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Built on the top of ‘data.table’, ‘dataMojo’ is a grammar of data manipulation with ‘data.table’, providing a consistent a series of utility functions that help you solve the most common data manipulation challenges:
long to wide or wide to longCalculate the row wise percentage of a frequency table
library(dataMojo)
library(data.table)
test_df <- data.frame(
Group = c("A", "B", "C"),
Female = c(2,3,5),
Male = c(10,11, 13)
)
print(test_df)
#> Group Female Male
#> 1 A 2 10
#> 2 B 3 11
#> 3 C 5 13
dataMojo::row_percent_convert(test_df, cols_rowsum = c("Female", "Male"))
#> Group Female Male
#> 1 A 0.1666667 0.8333333
#> 2 B 0.2142857 0.7857143
#> 3 C 0.2777778 0.7222222library(dataMojo)
library(data.table)
test_dt <- data.table::data.table(
Question = c(rep("Good", 3), rep("OK", 3), rep("Bad", 3)),
Gender = c(rep("F", 4), rep("M", 5))
)
print(test_dt)
#> Question Gender
#> 1: Good F
#> 2: Good F
#> 3: Good F
#> 4: OK F
#> 5: OK M
#> 6: OK M
#> 7: Bad M
#> 8: Bad M
#> 9: Bad M
dataMojo::pivot_percent_at(test_dt,
question_col = "Question", aggregated_by_cols = "Gender")
#> Gender Question.total Question.rate1valueGood Question.rate2valueOK
#> 1: F 4 75 25
#> 2: M 5 0 40
#> Question.rate3valueBad Question.count1valueGood Question.count2valueOK
#> 1: 0 3 1
#> 2: 60 0 2
#> Question.count3valueBad
#> 1: 0
#> 2: 3library(dataMojo)
library(data.table)
test_dt <- data.table(
Question1 = c(rep("Good", 3), rep("OK", 3), rep("Bad", 3)),
Question2 = c(rep("Good", 2), rep("OK", 2), rep("Bad", 5)),
Gender = c(rep("F", 4), rep("M", 5))
)
print(test_dt)
#> Question1 Question2 Gender
#> 1: Good Good F
#> 2: Good Good F
#> 3: Good OK F
#> 4: OK OK F
#> 5: OK Bad M
#> 6: OK Bad M
#> 7: Bad Bad M
#> 8: Bad Bad M
#> 9: Bad Bad M
dataMojo::pivot_percent_at_multi(test_dt,
question_col = c("Question1","Question2") , aggregated_by_cols = "Gender")
#> Gender Question1.total Question1.rate1valueGood Question1.rate2valueOK
#> 1: F 4 75 25
#> 2: M 5 0 40
#> Question1.rate3valueBad Question1.count1valueGood Question1.count2valueOK
#> 1: 0 3 1
#> 2: 60 0 2
#> Question1.count3valueBad Question2.total Question2.rate1valueGood
#> 1: 0 4 50
#> 2: 3 5 0
#> Question2.rate2valueOK Question2.rate3valueBad Question2.count1valueGood
#> 1: 50 0 2
#> 2: 0 100 0
#> Question2.count2valueOK Question2.count3valueBad
#> 1: 2 0
#> 2: 0 5This function is to calculate column-wise percentage in a new column
with desired numerator columns and denominator columns. If denominator
is 0, the percentage will be N/A.
library(dataMojo)
test_df <- data.frame(
hc1 = c(2, 0, 1, 5, 6, 7, 10),
hc2 = c(1, 0, 10, 12, 4, 1, 9 ),
total = c(10, 2, 0, 39, 23, 27, 30)
)
print(test_df)
#> hc1 hc2 total
#> 1 2 1 10
#> 2 0 0 2
#> 3 1 10 0
#> 4 5 12 39
#> 5 6 4 23
#> 6 7 1 27
#> 7 10 9 30
dataMojo::col_cal_percent(test_df,
new_col_name = "hc_percentage",
numerator_cols = c("hc1", "hc2"),
denominator_cols = "total"
)
#> hc1 hc2 total hc_percentage
#> 1 2 1 10 30%
#> 2 0 0 2 0%
#> 3 1 10 0 N/A
#> 4 5 12 39 44%
#> 5 6 4 23 43%
#> 6 7 1 27 30%
#> 7 10 9 30 63%Select variables in a data table. You can also use predicate functions like is.numeric to select variables based on their properties (e.g. 1:3 selects the first column to the third column).
library(dataMojo)
library(data.table)
data("dt_dates")
dt_dates <- setDT(dt_dates)
dataMojo::select_cols(dt_dates, c("Start_Date", "Full_name"))
#> Start_Date Full_name
#> 1: 2019-05-01 Joe, Smith
#> 2: 2019-08-04 Alex, Robinson
#> 3: 2019-07-05 David, Big
#> 4: 2019-07-04 Julia, Joe
#> 5: 2019-04-27 Jessa, OliverSplit a column with its special pattern, and assign to multiple columns respectively. For example, split full name column to first name and last name column.
data("dt_dates")
library(data.table)
data("dt_dates")
dataMojo::str_split_col(dt_dates,
by_col = "Full_name",
by_pattern = ", ",
match_to_names = c("First Name", "Last Name"))
#> Start_Date End_Date Full_name First Name Last Name
#> 1: 2019-05-01 2019-06-01 Joe, Smith Joe Smith
#> 2: 2019-08-04 2019-08-09 Alex, Robinson Alex Robinson
#> 3: 2019-07-05 2019-08-14 David, Big David Big
#> 4: 2019-07-04 2019-07-05 Julia, Joe Julia Joe
#> 5: 2019-04-27 2019-05-10 Jessa, Oliver Jessa Oliverfilter_all() is to return a data table with
ALL columns (greater than/ less than/ equal to) a
desired value.
data("dt_values")
dataMojo::filter_all(dt_values, operator = "l", .2)
#> A1 A2 A3
#> 1: 0.05785895 0.12946847 0.087393370
#> 2: 0.01923819 0.01278740 0.098913282
#> 3: 0.05195276 0.19132992 0.106693512
#> 4: 0.05032699 0.14571596 0.078407153
#> 5: 0.05952578 0.14576162 0.111872945
#> 6: 0.18180095 0.03566878 0.047573949
#> 7: 0.10973857 0.14381518 0.001265888filter_any() is to return a data table with
ANY columns (greater than/ less than/ equal to) a
desired value.
data("dt_values")
dataMojo::filter_any(dt_values, operator = "l", .1)
#> A1 A2 A3
#> 1: 0.0005183129 0.785432329 0.33682885
#> 2: 0.5106083730 0.089597210 0.35534382
#> 3: 0.0140479084 0.754373487 0.68909671
#> 4: 0.0646897766 0.659908085 0.33536504
#> 5: 0.0864958912 0.824531891 0.67044835
#> ---
#> 258: 0.0368269614 0.781635831 0.68857844
#> 259: 0.4405581164 0.008710776 0.06723523
#> 260: 0.0147206911 0.600409490 0.68254910
#> 261: 0.0277955788 0.508650963 0.28767138
#> 262: 0.9901734111 0.890964948 0.09758119Similarly, filter_all_at() is to return a data table
with ALL selected columns (greater than/ less than/
equal to) a desired value.
data("dt_values")
dataMojo::filter_all_at(dt_values, operator = "l", .1, c("A1", "A2"))
#> A1 A2 A3
#> 1: 0.01923819 0.01278740 0.09891328
#> 2: 0.01134451 0.04448781 0.83378764
#> 3: 0.05962021 0.04581089 0.60585367
#> 4: 0.06966295 0.08512458 0.67216791
#> 5: 0.04913060 0.08084439 0.53249534
#> 6: 0.03235521 0.08765999 0.71016331Similarly, filter_any_at() is to return a data table
with ANY selected columns (greater than/ less than/
equal to) a desired value.
data("dt_values")
dataMojo::filter_any_at(dt_values, operator = "l", .1, c("A1", "A2"))
#> A1 A2 A3
#> 1: 0.0005183129 0.785432329 0.33682885
#> 2: 0.5106083730 0.089597210 0.35534382
#> 3: 0.0140479084 0.754373487 0.68909671
#> 4: 0.0646897766 0.659908085 0.33536504
#> 5: 0.0864958912 0.824531891 0.67044835
#> ---
#> 183: 0.0158175936 0.416905575 0.79278071
#> 184: 0.0368269614 0.781635831 0.68857844
#> 185: 0.4405581164 0.008710776 0.06723523
#> 186: 0.0147206911 0.600409490 0.68254910
#> 187: 0.0277955788 0.508650963 0.28767138fill_NA_with() will fill NA value with a desired value
in the selected columns. If fill_cols is All
(same columns type), it will apply to the whole data table.
data("dt_missing")
dataMojo::fill_NA_with(dt_missing, fill_cols = c("Full_name"), fill_value = "pending")
#> Start_Date End_Date Full_name
#> 1: <NA> 2019-06-01 pending
#> 2: 2019-08-04 2019-08-09 pending
#> 3: 2019-07-05 2019-08-14 David, Big
#> 4: 2019-07-04 2019-07-05 Julia, Joe
#> 5: 2019-04-27 2019-05-10 Jessa, Oliverdt_group_by() is to group by desired columns and
summarize rows within groups.
data("dt_groups")
print(head(dt_groups))
#> A1 A2 group2 group1
#> 1: 0.6312317 0.5596497 1 1
#> 2: 0.9343597 0.8214651 2 2
#> 3: 0.1394824 0.7866118 3 3
#> 4: 0.8566525 0.1973685 4 4
#> 5: 0.9658633 0.6671387 5 5
#> 6: 0.4725889 0.3767837 1 6Now we see the dt_groups data table has A1, A2 as
numeric columns, and group1, group2 as group infomation.
data("dt_groups")
dataMojo::dt_group_by(dt_groups,
group_by_cols = c("group1", "group2"),
summarize_at = "A1",
operation = "mean")
#> group1 group2 summary_col
#> 1: 1 1 0.4953336
#> 2: 2 2 0.4948892
#> 3: 3 3 0.5314195
#> 4: 4 4 0.4958035
#> 5: 5 5 0.4825304
#> 6: 6 1 0.5213521
#> 7: 7 2 0.5305957
#> 8: 8 3 0.4768201
#> 9: 9 4 0.4855223
#> 10: 10 5 0.5002411Now we want to group by group1 and group2, then fetch the first
within each group, we can use get_row_group_by()
function.
data("dt_groups")
dataMojo::get_row_group_by(dt_groups,
group_by_cols = c("group1", "group2"),
fetch_row = "first")
#> group1 group2 A1 A2
#> 1: 1 1 0.6312317 0.5596497
#> 2: 2 2 0.9343597 0.8214651
#> 3: 3 3 0.1394824 0.7866118
#> 4: 4 4 0.8566525 0.1973685
#> 5: 5 5 0.9658633 0.6671387
#> 6: 6 1 0.4725889 0.3767837
#> 7: 7 2 0.3530244 0.6344632
#> 8: 8 3 0.2041025 0.7531322
#> 9: 9 4 0.8718080 0.6506606
#> 10: 10 5 0.3357608 0.9362194or last row with same example.
data("dt_groups")
dataMojo::get_row_group_by(dt_groups,
group_by_cols = c("group1", "group2"),
fetch_row = "last")
#> group1 group2 A1 A2
#> 1: 1 1 0.17294752 0.063375355
#> 2: 2 2 0.54620192 0.464936862
#> 3: 3 3 0.76486138 0.733507319
#> 4: 4 4 0.33303746 0.448011979
#> 5: 5 5 0.10455568 0.007968041
#> 6: 6 1 0.39483556 0.036755550
#> 7: 7 2 0.89792830 0.397020292
#> 8: 8 3 0.94427852 0.647780578
#> 9: 9 4 0.08840417 0.885425312
#> 10: 10 5 0.66508247 0.571804764long to wide or wide to longHere is an example of reshaping a data table from wide to long.
data("dt_dates")
print(head(dt_dates))
#> Start_Date End_Date Full_name First Name Last Name
#> 1: 2019-05-01 2019-06-01 Joe, Smith Joe Smith
#> 2: 2019-08-04 2019-08-09 Alex, Robinson Alex Robinson
#> 3: 2019-07-05 2019-08-14 David, Big David Big
#> 4: 2019-07-04 2019-07-05 Julia, Joe Julia Joe
#> 5: 2019-04-27 2019-05-10 Jessa, Oliver Jessa Oliver
dataMojo::reshape_longer(dt_dates,
keep_cols = "Full_name",
label_cols = c("Date_Type"),
value_cols = "Exact_date")
#> Warning in melt.data.table(dt, id.vars = keep_cols, variable.name =
#> label_cols, : 'measure.vars' [Start_Date, End_Date, First Name, Last Name, ...]
#> are not all of the same type. By order of hierarchy, the molten data value
#> column will be of type 'character'. All measure variables not of type
#> 'character' will be coerced too. Check DETAILS in ?melt.data.table for more on
#> coercion.
#> Full_name Date_Type Exact_date
#> 1: Joe, Smith Start_Date 18017
#> 2: Alex, Robinson Start_Date 18112
#> 3: David, Big Start_Date 18082
#> 4: Julia, Joe Start_Date 18081
#> 5: Jessa, Oliver Start_Date 18013
#> 6: Joe, Smith End_Date 18048
#> 7: Alex, Robinson End_Date 18117
#> 8: David, Big End_Date 18122
#> 9: Julia, Joe End_Date 18082
#> 10: Jessa, Oliver End_Date 18026
#> 11: Joe, Smith First Name Joe
#> 12: Alex, Robinson First Name Alex
#> 13: David, Big First Name David
#> 14: Julia, Joe First Name Julia
#> 15: Jessa, Oliver First Name Jessa
#> 16: Joe, Smith Last Name Smith
#> 17: Alex, Robinson Last Name Robinson
#> 18: David, Big Last Name Big
#> 19: Julia, Joe Last Name Joe
#> 20: Jessa, Oliver Last Name OliverHere is an example of reshaping a data table from long to wide.
data("dt_long")
print(head(dt_long))
#> Full_name Date_Type Exact_date
#> 1: Joe, Smith Start_Date 2019-05-01
#> 2: Alex, Robinson Start_Date 2019-08-04
#> 3: David, Big Start_Date 2019-07-05
#> 4: Julia, Joe Start_Date 2019-07-04
#> 5: Jessa, Oliver Start_Date 2019-04-27
#> 6: Joe, Smith End_Date 2019-06-01
dataMojo::reshape_wider(dt_long,
keep_cols = c("Full_name"),
col_label = c("Date_Type"),
col_value = "Exact_date")
#> Full_name Start_Date End_Date
#> 1: Alex, Robinson 2019-08-04 2019-08-09
#> 2: David, Big 2019-07-05 2019-08-14
#> 3: Jessa, Oliver 2019-04-27 2019-05-10
#> 4: Joe, Smith 2019-05-01 2019-06-01
#> 5: Julia, Joe 2019-07-04 2019-07-05row_expand_pattern() is to expand rows based on a
desired column.
data("starwars_simple")
starwars_simple[]
#> films
#> 1: The Empire Strikes Back, Revenge of the Sith, Return of the Jedi, A New Hope, The Force Awakens
#> 2: The Empire Strikes Back, Attack of the Clones, The Phantom Menace, Revenge of the Sith, Return of the Jedi, A New Hope
#> name height skin_color eye_color gender
#> 1: Luke Skywalker 172 fair blue masculine
#> 2: C-3PO 167 gold yellow masculine
row_expand_pattern(starwars_simple, "films", ", ", "film")[]
#> name height skin_color eye_color gender
#> 1: Luke Skywalker 172 fair blue masculine
#> 2: Luke Skywalker 172 fair blue masculine
#> 3: Luke Skywalker 172 fair blue masculine
#> 4: Luke Skywalker 172 fair blue masculine
#> 5: Luke Skywalker 172 fair blue masculine
#> 6: C-3PO 167 gold yellow masculine
#> 7: C-3PO 167 gold yellow masculine
#> 8: C-3PO 167 gold yellow masculine
#> 9: C-3PO 167 gold yellow masculine
#> 10: C-3PO 167 gold yellow masculine
#> 11: C-3PO 167 gold yellow masculine
#> film
#> 1: The Empire Strikes Back
#> 2: Revenge of the Sith
#> 3: Return of the Jedi
#> 4: A New Hope
#> 5: The Force Awakens
#> 6: The Empire Strikes Back
#> 7: Attack of the Clones
#> 8: The Phantom Menace
#> 9: Revenge of the Sith
#> 10: Return of the Jedi
#> 11: A New Hoperow_expand_dates() is to expand rows to each date given
start and end dates.
dt_dates_simple <- data.table(
Start_Date = as.Date(c("2020-02-03", "2020-03-01") ),
End_Date = as.Date(c("2020-02-05", "2020-03-02") ),
group = c("A", "B")
)
dt_dates_simple[]
#> Start_Date End_Date group
#> 1: 2020-02-03 2020-02-05 A
#> 2: 2020-03-01 2020-03-02 B
row_expand_dates(dt_dates_simple, "Start_Date", "End_Date", "Date")[]
#> Start_Date End_Date group Date
#> 1: 2020-02-03 2020-02-05 A 2020-02-03
#> 2: 2020-02-03 2020-02-05 A 2020-02-04
#> 3: 2020-02-03 2020-02-05 A 2020-02-05
#> 4: 2020-03-01 2020-03-02 B 2020-03-01
#> 5: 2020-03-01 2020-03-02 B 2020-03-02These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.