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 up 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")

Rows: 8 Columns: 63
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
dbl (63): id, baseline_sessions_n, intervention_sessions_n, time_1, time_2, ...

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

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. The output also says which 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)

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	11.481390	14.401475	6.150868	9.254059	9.579971	8.160149	10.124808	11.120275	9.202528	10.838353	9.181093	8.436128	9.977338	13.242464	9.317091	8.275580	9.207833	10.888414	7.225326	11.472184	8.570382	10.582867	11.03763	13.12591	17.99503	12.965307	23.83989	21.39564	22.652730	30.186799	29.49430	31.359282	29.31638	33.78262	38.94499	37.28258	42.56795	37.73358	44.34489	44.60556	49.51082	49.60417	48.72288	50.88207	51.22964	49.03761	51.27490	48.99979	51.13479	48.72249	50.63808	51.23539	54.45665	48.83957	50.35392	54.53799	52.76513	50.67702	50.17861	50.03028
2	18	19	9.114530	8.586483	11.009756	9.355582	8.094828	8.771899	8.953120	7.442692	11.056727	7.590044	11.086753	10.670066	7.842831	9.018026	11.266130	5.886133	7.387846	8.107445	12.111284	14.825195	13.106666	18.156120	21.65248	22.81874	24.84652	25.152196	29.22987	31.49869	30.092382	34.848345	29.83119	42.884047	40.93578	45.12095	48.62162	48.62702	47.45525	47.03118	48.66027	47.54405	50.79882	50.57596	49.92920	48.24046	49.40579	48.22480	47.97522	47.58815	44.74564	43.74299	49.14827	48.02379	44.89332	48.27857	47.22880	48.91035	44.84299	45.47214	49.48035	50.34650
3	5	18	7.243792	14.309134	7.978843	11.175362	12.287030	13.145834	11.694320	18.998364	16.125544	20.296444	21.905149	21.952354	27.114283	27.932517	27.447843	30.746875	36.685054	37.336880	39.197311	43.844702	44.751691	43.357550	46.34139	47.00592	44.49681	46.506577	44.03419	47.01678	48.690672	43.648066	47.70016	49.477448	47.99977	46.17302	48.51305	43.80151	48.55182	48.29836	43.90161	47.10333	51.73539	40.91064	45.88078	49.40663	45.65272	43.96433	46.60210	44.56747	42.59574	47.05461	46.58171	43.50139	42.44631	47.22240	44.16290	49.38115	44.99909	46.10792	47.08795	44.91932
4	32	20	11.296579	14.307367	14.810658	8.677719	9.533368	7.822653	9.561326	8.208895	12.897751	8.243799	8.547692	8.616214	7.586228	8.920517	9.198943	8.711324	11.026336	10.804031	11.591157	10.519060	9.027216	7.848482	12.31915	10.06748	12.19758	7.872384	8.83143	10.43239	8.886314	8.552978	11.83615	7.607858	11.13613	13.53312	14.89085	19.39188	22.94677	23.98795	20.03181	27.08285	28.20672	27.31684	34.86907	33.64589	36.78373	37.47689	40.52695	41.81391	40.74878	45.50657	45.75414	47.57176	56.02767	51.27312	51.97966	47.74709	48.65355	50.63484	48.44609	50.34281
5	19	20	10.667172	6.803895	8.303144	14.497614	6.867806	12.367478	9.413963	9.444261	8.744703	14.304808	10.960766	10.451372	14.827904	6.272452	10.096415	12.403348	8.463626	8.628488	9.839363	14.449422	13.048669	16.131181	18.19289	17.92118	23.90188	22.576320	23.05887	25.57293	29.038100	31.426959	33.96424	36.342143	38.46910	41.43815	40.76039	44.75332	47.46249	48.81603	52.23999	50.28782	51.07685	48.51878	51.01045	49.58853	50.63677	47.93697	45.46109	52.17792	48.00961	47.00786	50.80013	47.70737	54.89829	49.20719	52.96849	51.58046	47.74537	53.55861	48.21013	50.76616
6	24	20	13.027050	7.471434	13.679538	5.768483	10.788906	8.698977	9.322671	8.311932	12.865342	9.966903	6.926024	7.546386	10.175361	12.911465	5.829294	8.554141	10.513759	12.225228	9.298788	9.089913	9.930872	10.489717	12.34784	11.75163	10.44748	13.390600	16.23082	16.95081	21.799091	21.528074	21.87916	25.988930	26.66509	32.06949	32.79460	35.01925	37.48007	32.86721	35.36552	41.40063	46.80009	49.05867	46.94410	50.26605	54.22345	50.03206	51.80317	49.74147	48.77986	48.08299	48.62034	48.06852	48.06212	46.87093	50.26182	44.33751	51.32408	48.03641	54.78962	48.73745
7	10	19	9.695231	9.174433	10.636559	9.135522	7.362022	14.266673	9.447716	12.377972	6.093100	5.853903	9.539306	14.262163	13.727533	19.424575	20.669408	22.576336	26.741461	24.728567	28.852438	30.760060	31.627913	34.360552	36.09521	40.91820	39.43172	41.137066	43.04933	46.76154	46.199305	48.768979	47.36646	49.477421	52.47127	49.36562	44.54360	48.07476	46.38418	47.66100	45.35457	49.09778	48.01635	47.49901	49.15443	48.99818	47.30794	46.44190	49.66045	48.06729	46.28851	45.44424	48.75336	49.89107	49.41766	47.99784	50.40713	46.83389	46.45106	48.04787	48.95078	52.75730
8	13	20	10.596034	11.746395	10.903057	11.402866	8.263436	7.589180	8.232499	7.294619	10.028391	10.105379	9.796763	11.889425	7.041182	12.705354	13.625825	14.989743	19.613571	23.575270	22.594344	25.971062	26.860383	30.064456	29.58605	31.22675	34.26105	36.188052	38.32964	42.61264	43.274969	42.503742	42.50890	51.437160	53.72465	50.30605	48.78798	51.40765	53.23059	50.96061	53.23112	48.39759	48.72193	50.48096	51.47567	50.61417	48.02566	50.54120	50.11116	49.68551	50.81838	45.58281	50.14003	49.66069	47.78592	49.98868	52.46699	50.67714	49.14738	51.38654	47.67108	50.32172

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:

Each variable must have its own column.

Each observation must have its own row.

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	7.243792	14.30913	7.978843	11.17536	12.28703	13.14583	11.69432	18.99836	16.12554	20.29644	21.90515	21.95235	27.11428	27.93252	27.44784	30.74687	36.68505	37.33688	39.19731	43.8447	44.75169	43.35755	46.34139	47.00592	44.49681	46.50658	44.03419	47.01678	48.69067	43.64807	47.70016	49.47745	47.99977	46.17302	48.51305	43.80151	48.55182	48.29836	43.90161	47.10333	51.73539	40.91064	45.88078	49.40663	45.65272	43.96433	46.6021	44.56747	42.59574	47.05461	46.58171	43.50139	42.44631	47.2224	44.1629	49.38115	44.99909	46.10792	47.08795	44.91932

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	9.695231	9.174433	10.63656	9.135522	7.362022	14.26667	9.447716	12.37797	6.0931	5.853903	9.539306	14.26216	13.72753	19.42458	20.66941	22.57634	26.74146	24.72857	28.85244	30.76006	31.62791	34.36055	36.09521	40.9182	39.43172	41.13707	43.04933	46.76154	46.19931	48.76898	47.36646	49.47742	52.47127	49.36562	44.5436	48.07476	46.38418	47.661	45.35457	49.09778	48.01635	47.49901	49.15443	48.99818	47.30794	46.4419	49.66045	48.06729	46.28851	45.44424	48.75336	49.89107	49.41766	47.99784	50.40713	46.83389	46.45106	48.04787	48.95078	52.7573

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	11.48139	14.40148	6.150868	9.254059	9.579971	8.160149	10.124808	11.120275	9.202528	10.838353	9.181093	8.436128	9.977338	13.242464	9.317091	8.275580	9.207833	10.88841	7.225326	11.47218	8.570382	10.582867	11.03763	13.12591	17.99503	12.965307	23.83989	21.39564	22.652730	30.186799	29.49430	31.359282	29.31638	33.78262	38.94499	37.28258	42.56795	37.73358	44.34489	44.60556	49.51082	49.60417	48.72288	50.88207	51.22964	49.03761	51.27490	48.99979	51.13479	48.72249	50.63808	51.23539	54.45665	48.83957	50.35392	54.53799	52.76513	50.67702	50.17861	50.03028
4	32	20	11.29658	14.30737	14.810658	8.677719	9.533368	7.822653	9.561326	8.208895	12.897751	8.243799	8.547692	8.616214	7.586228	8.920517	9.198943	8.711324	11.026336	10.80403	11.591157	10.51906	9.027216	7.848482	12.31915	10.06748	12.19758	7.872384	8.83143	10.43239	8.886314	8.552978	11.83615	7.607858	11.13613	13.53312	14.89085	19.39188	22.94677	23.98795	20.03181	27.08285	28.20672	27.31684	34.86907	33.64589	36.78373	37.47689	40.52695	41.81391	40.74878	45.50657	45.75414	47.57176	56.02767	51.27312	51.97966	47.74709	48.65355	50.63484	48.44609	50.34281
8	13	20	10.59603	11.74640	10.903057	11.402866	8.263436	7.589180	8.232499	7.294619	10.028391	10.105379	9.796763	11.889425	7.041182	12.705354	13.625825	14.989743	19.613571	23.57527	22.594344	25.97106	26.860383	30.064456	29.58605	31.22675	34.26105	36.188052	38.32964	42.61264	43.274969	42.503742	42.50890	51.437160	53.72465	50.30605	48.78798	51.40765	53.23059	50.96061	53.23112	48.39759	48.72193	50.48096	51.47567	50.61417	48.02566	50.54120	50.11116	49.68551	50.81838	45.58281	50.14003	49.66069	47.78592	49.98868	52.46699	50.67714	49.14738	51.38654	47.67108	50.32172

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	11.481390	14.401475	6.150868	9.254059	9.579971	8.160149	10.124808	11.120275	9.202528	10.838353	9.181093	8.436128	9.977338	13.242464	9.317091	8.275580	9.207833	10.888414	7.225326	11.472184	8.570382	10.582867	11.03763	13.12591	17.99503	12.965307	23.83989	21.39564	22.652730	30.186799	29.49430	31.359282	29.31638	33.78262	38.94499	37.28258	42.56795	37.73358	44.34489	44.60556	49.51082	49.60417	48.72288	50.88207	51.22964	49.03761	51.27490	48.99979	51.13479	48.72249	50.63808	51.23539	54.45665	48.83957	50.35392	54.53799	52.76513	50.67702	50.17861	50.03028
3	5	18	7.243792	14.309134	7.978843	11.175362	12.287030	13.145834	11.694320	18.998364	16.125544	20.296444	21.905149	21.952354	27.114283	27.932517	27.447843	30.746875	36.685054	37.336880	39.197311	43.844702	44.751691	43.357550	46.34139	47.00592	44.49681	46.506577	44.03419	47.01678	48.690672	43.648066	47.70016	49.477448	47.99977	46.17302	48.51305	43.80151	48.55182	48.29836	43.90161	47.10333	51.73539	40.91064	45.88078	49.40663	45.65272	43.96433	46.60210	44.56747	42.59574	47.05461	46.58171	43.50139	42.44631	47.22240	44.16290	49.38115	44.99909	46.10792	47.08795	44.91932
4	32	20	11.296579	14.307367	14.810658	8.677719	9.533368	7.822653	9.561326	8.208895	12.897751	8.243799	8.547692	8.616214	7.586228	8.920517	9.198943	8.711324	11.026336	10.804031	11.591157	10.519060	9.027216	7.848482	12.31915	10.06748	12.19758	7.872384	8.83143	10.43239	8.886314	8.552978	11.83615	7.607858	11.13613	13.53312	14.89085	19.39188	22.94677	23.98795	20.03181	27.08285	28.20672	27.31684	34.86907	33.64589	36.78373	37.47689	40.52695	41.81391	40.74878	45.50657	45.75414	47.57176	56.02767	51.27312	51.97966	47.74709	48.65355	50.63484	48.44609	50.34281
5	19	20	10.667172	6.803895	8.303144	14.497614	6.867806	12.367478	9.413963	9.444261	8.744703	14.304808	10.960766	10.451372	14.827904	6.272452	10.096415	12.403348	8.463626	8.628488	9.839363	14.449422	13.048669	16.131181	18.19289	17.92118	23.90188	22.576320	23.05887	25.57293	29.038100	31.426959	33.96424	36.342143	38.46910	41.43815	40.76039	44.75332	47.46249	48.81603	52.23999	50.28782	51.07685	48.51878	51.01045	49.58853	50.63677	47.93697	45.46109	52.17792	48.00961	47.00786	50.80013	47.70737	54.89829	49.20719	52.96849	51.58046	47.74537	53.55861	48.21013	50.76616
6	24	20	13.027050	7.471434	13.679538	5.768483	10.788906	8.698977	9.322671	8.311932	12.865342	9.966903	6.926024	7.546386	10.175361	12.911465	5.829294	8.554141	10.513759	12.225228	9.298788	9.089913	9.930872	10.489717	12.34784	11.75163	10.44748	13.390600	16.23082	16.95081	21.799091	21.528074	21.87916	25.988930	26.66509	32.06949	32.79460	35.01925	37.48007	32.86721	35.36552	41.40063	46.80009	49.05867	46.94410	50.26605	54.22345	50.03206	51.80317	49.74147	48.77986	48.08299	48.62034	48.06852	48.06212	46.87093	50.26182	44.33751	51.32408	48.03641	54.78962	48.73745
8	13	20	10.596034	11.746395	10.903057	11.402866	8.263436	7.589180	8.232499	7.294619	10.028391	10.105379	9.796763	11.889425	7.041182	12.705354	13.625825	14.989743	19.613571	23.575270	22.594344	25.971062	26.860383	30.064456	29.58605	31.22675	34.26105	36.188052	38.32964	42.61264	43.274969	42.503742	42.50890	51.437160	53.72465	50.30605	48.78798	51.40765	53.23059	50.96061	53.23112	48.39759	48.72193	50.48096	51.47567	50.61417	48.02566	50.54120	50.11116	49.68551	50.81838	45.58281	50.14003	49.66069	47.78592	49.98868	52.46699	50.67714	49.14738	51.38654	47.67108	50.32172

Column selection

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

d %>% 
  select(id, time_1)

id	time_1
1	11.481390
2	9.114530
3	7.243792
4	11.296579
5	10.667172
6	13.027050
7	9.695231
8	10.596034

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
11.481390	14.401475	6.150868	9.254059	9.579971
9.114530	8.586483	11.009756	9.355582	8.094828
7.243792	14.309134	7.978843	11.175362	12.287030
11.296579	14.307367	14.810658	8.677719	9.533368
10.667172	6.803895	8.303144	14.497614	6.867806
13.027050	7.471434	13.679538	5.768483	10.788906
9.695231	9.174433	10.636559	9.135522	7.362022
10.596034	11.746395	10.903057	11.402866	8.263436

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	11.481390	14.401475	6.150868	9.254059	9.579971	8.160149	10.124808	11.120275	9.202528	10.838353	9.181093	8.436128	9.977338	13.242464	9.317091	8.275580	9.207833	10.888414	7.225326	11.472184	8.570382	10.582867	11.03763	13.12591	17.99503	12.965307	23.83989	21.39564	22.652730	30.186799	29.49430	31.359282	29.31638	33.78262	38.94499	37.28258	42.56795	37.73358	44.34489	44.60556	49.51082	49.60417	48.72288	50.88207	51.22964	49.03761	51.27490	48.99979	51.13479	48.72249	50.63808	51.23539	54.45665	48.83957	50.35392	54.53799	52.76513	50.67702	50.17861	50.03028
18	19	9.114530	8.586483	11.009756	9.355582	8.094828	8.771899	8.953120	7.442692	11.056727	7.590044	11.086753	10.670066	7.842831	9.018026	11.266130	5.886133	7.387846	8.107445	12.111284	14.825195	13.106666	18.156120	21.65248	22.81874	24.84652	25.152196	29.22987	31.49869	30.092382	34.848345	29.83119	42.884047	40.93578	45.12095	48.62162	48.62702	47.45525	47.03118	48.66027	47.54405	50.79882	50.57596	49.92920	48.24046	49.40579	48.22480	47.97522	47.58815	44.74564	43.74299	49.14827	48.02379	44.89332	48.27857	47.22880	48.91035	44.84299	45.47214	49.48035	50.34650
5	18	7.243792	14.309134	7.978843	11.175362	12.287030	13.145834	11.694320	18.998364	16.125544	20.296444	21.905149	21.952354	27.114283	27.932517	27.447843	30.746875	36.685054	37.336880	39.197311	43.844702	44.751691	43.357550	46.34139	47.00592	44.49681	46.506577	44.03419	47.01678	48.690672	43.648066	47.70016	49.477448	47.99977	46.17302	48.51305	43.80151	48.55182	48.29836	43.90161	47.10333	51.73539	40.91064	45.88078	49.40663	45.65272	43.96433	46.60210	44.56747	42.59574	47.05461	46.58171	43.50139	42.44631	47.22240	44.16290	49.38115	44.99909	46.10792	47.08795	44.91932
32	20	11.296579	14.307367	14.810658	8.677719	9.533368	7.822653	9.561326	8.208895	12.897751	8.243799	8.547692	8.616214	7.586228	8.920517	9.198943	8.711324	11.026336	10.804031	11.591157	10.519060	9.027216	7.848482	12.31915	10.06748	12.19758	7.872384	8.83143	10.43239	8.886314	8.552978	11.83615	7.607858	11.13613	13.53312	14.89085	19.39188	22.94677	23.98795	20.03181	27.08285	28.20672	27.31684	34.86907	33.64589	36.78373	37.47689	40.52695	41.81391	40.74878	45.50657	45.75414	47.57176	56.02767	51.27312	51.97966	47.74709	48.65355	50.63484	48.44609	50.34281
19	20	10.667172	6.803895	8.303144	14.497614	6.867806	12.367478	9.413963	9.444261	8.744703	14.304808	10.960766	10.451372	14.827904	6.272452	10.096415	12.403348	8.463626	8.628488	9.839363	14.449422	13.048669	16.131181	18.19289	17.92118	23.90188	22.576320	23.05887	25.57293	29.038100	31.426959	33.96424	36.342143	38.46910	41.43815	40.76039	44.75332	47.46249	48.81603	52.23999	50.28782	51.07685	48.51878	51.01045	49.58853	50.63677	47.93697	45.46109	52.17792	48.00961	47.00786	50.80013	47.70737	54.89829	49.20719	52.96849	51.58046	47.74537	53.55861	48.21013	50.76616
24	20	13.027050	7.471434	13.679538	5.768483	10.788906	8.698977	9.322671	8.311932	12.865342	9.966903	6.926024	7.546386	10.175361	12.911465	5.829294	8.554141	10.513759	12.225228	9.298788	9.089913	9.930872	10.489717	12.34784	11.75163	10.44748	13.390600	16.23082	16.95081	21.799091	21.528074	21.87916	25.988930	26.66509	32.06949	32.79460	35.01925	37.48007	32.86721	35.36552	41.40063	46.80009	49.05867	46.94410	50.26605	54.22345	50.03206	51.80317	49.74147	48.77986	48.08299	48.62034	48.06852	48.06212	46.87093	50.26182	44.33751	51.32408	48.03641	54.78962	48.73745
10	19	9.695231	9.174433	10.636559	9.135522	7.362022	14.266673	9.447716	12.377972	6.093100	5.853903	9.539306	14.262163	13.727533	19.424575	20.669408	22.576336	26.741461	24.728567	28.852438	30.760060	31.627913	34.360552	36.09521	40.91820	39.43172	41.137066	43.04933	46.76154	46.199305	48.768979	47.36646	49.477421	52.47127	49.36562	44.54360	48.07476	46.38418	47.66100	45.35457	49.09778	48.01635	47.49901	49.15443	48.99818	47.30794	46.44190	49.66045	48.06729	46.28851	45.44424	48.75336	49.89107	49.41766	47.99784	50.40713	46.83389	46.45106	48.04787	48.95078	52.75730
13	20	10.596034	11.746395	10.903057	11.402866	8.263436	7.589180	8.232499	7.294619	10.028391	10.105379	9.796763	11.889425	7.041182	12.705354	13.625825	14.989743	19.613571	23.575270	22.594344	25.971062	26.860383	30.064456	29.58605	31.22675	34.26105	36.188052	38.32964	42.61264	43.274969	42.503742	42.50890	51.437160	53.72465	50.30605	48.78798	51.40765	53.23059	50.96061	53.23112	48.39759	48.72193	50.48096	51.47567	50.61417	48.02566	50.54120	50.11116	49.68551	50.81838	45.58281	50.14003	49.66069	47.78592	49.98868	52.46699	50.67714	49.14738	51.38654	47.67108	50.32172

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
11.481390	14.401475	6.150868	9.254059	9.579971	8.160149	10.124808	11.120275	9.202528	10.838353	9.181093	8.436128	9.977338	13.242464	9.317091	8.275580	9.207833	10.888414	7.225326	11.472184	8.570382	10.582867	11.03763	13.12591	17.99503	12.965307	23.83989	21.39564	22.652730	30.186799	29.49430	31.359282	29.31638	33.78262	38.94499	37.28258	42.56795	37.73358	44.34489	44.60556	49.51082	49.60417	48.72288	50.88207	51.22964	49.03761	51.27490	48.99979	51.13479	48.72249	50.63808	51.23539	54.45665	48.83957	50.35392	54.53799	52.76513	50.67702	50.17861	50.03028
9.114530	8.586483	11.009756	9.355582	8.094828	8.771899	8.953120	7.442692	11.056727	7.590044	11.086753	10.670066	7.842831	9.018026	11.266130	5.886133	7.387846	8.107445	12.111284	14.825195	13.106666	18.156120	21.65248	22.81874	24.84652	25.152196	29.22987	31.49869	30.092382	34.848345	29.83119	42.884047	40.93578	45.12095	48.62162	48.62702	47.45525	47.03118	48.66027	47.54405	50.79882	50.57596	49.92920	48.24046	49.40579	48.22480	47.97522	47.58815	44.74564	43.74299	49.14827	48.02379	44.89332	48.27857	47.22880	48.91035	44.84299	45.47214	49.48035	50.34650
7.243792	14.309134	7.978843	11.175362	12.287030	13.145834	11.694320	18.998364	16.125544	20.296444	21.905149	21.952354	27.114283	27.932517	27.447843	30.746875	36.685054	37.336880	39.197311	43.844702	44.751691	43.357550	46.34139	47.00592	44.49681	46.506577	44.03419	47.01678	48.690672	43.648066	47.70016	49.477448	47.99977	46.17302	48.51305	43.80151	48.55182	48.29836	43.90161	47.10333	51.73539	40.91064	45.88078	49.40663	45.65272	43.96433	46.60210	44.56747	42.59574	47.05461	46.58171	43.50139	42.44631	47.22240	44.16290	49.38115	44.99909	46.10792	47.08795	44.91932
11.296579	14.307367	14.810658	8.677719	9.533368	7.822653	9.561326	8.208895	12.897751	8.243799	8.547692	8.616214	7.586228	8.920517	9.198943	8.711324	11.026336	10.804031	11.591157	10.519060	9.027216	7.848482	12.31915	10.06748	12.19758	7.872384	8.83143	10.43239	8.886314	8.552978	11.83615	7.607858	11.13613	13.53312	14.89085	19.39188	22.94677	23.98795	20.03181	27.08285	28.20672	27.31684	34.86907	33.64589	36.78373	37.47689	40.52695	41.81391	40.74878	45.50657	45.75414	47.57176	56.02767	51.27312	51.97966	47.74709	48.65355	50.63484	48.44609	50.34281
10.667172	6.803895	8.303144	14.497614	6.867806	12.367478	9.413963	9.444261	8.744703	14.304808	10.960766	10.451372	14.827904	6.272452	10.096415	12.403348	8.463626	8.628488	9.839363	14.449422	13.048669	16.131181	18.19289	17.92118	23.90188	22.576320	23.05887	25.57293	29.038100	31.426959	33.96424	36.342143	38.46910	41.43815	40.76039	44.75332	47.46249	48.81603	52.23999	50.28782	51.07685	48.51878	51.01045	49.58853	50.63677	47.93697	45.46109	52.17792	48.00961	47.00786	50.80013	47.70737	54.89829	49.20719	52.96849	51.58046	47.74537	53.55861	48.21013	50.76616
13.027050	7.471434	13.679538	5.768483	10.788906	8.698977	9.322671	8.311932	12.865342	9.966903	6.926024	7.546386	10.175361	12.911465	5.829294	8.554141	10.513759	12.225228	9.298788	9.089913	9.930872	10.489717	12.34784	11.75163	10.44748	13.390600	16.23082	16.95081	21.799091	21.528074	21.87916	25.988930	26.66509	32.06949	32.79460	35.01925	37.48007	32.86721	35.36552	41.40063	46.80009	49.05867	46.94410	50.26605	54.22345	50.03206	51.80317	49.74147	48.77986	48.08299	48.62034	48.06852	48.06212	46.87093	50.26182	44.33751	51.32408	48.03641	54.78962	48.73745
9.695231	9.174433	10.636559	9.135522	7.362022	14.266673	9.447716	12.377972	6.093100	5.853903	9.539306	14.262163	13.727533	19.424575	20.669408	22.576336	26.741461	24.728567	28.852438	30.760060	31.627913	34.360552	36.09521	40.91820	39.43172	41.137066	43.04933	46.76154	46.199305	48.768979	47.36646	49.477421	52.47127	49.36562	44.54360	48.07476	46.38418	47.66100	45.35457	49.09778	48.01635	47.49901	49.15443	48.99818	47.30794	46.44190	49.66045	48.06729	46.28851	45.44424	48.75336	49.89107	49.41766	47.99784	50.40713	46.83389	46.45106	48.04787	48.95078	52.75730
10.596034	11.746395	10.903057	11.402866	8.263436	7.589180	8.232499	7.294619	10.028391	10.105379	9.796763	11.889425	7.041182	12.705354	13.625825	14.989743	19.613571	23.575270	22.594344	25.971062	26.860383	30.064456	29.58605	31.22675	34.26105	36.188052	38.32964	42.61264	43.274969	42.503742	42.50890	51.437160	53.72465	50.30605	48.78798	51.40765	53.23059	50.96061	53.23112	48.39759	48.72193	50.48096	51.47567	50.61417	48.02566	50.54120	50.11116	49.68551	50.81838	45.58281	50.14003	49.66069	47.78592	49.98868	52.46699	50.67714	49.14738	51.38654	47.67108	50.32172

Select all variables end with 0:

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

time_10	time_20	time_30	time_40	time_50	time_60
10.838353	11.472184	30.186799	44.60556	48.72249	50.03028
7.590044	14.825195	34.848345	47.54405	43.74299	50.34650
20.296444	43.844702	43.648066	47.10333	47.05461	44.91932
8.243799	10.519060	8.552978	27.08285	45.50657	50.34281
14.304808	14.449422	31.426959	50.28782	47.00786	50.76616
9.966903	9.089913	21.528074	41.40063	48.08299	48.73745
5.853903	30.760060	48.768979	49.09778	45.44424	52.75730
10.105379	25.971062	42.503742	48.39759	45.58281	50.32172

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
14.401475	11.472184	8.570382	10.582867	11.03763	13.12591	17.99503	12.965307	23.83989	21.39564	22.652730
8.586483	14.825195	13.106666	18.156120	21.65248	22.81874	24.84652	25.152196	29.22987	31.49869	30.092382
14.309134	43.844702	44.751691	43.357550	46.34139	47.00592	44.49681	46.506577	44.03419	47.01678	48.690672
14.307367	10.519060	9.027216	7.848482	12.31915	10.06748	12.19758	7.872384	8.83143	10.43239	8.886314
6.803895	14.449422	13.048669	16.131181	18.19289	17.92118	23.90188	22.576320	23.05887	25.57293	29.038100
7.471434	9.089913	9.930872	10.489717	12.34784	11.75163	10.44748	13.390600	16.23082	16.95081	21.799091
9.174433	30.760060	31.627913	34.360552	36.09521	40.91820	39.43172	41.137066	43.04933	46.76154	46.199305
11.746395	25.971062	26.860383	30.064456	29.58605	31.22675	34.26105	36.188052	38.32964	42.61264	43.274969

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	11.481390	14.401475	6.150868	9.254059	9.579971	8.160149	10.124808	11.120275	9.202528	10.838353	9.181093	8.436128	9.977338	13.242464	9.317091	8.275580	9.207833	10.888414	7.225326	11.472184	8.570382	10.582867	11.03763	13.12591	17.99503	12.965307	23.83989	21.39564	22.652730	30.186799	29.49430	31.359282	29.31638	33.78262	38.94499	37.28258	42.56795	37.73358	44.34489	44.60556	49.51082	49.60417	48.72288	50.88207	51.22964	49.03761	51.27490	48.99979	51.13479	48.72249	50.63808	51.23539	54.45665	48.83957	50.35392	54.53799	52.76513	50.67702	50.17861	50.03028
2	18	19	9.114530	8.586483	11.009756	9.355582	8.094828	8.771899	8.953120	7.442692	11.056727	7.590044	11.086753	10.670066	7.842831	9.018026	11.266130	5.886133	7.387846	8.107445	12.111284	14.825195	13.106666	18.156120	21.65248	22.81874	24.84652	25.152196	29.22987	31.49869	30.092382	34.848345	29.83119	42.884047	40.93578	45.12095	48.62162	48.62702	47.45525	47.03118	48.66027	47.54405	50.79882	50.57596	49.92920	48.24046	49.40579	48.22480	47.97522	47.58815	44.74564	43.74299	49.14827	48.02379	44.89332	48.27857	47.22880	48.91035	44.84299	45.47214	49.48035	50.34650
3	5	18	7.243792	14.309134	7.978843	11.175362	12.287030	13.145834	11.694320	18.998364	16.125544	20.296444	21.905149	21.952354	27.114283	27.932517	27.447843	30.746875	36.685054	37.336880	39.197311	43.844702	44.751691	43.357550	46.34139	47.00592	44.49681	46.506577	44.03419	47.01678	48.690672	43.648066	47.70016	49.477448	47.99977	46.17302	48.51305	43.80151	48.55182	48.29836	43.90161	47.10333	51.73539	40.91064	45.88078	49.40663	45.65272	43.96433	46.60210	44.56747	42.59574	47.05461	46.58171	43.50139	42.44631	47.22240	44.16290	49.38115	44.99909	46.10792	47.08795	44.91932
4	32	20	11.296579	14.307367	14.810658	8.677719	9.533368	7.822653	9.561326	8.208895	12.897751	8.243799	8.547692	8.616214	7.586228	8.920517	9.198943	8.711324	11.026336	10.804031	11.591157	10.519060	9.027216	7.848482	12.31915	10.06748	12.19758	7.872384	8.83143	10.43239	8.886314	8.552978	11.83615	7.607858	11.13613	13.53312	14.89085	19.39188	22.94677	23.98795	20.03181	27.08285	28.20672	27.31684	34.86907	33.64589	36.78373	37.47689	40.52695	41.81391	40.74878	45.50657	45.75414	47.57176	56.02767	51.27312	51.97966	47.74709	48.65355	50.63484	48.44609	50.34281
5	19	20	10.667172	6.803895	8.303144	14.497614	6.867806	12.367478	9.413963	9.444261	8.744703	14.304808	10.960766	10.451372	14.827904	6.272452	10.096415	12.403348	8.463626	8.628488	9.839363	14.449422	13.048669	16.131181	18.19289	17.92118	23.90188	22.576320	23.05887	25.57293	29.038100	31.426959	33.96424	36.342143	38.46910	41.43815	40.76039	44.75332	47.46249	48.81603	52.23999	50.28782	51.07685	48.51878	51.01045	49.58853	50.63677	47.93697	45.46109	52.17792	48.00961	47.00786	50.80013	47.70737	54.89829	49.20719	52.96849	51.58046	47.74537	53.55861	48.21013	50.76616
6	24	20	13.027050	7.471434	13.679538	5.768483	10.788906	8.698977	9.322671	8.311932	12.865342	9.966903	6.926024	7.546386	10.175361	12.911465	5.829294	8.554141	10.513759	12.225228	9.298788	9.089913	9.930872	10.489717	12.34784	11.75163	10.44748	13.390600	16.23082	16.95081	21.799091	21.528074	21.87916	25.988930	26.66509	32.06949	32.79460	35.01925	37.48007	32.86721	35.36552	41.40063	46.80009	49.05867	46.94410	50.26605	54.22345	50.03206	51.80317	49.74147	48.77986	48.08299	48.62034	48.06852	48.06212	46.87093	50.26182	44.33751	51.32408	48.03641	54.78962	48.73745
7	10	19	9.695231	9.174433	10.636559	9.135522	7.362022	14.266673	9.447716	12.377972	6.093100	5.853903	9.539306	14.262163	13.727533	19.424575	20.669408	22.576336	26.741461	24.728567	28.852438	30.760060	31.627913	34.360552	36.09521	40.91820	39.43172	41.137066	43.04933	46.76154	46.199305	48.768979	47.36646	49.477421	52.47127	49.36562	44.54360	48.07476	46.38418	47.66100	45.35457	49.09778	48.01635	47.49901	49.15443	48.99818	47.30794	46.44190	49.66045	48.06729	46.28851	45.44424	48.75336	49.89107	49.41766	47.99784	50.40713	46.83389	46.45106	48.04787	48.95078	52.75730
8	13	20	10.596034	11.746395	10.903057	11.402866	8.263436	7.589180	8.232499	7.294619	10.028391	10.105379	9.796763	11.889425	7.041182	12.705354	13.625825	14.989743	19.613571	23.575270	22.594344	25.971062	26.860383	30.064456	29.58605	31.22675	34.26105	36.188052	38.32964	42.61264	43.274969	42.503742	42.50890	51.437160	53.72465	50.30605	48.78798	51.40765	53.23059	50.96061	53.23112	48.39759	48.72193	50.48096	51.47567	50.61417	48.02566	50.54120	50.11116	49.68551	50.81838	45.58281	50.14003	49.66069	47.78592	49.98868	52.46699	50.67714	49.14738	51.38654	47.67108	50.32172

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	11.481390	14.401475	6.150868	9.254059	9.579971	8.160149	10.124808	11.120275	9.202528	10.838353	9.181093	8.436128	9.977338	13.242464	9.317091	8.275580	9.207833	10.888414	7.225326	11.472184	8.570382	10.582867	11.03763	13.12591	17.99503	12.965307	23.83989	21.39564	22.652730	30.186799	29.49430	31.359282	29.31638	33.78262	38.94499	37.28258	42.56795	37.73358	44.34489	44.60556	49.51082	49.60417	48.72288	50.88207	51.22964	49.03761	51.27490	48.99979	51.13479	48.72249	50.63808	51.23539	54.45665	48.83957	50.35392	54.53799	52.76513	50.67702	50.17861	50.03028
2	18	19	9.114530	8.586483	11.009756	9.355582	8.094828	8.771899	8.953120	7.442692	11.056727	7.590044	11.086753	10.670066	7.842831	9.018026	11.266130	5.886133	7.387846	8.107445	12.111284	14.825195	13.106666	18.156120	21.65248	22.81874	24.84652	25.152196	29.22987	31.49869	30.092382	34.848345	29.83119	42.884047	40.93578	45.12095	48.62162	48.62702	47.45525	47.03118	48.66027	47.54405	50.79882	50.57596	49.92920	48.24046	49.40579	48.22480	47.97522	47.58815	44.74564	43.74299	49.14827	48.02379	44.89332	48.27857	47.22880	48.91035	44.84299	45.47214	49.48035	50.34650
3	5	18	7.243792	14.309134	7.978843	11.175362	12.287030	13.145834	11.694320	18.998364	16.125544	20.296444	21.905149	21.952354	27.114283	27.932517	27.447843	30.746875	36.685054	37.336880	39.197311	43.844702	44.751691	43.357550	46.34139	47.00592	44.49681	46.506577	44.03419	47.01678	48.690672	43.648066	47.70016	49.477448	47.99977	46.17302	48.51305	43.80151	48.55182	48.29836	43.90161	47.10333	51.73539	40.91064	45.88078	49.40663	45.65272	43.96433	46.60210	44.56747	42.59574	47.05461	46.58171	43.50139	42.44631	47.22240	44.16290	49.38115	44.99909	46.10792	47.08795	44.91932
4	32	20	11.296579	14.307367	14.810658	8.677719	9.533368	7.822653	9.561326	8.208895	12.897751	8.243799	8.547692	8.616214	7.586228	8.920517	9.198943	8.711324	11.026336	10.804031	11.591157	10.519060	9.027216	7.848482	12.31915	10.06748	12.19758	7.872384	8.83143	10.43239	8.886314	8.552978	11.83615	7.607858	11.13613	13.53312	14.89085	19.39188	22.94677	23.98795	20.03181	27.08285	28.20672	27.31684	34.86907	33.64589	36.78373	37.47689	40.52695	41.81391	40.74878	45.50657	45.75414	47.57176	56.02767	51.27312	51.97966	47.74709	48.65355	50.63484	48.44609	50.34281
5	19	20	10.667172	6.803895	8.303144	14.497614	6.867806	12.367478	9.413963	9.444261	8.744703	14.304808	10.960766	10.451372	14.827904	6.272452	10.096415	12.403348	8.463626	8.628488	9.839363	14.449422	13.048669	16.131181	18.19289	17.92118	23.90188	22.576320	23.05887	25.57293	29.038100	31.426959	33.96424	36.342143	38.46910	41.43815	40.76039	44.75332	47.46249	48.81603	52.23999	50.28782	51.07685	48.51878	51.01045	49.58853	50.63677	47.93697	45.46109	52.17792	48.00961	47.00786	50.80013	47.70737	54.89829	49.20719	52.96849	51.58046	47.74537	53.55861	48.21013	50.76616
6	24	20	13.027050	7.471434	13.679538	5.768483	10.788906	8.698977	9.322671	8.311932	12.865342	9.966903	6.926024	7.546386	10.175361	12.911465	5.829294	8.554141	10.513759	12.225228	9.298788	9.089913	9.930872	10.489717	12.34784	11.75163	10.44748	13.390600	16.23082	16.95081	21.799091	21.528074	21.87916	25.988930	26.66509	32.06949	32.79460	35.01925	37.48007	32.86721	35.36552	41.40063	46.80009	49.05867	46.94410	50.26605	54.22345	50.03206	51.80317	49.74147	48.77986	48.08299	48.62034	48.06852	48.06212	46.87093	50.26182	44.33751	51.32408	48.03641	54.78962	48.73745
7	10	19	9.695231	9.174433	10.636559	9.135522	7.362022	14.266673	9.447716	12.377972	6.093100	5.853903	9.539306	14.262163	13.727533	19.424575	20.669408	22.576336	26.741461	24.728567	28.852438	30.760060	31.627913	34.360552	36.09521	40.91820	39.43172	41.137066	43.04933	46.76154	46.199305	48.768979	47.36646	49.477421	52.47127	49.36562	44.54360	48.07476	46.38418	47.66100	45.35457	49.09778	48.01635	47.49901	49.15443	48.99818	47.30794	46.44190	49.66045	48.06729	46.28851	45.44424	48.75336	49.89107	49.41766	47.99784	50.40713	46.83389	46.45106	48.04787	48.95078	52.75730
8	13	20	10.596034	11.746395	10.903057	11.402866	8.263436	7.589180	8.232499	7.294619	10.028391	10.105379	9.796763	11.889425	7.041182	12.705354	13.625825	14.989743	19.613571	23.575270	22.594344	25.971062	26.860383	30.064456	29.58605	31.22675	34.26105	36.188052	38.32964	42.61264	43.274969	42.503742	42.50890	51.437160	53.72465	50.30605	48.78798	51.40765	53.23059	50.96061	53.23112	48.39759	48.72193	50.48096	51.47567	50.61417	48.02566	50.54120	50.11116	49.68551	50.81838	45.58281	50.14003	49.66069	47.78592	49.98868	52.46699	50.67714	49.14738	51.38654	47.67108	50.32172

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	11.481390	14.401475	6.150868	9.254059	9.579971	8.160149	10.124808	11.120275	9.202528	10.838353	9.181093	8.436128	9.977338	13.242464	9.317091	8.275580	9.207833	10.888414	7.225326	11.472184	8.570382	10.582867	11.03763	13.12591	17.99503	12.965307	23.83989	21.39564	22.652730	30.186799	29.49430	31.359282	29.31638	33.78262	38.94499	37.28258	42.56795	37.73358	44.34489	44.60556	49.51082	49.60417	48.72288	50.88207	51.22964	49.03761	51.27490	48.99979	51.13479	48.72249	50.63808	51.23539	54.45665	48.83957	50.35392	54.53799	52.76513	50.67702	50.17861	50.03028
2	18	19	9.114530	8.586483	11.009756	9.355582	8.094828	8.771899	8.953120	7.442692	11.056727	7.590044	11.086753	10.670066	7.842831	9.018026	11.266130	5.886133	7.387846	8.107445	12.111284	14.825195	13.106666	18.156120	21.65248	22.81874	24.84652	25.152196	29.22987	31.49869	30.092382	34.848345	29.83119	42.884047	40.93578	45.12095	48.62162	48.62702	47.45525	47.03118	48.66027	47.54405	50.79882	50.57596	49.92920	48.24046	49.40579	48.22480	47.97522	47.58815	44.74564	43.74299	49.14827	48.02379	44.89332	48.27857	47.22880	48.91035	44.84299	45.47214	49.48035	50.34650
3	5	18	7.243792	14.309134	7.978843	11.175362	12.287030	13.145834	11.694320	18.998364	16.125544	20.296444	21.905149	21.952354	27.114283	27.932517	27.447843	30.746875	36.685054	37.336880	39.197311	43.844702	44.751691	43.357550	46.34139	47.00592	44.49681	46.506577	44.03419	47.01678	48.690672	43.648066	47.70016	49.477448	47.99977	46.17302	48.51305	43.80151	48.55182	48.29836	43.90161	47.10333	51.73539	40.91064	45.88078	49.40663	45.65272	43.96433	46.60210	44.56747	42.59574	47.05461	46.58171	43.50139	42.44631	47.22240	44.16290	49.38115	44.99909	46.10792	47.08795	44.91932
4	32	20	11.296579	14.307367	14.810658	8.677719	9.533368	7.822653	9.561326	8.208895	12.897751	8.243799	8.547692	8.616214	7.586228	8.920517	9.198943	8.711324	11.026336	10.804031	11.591157	10.519060	9.027216	7.848482	12.31915	10.06748	12.19758	7.872384	8.83143	10.43239	8.886314	8.552978	11.83615	7.607858	11.13613	13.53312	14.89085	19.39188	22.94677	23.98795	20.03181	27.08285	28.20672	27.31684	34.86907	33.64589	36.78373	37.47689	40.52695	41.81391	40.74878	45.50657	45.75414	47.57176	56.02767	51.27312	51.97966	47.74709	48.65355	50.63484	48.44609	50.34281
5	19	20	10.667172	6.803895	8.303144	14.497614	6.867806	12.367478	9.413963	9.444261	8.744703	14.304808	10.960766	10.451372	14.827904	6.272452	10.096415	12.403348	8.463626	8.628488	9.839363	14.449422	13.048669	16.131181	18.19289	17.92118	23.90188	22.576320	23.05887	25.57293	29.038100	31.426959	33.96424	36.342143	38.46910	41.43815	40.76039	44.75332	47.46249	48.81603	52.23999	50.28782	51.07685	48.51878	51.01045	49.58853	50.63677	47.93697	45.46109	52.17792	48.00961	47.00786	50.80013	47.70737	54.89829	49.20719	52.96849	51.58046	47.74537	53.55861	48.21013	50.76616
6	24	20	13.027050	7.471434	13.679538	5.768483	10.788906	8.698977	9.322671	8.311932	12.865342	9.966903	6.926024	7.546386	10.175361	12.911465	5.829294	8.554141	10.513759	12.225228	9.298788	9.089913	9.930872	10.489717	12.34784	11.75163	10.44748	13.390600	16.23082	16.95081	21.799091	21.528074	21.87916	25.988930	26.66509	32.06949	32.79460	35.01925	37.48007	32.86721	35.36552	41.40063	46.80009	49.05867	46.94410	50.26605	54.22345	50.03206	51.80317	49.74147	48.77986	48.08299	48.62034	48.06852	48.06212	46.87093	50.26182	44.33751	51.32408	48.03641	54.78962	48.73745
7	10	19	9.695231	9.174433	10.636559	9.135522	7.362022	14.266673	9.447716	12.377972	6.093100	5.853903	9.539306	14.262163	13.727533	19.424575	20.669408	22.576336	26.741461	24.728567	28.852438	30.760060	31.627913	34.360552	36.09521	40.91820	39.43172	41.137066	43.04933	46.76154	46.199305	48.768979	47.36646	49.477421	52.47127	49.36562	44.54360	48.07476	46.38418	47.66100	45.35457	49.09778	48.01635	47.49901	49.15443	48.99818	47.30794	46.44190	49.66045	48.06729	46.28851	45.44424	48.75336	49.89107	49.41766	47.99784	50.40713	46.83389	46.45106	48.04787	48.95078	52.75730
8	13	20	10.596034	11.746395	10.903057	11.402866	8.263436	7.589180	8.232499	7.294619	10.028391	10.105379	9.796763	11.889425	7.041182	12.705354	13.625825	14.989743	19.613571	23.575270	22.594344	25.971062	26.860383	30.064456	29.58605	31.22675	34.26105	36.188052	38.32964	42.61264	43.274969	42.503742	42.50890	51.437160	53.72465	50.30605	48.78798	51.40765	53.23059	50.96061	53.23112	48.39759	48.72193	50.48096	51.47567	50.61417	48.02566	50.54120	50.11116	49.68551	50.81838	45.58281	50.14003	49.66069	47.78592	49.98868	52.46699	50.67714	49.14738	51.38654	47.67108	50.32172

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	11.481390	14.401475	6.150868	9.254059	9.579971	8.160149	10.124808	11.120275	9.202528	10.838353	9.181093	8.436128	9.977338	13.242464	9.317091	8.275580	9.207833	10.888414	7.225326	11.472184	8.570382	10.582867	11.03763	13.12591	17.99503	12.965307	23.83989	21.39564	22.652730	30.186799	29.49430	31.359282	29.31638	33.78262	38.94499	37.28258	42.56795	37.73358	44.34489	44.60556	49.51082	49.60417	48.72288	50.88207	51.22964	49.03761	51.27490	48.99979	51.13479	48.72249	50.63808	51.23539	54.45665	48.83957	50.35392	54.53799	52.76513	50.67702	50.17861	50.03028
2	18	19	9.114530	8.586483	11.009756	9.355582	8.094828	8.771899	8.953120	7.442692	11.056727	7.590044	11.086753	10.670066	7.842831	9.018026	11.266130	5.886133	7.387846	8.107445	12.111284	14.825195	13.106666	18.156120	21.65248	22.81874	24.84652	25.152196	29.22987	31.49869	30.092382	34.848345	29.83119	42.884047	40.93578	45.12095	48.62162	48.62702	47.45525	47.03118	48.66027	47.54405	50.79882	50.57596	49.92920	48.24046	49.40579	48.22480	47.97522	47.58815	44.74564	43.74299	49.14827	48.02379	44.89332	48.27857	47.22880	48.91035	44.84299	45.47214	49.48035	50.34650
3	5	18	7.243792	14.309134	7.978843	11.175362	12.287030	13.145834	11.694320	18.998364	16.125544	20.296444	21.905149	21.952354	27.114283	27.932517	27.447843	30.746875	36.685054	37.336880	39.197311	43.844702	44.751691	43.357550	46.34139	47.00592	44.49681	46.506577	44.03419	47.01678	48.690672	43.648066	47.70016	49.477448	47.99977	46.17302	48.51305	43.80151	48.55182	48.29836	43.90161	47.10333	51.73539	40.91064	45.88078	49.40663	45.65272	43.96433	46.60210	44.56747	42.59574	47.05461	46.58171	43.50139	42.44631	47.22240	44.16290	49.38115	44.99909	46.10792	47.08795	44.91932
4	32	20	11.296579	14.307367	14.810658	8.677719	9.533368	7.822653	9.561326	8.208895	12.897751	8.243799	8.547692	8.616214	7.586228	8.920517	9.198943	8.711324	11.026336	10.804031	11.591157	10.519060	9.027216	7.848482	12.31915	10.06748	12.19758	7.872384	8.83143	10.43239	8.886314	8.552978	11.83615	7.607858	11.13613	13.53312	14.89085	19.39188	22.94677	23.98795	20.03181	27.08285	28.20672	27.31684	34.86907	33.64589	36.78373	37.47689	40.52695	41.81391	40.74878	45.50657	45.75414	47.57176	56.02767	51.27312	51.97966	47.74709	48.65355	50.63484	48.44609	50.34281
5	19	20	10.667172	6.803895	8.303144	14.497614	6.867806	12.367478	9.413963	9.444261	8.744703	14.304808	10.960766	10.451372	14.827904	6.272452	10.096415	12.403348	8.463626	8.628488	9.839363	14.449422	13.048669	16.131181	18.19289	17.92118	23.90188	22.576320	23.05887	25.57293	29.038100	31.426959	33.96424	36.342143	38.46910	41.43815	40.76039	44.75332	47.46249	48.81603	52.23999	50.28782	51.07685	48.51878	51.01045	49.58853	50.63677	47.93697	45.46109	52.17792	48.00961	47.00786	50.80013	47.70737	54.89829	49.20719	52.96849	51.58046	47.74537	53.55861	48.21013	50.76616
6	24	20	13.027050	7.471434	13.679538	5.768483	10.788906	8.698977	9.322671	8.311932	12.865342	9.966903	6.926024	7.546386	10.175361	12.911465	5.829294	8.554141	10.513759	12.225228	9.298788	9.089913	9.930872	10.489717	12.34784	11.75163	10.44748	13.390600	16.23082	16.95081	21.799091	21.528074	21.87916	25.988930	26.66509	32.06949	32.79460	35.01925	37.48007	32.86721	35.36552	41.40063	46.80009	49.05867	46.94410	50.26605	54.22345	50.03206	51.80317	49.74147	48.77986	48.08299	48.62034	48.06852	48.06212	46.87093	50.26182	44.33751	51.32408	48.03641	54.78962	48.73745
7	10	19	9.695231	9.174433	10.636559	9.135522	7.362022	14.266673	9.447716	12.377972	6.093100	5.853903	9.539306	14.262163	13.727533	19.424575	20.669408	22.576336	26.741461	24.728567	28.852438	30.760060	31.627913	34.360552	36.09521	40.91820	39.43172	41.137066	43.04933	46.76154	46.199305	48.768979	47.36646	49.477421	52.47127	49.36562	44.54360	48.07476	46.38418	47.66100	45.35457	49.09778	48.01635	47.49901	49.15443	48.99818	47.30794	46.44190	49.66045	48.06729	46.28851	45.44424	48.75336	49.89107	49.41766	47.99784	50.40713	46.83389	46.45106	48.04787	48.95078	52.75730
8	13	20	10.596034	11.746395	10.903057	11.402866	8.263436	7.589180	8.232499	7.294619	10.028391	10.105379	9.796763	11.889425	7.041182	12.705354	13.625825	14.989743	19.613571	23.575270	22.594344	25.971062	26.860383	30.064456	29.58605	31.22675	34.26105	36.188052	38.32964	42.61264	43.274969	42.503742	42.50890	51.437160	53.72465	50.30605	48.78798	51.40765	53.23059	50.96061	53.23112	48.39759	48.72193	50.48096	51.47567	50.61417	48.02566	50.54120	50.11116	49.68551	50.81838	45.58281	50.14003	49.66069	47.78592	49.98868	52.46699	50.67714	49.14738	51.38654	47.67108	50.32172

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:

Create a new tibble called d_small starting with the tibble d “and then” (i..e, %>%)…
select variables id through time_3 “and then”…
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	11.48139	14.401475	6.150868
2	18	19	9.11453	8.586483	11.009756

What we need is:

id	baseline_sessions_n	intervention_sessions_n	time	on_task
1	22	20	1	11.481390
1	22	20	2	14.401475
1	22	20	3	6.150868
2	18	19	1	9.114530
2	18	19	2	8.586483
2	18	19	3	11.009756

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	11.481390
1	22	20	time_2	14.401475
1	22	20	time_3	6.150868
2	18	19	time_1	9.114530
2	18	19	time_2	8.586483
2	18	19	time_3	11.009756

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	11.481390
1	22	20	time_2	14.401475
1	22	20	time_3	6.150868
2	18	19	time_1	9.114530
2	18	19	time_2	8.586483
2	18	19	time_3	11.009756

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	11.481390
1	22	20	2	14.401475
1	22	20	3	6.150868
2	18	19	1	9.114530
2	18	19	2	8.586483
2	18	19	3	11.009756

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	11.481390
1	22	20	2	14.401475
1	22	20	3	6.150868
2	18	19	1	9.114530
2	18	19	2	8.586483
2	18	19	3	11.009756

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	11.481390
1	22	20	2	14.401475
1	22	20	3	6.150868
2	18	19	1	9.114530
2	18	19	2	8.586483
2	18	19	3	11.009756

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	11.48139	14.401475	6.150868
2	18	19	9.11453	8.586483	11.009756

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) %>% 
  head()

id	baseline_sessions_n	intervention_sessions_n	time	on_task	ontask_plus_1
1	22	20	1	11.481390	12.481390
1	22	20	2	14.401475	15.401475
1	22	20	3	6.150868	7.150868
1	22	20	4	9.254059	10.254059
1	22	20	5	9.579971	10.579971
1	22	20	6	8.160149	9.160149

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

d_longer %>% 
  head()

id	baseline_sessions_n	intervention_sessions_n	time	on_task
1	22	20	1	11.481390
1	22	20	2	14.401475
1	22	20	3	6.150868
1	22	20	4	9.254059
1	22	20	5	9.579971
1	22	20	6	8.160149

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 %>% 
  head()

id	baseline_sessions_n	intervention_sessions_n	time	on_task	phase
1	22	20	1	11.481390	Pre-Intervention
1	22	20	2	14.401475	Pre-Intervention
1	22	20	3	6.150868	Pre-Intervention
1	22	20	4	9.254059	Pre-Intervention
1	22	20	5	9.579971	Pre-Intervention
1	22	20	6	8.160149	Pre-Intervention

Plot the results:

Plotting is rarely done all at once. We usually build it step-by-step and layer-by-layer. Figure 1 is the first attempt.

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

Figure 1: First attempt at plotting. Messy, messy, messy!

That was not so great! Let’s make a separate plot for each person in Figure 2.

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

Figure 2: Plot each person’s data on separate facet plots

Better!

Let’s reorder the cases by the order in which the intervention is first implemented. The fct_reorder function is incredibly useful for creating plots. Because the id variable is just a number, we need to change it to a factor variable first. A factor is R’s way of grouping categorical data in any order we wish.

We need to reorder the id variable so that people with shorter baselines come before people with longer baselines. The first slot in the fct_reorder function is the variable we want to transform into a reordered factor. The second slot is another variable we want to order the factor factor by. Thus, the fct_reorder function usually takes this form: fct_reorder(variable_to_be_reordered, variable_to_sort_by)

In this case, we are assigning the reordered variable back to the same variable id that we started with. Putting it all together, we get Figure 3.

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 4. My priorities for a plot in a publication are quite different than a plot I am making just for my own use. I want the plot to be nearly self-explanatory. I want reader to understand the plot without needing to work hard. Thus, I prefer direct labels rather than plot legends that require looking things up. I also want my plot to be inclusive, so I choose a large font size from a legible font family. I also choose colors that are distinguishable by people different kinds of color blindness.

Code

# 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 = paste0("Child ", 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 == "Child 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, 24, 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 4: Effect on Intervention on Time on Task for Eight Children

For practice data wrangling, download this quarto document and complete the data-wrangling exercises therein.