231
8	13	20	40	48.398
8	13	20	41	48.722
8	13	20	42	50.481
8	13	20	43	51.476
8	13	20	44	50.614
8	13	20	45	48.026
8	13	20	46	50.541
8	13	20	47	50.111
8	13	20	48	49.685
8	13	20	49	50.818
8	13	20	50	45.583
8	13	20	51	50.140
8	13	20	52	49.661
8	13	20	53	47.786
8	13	20	54	49.989
8	13	20	55	52.467
8	13	20	56	50.677
8	13	20	57	49.147
8	13	20	58	51.386
8	13	20	59	47.671
8	13	20	60	50.322

We need to identify which rows are part of the pre-intervention phase, which are in the intervention phase, and which are in the post-intervention phase. Let’s use the case_when function, which is like a series of “if-then” statements followed by the desired result.

d_longer <- d_longer %>% 
  mutate(phase = case_when(
    time <= baseline_sessions_n ~ "Pre-Intervention",
    time <= baseline_sessions_n + intervention_sessions_n ~ "Intervention",
    time > baseline_sessions_n + intervention_sessions_n ~ "Post-Intervention" 
  ))

d_longer

id	baseline_sessions_n	intervention_sessions_n	time	on_task	phase
1	22	20	1	11.481	Pre-Intervention
1	22	20	2	14.402	Pre-Intervention
1	22	20	3	6.151	Pre-Intervention
1	22	20	4	9.254	Pre-Intervention
1	22	20	5	9.580	Pre-Intervention
1	22	20	6	8.160	Pre-Intervention
1	22	20	7	10.125	Pre-Intervention
1	22	20	8	11.120	Pre-Intervention
1	22	20	9	9.203	Pre-Intervention
1	22	20	10	10.838	Pre-Intervention
1	22	20	11	9.181	Pre-Intervention
1	22	20	12	8.436	Pre-Intervention
1	22	20	13	9.977	Pre-Intervention
1	22	20	14	13.242	Pre-Intervention
1	22	20	15	9.317	Pre-Intervention
1	22	20	16	8.276	Pre-Intervention
1	22	20	17	9.208	Pre-Intervention
1	22	20	18	10.888	Pre-Intervention
1	22	20	19	7.225	Pre-Intervention
1	22	20	20	11.472	Pre-Intervention
1	22	20	21	8.570	Pre-Intervention
1	22	20	22	10.583	Pre-Intervention
1	22	20	23	11.038	Intervention
1	22	20	24	13.126	Intervention
1	22	20	25	17.995	Intervention
1	22	20	26	12.965	Intervention
1	22	20	27	23.840	Intervention
1	22	20	28	21.396	Intervention
1	22	20	29	22.653	Intervention
1	22	20	30	30.187	Intervention
1	22	20	31	29.494	Intervention
1	22	20	32	31.359	Intervention
1	22	20	33	29.316	Intervention
1	22	20	34	33.783	Intervention
1	22	20	35	38.945	Intervention
1	22	20	36	37.283	Intervention
1	22	20	37	42.568	Intervention
1	22	20	38	37.734	Intervention
1	22	20	39	44.345	Intervention
1	22	20	40	44.606	Intervention
1	22	20	41	49.511	Intervention
1	22	20	42	49.604	Intervention
1	22	20	43	48.723	Post-Intervention
1	22	20	44	50.882	Post-Intervention
1	22	20	45	51.230	Post-Intervention
1	22	20	46	49.038	Post-Intervention
1	22	20	47	51.275	Post-Intervention
1	22	20	48	49.000	Post-Intervention
1	22	20	49	51.135	Post-Intervention
1	22	20	50	48.722	Post-Intervention
1	22	20	51	50.638	Post-Intervention
1	22	20	52	51.235	Post-Intervention
1	22	20	53	54.457	Post-Intervention
1	22	20	54	48.840	Post-Intervention
1	22	20	55	50.354	Post-Intervention
1	22	20	56	54.538	Post-Intervention
1	22	20	57	52.765	Post-Intervention
1	22	20	58	50.677	Post-Intervention
1	22	20	59	50.179	Post-Intervention
1	22	20	60	50.030	Post-Intervention
2	18	19	1	9.114	Pre-Intervention
2	18	19	2	8.586	Pre-Intervention
2	18	19	3	11.010	Pre-Intervention
2	18	19	4	9.356	Pre-Intervention
2	18	19	5	8.095	Pre-Intervention
2	18	19	6	8.772	Pre-Intervention
2	18	19	7	8.953	Pre-Intervention
2	18	19	8	7.443	Pre-Intervention
2	18	19	9	11.057	Pre-Intervention
2	18	19	10	7.590	Pre-Intervention
2	18	19	11	11.087	Pre-Intervention
2	18	19	12	10.670	Pre-Intervention
2	18	19	13	7.843	Pre-Intervention
2	18	19	14	9.018	Pre-Intervention
2	18	19	15	11.266	Pre-Intervention
2	18	19	16	5.886	Pre-Intervention
2	18	19	17	7.388	Pre-Intervention
2	18	19	18	8.107	Pre-Intervention
2	18	19	19	12.111	Intervention
2	18	19	20	14.825	Intervention
2	18	19	21	13.107	Intervention
2	18	19	22	18.156	Intervention
2	18	19	23	21.652	Intervention
2	18	19	24	22.819	Intervention
2	18	19	25	24.846	Intervention
2	18	19	26	25.152	Intervention
2	18	19	27	29.230	Intervention
2	18	19	28	31.499	Intervention
2	18	19	29	30.092	Intervention
2	18	19	30	34.848	Intervention
2	18	19	31	29.831	Intervention
2	18	19	32	42.884	Intervention
2	18	19	33	40.936	Intervention
2	18	19	34	45.121	Intervention
2	18	19	35	48.622	Intervention
2	18	19	36	48.627	Intervention
2	18	19	37	47.455	Intervention
2	18	19	38	47.031	Post-Intervention
2	18	19	39	48.660	Post-Intervention
2	18	19	40	47.544	Post-Intervention
2	18	19	41	50.799	Post-Intervention
2	18	19	42	50.576	Post-Intervention
2	18	19	43	49.929	Post-Intervention
2	18	19	44	48.240	Post-Intervention
2	18	19	45	49.406	Post-Intervention
2	18	19	46	48.225	Post-Intervention
2	18	19	47	47.975	Post-Intervention
2	18	19	48	47.588	Post-Intervention
2	18	19	49	44.746	Post-Intervention
2	18	19	50	43.743	Post-Intervention
2	18	19	51	49.148	Post-Intervention
2	18	19	52	48.024	Post-Intervention
2	18	19	53	44.893	Post-Intervention
2	18	19	54	48.279	Post-Intervention
2	18	19	55	47.229	Post-Intervention
2	18	19	56	48.910	Post-Intervention
2	18	19	57	44.843	Post-Intervention
2	18	19	58	45.472	Post-Intervention
2	18	19	59	49.480	Post-Intervention
2	18	19	60	50.346	Post-Intervention
3	5	18	1	7.244	Pre-Intervention
3	5	18	2	14.309	Pre-Intervention
3	5	18	3	7.979	Pre-Intervention
3	5	18	4	11.175	Pre-Intervention
3	5	18	5	12.287	Pre-Intervention
3	5	18	6	13.146	Intervention
3	5	18	7	11.694	Intervention
3	5	18	8	18.998	Intervention
3	5	18	9	16.125	Intervention
3	5	18	10	20.296	Intervention
3	5	18	11	21.905	Intervention
3	5	18	12	21.952	Intervention
3	5	18	13	27.114	Intervention
3	5	18	14	27.933	Intervention
3	5	18	15	27.448	Intervention
3	5	18	16	30.747	Intervention
3	5	18	17	36.685	Intervention
3	5	18	18	37.337	Intervention
3	5	18	19	39.197	Intervention
3	5	18	20	43.845	Intervention
3	5	18	21	44.752	Intervention
3	5	18	22	43.358	Intervention
3	5	18	23	46.341	Intervention
3	5	18	24	47.006	Post-Intervention
3	5	18	25	44.497	Post-Intervention
3	5	18	26	46.507	Post-Intervention
3	5	18	27	44.034	Post-Intervention
3	5	18	28	47.017	Post-Intervention
3	5	18	29	48.691	Post-Intervention
3	5	18	30	43.648	Post-Intervention
3	5	18	31	47.700	Post-Intervention
3	5	18	32	49.477	Post-Intervention
3	5	18	33	48.000	Post-Intervention
3	5	18	34	46.173	Post-Intervention
3	5	18	35	48.513	Post-Intervention
3	5	18	36	43.801	Post-Intervention
3	5	18	37	48.552	Post-Intervention
3	5	18	38	48.298	Post-Intervention
3	5	18	39	43.902	Post-Intervention
3	5	18	40	47.103	Post-Intervention
3	5	18	41	51.735	Post-Intervention
3	5	18	42	40.911	Post-Intervention
3	5	18	43	45.881	Post-Intervention
3	5	18	44	49.407	Post-Intervention
3	5	18	45	45.653	Post-Intervention
3	5	18	46	43.964	Post-Intervention
3	5	18	47	46.602	Post-Intervention
3	5	18	48	44.568	Post-Intervention
3	5	18	49	42.596	Post-Intervention
3	5	18	50	47.055	Post-Intervention
3	5	18	51	46.582	Post-Intervention
3	5	18	52	43.501	Post-Intervention
3	5	18	53	42.446	Post-Intervention
3	5	18	54	47.222	Post-Intervention
3	5	18	55	44.163	Post-Intervention
3	5	18	56	49.381	Post-Intervention
3	5	18	57	44.999	Post-Intervention
3	5	18	58	46.108	Post-Intervention
3	5	18	59	47.088	Post-Intervention
3	5	18	60	44.919	Post-Intervention
4	32	20	1	11.297	Pre-Intervention
4	32	20	2	14.307	Pre-Intervention
4	32	20	3	14.811	Pre-Intervention
4	32	20	4	8.678	Pre-Intervention
4	32	20	5	9.533	Pre-Intervention
4	32	20	6	7.823	Pre-Intervention
4	32	20	7	9.561	Pre-Intervention
4	32	20	8	8.209	Pre-Intervention
4	32	20	9	12.898	Pre-Intervention
4	32	20	10	8.244	Pre-Intervention
4	32	20	11	8.548	Pre-Intervention
4	32	20	12	8.616	Pre-Intervention
4	32	20	13	7.586	Pre-Intervention
4	32	20	14	8.921	Pre-Intervention
4	32	20	15	9.199	Pre-Intervention
4	32	20	16	8.711	Pre-Intervention
4	32	20	17	11.026	Pre-Intervention
4	32	20	18	10.804	Pre-Intervention
4	32	20	19	11.591	Pre-Intervention
4	32	20	20	10.519	Pre-Intervention
4	32	20	21	9.027	Pre-Intervention
4	32	20	22	7.848	Pre-Intervention
4	32	20	23	12.319	Pre-Intervention
4	32	20	24	10.068	Pre-Intervention
4	32	20	25	12.198	Pre-Intervention
4	32	20	26	7.872	Pre-Intervention
4	32	20	27	8.831	Pre-Intervention
4	32	20	28	10.432	Pre-Intervention
4	32	20	29	8.886	Pre-Intervention
4	32	20	30	8.553	Pre-Intervention
4	32	20	31	11.836	Pre-Intervention
4	32	20	32	7.608	Pre-Intervention
4	32	20	33	11.136	Intervention
4	32	20	34	13.533	Intervention
4	32	20	35	14.891	Intervention
4	32	20	36	19.392	Intervention
4	32	20	37	22.947	Intervention
4	32	20	38	23.988	Intervention
4	32	20	39	20.032	Intervention
4	32	20	40	27.083	Intervention
4	32	20	41	28.207	Intervention
4	32	20	42	27.317	Intervention
4	32	20	43	34.869	Intervention
4	32	20	44	33.646	Intervention
4	32	20	45	36.784	Intervention
4	32	20	46	37.477	Intervention
4	32	20	47	40.527	Intervention
4	32	20	48	41.814	Intervention
4	32	20	49	40.749	Intervention
4	32	20	50	45.507	Intervention
4	32	20	51	45.754	Intervention
4	32	20	52	47.572	Intervention
4	32	20	53	56.028	Post-Intervention
4	32	20	54	51.273	Post-Intervention
4	32	20	55	51.980	Post-Intervention
4	32	20	56	47.747	Post-Intervention
4	32	20	57	48.654	Post-Intervention
4	32	20	58	50.635	Post-Intervention
4	32	20	59	48.446	Post-Intervention
4	32	20	60	50.343	Post-Intervention
5	19	20	1	10.667	Pre-Intervention
5	19	20	2	6.804	Pre-Intervention
5	19	20	3	8.303	Pre-Intervention
5	19	20	4	14.498	Pre-Intervention
5	19	20	5	6.868	Pre-Intervention
5	19	20	6	12.367	Pre-Intervention
5	19	20	7	9.414	Pre-Intervention
5	19	20	8	9.444	Pre-Intervention
5	19	20	9	8.745	Pre-Intervention
5	19	20	10	14.305	Pre-Intervention
5	19	20	11	10.961	Pre-Intervention
5	19	20	12	10.451	Pre-Intervention
5	19	20	13	14.828	Pre-Intervention
5	19	20	14	6.272	Pre-Intervention
5	19	20	15	10.096	Pre-Intervention
5	19	20	16	12.403	Pre-Intervention
5	19	20	17	8.464	Pre-Intervention
5	19	20	18	8.629	Pre-Intervention
5	19	20	19	9.839	Pre-Intervention
5	19	20	20	14.449	Intervention
5	19	20	21	13.049	Intervention
5	19	20	22	16.131	Intervention
5	19	20	23	18.193	Intervention
5	19	20	24	17.921	Intervention
5	19	20	25	23.902	Intervention
5	19	20	26	22.576	Intervention
5	19	20	27	23.059	Intervention
5	19	20	28	25.573	Intervention
5	19	20	29	29.038	Intervention
5	19	20	30	31.427	Intervention
5	19	20	31	33.964	Intervention
5	19	20	32	36.342	Intervention
5	19	20	33	38.469	Intervention
5	19	20	34	41.438	Intervention
5	19	20	35	40.760	Intervention
5	19	20	36	44.753	Intervention
5	19	20	37	47.462	Intervention
5	19	20	38	48.816	Intervention
5	19	20	39	52.240	Intervention
5	19	20	40	50.288	Post-Intervention
5	19	20	41	51.077	Post-Intervention
5	19	20	42	48.519	Post-Intervention
5	19	20	43	51.011	Post-Intervention
5	19	20	44	49.589	Post-Intervention
5	19	20	45	50.637	Post-Intervention
5	19	20	46	47.937	Post-Intervention
5	19	20	47	45.461	Post-Intervention
5	19	20	48	52.178	Post-Intervention
5	19	20	49	48.010	Post-Intervention
5	19	20	50	47.008	Post-Intervention
5	19	20	51	50.800	Post-Intervention
5	19	20	52	47.707	Post-Intervention
5	19	20	53	54.898	Post-Intervention
5	19	20	54	49.207	Post-Intervention
5	19	20	55	52.968	Post-Intervention
5	19	20	56	51.581	Post-Intervention
5	19	20	57	47.745	Post-Intervention
5	19	20	58	53.559	Post-Intervention
5	19	20	59	48.210	Post-Intervention
5	19	20	60	50.766	Post-Intervention
6	24	20	1	13.027	Pre-Intervention
6	24	20	2	7.471	Pre-Intervention
6	24	20	3	13.680	Pre-Intervention
6	24	20	4	5.769	Pre-Intervention
6	24	20	5	10.789	Pre-Intervention
6	24	20	6	8.699	Pre-Intervention
6	24	20	7	9.323	Pre-Intervention
6	24	20	8	8.312	Pre-Intervention
6	24	20	9	12.865	Pre-Intervention
6	24	20	10	9.967	Pre-Intervention
6	24	20	11	6.926	Pre-Intervention
6	24	20	12	7.546	Pre-Intervention
6	24	20	13	10.175	Pre-Intervention
6	24	20	14	12.912	Pre-Intervention
6	24	20	15	5.829	Pre-Intervention
6	24	20	16	8.554	Pre-Intervention
6	24	20	17	10.514	Pre-Intervention
6	24	20	18	12.225	Pre-Intervention
6	24	20	19	9.299	Pre-Intervention
6	24	20	20	9.090	Pre-Intervention
6	24	20	21	9.931	Pre-Intervention
6	24	20	22	10.490	Pre-Intervention
6	24	20	23	12.348	Pre-Intervention
6	24	20	24	11.752	Pre-Intervention
6	24	20	25	10.447	Intervention
6	24	20	26	13.391	Intervention
6	24	20	27	16.231	Intervention
6	24	20	28	16.951	Intervention
6	24	20	29	21.799	Intervention
6	24	20	30	21.528	Intervention
6	24	20	31	21.879	Intervention
6	24	20	32	25.989	Intervention
6	24	20	33	26.665	Intervention
6	24	20	34	32.069	Intervention
6	24	20	35	32.795	Intervention
6	24	20	36	35.019	Intervention
6	24	20	37	37.480	Intervention
6	24	20	38	32.867	Intervention
6	24	20	39	35.365	Intervention
6	24	20	40	41.401	Intervention
6	24	20	41	46.800	Intervention
6	24	20	42	49.059	Intervention
6	24	20	43	46.944	Intervention
6	24	20	44	50.266	Intervention
6	24	20	45	54.224	Post-Intervention
6	24	20	46	50.032	Post-Intervention
6	24	20	47	51.803	Post-Intervention
6	24	20	48	49.742	Post-Intervention
6	24	20	49	48.780	Post-Intervention
6	24	20	50	48.083	Post-Intervention
6	24	20	51	48.620	Post-Intervention
6	24	20	52	48.069	Post-Intervention
6	24	20	53	48.062	Post-Intervention
6	24	20	54	46.871	Post-Intervention
6	24	20	55	50.262	Post-Intervention
6	24	20	56	44.337	Post-Intervention
6	24	20	57	51.324	Post-Intervention
6	24	20	58	48.036	Post-Intervention
6	24	20	59	54.790	Post-Intervention
6	24	20	60	48.737	Post-Intervention
7	10	19	1	9.695	Pre-Intervention
7	10	19	2	9.174	Pre-Intervention
7	10	19	3	10.637	Pre-Intervention
7	10	19	4	9.136	Pre-Intervention
7	10	19	5	7.362	Pre-Intervention
7	10	19	6	14.267	Pre-Intervention
7	10	19	7	9.448	Pre-Intervention
7	10	19	8	12.378	Pre-Intervention
7	10	19	9	6.093	Pre-Intervention
7	10	19	10	5.854	Pre-Intervention
7	10	19	11	9.539	Intervention
7	10	19	12	14.262	Intervention
7	10	19	13	13.727	Intervention
7	10	19	14	19.425	Intervention
7	10	19	15	20.669	Intervention
7	10	19	16	22.576	Intervention
7	10	19	17	26.741	Intervention
7	10	19	18	24.729	Intervention
7	10	19	19	28.852	Intervention
7	10	19	20	30.760	Intervention
7	10	19	21	31.628	Intervention
7	10	19	22	34.361	Intervention
7	10	19	23	36.095	Intervention
7	10	19	24	40.918	Intervention
7	10	19	25	39.432	Intervention
7	10	19	26	41.137	Intervention
7	10	19	27	43.049	Intervention
7	10	19	28	46.761	Intervention
7	10	19	29	46.199	Intervention
7	10	19	30	48.769	Post-Intervention
7	10	19	31	47.367	Post-Intervention
7	10	19	32	49.477	Post-Intervention
7	10	19	33	52.471	Post-Intervention
7	10	19	34	49.366	Post-Intervention
7	10	19	35	44.544	Post-Intervention
7	10	19	36	48.075	Post-Intervention
7	10	19	37	46.384	Post-Intervention
7	10	19	38	47.661	Post-Intervention
7	10	19	39	45.355	Post-Intervention
7	10	19	40	49.098	Post-Intervention
7	10	19	41	48.016	Post-Intervention
7	10	19	42	47.499	Post-Intervention
7	10	19	43	49.154	Post-Intervention
7	10	19	44	48.998	Post-Intervention
7	10	19	45	47.308	Post-Intervention
7	10	19	46	46.442	Post-Intervention
7	10	19	47	49.660	Post-Intervention
7	10	19	48	48.067	Post-Intervention
7	10	19	49	46.288	Post-Intervention
7	10	19	50	45.444	Post-Intervention
7	10	19	51	48.753	Post-Intervention
7	10	19	52	49.891	Post-Intervention
7	10	19	53	49.418	Post-Intervention
7	10	19	54	47.998	Post-Intervention
7	10	19	55	50.407	Post-Intervention
7	10	19	56	46.834	Post-Intervention
7	10	19	57	46.451	Post-Intervention
7	10	19	58	48.048	Post-Intervention
7	10	19	59	48.951	Post-Intervention
7	10	19	60	52.757	Post-Intervention
8	13	20	1	10.596	Pre-Intervention
8	13	20	2	11.746	Pre-Intervention
8	13	20	3	10.903	Pre-Intervention
8	13	20	4	11.403	Pre-Intervention
8	13	20	5	8.263	Pre-Intervention
8	13	20	6	7.589	Pre-Intervention
8	13	20	7	8.232	Pre-Intervention
8	13	20	8	7.295	Pre-Intervention
8	13	20	9	10.028	Pre-Intervention
8	13	20	10	10.105	Pre-Intervention
8	13	20	11	9.797	Pre-Intervention
8	13	20	12	11.889	Pre-Intervention
8	13	20	13	7.041	Pre-Intervention
8	13	20	14	12.705	Intervention
8	13	20	15	13.626	Intervention
8	13	20	16	14.990	Intervention
8	13	20	17	19.614	Intervention
8	13	20	18	23.575	Intervention
8	13	20	19	22.594	Intervention
8	13	20	20	25.971	Intervention
8	13	20	21	26.860	Intervention
8	13	20	22	30.064	Intervention
8	13	20	23	29.586	Intervention
8	13	20	24	31.227	Intervention
8	13	20	25	34.261	Intervention
8	13	20	26	36.188	Intervention
8	13	20	27	38.330	Intervention
8	13	20	28	42.613	Intervention
8	13	20	29	43.275	Intervention
8	13	20	30	42.504	Intervention
8	13	20	31	42.509	Intervention
8	13	20	32	51.437	Intervention
8	13	20	33	53.725	Intervention
8	13	20	34	50.306	Post-Intervention
8	13	20	35	48.788	Post-Intervention
8	13	20	36	51.408	Post-Intervention
8	13	20	37	53.231	Post-Intervention
8	13	20	38	50.961	Post-Intervention
8	13	20	39	53.231	Post-Intervention
8	13	20	40	48.398	Post-Intervention
8	13	20	41	48.722	Post-Intervention
8	13	20	42	50.481	Post-Intervention
8	13	20	43	51.476	Post-Intervention
8	13	20	44	50.614	Post-Intervention
8	13	20	45	48.026	Post-Intervention
8	13	20	46	50.541	Post-Intervention
8	13	20	47	50.111	Post-Intervention
8	13	20	48	49.685	Post-Intervention
8	13	20	49	50.818	Post-Intervention
8	13	20	50	45.583	Post-Intervention
8	13	20	51	50.140	Post-Intervention
8	13	20	52	49.661	Post-Intervention
8	13	20	53	47.786	Post-Intervention
8	13	20	54	49.989	Post-Intervention
8	13	20	55	52.467	Post-Intervention
8	13	20	56	50.677	Post-Intervention
8	13	20	57	49.147	Post-Intervention
8	13	20	58	51.386	Post-Intervention
8	13	20	59	47.671	Post-Intervention
8	13	20	60	50.322	Post-Intervention

Plot the results:

Plotting is rarely done all at once. We usually build it step-by-step and layer-by-layer. Here is the first attempt:

d_longer %>% 
  ggplot(aes(time, on_task)) +
  geom_line(aes(group = id, color = phase))

That was not so great! Let’s make a separate plot for each person:

d_longer %>% 
  ggplot(aes(time, on_task)) +
  geom_line(aes(group = id, color = phase)) + 
  facet_grid(rows = vars(id))

Better! Let’s reorder the cases by the order in which the intervention is first implemented:

d_longer %>% 
  mutate(id = fct_reorder(factor(id), baseline_sessions_n)) %>% 
  ggplot(aes(time, on_task)) +
  geom_line(aes(group = id, color = phase)) + 
  facet_grid(rows = vars(id))

Looks even better!

If I wanted a more polished plot for publication, I might create something like Figure 1.

# Some pre-processing for easier plotting
d_longer_processed <- d_longer %>% 
    mutate(id = factor(id) %>% fct_reorder(baseline_sessions_n),
         phase = fct_inorder(phase)) %>% 
  arrange(phase) %>% 
  mutate(id = factor(id, labels = LETTERS[1:8]))

# data for phase rectangles
d_phases <- d_longer_processed %>% 
  summarise(.by = c(id, phase),
            begin = min(time),
            end = max(time)) %>% 
  mutate(begin = ifelse(begin == 1, 0, begin),
         end = end + 1)

d_longer_processed %>%
  ggplot(aes(time, on_task)) +
    geom_vline(xintercept = rep(1:4, 12) + rep(seq(0,55,5), each = 4), linewidth = unit(.1, "mm"), color = "gray80") +
  ggtext::geom_richtext(
    data = . %>% filter(id == "E", time %in% c(10, 30, 50)),
    aes(label = phase, color = phase),
    label.color = NA,
    fill = scales::alpha("white", .5),
    label.padding = unit(0, "mm"),
    label.margin = unit(2, "mm"),
    vjust = c(0, 0, 1),
    angle = c(0, 21, 0),
    size = 5.5,
    family = "Roboto Condensed"
  ) +
  geom_rect(
    data = d_phases,
    aes(
      xmin = begin,
      xmax = end,
      ymin = 0,
      ymax = 60,
      fill = phase
    ),
    inherit.aes = FALSE
  ) +
  geom_line(aes(group = id, color = phase), linewidth = 1) +
  facet_grid(rows = vars(id)) +
  theme(strip.text.y = element_text(angle = 0),
        legend.position = "none") +
  labs(x = "Day", y = "Time on Task")  +
  theme_light(base_family = "Roboto Condensed", base_size = 18) +
  theme(legend.position = "none",
        panel.border = element_blank(),
        strip.text.y = element_text(angle = 0), 
        axis.text.y = element_text(vjust = c(0))) +
  scale_color_viridis_d(alpha = .9, begin = .1, end = .7) +
  scale_fill_viridis_d(alpha = .15, begin = .1, end = .7) +
  scale_y_continuous(
    limits = c(0, 60),
    breaks = seq(0, 50, 10),
    expand = expansion(0)
  ) +
  scale_x_continuous(limits = c(0, 61), breaks = seq(0, 60, 10), minor_breaks = seq(0,60, 5), expand = expansion(0)) +
   guides(
    x = guide_axis(minor.ticks = TRUE)
    # y = guide_axis(minor.ticks = TRUE)
  ) + 
  coord_cartesian(clip = "off")

Figure 1: Effect on Intervention on Time on Task for Eight Children