Server Side Programming Articles - Page 1533 of 2646

Suppose we have a list of numbers called nums where each number shows the maximum number of jumps we can make; we have to check whether we can reach to the last index starting at index 0 or not.So, if the input is like nums = [2, 5, 0, 2, 0], then the output will be True, as we can jump from index 0 to 1, then jump from index 1 to end.To solve this, we will follow these steps−n := size of numsarr := an array of size n and fill with falsearr[n - 1] := Truefor i in ... Read More

We might want to create a subset of an R data frame using one or more values of a particular column. For example, suppose we have a data frame df that contain columns C1, C2, C3, C4, and C5 and each of these columns contain values from A to Z. If we want to select rows using values A or B in column C1 then it can be done as df[df$C1=="A"|df$C1=="B",].Consider the below data frame −Exampleset.seed(99) x1

The contingency table considers the numerical values for two categorical variables. Often, we require contingency table for counts, especially in non-parametric analysis but it is also possible that we want to use means for our analysis. Hence, we can use cast function from reshape package which solves the problem of creating contingency table easily.Consider the below data frame −Example Live Demoset.seed(99) x1

Sometimes the data frame is filled with too many missing values/ NA’s and each column of the data frame contains at least one NA. In this case, we might want to find out how many missing values exists in each of the columns. Therefore, we can use colSums function along with is.na in the following manner: colSums(is.na(df)) #here df refers to data frame name.Consider the below data frame −Example Live Demoset.seed(109) x1

To simulate the normal distribution, we can use rnorm function in R but we cannot put a limit on the range of values for the simulation. If we want simulate this distribution for a fixed limit then truncnorm function of truncnorm package can be used. In this function, we can pass the limits with and without mean and standard deviation.Loading and installing truncnorm package −>install.packages("truncnorm") >library(truncnorm)Examplertruncnorm(n=10, a=0, b=10)[1] 0.76595522 0.33315633 1.29565988 0.67154230 0.04957334 0.38338705 [7] 0.75753005 0.65265304 0.63616552 0.45710877rtruncnorm(n=50, a=0, b=100)[1] 0.904997947 0.035692016 0.402963452 1.001102057 1.445190636 0.109245234 [7] 0.205630845 0.312428027 0.465876772 0.424647787 0.309222394 0.442172805 [13] 0.365503292 1.277570451 0.235747661 1.128447123 ... Read More

When we create a histogram using ggplot2 package, the area covered by the histogram is filled with grey color but we can remove that color to make the histogram look transparent. This can be done by using fill="transparent" and color="black" arguments in geom_histogram, we need to use color argument because if we don’t use then the borders of the histogram bars will also be removed and this color is not restricted to black color only.ExampleConsider the below data frame −set.seed(987) x

The percentiles divide a set of numeric values into hundred groups or individual values if the size of the values is 100. We can find percentiles for a numeric column of an R data frame, therefore, it is also possible to select values of a column based on these percentiles. For this purpose, we can use quantile function.ExampleConsider the below data frame −set.seed(111) x

If we have numbers then we might want to convert those numbers into words. For example, converting 1 to one. This might be required in cases where we have text data and numbers are part of the text. Therefore, it would be better to represent the numbers in text form to make the uniformity in the text. This can be done by using replace_number function qdap package.Installing and loading qdap package−install.packages("qdap") library("qdap")Examplereplace_number("1") [1] "one" replace_number("10") [1] "ten" replace_number("100") [1] "one hundred" replace_number("1000") [1] "one thousand" replace_number("1001") [1] "one thousand one" replace_number("12000") [1] "twelve thousand" replace_number("12214") [1] "twelve thousand two hundred ... Read More

Dealing with NA values is one of the boring and almost day to day task for an analyst and hence we need to replace it with the appropriate value. If in an R data frame, we have a Boolean column that represents TRUE and FALSE values, and we have only FALSE values then we might want to replace NA’s with TRUE. In this case, we can use single square bracket and is.na to set all NA’s to TRUE.Exampleset.seed(999) S.No.

Sometimes we have missing values that can be replaced with the values on the above row values, it often happens in situations when the data is recorded manually and the person responsible for it just mention the unique values because he or she understand the data characteristics. But if this data needs to be re-used by someone else then it does not make sense and we have to connect with the concerned person. If the concerned person tells us that the first value in each row can be filled for every NA in the same column then it can be ... Read More