5 R
5.1 Assignment
For assigning a value to a variable, i use <- unless the variable is a parameter or an argument - then i use =:
5.3 Numbers
5.3.3 Rounding
5.3.3.2 Significant digits
For printing only significant digits, there’s the function signif which takes two parameters: the initial number x and the number of significant digits digits:
5.3.3.3 Prefixes
For formatting with prefixes, one can use the function format_SI[57] from the package BAAQMD/strtools[58]:
number <- c(1234.56, 0.123456) # 1
librarian::shelf(c( # 2
"strtools" # 3
)) # 4
format_SI(number) # 5
## [1] "1k" "123m"
format_SI(number, fixed=TRUE) # 1
## [1] "1k" "0k"
format_SI(number, engineering=TRUE) # 1
## [1] "1.234560000k" "0.000123456k"
format_SI(number, digits=2) # 1
## [1] "1.23k" "123.46m"5.3.3.4 Powers and units
For using powers and units, there is the package formatdown[59]:
librarian::shelf(c( # 1
"formatdown" # 2
)) # 3
numbers <- c(123.456, 2e-6, 5e8, 0.23) # 4
format_power_numbers <- format_power(numbers) # 5
## Warning in format_power(numbers): `format_power()` is deprecated. Use `format_numbers()`
## which offers additional arguments and access to package options.
format_power_numbers_digits <- format_power(numbers, digits = 2) # 1
## Warning in format_power(numbers, digits = 2): `format_power()` is deprecated. Use `format_numbers()`
## which offers additional arguments and access to package options.
format_power_numbers_sci <- format_power(numbers, format="sci") # 1
## Warning in format_power(numbers, format = "sci"): `format_power()` is deprecated. Use `format_numbers()`
## which offers additional arguments and access to package options.
format_power_numbers_omit <- format_power(numbers, omit_power = c(-6, -6)) # 1
## Warning in format_power(numbers, omit_power = c(-6, -6)): `format_power()` is deprecated. Use `format_numbers()`
## which offers additional arguments and access to package options.
format_power_numbers_set <- format_power(numbers, set_power = 5) # 1
## Warning in format_power(numbers, set_power = 5): `format_power()` is deprecated. Use `format_numbers()`
## which offers additional arguments and access to package options.
units(numbers) <- "kg m-3" # 1
format_units_numbers <- format_units(x=numbers, unit="g l-1") # 2
## Warning in format_units(x = numbers, unit = "g l-1"): `format_units()` is deprecated. Use `format_numbers()`
## which offers additional arguments and access to package options.\(\small 123.5\), \(\small 2.000 \times 10^{-6}\), \(\small 500.0 \times 10^{6}\), \(\small 0.2300\)
\(\small 120\), \(\small 2.0 \times 10^{-6}\), \(\small 500 \times 10^{6}\), \(\small 0.23\)
\(\small 123.5\), \(\small 2.000 \times 10^{-6}\), \(\small 5.000 \times 10^{8}\), \(\small 0.2300\)
\(\small 123.5 \times 10^{0}\), \(\small 0.000002000\), \(\small 500.0 \times 10^{6}\), \(\small 230.0 \times 10^{-3}\)
\(\small 123.5\), \(\small 0.00000000002000 \times 10^{5}\), \(\small 5000 \times 10^{5}\), \(\small 0.2300\)
123.456000 [g/L], 0.000002 [g/L], 500000000.000000 [g/L], 0.230000 [g/L]
5.4 Time
5.4.1 Converting to time
string_of_time = "2020.7.08 16:43:59" # 1
str(string_of_time) # 2
## chr "2020.7.08 16:43:59"
# 1
librarian::shelf(c( # 2
"lubridate" # 3
)) # 4
# 5
string_of_time_as_time <- parse_date_time(string_of_time, c("%Y.%m.%d %H:%M", "%m.%d.%Y %H:%M", "%Y.%m.%d %H:%M:%S")) # 6
str(string_of_time_as_time) # 7
## POSIXct[1:1], format: "2020-07-08 16:43:59"[60].
5.5 Array
5.5.2 Referencing position
Referencing a position inside the array takes place using brackets whereas the first position has the index 1:
5.5.5 Adding column[61]
5.5.7 Importing data
Data can be imported from a text file into a data frame:
CO2_in_air <- read.table("co2_brw_surface-insitu_1_ccgg_DailyData.txt", header = TRUE, sep = "", dec = ".") # 1
# 2Data can also be imported from a comma-separated-values-(CSV-)file into a data frame:
5.5.8 Printing first rows
print(head(washing_cycles)) # 1
## Algus Kestus Temperatuur Pöördeid.min Kava Veenäit.enne
## 1 2018.12.24 19:05 0:29 40 1000 lühikava NA
## 2 2018.12.24 23:03 0:29 30 1000 lühikava NA
## 3 2018.12.24 0:27 1:54 40 1000 segakiud NA
## 4 2018.12.26 14:30 0:47 30 800 käsipesu NA
## 5 2018.12.26 23:35 0:47 40 800 villapesu NA
## 6 2018.12.27 0:58 0:29 30 1000 lühikava 279.396
## Veenäit.pärast Veekulu..l. kWh
## 1 NA 0.5
## 2 NA 0.2
## 3 NA 0.4
## 4 NA 0.4
## 5 NA 0.4
## 6 279.428 3.2 0.35.5.9 Editing the look of a cell
[62].
5.5.10 Editing the content
librarian::shelf("dplyr") # 1
mutate(data_frame, "array 2" = "mutated") # 2
## array array 2
## Caption of first row value_1 mutated
## Caption of second row value_3 mutated[63].
5.5.14 Subsetting
As washing_cycles also contains records with missing data i want them removed:
# 1
washing_cycles_with_full_records <- subset(washing_cycles, !is.na(`Veenäit.enne`) & "" != `Veenäit.pärast` & !is.na(`Veekulu..l.`) & !is.na(`kWh`)) # 2
# 3
print(head(washing_cycles_with_full_records)) # 4
## Algus Kestus Temperatuur Pöördeid.min Kava Veenäit.enne
## 6 2018.12.27 0:58 0:29 30 1000 lühikava 279.396
## 7 2018.12.27 19:32 0:44 30 1000 lühikava 279.439
## 9 2019.1.12 15:13 1:54 30 1000 sünteetika 280.071
## 10 2019.2.7 12:46 0:29 30 1000 lühikava 281.189
## 11 2019.2.16 21:56 0:29 30 1000 lühikava 281.522
## 12 2019.2.20 17:10 0:47 40 800 villapesu 281.698
## Veenäit.pärast Veekulu..l. kWh
## 6 279.428 3.2 0.3
## 7 279.459 2.0 0.1
## 9 280.13400000000001 6.3 1.5
## 10 281.21499999999997 2.6 0.4
## 11 281.54899999999998 2.7 0.3
## 12 281.745 4.7 0.9[65].
This was how to remove incomplete records by manually setting the columns that contain empty records. There is a move convenient method to do that without specifying columns:
[64].
I only want to see the data in the column Kava:
program_in_washing_cycles <- subset(washing_cycles_with_full_records, select = `Kava`) # 1
# 2
print(head(program_in_washing_cycles)) # 3
## Kava
## 6 lühikava
## 7 lühikava
## 9 sünteetika
## 10 lühikava
## 11 lühikava
## 12 villapesuI only want to see cycles from the rows 2 to 4 in the second column:
I want the last 216 rows to be removed:
number_of_rows_in_washing_cycles_with_full_records <- nrow(washing_cycles_with_full_records) # 1
data_frame_of_washing_cycles_with_full_records_without_last_records <- washing_cycles_with_full_records[ # 2
-c((number_of_rows_in_washing_cycles_with_full_records - 215):number_of_rows_in_washing_cycles_with_full_records), ] # 3
print(data_frame_of_washing_cycles_with_full_records_without_last_records) # 4
## Algus Kestus Temperatuur Pöördeid.min Kava Veenäit.enne
## 6 2018.12.27 0:58 0:29 30 1000 lühikava 279.396
## 7 2018.12.27 19:32 0:44 30 1000 lühikava 279.439
## 9 2019.1.12 15:13 1:54 30 1000 sünteetika 280.071
## Veenäit.pärast Veekulu..l. kWh
## 6 279.428 3.2 0.3
## 7 279.459 2.0 0.1
## 9 280.13400000000001 6.3 1.5I only want to see hot cycles:
hot_cycles <- subset(washing_cycles_with_full_records, `Temperatuur` > 40) # 1
print(hot_cycles) # 2
## Algus Kestus Temperatuur Pöördeid.min Kava
## 23 2019.4.19 15:13 2:10 60 1000 beebi
## 34 2019.7.6 16:05 1:54 60 1000 sünteetika
## 38 2019.7.10 13:30 1:54 60 1000 segakiud
## 65 2019.9.28 19:41 1:54 60 1000 segakiud, hygiene+
## 68 2019.9.29 16:39 2:10 90 400 autoclean
## 184 2020.11.15 19:19:59 1:54:59 60 1000 segakiud, hygiene+
## 203 2021.2.12 19:09:59 2:49:00 90 1300 Kochwäsche
## 206 2021.2.16 18:31:59 1:54:59 60 1000 sünteetika + hygiene+
## 320 11.12.2021 15:24:59 1:54:59 60 1000 segu, hygiene+
## 340 12.10.2021 23:22:00 2:49:00 90 1300 keedupesu, hygiene+
## 393 3.20.2022 14:42:00 2:59:00 90 1300 keedupesu, lisaloputus
## 425 6.24.2022 14:10:59 4:00:59 60 1300 puuvill, lisaloputus
## 427 6.24.2022 17:48:59 2:04:00 60 1000 segu, lisaloputus
## 439 7.17.2022 15:33:00 2:59:00 90 400 keedupesu
## Veenäit.enne Veenäit.pärast Veekulu..l. kWh
## 23 284.498 284.53699999999998 3.9 2.3
## 34 287.732 287.80200000000002 7.0 1.0
## 38 288.129 288.20600000000002 7.7 2.3
## 65 292.237 292.30799999999999 7.1 0.9
## 68 292.478 292.49799999999999 2.0 0.5
## 184 316.293 316.31200000000001 1.9 0.4
## 203 321.112 321.17500000000001 6.3 3.7
## 206 321.487 321.52199999999999 3.5 0.6
## 320 341.609 341.67599999999999 6.7 0.9
## 340 344.372 344.41000000000003 3.8 1.2
## 393 352.388 352.42599999999999 3.8 0.9
## 425 358.710 358.73599999999999 2.6 1.4
## 427 358.805 358.82999999999998 2.5 0.4
## 439 360.281 360.31 2.9 0.9I only want to see the indices of the cycles at the temperature of \(313.15 \times \mathrm{K}\):
5.5.15 Sorting
Displaying the indices of the descending sorted values of a vector:
librarian::shelf("dplyr") # 1
desc(as.matrix(subset(head(washing_cycles_with_full_records), select = `Kava`))) # 2
## [1] -1 -1 -5 -1 -1 -6Sorting values in ascending order according to the program:
head(arrange(washing_cycles_with_full_records, `Kava`)) # 1
## Algus Kestus Temperatuur Pöördeid.min Kava
## 1 2021.2.12 19:09:59 2:49:00 90 1300 Kochwäsche
## 2 7.13.2022 18:17:59 0:59:00 30 800 Trainers
## 3 2019.9.29 16:39 2:10 90 400 autoclean
## 4 2019.4.19 15:13 2:10 60 1000 beebi
## 5 7.17.2022 15:33:00 2:59:00 90 400 keedupesu
## 6 12.10.2021 23:22:00 2:49:00 90 1300 keedupesu, hygiene+
## Veenäit.enne Veenäit.pärast Veekulu..l. kWh
## 1 321.112 321.17500000000001 6.3 3.7
## 2 359.990 360.005 1.5 0.2
## 3 292.478 292.49799999999999 2.0 0.5
## 4 284.498 284.53699999999998 3.9 2.3
## 5 360.281 360.31 2.9 0.9
## 6 344.372 344.41000000000003 3.8 1.25.5.16 Totals
librarian::shelf("janitor") # 1
adorn_totals(dat = head(subset(x = washing_cycles_with_full_records, select = c(`Algus`, `Veekulu..l.`))), where = "row", fill = "", na.rm = TRUE, name = "Kokku", c(`Veekulu..l.`)) # 2
## Algus Veekulu..l.
## 2018.12.27 0:58 3.2
## 2018.12.27 19:32 2.0
## 2019.1.12 15:13 6.3
## 2019.2.7 12:46 2.6
## 2019.2.16 21:56 2.7
## 2019.2.20 17:10 4.7
## Kokku 21.5[67].
5.5.17 Mean
Mean row-wise can be calculated using rowMeans()[68].
t_1 <- c(7.508, 4.452, 3.434, 2.978, 2.752) # 1
t_2 <- c(7.775, 4.515, 3.434, 2.978, 2.752) # 2
t_3 <- c(7.685, 4.47, 3.603, 2.992, 2.732) # 3
# 4
data_frame_of_mass_time <- data.frame( # 5
t_1, # 6
t_2, # 7
t_3 # 8
) # 9
# 10
rowMeans(x = data_frame_of_mass_time[, c(1:3)]) # 11
## [1] 7.656000 4.479000 3.490333 2.982667 2.7453335.6 Functions
Functions can be made using the keyword function:
add <- function(first, second, digits = 2) { # 1
return(signif(first + second, digits = digits)) # 2
} # 3
# 4
add(first = 123, second = 456) # using the default value 2 for digits # 5
## [1] 580
# 1
sum <- add(first = 123, second = 456, digits = 1) # 2It is not possible to assign an argument with the same name as the parameter[69]. In the example above, the value for first could not be first although there might be an external variable first, id est first = first is not allowed. I have to use different names.
5.7 Square root
A square root can be calculated using the function sqrt():
The square root of \(\num{4}\) is \(\num{2}\).
5.8 Derivation
initial_function <- "x^3 + x^2" # 1
functionToUse <- parse(text = initial_function) # 2
# 3
librarian::shelf(c( # 4
"Ryacas" # 5
)) # 6
# 7
derivative = D(functionToUse, "x") # 8
string_of_derivative <- deparse(derivative) # 9The derivative of \(x^3 + x^2\) is \(3 * x^2 + 2 * x\).
equality <- paste(string_of_derivative, "== 0") # 1
print(equality) # 2
## [1] "3 * x^2 + 2 * x == 0"
print(paste("Solve(", equality, ", x)", sep = "")) # 1
## [1] "Solve(3 * x^2 + 2 * x == 0, x)"
print(y_rmvars(paste("Solve(", equality, ", x)", sep = ""))) # 1
## [1] "((Solve(3 * x^2 + 2 * x == 0, x)) /:: { _lhs == _rhs <- rhs })"
critical_places <- yac_str(y_rmvars(paste("Solve(", equality, ", x)", sep = ""))) # 1
print(critical_places) # 2
## [1] "{0,(-2)/3}"
critical_places_as_r <- as_r(critical_places) # 1
print(critical_places_as_r) # 2
## [1] "0" "(-2)/3"
critical_solution_1 <- (critical_places_as_r[1]) # 1
critical_solution_2 <- critical_places_as_r[2] # 2The critical solutions of \(x^3 + x^2\) are 0 and (-2)/3.
5.10 Strings
Strings can be written using either apostrophes or quotation marks.
For substituting something inside a string, gsub can be used[70]:
Here, in order to preserve a backslash, it has to be escaped as otherwise, it escapes the underscore. If I would turn off fixed, the function would work like with regular expressions.
5.12 Table
5.12.1 A user-friendly look
A table that is not just in R code but designed and all can be created using kable and kableExtra[71]. I have built a wrapper function print_table for that purpose so that I do not have to rewrite some general things from table to table. An example table is 5.1 on the page .
omega <- c(932.0058, 827.2861, 733.0383, 628.3185, 523.5988, 418.8790, 314.1593) # 1
omega_P <- c(0.03966657, 0.04155546, 0.05073632, 0.05411874, 0.05817764, 0.03878509, 0.01811760) # 2
# 3
data_frame_of_precession <- data.frame( # 4
omega, # 5
omega_P # 6
) # 7
# 8
colnames(data_frame_of_precession) <- c( # 9
"$\\frac{\\omega}{\\unit{\\per\\s}}$", # 10
"$\\frac{\\omega_\\text{P}}{\\unit{\\per\\s}}$" # 11
) # 12
# 13
print_table( # 14
table = data_frame_of_precession, # 15
caption = "Pretsessiooni nurkkiiruse sõltuvus güroskoobi nurkkiirusest." # 16
) # 17| \(\frac{\omega}{\unit{\per\s}}\) | \(\frac{\omega_\text{P}}{\unit{\per\s}}\) |
|---|---|
| 932.01 | 0.04 |
| 827.29 | 0.04 |
| 733.04 | 0.05 |
| 628.32 | 0.05 |
| 523.60 | 0.06 |
| 418.88 | 0.04 |
| 314.16 | 0.02 |
5.12.2 Untolerated symbols
I have to pay attention that there can’t be any underscores inside the table unless they are part of an equation. They can be escaped using gsub and the result is shown as the table 5.3 on the page .
print_table( # 1
table = sapply(data_frame, function(value) gsub("_", "\\_", value, fixed = TRUE)), # 2
caption = "Caption." # 3
) # 4| array | array 2 |
|---|---|
| value_1 | value_3 |
| value_3 | value_4 |
Inside the table, backslashes must be escaped.
5.12.3 Number of digits after comma
Tables 5.5 on the page and 5.7 on the page are for comparing the number of digits after comma. The table 5.5 has the default number of digits and the table 5.7 has another number of digits in every number after comma.
water_report <- head(subset(x = washing_cycles_with_full_records, select = c(`Algus`, `Veenäit.enne`, `Veenäit.pärast`))) # 1
# 2
librarian::shelf(c( # 3
'dplyr' # 4
)) # 5
# 6
water_report <- water_report %>% # 7
mutate(`Veenäit.pärast` = as.numeric(`Veenäit.pärast`)) # 8
# 9
colnames(water_report) <- c( # 10
"Start", # 11
"$\\mathrm{\\frac{\\text{Used water before}}{\\mathrm{m^3}}}$", # 12
"$\\mathrm{\\frac{\\text{Used water after}}{\\mathrm{m^3}}}$" # 13
) # 14
# 15
print_table( # 16
table = water_report, # 17
caption = "Water report with numbers with up to two digits after comma." # 18
) # 19| Start | \(\mathrm{\frac{\text{Used water before}}{\mathrm{m^3}}}\) | \(\mathrm{\frac{\text{Used water after}}{\mathrm{m^3}}}\) | |
|---|---|---|---|
| 6 | 2018.12.27 0:58 | 279.40 | 279.43 |
| 7 | 2018.12.27 19:32 | 279.44 | 279.46 |
| 9 | 2019.1.12 15:13 | 280.07 | 280.13 |
| 10 | 2019.2.7 12:46 | 281.19 | 281.21 |
| 11 | 2019.2.16 21:56 | 281.52 | 281.55 |
| 12 | 2019.2.20 17:10 | 281.70 | 281.74 |
[66(p. 38) to 42].
print_table( # 1
table = water_report, # 2
caption = "Water report with numbers with up to four digits after comma.", # 3
digits = 4 # 4
) # 5| Start | \(\mathrm{\frac{\text{Used water before}}{\mathrm{m^3}}}\) | \(\mathrm{\frac{\text{Used water after}}{\mathrm{m^3}}}\) | |
|---|---|---|---|
| 6 | 2018.12.27 0:58 | 279.396 | 279.428 |
| 7 | 2018.12.27 19:32 | 279.439 | 279.459 |
| 9 | 2019.1.12 15:13 | 280.071 | 280.134 |
| 10 | 2019.2.7 12:46 | 281.189 | 281.215 |
| 11 | 2019.2.16 21:56 | 281.522 | 281.549 |
| 12 | 2019.2.20 17:10 | 281.698 | 281.745 |
5.12.4 Additional header
It’s also possible for the table to have an additional header whose columns span over multiple columns in the first header[72] (the table 5.9 on the page ):
print_table( # 1
table = water_report, # 2
caption = "Water report with additional header.", # 3
additional_header = c("Spanned header" = 4) # 4
) # 5| Start | \(\mathrm{\frac{\text{Used water before}}{\mathrm{m^3}}}\) | \(\mathrm{\frac{\text{Used water after}}{\mathrm{m^3}}}\) | |
|---|---|---|---|
| 6 | 2018.12.27 0:58 | 279.40 | 279.43 |
| 7 | 2018.12.27 19:32 | 279.44 | 279.46 |
| 9 | 2019.1.12 15:13 | 280.07 | 280.13 |
| 10 | 2019.2.7 12:46 | 281.19 | 281.21 |
| 11 | 2019.2.16 21:56 | 281.52 | 281.55 |
| 12 | 2019.2.20 17:10 | 281.70 | 281.74 |
5.12.5 Look
It’s possible to change the look of a row (the table 5.11 on the page ):
print_table( # 1
table = water_report, # 2
caption = "Water report with coloured row." # 3
) %>% # 4
row_spec(2, color = "teal") # 5| Start | \(\mathrm{\frac{\text{Used water before}}{\mathrm{m^3}}}\) | \(\mathrm{\frac{\text{Used water after}}{\mathrm{m^3}}}\) | |
|---|---|---|---|
| 6 | 2018.12.27 0:58 | 279.40 | 279.43 |
| 7 | 2018.12.27 19:32 | 279.44 | 279.46 |
| 9 | 2019.1.12 15:13 | 280.07 | 280.13 |
| 10 | 2019.2.7 12:46 | 281.19 | 281.21 |
| 11 | 2019.2.16 21:56 | 281.52 | 281.55 |
| 12 | 2019.2.20 17:10 | 281.70 | 281.74 |
Here, %>% means piping.
And it’s possible to change the look of a column (the table 5.13 on the page ):
print_table( # 1
table = water_report, # 2
caption = "Water report with wider column." # 3
) %>% # 4
column_spec(1, width = "16em") # 5| Start | \(\mathrm{\frac{\text{Used water before}}{\mathrm{m^3}}}\) | \(\mathrm{\frac{\text{Used water after}}{\mathrm{m^3}}}\) | |
|---|---|---|---|
| 6 | 2018.12.27 0:58 | 279.40 | 279.43 |
| 7 | 2018.12.27 19:32 | 279.44 | 279.46 |
| 9 | 2019.1.12 15:13 | 280.07 | 280.13 |
| 10 | 2019.2.7 12:46 | 281.19 | 281.21 |
| 11 | 2019.2.16 21:56 | 281.52 | 281.55 |
| 12 | 2019.2.20 17:10 | 281.70 | 281.74 |
5.12.6 Landscape
If the table is too wide to fit the portrait format, it can be displayed in the landscape mode (the table 5.15 on the page ):
print_table( # 1
table = water_report, # 2
caption = "Water report as landscape." # 3
) %>% # 4
landscape() # 5| Start | \(\mathrm{\frac{\text{Used water before}}{\mathrm{m^3}}}\) | \(\mathrm{\frac{\text{Used water after}}{\mathrm{m^3}}}\) | |
|---|---|---|---|
| 6 | 2018.12.27 0:58 | 279.40 | 279.43 |
| 7 | 2018.12.27 19:32 | 279.44 | 279.46 |
| 9 | 2019.1.12 15:13 | 280.07 | 280.13 |
| 10 | 2019.2.7 12:46 | 281.19 | 281.21 |
| 11 | 2019.2.16 21:56 | 281.52 | 281.55 |
| 12 | 2019.2.20 17:10 | 281.70 | 281.74 |
5.12.7 Footnotes
Linked footnotes don’t work with kable. Footnotes can be created like this (the table 5.17 on the page ):
# 1
DATA_FRAME_OF_COMPARISON <- data.frame( # 2
0.3 # 3
) # 4
# 5
colnames(DATA_FRAME_OF_COMPARISON) <- c( # 6
paste("$\\frac{T_\\text{dew}}{}$", footnote_marker_number(1)) # 7
) # 8
# 9
print_table( # 10
table = DATA_FRAME_OF_COMPARISON, # 11
caption = "Water report with a footnote.", # 12
footnotes = c( # 13
"juhendi tabel 5.1" # 1 # 14
) # 15
) # 16| \(\frac{T_\text{dew}}{}\) <sup>1</sup> |
|---|
| 0.3 |
| 1 juhendi tabel 5.1 |
threeparttable must be set to TRUE for just in case the footnote is too long for the width of the paper[73].
5.12.8 Transposing
By default, I feed one-dimensional arrays to data frame and the values of these arrays will be displayed from top to down. If I want them to be displayed from left to right, I have to transform the table (the table 5.19 on the page ):
# 1
DATA_FRAME_OF_COMPARISON <- data.frame( # 2
0.3 # 3
) # 4
# 5
rownames(DATA_FRAME_OF_COMPARISON) <- c( # 6
"\"Pasco\" ilmajaam" # 7
) # 8
# 9
colnames(DATA_FRAME_OF_COMPARISON) <- c( # 10
paste("$\\frac{T_\\text{dew}}{}$", footnote_marker_number(1)) # 11
) # 12
# 13
print_table( # 14
DATA_FRAME_OF_COMPARISON, # 15
caption = "Table with rows and columns exchanged.", # 16
do_i_transpose = TRUE # 17
) # 18| “Pasco” ilmajaam | |
|---|---|
| \(\frac{T_\text{dew}}{}\) <sup>1</sup> | 0.3 |
5.12.9 Coloring according to values
It is possible to automatically show the values growing by different colors using the function spec_color of kableExtra as seen in the table 5.21 leheküljel .
Li <- c( # 1
4.5e-1, # acetate # 2
1.81, # bromide # 3
1.30e-2 # carbonate # 4
) # 5
# 6
Na <- c( # 7
5.04e-1, # acetate # 8
9.46e-1, # bromide # 9
3.07e-2 # carbonate # 10
) # 11
# 12
K <- c( # 13
2.69, # acetate # 14
6.78e-1, # bromide # 15
1.11 # carbonate # 16
) # 17
# 18
solubility <- data.frame( # 19
Li, # 20
Na, # 21
K # 22
) # 23
# 24
colnames(solubility) <- c( # 25
"\\ce{Li+}", # 26
"\\ce{Na+}", # 27
"\\ce{K+}" # 28
) # 29
rownames(solubility) <- c( # 30
"\\ce{[CH3CO2]-}", # 31
"\\ce{Br-}", # 32
"\\ce{[CO3]^2-}" # 33
) # 34
librarian::shelf(c( # 35
"dplyr" # 36
)) # 37
solubility <- mutate_all(.tbl=solubility, ~cell_spec(.x, color=spec_color(.x), font_size = spec_font_size(.x))) # 38
print_table(table=solubility, caption="Kilogrammi ühendi lahustuvus vees kilogrammi vee kohta[25, lk 4-43-4-114]. If the value of the solubility is unknown then the solubility is marked as follows: slightly soluble: , soluble: , very soluble: .") # 39| <span style=” color: rgba(60, 80, 139, 255) !important;font-size: 10px;” >0.45</span> | <span style=” color: rgba(31, 148, 140, 255) !important;font-size: 12px;” >0.504</span> | <span style=” color: rgba(253, 231, 37, 255) !important;font-size: 16px;” >2.69</span> | |
| <span style=” color: rgba(253, 231, 37, 255) !important;font-size: 16px;” >1.81</span> | <span style=” color: rgba(253, 231, 37, 255) !important;font-size: 16px;” >0.946</span> | <span style=” color: rgba(68, 1, 84, 255) !important;font-size: 8px;” >0.678</span> | |
| <span style=” color: rgba(68, 1, 84, 255) !important;font-size: 8px;” >0.013</span> | <span style=” color: rgba(68, 1, 84, 255) !important;font-size: 8px;” >0.0307</span> | <span style=” color: rgba(63, 72, 137, 255) !important;font-size: 10px;” >1.11</span> |
5.14 Plotting
5.14.1 One graph
An example of plotting data is putting data points to the plot - visible on the figure 5.1 on the page . The packages:
ggplot2- for plotting[75]
latex2exp- for using \(\mathrm{ \LaTeX }\) strings in labels
The parameters:
data- the data table
aes- the function that describes axis
x- data array for x axis
y- data array for y axis
geom_point- the function for plotting data points
shape- the shape of points
size- the size of points
color- the border color
fill- the fill color
labs- the function for creating labels for axis
TeX- the function for converting \(\mathrm{ \LaTeX }\) strings in labels
# 1
librarian::shelf(c( # 2
"Cairo", # 3
"ggplot2", # 4
"latex2exp" # 5
)) # 6
# 7
ggplot(data = washing_cycles_with_full_records, aes(x = `Temperatuur`, y = `Pöördeid.min`)) + geom_point(shape = 23, color = "#008000", fill = "#ff6600", size = 3) + # 8
labs(x = TeX("$\\frac{\\textit{t}}{^\\circ C}$"), y = TeX("$\\frac{\\textit{f}}{min}$")) # 9Figure 5.1: Washing cycles.
In order to use Unicode characters like the degree symbol, we need the library Cairo and the corresponding dev[76].
5.14.2 Additional data point
A single additional data point can be added using geom_point (figure 5.2 on the page )[77].
# 1
librarian::shelf(c( # 2
"Cairo", # 3
"ggplot2", # 4
"latex2exp" # 5
)) # 6
# 7
ggplot(data = washing_cycles_with_full_records, aes(x = `Temperatuur`, y = `Pöördeid.min`)) + # 8
geom_point(shape = 23, color = "#008000", fill = "#ff6600", size = 3) + # 9
geom_point(aes(x = 75, y = 900), shape = 23, size = 3) + # 10
labs(x = TeX("$\\frac{\\textit{t}}{^\\circ C}$"), y = TeX("$\\frac{\\textit{f}}{min}$")) # 11
## Warning in geom_point(aes(x = 75, y = 900), shape = 23, size = 3): All aesthetics have length 1, but the data has 219
## rows.
## ℹ Please consider using `annotate()` or provide
## this layer with data containing a single row.Figure 5.2: Additional data point.
5.14.3 Multiple graphs
Multiple graphs can be displayed on a same figure as seen on the figure 5.3 on the page .
librarian::shelf(c( # 1
"lubridate" # 2
)) # 3
# 4
washing_cycles_with_full_records <- washing_cycles_with_full_records %>% # 5
mutate(`Algus` = parse_date_time(x = washing_cycles_with_full_records$Algus, orders = c( # 6
"%Y-%m-%d %H", # 7
"%Y.%m.%d %H:%M", # 8
"%Y.%m.%d %H:%M:%S", # 9
"%m.%d.%Y %H:%M", # 10
"%m.%d.%Y %H:%M:%S" # 11
))) %>% # 12
mutate(`veekulu_dal` = (as.numeric(washing_cycles_with_full_records$Veenäit.pärast) - washing_cycles_with_full_records$Veenäit.enne) * 100) # 13
# 14
min_time = min(washing_cycles_with_full_records$Algus) # 15
max_time = max(washing_cycles_with_full_records$Algus) # 16
# 17
librarian::shelf(c( # 18
"ggplot2", # 19
"latex2exp" # 20
)) # 21
# 22
ggplot(data = washing_cycles_with_full_records, mapping = aes(x = `Algus`, y = `veekulu_dal`)) + # 23
geom_point(shape = 23, color = "#008000", fill = "#008000", size = 3) + # 24
labs(x = "Algus", y = TeX("$\\frac{\\textit{V} \\times 10^2}{m^3}$")) + # 25
geom_point(mapping = aes(x = `Algus`, y = `kWh`), color = "#ff6600", fill = "#ff6600", shape = 24, size = 3) + # 26
scale_y_continuous(sec.axis = sec_axis(~., name = TeX("\\frac{\\textit{W}}{kWh}"))) + # 27
theme( # 28
axis.title.y = element_text(colour = "#008000"), # 29
axis.text.y = element_text(colour = "#008000"), # 30
axis.ticks.y = element_line(colour = "#008000"), # 31
axis.title.y.right = element_text(colour = "#ff6600"), # 32
axis.ticks.y.right = element_line(colour = "#ff6600"), # 33
axis.text.y.right = element_text(colour = "#ff6600") # 34
) # 35
Figure 5.3: Water and electricity consumption between 2018-12-27 00:58:00 and 2022-07-17 15:33:00 .
5.14.4 Trend line
On the figure 5.4 on the page .
ggplot(data = washing_cycles_with_full_records, aes(x = `Temperatuur`, y = `Pöördeid.min`)) + geom_point(shape = 23, color = "#008000", fill = "#ff6600", size = 3) + # 1
labs(x = TeX("$\\frac{\\textit{t}}{\\degree C}$"), y = TeX("$\\frac{\\textit{f}}{min}$")) + # 2
geom_smooth() # 3
## `geom_smooth()` using method = 'loess' and formula
## = 'y ~ x'
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : pseudoinverse used at 29.7
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : neighborhood radius 10.3
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : reciprocal condition number 2.4663e-30
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : There are other near singularities as well. 100
## Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
## else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at
## 29.7
## Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
## else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 10.3
## Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
## else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
## number 2.4663e-30
## Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
## else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other near
## singularities as well. 100
Figure 5.4: Graph with a trend line.
5.14.5 Regression and correlation
The figure 5.5 on the page represents the correlation between the water and electricity consumption between 2018-12-27 00:58:00 and 2022-07-17 15:33:00. The plot displays the smooth regression line[78] and the correlation coefficient. There are also the data about the regression. Both labels have been positioned vertically.
For ggpubr, the dependency nloptr must be installed directly in Ubuntu[79]:
librarian::shelf(c( # 1
"ggplot2", # 2
"ggpmisc", #for stat_poly_line # 3
"ggpubr", # for stat_regline_equation # 4
"latex2exp" # 5
)) # 6
# 7
if (!decimal_separator_period) { # for stat_regline_equation and stat_cor # 8
options(OutDec = ".") # 9
} # 10
ggplot(data = washing_cycles_with_full_records, mapping = aes(x = `Veekulu..l.`, y = `kWh`)) + # 11
geom_point(shape = 23, color = "#ff6600", fill = "#ff6600", size = 3) + # 12
labs(x = TeX("$\\frac{\\textit{V}}{m^3}$"), y = TeX("$\\frac{\\textit{W}}{kWh}$")) + # 13
stat_poly_line() + # 14
stat_regline_equation(mapping = aes(x = `Veekulu..l.`, y = `kWh`, label = paste(after_stat(eq.label), after_stat(rr.label), after_stat(adj.rr.label), sep = "~~~~")), color = "#ff6600", label.y = 3) + # 15
stat_cor(aes(x = `Veekulu..l.`, y = `kWh`), color = "#ff6600", label.y = 2.5) # 16
Figure 5.5: Correlation between the water and electricity consumption between 2018-12-27 00:58:00 and 2022-07-17 15:33:00 .
It is possible to use different kind of trend lines as seen on the figure 5.6 on the page :
omega <- c(932.0058, 827.2861, 733.0383, 628.3185, 523.5988) # 1
h <- c(303.44868, 239.08893, 187.71603, 137.91380, 95.77349) # 2
# 3
data_frame_of_precession_with_height_without_outliers <- data.frame( # 4
omega, # 5
h # 6
) # 7
# 8
color_of_height <- "#ff00ff" # 9
color_x <- "#008000" # 10
# 11
ggplot( # 12
data <- data_frame_of_precession_with_height_without_outliers, # 13
mapping <- aes(x = omega, y = h) # 14
) + # 15
geom_point(shape = 23, size = 1) + # 16
labs(x = TeX("$\\frac{\\omega}{\\frac{1}{s}}$"), y = TeX("$\\frac{h}{m}$")) + # 17
theme( # 18
axis.title.x = element_text(colour = color_x), # 19
axis.text.x = element_text(colour = color_x), # 20
axis.ticks.x = element_line(colour = color_x), # 21
axis.title.y = element_text(colour = color_of_height), # 22
axis.text.y = element_text(colour = color_of_height), # 23
axis.ticks.y = element_line(colour = color_of_height) # 24
) + # 25
stat_poly_line(formula = y ~ poly(x, 2)) # 26
Figure 5.6: Polynomial trend line with the degree .
5.14.6 Error bars
The figure 5.7 on the page represents the water and electricity consumption between 2018-12-27 00:58:00 and 2022-07-17 15:33:00 with errorbars.
# 1
librarian::shelf(c( # 2
"ggplot2", # 3
"latex2exp" # 4
)) # 5
# 6
margin_of_V <- 1e-3 / 2 * 2 * 1e2 # 7
margin_of_W <- .1 / 2 # 8
# 9
color_x <- "#008000" # 10
color_y <- "#ff6600" # 11
# 12
ggplot(data = washing_cycles_with_full_records, mapping = aes(x = `veekulu_dal`, y = `kWh`)) + # 13
geom_point(shape = 23, size = 1) + # 14
labs(x = TeX("$\\frac{\\textit{V} \\times 10^2}{m^3}$"), y = TeX("$\\frac{\\textit{W}}{kWh}$")) + # 15
geom_errorbarh(aes(xmin = `veekulu_dal` - margin_of_V, xmax = `veekulu_dal` + margin_of_V, y = `kWh`), color = color_x) + # 16
geom_errorbar(aes(x = `veekulu_dal`, ymin = `kWh` - margin_of_W, ymax = `kWh` + margin_of_W), color = color_y) + # 17
theme(axis.title.x = element_text(colour = color_x), axis.text.x = element_text(colour = color_x), axis.ticks.x = element_line(colour = color_x), axis.title.y = element_text(colour = color_y), axis.text.y = element_text(colour = color_y), axis.ticks.y = element_line(colour = color_y)) # 18
Figure 5.7: Water and electricity consumption between 2018-12-27 00:58:00 and 2022-07-17 15:33:00 with errobars.
Joonisel 5.8 leheküljel on esitatud valgustugevuse mõõtmise tulemused keskväärtuse ja mõõtemääramatustega: mustaga on tähistatud A-, oranžiga B1-, roosaga B2-tüüpi ja rohelisega kogu mõõtemääramatus[80]. Sellelt jooniselt näeme, kui palju mingit tüüpi mõõtemääramatus antud juhul mõju avaldab.
librarian::shelf(c( # 1
"ggplot2", # 2
"latex2exp" # 3
)) # 4
color_x <- "#008000" # 5
illuminance <- c(923, 905, 593, 587) # 6
illuminance_data_frame = data.frame(illuminance) # 7
illuminance_mean <- mean(illuminance) # 8
illuminance_se <- as.numeric(mean_se(illuminance)["ymax"] - illuminance_mean) # 9
illuminance_size <- length(illuminance) # 10
illuminance_size_less_1 <- illuminance_size - 1 # 11
librarian::shelf(c( # 12
"distributions3" # 13
)) # 14
studentTDistribution = StudentsT(df = illuminance_size_less_1) # 15
alpha <- 0.05 # 16
probability_for_Student <- 1 - alpha / 2 # 17
caliper_number_student = quantile(studentTDistribution, probability_for_Student) # 18
illuminance_uncertainty_A <- caliper_number_student * illuminance_se # 19
# 20
precision_ratio <- .05 # 21
number_of_least_sigfig = 10 # 22
student_inf <- quantile(StudentsT(df = Inf), probability_for_Student) # 23
illuminance_uncertainty_B_1 <- student_inf * (illuminance_mean * precision_ratio + number_of_least_sigfig) / 3 # 24
# 25
illuminance_smallest_unit <- 1 # 26
probability <- 1 - alpha # 27
# 28
illuminance_uncertainty_B_2 <- probability * illuminance_smallest_unit / 2 # 29
# 30
illuminance_uncertainty <- sqrt(illuminance_uncertainty_A^2 + illuminance_uncertainty_B_1^2 + illuminance_uncertainty_B_2^2) # 31
# 32
ggplot(data = illuminance_data_frame, mapping = aes(x = `illuminance`, y = 0)) + # 33
geom_point(shape = 23, size = 1) + # 34
geom_point(aes(x = illuminance_mean, y = 0), shape = 23, size = 2, color = color_x) + # 35
labs(x = TeX("$\\frac{\\textit{q}}{lx}$")) + # 36
geom_errorbarh(aes(xmin = illuminance_mean - illuminance_uncertainty, xmax = illuminance_mean + illuminance_uncertainty), color = color_x) + # 37
geom_errorbarh(aes(xmin = illuminance_mean - illuminance_uncertainty_A, xmax = illuminance_mean + illuminance_uncertainty_A), height = .1) + # 38
geom_errorbarh(aes(xmin = illuminance_mean - illuminance_uncertainty_B_1, xmax = illuminance_mean + illuminance_uncertainty_B_1), color = "#ff6600", height = .2) + # 39
geom_errorbarh(aes(xmin = illuminance_mean - illuminance_uncertainty_B_2, xmax = illuminance_mean + illuminance_uncertainty_B_2), color = "#ff66ff", height = .4) + # 40
theme( # 41
axis.title.x = element_text(colour = color_x), # 42
axis.text.x = element_text(colour = color_x), # 43
axis.ticks.x = element_line(colour = color_x), # 44
axis.title.y=element_blank(), # 45
axis.text.y=element_blank(), # 46
axis.ticks.y=element_blank() # 47
) # 48
## Warning in geom_point(aes(x = illuminance_mean, y = 0), shape = 23, size = 2, : All aesthetics have length 1, but the data has 4
## rows.
## ℹ Please consider using `annotate()` or provide
## this layer with data containing a single row.
Figure 5.8: Valgustugevus koos keskmise valgustugevuse ja mõõtemääramatustega.
5.14.7 Accuracy
Joonisel 5.9 leheküljel on esitatud mõõdetud keskväärtus koos mõõtemääramatusega graafiliselt.
librarian::shelf(c( # 1
"ggplot2", # 2
"latex2exp" # 3
)) # 4
# 5
color_x <- "#008000" # 6
ruler_smallest_unit <- .5 # 7
ruler_relation = 4000 / 400 # 8
ruler_smallest_unit_actual <- ruler_smallest_unit * ruler_relation # 9
ruler_x <- 42 # 10
ruler_x_actual <- ruler_x * ruler_relation # 11
ruler_data_frame <- data.frame(ruler_x_actual) # 12
# 13
plot <- ggplot(data = ruler_data_frame, mapping = aes(x = `ruler_x_actual`, y = 0)) + # 14
geom_point(shape = 23, size = 1) + # 15
labs(x = TeX("$\\frac{\\textit{q}}{m}$")) + # 16
geom_errorbarh(aes(xmin = `ruler_x_actual` - ruler_smallest_unit_actual, xmax = `ruler_x_actual` + ruler_smallest_unit_actual), color = color_x) + # 17
theme( # 18
axis.title.x = element_text(colour = color_x), # 19
axis.text.x = element_text(colour = color_x), # 20
axis.ticks.x = element_line(colour = color_x), # 21
axis.title.y=element_blank(), # 22
axis.text.y=element_blank(), # 23
axis.ticks.y=element_blank() # 24
) # 25
# 26
plot # 27
Figure 5.9: Kahe kontrollpunkti omavaheline kaugus mõõtemääramatusega.
The figure 5.10 on the page shows how to specify the accuracy of the numbers on axis[34].
Figure 5.10: With specified accuracy.
5.15 Regression model[81]
angular_acceleration <- c(5.902952, 17.246897, 28.401369, 38.892302, 45.907424) # 1
torque <- c(0.001153365, 0.002174215, 0.003190228, 0.004201956, 0.005009516) # 2
formula = torque ~ angular_acceleration # 3
model = lm(formula = formula) # 4
# 5
b <- model$coefficients[1] # 6
k <- model$coefficients[2] # 7Regressioonisirge tõus on \qty{`r as.character(signif(x = k, digits = 3))`}{\kg\m\squared} ja see näitab inertsimomenti. Vabaliige on \qty{`r as.character(signif(x = b, digits = 3))`}{\N\m} ja see näitab hõõrdejõu momenti. # 1
# 2Regressioonisirge tõus on ja see näitab inertsimomenti. Vabaliige on ja see näitab hõõrdejõu momenti.
5.16 Correlation
In order to get the values of the result of a correlation analysis, one can use cor[82].
5.17 Linearizing
Lasen joonestada pretsessiooninurkkiiruse sõltuvuse graafiku rootori osakeste nurkkiirusest (joonis 5.11 leheküljel ).
librarian::shelf(c( # 1
"ggplot2", # 2
"latex2exp" # 3
)) # 4
# 5
color_x <- "#008000" # 6
color_y <- "#ff6600" # 7
# 8
number_of_rows_in_data_frame_of_precession <- nrow(data_frame_of_precession) # 9
data_frame_of_precession_without_outliers <- data_frame_of_precession[-c((number_of_rows_in_data_frame_of_precession - 1):number_of_rows_in_data_frame_of_precession), ] # 10
# 11
ggplot( # 12
data <- data_frame_of_precession_without_outliers, # 13
mapping <- aes(x = `$\\frac{\\omega}{\\unit{\\per\\s}}$`, y = `$\\frac{\\omega_\\text{P}}{\\unit{\\per\\s}}$`) # 14
) + # 15
geom_point(shape = 23, size = 1) + # 16
labs(x = TeX("$\\frac{\\omega}{\\frac{1}{s}}$"), y = TeX("$\\frac{\\omega_P}{\\frac{1}{s}}$")) + # 17
theme( # 18
axis.title.x = element_text(colour = color_x), # 19
axis.text.x = element_text(colour = color_x), # 20
axis.ticks.x = element_line(colour = color_x), # 21
axis.title.y = element_text(colour = color_y), # 22
axis.text.y = element_text(colour = color_y), # 23
axis.ticks.y = element_line(colour = color_y) # 24
) # 25
Figure 5.11: Pretsessiooninurkkiiruse sõltuvus rootori osakeste nurkkiirusest.
Teooria seosega (5.1) leheküljel ennustab pöördvõrdelist sõltuvust.
\[\begin{align} \omega_\text{P} = \frac{((\overrightarrow{r_\text{balancer; final}} - \overrightarrow{r_\text{balancer; initial}})) \times \overrightarrow{F_\text{balancer}}}{I \cdot \vec{\omega}}. \tag{5.1} \end{align}\]
Joonisel 5.11 leheküljel olev graafik lineariseeritud kujul[83] on esitatud joonisel 5.12 leheküljel .
data_frame_of_precession_without_outliers_linearized <- mutate(.data = data_frame_of_precession_without_outliers, linearized_omega = 1 / `$\\frac{\\omega}{\\unit{\\per\\s}}$`, .keep = "unused", .after = `$\\frac{\\omega}{\\unit{\\per\\s}}$`) # 1
colnames(data_frame_of_precession_without_outliers_linearized) <- c( # 2
"$\\frac{\\frac{1}{\\omega}}{\\unit{\\s}}$", # 3
"$\\frac{\\omega_\\text{P}}{\\unit{\\per\\s}}$" # 4
) # 5
# 6
ggplot( # 7
data <- data_frame_of_precession_without_outliers_linearized, # 8
mapping <- aes(x = `$\\frac{\\frac{1}{\\omega}}{\\unit{\\s}}$`, y = `$\\frac{\\omega_\\text{P}}{\\unit{\\per\\s}}$`) # 9
) + # 10
geom_point(shape = 23, size = 1) + # 11
labs(x = TeX("$\\frac{\\frac{1}{\\omega}}{s}$"), y = TeX("$\\frac{\\omega_P}{\\frac{1}{s}}$")) + # 12
theme( # 13
axis.title.x = element_text(colour = color_x), # 14
axis.text.x = element_text(colour = color_x), # 15
axis.ticks.x = element_line(colour = color_x), # 16
axis.title.y = element_text(colour = color_y), # 17
axis.text.y = element_text(colour = color_y), # 18
axis.ticks.y = element_line(colour = color_y) # 19
) # 20
Figure 5.12: Lineariseeritud nurkkiiruste graafik. Propelleri nurkkiiruse asemel on esitatud selle pöördväärtus, mis näitab, mitu sekundit ühe radiaani läbimine kestis.
5.18 Embedding
Including works for web output[84]:
5.19 Figures
5.19.1 Displaying a figure
For figures, i created a wrapper function include_external_graphics() for knitr’s include_graphics() because natively, include_graphics() doesn’t support all the necessary file types:
```{r label = "<label of figure>", echo=FALSE, fig.cap = "<caption of figure>"} # 1
include_external_graphics("<path-to-image-file>") # 2
# 3
``` # 4
# 5For this code chunk, i use the label and the following options:
- label
- the label that can be referenced must not include underscores but can include dashes
- fig.cap
- the caption of the figure. This must be present in order to reference it.
Other options are possible:
- fig.align
-
i set it to have the value
centerif i want the image to reside in center - fig.fullheight
-
i set it to
TRUEif the image would be printed out smaller otherwise - out.width
-
the width. Sometimes, the image is too wide for the page. Here, i set the width to be .92 of the height of the text part on the page as that image is rotated at a right angle:
.92\\textheight - out.height
-
sometimes, especially images with full size tend to be still a bit too large, so i set this parameter to have a value like
96% - out.extra:
-
for instance to turn the image with
anglethat has a value in degrees
5.19.2 Referencing a figure
Referencing a figure takes place using \@ref(fig:<label of figure>).
As an example, i refer to the figure 5.13.
Figure 5.13: The workflow from the written markdown to the ready output using bookdown.
5.19.3 Involving an external reference in the figure caption
If i want to include an external reference inside the figure caption, i had to use the function render_with_emojis inside the caption:
# 1
```{r label = "workflow", fig.cap = paste("The workflow from the written markdown to the ready output using *bookdown*", render_with_emojis("((ref:riederer-21))"), ".", sep = "")} # 2
include_external_graphics("rmd/workflow.png") # 3
# 4
``` # 5
# 6This is also obsolete because of numeric referencing.
Figure 5.14: The workflow from the written markdown to the ready output using bookdown((ref:riederer-21)).
Here, the function paste comes handy. paste glues strings together that can be fed into that function. The last parameter is sep which can be used to set the separator between the strings. Currently, i told the function to use an empty string as a separator.
5.19.4 Subfigures
Sometimes, it would be wasting having each figure below each other if they are narrow. In such a case, I want the images to be side by side. For that, I need to involve subfig as a dependency for the print output[85]:
Pildil 5.15 leheküljel kujutatud konteinerid sisaldavad kas segu, ühte ühendit, ühte elementi või nende kombinatsiooni. Sinised sfäärid tähistavad ühe elemendi aatomeid, rohelised sfäärid teise elemendi aatomeid.
include_external_graphics("rmd/alused-kordamine-ühendid-segu-element.svg") # 1
include_external_graphics("rmd/alused-ühendid-element.svg") # 2
Figure 5.15: Konteinerid.
As you see, there is no spacing between subfigures. I have not found a solution for that.
5.9 Comments
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