FreesearchR/app_docker/translations/translation_sw.csv

1	en	sw
2	Hello	Habari
3	Get started	Tuanze
4	File upload	Upakiaji wa faili
5	REDCap server export	Uhamishaji wa seva ya REDCap
6	Local or sample data	Taarifa za ndani au za mfano
7	Please be mindfull handling sensitive data	Tafadhali kumbuka kushughulikia data nyeti
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).	Programu ya ***FreesearchR*** huhifadhi data kwa uchanganuzi pekee, lakini tafadhali tumia tu na data nyeti unapoendesha ndani ya nchi. [Soma zaidi hapa](https://agdamsbo.github.io/FreesearchR/#run-locally-on-your-own-machine).
9	Quick overview	Muhtasari wa haraka
10	Select variables for final import	Chagua vigeu kwa ajili ya uingizaji wa mwisho
11	Exclude incomplete variables:	Usijumuishe vigeu visivyokamilika:
12	Manual selection:	Uchaguzi wa mikono:
13	Let's begin!	Tuanze!
14	Analysis validation	Uthibitishaji wa uchambuzi
15	Report	Ripoti
16	Choose your favourite output file format for further work, and download, when the analyses are done.	Chagua umbizo la faili la towe lako kwa kazi zaidi, na upakue, uchanganuzi utakapofanywa.
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>Programu ya <em><strong>FreesearchR</strong></em> huhifadhi data kwa ajili ya uchanganuzi pekee, lakini tafadhali tumia tu na data nyeti unapoendesha ndani ya nchi. <a href='https://agdamsbo.github.io/FreesearchR/#run-locally-on-your-own-machine'>Soma zaidi hapa</a></p>
19	Overview and filter	Muhtasari na chujio
20	Overview and filtering	Muhtasari na uchujaji
21	Visual overview	Muhtasari wa kuona
22	Filter data types	Chuja aina za data
23	Filter observations	Chuja uchunguzi
24	Apply filter on observation	Tumia kichujio unapotazama
25	Edit and create data	Edit and create data
26	Subset, rename and convert variables	Subset, rename and convert variables
27	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.).
28	Please note that data modifications are applied before any filtering.	Please note that data modifications are applied before any filtering.
29	Advanced data manipulation	Advanced data manipulation
30	Below options allow more advanced varaible manipulations.	Below options allow more advanced varaible manipulations.
31	Reorder factor levels	Reorder factor levels
32	Reorder the levels of factor/categorical variables.	Reorder the levels of factor/categorical variables.
33	New factor	New factor
34	Create factor/categorical variable from a continous variable (number/date/time).	Create factor/categorical variable from a continous variable (number/date/time).
35	New variable	New variable
36	Create a new variable based on an R-expression.	Create a new variable based on an R-expression.
37	Compare modified data to original	Compare modified data to original
38	Raw print of the original vs the modified data.	Raw print of the original vs the modified data.
39	Original data:	Data asili:
40	Modified data:	Data iliyobadilishwa:
41	New column name:	New column name:
42	Group calculation by:	Group calculation by:
43	Enter an expression to define new column:	Enter an expression to define new column:
44	Click on a column name to add it to the expression:	Click on a column name to add it to the expression:
45	Create column	Create column
46	New column name cannot be empty	New column name cannot be empty
47	Create a new column	Create a new column
48	Some operations are not allowed	Some operations are not allowed
49	Column added!	Column added!
50	Unique values:	Unique values:
51	Variable to cut:	Variable to cut:
52	Close intervals on the right	Close intervals on the right
53	Include lowest value	Include lowest value
54	Create factor variable	Create factor variable
55	Fixed breaks:	Fixed breaks:
56	Method:	Method:
57	Convert Numeric to Factor	Convert Numeric to Factor
58	Unique:	Unique:
59	Missing:	Missing:
60	Most Common:	Most Common:
61	Min:	Min:
62	Mean:	Mean:
63	Max:	Max:
64	Decimal separator:	Decimal separator:
65	First five rows are shown below:	First five rows are shown below:
66	Imported data	Imported data
67	www/intro.md	www/intro.md
68	Choose your data	Choose your data
69	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.
70	Factor variable to reorder:	Factor variable to reorder:
71	Sort by levels	Sort by levels
72	Sort by count	Sort by count
73	Create a new variable (otherwise replaces the one selected)	Create a new variable (otherwise replaces the one selected)
74	Update factor variable	Update factor variable
75	Sort count	Sort count
76	Levels	Levels
77	Count	Count
78	Update levels of a factor	Update levels of a factor
79	Update & select variables	Update & select variables
80	Date format:	Date format:
81	Date to use as origin to convert date/datetime:	Date to use as origin to convert date/datetime:
82	Apply changes	Apply changes
83	No data to display.	No data to display.
84	Data successfully updated!	Data successfully updated!
85	You removed {p_out} % of observations.	You removed {p_out} % of observations.
86	You removed {p_out} % of variables.	You removed {p_out} % of variables.
87	You can import {file_extensions_text} files	You can import {file_extensions_text} files
88	You can choose between these file types:	You can choose between these file types:
89	Rows to skip before reading data:	Rows to skip before reading data:
90	Missing values character(s):	Missing values character(s):
91	if several use a comma (',') to separate them	if several use a comma (',') to separate them
92	Encoding:	Encoding:
93	Upload a file:	Upload a file:
94	Browse...	Browse...
95	Select sheet to import:	Select sheet to import:
96	No file selected.	No file selected.
97	Evaluate	Evaluate
98	Visuals	Visuals
99	Regression	Regression
100	Download	Download
101	{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.
102	Prepare	Prepare
103	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.
104	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.
105	No variables have a correlation measure above the threshold.	No variables have a correlation measure above the threshold.
106	and	and
107	from each pair	from each pair
108	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.
109	Plot	Plot
110	Adjust settings, then press "Plot".	Adjust settings, then press "Plot".
111	Plot height (mm)	Plot height (mm)
112	Plot width (mm)	Plot width (mm)
113	File format	File format
114	Download plot	Download plot
115	Select variable	Select variable
116	Response variable	Response variable
117	Plot type	Plot type
118	Please select	Please select
119	Additional variables	Additional variables
120	Secondary variable	Secondary variable
121	No variable	No variable
122	Grouping variable	Grouping variable
123	No stratification	No stratification
124	Drawing the plot. Hold tight for a moment..	Drawing the plot. Hold tight for a moment..
125	#Plotting\n	#Plotting\n
126	Stacked horizontal bars	Stacked horizontal bars
127	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
128	Violin plot	Violin plot
129	A modern alternative to the classic boxplot to visualise data distribution	A modern alternative to the classic boxplot to visualise data distribution
130	Sankey plot	Sankey plot
131	A way of visualising change between groups	A way of visualising change between groups
132	Scatter plot	Scatter plot
133	A classic way of showing the association between to variables	A classic way of showing the association between to variables
134	Box plot	Box plot
135	A classic way to plot data distribution by groups	A classic way to plot data distribution by groups
136	Euler diagram	Euler diagram
137	Generate area-proportional Euler diagrams to display set relationships	Generate area-proportional Euler diagrams to display set relationships
138	Documentation	Documentation
139	Data is only stored for analyses and deleted when the app is closed.	Data is only stored for analyses and deleted when the app is closed.
140	Consider [running ***FreesearchR*** locally](https://agdamsbo.github.io/FreesearchR/#run-locally-on-your-own-machine) if working with sensitive data.	Consider [running ***FreesearchR*** locally](https://agdamsbo.github.io/FreesearchR/#run-locally-on-your-own-machine) if working with sensitive data.
141	Feedback	Feedback
142	License: AGPLv3	License: AGPLv3
143	Source	Source
144	Data includes {n_pairs} pairs of highly correlated variables.	Data includes {n_pairs} pairs of highly correlated variables.
145	Create plot	Create plot
146	Coefficients plot	Coefficients plot
147	Checks	Checks
148	Below you find a summary table for quick insigths, and on the right you can visualise data classes, browse observations and apply different data filters.	Below you find a summary table for quick insigths, and on the right you can visualise data classes, browse observations and apply different data filters.
149	Browse observations	Browse observations
150	Settings	Settings
151	The following error occured on determining correlations:	The following error occured on determining correlations:
152	We encountered the following error creating your report:	We encountered the following error creating your report:
153	No missing observations	No missing observations
154	There is a total of {p_miss} % missing observations.	There is a total of {p_miss} % missing observations.
155	Median:	Median:
156	Restore original data	Restore original data
157	Reset to original imported dataset. Careful! There is no un-doing.	Reset to original imported dataset. Careful! There is no un-doing.
158	Characteristics	Characteristics
159	Only factor/categorical variables are available for stratification. Go back to the 'Prepare' tab to reclass a variable if it's not on the list.	Only factor/categorical variables are available for stratification. Go back to the 'Prepare' tab to reclass a variable if it's not on the list.
160	Compare strata?	Compare strata?
161	Correlations	Correlations
162	To avoid evaluating the correlation of the outcome variable, this can be excluded from the plot or select 'none'.	To avoid evaluating the correlation of the outcome variable, this can be excluded from the plot or select 'none'.
163	Correlation cut-off	Correlation cut-off
164	Set the cut-off for considered 'highly correlated'.	Set the cut-off for considered 'highly correlated'.
165	Missings	Missings
166	Class	Class
167	Observations	Observations
168	Data classes and missing observations	Data classes and missing observations
169	Sure you want to reset data? This cannot be undone.	Sure you want to reset data? This cannot be undone.
170	Cancel	Cancel
171	Confirm	Confirm
172	The filtered data	The filtered data
173	Create new factor	Create new factor
174	This window is aimed at advanced users and require some *R*-experience!	This window is aimed at advanced users and require some *R*-experience!
175	Create new variables	Create new variables
176	Select data types to include	Select data types to include
177	Uploaded data overview	Uploaded data overview
178	Here is an overview of how your data is interpreted, and where data is missing. Use this information to consider if data is missing at random or if some observations are missing systematically wich may be caused by an observation bias.	Here is an overview of how your data is interpreted, and where data is missing. Use this information to consider if data is missing at random or if some observations are missing systematically wich may be caused by an observation bias.
179	Specify covariables	Specify covariables
180	If none are selected, all are included.	If none are selected, all are included.
181	Analyse	Analyse
182	Working...	Working...
183	Press 'Analyse' to create the regression model and after changing parameters.	Press 'Analyse' to create the regression model and after changing parameters.
184	Show p-value	Show p-value
185	Model checks	Model checks
186	Please confirm data reset!	Please confirm data reset!
187	Import data from REDCap	Import data from REDCap
188	REDCap server	REDCap server
189	Web address	Web address
190	Format should be either 'https://redcap.your.institution/' or 'https://your.institution/redcap/'	Format should be either 'https://redcap.your.institution/' or 'https://your.institution/redcap/'
191	API token	API token
192	The token is a string of 32 numbers and letters.	The token is a string of 32 numbers and letters.
193	Connect	Connect
194	Data import parameters	Data import parameters
195	Select fields/variables to import and click the funnel to apply optional filters	Select fields/variables to import and click the funnel to apply optional filters
196	Import	Import
197	Click to see data dictionary	Click to see data dictionary
198	Connected to server!	Connected to server!
199	The {data_rv$info$project_title} project is loaded.	The {data_rv$info$project_title} project is loaded.
200	Data dictionary	Data dictionary
201	Preview:	Preview:
202	Imported data set	Imported data set
203	Select fields/variables to import:	Select fields/variables to import:
204	Specify the data format	Specify the data format
205	Fill missing values?	Fill missing values?
206	Requested data was retrieved!	Requested data was retrieved!
207	Data retrieved, but it looks like only the ID was retrieved from the server. Please check with your REDCap administrator that you have required permissions for data access.	Data retrieved, but it looks like only the ID was retrieved from the server. Please check with your REDCap administrator that you have required permissions for data access.
208	Data retrieved, but it looks like not all requested fields were retrieved from the server. Please check with your REDCap administrator that you have required permissions for data access.	Data retrieved, but it looks like not all requested fields were retrieved from the server. Please check with your REDCap administrator that you have required permissions for data access.
209	Click to see the imported data	Click to see the imported data
210	Regression table	Regression table
211	Import a dataset from an environment	Import a dataset from an environment
212	Select a dataset:	Select a dataset:
213	List of datasets...	List of datasets...
214	No data selected!	No data selected!
215	No dataset here...	No dataset here...
216	Use a dataset from your environment or from the environment of a package.	Use a dataset from your environment or from the environment of a package.
217	Not a data.frame	Not a data.frame
218	Select source	Select source
219	Select a data source:	Select a data source:
220	Yes	Yes
221	No	No
222	Coefficient plot	Coefficient plot
223	Select outcome variable	Select outcome variable
224	Choose regression analysis	Choose regression analysis
225	Covariables to format as categorical	Covariables to format as categorical
226	Select variable to stratify baseline	Select variable to stratify baseline
227	Select models to plot	Select models to plot
228	Creating regression models failed with the following error:	Creating regression models failed with the following error:
229	Creating a regression table failed with the following error:	Creating a regression table failed with the following error:
230	Saving the plot. Hold on for a moment..	Saving the plot. Hold on for a moment..
231	Running model assumptions checks failed with the following error:	Running model assumptions checks failed with the following error:
232	Select checks to plot	Select checks to plot
233	Multivariable regression model checks	Multivariable regression model checks
234	Grouped by {get_label(data,ter)}	Grouped by {get_label(data,ter)}
235	Option to perform statistical comparisons between strata in baseline table.	Option to perform statistical comparisons between strata in baseline table.
236	Press 'Evaluate' to create the comparison table.	Press 'Evaluate' to create the comparison table.
237	The data includes {n_col} variables. Please limit to 100.	The data includes {n_col} variables. Please limit to 100.
238	Data import	Data import
239	Data import formatting	Data import formatting
240	Data modifications	Data modifications
241	Variables filter	Variables filter
242	Data filter	Data filter
243	Data characteristics table	Data characteristics table
244	The dataset without text variables	The dataset without text variables
245	Creating the table. Hold on for a moment..	Creating the table. Hold on for a moment..
246	Generating the report. Hold on for a moment..	Generating the report. Hold on for a moment..
247	We encountered the following error showing missingness:	We encountered the following error showing missingness:
248	We encountered the following error browsing your data:	We encountered the following error browsing your data:
249	Choose a name for the column to be created or modified, then enter an expression before clicking on the button below to create the variable, or cancel to exit without saving anything.	Choose a name for the column to be created or modified, then enter an expression before clicking on the button below to create the variable, or cancel to exit without saving anything.
250	Other	Other
251	Hour of the day	Hour of the day
252	Breaks	Breaks
253	By day of the week	By day of the week
254	By weekday	By weekday
255	By week number and year	By week number and year
256	By week number	By week number
257	By month and year	By month and year
258	By month only	By month only
259	By quarter of the year	By quarter of the year
260	By year	By year
261	Keep only most common	Keep only most common
262	Number	Number
263	Combine below percentage	Combine below percentage
264	Percentage	Percentage
265	By specified numbers	By specified numbers
266	By quantiles (groups of equal size)	By quantiles (groups of equal size)
267	Please fill in web address and API token, then press 'Connect'.	Please fill in web address and API token, then press 'Connect'.
268	There are more advanced options to modify factor/categorical variables as well as create new factor from an existing 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 an existing variable or new variables with R code. At the bottom you can restore the original data.
269	Text or character to split string by	Text or character to split string by
270	Split the variable	Split the variable
271	Variable to split:	Variable to split:
272	Direction:	Direction:
273	Split string to multiple columns. Keep number of rows.	Split string to multiple columns. Keep number of rows.
274	Split string to multiple observations (rows) in the same column. Also ads id and instance columns	Split string to multiple observations (rows) in the same column. Also ads id and instance columns
275	Split character string	Split character string
276	Split text	Split text
277	Split a character string by a common delimiter	Split a character string by a common delimiter
278	Select delimiter	Select delimiter
279	Browse data preview	Browse data preview
280	Original data	Original data
281	Preview of result	Preview of result
282	No character variables with accepted delimiters detected.	No character variables with accepted delimiters detected.
283	Split a text column by a recognised delimiter.	Split a text column by a recognised delimiter.
284	Apply split	Apply split
285	Stacked relative barplot	Stacked relative barplot
286	Create relative stacked barplots to show the distribution of categorical levels	Create relative stacked barplots to show the distribution of categorical levels
287	Side-by-side barplot	Side-by-side barplot
288	Create side-by-side barplot to show the distribution of categorical levels	Create side-by-side barplot to show the distribution of categorical levels
289	Select table theme	Select table theme
290	Level of detail	Level of detail
291	Minimal	Minimal
292	Extensive	Extensive
293	Letters	Letters
294	Words	Words
295	Shorten to first letters	Shorten to first letters
296	Shorten to first words	Shorten to first words
297	Missings across variables by the variable **'{input$missings_var}'**	Missings across variables by the variable **'{input$missings_var}'**
298	Missing vs non-missing observations in the variable **'{input$missings_var}'**	Missing vs non-missing observations in the variable **'{input$missings_var}'**
299	Evaluate missingness by either comparing missing values across variables (optionally grouped by af categorical or dichotomous variable) or compare variables grouped by the missing status (missing or not) of an outcome variable. If there is a significant difference i the missingness, this may cause a bias in you data and should be considered carefully interpreting the data and analyses as data may not be missing at random.	Evaluate missingness by either comparing missing values across variables (optionally grouped by af categorical or dichotomous variable) or compare variables grouped by the missing status (missing or not) of an outcome variable. If there is a significant difference i the missingness, this may cause a bias in you data and should be considered carefully interpreting the data and analyses as data may not be missing at random.
300	Calculating. Hold tight for a moment..	Calculating. Hold tight for a moment..
301	Overview of missing observations	Overview of missing observations
302	Analysis method for missingness overview	Analysis method for missingness overview
303	Overview of missings across variables	Overview of missings across variables
304	Overview of difference in variables by missing status in outcome	Overview of difference in variables by missing status in outcome
305	Select a variable for grouped overview	Select a variable for grouped overview
306	Select outcome variable for overview	Select outcome variable for overview
307	No outcome measure chosen	No outcome measure chosen
308	There is a significant difference in data missingness in {n_nonmcar} {ifelse(n_nonmcar==1,'variable','variables')} grouped by the selected outcome/grouping variable {outcome}.	There is a significant difference in data missingness in {n_nonmcar} {ifelse(n_nonmcar==1,'variable','variables')} grouped by the selected outcome/grouping variable {outcome}.