FreesearchR/inst/translations/translation_da.csv

1	en	da
2	Hello	Hej
3	Get started	Kom i gang
4	File upload	Upload fil
5	REDCap server export	Eksport fra REDCap server
6	Local or sample data	Lokal eller testdata
7	Please be mindfull handling sensitive data	Pas godt på og overvej nøje hvordan du håndterer personfølsomme 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).	***FreesearchR*** opbevarer alene data i forbindelse med din analyse, men du bør kun behandle personfølsomme data når du kører ***FreesearchR*** direkte på din egen maskine. [Læs mere her](https://agdamsbo.github.io/FreesearchR/#run-locally-on-your-own-machine).
9	Quick overview	Hurtigt overblik
10	Select variables for final import	Vælg variabler til den endelige import
11	Exclude incomplete variables:	Ekskluder inkomplette variabler:
12	Manual selection:	Manuel udvælgelse:
13	Let's begin!	Kom i gang!
14	Analysis validation	Analysevalidering
15	Report	Rapport
16	Choose your favourite output file format for further work, and download, when the analyses are done.	Vælge dit foretrukne dataformat, og hent data, når du er ærdig med databehandlingen.
17	www/intro.html	www/intro_da.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><em><strong>FreesearchR</strong></em> opbevarer alene data i forbindelse med din analyse, men du bør kun behandle personfølsomme data når du kører <em><strong>FreesearchR</strong></em> direkte på dine egen maskine. <a href='https://agdamsbo.github.io/FreesearchR/#run-locally-on-your-own-machine'>Læs mere her</a></p>
19	Overview and filter	Overblik og filtre
20	Overview and filtering	Overblik og filtrering
21	Visual overview	Visuelt overblik
22	Filter data types	Filtrer datatyper
23	Filter observations	Filtrer observationer
24	Apply filter on observation	Anvend filtre af observationer
25	Edit and create data	Ændr og opret variabler
26	Subset, rename and convert variables	Udvælg, omdøb og konverter variabler
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.).	Nedenfor er der mulighed for at lave simple ændringer i dit datasæt, såsom at redigere variabelnavne eller beskrivelser (bedre tabeller), eller omklassificering af variabler (numerisk, factoriel/kategorisk).
28	Please note that data modifications are applied before any filtering.	Bemærk at alle ændringer i data anvendes inden filtreringen.
29	Advanced data manipulation	Avanceret datamanipulation
30	Below options allow more advanced varaible manipulations.	Nedenfor er mulighed for avancerede ændringer i data.
31	Reorder factor levels	Arranger niveuer i faktor
32	Reorder the levels of factor/categorical variables.	Arranger rækkefølgen af niveauer i faktorielle/kategoriske variabler.
33	New factor	Ny faktor
34	Create factor/categorical variable from a continous variable (number/date/time).	Opret kategorisk variabel på baggrund af kontinuert variabel (numerisk/dato/tid).
35	New variable	Ny variabel
36	Create a new variable based on an R-expression.	Opret ny variabel baseret på R-kode.
37	Compare modified data to original	Sammenlign ændret data med det originale datasæt
38	Raw print of the original vs the modified data.	Simpel sammenligning af det originale og det ændrede datasæt.
39	Original data:	Original data:
40	Modified data:	Ændret data:
41	New column name:	Navn til ny variabel:
42	Group calculation by:	Grupper udregningen efter:
43	Enter an expression to define new column:	Indtast udregningen til at definere en ny variabel:
44	Click on a column name to add it to the expression:	Tryk på et variabel navn for at tilføje det til udregningen:
45	Create column	Opret variabel
46	New column name cannot be empty	Det nye variabelnavn kan ikke være tomt
47	Create a new column	Opret ny variabel
48	Some operations are not allowed	Nogle beregninger er ikke tilladte
49	Column added!	Variabel oprettet!
50	Unique values:	Unikke værdier:
51	Variable to cut:	Variabel, der skal deles:
52	Close intervals on the right	Luk intervaller til højre
53	Include lowest value	Inkluderer den laveste værdi
54	Create factor variable	Opret kategorisk variabel
55	Fixed breaks:	Faste niveauer:
56	Method:	Metode:
57	Convert Numeric to Factor	Konverter numerisk til kategorisk
58	Unique:	Unique:
59	Missing:	Manglende:
60	Most Common:	Mest hyppige:
61	Min:	Min:
62	Mean:	Mean:
63	Max:	Max:
64	Decimal separator:	Decimal adskiller:
65	First five rows are shown below:	De første fem rækker vises nedenfor:
66	Imported data	Importeret data
67	www/intro.md	www/intro.md
68	Choose your data	Vælg dine data
69	Upload a file, get data directly from REDCap or use local or sample data.	Upload en fil, hent data direkte fra en REDCap-server eller brug test-data eller lokal data.
70	Factor variable to reorder:	Kategoriske variabel der skal ændres:
71	Sort by levels	Sorter efter niveauer
72	Sort by count	Sorter efter antal
73	Create a new variable (otherwise replaces the one selected)	Opret en ny variabel (ellers erstattes den oprindelige)
74	Update factor variable	Updater faktor-variabel
75	Sort count	Sorter antal
76	Levels	Niveauer
77	Count	Antal
78	Update levels of a factor	Updater niveauerne for en faktor
79	Update & select variables	Opdater og vælg variabler
80	Date format:	Datoformat:
81	Date to use as origin to convert date/datetime:	Dato, der skal anvendes som udgangspunkt for konvertering af dato/datotid:
82	Apply changes	Anvend ændringer
83	No data to display.	Ingen data at vise.
84	Data successfully updated!	Data er opdateret!
85	You removed {p_out} % of observations.	Du har fjernet {p_out} % af observationerne.
86	You removed {p_out} % of variables.	Du har fjernet {p_out} % af variablerne.
87	You can import {file_extensions_text} files	Du kan vælge mellem disse filtyper: {file_extensions_text}
88	You can choose between these file types:	Du kan vælge mellem følgene filtyper:
89	Rows to skip before reading data:	Rækker der skal springes over:
90	Missing values character(s):	Tegn for manglende værdier:
91	if several use a comma (',') to separate them	hvis flere, så anvend et komma (',') som seperator
92	Encoding:	Kodning:
93	Upload a file:	Upload en fil:
94	Browse...	Vælg...
95	Select sheet to import:	Vælg ark:
96	No file selected.	Ingen fil valgt.
97	Evaluate	Evaluer
98	Visuals	Grafik
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} har {n} observationer og {n_var} variabler, med {n_complete} ({p_complete} %) komplette cases.
102	Prepare	Forbered
103	At 0, only complete variables are included; at 100, all variables are included.	Ved 0 inkluderes alene komplette variabler; ved 100 inkluderes alle variabler.
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.	De følgende variabel-par er stærkt korrelerede: {sentence_paste(.x,and_str)}.\nOvervej at fjerne en {more}fra datasættet for at sikre at prædiktorer er internt uafhængige.
105	No variables have a correlation measure above the threshold.	Ingen variabler er korrelerede over den angivne tærskelværdi.
106	and	og
107	from each pair	fra hvert par
108	Only non-text variables are available for plotting. Go the "Data" to reclass data to plot.	Kun variabler, der ikke er klassificerede som tekst er tilgængelige. Gå til fanen "Forbered" for at ændre klassifikationer.
109	Plot	Tegn
110	Adjust settings, then press "Plot".	Juster indstillingerne og tryk så "Tegn".
111	Plot height (mm)	Højde af grafik (mm)
112	Plot width (mm)	Bredde af grafik (mm)
113	File format	File format
114	Download plot	Download grafik
115	Select variable	Vælg variabel
116	Response variable	Svarvariable
117	Plot type	Type af grafik
118	Please select	Vælg
119	Additional variables	Yderligere variabler
120	Secondary variable	Sekundær variabel
121	No variable	Ingen variabel
122	Grouping variable	Variabel til gruppering
123	No stratification	Ingen stratificering
124	Drawing the plot. Hold tight for a moment..	Tegner grafikken. Spænd selen..
125	#Plotting\n	#Tegner\n
126	Stacked horizontal bars	Stablede horisontale søjler
127	A classical way of visualising the distribution of an ordinal scale like the modified Ranking Scale and known as Grotta bars	En klassisk visualisering af fordelingen af observationer på en ordinal kategorisk skala. Typisk brugt til modified Rankin Scale og kendes også som 'Grotta bars'
128	Violin plot	Violin-diagram
129	A modern alternative to the classic boxplot to visualise data distribution	Moderne alternativ til den klassiske box-plot og velegnet til at visualisere fordelingen af observationer
130	Sankey plot	Sankey-diagram
131	A way of visualising change between groups	Visualiserer ændring mellem grupper for samme type observationer
132	Scatter plot	Punkt-diagram
133	A classic way of showing the association between to variables	Visualiserer forholdet mellem to variabler
134	Box plot	Kasse-diagram
135	A classic way to plot data distribution by groups	Klassik måde at visualisere fordeling
136	Euler diagram	Eulerdiagram
137	Generate area-proportional Euler diagrams to display set relationships	Generer proportionelt Euler-diagram for at vise forhold mellem forskellige kategoriske observationer
138	Documentation	Dokumentation
139	Data is only stored for analyses and deleted when the app is closed.	Data opbevares alene til brug i analyser og slettes så snart appen lukkes.
140	Feedback	Feedback
141	License: AGPLv3	Licens: AGPLv3
142	Source	Kilde
143	Data includes {n_pairs} pairs of highly correlated variables.	Der er {n_pairs} variabel-par, der er stærkt internt korrelerede.
144	Create plot	Dan grafik
145	Coefficients plot	Koefficientgraf
146	Checks	Test af model
147	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.	Nedenfor er en opsummerende tabel, der giver hurtigt overblik. Til højre kan du få et visuelt overblik, gennemgå observationer og oprette datafiltre.
148	Browse observations	Gennemse observationer
149	Settings	Indstillinger
150	The following error occured on determining correlations:	Følgende fejl opstod i forbindelse med korrelationsanalysen:
151	We encountered the following error creating your report:	Følgende fejl opstod, da rapporten blev dannet:
152	No missing observations	Ingen manglende observationer
153	There is a total of {p_miss} % missing observations.	Der er i alt {p_miss} % manglende observationer.
154	Median:	Median:
155	Restore original data	Gendan originale data
156	Reset to original imported dataset. Careful! There is no un-doing.	Gendan det oprindeligt importerede datasæt. Forsigtig! Alle dine ændringer vil forsvinde.
157	Characteristics	Karakteristika
158	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.	Alene kategoriske variabler kan danne grundlag for stratificering. Mangler du en variabel, så gå til "Forbered" og omklassificer til kategorisk.
159	Compare strata?	Sammenlign strata?
160	Correlations	Korrelationer
161	To avoid evaluating the correlation of the outcome variable, this can be excluded from the plot or select 'none'.	For at udelukke svarvariablen fra korrelationsanalysen, så kan du vælge din svarvariabel eller vælge 'non', hvis du ikke vil angive en.
162	Correlation cut-off	Korrelationsgrænse
163	Set the cut-off for considered 'highly correlated'.	Angiv grænsen for. hvad, der tolkes som 'betydelig korrelation'.
164	Missings	Manglende observationer
165	Class	Klasse
166	Observations	Observationer
167	Data classes and missing observations	Dataklasser og manglende observationer
168	Sure you want to reset data? This cannot be undone.	Er du sikker på at du vil gendanne data? Det kan ikke fortrydes.
169	Cancel	Afbryd
170	Confirm	Bekræft
171	The filtered data	Filtreret data
172	Create new factor	Ny kategorisk variabel
173	This window is aimed at advanced users and require some *R*-experience!	Dette vindue er primært for avancerede brugere med nogen *R*-erfaring!
174	Create new variables	Opret nye variabler
175	Select data types to include	Vælg datatyper, der skal inkluderes
176	Uploaded data overview	Overblik over uploaded data
177	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.	Her har du en oversigt over hvordan data er blevet formateret, og hvor der er manglende observationer. Brug informationen til at overveje om manglende data mangler tilfældigt eller og der er et mønster, som kan være et udtryk for systematisk manglende data (observationsbias).
178	Specify covariables	Angiv kovariabler
179	If none are selected, all are included.	Hvis ingen er valgt inkluderes alle.
180	Analyse	Analysér
181	Working...	Arbejder...
182	Press 'Analyse' to create the regression model and after changing parameters.	Tryk 'Analysér' for at danne regressionsmodel og for at opdatere hvis parametre ændres.
183	Show p-value	Vis p-værdi
184	Model checks	Model-test
185	Please confirm data reset!	Bekræft gendannelse af data!
186	Import data from REDCap	Importér data fra REDCap
187	REDCap server	REDCap-server
188	Web address	Serveradresse
189	Format should be either 'https://redcap.your.institution/' or 'https://your.institution/redcap/'	Adressen skal være som 'https://redcap.your.institution/' eller 'https://your.institution/redcap/'
190	API token	API-nøgle
191	The token is a string of 32 numbers and letters.	En API-nøgle består af ialt 32 tal og bogstaver.
192	Connect	Forbind
193	Data import parameters	Data import parameters
194	Select fields/variables to import and click the funnel to apply optional filters	Vælg variabler, der skal importeres og tryk på tragten for at anvende valgfrie filtre
195	Import	Import
196	Click to see data dictionary	Tryk for at se metadata (Data Dictionary)
197	Connected to server!	Forbindelse til serveren oprettet!
198	The {data_rv$info$project_title} project is loaded.	{data_rv$info$project_title}-projektet er forbundet.
199	Data dictionary	Data dictionary
200	Preview:	Forsmag:
201	Imported data set	Importeret datasæt
202	Select fields/variables to import:	Vælg variabler, der skal importeres:
203	Specify the data format	Specificér dataformatet
204	Fill missing values?	Skal manglende observationer udfyldes?
205	Requested data was retrieved!	Det udvalgte data blev hentet!
206	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 er hentet, men det ser ud til kun at indeholde ID-variablen. Du skal kontakte din REDCap-administrator og sikre dig at du har adgang til faktisk at hente de udvalgte data.
207	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 er hentet, men det ser ud til kun at indeholde nogle af de udvalgte variabler. Du skal kontakte din REDCap-administrator og sikre dig at du har adgang til faktisk at hente de udvalgte data.
208	Click to see the imported data	Tryk for at se de importerede data
209	Regression table	Regressionstabel
210	Import a dataset from an environment	Importer et datasæt fra et kodemiljø
211	Select a dataset:	Vælg datasæt:
212	List of datasets...	Liste af datasæt...
213	No data selected!	Ingen data valgt!
214	No dataset here...	Ingen datasæt her...
215	Use a dataset from your environment or from the environment of a package.	Brug et datasæt fra dit lokale kodemiljø eller fra en tilgængelig pakke.
216	Not a data.frame	Ikke en data.frame
217	Select source	Vælg datakilde
218	Select a data source:	Vælg datakilde:
219	Yes	Ja
220	No	Nej
221	Coefficient plot	Koefficientplot
222	Select outcome variable	Vælg svarvariablen
223	Choose regression analysis	Vælg regressionsanalysen
224	Covariables to format as categorical	Kovariabler, der skal omklassificeres som kategoriske
225	Select variable to stratify baseline	Vælg variabel til a stratificere tabellen
226	Select models to plot	Vælg de modeller, der skal visualiseres
227	Creating regression models failed with the following error:	Oprettelsen af en regressionsmodel fejlede med den følgende besked:
228	Creating a regression table failed with the following error:	Oprettelsen af en regressionstabel fejlede med den følgende besked:
229	Saving the plot. Hold on for a moment..	Gemmer grafikken. Vent et øjeblik..
230	Running model assumptions checks failed with the following error:	Tjek af antagelser for regressionsmodellen fejlede med den følgende besked:
231	Select checks to plot	Vælg modeltests, der skal visualiseres
232	Multivariable regression model checks	Tests af multivariabel regressionsmodel
233	Grouped by {get_label(data,ter)}	Grupperet efter {get_label(data,ter)}
234	Option to perform statistical comparisons between strata in baseline table.	Mulighed for at udføre statistiske tests mellem strata i oversigtstabellen.
235	Press 'Evaluate' to create the comparison table.	Tryk 'Evaluér' for at oprette en oversigtstabel.
236	The data includes {n_col} variables. Please limit to 100.	Data indeholder {n_col} variabler. Begræns venligst til 100.
237	Data import	Data import
238	Data import formatting	Formatering af data ved import
239	Data modifications	Ændringer af data
240	Variables filter	Variables filter
241	Data filter	Data filter
242	Data characteristics table	Oversigtstabel
243	The dataset without text variables	Datasættet uden variabler formateret som tekst
244	Creating the table. Hold on for a moment..	Opretter tabellen. Vent et øjeblik..
245	Generating the report. Hold on for a moment..	Opretter rapporten. Vent et øjeblik..
246	We encountered the following error showing missingness:	Under analysen af manglende observationer opstod følgende fejl:
247	We encountered the following error browsing your data:	I forsøget på at vise en dataoversigt opstod følgende fejl:
248	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.	Vælg et navn til den nye variabel, skriv din formel og tryk så på knappen for at gemme variablen, eller annuler for at lukke uden at gemme.
249	Please fill in web address and API token, then press 'Connect'.	Udfyld serveradresse og API-nøgle, og tryk så 'Fobind'.
250	Other	Other
251	Hour of the day	Time på dagen
252	Breaks	Grupper
253	By day of the week	Efter ugedag
254	By weekday	Efter ugedag
255	By week number and year	Efter ugenummer og årstal
256	By month and year	Efter måned og årstal
257	By month only	Efter måned alene
258	By quarter of the year	Efter kvartal
259	By year	Efter år
260	Keep only most common	Behold kun de hyppigste
261	Number	Antal
262	Combine below percentage	Kombiner under procentsats
263	Percentage	Procentsats
264	By specified numbers	Efter specifikke værdier
265	By quantiles (groups of equal size)	I grupper af samme størrelse
266	By week number	Efter ugenummer alene
267	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.	Der er mere avancerede muligheder for at ændre kategoriske variable, oprette nye kategoriske variabler fra eksisterende data eller nye variable baseret på R-kode. Nederst kan du gendanne originale data.
268	Split the variable	Split the variable
269	Original data	Original data
270	Preview of result	Preview of result
271	No character variables with accepted delimiters detected.	No character variables with accepted delimiters detected.
272	Variable to split:	Variable to split:
273	Text or character to split string by	Text or character to split string by
274	Select delimiter	Select delimiter
275	Direction:	Direction:
276	Split string to multiple columns. Keep number of rows.	Split string to multiple columns. Keep number of rows.
277	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
278	Browse data preview	Browse data preview
279	Split character string	Split character string
280	Split text	Split text
281	Split a text column by a recognised delimiter.	Split a text column by a recognised delimiter.
282	Split a character string by a common delimiter	Split a character string by a common delimiter
283	Apply split	Apply split
284	Stacked relative barplot	Stacked relative barplot
285	Create relative stacked barplots to show the distribution of categorical levels	Create relative stacked barplots to show the distribution of categorical levels
286	Side-by-side barplot	Side-by-side barplot
287	Create side-by-side barplot to show the distribution of categorical levels	Create side-by-side barplot to show the distribution of categorical levels
288	Select table theme	Select table theme
289	Level of detail	Level of detail
290	Minimal	Minimal
291	Extensive	Extensive
292	Letters	Letters
293	Words	Words
294	Shorten to first letters	Shorten to first letters
295	Shorten to first words	Shorten to first words
296	Missings across variables by the variable **'{input$missings_var}'**	Missings across variables by the variable **'{input$missings_var}'**
297	Missing vs non-missing observations in the variable **'{input$missings_var}'**	Missing vs non-missing observations in the variable **'{input$missings_var}'**
298	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.
299	Calculating. Hold tight for a moment..	Calculating. Hold tight for a moment..
300	Overview of missing observations	Overview of missing observations
301	Analysis method for missingness overview	Analysis method for missingness overview
302	Overview of missings across variables	Overview of missings across variables
303	Overview of difference in variables by missing status in outcome	Overview of difference in variables by missing status in outcome
304	Select a variable for grouped overview	Select a variable for grouped overview
305	Select outcome variable for overview	Select outcome variable for overview
306	No outcome measure chosen	No outcome measure chosen
307	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}.
308	Include group differences	Include group differences
309	Error:	Error:
310	Download table	Download table
311	Welcome to FreesearchR	Welcome to FreesearchR
312	A free data analysis tool for clinicians, students, and learners	A free data analysis tool for clinicians, students, and learners
313	Core Features	Core Features
314	Import Data	Import Data
315	Load data from spreadsheets, REDCap servers, or try sample data. Multiple sources supported for maximum flexibility.	Load data from spreadsheets, REDCap servers, or try sample data. Multiple sources supported for maximum flexibility.
316	Data Management	Data Management
317	Filter, modify, and create new variables. Prepare your data efficiently for analysis.	Filter, modify, and create new variables. Prepare your data efficiently for analysis.
318	Descriptive Statistics	Descriptive Statistics
319	Evaluate data with descriptive analyses, inspect correlations, and handle missing observations effectively.	Evaluate data with descriptive analyses, inspect correlations, and handle missing observations effectively.
320	Additional Capabilities	Additional Capabilities
321	Data Visualization	Data Visualization
322	Create simple, clean plots for quick insights and overview	Create simple, clean plots for quick insights and overview
323	Regression Models	Regression Models
324	Build simple regression models for advanced analysis	Build simple regression models for advanced analysis
325	Export & Learn	Export & Learn
326	Download Results	Download Results
327	Export as comprehensive reports	Export as comprehensive reports
328	Get Modified Data	Get Modified Data
329	Save your processed datasets	Save your processed datasets
330	Reproducible Code	Reproducible Code
331	Learn and reproduce results	Learn and reproduce results
332	Full Documentation	Full Documentation
333	Share Feedback	Share Feedback
334	Contact Us	Contact Us
335	FreesearchR is available in multiple languages. To help with translations, please contact us at info@freesearchr.org	FreesearchR is available in multiple languages. To help with translations, please contact us at info@freesearchr.org
336	Home	Home
337	Start with FreesearchR for basic data evaluation and analysis.	Start with FreesearchR for basic data evaluation and analysis.
338	When you need more advanced tools, you'll be better prepared to use R directly.	When you need more advanced tools, you'll be better prepared to use R directly.
339	Consider running the FreesearchR app locally if working with sensitive data.	Consider running the FreesearchR app locally if working with sensitive data.
340	(Read more)	(Read more)