cleans readme

README.md

@@ -25,7 +25,7 @@ and hydrological data from publicly available repositories:
 
 ## Installation
 
-The stable release of the **climate** package from the [CRAN](https://CRAN.R-project.org) reposity can be installed with:
+The stable release of the **climate** package from the [CRAN](https://CRAN.R-project.org) repository can be installed with:
 
 ``` r
 install.packages("climate")
@@ -73,8 +73,6 @@ country in the Ogimet repository
 - **imgw_meteo_abbrev** - Dictionary explaining variables available for meteorological stations (from the IMGW-PIB repository)
 - **imgw_hydro_abbrev** - Dictionary explaining variables available for hydrological stations (from the IMGW-PIB repository)
 
-
-
 ## Example 1
 #### Download hourly dataset from NOAA ISH meteorological repository:
 
@@ -90,8 +88,6 @@ head(noaa)
 #   2019     1   1    3 16.85 52.417  84 5.2   4.6  5 240 1021.2       1900
 ```
 
-
-
 ## Example 2
 #### Finding a nearest meteorological stations in a given country using NOAA ISH data source:
 
@@ -118,9 +114,9 @@ nearest_stations_ogimet(country = "United+Kingdom",
 
 ![100 nearest stations to given coordinates in UK](http://iqdata.eu/kolokwium/uk.png)
 
-
 ## Example 3
 #### Downloading daily (or hourly) data from a global (OGIMET) repository knowing its ID (see also `nearest_stations_ogimet()`):
+
 ``` r
 library(climate)
 o = meteo_ogimet(date = c(Sys.Date() - 5, Sys.Date() - 1), 
@@ -178,12 +174,11 @@ head(h)
 ## Example 5
 #### Create Walter & Lieth climatic diagram based on downloaded data
 
-
 ``` r4
 library(climate)
 library(dplyr)
 
-df = meteo_imgw(interval = 'monthly', rank='synop', year = 1991:2019, station = "POZNAŃ") 
+df = meteo_imgw(interval = "monthly", rank = "synop", year = 1991:2019, station = "POZNAŃ") 
 df2 = select(df, station:t2m_mean_mon, rr_monthly)
 
 monthly_summary = df2 %>% 
@@ -239,7 +234,6 @@ ggplot(co2, aes(date, co2_avg)) +
 
 ![CO2 monthly concentration, Mauna Loa observatory](http://iqdata.eu/kolokwium/co2_chart.svg)
 
-
 ## Example 7
 #### Use "climate" inside python environment via rpy2
 
@@ -251,14 +245,14 @@ import pandas as pd
 import datetime as dt
 
 # load climate package (make sure that it was installed in R before)
-importr('climate')
+importr("climate")
 # test functionality e.g. with meteo_ogimet function for New York - La Guardia:
-df = robjects.r['meteo_ogimet'](interval = "daily", station = 72503,
-                                date = robjects.StrVector(['2022-05-01', '2022-06-15']))
+df = robjects.r["meteo_ogimet"](interval = "daily", station = 72503,
+                                date = robjects.StrVector(["2022-05-01", "2022-06-15"]))
 # optionally - transform object to pandas data frame and rename columns + fix datetime:
 res = pd.DataFrame(df).transpose()
 res.columns = df.colnames
-res['Date'] = pd.TimedeltaIndex(res['Date'], unit='d') + dt.datetime(1970,1,1)
+res["Date"] = pd.TimedeltaIndex(res["Date"], unit="d") + dt.datetime(1970,1,1)
 res.head
 
 >>> res[res.columns[0:7]].head()
@@ -293,4 +287,3 @@ LaTeX/BibTeX version can be obtained with:
 library(climate)
 citation("climate")
 ```
-