CourseKata - 2.4 Frequency Tables and Sorting Data Frames

2.4 Frequency Tables and Sorting Data Frames

Using `tally()` to Create Frequency Tables

We can use the tally() function to create a frequency table of the Age variable (in the MindsetMatters data frame). This will tell us how many housekeepers there were of each age.

tally(MindsetMatters$Age)

We don't have to use the $ notation. We could also specify the variable and data frame separately, like this:

tally(~ Age, data = MindsetMatters)

Age — counts from tally()
19	21	22	23	24	26	27	28	29	30	31	32	33	34	35	37
1	1	1	1	4	3	4	2	6	1	3	1	4	2	1	1
38	39	40	41	42	43	44	45	46	47	48	50	52	53	54	55
5	2	2	3	3	1	2	3	2	1	3	1	1	1	2	1
57	58	61	62	65	NA
1	1	1	1	1	1

The rows that start with 19, 38, and 57 represent the ages of the housekeepers and the numbers underneath them represent how many of each age are in the data frame. For example, there is one housekeeper who is 19 years old. There are two housekeepers who are 54 years old. There are three housekeepers who are 45 years old.

Try using the tally function to make a frequency table of housekeepers by Condition.

require(coursekata)
MindsetMatters <- Lock5withR::MindsetMatters %>%
    mutate(Condition = factor(Cond, levels = c(1, 0), labels = c("Informed", "Uninformed")))

# Use tally() with the MindsetMatters data frame to create a frequency table of housekeepers by Condition

# Use tally() with the MindsetMatters data frame to create a frequency table of housekeepers by Condition
tally(~Condition, data = MindsetMatters)

# Another solution
# tally(MindsetMatters$Condition)

ex() %>%
    check_function("tally") %>%
    check_result() %>%
    check_equal()

The output of tally() shows us that there are 41 housekeepers who were in the Informed condition and 34 in the Uninformed condition. Taking a look at this frequency table, we might wonder why there were slightly more housekeepers who were informed that their daily work of cleaning was equivalent to getting adequate exercise.

Using `arrange()` to Sort a Data Frame

Let’s turn our attention to two variables in the MindsetMatters data frame: Age (the age of the housekeepers, in years, at the start of the study) and Wt (their weight, in pounds, at the start of the study).

We might want to sort the whole data frame MindsetMatters by Age. But now we can’t use the sort() function—that only works with vectors, not with data frames. If we want to sort a whole data frame, we will use a different function, arrange().

The arrange() function works similarly to sort(), except now you have to specify both the name of the data frame and the name of the variable you want to use for sorting the rows.

arrange(MindsetMatters, Age)

Importantly, when you use arrange() to sort on one variable (e.g., Age), the order of the rows (which in this case is housekeepers) will change, but the contents of each row will stay the same.

The printout of MindsetMatters won’t stay arranged by age because we didn’t save our work. In order to save the new ordering, we need to assign the arranged version to an R object. We could assign it back to the existing object (MindsetMatters) or to a new object (e.g., Mindset2, MM2, MM_arrange or any other name you want to make up). If we assign it to the existing object it will revise what's in MindsetMatters to be in the new order. In general, it's a good practice to save a changed data frame to a new R object in case you want to go back to the original version.

Let’s use the assignment operator (<-) to assign the arranged data frame to MM_arrange. See if you can edit the code below to save the version of MindsetMatters that is arranged by Age into MM_arrange. Then print out the first six lines of MM_arrange using head().

require(coursekata)
MindsetMatters <- Lock5withR::MindsetMatters %>%
    mutate(Condition = factor(Cond, levels = c(1, 0), labels = c("Informed", "Uninformed")))

# save MindsetMatters, arranged by Age, to MM_arrange
arrange(MindsetMatters, Age)

# write code to print out the first 6 rows of MM_arrange

# save MindsetMatters, arranged by Age, to MM_arrange
MM_arrange <- arrange(MindsetMatters, Age)

# write code to print out the first 6 rows of MM_arrange
head(MM_arrange)

no_save <- "Make sure to both `arrange()` `MindsetMatters` by `Age` *and* save the arranged data frame to `MM_arrange`."
ex() %>% {
    check_object(., "MM_arrange") %>% check_equal(incorrect_msg = no_save)
    check_function(., "arrange") %>% check_arg("...") %>% check_equal()
    check_function(., "head") %>% check_result() %>% check_equal()
}

Data frame output
row	Cond	Age	Wt	Wt2	BMI	BMI2	Fat	Fat2	WHR	WHR2	Syst	Syst2	Diast	Diast2	Condition
1	0	19	123	124.2	19.6	19.7	26.6	NA	0.69	0.69	113	117	75	72	Uninformed
2	0	21	156	154.4	25.9	25.7	36.4	NA	0.78	0.78	116	135	67	65	Uninformed
3	1	22	127	124.6	25.6	25.2	34.6	31.6	0.74	0.73	110	103	65	69	Informed
4	1	23	161	161.4	26.8	26.9	38.1	37.1	0.90	0.86	126	101	74	64	Informed
5	0	24	90	91.8	16.5	16.8	NA	NA	0.73	0.73	NA	NA	78	76	Uninformed
6	1	24	166	169.0	28.5	29.0	41.3	41.1	0.88	0.90	114	123	56	55	Informed

Sorting a Data Frame in Descending Order

The function arrange() can also be used to arrange values in descending order by adding desc() around the variable name.

arrange(MindsetMatters, desc(Age))

Try arranging MindsetMatters by Wt in descending order. Save this to MM_desc. Print a few rows of MM_desc to check out what happened.

require(coursekata)
MindsetMatters <- Lock5withR::MindsetMatters %>%
    mutate(Condition = factor(Cond, levels = c(1, 0), labels = c("Informed", "Uninformed")))

# arrange MindsetMatters by Wt in descending order
MM_desc <-

# write code to print out a few rows of MM_desc

# arrange MindsetMatters by Wt in descending order
MM_desc <- arrange(MindsetMatters, desc(Wt))

# write code to print out a few rows of MM_desc
head(MM_desc)

no_save <- "Did you save the arranged dataset to `MM_desc`?"
ex() %>% {
    check_function(., "desc") %>%
        check_arg("x") %>%
        check_equal(eval = FALSE)
    check_function(., "arrange") %>%
        check_arg(".data") %>%
        check_equal(eval = FALSE)
    check_object(., "MM_desc") %>%
        check_equal(incorrect_msg = no_save)
    check_function(., "head") %>%
        check_arg("x") %>%
        check_equal()
}

Data frame output
row	Cond	Age	Wt	Wt2	BMI	BMI2	Fat	Fat2	WHR	WHR2	Syst	Syst2	Diast	Diast2	Condition
1	1	34	196	198.2	33.7	33.5	45.7	44.7	0.83	0.81	164	83	73	57	Informed
2	1	39	189	183.2	34.6	34.4	47.0	46.7	0.80	0.77	185	154	99	102	Informed
3	0	65	187	186.2	34.2	34.1	47.3	NA	0.89	NA	176	188	106	83	Uninformed
4	1	29	184	182.8	35.9	35.7	44.4	45.0	0.89	0.89	120	124	75	70	Informed
5	0	38	183	186.4	34.6	35.2	44.2	42.8	NA	NA	115	125	70	72	Uninformed
6	0	45	182	180.0	33.8	33.5	NA	45.6	0.85	0.88	145	141	96	84	Uninformed

2.3 A Data Frame Example: MindsetMatters 2.5 Measurement

19	21	22	23	24	26	27	28	29	30	31	32	33	34	35	37
1	1	1	1	4	3	4	2	6	1	3	1	4	2	1	1
38	39	40	41	42	43	44	45	46	47	48	50	52	53	54	55
5	2	2	3	3	1	2	3	2	1	3	1	1	1	2	1
57	58	61	62	65	NA
1	1	1	1	1	1

19	21	22	23	24	26	27	28	29	30	31	32	33	34	35	37
1	1	1	1	4	3	4	2	6	1	3	1	4	2	1	1
38	39	40	41	42	43	44	45	46	47	48	50	52	53	54	55
5	2	2	3	3	1	2	3	2	1	3	1	1	1	2	1
57	58	61	62	65	NA
1	1	1	1	1	1

2.4 Frequency Tables and Sorting Data Frames

Using tally() to Create Frequency Tables

Using arrange() to Sort a Data Frame

Sorting a Data Frame in Descending Order

Using `tally()` to Create Frequency Tables

Using `arrange()` to Sort a Data Frame

19	21	22	23	24	26	27	28	29	30	31	32	33	34	35	37
1	1	1	1	4	3	4	2	6	1	3	1	4	2	1	1
38	39	40	41	42	43	44	45	46	47	48	50	52	53	54	55
5	2	2	3	3	1	2	3	2	1	3	1	1	1	2	1
57	58	61	62	65	NA
1	1	1	1	1	1