FreesearchR/inst/translations/translation_sw.csv

1	en	sw
2	Hello	Habari
3	Get started	Get started
4	File upload	File upload
5	REDCap server export	REDCap server export
6	Local or sample data	Local or sample data
7	Please be mindfull handling sensitive data	Please be mindfull handling sensitive data
8	The ***FreesearchR*** app only stores data for analyses, but please only use with sensitive data when running locally. [Read more here](https://agdamsbo.github.io/FreesearchR/#run-locally-on-your-own-machine).	The ***FreesearchR*** app only stores data for analyses, but please only use with sensitive data when running locally. [Read more here](https://agdamsbo.github.io/FreesearchR/#run-locally-on-your-own-machine).
9	Quick overview	Quick overview
10	Select variables for final import	Select variables for final import
11	Exclude incomplete variables:	Exclude incomplete variables:
12	Manual selection:	Manual selection:
13	Let's begin!	Let's begin!
14	Analysis validation	Analysis validation
15	Report	Report
16	Choose your favourite output file format for further work, and download, when the analyses are done.	Choose your favourite output file format for further work, and download, when the analyses are done.
17	www/intro.html	www/intro_sw.html
18	<p>The <em><strong>FreesearchR</strong></em> app only stores data for analyses, but please only use with sensitive data when running locally. <a href='https://agdamsbo.github.io/FreesearchR/#run-locally-on-your-own-machine'>Read more here</a></p>	<p>The <em><strong>FreesearchR</strong></em> app only stores data for analyses, but please only use with sensitive data when running locally. <a href='https://agdamsbo.github.io/FreesearchR/#run-locally-on-your-own-machine'>Read more here</a></p>
19	Overview and filter	Overview and filter
20	Overview and filtering	Overview and filtering
21	Below you find a summary table for quick insigths, and on the right you can visualise data classes, browse data and apply different data filters.	Below you find a summary table for quick insigths, and on the right you can visualise data classes, browse data and apply different data filters.
22	Visual overview	Visual overview
23	Browse data	Browse data
24	Filter data types	Filter data types
25	Filter observations	Filter observations
26	Apply filter on observation	Apply filter on observation
27	Edit and create data	Edit and create data
28	Subset, rename and convert variables	Subset, rename and convert variables
29	Below, are several options for simple data manipulation like update variables by renaming, creating new labels (for nicer tables in the report) and changing variable classes (numeric, factor/categorical etc.).	Below, are several options for simple data manipulation like update variables by renaming, creating new labels (for nicer tables in the report) and changing variable classes (numeric, factor/categorical etc.).
30	There are more advanced options to modify factor/categorical variables as well as create new factor from a continous variable or new variables with R code. At the bottom you can restore the original data.	There are more advanced options to modify factor/categorical variables as well as create new factor from a continous variable or new variables with R code. At the bottom you can restore the original data.
31	Please note that data modifications are applied before any filtering.	Please note that data modifications are applied before any filtering.
32	Advanced data manipulation	Advanced data manipulation
33	Below options allow more advanced varaible manipulations.	Below options allow more advanced varaible manipulations.
34	Reorder factor levels	Reorder factor levels
35	Reorder the levels of factor/categorical variables.	Reorder the levels of factor/categorical variables.
36	New factor	New factor
37	Create factor/categorical variable from a continous variable (number/date/time).	Create factor/categorical variable from a continous variable (number/date/time).
38	New variable	New variable
39	Create a new variable based on an R-expression.	Create a new variable based on an R-expression.
40	Compare modified data to original	Compare modified data to original
41	Raw print of the original vs the modified data.	Raw print of the original vs the modified data.
42	Original data:	Original data:
43	Modified data:	Modified data:
44	New column name:	New column name:
45	Group calculation by:	Group calculation by:
46	Enter an expression to define new column:	Enter an expression to define new column:
47	Click on a column name to add it to the expression:	Click on a column name to add it to the expression:
48	Create column	Create column
49	Choose a name for the column to be created or modified,	Choose a name for the column to be created or modified,
50	then enter an expression before clicking on the button above to validate or on	then enter an expression before clicking on the button above to validate or on
51	to delete it.	to delete it.
52	New column name cannot be empty	New column name cannot be empty
53	Create a new column	Create a new column
54	Some operations are not allowed	Some operations are not allowed
55	Column added!	Column added!
56	Unique values:	Unique values:
57	Variable to cut:	Variable to cut:
58	Number of breaks:	Number of breaks:
59	Close intervals on the right	Close intervals on the right
60	Include lowest value	Include lowest value
61	Create factor variable	Create factor variable
62	Fixed breaks:	Fixed breaks:
63	Method:	Method:
64	Convert Numeric to Factor	Convert Numeric to Factor
65	Unique:	Unique:
66	Missing:	Missing:
67	Most Common:	Most Common:
68	Min:	Min:
69	Mean:	Mean:
70	Max:	Max:
71	Decimal separator:	Decimal separator:
72	First five rows are shown below:	First five rows are shown below:
73	Imported data	Imported data
74	www/intro.md	www/intro.md
75	Choose your data	Choose your data
76	Upload a file, get data directly from REDCap or use local or sample data.	Upload a file, get data directly from REDCap or use local or sample data.
77	Factor variable to reorder:	Factor variable to reorder:
78	Sort by levels	Sort by levels
79	Sort by count	Sort by count
80	Create a new variable (otherwise replaces the one selected)	Create a new variable (otherwise replaces the one selected)
81	Update factor variable	Update factor variable
82	Sort count	Sort count
83	Levels	Levels
84	Count	Count
85	Update levels of a factor	Update levels of a factor
86	Update & select variables	Update & select variables
87	Date format:	Date format:
88	Date to use as origin to convert date/datetime:	Date to use as origin to convert date/datetime:
89	Apply changes	Apply changes
90	No data to display.	No data to display.
91	Data successfully updated!	Data successfully updated!
92	You removed {p_out} % of observations.	You removed {p_out} % of observations.
93	You removed {p_out} % of variables.	You removed {p_out} % of variables.
94	You can import {file_extensions_text} files	You can import {file_extensions_text} files
95	You can choose between these file types:	You can choose between these file types:
96	Rows to skip before reading data:	Rows to skip before reading data:
97	Missing values character(s):	Missing values character(s):
98	if several use a comma (',') to separate them	if several use a comma (',') to separate them
99	Encoding:	Encoding:
100	Upload a file:	Upload a file:
101	Browse...	Browse...
102	Select sheet to import:	Select sheet to import:
103	No file selected.	No file selected.
104	Evaluate	Evaluate
105	Visuals	Visuals
106	Regression	Regression
107	Download	Download
108	{data_text} has {n} observations and {n_var} variables, with {n_complete} ({p_complete} %) complete cases.	{data_text} has {n} observations and {n_var} variables, with {n_complete} ({p_complete} %) complete cases.
109	Prepare	Prepare
110	At 0, only complete variables are included; at 100, all variables are included.	At 0, only complete variables are included; at 100, all variables are included.
111	The following variable pairs are highly correlated: {sentence_paste(.x,and_str)}.\nConsider excluding one {more}from the dataset to ensure variables are independent.	The following variable pairs are highly correlated: {sentence_paste(.x,and_str)}.\nConsider excluding one {more}from the dataset to ensure variables are independent.
112	No variables have a correlation measure above the threshold.	No variables have a correlation measure above the threshold.
113	and	and
114	from each pair	from each pair
115	Only non-text variables are available for plotting. Go the "Data" to reclass data to plot.	Only non-text variables are available for plotting. Go the "Data" to reclass data to plot.
116	Plot	Plot
117	Adjust settings, then press "Plot".	Adjust settings, then press "Plot".
118	Plot height (mm)	Plot height (mm)
119	Plot width (mm)	Plot width (mm)
120	File format	File format
121	Download plot	Download plot
122	Select variable	Select variable
123	Response variable	Response variable
124	Plot type	Plot type
125	Please select	Please select
126	Additional variables	Additional variables
127	Secondary variable	Secondary variable
128	No variable	No variable
129	Grouping variable	Grouping variable
130	No stratification	No stratification
131	Drawing the plot. Hold tight for a moment..	Drawing the plot. Hold tight for a moment..
132	#Plotting\n	#Plotting\n
133	Drawing the plot. Hold on for a moment..	Drawing the plot. Hold on for a moment..
134	Stacked horizontal bars	Stacked horizontal bars
135	A classical way of visualising the distribution of an ordinal scale like the modified Ranking Scale and known as Grotta bars	A classical way of visualising the distribution of an ordinal scale like the modified Ranking Scale and known as Grotta bars
136	Violin plot	Violin plot
137	A modern alternative to the classic boxplot to visualise data distribution	A modern alternative to the classic boxplot to visualise data distribution
138	Sankey plot	Sankey plot
139	A way of visualising change between groups	A way of visualising change between groups
140	Scatter plot	Scatter plot
141	A classic way of showing the association between to variables	A classic way of showing the association between to variables
142	Box plot	Box plot
143	A classic way to plot data distribution by groups	A classic way to plot data distribution by groups
144	Euler diagram	Euler diagram
145	Generate area-proportional Euler diagrams to display set relationships	Generate area-proportional Euler diagrams to display set relationships