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Main Index: R

Main Index: R

R

R2D-1, Douglas
R4D, Douglas (DC-3/ C-47)
R4D-8, Douglas (Super DC-3)
R5D, Douglas (DC-4)
Raab, 14 June 1809
Rabaul, Reduction of, Operation Cartwheel, (30 June 1943- January 1944)
Race to the Sea of September-October 1914
Racehorse, HMS (1900)
Racoon, HMS (1910)
Radcot Bridge, battle of, 19 December 1387
Radetzky von Radetz, Johann Joseph Wenzel Graf (1766-1858)
Radford, USS (DD-120/ AG-22)
Rafa, battle of, 9 January 1917
Rajah, HMS
Rajowka, battle of, 10 September 1708
Raleigh, USS (CL-7)
Ram Mk I, Cruiser Tank (Canada)
Ram Mk II, Cruiser Tank (Canada)
Ramcke, Hermann Bernhard, 1889-1968
Ramillies, battle of, 23 May 1706
Ramleh, battle of, 25 November 1177
Ramsay, Sir Bertram Home (1883–1945)
Ramsay, USS (DD-124/ DM-16)
Ramsey, HMS/ USS Meade (DD-274)
Ranee, HMS
Ranger, HMS(1895)
Rangoon, Short
Ranken, Harry Sherwood, VC MB ChB MRCP 1883-1914
Raphia, battle of, 22 June 217 BC
Rapid Deployment Force, United States (Longer article)
Rapido River, battle of, 20-22 Jan 1944
Rapp, Jean, Comte de, 1772-1821
Rappahannock Redoubts, action at, 7 November 1863
Rastatt, battle of, 5 July 1796
Rathburne, USS (DD-113/ APD-25)
Rattlesnake, HMS (1910)
Raudian Plain or Vercellae, battle of the, 30 July 101 BC
Ravager, HMS
Rava Ruska, battle of, 3-11 September 1914 (Poland)
Ravenna, battle, 11 April 1512
Ravi, battle of, 1306
Raymond, battle of, 12 May 1863
Reading, HMS/ USS Bailey (DD-269 )
Real IRA
Reaper, HMS
Reckless, Operation - Hollandia and Aitape, 22-27 April 1944
Recruit, HMS (1896)
Red Brigades
Reding, Teodoro, d.1809
Redinha, combat of, 12 March 1811
Redpole, HMS (1910)
Red River Campaign
Regen or Reinhausen, engagement of, 17 April 1809
Regensburg or Ratisbon, battle of, 23 April 1809
Reggiane Re.2000 Falco (Falcon)
Reggiane Re.2001 Falco II
Reggiane Re.2002 Ariete (Ram)
Reggiane Re.2003
Reggiane Re.2005 Sagittario (Archer)
Reichenbach, combat of, 22 May
Reichenbach, Convention of, 27 June 1813
Reid, USS (DD-21)
Reid, USS (DD-292)
Reille, Honoré Charles M. J., 1775-1860
Religion, First War of, 1562-3
Religion, Second War of, 1567-8
Religion, Third War of, 1568-70
Religion, Fourth War of, 1572-73
Religion, Fifth War of, 1575-76
Religion, Sixth War of, December 1576-September 1577
Religion, Seventh War of, 1580 ('Lover's War)
Religion, Eighth War of, 1585-89 (War of the Three Henrys)
Religion, Ninth War of, 1589-98
Renard, HMS (1909)
Renault AMC 34
Renault AMC 35
Renault AMR 33
Renault AMR 35
Renault FT-17 Light Tank (France)
Renault NC
Renault R35 Light Infantry Tank/ Char Léger MLE.1935 R
Renault UE Infantry Vehicle
Renchen, combat of, 26 June 1796
Rendova Island, invasion of, 30 June 1943
Rennell Island, battle of, 29-30 January 1943
Reno, USS (CL-96)
Reno, USS (DD-303)
Renshaw, USS (DD-176)
Renty, battle of, 13 August 1554
Republic P-43 Lancer
Republic XP-44 Rocket
Republic P-47 Thunderbolt
Republic XP-72
Resaca, battle of, 13-15 May 1864
Retiro, siege of, 13-14 August 1812
Reuben James, USS (DD-245)
Reval, action off, 29 July 1714
Revere, Paul, 1735-1818
Rheims, battle of, 13 March 1814
Rhine and German Fronts, War of the First Coalition
Rhodes, siege of, 88 B.C.
Rhyndacis, battle of the, 73 B.C.
Ribble, HMS (1904)
Rich Mountain, battle of, 12 July 1861
Richmond, Kentucky, battle of, 30 August 1862
Richmond, HMS/ USS Fairfax (DD-93)
Richmond, USS (CL-9)
Riedau, combat of, 1 May 1809
Rietfontein or Modderspruit, battle of, 24 October 1899
Rifle, Napoleonic
Rifleman, HMS (1910)
Rimini, battle of, 13-21 Sept 44
Ringgold, USS (DD-89)/ HMS Newark
Rio Mayor, skirmish of, 19 January 1811
Ripley, HMS/ USS Shubrick (DD-268)
Ripon, Blackburn
Ripon, Treaty of (26 October 1640)
River Class Destroyers/ E Class Destroyers (1912)
River Plate, battle of the, 13 December 1939
Riviera, HMS
Rivoli, battle of, 14 January 1797
Rivoli, combat of, 15 September 1799
Rizal, USS (DD-174/ DM-14)
Roanoke Island, battle of, 7-8 February 1862
Roast, Operation, 1-3 April 1945
Robert Smith, USS (DD-324)
Robinson, USS (DD-88)/ HMS Newmarket
Roc, Blackburn
Rochester, USS (CA-124)
Rockingham , HMS/ USS Swasey (DD-273)
Rodgers, USS (DD-254 )/ HMS Sherwood
Roe, USS (DD-24)
Roebuck, HMS (1901)
Roer, battle of the, 2 October 1794
Roi, battle of, 1 February 1944
Rokuhara, battle of, 20 June 1333
Rolica, battle of, 17 August 1808
Romagna or the Rivers, battle of, 22 September-21 December 1944
Romani, battle of, 3-9 August 1916
Romano-Chiusella, battle of, 26 May 1800
Romanus IV Diogenes, Byzantine Emperor (1067-1071)
Rome and Antiochus III, war between, 192-188 B.C.
Rome, sack of, 390 B.C.
Rome, Sulla's attack on or battle of the Esquiline Forum, 88 BC
Rome, siege of, 87 BC
Rommel, Erwin (1891-1944)
Rommel's First Offensive, March 24-May 30 1941
Rommel's Second Offensive, 21 January- 4 February 1942
Roncal, combat of, 12-13 May 1813
Roncesvalles, battle of, 25 July 1813
Ronin
Ronquillo, combat of, 25-26 March 1810
Rooilaagte, battle of, 25 November 1899
Roon class heavy cruisers
Roper, USS (DD-147/ APD-20)
Rosas, siege, 6 November- 5 December 1808
Roses, Wars of the, 1455-1485
Rossbach, battle of, 5 November 1757 (Germany)
Rossbrun, battle of, 26 July 1866
Rota, Avro
Rotary Engine
Rother, HMS (1904)
Rouen, siege of, 29 September-26 October 1562
Rouen, siege of, 11 November 1591 to April 1592
Rovereto, battle of, 4 September 1796
Rowan, USS (DD-64)
Rowley Burn, battle of, 633
Rowton Heath, battle of, 24 September 1645
Roxborough, HMS/ USS Foote (DD-169)
Royal Aircraft Factory
Royal Aircraft Factory B.E.1
Royal Aircraft Factory B.E.2
Royal Aircraft Factory B.E.2a
Royal Aircraft Factory B.E.2b
Royal Aircraft Factory B.E.2c
Royal Aircraft Factory B.E.2d
Royal Aircraft Factory B.E.2e
Royal Aircraft Factory B.E.2f
Royal Aircraft Factory B.E.2g
Royal Aircraft Factory B.E.2/ B.E.12 Squadrons
Royal Aircraft Factory B.E.3
Royal Aircraft Factory B.E.4
Royal Aircraft Factory B.E.5
Royal Aircraft Factory B.E.6
Royal Aircraft Factory B.E.7
Royal Aircraft Factory B.E.8
Royal Aircraft Factory B.E.8a
Royal Aircraft Factory B.E.9
Royal Aircraft Factory B.E.10
Royal Aircraft Factory B.E.12
Royal Aircraft Factory B.E.12a
Royal Aircraft Factory B.E.12b
Royal Aircraft Factory H.R.E.2
Royal Aircraft Factory H.R.E.6
Royal Aircraft Factory R.E.1
Royal Aircraft Factory R.E.3
Royal Aircraft Factory R.E.4
Royal Aircraft Factory R.E.5
Royal Aircraft Factory R.E.7
Royal Aircraft Factory R.E.8
Royal Aircraft Factory R.E.9
Royal Aircraft Factory SE.5a
Royalist HMS
Royal Marine, Operation: Mining the Rhine, May 1940
Royal Oak, HMS
Ruby, HMS (1910)
Ruler class escort carriers (UK)
Ruler, HMS
Runka
Rupert, Prince, count palatine of the Rhine, duke of Bavaria, duke of Cumberland, earl of Holderness (1619-1682)
Rupprecht, Crown Prince of Bavaria, 1869-1955
Ruspina, battle of, 46 BC
Russell, HMS
Russian, Napoleon's Campaign of 1812
Ruthven, battle of, 20 June 1306
Rutter, Operation, The Planned Attack on Dieppe, 7 July 1942
Ryan O-51 Dragonfly
Ryuho
Ryujo


R Tutorial

R is a programming language and software environment for statistical analysis, graphics representation and reporting. R was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, and is currently developed by the R Development Core Team. R is freely available under the GNU General Public License, and pre-compiled binary versions are provided for various operating systems like Linux, Windows and Mac. This programming language was named R, based on the first letter of first name of the two R authors (Robert Gentleman and Ross Ihaka), and partly a play on the name of the Bell Labs Language S.


PRODUCE FOR VICTORY

This exhibition was organized by the National Museum of American History, Smithsonian Institution, and the Smithsonian Institution Traveling Exhibition Service (SITES). It was designed, edited, and produced by the Office of Exhibits Central, Smithsonian Institution. This exhibition is no longer on view at the National Museum of American History. Please check the tour schedule for locations and dates for the traveling exhibition.

World War II posters helped to mobilize a nation. Inexpensive, accessible, and ever-present, the poster was an ideal agent for making war aims the personal mission of every citizen. Government agencies, businesses, and private organizations issued an array of poster images linking the military front with the home front--calling upon every American to boost production at work and at home.

Deriving their appearance from the fine and commercial arts, posters conveyed more than simple slogans. Posters expressed the needs and goals of the people who created them.


Wax Camps


In the 1960s and 1970s, Texas Historical Commission archeologists surveying the rugged west Texas lands along the Rio Grande river encountered a fascinating small-scale industry centered around an inconspicuous wild plant known as candelilla. While growth of the stalky, leafless plant is ruled largely by the whims of nature, the harvesting and extraction of the product—a high-quality wax—is dependent solely upon the ingenuity and sweat of Mexican laborers following decades-old traditions.

Then as now, the industry seems almost shockingly primitive in our crisply modern and mechanized world, particularly given the value and world-wide usage of the product. Wax makers (candelilleros)—many of them working alone in remote areas—cut massive stacks of the weed by hand, then boil it in jerry-rigged metal vats to extract the wax. Once cured, the raw wax is stuffed into huge burlap bags and hauled down dusty trails on the backs of small burros. Although a few workers have acquired trucks to expedite the process, paying for gasoline is another matter, and there are no trucks to equal the nimble burros in areas where there are no roads.

From these crude origins, the wax is delivered to buyers, refined in factories, and ultimately distributed into an international market where it will become a key ingredient in floor wax, cosmetics, candles, chewing gum, and other items. Though unknown to most of us, candelilla wax has touched all our lives in a small way through products such as these.

The late Curtis Tunnell, former State Archeologist of Texas and Executive Director of the Texas Historical Commission, was a leader of those early survey expeditions along the Rio Grande. Over a decade's time, he developed a fascination with the industry and an abiding admiration for the self-reliant wax makers. His conversations, interviews, and research led to a remarkable study published by the commission in 1981.

In the following exhibits, we take a look at the wax makers and their way of life as Tunnell observed and wrote about them roughly two decades ago. The text of his report is followed closely throughout. In the Introduction, we learn how an archeological survey project was transformed into a much richer, transcultural experience. The History section tracks the wax-making industry back to the turn of the century when enterprising businessmen with an eye for new opportunities moved onto the desert frontier. In "From Desert Plants to Dollars," a fully illustrated Techniques section takes the reader step-by-step through the wax-making process, followed by a look at the resourceful Candelilleros and their camps—fascinating constructions of desert plant materials and scavenged metal containers.

Historian and ethnographic researcher JoAnn Pospisil provides an update on the current state of the wax-making industry and new perspectives on traditionally held views about women's roles in the process. New restrictions and trade agreements such as NAFTA as well as border closings following the events of Septempber 11, 2001, have necessitated a number of shifts in the way the workers now operate.

Finally, in Tunnell's Journey, we look back over the life and career of Curtis Tunnell through recollections and tributes of some of his friends and colleagues at the THC.

The exhibits are illustrated by Tunnell's exceptional black and white photos and the charming drawings by Sharon Roos of the THC. New photos by Pospisil, Big Bend wildlife biologist Raymond Skiles, and others bring color and additional views of the industry, the people, and the ruggedly beautiful west Texas landscape to the presentation.

The Wax Camps exhibits on Texas Beyond History were supported by a grant through the Curtis Tunnell Memorial Award, Friends of the Texas Historical Commission. In these exhibits, TBH reflects Tunnell's appreciation for lesser-known, traditional cultures and pushes across political and geographic boundaries to provide a larger, more-meaningful context for understanding our state's rich cultural heritage.

In a small way, candelilla wax has touched all our lives, though unknown to most of us, through a variety of products.


2 Simple manipulations numbers and vectors

2.1 Vectors and assignment

R operates on named data structures. The simplest such structure is the numeric vector, which is a single entity consisting of an ordered collection of numbers. To set up a vector named x , say, consisting of five numbers, namely 10.4, 5.6, 3.1, 6.4 and 21.7, use the R command

This is an assignment statement using the function c() which in this context can take an arbitrary number of vector arguments and whose value is a vector got by concatenating its arguments end to end. 7

A number occurring by itself in an expression is taken as a vector of length one.

Notice that the assignment operator (&lsquo <- &rsquo), which consists of the two characters &lsquo < &rsquo (&ldquoless than&rdquo) and &lsquo - &rsquo (&ldquominus&rdquo) occurring strictly side-by-side and it &lsquopoints&rsquo to the object receiving the value of the expression. In most contexts the &lsquo = &rsquo operator can be used as an alternative.

Assignment can also be made using the function assign() . An equivalent way of making the same assignment as above is with:

The usual operator, <- , can be thought of as a syntactic short-cut to this.

Assignments can also be made in the other direction, using the obvious change in the assignment operator. So the same assignment could be made using

If an expression is used as a complete command, the value is printed and lost 8 . So now if we were to use the command

the reciprocals of the five values would be printed at the terminal (and the value of x , of course, unchanged).

would create a vector y with 11 entries consisting of two copies of x with a zero in the middle place.

2.2 Vector arithmetic

Vectors can be used in arithmetic expressions, in which case the operations are performed element by element. Vectors occurring in the same expression need not all be of the same length. If they are not, the value of the expression is a vector with the same length as the longest vector which occurs in the expression. Shorter vectors in the expression are recycled as often as need be (perhaps fractionally) until they match the length of the longest vector. In particular a constant is simply repeated. So with the above assignments the command

generates a new vector v of length 11 constructed by adding together, element by element, 2*x repeated 2.2 times, y repeated just once, and 1 repeated 11 times.

The elementary arithmetic operators are the usual + , - , * , / and ^ for raising to a power. In addition all of the common arithmetic functions are available. log , exp , sin , cos , tan , sqrt , and so on, all have their usual meaning. max and min select the largest and smallest elements of a vector respectively. range is a function whose value is a vector of length two, namely c(min(x), max(x)) . length(x) is the number of elements in x , sum(x) gives the total of the elements in x , and prod(x) their product.

Two statistical functions are mean(x) which calculates the sample mean, which is the same as sum(x)/length(x) , and var(x) which gives

or sample variance. If the argument to var() is an n-by-p matrix the value is a p-by-p sample covariance matrix got by regarding the rows as independent p-variate sample vectors.

sort(x) returns a vector of the same size as x with the elements arranged in increasing order however there are other more flexible sorting facilities available (see order() or sort.list() which produce a permutation to do the sorting).

Note that max and min select the largest and smallest values in their arguments, even if they are given several vectors. The parallel maximum and minimum functions pmax and pmin return a vector (of length equal to their longest argument) that contains in each element the largest (smallest) element in that position in any of the input vectors.

For most purposes the user will not be concerned if the &ldquonumbers&rdquo in a numeric vector are integers, reals or even complex. Internally calculations are done as double precision real numbers, or double precision complex numbers if the input data are complex.

To work with complex numbers, supply an explicit complex part. Thus

will give NaN and a warning, but

will do the computations as complex numbers.

2.3 Generating regular sequences

R has a number of facilities for generating commonly used sequences of numbers. For example 1:30 is the vector c(1, 2, &hellip, 29, 30) . The colon operator has high priority within an expression, so, for example 2*1:15 is the vector c(2, 4, &hellip, 28, 30) . Put n <- 10 and compare the sequences 1:n-1 and 1:(n-1) .

The construction 30:1 may be used to generate a sequence backwards.

The function seq() is a more general facility for generating sequences. It has five arguments, only some of which may be specified in any one call. The first two arguments, if given, specify the beginning and end of the sequence, and if these are the only two arguments given the result is the same as the colon operator. That is seq(2,10) is the same vector as 2:10 .

Arguments to seq() , and to many other R functions, can also be given in named form, in which case the order in which they appear is irrelevant. The first two arguments may be named from= value and to= value thus seq(1,30) , seq(from=1, to=30) and seq(to=30, from=1) are all the same as 1:30 . The next two arguments to seq() may be named by= value and length= value , which specify a step size and a length for the sequence respectively. If neither of these is given, the default by=1 is assumed.

generates in s3 the vector c(-5.0, -4.8, -4.6, &hellip, 4.6, 4.8, 5.0) . Similarly

generates the same vector in s4 .

The fifth argument may be named along= vector , which is normally used as the only argument to create the sequence 1, 2, &hellip, length( vector ) , or the empty sequence if the vector is empty (as it can be).

A related function is rep() which can be used for replicating an object in various complicated ways. The simplest form is

which will put five copies of x end-to-end in s5 . Another useful version is

which repeats each element of x five times before moving on to the next.

2.4 Logical vectors

As well as numerical vectors, R allows manipulation of logical quantities. The elements of a logical vector can have the values TRUE , FALSE , and NA (for &ldquonot available&rdquo, see below). The first two are often abbreviated as T and F , respectively. Note however that T and F are just variables which are set to TRUE and FALSE by default, but are not reserved words and hence can be overwritten by the user. Hence, you should always use TRUE and FALSE .

Logical vectors are generated by conditions. For example

sets temp as a vector of the same length as x with values FALSE corresponding to elements of x where the condition is not met and TRUE where it is.

The logical operators are < , <= , > , >= , == for exact equality and != for inequality. In addition if c1 and c2 are logical expressions, then c1 & c2 is their intersection (&ldquoand&rdquo), c1 | c2 is their union (&ldquoor&rdquo), and !c1 is the negation of c1 .

Logical vectors may be used in ordinary arithmetic, in which case they are coerced into numeric vectors, FALSE becoming 0 and TRUE becoming 1 . However there are situations where logical vectors and their coerced numeric counterparts are not equivalent, for example see the next subsection.

2.5 Missing values

In some cases the components of a vector may not be completely known. When an element or value is &ldquonot available&rdquo or a &ldquomissing value&rdquo in the statistical sense, a place within a vector may be reserved for it by assigning it the special value NA . In general any operation on an NA becomes an NA . The motivation for this rule is simply that if the specification of an operation is incomplete, the result cannot be known and hence is not available.

The function is.na(x) gives a logical vector of the same size as x with value TRUE if and only if the corresponding element in x is NA .

Notice that the logical expression x == NA is quite different from is.na(x) since NA is not really a value but a marker for a quantity that is not available. Thus x == NA is a vector of the same length as x all of whose values are NA as the logical expression itself is incomplete and hence undecidable.

Note that there is a second kind of &ldquomissing&rdquo values which are produced by numerical computation, the so-called Not a Number, NaN , values. Examples are

which both give NaN since the result cannot be defined sensibly.

In summary, is.na(xx) is TRUE both for NA and NaN values. To differentiate these, is.nan(xx) is only TRUE for NaN s.

Missing values are sometimes printed as <NA> when character vectors are printed without quotes.

2.6 Character vectors

Character quantities and character vectors are used frequently in R, for example as plot labels. Where needed they are denoted by a sequence of characters delimited by the double quote character, e.g., "x-values" , "New iteration results" .

Character strings are entered using either matching double ( " ) or single ( ' ) quotes, but are printed using double quotes (or sometimes without quotes). They use C-style escape sequences, using as the escape character, so is entered and printed as , and inside double quotes " is entered as " . Other useful escape sequences are , newline, , tab and  , backspace&mdashsee ?Quotes for a full list.

Character vectors may be concatenated into a vector by the c() function examples of their use will emerge frequently.

The paste() function takes an arbitrary number of arguments and concatenates them one by one into character strings. Any numbers given among the arguments are coerced into character strings in the evident way, that is, in the same way they would be if they were printed. The arguments are by default separated in the result by a single blank character, but this can be changed by the named argument, sep= string , which changes it to string , possibly empty.

makes labs into the character vector

Note particularly that recycling of short lists takes place here too thus c("X", "Y") is repeated 5 times to match the sequence 1:10 . 9

2.7 Index vectors selecting and modifying subsets of a data set

Subsets of the elements of a vector may be selected by appending to the name of the vector an index vector in square brackets. More generally any expression that evaluates to a vector may have subsets of its elements similarly selected by appending an index vector in square brackets immediately after the expression.

Such index vectors can be any of four distinct types.

    A logical vector. In this case the index vector is recycled to the same length as the vector from which elements are to be selected. Values corresponding to TRUE in the index vector are selected and those corresponding to FALSE are omitted. For example

creates (or re-creates) an object y which will contain the non-missing values of x , in the same order. Note that if x has missing values, y will be shorter than x . Also

creates an object z and places in it the values of the vector x+1 for which the corresponding value in x was both non-missing and positive.

selects the first 10 elements of x (assuming length(x) is not less than 10). Also

(an admittedly unlikely thing to do) produces a character vector of length 16 consisting of "x", "y", "y", "x" repeated four times.

gives y all but the first five elements of x .

The advantage is that alphanumeric names are often easier to remember than numeric indices. This option is particularly useful in connection with data frames, as we shall see later.

An indexed expression can also appear on the receiving end of an assignment, in which case the assignment operation is performed only on those elements of the vector. The expression must be of the form vector[ index_vector ] as having an arbitrary expression in place of the vector name does not make much sense here.

replaces any missing values in x by zeros and

2.8 Other types of objects

Vectors are the most important type of object in R, but there are several others which we will meet more formally in later sections.

  • matrices or more generally arrays are multi-dimensional generalizations of vectors. In fact, they are vectors that can be indexed by two or more indices and will be printed in special ways. See Arrays and matrices.
  • factors provide compact ways to handle categorical data. See Factors.
  • lists are a general form of vector in which the various elements need not be of the same type, and are often themselves vectors or lists. Lists provide a convenient way to return the results of a statistical computation. See Lists.
  • data frames are matrix-like structures, in which the columns can be of different types. Think of data frames as &lsquodata matrices&rsquo with one row per observational unit but with (possibly) both numerical and categorical variables. Many experiments are best described by data frames: the treatments are categorical but the response is numeric. See Data frames.
  • functions are themselves objects in R which can be stored in the project&rsquos workspace. This provides a simple and convenient way to extend R. See Writing your own functions.

Local Historical Societies

If you are unable to visit Pottsville yourself for in-depth research, you can contact the Schuylkill County Historical Society. The Society is located at 305 North Centre Street, Pottsville, PA 17901 telephone number (570) 622-7540 or email [email protected] Among the various materials at the Society are old newspapers, church and cemetery records, photographs, and other local resources. Please note the Society must charge a research fee in order to provide their services.

Other local historical societies and regional genealogy groups:

Ashland Area Historic Preservation Society -- coverage includes Ashland, Girardville, Gordon, and Lavelle. Address: 316-318 Centre Street, Ashland, PA 17936 telephone 570-875-2632. Find current events on their Facebook page.

Cressona Historical Society -- Address: 76 Pottsville St., Cressona, PA 17929.

Frackville Area Historical Society -- Address: 123 N 2nd St, Frackville, PA 17931 telephone 570-874-1579.

Mahanoy Area Historical Society -- this society covers the area for the Mahanoy Area School District, which includes Mahanoy City and Mahanoy Township, Gilberton, Maizeville, St. Nicholas, Wiggans, Delano, Barnesville, Park Place, and so forth. Address: 1-7 W. Centre Street, Mahanoy City, PA 17948 telephone 570-773-1295. Find current events or contact them on their Facebook page.

Minersville Area Historical Society -- coverage includes Minersville, Branch Twp, Cass Twp, Foster Twp, and Reilly Twp. Address: 100-102 S. Third Street, Minersville, PA 17954.

Northern Berks and Southern Schuylkill Historical Society -- Port Clinton, PA telephone 610-562-3749 or 610-562-9383.

Orwigsburg Historical Society -- coverage includes Orwigsburg, Deer Lake, Pinedale, McKeansburg, New Ringgold, and Auburn. Address: 109 E. Mifflin Street, Orwigsburg, PA 17961 telephone 570-617-7809. Find current events on their Facebook page.

Pinegrove Historical Society -- Visit them at the Hikes Homestead, 205 N. Tulpehocken Street, Pine Grove, PA 17963 telephone 570-345-0157. Find current events or contact them on their Facebook page.

Saint Clair Community and Historical Society -- a fairly new group, organized after the borough's 150th anniversary celebration in 2000. Find current events or contact them on their Facebook page.

Schuylkill Haven Area Historical Society -- dedicated to preserving the history of Schuylkill Haven and vicinity. Telephone 570-640-9397. Find current events or contact them on their Facebook page.

Schuylkill Historical Fire Society -- the society is located in Shenandoah, and offers free tours of its museum. Address: 105 South Jardin Street, Shenandoah, PA 17976 telephone 570-462-4400. Find current events or contact them on their Facebook page.

Greater Shenandoah Area Historical Society -- Covers Shenandoah and its surrounding patches including Brownsville, Lost Creek, Lost Creek #2, Raven Run, Shenandoah Heights, Turkey Run, and Upper and Lower William Penn. Address: 201 South Main Street, Shenandoah, PA 17976. Find current events or contact them on their Facebook page.

Tamaqua Historical Society -- 118 W. Broad St., Tamaqua, PA 18252 telephone 610-597-6722. Find current events or contact them on their Facebook page.


*Images of Each Index Page for Each County*

Below are links to the scanned Warrant Register pages for each county. These Warrant Registers serve as the basic index to the original land warrants, surveys and patents for about 70% of the land in the Commonwealth of Pennsylvania for the dates 1733-ca.1957. (For the pre-1733 period, consult the Old Rights Registers and the Proprietary Rights Index . For late-twentieth-century warrants and patents, contact the Pennsylvania State Archives at: (717) 783-2669 or (717) 783-3281.)

The scanned Warrant Registers for 1733-1957 are arranged alphabetically by the surname of the person who got the warrant (warrantee). More specifically, entries are grouped by the first letter of a person's surname, and thereunder arranged in rough chronological order by warrant date. Although the Warrant Registers also provide the name of the person who received the patent (patentee), they do not include an index by this name. You must use the Patent Indexes to look up properties by patentee name if that is all you know.

The geographical area covered by any particular warrant register includes the boundaries of the county as it existed at the time of the warrant. Parent counties contain entries for properties that eventually ended up in other counties, breaking off from the parent at a later date. For example, a 1765 warrant for land now in Lebanon County would be entered in the Lancaster County warrant register. When a new county was formed, the earlier records were not transferred from the parent county. So be aware of county boundary changes as you consult the Warrant Registers. You may want to refer to our list of county formation dates.

Steps for Using the Scanned Warrant Register pages:

  1. First, you will need to determine the name of the warrantee, the approximate date of the warrant, and the approximate location of the land from other sources.
  2. Based on this information, select the county below in which the land most likely was
    located.
  3. Click on the county name below and a list of pages in the warrant register for that county
    should appear.
  4. Follow the directions at the top of the county warrant register page.
  5. Please note: The page images are in Adobe pdf format and require the Adobe Acrobat Reader to be viewed and printed. The software is available for download free of charge from Adobe's website.
  6. To order copies of the records, provide the appropriate citations to the Archives at:
    Pennsylvania State Archives, 350 North Street, Harrisburg, PA 17120-0090 (717) 783-2669 or (717) 783-3281
  7. A current land records order form and price list may be accessed on our website
  8. Trouble? Use this email link to contact an archivist.

Other Related Indexes:
In addition to the county warrant registers, several other volumes index warrants and claims for the same time period. When searching for a particular individual or property, consult both the county warrant register and the pertinent index listed below if one covers the area and time period in which you are interested. The series descriptions to which this list is linked will provide more information on each index.

Certified Townships: Luzerne County [Connecticut Settlers], [c.1782-1810]. .
- for Bradford, Lackawanna, Luzerne and Wyoming Counties

Last Purchase Warrant Register, 1785-1821, 1785-1821.
- for Allegheny, Armstrong, Beaver, Bradford, Butler, Cameron, Clarion, Clearfield, Clinton, Crawford, Elk, Erie, Forest, Indiana, Jefferson, Lawrence, Lycoming, McKean, Mercer, Potter, Tioga, Venango and Warren Counties

Depreciation Land Register, [c.1785-1792].
- Allegheny, Armstrong, Beaver, Butler and Lawrence Counties

Donation Land Register, [c.1786-1810].
- Butler, Clarion, Crawford, Erie, Lawrence, Mercer, Venango and Warren Counties

Warrant Register Anomalies:
The county warrant registers have several anomalies with which the researcher should be familiar.

The entries for Allegheny County are divided into two sections: one section for warrants issued north and west of the Ohio River and another section for warrants issued south and east of the Ohio River. The warrants for each area are numbered separately.

The Northumberland Lottery warrant register indexes warrants that were issued for the eastern section of the Last Purchase (1784), primarily the region that is now Tioga County and the surrounding area. Following the Treaty of Fort Stanwix in 1784 by which Pennsylvania acquired the northwestern third of the state, the entire extent of the new acquisition was assigned to Northumberland County until it was later divided into regions covered by Allegheny and Lycoming Counties. The Commonwealth initially attempted to sell land in the 1784 purchase by lottery.

The Baynton and Wharton Warrant Register indexes warrants issued 1762-1767 to John Baynton and Samuel Wharton-who operated a Philadelphia-based mercantile firm-and to others who obtained warrants under their auspices for land primarily in Bedford, Blair, Franklin, Huntingdon and Mifflin Counties. Baynton and Wharton secured these warrants to protect their trade routes with the interior of the Commonwealth.


Drainage and soils

The dense river network that drains the republic includes two large swift rivers, the upper courses of the Syr Darya and the Amu Darya, together with their tributaries, notably the Vakhsh and Kofarnihon. The Amu Darya is formed by the confluence of the Panj and Vakhsh rivers the Panj forms much of the republic’s southern boundary. Most of the rivers flow east to west and eventually drain into the Aral Sea basin. The rivers have two high-water periods each year: in the spring, when rains fall and mountain snows melt, and in the summer, when the glaciers begin to melt. The summer flow is particularly helpful for irrigation purposes.

The few lakes in Tajikistan lie mostly in the Pamir region the largest is Lake Karakul, lying at an elevation of about 13,000 feet. Lake Sarez was formed in 1911 during an earthquake, when a colossal landslide dammed the Murgab River. The Zeravshan Range contains Iskanderkul, which, like most of the country’s lakes, is of glacial origin.

Tajikistan’s soil is poor in humus but rich in mineral nutrients. Sand, shingle, scree, bare rock, and permanent snow and ice cover about two-thirds of the surface.


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Judaism 101

Judaism 101 or "Jew FAQ" is an online encyclopedia of Judaism, covering Jewish beliefs, people, places, things, language, scripture, holidays, practices and customs. My goal is to make freely available a wide variety of basic, general information about Judaism, written from a traditional perspective in plain English.

The information in this site is written predominantly from the Orthodox viewpoint, because I believe that is a good starting point for any inquiry into Judaism. As recently as 300 years ago, this was the only Judaism, and it still is the only Judaism in many parts of the world. Be aware, however, that most Jews do not follow all of the traditions described here, or do not follow them in the precise form described here. The Conservative movement believes that these laws and traditions can change to suit the times, and Reform/Liberal/Progressive movements believe that individuals can make choices about what traditions to follow. However, what I present here is the starting point, the traditions that are being changed or chosen. On some pages, I have identified variations in practice or belief in other movements.