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An Na: 1x3

To calculate cases per person, we need to divide cases by population for each country and year.This is easiest if the cases and population variables are two columns in a data frame in which rows represent (country, year) combinations.

Neither table is particularly easy to work with.Since table2 has separate rows for cases and population we needed to generate a table with columns for cases and population where we couldcalculate cases per capita.table4a and table4b split the cases and population variables into different tables whichmade it easy to divide cases by population.However, we had to repeat this calculation for each row.

The functions pivot_longer() and pivot_wider() are not perfectly symmetrical because column type information is lost when a data frame is converted from wide to long.The function pivot_longer() stacks multiple columns which may have had multiple data types into a single column with a single data type.This transformation throws away the individual data types of the original columns.The function pivot_wider() creates column names from values in column.These column names will always be treated as character values by pivot_longer() so if the original variable used to create the column names did not have a character data type, then the round-trip will not reproduce the same dataset.

There is one difference, in the new data frame, year has a data type of character rather than numeric.The names_to column created from column names by pivot_longer() will be character by default, which is usually a safe assumption, since syntactically valid-column names can only be character values.

The original data types of column which pivot_wider() used to create the column names was not stored, so pivot_longer() has no idea that the column names in this case should be numeric values.In the current version of tidyr, the names_ptype argument does not convert the year column to a numeric vector, and it will raise an error.

The code fails because the column names 1999 and 2000 are not non-syntactic variable names.[^non-syntactic]When selecting variables from a data frame, tidyverse functions will interpret numbers, like 1999 and 2000, as column numbers.In this case, pivot_longer() tries to select the 1999th and 2000th column of the data frame.To select the columns 1999 and 2000, the names must be surrounded in backticks (```) or provided as strings.

This an example of turning an explicit missing value into an implicit missing value, which is discussed in the upcoming section, Missing Values section.The missing (male, pregnant) row represents an implicit missing value because the value of count can be inferred from its absence.In the tidy data, we can represent rows with missing values of count either explicitly with an NA (as in preg_tidy) or implicitly by the absence of a row (as in preg_tidy2).But in the wide data, the missing values can only be represented explicitly.

Though we have already done enough to make the data tidy, there are some other transformations that can clean the data further.If a variable takes two values, like pregnant and sex, it is often preferable to store them as logical vectors.

Apart from some minor memory savings, representing these variables as logical vectors results in more clear and concise code.Compare the filter() calls to select non-pregnant females from preg_tidy2 and preg_tidy.

In this example, one of the values, "d,e", has too few elements.The default for fill is similar to those in separate();it fills columns with missing values but emits a warning.In this example, the 2nd row of column three is NA.

The option fill = "left" also fills with missing values without emitting a warning, but this time from the left side.Now, the 2nd row of column one will be missing, and the other values in that row are shifted right.

The function extract() uses a regular expression to specify groups in character vector and split that single character vector into multiple columns.This is more flexible than separate() because it does not require a commonseparator or specific column positions.

In other words, with extract() and separate() only one column can be chosen,but there are many choices how to split that single column into different columns.With unite(), there are many choices as to which columns to include, but only onechoice as to how to combine their contents into a single vector.

The values_fill argument in pivot_wider() and the fill argument to complete() both set vales to replace NA.Both arguments accept named lists to set values for each column.Additionally, the values_fill argument of pivot_wider() accepts a single value.In complete(), the fill argument also sets a value to replace NAs but it is named list, allowing for different values for different variables.Also, both cases replace both implicit and explicit missing values.

The reasonableness of using na.rm = TRUE depends on how missing values are represented in this dataset.The main concern is whether a missing value means that there were no cases of TB or whether it means that the WHO does not have data on the number of TB cases.Here are some things we should look for to help distinguish between these cases.

If there are both explicit and implicit missing values, then it suggests that missing valuesare being used differently. In that case, it is likely that explicit missing values wouldmean no cases, and implicit missing values would mean no data on the number of cases.

All of these refer to (country, year) combinations for years prior to the existence of the country.For example, Timor-Leste achieved independence in 2002, so years prior to that are not included in the data.

A small multiples plot faceting by country is difficult given the number of countries.Focusing on those countries with the largest changes or absolute magnitudes after providing the context above is another option.

Since NA is not unique (i.e. one NA does not necessarily equal another NA) it seems to follow that when performing a join the rows should not match when both keys are NA. However when merging with dplyr NA will match NA when merging. What is the reasoning behind this behavior?

Exactly what matches what is to some extent a matter of definition. For all types, NA matches NA and no other value. For real and complex values, NaN values are regarded as matching any other NaN value, but not matching NA , where for complex x , real and imaginary parts must match both (unless containing at least one NA ).

From the SO thread posted by Yarnabrina, you can also see a way base R allows preventing NA from matching itself. dplyr has something similar (detailed further below in this post). The match function has an incomparables = parameter, so we can do this:

Use "never" to always treat two NA or NaN values as different, like joins for database sources, similarly to merge(incomparables = FALSE). The default, "na", always treats two NA or NaN values as equal, like merge(). Users and package authors can change the default behavior by calling pkgconfig::set_config("dplyr::na_matches" = "never").

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We have determined the structure of AlSb and GaSb (001) surfaces prepared by molecular beam epitaxy under typical Sb-rich device growth conditions. Within the range of flux and temperature where the diffraction pattern is nominally (1x3), we find that there are actually three distinct, stable (4x3) surface reconstructions. The three structures differ from any previously proposed for these growth conditions, with two of the reconstructions incorporating mixed III-V dimers within the Sb surface layer. These heterodimers appear to play an important role in island nucleation and growth.

I am having trouble including a conditional in my pipe. In my code, I am applying a function to data and, based on if there are any positive results, summarising the data. If there are no positive results, I need the output to be an "NA".

I realize I could handle this using a conditional outside of the pipe, but I'd rather keep it streamlined if possible. Really the thing that's driving me up the wall is that this code was working fine yesterday. I've tried updating all my packages and restarting R.

Funkcjonalna szafka zbudowana w systemie USM Haller. Elementem nośnym konstrukcji, a zarazem znakiem rozpoznawczym systemu USM, jest rama z chromowanych rurek połączonych kulkami, które również są chromowane. Szafka 1x3 to jedno z popularniejszych rozwiązań, często wybierane przez naszych klientów.

Ten model szafki może być wyposażony w kółka - szafka staje się wtedy mobilna. Jest to duży atut, biorąc pod uwagę, że sama szafka może ważyć (w zależności od wyposażenia dodatkowego) do 76 kg, a na każdej półce można bez obaw położyć kilkadziesiąt kilogramów. Przednie kółka są blokowane.

Szafka może być otwarta (także z tyłu, jeśli chcesz wyeksponować ścianę, lub uzyskać efekt lekkości) lub zamknięta szufladami bądź opadającymi drzwiczkami. W przypadku, kiedy konieczna jest zwiększona kontrola dostępu do zawartości mebla, należy wybrać wersję zamykaną na kluczyk. Szuflady mogą być wyposażone w wysokie burty.

Funkcjonalna szafka zbudowana w systemie USM Haller. Elementem nośnym konstrukcji, a zarazem znakiem rozpoznawczym systemu USM, jest rama z chromowanych rurek połączonych kulkami, które również są chromowane. Szafka 1x2 to jedno z popularniejszych rozwiązań, często wybierane przez naszych klientów. 041b061a72


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